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Wright L, Staatz CB, Silverwood RJ, Bann D. Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019. BMC Med 2023; 21:434. [PMID: 37957618 PMCID: PMC10644438 DOI: 10.1186/s12916-023-03103-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/04/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The widening of group-level socioeconomic differences in body mass index (BMI) has received considerable research attention. However, the predictive power of socioeconomic position (SEP) indicators at the individual level remains uncertain, as does the potential temporal variation in their predictive value. Examining this is important given the increasing incorporation of SEP indicators into predictive algorithms and calls to reduce social inequality to tackle the obesity epidemic. We thus investigated SEP differences in BMI over three decades of the obesity epidemic in England, comparing population-wide (SEP group differences in mean BMI) and individual-level (out-of-sample prediction of individuals' BMI) approaches to understanding social inequalities. METHODS We used repeated cross-sectional data from the Health Survey for England, 1991-2019. BMI (kg/m2) was measured objectively, and SEP was measured via educational attainment, occupational class, and neighbourhood index of deprivation. We ran random forest models for each survey year and measure of SEP adjusting for age and sex. RESULTS The mean and variance of BMI increased within each SEP group over the study period. Mean differences in BMI by SEP group also increased: differences between lowest and highest education groups were 1.0 kg/m2 (0.4, 1.6) in 1991 and 1.3 kg/m2 (0.7, 1.8) in 2019. At the individual level, the predictive capacity of SEP was low, though increased in later years: including education in models improved predictive accuracy (mean absolute error) by 0.14% (- 0.9, 1.08) in 1991 and 1.05% (0.18, 1.82) in 2019. Similar patterns were obtained for occupational class and neighbourhood deprivation and when analysing obesity as an outcome. CONCLUSIONS SEP has become increasingly important at the population (group difference) and individual (prediction) levels. However, predictive ability remains low, suggesting limited utility of including SEP in prediction algorithms. Assuming links are causal, abolishing SEP differences in BMI could have a large effect on population health but would neither reverse the obesity epidemic nor reduce much of the variation in BMI.
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Affiliation(s)
- Liam Wright
- Centre for Longitudinal Studies, Social Research Institute, University College London, 55-59 Gordon Square, London, WC1H 0NT, UK.
| | - Charis Bridger Staatz
- Centre for Longitudinal Studies, Social Research Institute, University College London, 55-59 Gordon Square, London, WC1H 0NT, UK
| | - Richard J Silverwood
- Centre for Longitudinal Studies, Social Research Institute, University College London, 55-59 Gordon Square, London, WC1H 0NT, UK
| | - David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College London, 55-59 Gordon Square, London, WC1H 0NT, UK
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Cuevas AG, Mann FD, Krueger RF. Discrimination Exposure and Polygenic Risk for Obesity in Adulthood: Testing Gene-Environment Correlations and Interactions. Lifestyle Genom 2023; 16:90-97. [PMID: 36750036 PMCID: PMC11078300 DOI: 10.1159/000529527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
INTRODUCTION Exposure to discrimination has emerged as a risk factor for obesity. It remains unclear, however, whether the genotype of the individual can modulate the sensitivity or response to discrimination exposure (gene × environment interaction) or increase the likelihood of experiencing discrimination (gene-environment correlation). METHODS This was an observational study of 4,102 white/European Americans in the Health and Retirement Study with self-reported, biological assessments, and genotyped data from 2006 to 2014. Discrimination was operationalized using the average of nine Everyday Discrimination Scale items. Polygenic risk scores (PRSs) for body mass index (BMI) and waist circumference (WC) were calculated using the weighted sum of risk alleles based on studies conducted by the Genetic Investigation of Anthropometric Traits (GIANT) consortium. RESULTS We found that greater PRS-BMI was significantly associated with more reports of discrimination (β = 0.04 ± 0.02; p = 0.037). Further analysis showed that measured BMI partially mediated the association between PRS-BMI and discrimination. There was no evidence that the association between discrimination and BMI, or the association between discrimination and WC, differed by PRS-BMI or PRS-WC, respectively. CONCLUSION Our findings suggest that individuals with genetic liability for obesity may experience greater discrimination in their lifetime, consistent with a gene-environment correlation hypothesis. There was no evidence of a gene-environment interaction. More genome-wide association studies in diverse populations are needed to improve generalizability of study findings. In the meantime, prevention and clinical intervention efforts that seek to reduce exposure to all forms of discrimination may help reduce obesity at the population level.
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Affiliation(s)
- Adolfo G. Cuevas
- Department of Social and Behavioral Sciences Department, School of Global Public Health, New York University
| | - Frank D. Mann
- Department of Family, Population, and Preventative Medicine, Program in Public Health, Stony Brook University
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Johnson W, Pereira SMP, Costa S, Baker JL, Norris T. The associations of maternal and paternal obesity with latent patterns of offspring BMI development between 7 and 17 years of age: pooled analyses of cohorts born in 1958 and 2001 in the United Kingdom. Int J Obes (Lond) 2023; 47:39-50. [PMID: 36357563 PMCID: PMC9834052 DOI: 10.1038/s41366-022-01237-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE We aimed to 1) describe how the UK obesity epidemic reflects a change over time in the proportion of the population demonstrating adverse latent patterns of BMI development and 2) investigate the potential roles of maternal and paternal BMI in this secular process. METHODS We used serial BMI data between 7 and 17 years of age from 13220 boys and 12711 girls. Half the sample was born in 1958 and half in 2001. Sex-specific growth mixture models were developed. The relationships of maternal and paternal BMI and weight status with class membership were estimated using the 3-step BCH approach, with covariate adjustment. RESULTS The selected models had five classes. For each sex, in addition to the two largest normal weight classes, there were "normal weight increasing to overweight" (17% of boys and 20% of girls), "overweight increasing to obesity" (8% and 6%), and "overweight decreasing to normal weight" (3% and 6%) classes. More than 1-in-10 children from the 2001 birth cohort were in the "overweight increasing to obesity" class, compared to less than 1-in-30 from the 1958 birth cohort. Approximately 75% of the mothers and fathers of this class had overweight or obesity. When considered together, both maternal and paternal BMI were associated with latent class membership, with evidence of negative departure from additivity (i.e., the combined effect of maternal and paternal BMI was smaller than the sum of the individual effects). The odds of a girl belonging to the "overweight increasing to obesity" class (compared to the largest normal weight class) was 13.11 (8.74, 19.66) times higher if both parents had overweight or obesity (compared to both parents having normal weight); the equivalent estimate for boys was 9.01 (6.37, 12.75). CONCLUSIONS The increase in obesity rates in the UK over more than 40 years has been partly driven by the growth of a sub-population demonstrating excess BMI gain during adolescence. Our results implicate both maternal and paternal BMI as correlates of this secular process.
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Affiliation(s)
- William Johnson
- grid.6571.50000 0004 1936 8542School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Snehal M. Pinto Pereira
- grid.83440.3b0000000121901201UCL Division of Surgery & Interventional Science, University College London, London, UK
| | - Silvia Costa
- grid.6571.50000 0004 1936 8542School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Jennifer L. Baker
- grid.411702.10000 0000 9350 8874Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | - Tom Norris
- grid.83440.3b0000000121901201UCL Division of Surgery & Interventional Science, University College London, London, UK
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Castro-de-Araujo LFS, Singh M, Zhou Y, Vinh P, Verhulst B, Dolan CV, Neale MC. MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives. Behav Genet 2023; 53:63-73. [PMID: 36322200 PMCID: PMC9823046 DOI: 10.1007/s10519-022-10122-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minică et al. in Behav Genet 48:337-349, https://doi.org/10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50, https://doi.org/10.1007/BF01067552 , 1993).
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Affiliation(s)
- Luis F. S. Castro-de-Araujo
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA ,grid.1008.90000 0001 2179 088XDepartment of Psychiatry, Austin Health, The University of Melbourne, Melbourne, VIC Australia
| | - Madhurbain Singh
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Yi Zhou
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Philip Vinh
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA
| | - Brad Verhulst
- grid.264756.40000 0004 4687 2082Department of Psychiatry and Behavioral Sciences, Texas A&M University, 2900 E 29th Street, Bryan, TX 77802 USA
| | - Conor V. Dolan
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1‑156, P.O. Box 980126, Richmond, VA 23298‑0126 USA ,grid.12380.380000 0004 1754 9227Department of Biological Psychology, Vrije Universiteit. Amsterdam, Transitorium 2B03, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
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Cui T, El Mekkaoui K, Reinvall J, Havulinna AS, Marttinen P, Kaski S. Gene-gene interaction detection with deep learning. Commun Biol 2022; 5:1238. [PMID: 36371468 PMCID: PMC9653457 DOI: 10.1038/s42003-022-04186-y] [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: 01/02/2022] [Accepted: 10/27/2022] [Indexed: 11/13/2022] Open
Abstract
The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.
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Affiliation(s)
- Tianyu Cui
- Department of Computer Science, Aalto University, Espoo, Finland.
| | | | - Jaakko Reinvall
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Aki S Havulinna
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Pekka Marttinen
- Department of Computer Science, Aalto University, Espoo, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Samuel Kaski
- Department of Computer Science, Aalto University, Espoo, Finland
- Department of Computer Science, University of Manchester, Manchester, UK
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Sharma T, Morassut RE, Langlois C, Meyre D. Body mass index trajectories and their predictors in undergraduate students from Canada: Results from the GENEiUS study. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2022:1-9. [PMID: 35930409 DOI: 10.1080/07448481.2022.2103384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 06/29/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
Objective: To explore the patterns and predictors of body mass index (BMI) change among undergraduate students from Ontario (Canada). Participants: 68 undergraduate students were followed longitudinally for 3 years with anthropometric data collected bi-annually. Methods: BMI measurements were plotted to generate individual BMI trajectory curves, which were categorized, based on the observed trajectory pattern. Within and between group comparisons of BMI were conducted via nonparametric paired tests. The association of baseline BMI, sex, and ethnicity with BMI trajectory type was assessed using multinomial logistic regression. Results: Four BMI trajectory types were observed: "stable weight" (n = 15, 22.1%), "weight gain" (n = 30, 44.1%), "weight loss" (n = 12, 17.6%), and "weight cycling" (n = 11, 16.2%) trajectories. Higher baseline BMI was significantly associated with the "weight gain," "weight loss," and the "weight cycling" trajectories as compared to the "stable weight" trajectory type. Conclusions: Our findings demonstrate an association between high baseline BMI and "nonstable" subsequent BMI change patterns among Canadian students.
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Affiliation(s)
- Tanmay Sharma
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Rita E Morassut
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Christine Langlois
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Molecular Medicine, Division of Biochemistry, Molecular Biology, and Nutrition, University Hospital of Nancy, Nancy, France
- Faculty of Medicine of Nancy INSERM UMR_S 1256, Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy, France
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7
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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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Abstract
Obesity is in theory defined on the basis of the excess health risk caused by adiposity exceeding the size normally found in the population, but for practical reasons, the World Health Organization (WHO) has defined obesity as a body mass index (weight (kg)/height (m)2) of 30 or above for adults. WHO considers the steep increases in prevalence of obesity in all age groups, especially since the 1970s as a global obesity epidemic. Today, approximately 650 million adult people and approximately 340 million children and adolescence (5-19 years) suffer from obesity. It is generally more prevalent among women and older age groups than among men and younger age groups. Beyond the necessity of availability of food, evidence about causes of obesity is still very limited. However, studies have shown that obesity 'runs in families', where both genetics and environmental, and especially social, factors play important roles. Obesity is associated with an increased risk of many adverse medical, mental and social consequences, including a strong relation to type 2 diabetes. Type 2 diabetes and related metabolic syndrome and diseases are major contributors to the excess morbidity and mortality associated with obesity.
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Leyden GM, Shapland CY, Davey Smith G, Sanderson E, Greenwood MP, Murphy D, Richardson TG. Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes. Am J Hum Genet 2022; 109:240-252. [PMID: 35090585 DOI: 10.1016/j.ajhg.2021.12.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/14/2021] [Indexed: 12/11/2022] Open
Abstract
Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10-14). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10-4).
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10
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BMI, Blood Pressure, and Plasma Lipids among Centenarians and Their Offspring. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:3836247. [PMID: 35096109 PMCID: PMC8794670 DOI: 10.1155/2022/3836247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/30/2021] [Indexed: 11/20/2022]
Abstract
Background The burden of cardiovascular diseases (CVDs) is increasing substantially due to population growth and aging. Determining effective prevention and understanding the underlying mechanisms remain desirable pursuits for increasing the quality of life. As centenarians and their offspring may have genetic advantages, they may present with healthier cardiovascular-related profiles. Methods We launched a cross-sectional household-based survey of centenarian families, including 253 centenarians, 217 centenarian offspring, and 116 offspring spouses without centenarian parents from county-level Chinese longevity city Rugao. Among offspring and offspring spouses were the following arrangements: 101 paired offspring and offspring spouses who lived together, 116 unpaired offspring, and 16 unpaired spouses. We investigated their cardiovascular-related health status including waist circumference, body mass index (BMI), blood pressure, and plasma lipids and compared results among centenarians, centenarian offspring, and offspring spouses. Results Centenarians ranged from 99 to 109 years with a median age of 100 years. Centenarian offspring, with a median age of 70 years, and offspring spouses, with a median age of 69 years, shared similar age. Results of blood pressure, plasma lipid levels, and BMI displayed no significant difference between centenarian offspring and offspring spouses. However, centenarians appeared to have lower waist circumference, BMI, TC, LDL-C, TG, and diastolic blood pressure but higher levels of systolic blood pressure (p < 0.05). Multivariate analysis showed the prevalence of obesity, hypertension, and dyslipidemia was similar between centenarian offspring and offspring spouses, while centenarians appeared to have a lower prevalence of obesity and a higher prevalence of hypertension (p < 0.05). Conclusions Centenarians and centenarian offspring did not present healthier BMI, blood pressure, or plasma lipids than offspring spouses. Further research on longevity and cardiovascular diseases are desirable.
