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Thanaj M, Basty N, Whitcher B, Sorokin EP, Liu Y, Srinivasan R, Cule M, Thomas EL, Bell JD. Precision MRI phenotyping of muscle volume and quality at a population scale. Front Physiol 2024; 15:1288657. [PMID: 38370011 PMCID: PMC10869600 DOI: 10.3389/fphys.2024.1288657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/09/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction: Magnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle volume measurements: total muscle volume, regional measurements, measurements of muscle quality: intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative and associate with relevant health conditions such as dynapenia and frailty. Methods: We have measured image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, derived from the neck-to-knee Dixon images in 44,520 UK Biobank participants. We further segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We defined dynapenia based on grip strength below sex-specific cut-off points and frailty based on five criteria (weight loss, exhaustion, grip strength, low physical activity and slow walking pace). We used logistic regression to investigate the association between muscle volume and quality measurements and dynapenia and frailty. Results: Muscle volumes were significantly higher in male compared with female participants, even after correcting for height while, IMAT (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants. From the overall cohort, 7.6% (N = 3,261) were identified with dynapenia, and 1.1% (N = 455) with frailty. Dynapenia and frailty were positively associated with age and negatively associated with physical activity levels. Additionally, reduced muscle volume and quality measurements were associated with both dynapenia and frailty. In dynapenia, muscle volume IDPs were most informative, particularly total muscle exhibiting odds ratios (OR) of 0.392, while for frailty, muscle quality was found to be most informative, in particular thigh IMAT volume indexed to height squared (OR = 1.396), both with p-values below the Bonferroni-corrected threshold (p < 8.8 × 10 - 5 ). Conclusion: Our fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. For dynapenia, muscle volumes particularly those including greater body coverage such as total muscle are the most informative, whilst, for frailty, markers of muscle quality were the most informative IDPs. These results suggest that different measurements may have varying diagnostic values for different health conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | | | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, CA, United States
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
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Bull CJ, Hazelwood E, Legge DN, Corbin LJ, Richardson TG, Lee M, Yarmolinsky J, Smith-Byrne K, Hughes DA, Johansson M, Peters U, Berndt SI, Brenner H, Burnett-Hartman A, Cheng I, Kweon SS, Le Marchand L, Li L, Newcomb PA, Pearlman R, McConnachie A, Welsh P, Taylor R, Lean MEJ, Sattar N, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Impact of weight loss on cancer-related proteins in serum: results from a cluster randomised controlled trial of individuals with type 2 diabetes. EBioMedicine 2024; 100:104977. [PMID: 38290287 PMCID: PMC10844806 DOI: 10.1016/j.ebiom.2024.104977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Type 2 diabetes is associated with higher risk of several cancer types. However, the biological intermediates driving this relationship are not fully understood. As novel interventions for treating and managing type 2 diabetes become increasingly available, whether they also disrupt the pathways leading to increased cancer risk is currently unknown. We investigated the effect of a type 2 diabetes intervention, in the form of intentional weight loss, on circulating proteins associated with cancer risk to gain insight into potential mechanisms linking type 2 diabetes and adiposity with cancer development. METHODS Fasting serum samples from participants with diabetes enrolled in the Diabetes Remission Clinical Trial (DiRECT) receiving the Counterweight-Plus weight-loss programme (intervention, N = 117, mean weight-loss 10 kg, 46% diabetes remission) or best-practice care by guidelines (control, N = 143, mean weight-loss 1 kg, 4% diabetes remission) were subject to proteomic analysis using the Olink Oncology-II platform (48% of participants were female; 52% male). To identify proteins which may be altered by the weight-loss intervention, the difference in protein levels between groups at baseline and 1 year was examined using linear regression. Mendelian randomization (MR) was performed to extend these results to evaluate cancer risk and elucidate possible biological mechanisms linking type 2 diabetes and cancer development. MR analyses were conducted using independent datasets, including large cancer meta-analyses, UK Biobank, and FinnGen, to estimate potential causal relationships between proteins modified during intentional weight loss and the risk of colorectal, breast, endometrial, gallbladder, liver, and pancreatic cancers. FINDINGS Nine proteins were modified by the intervention: glycoprotein Nmb; furin; Wnt inhibitory factor 1; toll-like receptor 3; pancreatic prohormone; erb-b2 receptor tyrosine kinase 2; hepatocyte growth factor; endothelial cell specific molecule 1 and Ret proto-oncogene (Holm corrected P-value <0.05). Mendelian randomization analyses indicated a causal relationship between predicted circulating furin and glycoprotein Nmb on breast cancer risk (odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.67-0.99, P-value = 0.03; and OR = 0.88, 95% CI = 0.78-0.99, P-value = 0.04 respectively), though these results were not supported in sensitivity analyses examining violations of MR assumptions. INTERPRETATION Intentional weight loss among individuals with recently diagnosed diabetes may modify levels of cancer-related proteins in serum. Further evaluation of the proteins identified in this analysis could reveal molecular pathways that mediate the effect of adiposity and type 2 diabetes on cancer risk. FUNDING The main sources of funding for this work were Diabetes UK, Cancer Research UK, World Cancer Research Fund, and Wellcome.
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Affiliation(s)
- Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Emma Hazelwood
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Danny N Legge
- School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Lee
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mattias Johansson
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea; Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; School of Public Health, University of Washington, Seattle, WA, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Alex McConnachie
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Roy Taylor
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Mike E J Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Neil Murphy
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK.
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Xu FQ, Xu QY, Zhu ZJ, Jin L, Ye TW, Du CF, Gao ZY, Huang XK, Zhang Z, Jin LM, Yao WF. Visceral and ectopic fat are more predictively associated with primary liver cancer than overall obesity from genetic sights: A Mendelian randomization study. Int J Cancer 2024; 154:530-537. [PMID: 37815300 DOI: 10.1002/ijc.34751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Several observational studies have reported an association between obesity and primary liver cancer (PLC), while the causality behind this association and the comparison of the risk effects of different obesity indicators on PLC remain unclear. In this study, we performed two-sample Mendelian randomization (MR) analyses to assess the associations of genetically determined liver fat, visceral adipose tissue (VAT), and body mass index (BMI) with the risk of PLC. The summary statistics of exposures were obtained from two genome-wide association studies (GWASs) based on the UK Biobank (UKB) imaging cohort and the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. GWAS summary statistics for PLC were obtained from FinnGen consortium R7 release data, including 304 PLC cases and 218 488 controls. Inverse-variance weighted (IVW) was used as the primary analysis, and a series of sensitivity analyses were performed to further verify the robustness of these findings. IVW analysis highlighted a significant association of genetically determined liver fat (OR per SD increase: 7.14; 95% CI: 5.10-9.99; P = 2.35E-30) and VAT (OR per SD increase: 5.70; 95% CI: 1.32-24.72; P = .020) with PLC but not of BMI with PLC. The findings were further confirmed by a series of MR methods. No evidence of horizontal pleiotropy between these associations existed. Our study suggested that genetically determined liver fat and VAT rather than BMI were associated with an increased risk of PLC, which suggested that visceral fat distribution is more predictive of the clinical risk of PLC than common in vitro measures.
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Affiliation(s)
- Fei-Qi Xu
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qing-Yun Xu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, China
| | - Zhang-Ji Zhu
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lei Jin
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Tai-Wei Ye
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cheng-Fei Du
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhen-Yu Gao
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiao-Kun Huang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhe Zhang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
- The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Li-Ming Jin
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Wei-Feng Yao
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Huang J, Lin Z, Lin J, Xie S, Xia S, Chen G, Zheng Z, Xu Z, Liu F, Wu H, Li S. Causal role of lipid metabolism in pulmonary alveolar proteinosis: an observational and mendelian randomisation study. Thorax 2024; 79:135-143. [PMID: 38124156 DOI: 10.1136/thorax-2023-220789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Pulmonary alveolar proteinosis (PAP) is a rare interstitial lung disease characterised by the accumulation of lipoprotein material in the alveoli. Although dyslipidaemia is a prominet feature, the causal effect of lipid traits on PAP remains unclear. This study aimed to explore the role of lipid traits in PAP and evaluate the potential of lipid-lowering drug targets in PAP. METHODS Clinical outcomes, lipid profiles and lung function tests were analysed in a clinical cohort of diagnosed PAP patients and propensity score-matched healthy controls. Genome-wide association study data on PAP, lipid metabolism, blood cells and variants of genes encoding potential lipid-lowering drug targets were obtained for Mendelian randomisation (MR) and mediation analyses. FINDINGS Observational results showed that higher levels of total cholesterol (TC), triglycerides and low-density lipoprotein (LDL) were associated with increased risks of PAP. Higher levels of TC and LDL were also associated with worse PAP severity. In MR analysis, elevated LDL was associated with an increased risk of PAP (OR: 4.32, 95% CI: 1.63 to 11.61, p=0.018). Elevated monocytes were associated with a lower risk of PAP (OR 0.34, 95% CI: 0.18 to 0.66, p=0.002) and mediated the risk impact of LDL on PAP. Genetic mimicry of PCSK9 inhibition was associated with a reduced risk of PAP (OR 0.03, p=0.007). INTERPRETATION Our results support the crucial role of lipid and metabolism-related traits in PAP risk, emphasising the monocyte-mediated, causal effect of elevated LDL in PAP genetics. PCSK9 mediates the development of PAP by raising LDL. These finding provide evidence for lipid-related mechanisms and promising lipid-lowering drug target for PAP.
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Affiliation(s)
- Junfeng Huang
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zikai Lin
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jinsheng Lin
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shuojia Xie
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shixin Xia
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Gengjia Chen
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Ziwen Zheng
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhe Xu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Fangcheng Liu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Hongkai Wu
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shiyue Li
- Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Guangzhou Medical University, Guangzhou, Guangdong, China
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Shu Y, Yang B, Liu X, Xu M, Deng C, Wu H. Causal effects from inflammatory bowel disease on liver function and disease: a two-sample Mendelian randomization study. Front Med (Lausanne) 2024; 10:1320842. [PMID: 38298515 PMCID: PMC10827874 DOI: 10.3389/fmed.2023.1320842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024] Open
Abstract
Background Accumulating evidence has shown that patients with inflammatory bowel disease (IBD) have liver function abnormalities and are susceptible to liver diseases. However, the existence of a causal relationship between IBD and liver function or disease remains unclear. Methods A two-sample Mendelian randomization (MR) analysis was performed using genetic associations from publicly available genome-wide association studies (GWAS). These associations encompass ulcerative colitis (UC), Crohn's disease (CD), liver function traits, and liver disease phenotypes. The liver function traits comprised hepatic biochemistries, percent liver fat, and liver iron content from the UK Biobank. Furthermore, the liver disease phenotypes included cholelithiasis, non-alcoholic fatty liver disease (NAFLD), primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC) in cohorts of European ancestry. The primary estimation used the inverse-variance weighted method, with GWAS of C-reactive protein (CRP) in the UK Biobank serving as a positive control outcome. Results Genetically predicted UC is causally associated with decreased levels of albumin (ALB) and liver iron content, while genetically predicted CD is causally associated with increased levels of alkaline phosphatase (ALP). Moreover, genetically predicted UC or CD increases the risk of PSC, and CD increases the risk of PBC. Neither UC nor CD causally increases the risk of cholelithiasis and NAFLD. Conclusion UC affects the levels of ALB and liver iron content, while CD affects the levels of ALP. Both UC and CD increase the risk of PSC, and CD increases the risk of PBC.
