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Morris DM, Wang C, Papanastasiou G, Gray CD, Xu W, Sjöström S, Badr S, Paccou J, Semple SIK, MacGillivray T, Cawthorn WP. A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data. Comput Struct Biotechnol J 2024; 24:89-104. [PMID: 38268780 PMCID: PMC10806280 DOI: 10.1016/j.csbj.2023.12.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/26/2024] Open
Abstract
Background Bone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT's true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances. Objective To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data. Materials and methods We studied males and females aged 60-69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10-12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis. Results Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics. Conclusions We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.
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Affiliation(s)
- David M. Morris
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Chengjia Wang
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Mathematics and Computer Sciences, Heriot-Watt University, Edinburgh EH14 1AS, UK
| | - Giorgos Papanastasiou
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- School of Computer Science and Electronic Engineering, Wivenhoe Park, The University of Essex, Colchester CO4 3SQ, UK
| | - Calum D. Gray
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Samuel Sjöström
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Sammy Badr
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Radiology and Musculoskeletal Imaging, F-59000 Lille, France
| | - Julien Paccou
- University of Lille, Marrow Adiposity and Bone Laboratory (MABlab) ULR 4490, F-59000 Lille, France
- CHU Lille, Department of Rheumatology, F-59000 Lille, France
| | - Scott IK Semple
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
- Edinburgh Imaging, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Tom MacGillivray
- Centre for Clinical Brain Sciences, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - William P. Cawthorn
- University/BHF Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh BioQuarter, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
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Tian H, Kong X, Han F, Xing F, Zhu S, Xu T, Wang W, Song N, Wu Y. Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study. Biol Trace Elem Res 2024; 202:2509-2517. [PMID: 37814169 DOI: 10.1007/s12011-023-03878-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05-1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.
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Affiliation(s)
- Huimin Tian
- Zhonglou District Center for Disease Control and Prevention, Changzhou, Jiangsu, China.
| | - Xiangjie Kong
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
| | - Fulei Han
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
| | - Fangjie Xing
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
| | - Shuai Zhu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
| | - Tao Xu
- Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
| | - Ning Song
- Department of Physiology, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Neurological Disorder, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
| | - Yili Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong, China
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Gagnon E, Bourgault J, Gobeil É, Thériault S, Arsenault BJ. Impact of loss-of-function in angiopoietin-like 4 on the human phenome. Atherosclerosis 2024; 393:117558. [PMID: 38703417 DOI: 10.1016/j.atherosclerosis.2024.117558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Carriers of the E40K loss-of-function variant in Angiopoietin-like 4 (ANGPTL4), have lower plasma triglyceride levels as well as lower rates of coronary artery disease (CAD) and type 2 diabetes (T2D). These genetic data suggest ANGPTL4 inhibition as a potential therapeutic target for cardiometabolic diseases. However, it is unknown whether the association between E40K and human diseases is due to linkage disequilibrium confounding. The broader impact of genetic ANGPTL4 inhibition is also unknown, raising uncertainties about the safety and validity of this target. METHODS To assess the impact of ANGPLT4 inhibition, we evaluated whether E40K and other loss-of-function variants in ANGPTL4 influenced a wide range of health markers and diseases using 29 publicly available genome-wide association meta-analyses of cardiometabolic traits and diseases, as well as 1589 diseases assessed in electronic health records within FinnGen (n = 309,154). To determine whether these relationships were likely causal, and not driven by other correlated variants, we used the Bayesian fine mapping algorithm CoPheScan. RESULTS The CoPheScan posterior probability of E40K being the causal variant for triglyceride levels was 99.99 %, validating the E40K to proxy lifelong lower activity of ANGPTL4. The E40K variant was associated with lower risk of CAD (odds ratio [OR] = 0.84, 95 % CI = 0.81 to 0.87, p=3.6e-21) and T2D (OR = 0.91, 95 % CI = 0.87 to 0.95, p=2.8e-05) in GWAS meta-analyses, with results replicated in FinnGen. These significant results were also replicated using other rare loss-of-function variants identified through whole exome sequencing in 488,278 participants of the UK Biobank. Using a Mendelian randomization study design, the E40K variant effect on cardiometabolic diseases was concordant with lipoprotein lipase enhancement (r = 0.82), but not hepatic lipase enhancement (r = -0.10), suggesting that ANGPTL4 effects on cardiometabolic diseases are potentially mainly mediated through lipoprotein lipase. After correction for multiple testing, the E40K variant did not significantly increase the risk of any of the 1589 diseases tested in FinnGen. CONCLUSIONS ANGPTL4 inhibition may represent a potentially safe and effective target for cardiometabolic diseases prevention or treatment.
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Affiliation(s)
- Eloi Gagnon
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Jérome Bourgault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Émilie Gobeil
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | - Sébastien Thériault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada; Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de Recherche de L'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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Nauffal V, Klarqvist MDR, Hill MC, Pace DF, Di Achille P, Choi SH, Rämö JT, Pirruccello JP, Singh P, Kany S, Hou C, Ng K, Philippakis AA, Batra P, Lubitz SA, Ellinor PT. Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping. Nat Med 2024:10.1038/s41591-024-03010-w. [PMID: 38806679 DOI: 10.1038/s41591-024-03010-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 04/22/2024] [Indexed: 05/30/2024]
Abstract
Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.
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Affiliation(s)
- Victor Nauffal
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Matthew C Hill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Danielle F Pace
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joel T Rämö
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James P Pirruccello
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cody Hou
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Anthony A Philippakis
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven A Lubitz
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
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Hu M, Li X, Wu J, Li B, Xia J, Yang Y, Yin C. Phenome-Wide Investigation of the Causal Associations Between Pre-Pregnancy Obesity Traits and Gestational Diabetes: A Two-Sample Mendelian Randomization Analyses. Reprod Sci 2024:10.1007/s43032-024-01577-w. [PMID: 38789873 DOI: 10.1007/s43032-024-01577-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024]
Abstract
Pre-pregnancy obesity was associated with gestational diabetes in observational studies, but whether this relationship is causal remains to be determined. To evaluate whether pre-pregnancy obesity traits causally affect gestational diabetes risk, a two-sample Mendelian randomization (MR) analysis was performed utilizing summary-level statistics from published genome-wide association studies (GWAS). Obesity-related traits included body mass index (BMI), overweight, obesity, obesity class 1, obesity class 2, obesity class 3, childhood obesity, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), percent liver fat, visceral adipose tissue volume, abdominal subcutaneous adipose tissue volume. Effect estimates were evaluated using the inverse-variance weighting method. Weighted median, MR-Egger, simple mode, and weighted mode were performed as sensitivity analyses. Genetically predicted pre-pregnancy BMI [odds ratio (OR) = 1.68; 95% confidence interval (CI): 1.45-1.95; P = 9.13 × 10-12], overweight (OR = 1.49; 95% CI: 1.21-1.85; P = 2.06 × 10-4), obesity (OR = 1.25; 95% CI: 1.18-1.33; P = 8.01 × 10-13), obesity class 1 (OR = 1.31; 95% CI: 1.17-1.46; P = 1.49 × 10-6), obesity class 2 (OR = 1.26; 95% CI: 1.16-1.37; P = 5.23 × 10-8), childhood obesity (OR = 1.33; 95% CI: 1.23-1.44; P = 4.06 × 10-12), and WHR (OR = 2.35; 95% CI: 1.44-3.83; P = 5.89 × 10-4) were associated with increased risk of gestational diabetes. No significant association was observed with obesity class 3, WC, HC, percent liver fat, visceral adipose tissue volume, or abdominal subcutaneous adipose tissue volume. Similar results were observed in sensitivity analyses. Therefore, genetically predicted pre-pregnancy obesity traits may increase the risk of gestational diabetes. Weight control before pregnancy may be beneficial to prevent gestational diabetes.
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Affiliation(s)
- Mengjin Hu
- Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Xiaosong Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100037, China
| | - Jiangong Wu
- Fenyang Center for Disease Control and Prevention, Fenyang, 032200, Shanxi Province, China
| | - Boyu Li
- Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Jinggang Xia
- Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yuejin Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100037, China.
| | - Chunlin Yin
- Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
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Shi H, Wu Q, Guo N, Liu L, Widjaja J, Yang J, Gu Y. Causal relationship of obesity and adiposity distribution on risk of ventral hernia. World J Surg 2024; 48:1141-1148. [PMID: 38520680 DOI: 10.1002/wjs.12137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/05/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE Ventral hernia (VH) is a common surgical disease. Previous studies suggested that obesity is an important risk factor for VH. However, the causal relationship between fat distribution and the risk of VH is still unclear. This study used Mendelian randomization (MR) to evaluate their causal relationship. METHODS We used the body mass index (BMI), body fat percentage, and body fat mass to represent general obesity and utilized the volume of abdominal subcutaneous adiposity tissue, visceral adiposity tissue, waist circumference, hip circumference, and waist-to-hip ratio to represent abdominal adiposity. The data were extracted from the large-scale genome-wide association study of European ancestry. We used two-sample MR to infer causality, using multivariate MR to correct the effects of confounding factors. RESULTS Increased BMI, body fat percentage, body fat mass, visceral adiposity tissue, waist circumference, and hip circumference rather than subcutaneous adiposity tissue or waist-to-hip ratio, were causally associated with a higher risk of VH. The results of multivariate MR suggested that body fat percentage was causally associated with a higher risk of VH after adjusting for body mass index, diabetes, and smoking. CONCLUSION General obesity, increased visceral adiposity tissue, waist circumference, and hip circumference rather than subcutaneous adiposity tissue or the waist-to-hip ratio were causally associated with a higher risk of VH. These findings provided a deeper understanding of the role that the distribution of adiposity plays in the mechanism of VH.
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Affiliation(s)
- Hekai Shi
- Department of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai, China
| | - Qian Wu
- Department of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai, China
| | - Nuojin Guo
- Department of Endocrinology, Shanghai East Hospital, Tongji University, Shanghai, China
| | - Ligang Liu
- Institute of Therapeutic Innovations and Outcomes, College of Pharmacy, The Ohio State University, Columbus, Ohio, USA
| | - Jason Widjaja
- Department of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai, China
| | - Jianjun Yang
- Department of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai, China
| | - Yan Gu
- Department of General Surgery, Fudan University Affiliated Huadong Hospital, Shanghai, China
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Helgesson S, Tarai S, Langner T, Ahlström H, Johansson L, Kullberg J, Lundström E. Spleen volume is independently associated with non-alcoholic fatty liver disease, liver volume and liver fibrosis. Heliyon 2024; 10:e28123. [PMID: 38665588 PMCID: PMC11043861 DOI: 10.1016/j.heliyon.2024.e28123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 04/28/2024] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) can lead to irreversible liver damage manifesting in systemic effects (e.g., elevated portal vein pressure and splenomegaly) with increased risk of deadly outcomes. However, the association of spleen volume with NAFLD and related type 2-diabetes (T2D) is not fully understood. The UK Biobank contains comprehensive health-data of 500,000 participants, including clinical data and MR images of >40,000 individuals. The present study estimated the spleen volume of 37,066 participants through automated deep learning-based image segmentation of neck-to-knee MR images. The aim was to investigate the associations of spleen volume with NAFLD, T2D and liver fibrosis, while adjusting for natural confounders. The recent redefinition and new designation of NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD), promoted by major organisations of studies on liver disease, was not employed as introduced after the conduct of this study. The results showed that spleen volume decreased with age, correlated positively with body size and was smaller in females compared to males. Larger spleens were observed in subjects with NAFLD and T2D compared to controls. Spleen volume was also positively and independently associated with liver fat fraction, liver volume and the fibrosis-4 score, with notable volumetric increases already at low liver fat fractions and volumes, but not independently associated with T2D. These results suggest a link between spleen volume and NAFLD already at an early stage of the disease, potentially due to initial rise in portal vein pressure.
