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      Changing genetic architecture of body mass index from infancy to early adulthood: an individual based pooled analysis of 25 twin cohorts

      research-article
      1 , 2 , , 1 , 3 , 4 , 5 , 6 , 7 , 7 , 8 , 9 , 10 , 11 , 12 , 12 , 13 , 14 , 14 , 15 , 16 , 17 , 16 , 17 , 18 , 18 , 18 , 19 , 19 , 20 , 21 , 22 , 22 , 23 , 24 , 25 , 24 , 26 , 27 , 28 , 29 , 30 , 31 , 30 , 32 , 33 , 34 , 21 , 35 , 21 , 36 , 37 , 38 , 39 , 38 , 40 , 41 , 42 , 43 , 44 , 44 , 45 , 46 , 46 , 47 , 47 , 48 , 48 , 18 , 49 , 50 , 4 , 7
      International Journal of Obesity (2005)
      Nature Publishing Group UK
      Risk factors, Epidemiology

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          Abstract

          Background

          Body mass index (BMI) shows strong continuity over childhood and adolescence and high childhood BMI is the strongest predictor of adult obesity. Genetic factors strongly contribute to this continuity, but it is still poorly known how their contribution changes over childhood and adolescence. Thus, we used the genetic twin design to estimate the genetic correlations of BMI from infancy to adulthood and compared them to the genetic correlations of height.

          Methods

          We pooled individual level data from 25 longitudinal twin cohorts including 38,530 complete twin pairs and having 283,766 longitudinal height and weight measures. The data were analyzed using Cholesky decomposition offering genetic and environmental correlations of BMI and height between all age combinations from 1 to 19 years of age.

          Results

          The genetic correlations of BMI and height were stronger than the trait correlations. For BMI, we found that genetic correlations decreased as the age between the assessments increased, a trend that was especially visible from early to middle childhood. In contrast, for height, the genetic correlations were strong between all ages. Age-to-age correlations between environmental factors shared by co-twins were found for BMI in early childhood but disappeared altogether by middle childhood. For height, shared environmental correlations persisted from infancy to adulthood.

          Conclusions

          Our results suggest that the genes affecting BMI change over childhood and adolescence leading to decreasing age-to-age genetic correlations. This change is especially visible from early to middle childhood indicating that new genetic factors start to affect BMI in middle childhood. Identifying mediating pathways of these genetic factors can open possibilities for interventions, especially for those children with high genetic predisposition to adult obesity.

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          Most cited references48

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          Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

          Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation.
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            Genetic studies of body mass index yield new insights for obesity biology.

            Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P  20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
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              Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

              Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.
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                Author and article information

