1
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Rivara AC, Russell EM, Carlson JC, Pomer A, Naseri T, Reupena MS, Manna SL, Viali S, Minster RL, Weeks DE, DeLany JP, Kershaw EE, McGarvey ST, Hawley NL. Associations between fasting glucose rate-of-change and the missense variant, rs373863828, in an adult Samoan cohort. PLoS One 2024; 19:e0302643. [PMID: 38829901 PMCID: PMC11146712 DOI: 10.1371/journal.pone.0302643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 04/09/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND The A allele of rs373863828 in CREB3 regulatory factor is associated with high Body Mass Index, but lower odds of type 2 diabetes. These associations have been replicated elsewhere, but to date all studies have been cross-sectional. Our aims were (1) to describe the development of type 2 diabetes and change in fasting glucose between 2010 and 2018 among a longitudinal cohort of adult Samoans without type 2 diabetes or who were not using diabetes medications at baseline, and (2) to examine associations between fasting glucose rate-of-change (mmol/L per year) and the A allele of rs373863828. METHODS We describe and test differences in fasting glucose, the development of type 2 diabetes, body mass index, age, smoking status, physical activity, urbanicity of residence, and household asset scores between 2010 and 2018 among a cohort of n = 401 adult Samoans, selected to have a ~2:2:1 ratio of GG:AG: AA rs373863828 genotypes. Multivariate linear regression was used to test whether fasting glucose rate-of-change was associated with rs373863828 genotype, and other baseline variables. RESULTS By 2018, fasting glucose and BMI significantly increased among all genotype groups, and a substantial portion of the sample developed type 2 diabetes mellitus. The A allele was associated with a lower fasting glucose rate-of-change (β = -0.05 mmol/L/year per allele, p = 0.058 among women; β = -0.004 mmol/L/year per allele, p = 0.863 among men), after accounting for baseline variables. Mean fasting glucose and mean BMI increased over an eight-year period and a substantial number of individuals developed type 2 diabetes by 2018. However, fasting glucose rate-of-change, and type 2 diabetes development was lower among females with AG and AA genotypes. CONCLUSIONS Further research is needed to understand the effect of the A allele on fasting glucose and type 2 diabetes development. Based on our observations that other risk factors increased over time, we advocate for the continued promotion for diabetes prevention and treatment programming, and the reduction of modifiable risk factors, in this setting.
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Affiliation(s)
- Anna C. Rivara
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Emily M. Russell
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Jenna C. Carlson
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Alysa Pomer
- Center of Surgery and Public Health, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Take Naseri
- Family Health Clinic, Apia, Samoa
- Naseri & Associates Health Consultancy Firm, Apia, Samoa
| | | | - Samantha L. Manna
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Satupaitea Viali
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America
- Oceania University of Medicine, Apia, Samoa
| | - Ryan L. Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Daniel E. Weeks
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - James P. DeLany
- Advent Health Orlando, Translational Research Institute, Orlando, FL, United States of America
| | - Erin E. Kershaw
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Stephen T. McGarvey
- Department of Epidemiology, International Health Institute, School of Public Health, Brown University, Providence, RI, United States of America
- Department of Anthropology, Brown University, Providence, RI, United States of America
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, United States of America
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2
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Zhang JZ, Heinsberg LW, Krishnan M, Hawley NL, Major TJ, Carlson JC, Hindmarsh JH, Watson H, Qasim M, Stamp LK, Dalbeth N, Murphy R, Sun G, Cheng H, Naseri T, Reupena MS, Kershaw EE, Deka R, McGarvey ST, Minster RL, Merriman TR, Weeks DE. Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries. Genet Epidemiol 2023; 47:105-118. [PMID: 36352773 PMCID: PMC9892232 DOI: 10.1002/gepi.22508] [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: 06/14/2022] [Revised: 09/02/2022] [Accepted: 10/05/2022] [Indexed: 11/11/2022]
Abstract
The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association and network analyses in a Samoa cohort (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 2976). An expanded set of phenotypes (adding estimated fat and fat-free mass, abdominal circumference, hip circumference, and abdominal-hip ratio) was tested in the Samoa cohort (n = 1496). In the Samoa cohort, we observed significant associations (log10 Bayes Factor [BF] ≥ 5.0) between rs373863828 and the overall phenotype panel (8.81), weight (8.30), and BMI (6.42). In the Aotearoa New Zealand cohort, we observed suggestive associations (1.5 < log10 BF < 5) between rs373863828 and the overall phenotype panel (4.60), weight (3.27), and BMI (1.80). In the combined cohort, we observed concordant signals with larger log10 BFs. In the Samoa-specific expanded phenotype analyses, we also observed significant associations between rs373863828 and fat mass (5.65), abdominal circumference (5.34), and hip circumference (5.09). Bayesian networks provided evidence for a direct association of rs373863828 with weight and indirect associations with height and BMI.
