1
|
Smith NC. Residential segregation and Black-White differences in physical and mental health: Evidence of a health paradox? Soc Sci Med 2024; 340:116417. [PMID: 38007966 DOI: 10.1016/j.socscimed.2023.116417] [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: 08/15/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 11/28/2023]
Abstract
Ample research finds that residential segregation is detrimental to Black Americans' physical health and exacerbates Black-White physical health disparities. However, less is known about how residential segregation may influence Black Americans' mental health and Black-White differences in mental health. Drawing on U.S. census data and a state representative study of Indiana residents (N = 2,685), I examine associations between residential segregation and multiple dimensions of physical and mental health. Consistent with past research, I find that residential segregation has an adverse association with physical health among Black respondents. In contrast, I find residential segregation to have a salubrious association with Black respondents' mental health, producing a Black mental health advantage at higher levels of segregation. I conclude by discussing the implications of these findings for research on residential segregation and health and the Black-White mental health paradox.
Collapse
Affiliation(s)
- Nicholas C Smith
- University of Maryland, Department of Sociology, 3141 Parren J. Mitchell Art-Sociology Building, RM 3137, College Park, MD, 20742, USA.
| |
Collapse
|
2
|
Salih A, Ardissino M, Wagen AZ, Bard A, Szabo L, Ryten M, Petersen SE, Altmann A, Raisi‐Estabragh Z. Genome-Wide Association Study of Pericardial Fat Area in 28 161 UK Biobank Participants. J Am Heart Assoc 2023; 12:e030661. [PMID: 37889180 PMCID: PMC10727393 DOI: 10.1161/jaha.123.030661] [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: 04/19/2023] [Accepted: 09/06/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND Pericardial adipose tissue (PAT) is the visceral adipose tissue compartment surrounding the heart. Experimental and observational research has suggested that greater PAT deposition might mediate cardiovascular disease, independent of general or subcutaneous adiposity. We characterize the genetic architecture of adiposity-adjusted PAT and identify causal associations between PAT and adverse cardiac magnetic resonance imaging measures of cardiac structure and function in 28 161 UK Biobank participants. METHODS AND RESULTS The PAT phenotype was extracted from cardiac magnetic resonance images using an automated image analysis tool previously developed and validated in this cohort. A genome-wide association study was performed with PAT area set as the phenotype, adjusting for age, sex, and other measures of obesity. Functional mapping and Bayesian colocalization were used to understand the biologic role of identified variants. Mendelian randomization analysis was used to examine potential causal links between genetically determined PAT and cardiac magnetic resonance-derived measures of left ventricular structure and function. We discovered 12 genome-wide significant variants, with 2 independent sentinel variants (rs6428792, P=4.20×10-9 and rs11992444, P=1.30×10-12) at 2 distinct genomic loci, that were mapped to 3 potentially causal genes: T-box transcription factor 15 (TBX15), tryptophanyl tRNA synthetase 2, mitochondrial (WARS2) and early B-cell factor-2 (EBF2) through functional annotation. Bayesian colocalization additionally suggested a role of RP4-712E4.1. Genetically predicted differences in adiposity-adjusted PAT were causally associated with adverse left ventricular remodeling. CONCLUSIONS This study provides insights into the genetic architecture determining differential PAT deposition, identifies causal links with left structural and functional parameters, and provides novel data about the pathophysiological importance of adiposity distribution.
Collapse
Affiliation(s)
- Ahmed Salih
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
| | - Maddalena Ardissino
- National Heart and Lung Institute, Imperial College LondonLondonUnited Kingdom
- Heart and Lung Research Institute, University of CambridgeCambridgeUnited Kingdom
| | - Aaron Z. Wagen
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUnited Kingdom
- Department of Clinical and Movement NeurosciencesQueen Square Institute of NeurologyLondonUnited Kingdom
- Neurodegeneration Biology LaboratoryThe Francis Crick InstituteLondonUnited Kingdom
| | - Andrew Bard
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
| | - Liliana Szabo
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
- Semmelweis University, Heart and Vascular CenterBudapestHungary
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUnited Kingdom
- NIHR Great Ormond Street Hospital Biomedical Research CentreUniversity College LondonLondonUnited Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
- Health Data Research UKLondonUnited Kingdom
- Alan Turing InstituteLondonUnited Kingdom
| | - André Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Zahra Raisi‐Estabragh
- William Harvey Research Institute, National Institute for Health and Care Research (NIHR) Barts Biomedical Research CentreQueen Mary University of London, Charterhouse SquareLondonUnited Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health National Health Service (NHS) Trust, West SmithfieldLondonUnited Kingdom
| |
Collapse
|
3
|
Wu Y, Tian H, Wang W, Li W, Duan H, Zhang D. DNA methylation and waist-to-hip ratio: an epigenome-wide association study in Chinese monozygotic twins. J Endocrinol Invest 2022; 45:2365-2376. [PMID: 35882828 DOI: 10.1007/s40618-022-01878-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/19/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Epigenetic signatures such as DNA methylation may be associated with specific obesity traits. We performed an epigenome-wide association study (EWAS) by combining with the waist-to-hip ratio (WHR)-discordant monozygotic (MZ) twin design in an attempt to identify genetically independent DNA methylation marks associated with abdominal obesity in Northern Han Chinese and to determine the causation underlying. METHODS A total of 60 WHR discordant MZ twin pairs were selected from the Qingdao Twin Registry, China. Generalized estimated equation (GEE) model was used to regress the methylation level of CpG sites on WHR. The Inference about Causation through Examination of FAmiliaL CONfounding (ICE FALCON) was used to assess the temporal relationship between methylation and WHR. Gene expression analysis was conducted to validate the results of differentially methylated analyses. RESULTS EWAS identified 92 CpG sites with the level of P < 10 - 4 which were annotated to 32 genes, especially CADPS2, TUSC5, ZCCHC14, CORO7, COL23A1, CACNA1C, CYP26B1, and BCAT1. ICE FALCON showed significant causality between DNA methylation of several genes and WHR (P < 0.05). In region-based analysis, 14 differentially methylated regions (DMRs) located at 15 genes (slk-corrected P < 0.05) were detected. The gene expression analysis identified the significant correlation between expression levels of 5 differentially methylated genes and WHR (P < 0.05). CONCLUSIONS Our study identifies the associations between specific epigenetic variations and WHR in Northern Han Chinese. These DNA methylation signatures may have value as diagnostic biomarkers and provide novel insights into the molecular mechanisms of pathogenesis.
Collapse
Affiliation(s)
- Y Wu
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, NO. 308 Ningxia Road, 266071, Qingdao, Shandong, China.
| | - H Tian
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, NO. 308 Ningxia Road, 266071, Qingdao, Shandong, China
| | - W Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, NO. 308 Ningxia Road, 266071, Qingdao, Shandong, China
| | - W Li
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - H Duan
- Qingdao Municipal Center for Disease Control and Prevention, Qingdao, Shandong, China
| | - D Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, NO. 308 Ningxia Road, 266071, Qingdao, Shandong, China
| |
Collapse
|
4
|
Bojarczuk A, Boulygina EA, Dzitkowska-Zabielska M, Łubkowska B, Leońska-Duniec A, Egorova ES, Semenova EA, Andryushchenko LB, Larin AK, Generozov EV, Cięszczyk P, Ahmetov II. Genome-Wide Association Study of Exercise-Induced Fat Loss Efficiency. Genes (Basel) 2022; 13:1975. [PMID: 36360211 PMCID: PMC9690053 DOI: 10.3390/genes13111975] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 04/21/2024] Open
Abstract
There is a wide range of individual variability in the change of body weight in response to exercise, and this variability partly depends on genetic factors. The study aimed to determine DNA polymorphisms associated with fat loss efficiency in untrained women with normal weight in response to a 12-week aerobic training program using the GWAS approach, followed by a cross-sectional study in athletes. The study involved 126 untrained young Polish women (age 21.4 ± 1.7 years; body mass index (BMI): 21.7 (2.4) kg/m2) and 550 Russian athletes (229 women, age 23.0 ± 4.1; 321 men, age 23.9 ± 4.7). We identified one genome-wide significant polymorphism (rs116143768) located in the ACSL1 gene (acyl-CoA synthetase long-chain family member 1, implicated in fatty acid oxidation), with a rare T allele associated with higher fat loss efficiency in Polish women (fat mass decrease: CC genotype (n = 122) -3.8%; CT genotype (n = 4) -31.4%; p = 1.18 × 10-9). Furthermore, male athletes with the T allele (n = 7) had significantly lower BMI (22.1 (3.1) vs. 25.3 (4.2) kg/m2, p = 0.046) than subjects with the CC genotype (n = 314). In conclusion, we have shown that the rs116143768 T allele of the ACSL1 gene is associated with higher fat loss efficiency in response to aerobic training in untrained women and lower BMI in physically active men.
Collapse
Affiliation(s)
- Aleksandra Bojarczuk
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
| | | | | | - Beata Łubkowska
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
| | - Agata Leońska-Duniec
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
| | - Emiliya S. Egorova
- Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia
| | - Ekaterina A. Semenova
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
- Research Institute of Physical Culture and Sport, Volga Region State University of Physical Culture, Sport and Tourism, 420138 Kazan, Russia
| | - Liliya B. Andryushchenko
- Department of Physical Education, Plekhanov Russian University of Economics, 115093 Moscow, Russia
| | - Andrey K. Larin
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
| | - Edward V. Generozov
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
| | - Pawel Cięszczyk
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
| | - Ildus I. Ahmetov
- Laboratory of Genetics of Aging and Longevity, Kazan State Medical University, 420012 Kazan, Russia
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
- Department of Physical Education, Plekhanov Russian University of Economics, 115093 Moscow, Russia
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 5AF, UK
| |
Collapse
|
5
|
Boulet N, Briot A, Galitzky J, Bouloumié A. The Sexual Dimorphism of Human Adipose Depots. Biomedicines 2022; 10:2615. [PMID: 36289874 PMCID: PMC9599294 DOI: 10.3390/biomedicines10102615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 08/21/2023] Open
Abstract
The amount and the distribution of body fat exhibit trajectories that are sex- and human species-specific and both are determinants for health. The enhanced accumulation of fat in the truncal part of the body as a risk factor for cardiovascular and metabolic diseases is well supported by epidemiological studies. In addition, a possible independent protective role of the gluteofemoral fat compartment and of the brown adipose tissue is emerging. The present narrative review summarizes the current knowledge on sexual dimorphism in fat depot amount and repartition and consequences on cardiometabolic and reproductive health. The drivers of the sex differences and fat depot repartition, considered to be the results of complex interactions between sex determination pathways determined by the sex chromosome composition, genetic variability, sex hormones and the environment, are discussed. Finally, the inter- and intra-depot heterogeneity in adipocytes and progenitors, emphasized recently by unbiased large-scale approaches, is highlighted.
Collapse
Affiliation(s)
| | | | | | - Anne Bouloumié
- Inserm, Unité Mixte de Recherche (UMR) 1297, Team 1, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université de Toulouse, F-31432 Toulouse, France
| |
Collapse
|
6
|
Exploring Lead loci shared between schizophrenia and Cardiometabolic traits. BMC Genomics 2022; 23:617. [PMID: 36008755 PMCID: PMC9414090 DOI: 10.1186/s12864-022-08766-4] [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: 01/16/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.
Collapse
|
7
|
Li H, Konja D, Wang L, Wang Y. Sex Differences in Adiposity and Cardiovascular Diseases. Int J Mol Sci 2022; 23:ijms23169338. [PMID: 36012601 PMCID: PMC9409326 DOI: 10.3390/ijms23169338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
Body fat distribution is a well-established predictor of adverse medical outcomes, independent of overall adiposity. Studying body fat distribution sheds insights into the causes of obesity and provides valuable information about the development of various comorbidities. Compared to total adiposity, body fat distribution is more closely associated with risks of cardiovascular diseases. The present review specifically focuses on the sexual dimorphism in body fat distribution, the biological clues, as well as the genetic traits that are distinct from overall obesity. Understanding the sex determinations on body fat distribution and adiposity will aid in the improvement of the prevention and treatment of cardiovascular diseases (CVD).