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Affiliation(s)
- Samuel Helgesson
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
| | - Sambit Tarai
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Håkan Ahlström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Joel Kullberg
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | - Elin Lundström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
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Cao M, Cui B. Clinically relevant plasma proteome for adiposity depots: evidence from systematic mendelian randomization and colocalization analyses. Cardiovasc Diabetol 2024; 23:126. [PMID: 38614964 PMCID: PMC11016216 DOI: 10.1186/s12933-024-02222-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/31/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND The accumulation of visceral and ectopic fat comprise a major cause of cardiometabolic diseases. However, novel drug targets for reducing unnecessary visceral and ectopic fat are still limited. Our study aims to provide a comprehensive investigation of the causal effects of the plasma proteome on visceral and ectopic fat using Mendelian randomization (MR) approach. METHODS We performed two-sample MR analyses based on five large genome-wide association study (GWAS) summary statistics of 2656 plasma proteins, to screen for causal associations of these proteins with traits of visceral and ectopic fat in over 30,000 participants of European ancestry, as well as to assess mediation effects by risk factors of outcomes. The colocalization analysis was conducted to examine whether the identified proteins and outcomes shared casual variants. RESULTS Genetically predicted levels of 14 circulating proteins were associated with visceral and ectopic fat (P < 4.99 × 10- 5, at a Bonferroni-corrected threshold). Colocalization analysis prioritized ten protein targets that showed effect on outcomes, including FST, SIRT2, DNAJB9, IL6R, CTSA, RGMB, PNLIPRP1, FLT4, PPY and IL6ST. MR analyses revealed seven risk factors for visceral and ectopic fat (P < 0.0024). Furthermore, the associations of CTSA, DNAJB9 and IGFBP1 with primary outcomes were mediated by HDL-C and SHBG. Sensitivity analyses showed little evidence of pleiotropy. CONCLUSIONS Our study identified candidate proteins showing putative causal effects as potential therapeutic targets for visceral and ectopic fat accumulation and outlined causal pathways for further prevention of downstream cardiometabolic diseases.
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Affiliation(s)
- Min Cao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Bin Cui
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wen J, Tian YE, Skampardoni I, Yang Z, Cui Y, Anagnostakis F, Mamourian E, Zhao B, Toga AW, Zaleskey A, Davatzikos C. The Genetic Architecture of Biological Age in Nine Human Organ Systems. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.08.23291168. [PMID: 37398441 PMCID: PMC10312870 DOI: 10.1101/2023.06.08.23291168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Filippos Anagnostakis
- Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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10
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Virostko J, Tirkes T. Cross-sectional imaging of the pancreas in diabetes. Abdom Radiol (NY) 2024:10.1007/s00261-024-04310-y. [PMID: 38557767 DOI: 10.1007/s00261-024-04310-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/04/2024]
Abstract
Diabetes mellitus presents a global health challenge characterized by dysregulated glucose metabolism and insulin resistance. Pancreas dysfunction contributes to the development and progression of diabetes. Cross-sectional imaging modalities have provided new insight into the structural and functional alterations of the pancreas in individuals with diabetes. This review summarizes MRI and CT studies that characterize pancreas alterations in both type 1 and type 2 diabetes and discusses future applications of these techniques.
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Affiliation(s)
- John Virostko
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, 10 E 24th Street, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA.
| | - Temel Tirkes
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medicine, Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
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11
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Cho JM, Koh JH, Kim SG, Lee S, Kim Y, Cho S, Kim K, Kim YC, Han SS, Lee H, Lee JP, Joo KW, Lim CS, Kim YS, Kim DK, Park S. Associations of MRI-derived kidney volume, kidney function, body composition and physical performance in ≈38 000 UK Biobank participants: a population-based observational study. Clin Kidney J 2024; 17:sfae068. [PMID: 38660121 PMCID: PMC11040514 DOI: 10.1093/ckj/sfae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Indexed: 04/26/2024] Open
Abstract
Background Kidney volume is used as a predictive and therapeutic marker for several clinical conditions. However, there is a lack of large-scale studies examining the relationship between kidney volume and various clinicodemographic factors, including kidney function, body composition and physical performance. Methods In this observational study, MRI-derived kidney volume measurements from 38 526 UK Biobank participants were analysed. Major kidney volume-related measures included body surface area (BSA)-adjusted total kidney volume (TKV) and the difference in bilateral kidneys. Multivariable-adjusted linear regression and cubic spline analyses were used to explore the association between kidney volume-related measures and clinicodemographic factors. Cox or logistic regression was used to identify the risks of death, non-kidney cancer, myocardial infarction, ischaemic stroke and chronic kidney disease (CKD). Results The median of BSA-adjusted TKV and the difference in kidney volume were 141.9 ml/m2 [interquartile range (IQR) 128.1-156.9] and 1.08-fold (IQR 1.04-1.15), respectively. Higher BSA-adjusted TKV was significantly associated with higher estimated glomerular filtration rate {eGFR; β = 0.43 [95% confidence interval (CI) 0.42-0.44]; P < .001}, greater muscle volume [β = 0.50 (95% CI 0.48-0.51); P < .001] and greater mean handgrip strength [β = 0.15 (95% CI 0.13-0.16); P < .001] but lower visceral adipose tissue volume [VAT; β = -0.09 (95% CI -0.11 to -0.07); P < .001] in adjusted models. A greater difference in bilateral kidney volumes was associated with lower eGFR, muscle volume and physical performance but with higher proteinuria and VAT. Higher BSA-adjusted TKV was significantly associated with a reduced risk of CKD [odds ratio (OR) 0.7 (95% CI 0.63-0.77); P < .001], while a greater difference in kidney volume was significantly associated with an increased risk of CKD [OR 1.13 (95% CI 1.07-1.20); P < .001]. Conclusion Higher BSA-adjusted TKV and lower differences in bilateral kidney volumes are associated with higher kidney function, muscle volume and physical performance and a reduced risk of CKD.
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Affiliation(s)
- Jeong Min Cho
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gyeonggi-do, Korea
| | - Jung Hun Koh
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Seong Geun Kim
- Department of Internal Medicine, Inje University Sanggye Paik Hospital, Seoul, Korea
| | - Soojin Lee
- Department of Internal Medicine, Uijeongbu Eulji University Medical Center, Uijeongbu, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Yaerim Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea
| | - Semin Cho
- Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gyeonggi-do, Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Korea
| | - Yong Chul Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Kwon Wook Joo
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Korea
| | - Yon Su Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Dong Ki Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Kidney Research Institute, Seoul National University, Seoul, Korea
| | - Sehoon Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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12
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Ren Z, Wesselius A, Stehouwer CDA, Brouwers MCGJ. Relationship between educational attainment and non-alcoholic fatty liver disease: A two-sample Mendelian randomization study. Dig Liver Dis 2024; 56:565-570. [PMID: 38104027 DOI: 10.1016/j.dld.2023.11.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Observational studies have identified an inverse association between education and non-alcoholic fatty liver disease (NAFLD). However, it is not possible to establish causality for this relationship. AIMS To gain more insight into the causal nature of the relationship between education and NAFLD. METHODS We performed two-sample Mendelian randomization (MR) analyses using summary-level, large-scale datasets to study the association of genetically predicted educational attainment (n = 1271 genetic instruments, obtained from 1,131,881 participants) with risk of NAFLD (i.e., liver fat [n = 32,858 participants] and electronic health record (EHR)-based NAFLD [n = 778,614 participants]). In sensitivity analyses, educational attainment was replaced by three education-related traits (i.e., genetically predicted cognition, math ability and highest math). RESULTS Inverse-variance weighted method showed a statistically significant association between genetically predicted educational attainment and liver fat (beta: -0.251, 95%CI: -0.305; -0.198) and EHR-based NAFLD (OR: 0.609, 95%CI: 0.547; 0.677). MR-Egger regression did not show statistically significant intercepts. Similar findings were obtained when other MR tests were used or when educational attainment was replaced by education-related traits. CONCLUSIONS This study suggests a causal, protective effect of higher education on NAFLD risk. Societal interventions targeted at people with low education are needed to alleviate the burden of NAFLD.
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Affiliation(s)
- Zhewen Ren
- Department of Internal Medicine, Division of Endocrinology and Metabolic Diseases, Maastricht University Medical Center, Maastricht, the Netherlands; Laboratory for Metabolism and Vascular Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands; NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Anke Wesselius
- NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands; Department of Epidemiology, Maastricht University, Maastricht, the Netherlands
| | - Coen D A Stehouwer
- Laboratory for Metabolism and Vascular Medicine, Maastricht University, Maastricht, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands; Department of Internal Medicine, Division of General Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Martijn C G J Brouwers
- Department of Internal Medicine, Division of Endocrinology and Metabolic Diseases, Maastricht University Medical Center, Maastricht, the Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands; CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands.
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13
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Peng Z, Dong X, Long Y, Li Z, Wang Y, Zhu W, Ding B. Causality between allergic diseases and kidney diseases: a two-sample Mendelian randomization study. Front Med (Lausanne) 2024; 11:1347152. [PMID: 38533318 PMCID: PMC10963543 DOI: 10.3389/fmed.2024.1347152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Abstract
Background Evidence from observational studies and clinical trials suggests that the allergic diseases (ADs) are associated with kidney diseases (KDs). However, the causal association between them remains to be determined. We used bidirectional two-sample Mendelian randomization (MR) analysis to evaluate the potential causality between them. Methods Mendelian randomization (MR) was performed using publicly available genome-wide association study (GWAS) summary datasets. Inverse variance weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods are used to evaluate the causality between ADs and KDs. Sensitivity and heterogeneity analyses were used to ensure the stability of the results. Results The MR results indicated that genetic susceptibility to ADs was associated with a higher risk of CKD [odds ratio (OR) = 1.124, 95% CI = 1.020-1.239, p = 0.019] and unspecified kidney failure (OR = 1.170, 95% CI = 1.004-1.363, p = 0.045) but not with kidney stone, ureter stone or bladder stone (OR = 1.001, 95% CI = 1.000-1.002, p = 0.216), other renal or kidney problem (OR = 1.000, 95% CI = 1.000-1.001, p = 0.339), urinary tract or kidney infection (OR = 1.000, 95% CI = 0.999-1.001, p = 0.604), kidney volume (OR = 0.996, 95% CI = 0.960-1.033, p = 0.812) and cyst of kidney (OR = 0.914, 95% CI = 0.756-1.105, p = 0.354). No causal evidence of KDs on ADs was found in present study. Conclusion Results from MR analysis indicate a causal association between ADs and CKD and unspecified kidney failure. These findings partly suggest that early monitoring of CKD risk in patients with ADs is intentional.