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Affiliation(s)
- Jerry Z. Zhang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Lacey W. Heinsberg
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Mohanraj Krishnan
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT
| | - Tanya J. Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Jenna C. Carlson
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Huti Watson
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti, New Zealand
| | - Muhammad Qasim
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti, New Zealand
| | - Lisa K. Stamp
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Guangyun Sun
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Hong Cheng
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | | | - Erin E. Kershaw
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Ranjan Deka
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI
- Department of Anthropology, Brown University, Providence, RI
| | - Ryan L. Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Tony R. Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama Birmingham, Birmingham, AL
| | - Daniel E. Weeks
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
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3
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Hawley NL, Duckham RL, Carlson JC, Naseri T, Reupena MS, Lameko V, Pomer A, Wetzel A, Selu M, Lupematisila V, Unasa F, Vesi L, Fatu T, Unasa S, Faasalele-Savusa K, Rivara AC, Russell E, Delany JP, Viali S, Kershaw EE, Minster RL, Weeks DE, McGarvey ST. The protective effect of rs373863828 on type 2 diabetes does not operate through a body composition pathway in adult Samoans. Obesity (Silver Spring) 2022; 30:2468-2476. [PMID: 36284436 PMCID: PMC10111239 DOI: 10.1002/oby.23559] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 04/01/2022] [Accepted: 05/09/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVE The aim of this study was to understand whether the paradoxical association of missense variant rs373863828 in CREB3 regulatory factor (CREBRF) with higher BMI but lower odds of diabetes is explained by either metabolically favorable body fat distribution or greater fat-free mass. METHODS This study explored the association of the minor allele with dual-energy x-ray absorptiometry-derived body composition in n = 421 Samoans and used path analysis to examine the mediating role of fat and fat-free mass on the relationship between rs373863828 and fasting glucose. RESULTS Among females, the rs373863828 minor A allele was associated with greater BMI. There was no association of genotype with percent body fat, visceral adiposity, or fat distribution in either sex. In both females and males, lean mass was greater with each A allele: 2.16 kg/copy (p = 0.0001) and 1.73 kg/copy (p = 0.02), respectively. Path analysis showed a direct negative effect of rs373863828 genotype on fasting glucose (p = 0.004) consistent with previous findings, but also an indirect positive effect on fasting glucose operating through fat-free mass (p = 0.027). CONCLUSIONS The protective effect of rs373863828 in CREBRF, common among Pacific Islanders, on type 2 diabetes does not operate through body composition. Rather, the variant's effects on body size/composition and fasting glucose likely operate via different, tissue-specific mechanisms.
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Affiliation(s)
- Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Rachel L. Duckham
- Institute of Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia
- Australian Institute for Musculoskeletal Science (AIMSS), The University of Melbourne and Western Health, St Albans, Victoria, Australia
| | - Jenna C. Carlson
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | - Alysa Pomer
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Abigail Wetzel
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Melania Selu
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | | | - Folla Unasa
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Lupesina Vesi
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Tracy Fatu
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Seipepa Unasa
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | | | - Anna C. Rivara
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Emily Russell
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - James P. Delany
- AdventHealth, Translational Research Institute, Orlando, FL, USA
| | | | - Erin E. Kershaw
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryan L. Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel E. Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
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4
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Li Y, Wang H, Chen H, Liao Y, Gou S, Yan Q, Zhuang Z, Li H, Wang J, Suo Y, Lan T, Liu Y, Zhao Y, Zou Q, Nie T, Hui X, Lai L, Wu D, Fan N. Generation of a genetically modified pig model with CREBRF R457Q variant. FASEB J 2022; 36:e22611. [PMID: 36250915 DOI: 10.1096/fj.202201117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022]
Abstract
Obesity is among the strongest risk factors for type 2 diabetes (T2D). The CREBRF missense allele rs373863828 (p. Arg457Gln, p. R457Q) is associated with increased body mass index but reduced risk of T2D in people of Pacific ancestry. To investigate the functional consequences of the CREBRF variant, we introduced the corresponding human mutation R457Q into the porcine genome. The CREBRFR457Q pigs displayed dramatically increased fat deposition, which was mainly distributed in subcutaneous adipose tissue other than visceral adipose tissue. The CREBRFR457Q variant promoted preadipocyte differentiation. The increased differentiation capacity of precursor adipocytes conferred pigs the unique histological phenotype that adipocytes had a smaller size but a greater number in subcutaneous adipose tissue (SAT) of CREBRFR457Q variant pigs. In addition, in SAT of CREBRFR457Q pigs, the contents of the peroxidative metabolites 4-hydroxy-nonenal and malondialdehyde were significantly decreased, while the activity of antioxidant enzymes, such as glutathione peroxidase, superoxide dismutase, and catalase, was increased, which was in accordance with the declined level of the reactive oxygen species (ROS) in CREBRFR457Q pigs. Together, these data supported a causal role of the CREBRFR457Q variant in the pathogenesis of obesity, partly via adipocyte hyperplasia, and further suggested that reduced oxidative stress in adipose tissue may mediate the relative metabolic protection afforded by this variant despite the related obesity.
