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Longitudinal relationships between glycemic status and body mass index in a multiethnic study: evidence from observational and genetic epidemiology. Sci Rep 2016; 6:30744. [PMID: 27480816 PMCID: PMC4969745 DOI: 10.1038/srep30744] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 07/07/2016] [Indexed: 12/12/2022] Open
Abstract
We investigated the relationship between glycemic status and BMI and its interaction with obesity single-nucleotide polymorphisms (SNPs) in a multi-ethnic longitudinal cohort at high-risk for dysglycemia. We studied 17 394 participants from six ethnicities followed-up for 3.3 years. Twenty-three obesity SNPs were genotyped and an unweighted genotype risk score (GRS) was calculated. Glycemic status was defined using an oral glucose tolerance test. Linear regression models were adjusted for age, sex and population stratification. Normal glucose tolerance (NGT) to dysglycemia transition was associated with baseline BMI and BMI change. Impaired fasting glucose/impaired glucose tolerance to type 2 diabetes transition was associated with baseline BMI but not BMI change. No simultaneous significant main genetic effects and interactions between SNPs/GRS and glycemic status or transition on BMI level and BMI change were observed. Our data suggests that the interplay between glycemic status and BMI trajectory may be independent of the effects of obesity genes. This implies that individuals with different glycemic statuses may be combined together in genetic association studies on obesity traits, if appropriate adjustments for glycemic status are performed. Implementation of population-wide weight management programs may be more beneficial towards individuals with NGT than those at a later disease stage.
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302
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Affiliation(s)
- Rui Feng
- Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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303
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Silventoinen K, Jelenkovic A, Sund R, Hur YM, Yokoyama Y, Honda C, Hjelmborg JVB, Möller S, Ooki S, Aaltonen S, Ji F, Ning F, Pang Z, Rebato E, Busjahn A, Kandler C, Saudino KJ, Jang KL, Cozen W, Hwang AE, Mack TM, Gao W, Yu C, Li L, Corley RP, Huibregtse BM, Christensen K, Skytthe A, Kyvik KO, Derom CA, Vlietinck RF, Loos RJ, Heikkilä K, Wardle J, Llewellyn CH, Fisher A, McAdams TA, Eley TC, Gregory AM, He M, Ding X, Bjerregaard-Andersen M, Beck-Nielsen H, Sodemann M, Tarnoki AD, Tarnoki DL, Stazi MA, Fagnani C, D'Ippolito C, Knafo-Noam A, Mankuta D, Abramson L, Burt SA, Klump KL, Silberg JL, Eaves LJ, Maes HH, Krueger RF, McGue M, Pahlen S, Gatz M, Butler DA, Bartels M, van Beijsterveldt TC, Craig JM, Saffery R, Freitas DL, Maia JA, Dubois L, Boivin M, Brendgen M, Dionne G, Vitaro F, Martin NG, Medland SE, Montgomery GW, Chong Y, Swan GE, Krasnow R, Magnusson PK, Pedersen NL, Tynelius P, Lichtenstein P, Haworth CM, Plomin R, Bayasgalan G, Narandalai D, Harden KP, Tucker-Drob EM, Öncel SY, Aliev F, Spector T, Mangino M, Lachance G, Baker LA, Tuvblad C, Duncan GE, Buchwald D, Willemsen G, Rasmussen F, Goldberg JH, Sørensen TI, Boomsma DI, Kaprio J. Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) study. Am J Clin Nutr 2016; 104:371-9. [PMID: 27413137 PMCID: PMC4962159 DOI: 10.3945/ajcn.116.130252] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 05/20/2016] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Both genetic and environmental factors are known to affect body mass index (BMI), but detailed understanding of how their effects differ during childhood and adolescence is lacking. OBJECTIVES We analyzed the genetic and environmental contributions to BMI variation from infancy to early adulthood and the ways they differ by sex and geographic regions representing high (North America and Australia), moderate (Europe), and low levels (East Asia) of obesogenic environments. DESIGN Data were available for 87,782 complete twin pairs from 0.5 to 19.5 y of age from 45 cohorts. Analyses were based on 383,092 BMI measurements. Variation in BMI was decomposed into genetic and environmental components through genetic structural equation modeling. RESULTS The variance of BMI increased from 5 y of age along with increasing mean BMI. The proportion of BMI variation explained by additive genetic factors was lowest at 4 y of age in boys (a(2) = 0.42) and girls (a(2) = 0.41) and then generally increased to 0.75 in both sexes at 19 y of age. This was because of a stronger influence of environmental factors shared by co-twins in midchildhood. After 15 y of age, the effect of shared environment was not observed. The sex-specific expression of genetic factors was seen in infancy but was most prominent at 13 y of age and older. The variance of BMI was highest in North America and Australia and lowest in East Asia, but the relative proportion of genetic variation to total variation remained roughly similar across different regions. CONCLUSIONS Environmental factors shared by co-twins affect BMI in childhood, but little evidence for their contribution was found in late adolescence. Our results suggest that genetic factors play a major role in the variation of BMI in adolescence among populations of different ethnicities exposed to different environmental factors related to obesity.
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Affiliation(s)
- Karri Silventoinen
- Departments of Social Research and Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan;
| | - Aline Jelenkovic
- Departments of Social Research and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Leioa, Spain
| | | | - Yoon-Mi Hur
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Yoshie Yokoyama
- Department of Public Health Nursing, Osaka City University, Osaka, Japan
| | - Chika Honda
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jacob vB Hjelmborg
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Sören Möller
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Syuichi Ooki
- Department of Health Science, Ishikawa Prefectural Nursing University, Kahoku, Ishikawa, Japan
| | - Sari Aaltonen
- Departments of Social Research and Public Health, University of Helsinki, Helsinki, Finland
| | - Fuling Ji
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Feng Ning
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Zengchang Pang
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Esther Rebato
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Leioa, Spain
| | | | | | - Kimberly J Saudino
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Kerry L Jang
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Wendy Cozen
- Department of Preventive Medicine, Keck School of Medicine of USC, and USC Norris Comprehensive Cancer Center, Los Angeles, CA
| | - Amie E Hwang
- Department of Preventive Medicine, Keck School of Medicine of USC, and
| | - Thomas M Mack
- Department of Preventive Medicine, Keck School of Medicine of USC, and USC Norris Comprehensive Cancer Center, Los Angeles, CA
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | | | | | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and Departments of Clinical Biochemistry and Pharmacology Clinical Genetics
| | - Axel Skytthe
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Kirsten O Kyvik
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Odense Patient data Explorative Network, and
| | - Catherine A Derom
- Centre of Human Genetics, University Hospitals Leuven, Leuven, Belgium; Department of Obstetrics and Gynaecology, Ghent University Hospitals, Ghent, Belgium
| | | | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kauko Heikkilä
- Public Health, University of Helsinki, Helsinki, Finland
| | - Jane Wardle
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Clare H Llewellyn
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Abigail Fisher
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Tom A McAdams
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom; MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | - Thalia C Eley
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Xiaohu Ding
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Morten Bjerregaard-Andersen
- Departments of Endocrinology and Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau; Research Center for Vitamins and Vaccines, Statens Serum Institute, Copenhagen, Denmark
| | | | - Morten Sodemann
- Departments of Endocrinology and Infectious Diseases, Odense University Hospital, Odense, Denmark
| | - Adam D Tarnoki
- Department of Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary; Hungarian Twin Registry, Budapest, Hungary
| | - David L Tarnoki
- Department of Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary; Hungarian Twin Registry, Budapest, Hungary
| | - Maria A Stazi
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | - Corrado Fagnani
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | - Cristina D'Ippolito
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | | | - David Mankuta
- Hadassah Hospital Obstetrics and Gynecology Department, Hebrew University Medical School, Jerusalem, Israel
| | - Lior Abramson
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | | | | | | | | | - Hermine H Maes
- Department of Human and Molecular Genetics, Massey Cancer Center, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Shandell Pahlen
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Margaret Gatz
- Department of Psychology, University of Southern California, Los Angeles, CA; Departments of Medical Epidemiology and Biostatistics and
| | - David A Butler
- Institute of Medicine, National Academy of Sciences Washington, DC
| | - Meike Bartels
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | | | - Jeffrey M Craig
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Richard Saffery
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Duarte L Freitas
- Department of Physical Education and Sport, University of Madeira, Funchal, Portugal
| | - José Antonio Maia
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, Porto, University of Porto, Portugal
| | - Lise Dubois
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Michel Boivin
- École de psychologie, Université Laval, Québec, Canada; Institute of Genetic, Neurobiological, and Social Foundations of Child Development, Tomsk State University, Russian Federation
| | - Mara Brendgen
- Département de psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | | | - Frank Vitaro
- École de psychoéducation, Université de Montréal, Montréal, Québec, Canada
| | | | | | - Grant W Montgomery
- Molecular Epidemiology Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Youngsook Chong
- Department of Psychology, Pusan National University, Busan, South Korea
| | - Gary E Swan
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ruth Krasnow
- Center for Health Sciences, SRI International, Menlo Park, CA
| | | | | | - Per Tynelius
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | | | - Claire Ma Haworth
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Robert Plomin
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | | | - Danshiitsoodol Narandalai
- Healthy Twin Association of Mongolia, Ulaanbaatar, Mongolia; Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX
| | | | - Sevgi Y Öncel
- Department of Statistics, Faculty of Arts and Sciences, Kırıkkale University, Kırıkkale, Turkey
| | - Fazil Aliev
- Department of Human and Molecular Genetics, Departments of Psychiatry and Psychology
| | - Timothy Spector
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Genevieve Lachance
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Laura A Baker
- Department of Psychology, University of Southern California, Los Angeles, CA
| | - Catherine Tuvblad
- Department of Psychology, University of Southern California, Los Angeles, CA; Örebro University, School of Law, Psychology and Social Work, Örebro, Sweden
| | - Glen E Duncan
- College of Medicine, Washington State University - Health Sciences Spokane, Spokane, WA
| | - Dedra Buchwald
- Washington State Twin Registry, Washington State University, Seattle, WA
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Finn Rasmussen
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Jack H Goldberg
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - Thorkild Ia Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Novo Nordisk Foundation Center for Basic Metabolic Research (Section on Metabolic Genetics) and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen,Denmark; Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, Copenhagen, The Capital Region, Denmark
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Jaakko Kaprio
- Public Health, University of Helsinki, Helsinki, Finland; National Institute for Health and Welfare, Helsinki, Finland; and Institute for Molecular Medicine FIMM, Helsinki, Finland
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304
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A GWA study reveals genetic loci for body conformation traits in Chinese Laiwu pigs and its implications for human BMI. Mamm Genome 2016; 27:610-621. [PMID: 27473603 DOI: 10.1007/s00335-016-9657-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 07/06/2016] [Indexed: 12/20/2022]
Abstract
Pigs share numerous physiological and phenotypic similarities with human and thus have been considered as a good model in nonrodent mammals for the study of genetic basis of human obesity. Researches on candidate genes for obesity traits have successfully identified some common genes between humans and pigs. However, few studies have assessed how many similarities exist between the genetic architecture of obesity in pigs and humans by large-scale comparative genomics. Here, we performed a genome-wide association study (GWAS) using the porcine 60 K SNP Beadchip for BMI and other four conformation traits at three different ages in a Chinese Laiwu pig population, which shows a large variability in fat deposition. In total, 35 SNPs were found to be significant at Bonferroni-corrected 5 % chromosome-wise level (P = 2.13 × 10-5) and 88 SNPs had suggestive (P < 10-4) association with the conformation traits. Some SNPs showed age-dependent association. Intriguingly, out of 32 regions associated with BMI in pigs, 18 were homologous with the loci for BMI in humans. Furthermore, five closest genes to GWAS peaks including HIF1AN, SMYD3, COX10, SLMAP, and GBE1 have been already associated with BMI in humans, which makes them very promising candidates for these QTLs. The result of GO analysis provided strong support to the fact that mitochondria and synapse play important roles in obesity susceptibility, which is consistent with previous findings on human obesity, and it also implicated new gene sets related to chromatin modification and Ig-like C2-type 5 domain. Therefore, these results not only provide new insights into the genetic architecture of BMI in pigs but also highlight that humans and pigs share the significant overlap of obesity-related genes.
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305
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Nivard MG, Middeldorp CM, Lubke G, Hottenga JJ, Abdellaoui A, Boomsma DI, Dolan CV. Detection of gene-environment interaction in pedigree data using genome-wide genotypes. Eur J Hum Genet 2016; 24:1803-1809. [PMID: 27436263 DOI: 10.1038/ejhg.2016.88] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 05/19/2016] [Accepted: 06/14/2016] [Indexed: 12/16/2022] Open
Abstract
Heritability may be estimated using phenotypic data collected in relatives or in distantly related individuals using genome-wide single nucleotide polymorphism (SNP) data. We combined these approaches by re-parameterizing the model proposed by Zaitlen et al and extended this model to include moderation of (total and SNP-based) genetic and environmental variance components by a measured moderator. By means of data simulation, we demonstrated that the type 1 error rates of the proposed test are correct and parameter estimates are accurate. As an application, we considered the moderation by age or year of birth of variance components associated with body mass index (BMI), height, attention problems (AP), and symptoms of anxiety and depression. The genetic variance of BMI was found to increase with age, but the environmental variance displayed a greater increase with age, resulting in a proportional decrease of the heritability of BMI. Environmental variance of height increased with year of birth. The environmental variance of AP increased with age. These results illustrate the assessment of moderation of environmental and genetic effects, when estimating heritability from combined SNP and family data. The assessment of moderation of genetic and environmental variance will enhance our understanding of the genetic architecture of complex traits.
