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Smelik M, Zhao Y, Li X, Loscalzo J, Sysoev O, Mahmud F, Mansour Aly D, Benson M. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Sci Rep 2024; 14:12710. [PMID: 38830935 PMCID: PMC11148091 DOI: 10.1038/s41598-024-63399-9] [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: 02/02/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024] Open
Abstract
Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.
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Affiliation(s)
- Martin Smelik
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
| | - Yelin Zhao
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
| | - Xinxiu Li
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oleg Sysoev
- Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Firoj Mahmud
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
| | - Dina Mansour Aly
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden
| | - Mikael Benson
- Medical Digital Twin Research Group, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Stockholm, Sweden.
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Ojima T, Namba S, Suzuki K, Yamamoto K, Sonehara K, Narita A, Kamatani Y, Tamiya G, Yamamoto M, Yamauchi T, Kadowaki T, Okada Y. Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses. Nat Genet 2024; 56:1100-1109. [PMID: 38862855 DOI: 10.1038/s41588-024-01782-y] [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: 07/17/2022] [Accepted: 04/26/2024] [Indexed: 06/13/2024]
Abstract
Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.
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Affiliation(s)
- Takafumi Ojima
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Shinichi Namba
- 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
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory of Children's Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, 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, Osaka, Japan.
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Mills C, Sud A, Everall A, Chubb D, Lawrence SED, Kinnersley B, Cornish AJ, Bentham R, Houlston RS. Genetic landscape of interval and screen detected breast cancer. NPJ Precis Oncol 2024; 8:122. [PMID: 38806682 PMCID: PMC11133314 DOI: 10.1038/s41698-024-00618-6] [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: 02/14/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024] Open
Abstract
Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.
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Affiliation(s)
- Charlie Mills
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre of Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Andrew Everall
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Daniel Chubb
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Samuel E D Lawrence
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
- University College London Cancer Institute, University College London, London, UK
| | - Alex J Cornish
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Robert Bentham
- University College London Cancer Institute, University College London, London, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK.
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Chen S, Tan ALM, Saad Menezes MC, Mao JF, Perry CL, Vella ME, Viswanadham VV, Kobren S, Churchill S, Kohane IS. Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders. NPJ Precis Oncol 2024; 8:120. [PMID: 38796637 PMCID: PMC11127926 DOI: 10.1038/s41698-024-00613-x] [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: 10/22/2023] [Accepted: 05/14/2024] [Indexed: 05/28/2024] Open
Abstract
A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient's immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.
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Affiliation(s)
- Siyuan Chen
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Maria C Saad Menezes
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Jenny F Mao
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
- Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT, 06511-8937, USA
| | - Cassandra L Perry
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Margaret E Vella
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Vinayak V Viswanadham
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Shilpa Kobren
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Susanne Churchill
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
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Shaked O, Loza BL, Olthoff KM, Reddy KR, Keating BJ, Testa G, Asrani SK, Shaked A. Donor and recipient genetics: implications for the development of post-transplant diabetes mellitus. Am J Transplant 2024:S1600-6135(24)00342-3. [PMID: 38782187 DOI: 10.1016/j.ajt.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/16/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
Post-transplant diabetes mellitus (PTDM) is a prevalent complication of liver transplantation and is associated with cardiometabolic complications. We studied the consequences of genetic effects of liver donors and recipients on PTDM outcomes, focusing on the diverse genetic pathways related to insulin that play a role in the development of PTDM. 1115 liver transplant recipients without a pre-transplant diagnosis of type 2 diabetes mellitus (T2D) and their paired donors recruited from two transplant centers had polygenic risk scores (PRS) for T2D, insulin secretion, and insulin sensitivity calculated. Among recipients in the highest T2D-PRS quintile, donor T2D-PRS did not contribute significantly to PTDM. However, in recipients with the lowest T2D genetic risk, donor livers with the highest T2D-PRS contributed to the development of PTDM (OR (95% CI)=3.79 (1.10-13.1), p=0.035). Recipient risk was linked to factors associated with insulin secretion (OR (95% CI) = 0.85 (0.74-0.98), p=0.02), while donor livers contributed to PTDM via gene pathways involved in insulin sensitivity (OR (95% CI)=0.86 (0.75-0.99), p=0.03). Recipient and donor PRS independently and collectively serve as predictors of PTDM onset. The genetically influenced biological pathways in recipients primarily pertain to insulin secretion, whereas the genetic makeup of donors exerts an influence on insulin sensitivity.
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Affiliation(s)
- Oren Shaked
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Bao-Li Loza
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kim M Olthoff
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Rajender Reddy
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brendan J Keating
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Abraham Shaked
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Zhang W, Sun J, Yu H, Shi M, Hu H, Yuan H. Causal relationship between type 2 diabetes mellitus and aortic dissection: insights from two-sample Mendelian randomization and mediation analysis. Front Endocrinol (Lausanne) 2024; 15:1405517. [PMID: 38803481 PMCID: PMC11128602 DOI: 10.3389/fendo.2024.1405517] [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: 03/23/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Objective Some evidence suggests a reduced prevalence of type 2 diabetes mellitus (T2DM) in patients with aortic dissection (AD), a catastrophic cardiovascular illness, compared to general population. However, the conclusions were inconsistent, and the causal relationship between T2DM and AD remains unclear. Methods In this study, we aimed to explore the causal relationship between T2DM and AD using bidirectional Mendelian randomization (MR) analysis. Mediation MR analysis was conducted to explore and quantify the possible mediation effects of 1400 metabolites in T2DM and AD. Results The results of 26 datasets showed no causal relationship between T2DM and AD (P>0.05). Only one dataset (ebi-a-GCST90006934) showed that T2DM was a protective factor for AD (I9-AORTDIS) (OR=0.815, 95%CI: 0.692-0.960, P=0.014), and did not show horizontal pleiotropy (P=0.808) and heterogeneity (P=0.525). Vanillic acid glycine plays a mediator in the causal relationship between T2DM and AD. The mediator effect for vanillic acid glycine levels was -0.023 (95%CI: -0.066-0.021). Conclusion From the perspective of MR analysis, there might not be a causal relationship between T2DM and AD, and T2DM might not be a protective factor for AD. If a causal relationship does exist between T2DM and AD, with T2DM serving as a protective factor, vanillic acid glycine may act as a mediator and enhance such a protective effect.
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Affiliation(s)
| | | | | | | | | | - Hong Yuan
- Department of Cardiovascular, First People’s Hospital of LinPing District, Hangzhou, China
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Zammit M, Agius R, Fava S, Vassallo J, Pace NP. Association between a polygenic lipodystrophy genetic risk score and diabetes risk in the high prevalence Maltese population. Acta Diabetol 2024; 61:555-564. [PMID: 38280973 DOI: 10.1007/s00592-023-02230-9] [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: 09/11/2023] [Accepted: 12/23/2023] [Indexed: 01/29/2024]
Abstract
BACKGROUND Type 2 diabetes (T2DM) is genetically heterogenous, driven by beta cell dysfunction and insulin resistance. Insulin resistance drives the development of cardiometabolic complications and is typically associated with obesity. A group of common variants at eleven loci are associated with insulin resistance and risk of both type 2 diabetes and coronary artery disease. These variants describe a polygenic correlate of lipodystrophy, with a high metabolic disease risk despite a low BMI. OBJECTIVES In this cross-sectional study, we sought to investigate the association of a polygenic risk score composed of eleven lipodystrophy variants with anthropometric, glycaemic and metabolic traits in an island population characterised by a high prevalence of both obesity and type 2 diabetes. METHODS 814 unrelated adults (n = 477 controls and n = 337 T2DM cases) of Maltese-Caucasian ethnicity were genotyped and associations with phenotypes explored. RESULTS A higher polygenic lipodystrophy risk score was correlated with lower adiposity indices (lower waist circumference and body mass index measurements) and higher HOMA-IR, atherogenic dyslipidaemia and visceral fat dysfunction as assessed by the visceral adiposity index in the DM group. In crude and covariate-adjusted models, individuals in the top quartile of polygenic risk had a higher T2DM risk relative to individuals in the first quartile of the risk score distribution. CONCLUSION This study consolidates the association between polygenic lipodystrophy risk alleles, metabolic syndrome parameters and T2DM risk particularly in normal-weight individuals. Our findings demonstrate that polygenic lipodystrophy risk alleles drive insulin resistance and diabetes risk independent of an increased BMI.
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Affiliation(s)
- Maria Zammit
- Department of Pathology, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Rachel Agius
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Stephen Fava
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Josanne Vassallo
- Department of Medicine, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta
| | - Nikolai Paul Pace
- Department of Anatomy, Faculty of Medicine and Surgery, University of Malta, Msida, MSD2080, Malta.
- Centre for Molecular Medicine and Biobanking, Faculty of Medicine and Surgery, University of Malta, Room 325, Msida, MSD2080, Malta.
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Wei Y, Hägg S, Mak JKL, Tuomi T, Zhan Y, Carlsson S. Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility. Eur J Epidemiol 2024:10.1007/s10654-024-01117-5. [PMID: 38555549 DOI: 10.1007/s10654-024-01117-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 03/15/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Smokers are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We investigated if the smoking-T2D association is mediated by alterations in the metabolome and assessed potential interaction with genetic susceptibility to diabetes or insulin resistance. METHODS In UK Biobank (n = 93,722), cross-sectional analyses identified 208 metabolites associated with smoking, of which 131 were confirmed in Mendelian Randomization analyses, including glycoprotein acetyls, fatty acids, and lipids. Elastic net regression was applied to create a smoking-related metabolic signature. We estimated hazard ratios (HR) of incident T2D in relation to baseline smoking/metabolic signature and calculated the proportion of the smoking-T2D association mediated by the signature. Additive interaction between the signature and genetic risk scores for T2D (GRS-T2D) and insulin resistance (GRS-IR) on incidence of T2D was assessed as relative excess risk due to interaction (RERI). FINDINGS The HR of T2D was 1·73 (95% confidence interval (CI) 1·54 - 1·94) for current versus never smoking, and 38·3% of the excess risk was mediated by the metabolic signature. The metabolic signature and its mediation role were replicated in TwinGene. The metabolic signature was associated with T2D (HR: 1·61, CI 1·46 - 1·77 for values above vs. below median), with evidence of interaction with GRS-T2D (RERI: 0·81, CI: 0·23 - 1·38) and GRS-IR (RERI 0·47, CI: 0·02 - 0·92). INTERPRETATION The increased risk of T2D in smokers may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D or insulin resistance.
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Affiliation(s)
- Yuxia Wei
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm, 17177, Sweden.
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tiinamaija Tuomi
- Department of Clinical Sciences in Malmö, Clinical Research Centre, Lund University, Malmö, Sweden
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
- Department of Endocrinology, Abdominal Center, Research Program for Diabetes and Obesity, Folkhälsan Research Center, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - Yiqiang Zhan
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm, 17177, Sweden
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Sofia Carlsson
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm, 17177, Sweden
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Benson M, Smelik M, Li X, Loscalzo J, Sysoev O, Mahmud F, Aly DM, Zhao Y. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. RESEARCH SQUARE 2024:rs.3.rs-3921099. [PMID: 38496611 PMCID: PMC10942575 DOI: 10.21203/rs.3.rs-3921099/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.