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Hüls A, Wright MN, Bogl LH, Kaprio J, Lissner L, Molnár D, Moreno LA, De Henauw S, Siani A, Veidebaum T, Ahrens W, Pigeot I, Foraita R. Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents. Int J Obes (Lond) 2021; 45:1321-1330. [PMID: 33753884 PMCID: PMC8159747 DOI: 10.1038/s41366-021-00795-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 02/03/2021] [Accepted: 02/23/2021] [Indexed: 02/01/2023]
Abstract
BACKGROUND Childhood obesity is a complex multifaceted condition, which is influenced by genetics, environmental factors, and their interaction. However, these interactions have mainly been studied in twin studies and evidence from population-based cohorts is limited. Here, we analyze the interaction of an obesity-related genome-wide polygenic risk score (PRS) with sociodemographic and lifestyle factors for BMI and waist circumference (WC) in European children and adolescents. METHODS The analyses are based on 8609 repeated observations from 3098 participants aged 2-16 years from the IDEFICS/I.Family cohort. A genome-wide polygenic risk score (PRS) was calculated using summary statistics from independent genome-wide association studies of BMI. Associations were estimated using generalized linear mixed models adjusted for sex, age, region of residence, parental education, dietary intake, relatedness, and population stratification. RESULTS The PRS was associated with BMI (beta estimate [95% confidence interval (95%-CI)] = 0.33 [0.30, 0.37], r2 = 0.11, p value = 7.9 × 10-81) and WC (beta [95%-CI] = 0.36 [0.32, 0.40], r2 = 0.09, p value = 1.8 × 10-71). We observed significant interactions with demographic and lifestyle factors for BMI as well as WC. Children from Southern Europe showed increased genetic liability to obesity (BMI: beta [95%-CI] = 0.40 [0.34, 0.45]) in comparison to children from central Europe (beta [95%-CI] = 0.29 [0.23, 0.34]), p-interaction = 0.0066). Children of parents with a low level of education showed an increased genetic liability to obesity (BMI: beta [95%-CI] = 0.48 [0.38, 0.59]) in comparison to children of parents with a high level of education (beta [95%-CI] = 0.30 [0.26, 0.34]), p-interaction = 0.0012). Furthermore, the genetic liability to obesity was attenuated by a higher intake of fiber (BMI: beta [95%-CI] interaction = -0.02 [-0.04,-0.01]) and shorter screen times (beta [95%-CI] interaction = 0.02 [0.00, 0.03]). CONCLUSIONS Our results highlight that a healthy childhood environment might partly offset a genetic predisposition to obesity during childhood and adolescence.
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Affiliation(s)
- Anke Hüls
- Department of Epidemiology and Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Marvin N Wright
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Leonie H Bogl
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria
- Institute of Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute of Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Lauren Lissner
- Department of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Dénes Molnár
- Department of Paediatrics, Medical School, University of Pécs, Pécs, Hungary
| | - Luis A Moreno
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Stefaan De Henauw
- Faculty of Medicine and Health Sciences, Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | | | - Toomas Veidebaum
- Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estonia
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Iris Pigeot
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
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12
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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: 33] [Impact Index Per Article: 11.0] [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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13
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Vogelezang S, Bradfield JP, Ahluwalia TS, Curtin JA, Lakka TA, Grarup N, Scholz M, van der Most PJ, Monnereau C, Stergiakouli E, Heiskala A, Horikoshi M, Fedko IO, Vilor-Tejedor N, Cousminer DL, Standl M, Wang CA, Viikari J, Geller F, Íñiguez C, Pitkänen N, Chesi A, Bacelis J, Yengo L, Torrent M, Ntalla I, Helgeland Ø, Selzam S, Vonk JM, Zafarmand MH, Heude B, Farooqi IS, Alyass A, Beaumont RN, Have CT, Rzehak P, Bilbao JR, Schnurr TM, Barroso I, Bønnelykke K, Beilin LJ, Carstensen L, Charles MA, Chawes B, Clément K, Closa-Monasterolo R, Custovic A, Eriksson JG, Escribano J, Groen-Blokhuis M, Grote V, Gruszfeld D, Hakonarson H, Hansen T, Hattersley AT, Hollensted M, Hottenga JJ, Hyppönen E, Johansson S, Joro R, Kähönen M, Karhunen V, Kiess W, Knight BA, Koletzko B, Kühnapfel A, Landgraf K, Langhendries JP, Lehtimäki T, Leinonen JT, Li A, Lindi V, Lowry E, Bustamante M, Medina-Gomez C, Melbye M, Michaelsen KF, Morgen CS, Mori TA, Nielsen TRH, Niinikoski H, Oldehinkel AJ, Pahkala K, Panoutsopoulou K, Pedersen O, Pennell CE, Power C, Reijneveld SA, Rivadeneira F, Simpson A, Sly PD, Stokholm J, Teo KK, Thiering E, Timpson NJ, Uitterlinden AG, van Beijsterveldt CEM, van Schaik BDC, Vaudel M, Verduci E, Vinding RK, Vogel M, Zeggini E, Sebert S, Lind MV, Brown CD, Santa-Marina L, Reischl E, Frithioff-Bøjsøe C, Meyre D, Wheeler E, Ong K, Nohr EA, Vrijkotte TGM, Koppelman GH, Plomin R, Njølstad PR, Dedoussis GD, Froguel P, Sørensen TIA, Jacobsson B, Freathy RM, Zemel BS, Raitakari O, Vrijheid M, Feenstra B, Lyytikäinen LP, Snieder H, Kirsten H, Holt PG, Heinrich J, Widén E, Sunyer J, Boomsma DI, Järvelin MR, Körner A, Davey Smith G, Holm JC, Atalay M, Murray C, Bisgaard H, McCarthy MI, Jaddoe VWV, Grant SFA, Felix JF. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet 2020; 16:e1008718. [PMID: 33045005 PMCID: PMC7581004 DOI: 10.1371/journal.pgen.1008718] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/22/2020] [Accepted: 03/16/2020] [Indexed: 01/22/2023] Open
Abstract
The genetic background of childhood body mass index (BMI), and the extent to which the well-known associations of childhood BMI with adult diseases are explained by shared genetic factors, are largely unknown. We performed a genome-wide association study meta-analysis of BMI in 61,111 children aged between 2 and 10 years. Twenty-five independent loci reached genome-wide significance in the combined discovery and replication analyses. Two of these, located near NEDD4L and SLC45A3, have not previously been reported in relation to either childhood or adult BMI. Positive genetic correlations of childhood BMI with birth weight and adult BMI, waist-to-hip ratio, diastolic blood pressure and type 2 diabetes were detected (Rg ranging from 0.11 to 0.76, P-values <0.002). A negative genetic correlation of childhood BMI with age at menarche was observed. Our results suggest that the biological processes underlying childhood BMI largely, but not completely, overlap with those underlying adult BMI. The well-known observational associations of BMI in childhood with cardio-metabolic diseases in adulthood may reflect partial genetic overlap, but in light of previous evidence, it is also likely that they are explained through phenotypic continuity of BMI from childhood into adulthood.
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Affiliation(s)
- Suzanne Vogelezang
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jonathan P. Bradfield
- Quantinuum Research LLC, San Diego, California, United States of America
- Center for Applied Genomics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Tarunveer S. Ahluwalia
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - John A. Curtin
- Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Timo A. Lakka
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Peter J. van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Claire Monnereau
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Evie Stergiakouli
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- School of Oral and Dental Sciences, University of Bristol, Bristol, United Kingdom
| | - Anni Heiskala
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Momoko Horikoshi
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Iryna O. Fedko
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Natalia Vilor-Tejedor
- ISGlobal, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Diana L. Cousminer
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany
| | - Carol A. Wang
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, Australia
| | - Jorma Viikari
- Department of Medicine, University of Turku, Turku, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
| | - Frank Geller
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Carmen Íñiguez
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Statistics and Computational Research–Universitat de València, València, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
| | - Niina Pitkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Alessandra Chesi
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Jonas Bacelis
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Obstetrics and Gynecology, Gothenburg Sweden
| | - Loic Yengo
- University Lille, Centre National de la Recherche Scientifique, Institut Pasteur de Lille, UMR 8199—European Genomic Institute for Diabetes, Lille, France
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Maties Torrent
- Area de Salut de Menorca ib-salut, Menorca, Spain
- Institut d'Investigacio Sanitaria Illes Balears (IdISBa), Palma de Mallorca, Spain
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Øyvind Helgeland
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Saskia Selzam
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Judith M. Vonk
- Department of Epidemiology, GRIAC (Groningen Research Institute for Asthma and COPD), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mohammed H. Zafarmand
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Obstetrics & Gynecology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Barbara Heude
- Université de Paris, CRESS, INSERM, INRA, Paris, France
| | - Ismaa Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Robin N. Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom
| | - Christian T. Have
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Rzehak
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Ludwig-Maximilians Universität München (LMU), Munich, Germany
| | - Jose Ramon Bilbao
- University of the Basque Country (UPV/EHU), Leioa, Spain
- Biocrues-Bizkaia Health Research Institute, Barakaldo, Spain
- CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Spain
| | - Theresia M. Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Inês Barroso
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Lawrence J. Beilin
- Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Lisbeth Carstensen
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Bo Chawes
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Karine Clément
- Nutrition and Obesities; systemic approaches research unit, Sorbonne University, INSERM, Pitie- Salpêtrière Hospital, Assistance Publique hôpital de Paris, Paris, France
| | - Ricardo Closa-Monasterolo
- Pediatrics, Nutrition and Development Research Unit, Universitat Rovira i Virgili, IISPV, Reus, Spain
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Johan G. Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Joaquin Escribano
- Pediatrics, Nutrition and Development Research Unit, Universitat Rovira i Virgili, IISPV, Reus, Spain
| | - Maria Groen-Blokhuis
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Ludwig-Maximilians Universität München (LMU), Munich, Germany
| | - Dariusz Gruszfeld
- Neonatal Department, Children's Memorial Health Institute, Warsaw, Poland
| | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States of America
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Andrew T. Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom
- NIHR Exeter Clinical Research Facility, College of Medicine and Health, University of Exeter, and Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom
| | - Mette Hollensted
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Danish Diabetes Academy, Odense, Denmark
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, Australia
- Population, Policy and Practice, UCL Institute of Child Health, University College London, London, United Kingdom
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Stefan Johansson
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Raimo Joro
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, United Kingdom
| | - Wieland Kiess
- Center for Pediatric Research, University Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
| | - Bridget A. Knight
- NIHR Exeter Clinical Research Facility, College of Medicine and Health, University of Exeter, and Royal Devon and Exeter NHS Foundation Trust, Exeter, United Kingdom
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Ludwig-Maximilians Universität München (LMU), Munich, Germany
| | - Andreas Kühnapfel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Kathrin Landgraf
- Center for Pediatric Research, University Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Integrated Research and Treatment Center (IFB) Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | | | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jaakko T. Leinonen
- Institute For Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Aihuali Li
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Virpi Lindi
- University of Eastern Finland Library Kuopio, Kuopio, Finland
| | - Estelle Lowry
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu University Hospital, Oulu, Finland
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carolina Medina-Gomez
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Mads Melbye
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Medicine, Stanford School of Medicine, Stanford, California, United States of America
| | - Kim F. Michaelsen
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Camilla S. Morgen
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- National Insitute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Trevor A. Mori
- Medical School, The University of Western Australia, Perth, Western Australia, Australia
| | - Tenna R. H. Nielsen
- Department of Pediatrics, Hvidovre Hospital, Hvidovre, Denmark
- The Children’s Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Harri Niinikoski
- Department of Physiology, University of Turku, Turku, Finland
- Department of Pediatrics, University of Turku, Turku, Finland
| | - Albertine J. Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center, Groningen, the Netherlands
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland
| | - Kalliope Panoutsopoulou
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Craig E. Pennell
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, Australia
| | - Christine Power
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Sijmen A. Reijneveld
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Angela Simpson
- Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Peter D. Sly
- Child Health Research Centre, University of Queensland, Brisbane, Australia
- World Health Organization, WHO Collaborating Centre for Children’s Health and Environment, Brisbane, Queensland, Australia
| | - Jakob Stokholm
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Kook K. Teo
- Department of Medicine, McMaster University, Hamilton, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Ludwig-Maximilians Universität München (LMU), Munich, Germany
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging NCHA), Leiden, the Netherlands
| | | | - Barbera D. C. van Schaik
- Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Elvira Verduci
- Department of Pediatrics, San Paolo Hospital, University of Milan, Milan, Italy
| | - Rebecca K. Vinding
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Mandy Vogel
- Center for Pediatric Research, University Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Integrated Research and Treatment Center (IFB) Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Eleftheria Zeggini
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom
- Institute of Translational Genomics, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Biocenter Oulu, Oulu University Hospital, Oulu, Finland
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, London, United Kingdom
| | - Mads V. Lind
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Christopher D. Brown
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Loreto Santa-Marina
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiologia y Salud Publica-CIBERESP), Barcelona, Spain
- Biodonostia Health Research Institute, San Sebastian, Spain
- Subdirección Salud Pública de Gipuzkoa, San Sebastian, Spain
| | - Eva Reischl
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Muenchen, Munich, Germany
| | - Christine Frithioff-Bøjsøe
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children’s Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen N, Denmark
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Eleanor Wheeler
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Ken Ong
- Medical Research Council Epidemiology Unit & Department of Paediatrics, University of Cambridge, Addenbrooke’s Hospital, Cambridge, England
| | - Ellen A. Nohr
- Research Unit for Gynaecology and Obstetrics, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Tanja G. M. Vrijkotte
- Department of Public Health, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Gerard H. Koppelman
- University Medical Center Groningen, University of Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC (Groningen Research Institute for Asthma and COPD), Groningen, the Netherlands
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Pål R. Njølstad
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Pediatrics and Adolescents, Haukeland University Hospital, Bergen, Norway
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - George D. Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Philippe Froguel
- University Lille, Centre National de la Recherche Scientifique, Institut Pasteur de Lille, UMR 8199—European Genomic Institute for Diabetes, Lille, France
- Section of Genomics of Common Disease, Department of Medicine, Imperial College London, London, United Kingdom
| | - Thorkild I. A. Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Obstetrics and Gynecology, Gothenburg Sweden
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Rachel M. Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, United Kingdom
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Babette S. Zemel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States of America
- Division of Gastroenterology, Hepatology and Nutrition, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Martine Vrijheid
- ISGlobal, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Holger Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Patrick G. Holt
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Joachim Heinrich
- Institute of Epidemiology, Helmholtz Zentrum München- German Research Center for Environmental Health, Neuherberg, Germany
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Inner City Clinic, University Hospital Munich, Ludwig-Maximilians-Universität of Munich, Munich, Germany
- Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Widén
- Institute For Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health research institute and Amsterdam Reproduction & Development research Institute, Amsterdam, the Netherlands
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, United Kingdom
| | - Antje Körner
- Center for Pediatric Research, University Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Integrated Research and Treatment Center (IFB) Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jens-Christian Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Children’s Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen N, Denmark
| | - Mustafa Atalay
- Institute of Biomedicine, Physiology, University of Eastern Finland, Kuopio, Finland
| | - Clare Murray
- Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Hans Bisgaard
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
- Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
| | | | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Struan F. A. Grant
- Center for Applied Genomics, Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Neonatal Department, Children's Memorial Health Institute, Warsaw, Poland
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- * E-mail:
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14
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Association of a genetic risk score with BMI along the life-cycle: Evidence from several US cohorts. PLoS One 2020; 15:e0239067. [PMID: 32941506 PMCID: PMC7497990 DOI: 10.1371/journal.pone.0239067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 08/29/2020] [Indexed: 12/27/2022] Open
Abstract
We use data from the National Longitudinal Study of Adolescent to Adult Health and from the Health and Retirement Study to explore how the effect of individuals’ genetic predisposition to higher BMI —measured by BMI polygenic scores— changes over the life-cycle for several cohorts. We find that the effect of BMI polygenic scores on BMI increases significantly as teenagers transition into adulthood (using the Add Health cohort, born 1974-83). However, this is not the case for individuals aged 55+ who were born in earlier HRS cohorts (1931-53), whose life-cycle pattern of genetic influence on BMI is remarkably stable as they move into old-age.