Collapse
|
8
|
Wan EYF, Fung WT, Yu EYT, Cheng WHG, Chan KS, Wang Y, Chan EWY, Wong ICK, Lam CLK. Association of genetic variants related to combined exposure to higher BMI and waist-to-hip ratio on lifelong cardiovascular risk in UK Biobank. Public Health Nutr 2022; 26:1-9. [PMID: 35621080 DOI: 10.1017/s1368980022001276] [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] [Indexed: 11/07/2022]
Abstract
OBJECTIVE This study examines the individual and combined association of BMI and waist-to-hip ratio (WHR) with CVD risk using genetic scores of the obesity measurements as proxies. DESIGN A 2 × 2 factorial analysis approach was applied, with participants divided into four groups of lifetime exposure to low BMI and WHR, high BMI, high WHR, and high BMI and WHR based on weighted genetic risk scores. The difference in CVD risk across groups was evaluated using multivariable logistic regression. SETTING Cohort study. PARTICIPANTS A total of 408 003 participants were included from the prospective observational UK Biobank study. RESULTS A total of 58 429 CVD events were recorded. Compared to the low BMI and WHR genetic scores group, higher BMI or higher WHR genetic scores were associated with an increase in CVD risk (high WHR: OR, 1·07; 95 % CI (1·04, 1·10)); high BMI: OR, 1·12; 95 % CI (1·09, 1·16). A weak additive effect on CVD risk was found between BMI and WHR (high BMI and WHR: OR, 1·16; 95 % CI (1·12, 1·19)). Subgroup analysis showed similar patterns between different sex, age (<65, ≥65 years old), smoking status, Townsend deprivation index, fasting glucose level and medication uses, but lower systolic blood pressure was associated with higher CVD risk in obese participants. CONCLUSIONS High BMI and WHR were associated with increased CVD risk, and their effects are weakly additive. Even though there were overlapping of effect, both BMI and WHR are important in assessing the CVD risk in the general population.
Collapse
Affiliation(s)
- Eric Yuk Fai Wan
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region, China
| | - Wing Tung Fung
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Esther Yee Tak Yu
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Will Ho Gi Cheng
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kam Suen Chan
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yuan Wang
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Esther Wai Yin Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region, China
| | - Ian Chi Kei Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region, China
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
| | - Cindy Lo Kuen Lam
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, China
| |
Collapse
|
9
|
Can adult polygenic scores improve prediction of body mass index in childhood? Int J Obes (Lond) 2022; 46:1375-1383. [PMID: 35505076 DOI: 10.1038/s41366-022-01130-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND/OBJECTIVES Modelling genetic pre-disposition may identify children at risk of obesity. However, most polygenic scores (PGSs) have been derived in adults, and lack validation during childhood. This study compared the utility of existing large-scale adult-derived PGSs to predict common anthropometric traits (body mass index (BMI), waist circumference, and body fat) in children and adults, and examined whether childhood BMI prediction could be improved by combining PGSs and non-genetic factors (maternal and earlier child BMI). SUBJECTS/METHODS Participants (n = 1365 children, and n = 2094 adults made up of their parents) were drawn from the Longitudinal Study of Australian Children. Children were weighed and measured every two years from 0-1 to 12-13 years, and adults were measured or self-reported measurements were obtained concurrently (average analysed). Participants were genotyped from blood or oral samples, and PGSs were derived based on published genome-wide association studies. We used linear regression to compare the relative utility of these PGSs to predict their respective traits at different ages. RESULTS BMI PGSs explained up to 12% of child BMI z-score variance in 10-13 year olds, compared with up to 15% in adults. PGSs for waist circumference and body fat explained less variance (up to 8%). An interaction between BMI PGSs and puberty (p = 0.001-0.002) suggests the effect of some variants may differ across the life course. Individual BMI measures across childhood predicted 10-60% of the variance in BMI at 12-13 years, and maternal BMI and BMI PGS each added 1-9% above this. CONCLUSION Adult-derived PGSs for BMI, particularly those derived by modelling between-variant interactions, may be useful for predicting BMI during adolescence with similar accuracy to that obtained in adulthood. The level of precision presented here to predict BMI during childhood may be relevant to public health, but is likely to be less useful for individual clinical purposes.
Collapse
|
10
|
Koprulu M, Zhao Y, Wheeler E, Dong L, Rocha N, Li C, Griffin JD, Patel S, Van de Streek M, Glastonbury CA, Stewart ID, Day FR, Luan J, Bowker N, Wittemans LBL, Kerrison ND, Cai L, Lucarelli DME, Barroso I, McCarthy MI, Scott RA, Saudek V, Small KS, Wareham NJ, Semple RK, Perry JRB, O’Rahilly S, Lotta LA, Langenberg C, Savage DB. Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution. J Clin Endocrinol Metab 2022; 107:1065-1077. [PMID: 34875679 PMCID: PMC8947777 DOI: 10.1210/clinem/dgab877] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Indexed: 11/25/2022]
Abstract
CONTEXT Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly noncoding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function (LoF) would be of most therapeutic benefit. OBJECTIVE This work aimed to identify genes/proteins involved in determining fat distribution. METHODS We combined the power of genome-wide analysis of array-based rare, nonsynonymous variants in 450 562 individuals in the UK Biobank with exome-sequence-based rare LoF gene burden testing in 184 246 individuals. RESULTS The data indicate that the LoF of 4 genes (PLIN1 [LoF variants, P = 5.86 × 10-7], INSR [LoF variants, P = 6.21 × 10-7], ACVR1C [LoF + moderate impact variants, P = 1.68 × 10-7; moderate impact variants, P = 4.57 × 10-7], and PDE3B [LoF variants, P = 1.41 × 10-6]) is associated with a beneficial effect on body mass index-adjusted waist-to-hip ratio and increased gluteofemoral fat mass, whereas LoF of PLIN4 (LoF variants, P = 5.86 × 10-7 adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B, and ACVR1C favorably affects metabolic phenotypes (eg, triglycerides [TGs] and high-density lipoprotein [HDL] cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counterintuitive insight into the potential consequences of targeting these molecules therapeutically.
Collapse
Affiliation(s)
- Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Liang Dong
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nuno Rocha
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Chen Li
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - John D Griffin
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development, and Medical, Cambridge, Massachusetts 02139, USA
| | - Satish Patel
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Marcel Van de Streek
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Felix R Day
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Nicholas Bowker
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, OX3 9DU, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Lina Cai
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Debora M E Lucarelli
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- D.M.E.L. is currently an employee of Enhanc3D Genomics Ltd
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, EX1 2HZ, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- M.McM.’s current address is Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Vladimir Saudek
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, SE1 7EH, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Stephen O’Rahilly
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health at Charité–Universitätsmedizin Berlin, 10117 Berlin, Germany
- Correspondence: Claudia Langenberg, MD, Dr Med, PhD, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - David B Savage
- University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, CB2 0QQ, UK
- David B. Savage, MBCHB, PhD, University of Cambridge Metabolic Research Laboratories, Wellcome Trust–MRC Institute of Metabolic Science, Box 289, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| |
Collapse
|
11
|
Ershova AI, Ivanova AA, Kiseleva AV, Sotnikova EA, Meshkov AN, Drapkina OM. From biobanking to personalized prevention of obesity, diabetes and metabolic syndrome. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2021-3123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
The growing prevalence of metabolic disorders creates an increasing demand for novel approaches to their prevention and therapy. Novel genetic diagnostic technologies are developed every year, which makes it possible to identify people who are at the highest genetic risk of diabetes, non-alcoholic fatty liver disease, and metabolic syndrome. Early intervention strategies can be used to prevent metabolic disorders in this group of people. Genetic risk scores (GRSs) are a powerful tool to identify people with a high genetic risk. Millions of genetic variants are analyzed in genome-wide association studies in order to combine them into GRSs. It has become possible to store and process such huge amounts of data with the help of biobanks, where biological samples are stored according to international standards. Genetic studies include more and more people every year that increases the predictive power of GRSs. It has already been demonstrated that the use of GRSs makes future preventive measures more effective. In the near future, GRSs are likely to become part of clinical guidelines so that they can be widely used to identify people at high risk for metabolic syndrome and its components.
Collapse
Affiliation(s)
- A. I. Ershova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. A. Ivanova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Medical Research Center for Therapy and Preventive Medicine
| | - E. A. Sotnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Pirogov Russian National Research Medical University
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
| |
Collapse
|
12
|
Robinette JW, Beam CR, Gruenewald TL. Can I Buy My Health? A Genetically Informed Study of Socioeconomic Status and Health. Ann Behav Med 2021; 56:418-427. [PMID: 34343242 DOI: 10.1093/abm/kaab064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND A large literature demonstrates associations between socioeconomic status (SES) and health, including physiological health and well-being. Moreover, gender differences are often observed among measures of both SES and health. However, relationships between SES and health are sometimes questioned given the lack of true experiments, and the potential biological and SES mechanisms explaining gender differences in health are rarely examined simultaneously. PURPOSE To use a national sample of twins to investigate lifetime socioeconomic adversity and a measure of physiological dysregulation separately by sex. METHODS Using the twin sample in the second wave of the Midlife in the United States survey (MIDUS II), biometric regression analysis was conducted to determine whether the established SES-physiological health association is observed among twins both before and after adjusting for potential familial-level confounds (additive genetic and shared environmental influences that may underly the SES-health link), and whether this association differs among men and women. RESULTS Although individuals with less socioeconomic adversity over the lifespan exhibited less physiological dysregulation among this sample of twins, this association only persisted among male twins after adjusting for familial influences. CONCLUSIONS Findings from the present study suggest that, particularly for men, links between socioeconomic adversity and health are not spurious or better explained by additive genetic or early shared environmental influences. Furthermore, gender-specific role demands may create differential associations between SES and health.
Collapse
Affiliation(s)
| | - Christopher R Beam
- Psychology Department, University of Southern California, Los Angeles, CA, USA
| | | |
Collapse
|
13
|
Pan DZ, Miao Z, Comenho C, Rajkumar S, Koka A, Lee SHT, Alvarez M, Kaminska D, Ko A, Sinsheimer JS, Mohlke KL, Mancuso N, Muñoz-Hernandez LL, Herrera-Hernandez M, Tusié-Luna MT, Aguilar-Salinas C, Pietiläinen KH, Pihlajamäki J, Laakso M, Garske KM, Pajukanta P. Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes. Genome Med 2021; 13:123. [PMID: 34340684 PMCID: PMC8327600 DOI: 10.1186/s13073-021-00939-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 07/14/2021] [Indexed: 12/14/2022] Open
Abstract
Background Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood. Methods We searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function. Results Here, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network. Conclusions Our study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00939-2.
Collapse
Affiliation(s)
- David Z Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.,Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Zong Miao
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.,Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA
| | - Caroline Comenho
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Sandhya Rajkumar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.,Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Dorota Kaminska
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Arthur Ko
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Janet S Sinsheimer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.,Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Linda Liliana Muñoz-Hernandez
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Ave. Morones Prieto 3000, Monterrey, N.L., México, 64710.,Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.,Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Miguel Herrera-Hernandez
- Departamento de Cirugía, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico
| | - Maria Teresa Tusié-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Carlos Aguilar-Salinas
- Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.,Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Obesity Center, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA. .,Bioinformatics Interdepartmental Program, UCLA, Los Angeles, USA. .,Institute for Precision Health at UCLA, Los Angeles, USA.
| |
Collapse
|
14
|
Dong SS, Zhu DL, Zhou XR, Rong Y, Zeng M, Chen JB, Jiang F, Tuo XM, Feng Z, Yang TL, Guo Y. An Intronic Risk SNP rs12454712 for Central Obesity Acts As an Allele-Specific Enhancer To Regulate BCL2 Expression. Diabetes 2021; 70:1679-1688. [PMID: 34035043 DOI: 10.2337/db20-1151] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022]
Abstract
Genome-wide association studies (GWAS) have reproducibly associated the single nucleotide polymorphism (SNP) rs12454712 with waist-to-hip ratio adjusted for BMI (WHRadjBMI), but the functional role underlying this intronic variant is unknown. Integrative genomic and epigenomic analyses supported rs12454712 as a functional independent variant. We further demonstrated that rs12454712 acted as an allele-specific enhancer regulating expression of its located gene BCL2 by using dual-luciferase reporter assays and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9. Specifically, the rs12454712-C allele can bind transcription factor ZNF329, which efficiently elevates the enhancer activity and increases BCL2 expression. Knocking down Bcl2 in 3T3-L1 cells led to the downregulation of adipogenic differentiation marker genes and increased cell apoptosis. A significant negative correlation between BCL2 expression in subcutaneous adipose tissues and obesity was observed. Our findings illustrate the molecular mechanisms behind the intronic SNP rs12454712 for central obesity, which would be a potential and promising target for developing appropriate therapies.