                Contributors
                karri.silventoinen@helsinki.fi
                Journal
                Int J Obes (Lond)
                Int J Obes (Lond)
                International Journal of Obesity (2005)
                Nature Publishing Group UK (London )
                0307-0565
                1476-5497
                9 August 2022
                9 August 2022
                2022
                : 46
                : 10
                : 1901-1909
                Affiliations
                [1 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Population Research Unit, Faculty of Social Sciences, , University of Helsinki, ; Helsinki, Finland
                [2 ]GRID grid.136593.b, ISNI 0000 0004 0373 3971, Center for Twin Research, , Osaka University Graduate School of Medicine, ; Osaka, Japan
                [3 ]GRID grid.11480.3c, ISNI 0000000121671098, Department of Physiology, Faculty of Medicine and Nursing, , University of the Basque Country, ; Leioa, Spain
                [4 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Public Health, , University of Helsinki, ; Helsinki, Finland
                [5 ]GRID grid.9668.1, ISNI 0000 0001 0726 2490, Institute of Clinical Medicine, , University of Eastern Finland, ; Kuopio, Finland
                [6 ]Department of Public Health Nursing, Osaka Metropolitan University, Osaka, Japan
                [7 ]GRID grid.452494.a, ISNI 0000 0004 0409 5350, Institute for Molecular Medicine Finland FIMM, ; Helsinki, Finland
                [8 ]GRID grid.415179.f, ISNI 0000 0001 0868 5401, UKK Institute – Centre for Health Promotion Research, ; Tampere, Finland
                [9 ]GRID grid.442963.e, ISNI 0000 0001 0690 8202, Faculty of Human Studies, , Shirayuri University, ; Tokyo, Japan
                [10 ]GRID grid.136304.3, ISNI 0000 0004 0370 1101, Center for Forensic Mental Health, , Chiba University, ; Chiba, Japan
                [11 ]GRID grid.412314.1, ISNI 0000 0001 2192 178X, Institute for Education and Human Development, , Ochanomizu University, ; Tokyo, Japan
                [12 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Department of Psychology, , University of Southern California, ; Los Angeles, CA USA
                [13 ]GRID grid.15895.30, ISNI 0000 0001 0738 8966, School of Law, Psychology and Social Work, , Örebro University, ; Örebro, Sweden
                [14 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Global Public Health, , Karolinska Institutet, ; Stockholm, Sweden
                [15 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), , Deakin University School of Medicine, ; Geelong, Australia
                [16 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Murdoch Childrens Research Institute, , Royal Children’s Hospital, ; Parkville, Victoria Australia
                [17 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Paediatrics, , University of Melbourne, ; Parkville, Victoria Australia
                [18 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Netherlands Twin Register, Department of Biological Psychology, , Vrije Universiteit, Amsterdam, ; Amsterdam, Netherlands
                [19 ]GRID grid.1049.c, ISNI 0000 0001 2294 1395, Genetic Epidemiology Department, , QIMR Berghofer Medical Research Institute, ; Brisbane, Australia
                [20 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Institute for Molecular Bioscience, , The University of Queensland, ; Brisbane, Australia
                [21 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
                [22 ]GRID grid.17635.36, ISNI 0000000419368657, Department of Psychology, , University of Minnesota, ; Minneapolis, MN USA
                [23 ]GRID grid.266097.c, ISNI 0000 0001 2222 1582, Department of Psychology, , University of California, Riverside, ; Riverside, CA 92521 USA
                [24 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, The Danish Twin Registry, Department of Public Health, Epidemiology, Biostatistics & Biodemography, , University of Southern Denmark Odense, ; Odense, Denmark
                [25 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Department of Clinical Biochemistry and Pharmacology and Department of Clinical Genetics, , Odense University Hospital, ; Odense, Denmark
                [26 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, Department of Clinical Research, , University of Southern Denmark, ; Odense, Denmark
                [27 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Odense Patient data Explorative Network (OPEN), , Odense University Hospital, ; Odense, Denmark
                [28 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Boston University, Department of Psychological and Brain Sciencies, ; Boston, MA USA
                [29 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, School of Epidemiology and Public Health, , University of Ottawa, Ottawa, ; Ontario, Canada
                [30 ]GRID grid.23856.3a, ISNI 0000 0004 1936 8390, École de psychologie, , Université Laval, ; Québec, Canada
                [31 ]GRID grid.38678.32, ISNI 0000 0001 2181 0211, Département de psychologie, , Université du Québec à Montréal, Montréal, ; Québec, Canada
                [32 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, École de psychoéducation, , Université de Montréal, ; Montréal, Québec Canada
                [33 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC), , Karolinska Institutet, ; Stockholm, Sweden
                [34 ]GRID grid.24381.3c, ISNI 0000 0000 9241 5705, Theme Women’s Health, , Karolinska University Hospital, Karolinska University Hospital, ; Stockholm, Sweden
                [35 ]GRID grid.24381.3c, ISNI 0000 0000 9241 5705, Pediatric Allergy and Pulmonology Unit at Astrid Lindgren Children’s Hospital, , Karolinska University Hospital, ; Stockholm, Sweden
                [36 ]GRID grid.266190.a, ISNI 0000000096214564, Institute for Behavioral Genetics, , University of Colorado, ; Boulder, Colorado USA
                [37 ]GRID grid.266190.a, ISNI 0000000096214564, Department of Psychology and Neuroscience, , University of Colorado, ; Boulder, Colorado USA
                [38 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, The Hebrew University of Jerusalem, ; Jerusalem, Israel
                [39 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Hadassah Hospital Obstetrics and Gynecology Department, , Hebrew University Medical School, ; Jerusalem, Israel
                [40 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, School of Psychological Science, , University of Bristol, ; Bristol, UK
                [41 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, , King’s College London, ; London, UK
                [42 ]GRID grid.418811.5, ISNI 0000 0004 9216 2620, Bandim Health Project, , INDEPTH Network, ; Bissau, Guinea-Bissau
                [43 ]Department of Endocrinology, Hospital of Southwest Jutland, Esbjerg, Denmark
                [44 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Department of Endocrinology, , Odense University Hospital, ; Odense, Denmark
                [45 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Department of Infectious Diseases, , Odense University Hospital, ; Odense, Denmark
                [46 ]GRID grid.30064.31, ISNI 0000 0001 2157 6568, Washington State Twin Registry, , Washington State University - Health Sciences Spokane, ; Spokane, WA USA
                [47 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Psychology, , Michigan State University, ; East Lansing, Michigan USA
                [48 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, , University College London, ; London, UK
                [49 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [50 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                Author information
                http://orcid.org/0000-0003-1759-3079
                http://orcid.org/0000-0002-6268-8117
                http://orcid.org/0000-0002-9510-4181
                http://orcid.org/0000-0002-9667-7555
                http://orcid.org/0000-0002-4140-8139
                http://orcid.org/0000-0003-3037-5287
                http://orcid.org/0000-0002-5580-1433
                http://orcid.org/0000-0002-5429-5292
                http://orcid.org/0000-0002-7315-7899
                http://orcid.org/0000-0003-0977-7249
                http://orcid.org/0000-0002-0756-3629
                http://orcid.org/0000-0002-0066-2827
                http://orcid.org/0000-0002-7099-7972
                http://orcid.org/0000-0003-4821-430X
                http://orcid.org/0000-0002-3716-2455
                Article
                1202
                10.1038/s41366-022-01202-3
                9492534
                35945263
                7b864b14-28d7-41cf-a516-8d3ce8c72aa1
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 March 2022
                : 22 July 2022
                : 25 July 2022
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                © Springer Nature Limited 2022

                Nutrition & Dietetics
                risk factors,epidemiology
                Nutrition & Dietetics
                risk factors, epidemiology

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