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Affiliation(s)
- Yufeng Shu
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Bocheng Yang
- Division of Plastic Surgery, Zhongshan Hospital Xiamen University, Xiamen, China
| | - Xuanyou Liu
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Meihua Xu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, China
| | - Chao Deng
- Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
| | - Hao Wu
- Department of Gastroenterology, Xiangya Hospital, Central South University, Changsha, China
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Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, Elliott P, Matthews PM, Lyons RA, Whetton AD, Lucassen A, Hurles ME, Chapman M, Roddam AW, Fitzpatrick NK, Hansell AL, Hardy R, Marioni RE, O’Donnell VB, Williams J, Lindgren CM, Effingham M, Sellors J, Danesh J, Collins R. Prospective study design and data analysis in UK Biobank. Sci Transl Med 2024; 16:eadf4428. [PMID: 38198570 PMCID: PMC11127744 DOI: 10.1126/scitranslmed.adf4428] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.
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Affiliation(s)
- Naomi E Allen
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ben Lacey
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Scotland
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
- Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Chemical Radiation Threats and Hazards, Imperial College London, UK
| | - Paul M Matthews
- UK Dementia Research Centre Institute and Department of Brain Sciences, Imperial College London, London, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, Wales
| | - Anthony D Whetton
- Veterinary Health Innovation Engine, University of Surrey, Guildford, UK
| | - Anneke Lucassen
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Southampton University, Southampton, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
| | | | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, Wales
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | | | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Rory Collins
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Xiang X, Wei Y, Zhao K. Reassessing the causal relationship between liver diseases and cardiovascular outcomes. J Hepatol 2024; 80:e20-e22. [PMID: 37586646 DOI: 10.1016/j.jhep.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023]
Affiliation(s)
- Xiqiao Xiang
- Department of PET-CT Imaging Centery, Shanghai Jiaotong University Affiliated Sixth People Hospital South Campus, Shanghai, 201499, China
| | - Yuanhao Wei
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Kun Zhao
- Department of PET-CT Imaging Centery, Shanghai Jiaotong University Affiliated Sixth People Hospital South Campus, Shanghai, 201499, China.
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59
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Zhou Y, Ni Y, Wang Z, Prud'homme GJ, Wang Q. Causal effects of non-alcoholic fatty liver disease on osteoporosis: a Mendelian randomization study. Front Endocrinol (Lausanne) 2023; 14:1283739. [PMID: 38149094 PMCID: PMC10749958 DOI: 10.3389/fendo.2023.1283739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Background Osteoporosis (OP) is a systemic skeletal disease characterized by compromised bone strength leading to an increased risk of fracture. There is an ongoing debate on whether non-alcoholic fatty liver disease (NAFLD) is an active contributor or an innocent bystander in the pathogenesis of OP. The aim of this study was to assess the causal association between NAFLD and OP. Methods We performed two-sample Mendelian randomization (MR) analyses to investigate the causal association between genetically predicted NAFLD [i.e., imaging-based liver fat content (LFC), chronically elevated serum alanine aminotransferase (cALT) and biopsy-confirmed NAFLD] and risk of OP. The inverse variant weighted method was performed as main analysis to obtain the causal estimates. Results Imaging-based LFC and biopsy-confirmed NAFLD demonstrated a suggestive causal association with OP ([odds ratio (OR): 1.003, 95% CI: 1.001-1.004, P < 0.001; OR: 1.001, 95% CI: 1.000-1.002, P = 0.031]). The association between cALT and OP showed a similar direction, but was not statistically significant (OR: 1.001, 95% CI: 1.000-1.002, P = 0.079). Repeated analyses after exclusion of genes associated with confounding factors showed consistent results. Sensitivity analysis indicated low heterogeneity, high reliability and low pleiotropy of the causal estimates. Conclusion The two-sample MR analyses suggest a causal association between genetically predicted NAFLD and OP.
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Affiliation(s)
- Yue Zhou
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Yunzhi Ni
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Zhihong Wang
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
| | - Gerald J Prud'homme
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Qinghua Wang
- Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical School, Fudan University, Shanghai, China
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Vanneste M, Hoskens H, Goovaerts S, Matthews H, Aponte JD, Cole J, Shriver M, Marazita ML, Weinberg SM, Walsh S, Richmond S, Klein OD, Spritz RA, Peeters H, Hallgrímsson B, Claes P. Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570544. [PMID: 38106188 PMCID: PMC10723447 DOI: 10.1101/2023.12.07.570544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Human craniofacial shape is highly variable yet highly heritable with genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the normal population. We compared three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores revealed a polygenic basis for normal facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples showed craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing new insights into the genetic intersection of complex traits and Mendelian disorders.
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Affiliation(s)
| | - Hanne Hoskens
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Seppe Goovaerts
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Harold Matthews
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Jose D Aponte
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joanne Cole
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Mark Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Ophir D Klein
- Department of Orofacial Sciences and Program in Craniofacial Biology, University of California, San Francisco, CA, 94143, USA
- Department of Pediatrics, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Richard A Spritz
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Benedikt Hallgrímsson
- Department of Cell Biology & Anatomy, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
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Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
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Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
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Shen M, Jiang L, Liu H, Dai H, Jiang H, Qian Y, Wang Z, Zheng S, Chen H, Yang T, Fu Q, Xu K. Interaction between the GCKR rs1260326 variant and serum HDL cholesterol contributes to HOMA-β and ISI Matusda in the middle-aged T2D individuals. J Hum Genet 2023; 68:835-842. [PMID: 37648893 DOI: 10.1038/s10038-023-01191-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/13/2023] [Accepted: 08/22/2023] [Indexed: 09/01/2023]
Abstract
This study aims to investigate the correlations between islet function/ insulin resistance and serum lipid levels, as well as to assess whether the strength of such correlations is affected by the GCKR rs1260326 variant in healthy and T2D individuals. We performed an oral glucose tolerance test (OGTT) on 4889 middle-aged adults, including 3135 healthy and 1754 T2D individuals from the REACTION population study in the Nanjing region. We also measured their serum lipid levels and genotyped for rs1260326. We found that serum high-density lipoprotein (HDL) cholesterol and triglyceride (TG) levels were independently correlated with indexes of islet function (HOMA-β and IGI [insulinogenic index]) and insulin resistance (HOMO-IR and ISIMatsuda) in both healthy and T2D individuals. The correlations were significantly decreased in T2D individuals, with significant heterogeneities compared to healthy controls (I2 > 75%, Phet < 0.05). Although no correlation was observed between serum total cholesterol (TC) level and islet function/ insulin resistance in healthy controls, significant correlations were found in T2D individuals, with significant heterogeneity to healthy controls in the correlation with ISIMatsuda(I2 = 85.3%, Phet = 0.009). Furthermore, we found significant interactions of the GCKR rs1260326 variant for the correlations between serum HDL cholesterol and HOMA-β/ISIMatsuda in T2D subjects (P = 0.015 and 0.038, respectively). These findings illustrate that distinct correlations between serum lipid levels and islet function/ insulin resistance occurred in T2D subjects compared to healthy individuals. Common gene variants, such as rs1260326, might interact substantially when studied in specific populations, especially T2D disease status.
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Affiliation(s)
- Min Shen
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Liying Jiang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hechun Liu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hao Dai
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hemin Jiang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yu Qian
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Zhixiao Wang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shuai Zheng
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Heng Chen
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Tao Yang
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Qi Fu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
| | - Kuanfeng Xu
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Gagnon E, Paulin A, Mitchell PL, Arsenault BJ. Disentangling the impact of gluteofemoral versus visceral fat accumulation on cardiometabolic health using sex-stratified Mendelian randomization. Atherosclerosis 2023; 386:117371. [PMID: 38029505 DOI: 10.1016/j.atherosclerosis.2023.117371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND AIMS Individuals with a higher abdominal adipose tissue accumulation are at higher risk of developing cardiometabolic diseases. For a given body mass index (BMI), women typically present lower abdominal adipose tissue accumulation compared to men. Whether abdominal adiposity is a causal driver of cardiometabolic risk, or a mere marker of ectopic fat deposition is debated. METHODS We investigated the sex-specific and sex-combined impact of height and BMI-adjusted gluteofemoral adipose tissue (GFATadj) adjusted abdominal subcutaneous adipose tissue (ASATadj) and adjusted visceral adipose tissue (VATadj) on cardiometabolic traits and diseases using Mendelian randomization. RESULTS Leveraging genome-wide summary statistics on GFATadj, ASATadj and VATadj from 39,076 UK Biobank participants with full-body magnetic resonance imaging available, we found that GFATadj is associated with a more favourable cardiometabolic risk profile including lower low density lipoprotein (LDL) cholesterol, triglycerides, fasting glucose, fasting insulin, liver enzyme levels and blood pressure as well as higher high density lipoprotein (HDL) cholesterol levels. GFATadj also is negatively associated with ischemic stroke, coronary artery disease, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). ASATadj is not associated with cardiometabolic traits and diseases, whereas VATadj is associated with liver fat accumulation but not with NAFLD or other cardiometabolic traits or diseases. Although the absolute effect sizes of GFATadj on LDL cholesterol were more pronounced in women compared to men, most associations did not differ by sex. CONCLUSIONS The inability of subcutaneous fat depots to efficiently store energy substrates could be the causal factor underlying the association of visceral lipid deposition with cardiometabolic health.
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Affiliation(s)
- Eloi Gagnon
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Audrey Paulin
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Patricia L Mitchell
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada
| | - Benoit J Arsenault
- Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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Liu J, Hu S, Chen L, Daly C, Prada Medina CA, Richardson TG, Traylor M, Dempster NJ, Mbasu R, Monfeuga T, Vujkovic M, Tsao PS, Lynch JA, Voight BF, Chang KM, Million VA, Cobbold JF, Tomlinson JW, van Duijn CM, Howson JMM. Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.30.23299247. [PMID: 38076879 PMCID: PMC10705663 DOI: 10.1101/2023.11.30.23299247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
BACKGROUND & AIMS Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity. METHODS We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD. RESULTS We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity. CONCLUSIONS This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.
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Affiliation(s)
- Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Sile Hu
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Lingyan Chen
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Charlotte Daly
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | | | - Tom G Richardson
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Traylor
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Niall J Dempster
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
| | - Richard Mbasu
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Thomas Monfeuga
- AI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Julie A Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - V A Million
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Department of Discovery Technology and Genomics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- AI & Digital Research, Research & Early Development, Novo Nordisk Research Centre Oxford, UK
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, School of Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Jeremy F Cobbold
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Jeremy W Tomlinson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Oxford, UK
| | | | - Joanna M M Howson
- Genetics Centre-of-Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
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Misicka E, Gunzler D, Albert J, Briggs FBS. Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk. Mult Scler Relat Disord 2023; 79:104964. [PMID: 37659350 PMCID: PMC10873055 DOI: 10.1016/j.msard.2023.104964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 07/24/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Epidemiologic studies have established obesity as a risk factor for multiple sclerosis (MS). These studies relied on body-mass index (BMI) and body size silhouettes as the primary measures of obesity. Unfortunately, the causal mechanisms through which obesity confers MS risk are not yet known. OBJECTIVES To investigate the causal effects of multiple specific measures of body fat on MS risk in populations of European descent, using Mendelian randomization (MR). METHODS MR is a genetic instrumental variable analysis utilizing genome-wide association (GWA) summary statistics to infer causality between phenotypes. MR analyses were performed to investigate the relationships between seven measures of body fat (BMI, waist-hip ratio, visceral adipose tissue [VAT], subcutaneous adipose tissue, and arm-, leg-, and trunk-fat to total body fat ratio) and MS risk. RESULTS Only BMI and VAT were significantly associated with MS risk in separate MR analyses (βBMI=0.27, pBMI<0.001; βVAT=0.28, pVAT=0.006). High correlation between BMI and VAT instruments suggest that two-sample MR associations for BMI and VAT likely capture the same causal mechanisms. CONCLUSIONS BMI and VAT were causally associated with MS risk in European populations, though their effects do not appear independent, suggesting overlap in the role of overall body mass and visceral obesity in MS pathogenesis.