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15
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Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity. Int J Obes (Lond) 2020; 44:2101-2112. [PMID: 32665611 PMCID: PMC7530941 DOI: 10.1038/s41366-020-0636-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 06/07/2020] [Accepted: 07/03/2020] [Indexed: 12/18/2022]
Abstract
Background/Objectives: Quantile-dependent expressivity occurs when a gene’s
phenotypic expression depends upon whether the trait (e.g., BMI) is high or
low relative to its distribution. We have previously shown that the obesity
effects of a genetic risk score (GRSBMI) increased significantly
with increasing quantiles of BMI. However, BMI is an inexact adiposity
measure and GRSBMI explains <3% of the BMI variance. The
purpose of this paper is to test BMI for quantile-dependent expressivity
using a more inclusive genetic measure
(h2, heritability in
the narrow sense), extend the result to other adiposity measures, and
demonstrate its consistency with purported gene-environment
interactions. Subjects/Methods: Quantile-specific offspring-parent regression slopes
(βOP) were obtained from quantile regression for
height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry
(DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures.
Heritability was estimated by 2βOP/(1+rspouse)
in 6,227 offspring-parent pairs from the Framingham Heart Study, where
rspouse is the spouse correlation. Results: Compared to h2 at the
10th percentile, genetic heritability was significantly
greater at the 90th population percentile for BMI (3.14-fold
greater, P<10−15), waist girth/ht (3.27-fold,
P<10−15), hip girth/ht (3.12-fold,
P=6.3×10−14), waist-to-hip ratio (1.75-fold,
P=0.01), sagittal diameter/ht (3.89-fold,
P=3.7×10−7), DXA total fat/ht2
(3.62-fold, P=0.0002), DXA leg fat/ht2 (3.29-fold,
P=2.0×10−11), DXA arm fat/ht2
(4.02-fold, P=0.001), CT-visceral fat/ht2 (3.03-fold, P=0.002),
and CT-subcutaneous fat/ht2 (3.54-fold, P=0.0004). External
validity was suggested by the phenomenon’s consistency with numerous
published reports. Quantile-dependent expressivity potentially explains
precision medicine markers for weight gain from overfeeding or antipsychotic
medications, and the modifying effects of physical activity, sleep, diet,
polycystic ovary syndrome, socioeconomic status, and depression on gene-BMI
relationships. Conclusion: Genetic heritabilities of anthropometric, CT, and DXA adiposity
measures increase with increasing adiposity. Some gene-environment
interactions may arise from analyzing subjects by characteristics that
distinguish high vs. low adiposity rather than the effects of environmental
stimuli on transcriptional and epigenetic processes.
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16
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Longitudinal association of a body mass index (BMI) genetic risk score with growth and BMI changes across the life course: The Cardiovascular Risk in Young Finns Study. Int J Obes (Lond) 2020; 44:1733-1742. [PMID: 32494039 DOI: 10.1038/s41366-020-0611-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 03/27/2020] [Accepted: 05/20/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND The role of genetic risk scores associated with adult body mass index (BMI) on BMI levels across the life course is unclear. We examined if a 97 single nucleotide polymorphism weighted genetic risk score (wGRS97) associated with age-related progression in BMI at different life stages and distinct developmental trajectories of BMI across the early life course. METHODS 2188 Cardiovascular Risk in Young Finns Study participants born pre-1980 who had genotype data and objective measurements of height and weight collected up to 8 times from age 6 to 49 years. Associations were examined using Individual Growth Curve analysis, Latent Class Growth Mixture Modelling, and Poisson modified regression. RESULTS The wGRS97 associated with BMI from age 6 years with peak effect sizes observed at age 30 years (females: 1.14 kg/m2; males: 1.09 kg/m2 higher BMI per standard deviation increase in wGRS97). The association between wGRS97 and BMI became stronger with age in childhood but slowed in adolescence, especially in females, and weakened at age 35-40 years. A higher wGRS97 associated with an increased BMI velocity in childhood and adulthood, but not with BMI change in adulthood. Compared with belonging to a 'normal stable' life-course trajectory group (normal BMI from childhood to adulthood), a one standard deviation higher wGRS97 associated with a 13-127% increased risk of belonging to a less favourable life-course BMI trajectory group. CONCLUSIONS Individuals with genetic susceptibility to higher adult BMI have higher levels and accelerated rates of increase in BMI in childhood/adolescence, and are at increased risk of having a less favourable life-course BMI trajectory.
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17
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Jackson SE, Llewellyn CH, Smith L. The obesity epidemic - Nature via nurture: A narrative review of high-income countries. SAGE Open Med 2020; 8:2050312120918265. [PMID: 32435480 PMCID: PMC7222649 DOI: 10.1177/2050312120918265] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 03/04/2020] [Indexed: 12/19/2022] Open
Abstract
Over the last three decades, the prevalence of obesity has increased rapidly in populations around the world. Despite a wealth of research, the relative contributions of the different mechanisms underlying this global epidemic are not fully understood. While there is growing consensus that the rapid rise in obesity prevalence has been driven by changes to the environment, it is evident that biology plays a central role in determining who develops obesity and who remains lean in the current obesogenic environment. This review summarises evidence on the extent to which genes and the environment influence energy intake and energy expenditure, and as a result, contribute to the ongoing global obesity epidemic. The concept of genetic susceptibility to the environment driving human variation in body weight is discussed.
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Affiliation(s)
- Sarah E Jackson
- Department of Behavioural Science and Health, University College London, London, UK
- Sarah E Jackson, Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK.
| | - Clare H Llewellyn
- Department of Behavioural Science and Health, University College London, London, UK
| | - Lee Smith
- Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge, UK
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18
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The CODATwins Project: The Current Status and Recent Findings of COllaborative Project of Development of Anthropometrical Measures in Twins. Twin Res Hum Genet 2019; 22:800-808. [PMID: 31364586 DOI: 10.1017/thg.2019.35] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural-geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.
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19
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Gene-Environment Interactions on Body Fat Distribution. Int J Mol Sci 2019; 20:ijms20153690. [PMID: 31357654 PMCID: PMC6696304 DOI: 10.3390/ijms20153690] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 02/08/2023] Open
Abstract
The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.
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20
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Brandkvist M, Bjørngaard JH, Ødegård RA, Åsvold BO, Sund ER, Vie GÅ. Quantifying the impact of genes on body mass index during the obesity epidemic: longitudinal findings from the HUNT Study. BMJ 2019; 366:l4067. [PMID: 31270083 PMCID: PMC6607203 DOI: 10.1136/bmj.l4067] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
OBJECTIVES To study the trajectories of body mass index (BMI) in Norway over five decades and to assess the differential influence of the obesogenic environment on BMI according to genetic predisposition. DESIGN Longitudinal study. SETTING General population of Nord-Trøndelag County, Norway. PARTICIPANTS 118 959 people aged 13-80 years who participated in a longitudinal population based health study (Nord-Trøndelag Health Study, HUNT), of whom 67 305 were included in analyses of association between genetic predisposition and BMI. MAIN OUTCOME MEASURE BMI. RESULTS Obesity increased in Norway starting between the mid-1980s and mid-1990s and, compared with older birth cohorts, those born after 1970 had a substantially higher BMI already in young adulthood. BMI differed substantially between the highest and lowest fifths of genetic susceptibility for all ages at each decade, and the difference increased gradually from the 1960s to the 2000s. For 35 year old men, the most genetically predisposed had 1.20 kg/m2 (95% confidence interval 1.03 to 1.37 kg/m2) higher BMI than those who were least genetically predisposed in the 1960s compared with 2.09 kg/m2 (1.90 to 2.27 kg/m2) in the 2000s. For women of the same age, the corresponding differences in BMI were 1.77 kg/m2 (1.56 to 1.97 kg/m2) and 2.58 kg/m2 (2.36 to 2.80 kg/m2). CONCLUSIONS This study provides evidence that genetically predisposed people are at greater risk for higher BMI and that genetic predisposition interacts with the obesogenic environment resulting in higher BMI, as observed between the mid-1980s and mid-2000s. Regardless, BMI has increased for both genetically predisposed and non-predisposed people, implying that the environment remains the main contributor.
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Affiliation(s)
- Maria Brandkvist
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway
- Department of Paediatrics, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Johan Håkon Bjørngaard
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway
| | - Rønnaug Astri Ødegård
- Department of Paediatrics, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Obesity Centre, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Erik R Sund
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Gunnhild Åberge Vie
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Postboks 8905, 7491 Trondheim, Norway
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21
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Pettersson E, Lichtenstein P, Larsson H, Song J, Agrawal A, Børglum AD, Bulik CM, Daly MJ, Davis LK, Demontis D, Edenberg HJ, Grove J, Gelernter J, Neale BM, Pardiñas AF, Stahl E, Walters JTR, Walters R, Sullivan PF, Posthuma D, Polderman TJC. Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls. Psychol Med 2019; 49:1166-1173. [PMID: 30221610 PMCID: PMC6421104 DOI: 10.1017/s0033291718002039] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 03/16/2018] [Accepted: 07/16/2018] [Indexed: 01/03/2023]
Abstract
BACKGROUND Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders. METHODS We assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia. RESULTS Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50. CONCLUSIONS Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.
