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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 PMCID: PMC11237142 DOI: 10.1038/s41467-024-49998-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Drouard G, Silventoinen K, Latvala A, Kaprio J. Genetic and Environmental Factors Underlying Parallel Changes in Body Mass Index and Alcohol Consumption: A 36-Year Longitudinal Study of Adult Twins. Obes Facts 2023; 16:224-236. [PMID: 36882010 PMCID: PMC10826601 DOI: 10.1159/000529835] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
INTRODUCTION While the genetic and environmental underpinnings of body weight and alcohol use are fairly well-known, determinants of simultaneous changes in these traits are still poorly known. We sought to quantify the environmental and genetic components underlying parallel changes in weight and alcohol consumption and to investigate potential covariation between them. METHODS The analysis comprised 4,461 adult participants (58% women) from the Finnish Twin Cohort with four measures of alcohol consumption and body mass index (BMI) over a 36-year follow-up. Trajectories of each trait were described by growth factors, defined as intercepts (i.e., baseline) and slopes (i.e., change over follow-up), using latent growth curve modeling. Growth values were used for male (190 monozygotic pairs, 293 dizygotic pairs) and female (316 monozygotic pairs, 487 dizygotic pairs) same-sex complete twin pairs in multivariate twin modeling. The variances and covariances of growth factors were then decomposed into genetic and environmental components. RESULTS The baseline heritabilities were similar in men (BMI: h2 = 79% [95% confidence interval: 74, 83]; alcohol consumption: h2 = 49% [32, 67]) and women (h2 = 77% [73, 81]; h2 = 45% [29, 61]). Heritabilities of BMI change were similar in men (h2 = 52% [42, 61]) and women (h2 = 57% [50, 63]), but the heritability of change in alcohol consumption was significantly higher (p = 0.03) in men (h2 = 45% [34, 54]) than in women (h2 = 31% [22, 38]). Significant additive genetic correlations between BMI at baseline and change in alcohol consumption were observed in both men (rA = -0.17 [-0.29, -0.04]) and women (rA = -0.18 [-0.31, -0.06]). Non-shared environmental factors affecting changes in alcohol consumption and BMI were correlated in men (rE = 0.18 [0.06, 0.30]). Among women, non-shared environmental factors affecting baseline alcohol consumption and the change in BMI were inversely correlated (rE = -0.11 [-0.20, -0.01]). CONCLUSIONS Based on genetic correlations, genetic variation underlying BMI may affect changes in alcohol consumption. Independent of genetic effects, change in BMI correlates with change in alcohol consumption in men, suggesting direct effects between them.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Karri Silventoinen
- Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Antti Latvala
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Institute of Criminology and Legal Policy, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Wittemans LBL, Nellaker C, Holmes C, Lindgren CM, Nicholson G. The genetic architecture of changes in adiposity during adulthood. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.09.23284364. [PMID: 36711652 PMCID: PMC9882550 DOI: 10.1101/2023.01.09.23284364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.
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Affiliation(s)
- Samvida S. Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | | | - Duncan S. Palmer
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, UK
| | - Laura B. L. Wittemans
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Christoffer Nellaker
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Chris Holmes
- Department of Statistics, University of Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Gillespie NA, Gentry AE, Kirkpatrick RM, Reynolds CA, Mathur R, Kendler KS, Maes HH, Webb BT, Peterson RE. Determining the stability of genome-wide factors in BMI between ages 40 to 69 years. PLoS Genet 2022; 18:e1010303. [PMID: 35951648 PMCID: PMC9398001 DOI: 10.1371/journal.pgen.1010303] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 08/23/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
- QIMR Berghofer Medical Research Institute, Herston, Australia
| | - Amanda Elswick Gentry
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Robert M. Kirkpatrick
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Chandra A. Reynolds
- Department of Psychology, University of California, Riverside, California, United States of America
| | - Ravi Mathur
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, North Carolina, United States of America
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Hermine H. Maes
- Virginia Institute for Psychiatric and Behavior Genetics, Departments of Human and Molecular Genetics, Psychiatry, & Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Bradley T. Webb
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
- GenOmics, Bioinformatics, and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, North Carolina, United States of America
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, United States of America
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Azzolini F, Berentsen GD, Skaug HJ, Hjelmborg JVB, Kaprio JA. The heritability of BMI varies across the range of BMI-a heritability curve analysis in a twin cohort. Int J Obes (Lond) 2022; 46:1786-1791. [PMID: 35817850 DOI: 10.1038/s41366-022-01172-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 06/09/2022] [Accepted: 06/14/2022] [Indexed: 12/18/2022]
Abstract
BACKGROUND The heritability of traits such as body mass index (BMI), a measure of obesity, is generally estimated using family and twin studies, and increasingly by molecular genetic approaches. These studies generally assume that genetic effects are uniform across all trait values, yet there is emerging evidence that this may not always be the case. METHOD/SUBJECTS This paper analyzes twin data using a recently developed measure of heritability called the heritability curve. Under the assumption that trait values in twin pairs are governed by a flexible Gaussian mixture distribution, heritability curves may vary across trait values. The data consist of repeated measures of BMI on 1506 monozygotic (MZ) and 2843 like-sexed dizygotic (DZ) adult twin pairs, gathered from multiple surveys in older Finnish Twin Cohorts. RESULTS The heritability curve and BMI value-specific MZ and DZ pairwise correlations were estimated, and these varied across the range of BMI. MZ correlations were highest at BMI values from 21 to 24, with a stronger decrease for women than for men at higher values. Models with additive and dominance effects fit best at low and high BMI values, while models with additive genetic and common environmental effects fit best in the normal range of BMI. CONCLUSIONS We demonstrate that twin and molecular genetic studies need to consider how genetic effects vary across trait values. Such variation may reconcile findings of traits with high heritability and major differences in mean values between countries or over time.
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Affiliation(s)
| | - Geir D Berentsen
- Department of Business and Management Science, NHH Norwegian School of Economics, Bergen, Norway
| | - Hans J Skaug
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Jacob V B Hjelmborg
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Jaakko A Kaprio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
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Silventoinen K, Konttinen H. Obesity and eating behavior from the perspective of twin and genetic research. Neurosci Biobehav Rev 2021; 109:150-165. [PMID: 31959301 DOI: 10.1016/j.neubiorev.2019.12.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/11/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022]
Abstract
Obesity has dramatically increased during the last decades and is currently one of the most serious global health problems. We present a hypothesis that obesity is a neuro-behavioral disease having a strong genetic background mediated largely by eating behavior and is sensitive to the macro-environment; we study this hypothesis from the perspective of genetic research. Genetic family and genome-wide-association studies have shown well that body mass index (BMI, kg/m2) is a highly heritable and polygenic trait. New genetic variation of BMI emerges after early childhood. Candidate genes of BMI notably express in brain tissue, supporting that this new variation is related to behavior. Obesogenic environments at both childhood family and societal levels reinforce the genetic susceptibility to obesity. Genetic factors have a clear influence on macro-nutrient intake and appetite-related eating behavior traits. Results on the gene-by-diet interactions in obesity are mixed, but emerging evidence suggests that eating behavior traits partly mediate the effect of genes on BMI. However, more rigorous prospective study designs controlling for measurement bias are still needed.
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Affiliation(s)
- Karri Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland.
| | - Hanna Konttinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
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Heitkamp M, Siegrist M, Molnos S, Brandmaier S, Wahl S, Langhof H, Grallert H, Halle M. Obesity Genes and Weight Loss During Lifestyle Intervention in Children With Obesity. JAMA Pediatr 2021; 175:e205142. [PMID: 33315090 PMCID: PMC7737153 DOI: 10.1001/jamapediatrics.2020.5142] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
IMPORTANCE Genome-wide association studies have identified genetic loci influencing obesity risk in children. However, the importance of these loci in the associations with weight reduction through lifestyle interventions has not been investigated in large intervention trials. OBJECTIVE To evaluate the associations between various obesity susceptibility loci and changes in body weight in children during an in-hospital, lifestyle intervention program. DESIGN, SETTING, AND PARTICIPANTS Long-term Effects of Lifestyle Intervention in Obesity and Genetic Influence in Children (LOGIC), an interventional prospective cohort study, enrolled 1429 children with overweight or obesity to participate in an in-hospital lifestyle intervention program. Genotyping of 56 validated obesity single-nucleotide variants (SNVs) was performed, and the associations between the SNVs and body weight reduction during the intervention were evaluated using linear mixed-effects models for each SNV. The LOGIC study was conducted from January 6, 2006, to October 19, 2013; data analysis was performed from July 15, 2015, to November 6, 2016. EXPOSURES A 4- to 6-week standardized in-hospital lifestyle intervention program (daily physical activity, calorie-restricted diet, and behavioral therapy). MAIN OUTCOMES AND MEASURES The association between 56 obesity-relevant SNVs and changes in body weight and body mass index. RESULTS Of 1429 individuals enrolled in the LOGIC Study, 1198 individuals (mean [SD] age, 14.0 [2.2] years; 670 [56%] girls) were genotyped. A mean (SD) decrease was noted in body weight of -8.7 (3.6) kg (95% CI, -15.7 to -1.8 kg), and body mass index (calculated as weight in kilograms divided by height in meters squared) decreased by -3.3 (1.1) (95% CI, -5.4 to -1.1) (both P < .05). Five of 56 obesity SNVs were statistically significantly associated with a reduction of body weight or body mass index (all P < 8.93 × 10-4 corresponding to Bonferroni correction for 56 tests). Compared with homozygous participants without the risk allele, homozygous carriers of the rs7164727 (LOC100287559: 0.42 kg; 95% CI, 0.31-0.53 kg, P = 4.00 × 10-4) and rs12940622 (RPTOR: 0.35 kg; 95% CI, 0.18-0.52 kg; P = 1.86 × 10-5) risk alleles had a lower reduction of body weight, whereas carriers of the rs13201877 (IFNGR1: 0.65 kg; 95% CI, 0.51-0.79 kg; P = 2.39 × 10-5), rs10733682 (LMX1B: 0.45 kg; 95% CI, 0.27-0.63 kg; P = 6.37 × 10-4), and rs2836754 (ETS2: 0.56 kg; 95% CI, 0.38-0.74 kg; P = 1.51 × 10-4) risk alleles were associated with a greater reduction of body weight after adjustment for age and sex. CONCLUSIONS AND RELEVANCE Genes appear to play a minor role in weight reduction by lifestyle in children with overweight or obesity. The findings suggest that environmental, social, and behavioral factors are more important to consider in obesity treatment strategies.