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Affiliation(s)
- Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands
| | - Christel M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands.,Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands.,Department of Childhood and Adolescent Psychiatry, GGZ Ingeest, VU University Medical Center, Amsterdam, The Netherlands
| | - Gitta Lubke
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands.,Department of Quantitative Psychology, University of Notre Dame, Notre Dame, IN, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands.,Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands.,Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands.,EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - Conor V Dolan
- Department of Biological Psychology, Vrije Universiteit Faculteit der Psychologie en Pedagogiek, VU University, Amsterdam, The Netherlands
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306
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Lindam A, Johansson S, Stephansson O, Wikström AK, Cnattingius S. High Maternal Body Mass Index in Early Pregnancy and Risks of Stillbirth and Infant Mortality-A Population-Based Sibling Study in Sweden. Am J Epidemiol 2016; 184:98-105. [PMID: 27358265 PMCID: PMC4945704 DOI: 10.1093/aje/kww046] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 04/26/2016] [Indexed: 12/16/2022] Open
Abstract
In a population-based case-control study, we investigated whether familial confounding influenced the associations between maternal overweight/obesity and risks of stillbirth and infant mortality by including both population and sister controls. Using nationwide data from the Swedish Medical Birth Register (1992–2011), we included all primiparous women with singleton births who also had a sister with a first birth during that time period. We used logistic regression analyses to calculate odds ratios (and 95% confidence intervals) adjusted for maternal age, height, smoking habits, education, and time period (5-year groups) of child's birth. Body mass index (BMI) was calculated as weight (kg)/height (m)2. Compared with population controls with a normal BMI (18.5–24.9), stillbirth risk increased with increasing BMI (BMI 25–29.9: odds ratio (OR) = 1.51 (95% confidence interval (CI): 1.21, 1.89); BMI 30–34.9: OR = 1.77 (95% CI: 1.24, 2.50); BMI ≥35: OR = 3.16 (95% CI: 2.10, 4.76)). The sister case-control analyses revealed similar results. Offspring of obese women (BMI ≥30) had an increased risk of infant mortality when population controls were used (OR = 2.41, 95% CI: 1.83, 3.16), and an even higher risk was obtained when sister controls were used (OR = 4.04, 95% CI: 2.25, 7.25). We conclude that obesity in early pregnancy is associated with increased risks of stillbirth and infant mortality independently of genetic and early environmental risk factors shared within families.
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Affiliation(s)
| | | | | | | | - Sven Cnattingius
- Correspondence to Prof. Sven Cnattingius, Clinical Epidemiology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital Solna, SE-171 76 Stockholm, Sweden (e-mail: )
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307
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Vashi N, Stryjecki C, Peralta-Romero J, Suarez F, Gomez-Zamudio J, Burguete-Garcia AI, Cruz M, Meyre D. Genetic markers of inflammation may not contribute to metabolic traits in Mexican children. PeerJ 2016; 4:e2090. [PMID: 27366637 PMCID: PMC4924140 DOI: 10.7717/peerj.2090] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/07/2016] [Indexed: 01/16/2023] Open
Abstract
Background: Low-grade chronic inflammation is a common feature of obesity and its cardio-metabolic complications. However, little is known about a possible causal role of inflammation in metabolic disorders. Mexico is among the countries with the highest obesity rates in the world and the admixed Mexican population is a relevant sample due to high levels of genetic diversity. Methods: Here, we studied 1,462 Mexican children recruited from Mexico City. Six genetic variants in five inflammation-related genes were genotyped: rs1137101 (leptin receptor (LEPR)), rs7305618 (hepatocyte nuclear factor 1 alpha (HNF1A)), rs1800629 (tumor necrosis factor alpha (TNFA)), rs1800896, rs1800871 (interleukin-10 (IL-10)), rs1862513 (resistin (RETN)). Ten continuous and eight binary traits were assessed. Linear and logistic regression models were used adjusting for age, sex, and recruitment centre. Results: We found that one SNP displayed a nominal evidence of association with a continuous trait: rs1800871 (IL-10) with LDL (beta = −0.068 ± 1.006, P = 0.01). Subsequently, we found one nominal association with a binary trait: rs7305618 (HNF1A) with family history of hypertension (odds-ratio = 1.389 [1.054–1.829], P = 0.02). However, no P-value passed the Bonferroni correction for multiple testing. Discussion: Our data in a Mexican children population are consistent with previous reports in European adults in failing to demonstrate an association between inflammation-associated single nucleotide polymorphisms (SNPs) and metabolic traits.
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Affiliation(s)
- Neeti Vashi
- Clinical Epidemiology & Biostatistics, McMaster University , Hamilton , Canada
| | - Carolina Stryjecki
- Clinical Epidemiology & Biostatistics, McMaster University , Hamilton , Canada
| | - Jesus Peralta-Romero
- Medical Research Unit in Biochemistry, Hospital de Especialidades, Centro Médico Nacional Siglo XXI del Instituto Mexicano del Seguro Social , Mexico City , Mexico
| | - Fernando Suarez
- Medical Research Unit in Biochemistry, Hospital de Especialidades, Centro Médico Nacional Siglo XXI del Instituto Mexicano del Seguro Social , Mexico City , Mexico
| | - Jaime Gomez-Zamudio
- Medical Research Unit in Biochemistry, Hospital de Especialidades, Centro Médico Nacional Siglo XXI del Instituto Mexicano del Seguro Social , Mexico City , Mexico
| | - Ana I Burguete-Garcia
- Centro de investigación sobre enfermedades infecciosas, Instituto Nacional de Salud Pública , Cuernavaca , Mexico
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Hospital de Especialidades, Centro Médico Nacional Siglo XXI del Instituto Mexicano del Seguro Social , Mexico City , Mexico
| | - David Meyre
- Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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308
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Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nat Rev Endocrinol 2016; 12:337-46. [PMID: 27062931 DOI: 10.1038/nrendo.2016.51] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Genomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.
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Affiliation(s)
- Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Helle Krogh Pedersen
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Adem Y Dawed
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Ewan R Pearson
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
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309
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Accuracy of heritability estimations in presence of hidden population stratification. Sci Rep 2016; 6:26471. [PMID: 27220488 PMCID: PMC4879529 DOI: 10.1038/srep26471] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 04/29/2016] [Indexed: 01/05/2023] Open
Abstract
The heritability of a trait is the proportion of its variance explained by genetic factors; it has historically been estimated using familial data. However, new methods have appeared for estimating heritabilities using genomewide data from unrelated individuals. A drawback of this strategy is that population stratification can bias the estimates. Indeed, an environmental factor associated with the phenotype may differ among population subgroups. This factor being associated both with the phenotype and the genetic variation in the population would be a confounder. A common solution consists in adjusting on the first Principal Components (PCs) of the genomic data. We study this procedure on simulated data and on 6000 individuals from the Three-City Study. We analyse the geographical coordinates of the birth cities, which are not genetically determined, but the heritability of which should be overestimated due to population stratification. We also analyse various anthropometric traits. The procedure fails to correct the bias in geographical coordinates heritability estimates. The heritability estimates of the anthropometric traits are affected by the inclusion of the first PC, but not by the following PCs, contrarily to geographical coordinates. We recommend to be cautious with heritability estimates obtained from a large population.
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Marcovecchio ML, Florio R, Verginelli F, De Lellis L, Capelli C, Verzilli D, Chiarelli F, Mohn A, Cama A. Low AMY1 Gene Copy Number Is Associated with Increased Body Mass Index in Prepubertal Boys. PLoS One 2016; 11:e0154961. [PMID: 27149670 PMCID: PMC4858278 DOI: 10.1371/journal.pone.0154961] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 04/21/2016] [Indexed: 01/22/2023] Open
Abstract
Background Genome-wide association studies have identified more than 60 single nucleotide polymorphisms associated with Body Mass Index (BMI). Additional genetic variants, such as copy number variations (CNV), have also been investigated in relation to BMI. Recently, the highly polymorphic CNV in the salivary amylase (AMY1) gene, encoding an enzyme implicated in the first step of starch digestion, has been associated with obesity in adults and children. We assessed the potential association between AMY1 copy number and a wide range of BMI in a population of Italian school-children. Methods 744 children (354 boys, 390 girls, mean age (±SD): 8.4±1.4years) underwent anthropometric assessments (height, weight) and collection of saliva samples for DNA extraction. AMY1 copies were evaluated by quantitative PCR. Results A significant increase of BMI z-score by decreasing AMY1 copy number was observed in boys (β: -0.117, p = 0.033), but not in girls. Similarly, waist circumference (β: -0.155, p = 0.003, adjusted for age) was negatively influenced by AMY1 copy number in boys. Boys with 8 or more AMY1 copy numbers presented a significant lower BMI z-score (p = 0.04) and waist circumference (p = 0.01) when compared to boys with less than 8 copy numbers. Conclusions In this pediatric-only, population-based study, a lower AMY1 copy number emerged to be associated with increased BMI in boys. These data confirm previous findings from adult studies and support a potential role of a higher copy number of the salivary AMY1 gene in protecting from excess weight gain.
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Affiliation(s)
- M. Loredana Marcovecchio
- Department of Paediatrics, University of Chieti, Chieti, Italy
- Clinical Research Centre, Centre of Excellence on Aging, University of Chieti, Chieti, Italy
| | - Rosalba Florio
- Department of Pharmacy, University of Chieti, Chieti, Italy
| | - Fabio Verginelli
- Department of Pharmacy, University of Chieti, Chieti, Italy
- Clinical Research Centre, Centre of Excellence on Aging, University of Chieti, Chieti, Italy
| | | | - Cristian Capelli
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | | | - Francesco Chiarelli
- Department of Paediatrics, University of Chieti, Chieti, Italy
- Clinical Research Centre, Centre of Excellence on Aging, University of Chieti, Chieti, Italy
| | - Angelika Mohn
- Department of Paediatrics, University of Chieti, Chieti, Italy
- Clinical Research Centre, Centre of Excellence on Aging, University of Chieti, Chieti, Italy
- * E-mail:
| | - Alessandro Cama
- Department of Pharmacy, University of Chieti, Chieti, Italy
- Clinical Research Centre, Centre of Excellence on Aging, University of Chieti, Chieti, Italy
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311
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Leońska-Duniec A, Ahmetov II, Zmijewski P. Genetic variants influencing effectiveness of exercise training programmes in obesity - an overview of human studies. Biol Sport 2016; 33:207-14. [PMID: 27601774 PMCID: PMC4993135 DOI: 10.5604/20831862.1201052] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Revised: 02/10/2016] [Accepted: 02/18/2016] [Indexed: 01/28/2023] Open
Abstract
Frequent and regular physical activity has significant benefits for health, including improvement of body composition and help in weight control. Consequently, promoting training programmes, particularly in those who are genetically predisposed, is a significant step towards controlling the presently increasing epidemic of obesity. Although the physiological responses of the human body to exercise are quite well described, the genetic background of these reactions still remains mostly unknown. This review not only summarizes the current evidence, through a literature review and the results of our studies on the influence of gene variants on the characteristics and range of the body's adaptive response to training, but also explores research organization problems, future trends, and possibilities. We describe the most reliable candidate genetic markers that are involved in energy balance pathways and body composition changes in response to training programmes, such as FTO, MC4R, ACE, PPARG, LEP, LEPR, ADRB2, and ADRB3. This knowledge can have an enormous impact not only on individualization of exercise programmes to make them more efficient and safer, but also on improved recovery, traumatology, medical care, diet, supplementation and many other areas. Nevertheless, the current studies still represent only the first steps towards a better understanding of the genetic factors that influence obesity-related traits, as well as gene variant x physical activity interactions, so further research is necessary.
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Affiliation(s)
- A Leońska-Duniec
- Faculty of Physical Culture and Health Promotion, University of Szczecin, Poland; Faculty of Tourism and Recreation, Gdansk University of Physical Education and Sport, Poland
| | - I I Ahmetov
- Sport Technology Research Center, Volga Region State Academy of Physical Culture, Sport and Tourism, Kazan, Russia; Laboratory of Molecular Genetics, Kazan State Medical University, Kazan, Russia
| | - P Zmijewski
- Department of Physiology, Institute of Sport, Warsaw, Poland
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312
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Robinson KN, Teran-Garcia M. From infancy to aging: Biological and behavioral modifiers of Fetuin-A. Biochimie 2016; 124:141-149. [DOI: 10.1016/j.biochi.2015.12.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 12/23/2015] [Indexed: 12/16/2022]
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Abstract
OBJECTIVE To test the relationship of anxiety to caloric intake and food cue perception in women and men. METHODS Fifty-five twins (26 complete, 3 incomplete pairs; 51% women) underwent 2 functional magnetic resonance imaging (fMRI) scans (before and after a standardized meal) and then ate at an ad libitum buffet to objectively assess food intake. State and trait anxiety were assessed using the State-Trait Anxiety Inventory. During the fMRI scans, participants viewed blocks of fattening and nonfattening food images, and nonfood objects. RESULTS In women, higher trait anxiety was associated with a higher body mass index (BMI) (r = 0.40, p = .010). Trait anxiety was positively associated with kilocalories consumed at the buffet (r = 0.53, p = .005) and percent kilocalories consumed from fat (r = 0.30, p = .006), adjusted for BMI. In within-pair models, which control for shared familial and genetic factors, higher trait anxiety remained associated with kilocalories consumed at the buffet (p = .66, p = .014), but not with BMI. In men, higher state anxiety was related to macronutrient choices, but not to total caloric intake or BMI. FMRI results revealed that women with high trait anxiety did not suppress activation by fattening food cues across brain regions associated with satiety perception after eating a standardized meal (low anxiety, mean difference = -15.4, p < .001; high anxiety, mean difference = -1.53, p = .82, adjusted for BMI). CONCLUSIONS In women, trait anxiety may promote excess caloric consumption through altered perception of high-calorie environmental food cues, placing women with genetic predispositions toward weight gain at risk of obesity. TRIAL REGISTRATION Clinicaltrials.govidentifier:NCT02483663.