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Affiliation(s)
- E. Pettersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - P. Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - H. Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - J. Song
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - A. Agrawal
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - A. D. Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - C. M. Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - M. J. Daly
- Analytic and Translational Genetics Unit (ATGU), Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - L. K. Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - D. Demontis
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - H. J. Edenberg
- Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Medical and Molecular Genetics, Indianapolis, IN, USA
| | - J. Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- BiRC-Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - J. Gelernter
- Yale University School of Medicine, Genetics and Neurobiology, New Haven, CT, USA
- US Department of Veterans Affairs, Psychiatry, West Haven, CT, USA
- Yale University School of Medicine, Psychiatry, New Haven, CT, USA
| | - B. M. Neale
- Analytic and Translational Genetics Unit (ATGU), Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - A. F. Pardiñas
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales
| | - E. Stahl
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - J. T. R. Walters
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales
| | - R. Walters
- Analytic and Translational Genetics Unit (ATGU), Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - P. F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - D. Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, VU University Medical Center (VUMC), Amsterdam, The Netherlands
| | - T. J. C. Polderman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
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22
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Silventoinen K, Jelenkovic A, Latvala A, Yokoyama Y, Sund R, Sugawara M, Tanaka M, Matsumoto S, Aaltonen S, Piirtola M, Freitas DL, Maia JA, Öncel SY, Aliev F, Ji F, Ning F, Pang Z, Rebato E, Saudino KJ, Cutler TL, Hopper JL, Ullemar V, Almqvist C, Magnusson PKE, Cozen W, Hwang AE, Mack TM, Willemsen G, Bartels M, van Beijsterveldt CEM, Nelson TL, Whitfield KE, Sung J, Kim J, Lee J, Lee S, Llewellyn CH, Fisher A, Medda E, Nisticò L, Toccaceli V, Baker LA, Tuvblad C, Corley RP, Huibregtse BM, Derom CA, Vlietinck RF, Loos RJF, Knafo-Noam A, Mankuta D, Abramson L, Burt SA, Klump KL, Silberg JL, Maes HH, Krueger RF, McGue M, Pahlen S, Gatz M, Butler DA, Harris JR, Nilsen TS, Harden KP, Tucker-Drob EM, Franz CE, Kremen WS, Lyons MJ, Lichtenstein P, Jeong HU, Hur YM, Boomsma DI, Sørensen TIA, Kaprio J. Parental Education and Genetics of BMI from Infancy to Old Age: A Pooled Analysis of 29 Twin Cohorts. Obesity (Silver Spring) 2019; 27:855-865. [PMID: 30950584 PMCID: PMC6478550 DOI: 10.1002/oby.22451] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 01/31/2019] [Indexed: 01/30/2023]
Abstract
OBJECTIVE The objective of this study was to analyze how parental education modifies the genetic and environmental variances of BMI from infancy to old age in three geographic-cultural regions. METHODS A pooled sample of 29 cohorts including 143,499 twin individuals with information on parental education and BMI from age 1 to 79 years (299,201 BMI measures) was analyzed by genetic twin modeling. RESULTS Until 4 years of age, parental education was not consistently associated with BMI. Thereafter, higher parental education level was associated with lower BMI in males and females. Total and additive genetic variances of BMI were smaller in the offspring of highly educated parents than in those whose parents had low education levels. Especially in North American and Australian children, environmental factors shared by co-twins also contributed to the higher BMI variation in the low education level category. In Europe and East Asia, the associations of parental education with mean BMI and BMI variance were weaker than in North America and Australia. CONCLUSIONS Lower parental education level is associated with higher mean BMI and larger genetic variance of BMI after early childhood, especially in the obesogenic macro-environment. The interplay among genetic predisposition, childhood social environment, and macro-social context is important for socioeconomic differences in BMI.
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Affiliation(s)
- Karri Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Aline Jelenkovic
- Department of Social Research, University of Helsinki, Helsinki, Finland
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Antti Latvala
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Yoshie Yokoyama
- Department of Public Health Nursing, Osaka City University, Osaka, Japan
| | - Reijo Sund
- Department of Social Research, University of Helsinki, Helsinki, Finland
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Masumi Sugawara
- Department of Psychology, Ochanomizu University, Tokyo, Japan
| | - Mami Tanaka
- Center for Forensic Mental Health, Chiba University, Chiba, Japan
| | - Satoko Matsumoto
- Institute for Education and Human Development, Ochanomizu University, Tokyo
| | - Sari Aaltonen
- Department of Social Research, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Maarit Piirtola
- Department of Social Research, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
| | - Duarte L Freitas
- Department of Physical Education and Sport, University of Madeira, Funchal, Portugal
| | - José A Maia
- CIFI2D, Faculty of Sport, Porto, University of Porto, Portugal
| | - Sevgi Y Öncel
- Department of Statistics, Faculty of Arts and Sciences, Kirikkale University, Kirikkale, Turkey
| | - Fazil Aliev
- Psychology and African American Studies, Viginia Commonwealth University, Richmond, VA, USA
| | - Fuling Ji
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Feng Ning
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Zengchang Pang
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Esther Rebato
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country UPV/EHU, Leioa, Spain
| | - Kimberly J Saudino
- Boston University, Department of Psychological and Brain Sciencies, Boston, MA, USA
| | - Tessa L Cutler
- The Australian Twin Registry, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - John L Hopper
- The Australian Twin Registry, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea
| | - Vilhelmina Ullemar
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pediatric Allergy and Pulmonology Unit at Astrid Lindgren Children’s Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Patrik KE Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wendy Cozen
- Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
- USC Norris Comprehensive Cancer Center, Los Angeles, California, USA
| | - Amie E Hwang
- Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Thomas M Mack
- Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
- USC Norris Comprehensive Cancer Center, Los Angeles, California, USA
| | - Gonneke Willemsen
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Meike Bartels
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - Tracy L Nelson
- Department of Health and Exercise Sciences and Colorado School of Public Health, Colorado State University, Fort Collins, Colorado, USA
| | | | - Joohon Sung
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea
- Institute of Health and Environment, Seoul National University, Seoul, South-Korea
| | - Jina Kim
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea
| | - Jooyeon Lee
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea
| | - Sooji Lee
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea
| | - Clare H Llewellyn
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Abigail Fisher
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Emanuela Medda
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità - Rome, Italy
| | - Lorenza Nisticò
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità - Rome, Italy
| | - Virgilia Toccaceli
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità - Rome, Italy
| | - Laura A Baker
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Catherine Tuvblad
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- School of Law, Psychology and Social Work, Örebro University, Örebro, Sweden
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA
| | - Brooke M Huibregtse
- Institute of Behavioral Science, University of Colorado, Boulder, Colorado, USA
| | - Catherine A Derom
- Centre of Human Genetics, University Hospitals Leuven, Leuven, Belgium
- Department of Obstetrics and Gynaecology, Ghent University Hospitals, Ghent, Belgium
| | | | - Ruth JF Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - David Mankuta
- Hadassah Hospital Obstetrics and Gynecology Department, Hebrew University Medical School, Jerusalem, Israel
| | - Lior Abramson
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Kelly L Klump
- Michigan State University, East Lansing, Michigan, USA
| | - Judy L Silberg
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Hermine H Maes
- Department of Human and Molecular Genetics, Psychiatry & Massey Cancer Center, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Shandell Pahlen
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Margaret Gatz
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - David A Butler
- Health and Medicine Division, The National Academies of Sciences, Engineering, and Medicine Washington, DC, USA
| | | | | | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA San Diego Center of Excellence for Stress and Mental Health, La Jolla, CA, USA
| | - Michael J Lyons
- Boston University, Department of Psychology, Boston, MA, USA
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Hoe-Uk Jeong
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Yoon-Mi Hur
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Thorkild IA Sørensen
- Novo Nordisk Foundation Centre for Basic Metabolic Research (Section of Metabolic Genetics), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland
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23
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Schrempft S, van Jaarsveld CHM, Fisher A, Herle M, Smith AD, Fildes A, Llewellyn CH. Variation in the Heritability of Child Body Mass Index by Obesogenic Home Environment. JAMA Pediatr 2018; 172:1153-1160. [PMID: 30285028 PMCID: PMC6396810 DOI: 10.1001/jamapediatrics.2018.1508] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/23/2018] [Indexed: 12/24/2022]
Abstract
Importance The early obesogenic home environment is consistently identified as a key influence on child weight trajectories, but little research has examined the mechanisms of that influence. Such research is essential for the effective prevention and treatment of overweight and obesity. Objective To test behavioral susceptibility theory's hypothesis that the heritability of body mass index (BMI) is higher among children who live in more obesogenic home environments. Design, Setting, and Participants This study was a gene-environment interaction twin study that used cross-sectional data from 925 families (1850 twins) in the Gemini cohort (a population-based prospective cohort of twins born in England and Wales between March and December 2007). Data were analyzed from July to October 2013 and in June 2018. Exposures Parents completed the Home Environment Interview, a comprehensive measure of the obesogenic home environment in early childhood. Three standardized composite scores were created to capture food, physical activity, and media-related influences in the home; these were summed to create an overall obesogenic risk score. The 4 composite scores were split on the mean, reflecting higher-risk and lower-risk home environments. Main Outcomes and Measures Quantitative genetic model fitting was used to estimate heritability of age-adjusted and sex-adjusted BMI (BMI SD score, estimated using British 1990 growth reference data) for children living in lower-risk and higher-risk home environments. Results Among 1850 twins (915 [49.5%] male and 935 [50.5%] female; mean [SD] age, 4.1 [0.4] years), the heritability of BMI SD score was significantly higher among children living in overall higher-risk home environments (86%; 95% CI, 68%-89%) compared with those living in overall lower-risk home environments (39%; 95% CI, 21%-57%). The findings were similar when examining the heritability of BMI in the separate food and physical activity environment domains. Conclusions and Relevance These findings support the hypothesis that obesity-related genes are more strongly associated with BMI in more obesogenic home environments. Modifying the early home environment to prevent weight gain may be particularly important for children genetically at risk for obesity.
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Affiliation(s)
- Stephanie Schrempft
- Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Cornelia H. M. van Jaarsveld
- Departments for Health Evidence and Primary and Community Care, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Abigail Fisher
- Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Moritz Herle
- University College London Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Andrea D. Smith
- Department of Behavioural Science and Health, University College London, London, United Kingdom
| | - Alison Fildes
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Clare H. Llewellyn
- Department of Behavioural Science and Health, University College London, London, United Kingdom
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24
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Pervasive Modulation of Obesity Risk by the Environment and Genomic Background. Genes (Basel) 2018; 9:genes9080411. [PMID: 30110940 PMCID: PMC6115725 DOI: 10.3390/genes9080411] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 07/30/2018] [Accepted: 07/30/2018] [Indexed: 12/22/2022] Open
Abstract
The prevalence of the so-called diseases of affluence, such as type 2 diabetes or hypertension, has increased dramatically in the last two generations. Although genome-wide association studies (GWAS) have discovered hundreds of genes involved in disease etiology, the sudden increase in disease incidence suggests a major role for environmental risk factors. Obesity constitutes a case example of a modern trait shaped by contemporary environment, although with considerable debates about the extent to which gene-by-environment (G×E) interactions accentuate obesity risk in individuals following obesogenic lifestyles. Although interaction effects have been robustly confirmed at the FTO locus, accumulating evidence at the genome-wide level implicates a role for polygenic risk-by-environment interactions. Through a variety of analyses using the UK Biobank, we confirm that the genomic background plays a major role in shaping the expressivity of alleles that increase body mass index (BMI).
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25
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Seyednasrollah F, Mäkelä J, Pitkänen N, Juonala M, Hutri-Kähönen N, Lehtimäki T, Viikari J, Kelly T, Li C, Bazzano L, Elo LL, Raitakari OT. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study. ACTA ACUST UNITED AC 2018; 10:CIRCGENETICS.116.001554. [PMID: 28620069 DOI: 10.1161/circgenetics.116.001554] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/06/2016] [Indexed: 12/21/2022]
Abstract
BACKGROUND Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. METHODS AND RESULTS A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P<0.0001) and validation data (AUC=0.769 versus AUC=0.747, P=0.026). WGRS97 improved the accuracy in training (AUC=0.782 versus AUC=0.744, P<0.0001) but not in validation data (AUC=0.749 versus AUC=0.747, P=0.785). Higher WGRS19 associated with higher body mass index at 9 years and WGRS97 at 6 years. Replication in BHS confirmed our findings that WGRS19 and WGRS97 are associated with body mass index. CONCLUSIONS WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity.
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Affiliation(s)
- Fatemeh Seyednasrollah
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Johanna Mäkelä
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.).
| | - Niina Pitkänen
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Markus Juonala
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Nina Hutri-Kähönen
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Terho Lehtimäki
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Jorma Viikari
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Tanika Kelly
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Changwei Li
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Lydia Bazzano
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Laura L Elo
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
| | - Olli T Raitakari
- From the Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Finland (F.S., J.M., L.L.E.); Department of Mathematics and Statistics (F.S.), Research Centre of Applied and Preventive Cardiovascular Medicine (N.P., O.T.R.), and Department of Medicine (M.J., J.V.), University of Turku, Finland; Division of Medicine (M.J., J.V.) and Clinical Physiology and Nuclear Medicine (O.T.R.), Turku University Hospital, Finland; Department of Pediatrics (N.H.-K.) and School of Medicine (T.L.), University of Tampere, Finland; Tampere University Hospital, Finland (N.H.-K.); Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland (T.L.); Tulane University Health Sciences Center, New Orleans, LA (T.K., L.B.); and Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (C.L.)
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26
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Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P, Mohan V, Diaz R, Engert JC, Yusuf S, Gerstein HC, Anand SS, Meyre D. Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. Am J Hum Genet 2017; 101:925-938. [PMID: 29220676 PMCID: PMC5812888 DOI: 10.1016/j.ajhg.2017.10.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 10/12/2017] [Indexed: 12/17/2022] Open
Abstract
A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10-4), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10-37; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.
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Affiliation(s)
- Arkan Abadi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sebastien Robiou du Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Ben Bolker
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Pardeep Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Gopalapuram, Chennai 600086, India
| | - Rafael Diaz
- Estudios Clínicos Latino America, Paraguay 160, S2000CVD Rosario, Santa Fe, Argentina
| | | | - Salim Yusuf
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Hertzel C Gerstein
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sonia S Anand
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
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Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study. Nutrients 2017; 9:nu9101107. [PMID: 29019927 PMCID: PMC5691723 DOI: 10.3390/nu9101107] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 10/04/2017] [Accepted: 10/09/2017] [Indexed: 01/02/2023] Open
Abstract
Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.