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10
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Arya P, Husain N, Kumar C, Shekhar R, Prakash V, Hameed S, Mohan L, Dikshit H. C-peptide Level in Patients With Uncontrolled Type 2 Diabetes Mellitus on Oral Anti-diabetic Drugs. Cureus 2024; 16:e56810. [PMID: 38654804 PMCID: PMC11036452 DOI: 10.7759/cureus.56810] [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] [Accepted: 03/23/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND In the development and progression of type 2 diabetes mellitus, β-cell dysfunction occurs after insulin resistance. Despite poor glycaemic control, there is a practice of increasing the dose of oral anti-diabetics or adding more drugs to the regimen due to the common perception that low endogenous insulin secretion is related to type 1 diabetes mellitus only and patient's poor compliance to injectables. Keeping this perspective in mind, this study was conducted to assess the prevalence of beta cell dysfunction by low serum C-peptide levels and its correlation with poor glycaemic control. MATERIALS AND METHODS A total of 134 patients with type 2 diabetes mellitus for more than 10 years on oral anti-diabetic drugs fulfilling our eligibility criteria were enrolled in our study. Blood samples for fasting blood sugar and fasting C-peptide level were taken before breakfast and uptake of anti-diabetic drugs. Correlation analysis was performed to evaluate the relationship between fasting C-peptide and glycaemic control with respect to glycated haemoglobin (HbA1c). RESULTS Of the patients, 19.40% had insufficient beta cell reserve serum levels (C-peptide < 0.5 ng/ml), of which most of the patients (14/26 = 53.85%) had poor glycaemic control (HbA1c < 8.0%). Overall, there was a significant correlation between poor glycaemic control with respect to HbA1c and low serum C-peptide levels (p < 0.05). We found a significant association of beta cell dysfunction (low fasting C-peptide level) with the use of insulin secretagogue. The proportion of patients with C-peptide levels less than 0.5 ng/ml was lower in patients using sodium-glucose cotransporter-2 (SGLT-2) inhibitors as compared to insulin secretagogue. CONCLUSION SGLT-2 inhibitors should be preferred over other anti-diabetic drugs as an add-on to existing metformin therapy. Insulin requirement must be assessed in patients who have long-term type 2 diabetes mellitus.
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Affiliation(s)
- Purnendu Arya
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Noor Husain
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Chakrapani Kumar
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Ravi Shekhar
- Department of Biochemistry, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Ved Prakash
- Department of Endocrinology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Saajid Hameed
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Lalit Mohan
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
| | - Harihar Dikshit
- Department of Pharmacology, Indira Gandhi Institute of Medical Sciences, Patna, IND
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11
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [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: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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12
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Zou X, Ji L. A second step towards precision medicine in diabetes. Nat Metab 2024; 6:10-11. [PMID: 38263316 DOI: 10.1038/s42255-023-00950-4] [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] [Indexed: 01/25/2024]
Affiliation(s)
- Xiantong Zou
- The Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Linong Ji
- The Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
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13
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Wang W, Li X, Chen F, Wei R, Chen Z, Li J, Qiao J, Pan Q, Yang W, Guo L. Secondary analysis of newly diagnosed type 2 diabetes subgroups and treatment responses in the MARCH cohort. Diabetes Metab Syndr 2024; 18:102936. [PMID: 38171152 DOI: 10.1016/j.dsx.2023.102936] [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: 04/14/2023] [Revised: 12/18/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE To incorporate new clusters in the MARCH (Metformin and AcaRbose in Chinese patients as the initial Hypoglycemic treatment) cohort of newly diagnosed type 2 diabetes (T2D) patients and compare the anti-glycemic effects of metformin and acarbose across different clusters. METHODS K-means cluster analysis was performed based on six clinical indicators. The diabetic clusters in the MARCH cohort were retrospectively associated with the response to metformin and acarbose. RESULTS A total of 590 newly diagnosed T2D patients were classified by data-driven clusters into the MARD (mild obesity-related diabetes) (34.1 %), MOD (mild obesity-related diabetes) (34.1 %), SIDD (severe insulin-deficient diabetes) (20.3 %) and SIRD (severe insulin-resistant diabetes) (11.5 %) subgroups at baseline. At 24 and 48 weeks, 346 participants had finished the follow-up. After the adjustment of age, gender, weight, baseline HbA1c, baseline fasting glucose and 2-h postprandial blood glucose (2hPG), metformin mainly decreased the fasting glucose (0.07 ± 0.89 vs -0.26 ± 0.83, P = 0.043) in the MARD subgroup presented with OGTT (oral glucose tolerance test) results compared with acarbose group at 24 weeks. Acarbose led to a greater decrease in 2hPG in the MOD subgroup compared with metformin group (0.08 ± 0.86 vs -0.24 ± 0.92, P = 0.037) at 24 weeks. There was a also significant interaction between cluster and treatment efficacy in HbA1c (glycated hemoglobin) reduction in metformin and acarbose groups at 24 and 48 weeks (pinteraction<0.001). CONCLUSIONS Metformin and acarbose affected different metabolic variables depending on the diabetes subtype.
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Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinyao Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
| | - Fei Chen
- College of Life Sciences, University of Chinese Academy of Sciences, China; China-Japan Friendship Hospital, Beijing, China
| | - Ran Wei
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhi Chen
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia
| | - Jingjing Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
| | - Jingtao Qiao
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
| | - Wenying Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
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14
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Dwibedi C, Ekström O, Brandt J, Adiels M, Franzén S, Abrahamsson B, Rosengren AH. Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology. Nat Metab 2024; 6:50-60. [PMID: 38177805 PMCID: PMC10822775 DOI: 10.1038/s42255-023-00943-3] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/08/2023] [Indexed: 01/06/2024]
Abstract
The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium-glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol-1; 95% confidence interval, -10.0 to -6.3 mmol mol-1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups ( NCT04451837 ).
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Affiliation(s)
- Chinmay Dwibedi
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Ola Ekström
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Jasmine Brandt
- Department of Clinical Chemistry and Pharmacology, Skåne University Hospital, Lund, Sweden
- Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden
| | - Martin Adiels
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Stefan Franzén
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- AstraZeneca, Gothenburg, Sweden
| | - Birgitta Abrahamsson
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Anders H Rosengren
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
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15
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Billings LK, Shi Z, Wei J, Rifkin AS, Zheng SL, Helfand BT, Ilbawi N, Dunnenberger HM, Hulick PJ, Qamar A, Xu J. Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes. J Clin Endocrinol Metab 2023; 109:107-113. [PMID: 37560999 DOI: 10.1210/clinem/dgad456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/10/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023]
Abstract
CONTEXT Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.
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Affiliation(s)
- Liana K Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Andrew S Rifkin
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Brian T Helfand
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Surgery, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nadim Ilbawi
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Henry M Dunnenberger
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Peter J Hulick
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Arman Qamar
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jianfeng Xu
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
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16
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Giaccherini M, Gori L, Gentiluomo M, Farinella R, Cervena K, Skieceviciene J, Dijk F, Capurso G, Vezakis A, Archibugi L, Chammas R, Hussein T, Tavano F, Hegyi P, Lovecek M, Izbicki JR, Brenner H, Mohelnikova-Duchonova B, Dell'Anna G, Kupcinskas J, Ermini S, Aoki MN, Neoptolemos JP, Gazouli M, Pasquali C, Pezzilli R, Talar-Wojnarowska R, Oliverius M, Al-Saeedi M, Lucchesi M, Furbetta N, Carrara S, van Eijck CHJ, Maleckas A, Milanetto AC, Lawlor RT, Schöttker B, Boggi U, Morelli L, Ginocchi L, Ponz de Leon Pisani R, Sperti C, Zerbi A, Arcidiacono PG, Uzunoglu FG, Bunduc S, Holleczek B, Gioffreda D, Małecka-Wojciesko E, Kiudelis M, Szentesi A, van Laarhoven HWM, Soucek P, Götz M, Erőss B, Cavestro GM, Basso D, Perri F, Landi S, Canzian F, Campa D. A scan of all coding region variants of the human genome, identifies 13q12.2-rs9579139 and 15q24.1-rs2277598 as novel risk loci for pancreatic ductal adenocarcinoma. Carcinogenesis 2023; 44:642-649. [PMID: 37670727 DOI: 10.1093/carcin/bgad056] [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: 02/16/2023] [Revised: 06/08/2023] [Accepted: 08/28/2023] [Indexed: 09/07/2023] Open
Abstract
Coding sequence variants comprise a small fraction of the germline genetic variability of the human genome. However, they often cause deleterious change in protein function and are therefore associated with pathogenic phenotypes. To identify novel pancreatic ductal adenocarcinoma (PDAC) risk loci, we carried out a complete scan of all common missense and synonymous SNPs and analysed them in a case-control study comprising four different populations, for a total of 14 538 PDAC cases and 190 657 controls. We observed a statistically significant association between 13q12.2-rs9581957-T and PDAC risk (P = 2.46 × 10-9), that is in linkage disequilibrium (LD) with a deleterious missense variant (rs9579139) of the URAD gene. Recent findings suggest that this gene is active in peroxisomes. Considering that peroxisomes have a key role as molecular scavengers, especially in eliminating reactive oxygen species, a malfunctioning URAD protein might expose the cell to a higher load of potentially DNA damaging molecules and therefore increase PDAC risk. The association was observed in individuals of European and Asian ethnicity. We also observed the association of the missense variant 15q24.1-rs2277598-T, that belongs to BBS4 gene, with increased PDAC risk (P = 1.53 × 10-6). rs2277598 is associated with body mass index and is in LD with diabetes susceptibility loci. In conclusion, we identified two missense variants associated with the risk of developing PDAC independently from the ethnicity highlighting the importance of conducting reanalysis of genome-wide association studies (GWASs) in light of functional data.