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Affiliation(s)
- Elina Misicka
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Douglas Gunzler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Center for Health Care Research and Policy, Case Western Reserve School of Medicine, Cleveland, OH, USA
| | - Jeffrey Albert
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Farren B S Briggs
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA.
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Mai Z, Mao H. Causal effects of nonalcoholic fatty liver disease on cerebral cortical structure: a Mendelian randomization analysis. Front Endocrinol (Lausanne) 2023; 14:1276576. [PMID: 38027213 PMCID: PMC10646496 DOI: 10.3389/fendo.2023.1276576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Background Previous studies have highlighted changes in the cerebral cortical structure and cognitive function among nonalcoholic fatty liver disease (NAFLD) patients. However, the impact of NAFLD on cerebral cortical structure and specific affected brain regions remains unclear. Therefore, we aimed to explore the potential causal relationship between NAFLD and cerebral cortical structure. Methods We conducted a Mendelian randomization (MR) study using genetic predictors of alanine aminotransferase (ALT), NAFLD, and percent liver fat (PLF) and combined them with genome-wide association study (GWAS) summary statistics from the ENIGMA Consortium. Several methods were used to assess the effect of NAFLD on full cortex and specific brain regions, along with sensitivity analyses. Results At the global level, PLF nominally decreased SA of full cortex; at the functional level, ALT presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars orbitalis, and TH of pericalcarine cortex. Besides, NAFLD presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of pars triangularis and TH of pericalcarine cortex, but increased TH of entorhinal cortex, lateral orbitofrontal cortex and temporal pole. Furthermore, PLF presented a nominal association with reduced SA of parahippocampal gyrus, TH of pars opercularis, TH of cuneus and lingual gyrus, but increased TH of entorhinal cortex. Conclusion NAFLD is suggestively associated with atrophy in specific functional regions of the human brain.
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Affiliation(s)
- Zhiliang Mai
- Department of Digestive Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Anatomy, Guangdong Medical University, Zhanjiang, China
| | - Hua Mao
- Department of Digestive Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Cao F, Pan F, Gong X, Wang W, Xu Y, Cao P, Wang Y. Causal relationship between gut microbiota with subcutaneous and visceral adipose tissue: a bidirectional two-sample Mendelian Randomization study. Front Microbiol 2023; 14:1285982. [PMID: 38029216 PMCID: PMC10644100 DOI: 10.3389/fmicb.2023.1285982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background Numerous studies have revealed associations between gut microbiota and adipose tissue. However, the specific functional bacterial taxa and their causal relationships with adipose tissue production in different regions of the body remain unclear. Methods We conducted a bidirectional two-sample Mendelian Randomization (MR) study using aggregated data from genome-wide association studies (GWAS) for gut microbiota and adipose tissue. We employed methods such as inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode to assess the causal relationships between gut microbiota and subcutaneous adipose tissue (SAT) as well as visceral adipose tissue (VAT). Cochran's Q test, MR-Egger regression intercept analysis, and MR-PRESSO were used to test for heterogeneity, pleiotropy, and outliers of the instrumental variables, respectively. Reverse MR was employed to evaluate the reverse causal relationships between SAT, VAT, and gut microbiota with significant associations. Results IVW results demonstrated that Betaproteobacteria were protective factors for SAT production (OR = 0.88, 95% CI: 0.80-0.96, p = 0.005) and VAT production (OR = 0.91, 95% CI: 0.83-0.99, p = 0.030). Various bacterial taxa including Ruminococcaceae UCG002 (OR = 0.94, 95% CI: 0.89-0.99, p = 0.017), Methanobacteria class (OR = 0.96, 95% CI: 0.92-1.00, p = 0.029), and Burkholderiales (OR = 0.90, 95% CI: 0.83-0.98, p = 0.012) were associated only with decreased SAT production. Rikenellaceae RC9 gut group (OR = 1.05, 95% CI: 1.02-1.10, p = 0.005), Eubacterium hallii group (OR = 1.08, 95% CI: 1.01-1.15, p = 0.028), Peptococcaceae (OR = 1.08, 95% CI: 1.01-1.17, p = 0.034), and Peptococcus (OR = 1.05, 95% CI: 1.00-1.10, p = 0.047) were risk factors for SAT production. Meanwhile, Eubacterium fissicatena group (OR = 0.95, 95% CI: 0.91-0.99, p = 0.019), Turicibacter (OR = 0.93, 95% CI: 0.88-0.99, p = 0.022), and Defluviitaleaceae UCG011 (OR = 0.94, 95% CI: 0.89-0.99, p = 0.024) were protective factors for VAT production. Furthermore, Bacteroidetes (OR = 1.09, 95% CI: 1.01-1.17, p = 0.018), Eubacterium eligens group (OR = 1.09, 95% CI: 1.01-1.19, p = 0.037), Alloprevotella (OR = 1.05, 95% CI: 1.00-1.10, p = 0.038), and Phascolarctobacterium (OR = 1.07, 95% CI: 1.00-1.15, p = 0.042) were associated with VAT accumulation. Additionally, reverse MR revealed significant associations between SAT, VAT, and Rikenellaceae RC9 gut group (IVW: OR = 1.57, 95% CI: 1.18-2.09, p = 0.002) as well as Betaproteobacteria (IVW: OR = 1.14, 95% CI: 1.01-1.29, p = 0.029), both acting as risk factors. Sensitivity analyzes during bidirectional MR did not identify heterogeneity or pleiotropy. Conclusion This study unveils complex causal relationships between gut microbiota and SAT/VAT, providing novel insights into the diagnostic and therapeutic potential of gut microbiota in obesity and related metabolic disorders.
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Affiliation(s)
- Feng Cao
- Department of General Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Feng Pan
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Gong
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wen Wang
- Department of General Practice, Anqing Hospital Affiliated Hospital of Anhui Medical University, Anqing, China
| | - Yanyan Xu
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Pengwei Cao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yong Wang
- Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of General Surgery, The Shenzhen Hospital of Southern Medical University, Shenzhen, China
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Wang Z, Liu Y, Zhang S, Yuan Y, Chen S, Li W, Zuo M, Xiang Y, Li T, Yang W, Yang Y, Liu Y. Effects of iron homeostasis on epigenetic age acceleration: a two-sample Mendelian randomization study. Clin Epigenetics 2023; 15:159. [PMID: 37805541 PMCID: PMC10559596 DOI: 10.1186/s13148-023-01575-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Epigenetic clocks constructed from DNA methylation patterns have emerged as excellent predictors of aging and aging-related health outcomes. Iron, a crucial element, is meticulously regulated within organisms, a phenomenon referred as iron homeostasis. Previous researches have demonstrated the sophisticated connection between aging and iron homeostasis. However, their causal relationship remains relatively unexplored. RESULTS Through two-sample Mendelian randomization (MR) utilizing the random effect inverse variance weighted (IVW) method, each standard deviation (SD) increase in serum iron was associated with increased GrimAge acceleration (GrimAA, BetaIVW = 0.27, P = 8.54E-03 in 2014 datasets; BetaIVW = 0.31, P = 1.25E-02 in 2021 datasets), HannumAge acceleration (HannumAA, BetaIVW = 0.32, P = 4.50E-03 in 2014 datasets; BetaIVW = 0.32, P = 8.03E-03 in 2021 datasets) and Intrinsic epigenetic age acceleration (IEAA, BetaIVW = 0.34, P = 5.33E-04 in 2014 datasets; BetaIVW = 0.49, P = 9.94E-04 in 2021 datasets). Similar results were also observed in transferrin saturation. While transferrin manifested a negative association with epigenetic age accelerations (EAAs) sensitivity analyses. Besides, lack of solid evidence to support a causal relationship from EAAs to iron-related biomarkers. CONCLUSIONS The results of present investigation unveiled the causality of iron overload on acceleration of epigenetic clocks. Researches are warranted to illuminate the underlying mechanisms and formulate strategies for potential interventions.
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Affiliation(s)
- Zhihao Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Liu
- Department of Thoracic Surgery and Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Shuxin Zhang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yunbo Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Siliang Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wenhao Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Mingrong Zuo
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yufan Xiang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Tengfei Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wanchun Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
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Stender S, Davey Smith G, Richardson TG. Genetic variation and elevated liver enzymes during childhood, adolescence and early adulthood. Int J Epidemiol 2023; 52:1341-1349. [PMID: 37105232 PMCID: PMC10555681 DOI: 10.1093/ije/dyad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Genetic factors influence the risk of fatty liver disease (FLD) in adults. The aim of this study was to test if, and when, genetic risk factors known to affect FLD in adults begin to exert their deleterious effects during childhood, adolescence and early adulthood. METHODS We included up to 4018 British children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Three genetic variants known to associate robustly with FLD in adults (PNPLA3 rs738409, TM6SF2 rs58542926 and HSD17B13 rs72613567) were tested for association with plasma levels of alanine transaminase (ALT) and aspartate transaminase (AST) during childhood (mean age: 9.9 years), early adolescence (15.5 years), late adolescence (17.8 years), and early adulthood (24.5 years). We also tested the associations of a 17-variant score and whole-genome polygenic risk scores (PRS) derived from associations in adults with plasma ALT and AST at the same four time points. Associations with elastography-derived liver steatosis and fibrosis were tested in early adulthood. RESULTS Genetic risk factors for FLD (individually, combined into a 3-variant score, a 17-variant score and as a genome-wide PRS), were associated with higher liver enzymes, beginning in childhood and throughout adolescence and early adulthood. The ALT-increasing effects of the genetic risk variants became larger with increasing age. The ALT-PRS was associated with liver steatosis in early adulthood. No genetic associations with fibrosis were observed. CONCLUSIONS Genetic factors that promote FLD in adults associate with elevated liver enzymes already during childhood, and their effects get amplified with increasing age.
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Affiliation(s)
- Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, UK
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Xia T, Du M, Li H, Wang Y, Zha J, Wu T, Ju S. Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk. Radiology 2023; 309:e231007. [PMID: 37874242 DOI: 10.1148/radiol.231007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.
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Affiliation(s)
- Tianyi Xia
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Mulong Du
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Huiqin Li
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Yuancheng Wang
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Junhao Zha
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Tong Wu
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
| | - Shenghong Ju
- From the Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, 87 Ding Jia Qiao Road, Nanjing 210009, China (T.X., Y.W., J.Z., T.W., S.J.); and Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China (M.D., H.L.)