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28
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Zhao W, Ware EB, He Z, Kardia SLR, Faul JD, Smith JA. Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14101153. [PMID: 28961216 PMCID: PMC5664654 DOI: 10.3390/ijerph14101153] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 09/25/2017] [Accepted: 09/26/2017] [Indexed: 12/22/2022]
Abstract
Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) (p = 0.07).
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Affiliation(s)
- Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Erin B Ware
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA.
| | - Zihuai He
- Department of Biostatistics, Columbia University, New York, NY 10032, USA.
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA.
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA.
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29
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Silventoinen K, Jelenkovic A, Sund R, Yokoyama Y, Hur YM, Cozen W, Hwang AE, Mack TM, Honda C, Inui F, Iwatani Y, Watanabe M, Tomizawa R, Pietiläinen KH, Rissanen A, Siribaddana SH, Hotopf M, Sumathipala A, Rijsdijk F, Tan Q, Zhang D, Pang Z, Piirtola M, Aaltonen S, Öncel SY, Aliev F, Rebato E, Hjelmborg JB, Christensen K, Skytthe A, Kyvik KO, Silberg JL, Eaves LJ, Cutler TL, Ordoñana JR, Sánchez-Romera JF, Colodro-Conde L, Song YM, Yang S, Lee K, Franz CE, Kremen WS, Lyons MJ, Busjahn A, Nelson TL, Whitfield KE, Kandler C, Jang KL, Gatz M, Butler DA, Stazi MA, Fagnani C, D’Ippolito C, Duncan GE, Buchwald D, Martin NG, Medland SE, Montgomery GW, Jeong HU, Swan GE, Krasnow R, Magnusson PKE, Pedersen NL, Dahl Aslan AK, McAdams TA, Eley TC, Gregory AM, Tynelius P, Baker LA, Tuvblad C, Bayasgalan G, Narandalai D, Spector TD, Mangino M, Lachance G, Burt SA, Klump KL, Harris JR, Brandt I, Nilsen TS, Krueger RF, McGue M, Pahlen S, Corley RP, Huibregtse BM, Bartels M, van Beijsterveldt CEM, Willemsen G, Goldberg JH, Rasmussen F, Tarnoki AD, Tarnoki DL, Derom CA, Vlietinck RF, Loos RJF, Hopper JL, Sung J, Maes HH, Turkheimer E, Boomsma DI, Sørensen TIA, Kaprio J. Differences in genetic and environmental variation in adult BMI by sex, age, time period, and region: an individual-based pooled analysis of 40 twin cohorts. Am J Clin Nutr 2017; 106:457-466. [PMID: 28679550 PMCID: PMC5525120 DOI: 10.3945/ajcn.117.153643] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 06/08/2017] [Indexed: 12/20/2022] Open
Abstract
Background: Genes and the environment contribute to variation in adult body mass index [BMI (in kg/m2)], but factors modifying these variance components are poorly understood.Objective: We analyzed genetic and environmental variation in BMI between men and women from young adulthood to old age from the 1940s to the 2000s and between cultural-geographic regions representing high (North America and Australia), moderate (Europe), and low (East Asia) prevalence of obesity.Design: We used genetic structural equation modeling to analyze BMI in twins ≥20 y of age from 40 cohorts representing 20 countries (140,379 complete twin pairs).Results: The heritability of BMI decreased from 0.77 (95% CI: 0.77, 0.78) and 0.75 (95% CI: 0.74, 0.75) in men and women 20-29 y of age to 0.57 (95% CI: 0.54, 0.60) and 0.59 (95% CI: 0.53, 0.65) in men 70-79 y of age and women 80 y of age, respectively. The relative influence of unique environmental factors correspondingly increased. Differences in the sets of genes affecting BMI in men and women increased from 20-29 to 60-69 y of age. Mean BMI and variances in BMI increased from the 1940s to the 2000s and were greatest in North America and Australia, followed by Europe and East Asia. However, heritability estimates were largely similar over measurement years and between regions. There was no evidence of environmental factors shared by co-twins affecting BMI.Conclusions: The heritability of BMI decreased and differences in the sets of genes affecting BMI in men and women increased from young adulthood to old age. The heritability of BMI was largely similar between cultural-geographic regions and measurement years, despite large differences in mean BMI and variances in BMI. Our results show a strong influence of genetic factors on BMI, especially in early adulthood, regardless of the obesity level in the population.
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Affiliation(s)
- Karri Silventoinen
- Departments of Social Research and .,Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Aline Jelenkovic
- Departments of Social Research and,Department of Genetics, Physical Anthropology, and Animal Physiology, University of the Basque Country, Leioa, Spain
| | - Reijo Sund
- Departments of Social Research and,Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Yoshie Yokoyama
- Department of Public Health Nursing, Osaka City University, Osaka, Japan
| | - Yoon-Mi Hur
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Wendy Cozen
- Department of Preventive Medicine, Keck School of Medicine,,Norris Comprehensive Cancer Center, and
| | - Amie E Hwang
- Department of Preventive Medicine, Keck School of Medicine
| | - Thomas M Mack
- Department of Preventive Medicine, Keck School of Medicine,,Norris Comprehensive Cancer Center, and
| | - Chika Honda
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Fujio Inui
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan;,Faculty of Health Science, Kio University, Nara, Japan
| | - Yoshinori Iwatani
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Mikio Watanabe
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Rie Tomizawa
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland;,Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Aila Rissanen
- Obesity Research Unit, Research Programs Unit, University of Helsinki, Helsinki, Finland;,Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Sisira H Siribaddana
- Institute of Research and Development, Battaramulla, Sri Lanka;,Faculty of Medicine and Allied Sciences, Rajarata University of Sri Lanka, Saliyapura, Sri Lanka
| | - Matthew Hotopf
- National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, Institute of Psychiatry Psychology and Neuroscience
| | - Athula Sumathipala
- Institute of Research and Development, Battaramulla, Sri Lanka;,Research Institute for Primary Care and Health Sciences, School for Primary Care Research, Faculty of Health, Keele University, Staffordshire, United Kingdom
| | - Fruhling Rijsdijk
- Medical Research Council Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, and
| | - Qihua Tan
- Unit of Epidemiology, Biostatistics, and Biodemography, Departments of Public Health and
| | - Dongfeng Zhang
- Department of Public Health, Qingdao University Medical College, Qingdao, China
| | - Zengchang Pang
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Maarit Piirtola
- Departments of Social Research and,Institute for Molecular Medicine, Helsinki, Finland
| | - Sari Aaltonen
- Departments of Social Research and,Public Health, and
| | - Sevgi Y Öncel
- Department of Statistics, Faculty of Arts and Sciences, Kırıkkale University, Kırıkkale, Turkey
| | - Fazil Aliev
- Faculty of Business, Karabuk University, Karabuk, Turkey;,Departments of Psychology and,African American Studies
| | - Esther Rebato
- Department of Genetics, Physical Anthropology, and Animal Physiology, University of the Basque Country, Leioa, Spain
| | | | - Kaare Christensen
- The Danish Twin Registry,,Departments of Clinical Biochemistry and Pharmacology and Clinical Genetics, and
| | | | - Kirsten O Kyvik
- Clinical Research, University of Southern Denmark, Odense, Denmark;,Odense Patient Data Explorative Network, Odense University Hospital, Odense, Denmark
| | - Judy L Silberg
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, and
| | - Lindon J Eaves
- Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, and
| | - Tessa L Cutler
- The Australian Twin Registry, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Juan R Ordoñana
- Department of Human Anatomy and Psychobiology, University of Murcia, Murcia, Spain;,Biomedical Research Institute of Murcia (IMIB)-Arrixaca, Murcia, Spain
| | - Juan F Sánchez-Romera
- Department of Human Anatomy and Psychobiology, University of Murcia, Murcia, Spain;,Biomedical Research Institute of Murcia (IMIB)-Arrixaca, Murcia, Spain
| | - Lucia Colodro-Conde
- Department of Human Anatomy and Psychobiology, University of Murcia, Murcia, Spain;,Quantitative Genetics Laboratory and
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sarah Yang
- Department of Epidemiology, School of Public Health, and,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Kayoung Lee
- Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, CA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, CA;,US Department of Veterans Affairs San Diego Center of Excellence for Stress and Mental Health, La Jolla, CA
| | | | | | - Tracy L Nelson
- Department of Health and Exercise Sciences, Colorado School of Public Health, Colorado State University, Aurora, CO
| | | | | | - Kerry L Jang
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; Departments of
| | - Margaret Gatz
- Department of Psychology, University of Southern California, Los Angeles, CA;,Medical Epidemiology and Biostatistics and
| | - David A Butler
- Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine, Washington, DC
| | - Maria A Stazi
- Italian National Institute of Health National Center for Epidemiology, Surveillance, and Health Promotion, Rome, Italy
| | - Corrado Fagnani
- Italian National Institute of Health National Center for Epidemiology, Surveillance, and Health Promotion, Rome, Italy
| | - Cristina D’Ippolito
- Italian National Institute of Health National Center for Epidemiology, Surveillance, and Health Promotion, Rome, Italy
| | - Glen E Duncan
- Washington State Twin Registry, Health Sciences, Washington State University, Spokane, WA
| | - Dedra Buchwald
- Washington State Twin Registry, Health Sciences, Washington State University, Spokane, WA
| | - Nicholas G Martin
- Genetic Epidemiology Department, Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, Australia
| | - Sarah E Medland
- Genetic Epidemiology Department, Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, Australia
| | - Grant W Montgomery
- Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Hoe-Uk Jeong
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Gary E Swan
- Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA
| | - Ruth Krasnow
- Center for Health Sciences, SRI International, Menlo Park, CA
| | | | | | - Anna K Dahl Aslan
- Medical Epidemiology and Biostatistics and,Institute of Gerontology and Aging Research Network, School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | - Tom A McAdams
- Medical Research Council Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, and
| | - Thalia C Eley
- Medical Research Council Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, and
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Per Tynelius
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Laura A Baker
- Department of Psychology, University of Southern California, Los Angeles, CA
| | - Catherine Tuvblad
- Department of Psychology, University of Southern California, Los Angeles, CA;,School of Law, Psychology, and Social Work, Örebro University, Örebro, Sweden
| | | | - Danshiitsoodol Narandalai
- Healthy Twin Association of Mongolia, Ulaanbaatar, Mongolia;,Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom;,National Institute for Health Research Biomedical Research Centre at Guy’s and St. Thomas’ Foundation Trust, London, United Kingdom
| | - Genevieve Lachance
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | | | | | | | | | | | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Shandell Pahlen
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Robin P Corley
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
| | - Brooke M Huibregtse
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
| | - Meike Bartels
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | | | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Jack H Goldberg
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - Finn Rasmussen
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Adam D Tarnoki
- Department of Radiology, Semmelweis University, Budapest, Hungary;,Hungarian Twin Registry, Budapest, Hungary
| | - David L Tarnoki
- Department of Radiology, Semmelweis University, Budapest, Hungary;,Hungarian Twin Registry, Budapest, Hungary
| | - Catherine A Derom
- Centre of Human Genetics, University Hospitals Leuven, Leuven, Belgium;,Department of Obstetrics and Gynaecology, Ghent University Hospitals, Ghent, Belgium
| | | | - Ruth JF Loos
- Charles Bronfman Institute for Personalized Medicine, Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John L Hopper
- Department of Epidemiology, School of Public Health, and,The Australian Twin Registry, Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Joohon Sung
- Department of Epidemiology, School of Public Health, and,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Hermine H Maes
- Departments of Human and Molecular Genetics and Psychiatry, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA
| | - Eric Turkheimer
- Department of Psychology, University of Virginia, Charlottesville, VA
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Thorkild IA Sørensen
- Section on Metabolic Genetics, Novo Nordisk Foundation Centre for Basic Metabolic Research, Copenhagen, Denmark;,Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; and,Department of Clinical Epidemiology, Bispebjerg and Frederiksberg Hospitals, Copenhagen, Denmark
| | - Jaakko Kaprio
- Public Health, and,Institute for Molecular Medicine, Helsinki, Finland
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30
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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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31
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Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci (Lond) 2017; 130:943-86. [PMID: 27154742 DOI: 10.1042/cs20160136] [Citation(s) in RCA: 227] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/24/2016] [Indexed: 12/19/2022]
Abstract
In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.
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32
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Abstract
PURPOSE OF REVIEW There is considerable variability in human body weight, despite the ubiquity of the 'obesogenic' environment. Human body weight has a strong genetic basis and it has been hypothesised that genetic susceptibility to the environment explains variation in human body weight, with differences in appetite being implicated as the mediating mechanism; so-called 'behavioural susceptibility theory' (BST), first described by Professor Jane Wardle. This review summarises the evidence for the role of appetite as a mediator of genetic risk of obesity. RECENT FINDINGS Variation in appetitive traits is observable from infancy, drives early weight gain and is highly heritable in infancy and childhood. Obesity-related common genetic variants identified through genome-wide association studies show associations with appetitive traits, and appetite mediates part of the observed association between genetic risk and adiposity. Obesity results from an interaction between genetic susceptibility to overeating and exposure to an 'obesogenic' food environment.