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Affiliation(s)
- Melanie Heitkamp
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany
| | - Monika Siegrist
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany
| | - Sophie Molnos
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Stefan Brandmaier
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany,Now with Roche Diagnostics, Bavaria, Germany
| | - Helmut Langhof
- Rehabilitation Clinic “Klinik Schönsicht,” Berchtesgaden, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany,German Center for Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin Halle
- Department of Prevention and Sports Medicine, Centre for Sports Cardiology, University Hospital “Klinikum rechts der Isar,” Technical University of Munich, Munich, Germany,German Center for Cardiovascular Research, partner site Munich Heart Alliance, Munich, Germany
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Wells JCK, Stock JT. Life History Transitions at the Origins of Agriculture: A Model for Understanding How Niche Construction Impacts Human Growth, Demography and Health. Front Endocrinol (Lausanne) 2020; 11:325. [PMID: 32508752 PMCID: PMC7253633 DOI: 10.3389/fendo.2020.00325] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/27/2020] [Indexed: 12/18/2022] Open
Abstract
Over recent millennia, human populations have regularly reconstructed their subsistence niches, changing both how they obtain food and the conditions in which they live. For example, over the last 12,000 years the vast majority of human populations shifted from foraging to practicing different forms of agriculture. The shift to farming is widely understood to have impacted several aspects of human demography and biology, including mortality risk, population growth, adult body size, and physical markers of health. However, these trends have not been integrated within an over-arching conceptual framework, and there is poor understanding of why populations tended to increase in population size during periods when markers of health deteriorated. Here, we offer a novel conceptual approach based on evolutionary life history theory. This theory assumes that energy availability is finite and must be allocated in competition between the functions of maintenance, growth, reproduction, and defence. In any given environment, and at any given stage during the life-course, natural selection favours energy allocation strategies that maximise fitness. We argue that the origins of agriculture involved profound transformations in human life history strategies, impacting both the availability of energy and the way that it was allocated between life history functions in the body. Although overall energy supply increased, the diet composition changed, while sedentary populations were challenged by new infectious burdens. We propose that this composite new ecological niche favoured increased energy allocation to defence (immune function) and reproduction, thus reducing the allocation to growth and maintenance. We review evidence in support of this hypothesis and highlight how further work could address both heterogeneity and specific aspects of the origins of agriculture in more detail. Our approach can be applied to many other transformations of the human subsistence niche, and can shed new light on the way that health, height, life expectancy, and fertility patterns are changing in association with globalization and nutrition transition.
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Affiliation(s)
- Jonathan C. K. Wells
- Childhood Nutrition Research Centre, Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- *Correspondence: Jonathan C. K. Wells
| | - Jay T. Stock
- Department of Anthropology, University of Western Ontario, London, ON, Canada
- Department of Archaeology, Max Planck Institute for the Science of Human History, Jena, Germany
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Johnson W, Hahn E, Gottschling J, Lenau F, Spinath FM, McGue M. SES-of-Origin and BMI in Youth: Comparing Germany and Minnesota. Behav Genet 2019; 49:24-48. [PMID: 30499035 PMCID: PMC6326974 DOI: 10.1007/s10519-018-9938-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 11/08/2018] [Indexed: 10/31/2022]
Abstract
Increasing obesity is a world-wide health concern. Its most commonly used indicator, body mass index (BMI), consistently shows considerable genetic and shared environmental variance throughout life, the latter particularly in youth. Several adult studies have observed less total and genetically influenced variance with higher attained SES. These studies offer clues about sources of the 'obesity epidemic' but analogous youth studies of SES-of-origin are needed. Genetic and environmental influences and moderating effects of SES may vary in countries with different health policies, lifestyles, and degrees/sources of social inequality, offering further clues to the sources of the obesity epidemic. We examined SES-of-origin moderation of BMI variance in the German TwinLife study's cohorts assessed around ages 5, 11, 17, and 23-24, and in the Minnesota Twin Family Study's (MTFS) 11- and 17-year-old birth cohorts assessed longitudinally around ages 11, 17, and 23-24, comparing male and female twins and their parents. Age for age, both sexes' means and variances were greater in MTFS than in TwinLife. We observed that SES generally moderated genetic influences, more strongly in females, similar to most adult studies of attained-SES moderation of BMI. We interpreted differences in our SES-of-origin observations in light of inevitably-missing covariance between SES-of-origin and BMI in the models, mother-father and parent-offspring BMI correlations, and parental attained-SES-BMI correlations. We suggest that one source of the present obesity epidemic is social change that amplifies expression of genes both constraining SES attainment and facilitating weight gain.
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Affiliation(s)
- Wendy Johnson
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
| | - Elisabeth Hahn
- Department of Psychology, Saarland University, Saarbrücken, Germany
| | - Juliana Gottschling
- Cognitive Science & Assessment, University of Luxembourg, Luxembourg City, Luxembourg
| | - Franziska Lenau
- Jugendwerk St. Josef - Haus Maria Rosenberg, Waldfischbach-Burgalben, Germany
| | - Frank M Spinath
- Department of Psychology, Saarland University, Saarbrücken, Germany
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, USA
- Department of Epidemiology, Biostatistics and Biodemography, University of Southern, Denmark, Odense, Denmark
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Trinh I, Gluscencova OB, Boulianne GL. An in vivo screen for neuronal genes involved in obesity identifies Diacylglycerol kinase as a regulator of insulin secretion. Mol Metab 2018; 19:13-23. [PMID: 30389349 PMCID: PMC6323187 DOI: 10.1016/j.molmet.2018.10.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 09/26/2018] [Accepted: 10/15/2018] [Indexed: 12/31/2022] Open
Abstract
Objective Obesity is a complex disorder involving many genetic and environmental factors that are required to maintain energy homeostasis. While studies in human populations have led to significant progress in the generation of an obesity gene map and broadened our understanding of the genetic basis of common obesity, there is still a large portion of heritability and etiology that remains unknown. Here, we have used the genetically tractable fruit fly, Drosophila melanogaster, to identify genes/pathways that function in the nervous system to regulate energy balance. Methods We performed an in vivo RNAi screen in Drosophila neurons and assayed for obese or lean phenotypes by measuring changes in levels of stored fats (in the form of triacylglycerides or TAG). Three rounds of screening were performed to verify the reproducibility and specificity of the adiposity phenotypes. Genes that produced >25% increase in TAG (206 in total) underwent a second round of screening to verify their effect on TAG levels by retesting the same RNAi line to validate the phenotype. All remaining hits were screened a third time by testing the TAG levels of additional RNAi lines against the genes of interest to rule out any off-target effects. Results We identified 24 genes including 20 genes that have not been previously associated with energy homeostasis. One identified hit, Diacylglycerol kinase (Dgk), has mammalian homologues that have been implicated in genome-wide association studies for metabolic defects. Downregulation of neuronal Dgk levels increases TAG and carbohydrate levels and these phenotypes can be recapitulated by reducing Dgk levels specifically within the insulin-producing cells that secrete Drosophila insulin-like peptides (dILPs). Conversely, overexpression of kinase-dead Dgk, but not wild-type, decreased circulating dILP2 and dILP5 levels resulting in lower insulin signalling activity. Despite having higher circulating dILP levels, Dgk RNAi flies have decreased pathway activity suggesting that they are insulin-resistant. Conclusion Altogether, we have identified several genes that act within the CNS to regulate energy homeostasis. One of these, Dgk, acts within the insulin-producing cells to regulate the secretion of dILPs and energy homeostasis in Drosophila. RNAi screen in neurons identifies 24 regulators of energy homeostasis. One of the hits, Dgk, affects lipid and carbohydrate homeostasis. Dgk acts within the IPCs to regulate dILP secretion and insulin signalling activity.
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Affiliation(s)
- Irene Trinh
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Canada; Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, M5G 0A6, Canada.
| | - Oxana B Gluscencova
- Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, M5G 0A6, Canada.
| | - Gabrielle L Boulianne
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Canada; Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, M5G 0A6, Canada.
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11
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Almeida SM, Furtado JM, Mascarenhas P, Ferraz ME, Ferreira JC, Monteiro MP, Vilanova M, Ferraz FP. Association between LEPR, FTO, MC4R, and PPARG-2 polymorphisms with obesity traits and metabolic phenotypes in school-aged children. Endocrine 2018; 60:466-478. [PMID: 29679223 PMCID: PMC5937906 DOI: 10.1007/s12020-018-1587-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 03/19/2018] [Indexed: 02/07/2023]
Abstract
PURPOSE Evaluate the relationship of leptin receptor (LEPR) rs1137101, fat mass obesity-associated (FTO) receptors 9939609, melanocortin-4 receptors (MC4R) rs2229616 and rs17782313, and proliferator-activated receptor-gamma (PPARG) rs1801282 with clinical and metabolic phenotypes in prepubertal children. RESEARCH QUESTION What is the effect of polymorphisms on clinical and metabolic phenotypes in prepubertal children? METHODS A cross-sectional descriptive study was performed to evaluate anthropometric features, percentage body fat (%BF), biochemical parameters, and genotype in 773 prepubertal children. RESULTS FTO rs9939609 was associated with an increase in body mass index (BMI) and BMI z-score (zBMI). MC4R rs17782313 was associated with a decrease in BMI and +0.06 units in zBMI. LEPR, and PPARG-2 polymorphisms were associated with decreases in BMI and an increase and decrease units in zBMI, respectively. The homozygous SNPs demonstrated increases (FTO rs993609 and MC4R rs17782313) and decreases (LEPR rs1137101, PPARG rs1801282) in zBMI than the homozygous form of the major allele. In the overweight/obese group, the MC4R rs17782313 CC genotype showed higher average weight, zBMI, waist circumference, waist-circumference-to-height ratio, and waist-hip ratio, and lower BMI, mid-upper arm circumference, calf circumference, and %BF (P< 0.05). FTO rs9939609 AT and AA genotypes were associated with lower triglycerides (P < 0.05). CONCLUSIONS We showed that MC4R rs17782313 and FTO rs9939609 were positively associated with zBMI, with weak and very weak effects, respectively, suggesting a very scarce contribution to childhood obesity. LEPR rs1137101 and PPARG-2 rs1801282 had weak and medium negative effects on zBMI, respectively, and may slightly protect against childhood obesity.
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Affiliation(s)
- Sílvia M Almeida
- Centro de Genética Médica e Nutrição Pediátrica Egas Moniz, Campus Universitário, Monte da Caparica, Portugal.