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Trejo MJ, Morrill KE, Klimentidis YC, Garcia DO. Examining genetic associations with hepatic steatosis in Mexican-origin adults. Ann Hepatol 2023; 28:101120. [PMID: 37271481 PMCID: PMC10486257 DOI: 10.1016/j.aohep.2023.101120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 06/06/2023]
Abstract
INTRODUCTION AND OBJECTIVES Various studies have identified single-nucleotide polymorphisms (SNPs) associated with nonalcoholic fatty liver disease (NAFLD) and related traits, including ones located in or near the LYPLAL1, GCKR, PPP1R3B, TM6SF2, MBOAT7, and PNPLA3 genes. However, these SNPs were identified primarily in populations of European ancestry. This study examined the associations of these previously identified SNPs with hepatic steatosis in a sample of Mexican-origin adults living in Southern Arizona. MATERIALS AND METHODS A total of 307 Mexican-origin adults between the ages of 18 and 64 with a body mass index (BMI) of 25 kg/m2 or higher were genotyped at the following SNPs: rs12137855 (LYPLAL1), rs1260326 (GCKR), rs4240624 (PPP1R3B), rs58542926 (TM6SF2), rs641738 (MBOAT7), and rs738409 (PNPLA3). Hepatic steatosis was assessed by transient elastography (FibroScan®) with controlled attenuation parameter. Regression models examined the association between each of the six SNPs and hepatic steatosis. BMI was examined as a potential modifier of the genetic associations. RESULTS Participants were, on average, 45 years old and mostly female (63%) with an overall mean hepatic steatosis of 288.1 dB/m. Models showed no associations between LYPLAL1, GCKR, PPP1R3B, TM6SF2, or MBOAT7 and hepatic steatosis. Only PNPLA3 was statistically significantly associated with hepatic steatosis in both unadjusted and adjusted models (p<0.01). There was no effect modification observed with BMI. CONCLUSIONS SNPs associated with NAFLD in populations of European descent did not strongly contribute to hepatic steatosis in individuals of Mexican-origin, except for rs738409 (PNPLA3). Further efforts are necessary to explore additional SNPs that may be associated with NAFLD in this high-risk population.
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Affiliation(s)
- Mario Jesus Trejo
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
| | - Kristin E Morrill
- Community & Systems Health Science Division, College of Nursing, University of Arizona, Tucson, AZ 85721, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA
| | - David O Garcia
- Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA
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Huang Y, Stinson SE, Juel HB, Lund MAV, Holm LA, Fonvig CE, Nielsen T, Grarup N, Pedersen O, Christiansen M, Chabanova E, Thomsen HS, Krag A, Stender S, Holm JC, Hansen T. An adult-based genetic risk score for liver fat associates with liver and plasma lipid traits in children and adolescents. Liver Int 2023; 43:1772-1782. [PMID: 37208954 DOI: 10.1111/liv.15613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND & AIMS Genome-wide association studies have identified steatogenic variants that also showed pleiotropic effects on cardiometabolic traits in adults. We investigated the effect of eight previously reported genome-wide significant steatogenic variants, individually and combined in a weighted genetic risk score (GRS), on liver and cardiometabolic traits, and the predictive ability of the GRS for hepatic steatosis in children and adolescents. APPROACH & RESULTS Children and adolescents with overweight (including obesity) from an obesity clinic group (n = 1768) and a population-based group (n = 1890) were included. Cardiometabolic risk outcomes and genotypes were obtained. Liver fat was quantified using 1 H-MRS in a subset of 727 participants. Variants in PNPLA3, TM6SF2, GPAM and TRIB1 were associated with higher liver fat (p < .05) and with distinct patterns of plasma lipids. The GRS was associated with higher liver fat content, plasma concentrations of alanine transaminase (ALT), aspartate aminotransferase (AST) and favourable plasma lipid levels. The GRS was associated with higher prevalence of hepatic steatosis (defined as liver fat ≥5.0%) (odds ratio per 1-SD unit: 2.17, p = 9.7E-10). A prediction model for hepatic steatosis including GRS alone yielded an area under the curve (AUC) of 0.78 (95% CI 0.76-0.81). Combining the GRS with clinical measures (waist-to-height ratio [WHtR] SDS, ALT, and HOMA-IR) increased the AUC up to 0.86 (95% CI 0.84-0.88). CONCLUSIONS The genetic predisposition for liver fat accumulation conferred risk of hepatic steatosis in children and adolescents. The liver fat GRS has potential clinical utility for risk stratification.
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Affiliation(s)
- Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sara E Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helene Baek Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten A V Lund
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Aas Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
| | - Cilius E Fonvig
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
- Department of Pediatrics, Kolding Hospital a Part of Lillebaelt Hospital, Kolding, Denmark
| | - Trine Nielsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Michael Christiansen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
| | - Elizaveta Chabanova
- Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Henrik S Thomsen
- Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
- Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
| | - Jens-Christian Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, accredited European Centre for Obesity Management, Department of Pediatrics, Copenhagen University Hospital Holbaek, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Brown OI, Drozd M, McGowan H, Giannoudi M, Conning-Rowland M, Gierula J, Straw S, Wheatcroft SB, Bridge K, Roberts LD, Levelt E, Ajjan R, Griffin KJ, Bailey MA, Kearney MT, Cubbon RM. Relationship Among Diabetes, Obesity, and Cardiovascular Disease Phenotypes: A UK Biobank Cohort Study. Diabetes Care 2023; 46:1531-1540. [PMID: 37368983 PMCID: PMC10369123 DOI: 10.2337/dc23-0294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE Obesity and diabetes frequently coexist, yet their individual contributions to cardiovascular risk remain debated. We explored cardiovascular disease biomarkers, events, and mortality in the UK Biobank stratified by BMI and diabetes. RESEARCH DESIGN AND METHODS A total of 451,355 participants were stratified by ethnicity-specific BMI categories (normal, overweight, obese) and diabetes status. We examined cardiovascular biomarkers including carotid intima-media thickness (CIMT), arterial stiffness, left ventricular ejection fraction (LVEF), and cardiac contractility index (CCI). Poisson regression models estimated adjusted incidence rate ratios (IRRs) for myocardial infarction, ischemic stroke, and cardiovascular death, with normal-weight nondiabetes as comparator. RESULTS Five percent of participants had diabetes (10% normal weight, 34% overweight, and 55% obese vs. 34%, 43%, and 23%, respectively, without diabetes). In the nondiabetes group, overweight/obesity was associated with higher CIMT, arterial stiffness, and CCI and lower LVEF (P < 0.005); these relationships were diminished in the diabetes group. Within BMI classes, diabetes was associated with adverse cardiovascular biomarker phenotype (P < 0.005), particularly in the normal-weight group. After 5,323,190 person-years follow-up, incident myocardial infarction, ischemic stroke, and cardiovascular mortality rose across increasing BMI categories without diabetes (P < 0.005); this was comparable in the diabetes groups (P-interaction > 0.05). Normal-weight diabetes had comparable adjusted cardiovascular mortality to obese nondiabetes (IRR 1.22 [95% CI 0.96-1.56]; P = 0.1). CONCLUSIONS Obesity and diabetes are additively associated with adverse cardiovascular biomarkers and mortality risk. While adiposity metrics are more strongly correlated with cardiovascular biomarkers than diabetes-oriented metrics, both correlate weakly, suggesting that other factors underpin the high cardiovascular risk of normal-weight diabetes.
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Affiliation(s)
- Oliver I. Brown
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Michael Drozd
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Hugo McGowan
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Marilena Giannoudi
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | | | - John Gierula
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Sam Straw
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Stephen B. Wheatcroft
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Katherine Bridge
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Lee D. Roberts
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Eylem Levelt
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Ramzi Ajjan
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Kathryn J. Griffin
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Marc A. Bailey
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Mark T. Kearney
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
| | - Richard M. Cubbon
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, U.K
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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Gagnon E, Manikpurage HD, Mitchell PL, Girard A, Gobeil É, Bourgault J, Bégin F, Marette A, Thériault S, Arsenault BJ. Large-scale metabolomic profiling and incident non-alcoholic fatty liver disease. iScience 2023; 26:107127. [PMID: 37456853 PMCID: PMC10339047 DOI: 10.1016/j.isci.2023.107127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/11/2023] [Accepted: 06/09/2023] [Indexed: 07/18/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent disease with no specific drug therapy. High-throughput metabolomics present an unprecedented opportunity to identify biomarkers and potentially causal risk factors for NAFLD. Here, we determined the impact of 21 circulating metabolites, 17 lipids, and 132 lipoprotein particle characteristics on NAFLD combining prospective observational and two-sample Mendelian randomization (MR) analyses in 121,032 UK Biobank participants. We identified several metabolic factors associated with NAFLD risk in observational and MR analyses including triglyceride-rich and high-density lipoprotein particles composition, as well as the ratio of polyunsaturated fatty acids to total fatty acids. This study, is one of the largest to investigate incident NAFLD, provides concordant observational and genetic evidence that therapies aimed at reducing circulating triglycerides and increasing large HDL particles, as well as interventions aimed at increasing polyunsaturated fatty acid content may warrant further investigation into NAFLD prevention and treatment.
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Affiliation(s)
- Eloi Gagnon
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Hasanga D. Manikpurage
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Patricia L. Mitchell
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Arnaud Girard
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Émilie Gobeil
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Jérôme Bourgault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - Frédéric Bégin
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
| | - André Marette
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec (QC), Canada
| | - Sébastien Thériault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec (QC), Canada
| | - Benoit J. Arsenault
- Centre de Recherche de L’Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec (QC), Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec (QC), Canada
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Wang D, Townsend LK, DesOrmeaux GJ, Frangos SM, Batchuluun B, Dumont L, Kuhre RE, Ahmadi E, Hu S, Rebalka IA, Gautam J, Jabile MJT, Pileggi CA, Rehal S, Desjardins EM, Tsakiridis EE, Lally JSV, Juracic ES, Tupling AR, Gerstein HC, Paré G, Tsakiridis T, Harper ME, Hawke TJ, Speakman JR, Blondin DP, Holloway GP, Jørgensen SB, Steinberg GR. GDF15 promotes weight loss by enhancing energy expenditure in muscle. Nature 2023; 619:143-150. [PMID: 37380764 PMCID: PMC10322716 DOI: 10.1038/s41586-023-06249-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 05/23/2023] [Indexed: 06/30/2023]
Abstract
Caloric restriction that promotes weight loss is an effective strategy for treating non-alcoholic fatty liver disease and improving insulin sensitivity in people with type 2 diabetes1. Despite its effectiveness, in most individuals, weight loss is usually not maintained partly due to physiological adaptations that suppress energy expenditure, a process known as adaptive thermogenesis, the mechanistic underpinnings of which are unclear2,3. Treatment of rodents fed a high-fat diet with recombinant growth differentiating factor 15 (GDF15) reduces obesity and improves glycaemic control through glial-cell-derived neurotrophic factor family receptor α-like (GFRAL)-dependent suppression of food intake4-7. Here we find that, in addition to suppressing appetite, GDF15 counteracts compensatory reductions in energy expenditure, eliciting greater weight loss and reductions in non-alcoholic fatty liver disease (NAFLD) compared to caloric restriction alone. This effect of GDF15 to maintain energy expenditure during calorie restriction requires a GFRAL-β-adrenergic-dependent signalling axis that increases fatty acid oxidation and calcium futile cycling in the skeletal muscle of mice. These data indicate that therapeutic targeting of the GDF15-GFRAL pathway may be useful for maintaining energy expenditure in skeletal muscle during caloric restriction.