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314
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Twin metabolomics: the key to unlocking complex phenotypes in nutrition research. Nutr Res 2016; 36:291-304. [DOI: 10.1016/j.nutres.2016.01.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2015] [Revised: 01/27/2016] [Accepted: 01/28/2016] [Indexed: 12/26/2022]
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315
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Bogl LH, Kaye SM, Rämö JT, Kangas AJ, Soininen P, Hakkarainen A, Lundbom J, Lundbom N, Ortega-Alonso A, Rissanen A, Ala-Korpela M, Kaprio J, Pietiläinen KH. Abdominal obesity and circulating metabolites: A twin study approach. Metabolism 2016; 65:111-21. [PMID: 26892522 DOI: 10.1016/j.metabol.2015.10.027] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 10/23/2015] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors. SUBJECTS AND METHODS Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (rg) and environmental (re) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs (intrapair BMI difference ≥3 kg/m(2)). RESULTS Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (rg=0.40), glycoprotein (rg=0.37), serum triglycerides (rg=0.36), BCAAs (rg=0.30-0.40), HDL particle diameter (rg=-0.33) and HDL cholesterol (rg=-0.30). The effect of acquired obesity within the discordant MZ pairs was particularly strong for atherogenic lipoproteins. CONCLUSIONS A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability.
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Affiliation(s)
- Leonie H Bogl
- Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland.
| | - Sanna M Kaye
- Obesity Research Unit, Research programs unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Joel T Rämö
- Obesity Research Unit, Research programs unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland; Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Antti J Kangas
- Computational Medicine, Institute of Health Sciences, University of Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Pasi Soininen
- Computational Medicine, Institute of Health Sciences, University of Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Antti Hakkarainen
- Helsinki Medical Imaging Center, University of Helsinki, Helsinki, Finland
| | - Jesper Lundbom
- Helsinki Medical Imaging Center, University of Helsinki, Helsinki, Finland
| | - Nina Lundbom
- Helsinki Medical Imaging Center, University of Helsinki, Helsinki, Finland
| | | | - Aila Rissanen
- Obesity Research Unit, Research programs unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland; Department of Psychiatry, Helsinki University Central Hospital, Helsinki, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Institute of Health Sciences, University of Oulu, Finland; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland; Oulu University Hospital, Oulu, Finland; Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Jaakko Kaprio
- Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland; Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland; Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research programs unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland; Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland; Department of Medicine, Division of Endocrinology, Helsinki University Central Hospital, Helsinki, Finland
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316
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Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models. Genetics 2016; 202:1313-28. [PMID: 26868768 DOI: 10.1534/genetics.115.183905] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 02/06/2016] [Indexed: 12/18/2022] Open
Abstract
Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G×E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametricG×Einteraction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence ofG×Einteractions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.
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317
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Devakumar D, Grijalva-Eternod C, Cortina-Borja M, Williams J, Fewtrell M, Wells J. Disentangling the associations between parental BMI and offspring body composition using the four-component model. Am J Hum Biol 2016; 28:524-33. [PMID: 26848813 PMCID: PMC4957621 DOI: 10.1002/ajhb.22825] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 08/15/2015] [Accepted: 12/08/2015] [Indexed: 12/12/2022] Open
Abstract
Objectives This study sets out to investigate the intergenerational associations between the body mass index (BMI) of parents and the body composition of their offspring. Methods The cross‐sectional data were analyzed for 511 parent–offspring trios from London and south‐east England. The offspring were aged 5–21 years. Parental BMI was obtained by recall and offspring fat mass and lean mass were obtained using the four‐component model. Multivariable regression analysis, with multiple imputation for missing paternal values was used. Sensitivity analyses for levels of non‐paternity were conducted. Results A positive association was seen between parental BMI and offspring BMI, fat mass index (FMI), and lean mass index (LMI). The mother's BMI was positively associated with the BMI, FMI, and LMI z‐scores of both daughters and sons and of a similar magnitude for both sexes. The father's BMI showed similar associations to the mother's BMI, with his son's BMI, FMI, and LMI z‐scores, but no association with his daughter. Sensitivity tests for non‐paternity showed that maternal coefficients remained greater than paternal coefficients throughout but there was no statistical difference at greater levels of non‐paternity. Conclusions We found variable associations between parental BMI and offspring body composition. Associations were generally stronger for maternal than paternal BMI, and paternal associations appeared to differ between sons and daughters. In this cohort, the mother's BMI was statistically significantly associated with her child's body composition but the father's BMI was only associated with the body composition of his sons. Am. J. Hum. Biol. 28:524–533, 2016. © 2016 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Delan Devakumar
- UCL Institute for Global Health, London, WC1N 1EH, United Kingdom
| | | | - Mario Cortina-Borja
- Clinical Epidemiology, Nutrition and Biostatistics Section, UCL Institute of Child Health, London, WC1N 1EH, United Kingdom
| | - Jane Williams
- Childhood Nutrition Research Centre, UCL Institute of Child Health, London, WC1N 1EH, United Kingdom
| | - Mary Fewtrell
- Childhood Nutrition Research Centre, UCL Institute of Child Health, London, WC1N 1EH, United Kingdom
| | - Jonathan Wells
- Childhood Nutrition Research Centre, UCL Institute of Child Health, London, WC1N 1EH, United Kingdom
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318
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Effects of Obesity Related Genetic Variations on Visceral and Subcutaneous Fat Distribution in a Chinese Population. Sci Rep 2016; 6:20691. [PMID: 26848030 PMCID: PMC4742921 DOI: 10.1038/srep20691] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 01/05/2016] [Indexed: 02/07/2023] Open
Abstract
Genome-wide association studies (GWAS) have uncovered numerous variants associated with body mass index (BMI), waist circumference, and waist-to-hip ratio. Our study aims to investigate how these variants are linked to fat distribution. We genotyped 56 validated variants of BMI, waist circumference, and waist-to-hip ratio in 2958 subjects from Chinese community-based populations and performed linear regression analyses to determine the association with visceral fat area (VFA) and subcutaneous fat area (SFA) imaged by magnetic resonance imaging (MRI). We found rs671 in ALDH2 exhibited the significant associations with VFA and the VFA-SFA ratio in all subjects (P = 9.64 × 10−5 and 6.54 × 10−4). rs17782313 near MC4R for VFA and rs4846567 near LYPLAL1 for SFA were found in females only (P = 2.93 × 10−4 and 0.0015), whereas rs671 in ALDH2 for VFA and the VFA-SFA ratio was restricted to males (P = 1.75 × 10−8 and 4.43 × 10−8). Given the robust association of rs671 with alcohol consumption, we next demonstrated the primary effects of rs671 on VFA and the VFA-SFA ratio were restricted to drinkers (P = 1.45 × 10−4 and 4.65 × 10−3). Our data implied that variants of MC4R and LYPLAL1 modulated body fat distribution with sexual dimorphism and that alcohol consumption may mediate the impact of the ALDH2 locus on visceral fat in a Chinese population.
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319
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Lill CM, Liu T, Norman K, Meyer A, Steinhagen-Thiessen E, Demuth I, Bertram L. Genetic Burden Analyses of Phenotypes Relevant to Aging in the Berlin Aging Study II (BASE-II). Gerontology 2016; 62:316-22. [PMID: 26821332 DOI: 10.1159/000438900] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 07/22/2015] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Body mass index (BMI), bone mineral density (BMD), and telomere length are phenotypes that modulate the course of aging. Over 40% of their phenotypic variance is determined by genetics. Genome-wide association studies (GWAS) have recently uncovered >100 independent single-nucleotide polymorphisms (SNPs) showing genome-wide significant (p < 5 × 10-8) association with these traits. OBJECTIVE To test the individual and combined impact of previously reported GWAS SNPs on BMI, BMD, and relative leukocyte telomere length (rLTL) in ∼1,750 participants of the Berlin Aging Study II (BASE-II), a cohort consisting predominantly of individuals >60 years of age. METHODS Linear regression analyses were performed on a total of 101 SNPs and BMI, BMD measurements of the femoral neck (FN) and lumbar spine (LS), and rLTL. The combined effect of all trait-specific SNPs was evaluated by generating a weighted genomic profile score (wGPS) used in the association analyses. The predictive capability of the wGPS was estimated by determining the area under the receiver operating curve (AUC) for osteoporosis status (determined by BMD) with and without the wGPS. RESULTS Five loci showed experiment-wide significant association with BMI (FTO rs1558902, p = 1.80 × 10-5) or BMD (MEPE rs6532023, pFN = 5.40 × 10-4, pLS = 1.09 × 10-4; TNFRSF11B rs2062377, pLS = 8.70 × 10-4; AKAP11 rs9533090, pLS = 1.05 × 10-3; SMG6 rs4790881, pFN = 3.41 × 10-4) after correction for multiple testing. Several additional loci showed nominally significant (p < 0.05) association with BMI and BMD. The trait-specific wGPS was highly significantly associated with BMD (p < 2 × 10-16) and BMI (p = 1.10 × 10-6). No significant association was detected for rLTL in either single-SNP or wGPS-based analyses. The AUC for osteoporosis improved modestly from 0.762 (95% CI 0.733-0.800) to 0.786 (95% CI 0.756-0.823) and 0.785 (95% CI 0.757-0.824) upon inclusion of the FN- and LS-BMD wGPS, respectively. CONCLUSION Our study provides an independent validation of previously reported genetic association signals for BMI and BMD in the BASE-II cohort. Additional studies are needed to pinpoint the factors underlying the proportion of phenotypic variance that remains unexplained by the current models.
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Affiliation(s)
- Christina M Lill
- Lx00FC;beck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Integrative and Experimental Genomics, University of Lx00FC;beck, Lx00FC;beck, Germany
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320
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Ropponen A, Silventoinen K, Koskenvuo M, Svedberg P, Kaprio J. Stability and change of body mass index as a predictor of disability pension. Scand J Public Health 2016; 44:369-76. [PMID: 26787552 DOI: 10.1177/1403494815622849] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2015] [Indexed: 11/15/2022]
Abstract
AIMS To investigate whether stability or change in body mass index (BMI) predict disability pension (DP) due to musculoskeletal diagnosis (MSD) when controlling for familial confounding. METHODS Our study cohort consisted of 17,169 Finnish twins born before 1958. Data on BMI and multiple covariates from questionnaires in 1975 and 1981 were included and DPs were collected from the national pension registers until the end of 2004. Cox proportional hazards regression models with Hazard Ratios (HR) and 95% Confidence Intervals (CI) were used for statistical analyses. RESULTS General DP was granted to 2853 individuals and DP due to MSD to 1143 individuals during the 23-year follow-up. A one-unit increase in BMI in both 1975 (HR 1.08, 95% CI 1.05, 1.10) and 1981 (HR 1.06, 95% CI 1.04, 1.07), as well as the stability of and change in BMI from 1975 to 1981 were all associated with an increased risk of DP. These associations held in the analyses controlling for multiple covariates (age, sex, socioeconomic status, education, marital status, leisure-time physical activity, and musculoskeletal pain), and mainly also familial confounding, that is, genetics and shared environment. HR for stable obesity was 2.28 (95% CI 1.69, 3.08) for DP due to MSD, and 1.91 (95% CI 1.56, 2.34) for general DP in the fully adjusted models. CONCLUSIONS BMI IS AN EARLY PREDICTOR OF GENERAL DP AND ALSO OF DP DUE TO MSD OWING TO THE INDEPENDENCY OF VARIOUS COVARIATES AND POTENTIALLY ALSO FAMILIAL CONFOUNDING, BMI MAY POSSIBLY HAVE A DIRECT EFFECT ON THE RISK OF DP.