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Affiliation(s)
- Yingying Li
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Chinese Academy of Sciences, Beijing, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Hai Wang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Huangyao Chen
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yuan Liao
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Institute of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Shixue Gou
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Quanmei Yan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Zhenpeng Zhuang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Hao Li
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Jiaowei Wang
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yangyang Suo
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Ting Lan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yang Liu
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yu Zhao
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Qingjian Zou
- Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, China
| | - Tao Nie
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Xiaoyan Hui
- School of Biomedical Sciences, the Chinese University of Hong Kong, Hong Kong SAR
| | - Liangxue Lai
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China.,Guangdong Provincial Key Laboratory of Large Animal Models for Biomedicine, Wuyi University, Jiangmen, China
| | - Donghai Wu
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Nana Fan
- CAS Key Laboratory of Regenerative Biology, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Sanya Institute of Swine Resource, Hainan Provincial Research Centre of Laboratory Animals, Sanya, China.,Research Unit of Generation of Large Animal Disease Models, Chinese Academy of Medical Sciences (2019RU015), Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
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5
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Lin M, Caberto C, Wan P, Li Y, Lum-Jones A, Tiirikainen M, Pooler L, Nakamura B, Sheng X, Porcel J, Lim U, Setiawan VW, Le Marchand L, Wilkens LR, Haiman CA, Cheng I, Chiang CWK. Population-specific reference panels are crucial for genetic analyses: an example of the CREBRF locus in Native Hawaiians. Hum Mol Genet 2021; 29:2275-2284. [PMID: 32491157 DOI: 10.1093/hmg/ddaa083] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 03/16/2020] [Accepted: 03/17/2020] [Indexed: 01/10/2023] Open
Abstract
Statistical imputation applied to genome-wide array data is the most cost-effective approach to complete the catalog of genetic variation in a study population. However, imputed genotypes in underrepresented populations incur greater inaccuracies due to ascertainment bias and a lack of representation among reference individuals, further contributing to the obstacles to study these populations. Here we examined the consequences due to the lack of representation by genotyping in a large number of self-reported Native Hawaiians (N = 3693) a functionally important, Polynesian-specific variant in the CREBRF gene, rs373863828. We found the derived allele was significantly associated with several adiposity traits with large effects (e.g. ~ 1.28 kg/m2 per allele in body mass index as the most significant; P = 7.5 × 10-5), consistent with the original findings in Samoans. Due to the current absence of Polynesian representation in publicly accessible reference sequences, rs373863828 or its proxies could not be tested through imputation using these existing resources. Moreover, the association signals at the entire CREBRF locus could not be captured by alternative approaches, such as admixture mapping. In contrast, highly accurate imputation can be achieved even if a small number (<200) of internally constructed Polynesian reference individuals were available; this would increase sample size and improve the statistical evidence of associations. Taken together, our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci such as CREBRF. Yet, they could be easily detected and prioritized with improved representation of diverse populations in sequencing studies.
