[Skip to Content]
[Skip to Content Landing]

Association of Body Mass Index With Cardiometabolic Disease in the UK BiobankA Mendelian Randomization Study

Educational Objective
To understand the role of mendelian randomization (MR) in the evaluation of the association between body mass index (BMI) and cardiometabolic traits.
1 Credit CME
Key Points

Question  Based on estimates derived using mendelian randomization, what is the association between body mass index and cardiometabolic traits?

Findings  In this population-based cohort study of approximately 120 000 individuals, associations were identified between body mass index and risk of hypertension, coronary heart disease, type 2 diabetes, and elevated systolic and diastolic blood pressures. No associations between body mass index and stroke or pulse rate were observed.

Meaning  Body mass index represents an important modifiable factor for ameliorating the risk of cardiometabolic disease in the general population.

Abstract

Importance  Higher body mass index (BMI) is a risk factor for cardiometabolic disease; however, the underlying causal associations remain unclear.

Objectives  To use UK Biobank data to report causal estimates of the association between BMI and cardiometabolic disease outcomes and traits, such as pulse rate, using mendelian randomization.

Design, Setting, and Participants  Cross-sectional baseline data from a population-based cohort study including 119 859 UK Biobank participants with complete phenotypic (medical and sociodemographic) and genetic data. Participants attended 1 of 22 assessment centers across the United Kingdom between 2006 and 2010. The present study was conducted from May 1 to July 11, 2016.

Main Outcomes and Measures  Prevalence of hypertension, coronary heart disease, and type 2 diabetes were determined at assessment, based on self-report. Blood pressure was measured clinically. Participants self-reported sociodemographic information pertaining to relevant confounders. A polygenic risk score comprising 93 single-nucleotide polymorphisms associated with BMI from previous genome-wide association studies was constructed, and the genetic risk score was applied to derive causal estimates using a mendelian randomization approach.

Results  Of the 119 859 individuals included in the study, 56 816 (47.4%) were men; mean (SD) age was 56.87 (7.93) years. Mendelian randomization analysis showed significant positive associations between genetically instrumented higher BMI and risk of hypertension (odds ratio [OR] per 1-SD higher BMI, 1.64; 95% CI, 1.48-1.83; P = 1.1 × 10−19), coronary heart disease (OR, 1.35; 95% CI, 1.09-1.69; P = .007) and type 2 diabetes (OR, 2.53; 95% CI, 2.04-3.13; P = 1.5 × 10−17), as well as systolic blood pressure (β = 1.65 mm Hg; 95% CI, 0.78-2.52 mm Hg; P = 2.0 × 10−04) and diastolic blood pressure (β  = 1.37 mm Hg; 95% CI, 0.88-1.85 mm Hg; P = 3.6 × 10−08). These associations were independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history.

Conclusions and Relevance  The results of this study add to the burgeoning evidence of an association between higher BMI and increased risk of cardiometabolic diseases. This finding has relevance for public health policies in many countries with increasing obesity levels.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 CME Credit™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

Article Information

Accepted for Publication: November 26, 2016.

Corresponding Author: Donald M. Lyall, PhD, Institute of Health and Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ, Scotland (donald.lyall@glasgow.ac.uk).

Published Online: July 5, 2017. doi:10.1001/jamacardio.2016.5804

Author Contributions: Drs Holmes, Sattar, and Pell contributed equally to the study. Dr Lyall had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Lyall, Sattar, Holmes.

Acquisition, analysis, or interpretation of data: Lyall, Celis-Morales, Ward, Iliodromiti, Anderson, Gill, Ntuk, Mackay, Holmes, Pell.

Drafting of the manuscript: Lyall.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Lyall.

Obtained funding: Pell.

Administrative, technical, or material support: Celis-Morales, Ward, Smith.

Study supervision: Sattar, Pell, Holmes.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Drs Sattar and Pell are members of UK Biobank scientific committees. This conflict of interest did not affect the present study with regard to motivation, analysis, or interpretation. No other disclosures were reported.

Funding/Support: This research received funding from the Welsh Assembly Government and the British Heart Foundation.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We are grateful to UK Biobank participants.

Additional Information: This research was conducted using UK Biobank application 7155. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council (MRC), Department of Health, Scottish Government and the Northwest Regional Development Agency.

