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Shi L, Yang C, Zhang M, Li K, Wang K, Jiao L, Liu R, Wang Y, Li M, Wang Y, Ma L, Hu S, Bian X. Dissecting the mechanism of atlastin-mediated homotypic membrane fusion at the single-molecule level. Nat Commun 2024; 15:2488. [PMID: 38509071 PMCID: PMC10954664 DOI: 10.1038/s41467-024-46919-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/13/2024] [Indexed: 03/22/2024] Open
Abstract
Homotypic membrane fusion of the endoplasmic reticulum (ER) is mediated by dynamin-like GTPase atlastin (ATL). This fundamental process relies on GTP-dependent domain rearrangements in the N-terminal region of ATL (ATLcyto), including the GTPase domain and three-helix bundle (3HB). However, its conformational dynamics during the GTPase cycle remain elusive. Here, we combine single-molecule FRET imaging and molecular dynamics simulations to address this conundrum. Different from the prevailing model, ATLcyto can form a loose crossover dimer upon GTP binding, which is tightened by GTP hydrolysis for membrane fusion. Furthermore, the α-helical motif between the 3HB and transmembrane domain, which is embedded in the surface of the lipid bilayer and self-associates in the crossover dimer, is required for ATL function. To recycle the proteins, Pi release, which disassembles the dimer, activates frequent relative movements between the GTPase domain and 3HB, and subsequent GDP dissociation alters the conformational preference of the ATLcyto monomer for entering the next reaction cycle. Finally, we found that two disease-causing mutations affect human ATL1 activity by destabilizing GTP binding-induced loose crossover dimer formation and the membrane-embedded helix, respectively. These results provide insights into ATL-mediated homotypic membrane fusion and the pathological mechanisms of related disease.
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Affiliation(s)
- Lijun Shi
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Chenguang Yang
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingyuan Zhang
- College of Life Sciences, Zhejiang University, Hangzhou, 310027, China
| | - Kangning Li
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Keying Wang
- College of Life Sciences, Zhejiang University, Hangzhou, 310027, China
| | - Li Jiao
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Ruming Liu
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Yunyun Wang
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, 300071, China
| | - Ming Li
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Wang
- College of Life Sciences, Zhejiang University, Hangzhou, 310027, China.
- The Provincial International Science and Technology Cooperation Base on Engineering Biology, International Campus of Zhejiang University, Haining, 314400, China.
| | - Lu Ma
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Shuxin Hu
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Xin Bian
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, 300071, China.
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2
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Zhao Y, Zhuang Z, Li Y, Xiao W, Song Z, Huang N, Wang W, Dong X, Jia J, Clarke R, Huang T. Elevated blood remnant cholesterol and triglycerides are causally related to the risks of cardiometabolic multimorbidity. Nat Commun 2024; 15:2451. [PMID: 38503751 PMCID: PMC10951224 DOI: 10.1038/s41467-024-46686-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 02/28/2024] [Indexed: 03/21/2024] Open
Abstract
The connection between triglyceride-rich lipoproteins and cardiometabolic multimorbidity, characterized by the concurrence of at least two of type 2 diabetes, ischemic heart disease, and stroke, has not been definitively established. We aim to examine the prospective associations between serum remnant cholesterol, triglycerides, and the risks of progression from first cardiometabolic disease to multimorbidity via multistate modeling in the UK Biobank. We also evaluate the causality of these associations via Mendelian randomization using 13 biologically relevant SNPs as the genetic instruments. Here we show that elevated remnant cholesterol and triglycerides are significantly associated with gradually higher risks of cardiometabolic multimorbidity, particularly the progression of ischemic heart disease to the multimorbidity of ischemic heart disease and type 2 diabetes. These results advocate for effective management of remnant cholesterol and triglycerides as a potential strategy in mitigating the risks of cardiometabolic multimorbidity.
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Affiliation(s)
- Yimin Zhao
- Department of Sports Medicine, Peking University Third Hospital, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhenhuang Zhuang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yueying Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wendi Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zimin Song
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wenxiu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Xue Dong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jinzhu Jia
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
- Center for Intelligent Public Health, Academy for Artificial Intelligence, Peking University, Beijing, China.
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3
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Deng B, Kong W, Shen X, Han C, Zhao Z, Chen S, Zhou C, Bae-Jump V. The role of DGAT1 and DGAT2 in regulating tumor cell growth and their potential clinical implications. J Transl Med 2024; 22:290. [PMID: 38500157 PMCID: PMC10946154 DOI: 10.1186/s12967-024-05084-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/10/2024] [Indexed: 03/20/2024] Open
Abstract
Lipid metabolism is widely reprogrammed in tumor cells. Lipid droplet is a common organelle existing in most mammal cells, and its complex and dynamic functions in maintaining redox and metabolic balance, regulating endoplasmic reticulum stress, modulating chemoresistance, and providing essential biomolecules and ATP have been well established in tumor cells. The balance between lipid droplet formation and catabolism is critical to maintaining energy metabolism in tumor cells, while the process of energy metabolism affects various functions essential for tumor growth. The imbalance of synthesis and catabolism of fatty acids in tumor cells leads to the alteration of lipid droplet content in tumor cells. Diacylglycerol acyltransferase 1 and diacylglycerol acyltransferase 2, the enzymes that catalyze the final step of triglyceride synthesis, participate in the formation of lipid droplets in tumor cells and in the regulation of cell proliferation, migration and invasion, chemoresistance, and prognosis in tumor. Several diacylglycerol acyltransferase 1 and diacylglycerol acyltransferase 2 inhibitors have been developed over the past decade and have shown anti-tumor effects in preclinical tumor models and improvement of metabolism in clinical trials. In this review, we highlight key features of fatty acid metabolism and different paradigms of diacylglycerol acyltransferase 1 and diacylglycerol acyltransferase 2 activities on cell proliferation, migration, chemoresistance, and prognosis in tumor, with the hope that these scientific findings will have potential clinical implications.
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Affiliation(s)
- Boer Deng
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Weimin Kong
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Xiaochang Shen
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Chao Han
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
| | - Ziyi Zhao
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Shuning Chen
- Department of Gynecologic Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People's Republic of China
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Chunxiao Zhou
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Victoria Bae-Jump
- Division of Gynecologic Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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4
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Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, Surendran P, Zhang W, Jousilahti P, Kristiansson K, Salomaa V, Goodwin M, Hughes DA, Boehnke M, Fernandes Silva L, Yin X, Mahajan A, Neville MJ, van Zuydam NR, de Mutsert R, Li-Gao R, Mook-Kanamori DO, Demirkan A, Liu J, Noordam R, Trompet S, Chen Z, Kartsonaki C, Li L, Lin K, Hagenbeek FA, Hottenga JJ, Pool R, Ikram MA, van Meurs J, Haller T, Milaneschi Y, Kähönen M, Mishra PP, Joshi PK, Macdonald-Dunlop E, Mangino M, Zierer J, Acar IE, Hoyng CB, Lechanteur YTE, Franke L, Kurilshikov A, Zhernakova A, Beekman M, van den Akker EB, Kolcic I, Polasek O, Rudan I, Gieger C, Waldenberger M, Asselbergs FW, Hayward C, Fu J, den Hollander AI, Menni C, Spector TD, Wilson JF, Lehtimäki T, Raitakari OT, Penninx BWJH, Esko T, Walters RG, Jukema JW, Sattar N, Ghanbari M, Willems van Dijk K, Karpe F, McCarthy MI, Laakso M, Järvelin MR, Timpson NJ, Perola M, Kooner JS, Chambers JC, van Duijn C, Slagboom PE, Boomsma DI, Danesh J, Ala-Korpela M, Butterworth AS, Kettunen J. Genome-wide characterization of circulating metabolic biomarkers. Nature 2024:10.1038/s41586-024-07148-y. [PMID: 38448586 DOI: 10.1038/s41586-024-07148-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/01/2024] [Indexed: 03/08/2024]
Abstract
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
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Affiliation(s)
- Minna K Karjalainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Savita Karthikeyan
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Public Health Specialty Training Programme, Cambridge, UK
| | - Eeva Sliz
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Elias Allara
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Wing Tung Fung
- 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
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Matt Goodwin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Jiangsu, China
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matt J Neville
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Natalie R van Zuydam
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Ayse Demirkan
- Surrey Institute for People-Centred AI, University of Surrey, Guildford, UK
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Pashupati P Mishra
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Erin Macdonald-Dunlop
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Jonas Zierer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carel B Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yara T E Lechanteur
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Center for Computational Biology, Leiden University Medical Center, Leiden, The Netherlands
- The Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ivana Kolcic
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Ozren Polasek
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anneke I den Hollander
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
- Genomics Research Center, Abbvie, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - James F Wilson
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Terho Lehtimäki
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tonu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fredrik Karpe
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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5
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Tangseefa P, Jin H, Zhang H, Xie M, Ibáñez CF. Human ACVR1C missense variants that correlate with altered body fat distribution produce metabolic alterations of graded severity in knock-in mutant mice. Mol Metab 2024; 81:101890. [PMID: 38307384 PMCID: PMC10863331 DOI: 10.1016/j.molmet.2024.101890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND & AIMS Genome-wide studies have identified three missense variants in the human gene ACVR1C, encoding the TGF-β superfamily receptor ALK7, that correlate with altered waist-to-hip ratio adjusted for body mass index (WHR/BMI), a measure of body fat distribution. METHODS To move from correlation to causation and understand the effects of these variants on fat accumulation and adipose tissue function, we introduced each of the variants in the mouse Acvr1c locus and investigated metabolic phenotypes in comparison with a null mutation. RESULTS Mice carrying the I195T variant showed resistance to high fat diet (HFD)-induced obesity, increased catecholamine-induced adipose tissue lipolysis and impaired ALK7 signaling, phenocopying the null mutants. Mice with the I482V variant displayed an intermediate phenotype, with partial resistance to HFD-induced obesity, reduction in subcutaneous, but not visceral, fat mass, decreased systemic lipolysis and reduced ALK7 signaling. Surprisingly, mice carrying the N150H variant were metabolically indistinguishable from wild type under HFD, although ALK7 signaling was reduced at low ligand concentrations. CONCLUSION Together, these results validate ALK7 as an attractive drug target in human obesity and suggest a lower threshold for ALK7 function in humans compared to mice.
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Affiliation(s)
- Pawanrat Tangseefa
- Chinese Institute for Brain Research, Zhongguancun Life Science Park, 102206 Beijing, China; Peking University School of Life Sciences, Peking-Tsinghua Center for Life Sciences, 100871 Beijing, China; PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Hong Jin
- Peking University School of Life Sciences, Peking-Tsinghua Center for Life Sciences, 100871 Beijing, China; PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Houyu Zhang
- Chinese Institute for Brain Research, Zhongguancun Life Science Park, 102206 Beijing, China; Peking University School of Psychological and Cognitive Sciences, 100871 Beijing, China
| | - Meng Xie
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China; Peking University School of Psychological and Cognitive Sciences, 100871 Beijing, China; Department of Biosciences and Nutrition, Karolinska Institute, Huddinge 14157, Sweden
| | - Carlos F Ibáñez
- Chinese Institute for Brain Research, Zhongguancun Life Science Park, 102206 Beijing, China; Peking University School of Life Sciences, Peking-Tsinghua Center for Life Sciences, 100871 Beijing, China; PKU-IDG/McGovern Institute for Brain Research, Beijing, China; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden; Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University, Stellenbosch 7600, South Africa.