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Affiliation(s)
- Zhe Peng
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xinyu Dong
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yingxin Long
- College of Pharmacy, Jinan University, Guangzhou, Guangdong, China
| | - Zunjiang Li
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yueyao Wang
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Wei Zhu
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
| | - Banghan Ding
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, Guangdong, China
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14
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024; 627:347-357. [PMID: 38374256 PMCID: PMC10937372 DOI: 10.1038/s41586-024-07019-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/03/2024] [Indexed: 02/21/2024]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kim M Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Giorgio E M Melloni
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nigel W Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
- Munich School for Data Science, Helmholtz Munich, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S K Lee
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J Y Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Anny H Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manonanthini Thangam
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K Das
- Section of Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Jost B Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Frederick K Kamanu
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Soochunhyang Institute of Medi-bio Science and Division of Endocrinology, Department of Internal Medicine, Soochunhyang University College of Medicine, Cheonan, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O Luk
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Olle Melander
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D Morris
- Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Filipe A Moura
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fraser J Pirie
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology, and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Touon, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C Engert
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Wayne H H Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Science and Engineering Research Board (SERB), Department of Science and Technology, Ministry of Science and Technology, Government of India, New Delhi, India
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michèle M Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sarah Finer
- Institute for Population Health Sciences, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Joshua C Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany.
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Zhang Z, Li M, Ji G, Zhang L. Causal relationship between sleep apnea and non-alcoholic fatty liver disease: A Mendelian randomization study. Eur J Clin Invest 2024; 54:e14116. [PMID: 37916519 DOI: 10.1111/eci.14116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Observational studies indicate that sleep apnea is associated with non-alcoholic fatty liver disease (NAFLD) and its related metabolic features, independent of confounding factors including obesity. However, the causal relationships remain to be determined. METHODS Univariable and multivariable Mendelian randomization (MR) analyses were performed to investigate the causal relationship between sleep apnea and NAFLD, along with its typical features including liver function, glycemic traits and lipid profiles. Summary-level data for sleep apnea were obtained from the Finngen consortium (33,423 cases and 307,648 controls). Summary-level data for NAFLD were available from a GWAS meta-analysis (8434 cases and 770,180 controls), and data for 12 NAFLD-related features from corresponding published GWASs. The inverse variance weighted (IVW) analysis was employed as the primary statistical method. Bidirectional MR and CAUSE analysis were conducted to avoid reverse causality and false positive findings. RESULTS In univariable MR analyses, we found evidence to support a causal effect of genetically predicted sleep apnea on NAFLD (OR = 1.50, 95% CI = 1.18-1.91) and HDL-C (β = -0.045, 95% CI = -0.090 to -0.001). In reverse MR, genetically predicted serum TG was associated with an increased risk of sleep apnea (OR = 1.07, 95% CI = 1.02-1.12), while genetically predicted HDL-C was associated with a decreased risk of sleep apnea (OR = 0.93, 95% CI = 0.89-0.98). After adjusting body mass index or educational attainment, none of these causal associations were retained. However, CAUSE method and MR analyses focusing on lipoprotein subfractions supported a causal effect of sleep apnea on HDL-C and HDL subfractions. CONCLUSION This MR study indicated that sleep apnea has no direct causal association with NAFLD, elevated liver enzymes and insulin resistance. Our results showed suggestive inverse associations of genetically predicted sleep apnea on HDL-C and HDL subfractions, indicating that both HDL-C levels and HDL function may be causally implicated in sleep apnea.
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Affiliation(s)
- Ziqi Zhang
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Meng Li
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guang Ji
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Li Zhang
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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16
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Mao Z, Gao ZX, Ji T, Huan S, Yin GP, Chen L. Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD. Lipids Health Dis 2024; 23:58. [PMID: 38395962 PMCID: PMC10885469 DOI: 10.1186/s12944-024-02043-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) patients have exhibited extra-hepatic neurological changes, but the causes and mechanisms remain unclear. This study investigates the causal effect of NAFLD on cortical structure through bidirectional two-sample Mendelian randomization analysis. METHODS Genetic data from 778,614 European individuals across four NAFLD studies were used to determine genetically predicted NAFLD. Abdominal MRI scans from 32,860 UK Biobank participants were utilized to evaluate genetically predicted liver fat and volume. Data from the ENIGMA Consortium, comprising 51,665 patients, were used to evaluate the associations between genetic susceptibility, NAFLD risk, liver fat, liver volume, and alterations in cortical thickness (TH) and surface area (SA). Inverse-variance weighted (IVW) estimation, Cochran Q, and MR-Egger were employed to assess heterogeneity and pleiotropy. RESULTS Overall, NAFLD did not significantly affect cortical SA or TH. However, potential associations were noted under global weighting, relating heightened NAFLD risk to reduced parahippocampal SA and decreased cortical TH in the caudal middle frontal, cuneus, lingual, and parstriangularis regions. Liver fat and volume also influenced the cortical structure of certain regions, although no Bonferroni-adjusted p-values reached significance. Two-step MR analysis revealed that liver fat, AST, and LDL levels mediated the impact of NAFLD on cortical structure. Multivariable MR analysis suggested that the impact of NAFLD on the cortical TH of lingual and parstriangularis was independent of BMI, obesity, hyperlipidemia, and diabetes. CONCLUSION This study provides evidence that NAFLD causally influences the cortical structure of the brain, suggesting the existence of a liver-brain axis in the development of NAFLD.
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Affiliation(s)
- Zun Mao
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Zhi-Xiang Gao
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Tong Ji
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, 210023, P. R. China
| | - Sheng Huan
- Department of Anesthesiology and Perioperative Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210000, P. R. China
| | - Guo-Ping Yin
- Department of Anesthesiology, Nanjing Second Hospital, Nanjing, 210000, P. R. China.
| | - Long Chen
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, College of Life Sciences, Nanjing Normal University, Nanjing, 210023, P. R. China.
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Yamazaki H, Streicher SA, Wu L, Fukuhara S, Wagner R, Heni M, Grossman SR, Lenz HJ, Setiawan VW, Le Marchand L, Huang BZ. Evidence for a causal link between intra-pancreatic fat deposition and pancreatic cancer: A prospective cohort and Mendelian randomization study. Cell Rep Med 2024; 5:101391. [PMID: 38280379 PMCID: PMC10897551 DOI: 10.1016/j.xcrm.2023.101391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/24/2023] [Accepted: 12/29/2023] [Indexed: 01/29/2024]
Abstract
Prior observational studies suggest an association between intra-pancreatic fat deposition (IPFD) and pancreatic ductal adenocarcinoma (PDAC); however, the causal relationship is unclear. To elucidate causality, we conduct a prospective observational study using magnetic resonance imaging (MRI)-measured IPFD data and also perform a Mendelian randomization study using genetic instruments for IPFD. In the observational study, we use UK Biobank data (N = 29,463, median follow-up: 4.5 years) and find that high IPFD (>10%) is associated with PDAC risk (adjusted hazard ratio [HR]: 3.35, 95% confidence interval [95% CI]: 1.60-7.00). In the Mendelian randomization study, we leverage eight out of nine IPFD-associated genetic variants (p < 5 × 10-8) from a genome-wide association study in the UK Biobank (N = 25,617) and find that genetically determined IPFD is associated with PDAC (odds ratio [OR] per 1-standard deviation [SD] increase in IPFD: 2.46, 95% CI: 1.38-4.40) in the Pancreatic Cancer Cohort Consortium I, II, III (PanScan I-III)/Pancreatic Cancer Case-Control Consortium (PanC4) dataset (8,275 PDAC cases and 6,723 non-cases). This study provides evidence for a potential causal role of IPFD in the pathogenesis of PDAC. Thus, reducing IPFD may lower PDAC risk.
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Affiliation(s)
- Hajime Yamazaki
- Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Center for Innovative Research for Communities and Clinical Excellence (CiRC2LE), Fukushima Medical University, Fukushima, Japan.
