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      Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change

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          Abstract

          In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.

          Please see later in the article for the Editors' Summary

          Abstract

          Background

          Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.

          Methods and Findings

          We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m 2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones ( p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R 2 = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites ( p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R 2 = 0.92).

          Conclusions

          Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.

          Please see later in the article for the Editors' Summary

          Editors' Summary

          Background

          Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m 2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.

          Why Was This Study Done?

          Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.

          What Did the Researchers Do and Find?

          The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m 2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m 2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m 2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.

          What Do These Findings Mean?

          These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.

          Additional Information

          Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001765.

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          Most cited references 12

          • Record: found
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          Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013.

          In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013. We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19,244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m(2) or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4-29·3) to 36·9% (36·3-37·4) in men, and from 29·8% (29·3-30·2) to 38·0% (37·5-38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9-24·7) of boys and 22·6% (21·7-23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7-8·6) to 12·9% (12·3-13·5) in 2013 for boys and from 8·4% (8·1-8·8) to 13·4% (13·0-13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Bill & Melinda Gates Foundation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            • Record: found
            • Abstract: found
            • Article: not found

            'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

            Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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              • Abstract: found
              • Article: not found

              Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI.

              Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits. We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO-BMI and BMI-trait associations. Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD [95% CI 0.013-0.064]; P = 0.003), glucose (0.024 [0.001-0.048]; P = 0.044), and triglycerides (0.028 [0.003-0.052]; P = 0.025) and lower HDL cholesterol (0.032 [0.008-0.057]; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase, gamma-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but did not reach P 12,000 individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes or obesity variant on secondary traits correlated with these conditions.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                PLoS
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                December 2014
                9 December 2014
                : 11
                : 12
                Affiliations
                [1 ]Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland
                [2 ]Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
                [3 ]NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
                [4 ]MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
                [5 ]Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
                [6 ]Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland
                [7 ]Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland
                [8 ]Department of Internal Medicine, Clinical Research Center and Biocenter Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
                [9 ]Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland
                [10 ]Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere, Tampere, Finland
                [11 ]University Heart Center Hamburg, Hamburg, Germany
                [12 ]Finnish Institute of Occupational Health, Helsinki, Finland
                [13 ]Department of Obstetrics and Gynecology, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
                [14 ]Department of Children, Young People and Families, National Institute for Health and Welfare, Oulu, Finland
                [15 ]Primary Health Care, School of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
                [16 ]Primary Health Care, Central Finland Central Hospital, Jyväskylä, Finland
                [17 ]Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, United Kingdom
                [18 ]Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland
                [19 ]Research Programs Unit Diabetes and Obesity, University of Helsinki, Helsinki, Finland
                [20 ]Wellcome Trust Sanger Institute, Hinxton, United Kingdom
                [21 ]Hjelt Institute, Department of Public Health, University of Helsinki, Helsinki, Finland
                [22 ]Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
                [23 ]Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
                [24 ]Oulu University Hospital, Oulu, Finland
                [25 ]Computational Medicine, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
                University Hospitals of Leicester NHS Trust, United Kingdom
                Author notes

                PW, AJK, PS, and MAK are shareholders of Brainshake Ltd, a startup company offering NMR-based metabolite profiling. SB has received research funding from Abbott, Abbott Diagnostics, Bayer, Boehringer Ingelheim, SIEMENS, and Thermo Fisher. SB has received honoraria for lectures from Abbott, Abbott Diagnostics, Astra Zeneca, Bayer, Boehringer Ingelheim, Medtronic, Pfizer, Roche, SIEMENS Diagnostics, SIEMENS, Thermo Fisher, and as member of Advisory Boards and for consulting for Boehringer Ingelheim, Bayer, Novartis, Roche, and Thermo Fisher. GDS is a member of the Editorial Board of PLOS Medicine. All other authors declare that no competing interests exist.

                Conceived and designed the experiments: PW QW AJK MT TT PS GDS MAK. Performed the experiments: AJK MT TT PS MAK. Analyzed the data: PW QW. Contributed reagents/materials/analysis tools: AJK RCR MT TT PS ASH MKa MJS SB TZ PE KPH SR VS OTR MRJ GDS MAK. Wrote the first draft of the manuscript: PW AJK RCR JS GDS MAK. Wrote the paper: PW QW AJK RCR JS MT TT PS ASH MKa JSV MJS MKä TL SM SB TZ JL AP PM MV PE KHP SR VS OTR MRJ GDS MAK. Agree with manuscript results and conclusions: PW QW AJK RCR JS MT TT PS ASH MKa JSV MJS MKä TL SM SB TZ JL AP PM MV PE KHP SR VS OTR MRJ GDS MAK. Enrolled patients: JSV MKä TL SM JL AP PM MV PE KHP VS OTR MRJ. All authors meet ICMJE criteria for authorship.

                Article
                PMEDICINE-D-14-02404
                10.1371/journal.pmed.1001765
                4260795
                25490400

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 18
                Funding
                This study was supported by the Academy of Finland (grant no. 137870, 139635, 250422, 136895, 263836, 134309, 117797, 41071, 266286, 104781, 120315, 129418), and the Responding to Public Health Challenges Research Programme of the Academy of Finland (129269, 129429, 126925, 121584, 124282, 129378), and the Center of Excellence in Complex Disease Genetics, the Sigrid Juselius Foundation, the Yrjö Jahnsson Foundation, the Finnish Foundation for Cardiovascular Research, the Finnish Diabetes Research Foundation; Oulu, Helsinki, Kuopio, Tampere, and Turku University Central Hospital Medical Funds, Biocenter Oulu, the Novo Nordisk Foundation, the Emil Aaltonen Foundation, the Paavo Nurmi Foundation, the Jenny and Antti Wihuri Foundation, the Juho Vainio Foundation, the Finnish Cultural Foundation, the Finnish Funding Agency for Technology and Innovation, Strategic Research Funding from the University of Oulu, the Social Insurance Institution of Finland, the UK National Institute of Health Research (NIHR) Biomedical Research Centre at Imperial College Health Care NHS Trust and Imperial College London, the Medical Research Council UK, the US National Heart, Lung, and Blood Institute (5R01HL087679), the US National Institute of Mental Health (1RL1MH083268), the European Network of Genomic and Genetic Epidemiology project, and the European Union Seventh Framework Programme (BiomarCaRE: HEALTH-F2-2011-278913 and EurHEALTHAgeing: 277849). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolomics
                Physiology
                Physiological Parameters
                Body Weight
                Weight Gain
                Weight Loss
                Medicine and Health Sciences
                Epidemiology
                Biomarker Epidemiology
                Cardiovascular Disease Epidemiology
                Genetic Epidemiology
                Molecular Epidemiology
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Type 2 Diabetes Risk
                Custom metadata
                The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. All data are available from the Institutional Data Access Committees of the Northern Finland Birth Cohort Studies (University of Oulu, Finland), the Cardiovascular Risk in Young Finns Study (University of Turku, Finland), and the FINRISK study committee at the National Institute for Health and Welfare (Helsinki, Finland) for researchers who meet the criteria for access to confidential data.

                Medicine

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