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Affiliation(s)
| | - Karri Silventoinen
- Population Research Unit, Department of Sociology, University of Helsinki, Finland
| | | | - Pia Svedberg
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Finland Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Finland Institute for Molecular Medicine, University of Helsinki, Finland
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Felix JF, Bradfield JP, Monnereau C, van der Valk RJP, Stergiakouli E, Chesi A, Gaillard R, Feenstra B, Thiering E, Kreiner-Møller E, Mahajan A, Pitkänen N, Joro R, Cavadino A, Huikari V, Franks S, Groen-Blokhuis MM, Cousminer DL, Marsh JA, Lehtimäki T, Curtin JA, Vioque J, Ahluwalia TS, Myhre R, Price TS, Vilor-Tejedor N, Yengo L, Grarup N, Ntalla I, Ang W, Atalay M, Bisgaard H, Blakemore AI, Bonnefond A, Carstensen L, Eriksson J, Flexeder C, Franke L, Geller F, Geserick M, Hartikainen AL, Haworth CMA, Hirschhorn JN, Hofman A, Holm JC, Horikoshi M, Hottenga JJ, Huang J, Kadarmideen HN, Kähönen M, Kiess W, Lakka HM, Lakka TA, Lewin AM, Liang L, Lyytikäinen LP, Ma B, Magnus P, McCormack SE, McMahon G, Mentch FD, Middeldorp CM, Murray CS, Pahkala K, Pers TH, Pfäffle R, Postma DS, Power C, Simpson A, Sengpiel V, Tiesler CMT, Torrent M, Uitterlinden AG, van Meurs JB, Vinding R, Waage J, Wardle J, Zeggini E, Zemel BS, Dedoussis GV, Pedersen O, Froguel P, Sunyer J, Plomin R, Jacobsson B, Hansen T, Gonzalez JR, Custovic A, Raitakari OT, Pennell CE, Widén E, Boomsma DI, Koppelman GH, Sebert S, Järvelin MR, Hyppönen E, McCarthy MI, Lindi V, Harri N, Körner A, Bønnelykke K, Heinrich J, Melbye M, Rivadeneira F, Hakonarson H, Ring SM, Smith GD, Sørensen TIA, Timpson NJ, Grant SFA, Jaddoe VWV. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet 2016; 25:389-403. [PMID: 26604143 PMCID: PMC4854022 DOI: 10.1093/hmg/ddv472] [Citation(s) in RCA: 225] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 11/15/2015] [Indexed: 12/24/2022] Open
Abstract
A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores. We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value < 5 × 10(-8)) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 × 10(-10)) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index.
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Affiliation(s)
- Janine F Felix
- The Generation R Study Group, Department of Pediatrics, Department of Epidemiology,
| | | | - Claire Monnereau
- The Generation R Study Group, Department of Pediatrics, Department of Epidemiology
| | | | | | | | - Romy Gaillard
- The Generation R Study Group, Department of Pediatrics, Department of Epidemiology
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Elisabeth Thiering
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany, Division of Metabolic and Nutritional Medicine, Dr von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Eskil Kreiner-Møller
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital
| | | | - Niina Pitkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, Institute of Clinical Medicine, Neurology
| | - Raimo Joro
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Alana Cavadino
- Centre for Environmental and Preventive Medicine, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK, Population, Policy and Practice, UCL Institute of Child Health
| | | | - Steve Franks
- Institute of Reproductive and Developmental Biology
| | - Maria M Groen-Blokhuis
- Department of Biological Psychology, VU University Amsterdam, NCA Neuroscience Campus Amsterdam, EMGO+ Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Diana L Cousminer
- Institute for Molecular Medicine, Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Julie A Marsh
- School of Women's and Infants' Health, The University of Western Australia, Perth, Australia
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, Department of Clinical Chemistry
| | - John A Curtin
- Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Jesus Vioque
- Universidad Miguel Hernandez, Elche-Alicante, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Tarunveer S Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, Steno Diabetes Center, Gentofte, Denmark
| | - Ronny Myhre
- Department of Genes and Envrionment, Division of Epidemiology
| | - Thomas S Price
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, USA
| | - Natalia Vilor-Tejedor
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain
| | - Loïc Yengo
- CNRS UMR8199, Pasteur Institute Lille, France, European Genomic Institute for Diabetes (EGID), Lille, France
| | - Niels Grarup
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ioanna Ntalla
- Department of Health Sciences, University of Leicester, Leicester, UK, Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Wei Ang
- School of Women's and Infants' Health, The University of Western Australia, Perth, Australia
| | - Mustafa Atalay
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital
| | - Alexandra I Blakemore
- Section of Investigative Medicine, Division of Diabetes, Endocrinology, and Metabolism, Faculty of Medicine, Imperial College, London, UK
| | - Amelie Bonnefond
- CNRS UMR8199, Pasteur Institute Lille, France, European Genomic Institute for Diabetes (EGID), Lille, France
| | - Lisbeth Carstensen
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | | | | | - Johan Eriksson
- National Institute for Health and Welfare, Helsinki, Finland
| | - Claudia Flexeder
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Frank Geller
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Mandy Geserick
- Center of Pediatric Research, Department of Women's and Child Health, LIFE Child (Leipzig Research Center for Civilization Diseases)
| | | | | | - Joel N Hirschhorn
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, USA, Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, USA, Department of Genetics, Harvard Medical School, Boston, USA
| | - Albert Hofman
- The Generation R Study Group, Department of Epidemiology
| | - Jens-Christian Holm
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, The Danish Childhood Obesity Biobank, Denmark, Institute of Medicine, Copenhagen University, Copenhagen, Denmark
| | - Momoko Horikoshi
- Wellcome Trust Centre for Human Genetics, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, NCA Neuroscience Campus Amsterdam, EMGO+ Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haja N Kadarmideen
- Department of Veterinary Clinical and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark
| | - Mika Kähönen
- Department of Clinical Physiology, University of Tampere School of Medicine, Tampere, Finland, Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Wieland Kiess
- Center of Pediatric Research, Department of Women's and Child Health
| | - Hanna-Maaria Lakka
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Timo A Lakka
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Alexandra M Lewin
- Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Liming Liang
- Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland, Department of Clinical Chemistry
| | - Baoshan Ma
- College of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning Province, China
| | - Per Magnus
- Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway
| | - Shana E McCormack
- Division of Human Genetics, Division of Endocrinology, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George McMahon
- MRC Integrative Epidemiology Unit at the University of Bristol
| | | | - Christel M Middeldorp
- Department of Biological Psychology, VU University Amsterdam, NCA Neuroscience Campus Amsterdam, EMGO+ Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Clare S Murray
- Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, Department of Health and Physical Activity, Paavo Nurmi Centre, Sports and Exercise Medicine Unit
| | - Tune H Pers
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, USA, Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Roland Pfäffle
- Center of Pediatric Research, Department of Women's and Child Health, CrescNet, Medical Faculty, University of Leipzig, Germany
| | - Dirkje S Postma
- Department of Pulmonology, GRIAC (Groningen Research Institute for Asthma and COPD)
| | - Christine Power
- Population, Policy and Practice, UCL Institute of Child Health
| | - Angela Simpson
- Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and
| | - Verena Sengpiel
- Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahlgrenska University Hosptial, Gothenburg, Sweden
| | - Carla M T Tiesler
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany, Division of Metabolic and Nutritional Medicine, Dr von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Maties Torrent
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain, Area de Salut de Menorca, ib-salut, Menorca, Spain
| | - André G Uitterlinden
- The Generation R Study Group, Department of Epidemiology, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce B van Meurs
- Department of Epidemiology, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rebecca Vinding
- Department of Pediatrics, Naestved Hospital, Naestved, Denmark, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital
| | - Johannes Waage
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital
| | - Jane Wardle
- Department of Epidemiology and Public Health, University College London, UK
| | - Eleftheria Zeggini
- Wellcome Trust Sanger Institute, The Morgan Building, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Babette S Zemel
- Division of Gastroenterology, Hepatology and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, PA, USA, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George V Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Oluf Pedersen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Philippe Froguel
- CNRS UMR8199, Pasteur Institute Lille, France, Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
| | - Jordi Sunyer
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Robert Plomin
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, UK
| | - Bo Jacobsson
- Department of Genes and Envrionment, Division of Epidemiology, Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahlgrenska University Hosptial, Gothenburg, Sweden
| | - Torben Hansen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Juan R Gonzalez
- CIBER Epidemiología y Salud Pública (CIBERESP), Spain, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain
| | - Adnan Custovic
- Centre for Respiratory Medicine and Allergy, Institute of Inflammation and Repair, University of Manchester and University Hospital of South Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, Department of Clinical Physiology and Nuclear Medicine
| | - Craig E Pennell
- School of Women's and Infants' Health, The University of Western Australia, Perth, Australia
| | - Elisabeth Widén
- Institute for Molecular Medicine, Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, NCA Neuroscience Campus Amsterdam, EMGO+ Institute for Health and Care Research, Amsterdam, the Netherlands
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC (Groningen Research Institute for Asthma and COPD), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sylvain Sebert
- Centre for Life Course Epidemiology, Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Centre for Life Course Epidemiology, Biocenter Oulu, University of Oulu, Oulu, Finland, Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London, UK, Unit of Primary Care, Oulu University Hospital, Oulu, Finland, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland
| | - Elina Hyppönen
- Population, Policy and Practice, UCL Institute of Child Health, School of Population Health and Sansom Institute, University of South Australia, Adelaide, Australia, South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK, Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Virpi Lindi
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Niinikoski Harri
- Department of Pediatrics, Turku University Hospital, University of Turku, Turku, Finland
| | - Antje Körner
- Center of Pediatric Research, Department of Women's and Child Health
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Mads Melbye
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA and
| | - Fernando Rivadeneira
- The Generation R Study Group, Department of Epidemiology, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, Department of Obstetrics and Gynecology, Sahlgrenska Academy, Sahlgrenska University Hosptial, Gothenburg, Sweden
| | - Susan M Ring
- MRC Integrative Epidemiology Unit at the University of Bristol, Avon Longitudinal Study of Parents and Children (ALSPAC), School of Social and Community Medicine, University of Bristol, Bristol, UK
| | | | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit at the University of Bristol, Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | | | - Struan F A Grant
- Center for Applied Genomics, Division of Human Genetics, Division of Endocrinology, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vincent W V Jaddoe
- The Generation R Study Group, Department of Pediatrics, Department of Epidemiology
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Sandholt CH, Grarup N, Pedersen O, Hansen T. Genome-wide association studies of human adiposity: Zooming in on synapses. Mol Cell Endocrinol 2015; 418 Pt 2:90-100. [PMID: 26427653 DOI: 10.1016/j.mce.2015.09.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 09/24/2015] [Accepted: 09/25/2015] [Indexed: 02/03/2023]
Abstract
The decade anniversary for genome-wide association studies (GWAS) is approaching, and this experimental approach has commenced a deeper understanding of the genetics underlying complex diseases. In obesity genetics the GIANT (Genetic Investigation of ANthropometric Traits) consortium has played a crucial role, recently with two comprehensive meta-analyses, one focusing on general obesity, analyzing body-mass index (BMI) and the other on fat distribution, focusing on waist-hip ratio adjusted for BMI. With the in silico methods applied in these two studies as the pivot, this review looks into some of the biological knowledge, beginning to emerge from the intricate genomic background behind the genetic determinants of human adiposity. These include synaptic dysfunction, where GWAS pinpoint potential new mechanisms in pathways already known to be linked with obesity.
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Affiliation(s)
- Camilla H Sandholt
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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323
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Llewellyn C, Wardle J. Behavioral susceptibility to obesity: Gene–environment interplay in the development of weight. Physiol Behav 2015; 152:494-501. [DOI: 10.1016/j.physbeh.2015.07.006] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 07/02/2015] [Accepted: 07/06/2015] [Indexed: 11/25/2022]
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324
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Hollensted M, Ahluwalia TS, Have CT, Grarup N, Fonvig CE, Nielsen TRH, Trier C, Paternoster L, Pedersen O, Holm JC, Sørensen TIA, Hansen T. Common variants in LEPR, IL6, AMD1, and NAMPT do not associate with risk of juvenile and childhood obesity in Danes: a case-control study. BMC MEDICAL GENETICS 2015; 16:105. [PMID: 26558825 PMCID: PMC4642628 DOI: 10.1186/s12881-015-0253-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 11/09/2015] [Indexed: 01/11/2023]
Abstract
Background Childhood obesity is a highly heritable disorder, for which the underlying genetic architecture is largely unknown. Four common variants involved in inflammatory-adipokine triggering (IL6 rs2069845, LEPR rs1137100, NAMPT rs3801266, and AMD1 rs2796749) have recently been associated with obesity and related traits in Indian children. The current study aimed to examine the effect of these variants on risk of childhood/juvenile onset obesity and on obesity-related quantitative traits in two Danish cohorts. Methods Genotype information was obtained for 1461 young Caucasian men from the Genetics of Overweight Young Adults (GOYA) study (overweight/obese: 739 and normal weight: 722) and the Danish Childhood Obesity Biobank (TDCOB; overweight/obese: 1022 and normal weight: 650). Overweight/obesity was defined as having a body mass index (BMI) ≥25 kg/m2; among children and youths, this cut-off was defined using age and sex-specific cut-offs corresponding to an adult body mass index ≥25 kg/m2. Risk of obesity was assessed using a logistic regression model whereas obesity-related quantitative measures were analyzed using a general linear model (based on z-scores) stratifying on the case status and adjusting for age and gender. Meta-analyses were performed using the fixed effects model. Results No statistically significant association with childhood/juvenile obesity was found for any of the four gene variants among the individual or combined analyses (rs2069845 OR: 0.94 CI: 0.85–1.04; rs1137100 OR: 1.01 CI: 0.90–1.14; rs3801266: 0.96 CI: 0.84–1.10; rs2796749 OR: 1.02 CI: 0.90–1.15; p > 0.05). However, among normal weight children and juvenile men, the LEPR rs1137100 A-allele significantly associated with lower BMI (β = −0.12, p = 0.0026). Conclusions The IL6, LEPR, NAMPT, and AMD1 gene variants previously found to associate among Indian children did not associate with risk of obesity or obesity-related quantitative measures among Caucasian children and juvenile men from Denmark.