- Instituto Universitário Egas Moniz, Campus Universitário, Monte da Caparica, Portugal.
| | - José M Furtado
- Centro de Genética Médica e Nutrição Pediátrica Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
- Instituto Universitário Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
| | - Paulo Mascarenhas
- Instituto Universitário Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
| | - Maria E Ferraz
- Centro de Genética Médica e Nutrição Pediátrica Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
| | - José C Ferreira
- Centro de Genética Médica e Nutrição Pediátrica Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
| | - Mariana P Monteiro
- Clinical and Experimental Endocrinology Group, Unit for Multidisciplinary Research in Biomedicine UMIB, ICBAS, University of Porto, Porto, Portugal
| | - Manuel Vilanova
- Instituto de Investigação e Inovação em Saúde, and IBMC-Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal
- Instituto de Ciências Biomédicas de Abel Salazar, Universidade do Porto, Porto, Portugal
| | - Fernando P Ferraz
- Centro de Genética Médica e Nutrição Pediátrica Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
- Instituto Universitário Egas Moniz, Campus Universitário, Monte da Caparica, Portugal
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12
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Hatoum AS, Rhee SH, Corley RP, Hewitt JK, Friedman NP. Etiology of Stability and Growth of Internalizing and Externalizing Behavior Problems Across Childhood and Adolescence. Behav Genet 2018; 48:298-314. [PMID: 29679193 DOI: 10.1007/s10519-018-9900-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Accepted: 04/12/2018] [Indexed: 10/17/2022]
Abstract
Internalizing and externalizing behaviors are heritable, and show genetic stability during childhood and adolescence. Less work has explored how genes influence individual differences in developmental trajectories. We estimated ACE biometrical latent growth curve models for the Teacher Report Form (TRF) and parent Child Behavior Checklist (CBCL) internalizing and externalizing scales from ages 7 to 16 years in 408 twin pairs from the Colorado Longitudinal Twin Study. We found that Intercept factors were highly heritable for both internalizing and externalizing behaviors (a2 = .61-.92), with small and nonsignificant environmental influences for teacher-rated data but significant nonshared environmental influences for parent-rated data. There was some evidence of heritability of decline in internalizing behavior (Slopes for teacher and parent ratings), but the Slope genetic variance was almost entirely shared with that for the Intercept when different than zero. These results suggest that genetic effects on these developmental trajectories operate primarily on initial levels and stability, with no significant unique genetic influences for change. Finally, cross-rater analyses of the growth factor scores revealed moderate to large genetic and environmental associations between growth factors derived from parents' and teachers' ratings, particularly the Intercepts.
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Affiliation(s)
- Alexander S Hatoum
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA. .,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, 80309, USA.
| | - Soo Hyun Rhee
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, 80309, USA
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, 80309, USA
| | - John K Hewitt
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, 80309, USA
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, 80309, USA
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13
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Gong J, Nishimura KK, Fernandez-Rhodes L, Haessler J, Bien S, Graff M, Lim U, Lu Y, Gross M, Fornage M, Yoneyama S, Isasi CR, Buzkova P, Daviglus M, Lin DY, Tao R, Goodloe R, Bush WS, Farber-Eger E, Boston J, Dilks HH, Ehret G, Gu CC, Lewis CE, Nguyen KDH, Cooper R, Leppert M, Irvin MR, Bottinger EP, Wilkens LR, Haiman CA, Park L, Monroe KR, Cheng I, Stram DO, Carlson CS, Jackson R, Kuller L, Houston D, Kooperberg C, Buyske S, Hindorff LA, Crawford DC, Loos RJ, Le Marchand L, Matise TC, North KE, Peters U. Trans-ethnic analysis of metabochip data identifies two new loci associated with BMI. Int J Obes (Lond) 2018; 42:384-390. [PMID: 29381148 PMCID: PMC5876082 DOI: 10.1038/ijo.2017.304] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 11/03/2017] [Accepted: 11/21/2017] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population. SUBJECTS Using eligible participants from the Population Architecture using Genomics and Epidemiology consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models. RESULTS We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 10-7). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue. CONCLUSION Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.
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Affiliation(s)
- Jian Gong
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Katherine K. Nishimura
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Lindsay Fernandez-Rhodes
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jeffery Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Stephanie Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Misa Graff
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Unhee Lim
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Myron Gross
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Myriam Fornage
- Health Science Center, University of Texas, Austin, Texas, United States of America
| | - Sachiko Yoneyama
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Martha Daviglus
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, U United States of America SA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ran Tao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - William S. Bush
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Eric Farber-Eger
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jonathan Boston
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Holli H. Dilks
- Sarah Cannon Research Institute, Nashville, Tennessee, United States of America
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Division of Cardiology, Geneva University Hospital, Geneva, Switzerland
| | - C. Charles Gu
- Department of Biostatistics, Washington University, St. Louis, Missouri, United States of America
| | - Cora E. Lewis
- Department of Medicine, University of Alabama, Birmingham, Alabama, United States of America
| | - Khanh-Dung H. Nguyen
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard Cooper
- Preventive Medicine and Epidemiology, Loyola University, Chicago, Illinois, United States of America
| | - Mark Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama, Birmingham, Alabama, United States of America
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Lynne R. Wilkens
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Christopher A. Haiman
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Lani Park
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Kristine R. Monroe
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Iona Cheng
- Cancer Prevention Institute of California, Fremont, California, United States of America
| | - Daniel O. Stram
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Christopher S. Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Rebecca Jackson
- Department of Internal Medicine, Ohio State Medical Center, Columbus, Ohio, United States of America
| | - Lew Kuller
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Denise Houston
- Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Steven Buyske
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Dana C. Crawford
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Loic Le Marchand
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kari E. North
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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14
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Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Longitudinal Analysis of Genetic Susceptibility and BMI Throughout Adult Life. Diabetes 2018; 67:248-255. [PMID: 29212779 PMCID: PMC5780056 DOI: 10.2337/db17-1156] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/21/2017] [Indexed: 01/01/2023]
Abstract
Little is known about the genetic influence on BMI trajectory throughout adulthood. We created a genetic risk score (GRS) comprising 97 adult BMI-associated variants among 9,971 women and 6,405 men of European ancestry. Serial measures of BMI were assessed from 18 (women) or 21 (men) years to 85 years of age. We also examined BMI change in early (from 18 or 21 to 45 years of age), middle (from 45 to 65 years of age), and late adulthood (from 65 to 80 years of age). GRS was positively associated with BMI across all ages, with stronger associations in women than in men. The associations increased from early to middle adulthood, peaked at 45 years of age in men and at 60 years of age in women (0.91 and 1.35 kg/m2 per 10-allele increment, respectively) and subsequently declined in late adulthood. For women, each 10-allele increment in the GRS was associated with an average BMI gain of 0.54 kg/m2 in early adulthood, whereas no statistically significant association was found for BMI change in middle or late adulthood or for BMI change in any life period in men. Our findings indicate that genetic predisposition exerts a persistent effect on adiposity throughout adult life and increases early adulthood weight gain in women.
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Affiliation(s)
- Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yan Zheng
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lu Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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15
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Konttinen H, Llewellyn C, Silventoinen K, Joensuu A, Männistö S, Salomaa V, Jousilahti P, Kaprio J, Perola M, Haukkala A. Genetic predisposition to obesity, restrained eating and changes in body weight: a population-based prospective study. Int J Obes (Lond) 2017; 42:858-865. [PMID: 29158543 DOI: 10.1038/ijo.2017.278] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 10/09/2017] [Accepted: 10/30/2017] [Indexed: 01/06/2023]
Abstract
OBJECTIVES There is no consensus on whether cognitive control over food intake (that is, restrained eating) is helpful, merely ineffective or actually harmful in weight management. We examined the interplay between genetic risk of obesity, restrained eating and changes in body weight and size. METHODS Participants were Finnish aged 25-74 years who attended the DIetary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome study at baseline in 2007 and follow-up in 2014. At baseline (n=5024), height, weight and waist circumference (WC) were measured in a health examination and participants self-reported their weight at age 20 years. At follow-up (n=3735), height, weight and WC were based on measured or self-reported information. We calculated 7-year change in body mass index (BMI) and WC and annual weight change from age 20 years to baseline. Three-Factor Eating Questionnaire-R18 was used to assess restrained eating. Genetic risk of obesity was assessed by calculating a polygenic risk score of 97 known BMI-related loci. RESULTS Cross-lagged autoregressive models indicated that baseline restrained eating was unrelated to 7-year change in BMI (β=0.00; 95% confidence interval (CI)=-0.01, 0.02). Instead, higher baseline BMI predicted greater 7-year increases in restrained eating (β=0.08; 95% CI=0.05, 0.11). Similar results were obtained with WC. Polygenic risk score correlated positively with restrained eating and obesity indicators in both study phases, but it did not predict 7-year change in BMI or WC. However, individuals with higher genetic risk of obesity tended to gain more weight from age 20 years to baseline, and this association was more pronounced in unrestrained eaters than in restrained eaters (P=0.038 for interaction). CONCLUSIONS Our results suggest that restrained eating is a marker for previous weight gain rather than a factor that leads to future weight gain in middle-aged adults. Genetic influences on weight gain from early to middle adulthood may vary according to restrained eating, but this finding needs to be replicated in future studies.
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Affiliation(s)
- H Konttinen
- Department of Social Research, University of Helsinki, Helsinki, Finland.,Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - C Llewellyn
- Department of Behavioural Science and Health, University College London, London, UK
| | - K Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
| | - A Joensuu
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - S Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - V Salomaa
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - P Jousilahti
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - J Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland
| | - M Perola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.,Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland.,Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - A Haukkala
- Department of Social Research, University of Helsinki, Helsinki, Finland
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16
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CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits. Nat Commun 2017; 8:744. [PMID: 28963451 PMCID: PMC5622064 DOI: 10.1038/s41467-017-00556-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 07/10/2017] [Indexed: 12/31/2022] Open
Abstract
There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m2). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (P = 2.5 × 10−10, 6.0 × 10−5, and 2.9 × 10−3). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders. Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remained largely unknown. Here, Kutalik and co-workers perform a large-scale genome-wide meta-analysis of structural variation and find rare CNVs associated with height, weight and BMI with large effect sizes.
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17
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The importance of gene-environment interactions in human obesity. Clin Sci (Lond) 2017; 130:1571-97. [PMID: 27503943 DOI: 10.1042/cs20160221] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/23/2016] [Indexed: 12/16/2022]
Abstract
The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.
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Genetic association of FTO/IRX region with obesity and overweight in the Polish population. PLoS One 2017; 12:e0180295. [PMID: 28662178 PMCID: PMC5491248 DOI: 10.1371/journal.pone.0180295] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 06/13/2017] [Indexed: 12/29/2022] Open
Abstract
Background/Objectives Genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI) in many different populations. Variants in the FTO locus are reported to be one of the strongest genetic predictors of obesity. Recent publications pointed also to a topologically associated domain (TAD) which is identified as a novel region affecting BMI. The TAD area encompasses the IRXB cluster (IRX3, IRX5, IRX6), FTO and RPGRIP1L genes. Subjects/Methods In this study, we investigated the relationship between variation of the FTO and IRX genes and obesity in Poles. We presented a case—control association analysis (normal versus overweight and/or obesity group) of Polish adult individuals (N = 5418). We determined whether or not the chromosomal region 16:53 500 000–55 500 000 contains polymorphic variants which are correlated with BMI in Polish population, including sex and age stratified analysis. Results The obtained results showed that the problem of weight-height abnormalities differently affects populations of Polish women and men (χ2 = 187.1; p<0.0001). From 353 SNPs enrolled to this study, 86 were statistically significant (highest χ2 = 15.72; p = 7.35E-05 observed for rs1558902). Linkage disequilibrium (LD) analysis revealed 61 blocks in the tested region of chromosome 16, with 24 SNPs located within the same block (block 8) of approximately 40 kb, in almost complete LD (|D’|>0.98, r2>0.80). We confirmed presence of the genetic susceptibility loci located in intron 1 of the FTO gene, which were correlated with BMI in our study group. For the first time, our analyses revealed strong association of FTO intronic variants (block 8) with overweight in group of men only. We have also identified association of the IRX region with overweight and/or obesity in Polish individuals. Conclusion Our study demonstrated how tested SNPs make differential contributions to obesity and overweight risk. We revealed sex dependent differences in the distribution of tested loci which are associated with BMI in the population of Poles.