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6
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Kumar V, Stewart JH. Obesity, bone marrow adiposity, and leukemia: Time to act. Obes Rev 2024; 25:e13674. [PMID: 38092420 DOI: 10.1111/obr.13674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/07/2023] [Accepted: 11/13/2023] [Indexed: 02/28/2024]
Abstract
Obesity has taken the face of a pandemic with less direct concern among the general population and scientific community. However, obesity is considered a low-grade systemic inflammation that impacts multiple organs. Chronic inflammation is also associated with different solid and blood cancers. In addition, emerging evidence demonstrates that individuals with obesity are at higher risk of developing blood cancers and have poorer clinical outcomes than individuals in a normal weight range. The bone marrow is critical for hematopoiesis, lymphopoiesis, and myelopoiesis. Therefore, it is vital to understand the mechanisms by which obesity-associated changes in BM adiposity impact leukemia development. BM adipocytes are critical to maintain homeostasis via different means, including immune regulation. However, obesity increases BM adiposity and creates a pro-inflammatory environment to upregulate clonal hematopoiesis and a leukemia-supportive environment. Obesity further alters lymphopoiesis and myelopoiesis via different mechanisms, which dysregulate myeloid and lymphoid immune cell functions mentioned in the text under different sequentially discussed sections. The altered immune cell function during obesity alters hematological malignancies and leukemia susceptibility. Therefore, obesity-induced altered BM adiposity, immune cell generation, and function impact an individual's predisposition and severity of leukemia, which should be considered a critical factor in leukemia patients.
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Affiliation(s)
- Vijay Kumar
- Department of Surgery, Laboratory of Tumor Immunology and Immunotherapy, Morehouse School of Medicine, Atlanta, Georgia, USA
| | - John H Stewart
- Department of Surgery, Laboratory of Tumor Immunology and Immunotherapy, Morehouse School of Medicine, Atlanta, Georgia, USA
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7
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Novakov V, Novakova O, Churnosova M, Aristova I, Ponomarenko M, Reshetnikova Y, Churnosov V, Sorokina I, Ponomarenko I, Efremova O, Orlova V, Batlutskaya I, Polonikov A, Reshetnikov E, Churnosov M. Polymorphism rs143384 GDF5 reduces the risk of knee osteoarthritis development in obese individuals and increases the disease risk in non-obese population. Arthroplasty 2024; 6:12. [PMID: 38424630 PMCID: PMC10905832 DOI: 10.1186/s42836-023-00229-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/26/2023] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND We investigated the effect of obesity on the association of genome-wide associative studies (GWAS)-significant genes with the risk of knee osteoarthritis (KOA). METHODS All study participants (n = 1,100) were divided into 2 groups in terms of body mass index (BMI): BMI ≥ 30 (255 KOA patients and 167 controls) and BMI < 30 (245 KOA and 433 controls). The eight GWAS-significant KOA single nucleotide polymorphisms (SNP) of six candidate genes, such as LYPLAL1 (rs2820436, rs2820443), SBNO1 (rs1060105, rs56116847), WWP2 (rs34195470), NFAT5 (rs6499244), TGFA (rs3771501), GDF5 (rs143384), were genotyped. Logistic regression analysis (gPLINK online program) was used for SNPs associations study with the risk of developing KOA into 2 groups (BMI ≥ 30 and BMI < 30) separately. The functional effects of KOA risk loci were evaluated using in silico bioinformatic analysis. RESULTS Multidirectional relationships of the rs143384 GDF5 with KOA in BMI-different groups were found: This SNP was KOA protective locus among individuals with BMI ≥ 30 (OR 0.41 [95%CI 0.20-0.94] recessive model) and was disorder risk locus among individuals with BMI < 30 (OR 1.32 [95%CI 1.05-1.65] allele model, OR 1.44 [95%CI 1.10-1.86] additive model, OR 1.67 [95%CI 1.10-2.52] dominant model). Polymorphism rs143384 GDF5 manifested its regulatory effects in relation to nine genes (GDF5, CPNE1, EDEM2, ERGIC3, GDF5OS, PROCR, RBM39, RPL36P4, UQCC1) in adipose tissue, which were involved in the regulation of pathways of apoptosis of striated muscle cells. CONCLUSIONS In summary, the effect of obesity on the association of the rs143384 GDF5 with KOA was shown: the "protective" value of this polymorphism in the BMI ≥ 30 group and the "risk" meaning in BMI < 30 cohort.
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Affiliation(s)
- Vitaly Novakov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Olga Novakova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Marina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Yuliya Reshetnikova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Vladimir Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Olga Efremova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Valentina Orlova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Irina Batlutskaya
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Alexey Polonikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
- Department of Biology, Medical Genetics and Ecology and Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, 305041, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia.
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8
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Margol AS, Molinaro AM, Onar-Thomas A, Resnick A, Hanson D, Kieran M, Mishra-Kalyani P, Rivera D, Barone A, Arons D, Meehan C, Prados M. Use of External Control Cohorts in Pediatric Brain Tumor Clinical Trials. J Clin Oncol 2024:JCO2301084. [PMID: 38394473 DOI: 10.1200/jco.23.01084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/18/2023] [Accepted: 01/03/2024] [Indexed: 02/25/2024] Open
Abstract
Why, when, and how to consider external control cohorts in pediatric brain tumor clinical trials.
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Affiliation(s)
- Ashley S Margol
- Keck School of Medicine of University of Southern California, Cancer and Blood Disease Institute at Children's Hospital Los Angeles, Los Angeles, CA
| | - Annette M Molinaro
- Division of Biomedical Statistics and Informatics, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA
| | | | - Adam Resnick
- Center for Data Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Derek Hanson
- Joseph M. Sanzari Children's Hospital at Hackensack University Medical Center, Hackensack, NJ
| | | | | | | | - Amy Barone
- US Food and Drug Administration, Washington, DC
| | | | | | - Michael Prados
- Departments of Neurosurgery and Pediatrics, University of California, San Francisco, San Francisco, CA
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9
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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10
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van Nassau SCMW, Bol GM, van der Baan FH, Roodhart JML, Vink GR, Punt CJA, May AM, Koopman M, Derksen JWG. Harnessing the Potential of Real-World Evidence in the Treatment of Colorectal Cancer: Where Do We Stand? Curr Treat Options Oncol 2024:10.1007/s11864-024-01186-4. [PMID: 38367182 DOI: 10.1007/s11864-024-01186-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 02/19/2024]
Abstract
OPINION STATEMENT Treatment guidelines for colorectal cancer (CRC) are primarily based on the results of randomized clinical trials (RCTs), the gold standard methodology to evaluate safety and efficacy of oncological treatments. However, generalizability of trial results is often limited due to stringent eligibility criteria, underrepresentation of specific populations, and more heterogeneity in clinical practice. This may result in an efficacy-effectiveness gap and uncertainty regarding meaningful benefit versus treatment harm. Meanwhile, conduct of traditional RCTs has become increasingly challenging due to identification of a growing number of (small) molecular subtypes. These challenges-combined with the digitalization of health records-have led to growing interest in use of real-world data (RWD) to complement evidence from RCTs. RWD is used to evaluate epidemiological trends, quality of care, treatment effectiveness, long-term (rare) safety, and quality of life (QoL) measures. In addition, RWD is increasingly considered in decision-making by clinicians, regulators, and payers. In this narrative review, we elaborate on these applications in CRC, and provide illustrative examples. As long as the quality of RWD is safeguarded, ongoing developments, such as common data models, federated learning, and predictive modelling, will further unfold its potential. First, whenever possible, we recommend conducting pragmatic trials, such as registry-based RCTs, to optimize generalizability and answer clinical questions that are not addressed in registrational trials. Second, we argue that marketing approval should be conditional for patients who would have been ineligible for the registrational trial, awaiting planned (non) randomized evaluation of outcomes in the real world. Third, high-quality effectiveness results should be incorporated in treatment guidelines to aid in patient counseling. We believe that a coordinated effort from all stakeholders is essential to improve the quality of RWD, create a learning healthcare system with optimal use of trials and real-world evidence (RWE), and ultimately ensure personalized care for every CRC patient.
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Affiliation(s)
- Sietske C M W van Nassau
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands.
| | - Guus M Bol
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Frederieke H van der Baan
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeanine M L Roodhart
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Geraldine R Vink
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
| | - Cornelis J A Punt
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anne M May
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Miriam Koopman
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Jeroen W G Derksen
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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11
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Chen J, Li XN, Lu CC, Yuan S, Yung G, Ye J, Tian H, Lin J. Considerations for master protocols using external controls. J Biopharm Stat 2024:1-23. [PMID: 38363805 DOI: 10.1080/10543406.2024.2311248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 01/24/2024] [Indexed: 02/18/2024]
Abstract
There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.
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Affiliation(s)
- Jie Chen
- Data Sciences, ECR Global, Shanghai, China
| | | | | | - Sammy Yuan
- Oncology Statistics, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | - Godwin Yung
- Product Development Data and Statistical Sciences, Genentech/Roche, South San Francisco, Cambridge, USA
| | - Jingjing Ye
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland, USA
| | - Hong Tian
- Global Statistics, BeiGene, Ridgefield Park, New Jersy, USA
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda, Cambridge, Massachusetts, USA
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12
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Huang A, Wu X, Lin J, Wei C, Xu W. Genetic insights into repurposing statins for hyperthyroidism prevention: a drug-target Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1331031. [PMID: 38425755 PMCID: PMC10902122 DOI: 10.3389/fendo.2024.1331031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Background Current therapeutic measures for thyroid dysfunction are limited and often accompanied by adverse effects. The use of lipid-lowering drugs like statins has recently been associated with lower thyroid eye diseases risk. Objective To investigate the implications of genetically proxied lipid-lowering drugs on thyroid dysfunction. Methods In this drug-target Mendelian randomization (MR) study, we utilized genetic variants within drug target genes associated with low-density lipoprotein (LDL) or triglyceride (TG), derived from a genome-wide association study (GWAS) meta-analysis (N ≤ 188,577), to simulate lifelong drug interventions. Genetic summary statistics for thyroid dysfunction outcomes were retrieved from GWAS datasets of Thyroid Omics Consortium (N ≤ 54,288) and UK Biobank (N = 484,598). Inverse-variance-weighted MR (IVW-MR) method was performed as primary analysis, followed by validation in colocalization analysis. A subsequent two-step MR analysis was conducted to identify biomarkers mediating the identified drug-outcome association. Results In IVW-MR analysis, genetic mimicry of 3-hydroxy-3-methylglutarylcoenzyme reductase (HMGCR) inhibitors (e.g. statins) was significantly associated with lower risk of hyperthyroidism in two independent datasets (OR1, 0.417 per 1-mmol/L lower in LDL-C; 95% CI 0.262 to 0.664; P1 = 2.262 × 10-4; OR2 0.996; 95% CI 0.993-0.998; P2 = 0.002). Two-step MR analysis revealed eighteen biomarkers linked to genetic mimicry of HMGCR inhibition, and identified insulin-like growth factor 1 (IGF-1) levels mediating 2.108% of the negative causal relationship between HMGCR inhibition and hyperthyroidism. Conclusion This study supports HMGCR inhibition as a promising therapeutic strategy for hyperthyroidism and suggests its underlying mechanisms may extend beyond lipid metabolism. Further investigations through laboratory studies and clinical trials are necessary to confirm and elucidate these findings.