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Affiliation(s)
| | - Leonardo Gori
- Department of Biology, University of Pisa, Pisa, Italy
| | | | | | - Klara Cervena
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- First Faculty of Medicine, Institute of Biology and Medical Genetics, Charles University, Prague, Czech Republic
| | - Jurgita Skieceviciene
- Department of Gastroenterology, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Frederike Dijk
- Department of Pathology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Gabriele Capurso
- Digestive and Liver Disease Unit, S. Andrea Hospital, "Sapienza" University of Rome, Rome, Italy
- Pancreato-Biliary Endoscopy and Endosonography Division, IRCCS San Raffaele Scientific Institute, Pancreas Translational and Clinical Research Center, Vita-Salute San Raffaele University, Milan, Italy
| | - Antonis Vezakis
- Department of Surgery, Aretaieio Hospital, Medical School, National and Kapodistrian University of Athens, Athens 11528, Greece
| | - Livia Archibugi
- Digestive and Liver Disease Unit, S. Andrea Hospital, "Sapienza" University of Rome, Rome, Italy
- Pancreato-Biliary Endoscopy and Endosonography Division, IRCCS San Raffaele Scientific Institute, Pancreas Translational and Clinical Research Center, Vita-Salute San Raffaele University, Milan, Italy
| | - Roger Chammas
- Departamento de Radiologia e Oncologia, Instituto Do Câncer Do Estado de São Paulo (ICESP), Center for Translational Research in Oncology (LIM24), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, Brazil
| | - Tamás Hussein
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Francesca Tavano
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, Foggia, Italy
| | - Péter Hegyi
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Martin Lovecek
- Department of Surgery I, University Hospital Olomouc, Olomouc, Czech Republic
| | - Jakob R Izbicki
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - 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), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Beatrice Mohelnikova-Duchonova
- Department of Oncology and Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital, Olomouc, Czech
| | - Giuseppe Dell'Anna
- Pancreatico/Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Juozas Kupcinskas
- Department of Gastroenterology, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Stefano Ermini
- Blood Transfusion Service, Azienda Ospedaliero-Universitaria Meyer, Children's Hospital, Florence, Italy
| | - Mateus Nóbrega Aoki
- Laboratory for Applied Science and Technology in Health, Carlos Chagas Institute, Oswaldo Cruz Foundation (Fiocruz), Curitiba, Brazil
| | - John P Neoptolemos
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | - Maria Gazouli
- Department of Basic Medical Sciences, Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Claudio Pasquali
- Department of Surgery, Oncology and Gastroenterology-DiSCOG, University of Padova, Padua, Italy
| | | | | | - Martin Oliverius
- Department of Surgery, University Hospital Kralovske Vinohrady, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Mohammed Al-Saeedi
- Department of General Surgery, University of Heidelberg, Heidelberg, Germany
| | - Maurizio Lucchesi
- Department of Medical Oncology, Oncology of Massa Carrara, Azienda USL Toscana Nord Ovest, Carrara, Italy
| | - Niccolò Furbetta
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Silvia Carrara
- Endoscopic Unit, Department of Gastroenterology, IRCCS Humanitas Research, Milan, Italy
| | - Casper H J van Eijck
- Department of Surgery, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Almantas Maleckas
- Department of Surgery, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Anna Caterina Milanetto
- Department of Surgery, Oncology and Gastroenterology-DiSCOG, University of Padova, Padua, Italy
| | - Rita T Lawlor
- ARC-Net Centre for Applied Research on Cancer and Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ugo Boggi
- Division of General and Transplant Surgery, Pisa University Hospital, Pisa, Italy
| | - Luca Morelli
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Laura Ginocchi
- Department of Medical Oncology, Oncology of Massa Carrara, Azienda USL Toscana Nord Ovest, Carrara, Italy
| | - Ruggero Ponz de Leon Pisani
- Pancreatico/Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Cosimo Sperti
- Department of Surgery, Oncology and Gastroenterology-DiSCOG, University of Padova, Padua, Italy
| | - Alessandro Zerbi
- Pancreatic Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Paolo Giorgio Arcidiacono
- Pancreatico/Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
| | - Faik G Uzunoglu
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefania Bunduc
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Center for Gastroenterology, Hepatology and Liver Transplant, Fundeni Clinical Institute, Bucharest, Romania
| | | | - Domenica Gioffreda
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, Foggia, Italy
| | | | - Mindaugas Kiudelis
- Department of Surgery, Institute for Digestive Research, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Andrea Szentesi
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
- Department of Medicine, Centre for Translational Medicine, University of Szeged, Szeged, Hungary
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Pavel Soucek
- Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic
| | - Mara Götz
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bálint Erőss
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
- Division of Pancreatic Diseases, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - Giulia Martina Cavestro
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Daniela Basso
- Department of Surgery, Oncology and Gastroenterology-DiSCOG, University of Padova, Padua, Italy
| | - Francesco Perri
- Division of Gastroenterology and Research Laboratory, Fondazione IRCCS "Casa Sollievo della Sofferenza" Hospital, San Giovanni Rotondo, Foggia, Italy
| | - Stefano Landi
- Department of Biology, University of Pisa, Pisa, Italy
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
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17
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Leslie RD, Ma RCW, Franks PW, Nadeau KJ, Pearson ER, Redondo MJ. Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes. Lancet Diabetes Endocrinol 2023; 11:848-860. [PMID: 37804855 DOI: 10.1016/s2213-8587(23)00159-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 10/09/2023]
Abstract
Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.
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Affiliation(s)
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China; Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Paul W Franks
- Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Kristen J Nadeau
- Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
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18
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Identifying genetic subtypes of disease from hospital diagnosis records. Nat Genet 2023; 55:1788-1789. [PMID: 37814054 DOI: 10.1038/s41588-023-01521-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
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19
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Jiang X, Zhang MJ, Zhang Y, Durvasula A, Inouye M, Holmes C, Price AL, McVean G. Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. Nat Genet 2023; 55:1854-1865. [PMID: 37814053 PMCID: PMC10632146 DOI: 10.1038/s41588-023-01522-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/31/2023] [Indexed: 10/11/2023]
Abstract
The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.
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Affiliation(s)
- Xilin Jiang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Department of Statistics, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yidong Zhang
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, 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
- British Heart Foundation Cambridge Centre of Research Excellence, Department of Clinical Medicine, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- The Alan Turing Institute, London, UK
| | - Chris Holmes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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20
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Lin X, Wang S, Huang J. The Association between the EAT-Lancet Diet and Diabetes: A Systematic Review. Nutrients 2023; 15:4462. [PMID: 37892537 PMCID: PMC10610026 DOI: 10.3390/nu15204462] [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: 09/23/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
Several studies have explored the association between diabetes and the EAT-Lancet diet. Thus, the objective of our study was to conduct a systematic review to analyze and summarize all clinical studies concerning the association between diabetes and the EAT-Lancet diet. We undertook a comprehensive search of the Embase, Cochrane, and PubMed databases up to 15 August 2023. All clinical studies concerning the association between diabetes and the EAT-Lancet diet were summarized and analyzed. In total, our systematic review included five studies of four prospective studies and one cross-sectional study, encompassing 259,315 participants. All the included studies were evaluated as high quality. The outcomes from all studies indicated that adherence to the EAT-Lancet diet was correlated with a reduced risk of diabetes. In conclusion, the EAT-Lancet diet may be an effective dietary intervention for diabetes. Nevertheless, the number of studies examining the association between diabetes and the EAT-Lancet diet is limited. Further high-quality studies are required to expand our understanding of the benefits of the EAT-Lancet diet for patients with diabetes.
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Affiliation(s)
| | - Shuai Wang
- Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310030, China
| | - Jinyu Huang
- Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310030, China
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21
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Verhoeff K, Marfil-Garza BA, Czarnecka Z, Cuesta-Gomez N, Jasra IT, Dadheech N, Senior PA, Shapiro AMJ. Stem Cell-Derived Islet Transplantation in Patients With Type 2 Diabetes: Can Diabetes Subtypes Guide Implementation? J Clin Endocrinol Metab 2023; 108:2772-2778. [PMID: 37170783 DOI: 10.1210/clinem/dgad257] [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: 11/23/2022] [Revised: 04/13/2023] [Accepted: 05/04/2023] [Indexed: 05/13/2023]
Abstract
Historically, only patients with brittle diabetes or severe recurrent hypoglycemia have been considered for islet transplantation (ITx). This population has been selected to optimize the risk-benefit profile, considering risks of long-term immunosuppression and limited organ supply. However, with the advent of stem cell (SC)-derived ITx and the potential for immunosuppression-free ITx, consideration of a broader recipient cohort may soon be justified. Simultaneously, the classical categorization of diabetes is being challenged by growing evidence in support of a clustering of disease subtypes that can be better categorized by the All New Diabetics in Scania (ANDIS) classification system. Using the ANDIS classification, 5 subtypes of diabetes have been described, each with unique causes and consequences. We evaluate consideration for ITx in the context of this broader patient population and the new classification of diabetes subtypes. In this review, we evaluate considerations for ITx based on novel diabetes subtypes, including their limitations, and we elaborate on unique transplant features that should now be considered to enable ITx in these "unconventional" patient cohorts. Based on evidence from those receiving whole pancreas transplant and our more than 20-year experience with ITx, we offer recommendations and potential research avenues to justify implementation of SC-derived ITx in broader populations of patients with all types of diabetes.
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Affiliation(s)
- Kevin Verhoeff
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Braulio A Marfil-Garza
- National Institute of Medical Sciences and Nutrition Salvador Zubiran, Mexico City, Department of Medicine Division of Endocrinology, University of Alberta, and CHRISTUS-LatAm Hub-Excellence and Innovation Center, Monterrey, Mexico
| | - Zofia Czarnecka
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Nerea Cuesta-Gomez
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Ila Tewari Jasra
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Nidheesh Dadheech
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - Peter A Senior
- Clinical Islet Transplant Programme, Department of Medicine Division of Endocrinology, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
| | - A M James Shapiro
- Department of Surgery, Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta T6G 2R3, Canada
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22
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Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WHH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC, Meigs JB, Udler MS. Precision subclassification of type 2 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:138. [PMID: 37798471 PMCID: PMC10556101 DOI: 10.1038/s43856-023-00360-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
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Affiliation(s)
- Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK.