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Affiliation(s)
- Dongdong Wang
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Logan K Townsend
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Geneviève J DesOrmeaux
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Sara M Frangos
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Battsetseg Batchuluun
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lauralyne Dumont
- Department of Pharmacology-Physiology, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Rune Ehrenreich Kuhre
- Global Obesity and Liver Disease Research, Global Drug Discovery, Novo Nordisk, Maaloev, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Elham Ahmadi
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sumei Hu
- Key Laboratory of Geriatric Nutrition and Health, Ministry of Education, Beijing Technology and Business University, Beijing, China
- Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Irena A Rebalka
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jaya Gautam
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Maria Joy Therese Jabile
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Chantal A Pileggi
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Sonia Rehal
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Eric M Desjardins
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Evangelia E Tsakiridis
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - James S V Lally
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Emma Sara Juracic
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - A Russell Tupling
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Hertzel C Gerstein
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, McMaster University, Hamilton Health Sciences, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Theodoros Tsakiridis
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
- Department of Oncology, McMaster University, Hamilton, Ontario, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
| | - Thomas J Hawke
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - John R Speakman
- Shenzhen Key Laboratory of Metabolic Health, Center for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
- CAS Center for Excellence in Animal Evolution and Genetics (CCEAEG), Kunming, China
| | - Denis P Blondin
- Department of Pharmacology-Physiology, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Division of Neurology, Department of Medicine, Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Graham P Holloway
- Department of Human Health and Nutritional Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Sebastian Beck Jørgensen
- Global Obesity and Liver Disease Research, Global Drug Discovery, Novo Nordisk, Maaloev, Denmark
- Bio Innovation Hub Transformational Research Unit, Novo Nordisk, Boston, MA, USA
| | - Gregory R Steinberg
- Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada.
- Division of Endocrinology and Metabolism, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada.
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Hilton C, Sabaratnam R, Drakesmith H, Karpe F. Iron, glucose and fat metabolism and obesity: an intertwined relationship. Int J Obes (Lond) 2023; 47:554-563. [PMID: 37029208 PMCID: PMC10299911 DOI: 10.1038/s41366-023-01299-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 03/08/2023] [Accepted: 03/16/2023] [Indexed: 04/09/2023]
Abstract
A bidirectional relationship exists between adipose tissue metabolism and iron regulation. Total body fat, fat distribution and exercise influence iron status and components of the iron-regulatory pathway, including hepcidin and erythroferrone. Conversely, whole body and tissue iron stores associate with fat mass and distribution and glucose and lipid metabolism in adipose tissue, liver, and muscle. Manipulation of the iron-regulatory proteins erythroferrone and erythropoietin affects glucose and lipid metabolism. Several lines of evidence suggest that iron accumulation and metabolism may play a role in the development of metabolic diseases including obesity, type 2 diabetes, hyperlipidaemia and non-alcoholic fatty liver disease. In this review we summarise the current understanding of the relationship between iron homoeostasis and metabolic disease.
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Affiliation(s)
- Catriona Hilton
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
| | - Rugivan Sabaratnam
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Hal Drakesmith
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
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Sun Z, Pan X, Tian A, Surakka I, Wang T, Jiao X, He S, Song J, Tian X, Tong D, Wen J, Zhang Y, Liu W, Chen P. Genetic variants in HFE are associated with non-alcoholic fatty liver disease in lean individuals. JHEP Rep 2023; 5:100744. [PMID: 37235137 PMCID: PMC10206181 DOI: 10.1016/j.jhepr.2023.100744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/13/2023] [Accepted: 03/07/2023] [Indexed: 05/28/2023] Open
Abstract
Background & Aims Around 20% of patients with non-alcoholic fatty liver disease (NAFLD) are lean. Increasing evidence suggests that lean NAFLD is a unique subtype of the disease. We aimed to explore the metabolic profile, genetic basis, causal risk factors, and clinical sequelae underlying lean NAFLD. Methods NAFLD was diagnosed by whole liver proton density fat fraction ≥5%. Whole liver proton density fat fraction and hepatic iron were quantified using magnetic resonance imaging in the UK Biobank. Individuals in this study were stratified according to the World Health Organization criteria of obesity, into lean, overweight, and obese. Mediation analysis, Mendelian randomisation analysis, and Bayesian networks were used to identify a risk factor or a clinical sequela of lean/obese NAFLD. Results Lean NAFLD manifested a distinct metabolic profile, featured by elevated hepatic iron and fasting glucose. Four loci, namely, HFE rs1800562, SLC17A3-SLC17A2-TRIM38 rs9348697, PNPLA3 rs738409, and TM6SF2 rs58542926, were associated with lean NAFLD (p <5 × 10-8). HFE rs1800562 was specifically associated with lean NAFLD and demonstrated a significant mediation effect through elevating hepatic iron. Type 2 diabetes was the most pronounced clinical sequela of lean NAFLD, followed by liver cirrhosis. Conclusions Our study suggested that HFE plays a potential steatogenic role rather than regulating iron homoeostasis in patients with lean NAFLD. The increased liver iron deposition is associated with lean NAFLD, whereas obese NAFLD is not related to hepatic iron. The clinical management of patients with lean NAFLD shall be concerned with the prevention and treatment of type 2 diabetes and liver cirrhosis. Impact and implications Lean NAFLD has a distinct natural history from obese NAFLD. This study underscored liver iron content and the genetic variant of the iron homoeostasis gene HFE as major risks of lean NAFLD, in addition to the unique metabolic profile. The development of type 2 diabetes or liver cirrhosis shall be closely monitored and prevented in patients with lean NAFLD.
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Affiliation(s)
- Zewen Sun
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Xingchen Pan
- Department of Molecular Biology, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Aowen Tian
- Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, China
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Tao Wang
- Software College of Jilin University, Changchun, China
| | - Xu Jiao
- Software College of Jilin University, Changchun, China
| | - Shanshan He
- Software College of Jilin University, Changchun, China
| | - Jinfang Song
- Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China
| | - Xin Tian
- Department of Pediatrics, The Second Hospital of Jilin University, Changchun, China
| | - Dan Tong
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Jianping Wen
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - Yonggang Zhang
- The Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Wanqing Liu
- Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI, USA
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Peng Chen
- Department of Genetics, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
- Department of Pathology, College of Basic Medical Sciences, Jilin University, Changchun, China
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
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Aberra YT, Ma L, Björkegren JLM, Civelek M. Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation. eLife 2023; 12:e79834. [PMID: 37326626 PMCID: PMC10275637 DOI: 10.7554/elife.79834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity.
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Affiliation(s)
- Yonathan Tamrat Aberra
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Johan LM Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Medicine, Karolinska Institutet, HuddingeStockholmSweden
| | - Mete Civelek
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
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80
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Gentiluomo M, Corradi C, Arcidiacono PG, Crippa S, Falconi M, Belfiori G, Farinella R, Apadula L, Lauri G, Bina N, Rizzato C, Canzian F, Morelli L, Capurso G, Campa D. Role of pancreatic ductal adenocarcinoma risk factors in intraductal papillary mucinous neoplasm progression. Front Oncol 2023; 13:1172606. [PMID: 37346070 PMCID: PMC10280811 DOI: 10.3389/fonc.2023.1172606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/16/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction Pancreatic ductal adenocarcinoma (PDAC) is lethal due to its late diagnosis and lack of successful treatments. A possible strategy to reduce its death burden is prevention. Intraductal papillary mucinous neoplasms (IPMNs) are precursors of PDAC. It is difficult to estimate the incidence of IPMNs because they are asymptomatic. Two recent studies reported pancreatic cysts in 3% and 13% of scanned subjects. The possibility of identifying a subgroup of IPMN patients with a higher probability of progression into cancer could be instrumental in increasing the survival rate. In this study, genetic and non-genetic PDAC risk factors were tested in a group of IPMN patients under surveillance. Methods A retrospective study was conducted on 354 IPMN patients enrolled in two Italian centres with an average follow-up of 64 months. With the use of DNA extracted from blood, collected at IPMN diagnosis, all patients were genotyped for 30 known PDAC risk loci. The polymorphisms were analysed individually and grouped in an unweighted polygenic score (PGS) in relation to IPMN progression. The ABO blood group and non-genetic PDAC risk factors were also analysed. IPMN progression was defined based on the development of worrisome features and/or high-risk stigmata during follow-up. Results Two genetic variants (rs1517037 and rs10094872) showed suggestive associations with an increment of IPMN progression. After correction for multiple testing, using the Bonferroni correction, none of the variants showed a statistically significant association. However, associations were observed for the non-genetic variables, such as smoking status, comparing heavy smokers with light smokers (HR = 3.81, 95% 1.43-10.09, p = 0.007), and obesity (HR = 2.46, 95% CI 1.22-4.95, p = 0.012). Conclusion In conclusion, this study is the first attempt to investigate the presence of shared genetic background between PDAC risk and IPMN progression; however, the results suggest that the 30 established PDAC susceptibility polymorphisms are not associated with clinical IPMN progression in a sample of 354 patients. However, we observed indications of cigarette smoking and body mass index (BMI) involvement in IPMN progression. The biological mechanism that could link these two risk factors to progression could be chronic inflammation, of which both smoking and obesity are strong promoters.
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Affiliation(s)
| | | | - Paolo Giorgio Arcidiacono
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Stefano Crippa
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Massimo Falconi
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | - Giulio Belfiori
- Unit of Pancreatic Surgery, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Vita-Salute University, Milan, Italy
| | | | - Laura Apadula
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Gaetano Lauri
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Niccolò Bina
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
| | - Cosmeri Rizzato
- Department of Translational Research and of New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Luca Morelli
- General Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Gabriele Capurso
- Pancreato-Biliary Endoscopy and Endosonography Division, Pancreas Translational and Clinical Research Center, Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy
- Digestive and Liver Disease Unit, Sant’Andrea University Hospital, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
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Yamazaki H, Streicher SA, Wu L, Fukuhara S, Wagner R, Heni M, Grossman SR, Lenz HJ, Setiawan VW, Marchand LL, Huang BZ. Genetic Evidence Causally Linking Pancreas Fat to Pancreatic Cancer: A Mendelian Randomization Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.20.23288770. [PMID: 37163062 PMCID: PMC10168411 DOI: 10.1101/2023.04.20.23288770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background & Aims Pancreatic ductal adenocarcinoma (PDAC) is highly lethal, and any clues to understanding its elusive etiology could lead to breakthroughs in prevention, early detection, or treatment. Observational studies have shown a relationship between pancreas fat accumulation and PDAC, but the causality of this link is unclear. We therefore investigated whether pancreas fat is causally associated with PDAC using two-sample Mendelian randomization. Methods We leveraged eight genetic variants associated with pancreas fat (P<5×10 -8 ) from a genome-wide association study (GWAS) in the UK Biobank (25,617 individuals), and assessed their association with PDAC in the Pancreatic Cancer Cohort Consortium I-III and the Pancreatic Cancer Case-Control Consortium dataset (8,275 PDAC cases and 6,723 non-cases). Causality was assessed using the inverse-variance weighted method. Although none of these genetic variants were associated with body mass index (BMI) at genome-wide significance, we further conducted a sensitivity analysis excluding genetic variants with a nominal BMI association in GWAS summary statistics from the UK Biobank and the Genetic Investigation of Anthropometric Traits consortium dataset (806,834 individuals). Results Genetically determined higher levels of pancreas fat using the eight genetic variants was associated with increased risk of PDAC. For one standard deviation increase in pancreas fat levels (i.e., 7.9% increase in pancreas fat fraction), the odds ratio of PDAC was 2.46 (95%CI:1.38-4.40, P=0.002). Similar results were obtained after excluding genetic variants nominally linked to BMI (odds ratio:3.79, 95%CI:1.66-8.65, P=0.002). Conclusions This study provides genetic evidence for a causal role of pancreas fat in the pathogenesis of PDAC. Thus, reducing pancreas fat could lower the risk of PDAC.