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Fernández-Rhodes L, Gong J, Haessler J, Franceschini N, Graff M, Nishimura KK, Wang Y, Highland HM, Yoneyama S, Bush WS, Goodloe R, Ritchie MD, Crawford D, Gross M, Fornage M, Buzkova P, Tao R, Isasi C, Avilés-Santa L, Daviglus M, Mackey RH, Houston D, Gu CC, Ehret G, Nguyen KDH, Lewis CE, Leppert M, Irvin MR, Lim U, Haiman CA, Le Marchand L, Schumacher F, Wilkens L, Lu Y, Bottinger EP, Loos RJL, Sheu WHH, Guo X, Lee WJ, Hai Y, Hung YJ, Absher D, Wu IC, Taylor KD, Lee IT, Liu Y, Wang TD, Quertermous T, Juang JMJ, Rotter JI, Assimes T, Hsiung CA, Chen YDI, Prentice R, Kuller LH, Manson JE, Kooperberg C, Smokowski P, Robinson WR, Gordon-Larsen P, Li R, Hindorff L, Buyske S, Matise TC, Peters U, North KE. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci. Hum Genet 2017; 136:771-800. [PMID: 28391526 PMCID: PMC5485655 DOI: 10.1007/s00439-017-1787-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 03/23/2017] [Indexed: 11/26/2022]
Abstract
Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jian Gong
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mariaelisa Graff
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katherine K Nishimura
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yujie Wang
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heather M Highland
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sachiko Yoneyama
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William S Bush
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| | - Marylyn D Ritchie
- Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - Dana Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Myriam Fornage
- Center for Human Genetics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Petra Buzkova
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Ran Tao
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carmen Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Martha Daviglus
- Insitute of Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Rachel H Mackey
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Denise Houston
- Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Geneva University Hospital, Geneva, OH, Switzerland
| | - Khanh-Dung H Nguyen
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Cora E Lewis
- Department of Medicine, University of Alabama, Birmingham, AL, USA
| | - Mark Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | | | - Unhee Lim
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Fredrick Schumacher
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lynne Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yingchang Lu
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erwin P Bottinger
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth J L Loos
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wayne H-H Sheu
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yang Hai
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - I-Chien Wu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Yeheng Liu
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tzung-Dau Wang
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jyh-Ming J Juang
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Themistocles Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chao A Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan
| | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ross Prentice
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lewis H Kuller
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - JoAnn E Manson
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Paul Smokowski
- School of Social Welfare, The University of Kansas, Lawrence, KS, USA
| | - Whitney R Robinson
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lucia Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Steven Buyske
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, USA
| | - Tara C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Petersen JD, Kyvik KO, Heitmann BL, Vámosi ME. The association between parental separation during childhood and obesity in adulthood: a Danish twin study. Obes Sci Pract 2017; 2:436-443. [PMID: 28090349 PMCID: PMC5192531 DOI: 10.1002/osp4.79] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 09/13/2016] [Accepted: 10/01/2016] [Indexed: 11/08/2022] Open
Abstract
Objective The purpose of this study was to examine if parental separation during childhood is associated with obesity in adulthood. Methods A co‐twin case–control study of 146 adult same‐sexed twin pairs with discordant body mass index (BMI) (i.e. one of the twins should have a BMI of 20–25 kg/m2, and the co‐twin's BMI ≥ 30 kg/m2) was selected from Danish Twin Registry (DTR). In total of 236 eligible twin individuals participated in the study. Childhood parental separation (defined as separation from one of the biological parents, regardless of the reason for separation) for at least one year prior to age 17 was self‐reported. The statistical analysis includes logistic and linear regression models using STATA 13.0. Results There were no differences in the odds of developing obesity in adulthood between the twin who stayed with a father and the co‐twin who was separated from him for at least 1 year prior to age 17 [OR = 1.22, 95%CI (0.46–3.34), p = 0.65]. Separation from a mother also showed no differences in the odds for developing obesity [OR = 0.90, 95%CI (0.32–2.46), p = 0.82]. Conclusions Because of the limited number of discordant twin pairs for childhood parental separation, we cannot provide evidence to suggest that separation from parents in childhood was associated with developing obesity in adulthood. Further studies of pooling discordant twins from several countries should be considered.
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Affiliation(s)
- J. D. Petersen
- Research Unit for General Practice, Department of Public HealthUniversity of Southern DenmarkOdense CDenmark
- Research Unit for Dietary Studies at the Parker InstituteBispebjerg and Frederiksberg HospitalCopenhagenDenmark
| | - K. O. Kyvik
- Department of Clinical ResearchUniversity of Southern DenmarkOdenseDenmark
- Odense Patient Data Explorative NetworkOdense University HospitalOdenseDenmark
- The Danish Twin Registry, Epidemiology, Biostatistics and Biodemography, Institute of Public HealthUniversity of Southern DenmarkOdenseDenmark
| | - B. L. Heitmann
- Research Unit for Dietary Studies at the Parker InstituteBispebjerg and Frederiksberg HospitalCopenhagenDenmark
- The Boden Institute of Obesity, Nutrition Exercise and Eating DisordersUniversity of SydneyAustralia
- The National Institute of Public HealthUniversity of Southern DenmarkDenmark
- Department of Public Health, Section for General PracticeUniversity of CopenhagenDenmark
| | - M. E. Vámosi
- Institute of Public Health, Department of Nursing ScienceUniversity of AarhusDenmark
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Li A, Teo KK, Morrison KM, McDonald SD, Atkinson SA, Anand SS, Meyre D. A genetic link between prepregnancy body mass index, postpartum weight retention, and offspring weight in early childhood. Obesity (Silver Spring) 2017; 25:236-243. [PMID: 27883278 DOI: 10.1002/oby.21707] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 08/22/2016] [Accepted: 09/09/2016] [Indexed: 02/04/2023]
Abstract
OBJECTIVE The effects of maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG) on maternal and offspring obesity traits, as well as the maternal and offspring genetic contribution to GWG and postpartum weight retention, were examined. METHODS Blood samples from mothers (n = 608) and offspring (n = 541) were genotyped for 83 BMI-associated SNPs and 47 waist-to-hip ratio (WHR)-associated SNPs. Linear regression and mixed-effects regression models were performed to examine clinical epidemiological and genetic associations with unweighted and weighted BMI and WHR genetic risk scores (GRS). RESULTS Prepregnancy BMI was positively associated with offspring weight and BMI Z-score from birth to 5 years. GWG was positively associated with maternal postpartum weight retention at 1 and 5 years and with offspring weight Z-score from birth to 5 years old. The maternal unweighted BMI GRS was associated with prepregnancy BMI, postpartum weight retention at 5 years, and offspring weight Z-score from birth to 5 years old, but not associated with GWG. Both maternal and offspring unweighted WHR GRSs were negatively associated with GWG. CONCLUSIONS Maternal BMI-associated SNPs may contribute to the genetic link between prepregnancy BMI variation, long-term postpartum weight retention, and offspring birth weight and longitudinal weight. Maternal and offspring WHR-associated SNPs may contribute to GWG variation.
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Affiliation(s)
- Aihua Li
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Koon K Teo
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Katherine M Morrison
- Department of Pediatrics, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Sarah D McDonald
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
| | - Stephanie A Atkinson
- Department of Pediatrics, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Sonia S Anand
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
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Pek SLT, Sum CF, Lin MX, Cheng AKS, Wong MTK, Lim SC, Tavintharan S. Circulating and visceral adipose miR-100 is down-regulated in patients with obesity and Type 2 diabetes. Mol Cell Endocrinol 2016; 427:112-23. [PMID: 26973292 DOI: 10.1016/j.mce.2016.03.010] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 03/04/2016] [Accepted: 03/08/2016] [Indexed: 11/30/2022]
Abstract
Obesity is a major public health problem conferring substantial excess risk for Type 2 diabetes (T2D). The role of microRNAs (miRNAs) in obesity and adipose tissue is not clearly defined. We hypothesize that circulating miRNA expression profiles vary according to differences in body mass index (BMI) and T2D and circulating miRNAs may reflect adipose tissue expression. Compared to healthy, lean individuals, circulating miR-100 was significantly lower in obese normoglycemic subjects and subjects with T2D. In visceral adipose tissue, expression of miR-100 was lower from obese subjects with T2D compared to obese subjects without T2D. miR-100 expression was significantly lower after adipogenic induction in human visceral, subcutaneous adipocytes and 3T3-L1 adipocytes. miR-100 reduced expression of mammalian target of rapamycin (mTOR) and Insulin Growth Factor Receptor (IGFR) directly. Differentiation of 3T3-L1 was accelerated by inhibition of miR-100 and reduced by miR-100 mimic transfection. Our data provide the first evidence of an association of circulating miR-100 with obesity and diabetes. Additionally, our in-vitro findings, and the miR-100 expression patterns in site-specific adipose tissue suggest miR-100 to modulate IGFR, mTOR and mediate adipogenesis.
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Affiliation(s)
| | - Chee Fang Sum
- Diabetes Centre, Khoo Teck Puat Hospital, 768828, Singapore; Division of Endocrinology, Khoo Teck Puat Hospital, 768828, Singapore.
| | | | | | | | - Su Chi Lim
- Clinical Research Unit, Khoo Teck Puat Hospital, 768828, Singapore; Diabetes Centre, Khoo Teck Puat Hospital, 768828, Singapore; Division of Endocrinology, Khoo Teck Puat Hospital, 768828, Singapore.
| | - Subramaniam Tavintharan
- Clinical Research Unit, Khoo Teck Puat Hospital, 768828, Singapore; Diabetes Centre, Khoo Teck Puat Hospital, 768828, Singapore; Division of Endocrinology, Khoo Teck Puat Hospital, 768828, Singapore.