| | - Samantha A Streicher
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Shunichi Fukuhara
- Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Center for Innovative Research for Communities and Clinical Excellence (CiRC2LE), Fukushima Medical University, Fukushima, Japan; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Róbert Wagner
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany; Institute for Clinical Diabetology, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine University, Düsseldorf, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Martin Heni
- Division of Endocrinology and Diabetology, Department of Internal Medicine I, Ulm University, Ulm, Germany; Institute for Clinical Chemistry and Pathobiochemistry, Department for Diagnostic Laboratory Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Steven R Grossman
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heinz-Josef Lenz
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Veronica Wendy Setiawan
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loïc Le Marchand
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Brian Z Huang
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA; Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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18
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Thanaj M, Basty N, Whitcher B, Sorokin EP, Liu Y, Srinivasan R, Cule M, Thomas EL, Bell JD. Precision MRI phenotyping of muscle volume and quality at a population scale. Front Physiol 2024; 15:1288657. [PMID: 38370011 PMCID: PMC10869600 DOI: 10.3389/fphys.2024.1288657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: Magnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle volume measurements: total muscle volume, regional measurements, measurements of muscle quality: intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative and associate with relevant health conditions such as dynapenia and frailty. Methods: We have measured image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, derived from the neck-to-knee Dixon images in 44,520 UK Biobank participants. We further segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We defined dynapenia based on grip strength below sex-specific cut-off points and frailty based on five criteria (weight loss, exhaustion, grip strength, low physical activity and slow walking pace). We used logistic regression to investigate the association between muscle volume and quality measurements and dynapenia and frailty. Results: Muscle volumes were significantly higher in male compared with female participants, even after correcting for height while, IMAT (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants. From the overall cohort, 7.6% (N = 3,261) were identified with dynapenia, and 1.1% (N = 455) with frailty. Dynapenia and frailty were positively associated with age and negatively associated with physical activity levels. Additionally, reduced muscle volume and quality measurements were associated with both dynapenia and frailty. In dynapenia, muscle volume IDPs were most informative, particularly total muscle exhibiting odds ratios (OR) of 0.392, while for frailty, muscle quality was found to be most informative, in particular thigh IMAT volume indexed to height squared (OR = 1.396), both with p-values below the Bonferroni-corrected threshold (p < 8.8 × 10 - 5 ). Conclusion: Our fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. For dynapenia, muscle volumes particularly those including greater body coverage such as total muscle are the most informative, whilst, for frailty, markers of muscle quality were the most informative IDPs. These results suggest that different measurements may have varying diagnostic values for different health conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | | | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Bull CJ, Hazelwood E, Legge DN, Corbin LJ, Richardson TG, Lee M, Yarmolinsky J, Smith-Byrne K, Hughes DA, Johansson M, Peters U, Berndt SI, Brenner H, Burnett-Hartman A, Cheng I, Kweon SS, Le Marchand L, Li L, Newcomb PA, Pearlman R, McConnachie A, Welsh P, Taylor R, Lean MEJ, Sattar N, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Impact of weight loss on cancer-related proteins in serum: results from a cluster randomised controlled trial of individuals with type 2 diabetes. EBioMedicine 2024; 100:104977. [PMID: 38290287 PMCID: PMC10844806 DOI: 10.1016/j.ebiom.2024.104977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Type 2 diabetes is associated with higher risk of several cancer types. However, the biological intermediates driving this relationship are not fully understood. As novel interventions for treating and managing type 2 diabetes become increasingly available, whether they also disrupt the pathways leading to increased cancer risk is currently unknown. We investigated the effect of a type 2 diabetes intervention, in the form of intentional weight loss, on circulating proteins associated with cancer risk to gain insight into potential mechanisms linking type 2 diabetes and adiposity with cancer development. METHODS Fasting serum samples from participants with diabetes enrolled in the Diabetes Remission Clinical Trial (DiRECT) receiving the Counterweight-Plus weight-loss programme (intervention, N = 117, mean weight-loss 10 kg, 46% diabetes remission) or best-practice care by guidelines (control, N = 143, mean weight-loss 1 kg, 4% diabetes remission) were subject to proteomic analysis using the Olink Oncology-II platform (48% of participants were female; 52% male). To identify proteins which may be altered by the weight-loss intervention, the difference in protein levels between groups at baseline and 1 year was examined using linear regression. Mendelian randomization (MR) was performed to extend these results to evaluate cancer risk and elucidate possible biological mechanisms linking type 2 diabetes and cancer development. MR analyses were conducted using independent datasets, including large cancer meta-analyses, UK Biobank, and FinnGen, to estimate potential causal relationships between proteins modified during intentional weight loss and the risk of colorectal, breast, endometrial, gallbladder, liver, and pancreatic cancers. FINDINGS Nine proteins were modified by the intervention: glycoprotein Nmb; furin; Wnt inhibitory factor 1; toll-like receptor 3; pancreatic prohormone; erb-b2 receptor tyrosine kinase 2; hepatocyte growth factor; endothelial cell specific molecule 1 and Ret proto-oncogene (Holm corrected P-value <0.05). Mendelian randomization analyses indicated a causal relationship between predicted circulating furin and glycoprotein Nmb on breast cancer risk (odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.67-0.99, P-value = 0.03; and OR = 0.88, 95% CI = 0.78-0.99, P-value = 0.04 respectively), though these results were not supported in sensitivity analyses examining violations of MR assumptions. INTERPRETATION Intentional weight loss among individuals with recently diagnosed diabetes may modify levels of cancer-related proteins in serum. Further evaluation of the proteins identified in this analysis could reveal molecular pathways that mediate the effect of adiposity and type 2 diabetes on cancer risk. FUNDING The main sources of funding for this work were Diabetes UK, Cancer Research UK, World Cancer Research Fund, and Wellcome.
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Affiliation(s)
- Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Emma Hazelwood
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Danny N Legge
- School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Lee
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mattias Johansson
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea; Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; School of Public Health, University of Washington, Seattle, WA, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Alex McConnachie
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Roy Taylor
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Mike E J Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Neil Murphy
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK.
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20
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Xu FQ, Xu QY, Zhu ZJ, Jin L, Ye TW, Du CF, Gao ZY, Huang XK, Zhang Z, Jin LM, Yao WF. Visceral and ectopic fat are more predictively associated with primary liver cancer than overall obesity from genetic sights: A Mendelian randomization study. Int J Cancer 2024; 154:530-537. [PMID: 37815300 DOI: 10.1002/ijc.34751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Several observational studies have reported an association between obesity and primary liver cancer (PLC), while the causality behind this association and the comparison of the risk effects of different obesity indicators on PLC remain unclear. In this study, we performed two-sample Mendelian randomization (MR) analyses to assess the associations of genetically determined liver fat, visceral adipose tissue (VAT), and body mass index (BMI) with the risk of PLC. The summary statistics of exposures were obtained from two genome-wide association studies (GWASs) based on the UK Biobank (UKB) imaging cohort and the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. GWAS summary statistics for PLC were obtained from FinnGen consortium R7 release data, including 304 PLC cases and 218 488 controls. Inverse-variance weighted (IVW) was used as the primary analysis, and a series of sensitivity analyses were performed to further verify the robustness of these findings. IVW analysis highlighted a significant association of genetically determined liver fat (OR per SD increase: 7.14; 95% CI: 5.10-9.99; P = 2.35E-30) and VAT (OR per SD increase: 5.70; 95% CI: 1.32-24.72; P = .020) with PLC but not of BMI with PLC. The findings were further confirmed by a series of MR methods. No evidence of horizontal pleiotropy between these associations existed. Our study suggested that genetically determined liver fat and VAT rather than BMI were associated with an increased risk of PLC, which suggested that visceral fat distribution is more predictive of the clinical risk of PLC than common in vitro measures.
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Affiliation(s)
- Fei-Qi Xu
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qing-Yun Xu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, China
| | - Zhang-Ji Zhu
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lei Jin
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Tai-Wei Ye
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cheng-Fei Du
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhen-Yu Gao
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiao-Kun Huang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhe Zhang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Li-Ming Jin
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wei-Feng Yao
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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21
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Huang J, Lin Z, Lin J, Xie S, Xia S, Chen G, Zheng Z, Xu Z, Liu F, Wu H, Li S. Causal role of lipid metabolism in pulmonary alveolar proteinosis: an observational and mendelian randomisation study. Thorax 2024; 79:135-143. [PMID: 38124156 DOI: 10.1136/thorax-2023-220789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Pulmonary alveolar proteinosis (PAP) is a rare interstitial lung disease characterised by the accumulation of lipoprotein material in the alveoli. Although dyslipidaemia is a prominet feature, the causal effect of lipid traits on PAP remains unclear. This study aimed to explore the role of lipid traits in PAP and evaluate the potential of lipid-lowering drug targets in PAP. METHODS Clinical outcomes, lipid profiles and lung function tests were analysed in a clinical cohort of diagnosed PAP patients and propensity score-matched healthy controls. Genome-wide association study data on PAP, lipid metabolism, blood cells and variants of genes encoding potential lipid-lowering drug targets were obtained for Mendelian randomisation (MR) and mediation analyses. FINDINGS Observational results showed that higher levels of total cholesterol (TC), triglycerides and low-density lipoprotein (LDL) were associated with increased risks of PAP. Higher levels of TC and LDL were also associated with worse PAP severity. In MR analysis, elevated LDL was associated with an increased risk of PAP (OR: 4.32, 95% CI: 1.63 to 11.61, p=0.018). Elevated monocytes were associated with a lower risk of PAP (OR 0.34, 95% CI: 0.18 to 0.66, p=0.002) and mediated the risk impact of LDL on PAP. Genetic mimicry of PCSK9 inhibition was associated with a reduced risk of PAP (OR 0.03, p=0.007). INTERPRETATION Our results support the crucial role of lipid and metabolism-related traits in PAP risk, emphasising the monocyte-mediated, causal effect of elevated LDL in PAP genetics. PCSK9 mediates the development of PAP by raising LDL. These finding provide evidence for lipid-related mechanisms and promising lipid-lowering drug target for PAP.
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Affiliation(s)
- Junfeng Huang
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zikai Lin
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jinsheng Lin
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shuojia Xie
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shixin Xia
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Gengjia Chen
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Ziwen Zheng
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhe Xu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Fangcheng Liu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Hongkai Wu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shiyue Li
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
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22
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Shu Y, Yang B, Liu X, Xu M, Deng C, Wu H. Causal effects from inflammatory bowel disease on liver function and disease: a two-sample Mendelian randomization study. Front Med (Lausanne) 2024; 10:1320842. [PMID: 38298515 PMCID: PMC10827874 DOI: 10.3389/fmed.2023.1320842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024] Open
Abstract
Background Accumulating evidence has shown that patients with inflammatory bowel disease (IBD) have liver function abnormalities and are susceptible to liver diseases. However, the existence of a causal relationship between IBD and liver function or disease remains unclear. Methods A two-sample Mendelian randomization (MR) analysis was performed using genetic associations from publicly available genome-wide association studies (GWAS). These associations encompass ulcerative colitis (UC), Crohn's disease (CD), liver function traits, and liver disease phenotypes. The liver function traits comprised hepatic biochemistries, percent liver fat, and liver iron content from the UK Biobank. Furthermore, the liver disease phenotypes included cholelithiasis, non-alcoholic fatty liver disease (NAFLD), primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC) in cohorts of European ancestry. The primary estimation used the inverse-variance weighted method, with GWAS of C-reactive protein (CRP) in the UK Biobank serving as a positive control outcome. Results Genetically predicted UC is causally associated with decreased levels of albumin (ALB) and liver iron content, while genetically predicted CD is causally associated with increased levels of alkaline phosphatase (ALP). Moreover, genetically predicted UC or CD increases the risk of PSC, and CD increases the risk of PBC. Neither UC nor CD causally increases the risk of cholelithiasis and NAFLD. Conclusion UC affects the levels of ALB and liver iron content, while CD affects the levels of ALP. Both UC and CD increase the risk of PSC, and CD increases the risk of PBC.
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Affiliation(s)
- Yufeng Shu
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Bocheng Yang
- Division of Plastic Surgery, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Xuanyou Liu
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Meihua Xu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, China
| | - Chao Deng
- Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Wu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, China
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23
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Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, Elliott P, Matthews PM, Lyons RA, Whetton AD, Lucassen A, Hurles ME, Chapman M, Roddam AW, Fitzpatrick NK, Hansell AL, Hardy R, Marioni RE, O’Donnell VB, Williams J, Lindgren CM, Effingham M, Sellors J, Danesh J, Collins R. Prospective study design and data analysis in UK Biobank. Sci Transl Med 2024; 16:eadf4428. [PMID: 38198570 PMCID: PMC11127744 DOI: 10.1126/scitranslmed.adf4428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.
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Affiliation(s)
- Naomi E Allen
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ben Lacey
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Scotland
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
- Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Chemical Radiation Threats and Hazards, Imperial College London, UK
| | - Paul M Matthews
- UK Dementia Research Centre Institute and Department of Brain Sciences, Imperial College London, London, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, Wales
| | - Anthony D Whetton
- Veterinary Health Innovation Engine, University of Surrey, Guildford, UK
| | - Anneke Lucassen
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Southampton University, Southampton, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
| | | | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, Wales
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | | | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Rory Collins
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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24
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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25
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Xiang X, Wei Y, Zhao K. Reassessing the causal relationship between liver diseases and cardiovascular outcomes. J Hepatol 2024; 80:e20-e22. [PMID: 37586646 DOI: 10.1016/j.jhep.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023]
Affiliation(s)
- Xiqiao Xiang
- Department of PET-CT Imaging Centery, Shanghai Jiaotong University Affiliated Sixth People Hospital South Campus, Shanghai, 201499, China
| | - Yuanhao Wei
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Kun Zhao
- Department of PET-CT Imaging Centery, Shanghai Jiaotong University Affiliated Sixth People Hospital South Campus, Shanghai, 201499, China.