Collapse
Affiliation(s)
- Shan-Shan Dong
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Research Institute of Xi'an Jiaotong University, Hangzhou, Zhejiang, China
| | - Dong-Li Zhu
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiao-Rong Zhou
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yu Rong
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Mengqi Zeng
- Center for Mitochondrial Biology and Medicine, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jia-Bin Chen
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Feng Jiang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiao-Mei Tuo
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zhihui Feng
- Center for Mitochondrial Biology and Medicine, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tie-Lin Yang
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yan Guo
- Biomedical Informatics & Genomics Center, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| |
Collapse
|
15
|
Sun C, Kovacs P, Guiu-Jurado E. Genetics of Body Fat Distribution: Comparative Analyses in Populations with European, Asian and African Ancestries. Genes (Basel) 2021; 12:genes12060841. [PMID: 34072523 PMCID: PMC8228180 DOI: 10.3390/genes12060841] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 12/16/2022] Open
Abstract
Preferential fat accumulation in visceral vs. subcutaneous depots makes obese individuals more prone to metabolic complications. Body fat distribution (FD) is regulated by genetics. FD patterns vary across ethnic groups independent of obesity. Asians have more and Africans have less visceral fat compared with Europeans. Consequently, Asians tend to be more susceptible to type 2 diabetes even with lower BMIs when compared with Europeans. To date, genome-wide association studies (GWAS) have identified more than 460 loci related to FD traits. However, the majority of these data were generated in European populations. In this review, we aimed to summarize recent advances in FD genetics with a focus on comparisons between European and non-European populations (Asians and Africans). We therefore not only compared FD-related susceptibility loci identified in three ethnicities but also discussed whether known genetic variants might explain the FD pattern heterogeneity across different ancestries. Moreover, we describe several novel candidate genes potentially regulating FD, including NID2, HECTD4 and GNAS, identified in studies with Asian populations. It is of note that in agreement with current knowledge, most of the proposed FD candidate genes found in Asians belong to the group of developmental genes.
Collapse
Affiliation(s)
- Chang Sun
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Esther Guiu-Jurado
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103 Leipzig, Germany
- Deutsches Zentrum für Diabetesforschung, 85764 Neuherberg, Germany
| |
Collapse
|
16
|
SAINI SIMMI, WALIA GAGANDEEPKAUR, SACHDEVA MOHINDERPAL, GUPTA VIPIN. Genomics of body fat distribution. J Genet 2021. [DOI: 10.1007/s12041-021-01281-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
17
|
Mušo M, Dumbell R, Pulit S, Sinnott-Armstrong N, Laber S, Zolkiewski L, Bentley L, Claussnitzer M, Cox RD. A lead candidate functional single nucleotide polymorphism within the WARS2 gene associated with waist-hip-ratio does not alter RNA stability. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2020; 1863:194640. [PMID: 33007465 PMCID: PMC7695619 DOI: 10.1016/j.bbagrm.2020.194640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 09/22/2020] [Accepted: 09/22/2020] [Indexed: 11/06/2022]
Abstract
We have prioritised a single nucleotide polymorphism (SNP) rs2645294 as one candidate functional SNP in the TBX15-WARS2 waist-hip-ratio locus using posterior probability analysis. This SNP is located in the 3' untranslated region of the WARS2 (tryptophanyl tRNA synthetase 2, mitochondrial) gene with which it has an expression quantitative trait in subcutaneous white adipose tissue. We show that transcripts of the WARS2 gene in a human white adipose cell line, heterozygous for the rs2645294 SNP, showed allelic imbalance. We tested whether the rs2645294 SNP altered WARS2 RNA stability using three different methods: actinomycin-D inhibition and RNA decay, mature and nascent RNA analysis and luciferase reporter assays. We found no evidence of a difference in RNA stability between the rs2645294 alleles indicating that the allelic expression imbalance was likely due to transcriptional regulation.
Collapse
Affiliation(s)
- Milan Mušo
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Rebecca Dumbell
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Sara Pulit
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands; Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK; Program in Medical Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Samantha Laber
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Louisa Zolkiewski
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Liz Bentley
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK
| | - Melina Claussnitzer
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Gerontology Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Institute of Nutritional Science, University of Hohenheim, Stuttgart, Germany
| | - Roger D Cox
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire OX11 0RD, UK.
| |
Collapse
|
18
|
Abstract
Obesity represents a major health burden to both developed and developing countries. Furthermore, the incidence of obesity is increasing in children. Obesity contributes substantially to mortality in the United States by increasing the risk for type 2 diabetes, cardiovascular-related diseases, and other comorbidities. Despite environmental changes over past decades, including increases in high-calorie foods and sedentary lifestyles, there is very clear evidence of a genetic predisposition to obesity risk. Childhood obesity cases can be categorized in one of two ways: syndromic or non-syndromic. Syndromic obesity includes disorders such as Prader-Willi syndrome, Bardet-Biedl syndrome, and Alström syndrome. Non-syndromic cases of obesity can be further separated into rarer instances of monogenic obesity and much more common forms of polygenic obesity. The advent of genome-wide association studies (GWAS) and next-generation sequencing has driven significant advances in our understanding of the genetic contribution to childhood obesity. Many rare and common genetic variants have been shown to contribute to the heritability in obesity, although the molecular mechanisms underlying most of these variants remain unclear. An important caveat of GWAS efforts is that they do not strictly represent gene target discoveries, rather simply the uncovering of robust genetic signals. One clear example of this is with progress in understanding the key obesity signal harbored within an intronic region of the FTO gene. It has been shown that the non-coding region in which the variant actually resides in fact influences the expression of genes distal to FTO instead, specifically IRX3 and IRX5. Such discoveries suggest that associated non-coding variants can be embedded within or next to one gene, but commonly influence the expression of other, more distal effector genes. Advances in genetics and genomics are therefore contributing to a deeper understanding of childhood obesity, allowing for development of clinical tools and therapeutic agents.
Collapse
|
19
|
Robinette JW, Boardman JD, Crimmins E. Perceived neighborhood social cohesion and cardiometabolic risk: a gene × environment study. BIODEMOGRAPHY AND SOCIAL BIOLOGY 2020; 65:1-15. [PMID: 32065540 DOI: 10.1080/19485565.2019.1568672] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
People living in socially cohesive neighborhoods generally have better health. We extend this research by evaluating the hypothesis that perceived neighborhood cohesion may influence health by attenuating genetic liability for cardiometabolic risk factors. Using data from the Health and Retirement Study (n = 6615; mean age 69.7), we conducted a gene × environment interaction study hypothesizing that perceived neighborhood cohesion would attenuate the link between polygenic scores for waist-to-hip ratio (WHR) and body mass index and a measure of multisystem cardiometabolic risk (systolic and diastolic blood pressure, heart rate, A1c, C-reactive protein, and total and high-density lipoprotein cholesterol). In support of the hypothesis, results indicated that among people perceiving low neighborhood cohesion, higher WHR polygenic scores were associated with greater cardiometabolic risk. In contrast, the genetic-cardiometabolic risk link was much attenuated among those living in neighborhoods perceived as socially cohesive. Our results support community-level interventions to enhance the social cohesiveness of individuals' neighborhoods which may provide health benefits by reducing the risks associated with known genetic risk factors.
Collapse
Affiliation(s)
- Jennifer W Robinette
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| | - Jason D Boardman
- Institute of Behavioral Science and Department of Sociology, University of Colorado, Boulder, Boulder, Colorado, USA
| | - Eileen Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, California, USA
| |
Collapse
|
20
|
Sun W, von Meyenn F, Peleg‐Raibstein D, Wolfrum C. Environmental and Nutritional Effects Regulating Adipose Tissue Function and Metabolism Across Generations. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2019; 6:1900275. [PMID: 31179229 PMCID: PMC6548959 DOI: 10.1002/advs.201900275] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 03/21/2019] [Indexed: 05/12/2023]
Abstract
The unabated rise in obesity prevalence during the last 40 years has spurred substantial interest in understanding the reasons for this epidemic. Studies in mice and humans have demonstrated that obesity is a highly heritable disease; however genetic variations within specific populations have so far not been able to explain this phenomenon to its full extent. Recent work has demonstrated that environmental cues can be sensed by an organism to elicit lasting changes, which in turn can affect systemic energy metabolism by different epigenetic mechanisms such as changes in small noncoding RNA expression, DNA methylation patterns, as well as histone modifications. These changes can directly modulate cellular function in response to environmental cues, however research during the last decade has demonstrated that some of these modifications might be transmitted to subsequent generations, thus modulating energy metabolism of the progeny in an inter- as well as transgenerational manner. In this context, adipose tissue has become a focus of research due to its plasticity, which allows the formation of energy storing (white) as well as energy wasting (brown/brite/beige) cells within the same depot. In this Review, the effects of environmental induced obesity with a particular focus on adipose tissue are discussed.
Collapse
Affiliation(s)
- Wenfei Sun
- Department of Health Science and TechnologiesETH ZürichSchorenstrasse 16SchwerzenbachCH‐8603Switzerland
| | - Ferdinand von Meyenn
- Department of Health Science and TechnologiesETH ZürichSchorenstrasse 16SchwerzenbachCH‐8603Switzerland
| | - Daria Peleg‐Raibstein
- Department of Health Science and TechnologiesETH ZürichSchorenstrasse 16SchwerzenbachCH‐8603Switzerland
| | - Christian Wolfrum
- Department of Health Science and TechnologiesETH ZürichSchorenstrasse 16SchwerzenbachCH‐8603Switzerland
| |
Collapse
|
21
|
Raboin MJ, Letaw J, Mitchell AD, Toffey D, McKelvey J, Roberts CT, Curran JE, Vinson A. Genetic Architecture of Human Obesity Traits in the Rhesus Macaque. Obesity (Silver Spring) 2019; 27:479-488. [PMID: 30741480 PMCID: PMC6389383 DOI: 10.1002/oby.22392] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 06/20/2018] [Accepted: 10/31/2018] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Whereas the metabolic consequences of obesity have been studied extensively in the rhesus macaque, corollary genetic studies of obesity are nonexistent. This study assessed genetic contributions to spontaneous adiposity in this species. METHODS Phenotypic variation by age class and sex for BMI, waist to height ratio, waist to thigh ratio, and waist circumference was assessed in 583 macaques. Total and sex-specific heritability for all traits was estimated, including waist to thigh ratio adjusted for BMI, as well as genotypic and phenotypic correlations. In addition, functional genetic variation at BDNF, FTO, LEP, LEPR, MC4R, PCSK1, POMC, and SIM1 was assessed in four animals with extreme spontaneous adiposity. RESULTS Trait heritability in the combined sample was low to moderate (0.14-0.32), whereas sex-specific heritability was more substantial (0.20-0.67). Heritability was greater in females for all traits except BMI. All traits were robustly correlated, with genetic correlations of 0.63 to 0.93 indicating substantial pleiotropy. Likely functional variants were discovered in the four macaques at all eight human obesity genes, including six missense mutations in BDNF, FTO, LEP, LEPR, and PCSK1 and, notably, one nonsense mutation in LEPR. CONCLUSIONS A moderate polygenic contribution to adiposity in rhesus macaques was found, as well as mutations with potentially larger effects in multiple genes that influence obesity in humans.
Collapse
Affiliation(s)
- Michael J Raboin
- Primate Genetics Section, Oregon National Primate Research Center/Oregon Health & Science University, Beaverton, OR, U.S
| | - John Letaw
- Primate Genetics Section, Oregon National Primate Research Center/Oregon Health & Science University, Beaverton, OR, U.S
| | - Asia D. Mitchell
- Primate Genetics Section, Oregon National Primate Research Center/Oregon Health & Science University, Beaverton, OR, U.S
| | - David Toffey
- Primate Genetics Section, Oregon National Primate Research Center/Oregon Health & Science University, Beaverton, OR, U.S
| | - Jessica McKelvey
- Division of Cardiometabolic Health, Oregon National Primate Research Center, Beaverton, OR, U.S
| | - Charles T. Roberts
- Division of Cardiometabolic Health, Oregon National Primate Research Center, Beaverton, OR, U.S
| | - Joanne E. Curran
- South Texas Diabetes & Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, U.S
| | - Amanda Vinson
- Primate Genetics Section, Oregon National Primate Research Center/Oregon Health & Science University, Beaverton, OR, U.S
- Division of Cardiometabolic Health, Oregon National Primate Research Center, Beaverton, OR, U.S
- Amanda Vinson: (corresponding author), Oregon National Primate Research Center, 505 NW 185 Ave., Mail code L584, Beaverton, OR 97006
| |
Collapse
|
22
|
Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, Yengo L, Ferreira T, Marouli E, Ji Y, Yang J, Jones S, Beaumont R, Croteau-Chonka DC, Winkler TW, Hattersley AT, Loos RJF, Hirschhorn JN, Visscher PM, Frayling TM, Yaghootkar H, Lindgren CM. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019; 28:166-174. [PMID: 30239722 PMCID: PMC6298238 DOI: 10.1093/hmg/ddy327] [Citation(s) in RCA: 627] [Impact Index Per Article: 125.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/27/2018] [Accepted: 09/03/2018] [Indexed: 01/08/2023] Open
Abstract
More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.