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Affiliation(s)
- Mette Hollensted
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark.
| | - Tarunveer S Ahluwalia
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark. .,COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Ledreborg Allé 34, DK-2820, Copenhagen, Denmark. .,Steno Diabetes Center, Gentofte, Denmark.
| | - Christian Theil Have
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark.
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark.
| | - Cilius Esmann Fonvig
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark. .,The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark.
| | - Tenna Ruest Haarmark Nielsen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark. .,The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark.
| | - Cæcilie Trier
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark. .,The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark.
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark.
| | - Jens-Christian Holm
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | - Thorkild I A Sørensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark. .,MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK. .,Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark.
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, DIKU Building, Universitetsparken 1, 2100, Copenhagen, Denmark.
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Han TS, Hart CL, Haig C, Logue J, Upton MN, Watt GCM, Lean MEJ. Contributions of maternal and paternal adiposity and smoking to adult offspring adiposity and cardiovascular risk: the Midspan Family Study. BMJ Open 2015; 5:e007682. [PMID: 26525718 PMCID: PMC4636631 DOI: 10.1136/bmjopen-2015-007682] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVE Obesity has some genetic basis but requires interaction with environmental factors for phenotypic expression. We examined contributions of gender-specific parental adiposity and smoking to adiposity and related cardiovascular risk in adult offspring. DESIGN Cross-sectional general population survey. SETTING Scotland. PARTICIPANTS 1456 of the 1477 first generation families in the Midspan Family Study: 2912 parents (aged 45-64 years surveyed between 1972 and 1976) who had 1025 sons and 1283 daughters, aged 30-59 years surveyed in 1996. MAIN MEASURES Offspring body mass index (BMI), waist circumference (WC), cardiometabolic risk (lipids, blood pressure and glucose) and cardiovascular disease as outcome measures, and parental BMI and smoking as determinants. All analyses adjusted for age, socioeconomic status and family clustering and offspring birth weight. RESULTS Regression coefficients for BMI associations between father-son (0.30) and mother-daughter (0.33) were greater than father-daughter (0.23) or mother-son (0.22). Regression coefficient for the non-genetic, shared-environment or assortative-mating relationship between BMIs of fathers and mothers was 0.19. Heritability estimates for BMI were greatest among women with mothers who had BMI either <25 or ≥30 kg/m(2). Compared with offspring without obese parents, offspring with two obese parents had adjusted OR of 10.25 (95% CI 6.56 to 13.93) for having WC ≥102 cm for men, ≥88 cm women, 2.46 (95% CI 1.33 to 4.57) for metabolic syndrome and 3.03 (95% CI 1.55 to 5.91) for angina and/or myocardial infarct (p<0.001). Neither parental adiposity nor smoking history determined adjusted offspring individual cardiometabolic risk factors, diabetes or stroke. Maternal, but not paternal, smoking had significant effects on WC in sons (OR=1.50; 95% CI 1.13 to 2.01) and daughters (OR=1.42; 95% CI 1.10 to 1.84) and metabolic syndrome OR=1.68; 95% CI 1.17 to 2.40) in sons. CONCLUSIONS There are modest genetic/epigenetic influences on the environmental factors behind adverse adiposity. Maternal smoking appears a specific hazard on obesity and metabolic syndrome. A possible epigenetic mechanism linking maternal smoking to obesity and metabolic syndrome in offspring is proposed. Individuals with family histories of obesity should be targeted from an early age to prevent obesity and complications.
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Affiliation(s)
- T S Han
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL) & Ashford and St Peter's NHS Foundation Trust, Surrey, UK
| | - C L Hart
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - C Haig
- Robertson Centre for Biostatics, University of Glasgow, Glasgow, UK
| | - J Logue
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - M N Upton
- Woodlands Family Medical Centre, Stockton-on-Tees, UK
| | - G C M Watt
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - M E J Lean
- School of Medicine, Royal Infirmary, University of Glasgow, Glasgow, UK
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326
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Pietiläinen KH, Ismail K, Järvinen E, Heinonen S, Tummers M, Bollepalli S, Lyle R, Muniandy M, Moilanen E, Hakkarainen A, Lundbom J, Lundbom N, Rissanen A, Kaprio J, Ollikainen M. DNA methylation and gene expression patterns in adipose tissue differ significantly within young adult monozygotic BMI-discordant twin pairs. Int J Obes (Lond) 2015; 40:654-61. [PMID: 26499446 DOI: 10.1038/ijo.2015.221] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2015] [Revised: 08/29/2015] [Accepted: 09/21/2015] [Indexed: 11/09/2022]
Abstract
BACKGROUND Little is known about epigenetic alterations associated with subcutaneous adipose tissue (SAT) in obesity. Our aim was to study genome-wide DNA methylation and gene expression differences in SAT in monozygotic (MZ) twin pairs who are discordant for body mass index (BMI). This design completely matches lean and obese groups for genetic background, age, gender and shared environment. METHODS 14We analyzed DNA methylome and gene expression from SAT, together with body composition (magnetic resonance imaging/spectroscopy) and glucose tolerance test, lipids and C-reactive protein from 26 rare BMI-discordant (intrapair difference in BMI ⩾3 kg m(-2)) MZ twin pairs identified from 10 birth cohorts of young adult Finnish twins. RESULTS We found 17 novel obesity-associated genes that were differentially methylated across the genome between heavy and lean co-twins. Nine of them were also differentially expressed. Pathway analyses indicated that dysregulation of SAT in obesity includes a paradoxical downregulation of lipo/adipogenesis and upregulation of inflammation and extracellular matrix remodeling. Furthermore, CpG sites whose methylation correlated with metabolically harmful fat depots (intra-abdominal and liver fat) also correlated with measures of insulin resistance, dyslipidemia and low-grade inflammation, thus suggesting that epigenetic alterations in SAT are associated with the development of unhealthy obesity. CONCLUSION This is the first study in BMI-discordant MZ twin pairs reporting genome-wide DNA methylation and expression profiles in SAT. We found a number of novel genes and pathways whose methylation and expression patterns differ within the twin pairs, suggesting that the pathological adaptation of SAT to obesity is, at least in part, epigenetically regulated.
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Affiliation(s)
- K H Pietiläinen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland.,Endocrinology, Abdominal Center, Helsinki University Central Hospital, and University of Helsinki, Helsinki, Finland.,Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - K Ismail
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - E Järvinen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - S Heinonen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - M Tummers
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - S Bollepalli
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - R Lyle
- Oslo University Hospital and University of Oslo, Department of Medical Genetics, Oslo, Norway
| | - M Muniandy
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland.,Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - E Moilanen
- The Immunopharmacology Research Group, University of Tampere School of Medicine and Tampere University Hospital, Tampere, Finland
| | - A Hakkarainen
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - J Lundbom
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany.,German Center for Diabetes Research, Partner Düsseldorf, Düsseldorf, Germany
| | - N Lundbom
- HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A Rissanen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - J Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland.,Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,National Institute for Health and Welfare, Helsinki, Finland
| | - M Ollikainen
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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327
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Kvaløy K, Holmen J, Hveem K, Holmen TL. Genetic Effects on Longitudinal Changes from Healthy to Adverse Weight and Metabolic Status – The HUNT Study. PLoS One 2015; 10:e0139632. [PMID: 26445370 PMCID: PMC4596824 DOI: 10.1371/journal.pone.0139632] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 09/14/2015] [Indexed: 01/11/2023] Open
Abstract
Introduction The complexity of obesity and onset and susceptibility of cardio-metabolic disorders are still poorly understood and is addressed here through studies of genetic influence on weight gain and increased metabolic risk longitudinally. Subjects/Methods Twenty seven previously identified obesity, eating disorder or metabolic risk susceptibility SNPs were tested for association with weight or metabolically related traits longitudinally in 3999 adults participating both in the HUNT2 (1995–97) and HUNT3 (2006–08) surveys. Regression analyses were performed with changes from normal weight to overweight/obesity or from metabolically healthy to adverse developments with regards to blood pressure, glucose, HDL cholesterol, triglycerides or metabolic syndrome as outcomes. Additionally, a sub-sample of 1380 adolescents was included for testing association of nine SNPs with longitudinal weight gain into young adulthood. Results The most substantial effect on BMI-based weight gain from normal to overweight/obesity in adults was observed for the DRD2 variant (rs6277)(OR: 0.79, 95% CI: 0.69–0.90, P = 3.9x10-4, adj. P = 0.015). DRD2 was not associated with BMI on a cross-sectional level. In the adolescent sample, FTO (rs1121980) was associated with change to overweight at adulthood in the combined male-female sample (OR: 1.27, 95% CI: 1.09–1.49, P = 3.0x10-3, adj. P = 0.019) and in females (OR: 1.53, 95% CI: 1.23–1.91, P = 1.8x10-4, adj. P = 0.003). When testing for association to longitudinal adverse developments with regard to blood pressure, blood lipids and glucose, only rs964184 (ZNF259/APOA5) was significantly associated to unfavourable triglyceride changes (OR: 1.66, 95% CI: 1.36–2.03, P = 5.7x10-7, adj. P = 0.001). Pleiotropic effects on metabolic traits, however, were observed for several genetic loci cross-sectionally, ZNF259/APOA5, LPL and GRB14 being the most important. Conclusions DRD2 exhibits effects on weight gain from normal weight to overweight/obesity in adults, while, FTO is associated to weight gain from adolescence to young adulthood. Unhealthy longitudinal triglyceride development is strongly affected by ZNF259/APOA. Our main finding, linking the DRD2 variant directly to the longitudinal weight gain observed, has not previously been identified. It suggests a genetic pre-disposition involving the dopaminergic signalling pathways known to play a role in food reward and satiety linked mechanisms.
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Affiliation(s)
- Kirsti Kvaløy
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
| | - Jostein Holmen
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristian Hveem
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Turid Lingaas Holmen
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
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328
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Karaderi T, Drong AW, Lindgren CM. Insights into the Genetic Susceptibility to Type 2 Diabetes from Genome-Wide Association Studies of Obesity-Related Traits. Curr Diab Rep 2015; 15:83. [PMID: 26363598 PMCID: PMC4568008 DOI: 10.1007/s11892-015-0648-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Obesity and type 2 diabetes (T2D) are common and complex metabolic diseases, which are caused by an interchange between environmental and genetic factors. Recently, a number of large-scale genome-wide association studies (GWAS) have improved our knowledge of the genetic architecture and biological mechanisms of these diseases. Currently, more than ~250 genetic loci have been found for monogenic, syndromic, or common forms of T2D and/or obesity-related traits. In this review, we discuss the implications of these GWAS for obesity and T2D, and investigate the overlap of loci for obesity-related traits and T2D, highlighting potential mechanisms that affect T2D susceptibility.
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Affiliation(s)
- Tugce Karaderi
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, Oxford, UK.
| | - Alexander W Drong
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, Oxford, UK.
| | - Cecilia M Lindgren
- Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, Oxford, UK.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Big Data Institute, University of Oxford, Oxford, UK.
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329
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Appetitive traits as behavioural pathways in genetic susceptibility to obesity: a population-based cross-sectional study. Sci Rep 2015; 5:14726. [PMID: 26423639 PMCID: PMC4589697 DOI: 10.1038/srep14726] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 09/09/2015] [Indexed: 11/13/2022] Open
Abstract
The mechanisms through which genes influence body weight are not well understood, but appetite has been implicated as one mediating pathway. Here we use data from two independent population-based Finnish cohorts (4632 adults aged 25–74 years from the DILGOM study and 1231 twin individuals aged 21–26 years from the FinnTwin12 study) to investigate whether two appetitive traits mediate the associations between known obesity-related genetic variants and adiposity. The results from structural equation modelling indicate that the effects of a polygenic risk score (90 obesity-related loci) on measured body mass index and waist circumference are partly mediated through higher levels of uncontrolled eating (βindirect = 0.030–0.032, P < 0.001 in DILGOM) and emotional eating (βindirect = 0.020–0.022, P < 0.001 in DILGOM and βindirect = 0.013–0.015, P = 0.043–0.044 in FinnTwin12). Our findings suggest that genetic predispositions to obesity may partly exert their effects through appetitive traits reflecting lack of control over eating or eating in response to negative emotions. Obesity prevention and treatment studies should examine the impact of targeting these eating behaviours, especially among individuals having a high genetic predisposition to obesity.