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McGuire AB, Rafi SK, Manzardo AM, Butler MG. Morphometric Analysis of Recognized Genes for Autism Spectrum Disorders and Obesity in Relationship to the Distribution of Protein-Coding Genes on Human Chromosomes. Int J Mol Sci 2016; 17:E673. [PMID: 27164088 PMCID: PMC4881499 DOI: 10.3390/ijms17050673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 04/11/2016] [Accepted: 04/28/2016] [Indexed: 12/20/2022] Open
Abstract
Mammalian chromosomes are comprised of complex chromatin architecture with the specific assembly and configuration of each chromosome influencing gene expression and function in yet undefined ways by varying degrees of heterochromatinization that result in Giemsa (G) negative euchromatic (light) bands and G-positive heterochromatic (dark) bands. We carried out morphometric measurements of high-resolution chromosome ideograms for the first time to characterize the total euchromatic and heterochromatic chromosome band length, distribution and localization of 20,145 known protein-coding genes, 790 recognized autism spectrum disorder (ASD) genes and 365 obesity genes. The individual lengths of G-negative euchromatin and G-positive heterochromatin chromosome bands were measured in millimeters and recorded from scaled and stacked digital images of 850-band high-resolution ideograms supplied by the International Society of Chromosome Nomenclature (ISCN) 2013. Our overall measurements followed established banding patterns based on chromosome size. G-negative euchromatic band regions contained 60% of protein-coding genes while the remaining 40% were distributed across the four heterochromatic dark band sub-types. ASD genes were disproportionately overrepresented in the darker heterochromatic sub-bands, while the obesity gene distribution pattern did not significantly differ from protein-coding genes. Our study supports recent trends implicating genes located in heterochromatin regions playing a role in biological processes including neurodevelopment and function, specifically genes associated with ASD.
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Affiliation(s)
| | | | - Ann M Manzardo
- Departments of Psychiatry & Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, KS 66160, USA.
| | - Merlin G Butler
- Departments of Psychiatry & Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, KS 66160, USA.
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Liao C, Gao W, Cao W, Lv J, Yu C, Wang S, Zhou B, Pang Z, Cong L, Dong Z, Wu F, Wang H, Wu X, Jiang G, Wang X, Wang B, Li L. The association of cigarette smoking and alcohol drinking with body mass index: a cross-sectional, population-based study among Chinese adult male twins. BMC Public Health 2016; 16:311. [PMID: 27068329 PMCID: PMC4827244 DOI: 10.1186/s12889-016-2967-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Accepted: 03/17/2016] [Indexed: 01/10/2023] Open
Abstract
Background Obesity is a multifactorial abnormality which has an underlying genetic control but requires environmental influences to trigger. Numerous epidemiological studies have examined the roles of physical inactivity and dietary factors in obesity development. Interactions between obesity-related genes and these lifestyles have also been confirmed. However, less attention has been paid to these complex relationship between cigarette smoking, alcohol drinking and obesity. The purpose of this study was to assess whether cigarette smoking and alcohol drinking were associated with body mass index (BMI), and whether these lifestyle factors modified the genetic variance of BMI. Methods Subjects were twins recruited through the Chinese National Twin Registry, aged 18 to 79 years, and the sample comprised 6121 complete male twin pairs. Information on height, weight, cigarette smoking and alcohol drinking status were assessed with self-report questionnaires. The associations of cigarette smoking and alcohol drinking with BMI were evaluated by linear regression models. Further, structure equation models were conducted to estimate whether cigarette smoking and alcohol drinking status modified the degree of genetic variance of BMI. Results After adjustment for a variety of socio-demographic and lifestyle factors, former smokers had higher BMI (β = 0.475; 95 % CI, 0.196 to 0.754) whereas moderate to heavy smokers had lower BMI (β = −0.115; 95 % CI, −0.223 to −0.007) when compared with nonsmokers. BMI decreased with increased cigarette pack-years (β = −0.008; 95 % CI, −0.013 to −0.003). These effects still existed substantially in within-MZ twin pair analyses. By contrast, current alcohol drinking had no significant influence on BMI when additionally controlled for shared factors in within-pair analyses. Genetic modification by alcohol drinking was statistically significant for BMI (β = −0.137; 95 % CI, −0.215 to −0.058), with the intake of alcohol decreasing the additive genetic component of BMI. Conclusions Cigarette smoking was negatively associated with BMI independent of genetic influences. The influence of genes on BMI was moderated by alcohol drinking, such that for individuals who were regular drinkers, genetic factors became less influential. Our findings highlight gene-alcohol interaction in finding candidate genes of BMI and elucidating the etiological factors of obesity.
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Affiliation(s)
- Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Shengfeng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Bin Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zengchang Pang
- Qingdao Center for Diseases Control and Prevention, Qingdao, 266033, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Zhong Dong
- Beijing Center for Disease Control and Prevention, Beijing, 100013, China
| | - Fan Wu
- Shanghai Center for Disease Control and Prevention, Shanghai, 200336, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Guohong Jiang
- Tianjin Center for Disease Control and Prevention, Tianjin, 300011, China
| | - Xiaojie Wang
- Qinghai Center for Disease Control and Prevention, Xining, 810007, China
| | - Binyou Wang
- Harbin Medical University, Harbin, 150081, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
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Li S, Kyvik KO, Pang Z, Zhang D, Duan H, Tan Q, Hjelmborg J, Kruse T, Dalgård C. Genetic and Environmental Regulation on Longitudinal Change of Metabolic Phenotypes in Danish and Chinese Adult Twins. PLoS One 2016; 11:e0148396. [PMID: 26862898 PMCID: PMC4749287 DOI: 10.1371/journal.pone.0148396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 01/18/2016] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The rate of change in metabolic phenotypes can be highly indicative of metabolic disorders and disorder-related modifications. We analyzed data from longitudinal twin studies on multiple metabolic phenotypes in Danish and Chinese twins representing two populations of distinct ethnic, cultural, social-economic backgrounds and geographical environments. MATERIALS AND METHODS The study covered a relatively large sample of 502 pairs of Danish adult twins followed up for a long period of 12 years with a mean age at intake of 38 years (range: 18-65) and a total of 181 Chinese adult twin pairs traced for about 7 years with a mean baseline age of 39.5 years (range: 23-64). The classical twin models were fitted to the longitudinal change in each phenotype (Δphenotype) to estimate the genetic and environmental contributions to the variation in Δphenotype. RESULTS Moderate to high contributions by the unique environment were estimated for all phenotypes in both Danish (from 0.51 for low density lipoprotein cholesterol up to 0.72 for triglycerides) and Chinese (from 0.41 for triglycerides up to 0.73 for diastolic blood pressure) twins; low to moderate genetic components were estimated for long-term change in most of the phenotypes in Danish twins except for triglycerides and hip circumference. Compared with Danish twins, the Chinese twins tended to have higher genetic control over the longitudinal changes in lipids (except high density lipoprotein cholesterol) and glucose, higher unique environmental contribution to blood pressure but no genetic contribution to longitudinal change in body mass traits. CONCLUSION Our results emphasize the major contribution of unique environment to the observed intra-individual variation in all metabolic phenotypes in both samples, and meanwhile reveal differential patterns of genetic and common environmental regulation on changes over time in metabolic phenotypes across the two samples.
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Affiliation(s)
- Shuxia Li
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- * E-mail:
| | - Kirsten Ohm Kyvik
- Department of Clinical Research, University of Southern Denmark, and Odense Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Zengchang Pang
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Dongfeng Zhang
- Department of Public Health, Qingdao University Medical College, Qingdao, China
| | - Haiping Duan
- Qingdao Center for Disease Control and Prevention, Qingdao, China
| | - Qihua Tan
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Jacob Hjelmborg
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Torben Kruse
- Unit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Christine Dalgård
- Environmental Medicine, Department of Public Health, University of Southern Denmark, Odense, Denmark
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Kronenberg F, Paulweber B, Lamina C. [Genomwide association studies on obesity: what can we learn from these studies]. Wien Med Wochenschr 2016; 166:88-94. [PMID: 26795628 DOI: 10.1007/s10354-015-0429-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 12/30/2015] [Indexed: 01/10/2023]
Abstract
The introduction of genome-wide association studies resulted in a tremendous increase in the number of genes associated with obesity and related phenotypes (BMI, waist and waist-hip-ratio). Despite this enormous gain in knowledge the search for genes is only started since only a small fraction of the heritability of these phenotypes is explained yet: each single gene of the 97 hitherto known BMI-associated genes and 49 waist-hip-ratio-associated genes explains only a tiny fraction of the variance of these phenotypes. Sex-specific differences are mainly known for waist-hip-ratio and ̴40% of the genes showed only an effect in women but no or a markedly smaller effect in men. The functional characterization of the identified genes will take a lot of time. It is unclear whether and how fast the findings will result in therapeutic consequences. It is of utmost importance that we understand the involved mechanisms before new therapeutic strategies can be developed.
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Affiliation(s)
- Florian Kronenberg
- Division für Genetische Epidemiologie, Department für Medizinische Genetik, Molekulare and Klinische Pharmakologie, Medizinische Universität Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Österreich.
| | - Bernhard Paulweber
- Universitätsklinik für Innere Medizin I der Paracelsus Medizinischen Privatuniversität Salzburg, St. Johanns-Spital, Müllner Hauptstraße 48, 5020, Salzburg, Österreich
| | - Claudia Lamina
- Division für Genetische Epidemiologie, Department für Medizinische Genetik, Molekulare and Klinische Pharmakologie, Medizinische Universität Innsbruck, Schöpfstr. 41, 6020, Innsbruck, Österreich
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Moreira OC, Oliveira CEPD, Matos DGD, Hickner RC, Oliveira RARD, Aidar FJ, Rodríguez-Gázquez MDLÁ. Prevalence of coronary heart disease risk factors in physical education students. MOTRIZ: REVISTA DE EDUCACAO FISICA 2015. [DOI: 10.1590/s1980-65742015000400011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Abstract To establish the prevalence of coronary risk in physical education students, and compare risk between the genders and the years of course. We evaluated 246 physical education students using RISKO questionnaire to determine eight risk factors: age, heredity, body weight, smoking, physical inactivity, hypercholesterolemia, hypertension and sex. Students had mean coronary risk score of 16.03 ± 3.52 points, rated "below-average risk." Men had significantly greater risk compared to women. No difference was found between the years of course. The prevalence of risk factors were heritability (58.37%), physical inactivity (32.65%), hypercholesterolemia (32.24%), overweight (27.35%), smoking (3.67%) and hypertension (2.45%). The coronary risk of physical education students was rated as below average, being higher among men than women, and no difference in risk between years of course. The most prevalent risk factors were heredity, physical inactivity, overweight and hypercholesterolemia.