Collapse
Affiliation(s)
- Sara L Pulit
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
| | - Charli Stoneman
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Andrew P Morris
- Biostatistics Department, University of Liverpool, Liverpool, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Andrew R Wood
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Craig A Glastonbury
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Jessica Tyrrell
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Loïc Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
| | - Eirini Marouli
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yingjie Ji
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Samuel Jones
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Robin Beaumont
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Damien C Croteau-Chonka
- Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | | | - Andrew T Hattersley
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Timothy M Frayling
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Hanieh Yaghootkar
- University of Exeter Medical School, University of Exeter, Royal Devon and Exeter NHS Trust, Exeter, UK
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| |
Collapse
|
23
|
Saini S, Walia GK, Sachdeva MP, Gupta V. Genetics of obesity and its measures in India. J Genet 2018. [DOI: 10.1007/s12041-018-0987-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
24
|
A fine-mapping study of central obesity loci incorporating functional annotation and imputation. Eur J Hum Genet 2018; 26:1369-1377. [PMID: 29967334 DOI: 10.1038/s41431-018-0168-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 03/08/2018] [Accepted: 04/11/2018] [Indexed: 01/02/2023] Open
Abstract
A recent genome-wide association study (GWAS) of central obesity identified 27 loci, from sex-combined analysis, associated with waist-to-hip ratio adjusted for body-mass index (WHRadjBMI) in European-ancestry individuals. Nevertheless, the identified variants may not be the biological causal ones due to the presence of linkage disequilibrium (LD). To better understand the mechanisms underlying the identified loci from the GWAS meta-analysis, we first imputed summary statistics at GWAS loci to increase genetic resolution, and then we applied a Bayesian statistical fine-mapping method through PAINTOR, incorporating LD structure and functional annotations to select and prioritize the most plausible causal variants across WHRadjBMI-associated regions. Using adipose tissue- and cell-specific annotations that showed significant associations with WHRadjBMI, we identified 33 single-nucleotide polymorphisms (SNPs) from 27 sex-combined fine-mapping loci with posterior probability of causality greater than 0.9. Six of the selected 33 SNPs belong to at least one of the top five identified annotations. SNPs rs1440372 (SMAD6) and rs12608504 (JUND) are particularly important since they not only have associated functional annotations but are also GWA hits in the original study. Incorporation of functional annotations helps identify additional plausible causal variants, such as rs2213731 (DNM3-PIGC) and rs4531856 (JUND), that did not reach genome-wide significance in GWAS. Our results provide promising candidates for future functional validation experiments.
Collapse
|
25
|
Robinette JW, Boardman JD, Crimmins E. Perceived neighborhood social cohesion and cardiometabolic risk: a gene × environment study. BIODEMOGRAPHY AND SOCIAL BIOLOGY 2018; 64:173-186. [PMID: 31852333 PMCID: PMC6927540 DOI: 10.1080/19485565.2019.1579084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
People living in socially cohesive neighborhoods generally have better health. We extend this research by evaluating the hypothesis that perceived neighborhood cohesion may influence health by attenuating genetic liability for cardiometabolic risk factors. Using data from the Health and Retirement Study (n = 6,615; mean age 69.7), we conducted a gene × environment interaction study hypothesizing that perceived neighborhood cohesion would attenuate the link between polygenic scores for waist-to-hip ratio (WHR) and body mass index and a measure of multisystem cardiometabolic risk (systolic blood pressure [SBP] and diastolic blood pressure [DBP], heart rate, A1c, C-reactive protein, and total and high-density lipoprotein cholesterol). In support of the hypothesis, results indicated that among people perceiving low neighborhood cohesion, higher WHR polygenic scores were associated with greater cardiometabolic risk. In contrast, the genetic-cardiometabolic risk link was much attenuated among those living in neighborhoods perceived as socially cohesive. Our results support community-level interventions to enhance the social cohesiveness of individuals' neighborhoods which may provide health benefits by reducing the risks associated with known genetic risk factors.
Collapse
Affiliation(s)
- Jennifer W. Robinette
- Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA 90089-0191
| | - Jason D. Boardman
- Institute of Behavioral Science and Department of Sociology University of Colorado, Boulder, 1440 15th Street, Boulder, CO 80309
| | - Eileen Crimmins
- Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA 90089-0191
| |
Collapse
|
26
|
Loos RJ. The genetics of adiposity. Curr Opin Genet Dev 2018; 50:86-95. [PMID: 29529423 PMCID: PMC6089650 DOI: 10.1016/j.gde.2018.02.009] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 02/09/2018] [Accepted: 02/14/2018] [Indexed: 02/08/2023]
Abstract
Genome-wide discovery efforts have identified more than 500 genetic loci associated with adiposity traits. The vast majority of these loci were found through large-scale meta-analyses for body mass index (BMI) and waist-to-hip ratio (WHR), and in European ancestry populations. However, alternative approaches, focusing on non-European ancestry populations, more refined adiposity measures, and low-frequency (minor allele frequency (MAF)<5%) coding variants, identified additional novel loci that had not been identified before. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with fat distribution point to pathways involved in adipocyte biology. Pinpointing the causal gene within each locus remains challenging, but is a critical step towards translation of genome-wide association study (GWAS) loci into new biology. Ultimately, new genes may provide pharmacological targets for the development of weight loss drugs.
Collapse
Affiliation(s)
- Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
27
|
Goutzelas Y, Kotsa K, Vasilopoulos Y, Tsekmekidou X, Stamatis C, Yovos JG, Sarafidou T, Mamuris Z. Association analysis of FTO gene polymorphisms with obesity in Greek adults. Gene 2017; 613:10-13. [DOI: 10.1016/j.gene.2017.02.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 11/18/2016] [Accepted: 02/27/2017] [Indexed: 02/08/2023]
|
28
|
SNP-SNP interactions between WNT4 and WNT5A were associated with obesity related traits in Han Chinese Population. Sci Rep 2017; 7:43939. [PMID: 28272483 PMCID: PMC5341019 DOI: 10.1038/srep43939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/31/2017] [Indexed: 12/02/2022] Open
Abstract
Considering the biological roles of WNT4 and WNT5A involved in adipogenesis, we aimed to investigate whether SNPs in WNT4 and WNT5A contribute to obesity related traits in Han Chinese population. Targeted genomic sequence for WNT4 and WNT5A was determined in 100 Han Chinese subjects and tag SNPs were selected. Both single SNP and SNP × SNP interaction association analyses with body mass index (BMI) were evaluated in the 100 subjects and another independent sample of 1,627 Han Chinese subjects. Meta-analyses were performed and multiple testing corrections were carried out using the Bonferroni method. Consistent with the Genetic Investigation of ANthropometric Traits (GIANT) dataset results, we didn’t detect significant association signals in single SNP association analyses. However, the interaction between rs2072920 and rs11918967, was associated with BMI after multiple testing corrections (combined P = 2.20 × 10−4). The signal was also significant in each contributing data set. SNP rs2072920 is located in the 3′-UTR of WNT4 and SNP rs11918967 is located in the intron of WNT5A. Functional annotation results revealed that both SNPs might be involved in transcriptional regulation of gene expression. Our results suggest that a combined effect of SNPs via WNT4-WNT5A interaction may affect the variation of BMI in Han Chinese population.
Collapse
|
29
|
Pulit SL, Karaderi T, Lindgren CM. Sexual dimorphisms in genetic loci linked to body fat distribution. Biosci Rep 2017; 37:BSR20160184. [PMID: 28073971 PMCID: PMC5291139 DOI: 10.1042/bsr20160184] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 01/07/2017] [Accepted: 01/10/2017] [Indexed: 01/02/2023] Open
Abstract
Obesity is a chronic condition associated with increased morbidity and mortality and is a risk factor for a number of other diseases including type 2 diabetes and cardiovascular disease. Obesity confers an enormous, costly burden on both individuals and public health more broadly. Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes. Body fat distribution is distinct from overall obesity in measurement, but studies of body fat distribution can yield insights into the risk factors for and causes of overall obesity. Sexual dimorphism in body fat distribution is present throughout life. Though sexual dimorphism is subtle in early stages of life, it is attenuated in puberty and during menopause. This phenomenon could be, at least in part, due to the influence of sex hormones on the trait. Findings from recent large genome-wide association studies (GWAS) for various measures of body fat distribution (including waist-to-hip ratio, hip or waist circumference, trunk fat percentage and the ratio of android and gynoid fat percentage) emphasize the strong sexual dimorphism in the genetic regulation of fat distribution traits. Importantly, sexual dimorphism is not observed for overall obesity (as assessed by body mass index or total fat percentage). Notably, the genetic loci associated with body fat distribution, which show sexual dimorphism, are located near genes that are expressed in adipose tissues and/or adipose cells. Considering the epidemiological and genetic evidence, sexual dimorphism is a prominent feature of body fat distribution. Research that specifically focuses on sexual dimorphism in fat distribution can provide novel insights into human physiology and into the development of obesity and its comorbidities, as well as yield biological clues that will aid in the improvement of disease prevention and treatment.
Collapse
Affiliation(s)
- Sara L Pulit
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tugce Karaderi
- Department of Biological Sciences, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, Cyprus
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, U.K.
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K
| |
Collapse
|
30
|
Dong SS, Guo Y, Zhu DL, Chen XF, Wu XM, Shen H, Chen XD, Tan LJ, Tian Q, Deng HW, Yang TL. Epigenomic elements analyses for promoters identify ESRRG as a new susceptibility gene for obesity-related traits. Int J Obes (Lond) 2016; 40:1170-6. [PMID: 27113491 PMCID: PMC4935547 DOI: 10.1038/ijo.2016.44] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 02/23/2016] [Accepted: 02/28/2016] [Indexed: 12/22/2022]
Abstract
OBJECTIVES With ENCODE epigenomic data and results from published genome-wide association studies (GWASs), we aimed to find regulatory signatures of obesity genes and discover novel susceptibility genes. METHODS Obesity genes were obtained from public GWAS databases and their promoters were annotated based on the regulatory element information. Significantly enriched or depleted epigenomic elements in the promoters of obesity genes were evaluated and all human genes were then prioritized according to the existence of the selected elements to predict new candidate genes. Top-ranked genes were subsequently applied to validate their associations with obesity-related traits in three independent in-house GWAS samples. RESULTS We identified RAD21 and EZH2 as over-represented, and STAT2 (signal transducer and activator of transcription 2) and IRF3 (interferon regulatory transcription factor 3) as depleted transcription factors. Histone modification of H3K9me3 and chromatin state segmentation of 'poised promoter' and 'repressed' were over-represented. All genes were prioritized and we selected the top five genes for validation at the population level. Combining results from the three GWAS samples, rs7522101 in ESRRG (estrogen-related receptor-γ) remained significantly associated with body mass index after multiple testing corrections (P=7.25 × 10(-5)). It was also associated with β-cell function (P=1.99 × 10(-3)) and fasting glucose level (P<0.05) in the meta-analyses of glucose and insulin-related traits consortium (MAGIC) data set.Cnoclusions:In summary, we identified epigenomic characteristics for obesity genes and suggested ESRRG as a novel obesity-susceptibility gene.
Collapse
Affiliation(s)
- S-S Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Y Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - D-L Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - X-F Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - X-M Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - H Shen
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - X-D Chen
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, People's Republic of China
| | - L-J Tan
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, People's Republic of China
| | - Q Tian
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - H-W Deng
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - T-L Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China
| |
Collapse
|
31
|
Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project. Eur J Hum Genet 2016; 24:1181-7. [PMID: 26757982 DOI: 10.1038/ejhg.2015.272] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Revised: 11/10/2015] [Accepted: 11/26/2015] [Indexed: 02/06/2023] Open
Abstract
Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.
Collapse
|
32
|
Bosch TA, Chow L, Dengel DR, Melhorn SJ, Webb M, Yancey D, Callahan H, De Leon MRB, Tyagi V, Schur EA. In adult twins, visceral fat accumulation depends more on exceeding sex-specific adiposity thresholds than on genetics. Metabolism 2015; 64:991-8. [PMID: 26117000 PMCID: PMC4546509 DOI: 10.1016/j.metabol.2015.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 05/30/2015] [Accepted: 06/02/2015] [Indexed: 10/23/2022]
Abstract
OBJECTIVE We recently reported sex-specific percent body fat (%BF) thresholds (males=23%, females=38%) above which, visceral adipose tissue (VAT) significantly increases. Using monozygotic (MZ) and dizygotic (DZ) twins, we examined the influence of genetics on regional fat distribution measured by dual-energy X-ray absorptiometry, above and below these sex-specific thresholds for VAT accumulation. METHODS Fifty-eight twin pairs (44 MZ, 14 DZ) were recruited from the University of Washington Twin Registry. Segmented linear regression was used to assess the threshold between VAT mass and %BF by sex and by zygosity. To assess the effect of genetics on VAT accumulation, Dunnett's T3 compared MZ and DZ pairs whether the twin pairs were both above the adiposity threshold or not. RESULTS %BF thresholds for VAT accumulation were identified (%BF: M=20.6%, F=39.4%). Zygosity-specific thresholds were not significantly different (p>0.05). If at least one twin was below threshold, DZ twins still exhibited greater within-pair differences than MZ pairs in %BF (p=0.023) but not VAT (p=0.121). CONCLUSIONS Using a twin study approach, we observed no difference by zygosity for the threshold as which VAT accumulates. Additionally, for the first time we observed that while total BF is influenced by genetics, VAT accumulation may depend more on whether a person's %BF is above their sex-specific adiposity threshold. These results suggest that there may not be a genetic predisposition for VAT accumulation but rather it is a result of a predisposition for total fat accumulation.