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330
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Takeuchi H, Taki Y, Sekiguchi A, Nouchi R, Kotozaki Y, Nakagawa S, Miyauchi CM, Iizuka K, Yokoyama R, Shinada T, Yamamoto Y, Hanawa S, Araki T, Hashizume H, Kunitoki K, Sassa Y, Kawashima R. Amygdala and cingulate structure is associated with stereotype on sex-role. Sci Rep 2015; 5:14220. [PMID: 26420574 PMCID: PMC4588577 DOI: 10.1038/srep14220] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 08/21/2015] [Indexed: 11/23/2022] Open
Abstract
Sex-role egalitarianism (SRE) is the belief that the sex of an individual should not influence the perception of his or her rights, abilities, obligations, and opportunities. Thus, low SRE reflects a more conservative stereotypical view on sex-role. Here we investigated anatomical correlates of individual differences in SRE in the present study. We used voxel-based morphometry, a questionnaire to determine an individual’s SRE and associated psychological measures, and determined the association of SRE with gray matter structures and their cognitive nature in healthy individuals (375 men and 306 women; age, 20.6 ± 1.8 years). We demonstrated that higher SRE was associated with smaller regional gray matter density (rGMD) in the anterior part of the posterior cingulate cortex (PCC) and higher rGMD in the right amygdala. Post-hoc analyses revealed psychological measures characterized by contentious interpersonal orientations, such as contentious achievement motivation, were associated with lower SRE and higher rGMD in the anterior part of PCC. Depressive tendencies were associated with lower SRE and higher rGMD in the right amygdala. These findings suggest that variations in stereotype on sex role have roots in the limbic brain structures linked to contentious interpersonal orientation (cingulate) and negative mood (amygdala).
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Affiliation(s)
- Hikaru Takeuchi
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Yasuyuki Taki
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.,Department of Radiology and Nuclear Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Atsushi Sekiguchi
- Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.,Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Rui Nouchi
- Human and Social Response Research Division, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Yuka Kotozaki
- Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Seishu Nakagawa
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Carlos Makoto Miyauchi
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Graduate Schools for Law and Politics, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Kunio Iizuka
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Ryoichi Yokoyama
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Japan Society for the Promotion of Science, Tokyo, Japan
| | - Takamitsu Shinada
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Yuki Yamamoto
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Sugiko Hanawa
- Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Tsuyoshi Araki
- Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Hiroshi Hashizume
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | | | - Yuko Sassa
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Ryuta Kawashima
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.,Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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331
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Dinescu D, Horn EE, Duncan G, Turkheimer E. Socioeconomic modifiers of genetic and environmental influences on body mass index in adult twins. Health Psychol 2015; 35:157-66. [PMID: 26348497 DOI: 10.1037/hea0000255] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Individual measures of socioeconomic status (SES) suppress genetic variance in body mass index (BMI). Our objective was to examine the influence of both individual-level (i.e., educational attainment, household income) and macrolevel (i.e., neighborhood socioeconomic advantage) SES indicators on genetic contributions to BMI. METHOD The study used education level data from 4,162 monozygotic (MZ) and 1,900 dizygotic (DZ) same-sex twin pairs (64% female), income level data from 3,498 MZ and 1,534 DZ pairs (65% female), and neighborhood-level socioeconomic deprivation data from 2,327 MZ and 948 DZ pairs (65% female). Covariates included age (M = 40.4 ± 17.5 years), sex, and ethnicity. The cotwin control model was used to evaluate the mechanisms through which SES influences BMI (e.g., through genetic vs. environmental pathways), and a gene-by-environment interaction model was used to test whether residual variance in BMI, after controlling for the main effects of SES, was moderated by socioeconomic measures. RESULTS SES significantly predicted BMI. The association was noncausal, however, and instead was driven primarily through a common underlying genetic background that tended to grow less influential as SES increased. After controlling for the main effect of SES, both genetic and nonshared environmental variance decreased with increasing SES. CONCLUSIONS The impact of individual and macrolevel SES on BMI extends beyond its main effects. The influence of genes on BMI is moderated by individual and macrolevel measures of SES, such that when SES is higher, genetic factors become less influential.
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Affiliation(s)
| | - Erin E Horn
- Department of Psychology, University of Virginia
| | - Glen Duncan
- Department of Epidemiology, Nutritional Sciences Program, University of Washington
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332
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Machal J, Novak J, Hezova R, Zlamal F, Vasku A, Slaby O, Bienertova-Vasku J. Polymorphism in miR-31 and miR-584 binding site in the angiotensinogen gene differentially influences body fat distribution in both sexes. GENES AND NUTRITION 2015; 10:488. [PMID: 26319141 DOI: 10.1007/s12263-015-0488-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 08/17/2015] [Indexed: 01/22/2023]
Abstract
Angiotensinogen (AGT), its active fragments and microRNA-31 (miR-31) play an important role in adipocyte differentiation. AGT contains a miR-31 polymorphic binding site. We hypothesize that the rs7079 polymorphism in the miR-31/584 binding site of the AGT gene could influence body fat distribution. A total of 751 subjects (195 men, 556 women) were enrolled in the study. The rs7079 genotypes were determined by qRT-PCR. Anthropometric measurements were taken on all subjects, who were subsequently divided into two groups: obese (>30 kg m(-2)) and non-obese (<30 kg m(-2)). Linear regression models were created to determine the contributions of sex, obesity status and rs7079 to all measured parameters. Adding the rs7079 genotype significantly contributed to the linear regression model for waist circumference (p = 0.013), hip circumference (p = 0.018) and supraspinal skin-fold thickness (p = 1 × 10(-3)). Differences between sexes and between the obese and non-obese groups were observed. Waist circumference was lower in men carrying the A allele (p = 0.022); hip circumference was higher only in obese women carrying the A allele (p = 0.015). While men carrying the A allele had lower supraspinal skin-fold thickness (p = 0.022), this parameter was found to be higher in A allele carrying women (p = 3 × 10(-3)). The higher total sum of skin-fold thickness in A allele carrying women was restricted to obese individuals (p = 0.028). The presence of the A allele was associated with both lower tricipital skin-fold thickness in non-obese women (p = 0.023) and a trend of higher thickness in non-obese men (p = 0.065). Significant associations of rs7079 in the AGT gene and body fat distribution were observed. The distribution followed opposing patterns in both sexes.
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Affiliation(s)
- Jan Machal
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Building A18, 625 00, Brno, Czech Republic
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333
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Iranzo-Tatay C, Gimeno-Clemente N, Livianos-Aldana L, Rojo-Moreno L. [Genetic and environmental contributions to body mass index in a Spanish adolescent twin sample]. Med Clin (Barc) 2015; 145:153-9. [PMID: 25066500 DOI: 10.1016/j.medcli.2014.05.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 05/17/2014] [Accepted: 05/22/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND AND OBJECTIVE Twin and family studies support large genetic influences on variability in body mass index (BMI), with heritability estimates ranging from 47% to over 90%. Our objective was to study the relative contributions of genetics and environment to BMI, evaluating sex differences, in an adolescent twin sample from Valencia, Spain. MATERIAL AND METHODS Five hundred eighty-four pairs of adolescent twins between 13 and 18 years of age completed the study (82 monozygotic [MZ] and 87 dizygotic [DZ] pairs of male twins, 118 MZ and 102 DZ pairs of female twins, and 195 opposite-sex pairs of DZ twins). To determine zygosity, teachers responded a questionnaire on physical similarity. They also measured the participant's height and weight. BMI was calculated and weight status was determined according to age. We used twin models to assess genetic and environmental (common and unique) factors affecting BMI. RESULTS There was a 7.1% frequency of overweight and 2.8% of obesity. The estimated heritability of BMI was 88.0% in boys and 72.1% in girls, with the remaining variance attributable to non-shared environment in boys (12.0%) and 8.8% in girls. It was only in girls that common environment had an effect on BMI. CONCLUSIONS Genetics appears to play an important role in explaining the variability in BMI in the adolescence, with slight variations between boys and girls. Common environmental factors exert their influence on BMI only in girls.
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Affiliation(s)
- Carmen Iranzo-Tatay
- Servicio de Psiquiatría, Hospital Universitario y Politécnico La Fe, Valencia, España.
| | - Natalia Gimeno-Clemente
- Grupo de investigación Psiquiátrica, Sección de Psiquiatría Infanto-Juvenil, Hospital Universitario La Fe, Valencia, España
| | - Lorenzo Livianos-Aldana
- Servicio de Psiquiatría, Hospital Universitario y Politécnico La Fe, Valencia, España; Consorcio de Investigación Biomédica de Epidemiología y Salud Pública (CIBERESP); Departamento de Psiquiatría, Facultad de Medicina, Universidad de Valencia, Valencia, España
| | - Luis Rojo-Moreno
- Servicio de Psiquiatría, Hospital Universitario y Politécnico La Fe, Valencia, España; Consorcio de Investigación Biomédica de Epidemiología y Salud Pública (CIBERESP); Departamento de Psiquiatría, Facultad de Medicina, Universidad de Valencia, Valencia, España
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334
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Kerekes N, Tajnia A, Lichtenstein P, Lundström S, Anckarsäter H, Nilsson T, Råstam M. Neurodevelopmental problems and extremes in BMI. PeerJ 2015. [PMID: 26207189 PMCID: PMC4511820 DOI: 10.7717/peerj.1024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background. Over the last few decades, an increasing number of studies have suggested a connection between neurodevelopmental problems (NDPs) and body mass index (BMI). Attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) both seem to carry an increased risk for developing extreme BMI. However, the results are inconsistent, and there have been only a few studies of the general population of children. Aims. We had three aims with the present study: (1) to define the prevalence of extreme (low or high) BMI in the group of children with ADHD and/or ASDs compared to the group of children without these NDPs; (2) to analyze whether extreme BMI is associated with the subdomains within the diagnostic categories of ADHD or ASD; and (3) to investigate the contribution of genetic and environmental factors to BMI in boys and girls at ages 9 and 12. Method. Parents of 9- or 12-year-old twins (n = 12,496) were interviewed using the Autism-Tics, ADHD and other Comorbidities (A-TAC) inventory as part of the Child and Adolescent Twin Study in Sweden (CATSS). Univariate and multivariate generalized estimated equation models were used to analyze associations between extremes in BMI and NDPs. Results. ADHD screen-positive cases followed BMI distributions similar to those of children without ADHD or ASD. Significant association was found between ADHD and BMI only among 12-year-old girls, where the inattention subdomain of ADHD was significantly associated with the high extreme BMI. ASD scores were associated with both the low and the high extremes of BMI. Compared to children without ADHD or ASD, the prevalence of ASD screen-positive cases was three times greater in the high extreme BMI group and double as much in the low extreme BMI group. Stereotyped and repetitive behaviors were significantly associated with high extreme BMIs. Conclusion. Children with ASD, with or without coexisting ADHD, are more prone to have low or high extreme BMIs than children without ADHD or ASD.
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Affiliation(s)
- Nóra Kerekes
- CELAM (Centre for Ethics, Law and Mental Health), Institute of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden ; Swedish Prison and Probation Services, R&E , Sweden
| | - Armin Tajnia
- CELAM (Centre for Ethics, Law and Mental Health), Institute of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm , Sweden
| | - Sebastian Lundström
- CELAM (Centre for Ethics, Law and Mental Health), Institute of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden ; Swedish Prison and Probation Services, R&E , Sweden ; Gillberg Neuropsychiatry Centre, Institution of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden
| | - Henrik Anckarsäter
- CELAM (Centre for Ethics, Law and Mental Health), Institute of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden
| | - Thomas Nilsson
- CELAM (Centre for Ethics, Law and Mental Health), Institute of Neuroscience and Physiology, University of Gothenburg , Gothenburg , Sweden
| | - Maria Råstam
- Department of Clinical Sciences, Lund University , Lund , Sweden
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335
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Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A, Harris SE, Liewald D, Henders AK, Mendelson MM, Liu C, Joehanes R, Liang L, Levy D, Martin NG, Starr JM, Wijmenga C, Wray NR, Yang J, Montgomery GW, Franke L, Deary IJ, Visscher PM. Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations. Am J Hum Genet 2015; 97:75-85. [PMID: 26119815 PMCID: PMC4572498 DOI: 10.1016/j.ajhg.2015.05.014] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 05/21/2015] [Indexed: 01/23/2023] Open
Abstract
We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction.
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Affiliation(s)
- Sonia Shah
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Marc J Bonder
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Riccardo E Marioni
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Zhihong Zhu
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Allan F McRae
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Dave Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Anjali K Henders
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Michael M Mendelson
- Framingham Heart Study and Boston University School of Medicine, Boston, MA 01702, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, USA; Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Roby Joehanes
- Hebrew Senior Life, Harvard Medical School, Boston, MA 02131, USA
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Daniel Levy
- Population Studies Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, MD 20892-7936, USA
| | - Nicholas G Martin
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Cisca Wijmenga
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Naomi R Wray
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Jian Yang
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia
| | - Grant W Montgomery
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen 9713 AV, the Netherlands
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Peter M Visscher
- Queensland Brain Institute, University of Queensland, Brisbane 4072, Australia; University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD 4072, Australia; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK.