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, Arruda ALDSV, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Lithgart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Metspalu A, Mo H, Morris AD, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.31.23287839. [PMID: 37034649 PMCID: PMC10081410 DOI: 10.1101/2023.03.31.23287839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J. Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing City, China
| | - Kim M. Lorenz
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Nigel W. Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza de S. V. Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S. K. Lee
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H. Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E. Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P. Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto at Mississsauga, Mississauga, ON, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S. Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J. Y. Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Institute of Data Science, Korea University, Seoul, South Korea
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Anny H. Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E. Cade
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K. Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Linda S. Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A. Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S. Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S. Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A. Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K. Das
- Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H. Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P. Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S. Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S. Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C. Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A. Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Jost B. Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Fouad R. Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M. Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N. Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G. Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A. Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Severance Biomedical Science Institute and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R. Lee
- USC-Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Lithgart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre For Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E. Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian’an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A. Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D. Morris
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Jerry L. Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R. Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilians Universität München, Munich, Germany
| | - Fraser J. Pirie
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G. Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Dusseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig Maximilians Universität München, Munich, Germany
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C. Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S. Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B. van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R. Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S. Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | | | | | | | | | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A. Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L. Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, US
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M. Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C. Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Wayne H. H. Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M. Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O. Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Lund University Diabetes Centre, Malmö, Sweden
| | - Giriraj R. Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Francis S. Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Michèle M. Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
- Deceased
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, The University of Chicago, Chicago, IL, USA
| | - Ayesha A. Motala
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S. Kooner
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J. Wareham
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Mark O. Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D. Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Joshua C. Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hosptial, London NorthWest Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E. Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S. Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - James B. Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Present address: Genentech, South San Francisco, CA, USA
| | - Cassandra N. Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F. Voight
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
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83
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Basty N, Thanaj M, Cule M, Sorokin EP, Liu Y, Thomas EL, Bell JD, Whitcher B. Artifact-free fat-water separation in Dixon MRI using deep learning. JOURNAL OF BIG DATA 2023; 10:4. [PMID: 36686622 PMCID: PMC9835035 DOI: 10.1186/s40537-022-00677-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/25/2022] [Indexed: 06/17/2023]
Abstract
Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-022-00677-1.
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Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | | | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, USA
| | - E. Louise Thomas
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Jimmy D. Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, University of Westminster, London, UK
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84
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Park J, MacLean MT, Lucas AM, Torigian DA, Schneider CV, Cherlin T, Xiao B, Miller JE, Bradford Y, Judy RL, Verma A, Damrauer SM, Ritchie MD, Witschey WR, Rader DJ. Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank. Cell Rep Med 2022; 3:100855. [PMID: 36513072 PMCID: PMC9798024 DOI: 10.1016/j.xcrm.2022.100855] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 08/22/2022] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.
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Affiliation(s)
- Joseph Park
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew T MacLean
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia M Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew A Torigian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Carolin V Schneider
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tess Cherlin
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brenda Xiao
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason E Miller
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Renae L Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Damrauer
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Surgery, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter R Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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85
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He H, Liao S, Zeng Y, Liang L, Chen J, Tao C. Causal relationships between metabolic-associated fatty liver disease and iron status: Two-sample Mendelian randomization. Liver Int 2022; 42:2759-2768. [PMID: 36226474 DOI: 10.1111/liv.15455] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND & AIMS Dysregulated iron homeostasis plays an important role in the hepatic manifestation of metabolic-associated fatty liver disease (MAFLD). We investigated the causal effects of five iron metabolism markers, regular iron supplementation and MAFLD risk. METHODS Genetic summary statistics were obtained from open genome-wide association study databases. Two-sample bidirectional Mendelian randomization analysis was performed to estimate the causal effect between iron status and MAFLD, including Mendelian randomization inverse-variance weighted, weighted median methods and Mendelian randomization-Egger regression. The Mendelian randomization-PRESSO outlier test, Cochran's Q test and Mendelian randomization-Egger regression were used to assess outliers, heterogeneity and pleiotropy respectively. RESULTS Mendelian randomization inverse-variance weighted results showed that the genetically predicted per standard deviation increase in liver iron (Data set 2: odds ratio 1.193, 95% confidence interval [CI] 1.074-1.326, p = .001) was associated with an increased MAFLD risk, consistent with the weighted median estimates and Mendelian randomization-Egger regression, although Data set 1 was not significant. Mendelian randomization inverse-variance weighted analysis showed that genetically predicted MAFLD was significantly associated with increased serum ferritin levels in both datasets (Dataset 1: β = .038, 95% CI = .014 to .062, p = .002; Dataset 2: β = .081, 95% CI = .025 to .136, p = .004), and a similar result was observed with the weighted median methods for Dataset 2 instead of Mendelian randomization-Egger regression. CONCLUSIONS This study uncovered genetically predicted causal associations between iron metabolism status and MAFLD. These findings underscore the need for improved guidelines for managing MAFLD risk by emphasizing hepatic iron levels as a risk factor and ferritin levels as a prognostic factor.
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Affiliation(s)
- He He
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Sichuan, China
| | - Shenling Liao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Sichuan, China
| | - Yuping Zeng
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Sichuan, China
| | - Libo Liang
- Department of International Medical Centre, West China Hospital, Sichuan University, Sichuan, China
| | - Jie Chen
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Sichuan, China
| | - Chuanmin Tao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Sichuan, China
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86
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van der Meer D, Gurholt TP, Sønderby IE, Shadrin AA, Hindley G, Rahman Z, de Lange AMG, Frei O, Leinhard OD, Linge J, Simon R, Beck D, Westlye LT, Halvorsen S, Dale AM, Karlsen TH, Kaufmann T, Andreassen OA. The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. Commun Biol 2022; 5:1271. [PMID: 36402844 PMCID: PMC9675774 DOI: 10.1038/s42003-022-04237-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 11/08/2022] [Indexed: 11/21/2022] Open
Abstract
Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2 = .25 vs. .13, p = 1.8x10-7). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg = .49, p = 2.7x10-22). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
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Affiliation(s)
- Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ida E Sønderby
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy Hindley
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Zillur Rahman
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Dept. of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Dept. of Psychiatry, University of Oxford, Oxford, UK
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olof D Leinhard
- AMRA Medical, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Jennifer Linge
- AMRA Medical, Linköping, Sweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Rozalyn Simon
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dani Beck
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Sigrun Halvorsen
- Department of Cardiology, Oslo University Hospital Ullevål, and University of Oslo, Oslo, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA, 92037, USA
| | - Tom H Karlsen
- Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway
- Research Institute for Internal Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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87
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Tang Q, Yang Q, Yu X, Liu Y, Tong Z, Li B, Chen Y, Yu EY, Li W. Association of demographic and clinical factors with risk of acute pancreatitis: An exposure-wide Mendelian randomization study. Mol Genet Genomic Med 2022; 11:e2091. [PMID: 36345251 PMCID: PMC9834139 DOI: 10.1002/mgg3.2091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 05/27/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The incidence of acute pancreatitis (AP) is increasing over years, which brings enormous economy and health burden. However, the aetiologies of AP and underlying mechanisms are still unclear. Here, we performed a two-sample Mendelian randomization (MR) analysis to investigate the associations between all reported possible risk factors and AP using publicly available genome-wide association study summary statistics. METHODS A series of quality control steps were taken in our analysis to select eligible instrumental single nucleotide polymorphisms which were strongly associated with exposures. To make the conclusions more robust and reliable, we utilized several analytical methods (inverse-variance weighting, MR-PRESSO method, weighted median, MR-Egger regression) that are based on different assumptions of two-sample MR analysis. The MR-Egger intercept test, radial regression and leave-one-out sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of these genetic variants on each exposure. A two-step MR method was applied to explore mediators in significant associations. RESULTS Genetic predisposition to cholelithiasis (effect estimate: 17.30, 95% CI: 12.25-22.36, p = 1.95 E-11), body mass index (0.32, 95% CI: 0.13-0.51, p < 0.001), body fat percentage (0.57, 95% CI: 0.31-0.83, p = 1.31 E-05), trunk fat percentage (0.36, 95% CI: 0.14-0.59, p < 0.005), ever smoked (1.61, 95% CI: 0.45-2.77, p = 0.007), and limbs fat percentage (0.55, 95% CI: 0.41-0.69, p < 0.001) were associated with an increased risk of AP. In addition, whole-body fat-free mass (-0.32, 95% CI: -0.55 to -0.10, p = 0.004) was associated with a decrease risk of AP. CONCLUSION Genetic predisposition to cholelithiasis, obesity and smoking could be causally associated with an increased risk of AP, and whole body fat-free mass could be associated with a decreased risk of AP.
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Affiliation(s)
- Qiu‐Yi Tang
- Medical School of Southeast UniversityNanjingChina
| | - Qi Yang
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | | | - Yu‐Xiu Liu
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Zhi‐Hui Tong
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Bai‐Qiang Li
- Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
| | - Ya‐Ting Chen
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public HealthSoutheast UniversityNanjingChina
| | - Evan Yi‐Wen Yu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public HealthSoutheast UniversityNanjingChina,CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in MetabolismMaastricht UniversityMaastrichtthe Netherlands
| | - Wei‐Qin Li
- Medical School of Southeast UniversityNanjingChina,Center of Severe Acute Pancreatitis (CSAP), Department of Critical Care MedicineJinling HospitalNanjingChina
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88
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Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies. Sci Rep 2022; 12:18733. [PMID: 36333523 PMCID: PMC9636393 DOI: 10.1038/s41598-022-23632-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022] Open
Abstract
Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.
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89
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Sveinbjornsson G, Ulfarsson MO, Thorolfsdottir RB, Jonsson BA, Einarsson E, Gunnlaugsson G, Rognvaldsson S, Arnar DO, Baldvinsson M, Bjarnason RG, Eiriksdottir T, Erikstrup C, Ferkingstad E, Halldorsson GH, Helgason H, Helgadottir A, Hindhede L, Hjorleifsson G, Jones D, Knowlton KU, Lund SH, Melsted P, Norland K, Olafsson I, Olafsson S, Oskarsson GR, Ostrowski SR, Pedersen OB, Snaebjarnarson AS, Sigurdsson E, Steinthorsdottir V, Schwinn M, Thorgeirsson G, Thorleifsson G, Jonsdottir I, Bundgaard H, Nadauld L, Bjornsson ES, Rulifson IC, Rafnar T, Norddahl GL, Thorsteinsdottir U, Sulem P, Gudbjartsson DF, Holm H, Stefansson K. Multiomics study of nonalcoholic fatty liver disease. Nat Genet 2022; 54:1652-1663. [PMID: 36280732 PMCID: PMC9649432 DOI: 10.1038/s41588-022-01199-5] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 09/02/2022] [Indexed: 11/09/2022]
Abstract
Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options.
