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Wang Q, McCormick S, Leask MP, Watson H, O'Sullivan C, Krebs JD, Hall R, Whitfield P, Merry TL, Murphy R, Shepherd PR. A Polynesian-specific SLC22A3 variant associates with low plasma lipoprotein(a) concentrations independent of apo(a) isoform size in males. Biosci Rep 2024; 44:BSR20240403. [PMID: 38896441 DOI: 10.1042/bsr20240403] [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: 04/09/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 06/21/2024] Open
Abstract
Lipoprotein(a) (Lp(a)) is a low-density lipoprotein (LDL)-like particle in which the apolipoprotein B component is covalently linked to apolipoprotein(a) (apo(a)). Lp(a) is a well-established independent risk factor for cardiovascular diseases. Plasma Lp(a) concentrations vary enormously between individuals and ethnic groups. Several nucleotide polymorphisms in the SLC22A3 gene associate with Lp(a) concentration in people of different ethnicities. We investigated the association of a Polynesian-specific (Māori and Pacific peoples) SLC22A3 gene coding variant p.Thr44Met) with the plasma concentration of Lp(a) in a cohort of 302 healthy Polynesian males. An apo(a)-size independent assay assessed plasma Lp(a) concentrations; all other lipid and apolipoprotein concentrations were measured using standard laboratory techniques. Quantitative real-time polymerase chain reaction was used to determine apo(a) isoforms. The range of metabolic (HbA1c, blood pressure, and blood lipids) and blood lipid variables were similar between the non-carriers and carriers in age, ethnicity and BMI adjusted models. However, rs8187715 SLC22A3 variant was significantly associated with lower Lp(a) concentrations. Median Lp(a) concentration was 10.60 nmol/L (IQR: 5.40-41.00) in non-carrier group, and was 7.60 nmol/L (IQR: 5.50-12.10) in variant carrier group (P<0.05). Lp(a) concentration inversely correlated with apo(a) isoform size. After correction for apo(a) isoform size, metabolic parameters and ethnicity, the association between the SLC22A3 variant and plasma Lp(a) concentration remained. The present study is the first to identify the association of this gene variant and low plasma Lp(a) concentrations. This provides evidence for better guidance on ethnic specific cut-offs when defining 'elevated' and 'normal' plasma Lp(a) concentrations in clinical applications.
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Affiliation(s)
- Qian Wang
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre, New Zealand
| | - Sally McCormick
- Maurice Wilkins Centre, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Huti Watson
- Paratene Ngata Research Centre, Ngati Porou Oranga, Te Puia Springs, New Zealand
| | - Conor O'Sullivan
- Maurice Wilkins Centre, New Zealand
- Moko Foundation, Kaitaia, New Zealand
| | - Jeremy D Krebs
- Centre for Endocrine, Diabetes and Obesity Research, Te Whatu Ora New Zealand Capital, Coast and Hutt Valley, Wellington, New Zealand
- Department of Medicine, University of Otago, Wellington, New Zealand
| | - Rosemary Hall
- Department of Medicine, University of Otago, Wellington, New Zealand
| | | | - Troy L Merry
- Maurice Wilkins Centre, New Zealand
- Department of Nutrition, University of Auckland, New Zealand
| | - Rinki Murphy
- Maurice Wilkins Centre, New Zealand
- Auckland Diabetes Center, Te Whatu Ora Health New Zealand, Te Tokai Tumai, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Peter R Shepherd
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre, New Zealand
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2
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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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3
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Amitrano F, Krishnan M, Murphy R, Okesene-Gafa KAM, Ji M, Thompson JMD, Taylor RS, Merriman TR, Rush E, McCowan M, McCowan LME, McKinlay CJD. The impact of CREBRF rs373863828 Pacific-variant on infant body composition. Sci Rep 2024; 14:8825. [PMID: 38627436 PMCID: PMC11021527 DOI: 10.1038/s41598-024-59417-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
In Māori and Pacific adults, the CREBRF rs373863828 minor (A) allele is associated with increased body mass index (BMI) but reduced incidence of type-2 and gestational diabetes mellitus. In this prospective cohort study of Māori and Pacific infants, nested within a nutritional intervention trial for pregnant women with obesity and without pregestational diabetes, we investigated whether the rs373863828 A allele is associated with differences in growth and body composition from birth to 12-18 months' corrected age. Infants with and without the variant allele were compared using generalised linear models adjusted for potential confounding by gestation length, sex, ethnicity and parity, and in a secondary analysis, additionally adjusted for gestational diabetes. Carriage of the rs373863828 A allele was not associated with altered growth and body composition from birth to 6 months. At 12-18 months, infants with the rs373863828 A allele had lower whole-body fat mass [FM 1.4 (0.7) vs. 1.7 (0.7) kg, aMD -0.4, 95% CI -0.7, 0.0, P = 0.05; FM index 2.2 (1.1) vs. 2.6 (1.0) kg/m2 aMD -0.6, 95% CI -1.2,0.0, P = 0.04]. However, this association was not significant after adjustment for gestational diabetes, suggesting that it may be mediated, at least in part, by the beneficial effect of CREBRF rs373863828 A allele on maternal glycemic status.