| | - Robert Wagner
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Caroline C Wang
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Raymond J Kreienkamp
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Sara J Cromer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anne Cathrine Baun Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Aaron Leong
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Aaron J Deutsch
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Liana K Billings
- Division of Endocrinology, Diabetes and Metabolism, NorthShore University Health System, Skokie, IL, USA
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Robert H Eckel
- Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Miaoli County, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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23
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Diabetes Care 2023; 46:e151-e199. [PMID: 37471273 PMCID: PMC10516260 DOI: 10.2337/dci23-0036] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/11/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B. Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA
| | - George L. Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, IL
| | - David E. Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA
| | - Andrea R. Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E. Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL
| | - David M. Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA
| | - M. Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC
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24
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Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, Clark AL, Colclough K, Corcoy R, Cromer SJ, Duan D, Felton JL, Francis EC, Gillard P, Gingras V, Gaillard R, Haider E, Hughes A, Ikle JM, Jacobsen LM, Kahkoska AR, Kettunen JLT, Kreienkamp RJ, Lim LL, Männistö JME, Massey R, Mclennan NM, Miller RG, Morieri ML, Most J, Naylor RN, Ozkan B, Patel KA, Pilla SJ, Prystupa K, Raghavan S, Rooney MR, Schön M, Semnani-Azad Z, Sevilla-Gonzalez M, Svalastoga P, Takele WW, Tam CHT, Thuesen ACB, Tosur M, Wallace AS, Wang CC, Wong JJ, Yamamoto JM, Young K, Amouyal C, Andersen MK, Bonham MP, Chen M, Cheng F, Chikowore T, Chivers SC, Clemmensen C, Dabelea D, Dawed AY, Deutsch AJ, Dickens LT, DiMeglio LA, Dudenhöffer-Pfeifer M, Evans-Molina C, Fernández-Balsells MM, Fitipaldi H, Fitzpatrick SL, Gitelman SE, Goodarzi MO, Grieger JA, Guasch-Ferré M, Habibi N, Hansen T, Huang C, Harris-Kawano A, Ismail HM, Hoag B, Johnson RK, Jones AG, Koivula RW, Leong A, Leung GKW, Libman IM, Liu K, Long SA, Lowe WL, Morton RW, Motala AA, Onengut-Gumuscu S, Pankow JS, Pathirana M, Pazmino S, Perez D, Petrie JR, Powe CE, Quinteros A, Jain R, Ray D, Ried-Larsen M, Saeed Z, Santhakumar V, Kanbour S, Sarkar S, Monaco GSF, Scholtens DM, Selvin E, Sheu WHH, Speake C, Stanislawski MA, Steenackers N, Steck AK, Stefan N, Støy J, Taylor R, Tye SC, Ukke GG, Urazbayeva M, Van der Schueren B, Vatier C, Wentworth JM, Hannah W, White SL, Yu G, Zhang Y, Zhou SJ, Beltrand J, Polak M, Aukrust I, de Franco E, Flanagan SE, Maloney KA, McGovern A, Molnes J, Nakabuye M, Njølstad PR, Pomares-Millan H, Provenzano M, Saint-Martin C, Zhang C, Zhu Y, Auh S, de Souza R, Fawcett AJ, Gruber C, Mekonnen EG, Mixter E, Sherifali D, Eckel RH, Nolan JJ, Philipson LH, Brown RJ, Billings LK, Boyle K, Costacou T, Dennis JM, Florez JC, Gloyn AL, Gomez MF, Gottlieb PA, Greeley SAW, Griffin K, Hattersley AT, Hirsch IB, Hivert MF, Hood KK, Josefson JL, Kwak SH, Laffel LM, Lim SS, Loos RJF, Ma RCW, Mathieu C, Mathioudakis N, Meigs JB, Misra S, Mohan V, Murphy R, Oram R, Owen KR, Ozanne SE, Pearson ER, Perng W, Pollin TI, Pop-Busui R, Pratley RE, Redman LM, Redondo MJ, Reynolds RM, Semple RK, Sherr JL, Sims EK, Sweeting A, Tuomi T, Udler MS, Vesco KK, Vilsbøll T, Wagner R, Rich SS, Franks PW. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat Med 2023; 29:2438-2457. [PMID: 37794253 PMCID: PMC10735053 DOI: 10.1038/s41591-023-02502-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/14/2023] [Indexed: 10/06/2023]
Abstract
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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Affiliation(s)
- Deirdre K Tobias
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Merino
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Abrar Ahmad
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Catherine Aiken
- Department of Obstetrics and Gynaecology, The Rosie Hospital, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Jamie L Benham
- Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Dhanasekaran Bodhini
- Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India
| | - Amy L Clark
- Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children's Hospital, St. Louis, MO, USA
| | - Kevin Colclough
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Rosa Corcoy
- CIBER-BBN, ISCIII, Madrid, Spain
- Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Sara J Cromer
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jamie L Felton
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ellen C Francis
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA
| | | | - Véronique Gingras
- Department of Nutrition, Université de Montréal, Montreal, Quebec, Quebec, Canada
- Research Center, Sainte-Justine University Hospital Center, Montreal, Quebec, Quebec, Canada
| | - Romy Gaillard
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eram Haider
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Alice Hughes
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennifer M Ikle
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jarno L T Kettunen
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Raymond J Kreienkamp
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Asia Diabetes Foundation, Hong Kong SAR, China
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jonna M E Männistö
- Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Robert Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Niamh-Maire Mclennan
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mario Luca Morieri
- Metabolic Disease Unit, University Hospital of Padova, Padova, Italy
- Department of Medicine, University of Padova, Padova, Italy
| | - Jasper Most
- Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Rochelle N Naylor
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kashyap Amratlal Patel
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sridharan Raghavan
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Pernille Svalastoga
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Wubet Worku Takele
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Claudia Ha-Ting Tam
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Anne Cathrine B Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mustafa Tosur
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Children's Nutrition Research Center, USDA/ARS, Houston, TX, USA
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Caroline C Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessie J Wong
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Katherine Young
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Chloé Amouyal
- Department of Diabetology, APHP, Paris, France
- Sorbonne Université, INSERM, NutriOmic team, Paris, France
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maxine P Bonham
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | - Mingling Chen
- Monash Centre for Health Research and Implementation, Monash University, Clayton, Victoria, Australia
| | - Feifei Cheng
- Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Tinashe Chikowore
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sian C Chivers
- Department of Women and Children's Health, King's College London, London, UK
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Aaron J Deutsch
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Laura T Dickens
- Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - María Mercè Fernández-Balsells
- Biomedical Research Institute Girona, IdIBGi, Girona, Spain
- Diabetes, Endocrinology and Nutrition Unit Girona, University Hospital Dr Josep Trueta, Girona, Spain
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Stephanie L Fitzpatrick
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Stephen E Gitelman
- University of California at San Francisco, Department of Pediatrics, Diabetes Center, San Francisco, CA, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jessica A Grieger
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nahal Habibi
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chuiguo Huang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Arianna Harris-Kawano
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children's Hospital, Sioux Falls, SD, USA
- University of South Dakota School of Medicine, E Clark St, Vermillion, SD, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Robert W Koivula
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Aaron Leong
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gloria K W Leung
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | | | - Kai Liu
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Robert W Morton
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Maleesa Pathirana
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sofia Pazmino
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Dianna Perez
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John R Petrie
- School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Camille E Powe
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alejandra Quinteros
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Rashmi Jain
- Sanford Children's Specialty Clinic, Sioux Falls, SD, USA
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mathias Ried-Larsen
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
- Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Zeb Saeed
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vanessa Santhakumar
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kanbour
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- AMAN Hospital, Doha, Qatar
| | - Sudipa Sarkar
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Taiwan
- Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nele Steenackers
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
| | - Julie Støy
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | | | - Sok Cin Tye
- Sections on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Marzhan Urazbayeva
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Gastroenterology, Baylor College of Medicine, Houston, TX, USA
| | - Bart Van der Schueren
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
- Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium
| | - Camille Vatier
- Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, France
- Department of Endocrinology, Diabetology and Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, National Reference Center for Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Paris, France
| | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, Victoria, Australia
- Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- University of Melbourne Department of Medicine, Parkville, Victoria, Australia
| | - Wesley Hannah
- Deakin University, Melbourne, Victoria, Australia
- Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Sara L White
- Department of Women and Children's Health, King's College London, London, UK
- Department of Diabetes and Endocrinology, Guy's and St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Gechang Yu
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yingchai Zhang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shao J Zhou
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
| | - Jacques Beltrand
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Michel Polak
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Ingvild Aukrust
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Elisa de Franco
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Kristin A Maloney
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew McGovern
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Janne Molnes
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Mariam Nakabuye
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pål Rasmus Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Hugo Pomares-Millan
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Cécile Saint-Martin
- Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Cuilin Zhang
- Global Center for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Sungyoung Auh
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Russell de Souza
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Andrea J Fawcett
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Clinical and Organizational Development, Chicago, IL, USA
| | | | - Eskedar Getie Mekonnen
- College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Emily Mixter
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Diana Sherifali
- Population Health Research Institute, Hamilton, Ontario, Canada
- School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert H Eckel
- Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Aurora, CO, USA
| | - John J Nolan
- Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Department of Endocrinology, Wexford General Hospital, Wexford, Ireland
| | - Louis H Philipson
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Rebecca J Brown
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Liana K Billings
- Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA
- Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Kristen Boyle
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - John M Dennis
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jose C Florez
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Faculty of Health, Aarhus University, Aarhus, Denmark
| | - Peter A Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Siri Atma W Greeley
- Departments of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Kurt Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Irl B Hirsch
- University of Washington School of Medicine, Seattle, WA, USA
| | - Marie-France Hivert
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Medicine, Universite de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Korey K Hood
- Stanford University School of Medicine, Stanford, CA, USA
| | - Jami L Josefson
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Siew S Lim
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ronald C W Ma
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | | | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK
| | - Viswanathan Mohan
- Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Rinki Murphy
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand
- Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand
| | - Richard Oram
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Katharine R Owen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Susan E Ozanne
- University of Cambridge, Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Toni I Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rodica Pop-Busui
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Rebecca M Reynolds
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Emily K Sims
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianne Sweeting
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Tiinamaija Tuomi
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Miriam S Udler
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kimberly K Vesco
- Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Tina Vilsbøll
- Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Robert Wagner
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark.
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Huang L, Liu P, Du Y, Pan D, Lee A, Wolfe SA, Wang YX. A brown fat-enriched adipokine, ASRA, is a leptin receptor antagonist that stimulates appetite. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557454. [PMID: 37745491 PMCID: PMC10515849 DOI: 10.1101/2023.09.12.557454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The endocrine control of food intake remains incompletely understood, and whether the leptin receptor-mediated anorexigenic pathway in the hypothalamus is negatively regulated by a humoral factor is unknown. Here we identify an appetite-stimulating factor - ASRA - that acts as a leptin receptor antagonist. ASRA encodes an 8 kD protein that is abundantly and selectively expressed in adipose tissue and to a lesser extent, in liver, and is upregulated during fasting and cold. ASRA protein associates with autophagosomes and its secretion is induced by energy deficiency. Overexpression of ASRA in mice attenuates leptin receptor signaling leading to elevated blood glucose and development of severe hyperphagic obesity, whereas either adipose- or liver-specific ASRA knockout mice display increased leptin sensitivity, improved glucose homeostasis, reduced food intake, and resistance to high fat diet-induced obesity. Furthermore, ASRA is indispensable for cold-evoked feeding response. Recombinant ASRA (rASRA) protein binds to leptin receptor and suppresses leptin receptor signaling in cultured cells. In vivo, rASRA promotes food intake and increases blood glucose in a leptin receptor signaling-dependent manner. Our studies collectively show that ASRA, acting as a peripheral signal of energy deficit, stimulates appetite and regulates glucose metabolism by antagonizing leptin receptor signaling, thus revealing a previously unknown endocrine mechanism that has important implications for our understanding of leptin resistance.
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Affiliation(s)
- Lei Huang
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- These authors contributed equally to this work: Lei Huang, Pengpeng Liu, and Yong Du
| | - Pengpeng Liu
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, USA
- These authors contributed equally to this work: Lei Huang, Pengpeng Liu, and Yong Du
| | - Yong Du
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- These authors contributed equally to this work: Lei Huang, Pengpeng Liu, and Yong Du
| | - Dongning Pan
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Present address: Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, Fudan University Shanghai Medical College, Shanghai, China
| | - Alexandra Lee
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Scot A. Wolfe
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Yong-Xu Wang
- Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
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Wang W, Jia T, Liu Y, Deng H, Chen Z, Wang J, Geng Z, Wei R, Qiao J, Ma Y, Jiang X, Xu W, Shao J, Zhou K, Li Y, Pan Q, Yang W, Weng J, Guo L. Data-driven subgroups of newly diagnosed type 2 diabetes and the relationship with cardiovascular diseases at genetic and clinical levels in Chinese adults. Diabetes Metab Syndr 2023; 17:102850. [PMID: 37683311 DOI: 10.1016/j.dsx.2023.102850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/20/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND To subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. METHODS The K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. RESULTS Four clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. CONCLUSION There are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.