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Affiliation(s)
| | - Magnus O Ulfarsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | - David O Arnar
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland.,Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Ragnar G Bjarnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.,Children's Medical Center, Landspítali-The National University Hospital of Iceland, Reykjavík, Iceland
| | | | | | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | | | | | | | | | - Lotte Hindhede
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | | | - David Jones
- Intermountain Healthcare, St. George, UT, USA
| | | | | | - Pall Melsted
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Mechanical Engineering, Industrial Engineering and Computer Science, University of Iceland, Reykjavik, Iceland
| | | | - Isleifur Olafsson
- Clinical Laboratory Services, Diagnostics and Blood Bank, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Sigurdur Olafsson
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Cophenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | | | - Emil Sigurdsson
- Development Centre for Primary Health Care in Iceland, Reykjavík, Iceland.,Department of Family Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Michael Schwinn
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Cophenhagen, Denmark
| | - Gudmundur Thorgeirsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Ingileif Jonsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Einar S Bjornsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.,Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland.,Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland. .,Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
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90
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Karabegović I, Abozaid Y, Maas SCE, Labrecque J, Bos D, De Knegt RJ, Ikram MA, Voortman T, Ghanbari M. Plasma MicroRNA Signature of Alcohol Consumption: The Rotterdam Study. J Nutr 2022; 152:2677-2688. [PMID: 36130258 PMCID: PMC9839997 DOI: 10.1093/jn/nxac216] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/16/2022] [Accepted: 09/13/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) represent a class of noncoding RNAs that regulate gene expression and are implicated in the pathogenesis of different diseases. Alcohol consumption might affect the expression of miRNAs, which in turn could play a role in risk of diseases. OBJECTIVES We investigated whether plasma concentrations of miRNAs are altered by alcohol consumption. Given the existing evidence showing the link between alcohol and liver diseases, we further explored the extent to which these associations are mediated by miRNAs. METHODS Profiling of plasma miRNAs was conducted using the HTG EdgeSeq miRNA Whole Transcriptome Assay in 1933 participants of the Rotterdam Study. Linear regression was implemented to explore the link between alcohol consumption (glasses/d) and miRNA concentrations, adjusted for age, sex, cohort, BMI, and smoking. Sensitivity analysis for alcohol categories (nondrinkers, light drinkers, and heavy drinkers) was performed, where light drinkers corresponded to 0-2 glasses/d in men and 0-1 glasses/d in women, and heavy drinkers to >2 glasses/d in men and >1 glass/d in women. Moreover, we utilized the alcohol-associated miRNAs to explore their potential mediatory role between alcohol consumption and liver-related traits. Finally, we retrieved putative target genes of identified miRNAs to gain an understanding of the molecular pathways concerning alcohol consumption. RESULTS Plasma concentrations of miR-193b-3p, miR-122-5p, miR-3937, and miR-4507 were significantly associated with alcohol consumption surpassing the Bonferroni-corrected P < 8.46 × 10-5. The top significant association was observed for miR-193b-3p (β = 0.087, P = 2.90 × 10-5). Furthermore, a potential mediatory role of miR-3937 and miR-122-5p was observed between alcohol consumption and liver traits. Pathway analysis of putative target genes revealed involvement in biological regulation and cellular processes. CONCLUSIONS This study indicates that alcohol consumption is associated with plasma concentrations of 4 miRNAs. We outline a potential mediatory role of 2 alcohol-associated miRNAs (miR-3937 and miR-122-5p), laying the groundwork for further exploration of miRNAs as potential mediators between lifestyle factors and disease development.
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Affiliation(s)
- Irma Karabegović
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Yasir Abozaid
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Silvana C E Maas
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands,Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Jeremy Labrecque
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands,Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Robert J De Knegt
- Department of Gastroenterology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands,Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
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91
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Trumpff C, Rausser S, Haahr R, Karan KR, Gouspillou G, Puterman E, Kirschbaum C, Picard M. Dynamic behavior of cell-free mitochondrial DNA in human saliva. Psychoneuroendocrinology 2022; 143:105852. [PMID: 35834882 PMCID: PMC9880596 DOI: 10.1016/j.psyneuen.2022.105852] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 05/20/2022] [Accepted: 06/23/2022] [Indexed: 01/31/2023]
Abstract
Mitochondria contain their own genome that can be released in multiple biofluids such as blood and cerebrospinal fluid, as cell-free mitochondrial DNA (cf-mtDNA). In clinical studies, blood cf-mtDNA predicts mortality and higher cf-mtDNA levels are associated with mental and physical stress. However, the dynamics of cf-mtDNA has not been defined, and whether it can be measured non-invasively like other neuroendocrine markers in saliva has not been examined. Here we report cf-mtDNA in human saliva and establish its natural within-person dynamic behavior across multiple weeks. In a small proof-of-principle cohort of healthy adults, we first develop an approach to rapidly quantify salivary cf-mtDNA without DNA isolation, and demonstrate the existence of salivary cf-mtDNA. We then deploy this approach to perform an intensive repeated-measures analysis of two healthy men studied at 4 daily timepoints over 53-60 consecutive days (n = 212-220 observations each) with parallel measures of steroid hormones, self-reported daily mood, and health-related behaviors. Salivary cf-mtDNA exhibited a robust awakening response reaching up to two orders of magnitude 30-45 min after awakening, varied from day-to-day, and moderately correlated with the cortisol awakening response. In exploratory analyses, no consistent association with self-reported daily mood/health-related behaviors were found, although this requires further examination in future studies. Dynamic variation in cf-mtDNA was inversely related with salivary interleukin 6 (IL-6), inconsistent with a pro-inflammatory effect of salivary cf-mtDNA. The highly dynamic behavior of salivary cf-mtDNA opens the door to non-invasive studies examining the relevance of mtDNA signaling in relation to human health.
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Affiliation(s)
- Caroline Trumpff
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, USA
| | - Shannon Rausser
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, USA
| | - Rachel Haahr
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, USA
| | - Kalpita R Karan
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, USA
| | - Gilles Gouspillou
- Département des Sciences de l'Activité Physique, Faculté des Sciences, Université du Québec à Montréal (UQAM), Montreal, Québec, Canada
| | - Eli Puterman
- School of Kinesiology, University of British Columbia, Vancouver, Canada
| | | | - Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, USA; Department of Neurology, H. Houston Merritt Center, Columbia University Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, USA; New York State Psychiatric Institute, New York, USA.
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92
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Chen L, Fan Z, Sun X, Qiu W, Mu W, Chai K, Cao Y, Wang G, Lv G. Examination on the risk factors of cholangiocarcinoma: A Mendelian randomization study. Front Pharmacol 2022; 13:900424. [PMID: 36091764 PMCID: PMC9462706 DOI: 10.3389/fphar.2022.900424] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 07/26/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Several risk factors have been identified for CCA, however, whether such associations were causal remains unknown. Methods: Mendelian randomization (MR) has been applied to examine the causal relationship between 26 putative risk factors and CCA. The genetic variants for each risk factor were extracted from their corresponding genome-wide association study (GWAS) if they reached the genome-wide significance (p-value < 5 × 10−8). The genetic associations with CCA were obtained from the publicly available GWAS with the largest sample size. Mainly, inverse-variance weighted (IVW) has been adopted to estimate the causal effect on CCA. Both multivariable and mediation MR analyses were carried out to detect independent factors. Results: Three putative risk factors can causally elevate the risk of CCA after FDR correction, including liver fat content (LFC), non-alcoholic fatty liver disease (NAFLD), and cholelithiasis. The odds of CCA would increase per 1-SD increase in the liver fat content (LFC) (OR = 2.12 [1.66, 2.71]) and logOR of NAFLD. The genetic liability to cholelithiasis would increase the risk of CCA as well (OR = 2.17 [1.47, 3.20]). They were still significant in other methods. The multivariable MR analysis indicated that genetically-elevated LFC should increase the risk of CCA independently of cholelithiasis (OR = 1.88 [1.39, 2.55]). In the mediation MR analysis, the indirect effect was not significant when treating cholelithiasis as the mediator (indirect OR = 0.95 [0.85, 1.07]). Conclusion: This MR study identified that gallstone and liver fat accumulation are two independent risk factors of CCA, suggesting two modifiable ways of preventing CCA.
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93
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Bešević J, Lacey B, Conroy M, Omiyale W, Feng Q, Collins R, Allen N. New Horizons: the value of UK Biobank to research on endocrine and metabolic disorders. J Clin Endocrinol Metab 2022; 107:2403-2410. [PMID: 35793237 PMCID: PMC9387695 DOI: 10.1210/clinem/dgac407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Indexed: 11/24/2022]
Abstract
UK Biobank is an intensively characterized prospective study of 500 000 men and women, aged 40 to 69 years when recruited, between 2006 and 2010, from the general population of the United Kingdom. Established as an open-access resource for researchers worldwide to perform health research that is in the public interest, UK Biobank has collected (and continues to collect) a vast amount of data on genetic, physiological, lifestyle, and environmental factors, with prolonged follow-up of heath conditions through linkage to administrative electronic health records. The study has already demonstrated its unique value in enabling research into the determinants of common endocrine and metabolic diseases. The importance of UK Biobank, heralded as a flagship project for UK health research, will only increase over time as the number of incident disease events accrue, and the study is enhanced with additional data from blood assays (such as whole-genome sequencing, metabolomics, and proteomics), wearable technologies (including physical activity and cardiac monitors), and body imaging (magnetic resonance imaging and dual-energy X-ray absorptiometry). This unique research resource is likely to transform our understanding of the causes, diagnosis, and treatment of many endocrine and metabolic disorders.
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Affiliation(s)
- Jelena Bešević
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Ben Lacey
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Megan Conroy
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Wemimo Omiyale
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Qi Feng
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Rory Collins
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
- UK Biobank, Stockport, Greater Manchester, UK
| | - Naomi Allen
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
- UK Biobank, Stockport, Greater Manchester, UK
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94
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Mann JP, Hoare M. A minority of somatically mutated genes in pre-existing fatty liver disease have prognostic importance in the development of NAFLD. Liver Int 2022; 42:1823-1835. [PMID: 35474605 PMCID: PMC9544140 DOI: 10.1111/liv.15283] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Understanding the genetics of liver disease has the potential to facilitate clinical risk stratification. We recently identified acquired somatic mutations in six genes and one lncRNA in pre-existing fatty liver disease. We hypothesised that germline variation in these genes might be associated with the risk of developing steatosis and contribute to the prediction of disease severity. METHODS Genome-wide association study (GWAS) summary statistics were extracted from seven studies (>1.7 million participants) for variants near ACVR2A, ALB, CIDEB, FOXO1, GPAM, NEAT1 and TNRC6B for: aminotransferases, liver fat, HbA1c, diagnosis of NAFLD, ARLD and cirrhosis. Findings were replicated using GWAS data from multiple independent cohorts. A phenome-wide association study was performed to examine for related metabolic traits, using both common and rare variants, including gene-burden testing. RESULTS There was no evidence of association between rare germline variants or SNPs near five genes (ACVR2A, ALB, CIDEB, FOXO1 and TNRC6B) and risk or severity of liver disease. Variants in GPAM (proxies for p.Ile43Val) were associated with liver fat (p = 3.6 × 10-13 ), ALT (p = 2.8 × 10-39 ) and serum lipid concentrations. Variants in NEAT1 demonstrated borderline significant associations with ALT (p = 1.9 × 10-11 ) and HbA1c, but not with liver fat, as well as influencing waist-to-hip ratio, adjusted for BMI. CONCLUSIONS Despite the acquisition of somatic mutations at these loci during progressive fatty liver disease, we did not find associations between germline variation and markers of liver disease, except in GPAM. In the future, larger sample sizes may identify associations. Currently, germline polygenic risk scores will not capture data from genes affected by somatic mutations.