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Affiliation(s)
- Anqi Huang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Shantou University Medical College, Shantou, China
| | - Xinyi Wu
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Shantou University Medical College, Shantou, China
| | - Jiaqi Lin
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Shantou University Medical College, Shantou, China
| | - Chiju Wei
- Multidisciplinary Research Center, Shantou University, Shantou, China
| | - Wencan Xu
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
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13
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Loh WJ, Yaligar J, Hooper AJ, Sadananthan SA, Kway Y, Lim SC, Watts GF, Velan SS, Leow MKS, Khoo J. Clinical and imaging features of women with polygenic partial lipodystrophy: a case series. Nutr Diabetes 2024; 14:3. [PMID: 38321009 PMCID: PMC10847407 DOI: 10.1038/s41387-024-00260-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 01/13/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Familial partial lipodystrophy (FPLD) is an inherited disorder of white adipose tissue that causes premature cardiometabolic disease. There is no clear diagnostic criteria for FPLD, and this may explain the under-detection of this condition. AIM This pilot study aimed to describe the clinical features of women with FPLD and to explore the value of adipose tissue measurements that could be useful in diagnosis. METHODS In 8 women with FPLD and 4 controls, skinfold measurements, DXA and whole-body MRI were undertaken. RESULTS Whole genome sequencing was negative for monogenic metabolic causes, but polygenic scores for partial lipodystrophy were elevated in keeping with FPLD type 1. The mean age of diagnosis of DM was 31 years in the FPLD group. Compared with controls, the FPLD group had increased HOMA-IR (10.3 vs 2.9, p = 0.028) and lower mean thigh skinfold thickness (19.5 mm vs 48.2 mm, p = 0.008). The FPLD group had lower percentage of leg fat and an increased ratio of trunk to leg fat percentage on DXA. By MRI, the FPLD group had decreased subcutaneous adipose tissue (SAT) volume in the femoral and calf regions (p < 0.01); abdominal SAT, visceral adipose tissue, and femoral and calf muscle volumes were not different from controls. CONCLUSION Women with FPLD1 in Singapore have significant loss of adipose but not muscle tissue in lower limbs and have early onset of diabetes. Reduced thigh skinfold, and increased ratio of trunk to leg fat percentage on DXA are potentially clinically useful markers to identify FPLD1.
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Affiliation(s)
- Wann Jia Loh
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
| | - Jadegoud Yaligar
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Amanda J Hooper
- Department of Biochemistry, Pathwest and Fiona Stanley Hospital Network, Perth, Australia
- School of Medicine, University of Western Australia, Perth, Australia
| | - Suresh Anand Sadananthan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Yeshe Kway
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, Australia
- Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, Australia
| | - Sambasivam Sendhil Velan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Melvin Khee Shing Leow
- Duke-NUS Medical School, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
- LKC School of Medicine, NTU, Singapore, Singapore
| | - Joan Khoo
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore
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14
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Manco L, Albuquerque D, Rodrigues D, Machado-Rodrigues AM, Padez C. Protective Association of APOC1/rs4420638 with Risk of Obesity: A case-control Study in Portuguese Children. Biochem Genet 2024; 62:254-263. [PMID: 37328602 PMCID: PMC10902077 DOI: 10.1007/s10528-023-10427-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023]
Abstract
The association of the rs4420638 polymorphism, near the APOC1 gene, was examined with the risk of obesity among Portuguese children. A sample of 446 Portuguese individuals (231 boys and 215 girls) of European descent, aged 3.2 to 13.7 years old (mean age: 7.98 years), were selected to conduct a case-control study. Body mass index (BMI), BMI Z-scores, and waist circumference were calculated. Genotyping was performed by real time PCR using a pre-designed TaqMan probe. Logistic regression and the nonparametric Mann-Whitney test were used to test the associations. The association results revealed a significant protective effect from the minor G-allele of SNP rs4420638 against obesity, with an odds ratio (OR) of 0.619 (95% CI 0.421-0.913; p = 0.0155) in the additive model, and OR of 0.587 (95% CI 0.383-0.9; p = 0.0145) in the dominant model. Moreover, comparing genotype groups (AA vs. AG + GG), significantly lower values (p < 0.05) for the anthropometric traits weight, height, BMI, BMI Z-score and waist circumference, were observed in the carriers of allele G. The present study provides further evidence for the APOE/APOC1 candidate-region association with the risk of obesity. This was the first study to describe the protective association of the rs4420638 minor G-allele against obesity in childhood exclusively.
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Affiliation(s)
- Licínio Manco
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal.
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal.
| | - David Albuquerque
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
| | - Daniela Rodrigues
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
| | - Aristides M Machado-Rodrigues
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
- Faculty of Sport Sciences and Physical Education, University of Coimbra, Coimbra, Portugal
| | - Cristina Padez
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal
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15
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Zhang T, Chen Y, Li X, Zhang J, Duan L. Genetic associations and potential mediators between psychiatric disorders and irritable bowel syndrome: a Mendelian randomization study with mediation analysis. Front Psychiatry 2024; 15:1279266. [PMID: 38352653 PMCID: PMC10861787 DOI: 10.3389/fpsyt.2024.1279266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Objective Potential causal associations between psychiatric disorders and irritable bowel syndrome have been demonstrated in observational studies; however, these studies are susceptible to underlying confounding and reverse causation biases. We aimed to assess the causal effects of psychiatric disorders on irritable bowel syndrome (IBS) and the potential mediators from a genetic perspective by conducting a Mendelian randomization (MR) study with mediation analysis. Method Genetic instruments associated with psychiatric disorders, potential mediators, and IBS were obtained from large-scale genome-wide association studies (GWAS). Three MR methods - the inverse-variance weighted (IVW) method, MR-Egger method, and weighted median method, were used to investigate causal association estimates. Heterogeneity among different genetic instrumental variables (IVs) was assessed using Q tests. Additionally, the MR-PRESSO and MR-Pleiotropy methods were used to verify horizontal pleiotropy and detect outliers that might bias the results, which were removed from further analysis. Consequently, we used MR mediation analysis to investigate potential mediators in the causal associations between psychiatric disorders and IBS. Results MR provided evidence of the causal effects of genetically predicted broad depression, major depressive disorder (MDD), anxiety disorder, post-traumatic stress disorder (PTSD), and schizophrenia on IBS. The results of MR mediation analysis demonstrated that the reduction in acetate levels mediated 12.6% of the effects of broad depression on IBS; insomnia mediated 16.00%, 16.20%, and 27.14% of the effects of broad depression, MDD, and PTSD on IBS, respectively; and the increase in blood β-hydroxybutyrate levels mediated 50.76% of the effects of schizophrenia on IBS. Conclusion Our study confirmed the brain-gut axis involvement and potential modulators in the pathophysiology of psychiatric disorder-induced IBS from a genetic perspective, and suggests potential therapeutic targets for the disrupted brain-gut axis.
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Affiliation(s)
| | | | | | | | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
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16
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Alves M, Laranjeira F, Correia-da-Silva G. Understanding Hypertriglyceridemia: Integrating Genetic Insights. Genes (Basel) 2024; 15:190. [PMID: 38397180 PMCID: PMC10887881 DOI: 10.3390/genes15020190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
Hypertriglyceridemia is an exceptionally complex metabolic disorder characterized by elevated plasma triglycerides associated with an increased risk of acute pancreatitis and cardiovascular diseases such as coronary artery disease. Its phenotype expression is widely heterogeneous and heavily influenced by conditions as obesity, alcohol consumption, or metabolic syndromes. Looking into the genetic underpinnings of hypertriglyceridemia, this review focuses on the genetic variants in LPL, APOA5, APOC2, GPIHBP1 and LMF1 triglyceride-regulating genes reportedly associated with abnormal genetic transcription and the translation of proteins participating in triglyceride-rich lipoprotein metabolism. Hypertriglyceridemia resulting from such genetic abnormalities can be categorized as monogenic or polygenic. Monogenic hypertriglyceridemia, also known as familial chylomicronemia syndrome, is caused by homozygous or compound heterozygous pathogenic variants in the five canonical genes. Polygenic hypertriglyceridemia, also known as multifactorial chylomicronemia syndrome in extreme cases of hypertriglyceridemia, is caused by heterozygous pathogenic genetic variants with variable penetrance affecting the canonical genes, and a set of common non-pathogenic genetic variants (polymorphisms, using the former nomenclature) with well-established association with elevated triglyceride levels. We further address recent progress in triglyceride-lowering treatments. Understanding the genetic basis of hypertriglyceridemia opens new translational opportunities in the scope of genetic screening and the development of novel therapies.
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Affiliation(s)
- Mara Alves
- Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal;
| | - Francisco Laranjeira
- CGM—Centro de Genética Médica Jacinto de Magalhães, Centro Hospitalar Universitário de Santo António (CHUdSA), 4099-028 Porto, Portugal;
- UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-346 Porto, Portugal
- ITR—Laboratory for Integrative and Translational Research in Population Health, 4050-600 Porto, Portugal
| | - Georgina Correia-da-Silva
- UCIBIO Applied Molecular Biosciences Unit and Associate Laboratory i4HB—Institute for Health and Bioeconomy Laboratory of Biochemistry, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
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17
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Brown EA, Kales S, Boyle MJ, Vitti J, Kotliar D, Schaffner S, Tewhey R, Sabeti PC. Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression. Hum Mol Genet 2024; 33:270-283. [PMID: 37930192 PMCID: PMC10800014 DOI: 10.1093/hmg/ddad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
While genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.
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Affiliation(s)
- Elizabeth A Brown
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Susan Kales
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Michael James Boyle
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
| | - Joseph Vitti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Dylan Kotliar
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Steve Schaffner
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
| | - Ryan Tewhey
- The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, United States
| | - Pardis C Sabeti
- The Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
- Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, United States
- Howard Hughes Medical Institute, Harvard University, 26 Oxford Street, Cambridge, MA 02138, United States
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18
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van Zwol W, van de Sluis B, Ginsberg HN, Kuivenhoven JA. VLDL Biogenesis and Secretion: It Takes a Village. Circ Res 2024; 134:226-244. [PMID: 38236950 DOI: 10.1161/circresaha.123.323284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/21/2023] [Indexed: 01/23/2024]
Abstract
The production and secretion of VLDLs (very-low-density lipoproteins) by hepatocytes has a direct impact on liver fat content, as well as the concentrations of cholesterol and triglycerides in the circulation and thus affects both liver and cardiovascular health, respectively. Importantly, insulin resistance, excess caloric intake, and lack of physical activity are associated with overproduction of VLDL, hepatic steatosis, and increased plasma levels of atherogenic lipoproteins. Cholesterol and triglycerides in remnant particles generated by VLDL lipolysis are risk factors for atherosclerotic cardiovascular disease and have garnered increasing attention over the last few decades. Presently, however, increased risk of atherosclerosis is not the only concern when considering today's cardiometabolic patients, as they often also experience hepatic steatosis, a prevalent disorder that can progress to steatohepatitis and cirrhosis. This duality of metabolic risk highlights the importance of understanding the molecular regulation of the biogenesis of VLDL, the lipoprotein that transports triglycerides and cholesterol out of the liver. Fortunately, there has been a resurgence of interest in the intracellular assembly, trafficking, degradation, and secretion of VLDL by hepatocytes, which has led to many exciting new molecular insights that are the topic of this review. Increasing our understanding of the biology of this pathway will aid to the identification of novel therapeutic targets to improve both the cardiovascular and the hepatic health of cardiometabolic patients. This review focuses, for the first time, on this duality.