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Affiliation(s)
- Clare H Llewellyn
- Department of Behavioural Science and Health, University College London, London, UK.
| | - Alison Fildes
- Department of Behavioural Science and Health, University College London, London, UK
- School of Psychology, University of Leeds, Leeds, UK
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33
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Mäkelä J, Lagström H, Pitkänen N, Kuulasmaa T, Kaljonen A, Laakso M, Niinikoski H. Genetic risk clustering increases children's body weight at 2 years of age - the STEPS Study. Pediatr Obes 2016; 11:459-467. [PMID: 26663901 DOI: 10.1111/ijpo.12087] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 10/20/2015] [Accepted: 10/24/2015] [Indexed: 12/21/2022]
Abstract
BACKGROUND Genetic determinants have an impact on adult weight but the association between genetic determinants and weight at young age is still poorly understood. OBJECTIVE The objective of this study was to examine the association between genetic risk scores and early growth from birth to 2 years of age. METHODS Genetic risk scores of 83 adiposity-related or obesity-related single nucleotide polymorphisms (SNPs) (genetic risk score [GRS]83) were calculated for 1278 children. Specific phenotype score for 16 weight-related SNPs (weightGRS) was calculated. Anthropometric data were obtained at birth, 13 months and 2 years of age. RESULTS The GRS83 was associated with weight at 13 months (β = 0.080, P = 0.015) and 2 years (β = 0.080, P = 0.017) of age and with weight gain from birth to 13 months (β = 0.069, P = 0.036) and to 2 years of age (β = 0.074, P = 0.028). At 2 years of age, the GRS83 was also associated with weight for height (β = 0.065, P = 0.046), weight-for-height standard deviation score (SDS) (β = 0.074, P = 0.022) and body mass index SDS (β = 0.068, P = 0.045). WeightGRS was associated with higher body weight at 13 months (β = 0.081, P = 0.014) and 2 years of age (β = 0.086, P = 0.011). The genetic effect on weight varied from 0.69 to 1.89 kg at 2 years of age according to number of risk alleles. Children with high genetic risk for adiposity were heavier than children with low genetic risk at 2 years of age (12.8 vs. 13.4 kg, P = 0.017). CONCLUSION The GRS 83 revealed increased genetic risk for higher weight in children already at 13 months and 2 years of age, which may result in increased obesity risk later in life.
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Affiliation(s)
- J Mäkelä
- Turku Institute for Child and Youth Research, University of Turku, Turku, Finland.,Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - H Lagström
- Turku Institute for Child and Youth Research, University of Turku, Turku, Finland
| | - N Pitkänen
- Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - T Kuulasmaa
- Institute of Clinical Medicine/Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - A Kaljonen
- Turku Institute for Child and Youth Research, University of Turku, Turku, Finland
| | - M Laakso
- Institute of Clinical Medicine/Internal Medicine, University of Eastern Finland, Kuopio, Finland.,Institute of Clinical Medicine/Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - H Niinikoski
- Department of Pediatrics, University of Turku, Turku, Finland.,Department of Physiology, University of Turku, Turku, Finland
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34
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Vaezghasemi M, Razak F, Ng N, Subramanian SV. Inter-individual inequality in BMI: An analysis of Indonesian Family Life Surveys (1993-2007). SSM Popul Health 2016; 2:876-888. [PMID: 29349195 PMCID: PMC5757920 DOI: 10.1016/j.ssmph.2016.09.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 09/12/2016] [Accepted: 09/30/2016] [Indexed: 11/28/2022] Open
Abstract
Widening inequalities in mean Body Mass Index (BMI) between social and economic groups are well documented. However, whether changes in mean BMI are followed by changes in dispersion (or variance) and whether these inequalities are also occurring within social groups or across individuals remain understudied. In addition, a substantial body of literature exists on the global increase in mean BMI and prevalence of overweight and obesity. However, whether this weight gain is shared proportionately across the whole spectrum of BMI distribution, also remains understudied. We examined changes in the distribution of BMI at the population level over time to understand how changes in the dispersion reflect between-group compared to within-group inequalities in weight gain. Moreover, we investigated the entire distribution of BMI to determine in which percentiles the most weight gain is occurring over time. Utilizing four waves (from 1993 to 2007) of Indonesian Family Life Surveys (IFLS), we estimated changes in the mean and the variance of BMI over time and across various socioeconomic groups based on education and households' expenditure per capita in 53,648 men and women aged 20-50 years. An increase in mean and standard deviation was observed among men (by 4.3% and 25%, respectively) and women (by 7.3% and 20%, respectively) over time. Quantile-Quantile plots showed that higher percentiles had greater increases in BMI compared to the segment of the population at lower percentiles. While between socioeconomic group differences decreased over time, within-group differences increased and were more prominent among individuals with poor education and lower per capita expenditures. Population changes in BMI cannot be fully described by average trends or single parameters such as the mean BMI. Moreover, greater increases in within-group dispersion compared with between-group differences imply that growing inequalities are not merely driven by these socioeconomic factors at the population level.
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Affiliation(s)
- Masoud Vaezghasemi
- Epidemiology and Global Health Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.,Umeå Centre for Gender Studies, Umeå University, Umeå, Sweden
| | - Fahad Razak
- Department of Medicine, University of Toronto, Toronto, Canada.,Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Ontario, Canada.,Harvard Center for Population and Development Studies, Cambridge, MA, USA
| | - Nawi Ng
- Epidemiology and Global Health Unit, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - S V Subramanian
- Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, USA
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35
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Silventoinen K, Huppertz C, van Beijsterveldt CEM, Bartels M, Willemsen G, Boomsma DI. The genetic architecture of body mass index from infancy to adulthood modified by parental education. Obesity (Silver Spring) 2016; 24:2004-11. [PMID: 27474859 DOI: 10.1002/oby.21588] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 05/12/2016] [Accepted: 05/31/2016] [Indexed: 01/17/2023]
Abstract
OBJECTIVE A higher prevalence of obesity in lower socioeconomic classes is common in Western societies. This study examined the role of gene-environment interactions in the association between parental education and body mass index (BMI) from infancy to the onset of adulthood. METHODS Parentally reported BMI from 1 to 13 and self-reported BMI from 14 to 20 years of age were collected in 16,646 complete Dutch twin pairs and analyzed by genetic twin modeling. RESULTS At 7 to 8 years of age, children whose parents had middle or low educational levels had more excess weight than the children of more highly educated parents, and the difference increased until 18 to 20 years of age. The major part of the BMI variation was explained by additive genetic factors (a(2) = 0.55-0.85), but environmental factors common for co-twins also played a significant role, especially from 3 to 7-8 years of age (c(2) = 0.15-0.29). The genetic variation in BMI was higher in children whose parents had middle or low educational levels compared with children whose parents had a high educational level. CONCLUSIONS The interaction between genetic factors and the childhood social environment may contribute to the formation of socioeconomic differences in obesity.
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Affiliation(s)
- Karri Silventoinen
- Department of Social Research, University of Helsinki, Helsinki, Finland
| | - Charlotte Huppertz
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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36
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Silventoinen K, Jelenkovic A, Sund R, Hur YM, Yokoyama Y, Honda C, Hjelmborg JVB, Möller S, Ooki S, Aaltonen S, Ji F, Ning F, Pang Z, Rebato E, Busjahn A, Kandler C, Saudino KJ, Jang KL, Cozen W, Hwang AE, Mack TM, Gao W, Yu C, Li L, Corley RP, Huibregtse BM, Christensen K, Skytthe A, Kyvik KO, Derom CA, Vlietinck RF, Loos RJ, Heikkilä K, Wardle J, Llewellyn CH, Fisher A, McAdams TA, Eley TC, Gregory AM, He M, Ding X, Bjerregaard-Andersen M, Beck-Nielsen H, Sodemann M, Tarnoki AD, Tarnoki DL, Stazi MA, Fagnani C, D'Ippolito C, Knafo-Noam A, Mankuta D, Abramson L, Burt SA, Klump KL, Silberg JL, Eaves LJ, Maes HH, Krueger RF, McGue M, Pahlen S, Gatz M, Butler DA, Bartels M, van Beijsterveldt TC, Craig JM, Saffery R, Freitas DL, Maia JA, Dubois L, Boivin M, Brendgen M, Dionne G, Vitaro F, Martin NG, Medland SE, Montgomery GW, Chong Y, Swan GE, Krasnow R, Magnusson PK, Pedersen NL, Tynelius P, Lichtenstein P, Haworth CM, Plomin R, Bayasgalan G, Narandalai D, Harden KP, Tucker-Drob EM, Öncel SY, Aliev F, Spector T, Mangino M, Lachance G, Baker LA, Tuvblad C, Duncan GE, Buchwald D, Willemsen G, Rasmussen F, Goldberg JH, Sørensen TI, Boomsma DI, Kaprio J. Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) study. Am J Clin Nutr 2016; 104:371-9. [PMID: 27413137 PMCID: PMC4962159 DOI: 10.3945/ajcn.116.130252] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Accepted: 05/20/2016] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Both genetic and environmental factors are known to affect body mass index (BMI), but detailed understanding of how their effects differ during childhood and adolescence is lacking. OBJECTIVES We analyzed the genetic and environmental contributions to BMI variation from infancy to early adulthood and the ways they differ by sex and geographic regions representing high (North America and Australia), moderate (Europe), and low levels (East Asia) of obesogenic environments. DESIGN Data were available for 87,782 complete twin pairs from 0.5 to 19.5 y of age from 45 cohorts. Analyses were based on 383,092 BMI measurements. Variation in BMI was decomposed into genetic and environmental components through genetic structural equation modeling. RESULTS The variance of BMI increased from 5 y of age along with increasing mean BMI. The proportion of BMI variation explained by additive genetic factors was lowest at 4 y of age in boys (a(2) = 0.42) and girls (a(2) = 0.41) and then generally increased to 0.75 in both sexes at 19 y of age. This was because of a stronger influence of environmental factors shared by co-twins in midchildhood. After 15 y of age, the effect of shared environment was not observed. The sex-specific expression of genetic factors was seen in infancy but was most prominent at 13 y of age and older. The variance of BMI was highest in North America and Australia and lowest in East Asia, but the relative proportion of genetic variation to total variation remained roughly similar across different regions. CONCLUSIONS Environmental factors shared by co-twins affect BMI in childhood, but little evidence for their contribution was found in late adolescence. Our results suggest that genetic factors play a major role in the variation of BMI in adolescence among populations of different ethnicities exposed to different environmental factors related to obesity.