Collapse
Affiliation(s)
- Tyler A Bosch
- Department of Medicine, University of Minnesota Medical School, MMC 101, 420 Delaware St SE, Minneapolis, MN 55455, USA.
| | - Lisa Chow
- Department of Medicine, University of Minnesota Medical School, MMC 101, 420 Delaware St SE, Minneapolis, MN 55455, USA
| | - Donald R Dengel
- School of Kinesiology, University of Minnesota, 1900 University Avenue SE, Minneapolis, MN 55455, USA
| | - Susan J Melhorn
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| | - Mary Webb
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| | - Danielle Yancey
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| | - Holly Callahan
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| | - Mary Rosalyn B De Leon
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| | - Vidhi Tyagi
- Simmons College, 300 Fenway, Boston, MA 02115, USA
| | - Ellen A Schur
- Department of Medicine, University of Washington, Box 359780, 325 Ninth Avenue, Seattle, WA 98109, USA
| |
Collapse
|
33
|
Horikoshi M, Mӓgi R, van de Bunt M, Surakka I, Sarin AP, Mahajan A, Marullo L, Thorleifsson G, Hӓgg S, Hottenga JJ, Ladenvall C, Ried JS, Winkler TW, Willems SM, Pervjakova N, Esko T, Beekman M, Nelson CP, Willenborg C, Wiltshire S, Ferreira T, Fernandez J, Gaulton KJ, Steinthorsdottir V, Hamsten A, Magnusson PKE, Willemsen G, Milaneschi Y, Robertson NR, Groves CJ, Bennett AJ, Lehtimӓki T, Viikari JS, Rung J, Lyssenko V, Perola M, Heid IM, Herder C, Grallert H, Müller-Nurasyid M, Roden M, Hypponen E, Isaacs A, van Leeuwen EM, Karssen LC, Mihailov E, Houwing-Duistermaat JJ, de Craen AJM, Deelen J, Havulinna AS, Blades M, Hengstenberg C, Erdmann J, Schunkert H, Kaprio J, Tobin MD, Samani NJ, Lind L, Salomaa V, Lindgren CM, Slagboom PE, Metspalu A, van Duijn CM, Eriksson JG, Peters A, Gieger C, Jula A, Groop L, Raitakari OT, Power C, Penninx BWJH, de Geus E, Smit JH, Boomsma DI, Pedersen NL, Ingelsson E, Thorsteinsdottir U, Stefansson K, Ripatti S, Prokopenko I, McCarthy MI, Morris AP. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation. PLoS Genet 2015; 11:e1005230. [PMID: 26132169 PMCID: PMC4488845 DOI: 10.1371/journal.pgen.1005230] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 04/18/2015] [Indexed: 11/19/2022] Open
Abstract
Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated. Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
Collapse
Affiliation(s)
- Momoko Horikoshi
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Reedik Mӓgi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Ida Surakka
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | | | - Sara Hӓgg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas W. Winkler
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Sara M. Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Children’s Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Marian Beekman
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Disease Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Christina Willenborg
- Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Steven Wiltshire
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Teresa Ferreira
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Juan Fernandez
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Kyle J. Gaulton
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Anders Hamsten
- Cardiovascular Genetics and Genomics Group, Atherosclerosis Research Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Neil R. Robertson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Amanda J. Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Terho Lehtimӓki
- Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland
| | - Jorma S. Viikari
- Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland
| | - Johan Rung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Steno Diabetes Center A/S, Gentofte, Denmark
| | - Markus Perola
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Iris M. Heid
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Düsseldorf, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD e.V.), Partner Düsseldorf, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Elina Hypponen
- School of Population Health, University of South Australia, Adelaide, Australia
- Centre for Paediatric Epidemiology and Biostatistics, University College London Institute of Child Health, London, United Kingdom
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Center for Medical Systems Biology, Leiden, The Netherlands
| | - Elisabeth M. van Leeuwen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lennart C. Karssen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | - Anton J. M. de Craen
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Joris Deelen
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Aki S. Havulinna
- Unit of Chronic Disease Epidemiology and Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Matthew Blades
- Bioinformatics and Biostatistics Support Hub (B/BASH), University of Leicester, Leicester, United Kingdom
| | - Christian Hengstenberg
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Munich, Munich, Germany
| | - Jeanette Erdmann
- Institute for Integrative and Experimental Genomics, University of Lübeck, Lübeck, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- DZHK German Center for Cardiovascular Research, Partner Site Munich, Munich, Germany
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
- The Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Martin D. Tobin
- Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research Leicester Cardiovascular Disease Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden
| | - Veikko Salomaa
- Unit of Chronic Disease Epidemiology and Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Cecilia M. Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - P. Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Center for Medical Systems Biology, Leiden, The Netherlands
| | - Johan G. Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Vasa Central Hospital, Vasa, Finland
- Department of Health Promotion and Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Annette Peters
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Antti Jula
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland
| | - Leif Groop
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Olli T. Raitakari
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Chris Power
- Centre for Paediatric Epidemiology and Biostatistics, University College London Institute of Child Health, London, United Kingdom
| | | | - Eco de Geus
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
- EMGO Institute for Health and Care Research, VU University & VU University Medical Center, Amsterdam, The Netherlands
| | - Johannes H. Smit
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Medical Sciences, Molecular Epidemiology, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Unnur Thorsteinsdottir
- deCode Genetic - Amgen Inc, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCode Genetic - Amgen Inc, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
- The Department of Public Health, University of Helsinki, Helsinki, Finland
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
| | - Inga Prokopenko
- Deparment of Genomics of Common Disease, School of Public Health, Imperial College London, London, United Kingdom
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom
| | | |
Collapse
|
34
|
Zhou B, Gao W, Lv J, Yu C, Wang S, Liao C, Pang Z, Cong L, Dong Z, Wu F, Wang H, Wu X, Jiang G, Wang X, Wang B, Cao W, Li L. Genetic and Environmental Influences on Obesity-Related Phenotypes in Chinese Twins Reared Apart and Together. Behav Genet 2015; 45:427-37. [DOI: 10.1007/s10519-015-9711-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2014] [Accepted: 01/30/2015] [Indexed: 10/23/2022]
|
35
|
Wang T, Jia W, Hu C. Advancement in genetic variants conferring obesity susceptibility from genome-wide association studies. Front Med 2014; 9:146-61. [PMID: 25556696 DOI: 10.1007/s11684-014-0373-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 08/25/2014] [Indexed: 12/18/2022]
Abstract
Obesity prevalence has increased in recent years. Lifestyle change fuels obesity, but genetic factors cause more than 50% of average variations in obesity. The advent of genome-wide association studies (GWAS) has hastened the progress of polygenic obesity research. As of this writing, more than 73 obesity susceptibility loci have been identified in ethnic groups through GWAS. The identified loci explain only 2% to 4% of obesity heritability, thereby indicating that a large proportion of loci remain undiscovered. Thus, the next step is to identify and confirm novel loci, which may exhibit smaller effects and lower allele frequencies than established loci. However, achieving these tasks has been difficult for researchers. GWAS help researchers discover the causal loci. Moreover, numerous biological studies have been performed on the polygenic effects on obesity, such as studies on fat mass- and obesity-associated gene (FTO), but the role of these polygenic effects in the mechanism of obesity remains unclear. Thus, obesity-causing variations should be identified, and insights into the biology of polygenic effects on obesity are needed.
Collapse
Affiliation(s)
- Tao Wang
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | | | | |
Collapse
|
36
|
Shi J, Li L, Hong J, Qi L, Cui B, Gu W, Zhang Y, Miao L, Wang R, Wang W, Ning G. Genetic variants determining body fat distribution and sex hormone-binding globulin among Chinese female young adults. J Diabetes 2014; 6:514-8. [PMID: 24628818 DOI: 10.1111/1753-0407.12146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Revised: 03/01/2014] [Accepted: 03/03/2014] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Measures of body fat distribution (i.e. waist : hip ratio [WHR]) are major risk factors for diabetes, independent of overall adiposity. The genetic variants related to body fat distribution show sexual dimorphism and particularly affect females. Substantial literature supports a role for sex hormone-binding globulin (SHBG) in the maintenance of glucose homeostasis. The aim of the present study was to examine the association of the genetic risk score of body fat distribution with SHBG levels and insulin resistance in young (14-30 years) Chinese females. METHODS In all, 675 young Chinese females were evaluated in the present study. A genetic risk score (GRS) was calculated on the basis of 12 established variants associated with body fat distribution. The main outcome variable was serum SHBG levels and homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS The GRS of body fat distribution was significantly associated with decreasing serum SHBG levels (P = 0.018), independent of body mass index and WHR. In addition, the GRS and SHBG showed additive effects on HOMA-IR (P = 0.004). CONCLUSIONS The GRS of body fat distribution reflects serum SHBG levels, and the GRS and SHBG jointly influence the risk of insulin resistance.
Collapse
Affiliation(s)
- Juan Shi
- Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrinology and Metabolism, Endocrine and Metabolic E-Institutes of Shanghai Universities (EISU) and Key Laboratory for Endocrinology and Metabolism of Chinese Health Ministry, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
37
|
Liu CT, Buchkovich ML, Winkler TW, Heid IM, Borecki IB, Fox CS, Mohlke KL, North KE, Adrienne Cupples L. Multi-ethnic fine-mapping of 14 central adiposity loci. Hum Mol Genet 2014; 23:4738-44. [PMID: 24760767 PMCID: PMC4119415 DOI: 10.1093/hmg/ddu183] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Revised: 04/15/2014] [Accepted: 04/16/2014] [Indexed: 01/04/2023] Open
Abstract
The Genetic Investigation of Anthropometric Traits (GIANT) consortium identified 14 loci in European Ancestry (EA) individuals associated with waist-to-hip ratio (WHR) adjusted for body mass index. These loci are wide and narrowing the signals remains necessary. Twelve of 14 loci identified in GIANT EA samples retained strong associations with WHR in our joint EA/individuals of African Ancestry (AA) analysis (log-Bayes factor >6.1). Trans-ethnic analyses at five loci (TBX15-WARS2, LYPLAL1, ADAMTS9, LY86 and ITPR2-SSPN) substantially narrowed the signals to smaller sets of variants, some of which are in regions that have evidence of regulatory activity. By leveraging varying linkage disequilibrium structures across different populations, single-nucleotide polymorphisms (SNPs) with strong signals and narrower credible sets from trans-ethnic meta-analysis of central obesity provide more precise localizations of potential functional variants and suggest a possible regulatory role. Meta-analysis results for WHR were obtained from 77 167 EA participants from GIANT and 23 564 AA participants from the African Ancestry Anthropometry Genetics Consortium. For fine mapping we interrogated SNPs within ± 250 kb flanking regions of 14 previously reported index SNPs from loci discovered in EA populations by performing trans-ethnic meta-analysis of results from the EA and AA meta-analyses. We applied a Bayesian approach that leverages allelic heterogeneity across populations to combine meta-analysis results and aids in fine-mapping shared variants at these locations. We annotated variants using information from the ENCODE Consortium and Roadmap Epigenomics Project to prioritize variants for possible functionality.
Collapse
Affiliation(s)
- Ching-Ti Liu
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Martin L Buchkovich
- Department of Genetics, University of North Carolina at Chapel Hill, NC, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany Institute of Epidemiology, Helmholtz ZentrumMuenchen-German Research Center for Environmental Health, Neuherberg, Germany
| | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University, St Louis, MO, USA
| | | | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, NC, USA
| | - Kari E North
- Department of Epidemiology and Carolina Population Center, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University, Boston, MA, USA NHLBI Framingham Heart Study, Framingham, MA, USA
| |
Collapse
|
38
|
Schleinitz D, Böttcher Y, Blüher M, Kovacs P. The genetics of fat distribution. Diabetologia 2014; 57:1276-86. [PMID: 24632736 DOI: 10.1007/s00125-014-3214-z] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Accepted: 02/18/2014] [Indexed: 12/22/2022]
Abstract
Fat stored in visceral depots makes obese individuals more prone to complications than subcutaneous fat. There is good evidence that body fat distribution (FD) is controlled by genetic factors. WHR, a surrogate measure of FD, shows significant heritability of up to ∼60%, even after adjusting for BMI. Genetic variants have been linked to various forms of altered FD such as lipodystrophies; however, the polygenic background of visceral obesity has only been sparsely investigated in the past. Recent genome-wide association studies (GWAS) for measures of FD revealed numerous loci harbouring genes potentially regulating FD. In addition, genes with fat depot-specific expression patterns (in particular subcutaneous vs visceral adipose tissue) provide plausible candidate genes involved in the regulation of FD. Many of these genes are differentially expressed in various fat compartments and correlate with obesity-related traits, thus further supporting their role as potential mediators of metabolic alterations associated with a distinct FD. Finally, developmental genes may at a very early stage determine specific FD in later life. Indeed, genes such as TBX15 not only manifest differential expression in various fat depots, but also correlate with obesity and related traits. Moreover, recent GWAS identified several polymorphisms in developmental genes (including TBX15, HOXC13, RSPO3 and CPEB4) strongly associated with FD. More accurate methods, including cardiometabolic imaging, for assessment of FD are needed to promote our understanding in this field, where the main focus is now to unravel the yet unknown biological function of these novel 'fat distribution genes'.