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336
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Stegemann R, Buchner DA. Transgenerational inheritance of metabolic disease. Semin Cell Dev Biol 2015; 43:131-140. [PMID: 25937492 PMCID: PMC4626440 DOI: 10.1016/j.semcdb.2015.04.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 04/20/2015] [Accepted: 04/22/2015] [Indexed: 02/05/2023]
Abstract
Metabolic disease encompasses several disorders including obesity, type 2 diabetes, and dyslipidemia. Recently, the incidence of metabolic disease has drastically increased, driven primarily by a worldwide obesity epidemic. Transgenerational inheritance remains controversial, but has been proposed to contribute to human metabolic disease risk based on a growing number of proof-of-principle studies in model organisms ranging from Caenorhabditis elegans to Mus musculus to Sus scrofa. Collectively, these studies demonstrate that heritable risk is epigenetically transmitted from parent to offspring over multiple generations in the absence of a continued exposure to the triggering stimuli. A diverse assortment of initial triggers can induce transgenerational inheritance including high-fat or high-sugar diets, low-protein diets, various toxins, and ancestral genetic variants. Although the mechanistic basis underlying the transgenerational inheritance of disease risk remains largely unknown, putative molecules mediating transmission include small RNAs, histone modifications, and DNA methylation. Due to the considerable impact of metabolic disease on human health, it is critical to better understand the role of transgenerational inheritance of metabolic disease risk to open new avenues for therapeutic intervention and improve upon the current methods for clinical diagnoses and treatment.
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Affiliation(s)
- Rachel Stegemann
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - David A Buchner
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH 44106, United States; Department of Biological Chemistry, Case Western Reserve University, Cleveland, OH 44106, United States.
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337
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Ajslev TA, Ängquist L, Silventoinen K, Baker JL, Sørensen TIA. Stable intergenerational associations of childhood overweight during the development of the obesity epidemic. Obesity (Silver Spring) 2015; 23:1279-87. [PMID: 25959297 DOI: 10.1002/oby.21060] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 01/25/2015] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The obesity epidemic may have developed as a response to the obesogenic environment among the genetically predisposed. This investigation examined whether the intergenerational resemblances in childhood overweight changed across the development of the obesity epidemic in groups of children born to parents with and without childhood overweight. METHODS The study population was from the Copenhagen School Health Records Register, which includes age- and sex-specific body mass index (BMI; kg/m(2) ) of children. This study used BMI values from 7-year-old children born 1952-1989 and from their parents at ages 7 and 13 years. The available number of parent-child pairs ranged from 17,926 through 42,184. The odds ratios of childhood overweight (BMI z-score >90th percentile) were calculated using logistic regression by parental BMI groups (BMI > or ≤90th percentile) and child birth year intervals. RESULTS Stable levels in parent-child overweight associations were observed across child BMI groups born to parents with and without childhood overweight. A slight upward odds ratio trend was observed across time in children born to two overweight parents at age 13, but not at age 7 years. CONCLUSIONS Parent-child resemblance in childhood overweight showed small changes during the development of the obesity epidemic, suggesting that the obesogenic environment inducing the epidemic in Denmark influenced children irrespective of their familial predisposition.
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Affiliation(s)
- Teresa A Ajslev
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg University Hospital, The Capital Region, Copenhagen, Denmark
| | - Lars Ängquist
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg University Hospital, The Capital Region, Copenhagen, Denmark
| | - Karri Silventoinen
- Population Research Unit, Department of Social Research, University of Helsinki, Helsinki, Finland
| | - Jennifer L Baker
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg University Hospital, The Capital Region, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Thorkild I A Sørensen
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg University Hospital, The Capital Region, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- MRC Integrative Epidemiology Unit, Bristol University, Bristol, UK
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338
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The CODATwins Project: The Cohort Description of Collaborative Project of Development of Anthropometrical Measures in Twins to Study Macro-Environmental Variation in Genetic and Environmental Effects on Anthropometric Traits. Twin Res Hum Genet 2015; 18:348-60. [PMID: 26014041 DOI: 10.1017/thg.2015.29] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.
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339
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Associations Between Fast-Food Consumption and Body Mass Index: A Cross-Sectional Study in Adult Twins. Twin Res Hum Genet 2015; 18:375-82. [PMID: 26005202 DOI: 10.1017/thg.2015.33] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Obesity is a substantial health problem in the United States, and is associated with many chronic diseases. Previous studies have linked poor dietary habits to obesity. This cross-sectional study aimed to identify the association between body mass index (BMI) and fast-food consumption among 669 same-sex adult twin pairs residing in the Puget Sound region around Seattle, Washington. We calculated twin-pair correlations for BMI and fast-food consumption. We next regressed BMI on fast-food consumption using generalized estimating equations (GEE), and finally estimated the within-pair difference in BMI associated with a difference in fast-food consumption, which controls for all potential genetic and environment characteristics shared between twins within a pair. Twin-pair correlations for fast-food consumption were similar for identical (monozygotic; MZ) and fraternal (dizygotic; DZ) twins, but were substantially higher in MZ than DZ twins for BMI. In the unadjusted GEE model, greater fast-food consumption was associated with larger BMI. For twin pairs overall, and for MZ twins, there was no association between within-pair differences in fast-food consumption and BMI in any model. In contrast, there was a significant association between within-pair differences in fast-food consumption and BMI among DZ twins, suggesting that genetic factors play a role in the observed association. Thus, although variance in fast-food consumption itself is largely driven by environmental factors, the overall association between this specific eating behavior and BMI is largely due to genetic factors.
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340
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Wu SH, Guo Q, Viken RJ, Reed T, Dai J. Heritability of usual alcohol intoxication and hangover in male twins: the NAS-NRC Twin Registry. Alcohol Clin Exp Res 2015; 38:2307-13. [PMID: 25156618 DOI: 10.1111/acer.12487] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2013] [Accepted: 05/02/2014] [Indexed: 11/29/2022]
Abstract
BACKGROUND Alcohol consumption is influenced by heritable factors. The genetic influence on usual high-density drinking, including alcohol intoxication and hangover, is unknown. We aim to estimate the heritability of usual high-density drinking. METHODS A total of 13,511 male twins in this cross-sectional study were included from the National Academy of Sciences-National Research Council (NAS-NRC) Twin Registry. Data on the frequency of alcohol intoxication and alcohol hangover over the past year, that is, usual high-density drinking (phenotypes), were collected through a self-administered questionnaire when twins were middle-aged in 1972. Structural equation modeling was used to estimate the variance components of phenotypes. RESULTS The mean of the frequency of usual high-density drinking in the entire twin population was 0.16 times per month for intoxication and 0.18 times per month for hangover. The heritability of usual alcohol intoxication was 50.7% (95% confidence interval [CI] 46.2 to 55.0) before and 49.9% (95% CI 45.3 to 54.2) after the body mass index (BMI) adjustment. The heritability of usual hangover was 55.4% (95% CI 51.2 to 58.6) before and 54.8% (95% CI 50.6 to 58.8) after adjustment for BMI. Unshared environmental factors between co-twins explained the remaining variance in alcohol intoxication and in hangover. CONCLUSIONS Both genetic and unshared environmental factors have important influences on usual alcohol intoxication and hangover. These findings are important in understanding the occurrence of and developing interventions for usual high-density drinking.
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Affiliation(s)
- Sheng-Hui Wu
- Division of Epidemiology, Department of Medicine, Institute of Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee
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341
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Zhang D, Li Z, Wang H, Yang M, Liang L, Fu J, Wang C, Ling J, Zhang Y, Zhang S, Xu Y, Zhu Y, Lai M. Interactions between obesity-related copy number variants and dietary behaviors in childhood obesity. Nutrients 2015; 7:3054-66. [PMID: 25912042 PMCID: PMC4425189 DOI: 10.3390/nu7043054] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 04/03/2015] [Accepted: 04/14/2015] [Indexed: 01/10/2023] Open
Abstract
Copy number variants (CNVs) have been implicated as an important genetic marker of obesity, and gene-environment interaction has been found to modulate risk of obesity. To evaluate the associations between CNVs and childhood obesity, as well as the interactions between CNVs and dietary behaviors, we recruited 534 obese children and 508 controls from six cities in China and six candidate CNVs were screened through published genome-wide studies (GWAS) on childhood obesity. We found three loci (10q11.22, 4q25 and 11q11) to be significantly associated with obesity after false discovery rate (FDR) correction (all the p ≤ 0.05). Cumulative effect of the three positive loci was measured by the genetic risk score (GRS), showing a significant relationship with the risk of obesity (Ptrend < 0.001). The OR of obesity increased to 21.38 (95% CI = 21.19-21.55) among the 10q11.22 deletion carriers who had meat-based diets, indicating prominent multiplicative interaction (MI) between deletions of 10q11.22 and preference for a meat-based diet. Simultaneous deletions of 5q13.2 and duplications of 6q14.1 had significant MI with a preference for salty foods. Our results suggested that CNVs may contribute to the genetic susceptibility of childhood obesity, and the CNV-diet interactions modulate the risk of obesity.
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Affiliation(s)
- Dandan Zhang
- Department of Pathology, Zhejiang University School of Medicine, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
- Key Laboratory of Disease Proteomics of Zhejiang Province, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Zhenli Li
- Department of Pathology, Zhejiang University School of Medicine, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
- Key Laboratory of Disease Proteomics of Zhejiang Province, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Hao Wang
- Department of Pathology, Zhejiang University School of Medicine, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
- Key Laboratory of Disease Proteomics of Zhejiang Province, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Min Yang
- Department of Nutrition, Zhejiang University School of Public Health, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Li Liang
- Department of Pediatrics, the First Affiliated Hospital of College of Medicine, Zhejiang University, 79 Qing-chun Road, Hangzhou 310003, Zhejiang, China.
| | - Junfen Fu
- Department of Endocrinology, Children's Hospital of College of Medicine, Zhejiang University, 25 Guang-fu Road, Hangzhou 310003, Zhejiang, China.
| | - Chunling Wang
- Department of Pediatrics, the First Affiliated Hospital of College of Medicine, Zhejiang University, 79 Qing-chun Road, Hangzhou 310003, Zhejiang, China.
| | - Jie Ling
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Yan Zhang
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Shuai Zhang
- Department of Pathology, Zhejiang University School of Medicine, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
- Key Laboratory of Disease Proteomics of Zhejiang Province, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Yuyang Xu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
| | - Maode Lai
- Department of Pathology, Zhejiang University School of Medicine, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
- Key Laboratory of Disease Proteomics of Zhejiang Province, 866 Yu-hang-tang Road, Hangzhou 310058, Zhejiang, China.
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342
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Horn EE, Turkheimer E, Strachan E, Duncan GE. Behavioral and Environmental Modification of the Genetic Influence on Body Mass Index: A Twin Study. Behav Genet 2015; 45:409-26. [PMID: 25894925 DOI: 10.1007/s10519-015-9718-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Accepted: 03/30/2015] [Indexed: 02/04/2023]
Abstract
Body mass index (BMI) has a strong genetic basis, with a heritability around 0.75, but is also influenced by numerous behavioral and environmental factors. Aspects of the built environment (e.g., environmental walkability) are hypothesized to influence obesity by directly affecting BMI, by facilitating or inhibiting behaviors such as physical activity that are related to BMI, or by suppressing genetic tendencies toward higher BMI. The present study investigated relative influences of physical activity and walkability on variance in BMI using 5079 same-sex adult twin pairs (70 % monozygotic, 65 % female). High activity and walkability levels independently suppressed genetic variance in BMI. Estimating their effects simultaneously, however, suggested that the walkability effect was mediated by activity. The suppressive effect of activity on variance in BMI was present even with a tendency for low-BMI individuals to select into environments that require higher activity levels. Overall, our results point to community- or macro-level interventions that facilitate individual-level behaviors as a plausible approach to addressing the obesity epidemic among US adults.
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Affiliation(s)
- Erin E Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, USA,
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343
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Ouwens M, Schiffer A, Visser L, Raeijmaekers N, Nyklíček I. Mindfulness and eating behaviour styles in morbidly obese males and females. Appetite 2015; 87:62-7. [DOI: 10.1016/j.appet.2014.11.030] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 11/26/2014] [Accepted: 11/30/2014] [Indexed: 10/24/2022]
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344
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Nead KT, Li A, Wehner MR, Neupane B, Gustafsson S, Butterworth A, Engert JC, Davis AD, Hegele RA, Miller R, den Hoed M, Khaw KT, Kilpeläinen TO, Wareham N, Edwards TL, Hallmans G, Varga TV, Kardia SLR, Smith JA, Zhao W, Faul JD, Weir D, Mi J, Xi B, Quinteros SC, Cooper C, Sayer AA, Jameson K, Grøntved A, Fornage M, Sidney S, Hanis CL, Highland HM, Häring HU, Heni M, Lasky-Su J, Weiss ST, Gerhard GS, Still C, Melka MM, Pausova Z, Paus T, Grant SFA, Hakonarson H, Price RA, Wang K, Scherag A, Hebebrand J, Hinney A, Franks PW, Frayling TM, McCarthy MI, Hirschhorn JN, Loos RJ, Ingelsson E, Gerstein HC, Yusuf S, Beyene J, Anand SS, Meyre D. Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals. Hum Mol Genet 2015; 24:3582-94. [PMID: 25784503 DOI: 10.1093/hmg/ddv097] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 03/13/2015] [Indexed: 12/31/2022] Open
Abstract
Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m(2)], but their contribution to common obesity (BMI ≥ 30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06-1.24, P = 6.08 × 10(-6)) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04-1.10, P = 3.00 × 10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00-0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00-0.03; P = 5.57 × 10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.
