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      Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts

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          Abstract

          A mendelian randomization study based on data from multiple cohorts conducted by Karani Santhanakrishnan Vimaleswaran and colleagues re-examines the causal nature of the relationship between vitamin D levels and obesity.

          Abstract

          Background

          Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.

          Methods and Findings

          We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects.

          Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium ( n = 123,864). Each 1 kg/m 2 higher BMI was associated with 1.15% lower 25(OH)D ( p = 6.52×10 −27). The BMI allele score was associated both with BMI ( p = 6.30×10 −62) and 25(OH)D (−0.06% [95% CI −0.10 to −0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D ( p≤8.07×10 −57 for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: −4.2 [95% CI −7.1 to −1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium ( p≥0.57 for both vitamin D scores).

          Conclusions

          On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.

          Please see later in the article for the Editors' Summary

          Editors' Summary

          Background

          Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the US, for example, a third of the adult population is now obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m 2. Although there is a genetic contribution to obesity, people generally become obese by consuming food and drink that contains more energy than they need for their daily activities. Thus, obesity can be prevented by having a healthy diet and exercising regularly. Compared to people with a healthy weight, obese individuals have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. They also have a higher risk of vitamin D deficiency, another increasingly common public health concern. Vitamin D, which is essential for healthy bones as well as other functions, is made in the skin after exposure to sunlight but can also be obtained through the diet and through supplements.

          Why Was This Study Done?

          Observational studies cannot prove that obesity causes vitamin D deficiency because obese individuals may share other characteristics that reduce their circulating 25-hydroxy vitamin D [25(OH)D] levels (referred to as confounding). Moreover, observational studies cannot indicate whether the larger vitamin D storage capacity of obese individuals (vitamin D is stored in fatty tissues) lowers their 25(OH)D levels or whether 25(OH)D levels influence fat accumulation (reverse causation). If obesity causes vitamin D deficiency, monitoring and treating vitamin D deficiency might alleviate some of the adverse health effects of obesity. Conversely, if low vitamin D levels cause obesity, encouraging people to take vitamin D supplements might help to control the obesity epidemic. Here, the researchers use bi-directional “Mendelian randomization” to examine the direction and causality of the relationship between BMI and 25(OH)D. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants do not change over time and are inherited randomly, they are not prone to confounding and are free from reverse causation. Thus, if a lower vitamin D status leads to obesity, genetic variants associated with lower 25(OH)D concentrations should be associated with higher BMI, and if obesity leads to a lower vitamin D status, then genetic variants associated with higher BMI should be associated with lower 25(OH)D concentrations.

          What Did the Researchers Do and Find?

          The researchers created a “BMI allele score” based on 12 BMI-related gene variants and two “25(OH)D allele scores,” which are based on gene variants that affect either 25(OH)D synthesis or breakdown. Using information on up to 42,024 participants from 21 studies, the researchers showed that the BMI allele score was associated with both BMI and with 25(OH)D levels among the study participants. Based on this information, they calculated that each 10% increase in BMI will lead to a 4.2% decrease in 25(OH)D concentrations. By contrast, although both 25(OH)D allele scores were strongly associated with 25(OH)D levels, neither score was associated with BMI. This lack of an association between 25(OH)D allele scores and obesity was confirmed using data from more than 100,000 individuals involved in 46 studies that has been collected by the GIANT (Genetic Investigation of Anthropometric Traits) consortium.

          What Do These Findings Mean?

          These findings suggest that a higher BMI leads to a lower vitamin D status whereas any effects of low vitamin D status on BMI are likely to be small. That is, these findings provide evidence for obesity as a causal factor in the development of vitamin D deficiency but not for vitamin D deficiency as a causal factor in the development of obesity. These findings suggest that population-level interventions to reduce obesity should lead to a reduction in the prevalence of vitamin D deficiency and highlight the importance of monitoring and treating vitamin D deficiency as a means of alleviating the adverse influences of obesity on health.

          Additional Information

          Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001383.