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Affiliation(s)
- Karri Silventoinen
- Departments of Social Research and Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan;
| | - Aline Jelenkovic
- Departments of Social Research and Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Leioa, Spain
| | | | - Yoon-Mi Hur
- Department of Education, Mokpo National University, Jeonnam, South Korea
| | - Yoshie Yokoyama
- Department of Public Health Nursing, Osaka City University, Osaka, Japan
| | - Chika Honda
- Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Jacob vB Hjelmborg
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Sören Möller
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Syuichi Ooki
- Department of Health Science, Ishikawa Prefectural Nursing University, Kahoku, Ishikawa, Japan
| | - Sari Aaltonen
- Departments of Social Research and Public Health, University of Helsinki, Helsinki, Finland
| | - Fuling Ji
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Feng Ning
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Zengchang Pang
- Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao, China
| | - Esther Rebato
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Leioa, Spain
| | | | | | - Kimberly J Saudino
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
| | - Kerry L Jang
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Wendy Cozen
- Department of Preventive Medicine, Keck School of Medicine of USC, and USC Norris Comprehensive Cancer Center, Los Angeles, CA
| | - Amie E Hwang
- Department of Preventive Medicine, Keck School of Medicine of USC, and
| | - Thomas M Mack
- Department of Preventive Medicine, Keck School of Medicine of USC, and USC Norris Comprehensive Cancer Center, Los Angeles, CA
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | | | | | - Kaare Christensen
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and Departments of Clinical Biochemistry and Pharmacology Clinical Genetics
| | - Axel Skytthe
- The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics and Biodemography, and
| | - Kirsten O Kyvik
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Odense Patient data Explorative Network, and
| | - Catherine A Derom
- Centre of Human Genetics, University Hospitals Leuven, Leuven, Belgium; Department of Obstetrics and Gynaecology, Ghent University Hospitals, Ghent, Belgium
| | | | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kauko Heikkilä
- Public Health, University of Helsinki, Helsinki, Finland
| | - Jane Wardle
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Clare H Llewellyn
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Abigail Fisher
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom
| | - Tom A McAdams
- Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom; MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | - Thalia C Eley
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia
| | - Xiaohu Ding
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Morten Bjerregaard-Andersen
- Departments of Endocrinology and Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau; Research Center for Vitamins and Vaccines, Statens Serum Institute, Copenhagen, Denmark
| | | | - Morten Sodemann
- Departments of Endocrinology and Infectious Diseases, Odense University Hospital, Odense, Denmark
| | - Adam D Tarnoki
- Department of Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary; Hungarian Twin Registry, Budapest, Hungary
| | - David L Tarnoki
- Department of Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary; Hungarian Twin Registry, Budapest, Hungary
| | - Maria A Stazi
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | - Corrado Fagnani
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | - Cristina D'Ippolito
- Istituto Superiore di Sanità - National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
| | | | - David Mankuta
- Hadassah Hospital Obstetrics and Gynecology Department, Hebrew University Medical School, Jerusalem, Israel
| | - Lior Abramson
- The Hebrew University of Jerusalem, Jerusalem, Israel
| | | | | | | | | | - Hermine H Maes
- Department of Human and Molecular Genetics, Massey Cancer Center, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA
| | - Robert F Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Shandell Pahlen
- Department of Psychology, University of Minnesota, Minneapolis, MN
| | - Margaret Gatz
- Department of Psychology, University of Southern California, Los Angeles, CA; Departments of Medical Epidemiology and Biostatistics and
| | - David A Butler
- Institute of Medicine, National Academy of Sciences Washington, DC
| | - Meike Bartels
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | | | - Jeffrey M Craig
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Richard Saffery
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Duarte L Freitas
- Department of Physical Education and Sport, University of Madeira, Funchal, Portugal
| | - José Antonio Maia
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, Porto, University of Porto, Portugal
| | - Lise Dubois
- School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Michel Boivin
- École de psychologie, Université Laval, Québec, Canada; Institute of Genetic, Neurobiological, and Social Foundations of Child Development, Tomsk State University, Russian Federation
| | - Mara Brendgen
- Département de psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | | | - Frank Vitaro
- École de psychoéducation, Université de Montréal, Montréal, Québec, Canada
| | | | | | - Grant W Montgomery
- Molecular Epidemiology Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Youngsook Chong
- Department of Psychology, Pusan National University, Busan, South Korea
| | - Gary E Swan
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ruth Krasnow
- Center for Health Sciences, SRI International, Menlo Park, CA
| | | | | | - Per Tynelius
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | | | - Claire Ma Haworth
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Robert Plomin
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, and
| | | | - Danshiitsoodol Narandalai
- Healthy Twin Association of Mongolia, Ulaanbaatar, Mongolia; Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX
| | | | - Sevgi Y Öncel
- Department of Statistics, Faculty of Arts and Sciences, Kırıkkale University, Kırıkkale, Turkey
| | - Fazil Aliev
- Department of Human and Molecular Genetics, Departments of Psychiatry and Psychology
| | - Timothy Spector
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Genevieve Lachance
- Department of Twin Research and Genetic Epidemiology, King's College, London, United Kingdom
| | - Laura A Baker
- Department of Psychology, University of Southern California, Los Angeles, CA
| | - Catherine Tuvblad
- Department of Psychology, University of Southern California, Los Angeles, CA; Örebro University, School of Law, Psychology and Social Work, Örebro, Sweden
| | - Glen E Duncan
- College of Medicine, Washington State University - Health Sciences Spokane, Spokane, WA
| | - Dedra Buchwald
- Washington State Twin Registry, Washington State University, Seattle, WA
| | - Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Finn Rasmussen
- Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Jack H Goldberg
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
| | - Thorkild Ia Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Novo Nordisk Foundation Center for Basic Metabolic Research (Section on Metabolic Genetics) and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen,Denmark; Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, Copenhagen, The Capital Region, Denmark
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
| | - Jaakko Kaprio
- Public Health, University of Helsinki, Helsinki, Finland; National Institute for Health and Welfare, Helsinki, Finland; and Institute for Molecular Medicine FIMM, Helsinki, Finland
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Reynolds CA, Gatz M, Christensen K, Christiansen L, Dahl Aslan AK, Kaprio J, Korhonen T, Kremen WS, Krueger R, McGue M, Neiderhiser JM, Pedersen NL. Gene-Environment Interplay in Physical, Psychological, and Cognitive Domains in Mid to Late Adulthood: Is APOE a Variability Gene? Behav Genet 2016; 46:4-19. [PMID: 26538244 PMCID: PMC4858319 DOI: 10.1007/s10519-015-9761-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 10/14/2015] [Indexed: 10/22/2022]
Abstract
Despite emerging interest in gene-environment interaction (GxE) effects, there is a dearth of studies evaluating its potential relevance apart from specific hypothesized environments and biometrical variance trends. Using a monozygotic within-pair approach, we evaluated evidence of G×E for body mass index (BMI), depressive symptoms, and cognition (verbal, spatial, attention, working memory, perceptual speed) in twin studies from four countries. We also evaluated whether APOE is a 'variability gene' across these measures and whether it partly represents the 'G' in G×E effects. In all three domains, G×E effects were pervasive across country and gender, with small-to-moderate effects. Age-cohort trends were generally stable for BMI and depressive symptoms; however, they were variable-with both increasing and decreasing age-cohort trends-for different cognitive measures. Results also suggested that APOE may represent a 'variability gene' for depressive symptoms and spatial reasoning, but not for BMI or other cognitive measures. Hence, additional genes are salient beyond APOE.
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Affiliation(s)
- Chandra A Reynolds
- Department of Psychology, University of California Riverside, 900 University Ave., Riverside, CA, 92521, USA.
| | - Margaret Gatz
- Department of Psychology, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177, Stockholm, Sweden
| | - Kaare Christensen
- Epidemiology, Biostatistics and Bio-demography, Institute of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
- Department of Clinical Genetics and Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Lene Christiansen
- Epidemiology, Biostatistics and Bio-demography, Institute of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
| | - Anna K Dahl Aslan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177, Stockholm, Sweden
- Institute of Gerontology, School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | - Jaakko Kaprio
- Department of Public Health & Institute for Molecular Medicine FIMM, University of Helsinki, 00014, Helsinki, Finland
| | - Tellervo Korhonen
- Department of Public Health, University of Helsinki, 00014, Helsinki, Finland
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, 70211, Kuopio, Finland
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Robert Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Matt McGue
- Epidemiology, Biostatistics and Bio-demography, Institute of Public Health, University of Southern Denmark, 5000, Odense C, Denmark
- Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Jenae M Neiderhiser
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Nancy L Pedersen
- Department of Psychology, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 17177, Stockholm, Sweden
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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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Liu H, Guo G. Lifetime Socioeconomic Status, Historical Context, and Genetic Inheritance in Shaping Body Mass in Middle and Late Adulthood. AMERICAN SOCIOLOGICAL REVIEW 2015; 80:705-737. [PMID: 27231400 PMCID: PMC4878452 DOI: 10.1177/0003122415590627] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
This study demonstrates body mass in middle and late adulthood as a consequence of the complex interplay among individuals' genes, lifetime socioeconomic experiences, and the historical context in which they live. Drawing on approximately 9,000 genetic samples from the Health and Retirement Study, we first investigate how socioeconomic status (SES) over the life course moderates the impact of 32 established obesity-related genetic variants on body mass index (BMI) in middle and late adulthood. Further, we consider differences across birth cohorts in the genetic influence on BMI and cohort variations in the moderating effects of life-course SES on the genetic influence. Our analyses suggest that persistently low SES over the life course or downward mobility (e.g., high SES in childhood but low SES in adulthood) amplified the genetic influence on BMI, while persistently high SES or upward mobility (e.g., low SES in childhood but high SES in adulthood) compensated for such influence. For more recent birth cohorts, while the genetic influence on BMI became stronger, the moderating effects of lifetime SES on the genetic influence were weaker compared to earlier cohorts. We discuss these findings in light of social changes during the obesity epidemic in the United States.
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Affiliation(s)
- Hexuan Liu
- Department of Sociology, the University of North Carolina at Chapel
Hill
- Carolina Population Center, the University of North Carolina at
Chapel Hill
| | - Guang Guo
- Department of Sociology, the University of North Carolina at Chapel
Hill
- Carolina Center for Genome Sciences, the University of North
Carolina at Chapel Hill
- Carolina Population Center, the University of North Carolina at
Chapel Hill
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40
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The CODATwins Project: The Cohort Description of Collaborative Project of Development of Anthropometrical Measures in Twins to Study Macro-Environmental Variation in Genetic and Environmental Effects on Anthropometric Traits. Twin Res Hum Genet 2015; 18:348-60. [PMID: 26014041 DOI: 10.1017/thg.2015.29] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.
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41
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Manning S, Pucci A, Batterham RL. Roux-en-Y gastric bypass: effects on feeding behavior and underlying mechanisms. J Clin Invest 2015; 125:939-48. [PMID: 25729850 DOI: 10.1172/jci76305] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Bariatric surgery is the most effective treatment for severe obesity, producing marked sustained weight loss with associated reduced morbidity and mortality. Roux-en-Y gastric bypass surgery (RYGBP), the most commonly performed procedure, was initially viewed as a hybrid restrictive-malabsorptive procedure. However, over the last decade, it has become apparent that alternative physiologic mechanisms underlie its beneficial effects. RYGBP-induced altered feeding behavior, including reduced appetite and changes in taste/food preferences, is now recognized as a key driver of the sustained postoperative weight loss. The brain ultimately determines feeding behavior, and here we review the mechanisms by which RYGBP may affect central appetite-regulating pathways.
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Krishna A, Razak F, Lebel A, Smith GD, Subramanian SV. Trends in group inequalities and interindividual inequalities in BMI in the United States, 1993-2012. Am J Clin Nutr 2015; 101:598-605. [PMID: 25733645 DOI: 10.3945/ajcn.114.100073] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Marked increases in mean body mass index (BMI) and prevalence of obesity and overweight in the United States are well known. However, whether these average increases were accompanied by changing dispersion (or SD) remains understudied. OBJECTIVE We investigated population-level changes in the BMI distribution over time to understand how changes in dispersion reflect between-group compared with within-group inequalities in weight gain in the United States. DESIGN Using data from the Behavioral Risk Factor Surveillance System survey (1993-2012), we analyzed associations between mean, SD, and median BMI and BMI at the 5th and 95th percentiles for 3,050,992 non-Hispanic white, non-Hispanic black, and Hispanic men and women aged 25-64 y. RESULTS Overall, an increase of 1.0 in mean BMI (in kg/m²) was associated with an increase of 0.70 (95% CI: 0.67, 0.73) in the SD of BMI. A change of 1.0 in median BMI was associated with a change of 0.18 (95% CI: 0.14, 0.21) in the BMI value at the 5th percentile compared with a change of 2.94 (95% CI: 2.81, 3.07) at the 95th percentile. Quantile-quantile plots showed unequal changes in the BMI distribution, with pronounced changes at higher percentiles. Similar patterns were observed in subgroups stratified by sex, race-ethnicity, and education with non-Hispanic black women and women with less than a high school education having highest mean BMI, SD of BMI, and BMI values at the 5th and 95th percentiles. CONCLUSIONS Mean BMI and the percentage of overweight and obese individuals do not fully describe population changes in BMI. Increases in within-group inequality in BMI represent an underrecognized characteristic of population-level weight gain. Crucially, similar increases in dispersion within groups suggest that growing inequalities in BMI at the population level are not driven by these socioeconomic and demographic factors. Future research should focus on understanding factors driving inequalities in weight gain between individuals.
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Affiliation(s)
- Aditi Krishna
- From the Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA (AK and SVS); the Department of Medicine, University of Toronto, Toronto, Canada (FR); the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Canada (FR); the Harvard Center for Population and Development Studies. Boston, MA (FR); Laval University, Quebec, Canada (AL); the Quebec Heart and Lung Institute Research Center, Quebec, Canada (AL); and the School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom (GDS)
| | - Fahad Razak
- From the Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA (AK and SVS); the Department of Medicine, University of Toronto, Toronto, Canada (FR); the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Canada (FR); the Harvard Center for Population and Development Studies. Boston, MA (FR); Laval University, Quebec, Canada (AL); the Quebec Heart and Lung Institute Research Center, Quebec, Canada (AL); and the School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom (GDS)
| | - Alexandre Lebel
- From the Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA (AK and SVS); the Department of Medicine, University of Toronto, Toronto, Canada (FR); the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Canada (FR); the Harvard Center for Population and Development Studies. Boston, MA (FR); Laval University, Quebec, Canada (AL); the Quebec Heart and Lung Institute Research Center, Quebec, Canada (AL); and the School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom (GDS)
| | - George Davey Smith
- From the Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA (AK and SVS); the Department of Medicine, University of Toronto, Toronto, Canada (FR); the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Canada (FR); the Harvard Center for Population and Development Studies. Boston, MA (FR); Laval University, Quebec, Canada (AL); the Quebec Heart and Lung Institute Research Center, Quebec, Canada (AL); and the School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom (GDS)
| | - S V Subramanian
- From the Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA (AK and SVS); the Department of Medicine, University of Toronto, Toronto, Canada (FR); the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael's Hospital, Toronto, Canada (FR); the Harvard Center for Population and Development Studies. Boston, MA (FR); Laval University, Quebec, Canada (AL); the Quebec Heart and Lung Institute Research Center, Quebec, Canada (AL); and the School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom (GDS)
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Ajslev TA, Ängquist L, Silventoinen K, Baker JL, Sørensen TIA. Trends in parent-child correlations of childhood body mass index during the development of the obesity epidemic. PLoS One 2014; 9:e109932. [PMID: 25329656 PMCID: PMC4201474 DOI: 10.1371/journal.pone.0109932] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Accepted: 09/12/2014] [Indexed: 01/19/2023] Open
Abstract
Background The intergenerational resemblance in body mass index may have increased during the development of the obesity epidemic due to changes in environment and/or expression of genetic predisposition. Objectives This study investigates trends in intergenerational correlations of childhood body mass index (BMI; kg/m2) during the emergence of the obesity epidemic. Methods The study population was derived from the Copenhagen School Health Records Register, which includes height and weight measurements since birth year 1930. Mothers and fathers with BMIs available at ages 7 (n = 25,923 and n = 20,972) or 13 years (n = 26,750 and n = 21,397), respectively, were linked through the civil registration system introduced in 1968 to their children with BMIs available at age 7 years. Age- and sex-specific BMI z-scores were calculated. Correlations were estimated across eight intervals of child birth years (1952–1989) separately by sex. Trends in these correlations were examined. Whereas the mother-child correlations reflected the biological relationship, a likely decline in the assignment of non-biological fathers through the registration system across time must be considered when interpreting the father-child correlations. Results The BMI correlations between mothers and sons ranged from 0.29–0.36 and they decreased marginally, albeit significantly across time at ages 7–7 years (−0.002/year, p = 0.006), whereas those at 13–7 years remained stable (<0.0004/year, p = 0.96). Mother-daughter correlations ranged from 0.30–0.34, and they were stable at ages 7–7 years (0.0001/year, p = 0.84) and at 13–7 years (0.0004/year, p = 0.56). In contrast, father-son correlations increased significantly during this period, both at ages 7–7 (0.002/year, p = 0.007) and at ages 13–7 years (0.003/year, p<0.001), whereas the increase in father-daughter correlations were insignificant both at ages 7–7 (0.001/year, p = 0.37) and at ages 13–7 years (0.001/year, p = 0.18). Conclusion During the obesity epidemics development, the intergenerational resemblance with mothers remained stable, whereas the father-child BMI resemblance increased, possibly reflecting changes in family relationships, and unlikely to have influenced the epidemic.