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26
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Zhou Y, Ni Y, Wang Z, Prud'homme GJ, Wang Q. Causal effects of non-alcoholic fatty liver disease on osteoporosis: a Mendelian randomization study. Front Endocrinol (Lausanne) 2023; 14:1283739. [PMID: 38149094 PMCID: PMC10749958 DOI: 10.3389/fendo.2023.1283739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Background Osteoporosis (OP) is a systemic skeletal disease characterized by compromised bone strength leading to an increased risk of fracture. There is an ongoing debate on whether non-alcoholic fatty liver disease (NAFLD) is an active contributor or an innocent bystander in the pathogenesis of OP. The aim of this study was to assess the causal association between NAFLD and OP. Methods We performed two-sample Mendelian randomization (MR) analyses to investigate the causal association between genetically predicted NAFLD [i.e., imaging-based liver fat content (LFC), chronically elevated serum alanine aminotransferase (cALT) and biopsy-confirmed NAFLD] and risk of OP. The inverse variant weighted method was performed as main analysis to obtain the causal estimates. Results Imaging-based LFC and biopsy-confirmed NAFLD demonstrated a suggestive causal association with OP ([odds ratio (OR): 1.003, 95% CI: 1.001-1.004, P < 0.001; OR: 1.001, 95% CI: 1.000-1.002, P = 0.031]). The association between cALT and OP showed a similar direction, but was not statistically significant (OR: 1.001, 95% CI: 1.000-1.002, P = 0.079). Repeated analyses after exclusion of genes associated with confounding factors showed consistent results. Sensitivity analysis indicated low heterogeneity, high reliability and low pleiotropy of the causal estimates. Conclusion The two-sample MR analyses suggest a causal association between genetically predicted NAFLD and OP.
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Affiliation(s)
- Yue Zhou
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Yunzhi Ni
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Zhihong Wang
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Gerald J Prud'homme
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Qinghua Wang
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
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Vanneste M, Hoskens H, Goovaerts S, Matthews H, Aponte JD, Cole J, Shriver M, Marazita ML, Weinberg SM, Walsh S, Richmond S, Klein OD, Spritz RA, Peeters H, Hallgrímsson B, Claes P. Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570544. [PMID: 38106188 PMCID: PMC10723447 DOI: 10.1101/2023.12.07.570544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.
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Affiliation(s)
| | - Hanne Hoskens
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Seppe Goovaerts
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Harold Matthews
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Jose D Aponte
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joanne Cole
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Mark Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Ophir D Klein
- Department of Orofacial Sciences and Program in Craniofacial Biology, University of California, San Francisco, CA, 94143, USA
- Department of Pediatrics, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Richard A Spritz
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Benedikt Hallgrímsson
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
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28
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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29
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Shen M, Jiang L, Liu H, Dai H, Jiang H, Qian Y, Wang Z, Zheng S, Chen H, Yang T, Fu Q, Xu K. Interaction between the GCKR rs1260326 variant and serum HDL cholesterol contributes to HOMA-β and ISI Matusda in the middle-aged T2D individuals. J Hum Genet 2023; 68:835-842. [PMID: 37648893 DOI: 10.1038/s10038-023-01191-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/13/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
This study aims to investigate the correlations between islet function/ insulin resistance and serum lipid levels, as well as to assess whether the strength of such correlations is affected by the GCKR rs1260326 variant in healthy and T2D individuals. We performed an oral glucose tolerance test (OGTT) on 4889 middle-aged adults, including 3135 healthy and 1754 T2D individuals from the REACTION population study in the Nanjing region. We also measured their serum lipid levels and genotyped for rs1260326. We found that serum high-density lipoprotein (HDL) cholesterol and triglyceride (TG) levels were independently correlated with indexes of islet function (HOMA-β and IGI [insulinogenic index]) and insulin resistance (HOMO-IR and ISIMatsuda) in both healthy and T2D individuals. The correlations were significantly decreased in T2D individuals, with significant heterogeneities compared to healthy controls (I2 > 75%, Phet < 0.05). Although no correlation was observed between serum total cholesterol (TC) level and islet function/ insulin resistance in healthy controls, significant correlations were found in T2D individuals, with significant heterogeneity to healthy controls in the correlation with ISIMatsuda(I2 = 85.3%, Phet = 0.009). Furthermore, we found significant interactions of the GCKR rs1260326 variant for the correlations between serum HDL cholesterol and HOMA-β/ISIMatsuda in T2D subjects (P = 0.015 and 0.038, respectively). These findings illustrate that distinct correlations between serum lipid levels and islet function/ insulin resistance occurred in T2D subjects compared to healthy individuals. Common gene variants, such as rs1260326, might interact substantially when studied in specific populations, especially T2D disease status.
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Affiliation(s)
- Min Shen
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Liying Jiang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hechun Liu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hao Dai
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hemin Jiang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yu Qian
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhixiao Wang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shuai Zheng
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Heng Chen
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Tao Yang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Qi Fu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Kuanfeng Xu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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30
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Gagnon E, Paulin A, Mitchell PL, Arsenault BJ. Disentangling the impact of gluteofemoral versus visceral fat accumulation on cardiometabolic health using sex-stratified Mendelian randomization. Atherosclerosis 2023; 386:117371. [PMID: 38029505 DOI: 10.1016/j.atherosclerosis.2023.117371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND AIMS Individuals with a higher abdominal adipose tissue accumulation are at higher risk of developing cardiometabolic diseases. For a given body mass index (BMI), women typically present lower abdominal adipose tissue accumulation compared to men. Whether abdominal adiposity is a causal driver of cardiometabolic risk, or a mere marker of ectopic fat deposition is debated. METHODS We investigated the sex-specific and sex-combined impact of height and BMI-adjusted gluteofemoral adipose tissue (GFATadj) adjusted abdominal subcutaneous adipose tissue (ASATadj) and adjusted visceral adipose tissue (VATadj) on cardiometabolic traits and diseases using Mendelian randomization. RESULTS Leveraging genome-wide summary statistics on GFATadj, ASATadj and VATadj from 39,076 UK Biobank participants with full-body magnetic resonance imaging available, we found that GFATadj is associated with a more favourable cardiometabolic risk profile including lower low density lipoprotein (LDL) cholesterol, triglycerides, fasting glucose, fasting insulin, liver enzyme levels and blood pressure as well as higher high density lipoprotein (HDL) cholesterol levels. GFATadj also is negatively associated with ischemic stroke, coronary artery disease, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). ASATadj is not associated with cardiometabolic traits and diseases, whereas VATadj is associated with liver fat accumulation but not with NAFLD or other cardiometabolic traits or diseases. Although the absolute effect sizes of GFATadj on LDL cholesterol were more pronounced in women compared to men, most associations did not differ by sex. CONCLUSIONS The inability of subcutaneous fat depots to efficiently store energy substrates could be the causal factor underlying the association of visceral lipid deposition with cardiometabolic health.
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Affiliation(s)
- Eloi Gagnon
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Audrey Paulin
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Patricia L Mitchell
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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Liu J, Hu S, Chen L, Daly C, Prada Medina CA, Richardson TG, Traylor M, Dempster NJ, Mbasu R, Monfeuga T, Vujkovic M, Tsao PS, Lynch JA, Voight BF, Chang KM, Million VA, Cobbold JF, Tomlinson JW, van Duijn CM, Howson JMM. Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.30.23299247. [PMID: 38076879 PMCID: PMC10705663 DOI: 10.1101/2023.11.30.23299247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
BACKGROUND & AIMS Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity. METHODS We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD. RESULTS We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity. CONCLUSIONS This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.
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Affiliation(s)
- Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Sile Hu
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Lingyan Chen
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Charlotte Daly
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | | | - Tom G Richardson
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Traylor
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Niall J Dempster
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Richard Mbasu
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Thomas Monfeuga
- AI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Julie A Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - V A Million
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- AI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Jeremy F Cobbold
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Jeremy W Tomlinson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
| | | | - Joanna M M Howson
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
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Misicka E, Gunzler D, Albert J, Briggs FBS. Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk. Mult Scler Relat Disord 2023; 79:104964. [PMID: 37659350 PMCID: PMC10873055 DOI: 10.1016/j.msard.2023.104964] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Epidemiologic studies have established obesity as a risk factor for multiple sclerosis (MS). These studies relied on body-mass index (BMI) and body size silhouettes as the primary measures of obesity. Unfortunately, the causal mechanisms through which obesity confers MS risk are not yet known. OBJECTIVES To investigate the causal effects of multiple specific measures of body fat on MS risk in populations of European descent, using Mendelian randomization (MR). METHODS MR is a genetic instrumental variable analysis utilizing genome-wide association (GWA) summary statistics to infer causality between phenotypes. MR analyses were performed to investigate the relationships between seven measures of body fat (BMI, waist-hip ratio, visceral adipose tissue [VAT], subcutaneous adipose tissue, and arm-, leg-, and trunk-fat to total body fat ratio) and MS risk. RESULTS Only BMI and VAT were significantly associated with MS risk in separate MR analyses (βBMI=0.27, pBMI<0.001; βVAT=0.28, pVAT=0.006). High correlation between BMI and VAT instruments suggest that two-sample MR associations for BMI and VAT likely capture the same causal mechanisms. CONCLUSIONS BMI and VAT were causally associated with MS risk in European populations, though their effects do not appear independent, suggesting overlap in the role of overall body mass and visceral obesity in MS pathogenesis.
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Affiliation(s)
- Elina Misicka
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Douglas Gunzler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Center for Health Care Research and Policy, Case Western Reserve School of Medicine, Cleveland, OH, USA
| | - Jeffrey Albert
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Farren B S Briggs
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.
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Mai Z, Mao H. Causal effects of nonalcoholic fatty liver disease on cerebral cortical structure: a Mendelian randomization analysis. Front Endocrinol (Lausanne) 2023; 14:1276576. [PMID: 38027213 PMCID: PMC10646496 DOI: 10.3389/fendo.2023.1276576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Background Previous studies have highlighted changes in the cerebral cortical structure and cognitive function among nonalcoholic fatty liver disease (NAFLD) patients. However, the impact of NAFLD on cerebral cortical structure and specific affected brain regions remains unclear. Therefore, we aimed to explore the potential causal relationship between NAFLD and cerebral cortical structure. Methods We conducted a Mendelian randomization (MR) study using genetic predictors of alanine aminotransferase (ALT), NAFLD, and percent liver fat (PLF) and combined them with genome-wide association study (GWAS) summary statistics from the ENIGMA Consortium. Several methods were used to assess the effect of NAFLD on full cortex and specific brain regions, along with sensitivity analyses. Results At the global level, PLF nominally decreased SA of full cortex; at the functional level, ALT presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars orbitalis, and TH of pericalcarine cortex. Besides, NAFLD presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars triangularis and TH of pericalcarine cortex, but increased TH of entorhinal cortex, lateral orbitofrontal cortex and temporal pole. Furthermore, PLF presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of cuneus and lingual gyrus, but increased TH of entorhinal cortex. Conclusion NAFLD is suggestively associated with atrophy in specific functional regions of the human brain.