          • The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish); a data brief provides information about the vitamin D status of the US population

          • The World Health Organization provides information on obesity (in several languages)

          • The UK National Health Service Choices website provides detailed information about obesity and a link to a personal story about losing weight; it also provides information about vitamin D

          • The International Obesity Taskforce provides information about the global obesity epidemic

          • The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)

          • The US Office of Dietary Supplements provides information about vitamin D (in English and Spanish)

          • MedlinePlus has links to further information about obesity and about vitamin D (in English and Spanish)

          • Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

          • Overview and details of the collaborative large-scale genetic association study (D-CarDia) provide information about vitamin D and the risk of cardiovascular disease, diabetes and related traits

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          Most cited references51

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          Introduction to Meta-Analysis

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            Mendelian randomization: using genes as instruments for making causal inferences in epidemiology.

            Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.
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              '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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                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
                February 2013
                February 2013
                5 February 2013
                : 10
                : 2
                : e1001383
                Affiliations
                [1 ]Centre for Paediatric Epidemiology and Biostatistics and MRC Centre of Epidemiology for Child Health, UCL Institute of Child Health, London, United Kingdom
                [2 ]Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
                [3 ]Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
                [4 ]National Institute for Health and Welfare, Helsinki, Finland
                [5 ]Department of Internal Medicine, Division of Endocrinology and Metabolism, Medical University of Graz, Austria
                [6 ]Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands
                [7 ]Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [8 ]Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
                [9 ]Department of Epidemiology, Biostatistics and Occupational Health, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
                [10 ]Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
                [11 ]Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston Salem, North Carolina, United States of America
                [12 ]Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, United Kingdom
                [13 ]Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
                [14 ]Center for Bone and Arthritis Research, Department of Internal Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
                [15 ]Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
                [16 ]Andrija Stampar School of Public Health, Medical School University of Zagreb, Zagreb, Croatia
                [17 ]University of Maryland School of Medicine, Division of Endocrinology, Baltimore, Maryland, United States of America
                [18 ]Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, United Kingdom
                [19 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
                [20 ]Oxford NIHR Biomedical Research Centre, Churchill Hospital, Headington, Oxford, United Kingdom
                [21 ]National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
                [22 ]Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland
                [23 ]LURIC Study non-profit LLC, Freiburg, Germany and Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
                [24 ]MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
                [25 ]NIHR Musculoskeletal BRU, Botnar Research Centre, Oxford, United Kingdom
                [26 ]MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
                [27 ]Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
                [28 ]Department of Medicine, University of Turku and Turku University Hospital, Turku, Finland
                [29 ]Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston Salem, North Carolina, United States of America
                [30 ]Clinical Research Branch, Harbor Hospital, Baltimore, Maryland, United States of America
                [31 ]Department of Medical Sciences, Uppsala University, Uppsala, Sweden
                [32 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                [33 ]Colon Cancer Genetics Group and Academic Coloproctology, Institute of Genetics and Molecular Medicine, University of Edinburgh, United Kingdom
                [34 ]MRC Human Genetics Unit Western General Hospital Edinburgh, United Kingdom
                [35 ]Institute of Biomedicine, University of Oulu, Oulu, Finland
                [36 ]Department of Psychiatry, Kuopio University Hospital, Kuopio, Finland
                [37 ]Department of Public Health Science and General Practice, University of Oulu, Oulu, Finland
                [38 ]Department of Obstetrics and Gynaecology and Public Health and General Practice, University of Oulu, Oulu, Finland
                [39 ]MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
                [40 ]Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
                [41 ]Department of Clinical Chemistry, Fimlab Laboratories, Tampere University Hospital and University of Tampere, Tampere, Finland
                [42 ]Department of Haematology, University of Cambridge, United Kingdom
                [43 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
                [44 ]NHS Blood and Transplant, Cambridge, United Kingdom
                [45 ]Synlab Academy, Mannheim, Germany
                [46 ]Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
                [47 ]Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [48 ]Department of Biostatistics and Epidemiology, School of Public Health, MRC-HPA Centre for Environment and Health, Imperial College, Faculty of Medicine, London, United Kingdom
                [49 ]Department of Children, Young People and Families, National Institute for Health and Welfare, Oulu, Finland
                [50 ]Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
                [51 ]Quantitative Sciences, GlaxoSmithKline, Stevenage, United Kingdom
                [52 ]Genetic Epidemiology Group, Department of Epidemiology and Public Health, Division of Population Health, University College London, London, United Kingdom
                [53 ]Division of Medicine, Centre for Clinical Pharmacology, University College London, London, United Kingdom
                Centre for Biomedicine, EURAC, Italy
                Author notes