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Affiliation(s)
- Kevin T Nead
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Aihua Li
- Department of Clinical Epidemiology and Biostatistics
| | - Mackenzie R Wehner
- Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Binod Neupane
- Department of Clinical Epidemiology and Biostatistics
| | - Stefan Gustafsson
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Adam Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - James C Engert
- Population Health Research Institute, McMaster University, and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON, Canada L8L 2X
| | | | - Robert A Hegele
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada L8S 4L8, Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala SE 751 05, Sweden
| | | | - Marcel den Hoed
- The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada H3H 2R9, Six Nations Health Services, Ohsweken, Canada N0A 1M0
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Tuomas O Kilpeläinen
- The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada H3H 2R9, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, London, ON, Canada N6A 5K8
| | - Nick Wareham
- The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada H3H 2R9
| | - Todd L Edwards
- Department of Medicine, University of Western Ontario, London, ON, Canada N6A 3K7
| | - Göran Hallmans
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Tibor V Varga
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sharon L R Kardia
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen 2100, Denmark
| | - Jennifer A Smith
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen 2100, Denmark
| | - Wei Zhao
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Copenhagen 2100, Denmark
| | - Jessica D Faul
- Center for Human Genetics Research, Vanderbilt Epidemiology Center, Department of Medicine, Vanderbilt University, Nashville, TN 37235, USA
| | - David Weir
- Center for Human Genetics Research, Vanderbilt Epidemiology Center, Department of Medicine, Vanderbilt University, Nashville, TN 37235, USA
| | - Jie Mi
- Department of Public Health and Clinical Medicine, Umeå University, Umeå 901 87, Sweden
| | - Bo Xi
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö 205 02, Sweden
| | | | - Cyrus Cooper
- Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA, Department of Epidemiology, Capital Institute of Pediatrics, Beijing 100020, China, Department of Maternal and Child Health Care, School of Public Health, Shandong University, Jinan 250100, China
| | - Avan Aihie Sayer
- Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
| | - Karen Jameson
- Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA
| | - Anders Grøntved
- Unidad de Genómica de Poblaciones Aplicada a la Salud, Facultad de Química, Universidad Nacional Autónoma de México, Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Myriam Fornage
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK
| | - Stephen Sidney
- National Institute for Health Research Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton General Hospital, Southampton SO16 6YD, UK
| | - Craig L Hanis
- National Institute for Health Research Biomedical Research Unit, University of Oxford, Oxford OX3 7LE, UK
| | - Heather M Highland
- National Institute for Health Research Biomedical Research Unit, University of Oxford, Oxford OX3 7LE, UK
| | - Hans-Ulrich Häring
- Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense DK-5230, Denmark, University of Texas Health Science Center at Houston Institute of Molecular Medicine and Division of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
| | - Martin Heni
- Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense DK-5230, Denmark, University of Texas Health Science Center at Houston Institute of Molecular Medicine and Division of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
| | - Jessica Lasky-Su
- Division of Research, Kaiser Permanente of Northern California, Oakland, CA 94612, USA, The Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Scott T Weiss
- Division of Research, Kaiser Permanente of Northern California, Oakland, CA 94612, USA, The Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Glenn S Gerhard
- Internal Medicine IV (Endocrinology, Diabetology, Angiology, Nephrology, and Clinical Chemistry), University Hospital of Tuebingen, Tübingen 72076, Germany
| | | | - Melkaey M Melka
- The Department of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zdenka Pausova
- The Department of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tomáš Paus
- Center for Genomic Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Struan F A Grant
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Department of Pathology and Laboratory Medicine, Pennsylvania State University, Hershey, PA 17033, USA, Geisinger Obesity Institute, Danville, PA 17822, USA
| | - Hakon Hakonarson
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, Department of Pathology and Laboratory Medicine, Pennsylvania State University, Hershey, PA 17033, USA, Geisinger Obesity Institute, Danville, PA 17822, USA
| | - R Arlen Price
- The Hospital for Sick Children, Department of Physiology, University of Toronto, Toronto, ON, Canada M5G 1X
| | - Kai Wang
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK, Rotman Research Institute, University of Toronto, Toronto, Canada M6A 2E1
| | - Andre Scherag
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | | | | | | | - Paul W Franks
- Department of Medicine, University of Western Ontario, London, ON, Canada N6A 3K7, MRC Epidemiology Unit, University of Cambridge, Cambridge, UK, Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy M Frayling
- Zilkha Neurogenetic Institute, Department of Psychiatry and Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Mark I McCarthy
- Clinical Epidemiology, Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena 07740, Germany
| | - Joel N Hirschhorn
- Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen 45141, Germany, Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA, Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 4TH, UK
| | - Ruth J Loos
- The Research Institute of the McGill University Health Centre, McGill University, Montreal, QC, Canada H3H 2R9, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 9DU, UK
| | - Erik Ingelsson
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Hertzel C Gerstein
- Department of Clinical Epidemiology and Biostatistics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Divisions of Genetics and Endocrinology, Children's Hospital, Boston, MA 02115, USA
| | - Salim Yusuf
- Department of Clinical Epidemiology and Biostatistics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Divisions of Genetics and Endocrinology, Children's Hospital, Boston, MA 02115, USA
| | - Joseph Beyene
- Department of Clinical Epidemiology and Biostatistics
| | - Sonia S Anand
- Department of Clinical Epidemiology and Biostatistics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA, Divisions of Genetics and Endocrinology, Children's Hospital, Boston, MA 02115, USA
| | - David Meyre
- Department of Clinical Epidemiology and Biostatistics, Divisions of Genetics and Endocrinology, Children's Hospital, Boston, MA 02115, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA,
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345
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van Dongen J, Willemsen G, Heijmans BT, Neuteboom J, Kluft C, Jansen R, Penninx BWJ, Slagboom PE, de Geus EJC, Boomsma DI. Longitudinal weight differences, gene expression and blood biomarkers in BMI-discordant identical twins. Int J Obes (Lond) 2015; 39:899-909. [PMID: 25765203 PMCID: PMC4471109 DOI: 10.1038/ijo.2015.24] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Revised: 11/28/2014] [Accepted: 12/18/2014] [Indexed: 12/31/2022]
Abstract
Background BMI discordant monozygotic (MZ) twins allows an examination of the causes and consequences of adiposity in a genetically controlled design. Few studies have examined longitudinal BMI discordance in MZ pairs. Objectives To study the development over time of BMI discordance in adolescent and adult MZ twin pairs, and to examine lifestyle, metabolic, inflammatory, and gene expression differences associated with concurrent and long-term BMI discordance in MZ pairs. Subjects/Methods BMI data from 2775 MZ twin pairs, collected in eight longitudinal surveys and a biobank project between 1991 and 2011, were analyzed to characterize longitudinal discordance. Lifestyle characteristics were compared within discordant pairs (ΔBMI ≥ 3 kg/m2) and biomarkers (lipids, glucose, insulin, CRP, fibrinogen, IL-6, TNF-α and sIL-6R and liver enzymes AST, ALT and GGT) and gene expression were compared in peripheral blood from discordant pairs who participated in the NTR biobank project. Results The prevalence of discordance ranged from 3.2% in 1991 (mean age=17, SD=2.4) to 17.4% (N=202 pairs) in 2009 (mean age=35, SD=15), and was 16.5% (N=174) among pairs participating in the biobank project (mean age=35, SD=12). Of 699 MZ with BMI data from 3-5 time points, 17 pairs (2.4%) were long-term discordant (at all available time points; mean follow-up range=6.4 years). Concurrently discordant pairs showed significant differences in self-ratings of which twin eats most (p=2.3×10−13), but not in leisure time exercise activity (p=0.28) and smoking (p>0.05). Ten out of 14 biomarkers showed significantly more unfavorable levels in the heavier of twin of the discordant pairs (p-values < 0.001); most of these biomarker differences were largest in longitudinally discordant pairs. No significant gene expression differences were identified, although high ranking genes were enriched for Gene Ontology (GO) terms highlighting metabolic gene regulation and inflammation pathways. Conclusions BMI discordance is uncommon in adolescent identical pairs but increases with higher pair-mean of BMI at older ages, although long-term BMI discordance is rare. In discordant pairs, the heavier twin had a more unfavorable blood biomarker profile than the genetically matched leaner twin, in support of causal effects of obesity.
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Affiliation(s)
- J van Dongen
- 1] Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands [2] EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - G Willemsen
- 1] Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands [2] EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - B T Heijmans
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - J Neuteboom
- Good Biomarker Sciences, Leiden, The Netherlands
| | - C Kluft
- Good Biomarker Sciences, Leiden, The Netherlands
| | - R Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - B W J Penninx
- 1] EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands [2] Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - P E Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - E J C de Geus
- 1] Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands [2] EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
| | - D I Boomsma
- 1] Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands [2] EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
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346
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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]
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347
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Genetic contribution to the variance of blood pressure and heart rate: a systematic review and meta-regression of twin studies. Twin Res Hum Genet 2015; 18:158-70. [PMID: 25744168 DOI: 10.1017/thg.2015.8] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The genetic contribution of blood pressure and heart rate (HR) varied widely between studies. Demographic factors such as ethnicity, age and/or sex might explain some of the heterogeneity. We performed a systematic review focusing on four phenotypes: systolic blood pressure (SBP), diastolic blood pressure (DBP), HR and pulse pressure (PP). Meta-regression was conducted to analyze potential factors in relation to SBP and DBP heritability. A total of 10,613 independent twins that came from 17 studies were included in the analysis. The weighted mean value of heritability for SBP and DBP was 0.54 (95% CIs: 0.48-0.60) and 0.49 (95% CIs: 0.42-0.56). Comparatively, three studies of HR and four studies of PP heritability were limited for the heterogeneity test. Meta-regression showed that, on average, SBP heritability with additive genes/unique environment (AE) model tend to have a higher heritability than additive genes/shared environment/unique environment (ACE) model (coefficient = 0.0947, p = .0142). A similar result was found for DBP as well. No other factors such as sex, age, ethnicity, publication year were significantly associated with heritability variance. Our study shows heritability estimates based on twin studies of both SBP and DBP are around 50%, using an AE rather than an ACE model; the variance due to C ended up in A, suggesting that the AE model may overestimate heritability if a small contribution of shared environment exists.
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348
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Oh EC, Vasanth S, Katsanis N. Metabolic regulation and energy homeostasis through the primary Cilium. Cell Metab 2015; 21:21-31. [PMID: 25543293 PMCID: PMC4370781 DOI: 10.1016/j.cmet.2014.11.019] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 09/19/2014] [Accepted: 11/20/2014] [Indexed: 02/07/2023]
Abstract
Obesity and diabetes represent a significant healthcare concern. In contrast to genome-wide association studies that, some exceptions notwithstanding, have offered modest clues about pathomechanism, the dissection of rare disorders in which obesity represents a core feature have highlighted key molecules and structures critical to energy regulation. Here we focus on the primary cilium, an organelle whose roles in energy homeostasis have been underscored by the high incidence of obesity and type II diabetes in patients and mouse mutants with compromised ciliary function. We discuss recent evidence linking ciliary dysfunction to metabolic defects and we explore the contribution of neuronal and nonneuronal cilia to these phenotypes.
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Affiliation(s)
- Edwin C Oh
- Center for Human Disease Modeling, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Shivakumar Vasanth
- Center for Human Disease Modeling, Duke University School of Medicine, Durham, NC 27710, USA
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University School of Medicine, Durham, NC 27710, USA.
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349
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Duncan GE, Cash SW, Horn EE, Turkheimer E. Quasi-causal associations of physical activity and neighborhood walkability with body mass index: a twin study. Prev Med 2015; 70:90-5. [PMID: 25482422 PMCID: PMC4274205 DOI: 10.1016/j.ypmed.2014.11.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 09/17/2014] [Accepted: 11/26/2014] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Physical activity, neighborhood walkability, and body mass index (BMI, kg/m(2)) associations were tested using quasi-experimental twin methods. We hypothesized that physical activity and walkability were independently associated with BMI within twin pairs, controlling for genetic and environmental background shared between them. METHODS Data were from 6376 (64% female; 58% identical) same-sex pairs, University of Washington Twin Registry, 2008-2013. Neighborhood walking, moderate-to-vigorous physical activity (MVPA), and BMI were self-reported. Residential address was used to calculate walkability. Phenotypic (non-genetically informed) and biometric (genetically informed) regression was employed, controlling for age, sex, and race. RESULTS Walking and MVPA were associated with BMI in phenotypic analyses; associations were attenuated but significant in biometric analyses (Ps<0.05). Walkability was not associated with BMI, however, was associated with walking (but not MVPA) in both phenotypic and biometric analyses (Ps<0.05), with no attenuation accounting for shared genetic and environmental background. CONCLUSIONS The association between activity and BMI is largely due to shared genetic and environmental factors, but a significant causal relationship remains accounting for shared background. Although walkability is not associated with BMI, it is associated with neighborhood walking (but not MVPA) accounting for shared background, suggesting a causal relationship between them.
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Affiliation(s)
- Glen E Duncan
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States; Nutritional Sciences Program, School of Public Health, University of Washington, Seattle, WA, United States.
| | - Stephanie Whisnant Cash
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Erin E Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Eric Turkheimer
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
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350
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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.
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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
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