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Affiliation(s)
| | - Mohanraj Krishnan
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Karaponi A M Okesene-Gafa
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Maria Ji
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - John M D Thompson
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand
| | - Rennae S Taylor
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
| | - Tony R Merriman
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Elaine Rush
- Faculty of Health and Environmental Science, Auckland University of Technology, Auckland, New Zealand
| | - Megan McCowan
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Lesley M E McCowan
- Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand
| | - Christopher J D McKinlay
- Te Whatu Ora, Counties Manukau, Auckland, New Zealand.
- Department of Paediatrics: Child and Youth Health, University of Auckland, Auckland, New Zealand.
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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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5
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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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6
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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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7
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Lee K, Vakili S, Burden HJ, Adams S, Smith GC, Kulatea B, Wright-McNaughton M, Sword D, Watene-O'Sullivan C, Atiola RD, Paul RG, Plank LD, Kallingappa P, King F, Wilcox P, Merriman TR, Krebs JD, Hall RM, Murphy R, Merry TL, Shepherd PR. The minor allele of the CREBRF rs373863828 p.R457Q coding variant is associated with reduced levels of myostatin in males: Implications for body composition. Mol Metab 2022; 59:101464. [PMID: 35218947 PMCID: PMC8927835 DOI: 10.1016/j.molmet.2022.101464] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 02/16/2022] [Accepted: 02/16/2022] [Indexed: 12/04/2022] Open
Abstract
Objective The minor allele (A) of the rs373863828 variant (p.Arg457Gln) in CREBRF is restricted to indigenous peoples of the Pacific islands (including New Zealand Māori and peoples of Polynesia), with a frequency of up to 25% in these populations. This allele associates with a large increase in body mass index (BMI) but with significantly lower risk of type-2 diabetes (T2D). It remains unclear whether the increased BMI is driven by increased adiposity or by increased lean mass. Methods We undertook body composition analysis using DXA in 189 young men of Māori and Pacific descent living in Aotearoa New Zealand. Further investigation was carried out in two orthologous Arg458Gln knockin mouse models on FVB/NJ and C57BL/6j backgrounds. Results The rs373863828 A allele was associated with lower fat mass when adjusted for BMI (p < 0.05) and was associated with significantly lower circulating levels of the muscle inhibitory hormone myostatin (p < 0.05). Supporting the human data, significant reductions in adipose tissue mass were observed in the knockin mice. This was more significant in older mice in both backgrounds and appeared to be the result of reduced age-associated increases in fat mass. The older male knockin mice on C57BL/6j background also had increased grip strength (p < 0.01) and lower levels of myostatin (p < 0.05). Conclusion Overall, these results prove that the rs373863828 A-allele is associated with a reduction of myostatin levels which likely contribute to an age-dependent lowering of fat mass, at least in males. The CREBRF p.457Gln variant associates with decreased fat mass in males. Consistent with this CREBRF p.457Gln associates with decreased myostatin levels. These effects are more obvious with age.
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Affiliation(s)
- Kate Lee
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Sanaz Vakili
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Hannah J Burden
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Shannon Adams
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Greg C Smith
- Department of Pharmacology, School of Medical Sciences, UNSW Australia, Kensington, Australia
| | - Braydon Kulatea
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | | | - Danielle Sword
- Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | | | - Robert D Atiola
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Ryan G Paul
- Waikato Medical Research Centre, University of Waikato, Hamilton, New Zealand
| | - Lindsay D Plank
- Department of Surgery, School of Medicine, The University of Auckland, Auckland, New Zealand
| | - Prasanna Kallingappa
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Frances King
- Ngati Porou Hauora, Te Puia Springs, New Zealand
| | - Phillip Wilcox
- Department of Mathematics and Statistics, University of Otago, New Zealand
| | - Tony R Merriman
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Biochemistry, School of Biomedical Sciences, University of Otago, New Zealand; Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Alabama, United States
| | - Jeremy D Krebs
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | - Rosemary M Hall
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Medicine, University of Otago Wellington, Wellington, New Zealand
| | - Rinki Murphy
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Troy L Merry
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand; Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Peter R Shepherd
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand.
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8
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Limited Metabolic Effect of the CREBRF R457Q Obesity Variant in Mice. Cells 2022; 11:cells11030497. [PMID: 35159305 PMCID: PMC8833978 DOI: 10.3390/cells11030497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 11/17/2022] Open
Abstract
The Arg457Gln missense variant in the CREBRF gene has previously been identified as driving excess body weight in Pacific/Oceanic populations. Intriguingly, Arg457Gln variant carriers also demonstrate paradoxical reductions in diabetes risk, indicating that the gene has a critical role in whole-body metabolism. To study the function of this variant in more detail, we generated mice on an FVB/N background with the Crebrf Arg458Gln variant knocked in to replace the endogenous Crebrf. The whole-body metabolic phenotype was characterized for male and female mice on a regular chow diet or an 8-week high-fat challenge. Regular assessment of body composition found that the Crebrf variant had no influence on total body weight or fat mass at any time point. Glucose tolerance tests demonstrated no obvious genotype effect on glucose homeostasis, with indirect calorimetry measures of whole-body energy expenditure likewise unaffected. Male chow-fed variant carriers displayed a trend towards increased lean mass and significantly reduced sensitivity to insulin administration. Overall, this novel mouse model showed only limited phenotypic effects associated with the Crebrf missense variant. The inability to recapitulate results of human association studies may invite reconsideration of the precise mechanistic link between CREBRF function and the risks of obesity and diabetes in variant allele carriers.
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