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Affiliation(s)
- Willemien van Zwol
- Department of Paediatrics, University Medical Center Groningen, University of Groningen, the Netherlands (W.v.Z., B.v.d.S., J.A.K.)
| | - Bart van de Sluis
- Department of Paediatrics, University Medical Center Groningen, University of Groningen, the Netherlands (W.v.Z., B.v.d.S., J.A.K.)
| | - Henry N Ginsberg
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY (H.N.G.)
| | - Jan Albert Kuivenhoven
- Department of Paediatrics, University Medical Center Groningen, University of Groningen, the Netherlands (W.v.Z., B.v.d.S., J.A.K.)
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19
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Opsahl JO, Fragoso-Bargas N, Lee Y, Carlsen EØ, Lekanova N, Qvigstad E, Sletner L, Jenum AK, Lee-Ødegård S, Prasad RB, Birkeland KI, Moen GH, Sommer C. Epigenome-wide association study of DNA methylation in maternal blood leukocytes with BMI in pregnancy and gestational weight gain. Int J Obes (Lond) 2024:10.1038/s41366-024-01458-x. [PMID: 38219005 DOI: 10.1038/s41366-024-01458-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 01/15/2024]
Abstract
OBJECTIVES We aimed to discover CpG sites with differential DNA methylation in peripheral blood leukocytes associated with body mass index (BMI) in pregnancy and gestational weight gain (GWG) in women of European and South Asian ancestry. Furthermore, we aimed to investigate how the identified sites were associated with methylation quantitative trait loci, gene ontology, and cardiometabolic parameters. METHODS In the Epigenetics in pregnancy (EPIPREG) sample we quantified maternal DNA methylation in peripheral blood leukocytes in gestational week 28 with Illumina's MethylationEPIC BeadChip. In women with European (n = 303) and South Asian (n = 164) ancestry, we performed an epigenome-wide association study of BMI in gestational week 28 and GWG between gestational weeks 15 and 28 using a meta-analysis approach. Replication was performed in the Norwegian Mother, Father, and Child Cohort Study, the Study of Assisted Reproductive Technologies (MoBa-START) (n = 877, mainly European/Norwegian). RESULTS We identified one CpG site significantly associated with GWG (p 5.8 × 10-8) and five CpG sites associated with BMI at gestational week 28 (p from 4.0 × 10-8 to 2.1 × 10-10). Of these, we were able to replicate three in MoBa-START; cg02786370, cg19758958 and cg10472537. Two sites are located in genes previously associated with blood pressure and BMI. DNA methylation at the three replicated CpG sites were associated with levels of blood pressure, lipids and glucose in EPIPREG (p from 1.2 × 10-8 to 0.04). CONCLUSIONS We identified five CpG sites associated with BMI at gestational week 28, and one with GWG. Three of the sites were replicated in an independent cohort. Several genetic variants were associated with DNA methylation at cg02786379 and cg16733643 suggesting a genetic component influencing differential methylation. The identified CpG sites were associated with cardiometabolic traits. CLINICALTRIALS GOV REGISTRATION NO Not applicable.
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Affiliation(s)
- J O Opsahl
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - N Fragoso-Bargas
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Y Lee
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - E Ø Carlsen
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - N Lekanova
- Department of Biosciences, The Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - E Qvigstad
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - L Sletner
- Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
| | - A K Jenum
- General Practice Research Unit, Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - S Lee-Ødegård
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - R B Prasad
- Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - K I Birkeland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - G-H Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Institute of Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - C Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
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20
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Neehal N, Anand V, Bennett KP. Framework for Research in Equitable Synthetic Control Arms. AMIA Annu Symp Proc 2024; 2023:530-539. [PMID: 38222411 PMCID: PMC10785851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Randomized Clinical Trials (RCTs) measure an intervention's efficacy, but they may not be generalizable to a desired target population if the RCT is not equitable. Thus, representativeness of RCTs has become a national priority. Synthetic Controls (SCs) that incorporate observational data into RCTs have shown great potential to produce more efficient studies, but their equity is rarely considered. Here, we examine how to improve treatment effect estimation and equity of a trial by augmenting "on-trial" concurrent controls with SCs to form a Hybrid Control Arm (HCA). We introduce FRESCA - a framework to evaluate HCA construction methods using RCT simulations. FRESCA shows that doing propensity and equity adjustment when constructing the HCA leads to accurate population treatment effect estimates while meeting equity goals with potentially less "on-trial" patients. This work represents the first investigation of equity in HCA design that provides definitions, metrics, compelling questions, and resources for future work.
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Affiliation(s)
| | - Vibha Anand
- Center for Computational Health, IBM T.J. Watson Research Center, Cambridge, MA
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21
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Gholami M. Genetic variants and haplotype structures of miRNA host genes in cancer and obesity. J Biomol Struct Dyn 2024:1-7. [PMID: 38174558 DOI: 10.1080/07391102.2023.2300056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
Cancer and obesity are two important public health problems. This study aimed to investigate the role of genetic variants and haplotypes of miRNA host genes in cancer and obesity. Data from the catalog of genome-wide association studies (GWAS) were used to find significant variants (index). Then, 1000-genome phase 3 data were used to find haplotypic variants (proxy) associated with these diseases. The candidate variants and haplotypes were identified from proxy and index variants. Finally, SNP function analysis was performed. All GWAS-significant cancer-associated miRNA host gene variants, including MIR4713HG, MIR663AHG, MIR99AHG and MIR4435-2HG, were also significantly associated with obesity. The rs703764 variant was common between cutaneous melanoma and obesity traits in the European population (P ≤ 5E-8). The rs2414098 variant was associated with endometrial cancer (P ≤ 5E-13), and the rs7173595 variant was associated with waist-hip ratio (P ≤ 5E-13) and new CGGCATCA haplotypic located at MIR4713HG was identified in the European population. In addition, the ATCTTGTT haplotype for rs17041868 in MIR4435-2HG was identified to be associated with obesity traits (waist-hip ratio and BMI) in the European population (P ≤ 5E-8). This study found that rs703764 is a common genetic marker between cancer and obesity. The CGGCATCA haplotype is common between endometrial cancer and waist-hip ratio. Also, ATCTTGTT haplotype is associated with obesity traits. These results indicate that the variants and haplotypes of miRNAs host genes play an important role between cancer and obesity in the European population. It is suggested to investigate the effect of these structures in other populations.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Morteza Gholami
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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22
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Daubney ER, D'Urso S, Cuellar-Partida G, Rajbhandari D, Peach E, de Guzman E, McArthur C, Rhodes A, Meyer J, Finfer S, Myburgh J, Cohen J, Schirra HJ, Venkatesh B, Evans DM. A Genome-Wide Association Study of Serum Metabolite Profiles in Septic Shock Patients. Crit Care Explor 2024; 6:e1030. [PMID: 38239409 PMCID: PMC10796137 DOI: 10.1097/cce.0000000000001030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
OBJECTIVES We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology. DESIGN Retrospective multicenter cohort studies of septic shock patients. SETTING All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion. PATIENTS Adult, critically ill, mechanically ventilated patients with septic shock (n = 230) who were a subset of the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock trial (ClinicalTrials.gov number: NCT01448109). INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A genome-wide association study was conducted for a range of serum metabolite levels for participants. Genome-wide significant associations (p ≤ 5 × 10-8) were found for the two major ketone bodies (3-hydroxybutyrate [rs2456680] and acetoacetate [rs2213037] and creatinine (rs6851961). One of these single-nucleotide polymorphisms (SNPs) (rs2213037) was located in the alcohol dehydrogenase cluster of genes, which code for enzymes related to the metabolism of acetoacetate and, therefore, presents a plausible association for this metabolite. None of the three SNPs showed strong associations with risk of sepsis, 28- or 90-day mortality, or Acute Physiology and Chronic Health Evaluation score (a measure of sepsis severity). CONCLUSIONS We suggest that the genetic associations with metabolites may reflect a starvation response rather than processes involved in sepsis pathophysiology. However, our results require further investigation and replication in both healthy and diseased cohorts including those of different ancestry.
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Affiliation(s)
- Emily R Daubney
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Shannon D'Urso
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | | | | | - Elizabeth Peach
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
| | - Erika de Guzman
- Australian Translational Genomics Centre, Queensland University of Technology, Brisbane, QLD, Australia
| | - Colin McArthur
- Department of Critical Care Medicine, Auckland City Hospital, Auckland, New Zealand
| | - Andrew Rhodes
- Department of Adult Critical Care, St George's University Hospitals NHS Foundation Trust and St George's University of London, London, United Kingdom
| | - Jason Meyer
- The George Institute for Global Health, Sydney, NSW, Australia
- Intensive Care Unit, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Simon Finfer
- The George Institute for Global Health, Sydney, NSW, Australia
- School of Public Health, Imperial College London, London, United Kingdom
| | - John Myburgh
- The George Institute for Global Health, Sydney, NSW, Australia
- St George Hospital, Sydney, NSW, Australia
| | - Jeremy Cohen
- Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
- Intensive Care Unit, The Wesley Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Horst Joachim Schirra
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Griffith School of Environment and Science-Chemical Sciences, Griffith University, Brisbane, QLD, Australia
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD, Australia
| | - Balasubramanian Venkatesh
- The George Institute for Global Health, Sydney, NSW, Australia
- Intensive Care Unit, Princess Alexandra Hospital, Brisbane, QLD, Australia
- Intensive Care Unit, The Wesley Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Faculty of Health, University of New South Wales, Sydney, NSW, Australia
| | - David M Evans
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Frazer Institute, University of Queensland, Brisbane, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
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23
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Siu DHW, Lin FPY, Cho D, Lord SJ, Heller GZ, Simes RJ, Lee CK. Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials. JCO Precis Oncol 2024; 8:e2300317. [PMID: 38190581 DOI: 10.1200/po.23.00317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/03/2023] [Accepted: 10/13/2023] [Indexed: 01/10/2024] Open
Abstract
Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.
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Affiliation(s)
- Derrick H W Siu
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
- Department of Medical Oncology, Illawarra Cancer Care Centre, Wollongong, NSW, Australia
| | - Frank P Y Lin
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Doah Cho
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
| | - Sarah J Lord
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
- School of Medicine, University of Notre Dame, Sydney, NSW, Australia
| | - Gillian Z Heller
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
- Mathematics and Statistics, Macquarie University, Macquarie Park, NSW, Australia
| | - R John Simes
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
| | - Chee Khoon Lee
- National Health and Medical Research Council Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia
- Cancer Care Centre, St George Hospital, Kogarah, NSW, Australia
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24
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Trichia E, Koulman A, Stewart ID, Brage S, Griffin SJ, Griffin JL, Khaw K, Langenberg C, Wareham NJ, Imamura F, Forouhi NG. Plasma Metabolites Related to the Consumption of Different Types of Dairy Products and Their Association with New-Onset Type 2 Diabetes: Analyses in the Fenland and EPIC-Norfolk Studies, United Kingdom. Mol Nutr Food Res 2024; 68:e2300154. [PMID: 38054622 PMCID: PMC10909549 DOI: 10.1002/mnfr.202300154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/07/2023] [Indexed: 12/07/2023]
Abstract
SCOPE To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk. METHODS AND RESULTS Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers. CONCLUSION Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.