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Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Tong Jia
- Institute for Artificial Intelligence, Peking University, Beijing, China
| | - Yiying Liu
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of Chinese Academy of Sciences, China
| | - Hongrong Deng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zihao Chen
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Jing Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Zhaoxu Geng
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Ran Wei
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Jingtao Qiao
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yanhua Ma
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Xun Jiang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Wen Xu
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jian Shao
- No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, 510005, Guangdong Province, China
| | - Kaixin Zhou
- The Fifth People's Hospital of Chongqing, Chongqing, China
| | - Ying Li
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
| | - Wenying Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Jianping Weng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
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Christiansen CE, Arathimos R, Pain O, Molokhia M, Bell JT, Lewis CM. Stratified genome-wide association analysis of type 2 diabetes reveals subgroups with genetic and environmental heterogeneity. Hum Mol Genet 2023; 32:2638-2645. [PMID: 37364045 PMCID: PMC10407708 DOI: 10.1093/hmg/ddad093] [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: 10/17/2022] [Revised: 04/18/2023] [Accepted: 05/31/2023] [Indexed: 06/28/2023] Open
Abstract
Type 2 diabetes (T2D) is a heterogeneous illness caused by genetic and environmental factors. Previous genome-wide association studies (GWAS) have identified many genetic variants associated with T2D and found evidence of differing genetic profiles by age-at-onset. This study seeks to explore further the genetic and environmental drivers of T2D by analyzing subgroups on the basis of age-at-onset of diabetes and body mass index (BMI). In the UK Biobank, 36 494 T2D cases were stratified into three subgroups, and GWAS was performed for all T2D cases and for each subgroup relative to 421 021 controls. Altogether, 18 single nucleotide polymorphisms were significantly associated with T2D genome-wide in one or more subgroups and also showed evidence of heterogeneity between the subgroups (Cochrane's Q P < 0.01), with two SNPs remaining significant after multiple testing (in CDKN2B and CYTIP). Combined risk scores, on the basis of genetic profile, BMI and age, resulted in excellent diabetes prediction [area under the ROC curve (AUC) = 0.92]. A modest improvement in prediction (AUC = 0.93) was seen when the contribution of genetic and environmental factors was evaluated separately for each subgroup. Increasing sample sizes of genetic studies enables us to stratify disease cases into subgroups, which have sufficient power to highlight areas of genetic heterogeneity. Despite some evidence that optimizing combined risk scores by subgroup improves prediction, larger sample sizes are likely needed for prediction when using a stratification approach.
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Affiliation(s)
- Colette E Christiansen
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, UK
- School of Mathematics and Statistics, The Open University, Milton Keynes, MK7 6AA, UK
| | - Ryan Arathimos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing’s College London, London, SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust UK, London, SE5 8AF, UK
| | - Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing’s College London, London, SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust UK, London, SE5 8AF, UK
| | - Mariam Molokhia
- School of Population Health and Environmental Sciences, King’s College London, London, SE1 1UL, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing’s College London, London, SE5 8AF, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust UK, London, SE5 8AF, UK
- Department of Medical and Molecular Genetics, Faculty of Life Sciences & Medicine, King’s College London, London, SE1 9RT, UK
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28
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Niu M, Zhao Y, Jia Y, Xiang L, Dai X, Chen H. Whole-genome sequencing study to identify candidate markers indicating susceptibility to type 2 diabetes in Bama miniature pigs. Animal Model Exp Med 2023; 6:283-293. [PMID: 37132291 PMCID: PMC10486338 DOI: 10.1002/ame2.12317] [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: 01/07/2023] [Accepted: 03/08/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Hundreds of single-nucleotide polymorphism (SNP) sites have been found to be potential genetic markers of type 2 diabetes mellitus (T2DM). However, SNPs related to T2DM in minipigs have been less reported. This study aimed to screen the T2DM-susceptible candidate SNP loci in Bama minipigs so as to improve the success rate of the minipig T2DM model. METHODS The genomic DNAs of three Bama minipigs with T2DM, six sibling low-susceptibility minipigs with T2DM, and three normal control minipigs were compared by whole-genome sequencing. The T2DM Bama minipig-specific loci were obtained, and their functions were annotated. Meanwhile, the Biomart software was used to perform homology alignment with T2DM-related loci obtained from the human genome-wide association study to screen candidate SNP markers for T2DM in Bama miniature pigs. RESULTS Whole-genome resequencing detected 6960 specific loci in the minipigs with T2DM, and 13 loci corresponding to 9 diabetes-related genes were selected. Further, a set of 122 specific loci in 69 orthologous genes of human T2DM candidate genes were obtained in the pigs. Collectively, a batch of T2DM-susceptible candidate SNP markers in Bama minipigs, covering 16 genes and 135 loci, was established. CONCLUSIONS Whole-genome sequencing and comparative genomics analysis of the orthologous genes in pigs that corresponded to the human T2DM-related variant loci successfully screened out T2DM-susceptible candidate markers in Bama miniature pigs. Using these loci to predict the susceptibility of the pigs before constructing an animal model of T2DM may help to establish an ideal animal model.
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Affiliation(s)
- Miaomiao Niu
- Laboratory Animal CenterChinese PLA General HospitalBeijingPR China
| | - Yuqiong Zhao
- Laboratory Animal CenterChinese PLA General HospitalBeijingPR China
| | - Yunxiao Jia
- Laboratory Animal CenterChinese PLA General HospitalBeijingPR China
| | - Lei Xiang
- Beijing Institute of Orthopaedic TraumaBeijing Jishuitan HospitalBeijingPR China
| | - Xin Dai
- Laboratory Animal CenterChinese PLA General HospitalBeijingPR China
| | - Hua Chen
- Laboratory Animal CenterChinese PLA General HospitalBeijingPR China
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29
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Xue Q, Li X, Wang X, Ma H, Heianza Y, Qi L. Subtypes of Type 2 Diabetes and Incident Cardiovascular Disease Risk: UK Biobank and All of Us Cohorts. Mayo Clin Proc 2023; 98:1192-1204. [PMID: 37422735 DOI: 10.1016/j.mayocp.2023.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 12/02/2022] [Accepted: 01/31/2023] [Indexed: 07/10/2023]
Abstract
OBJECTIVE To characterize and validate the subtypes of type 2 diabetes (T2D) using a novel clustering algorithm and to further assess their associations with the risk of incident cardiovascular disease (CVD) events. METHODS Unsupervised k-means clustering based on glycated hemoglobin level, age at onset of T2D, body mass index, and estimated glomerular filtration rate was conducted among participants with T2D from the UK Biobank (March 13, 2006, to October 1, 2010) and replicated in the All of Us cohort (May 30, 2017, to April 1, 2021). RESULTS Five distinct T2D clusters were identified in the UK Biobank and validated in the All of Us cohort, characterizing the phenotypically heterogeneous subtypes. With a median follow-up of 11.69 years for patients with T2D in the UK Biobank, risks of incident CVD events varied considerably between the clusters after adjustment for potential confounders and multiple testing (all P<.001). With cluster 1 characterized by early onset of T2D and mild abnormalities of other variables as the reference, patients in cluster 5 characterized by poor renal function had the highest risk of CVD events (hazard ratio [95% CI], 1.72 [1.45 to 2.03], 2.41 [1.93 to 3.02], and 1.62 [1.35 to 1.94] for composite CVD event, CVD mortality, and CVD incidence, respectively; all P<.001), followed by cluster 4 characterized by poor glycemic control and cluster 3 characterized by severe obesity. No consistently significant difference was found between cluster 2 characterized by late onset of T2D and cluster 1. CONCLUSION Our study, using a novel clustering algorithm to identify robust subtypes of T2D, found heterogeneous associations with incident CVD risk among patients with diabetes.
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Affiliation(s)
- Qiaochu Xue
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Xiang Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Xuan Wang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Hao Ma
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
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30
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Sacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark Å, Metzger BE, Nathan DM, Kirkman MS. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 2023:hvad080. [PMID: 37473453 DOI: 10.1093/clinchem/hvad080] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. APPROACH An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association. CONTENT Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed. SUMMARY The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.
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Affiliation(s)
- David B Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Mark Arnold
- Department of Chemistry, University of Iowa, Iowa City, IA, United States
| | - George L Bakris
- Department of Medicine, American Heart Association Comprehensive Hypertension Center, Section of Endocrinology, Diabetes and Metabolism, University of Chicago Medicine, Chicago, ILUnited States
| | - David E Bruns
- Department of Pathology, University of Virginia Medical School, Charlottesville, VA, United States
| | - Andrea R Horvath
- New South Wales Health Pathology Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Åke Lernmark
- Department of Clinical Sciences, Lund University/CRC, Skane University Hospital Malmö, Malmö, Sweden
| | - Boyd E Metzger
- Division of Endocrinology, Northwestern University, The Feinberg School of Medicine, Chicago, IL, United States
| | - David M Nathan
- Massachusetts General Hospital Diabetes Center and Harvard Medical School, Boston, MA, United States
| | - M Sue Kirkman
- Department of Medicine, University of North Carolina, Chapel Hill, NC, United States
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Yajnik CS, Wagh R, Kunte P, Asplund O, Ahlqvist E, Bhat D, Shukla SR, Prasad RB. Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2023; 14:100182. [PMID: 37492423 PMCID: PMC10363502 DOI: 10.1016/j.lansea.2023.100182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/08/2022] [Accepted: 03/09/2023] [Indexed: 07/27/2023]
Abstract
Background A machine-learning approach identified five subgroups of diabetes in Europeans which included severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) with partially distinct genetic aetiologies. We previously validated four of the non-autoimmune subgroups in people with young-onset type 2 diabetes (T2D) from the Indian WellGen study. Here, we aimed to apply European-derived centroids and genetic risk scores (GRSs) to the unselected (for age) WellGen to test their applicability and investigate the genetic aetiology of the Indian T2D subgroups. Methods We applied European derived centroids and GRSs to T2D participants of Indian ancestry (WellGen, n = 2217, 821 genotyped) and compared them with normal glucose tolerant controls (Pune Maternal Nutrition Study, n = 461). Findings SIDD was the predominant subgroup followed by MOD, whereas SIRD and MARD were less frequent. Weighted-GRS for T2D, obesity and lipid-related traits associated with T2D. We replicated some of the previous associations of GRS for T2D, insulin secretion, and BMI with SIDD and MOD. Unique to Indian subgroups was the association of GRS for (a) proinsulin with MOD and MARD, (b) liver-lipids with SIDD, SIRD and MOD, and (c) opposite effect of beta-cell GRS with SIDD and MARD, obesity GRS with MARD compared to Europeans. Genetic variants of fucosyltransferases were associated with T2D and MOD in Indians but not Europeans. Interpretation The similarities emphasise the applicability of some of the European-derived GRSs to T2D and its subgroups in India while the differences highlight the need for large-scale studies to identify aetiologies in diverse ancestries. The data provide robust evidence for genetically distinct aetiologies for the T2D subgroups and at least partly mirror those seen in Europeans. Funding Vetenskapsrådet, Diabetes Wellness, and Hjärt-Lungfonden (Sweden), DST (India), Wellcome Trust, Crafoord Foundation and Albert Påhlsson Foundation.