References
1.
De Schutter  A, Lavie  CJ, Milani  RV.  The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease—the obesity paradox.  Prog Cardiovasc Dis. 2014;56(4):401-408.PubMedGoogle ScholarCrossref
2.
World Health Organization. Obesity and overweight. http://www.who.int/mediacentre/factsheets/fs311/en/. Updated June 2016. Accessed May 12, 2016.
3.
Hall  ME, do Carmo  JM, da Silva  AA, Juncos  LA, Wang  Z, Hall  JE.  Obesity, hypertension, and chronic kidney disease.  Int J Nephrol Renovasc Dis. 2014;7:75-88.PubMedGoogle ScholarCrossref
4.
Eckel  RH, Kahn  SE, Ferrannini  E,  et al; Endocrine Society; American Diabetes Association; European Association for the Study of Diabetes.  Obesity and type 2 diabetes: what can be unified and what needs to be individualized?  Diabetes Care. 2011;34(6):1424-1430.PubMedGoogle ScholarCrossref
5.
Lu  Y, Hajifathalian  K, Ezzati  M, Woodward  M, Rimm  EB, Danaei  G; Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects).  Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants.  Lancet. 2014;383(9921):970-983.PubMedGoogle ScholarCrossref
6.
Benito-León  J, Mitchell  AJ, Hernández-Gallego  J, Bermejo-Pareja  F.  Obesity and impaired cognitive functioning in the elderly: a population-based cross-sectional study (NEDICES).  Eur J Neurol. 2013;20(6):899-906, e76-e77.PubMedGoogle ScholarCrossref
7.
Yang  J, Manolio  TA, Pasquale  LR,  et al.  Genome partitioning of genetic variation for complex traits using common SNPs.  Nat Genet. 2011;43(6):519-525.PubMedGoogle ScholarCrossref
8.
Davey Smith  G, Hemani  G.  Mendelian randomization: genetic anchors for causal inference in epidemiological studies.  Hum Mol Genet. 2014;23(R1):R89-R98.PubMedGoogle ScholarCrossref
9.
Smith  GD, Ebrahim  S.  Mendelian randomization: can genetic epidemiology contribute to understanding environmental determinants of disease?  Int J Epidemiol. 2003;32(1):1-22.PubMedGoogle ScholarCrossref
10.
Nordestgaard  BG, Palmer  TM, Benn  M,  et al.  The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a mendelian randomisation approach.  PLoS Med. 2012;9(5):e1001212.PubMedGoogle ScholarCrossref
11.
Holmes  MV, Lange  LA, Palmer  T,  et al.  Causal effects of body mass index on cardiometabolic traits and events: a mendelian randomization analysis.  Am J Hum Genet. 2014;94(2):198-208.PubMedGoogle ScholarCrossref
12.
Hägg  S, Fall  T, Ploner  A,  et al; European Network for Genetic and Genomic Epidemiology Consortium.  Adiposity as a cause of cardiovascular disease: a mendelian randomization study.  Int J Epidemiol. 2015;44(2):578-586.PubMedGoogle ScholarCrossref
13.
Locke  AE, Kahali  B, Berndt  SI,  et al; LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium.  Genetic studies of body mass index yield new insights for obesity biology.  Nature. 2015;518(7538):197-206.PubMedGoogle ScholarCrossref
14.
Böhm  M, Reil  J-C, Deedwania  P, Kim  JB, Borer  JS.  Resting heart rate: risk indicator and emerging risk factor in cardiovascular disease.  Am J Med. 2015;128(3):219-228.PubMedGoogle ScholarCrossref
15.
Sudlow  C, Gallacher  J, Allen  N,  et al.  UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.  PLoS Med. 2015;12(3):e1001779.PubMedGoogle ScholarCrossref
16.
Allen  N, Sudlow  C, Downey  P,  et al.  UK Biobank: current status and what it means for epidemiology.  Health Policy Technol. 2012;1:123-126.Google ScholarCrossref
17.
Bhatnagar  P, Scarborough  P, Smeeton  NC, Allender  S.  The incidence of all stroke and stroke subtype in the United Kingdom, 1985 to 2008: a systematic review.  BMC Public Health. 2010;10:539.PubMedGoogle ScholarCrossref
18.
Celis-Morales  CA, Perez-Bravo  F, Ibañez  L, Salas  C, Bailey  MES, Gill  JMR.  Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers.  PLoS One. 2012;7(5):e36345.PubMedGoogle ScholarCrossref