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Affiliation(s)
- Jake P. Mann
- Institute of Metabolic ScienceUniversity of CambridgeCambridgeUK
- School of Clinical MedicineUniversity of CambridgeCambridgeUK
| | - Matthew Hoare
- School of Clinical MedicineUniversity of CambridgeCambridgeUK
- CRUK Cambridge InstituteUniversity of CambridgeCambridgeUK
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95
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Nádasdi Á, Gál V, Masszi T, Patócs A, Igaz P, Somogyi A, Firneisz G. Combined effect of pancreatic lipid content and gene variants (TCF7L2, WFS1 and 11BHSD1) on B-cell function in Middle Aged Women in a Post Hoc Analysis. Diabetol Metab Syndr 2022; 14:106. [PMID: 35897035 PMCID: PMC9331183 DOI: 10.1186/s13098-022-00876-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND TCF7L2 rs7903146 and PNPLA3 rs738409 gene variants confer the strongest risk for type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), respectively. Pancreatic triacylglycerol content (PTGC) was reported to have a role in T2DM development. We aimed to assess the correlation between PTGC and hepatic triacylglycerol content (HTGC) stratified by PNPLA3 rs738409 genotype and subsequently interactions between PTGC and gene variants associated with β-cell dysfunction (TCF7L2, WFS1) and visceral adiposity (11ΒHSD1) on β-cell function were also tested. METHODS PTGC and HTGC were assessed using MR in a post-hoc analysis of a genotype-based (PNPLA3 rs738409) recall study of 39 (lipid- and glucose lowering) drug-naïve women. Oral glucose tolerance test, HbA1c, insulin indices, anthropometric data were evaluated. The effect of minor allele carrying of TCF7L2 (rs7903146); WFS1 (rs1801214) and 11ΒHSD1 (rs4844880) variants in combination with PTGC was studied on surrogate markers of β-cell function. We used Spearman's rank-order, Mann-Whitney-U tests, and linear regression models. RESULTS PTGC and HTGC values were correlated after stratification by the rs738409 variant (only in CC genotype group R = 0.67, p = 10- 4). PTGC and HbA1c values correlated in the entire study population (R = 0.58, p = 10- 4). Insulin resistance, sensitivity and disposition indices were correlated with PTGC (HOMA2-IR: R = 0.42, p = 0.008; TyG: R = 0.38, p = 0.018; Matsuda: R= - 0.48, p = 0.002; DIbasal: R=-0.33, p = 0.039; ISSI-2: R=-0.35, p = 0.028). Surrogate markers of β-cell function (HOMA2-B, AUCinsulin/AUCglucose) correlated significantly with PTGC in subjects with the following genotypes rs7903146: CC R = 0.51, p = 0.022; rs18001214: CT + CC R = 0.55, p = 0.013; rs4844880: TA + AA R = 0.56, p = 0.016. The strongest interactions were found between PTGC and TCF7L2 rs7903146 effect on HOMA2-B (p = 0.001) and AUCinsulin/AUCglucose (p = 0.013). CONCLUSIONS The PNPLA3 rs738409 genotype has a major effect on the correlation between PTGC and HTGC. Furthermore we first report the combined effect of PTGC and individual risk gene variants of TCF7L2, WFS1 and 11ΒHSD1 on β-cell dysfunction. The correlation between pancreatic lipid accumulation and HbA1c also indicates an important role for the latter pathology.
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Affiliation(s)
- Ákos Nádasdi
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Szentkirályi St 46, 1088, Budapest, Hungary
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
| | - Tamás Masszi
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Szentkirályi St 46, 1088, Budapest, Hungary
| | - Attila Patócs
- MTA-SE Hereditary Tumors Research Group, Eötvös Loránd Research Network, Budapest, Hungary
- Department of Laboratory Medicine, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Peter Igaz
- MTA-SE Molecular Medicine Research Group, Eötvös Loránd Research Network, Budapest, Hungary
- Department of Endocrinology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Anikó Somogyi
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Szentkirályi St 46, 1088, Budapest, Hungary
| | - Gábor Firneisz
- Department of Internal Medicine and Haematology, Faculty of Medicine, Semmelweis University, Szentkirályi St 46, 1088, Budapest, Hungary.
- MTA-SE Molecular Medicine Research Group, Eötvös Loránd Research Network, Budapest, Hungary.
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96
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Agrawal S, Wang M, Klarqvist MDR, Smith K, Shin J, Dashti H, Diamant N, Choi SH, Jurgens SJ, Ellinor PT, Philippakis A, Claussnitzer M, Ng K, Udler MS, Batra P, Khera AV. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat Commun 2022; 13:3771. [PMID: 35773277 PMCID: PMC9247093 DOI: 10.1038/s41467-022-30931-2] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 12/11/2022] Open
Abstract
For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
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Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Kirk Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joseph Shin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hesam Dashti
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sean J Jurgens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melina Claussnitzer
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
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97
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Sorokin EP, Basty N, Whitcher B, Liu Y, Bell JD, Cohen RL, Cule M, Thomas EL. Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis. Am J Hum Genet 2022; 109:1092-1104. [PMID: 35568031 PMCID: PMC9247824 DOI: 10.1016/j.ajhg.2022.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.
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Affiliation(s)
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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98
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Picard M. Why Do We Care More About Disease than Health? PHENOMICS (CHAM, SWITZERLAND) 2022; 2:145-155. [PMID: 36939781 PMCID: PMC9590501 DOI: 10.1007/s43657-021-00037-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/26/2021] [Accepted: 12/03/2021] [Indexed: 04/30/2023]
Abstract
Modern Western biomedical research and clinical practice are primarily focused on disease. This disease-centric approach has yielded an impressive amount of knowledge around what goes wrong in illness. However, in comparison, researchers and physicians know little about health. What is health? How do we quantify it? And how do we improve it? We currently do not have good answers to these questions. Our lack of fundamental knowledge about health is partly driven by three main factors: (i) a lack of understanding of the dynamic processes that cause variations in health/disease states over time, (ii) an excessive focus on genes, and (iii) a pervasive psychological bias towards additive solutions. Here I briefly discuss potential reasons why scientists and funders have generally adopted a gene- and disease-centric framework, how medicine has ended up practicing "diseasecare" rather than healthcare, and present cursory evidence that points towards an alternative energetic view of health. Understanding the basis of human health with a similar degree of precision that has been deployed towards mapping disease processes could bring us to a point where we can actively support and promote human health across the lifespan, before disease shows up on a scan or in bloodwork.
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Affiliation(s)
- Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, NY 10032 USA
- Department of Neurology, Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY 10032 USA
- New York State Psychiatric Institute, New York, NY 10032 USA
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99
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O’Nunain K, Park C, Urquijo H, Leyden GM, Hughes AD, Davey Smith G, Richardson TG. A lifecourse mendelian randomization study highlights the long-term influence of childhood body size on later life heart structure. PLoS Biol 2022; 20:e3001656. [PMID: 35679339 PMCID: PMC9182693 DOI: 10.1371/journal.pbio.3001656] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
Children with obesity typically have larger left ventricular heart dimensions during adulthood. However, whether this is due to a persistent effect of adiposity extending into adulthood is challenging to disentangle due to confounding factors throughout the lifecourse. We conducted a multivariable mendelian randomization (MR) study to separate the independent effects of childhood and adult body size on 4 magnetic resonance imaging (MRI) measures of heart structure and function in the UK Biobank (UKB) study. Strong evidence of a genetically predicted effect of childhood body size on all measures of adulthood heart structure was identified, which remained robust upon accounting for adult body size using a multivariable MR framework (e.g., left ventricular end-diastolic volume (LVEDV), Beta = 0.33, 95% confidence interval (CI) = 0.23 to 0.43, P = 4.6 × 10-10). Sensitivity analyses did not suggest that other lifecourse measures of body composition were responsible for these effects. Conversely, evidence of a genetically predicted effect of childhood body size on various other MRI-based measures, such as fat percentage in the liver (Beta = 0.14, 95% CI = 0.05 to 0.23, P = 0.002) and pancreas (Beta = 0.21, 95% CI = 0.10 to 0.33, P = 3.9 × 10-4), attenuated upon accounting for adult body size. Our findings suggest that childhood body size has a long-term (and potentially immutable) influence on heart structure in later life. In contrast, effects of childhood body size on other measures of adulthood organ size and fat percentage evaluated in this study are likely explained by the long-term consequence of remaining overweight throughout the lifecourse.
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Affiliation(s)
- Katie O’Nunain
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Chloe Park
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Helena Urquijo
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Genevieve M. Leyden
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, United Kingdom
| | - Alun D. Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - George Davey Smith
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Tom G. Richardson
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Novo Nordisk Research Centre, Headington, Oxford, United Kingdom
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100
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Chen L, Fan Z, Sun X, Qiu W, Chen Y, Zhou J, Lv G. Mendelian Randomization Rules Out Causation Between Inflammatory Bowel Disease and Non-Alcoholic Fatty Liver Disease. Front Pharmacol 2022; 13:891410. [PMID: 35662732 PMCID: PMC9161361 DOI: 10.3389/fphar.2022.891410] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022] Open
Abstract
Background: Inflammatory bowel disease (IBD) and non-alcoholic fatty liver disease (NAFLD) usually co-exist clinically. However, whether such association is causal is still unknown. Methods: Genetic variants were extracted as instrumental variables from the largest genome-wide association study (GWAS) of IBD, Crohn’s disease (CD) and ulcerative colitis (UC) with 25,042 cases and 34,915 controls (GWAS p-value < 5 × 10−8). Information of genetic variants in NAFLD was extracted from a GWAS with 1,483 cases and 17,781controls. Also, liver fat content (LFC) was included as the outcome. Then, a bi-direction Mendelian randomization (MR) was carried out to appraise the causal relationship between NAFLD on IBD. Besides, a multivariable MR (MVMR) design was carried to adjust for body mass index (BMI) and type 2 diabetes (T2D) as well. Results: Generally, IBD might not affect the risk of NAFLD (OR = 0.994 [0.970, 1.019]), together with its subtypes including UC and CD. However, genetically-elevated risk of IBD might cause liver fat accumulation (beta = 0.019, p-value = 0.016) while turning insignificant at Bonferroni correction. Besides, no causal effect of NAFLD on IBD was observed (OR = 0.968 [0.928, 1.009]), together with UC and CD. Also, genetically-elevated LFC could not impact IBD, UC and CD either. The MR CAUSE analysis supported these null associations and MVMR analysis also supported such null associations even after adjusting for BMI and T2D. Conclusion: This MR study ruled out the causal relationship between IBD and NAFLD, suggesting therapeutics targeting NAFLD might not work for IBD and vice versa.
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