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Affiliation(s)
- Teresa A. Ajslev
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg Hospital, Frederiksberg, Denmark
- * E-mail:
| | - Lars Ängquist
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg Hospital, Frederiksberg, Denmark
| | - Karri Silventoinen
- Population Research Unit, Department of Social Research, University of Helsinki, Helsinki, Finland
| | - Jennifer L. Baker
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg Hospital, Frederiksberg, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I. A. Sørensen
- Institute of Preventive Medicine, Frederiksberg and Bispebjerg Hospital, Frederiksberg, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences University of Copenhagen, Copenhagen, Denmark
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Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index. Demography 2014; 51:119-39. [PMID: 24281739 DOI: 10.1007/s13524-013-0259-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.
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Marigorta UM, Gibson G. A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects. Front Genet 2014; 5:225. [PMID: 25101110 PMCID: PMC4104702 DOI: 10.3389/fgene.2014.00225] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 06/28/2014] [Indexed: 01/16/2023] Open
Abstract
The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.
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Affiliation(s)
- Urko M Marigorta
- Center for Integrative Genomics, School of Biology, Georgia Institute of Technology Atlanta, GA, USA
| | - Greg Gibson
- Center for Integrative Genomics, School of Biology, Georgia Institute of Technology Atlanta, GA, USA
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Al-Daghri NM, Guerini FR, Al-Attas OS, Alokail MS, Alkharfy KM, Draz HM, Agliardi C, Costa AS, Saulle I, Mohammed AK, Biasin M, Clerici M. Vitamin D receptor gene polymorphisms are associated with obesity and inflammosome activity. PLoS One 2014; 9:e102141. [PMID: 25020064 PMCID: PMC4096505 DOI: 10.1371/journal.pone.0102141] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Accepted: 06/13/2014] [Indexed: 01/29/2023] Open
Abstract
To explore the mechanisms underlying the suggested role of the vitamin D/vitamin D receptor (VDR) complex in the pathogenesis of obesity we performed genetic and immunologic analyses in obese and non-obese Saudi individuals without other concomitant chronic diseases. Genomic DNA was genotyped for gene single nucleotide polymorphisms (SNPs) of VDR by allelic discrimination in 402 obese (body mass index –BMI≥30 kg/m2) and 489 non-obese (BMI<30 kg/m2) Saudis. Q-PCR analyses were performed using an ABI Prism 7000 Sequence Detection System. The inflammosome pathway was analysed by PCR, cytokines and plasma lipopolysaccaride (LPS) concentrations with ELISA assays. Results showed that the VDR SNPs rs731236 (G) (TaqI) and rs1544410 (T) (Bsm-I) minor allele polymorphisms are significantly more frequent in obese individuals (p = 0.009, β = 0.086 and p = 0.028, β = 0.072, respectively). VDR haplotypes identified are positively (GTA) (p = 0.008, β = 1.560); or negatively (ACC) (p = 0.044, β = 0.766) associated with obesity and higher BMI scores. The GTA "risk" haplotype was characterized by an up-regulation of inflammosome components, a higher production of proinflammatory cytokines (p<0.05) and a lower VDR expression. Plasma LPS concentration was also increased in GTA obese individuals (p<0.05), suggesting an alteration of gut permeability leading to microbial translocation. Data herein indicate that polymorphisms affecting the vitamin D/VDR axis play a role in obesity that is associated with an ongoing degree of inflammation, possibly resulting from alterations of gut permeability and microbial translocation. These results could help the definition of VDR fingerprints that predict an increased risk of developing obesity and might contribute to the identification of novel therapeutic strategies for this metabolic condition.
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Affiliation(s)
- Nasser M. Al-Daghri
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- Prince Mutaib Chair for Biomarkers of Osteoporosis, College of Science, King Saud University, Riyadh, KSA
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh, KSA
- * E-mail:
| | - Franca R. Guerini
- Don Gnocchi Foundation, ONLUS, Milano and Università degli Studi di Milano, Milano, Italy
| | - Omar S. Al-Attas
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- Prince Mutaib Chair for Biomarkers of Osteoporosis, College of Science, King Saud University, Riyadh, KSA
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh, KSA
| | - Majed S. Alokail
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- Prince Mutaib Chair for Biomarkers of Osteoporosis, College of Science, King Saud University, Riyadh, KSA
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh, KSA
| | - Khalid M. Alkharfy
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- Prince Mutaib Chair for Biomarkers of Osteoporosis, College of Science, King Saud University, Riyadh, KSA
- Clinical Pharmacy Department, College of Pharmacy, King Saud University, Riyadh, KSA
| | - Hossam M. Draz
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- INRS-Institute Armand Frappier, University of Quebec, Laval, Quebec, Canada
| | - Cristina Agliardi
- Don Gnocchi Foundation, ONLUS, Milano and Università degli Studi di Milano, Milano, Italy
| | - Andrea S. Costa
- Don Gnocchi Foundation, ONLUS, Milano and Università degli Studi di Milano, Milano, Italy
| | - Irma Saulle
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milano, Italy
| | - Abdul Khader Mohammed
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
| | - Mara Biasin
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milano, Italy
| | - Mario Clerici
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA)
- Don Gnocchi Foundation, ONLUS, Milano and Università degli Studi di Milano, Milano, Italy
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milano, Italy
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Al-Daghri NM, Alkharfy KM, Al-Attas OS, Krishnaswamy S, Mohammed AK, Albagha OM, Alenad AM, Chrousos GP, Alokail MS. Association between type 2 diabetes mellitus-related SNP variants and obesity traits in a Saudi population. Mol Biol Rep 2014; 41:1731-40. [PMID: 24435973 DOI: 10.1007/s11033-014-3022-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Accepted: 01/02/2014] [Indexed: 10/25/2022]
Abstract
Obesity, commonly measured as body mass index (BMI), has been on a rapid rise around the world and is an underlying cause of several chronic non-communicable diseases, including type 2 diabetes mellitus (T2DM). In addition to the environmental factors, genetic factors may also contribute to the ongoing obesity epidemic in Saudi Arabia. This study investigated the association between variants of 36 previously established T2DM SNPs and obesity phenotypes in a population of Saudi subjects. Study subjects consisted of 975 obese (BMI: ≥30), 825 overweight (25-30) and 423 lean controls (18-25) and of these 927 had a history of T2DM. Subjects were genotyped for 36 SNPs, which have been previously proved to be T2DM linked, using the KASPar method and the means of BMI and waist circumference (WC) corresponding to each of the genotypes were compared by additive, recessive and dominant genetic models. Five and seven of 36 T2DM-related SNPs were significantly associated with the BMI and WC, respectively. Variants of SNPs rs7903146, rs1552224 and rs11642841 in the control group and rs7903146 in T2DM group showed significant association with both BMI and WC. Variant of SNP rs10440833 was significantly associated with BMI in T2DM group of both males [OR = 1.8 (1.0, 3.3); P = 0.04] and females [OR = 2.0 (1.0, 3.9); P = 0.04]. Genetic risk scores explained 19 and 14% of WC and hip size variance in this population. Variants of a number of established T2DM related SNPs were associated with obesity phenotypes and may be significant hereditary factors in the pathogenesis of T2DM.
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Affiliation(s)
- Nasser M Al-Daghri
- Biomarkers Research Program, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia,
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Min J, Chiu DT, Wang Y. Variation in the heritability of body mass index based on diverse twin studies: a systematic review. Obes Rev 2013; 14:871-82. [PMID: 23980914 PMCID: PMC4346225 DOI: 10.1111/obr.12065] [Citation(s) in RCA: 93] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Revised: 06/27/2013] [Accepted: 07/15/2013] [Indexed: 12/22/2022]
Abstract
OBJECTIVES Over the past three decades, twin studies have shown variation in the heritability of obesity. This study examined the difference of body mass index (BMI) heritability (BMI-H) by population characteristics, such as sex, age, time period of observation and average BMI, as well as by broad social-environmental factors as indicated by country-level gross domestic product (GDP) per capita and GDP growth rate. METHODS Twin studies that reported BMI-H and were published in English from January 1990 to February 2011 after excluding those with disease, special occupations or combined heritability estimates for country/ethnic groups were searched in PubMed. 32 studies were identified from Finland (7), the United Kingdom (6), the United States (3), Denmark (3), China (3), Netherlands (2), South Korea (2), Sweden (2) and four from other countries. Meta-regression models with random effects were used to assess variation in BMI-H. RESULTS Heterogeneity of BMI-H is significantly attributable to variations in age (<20, 20-55 and ≥56 years), time period of observation (i.e. year of data collection), average BMI and GDP (≤$20,000, $20,001-26,000 and >$26,000). BMI-H was higher in adolescents (<20 years), in studies done in past years, and in populations with higher average BMIs or higher GDP per capita (≥$26,000) than their counterparts. Consistent lowering effects of high GDP growth rate (>median) on BMI-H were shown through stratified analyses by GDP. BMI-H was lower in countries of mid-level GDP, particularly those experiencing rapid economic growth. CONCLUSIONS BMI-H is sensitive to age, time period of observation, average BMI, GDP and rapid economic growth.
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Affiliation(s)
- J Min
- Johns Hopkins Global Center on Childhood Obesity, Department of International Health Human Nutrition Program, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA; Department of Preventive Medicine, School of Medicine, Ewha Womans University, Seoul, Korea
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Demerath EW, Choh AC, Johnson W, Curran JE, Lee M, Bellis C, Dyer TD, Czerwinski SA, Blangero J, Towne B. The positive association of obesity variants with adulthood adiposity strengthens over an 80-year period: a gene-by-birth year interaction. Hum Hered 2013; 75:175-85. [PMID: 24081233 DOI: 10.1159/000351742] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE To test the hypothesis that the statistical effect of obesity-related genetic variants on adulthood adiposity traits depends on birth year. METHODS The study sample included 907 related, non-Hispanic White participants in the Fels Longitudinal Study, born between 1901 and 1986, and aged 25-64.99 years (474 females; 433 males) at the time of measurement. All had both genotype data from which a genetic risk score (GRS) composed of 32 well-replicated obesity-related common single nucleotide polymorphisms was created, and phenotype data [including body mass index (BMI), waist circumference, and the sum of four subcutaneous skinfolds]. Maximum likelihood-based variance components analysis was used to estimate trait heritabilities, main effects of GRS and birth year, GRS-by-birth year interaction, sex, and age. RESULTS Positive GRS-by-birth year interaction effects were found for BMI (p < 0.001), waist circumference (p = 0.007), and skinfold thickness (p < 0.007). For example, each one-allele increase in GRS was estimated to result in a 0.16 increase in BMI among males born in 1930 compared to a 0.47 increase among those born in 1970. CONCLUSIONS These novel findings suggest the influence of common obesity susceptibility variants has increased during the obesity epidemic.
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Affiliation(s)
- Ellen W Demerath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minn., USA
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Brunkwall L, Ericson U, Hellstrand S, Gullberg B, Orho-Melander M, Sonestedt E. Genetic variation in the fat mass and obesity-associated gene (FTO) in association with food preferences in healthy adults. Food Nutr Res 2013; 57:20028. [PMID: 23589710 PMCID: PMC3625705 DOI: 10.3402/fnr.v57i0.20028] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Revised: 03/04/2013] [Accepted: 03/14/2013] [Indexed: 12/05/2022] Open
Abstract
Background Earlier studies have indicated that the fat mass and obesity-associated gene (FTO) is not only associated with BMI and weight but also with appetite and dietary intake. Objectives We investigated if the FTO rs9939609 associates with food preferences in healthy adults with no cancer, cardiovascular disease, or diabetes. Additionally, we challenged the question if the associations are modified by obesity status (BMI ≤25 or >25 kg/m2). Design The analyses are made with 22,799 individuals from the Swedish population-based Malmö Diet and Cancer Cohort Study, who were born between 1923 and 1945. To investigate food preference, 27 food groups conducted from a modified diet history method including a 7-day registration of cooked meals and cold beverages were used in the analyses. Bonferroni correction was used to correct for multiple testing, resulting in a cut-off value for significance level of p<0.002. Results We observed that the obesity susceptible A-allele carriers reported a higher consumption of biscuits and pastry but lower consumption of soft drinks (P for trend <0.0001 for both) as compared to TT genotype carriers. In contrast to our hypothesis, the results did not significantly differ depending on obesity status except for consumption of juice, where only the overweight individuals with A-allele had a higher consumption as compared to TT carriers (P for interaction=0.04). Conclusion Our results indicate that the FTO A-allele may associate with certain food preference and in particular with certain energy-dense foods.
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Affiliation(s)
- Louise Brunkwall
- Diabetes and Cardiovascular Disease - Genetic Epidemiology, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
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