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Affiliation(s)
- Zhiliang Mai
- Department of Digestive Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Anatomy, Guangdong Medical University, Zhanjiang, China
| | - Hua Mao
- Department of Digestive Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Cao F, Pan F, Gong X, Wang W, Xu Y, Cao P, Wang Y. Causal relationship between gut microbiota with subcutaneous and visceral adipose tissue: a bidirectional two-sample Mendelian Randomization study. Front Microbiol 2023; 14:1285982. [PMID: 38029216 PMCID: PMC10644100 DOI: 10.3389/fmicb.2023.1285982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background Numerous studies have revealed associations between gut microbiota and adipose tissue. However, the specific functional bacterial taxa and their causal relationships with adipose tissue production in different regions of the body remain unclear. Methods We conducted a bidirectional two-sample Mendelian Randomization (MR) study using aggregated data from genome-wide association studies (GWAS) for gut microbiota and adipose tissue. We employed methods such as inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode to assess the causal relationships between gut microbiota and subcutaneous adipose tissue (SAT) as well as visceral adipose tissue (VAT). Cochran's Q test, MR-Egger regression intercept analysis, and MR-PRESSO were used to test for heterogeneity, pleiotropy, and outliers of the instrumental variables, respectively. Reverse MR was employed to evaluate the reverse causal relationships between SAT, VAT, and gut microbiota with significant associations. Results IVW results demonstrated that Betaproteobacteria were protective factors for SAT production (OR = 0.88, 95% CI: 0.80-0.96, p = 0.005) and VAT production (OR = 0.91, 95% CI: 0.83-0.99, p = 0.030). Various bacterial taxa including Ruminococcaceae UCG002 (OR = 0.94, 95% CI: 0.89-0.99, p = 0.017), Methanobacteria class (OR = 0.96, 95% CI: 0.92-1.00, p = 0.029), and Burkholderiales (OR = 0.90, 95% CI: 0.83-0.98, p = 0.012) were associated only with decreased SAT production. Rikenellaceae RC9 gut group (OR = 1.05, 95% CI: 1.02-1.10, p = 0.005), Eubacterium hallii group (OR = 1.08, 95% CI: 1.01-1.15, p = 0.028), Peptococcaceae (OR = 1.08, 95% CI: 1.01-1.17, p = 0.034), and Peptococcus (OR = 1.05, 95% CI: 1.00-1.10, p = 0.047) were risk factors for SAT production. Meanwhile, Eubacterium fissicatena group (OR = 0.95, 95% CI: 0.91-0.99, p = 0.019), Turicibacter (OR = 0.93, 95% CI: 0.88-0.99, p = 0.022), and Defluviitaleaceae UCG011 (OR = 0.94, 95% CI: 0.89-0.99, p = 0.024) were protective factors for VAT production. Furthermore, Bacteroidetes (OR = 1.09, 95% CI: 1.01-1.17, p = 0.018), Eubacterium eligens group (OR = 1.09, 95% CI: 1.01-1.19, p = 0.037), Alloprevotella (OR = 1.05, 95% CI: 1.00-1.10, p = 0.038), and Phascolarctobacterium (OR = 1.07, 95% CI: 1.00-1.15, p = 0.042) were associated with VAT accumulation. Additionally, reverse MR revealed significant associations between SAT, VAT, and Rikenellaceae RC9 gut group (IVW: OR = 1.57, 95% CI: 1.18-2.09, p = 0.002) as well as Betaproteobacteria (IVW: OR = 1.14, 95% CI: 1.01-1.29, p = 0.029), both acting as risk factors. Sensitivity analyzes during bidirectional MR did not identify heterogeneity or pleiotropy. Conclusion This study unveils complex causal relationships between gut microbiota and SAT/VAT, providing novel insights into the diagnostic and therapeutic potential of gut microbiota in obesity and related metabolic disorders.
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Affiliation(s)
- Feng Cao
- Department of General Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Feng Pan
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Gong
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wen Wang
- Department of General Practice, Anqing Hospital Affiliated Hospital of Anhui Medical University, Anqing, China
| | - Yanyan Xu
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Pengwei Cao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yong Wang
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of General Surgery, The Shenzhen Hospital of Southern Medical University, Shenzhen, China
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Wang Z, Liu Y, Zhang S, Yuan Y, Chen S, Li W, Zuo M, Xiang Y, Li T, Yang W, Yang Y, Liu Y. Effects of iron homeostasis on epigenetic age acceleration: a two-sample Mendelian randomization study. Clin Epigenetics 2023; 15:159. [PMID: 37805541 PMCID: PMC10559596 DOI: 10.1186/s13148-023-01575-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Epigenetic clocks constructed from DNA methylation patterns have emerged as excellent predictors of aging and aging-related health outcomes. Iron, a crucial element, is meticulously regulated within organisms, a phenomenon referred as iron homeostasis. Previous researches have demonstrated the sophisticated connection between aging and iron homeostasis. However, their causal relationship remains relatively unexplored. RESULTS Through two-sample Mendelian randomization (MR) utilizing the random effect inverse variance weighted (IVW) method, each standard deviation (SD) increase in serum iron was associated with increased GrimAge acceleration (GrimAA, BetaIVW = 0.27, P = 8.54E-03 in 2014 datasets; BetaIVW = 0.31, P = 1.25E-02 in 2021 datasets), HannumAge acceleration (HannumAA, BetaIVW = 0.32, P = 4.50E-03 in 2014 datasets; BetaIVW = 0.32, P = 8.03E-03 in 2021 datasets) and Intrinsic epigenetic age acceleration (IEAA, BetaIVW = 0.34, P = 5.33E-04 in 2014 datasets; BetaIVW = 0.49, P = 9.94E-04 in 2021 datasets). Similar results were also observed in transferrin saturation. While transferrin manifested a negative association with epigenetic age accelerations (EAAs) sensitivity analyses. Besides, lack of solid evidence to support a causal relationship from EAAs to iron-related biomarkers. CONCLUSIONS The results of present investigation unveiled the causality of iron overload on acceleration of epigenetic clocks. Researches are warranted to illuminate the underlying mechanisms and formulate strategies for potential interventions.
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Affiliation(s)
- Zhihao Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Shuxin Zhang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yunbo Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Siliang Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wenhao Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Mingrong Zuo
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yufan Xiang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Tengfei Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wanchun Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
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Stender S, Davey Smith G, Richardson TG. Genetic variation and elevated liver enzymes during childhood, adolescence and early adulthood. Int J Epidemiol 2023; 52:1341-1349. [PMID: 37105232 PMCID: PMC10555681 DOI: 10.1093/ije/dyad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Genetic factors influence the risk of fatty liver disease (FLD) in adults. The aim of this study was to test if, and when, genetic risk factors known to affect FLD in adults begin to exert their deleterious effects during childhood, adolescence and early adulthood. METHODS We included up to 4018 British children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Three genetic variants known to associate robustly with FLD in adults (PNPLA3 rs738409, TM6SF2 rs58542926 and HSD17B13 rs72613567) were tested for association with plasma levels of alanine transaminase (ALT) and aspartate transaminase (AST) during childhood (mean age: 9.9 years), early adolescence (15.5 years), late adolescence (17.8 years), and early adulthood (24.5 years). We also tested the associations of a 17-variant score and whole-genome polygenic risk scores (PRS) derived from associations in adults with plasma ALT and AST at the same four time points. Associations with elastography-derived liver steatosis and fibrosis were tested in early adulthood. RESULTS Genetic risk factors for FLD (individually, combined into a 3-variant score, a 17-variant score and as a genome-wide PRS), were associated with higher liver enzymes, beginning in childhood and throughout adolescence and early adulthood. The ALT-increasing effects of the genetic risk variants became larger with increasing age. The ALT-PRS was associated with liver steatosis in early adulthood. No genetic associations with fibrosis were observed. CONCLUSIONS Genetic factors that promote FLD in adults associate with elevated liver enzymes already during childhood, and their effects get amplified with increasing age.
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Affiliation(s)
- Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
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Xia T, Du M, Li H, Wang Y, Zha J, Wu T, Ju S. Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk. Radiology 2023; 309:e231007. [PMID: 37874242 DOI: 10.1148/radiol.231007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.
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Affiliation(s)
- Tianyi Xia
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Mulong Du
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Huiqin Li
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Yuancheng Wang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Junhao Zha
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Tong Wu
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Shenghong Ju
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
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Trejo MJ, Morrill KE, Klimentidis YC, Garcia DO. Examining genetic associations with hepatic steatosis in Mexican-origin adults. Ann Hepatol 2023; 28:101120. [PMID: 37271481 PMCID: PMC10486257 DOI: 10.1016/j.aohep.2023.101120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023]
Abstract
INTRODUCTION AND OBJECTIVES Various studies have identified single-nucleotide polymorphisms (SNPs) associated with nonalcoholic fatty liver disease (NAFLD) and related traits, including ones located in or near the LYPLAL1, GCKR, PPP1R3B, TM6SF2, MBOAT7, and PNPLA3 genes. However, these SNPs were identified primarily in populations of European ancestry. This study examined the associations of these previously identified SNPs with hepatic steatosis in a sample of Mexican-origin adults living in Southern Arizona. MATERIALS AND METHODS A total of 307 Mexican-origin adults between the ages of 18 and 64 with a body mass index (BMI) of 25 kg/m2 or higher were genotyped at the following SNPs: rs12137855 (LYPLAL1), rs1260326 (GCKR), rs4240624 (PPP1R3B), rs58542926 (TM6SF2), rs641738 (MBOAT7), and rs738409 (PNPLA3). Hepatic steatosis was assessed by transient elastography (FibroScan®) with controlled attenuation parameter. Regression models examined the association between each of the six SNPs and hepatic steatosis. BMI was examined as a potential modifier of the genetic associations. RESULTS Participants were, on average, 45 years old and mostly female (63%) with an overall mean hepatic steatosis of 288.1 dB/m. Models showed no associations between LYPLAL1, GCKR, PPP1R3B, TM6SF2, or MBOAT7 and hepatic steatosis. Only PNPLA3 was statistically significantly associated with hepatic steatosis in both unadjusted and adjusted models (p<0.01). There was no effect modification observed with BMI. CONCLUSIONS SNPs associated with NAFLD in populations of European descent did not strongly contribute to hepatic steatosis in individuals of Mexican-origin, except for rs738409 (PNPLA3). Further efforts are necessary to explore additional SNPs that may be associated with NAFLD in this high-risk population.