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Affiliation(s)
- Meng Lin
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christian Caberto
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Peggy Wan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA
| | - Annette Lum-Jones
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Maarit Tiirikainen
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Loreall Pooler
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Brooke Nakamura
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Xin Sheng
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jacqueline Porcel
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Unhee Lim
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Veronica Wendy Setiawan
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI 96813, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94518, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.,Quantitative Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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6
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Arslanian KJ, Fidow UT, Atanoa T, Unasa-Apelu F, Naseri T, Wetzel AI, Pomer A, Duckham RL, McGarvey ST, Strayer JA, Kershaw EE, Weeks DE, Hawley NL. A missense variant in CREBRF, rs373863828, is associated with fat-free mass, not fat mass in Samoan infants. Int J Obes (Lond) 2021; 45:45-55. [PMID: 32884101 PMCID: PMC8329753 DOI: 10.1038/s41366-020-00659-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 07/17/2020] [Accepted: 08/15/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND/OBJECTIVES In Samoa, where 80% of the adult population is living with obesity, understanding the determinants of adiposity and growth during infancy may inform prevention efforts. We examined the association of a missense variant, rs373863828, in the CREBRF gene with body composition in Samoan infants. Adults with one or more copies of the rs373863828 minor allele (A) have higher odds of obesity, based on body-mass index (BMI), but paradoxically decreased odds of diabetes compared to those without the allele. Our study may offer novel insight into the natural history and pathogenesis of this unexpected relationship. SUBJECTS/METHODS In a prospective study, we measured body composition in early infancy, and at 2- and 4-months of age using anthropometry and dual-energy x-ray absorptiometry (DXA). We genotyped subjects at the CREBRF rs373863828 locus and compared infants with (AA/AG) and without (GG) the variant. In longitudinal analyses, we calculated the absolute change in each outcome from the early infant to the 4-month assessment, adjusting for baseline and other covariates. RESULTS In cross-sectional analyses, there was no significant difference in infant BMI or fat mass by genotype. After adjusting for covariates, infants with the variant had 4.0 ± 1.8 g more bone mass (p = 0.026) and 210.9 ± 79.6 g more lean mass (p = 0.009) at 4-months and accumulated 176.9 ± 73.0 g more lean mass between the early infant and 4-month assessment (p = 0.017). CONCLUSIONS The CREBRF rs373863828 minor allele (A) was not associated with increased BMI or adiposity in Samoan infants, but instead with increased lean and bone mass. Our findings suggest that lean (i.e., muscle) and bone mass accretion should be explored as pathways to explain the "protective" effect of the CREBRF variant against diabetes.
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Affiliation(s)
- K J Arslanian
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
- Department of Anthropology, Yale University, New Haven, CT, USA
| | - U T Fidow
- Obstetrics & Gynecology, Tupua Tamasese Meaole Hospital, Samoa National Health Services, Apia, Samoa
| | - T Atanoa
- Community Studies Program, University of California-Santa Cruz, Santa Cruz, CA, USA
| | - F Unasa-Apelu
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - T Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | - A I Wetzel
- International Health Institute, Brown University School of Public Health, Providence, RI, USA
| | - A Pomer
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - R L Duckham
- Deakin University, Institute for Physical Activity and Nutrition, Geelong, VIC, Australia
- Australian Institute for Musculoskeletal Sciences (AIMSS), The University of Melbourne and Western Health, St. Albans, Australia
| | - S T McGarvey
- International Health Institute, Brown University School of Public Health, Providence, RI, USA
- Departments of Epidemiology and Anthropology, Brown University, Providence, RI, USA
| | - J A Strayer
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - E E Kershaw
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - D E Weeks
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - N L Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
- Department of Anthropology, Yale University, New Haven, CT, USA.
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7
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Hawley NL, Pomer A, Rivara AC, Rosenthal SL, Duckham RL, Carlson JC, Naseri T, Reupena MS, Selu M, Lupematisila V, Unasa F, Vesi L, Fatu T, Unasa S, Faasalele-Savusa K, Wetzel AI, Soti-Ulberg C, Prescott AT, Siufaga G, Penaia C, To SB, LaMonica LC, Lameko V, Choy CC, Crouter SE, Redline S, Deka R, Kershaw EE, Urban Z, Minster RL, Weeks DE, McGarvey ST. Exploring the Paradoxical Relationship of a Creb 3 Regulatory Factor Missense Variant With Body Mass Index and Diabetes Among Samoans: Protocol for the Soifua Manuia (Good Health) Observational Cohort Study. JMIR Res Protoc 2020; 9:e17329. [PMID: 32706746 PMCID: PMC7413272 DOI: 10.2196/17329] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 02/24/2020] [Accepted: 03/02/2020] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The prevalence of obesity and diabetes in Samoa, like many other Pacific Island nations, has reached epidemic proportions. Although the etiology of these conditions can be largely attributed to the rapidly changing economic and nutritional environment, a recently identified genetic variant, rs373863828 (CREB 3 regulatory factor, CREBRF: c.1370G>A p.