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Obesity, More than a ‘Cosmetic’ Problem. Current Knowledge and Future Prospects of Human Obesity Genetics. Biochem Genet 2015; 54:1-28. [DOI: 10.1007/s10528-015-9700-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 10/30/2015] [Indexed: 12/17/2022]
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Liao C, Gao W, Cao W, Lv J, Yu C, Wang S, Zhou B, Pang Z, Cong L, Wang H, Wu X, Li L. Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study. PLoS One 2015; 10:e0140595. [PMID: 26556598 PMCID: PMC4640552 DOI: 10.1371/journal.pone.0140595] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 09/27/2015] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To quantitate and compare the associations of various body composition measurements with serum metabolites and to what degree genetic or environmental factors affect obesity-metabolite relation. METHODS Body mass index (BMI), waist circumference (WC), lean body mass (LBM), percent body fat (PBF), fasting serum high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), glucose, insulin and lifestyle factors were assessed in 903 twins from Chinese National Twin Registry (CNTR). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting serum glucose and insulin. Linear regression models and bivariate structural equation models were used to examine the relation of various body composition measurements with serum metabolite levels and genetic/environmental influences on these associations, respectively. RESULTS At individual level, adiposity measurements (BMI, WC and PBF) showed significant associations with serum metabolite concentrations in both sexes and the associations still existed in male twins when using within-MZ twin pair comparison analyses. Associations of BMI with TG, insulin and HOMA-IR were significantly stronger in male twins compared to female twins (BMI-by-sex interaction p = 0.043, 0.020 and 0.019, respectively). Comparison of various adiposity measurements with levels of serum metabolites revealed that WC explained the largest fraction of variance in serum LDL-C, TG, TC and glucose concentrations while BMI performed best in explaining variance in serum HDL-C, insulin and HOMA-IR levels. Of these phenotypic correlations, 64-81% were attributed to genetic factors, whereas 19-36% were attributed to unique environmental factors. CONCLUSIONS We observed different associations between adiposity and serum metabolite profile and demonstrated that WC and BMI explained the largest fraction of variance in serum lipid profile and insulin resistance, respectively. To a large degree, shared genetic factors contributed to these associations with the remaining explained by twin-specific environmental factors.
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Affiliation(s)
- Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- * E-mail: (LML); (WJG)
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- 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
| | - Shengfeng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Bin Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Diseases Control and Prevention, Qingdao, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- * E-mail: (LML); (WJG)
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Heritability of gestational weight gain--a Swedish register-based twin study. Twin Res Hum Genet 2015; 18:410-8. [PMID: 26111621 DOI: 10.1017/thg.2015.38] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Gestational weight gain (GWG) is a complex trait involving intrauterine environmental, maternal environmental, and genetic factors. However, the extent to which these factors contribute to the total variation in GWG is unclear. We therefore examined the genetic and environmental influences on the variation in GWG in the first and second pregnancy in monozygotic (MZ) and dizygotic (DZ) twin mother-pairs. Further, we explored if any co-variance existed between factors influencing the variation in GWG of the mothers’ first and second pregnancies. By using Swedish nationwide record-linkage data, we identified 694 twin mother-pairs with complete data on their first pregnancy and 465 twin mother-pairs with complete data on their second pregnancy during 1982–2010. For a subanalysis, 143 twin mother-pairs had complete data on two consecutive pregnancies during the study period. We used structural equation modeling (SEM) to assess the contribution of genetic, shared, and unique environmental factors to the variation in GWG. A bivariate Cholesky decomposition model was used for the subanalysis. We found that genetic factors explained 43% (95% CI: 36–51%) of the variation in GWG in the first pregnancy and 26% (95% CI: 16–36%) in the second pregnancy. The remaining variance was explained by unique environmental factors. Both overlapping and distinct genetic and unique environmental factors influenced GWG in the first and the second pregnancy. This study showed that GWG has a moderate heritability, suggesting that a large part of the variation in the trait can be explained by unique environmental factors.
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Amaral WZ, Krueger RF, Ryff CD, Coe CL. Genetic and environmental determinants of population variation in interleukin-6, its soluble receptor and C-reactive protein: insights from identical and fraternal twins. Brain Behav Immun 2015; 49:171-81. [PMID: 26086344 PMCID: PMC4567498 DOI: 10.1016/j.bbi.2015.05.010] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 05/19/2015] [Accepted: 05/25/2015] [Indexed: 12/26/2022] Open
Abstract
Interleukin-6 and C-reactive protein are commonly assessed biomarkers linked to illness, obesity, and stressful life events. However, relatively little is known about their heritability. By comparing Caucasian twins from the Midlife in the US project (MIDUS), we estimated the heritability of IL-6, its soluble receptor, and CRP. Based on the hypothesis that adiposity might contribute more to IL-6 than to sIL-6r, we fit heritability models quantifying the extent to which each reflected genetic and environmental factors shared with obesity. Genetic influences on IL-6 and its receptor proved to be distinct. Further, the appearance of a heritable basis for IL-6 was mediated largely via shared paths with obesity. Supporting this conclusion, we confirmed that when unrelated adult controls are carefully matched to twin participants on BMI, age, gender and socioeconomic indices, their IL-6 is similar to the corresponding twins. In contrast, the effect of BMI on CRP was split between shared genetics and environmental influences. In conclusion, IL-6 is strongly affected by factors associated with obesity accounting for its lability and responsiveness to diet, life style and contemporaneous events.
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Affiliation(s)
- Wellington Z Amaral
- Department of Psychology, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Carol D Ryff
- Department of Psychology, University of Wisconsin-Madison, Madison, WI 53715, USA.
| | - Christopher L Coe
- Department of Psychology, University of Wisconsin-Madison, Madison, WI 53715, USA.
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Moreira OC, Oliveira RARD, Oliveira CEPD, Doimo LA, Amorim PRDS, Marins JCB. Anthropometric, cardiovascular and functional variables as indicators of health related physical fitness in university professors. FISIOTERAPIA EM MOVIMENTO 2015. [DOI: 10.1590/0103-5150.028.003.ao13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
AbstractObjective To verify the behavior of anthropometric, cardiovascular and functional variables as indicators of health-related physical fitness in university professors and perform a comparison of these variables between sexes.Materials and methods We conducted an observational epidemiological cross-sectional study in 145 professors (45.86 ± 9.7 years), 103 men (71.03%), which were evaluated by measuring heart rate (HR) and systolic (SBP) and diastolic (DBP) pressure at rest, body weight, height, body mass index (BMI), body fat percentage (BF%), handgrip strength (HGS), flexibility and cardiorespiratory fitness (CRF). We proceeded to the descriptive analysis, Student t-test for comparison between sexes and multiple regression analysis to verify the association between the variables analyzed. It was adopted a significance level of p < 0.05.Results The sex affected all variables. Women had better levels of BMI, flexibility, SBP and DBP. The BF% and CRF were associated with SBP and BMI in both sexes.Conclusion The behavior of anthropometric, cardiovascular and functional variables indicated unsatisfactory values for flexibility, HGS and BMI, with the worst levels among men. Furthermore, the variables that showed better association with HRPF were BF% and CRF.
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Huggins GS, Berger S, McCaffery JM. Can Genetics Modify the Influence of Healthy Lifestyle on Lipids in the Context of Obesity and Type 2 Diabetes? CURRENT CARDIOVASCULAR RISK REPORTS 2015. [DOI: 10.1007/s12170-015-0464-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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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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Changes in Weight and Cardiovascular Disease Risk Factors in Monozygotic Twins: The Healthy Twin Study. Twin Res Hum Genet 2015; 18:151-7. [DOI: 10.1017/thg.2014.90] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
We aimed to assess the non-genetic contribution to the associations between the change in weight and changes in cardiovascular disease (CVD) risk factors. This analysis included 194 Korean monozygotic (MZ) twin pairs (116 men, 272 women; mean age, 38.5 ± 6.8 years) who were first examined for weight and CVD risk factors (blood pressure (BP), glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL)) between December 2005 and December 2008, and returned for a repeat examination after 2.7 ± 0.9 years. The within-pair correlations were 0.21 for the change in weight and 0.05-0.42 for the changes in CVD risk factors. Bivariate analyses showed significant environmental correlations shared between the change in weight and the changes in CVD risk factors (p < .05), except for glucose, while there were no significant genetic effects shared between the phenotypes. After adjusting for baseline values of weight, smoking, and alcohol consumption, diastolic blood pressure (DBP), TG, TC, and LDL significantly increased by 1.6 mmHg, 0.09 mmol/L, 0.10 mmol/L, and 0.09 mmol/L, respectively, per 1 kg increase in within-pair differences in weight change. In Korean MZ twins, similarity between twins for changes in weight and CVD risk factors were small to moderate, and non-genetic factors were responsible for the associations between the change in weight and changes in DBP, TG, TC, and LDL.
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Butler MG, McGuire A, Manzardo AM. Clinically relevant known and candidate genes for obesity and their overlap with human infertility and reproduction. J Assist Reprod Genet 2015; 32:495-508. [PMID: 25631154 DOI: 10.1007/s10815-014-0411-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 12/11/2014] [Indexed: 10/24/2022] Open
Abstract
PURPOSE Obesity is a growing public health concern now reaching epidemic status worldwide for children and adults due to multiple problems impacting on energy intake and expenditure with influences on human reproduction and infertility. A positive family history and genetic factors are known to play a role in obesity by influencing eating behavior, weight and level of physical activity and also contributing to human reproduction and infertility. Recent advances in genetic technology have led to discoveries of new susceptibility genes for obesity and causation of infertility. The goal of our study was to provide an update of clinically relevant candidate and known genes for obesity and infertility using high resolution chromosome ideograms with gene symbols and tabular form. METHODS We used computer-based internet websites including PubMed to search for combinations of key words such as obesity, body mass index, infertility, reproduction, azoospermia, endometriosis, diminished ovarian reserve, estrogen along with genetics, gene mutations or variants to identify evidence for development of a master list of recognized obesity genes in humans and those involved with infertility and reproduction. Gene symbols for known and candidate genes for obesity were plotted on high resolution chromosome ideograms at the 850 band level. Both infertility and obesity genes were listed separately in alphabetical order in tabular form and those highlighted when involved with both conditions. RESULTS By searching the medical literature and computer generated websites for key words, we found documented evidence for 370 genes playing a role in obesity and 153 genes for human reproduction or infertility. The obesity genes primarily affected common pathways in lipid metabolism, deposition or transport, eating behavior and food selection, physical activity or energy expenditure. Twenty-one of the obesity genes were also associated with human infertility and reproduction. Gene symbols were plotted on high resolution ideograms and their name, precise chromosome band location and description were summarized in tabular form. CONCLUSIONS Meaningful correlations in the obesity phenotype and associated human infertility and reproduction are represented with the location of genes on chromosome ideograms along with description of the gene and position in tabular form. These high resolution chromosome ideograms and tables will be useful in genetic awareness and counseling, diagnosis and treatment to improve clinical outcomes.