Collapse
Affiliation(s)
- Dorit Schleinitz
- Integrated Research and Treatment Center (IFB) AdiposityDiseases, University of Leipzig, Liebigstr. 21, 04103, Leipzig, Germany
| | | | | | | |
Collapse
|
39
|
Al-Sinani S, Al-Shafaee M, Al-Mamari A, Woodhouse N, El-Shafie O, Hassan MO, Al-Yahyaee S, Albarwani S, Jaju D, Al-Hashmi K, Al-Abri M, Rizvi S, Bayoumi R. Impaired Fasting Glucose in Omani Adults with no Family History of Type 2 Diabetes. Sultan Qaboos Univ Med J 2014; 14:e183-e189. [PMID: 24790740 PMCID: PMC3997534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 01/05/2014] [Accepted: 01/30/2014] [Indexed: 06/03/2023] Open
Abstract
OBJECTIVES The aim of this study was to estimate the prevalence of impaired fasting glucose (IFG) among Omani adults with no family history (FH) of diabetes and to investigate the factors behind the risk of developing type 2 diabetes (T2D), while excluding a FH of diabetes. METHODS A total of 1,182 Omani adults, aged ≥40 years, visited the Family Medicine & Community Health Clinic at Sultan Qaboos University Hospital, Oman, on days other than the Diabetes Clinic days, from July 2010 to July 2011. The subjects were interviewed and asked if they had T2D or a FH of T2D. RESULTS Only 191 (16%) reported no personal history of T2D or FH of the disease. Of these, anthropometric and biochemical data was complete in 159 subjects. Of these a total of 42 (26%) had IFG according to the American Diabetes Association criteria. Body mass index, fasting insulin, haemoglobin A1C and blood pressure (BP), were significantly higher among individuals with IFG (P <0.01, P <0.05, P <0.01 and P <0.01, respectively). In addition, fasting insulin, BP and serum lipid profile were correlated with obesity indices (P <0.05). Obesity indices were strongly associated with the risk of IFG among Omanis, with waist circumference being the strongest predictor. CONCLUSION Despite claiming no FH of diabetes, a large number of Omani adults in this study had a high risk of developing diabetes. This is possibly due to environmental factors and endogamy. The high prevalence of obesity combined with genetically susceptible individuals is a warning that diabetes could be a future epidemic in Oman.
Collapse
Affiliation(s)
- Sawsan Al-Sinani
- Departments of Biochemistry, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Mohammed Al-Shafaee
- Family Medicine & Public Health, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Ali Al-Mamari
- Departments of Medicine, Sultan Qaboos University Hospital, Muscat, Oman
| | - Nicolas Woodhouse
- Medicine, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Omayma El-Shafie
- Departments of Medicine, Sultan Qaboos University Hospital, Muscat, Oman
| | - Mohammed O. Hassan
- Physiology, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Said Al-Yahyaee
- Genetics, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Sulayma Albarwani
- Physiology, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Deepali Jaju
- Clinical Physiology, Sultan Qaboos University Hospital, Muscat, Oman
| | - Khamis Al-Hashmi
- Medicine, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Mohammed Al-Abri
- Clinical Physiology, Sultan Qaboos University Hospital, Muscat, Oman
| | - Syed Rizvi
- Family Medicine & Public Health, College of Medicine & Health Sciences, Sultan Qaboos University
| | - Riad Bayoumi
- Departments of Biochemistry, College of Medicine & Health Sciences, Sultan Qaboos University
| |
Collapse
|
40
|
Yoneyama S, Guo Y, Lanktree MB, Barnes MR, Elbers CC, Karczewski KJ, Padmanabhan S, Bauer F, Baumert J, Beitelshees A, Berenson GS, Boer JM, Burke G, Cade B, Chen W, Cooper-Dehoff RM, Gaunt TR, Gieger C, Gong Y, Gorski M, Heard-Costa N, Johnson T, Lamonte MJ, Mcdonough C, Monda KL, Onland-Moret NC, Nelson CP, O'Connell JR, Ordovas J, Peter I, Peters A, Shaffer J, Shen H, Smith E, Speilotes L, Thomas F, Thorand B, Monique Verschuren WM, Anand SS, Dominiczak A, Davidson KW, Hegele RA, Heid I, Hofker MH, Huggins GS, Illig T, Johnson JA, Kirkland S, König W, Langaee TY, Mccaffery J, Melander O, Mitchell BD, Munroe P, Murray SS, Papanicolaou G, Redline S, Reilly M, Samani NJ, Schork NJ, Van Der Schouw YT, Shimbo D, Shuldiner AR, Tobin MD, Wijmenga C, Yusuf S, Hakonarson H, Lange LA, Demerath EW, Fox CS, North KE, Reiner AP, Keating B, Taylor KC. Gene-centric meta-analyses for central adiposity traits in up to 57 412 individuals of European descent confirm known loci and reveal several novel associations. Hum Mol Genet 2014; 23:2498-510. [PMID: 24345515 PMCID: PMC3988452 DOI: 10.1093/hmg/ddt626] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2013] [Revised: 11/18/2013] [Accepted: 12/09/2013] [Indexed: 12/15/2022] Open
Abstract
Waist circumference (WC) and waist-to-hip ratio (WHR) are surrogate measures of central adiposity that are associated with adverse cardiovascular events, type 2 diabetes and cancer independent of body mass index (BMI). WC and WHR are highly heritable with multiple susceptibility loci identified to date. We assessed the association between SNPs and BMI-adjusted WC and WHR and unadjusted WC in up to 57 412 individuals of European descent from 22 cohorts collaborating with the NHLBI's Candidate Gene Association Resource (CARe) project. The study population consisted of women and men aged 20-80 years. Study participants were genotyped using the ITMAT/Broad/CARE array, which includes ∼50 000 cosmopolitan tagged SNPs across ∼2100 cardiovascular-related genes. Each trait was modeled as a function of age, study site and principal components to control for population stratification, and we conducted a fixed-effects meta-analysis. No new loci for WC were observed. For WHR analyses, three novel loci were significantly associated (P < 2.4 × 10(-6)). Previously unreported rs2811337-G near TMCC1 was associated with increased WHR (β ± SE, 0.048 ± 0.008, P = 7.7 × 10(-9)) as was rs7302703-G in HOXC10 (β = 0.044 ± 0.008, P = 2.9 × 10(-7)) and rs936108-C in PEMT (β = 0.035 ± 0.007, P = 1.9 × 10(-6)). Sex-stratified analyses revealed two additional novel signals among females only, rs12076073-A in SHC1 (β = 0.10 ± 0.02, P = 1.9 × 10(-6)) and rs1037575-A in ATBDB4 (β = 0.046 ± 0.01, P = 2.2 × 10(-6)), supporting an already established sexual dimorphism of central adiposity-related genetic variants. Functional analysis using ENCODE and eQTL databases revealed that several of these loci are in regulatory regions or regions with differential expression in adipose tissue.
Collapse
Affiliation(s)
| | - Yiran Guo
- Center for Applied Genomics, Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Abramson Research Center, Philadelphia, PA 19104, USA
- BGI-Shenzhen, Beishan Beishan Industrial Zone,Yantian District, Shenzhen 518083, China
| | | | - Michael R. Barnes
- National Institute for Health Biomedical Research Unit
- London School of Medicine
| | - Clara C. Elbers
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | | | | | - Florianne Bauer
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | | | | | - Gerald S. Berenson
- Department of Epidemiology, Tulane University, New Orleans, LA 70112, USA
| | - Jolanda M.A. Boer
- Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, 3720 BA Bilthoven, The Netherlands
| | | | - Brian Cade
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
- Division of Sleep Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Wei Chen
- Department of Epidemiology, Tulane University, New Orleans, LA 70112, USA
| | - Rhonda M. Cooper-Dehoff
- Department of Pharmacotherapy and Translational Research
- Center for Pharmacogenomics, University of Florida, Gainesville, FL 32610, USA
| | - Tom R. Gaunt
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, Bristol BS8 2BN, UK
| | | | - Yan Gong
- Department of Pharmacotherapy and Translational Research
- Center for Pharmacogenomics, University of Florida, Gainesville, FL 32610, USA
| | - Mathias Gorski
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany
- Department of Internal Medicine II, University Medical Center Regensburg, 93053 Regensburg, Germany
| | | | - Toby Johnson
- Clinical Pharmacology and The Genome Centre, William Harvey Research Institute, Barts
- London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Michael J. Lamonte
- Department of Social and Preventive Medicine, SUNY-Buffalo School of Public Health and Health Professions, Buffalo, NY 14214, USA
| | - Caitrin Mcdonough
- Department of Pharmacotherapy and Translational Research
- Center for Pharmacogenomics, University of Florida, Gainesville, FL 32610, USA
| | - Keri L. Monda
- Gillings School of Global Public Health
- The Center for Observational Research, Amgen, Inc., Thousand Oaks, CA 91320, USA
| | - N. Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | - Christopher P. Nelson
- Department of Cardiovascular Science, University of Leicester, Leicester LE3 9QP, UK
- Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester LE3 9QP, UK
| | | | - Jose Ordovas
- Nutrition and Genomics Laboratory, Tufts University, Boston, MA 02111, USA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Jonathan Shaffer
- Division of General Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY 10032, USA
| | | | - Erin Smith
- Department of Pediatrics and Rady's Children's Hospital, University of California at San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Liz Speilotes
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Internal Medicine, Division of Gastroenterology
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- The Broad Institute, Cambridge, MA 02141, USA
| | - Fridtjof Thomas
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | | | - W. M. Monique Verschuren
- Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, 3720 BA Bilthoven, The Netherlands
| | - Sonia S. Anand
- Population Health Research Institute, Hamilton Health Sciences, Department of Medicine, and
- Population Genomics Program, Department of Clinical Epidemiology, McMaster University, Hamilton, ON, CanadaL8S4L8
| | - Anna Dominiczak
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Karina W. Davidson
- Division of General Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY 10032, USA
| | - Robert A. Hegele
- Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, CanadaN6A 5C1
| | - Iris Heid
- Institute of Genetic Epidemiology
- Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, 93053 Regensburg, Germany
| | - Marten H. Hofker
- Department of Molecular Genetics, University Medical Center Groningen, Groningen University, 9700 AB Groningen, The Netherlands
| | - Gordon S. Huggins
- Center for Translational Genomics, Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA 02111, USA
| | - Thomas Illig
- Research Unit for Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, 85764 Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, 30625 Hannover, Germany
| | - Julie A. Johnson
- Department of Pharmacotherapy and Translational Research
- Center for Pharmacogenomics, University of Florida, Gainesville, FL 32610, USA
| | - Susan Kirkland
- Department of Community Health & Epidemiology, Dalhousie University, Halifax, NS, Canada
| | | | - Wolfgang König
- Department of Internal Medicine II, Cardiology, University of Ulm Medical Center, Ulm 89081, Germany
| | - Taimour Y. Langaee
- Department of Pharmacotherapy and Translational Research
- Center for Pharmacogenomics, University of Florida, Gainesville, FL 32610, USA
| | - Jeanne Mccaffery
- Weight Control and Diabetes Research Center, The Miriam Hospital and
- Warren Alpert School of Medicine at Brown University, Providence, RI 02906, USA
| | - Olle Melander
- Department of Clinical Sciences, Hypertension & Cardiovascular Disease, Lund University, SE 20502 Malmo, Sweden
| | | | - Patricia Munroe
- Clinical Pharmacology and The Genome Centre, William Harvey Research Institute, Barts
- London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Sarah S. Murray
- The Scripps Research Institute, Scripps Health, La Jolla, CA 92037, USA
| | - George Papanicolaou
- Division of Prevention and Population Sciences, NHLBI, NIH, Bethesda, MD 20824, USA
| | - Susan Redline
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
- Division of Sleep Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Muredach Reilly
- Cardiovascular Institute, University of Pennsylvania Medical Center, Philadelphia, PA 19104, USA
| | - Nilesh J. Samani
- Department of Cardiovascular Science, University of Leicester, Leicester LE3 9QP, UK
- Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester LE3 9QP, UK
| | - Nicholas J. Schork
- The Scripps Research Institute, Scripps Health, La Jolla, CA 92037, USA
- Scripps Translational Science Institute, La Jolla, CA 92037, USA
| | - Yvonne T. Van Der Schouw
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | - Daichi Shimbo
- Division of General Medicine, Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY 10032, USA
| | - Alan R. Shuldiner
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD 21201, USA
| | - Martin D. Tobin
- Department of Health Sciences
- Department of Genetics, University of Leicester, Leicester LE1 7RH, UK
| | - Cisca Wijmenga
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9700 RB, The Netherlands
| | - Salim Yusuf
- Population Health Research Institute, Hamilton Health Sciences, Department of Medicine, and
- Population Genomics Program, Department of Clinical Epidemiology, McMaster University, Hamilton, ON, CanadaL8S4L8
| | | | | | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Abramson Research Center, Philadelphia, PA 19104, USA
| | - Leslie A. Lange
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ellen W Demerath
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55454, USA
| | - Caroline S. Fox
- Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Kari E North
- Gillings School of Global Public Health
- Carolina Center for Genome Sciences, Chapel Hill, NC 27599, USA
| | - Alex P. Reiner
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA 98195, USA
| | - Brendan Keating
- Center for Applied Genomics, Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Abramson Research Center, Philadelphia, PA 19104, USA
| | - Kira C. Taylor
- Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA
| |
Collapse
|
41
|
Genotype by energy expenditure interaction and body composition traits: The Portuguese Healthy Family Study. BIOMED RESEARCH INTERNATIONAL 2014; 2014:845207. [PMID: 24791001 PMCID: PMC3984825 DOI: 10.1155/2014/845207] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 02/12/2014] [Accepted: 02/25/2014] [Indexed: 01/09/2023]
Abstract
Background and Aims. Energy expenditure has been negatively correlated with fat accumulation. However, this association is highly variable. In the present study we applied a genotype by environment interaction method to examine the presence of Genotype x by Total Daily Energy Expenditure and Genotype x by Daily Energy Expenditure interactions in the expression of different body composition traits. Methods and Results. A total of 958 subjects from 294 families of The Portuguese Healthy Family Study were included in the analysis. TDEE and DEE were assessed using a physical activity recall. Body fat percentages were measured with a bioelectrical impedance scale. GxTDEE and GxDEE examinations were performed using SOLAR 4.0 software. All BC traits were significantly heritable, with heritabilities ranging from 21% to 34%. The GxTDEE and GxDEE interaction models fitted the data better than the polygenic model for all traits. For all traits, a significant GxTDEE and GxDEE interaction was due to variance heterogeneity among distinct levels of TDEE and DEE. For WC, GxTDEE was also significant due to the genetic correlation function. Conclusions. TDEE and DEE are environmental constraints associated with the expression of individuals' BC genotypes, leading to variability in the phenotypic expression of BC traits.