19.
Townsend  P. Deprivation.  J Soc Policy. 1987;16(2):125–146.Google ScholarCrossref
20.
Genotyping and quality control of UK Biobank, a large-scale, extensive phenotyped prospective resource. US Biobank. http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf. Published 2015. Accessed January 31, 2017.
21.
Rodriguez  S, Gaunt  TR, Day  INM.  Hardy-Weinberg equilibrium testing of biological ascertainment for mendelian randomization studies.  Am J Epidemiol. 2009;169(4):505-514.PubMedGoogle ScholarCrossref
22.
StataCorp. Stata Statistical Software: Release 13. College Station, TX: StataCorp Inc; 2013.
23.
Purcell  S, Neale  B, Todd-Brown  K,  et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses.  Am J Hum Genet. 2007;81(3):559-575.PubMedGoogle ScholarCrossref
24.
Williams  SM, Haines  JL.  Correcting away the hidden heritability.  Ann Hum Genet. 2011;75(3):348-350.PubMedGoogle ScholarCrossref
25.
Baum  CF, Schaffer  ME, Stillman  S. IVREG2: Stata module for extended instrumental variables/2SLS and GMM estimation. http://ideas.repec.org/c/boc/bocode/s425401.html. Published 2016. Accessed February 11, 2016.
26.
Burgess  S, Thompson  SG; CRP CHD Genetics Collaboration.  Avoiding bias from weak instruments in mendelian randomization studies.  Int J Epidemiol. 2011;40(3):755-764.PubMedGoogle ScholarCrossref
27.
Palmer  TM, Sterne  JAC, Harbord  RM,  et al.  Instrumental variable estimation of causal risk ratios and causal odds ratios in mendelian randomization analyses.  Am J Epidemiol. 2011;173(12):1392-1403.PubMedGoogle ScholarCrossref
28.
White  J, Swerdlow  DI, Preiss  D,  et al.  Association of lipid fractions with risks for coronary artery disease and diabetes.  JAMA Cardiol. 2016;1(6):692-699.PubMedGoogle ScholarCrossref
29.
Bowden  J, Davey Smith  G, Burgess  S.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.  Int J Epidemiol. 2015;44(2):512-525.PubMedGoogle ScholarCrossref
30.
Fall  T, Hägg  S, Mägi  R,  et al; European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium.  The role of adiposity in cardiometabolic traits: a mendelian randomization analysis.  PLoS Med. 2013;10(6):e1001474.PubMedGoogle ScholarCrossref
31.
Timpson  NJ, Harbord  R, Davey Smith  G, Zacho  J, Tybjaerg-Hansen  A, Nordestgaard  BG.  Does greater adiposity increase blood pressure and hypertension risk? mendelian randomization using the FTO/MC4R genotype.  Hypertension. 2009;54(1):84-90.PubMedGoogle ScholarCrossref
32.
Lyall  DM, Cullen  B, Allerhand  M,  et al.  Cognitive test scores in UK Biobank: data reduction in 480,416 participants and longitudinal stability in 20,346 participants.  PLoS One. 2016;11(4):e0154222.PubMedGoogle ScholarCrossref
33.
Collins  R.  What makes UK Biobank special?  Lancet. 2012;379(9822):1173-1174.PubMedGoogle ScholarCrossref
34.
Bhatnagar  P, Wickramasinghe  K, Williams  J, Rayner  M, Townsend  N.  The epidemiology of cardiovascular disease in the UK 2014.  Heart. 2015;101(15):1182-1189.PubMedGoogle ScholarCrossref
35.
National Health Service. Health survey for England, 2014: trend tables. http://content.digital.nhs.uk/catalogue/PUB19297. Published December 16, 2015. Accessed September 7, 2016.
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_LoginSubscribe_Purchase
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_LoginSubscribe_Purchase
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right

Name Your Search

Save Search
With a personal account, you can:
  • Track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
jn-learning_Modal_SaveSearch_NoAccess_Purchase

Lookup An Activity

or

My Saved Searches

You currently have no searches saved.

With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Topics
State Requirements