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Affiliation(s)
- Merlin G Butler
- Departments of Psychiatry & Behavioral Sciences and Pediatrics, University of Kansas Medical Center, 3901 Rainbow Boulevard, MS 4015, Kansas City, KS, 66160, USA,
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Feng Y, Wang F, Pan H, Qiu S, Lü J, Wu L, Wang J, Lu C. Obesity-associated gene FTO rs9939609 polymorphism in relation to the risk of tuberculosis. BMC Infect Dis 2014; 14:592. [PMID: 25377722 PMCID: PMC4226896 DOI: 10.1186/s12879-014-0592-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Accepted: 10/27/2014] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Obesity is known to affect cell-mediated immune responses. Recent studies have revealed that genetic polymorphisms in the fat mass and obesity associated (FTO) gene are related to human obesity. We hypothesize that this gene may also play a role in the risk of immune-related infectious diseases such as tuberculosis. METHODS This case-control study included 1625 pulmonary tuberculosis cases and 1570 unaffected controls recruited from the Jiangsu province in China. Single nucleotide polymorphisms (SNPs), rs9939609 and rs8050136, in the FTO gene were genotyped using TaqMan allelic discrimination assays. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using the unconditional logistic regression model. RESULTS We observed a significant association between the genetic polymorphism rs9939609 and tuberculosis risk. Compared with the common genotype TT, individuals carrying AA had a significantly increased risk, with an OR of 3.77 (95% CI: 2.26-6.28). After adjusting for potential confounders, the relationship remains significant. An additive model showed that carriers of an allele A had a 26% increased risk of tuberculosis compared with the T allele (OR: 1.26, 95% CI: 1.08-1.48). Compared with the common haplotype rs9939609T-rs8050136C, the haplotype rs9939609A-rs8050136C was related to an increased risk of tuberculosis (OR = 6.09, 95% CI: 3.27-12.34). CONCLUSIONS The FTO polymorphism rs9939609 is associated with a risk of pulmonary tuberculosis in the Chinese population.
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Wehby GL, Prater KN, Ryckman KK, Kummet C, Murray JC. Candidate gene study for smoking, alcohol use, and body weight in a sample of pregnant women. J Matern Fetal Neonatal Med 2014; 28:804-11. [PMID: 25014319 DOI: 10.3109/14767058.2014.932768] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Prenatal smoking, alcohol use, and obesity have significant effects on maternal and fetal health. However, not much is known about the genetic contributions to these risk factors among pregnant women. We evaluate the associations between several candidate genes and smoking, alcohol use, pre-pregnancy body weight, and weight gain during pregnancy in a sample of pregnant women. METHODS The study analyzes a sample of about 1900 mothers from the Danish National Birth Cohort. We test the association between 1450 SNPs in/near 117 genes/loci and various risk factor measures. RESULTS Only a few SNPs in FTO were significantly associated with pre-pregnancy obesity and body mass index (4 and 2 SNPs, respectively) after SNP-level correction for multiple testing. A few loci were significantly related to various smoking measures (any smoking, quitting and cigarette number) with gene/locus-level correction for multiple testing, but not after SNP-level correction. Similarly, some loci were significant for the alcohol measures at the gene/locus-level but not at SNP-level correction. CONCLUSION The study suggests that the majority of the evaluated candidate genes may not play an important role in influencing these risk factors among pregnant women, highlighting the importance of other genetic factors and non-genetic contributors to their etiology.
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Tan Q, B Hjelmborg JV, Thomassen M, Jensen AK, Christiansen L, Christensen K, Zhao JH, Kruse TA. Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis. BMC Proc 2014; 8:S82. [PMID: 25519411 PMCID: PMC4144324 DOI: 10.1186/1753-6561-8-s1-s82] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.
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Affiliation(s)
- Qihua Tan
- Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark
| | - Jacob V B Hjelmborg
- Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark
| | - Mads Thomassen
- Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark
| | - Andreas Kryger Jensen
- Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark
| | - Lene Christiansen
- Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark
| | - Kaare Christensen
- Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark ; Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, J. B. Winsloews Vej 9B, 5000 Odense C, Denmark
| | - Jing Hua Zhao
- MRC Epidemiology Unit and Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Torben A Kruse
- Institute of Clinical Research, Unit of Human Genetics, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, Denmark
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Gilthorpe MS, Dahly DL, Tu YK, Kubzansky LD, Goodman E. Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures. J Dev Orig Health Dis 2014; 5:197-205. [PMID: 24901659 PMCID: PMC4098080 DOI: 10.1017/s2040174414000130] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2013] [Revised: 01/26/2014] [Accepted: 01/30/2014] [Indexed: 12/03/2022]
Abstract
Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance-covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance-covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models.
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Affiliation(s)
- M. S. Gilthorpe
- Division of Epidemiology & Biostatistics, School
of Medicine, University of Leeds,
Leeds, UK
| | - D. L. Dahly
- Department of Epidemiology and Public Health, University
College Cork, Cork, Ireland
| | - Y.-K. Tu
- Institute of Epidemiology & Preventive Medicine,
College of Public Health, National Taiwan
University, Taipei, Taiwan
| | - L. D. Kubzansky
- Department of Social and Behavioral Sciences, Harvard
School of Public Health, Boston,
MA, USA
| | - E. Goodman
- Mass General Hospital for Children, Department of
Pediatrics, Harvard Medical School,
Boston, MA, USA
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Abstract
Evolution has molded metabolic thrift within humans, a genetic heritage that, when thrust into our modern "obesogenic" environment, creates the current obesity crisis. Modern genetic analysis has identified genetic and epigenetic contributors to obesity, an understanding of which will guide the development of environmental, pharmacologic, and genetic therapeutic interventions.
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Silventoinen K, Tynelius P, Rasmussen F. Weight status in young adulthood and survival after cardiovascular diseases and cancer. Int J Epidemiol 2014; 43:1197-204. [PMID: 24733247 DOI: 10.1093/ije/dyu091] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Some studies have suggested that overweight is associated with lower mortality, but these results may be affected by reverse causality. We analysed how body mass index (BMI) in young adulthood is associated with mortality in the general population and after the diagnoses of coronary heart disease (CHD), stroke and cancer. METHODS BMI was measured at an average age of 18 years in 734 438 Swedish men born in 1950-65. Diagnoses of CHD, stroke and cancer as well as all-cause mortality were derived from registers covering the whole population, up to 31 December 2010. The follow-up of 24.56 million person-years included 33 067 cases of mortality and 19 843 CHD, 13 578 stroke and 27 365 cancer diagnoses. Hazard ratios (HR) [with 95% confidence intervals (CI)] were estimated by the Cox proportional hazards model. RESULTS Higher mortality in the whole cohort (HR = 1.26, 1.21-1.32) as well as after the diagnosis of CHD (HR = 1.33, 1.09-1.63) or cancer (HR = 1.13, 1.01-1.25) was found in moderately overweight men (BMI 25.0-27.4 kg/m(2)) as compared with normal weight men (BMI 20.1-22.4 kg/m(2)); for stroke patients the result for the same BMI categories was not statistically significant (HR = 1.17, 0.94-1.45). Mortality increased with increasing weight status and was highest in obese men (BMI >30 kg/m(2)): HR = 2.17 (2.02-2.34) for the whole cohort, 2.35 (1.81-3.05) after the diagnosis of CHD, 2.08 (1.56-2.77) after stroke and 1.68 (1.40-2.01) after cancer. CONCLUSIONS Even moderate overweight in young adulthood increases all-cause mortality and mortality after the diagnosis of CHD, stroke and cancer in men. Preventing overweight in young adulthood remains as an important public health issue.
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Affiliation(s)
- Karri Silventoinen
- Department of Social Research and Department of Public Health, University of Helsinki, Helsinki, Finland and Department of Public Health Sciences, Karolinska Institutet, Stockholm, SwedenDepartment of Social Research and Department of Public Health, University of Helsinki, Helsinki, Finland and Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Per Tynelius
- Department of Social Research and Department of Public Health, University of Helsinki, Helsinki, Finland and Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Finn Rasmussen
- Department of Social Research and Department of Public Health, University of Helsinki, Helsinki, Finland and Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
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43
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Lanfer A, Mehlig K, Heitmann BL, Lissner L. Does change in hip circumference predict cardiovascular disease and overall mortality in Danish and Swedish women? Obesity (Silver Spring) 2014; 22:957-63. [PMID: 23963732 DOI: 10.1002/oby.20604] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2013] [Accepted: 08/12/2013] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Accumulating evidence consistently shows that small hip circumference (HC) is related to increased risk of cardiovascular disease (CVD), coronary heart disease, diabetes, and premature death in women. This study aims to clarify whether this inverse association can be found in both normal- and overweight individuals and if change in HC over time relates to morbidity and mortality risk. METHODS HC and 6-year change in HC in relation to the risk for all-cause mortality and CVD morbidity and mortality was investigated in a pooled sample of 2,867 women from the DANISH MONICA study and the Prospective Population Study of Women in Gothenburg with a total of 66,627 person-years of follow-up. RESULTS Baseline HC was significantly and inversely associated with all-cause and CVD-specific mortality after adjustment for BMI, waist circumference (WC), and other covariates. In stratified analyses, the inverse association was weaker in women with a BMI of more than 25 kg/m2. Six-year change in hip size was not associated with mortality or morbidity endpoints. CONCLUSIONS Our results imply the existence of a basal risk associated with small hip size, which is, however, independent from changes in gluteofemoral body mass and therefore unlikely to be modifiable.
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Affiliation(s)
- Anne Lanfer
- Department of Public Health and Community Medicine, University of Gothenburg, 405 30, Gothenburg, Sweden
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Lucke-Wold BP, DiPasquale K, Logsdon AF, Nguyen L, Lucke-Wold AN, Turner RC, Huber JD, Rosen CL. Metabolic Syndrome and its Profound Effect on Prevalence of Ischemic Stroke. AMERICAN MEDICAL STUDENT RESEARCH JOURNAL 2014; 1:29-38. [PMID: 27284575 PMCID: PMC4896644 DOI: 10.15422/amsrj.2014.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Ischemic stroke represents a leading cause of death worldwide and the leading cause of disability in the United States. Greater than 8% of all deaths are attributed to ischemic stroke. This rate is consistent with the heightened burden of cardiovascular disease deaths. Treatments for acute ischemic stroke remain limited to tissue plasminogen activator and mechanical thrombolysis, both of which require significant medical expertise and can only be applied to a select number of patients based on time of presentation, imaging, and absence of contraindications. Over 1,000 compounds that were successful in treating ischemic stroke in animal models have failed to correlate to success in clinical trials. The search for alternative treatments is ongoing, drawing greater attention to the importance of preclinical models that more accurately represent the clinical population through incorporation of common risk factors. This work reviews the contribution of these commonly observed risk factors in the clinical population highlighting both the pathophysiology as well as current clinical diagnosis and treatment standards. We also highlight future potential therapeutic targets, areas requiring further investigation, and recent changes in best-practice clinical care.