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Affiliation(s)
- Chittaranjan S. Yajnik
- Diabetes Unit, Kamalnayan Bajaj Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, 411011, India
| | - Rucha Wagh
- Diabetes Unit, Kamalnayan Bajaj Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, 411011, India
- Symbiosis School of Biological Sciences, Symbiosis International (Deemed) University, Pune, 411021, India
| | - Pooja Kunte
- Diabetes Unit, Kamalnayan Bajaj Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, 411011, India
- Diabetes and Islet Biology Group, School of Medicine, Western Sydney University, Campbelltown Campus, Sydney, 2560, NSW, Australia
| | - Olof Asplund
- Department of Clinical Sciences, Diabetes and Endocrinology, CRC, Lund University, Malmö SE-205 02, Sweden
| | - Emma Ahlqvist
- Department of Clinical Sciences, Diabetes and Endocrinology, CRC, Lund University, Malmö SE-205 02, Sweden
| | - Dattatrey Bhat
- Diabetes Unit, Kamalnayan Bajaj Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, 411011, India
| | - Sharvari R. Shukla
- Diabetes Unit, Kamalnayan Bajaj Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, 411011, India
- Symbiosis Statistical Institute, Symbiosis International University, Pune, 411005, India
| | - Rashmi B. Prasad
- Department of Clinical Sciences, Diabetes and Endocrinology, CRC, Lund University, Malmö SE-205 02, Sweden
- Institute for Molecular Medicine Finland FIMM, Helsinki University, 00290, Helsinki, Finland
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32
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Zhao S, Li H, Jing X, Zhang X, Li R, Li Y, Liu C, Chen J, Li G, Zheng W, Li Q, Wang X, Wang L, Sun Y, Xu Y, Wang S. Identifying subgroups of patients with type 2 diabetes based on real-world traditional chinese medicine electronic medical records. Front Pharmacol 2023; 14:1210667. [PMID: 37456755 PMCID: PMC10339739 DOI: 10.3389/fphar.2023.1210667] [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: 04/23/2023] [Accepted: 06/15/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction: Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and Type 2 diabetes patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical Type 2 diabetes population to help identify more accurate disease subtypes for personalized treatment. Methods: Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 Type 2 diabetes inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the electronic medical records, molecular interaction networks and domain knowledge. Results: Of the 2137 Type 2 diabetes patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained eight non-overlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were six overlapping pathways and eight overlapping Gene Ontology terms among the herbs, comorbidities, and Type 2 diabetes in H3. Discussion: Our results demonstrate that identification of the Type 2 diabetes subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.
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Affiliation(s)
- Shuai Zhao
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Hengfei Li
- Department of Infectious Diseases, Hubei Provincial Hospital of Traditional Chinese Medicine (Affiliated Hospital of Hubei University of Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine), Wuhan, China
| | - Xuan Jing
- Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, China
| | - Xuebin Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ronghua Li
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yinghao Li
- Institute of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Chenguang Liu
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jie Chen
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Guoxia Li
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Wenfei Zheng
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qian Li
- Department of Nursing, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xue Wang
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Letian Wang
- Institute of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuanyuan Sun
- Department of Obstetrics and Gynecology, Weifang Fangzi District People’s Hospital, Weifang, China
| | - Yunsheng Xu
- Department of Endocrinology, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Shihua Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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Cronjé HT, Mi MY, Austin TR, Biggs ML, Siscovick DS, Lemaitre RN, Psaty BM, Tracy RP, Djoussé L, Kizer JR, Ix JH, Rao P, Robbins JM, Barber JL, Sarzynski MA, Clish CB, Bouchard C, Mukamal KJ, Gerszten RE, Jensen MK. Plasma Proteomic Risk Markers of Incident Type 2 Diabetes Reflect Physiologically Distinct Components of Glucose-Insulin Homeostasis. Diabetes 2023; 72:666-673. [PMID: 36749929 PMCID: PMC10130486 DOI: 10.2337/db22-0628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/02/2023] [Indexed: 02/09/2023]
Abstract
High-throughput proteomics allows researchers to simultaneously explore the roles of thousands of biomarkers in the pathophysiology of diabetes. We conducted proteomic association studies of incident type 2 diabetes and physiologic responses to an intravenous glucose tolerance test (IVGTT) to identify novel protein contributors to glucose homeostasis and diabetes risk. We tested 4,776 SomaScan proteins measured in relation to 18-year incident diabetes risk in participants from the Cardiovascular Health Study (N = 2,631) and IVGTT-derived measures in participants from the HERITAGE Family Study (N = 752). We characterize 51 proteins that were associated with longitudinal diabetes risk, using their respective 39, 9, and 8 concurrent associations with insulin sensitivity index (SI), acute insulin response to glucose (AIRG), and glucose effectiveness (SG). Twelve of the 51 diabetes associations appear to be novel, including β-glucuronidase, which was associated with increased diabetes risk and lower SG, suggesting an alternative pathway to insulin for glucose disposal; and plexin-B2, which also was associated with increased diabetes risk, but with lower AIRG, and not with SI, indicating a mechanism related instead to pancreatic dysfunction. Other novel protein associations included alcohol dehydrogenase-1C, fructose-bisphosphate aldolase-B, sorbitol dehydrogenase with elevated type 2 diabetes risk, and a leucine-rich repeat containing protein-15 and myocilin with decreased risk. ARTICLE HIGHLIGHTS Plasma proteins are associated with the risk of incident diabetes in older adults independent of various demographic, lifestyle, and biochemical risk factors. These same proteins are associated with subtle differences in measures of glucose homeostasis earlier in life. Proteins that are associated with lower insulin sensitivity in individuals without diabetes tend to be associated with appropriate compensatory mechanisms, such as a stronger acute insulin response or higher glucose effectiveness. Proteins that are associated with future diabetes risk, but not with insulin insensitivity, tend to be associated with lower glucose effectiveness and/or impaired acute insulin response.
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Affiliation(s)
- Héléne T. Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Michael Y. Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Mary L. Biggs
- Department of Biostatistics, University of Washington, Seattle, WA
| | | | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, Health Systems and Population Health, University of Washington, Seattle, WA
| | - Russell P. Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT
| | - Luc Djoussé
- Division of Aging, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Jorge R. Kizer
- Cardiology Section San Francisco Veterans Affairs Health Care System, San Francisco, CA
- Department of Medicine, Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Joachim H. Ix
- Division of Nephrology-Hypertension, University of California, San Diego, La Jolla, CA
| | - Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jeremy M. Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jacob L. Barber
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Mark A. Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | | | | | - Kenneth J. Mukamal
- Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Majken K. Jensen
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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34
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Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WHH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC, Meigs JB, Udler MS. Systematic review of precision subclassification of type 2 diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.19.23288577. [PMID: 37131632 PMCID: PMC10153304 DOI: 10.1101/2023.04.19.23288577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.
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Yan X, Li X, Liu B, Huang J, Xiang Y, Hu Y, Tang X, Zhang Z, Huang G, Xie Z, Zhou H, Liu Z, Wang X, Leslie RD, Zhou Z. Combination therapy with saxagliptin and vitamin D for the preservation of β-cell function in adult-onset type 1 diabetes: a multi-center, randomized, controlled trial. Signal Transduct Target Ther 2023; 8:158. [PMID: 37076476 PMCID: PMC10115841 DOI: 10.1038/s41392-023-01369-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/21/2023] Open
Abstract
Disease modifying therapies aiming to preserve β-cell function in patients with adult-onset autoimmune type 1 diabetes are lacking. Here, we conducted a multi-centre, randomized, controlled trial to assess the β-cell preservation effects of saxagliptin alone and saxagliptin combined with vitamin D as adjunctive therapies in adult-onset autoimmune type 1 diabetes. In this 3-arm trial, 301 participants were randomly assigned to a 24-month course of the conventional therapy (metformin with or without insulin) or adjunctive saxagliptin or adjunctive saxagliptin plus vitamin D to the conventional therapy. The primary endpoint was the change from baseline to 24 months in the fasting C-peptide. The secondary endpoints included the area under the concentration-time curve (AUC) for C-peptide level in a 2-h mixed-meal tolerance test, glycemic control, total daily insulin use and safety, respectively. The primary endpoint was not achieved in saxagliptin plus vitamin D group (P = 0.18) and saxagliptin group (P = 0.26). However, compared with the conventional therapy, 2-h C-peptide AUC from 24 months to baseline decreased less with saxagliptin plus vitamin D (-276 pmol/L vs. -419 pmol/L; P = 0.01), and not to the same degree with saxagliptin alone (-314 pmol/L; P = 0.14). Notably, for participants with higher glutamic acid decarboxylase antibody (GADA) levels, the decline of β-cell function was much lower in saxagliptin plus vitamin D group than in the conventional therapy group (P = 0.001). Insulin dose was significantly reduced in both active treatment groups than in the conventional therapy group despite all groups having similar glycemic control. In conclusion, the combination of saxagliptin and vitamin D preserves pancreatic β-cell function in adult-onset autoimmune type 1 diabetes, an effect especially efficacious in individuals with higher GADA levels. Our results provide evidence for a novel adjunct to insulin and metformin as potential initial treatment for adult-onset type 1 diabetes. (ClinicalTrials.gov identifier: NCT02407899).
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Affiliation(s)
- Xiang Yan
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Bingwen Liu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jiaqi Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yufei Xiang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yuhang Hu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaohan Tang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ziwei Zhang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Gan Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Houde Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhenqi Liu
- Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia Health System, Charlottesville, VA, USA
| | - Xiangbing Wang
- Division of Endocrinology, Metabolism and Nutrition, Rutgers University-Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Richard David Leslie
- Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK.
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
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Zhang S, Stubbendorff A, Olsson K, Ericson U, Niu K, Qi L, Borné Y, Sonestedt E. Adherence to the EAT-Lancet diet, genetic susceptibility, and risk of type 2 diabetes in Swedish adults. Metabolism 2023; 141:155401. [PMID: 36682448 DOI: 10.1016/j.metabol.2023.155401] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/11/2023] [Accepted: 01/14/2023] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND AIMS In 2019, the EAT-Lancet Commission proposed a mainly plant-based diet that nurtures human health and supports environmental sustainability. However, its association with type 2 diabetes (T2D) has not been widely studied, and it remains unclear whether genetic susceptibility for T2D can modify this association. The aim was therefore to investigate the association between the EAT-Lancet diet and risk of T2D and assess whether the association differs by the genetic predisposition to T2D. METHODS A total of 24,494 participants from the Malmö Diet and Cancer study were analyzed. Dietary intake was assessed using a modified diet history methodology, and an EAT-Lancet diet index (range from 0 to 42 points) was constructed based on the EAT-Lancet reference diet. National and local registers were used to identify T2D cases during follow-up. Cox proportional hazards regression model was applied to estimate the association between the EAT-Lancet diet index and risk of T2D. Genetic predisposition to T2D was captured based on 116 single nucleotide polymorphisms. RESULTS During a median of 24.3 years of follow-up, 4197 (17.1 %) T2D cases were documented. Compared with those with the lowest adherence to the EAT-Lancet diet (≤13 points), participants who had the highest adherence (≥23 points) showed an 18 % (95 % CI: 4 %-30 %) lower risk of T2D (P for trend <0.01). There was no significant multiplicative interaction between genetic predisposition to T2D and the EAT-Lancet diet index (P = 0.59). Also, no significant additive interaction between the genetic risk and the EAT-Lancet diet was seen (P = 0.44). The highest risk was observed among the 22.9 % of the individuals with high genetic risk and low EAT-Lancet diet score (HR = 1.79; 95 % CI: 1.63, 1.96). CONCLUSIONS Our findings indicate that high adherence to the EAT-Lancet diet was associated with decreased risk of incident T2D among people with different genetic risks.