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Affiliation(s)
- Mario Jesus Trejo
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
| | - Kristin E Morrill
- Community & Systems Health Science Division, College of Nursing, University of Arizona, Tucson, AZ 85721, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA
| | - David O Garcia
- Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA
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Huang Y, Stinson SE, Juel HB, Lund MAV, Holm LA, Fonvig CE, Nielsen T, Grarup N, Pedersen O, Christiansen M, Chabanova E, Thomsen HS, Krag A, Stender S, Holm JC, Hansen T. An adult-based genetic risk score for liver fat associates with liver and plasma lipid traits in children and adolescents. Liver Int 2023; 43:1772-1782. [PMID: 37208954 DOI: 10.1111/liv.15613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND & AIMS Genome-wide association studies have identified steatogenic variants that also showed pleiotropic effects on cardiometabolic traits in adults. We investigated the effect of eight previously reported genome-wide significant steatogenic variants, individually and combined in a weighted genetic risk score (GRS), on liver and cardiometabolic traits, and the predictive ability of the GRS for hepatic steatosis in children and adolescents. APPROACH & RESULTS Children and adolescents with overweight (including obesity) from an obesity clinic group (n = 1768) and a population-based group (n = 1890) were included. Cardiometabolic risk outcomes and genotypes were obtained. Liver fat was quantified using 1 H-MRS in a subset of 727 participants. Variants in PNPLA3, TM6SF2, GPAM and TRIB1 were associated with higher liver fat (p < .05) and with distinct patterns of plasma lipids. The GRS was associated with higher liver fat content, plasma concentrations of alanine transaminase (ALT), aspartate aminotransferase (AST) and favourable plasma lipid levels. The GRS was associated with higher prevalence of hepatic steatosis (defined as liver fat ≥5.0%) (odds ratio per 1-SD unit: 2.17, p = 9.7E-10). A prediction model for hepatic steatosis including GRS alone yielded an area under the curve (AUC) of 0.78 (95% CI 0.76-0.81). Combining the GRS with clinical measures (waist-to-height ratio [WHtR] SDS, ALT, and HOMA-IR) increased the AUC up to 0.86 (95% CI 0.84-0.88). CONCLUSIONS The genetic predisposition for liver fat accumulation conferred risk of hepatic steatosis in children and adolescents. The liver fat GRS has potential clinical utility for risk stratification.
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Affiliation(s)
- Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sara E Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helene Baek Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten A V Lund
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Aas Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
| | - Cilius E Fonvig
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
- Department of Pediatrics, Kolding Hospital a Part of Lillebaelt Hospital, Kolding, Denmark
| | - Trine Nielsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Michael Christiansen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
| | - Elizaveta Chabanova
- Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Henrik S Thomsen
- Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
- Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
| | - Jens-Christian Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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40
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Brown OI, Drozd M, McGowan H, Giannoudi M, Conning-Rowland M, Gierula J, Straw S, Wheatcroft SB, Bridge K, Roberts LD, Levelt E, Ajjan R, Griffin KJ, Bailey MA, Kearney MT, Cubbon RM. Relationship Among Diabetes, Obesity, and Cardiovascular Disease Phenotypes: A UK Biobank Cohort Study. Diabetes Care 2023; 46:1531-1540. [PMID: 37368983 PMCID: PMC10369123 DOI: 10.2337/dc23-0294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE Obesity and diabetes frequently coexist, yet their individual contributions to cardiovascular risk remain debated. We explored cardiovascular disease biomarkers, events, and mortality in the UK Biobank stratified by BMI and diabetes. RESEARCH DESIGN AND METHODS A total of 451,355 participants were stratified by ethnicity-specific BMI categories (normal, overweight, obese) and diabetes status. We examined cardiovascular biomarkers including carotid intima-media thickness (CIMT), arterial stiffness, left ventricular ejection fraction (LVEF), and cardiac contractility index (CCI). Poisson regression models estimated adjusted incidence rate ratios (IRRs) for myocardial infarction, ischemic stroke, and cardiovascular death, with normal-weight nondiabetes as comparator. RESULTS Five percent of participants had diabetes (10% normal weight, 34% overweight, and 55% obese vs. 34%, 43%, and 23%, respectively, without diabetes). In the nondiabetes group, overweight/obesity was associated with higher CIMT, arterial stiffness, and CCI and lower LVEF (P < 0.005); these relationships were diminished in the diabetes group. Within BMI classes, diabetes was associated with adverse cardiovascular biomarker phenotype (P < 0.005), particularly in the normal-weight group. After 5,323,190 person-years follow-up, incident myocardial infarction, ischemic stroke, and cardiovascular mortality rose across increasing BMI categories without diabetes (P < 0.005); this was comparable in the diabetes groups (P-interaction > 0.05). Normal-weight diabetes had comparable adjusted cardiovascular mortality to obese nondiabetes (IRR 1.22 [95% CI 0.96-1.56]; P = 0.1). CONCLUSIONS Obesity and diabetes are additively associated with adverse cardiovascular biomarkers and mortality risk. While adiposity metrics are more strongly correlated with cardiovascular biomarkers than diabetes-oriented metrics, both correlate weakly, suggesting that other factors underpin the high cardiovascular risk of normal-weight diabetes.
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Affiliation(s)
- Oliver I. Brown
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Michael Drozd
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Hugo McGowan
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Marilena Giannoudi
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | | | - John Gierula
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Sam Straw
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Stephen B. Wheatcroft
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Katherine Bridge
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Lee D. Roberts
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Eylem Levelt
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Ramzi Ajjan
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Kathryn J. Griffin
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Marc A. Bailey
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Mark T. Kearney
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Richard M. Cubbon
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
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41
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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42
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Gagnon E, Manikpurage HD, Mitchell PL, Girard A, Gobeil É, Bourgault J, Bégin F, Marette A, Thériault S, Arsenault BJ. Large-scale metabolomic profiling and incident non-alcoholic fatty liver disease. iScience 2023; 26:107127. [PMID: 37456853 PMCID: PMC10339047 DOI: 10.1016/j.isci.2023.107127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/11/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent disease with no specific drug therapy. High-throughput metabolomics present an unprecedented opportunity to identify biomarkers and potentially causal risk factors for NAFLD. Here, we determined the impact of 21 circulating metabolites, 17 lipids, and 132 lipoprotein particle characteristics on NAFLD combining prospective observational and two-sample Mendelian randomization (MR) analyses in 121,032 UK Biobank participants. We identified several metabolic factors associated with NAFLD risk in observational and MR analyses including triglyceride-rich and high-density lipoprotein particles composition, as well as the ratio of polyunsaturated fatty acids to total fatty acids. This study, is one of the largest to investigate incident NAFLD, provides concordant observational and genetic evidence that therapies aimed at reducing circulating triglycerides and increasing large HDL particles, as well as interventions aimed at increasing polyunsaturated fatty acid content may warrant further investigation into NAFLD prevention and treatment.
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Affiliation(s)
- Eloi Gagnon
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Hasanga D. Manikpurage
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Patricia L. Mitchell
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Arnaud Girard
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Émilie Gobeil
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Jérôme Bourgault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Frédéric Bégin
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - André Marette
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec (QC), Canada
| | - Sébastien Thériault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec (QC), Canada
| | - Benoit J. Arsenault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec (QC), Canada
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43
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Wang D, Townsend LK, DesOrmeaux GJ, Frangos SM, Batchuluun B, Dumont L, Kuhre RE, Ahmadi E, Hu S, Rebalka IA, Gautam J, Jabile MJT, Pileggi CA, Rehal S, Desjardins EM, Tsakiridis EE, Lally JSV, Juracic ES, Tupling AR, Gerstein HC, Paré G, Tsakiridis T, Harper ME, Hawke TJ, Speakman JR, Blondin DP, Holloway GP, Jørgensen SB, Steinberg GR. GDF15 promotes weight loss by enhancing energy expenditure in muscle. Nature 2023; 619:143-150. [PMID: 37380764 PMCID: PMC10322716 DOI: 10.1038/s41586-023-06249-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 05/23/2023] [Indexed: 06/30/2023]
Abstract
Caloric restriction that promotes weight loss is an effective strategy for treating non-alcoholic fatty liver disease and improving insulin sensitivity in people with type 2 diabetes1. Despite its effectiveness, in most individuals, weight loss is usually not maintained partly due to physiological adaptations that suppress energy expenditure, a process known as adaptive thermogenesis, the mechanistic underpinnings of which are unclear2,3. Treatment of rodents fed a high-fat diet with recombinant growth differentiating factor 15 (GDF15) reduces obesity and improves glycaemic control through glial-cell-derived neurotrophic factor family receptor α-like (GFRAL)-dependent suppression of food intake4-7. Here we find that, in addition to suppressing appetite, GDF15 counteracts compensatory reductions in energy expenditure, eliciting greater weight loss and reductions in non-alcoholic fatty liver disease (NAFLD) compared to caloric restriction alone. This effect of GDF15 to maintain energy expenditure during calorie restriction requires a GFRAL-β-adrenergic-dependent signalling axis that increases fatty acid oxidation and calcium futile cycling in the skeletal muscle of mice. These data indicate that therapeutic targeting of the GDF15-GFRAL pathway may be useful for maintaining energy expenditure in skeletal muscle during caloric restriction.
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Affiliation(s)
- Dongdong Wang
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Logan K Townsend
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Geneviève J DesOrmeaux
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Sara M Frangos
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Battsetseg Batchuluun
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lauralyne Dumont
- Department of Pharmacology-Physiology, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Rune Ehrenreich Kuhre
- Global Obesity and Liver Disease Research, Global Drug Discovery, Novo Nordisk, Maaloev, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Elham Ahmadi
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sumei Hu
- Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, Beijing Technology and Business University, Beijing, China
- Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Irena A Rebalka
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jaya Gautam
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Maria Joy Therese Jabile
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Chantal A Pileggi
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Sonia Rehal
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Eric M Desjardins
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Evangelia E Tsakiridis
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - James S V Lally
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Emma Sara Juracic
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - A Russell Tupling
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Hertzel C Gerstein
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Theodoros Tsakiridis
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Department of Oncology, McMaster University, Hamilton, Ontario, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Thomas J Hawke
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - John R Speakman
- Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
- CAS Center for Excellence in Animal Evolution and Genetics (CCEAEG), Kunming, China
| | - Denis P Blondin
- Department of Pharmacology-Physiology, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Division of Neurology, Department of Medicine, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Graham P Holloway
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Sebastian Beck Jørgensen
- Global Obesity and Liver Disease Research, Global Drug Discovery, Novo Nordisk, Maaloev, Denmark
- Bio Innovation Hub Transformational Research Unit, Novo Nordisk, Boston, MA, USA
| | - Gregory R Steinberg
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada.