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Affiliation(s)
- Brandon P Lucke-Wold
- Department of Neurosurgery, West Virginia University, School of Medicine, Morgantown, West Virginia; The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia
| | - Kenneth DiPasquale
- The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia; Department of Basic Pharmaceutical Sciences, West Virginia University, School of Pharmacy, Morgantown, West Virginia
| | - Aric F Logsdon
- The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia; Department of Basic Pharmaceutical Sciences, West Virginia University, School of Pharmacy, Morgantown, West Virginia
| | - Linda Nguyen
- Department of Basic Pharmaceutical Sciences, West Virginia University, School of Pharmacy, Morgantown, West Virginia
| | - A Noelle Lucke-Wold
- The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia; West Virginia University, School of Nursing, Morgantown, West Virginia
| | - Ryan C Turner
- Department of Neurosurgery, West Virginia University, School of Medicine, Morgantown, West Virginia; The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia
| | - Jason D Huber
- The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia; Department of Basic Pharmaceutical Sciences, West Virginia University, School of Pharmacy, Morgantown, West Virginia
| | - Charles L Rosen
- Department of Neurosurgery, West Virginia University, School of Medicine, Morgantown, West Virginia; The Center for Neuroscience, West Virginia University, School of Medicine, Morgantown, West Virginia
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45
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Latent Growth Curve Models with Random and Fixed Effects. EMERGING METHODS IN FAMILY RESEARCH 2014. [DOI: 10.1007/978-3-319-01562-0_1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Multifactorial analysis of changes in body mass index across the adult life course: a study with 65 years of follow-up. Int J Obes (Lond) 2013; 38:1133-41. [PMID: 24193660 PMCID: PMC4012011 DOI: 10.1038/ijo.2013.204] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Revised: 09/26/2013] [Accepted: 10/20/2013] [Indexed: 12/04/2022]
Abstract
Background Although the negative consequences on health of being obese are well known, most adults gain weight across the life span. The general increase in body mass index (BMI) is mainly considered to originate from behavioral and environmental changes, but few studies have evaluated the influence of these factors on change in BMI in the presence of genetic risk. We aimed to study the influence of multifactorial causes of change in BMI, over 65 years. Methods and Findings Totally, 6,130 participants from TwinGene, who had up to 5 assessments, and 536 from the Swedish Adoption/Twin Study of Aging, who had up to 12 assessments, ranging over 65 years were included. The influence of lifestyle factors, birth cohort, cardiometabolic diseases, and an individual obesity genetic risk score based on 32 single nucleotide polymorphisms on change in BMI was evaluated with a growth model. For both sexes, BMI increased from early adulthood to age 65 years, after which the increase leveled off; BMI declined after age 80 years. A higher obesity genetic risk score, birth after 1925, and cardiometabolic diseases were associated with higher average BMI and a steeper increase in BMI prior to age 65 years. Among men, few factors were identified that influence BMI trajectories in late life, while for women, type 2 diabetes mellitus and dementia were associated with a steeper decrease in BMI after the age of 65 years. Conclusions There are two turning points in BMI in late adulthood, one at age 65 years and one at age 80 years. Factors associated with an increase in BMI in midlife, were not associated with an increase in BMI after the age of 65 years. These findings indicate that the causes and consequences of change in BMI differ across the life span. Current health recommendations need to be adjusted accordingly.
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Gong J, Schumacher F, Lim U, Hindorff L, Haessler J, Buyske S, Carlson C, Rosse S, Bůžková P, Fornage M, Gross M, Pankratz N, Pankow J, Schreiner P, Cooper R, Ehret G, Gu C, Houston D, Irvin M, Jackson R, Kuller L, Henderson B, Cheng I, Wilkens L, Leppert M, Lewis C, Li R, Nguyen KD, Goodloe R, Farber-Eger E, Boston J, Dilks H, Ritchie M, Fowke J, Pooler L, Graff M, Fernandez-Rhodes L, Cochrane B, Boerwinkle E, Kooperberg C, Matise T, Le Marchand L, Crawford D, Haiman C, North K, Peters U. Fine Mapping and Identification of BMI Loci in African Americans. Am J Hum Genet 2013; 93:661-71. [PMID: 24094743 DOI: 10.1016/j.ajhg.2013.08.012] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2013] [Revised: 08/07/2013] [Accepted: 08/16/2013] [Indexed: 10/26/2022] Open
Abstract
Genome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these GWAS loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI GWAS loci identified in EA studies in 29,151 AAs from the Population Architecture using Genomics and Epidemiology (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8 × 10(-5). Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r(2) > 0.5) with the most significant SNP in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6 × 10(-8)) and DHX34 (rs4802349, p = 1.2 × 10(-7)), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci.
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48
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Park SL, Cheng I, Pendergrass SA, Kucharska-Newton AM, Lim U, Ambite JL, Caberto CP, Monroe KR, Schumacher F, Hindorff LA, Oetjens MT, Wilson S, Goodloe RJ, Love SA, Henderson BE, Kolonel LN, Haiman CA, Crawford DC, North KE, Heiss G, Ritchie MD, Wilkens LR, Le Marchand L. Association of the FTO obesity risk variant rs8050136 with percentage of energy intake from fat in multiple racial/ethnic populations: the PAGE study. Am J Epidemiol 2013; 178:780-90. [PMID: 23820787 DOI: 10.1093/aje/kwt028] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Common obesity risk variants have been associated with macronutrient intake; however, these associations' generalizability across populations has not been demonstrated. We investigated the associations between 6 obesity risk variants in (or near) the NEGR1, TMEM18, BDNF, FTO, MC4R, and KCTD15 genes and macronutrient intake (carbohydrate, protein, ethanol, and fat) in 3 Population Architecture using Genomics and Epidemiology (PAGE) studies: the Multiethnic Cohort Study (1993-2006) (n = 19,529), the Atherosclerosis Risk in Communities Study (1987-1989) (n = 11,114), and the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study, which accesses data from the Third National Health and Nutrition Examination Survey (1991-1994) (n = 6,347). We used linear regression, with adjustment for age, sex, and ethnicity, to estimate the associations between obesity risk genotypes and macronutrient intake. A fixed-effects meta-analysis model showed that the FTO rs8050136 A allele (n = 36,973) was positively associated with percentage of calories derived from fat (βmeta = 0.2244 (standard error, 0.0548); P = 4 × 10(-5)) and inversely associated with percentage of calories derived from carbohydrate (βmeta = -0.2796 (standard error, 0.0709); P = 8 × 10(-5)). In the Multiethnic Cohort Study, percentage of calories from fat assessed at baseline was a partial mediator of the rs8050136 effect on body mass index (weight (kg)/height (m)(2)) obtained at 10 years of follow-up (mediation of effect = 0.0823 kg/m(2), 95% confidence interval: 0.0559, 0.1128). Our data provide additional evidence that the association of FTO with obesity is partially mediated by dietary intake.
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Affiliation(s)
- Sungshim Lani Park
- Epidemiology Program, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI 96813, USA.
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Zhou X, Chai Y, Chen K, Yang Y, Liu Z. A meta-analysis of reference values of leptin concentration in healthy postmenopausal women. PLoS One 2013; 8:e72734. [PMID: 24023638 PMCID: PMC3758328 DOI: 10.1371/journal.pone.0072734] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Accepted: 07/12/2013] [Indexed: 02/07/2023] Open
Abstract
Objective There are numerous reports about the leptin concentration (LC) in postmenopausal women (PW). Changes in LC can elicit different clinical outcomes. We systematically analyzed the LC in PW. Methods A search was conducted in original English-language studies published from 1994 to October 2012 in the following databases: Medline (78), Cochrane Center (123) Embase (505), Biological abstracts (108), Cochrane (53) and Science Finder Scholar (0). A meta-analysis was undertaken on the correction coefficient (r) between the serum LC and body mass index (BMI) for healthy PW across studies containing a dataset and sample size. Pre-analytical and analytical variations were examined. Pre-analytical variables included fasting status (FS) and sampling timing. Analytical variation comprised assay methodology, LC in those undertaking hormone replacement therapy (HRT) and those not having HRT as well as LC change according to age. Results Twenty-seven studies met the inclusion criteria. Eighteen studies detected LC in the morning in a FS, 15 studies denoted the r between leptin and the BMI. A combined r was counted for the 15 studies (r = 0.51 [95% confidence interval (CI), 0.46–0.54], P = 0.025), and if sampling collection was in the FSat morning, a combined r was form 10 studies (r = 0.54 [95% CI, 0.45–0.54], P = 0.299) and heterogeneity was diminished. LC did not change between HRT users and non-users in 7 studies. Five studies analyzed changes in LC according to age. Conclusion Based on all studies that investigated both LC and BMI, LC was positively correlated with the BMI. No studies established reference ranges according to the Clinical and Laboratory Standards Institute (CLSI) in healthy PW, and there was a wide variation in LC values. These differences suggest that caution should be used in the interpretation and comparison between studies.
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Affiliation(s)
- Xi Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of the Medical College, Xi'an Jiaotong University, China
- Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - YanLan Chai
- Department of Radiation Oncology, The First Affiliated Hospital of the Medical College, Xi'an Jiaotong University, China
| | - Ke Chen
- Department of Physiology and Pathophysiology, Health Science Center, Xi'an Jiaotong University, China
| | - YunYi Yang
- Department of Radiation Oncology, The First Affiliated Hospital of the Medical College, Xi'an Jiaotong University, China
| | - Zi Liu
- Department of Radiation Oncology, The First Affiliated Hospital of the Medical College, Xi'an Jiaotong University, China
- * E-mail:
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Sapkota Y, Yasui Y, Lai R, Sridharan M, Robson PJ, Cass CE, Mackey JR, Damaraju S. Identification of a breast cancer susceptibility locus at 4q31.22 using a genome-wide association study paradigm. PLoS One 2013; 8:e62550. [PMID: 23717390 PMCID: PMC3661567 DOI: 10.1371/journal.pone.0062550] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 03/22/2013] [Indexed: 11/26/2022] Open
Abstract
More than 40 single nucleotide polymorphisms (SNPs) for breast cancer susceptibility were identified by genome-wide association studies (GWASs). However, additional SNPs likely contribute to breast cancer susceptibility and overall genetic risk, prompting this investigation for additional variants. Six putative breast cancer susceptibility SNPs identified in a two-stage GWAS that we reported earlier were replicated in a follow-up stage 3 study using an independent set of breast cancer cases and controls from Canada, with an overall cumulative sample size of 7,219 subjects across all three stages. The study design also encompassed the 11 variants from GWASs previously reported by various consortia between the years 2007–2009 to (i) enable comparisons of effect sizes, and (ii) identify putative prognostic variants across studies. All SNP associations reported with breast cancer were also adjusted for body mass index (BMI). We report a strong association with 4q31.22-rs1429142 (combined per allele odds ratio and 95% confidence interval = 1.28 [1.17–1.41] and Pcombined = 1.5×10−7), when adjusted for BMI. Ten of the 11 breast cancer susceptibility loci reported by consortia also showed associations in our predominantly Caucasian study population, and the associations were independent of BMI; four FGFR2 SNPs and TNRC9-rs3803662 were among the most notable associations. Since the original report by Garcia-Closas et al. 2008, this is the second study to confirm the association of 8q24.21-rs13281615 with breast cancer outcomes.
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Affiliation(s)
- Yadav Sapkota
- Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Yutaka Yasui
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond Lai
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Malinee Sridharan
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Paula J. Robson
- Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Alberta, Canada
- Alberta Health Services – Cancer Care, Edmonton, Alberta, Canada
| | - Carol E. Cass
- Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
| | - John R. Mackey
- Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Oncology, University of Alberta, Edmonton, Alberta, Canada
| | - Sambasivarao Damaraju
- Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- * E-mail:
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