Collapse
|
42
|
Steffen LM, Sinaiko AR, Zhou X, Moran A, Jacobs Jr DR, Korenfeld Y, Dengel DR, Chow LS, Steinberger J. Relation of adiposity, television and screen time in offspring to their parents. BMC Pediatr 2013; 13:133. [PMID: 24004899 PMCID: PMC3766692 DOI: 10.1186/1471-2431-13-133] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2013] [Accepted: 08/24/2013] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Few studies have examined the relations of adiposity and lifestyle factors in young offspring with their parents as children (parentschild) or at their current age (parentsadult). Therefore, we compared measures of adiposity and lifestyle in parentschild and parentsadult with their offspring. METHODS Two generations (one parent and his/her offspring) participated in this study: 234 parents from a previously established cohort and 382 offspring. Parentsadult and offspring underwent measurements for height, weight, waist circumference, % body fat, visceral fat, and lifestyle habits. Participants were classified as normal weight, overweight, obese based on age-specific BMI criteria. Mixed model linear regression analysis evaluated the associations of adiposity and lifestyle factors of parentschild and parentsadult with that of their offspring, adjusting for age, sex, race, and family membership. RESULTS The prevalence of obesity was greater among offspring mean age 12.3 years compared to their parentschild mean age 12.6 years (18.4% vs 10.1%, p<0.001) even though hours of television (TV) watching were similar between the two generations as children (p=0.80). Sixty percent of parents (as children and adults) and offspring reported more than 2 hours of TV/day. Offspring of parents who were overweight and obese as children had greater BMI (all p<0.001) than offspring of parents who were normal weight as children. For both parentadult and offspring, adiposity was greater with greater total screen time. CONCLUSIONS Identifying high-risk families is important for early intervention of overweight, especially in children.
Collapse
Affiliation(s)
- Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Alan R Sinaiko
- Department of Pediatrics, University of Minnesota School of Medicine, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Xia Zhou
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Antoinette Moran
- Department of Pediatrics, University of Minnesota School of Medicine, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - David R Jacobs Jr
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Yoel Korenfeld
- Department of Pediatrics, University of Minnesota School of Medicine, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Donald R Dengel
- School of Kinesiology, University of Minnesota, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Lisa S Chow
- Division of Endocrinology, Diabetes and Metabolism, University of Minnesota School of Medicine, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| | - Julia Steinberger
- Department of Pediatrics, University of Minnesota School of Medicine, 1300 South Second St Suite 300, Minneapolis, MN 55454, USA
| |
Collapse
|
43
|
Liu CT, Monda KL, Taylor KC, Lange L, Demerath EW, Palmas W, Wojczynski MK, Ellis JC, Vitolins MZ, Liu S, Papanicolaou GJ, Irvin MR, Xue L, Griffin PJ, Nalls MA, Adeyemo A, Liu J, Li G, Ruiz-Narvaez EA, Chen WM, Chen F, Henderson BE, Millikan RC, Ambrosone CB, Strom SS, Guo X, Andrews JS, Sun YV, Mosley TH, Yanek LR, Shriner D, Haritunians T, Rotter JI, Speliotes EK, Smith M, Rosenberg L, Mychaleckyj J, Nayak U, Spruill I, Garvey WT, Pettaway C, Nyante S, Bandera EV, Britton AF, Zonderman AB, Rasmussen-Torvik LJ, Chen YDI, Ding J, Lohman K, Kritchevsky SB, Zhao W, Peyser PA, Kardia SLR, Kabagambe E, Broeckel U, Chen G, Zhou J, Wassertheil-Smoller S, Neuhouser ML, Rampersaud E, Psaty B, Kooperberg C, Manson JE, Kuller LH, Ochs-Balcom HM, Johnson KC, Sucheston L, Ordovas JM, Palmer JR, Haiman CA, McKnight B, Howard BV, Becker DM, Bielak LF, Liu Y, Allison MA, Grant SFA, Burke GL, Patel SR, Schreiner PJ, Borecki IB, Evans MK, Taylor H, Sale MM, Howard V, Carlson CS, Rotimi CN, Cushman M, Harris TB, Reiner AP, Cupples LA, North KE, Fox CS. Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet 2013; 9:e1003681. [PMID: 23966867 PMCID: PMC3744443 DOI: 10.1371/journal.pgen.1003681] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Accepted: 06/13/2013] [Indexed: 01/18/2023] Open
Abstract
Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values<5.0 × 10(-6) were followed-up (stage 2) in AA with WC and with WHR. Additionally, we interrogated genomic regions of previously identified European ancestry (EA) WHR loci among AA. In joint analysis of association results including both Stage 1 and 2 cohorts, 2 SNPs demonstrated association, rs2075064 at LHX2, p = 2.24×10(-8) for WC-adjusted-for-BMI, and rs6931262 at RREB1, p = 2.48×10(-8) for WHR-adjusted-for-BMI. However, neither signal was genome-wide significant after double GC-correction (LHX2: p = 6.5 × 10(-8); RREB1: p = 5.7 × 10(-8)). Six of fourteen previously reported loci for waist in EA populations were significant (p<0.05 divided by the number of independent SNPs within the region) in AA studied here (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, ITPR2-SSPN). Further, we observed associations with metabolic traits: rs13389219 at GRB14 associated with HDL-cholesterol, triglycerides, and fasting insulin, and rs13060013 at ADAMTS9 with HDL-cholesterol and fasting insulin. Finally, we observed nominal evidence for sexual dimorphism, with stronger results in AA women at the GRB14 locus (p for interaction = 0.02). In conclusion, we identified two suggestive loci associated with fat distribution in AA populations in addition to confirming 6 loci previously identified in populations of EA. These findings reinforce the concept that there are fat distribution loci that are independent of generalized adiposity.
Collapse
Affiliation(s)
- Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Keri L. Monda
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Kira C. Taylor
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Epidemiology and Population Health, University of Louisville, Louisville, Kentucky, United States of America
| | - Leslie Lange
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Ellen W. Demerath
- University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Walter Palmas
- Department of Medicine, Columbia University, New York, New York, United States of America
| | - Mary K. Wojczynski
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jaclyn C. Ellis
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Mara Z. Vitolins
- Department of Epidemiology & Prevention, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America
| | - Simin Liu
- Departments of Epidemiology, Medicine, and Obstetrics and Gynecology and Center for Metabolic Disease Prevention, Los Angeles, California, United States of America
| | - George J. Papanicolaou
- Division of Cardiovascular Sciences, Prevention and Population Sciences Program, National Heart, Lung, & Blood Institute, Bethesda, Maryland, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, UAB, Birmingham, Alabama, United States of America
| | - Luting Xue
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Paula J. Griffin
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Michael A. Nalls
- Laboratory of Neurogenetics, National Institute of Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Jiankang Liu
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Guo Li
- University of Washington, Seattle, Washington, United States of America
| | - Edward A. Ruiz-Narvaez
- Slone Epidemiology Center, Boston University, Boston, Massachusetts, United States of America
| | - Wei-Min Chen
- Center for Public Health and Genomics and Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Fang Chen
- Center for Public Health and Genomics and Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Brian E. Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Robert C. Millikan
- Department of Epidemiology, Gillings School of Global Public Health, and Lineberger Comprehensive Cancer Center, UNC at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Christine B. Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - Sara S. Strom
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Xiuqing Guo
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Jeanette S. Andrews
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Yan V. Sun
- Department of Epidemiolgy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Thomas H. Mosley
- Division of Geriatrics, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Lisa R. Yanek
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Talin Haritunians
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Jerome I. Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | | | - Megan Smith
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Lynn Rosenberg
- Slone Epidemiology Center, Boston University, Boston, Massachusetts, United States of America
| | - Josyf Mychaleckyj
- Center for Public Health and Genomics and Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Uma Nayak
- Center for Public Health and Genomics and Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Ida Spruill
- Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - W. Timothy Garvey
- Department of Epidemiology, UAB School of Public Health, Birmingham, Alabama, United States of America
| | - Curtis Pettaway
- Department of Urology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Sarah Nyante
- Department of Epidemiology, Gillings School of Global Public Health, and Lineberger Comprehensive Cancer Center, UNC at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Elisa V. Bandera
- The Cancer Institute of New Jersey, New Brunswick, New Jersey, United States of America
| | - Angela F. Britton
- Laboratory of Neurogenetics, National Institute of Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Alan B. Zonderman
- Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, NIH Biomedical Center, Baltimore, Maryland, United States of America
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Yii-Der Ida Chen
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Jingzhong Ding
- Department of Internal Medicine/Geriatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Kurt Lohman
- Department of Epidemiology & Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Stephen B. Kritchevsky
- Department of Internal Medicine/Geriatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Wei Zhao
- Department of Epidemiolgy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Patricia A. Peyser
- Department of Epidemiolgy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sharon L. R. Kardia
- Department of Epidemiolgy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Edmond Kabagambe
- Department of Epidemiology, UAB, Birmingham, Alabama, United States of America
| | - Ulrich Broeckel
- Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America
| | - Marian L. Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Evadnie Rampersaud
- Miami Institute for Human Genomics, Miami, Florida, United States of America
- John T. McDonald Department of Human Genetics, University of Miami, Miami, Florida, United States of America
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lewis H. Kuller
- Department of Epidemiology, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America
| | - Heather M. Ochs-Balcom
- Department of Social and Preventive Medicine, University at Buffalo, Buffalo, New York, United States of America
| | - Karen C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Lara Sucheston
- Department of Biostatistics, University of Buffalo School of Public Health and Health Professions, New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, New York, United States of America
| | - Jose M. Ordovas
- Tufts University, Boston, Massachusetts, United States of America
| | - Julie R. Palmer
- Slone Epidemiology Center, Boston University, Boston, Massachusetts, United States of America
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Barbara V. Howard
- MedStar Health Research Institute and Georgetown University, Hyattsville, Maryland, United States of America
| | - Diane M. Becker
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Lawrence F. Bielak
- Department of Epidemiolgy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Matthew A. Allison
- University of California at San Diego Department of Preventive Medicine, La Jolla, California, United States of America
| | - Struan F. A. Grant
- Division of Human Genetics, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, United States of America
| | - Gregory L. Burke
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Sanjay R. Patel
- Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Pamela J. Schreiner
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Ingrid B. Borecki
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Michele K. Evans
- Health Disparities Research Section, Clinical Research Branch, National Institute on Aging, National Institutes of Health, NIH Biomedical Center, Baltimore, Maryland, United States of America
| | - Herman Taylor
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Michele M. Sale
- Center for Public Genomics, Department of Biochemistry and Molecular Genetics and Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
| | - Virginia Howard
- Department of Epidemiology, UAB School of Public Health, Birmingham, Alabama, United States of America
| | - Christopher S. Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, Vermont, United States of America
| | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland, United States of America
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
- NHLBI's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Caroline S. Fox
- NHLBI's Framingham Heart Study, Framingham, Massachusetts, United States of America
- NHLBI's Center for Population Studies, Framingham, Massachusetts, United States of America
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| |
Collapse
|
44
|
Price M, Raffelsbauer D. Genetics and environmental factors in obesity and diabetes: Complex problems, complex solutions. ACTA ACUST UNITED AC 2013. [DOI: 10.1179/2047480612z.00000000065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
|
45
|
Vik KL, Romundstad P, Nilsen TIL. Tracking of cardiovascular risk factors across generations: family linkage within the population-based HUNT study, Norway. J Epidemiol Community Health 2013; 67:564-70. [PMID: 23661719 DOI: 10.1136/jech-2012-201634] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BACKGROUND Parent-offspring studies have shown that cardiovascular risk factors cluster within families. However, most studies have assessed the offspring cardiovascular risk factor level at a young age, and whether an association persists into the offspring's adult life is less clear. This study linked information between parents and their adult offspring to investigate the intergenerational association of anthropometric measures, blood pressure, blood lipid levels and physical activity. METHODS The study population consisted of parent and adult offspring pairs (11,931 fathers-sons, 12,563 fathers-daughters, 15,626 mothers-sons and 16,449 mothers-daughters) who participated in the second and third cross-sectional waves of the Nord-Trøndelag Health Study (HUNT 2, 1995-1997 and HUNT 3, 2006-2008). A general linear model and logistic regression were used to estimate the association between the parent and offspring risk factor levels. RESULTS All continuously measured cardiovascular risk factors under study showed a statistically significant positive association between parents and offspring, except the waist-hip ratio. Adjusted coefficients from linear regression ranged from 0.09 (95% CI 0.07 to 0.11) for waist circumference to 0.29 (95% CI 0.27 to 0.32) for body weight. Moreover, offspring were two to three times more likely to be obese, have a high cholesterol level, or hypertension when comparing extreme categories of the corresponding parental risk factor level. Physically active parents had a lower risk of having physically inactive offspring. CONCLUSIONS The results suggested that cardiovascular risk factors track across generations and persist into the offspring's adult life.