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Affiliation(s)
- Eirini Trichia
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Albert Koulman
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Isobel D. Stewart
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Soren Brage
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Simon J. Griffin
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | | | - Kay‐Tee Khaw
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Claudia Langenberg
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Nicholas J. Wareham
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Fumiaki Imamura
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Nita G. Forouhi
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
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25
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Moritz L, Schumann A, Pohl M, Köttgen A, Hannibal L, Spiekerkoetter U. A systematic review of metabolomic findings in adult and pediatric renal disease. Clin Biochem 2024; 123:110703. [PMID: 38097032 DOI: 10.1016/j.clinbiochem.2023.110703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 12/03/2023] [Accepted: 12/07/2023] [Indexed: 12/29/2023]
Abstract
Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.
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Affiliation(s)
- Lennart Moritz
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany; Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Anke Schumann
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany; Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Martin Pohl
- Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Luciana Hannibal
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
| | - Ute Spiekerkoetter
- Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
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26
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Edwards D, Best N, Crawford J, Zi L, Shelton C, Fowler A. Using Bayesian Dynamic Borrowing to Maximize the Use of Existing Data: A Case-Study. Ther Innov Regul Sci 2024; 58:1-10. [PMID: 37910271 PMCID: PMC10764450 DOI: 10.1007/s43441-023-00585-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/25/2023] [Indexed: 11/03/2023]
Abstract
Bayesian Dynamic Borrowing (BDB) designs are being increasingly used in clinical drug development. These methods offer a mathematically rigorous and robust approach to increase efficiency and strengthen evidence by integrating existing trial data into a new clinical trial. The regulatory acceptability of BDB is evolving and varies between and within regulatory agencies. This paper describes how BDB can be used to design a new randomised clinical trial including external data to supplement the planned sample size and discusses key considerations related to data re-use and BDB in drug development programs. A case-study illustrating the planning and evaluation of a BDB approach to support registration of a new medicine with the Center for Drug Evaluation in China will be presented. Key steps and considerations for the use of BDB will be discussed and evaluated, including how to decide whether it is appropriate to borrow external data, which external data can be re-used, the weight to put on the external data and how to decide if the new study has successfully demonstrated treatment benefit.
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Affiliation(s)
- Dawn Edwards
- GSK, 980 Great West Road, Brentford, TW8 9GS, Middlesex, UK.
| | - N Best
- GSK, 980 Great West Road, Brentford, TW8 9GS, Middlesex, UK
| | - J Crawford
- GSK, 980 Great West Road, Brentford, TW8 9GS, Middlesex, UK
| | | | | | - A Fowler
- GSK, 980 Great West Road, Brentford, TW8 9GS, Middlesex, UK
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27
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Zhu Y, Wang Q, Dai H, Hou T, Wang T, Zhao Z, Li M, Miao W, Yang J, Lu J, Xu Y, Chen Y, Ning G, Zheng J, Bi Y, Xu M, Wang W. Sex-specific causality of MRI-derived body compositions on glycaemic traits: Mendelian randomization and observational study. Diabetes Obes Metab 2024; 26:373-384. [PMID: 37920887 DOI: 10.1111/dom.15326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 11/04/2023]
Abstract
AIM To investigate the sex-specific causality of body compositions in type 2 diabetes and related glycaemic traits using Mendelian randomization (MR). MATERIALS AND METHODS We leveraged sex-specific summary-level statistics from genome-wide association studies for three adipose deposits adjusted for body mass index and height, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VATadj) and gluteofemoral adipose tissue (GFATadj), measured by MRI (20 038 women; 19 038 men), and fat mass-adjusted appendicular lean mass (ALMadj) (244 730 women; 205 513 men) in the UK Biobank. Sex-specific statistics of type 2 diabetes were from the Diabetes Genetics Replication and Meta-analysis Consortium and those for fasting glucose and insulin were from the Meta-analyses of Glucose and Insulin-related Traits Consortium. Univariable and multivariable MR (MVMR) were performed. We also performed MR analyses of anthropometric traits and genetic association analyses using individual-level data of body composition as validation. RESULTS Univariable MR analysis showed that, in women, higher GFATadj and ALMadj exerted a causally protective effect on type 2 diabetes (GFATadj: odds ratio [OR] 0.59, 95% confidence interval [CI; 0.50, 0.69]; ALMadj: OR 0.84, 95% CI [0.77, 0.91]) and VATadj to be riskier in glycaemic traits. MVMR showed that GFATadj retained a robust effect on type 2 diabetes (OR 0.57, 95% CI [0.42, 0.77]; P = 2.6 × 10-4 ) in women, while it was nominally significant in men (OR 0.58, 95% CI [0.35, 0.96]; P = 3.3 × 10-2 ), after adjustment for ASATadj and VATadj. MR analyses of anthropometric measures and genetic association analyses of glycaemic traits confirmed the results. CONCLUSIONS Body composition has a sex-specific effect on type 2 diabetes, and higher GFATadj has an independent protective effect on type 2 diabetes in both sexes.
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Affiliation(s)
- Yijie Zhu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qi Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huajie Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianzhichao Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Miao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Yang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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28
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Carrasco-Zanini J, Pietzner M, Wheeler E, Kerrison ND, Langenberg C, Wareham NJ. Multi-omic prediction of incident type 2 diabetes. Diabetologia 2024; 67:102-112. [PMID: 37889320 PMCID: PMC10709231 DOI: 10.1007/s00125-023-06027-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
AIMS/HYPOTHESIS The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. METHODS We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c. RESULTS Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period. CONCLUSIONS/INTERPRETATION Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, 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
| | - Maik Pietzner
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, 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, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, 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.
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK.
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Jiang S, Ren Z, Yang Y, Liu Q, Zhou S, Xiao Y. The GPIHBP1-LPL complex and its role in plasma triglyceride metabolism: Insights into chylomicronemia. Biomed Pharmacother 2023; 169:115874. [PMID: 37951027 DOI: 10.1016/j.biopha.2023.115874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/13/2023] Open
Abstract
GPIHBP1 is a protein found in the endothelial cells of capillaries that is anchored by glycosylphosphatidylinositol and binds to high-density lipoproteins. GPIHBP1 attaches to lipoprotein lipase (LPL), subsequently carrying the enzyme and anchoring it to the capillary lumen. Enabling lipid metabolism is essential for the marginalization of lipoproteins alongside capillaries. Studies underscore the significance of GPIHBP1 in transporting, stabilizing, and aiding in the marginalization of LPL. The intricate interplay between GPIHBP1 and LPL has provided novel insights into chylomicronemia in recent years. Mutations hindering the formation or reducing the efficiency of the GPIHBP1-LPL complex are central to the onset of chylomicronemia. This review delves into the structural nuances of the GPIHBP1-LPL interaction, the consequences of mutations in the complex leading to chylomicronemia, and cutting-edge advancements in chylomicronemia treatment.
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Affiliation(s)
- Shali Jiang
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China; Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, PR China
| | - Zhuoqun Ren
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China; Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, PR China
| | - Yutao Yang
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China; Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, PR China
| | - Qiming Liu
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China
| | - Shenghua Zhou
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China
| | - Yichao Xiao
- Department of Cardiovascular Medicine, Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, PR China.
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30
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Bi D, Liu M, Lin J, Liu R. BEATS: Bayesian hybrid design with flexible sample size adaptation for time-to-event endpoints. Stat Med 2023; 42:5708-5722. [PMID: 37858287 DOI: 10.1002/sim.9936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/17/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta-analytic predictive prior (BaSe-MAP) to incorporate time-to-event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.
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Affiliation(s)
- Dehua Bi
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Meizi Liu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Rachael Liu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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31
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Bu SY. Role of Dgat2 in Glucose Uptake and Fatty Acid Metabolism in C2C12 Skeletal Myotubes. J Microbiol Biotechnol 2023; 33:1563-1575. [PMID: 37644753 PMCID: PMC10772559 DOI: 10.4014/jmb.2307.07018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 08/31/2023]
Abstract
Acyl-coenzyme A (CoA):diacylglycerol acyltransferase 2 (DGAT2) catalyzes the last stage of triacylglycerol (TAG) synthesis, a process that forms ester bonds with diacylglycerols (DAG) and fatty acyl-CoA substrates. The enzymatic role of Dgat2 has been studied in various biological species. Still, the full description of how Dgat2 channels fatty acids in skeletal myocytes and the consequence thereof in glucose uptake have yet to be well established. Therefore, this study explored the mediating role of Dgat2 in glucose uptake and fatty acid partitioning under short interfering ribonucleic acid (siRNA)-mediated Dgat2 knockdown conditions. Cells transfected with Dgat2 siRNA downregulated glucose transporter type 4 (Glut4) messenger RNA (mRNA) expression and decreased the cellular uptake of [1-14C]-labeled 2-deoxyglucose up to 24.3% (p < 0.05). Suppression of Dgat2 deteriorated insulininduced Akt phosphorylation. Dgat2 siRNA reduced [1-14C]-labeled oleic acid incorporation into TAG, but increased the level of [1-14C]-labeled free fatty acids at 3 h after initial fatty acid loading. In an experiment of chasing radioisotope-labeled fatty acids, Dgat2 suppression augmented the level of cellular free fatty acids. It decreased the level of re-esterification of free fatty acids to TAG by 67.6% during the chase period, and the remaining pulses of phospholipids and cholesteryl esters were decreased by 34.5% and 61%, respectively. Incorporating labeled fatty acids into beta-oxidation products increased in Dgat2 siRNA transfected cells without gene expression involving fatty acid oxidation. These results indicate that Dgat2 has regulatory function in glucose uptake, possibly through the reaction of TAG with endogenously released or recycled fatty acids.
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Affiliation(s)
- So Young Bu
- Department of Food and Nutrition, College of Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
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Griseti E, Bello AA, Bieth E, Sabbagh B, Iacovoni JS, Bigay J, Laurell H, Čopič A. Molecular mechanisms of perilipin protein function in lipid droplet metabolism. FEBS Lett 2023. [PMID: 38140813 DOI: 10.1002/1873-3468.14792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Perilipins are abundant lipid droplet (LD) proteins present in all metazoans and also in Amoebozoa and fungi. Humans express five perilipins, which share a similar domain organization: an amino-terminal PAT domain and an 11-mer repeat region, which can fold into amphipathic helices that interact with LDs, followed by a structured carboxy-terminal domain. Variations of this organization that arose during vertebrate evolution allow for functional specialization between perilipins in relation to the metabolic needs of different tissues. We discuss how different features of perilipins influence their interaction with LDs and their cellular targeting. PLIN1 and PLIN5 play a direct role in lipolysis by regulating the recruitment of lipases to LDs and LD interaction with mitochondria. Other perilipins, particularly PLIN2, appear to protect LDs from lipolysis, but the molecular mechanism is not clear. PLIN4 stands out with its long repetitive region, whereas PLIN3 is most widely expressed and is used as a nascent LD marker. Finally, we discuss the genetic variability in perilipins in connection with metabolic disease, prominent for PLIN1 and PLIN4, underlying the importance of understanding the molecular function of perilipins.