[R457Q]) is associated with increased odds of obesity, but paradoxically, decreased odds of diabetes. OBJECTIVE The overarching goal of the Soifua Manuia (Good Health) study was to precisely characterize the association of the CREBRF variant with metabolic (body composition and glucose homeostasis) and behavioral traits (dietary intake, physical activity, sleep, and weight control behaviors) that influence energy homeostasis in 500 adults. METHODS A cohort of adult Samoans who participated in a genome-wide association study of adiposity in Samoa in 2010 was followed up, based on the presence or absence of the CREBRF variant, between August 2017 and March 2019. Over a period of 7-10 days, each participant completed the main study protocol, which consisted of anthropometric measurements (weight, height, circumferences, and skinfolds), body composition assessment (bioelectrical impedance and dual-energy x-ray absorptiometry), point-of-care glycated hemoglobin measurement, a fasting blood draw and oral glucose tolerance test, urine collection, blood pressure measurement, hand grip strength measurement, objective physical activity and sleep apnea monitoring, and questionnaire measures (eg, health interview, cigarette and alcohol use, food frequency questionnaire, socioeconomic position, stress, social support, food and water insecurity, sleep, body image, and dietary preferences). In January 2019, a subsample of the study participants (n=118) completed a buttock fat biopsy procedure to collect subcutaneous adipose tissue samples. RESULTS Enrollment of 519 participants was completed in March 2019. Data analyses are ongoing, with results expected in 2020 and 2021. CONCLUSIONS While the genetic variant rs373863828, in CREBRF, has the largest known effect size of any identified common obesity gene, very little is currently understood about the mechanisms by which it confers increased odds of obesity but paradoxically lowered odds of type 2 diabetes. The results of this study will provide insights into how the gene functions on a whole-body level, which could provide novel targets to prevent or treat obesity, diabetes, and associated metabolic disorders. This study represents the human arm of a comprehensive and integrated approach involving humans as well as preclinical models that will provide novel insights into metabolic disease. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/17329.
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Affiliation(s)
- Nicola L Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | - Alysa Pomer
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | - Anna C Rivara
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | - Samantha L Rosenthal
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rachel L Duckham
- Institute of Physical Activity and Nutrition, Deakin University, Geelong, Australia
- Australian Institute for Musculoskeletal Sciences, The University of Melbourne and Western Health, St Albans, Australia
| | - Jenna C Carlson
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | | | | | - Melania Selu
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | | | - Folla Unasa
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Lupesina Vesi
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Tracy Fatu
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Seipepa Unasa
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | | | - Abigail I Wetzel
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, United States
| | | | - Angela T Prescott
- Department of Plastic and Reconstructive Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Gloria Siufaga
- Obesity, Lifestyle and Genetic Adaptations Study Group, Apia, Samoa
| | - Corina Penaia
- Asian Pacific Islander Forward Movement, Los Angeles, CA, United States
| | - Sophie B To
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, United States
| | - Lauren C LaMonica
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | | | - Courtney C Choy
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, United States
| | - Scott E Crouter
- Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee Knoxville, Knoxville, TN, United States
| | - Susan Redline
- Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Ranjan Deka
- Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH, United States
| | - Erin E Kershaw
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zsolt Urban
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ryan L Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Stephen T McGarvey
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, United States
- Department of Anthropology, Brown University, Providence, RI, United States
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8
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Barroso I, McCarthy MI. The Genetic Basis of Metabolic Disease. Cell 2019; 177:146-161. [PMID: 30901536 PMCID: PMC6432945 DOI: 10.1016/j.cell.2019.02.024] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/11/2019] [Accepted: 02/14/2019] [Indexed: 02/06/2023]
Abstract
Recent developments in genetics and genomics are providing a detailed and systematic characterization of the genetic underpinnings of common metabolic diseases and traits, highlighting the inherent complexity within systems for homeostatic control and the many ways in which that control can fail. The genetic architecture underlying these common metabolic phenotypes is complex, with each trait influenced by hundreds of loci spanning a range of allele frequencies and effect sizes. Here, we review the growing appreciation of this complexity and how this has fostered the implementation of genome-scale approaches that deliver robust mechanistic inference and unveil new strategies for translational exploitation.
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Affiliation(s)
- Inês Barroso
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK; Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK
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9
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Hawley NL, McGarvey ST. Human biology of the Pacific. Ann Hum Biol 2018; 45:171-174. [PMID: 29877156 DOI: 10.1080/03014460.2018.1477483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Nicola L Hawley
- a Department of Chronic Disease Epidemiology , Yale School of Public Health , New Haven , CT , USA
| | - Stephen T McGarvey
- b Epidemiology and Anthropology , International Health Institute, Brown University School of Public Health , Providence , RI , USA
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