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Affiliation(s)
- Shunming Zhang
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China; Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden.
| | - Anna Stubbendorff
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Kjell Olsson
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Ulrika Ericson
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yan Borné
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Emily Sonestedt
- Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden.
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Pigeyre M, Gerstein H, Ahlqvist E, Hess S, Paré G. Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial. Diabetologia 2023; 66:1045-1051. [PMID: 36854916 DOI: 10.1007/s00125-023-05887-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/18/2023] [Indexed: 03/02/2023]
Abstract
AIMS/HYPOTHESIS Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). METHODS Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. RESULTS A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. CONCLUSIONS/INTERPRETATION We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. TRIAL REGISTRATION ORIGIN trial, ClinicalTrials.gov NCT00069784.
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Affiliation(s)
- Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
| | - Hertzel Gerstein
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Sibylle Hess
- Global Medical Diabetes, Sanofi, Frankfurt, Germany
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
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Wei Y, Richardson TG, Zhan Y, Carlsson S. Childhood adiposity and novel subtypes of adult-onset diabetes: a Mendelian randomisation and genome-wide genetic correlation study. Diabetologia 2023; 66:1052-1056. [PMID: 36843089 PMCID: PMC10163070 DOI: 10.1007/s00125-023-05883-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/24/2023] [Indexed: 02/28/2023]
Abstract
AIMS/HYPOTHESIS We investigated whether the impacts of childhood adiposity on adult-onset diabetes differ across proposed diabetes subtypes using a Mendelian randomisation (MR) design. METHODS We performed MR analysis using data from European genome-wide association studies of childhood adiposity, latent autoimmune diabetes in adults (LADA, proxy for severe autoimmune diabetes), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). RESULTS Higher levels of childhood adiposity had positive genetically predicted effects on LADA (OR 1.62, 95% CI 1.05, 2.52), SIDD (OR 2.11, 95% CI 1.18, 3.80), SIRD (OR 2.76, 95% CI 1.60, 4.75) and MOD (OR 7.30, 95% CI 4.17, 12.78), but not MARD (OR 1.06, 95% CI 0.70, 1.60). CONCLUSIONS/INTERPRETATION Childhood adiposity is a risk factor not only for adult-onset diabetes primarily characterised by obesity or insulin resistance, but also for subtypes primarily characterised by insulin deficiency or autoimmunity. These findings emphasise the importance of preventing childhood obesity.
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Affiliation(s)
- Yuxia Wei
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Yiqiang Zhan
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Sofia Carlsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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de Klerk JA, Beulens JWJ, Mei H, Bijkerk R, van Zonneveld AJ, Koivula RW, Elders PJM, 't Hart LM, Slieker RC. Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study. Diabetologia 2023; 66:1057-1070. [PMID: 36826505 PMCID: PMC10163084 DOI: 10.1007/s00125-023-05886-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/17/2023] [Indexed: 02/25/2023]
Abstract
AIMS/HYPOTHESIS The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. METHODS Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data. RESULTS Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10-15), without evidence for reverse causality. CONCLUSIONS/INTERPRETATION Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.
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Affiliation(s)
- Juliette A de Klerk
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Joline W J Beulens
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Roel Bijkerk
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Anton Jan van Zonneveld
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Robert W Koivula
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Malmö, Sweden
| | - Petra J M Elders
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands.
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.
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Broadaway KA, Yin X, Williamson A, Parsons VA, Wilson EP, Moxley AH, Vadlamudi S, Varshney A, Jackson AU, Ahuja V, Bornstein SR, Corbin LJ, Delgado GE, Dwivedi OP, Fernandes Silva L, Frayling TM, Grallert H, Gustafsson S, Hakaste L, Hammar U, Herder C, Herrmann S, Højlund K, Hughes DA, Kleber ME, Lindgren CM, Liu CT, Luan J, Malmberg A, Moissl AP, Morris AP, Perakakis N, Peters A, Petrie JR, Roden M, Schwarz PEH, Sharma S, Silveira A, Strawbridge RJ, Tuomi T, Wood AR, Wu P, Zethelius B, Baldassarre D, Eriksson JG, Fall T, Florez JC, Fritsche A, Gigante B, Hamsten A, Kajantie E, Laakso M, Lahti J, Lawlor DA, Lind L, März W, Meigs JB, Sundström J, Timpson NJ, Wagner R, Walker M, Wareham NJ, Watkins H, Barroso I, O'Rahilly S, Grarup N, Parker SC, Boehnke M, Langenberg C, Wheeler E, Mohlke KL. Loci for insulin processing and secretion provide insight into type 2 diabetes risk. Am J Hum Genet 2023; 110:284-299. [PMID: 36693378 PMCID: PMC9943750 DOI: 10.1016/j.ajhg.2023.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023] Open
Abstract
Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.
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Affiliation(s)
- K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Victoria A Parsons
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Emma P Wilson
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Anne H Moxley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Vasudha Ahuja
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Stefan R Bornstein
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Laura J Corbin
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Om P Dwivedi
- University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | | | | | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Stefan Gustafsson
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Ulf Hammar
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Christian Herder
- German Center for Diabetes Research, 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
| | - Sandra Herrmann
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
| | | | - David A Hughes
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus E Kleber
- Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany; SYNLAB MVZ Humangenetik Mannheim, Mannheim, BW, Germany
| | - Cecilia M Lindgren
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK; Broad Institute, Cambridge, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Anni Malmberg
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Angela P Moissl
- Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany; Competence Cluster for Nutrition and Cardiovascular Health, Halle-Jena-Leipzig, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nikolaos Perakakis
- Department of Internal Medicine, Metabolic and Vascular Medicine, MedicCal Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - John R Petrie
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Michael Roden
- 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; German Center for Diabetes Research, Neuherberg, Germany
| | - Peter E H Schwarz
- Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany; Helmholtz Zentrum München, Paul Langerhans Institute Dresden, University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research, Neuherberg, Germany; Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Chair of Food Chemistry and Molecular Sensory Science, Technische Universität München, Freising, Germany
| | - Angela Silveira
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden; Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rona J Strawbridge
- Institute of Health and Wellbeing, Mental Health and Wellbeing, University of Glasgow, Glasgow, UK; Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Andrew R Wood
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Björn Zethelius
- Department of Geriatrics, Uppsala University, Uppsala, Sweden
| | - Damiano Baldassarre
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy; Cardiovascular Prevention Area, Centro Cardiologico Monzino I.R.C.C.S., Milan, Italy
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Folkhälsan Research Centre, Helsinki, Finland; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andreas Fritsche
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Bruna Gigante
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Deborah A Lawlor
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lars Lind
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Winfried März
- Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, Germany; Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany
| | - James B Meigs
- Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Johan Sundström
- Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robert Wagner
- Department of Internal Medicine, Diabetology, Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of Tübingen, Tübingen, Germany; German Center for Diabetes Research, Neuherberg, Germany
| | - Mark Walker
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK; Health Data Research UK, Gibbs Building, London, UK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research, Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen O'Rahilly
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephen Cj Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 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, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
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Metabotyping: a tool for identifying subgroups for tailored nutrition advice. Proc Nutr Soc 2023:1-12. [PMID: 36727494 DOI: 10.1017/s0029665123000058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.
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42
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Cook TW, Wilstermann AM, Mitchell JT, Arnold NE, Rajasekaran S, Bupp CP, Prokop JW. Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics. Biomolecules 2023; 13:257. [PMID: 36830626 PMCID: PMC9953665 DOI: 10.3390/biom13020257] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/20/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Insulin is amongst the human genome's most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion transcript INS-IGF2. This INS-IGF2 transcript splice site was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS-IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic RNAseq datasets with an elevation of the 3'UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.
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Affiliation(s)
- Taylor W. Cook
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
| | | | - Jackson T. Mitchell
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
| | - Nicholas E. Arnold
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
| | - Surender Rajasekaran
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Office of Research, Corewell Health, Grand Rapids, MI 49503, USA
| | - Caleb P. Bupp
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Division of Medical Genetics, Corewell Health, Grand Rapids, MI 49503, USA
| | - Jeremy W. Prokop
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, USA
- Office of Research, Corewell Health, Grand Rapids, MI 49503, USA
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Novel loci for hyperglycemia identified by QTL mapping of longitudinal phenotypes and congenic analysis. Sci Rep 2023; 13:1315. [PMID: 36693911 PMCID: PMC9873599 DOI: 10.1038/s41598-023-28189-9] [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: 03/07/2022] [Accepted: 01/13/2023] [Indexed: 01/25/2023] Open
Abstract
We previously reported that four hyperglycemia loci are located on three chromosomes in the Nagoya-Shibata-Yasuda (NSY) mouse model, commonly used to study type 2 diabetes. However, we did not search for hyperglycemia loci across all chromosomes. In this study, we performed quantitative trait loci (QTLs) mapping of longitudinal phenotypes from crosses between NSY (hyperglycemic) and C3H (normoglycemic) mice. We identified four new QTLs for hyperglycemia, namely Nidd5nsy, Nidd6nsy, Nidd1c3h, and Nidd2c3h, on Chromosome 1, 4, 10, and 13, respectively. These QTLs were associated with hyperglycemia in young mice and had attenuated effects in older mice. Nidd5nsy and Nidd6nsy were hyperglycemic with NSY alleles, and Nidd1c3h and Nidd2c3h were hyperglycemic with C3H alleles. We further bred Nidd5nsy congenic mice and demonstrated that Nidd5nsy has a strong effect on hyperglycemia when young, accompanied by insulin resistance and visceral fat accumulation. These results showed that the effects of individual QTLs strengthened or weakened with age, and that the sum of the effects of QTLs captured the age-related deterioration of glucose tolerance in individuals. Our results support the importance of longitudinal phenotypes in the genetic analysis of polygenic traits and have implications for the genetic basis and pathogenesis of type 2 diabetes in humans.
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Timasheva Y, Balkhiyarova Z, Avzaletdinova D, Rassoleeva I, Morugova TV, Korytina G, Prokopenko I, Kochetova O. Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes. Int J Mol Sci 2023; 24:ijms24020984. [PMID: 36674502 PMCID: PMC9866792 DOI: 10.3390/ijms24020984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 01/07/2023] Open
Abstract
We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10-5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10-4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10-5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.