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Affiliation(s)
- Elena Griseti
- Institut des Maladies Métaboliques et Cardiovasculaires - I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS), France
| | - Abdoul Akim Bello
- Institut de Pharmacologie Moléculaire et Cellulaire - IPMC, Université Côte d'Azur, CNRS, Valbonne, France
| | - Eric Bieth
- Institut des Maladies Métaboliques et Cardiovasculaires - I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS), France
- Departement de Génétique Médicale, Centre Hospitalier Universitaire de Toulouse, France
| | - Bayane Sabbagh
- Centre de Recherche en Biologie Cellulaire de Montpellier - CRBM, Université de Montpellier, CNRS, France
| | - Jason S Iacovoni
- Institut des Maladies Métaboliques et Cardiovasculaires - I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS), France
| | - Joëlle Bigay
- Institut de Pharmacologie Moléculaire et Cellulaire - IPMC, Université Côte d'Azur, CNRS, Valbonne, France
| | - Henrik Laurell
- Institut des Maladies Métaboliques et Cardiovasculaires - I2MC, Université de Toulouse, Inserm, Université Toulouse III - Paul Sabatier (UPS), France
| | - Alenka Čopič
- Centre de Recherche en Biologie Cellulaire de Montpellier - CRBM, Université de Montpellier, CNRS, France
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Baron C, Cherkaoui S, Therrien-Laperriere S, Ilboudo Y, Poujol R, Mehanna P, Garrett ME, Telen MJ, Ashley-Koch AE, Bartolucci P, Rioux JD, Lettre G, Rosiers CD, Ruiz M, Hussin JG. Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results. iScience 2023; 26:108473. [PMID: 38077122 PMCID: PMC10709128 DOI: 10.1016/j.isci.2023.108473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/24/2023] [Accepted: 11/13/2023] [Indexed: 12/20/2023] Open
Abstract
Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.
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Affiliation(s)
- Cantin Baron
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | - Sarah Cherkaoui
- Montreal Heart Institute, Montréal, QC, Canada
- Division of Oncology and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, France
| | | | - Yann Ilboudo
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | | | | | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Marilyn J. Telen
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | | | - Pablo Bartolucci
- Université Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center – UMGGR, Créteil, France
- Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, France
| | - John D. Rioux
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Christine Des Rosiers
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Matthieu Ruiz
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Julie G. Hussin
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
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Biswas S, Hilser JR, Woodward NC, Wang Z, Gukasyan J, Nemet I, Schwartzman WS, Huang P, Han Y, Fouladian Z, Charugundla S, Spencer NJ, Pan C, Tang WW, Lusis AJ, Hazen SL, Hartiala JA, Allayee H. Effect of Genetic and Dietary Perturbation of Glycine Metabolism on Atherosclerosis in Humans and Mice. medRxiv 2023:2023.12.08.23299748. [PMID: 38168321 PMCID: PMC10760269 DOI: 10.1101/2023.12.08.23299748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Objective Epidemiological and genetic studies have reported inverse associations between circulating glycine levels and risk of coronary artery disease (CAD). However, these findings have not been consistently observed in all studies. We sought to evaluate the causal relationship between circulating glycine levels and atherosclerosis using large-scale genetic analyses in humans and dietary supplementation experiments in mice. Methods Serum glycine levels were evaluated for association with prevalent and incident CAD in the UK Biobank. A multi-ancestry genome-wide association study (GWAS) meta-analysis was carried out to identify genetic determinants for circulating glycine levels, which were then used to evaluate the causal relationship between glycine and risk of CAD by Mendelian randomization (MR). A glycine feeding study was carried out with atherosclerosis-prone apolipoprotein E deficient (ApoE-/-) mice to determine the effects of increased circulating glycine levels on amino acid metabolism, metabolic traits, and aortic lesion formation. Results Among 105,718 subjects from the UK Biobank, elevated serum glycine levels were associated with significantly reduced risk of prevalent CAD (Quintile 5 vs. Quintile 1 OR=0.76, 95% CI 0.67-0.87; P<0.0001) and incident CAD (Quintile 5 vs. Quintile 1 HR=0.70, 95% CI 0.65-0.77; P<0.0001) in models adjusted for age, sex, ethnicity, anti-hypertensive and lipid-lowering medications, blood pressure, kidney function, and diabetes. A meta-analysis of 13 GWAS datasets (total n=230,947) identified 61 loci for circulating glycine levels, of which 26 were novel. MR analyses provided modest evidence that genetically elevated glycine levels were causally associated with reduced systolic blood pressure and risk of type 2 diabetes, but did provide evidence for an association with risk of CAD. Furthermore, glycine-supplementation in ApoE-/- mice did not alter cardiometabolic traits, inflammatory biomarkers, or development of atherosclerotic lesions. Conclusions Circulating glycine levels were inversely associated with risk of prevalent and incident CAD in a large population-based cohort. While substantially expanding the genetic architecture of circulating glycine levels, MR analyses and in vivo feeding studies in humans and mice, respectively, did not provide evidence that the clinical association of this amino acid with CAD represents a causal relationship, despite being associated with two correlated risk factors.
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Affiliation(s)
- Subarna Biswas
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - James R. Hilser
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Nicholas C. Woodward
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zeneng Wang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Janet Gukasyan
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Ina Nemet
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
| | - William S. Schwartzman
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Pin Huang
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Yi Han
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zachary Fouladian
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Sarada Charugundla
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Neal J. Spencer
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Calvin Pan
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - W.H. Wilson Tang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Aldons J. Lusis
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Stanley L. Hazen
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Jaana A. Hartiala
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Hooman Allayee
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
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Khan A, Unlu G, Lin P, Liu Y, Kilic E, Kenny TC, Birsoy K, Gamazon ER. GeneMAP: A discovery platform for metabolic gene function. bioRxiv 2023:2023.12.07.570588. [PMID: 38106122 PMCID: PMC10723489 DOI: 10.1101/2023.12.07.570588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Organisms maintain metabolic homeostasis through the combined functions of small molecule transporters and enzymes. While many of the metabolic components have been well-established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Associations Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions, even pinpointing genes that are distant from the variants implicated by GWAS. In particular, our work identified SLC25A48 as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite, betaine. Rare variant testing and polygenic risk score analyses have elucidated choline-relevant phenomic consequences of SLC25A48 dysfunction. Altogether, our study proposes SLC25A48 as a mitochondrial choline transporter and provides a discovery platform for metabolic gene function.
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36
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Yang C, Li Q, Lin Y, Wang Y, Shi H, Xiang H, Zhu J. Diacylglycerol acyltransferase 2 promotes the adipogenesis of intramuscular preadipocytes in goat. Anim Biotechnol 2023; 34:2376-2383. [PMID: 35749715 DOI: 10.1080/10495398.2022.2091586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Diacylglycerol acyltransferase 2 (DGAT2) is the key enzyme that catalyzes the last step of triglyceride synthesis. However, its role in intramuscular fat (IMF) deposition in goat remains unclear. The purpose of this study was to explore the role of DGAT2 in regulating goat IMF deposition. In the present study, the expression of DGAT2 was highest in goat triceps brachii, and highest on the first day after oleic acid induction in goat intramuscular preadipocytes. The overexpression of DGAT2 promoted the accumulation of lipid droplets and triglyceride synthesis, accompanied by the expression upregulation of DGAT1, TIP47, ACC and ACOX1 significantly, and expression downregulation of AGPAT6, LPIN1, LPL, HSL, ATGL and ADRP significantly. In contrast, the silencing of DGAT2 decreased the accumulation of lipid droplets, inhibited the expression of DGAT1, GPAM, ADRP, AGPAT6, LPL, HSL, ATGL, ACC, FASN, ACOX1 significantly, and enhanced that of TIP47 significantly. Overall, these data underscore DGAT2 may play a potentially important role in lipid droplets formation and triglyceride accumulation, so as to maintain intramuscular fat deposition, beyond triglyceride synthesis in goat.
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Affiliation(s)
- Changheng Yang
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
| | - Qi Li
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
| | - Yaqiu Lin
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Ministry of Education, Southwest Minzu University, Chengdu, China
| | - Yong Wang
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Ministry of Education, Southwest Minzu University, Chengdu, China
| | - Hengbo Shi
- College of Animal Science, Zhejiang University, Hangzhou, China
| | - Hua Xiang
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Ministry of Education, Southwest Minzu University, Chengdu, China
| | - Jiangjiang Zhu
- Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization Key Laboratory of Sichuan Province, Southwest Minzu University, Chengdu, China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Ministry of Education, Southwest Minzu University, Chengdu, China
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Kumari RM, Khatri A, Chaudhary R, Choudhary V. Concept of lipid droplet biogenesis. Eur J Cell Biol 2023; 102:151362. [PMID: 37742390 DOI: 10.1016/j.ejcb.2023.151362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/26/2023] Open
Abstract
Lipid droplets (LD) are functionally conserved fat storage organelles found in all cell types. LDs have a unique structure comprising of a hydrophobic core of neutral lipids (fat), triacylglycerol (TAG) and cholesterol esters (CE) surrounded by a phospholipid monolayer. LD surface is decorated by a multitude of proteins and enzymes rendering this compartment functional. Accumulating evidence suggests that LDs originate from discrete ER-subdomains, demarcated by the lipodystrophy protein seipin, however, the mechanisms of which are not well understood. LD biogenesis factors together with biophysical properties of the ER membrane orchestrate spatiotemporal regulation of LD nucleation and growth at specific ER subdomains in response to metabolic cues. Defects in LD formation manifests in several human pathologies, including obesity, lipodystrophy, ectopic fat accumulation, and insulin resistance. Here, we review recent advances in understanding the molecular events during initial stages of eukaryotic LD assembly and discuss the critical role of factors that ensure fidelity of this process.
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Affiliation(s)
- R Mankamna Kumari
- Lipid Metabolism Laboratory, Department of Biotechnology, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India
| | - Amit Khatri
- Lipid Metabolism Laboratory, Department of Biotechnology, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India
| | - Ritika Chaudhary
- Lipid Metabolism Laboratory, Department of Biotechnology, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India
| | - Vineet Choudhary
- Lipid Metabolism Laboratory, Department of Biotechnology, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India.
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Gale RP, Zhang MJ, Lazarus HM. The role of randomized controlled trials, registries, observational databases in evaluating new interventions. Best Pract Res Clin Haematol 2023; 36:101523. [PMID: 38092482 DOI: 10.1016/j.beha.2023.101523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/17/2023] [Indexed: 12/18/2023]
Abstract
Approaches to comparing safety and efficacy of interventions include analyzing data from randomized controlled trials (RCTs), registries and observational databases (ODBs). RCTs are regarded as the gold standard but data from such trials are sometimes unavailable because a disease is uncommon, because the intervention is uncommon, because of structural limitations or because randomization cannot be done for practical or (seemingly) ethical reasons. There are many examples of an unproved intervention being so widely-believed to be effective that clinical trialists and potential subjects decline randomization. Often, when a RCT is finally done the intervention is proved ineffective or even harmful. These situations are termed medical reversals and are not uncommon [1,2]. There is also the dilemma of when seemingly similar RCTs report discordant conclisions Data from high-quality registries, especially ODBs can be used when data from RCTs are unavailable but also have limitations. Biases and confounding co-variates may be unknown, difficult or impossible to identify and/or difficult to adjust for adequately. However, ODBs sometimes have large numbers of diverse subjects and often give answers more useful to clinicians than RCTs. Side-by-side comparisons suggest analyses from high-quality ODBs often give similar conclusions from high quality RCTs. Meta-analyses combining data from RCTs, registries and ODBs are sometimes appropriate. We suggest increased use of registries and ODBs to compare efficacy of interventions.