Collapse
Affiliation(s)
- Kirsti L Vik
- Department of Human Movement Science, Norwegian University of Science and Technology, Trondheim Norway.
| | | | | |
Collapse
|
46
|
Fat depot-specific mRNA expression of novel loci associated with waist-hip ratio. Int J Obes (Lond) 2013; 38:120-5. [PMID: 23670221 DOI: 10.1038/ijo.2013.56] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2012] [Revised: 02/08/2013] [Accepted: 03/10/2013] [Indexed: 12/11/2022]
Abstract
OBJECTIVE We hypothesized that genes within recently identified loci associated with waist-hip ratio (WHR) exhibit fat depot-specific mRNA expression, which correlates with obesity-related traits. METHODS Adipose tissue (AT) mRNA expression of 6 genes (TBX15/WARS2, STAB1, PIGC, ZNRF3 and GRB14) within these loci showing coincident cis-expression quantitative trait loci was measured in 222 paired samples of human visceral (vis) and subcutaneous (sc) AT. The relationship of mRNA expression levels with obesity-related quantitative traits was assessed by Pearson's correlation analyses. Multivariate linear relationships were assessed by generalized linear regression models. RESULTS Whereas only PIGC, ZNFR3 and STAB1 mRNA expression in sc AT correlated nominally with WHR (P<0.05, adjusted for age and sex), mRNA expression of all studied genes in at least one of the fat depots correlated significantly with vis and/or sc fat area (P ranging from 0.05 to 4.0 × 10(6), adjusted for age and sex). Consistently, the transcript levels of WARS, PIGC and GRB14 were nominally associated with body mass index (BMI) (P ranging from 0.02 to 9.2 × 10(5), adjusted for age and sex). Moreover, independent of sex, obesity and diabetes status, differential expression between vis and sc AT was observed for all tested genes (P<0.01). Finally, the rs10195252 T-allele was nominally associated with increased GRB14 sc mRNA expression (P=0.025 after adjusting for age, sex and BMI). CONCLUSIONS Our data including the inter-depot variability of mRNA expression suggests that genes within the WHR-associated loci might be involved in the regulation of fat distribution.
Collapse
|
47
|
Kitamoto A, Kitamoto T, Mizusawa S, Teranishi H, So R, Matsuo T, Nakata Y, Hyogo H, Ochi H, Nakamura T, Kamohara S, Miyatake N, Kotani K, Komatsu R, Itoh N, Mineo I, Wada J, Yoneda M, Nakajima A, Funahashi T, Miyazaki S, Tokunaga K, Masuzaki H, Ueno T, Chayama K, Hamaguchi K, Yamada K, Hanafusa T, Oikawa S, Sakata T, Tanaka K, Matsuzawa Y, Nakao K, Sekine A, Hotta K. NUDT3 rs206936 is associated with body mass index in obese Japanese women. Endocr J 2013; 60:991-1000. [PMID: 23708086 DOI: 10.1507/endocrj.ej13-0100] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The predominant risk factor of metabolic syndrome is intra-abdominal fat accumulation, which is determined by waist circumference, waist-hip ratio measurements and visceral fat area (VFA); the latter can be accurately measured by performing computed tomography (CT). In addition to environmental factors, genetic factors play an important role in obesity and fat distribution. New genetic loci associated with body mass index (BMI) and adiposity have been identified by genome-wide association studies (GWASs). This study utilized CT to investigate whether single nucleotide polymorphisms (SNPs) that confer susceptibility to higher BMI are associated with VFA, subcutaneous fat area (SFA), and the ratio of VFA to SFA (V/S ratio). We measured the VFA and SFA of 1424 obese Japanese subjects (BMI ≥ 25 kg/m(2), 635 men and 789 women) who were genotyped for 13 single nucleotide polymorphisms (SNPs) reported by recent GWASs, namely, TNNI3K rs1514175, PTBP2 rs1555543, ADCY3 rs713586, IRS1 rs2943650, POC5 rs2112347, NUDT3 rs206936, LINGO2 rs10968576, STK33 rs4929949, MTIF3 rs4771122, SPRY2 rs534870, MAP2K5 rs2241423, QPCTL rs2287019, and ZC3H4 rs3810291. The G-allele of NUDT3 rs206936 was significantly associated with increased BMI (P = 5.3 × 10(-5)) and SFA (P = 0.00039) in the obese Japanese women. After adjustment with BMI, the association between rs206936 and SFA was not observed. This significant association was not observed in the men. The other SNPs analyzed were not significantly associated with BMI, VFA, SFA, or V/S ratio. Our results suggest that NUDT3 rs206936 is associated with BMI in Japanese women.
Collapse
Affiliation(s)
- Aya Kitamoto
- EBM Research Center, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
48
|
Hotta K, Kitamoto A, Kitamoto T, Mizusawa S, Teranishi H, So R, Matsuo T, Nakata Y, Hyogo H, Ochi H, Nakamura T, Kamohara S, Miyatake N, Kotani K, Itoh N, Mineo I, Wada J, Yoneda M, Nakajima A, Funahashi T, Miyazaki S, Tokunaga K, Masuzaki H, Ueno T, Chayama K, Hamaguchi K, Yamada K, Hanafusa T, Oikawa S, Sakata T, Tanaka K, Matsuzawa Y, Nakao K, Sekine A. Replication study of 15 recently published Loci for body fat distribution in the Japanese population. J Atheroscler Thromb 2012; 20:336-50. [PMID: 23221025 DOI: 10.5551/jat.14589] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
AIM Visceral fat accumulation plays an integral role in morbidity and mortality rates by increasing the risk of developing metabolic disorders such as type 2 diabetes, dyslipidemia, and hypertension. New genetic loci associated with fat distribution, measured by waist-hip ratios and computed tomography (CT), have recently been identified by genome-wide association studies in European-descent populations. This study used CT to investigate whether single nucleotide polymorphisms (SNPs) that confer susceptibility to fat distribution are associated with visceral fat area (VFA) and subcutaneous fat area (SFA) in the Japanese population. METHODS We measured the VFAs and SFAs of 1424 obese Japanese subjects (BMI≥25 kg/m(2), 635 men and 789 women) that were genotyped at 15 SNPs, namely, TBX15 rs984222, DNM3 rs1011731, LYPLAL1 rs4846567, GRB14 rs10195252, NISCH rs6784615, ADAMTS9 rs6795735, CPEB4 rs6861681, LY86 rs1294421, VEGFA rs6905288, RSPO3 rs9491696, NFE2L3 rs1055144, ITPR2 rs718314, HOXC13 rs1443512, ZNRF3 rs4823006 and THNSL2 rs1659258. RESULTS The G-allele of LYPLAL1 rs4846567 was borderline associated with an increased ratio of VFA to SFA (V/S ratio; p= 0.0020). LYPLAL1 rs4846567 had a stronger effect on the V/S ratio in women (p= 0.0078) than in men (p= 0.12); however, neither result was significant after Bonferroni correction for multiple comparisons. NISCH rs6784615 was nominally associated with increased VFA (p=0.040) and V/S ratio (p= 0.020). The other SNPs analyzed were not significantly associated with body mass index (BMI), VFA, or SFA. CONCLUSION Our results suggest that LYPLAL1 rs4846567 and NISCH rs6784615 may influence fat distribution in the Japanese population.
Collapse
Affiliation(s)
- Kikuko Hotta
- EBM Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
49
|
Positive natural selection of TRIB2, a novel gene that influences visceral fat accumulation, in East Asia. Hum Genet 2012; 132:201-17. [PMID: 23108367 DOI: 10.1007/s00439-012-1240-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Accepted: 10/16/2012] [Indexed: 10/27/2022]
Abstract
Accumulation of visceral fat increases cardiovascular mortality in industrialized societies. However, during the evolution of the modern human, visceral fat may have acted as energy storage facility to survive in times of famine. Therefore, past natural selection might contribute to shaping the variation of visceral fat accumulation in present populations. Here, we report that the gene encoding tribbles homolog 2 (TRIB2) influenced visceral fat accumulation and was operated by recent positive natural selection in East Asians. Our candidate gene association analysis on 11 metabolic traits of 5,810 East Asians revealed that rs1057001, a T/A transversion polymorphism in 3'untranslated region (UTR) of TRIB2, was strongly associated with visceral fat area (VFA) and waist circumference adjusted for body mass index (P = 2.7 × 10(-6) and P = 9.0 × 10(-6), respectively). rs1057001 was in absolute linkage disequilibrium with a conserved insertion-deletion polymorphism in the 3'UTR and was associated with allelic imbalance of TRIB2 transcript levels in adipose tissues. rs1057001 showed high degree of interpopulation variation of the allele frequency; the low-VFA-associated A allele was found with high frequencies in East Asians. Haplotypes containing the rs1057001 A allele exhibited a signature of a selective sweep, which may have occurred 16,546-27,827 years ago in East Asians. Given the predominance of the thrifty gene hypothesis, it is surprising that the apparently non-thrifty allele was selectively favored in the evolution of modern humans. Environmental/physiological factors other than famine would be needed to explain the non-neutral evolution of TRIB2 in East Asians.
Collapse
|
50
|
Abstract
Obesity is a complex disease that affects all ethnic populations worldwide. The etiology of this disease is based on the interaction of genetic factors, environment and lifestyles indicators. Genetic contribution to the epidemic has gained attention from 2 sources: monogenic syndromes that display severe obesity, and the polygenic model of common obesity. Single mutations can render a syndrome with severe obesity resulting from alteration in central o peripheral appetite control mechanisms. The interaction of several polymorphisms and epigenetic modifications constitute the basic plot for common obesity, molecular ingredients that should not confuse the investigator-they make this riddle even harder to decipher.
Collapse
|