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Affiliation(s)
- Yanina Timasheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
- Department of Medical Genetics and Fundamental Medicine, Bashkir State Medical University, 450008 Ufa, Russia
- Correspondence:
| | - Zhanna Balkhiyarova
- Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Diana Avzaletdinova
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Irina Rassoleeva
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Tatiana V. Morugova
- Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia
| | - Gulnaz Korytina
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK
| | - Olga Kochetova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia
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Dawed AY, Haider E, Pearson ER. Precision Medicine in Diabetes. Handb Exp Pharmacol 2023; 280:107-129. [PMID: 35704097 DOI: 10.1007/164_2022_590] [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] [Indexed: 10/18/2022]
Abstract
Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.
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Affiliation(s)
- Adem Y Dawed
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Eram Haider
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ewan R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.
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Mansour A, Mousa M, Abdelmannan D, Tay G, Hassoun A, Alsafar H. Microvascular and macrovascular complications of type 2 diabetes mellitus: Exome wide association analyses. Front Endocrinol (Lausanne) 2023; 14:1143067. [PMID: 37033211 PMCID: PMC10076756 DOI: 10.3389/fendo.2023.1143067] [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: 01/12/2023] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a chronic, metabolic disorder in which concomitant insulin resistance and β-cell impairment lead to hyperglycemia, influenced by genetic and environmental factors. T2DM is associated with long-term complications that have contributed to the burden of morbidity and mortality worldwide. The objective of this manuscript is to conduct an Exome-Wide Association Study (EWAS) on T2DM Emirati individuals to improve our understanding on diabetes-related complications to improve early diagnostic methods and treatment strategies. METHODS This cross-sectional study recruited 310 Emirati participants that were stratified according to their medically diagnosed diabetes-related complications: diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, and cardiovascular complications. The Illumina's Infinium Exome-24 array was used and 39,840 SNPs remained for analysis after quality control. FINDINGS The analysis revealed the associations of various genes with each complication category: 1) diabetic retinopathy was associated to SHANK3 gene in locus 22q13.33 (SNP rs9616915; p=5.18 x10-4), ZSCAN5A gene in locus 19q13.43 (SNP rs7252603; p=7.55 x10-4), and DCP1B gene in locus 12p13.33 (SNPs rs715146, rs1044950, rs113147414, rs34730825; p=7.62 x10-4); 2) diabetic neuropathy was associated to ADH4 gene in locus 4q23 (SNP rs4148883; p=1.23 x10-4), SLC11A1 gene in locus 2q35 (SNP rs17235409; p=1.85 x10-4), and MATN4 gene in locus 20q13.12 (SNP rs2072788; p=2.68 x10-4); 3) diabetic nephropathy was associated to PPP1R3A gene in locus 7q31.1 (SNP rs1799999; p=1.91 x10-4), ZNF136 gene in locus 19p13.2 (SNP rs140861589; p=2.80 x10-4), and HSPA12B gene in locus 20p13 (SNP rs6076550; p=2.86 x10-4); and 4) cardiovascular complications was associated to PCNT gene in locus 21q22.3 (SNPs rs7279204, rs6518289, rs2839227, rs2839223; p=2.18 x10-4,3.04 x10-4,4.51 x10-4,5.22 x10-4 respectively), SEPT14 gene in locus 7p11.2 (SNP rs146350220; p=2.77 x10-4), and WDR73 gene in locus 15q25.2 (SNP rs72750868; p=4.47 x10-4). INTERPRETATION We have identified susceptibility loci associated with each category of T2DM-related complications in the Emirati population. Given that only 16% of the markers from the Illumina's Infinium Exome chip passed quality control assessment, this demonstrates that multiple variants were, either, monomorphic in the Arab population or were not genotyped due to the use of a Euro-centric EWAS array that limits the possibility of including targeted ethnic-specific SNPs. Our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci associated to T2DM complications. Further effort must be conducted to improve the representation of diverse populations in genotyping and sequencing studies.
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Affiliation(s)
- Afnan Mansour
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Dima Abdelmannan
- Dubai Health Authority, Dubai Diabetes Center, Dubai, United Arab Emirates
| | - Guan Tay
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Ahmed Hassoun
- Fakeeh University Hospital, Dubai, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, College of Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- *Correspondence: Habiba Alsafar,
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47
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Uglebjerg N, Ahmadizar F, Aly DM, Cañadas-Garre M, Hill C, Naber A, Oddsson A, Singh SS, Smyth L, Trégouët DA, Chaker L, Ghanbari M, Steinthorsdottir V, Ahlqvist E, Hadjadj S, Van Hoek M, Kavousi M, McKnight AJ, Sijbrands EJ, Stefansson K, Simons M, Rossing P, Ahluwalia TS. Four missense genetic variants in CUBN are associated with higher levels of eGFR in non-diabetes but not in diabetes mellitus or its subtypes: A genetic association study in Europeans. Front Endocrinol (Lausanne) 2023; 14:1081741. [PMID: 36926036 PMCID: PMC10011651 DOI: 10.3389/fendo.2023.1081741] [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: 10/27/2022] [Accepted: 02/07/2023] [Indexed: 03/08/2023] Open
Abstract
AIM Rare genetic variants in the CUBN gene encoding the main albumin-transporter in the proximal tubule of the kidneys have previously been associated with microalbuminuria and higher urine albumin levels, also in diabetes. Sequencing studies in isolated proteinuria suggest that these variants might not affect kidney function, despite proteinuria. However, the relation of these CUBN missense variants to the estimated glomerular filtration rate (eGFR) is largely unexplored. We hereby broadly examine the associations between four CUBN missense variants and eGFRcreatinine in Europeans with Type 1 (T1D) and Type 2 Diabetes (T2D). Furthermore, we sought to deepen our understanding of these variants in a range of single- and aggregate- variant analyses of other kidney-related traits in individuals with and without diabetes mellitus. METHODS We carried out a genetic association-based linear regression analysis between four CUBN missense variants (rs141640975, rs144360241, rs45551835, rs1801239) and eGFRcreatinine (ml/min/1.73 m2, CKD-EPIcreatinine(2012), natural log-transformed) in populations with T1D (n ~ 3,588) or T2D (n ~ 31,155) from multiple European studies and in individuals without diabetes from UK Biobank (UKBB, n ~ 370,061) with replication in deCODE (n = 127,090). Summary results of the diabetes-group were meta-analyzed using the fixed-effect inverse-variance method. RESULTS Albeit we did not observe associations between eGFRcreatinine and CUBN in the diabetes-group, we found significant positive associations between the minor alleles of all four variants and eGFRcreatinine in the UKBB individuals without diabetes with rs141640975 being the strongest (Effect=0.02, PeGFR_creatinine=2.2 × 10-9). We replicated the findings for rs141640975 in the Icelandic non-diabetes population (Effect=0.026, PeGFR_creatinine=7.7 × 10-4). For rs141640975, the eGFRcreatinine-association showed significant interaction with albuminuria levels (normo-, micro-, and macroalbuminuria; p = 0.03). An aggregated genetic risk score (GRS) was associated with higher urine albumin levels and eGFRcreatinine. The rs141640975 variant was also associated with higher levels of eGFRcreatinine-cystatin C (ml/min/1.73 m2, CKD-EPI2021, natural log-transformed) and lower circulating cystatin C levels. CONCLUSIONS The positive associations between the four CUBN missense variants and eGFR in a large population without diabetes suggests a pleiotropic role of CUBN as a novel eGFR-locus in addition to it being a known albuminuria-locus. Additional associations with diverse renal function measures (lower cystatin C and higher eGFRcreatinine-cystatin C levels) and a CUBN-focused GRS further suggests an important role of CUBN in the future personalization of chronic kidney disease management in people without diabetes.
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Affiliation(s)
- Nicoline Uglebjerg
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Data Science & Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, Netherlands
| | - Dina M. Aly
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Marisa Cañadas-Garre
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
- GENYO Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, Granada, Spain
- Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain
| | - Claire Hill
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Annemieke Naber
- Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Sunny S. Singh
- Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Laura Smyth
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - David-Alexandre Trégouët
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Population Health Research Center, Bordeaux, France
| | - Layal Chaker
- Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Emma Ahlqvist
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Samy Hadjadj
- Nantes Université, Centre Hospitalier Universitaire Nantes, Centre National de la Recherche Scientifique, INSERM, l’institut du thorax, Nantes, France
| | - Mandy Van Hoek
- Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Amy Jayne McKnight
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
| | - Eric J. Sijbrands
- Department of Internal Medicine, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Kari Stefansson
- deCODE Genetics, Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Matias Simons
- Institute of Human Genetics, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Rossing
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Tarunveer S. Ahluwalia
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- *Correspondence: Tarunveer S. Ahluwalia,
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48
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Nachmanson D, Pagadala M, Steward J, Cheung C, Bruce LK, Lee NQ, O'Keefe TJ, Lin GY, Hasteh F, Morris GP, Carter H, Harismendy O. Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes. J Transl Med 2022; 20:623. [PMID: 36575447 PMCID: PMC9793518 DOI: 10.1186/s12967-022-03810-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
PURPOSE The contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology. EXPERIMENTAL DESIGN Blood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE). RESULTS Tissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2-3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively. CONCLUSIONS Archival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.
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Affiliation(s)
- Daniela Nachmanson
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Meghana Pagadala
- Biomedical Science Graduate Program, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Joseph Steward
- Moores Cancer Center, University of California San Diego, 3855 Health Science Drive, San Diego, CA, 92093, USA
| | - Callie Cheung
- Moores Cancer Center, University of California San Diego, 3855 Health Science Drive, San Diego, CA, 92093, USA
| | - Lauryn Keeler Bruce
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Nicole Q Lee
- Moores Cancer Center, University of California San Diego, 3855 Health Science Drive, San Diego, CA, 92093, USA
| | - Thomas J O'Keefe
- Department of Surgery, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Grace Y Lin
- Department of Pathology, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Farnaz Hasteh
- Department of Pathology, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
| | - Hannah Carter
- Moores Cancer Center, University of California San Diego, 3855 Health Science Drive, San Diego, CA, 92093, USA
- Division of Medical Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Olivier Harismendy
- Moores Cancer Center, University of California San Diego, 3855 Health Science Drive, San Diego, CA, 92093, USA.
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA.
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49
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Lamri A, De Paoli M, De Souza R, Werstuck G, Anand S, Pigeyre M. Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits. Front Cardiovasc Med 2022; 9:964743. [PMID: 36505380 PMCID: PMC9729955 DOI: 10.3389/fcvm.2022.964743] [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: 06/08/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022] Open
Abstract
There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.
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Affiliation(s)
- Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada
| | - Monica De Paoli
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Russell De Souza
- Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Geoff Werstuck
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Sonia Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Marie Pigeyre
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,*Correspondence: Marie Pigeyre
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50
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Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, Thangam M, Ahlqvist E, Malik RA, Albagha OME, Abou-Samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun 2022; 13:7121. [PMID: 36402758 PMCID: PMC9675829 DOI: 10.1038/s41467-022-34754-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/07/2022] [Indexed: 11/20/2022] Open
Abstract
Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.
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Affiliation(s)
- Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Nisha Stephan
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Emma Ahlqvist
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | | | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar.
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