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Affiliation(s)
- Robert Peter Gale
- Centre for Haematology, Department of Immunology and Inflammation, Imperial College of Science, Technology and Medicine, London, UK.
| | - Mei-Jie Zhang
- Center for International Blood and Marrow Research (CIBMTR), Medical College of Wisconsin, Milwaukee, WI, USA
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McGough SF, Shamas N, Wang J, Jaber M, Swarup B, Blanchet Zumofen MH, Lautié B, Parreira J, Wei MC, Shewade A. Comparative effectiveness between mosunetuzumab monotherapy clinical trial and real-world data in relapsed/refractory follicular lymphoma in third or subsequent lines of systemic therapy. Leuk Lymphoma 2023; 64:2269-2278. [PMID: 37840271 DOI: 10.1080/10428194.2023.2262066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023]
Abstract
A comparison of clinical outcomes in the third or subsequent line (3 L+) of systemic therapy between a real-world data (RWD) external control cohort and a mosunetuzumab single-arm clinical trial cohort is presented. Data for 3 L + patients with relapsed/refractory follicular lymphoma (FL) were obtained from the mosunetuzumab single-arm trial (n = 90) and a US electronic health records database (n = 158), with patients meeting key eligibility criteria from the trial, balanced on pre-specified prognostic factors. Overall response and complete response rates were 80% and 60% in the mosunetuzumab cohort and 75% and 33% in the RWD cohort, odds ratios of 1.23 (95% CI, 0.52-2.93) and 3.18 (95% CI, 1.41-7.17), respectively. Hazard ratios for progression-free survival and overall survival were 0.82 (95% CI, 0.53-1.27) and 0.43 (95% CI, 0.19-0.94). These findings support a clinically meaningful benefit of mosunetuzumab monotherapy as a chemotherapy-free option for the 3 L + FL population.
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Affiliation(s)
| | | | - Jue Wang
- Genentech, Inc., South San Francisco, California, USA
| | | | | | | | | | | | - Michael C Wei
- Genentech, Inc., South San Francisco, California, USA
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40
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Elmaleh-Sachs A, Schwartz JL, Bramante CT, Nicklas JM, Gudzune KA, Jay M. Obesity Management in Adults: A Review. JAMA 2023; 330:2000-2015. [PMID: 38015216 DOI: 10.1001/jama.2023.19897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Importance Obesity affects approximately 42% of US adults and is associated with increased rates of type 2 diabetes, hypertension, cardiovascular disease, sleep disorders, osteoarthritis, and premature death. Observations A body mass index (BMI) of 25 or greater is commonly used to define overweight, and a BMI of 30 or greater to define obesity, with lower thresholds for Asian populations (BMI ≥25-27.5), although use of BMI alone is not recommended to determine individual risk. Individuals with obesity have higher rates of incident cardiovascular disease. In men with a BMI of 30 to 39, cardiovascular event rates are 20.21 per 1000 person-years compared with 13.72 per 1000 person-years in men with a normal BMI. In women with a BMI of 30 to 39.9, cardiovascular event rates are 9.97 per 1000 person-years compared with 6.37 per 1000 person-years in women with a normal BMI. Among people with obesity, 5% to 10% weight loss improves systolic blood pressure by about 3 mm Hg for those with hypertension, and may decrease hemoglobin A1c by 0.6% to 1% for those with type 2 diabetes. Evidence-based obesity treatment includes interventions addressing 5 major categories: behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures. Comprehensive obesity care plans combine appropriate interventions for individual patients. Multicomponent behavioral interventions, ideally consisting of at least 14 sessions in 6 months to promote lifestyle changes, including components such as weight self-monitoring, dietary and physical activity counseling, and problem solving, often produce 5% to 10% weight loss, although weight regain occurs in 25% or more of participants at 2-year follow-up. Effective nutritional approaches focus on reducing total caloric intake and dietary strategies based on patient preferences. Physical activity without calorie reduction typically causes less weight loss (2-3 kg) but is important for weight-loss maintenance. Commonly prescribed medications such as antidepressants (eg, mirtazapine, amitriptyline) and antihyperglycemics such as glyburide or insulin cause weight gain, and clinicians should review and consider alternatives. Antiobesity medications are recommended for nonpregnant patients with obesity or overweight and weight-related comorbidities in conjunction with lifestyle modifications. Six medications are currently approved by the US Food and Drug Administration for long-term use: glucagon-like peptide receptor 1 (GLP-1) agonists (semaglutide and liraglutide only), tirzepatide (a glucose-dependent insulinotropic polypeptide/GLP-1 agonist), phentermine-topiramate, naltrexone-bupropion, and orlistat. Of these, tirzepatide has the greatest effect, with mean weight loss of 21% at 72 weeks. Endoscopic procedures (ie, intragastric balloon and endoscopic sleeve gastroplasty) can attain 10% to 13% weight loss at 6 months. Weight loss from metabolic and bariatric surgeries (ie, laparoscopic sleeve gastrectomy and Roux-en-Y gastric bypass) ranges from 25% to 30% at 12 months. Maintaining long-term weight loss is difficult, and clinical guidelines support the use of long-term antiobesity medications when weight maintenance is inadequate with lifestyle interventions alone. Conclusion and Relevance Obesity affects approximately 42% of adults in the US. Behavioral interventions can attain approximately 5% to 10% weight loss, GLP-1 agonists and glucose-dependent insulinotropic polypeptide/GLP-1 receptor agonists can attain approximately 8% to 21% weight loss, and bariatric surgery can attain approximately 25% to 30% weight loss. Comprehensive, evidence-based obesity treatment combines behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures as appropriate for individual patients.
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Affiliation(s)
- Arielle Elmaleh-Sachs
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- Family Health Centers at NYU Langone, New York, New York
| | - Jessica L Schwartz
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Carolyn T Bramante
- Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis
| | - Jacinda M Nicklas
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora
| | - Kimberly A Gudzune
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Melanie Jay
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- New York Harbor Veteran Affairs, New York, New York
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Zhao J, Zeng J, Zhu C, Li X, Liu D, Zhang J, Li F, Targher G, Fan JG. Genetically predicted plasma levels of amino acids and metabolic dysfunction-associated fatty liver disease risk: a Mendelian randomization study. BMC Med 2023; 21:469. [PMID: 38017422 PMCID: PMC10685523 DOI: 10.1186/s12916-023-03185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 11/21/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Emerging metabolomics-based studies suggested links between amino acid metabolism and metabolic dysfunction-associated fatty liver disease (MAFLD) risk; however, whether there exists an aetiological role of amino acid metabolism in MAFLD development remains unknown. The aim of the present study was to assess the causal relationship between circulating levels of amino acids and MAFLD risk. METHODS We conducted a two-sample Mendelian randomization (MR) analysis using summary-level data from genome-wide association studies (GWAS) to evaluate the causal relationship between genetically predicted circulating levels of amino acids and the risk of MAFLD. In the discovery MR analysis, we used data from the largest MAFLD GWAS (8434 cases and 770,180 controls), while in the replication MR analysis, we used data from a GWAS on MAFLD (1483 cases and 17,781 controls) where MAFLD cases were diagnosed using liver biopsy. We used Wald ratios or inverse variance-weighted (IVW) methods in the MR main analysis and weighted median and MR-Egger regression analyses in sensitivity analyses. Furthermore, we performed a conservative MR analysis by restricting genetic instruments to those directly involved in amino acid metabolism pathways. RESULTS We found that genetically predicted higher alanine (OR = 1.43, 95% CI 1.13-1.81) and lower glutamine (OR = 0.83, 95% CI 0.73-0.96) levels were associated with a higher risk of developing MAFLD based on the results from the MR main and conservative analysis. The results from MR sensitivity analyses and complementary analysis using liver proton density fat fraction as a continuous outcome proxying for MAFLD supported the main findings. CONCLUSIONS Novel causal metabolites related to MAFLD development were uncovered through MR analysis, suggesting future potential for evaluating these metabolites as targets for MAFLD prevention or treatment.
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Affiliation(s)
- Jian Zhao
- The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China.
- Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
| | - Jing Zeng
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China
| | - Cairong Zhu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuechao Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Dong Liu
- The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China
| | - Jun Zhang
- The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China
- Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Li
- The Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China
- Department of Maternal and Child Health, School of Public Health, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatric & Child Primary Care, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- Metabolic Diseases Research Unit, IRCCS Ospedale Sacro Cuore - Don Calabria, Negrar di Valpolicella, Italy
| | - Jian-Gao Fan
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665, Kongjiang Road, Yangpu District, Shanghai, 200092, China.
- Shanghai Key Lab of Pediatric Gastroenterology and Nutrition, Shanghai, China.
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Zhang T, Cao Y, Zhao J, Yao J, Liu G. Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke. J Transl Med 2023; 21:822. [PMID: 37978512 PMCID: PMC10655369 DOI: 10.1186/s12967-023-04677-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke. METHODS Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin. RESULTS After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes. CONCLUSIONS The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.
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Affiliation(s)
- Tianlong Zhang
- Department of Critical Medicine, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yina Cao
- Department of Neurology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Jiali Yao
- Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China.
| | - Gang Liu
- Department of Infection Control, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
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Zhang Y, Qi Q. Can healthy lifestyle offset the genetic predisposition to obesity to prevent coronary heart disease? Am J Clin Nutr 2023; 118:841-842. [PMID: 37923496 DOI: 10.1016/j.ajcnut.2023.08.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 11/07/2023] Open
Affiliation(s)
- Yanbo Zhang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States.
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Wang M, Au Yeung SL, Luo S, Jang H, Ho HS, Sharp SJ, Wijndaele K, Brage S, Wareham NJ, Kim Y. Adherence to a healthy lifestyle, genetic susceptibility to abdominal obesity, cardiometabolic risk markers, and risk of coronary heart disease. Am J Clin Nutr 2023; 118:911-920. [PMID: 37923500 DOI: 10.1016/j.ajcnut.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/20/2023] [Accepted: 08/01/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Little is known about whether the association between genetic susceptibility to high waist-to-hip ratio (WHR), a measure of abdominal obesity, and incident coronary heart disease (CHD) is modified by adherence to a healthy lifestyle. OBJECTIVES To explore the interplay of genetic susceptibility to high WHR and adherence to a healthy lifestyle on incident CHD. METHODS This study included 282,316 white British individuals from the UK Biobank study. Genetic risk for high WHR was estimated in the form of weighted polygenic risk scores (PRSs), calculated based on 156 single-nucleotide polymorphisms. Lifestyle scores were calculated based on 5 healthy lifestyle factors: regular physical activity, no current smoking, a healthy diet, <3 times/wk of alcohol consumption and 7-9 h/d of sleep. Incident CHD (n = 11,635) was accrued over a median 13.8 y of follow-up, and 12 individual cardiovascular disease risk markers assessed at baseline. RESULTS Adhering to a favorable lifestyle (4-5 healthy factors) was associated with a 25% (hazard ratio: 0.75, 95% confidence interval: 0.70, 0.81) lower hazard of CHD compared with an unfavorable lifestyle (0-1 factor), independent of PRS for high WHR. Estimated 12-y absolute risk of CHD was lower for a favorable lifestyle at high genetic risk (1.73%) and medium genetic risk (1.67%) than for an unfavorable lifestyle at low genetic risk (2.08%). Adhering to a favorable lifestyle was associated with healthier levels of cardiovascular disease risk markers (except random glucose and high-density lipoprotein), independent of PRS for high WHR. CONCLUSIONS Individuals who have high or medium genetic risk of abdominal obesity but adhere to a healthy lifestyle may have a lower risk of developing CHD, compared with those who have low genetic risk and an unhealthy lifestyle. Future clinical trials of lifestyle modification could be implemented for individuals at high genetic risk of abdominal obesity for the primary prevention of CHD events.