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
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Hilton C, Sabaratnam R, Drakesmith H, Karpe F. Iron, glucose and fat metabolism and obesity: an intertwined relationship. Int J Obes (Lond) 2023; 47:554-563. [PMID: 37029208 PMCID: PMC10299911 DOI: 10.1038/s41366-023-01299-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 03/08/2023] [Accepted: 03/16/2023] [Indexed: 04/09/2023]
Abstract
A bidirectional relationship exists between adipose tissue metabolism and iron regulation. Total body fat, fat distribution and exercise influence iron status and components of the iron-regulatory pathway, including hepcidin and erythroferrone. Conversely, whole body and tissue iron stores associate with fat mass and distribution and glucose and lipid metabolism in adipose tissue, liver, and muscle. Manipulation of the iron-regulatory proteins erythroferrone and erythropoietin affects glucose and lipid metabolism. Several lines of evidence suggest that iron accumulation and metabolism may play a role in the development of metabolic diseases including obesity, type 2 diabetes, hyperlipidaemia and non-alcoholic fatty liver disease. In this review we summarise the current understanding of the relationship between iron homoeostasis and metabolic disease.
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Affiliation(s)
- Catriona Hilton
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
| | - Rugivan Sabaratnam
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Hal Drakesmith
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
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45
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Sun Z, Pan X, Tian A, Surakka I, Wang T, Jiao X, He S, Song J, Tian X, Tong D, Wen J, Zhang Y, Liu W, Chen P. Genetic variants in HFE are associated with non-alcoholic fatty liver disease in lean individuals. JHEP Rep 2023; 5:100744. [PMID: 37235137 PMCID: PMC10206181 DOI: 10.1016/j.jhepr.2023.100744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/13/2023] [Accepted: 03/07/2023] [Indexed: 05/28/2023] Open
Abstract
Background & Aims Around 20% of patients with non-alcoholic fatty liver disease (NAFLD) are lean. Increasing evidence suggests that lean NAFLD is a unique subtype of the disease. We aimed to explore the metabolic profile, genetic basis, causal risk factors, and clinical sequelae underlying lean NAFLD. Methods NAFLD was diagnosed by whole liver proton density fat fraction ≥5%. Whole liver proton density fat fraction and hepatic iron were quantified using magnetic resonance imaging in the UK Biobank. Individuals in this study were stratified according to the World Health Organization criteria of obesity, into lean, overweight, and obese. Mediation analysis, Mendelian randomisation analysis, and Bayesian networks were used to identify a risk factor or a clinical sequela of lean/obese NAFLD. Results Lean NAFLD manifested a distinct metabolic profile, featured by elevated hepatic iron and fasting glucose. Four loci, namely, HFE rs1800562, SLC17A3-SLC17A2-TRIM38 rs9348697, PNPLA3 rs738409, and TM6SF2 rs58542926, were associated with lean NAFLD (p <5 × 10-8). HFE rs1800562 was specifically associated with lean NAFLD and demonstrated a significant mediation effect through elevating hepatic iron. Type 2 diabetes was the most pronounced clinical sequela of lean NAFLD, followed by liver cirrhosis. Conclusions Our study suggested that HFE plays a potential steatogenic role rather than regulating iron homoeostasis in patients with lean NAFLD. The increased liver iron deposition is associated with lean NAFLD, whereas obese NAFLD is not related to hepatic iron. The clinical management of patients with lean NAFLD shall be concerned with the prevention and treatment of type 2 diabetes and liver cirrhosis. Impact and implications Lean NAFLD has a distinct natural history from obese NAFLD. This study underscored liver iron content and the genetic variant of the iron homoeostasis gene HFE as major risks of lean NAFLD, in addition to the unique metabolic profile. The development of type 2 diabetes or liver cirrhosis shall be closely monitored and prevented in patients with lean NAFLD.
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Affiliation(s)
- Zewen Sun
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Xingchen Pan
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Aowen Tian
- Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, China
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Tao Wang
- Software College of Jilin University, Changchun, China
| | - Xu Jiao
- Software College of Jilin University, Changchun, China
| | - Shanshan He
- Software College of Jilin University, Changchun, China
| | - Jinfang Song
- Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China
| | - Xin Tian
- Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China
| | - Dan Tong
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Jianping Wen
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Yonggang Zhang
- The Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Wanqing Liu
- Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI, USA
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Peng Chen
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
- Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, China
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
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Aberra YT, Ma L, Björkegren JLM, Civelek M. Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation. eLife 2023; 12:e79834. [PMID: 37326626 PMCID: PMC10275637 DOI: 10.7554/elife.79834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity.
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Affiliation(s)
- Yonathan Tamrat Aberra
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Johan LM Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Medicine, Karolinska Institutet, HuddingeStockholmSweden
| | - Mete Civelek
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
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Gentiluomo M, Corradi C, Arcidiacono PG, Crippa S, Falconi M, Belfiori G, Farinella R, Apadula L, Lauri G, Bina N, Rizzato C, Canzian F, Morelli L, Capurso G, Campa D. Role of pancreatic ductal adenocarcinoma risk factors in intraductal papillary mucinous neoplasm progression. Front Oncol 2023; 13:1172606. [PMID: 37346070 PMCID: PMC10280811 DOI: 10.3389/fonc.2023.1172606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/16/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction Pancreatic ductal adenocarcinoma (PDAC) is lethal due to its late diagnosis and lack of successful treatments. A possible strategy to reduce its death burden is prevention. Intraductal papillary mucinous neoplasms (IPMNs) are precursors of PDAC. It is difficult to estimate the incidence of IPMNs because they are asymptomatic. Two recent studies reported pancreatic cysts in 3% and 13% of scanned subjects. The possibility of identifying a subgroup of IPMN patients with a higher probability of progression into cancer could be instrumental in increasing the survival rate. In this study, genetic and non-genetic PDAC risk factors were tested in a group of IPMN patients under surveillance. Methods A retrospective study was conducted on 354 IPMN patients enrolled in two Italian centres with an average follow-up of 64 months. With the use of DNA extracted from blood, collected at IPMN diagnosis, all patients were genotyped for 30 known PDAC risk loci. The polymorphisms were analysed individually and grouped in an unweighted polygenic score (PGS) in relation to IPMN progression. The ABO blood group and non-genetic PDAC risk factors were also analysed. IPMN progression was defined based on the development of worrisome features and/or high-risk stigmata during follow-up. Results Two genetic variants (rs1517037 and rs10094872) showed suggestive associations with an increment of IPMN progression. After correction for multiple testing, using the Bonferroni correction, none of the variants showed a statistically significant association. However, associations were observed for the non-genetic variables, such as smoking status, comparing heavy smokers with light smokers (HR = 3.81, 95% 1.43-10.09, p = 0.007), and obesity (HR = 2.46, 95% CI 1.22-4.95, p = 0.012). Conclusion In conclusion, this study is the first attempt to investigate the presence of shared genetic background between PDAC risk and IPMN progression; however, the results suggest that the 30 established PDAC susceptibility polymorphisms are not associated with clinical IPMN progression in a sample of 354 patients. However, we observed indications of cigarette smoking and body mass index (BMI) involvement in IPMN progression. The biological mechanism that could link these two risk factors to progression could be chronic inflammation, of which both smoking and obesity are strong promoters.
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Affiliation(s)
| | | | - Paolo Giorgio Arcidiacono
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Crippa
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Massimo Falconi
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Giulio Belfiori
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | | | - Laura Apadula
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Gaetano Lauri
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Niccolò Bina
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Cosmeri Rizzato
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luca Morelli
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Gabriele Capurso
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
- Digestive and Liver Disease Unit, Sant’Andrea University Hospital, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
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48
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Yamazaki H, Streicher SA, Wu L, Fukuhara S, Wagner R, Heni M, Grossman SR, Lenz HJ, Setiawan VW, Marchand LL, Huang BZ. Genetic Evidence Causally Linking Pancreas Fat to Pancreatic Cancer: A Mendelian Randomization Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.20.23288770. [PMID: 37163062 PMCID: PMC10168411 DOI: 10.1101/2023.04.20.23288770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background & Aims Pancreatic ductal adenocarcinoma (PDAC) is highly lethal, and any clues to understanding its elusive etiology could lead to breakthroughs in prevention, early detection, or treatment. Observational studies have shown a relationship between pancreas fat accumulation and PDAC, but the causality of this link is unclear. We therefore investigated whether pancreas fat is causally associated with PDAC using two-sample Mendelian randomization. Methods We leveraged eight genetic variants associated with pancreas fat (P<5×10 -8 ) from a genome-wide association study (GWAS) in the UK Biobank (25,617 individuals), and assessed their association with PDAC in the Pancreatic Cancer Cohort Consortium I-III and the Pancreatic Cancer Case-Control Consortium dataset (8,275 PDAC cases and 6,723 non-cases). Causality was assessed using the inverse-variance weighted method. Although none of these genetic variants were associated with body mass index (BMI) at genome-wide significance, we further conducted a sensitivity analysis excluding genetic variants with a nominal BMI association in GWAS summary statistics from the UK Biobank and the Genetic Investigation of Anthropometric Traits consortium dataset (806,834 individuals). Results Genetically determined higher levels of pancreas fat using the eight genetic variants was associated with increased risk of PDAC. For one standard deviation increase in pancreas fat levels (i.e., 7.9% increase in pancreas fat fraction), the odds ratio of PDAC was 2.46 (95%CI:1.38-4.40, P=0.002). Similar results were obtained after excluding genetic variants nominally linked to BMI (odds ratio:3.79, 95%CI:1.66-8.65, P=0.002). Conclusions This study provides genetic evidence for a causal role of pancreas fat in the pathogenesis of PDAC. Thus, reducing pancreas fat could lower the risk of PDAC.
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49
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, Arruda ALDSV, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Lithgart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Metspalu A, Mo H, Morris AD, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.31.23287839. [PMID: 37034649 PMCID: PMC10081410 DOI: 10.1101/2023.03.31.23287839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing City, China
| | - Kim M. Lorenz
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza de S. V. Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S. K. Lee
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H. Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E. Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P. Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississsauga, Mississauga, ON, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S. Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J. Y. Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Institute of Data Science, Korea University, Seoul, South Korea
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Anny H. Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K. Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Linda S. Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A. Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S. Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S. Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A. Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K. Das
- Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H. Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P. Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S. Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S. Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C. Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A. Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jost B. Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Fouad R. Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M. Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N. Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G. Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A. Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Severance Biomedical Science Institute and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R. Lee
- USC-Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Lithgart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre For Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E. Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A. Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D. Morris
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Jerry L. Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R. Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians Universität München, Munich, Germany
| | - Fraser J. Pirie
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G. Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig Maximilians Universität München, Munich, Germany
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C. Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B. van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R. Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S. Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | | | | | | | | | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A. Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L. Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, US
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M. Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C. Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Wayne H. H. Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M. Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Lund University Diabetes Centre, Malmö, Sweden
| | - Giriraj R. Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Michèle M. Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Deceased
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S. Kooner
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J. Wareham
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Mark O. Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Joshua C. Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E. Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S. Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F. Voight
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
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50
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Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. JOURNAL OF BIG DATA 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-022-00677-1.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
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