                LTH is currently supported by a Canada Institute of Research (CIHR) Fellowship award. CC has received honoraria and consulting fees from Amgen, Eli Lilly, Medtronic, Merck, Novartis, and Servier. WM is an employee of synlab laboratory services GmbH. Synlab offers vitamin D testing. TJW is on the scientific advisory board for Diasorin Inc. and has received research support from them. JCW is 90% employed by GlaxoSmithKline (GSK) whilst maintaining a 10% appointment at London School of Hygiene & Tropical Medicine (LSHTM), and holds GSK shares. All other authors declare that no competing interests exist.

                Conceived and designed the experiments: KSV DJB MM OR JV BDM EAM NJW TM TL WHO CC TJW MRJ JCW ADH EH. Performed the experiments: KSV DJB CL ET SP LTH JDC KHL ZD RL ARW KM LZ LMY JD MCK KJ VS BDM EAS DKH TL PK CC RJFL JCW ADH EH. Analyzed the data: KSV DJB CL ET SP LTH JDC KHL ZD RL ARW KM LZ LMY JD MCK KJ VS BDM EAS DKH TL PK CC RJFL JCW ADH EH. Contributed reagents/materials/analysis tools: KSV DJB SP LTH KM LMY MM JD MK NA OR JV SBK HM EI LB LL VS BDM KH EAS DKH TL PK CC WM RJFL CP MRJ JCW ADH EH. Wrote the first draft of the manuscript: KSV DJB EH. Contributed to the writing of the manuscript: KSV DJB EH. ICMJE criteria for authorship read and met: KSV DJB CL ET SP LTH JDC ZD RL DKH ARW KM LV LZ LMY MM JD MK MEK KJ NA OR JV KKL LF HM EI LB LL ML VS HC MD BDM KHH AP ALH EAS ET AJ NJW CO TMF SBK TDS JBR TL WHO PK CC WM CP RJFL TJW MRJ JCW ADH EH. Agree with manuscript results and conclusions: KSV DJB CL ET SP LTH JDC ZD RL DKH ARW KM LV LZ LMY MM JD MK MEK KJ NA OR JV KKL LF HM EI LB LL ML VS HC MD BDM KHH AP ALH EAS ET AJ NJW CO TMF SBK TDS JBR TL WHO PK CC WM CP RJFL TJW MRJ JCW ADH EH. Obtained the data: KSV DJB SP LTH ZD RL KM LV MM MK MCK KJ NA OR JV SBK LF HM EI LB LL ML VS HC MD BDM AP AH ET NJW CO TMF DKH JBR TL WHO PK CC WM CP ADH MRJ EH. Provided the administrative, technical, or material support: KSV DJB KM MK OR JV SBK LF HM EI LL VS TMF DKH TL CP EH. Supervised the study: JD OR JV SBK HC MD BDM EAS AJ ET TS TMF JBR TL TJW MRJ JCW ADH EH.

                ¶ Membership of the Genetic Investigation of Anthropometric Traits (GIANT) consortium is provided in the Acknowledgments.

                Article
                PMEDICINE-D-12-01552
                10.1371/journal.pmed.1001383
                3564800
                23393431
                70d43c3b-c930-4c26-b878-d5e52318ba93
                Copyright @ 2013

                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.

                History
                : 31 May 2012
                : 24 December 2012
                Page count
                Pages: 13
                Funding
                The authors thank the British Heart Foundation (grant PG/09/023) and the UK Medical Research Council (MRC; grant G0601653) for funding this work. ADH is a British Heart Foundation Senior Research Fellow (Award FS05/125). EH is a Department of Health (UK) Public Health Career Scientist. This work was undertaken at the Centre for Paediatric Epidemiology and Biostatistics, which benefits from funding support from the MRC in its capacity as the MRC Centre of Epidemiology for Child Health. Research at the University College London Institute of Child Health and Great Ormond Street Hospital for Children NHS Trust benefits from R&D funding received from the NHS Executive. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Genetics
                Population Genetics
                Genetic Polymorphism
                Population Biology
                Epidemiology
                Genetic Epidemiology

                Medicine
                Medicine

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