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Affiliation(s)
- Mengyao Wang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Shiu Lun Au Yeung
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Haeyoon Jang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Hin Sheung Ho
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Katrien Wijndaele
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom.
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Pray R, Riskin S. The History and Faults of the Body Mass Index and Where to Look Next: A Literature Review. Cureus 2023; 15:e48230. [PMID: 38050494 PMCID: PMC10693914 DOI: 10.7759/cureus.48230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/03/2023] [Indexed: 12/06/2023] Open
Abstract
Body mass index (BMI) is an anthropometric index that is commonly used in the medical setting and is a factor in assessing various disease risks but its origins are unknown by many. More importantly, BMI does not properly assess body fat percentage and muscle mass or distinguish abdominal fat from gluteofemoral fat, which is important to note because abdominal fat is associated with insulin resistance, metabolic disease, and cardiovascular complications. Using a less accurate index to assess the relationship between weight and disease risk is conceptually invalid because the use of BMI ultimately trickles into patient treatment, preventive medicine, and overall health outcomes. Several different anthropometric indices that more accurately assess abdominal adiposity through the incorporation of waist circumference exist and have been extensively studied, such as waist-to-hip ratio, waist-to-height ratio, and a body shape index. It is important that we consider replacing BMI's usage in the healthcare setting with a different anthropometric index: one that considers height, sex, and race differences, accounts for abdominal adiposity, and more accurately predicts the relationship between obesity, mortality, and diseases such as cardiovascular disease, hypertension, insulin resistance, and diabetes.
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Affiliation(s)
- Rachel Pray
- Medicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Clearwater, USA
| | - Suzanne Riskin
- Internal Medicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Clearwater, USA
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Szczerbinski L, Florez JC. Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future. Lancet Diabetes Endocrinol 2023; 11:861-878. [PMID: 37804854 DOI: 10.1016/s2213-8587(23)00232-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 10/09/2023]
Abstract
Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.
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Affiliation(s)
- Lukasz Szczerbinski
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jose C Florez
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Skarbinski J, Fischer H, Hong V, Liu L, Yau VM, Incerti D, Qian L, Ackerson BK, Amsden LB, Shaw SF, Tartof SY. Real-World Evidence to Supplement Randomized Clinical Trials: Tocilizumab for Severe COVID-19 Pneumonia vs. a Cohort Receiving Standard of Care. Clin Pharmacol Ther 2023; 114:1073-1081. [PMID: 37571812 DOI: 10.1002/cpt.3020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Randomized controlled trials (RCTs) remain the gold standard for evaluating treatment efficacy, but real-world evidence can supplement RCT results. Tocilizumab was not found to reduce 28-day mortality in a phase III, double-blind, placebo-controlled trial (COVACTA) among hospitalized patients with severe coronavirus disease 2019 (COVID-19) pneumonia. We created a real-world external comparator arm mirroring the COVACTA trial to confirm findings and assess the feasibility of using an external comparator arm to supplement an RCT. Eligible COVACTA participants in both the tocilizumab treatment and placebo arms were matched 1:1 using propensity score matching to persons without tocilizumab exposure in an external comparator arm. Adjusted Cox proportional hazard models estimated differences in 28-day mortality comparing COVACTA participants to matched external comparator arm participants. Patients in the COVACTA tocilizumab treatment arm had a similar risk of death compared with patients in the external comparator arm (hazard ratio (HR): 1.09, 95% confidence interval (CI): 0.64-1.84) with similar estimated 28-day mortality in the COVACTA tocilizumab treatment arm compared with the external comparator arm (18%, 95% CI: 13-24 vs. 19%, 95% CI: 13-24, P > 0.9). COVACTA placebo treatment arm participants had a similar risk of mortality (adjusted HR: 0.69, 95% CI: 0.32-1.46) compared with the external comparator arm. Using an external comparator arm has the potential to supplement RCT data and support results of primary RCT analyses.
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Affiliation(s)
- Jacek Skarbinski
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- Department of Infectious Diseases, Oakland Medical Center, Kaiser Permanente Northern California, Oakland, California, USA
| | - Heidi Fischer
- Department of Research & Evaluation, Kaiser Permanente Southern California, California, Pasadena, USA
| | - Vennis Hong
- Department of Research & Evaluation, Kaiser Permanente Southern California, California, Pasadena, USA
| | - Liyan Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Vincent M Yau
- Genentech, a Member of the Roche Group, South San Francisco, California, USA
| | - Devin Incerti
- Genentech, a Member of the Roche Group, South San Francisco, California, USA
| | - Lei Qian
- Department of Research & Evaluation, Kaiser Permanente Southern California, California, Pasadena, USA
| | - Bradley K Ackerson
- Southern California Permanente Medical Group, Harbor City, California, USA
| | - Laura B Amsden
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Sally F Shaw
- Department of Research & Evaluation, Kaiser Permanente Southern California, California, Pasadena, USA
| | - Sara Y Tartof
- Department of Research & Evaluation, Kaiser Permanente Southern California, California, Pasadena, USA
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA
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Ottensmann L, Tabassum R, Ruotsalainen SE, Gerl MJ, Klose C, Widén E, Simons K, Ripatti S, Pirinen M. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun 2023; 14:6934. [PMID: 37907536 PMCID: PMC10618167 DOI: 10.1038/s41467-023-42532-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023] Open
Abstract
The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.
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Affiliation(s)
- Linda Ottensmann
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Rubina Tabassum
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | | | - Elisabeth Widén
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Samuli Ripatti
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland.
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
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Huang B, DePaolo J, Judy RL, Shakt G, Witschey WR, Levin MG, Gershuni VM. Relationships between body fat distribution and metabolic syndrome traits and outcomes: A mendelian randomization study. PLoS One 2023; 18:e0293017. [PMID: 37883456 PMCID: PMC10602264 DOI: 10.1371/journal.pone.0293017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/04/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Obesity is a complex, multifactorial disease associated with substantial morbidity and mortality worldwide. Although it is frequently assessed using BMI, many epidemiological studies have shown links between body fat distribution and obesity-related outcomes. This study examined the relationships between body fat distribution and metabolic syndrome traits using Mendelian Randomization (MR). METHODS/FINDINGS Genetic variants associated with visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT), as well as their relative ratios, were identified from a genome wide association study (GWAS) performed with the United Kingdom BioBank. GWAS summary statistics for traits and outcomes related to metabolic syndrome were obtained from the IEU Open GWAS Project. Two-sample MR and BMI-controlled multivariable MR (MVMR) were performed to examine relationships between each body fat measure and ratio with the outcomes. Increases in absolute GFAT were associated with a protective cardiometabolic profile, including lower low density lipoprotein cholesterol (β: -0.19, [95% CI: -0.28, -0.10], p < 0.001), higher high density lipoprotein cholesterol (β: 0.23, [95% CI: 0.03, 0.43], p = 0.025), lower triglycerides (β: -0.28, [95% CI: -0.45, -0.10], p = 0.0021), and decreased systolic (β: -1.65, [95% CI: -2.69, -0.61], p = 0.0019) and diastolic blood pressures (β: -0.95, [95% CI: -1.65, -0.25], p = 0.0075). These relationships were largely maintained in BMI-controlled MVMR analyses. Decreases in relative GFAT were linked with a worse cardiometabolic profile, with higher levels of detrimental lipids and increases in systolic and diastolic blood pressures. CONCLUSION A MR analysis of ASAT, GFAT, and VAT depots and their relative ratios with metabolic syndrome related traits and outcomes revealed that increased absolute and relative GFAT were associated with a favorable cardiometabolic profile independently of BMI. These associations highlight the importance of body fat distribution in obesity and more precise means to categorize obesity beyond BMI.
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Affiliation(s)
- Brian Huang
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - John DePaolo
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Renae L. Judy
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Gabrielle Shakt
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Walter R. Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Michael G. Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States of America
| | - Victoria M. Gershuni
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
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Björnson E, Adiels M, Taskinen MR, Burgess S, Rawshani A, Borén J, Packard CJ. Triglyceride-rich lipoprotein remnants, low-density lipoproteins, and risk of coronary heart disease: a UK Biobank study. Eur Heart J 2023; 44:4186-4195. [PMID: 37358553 PMCID: PMC10576615 DOI: 10.1093/eurheartj/ehad337] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 03/04/2023] [Accepted: 05/16/2023] [Indexed: 06/27/2023] Open
Abstract
AIMS The strength of the relationship of triglyceride-rich lipoproteins (TRL) with risk of coronary heart disease (CHD) compared with low-density lipoprotein (LDL) is yet to be resolved. METHODS AND RESULTS Single-nucleotide polymorphisms (SNPs) associated with TRL/remnant cholesterol (TRL/remnant-C) and LDL cholesterol (LDL-C) were identified in the UK Biobank population. In a multivariable Mendelian randomization analysis, TRL/remnant-C was strongly and independently associated with CHD in a model adjusted for apolipoprotein B (apoB). Likewise, in a multivariable model, TRL/remnant-C and LDL-C also exhibited independent associations with CHD with odds ratios per 1 mmol/L higher cholesterol of 2.59 [95% confidence interval (CI): 1.99-3.36] and 1.37 [95% CI: 1.27-1.48], respectively. To examine the per-particle atherogenicity of TRL/remnants and LDL, SNPs were categorized into two clusters with differing effects on TRL/remnant-C and LDL-C. Cluster 1 contained SNPs in genes related to receptor-mediated lipoprotein removal that affected LDL-C more than TRL/remnant-C, whereas cluster 2 contained SNPs in genes related to lipolysis that had a much greater effect on TRL/remnant-C. The CHD odds ratio per standard deviation (Sd) higher apoB for cluster 2 (with the higher TRL/remnant to LDL ratio) was 1.76 (95% CI: 1.58-1.96), which was significantly greater than the CHD odds ratio per Sd higher apoB in cluster 1 [1.33 (95% CI: 1.26-1.40)]. A concordant result was obtained by using polygenic scores for each cluster to relate apoB to CHD risk. CONCLUSION Distinct SNP clusters appear to impact differentially on remnant particles and LDL. Our findings are consistent with TRL/remnants having a substantially greater atherogenicity per particle than LDL.
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Affiliation(s)
- Elias Björnson
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
| | - Martin Adiels
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
- School of Public Health and Community Medicine, University of Gothenburg, Medicinaregatan 18A, 41390 Gothenburg, Sweden
| | - Marja-Riitta Taskinen
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Biomedicum 1, Haartmanninkatu 8, 00290 Helsinki, Finland
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Robinson Way, Cambridge, CB2 0SR, UK
- Cardiovascular Epidemiology Unit, Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Papworth Road, Cambridge, CB2 0BD, UK
| | - Aidin Rawshani
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, 41345 Gothenburg, Sweden
| | - Chris J Packard
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, G12 8TA Glasgow, UK
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