A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank

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Authors: Hyppönen E, Mulugeta A, Zhou A, Santhanakrishnan VK.
Publication: Lancet Digital Health
Year: 2019


Mendelian randomisation allows for the testing of causal effects in situations where clinical trials are challenging to do. In this hypothesis-free, data-driven phenome-wide association study (PheWAS), we sought to assess possible associations of high body-mass index (BMI) with multiple disease outcomes.


For this registry-based case-control PheWAS, we used genome-wide data available from the UK Biobank to construct a genetic risk score of 76 variants related to BMI. Eligible UK Biobank participants were aged 37-73 years during recruitment, were white British, were unrelated to each other, and had available genetic information. Disease outcomes from these participants were mapped to a phenotype code (phecode). Participants with a phecode of interest were recoded as cases, whereas participants without a phecode of interest or any codes under a parent phecode were classified as controls. We did a PheWAS to analyse possible associations between the BMI genetic risk score and a range of disease outcomes. Disease associations passing stringent correction for multiple testing (Bonferroni corrected threshold p<5·4 × 10-5, false discovery rate corrected p<0·0074) were assessed for causal association with use of inverse-variance weighted mendelian randomisation. We did sensitivity analyses to assess pleiotropy and stability of estimation with use of weighted median, weighted mode, Egger regression, and mendelian randomisation pleiotropy residual sum and outlier methods.


Our study population comprised 337 536 UK Biobank participants, and analyses were done for 925 unique phecodes from 17 different disease categories. After Bonferroni correction, PheWAS identified that BMI genetic risk score was associated with hospital-diagnosed obesity and 58 other outcomes; 30 distinct disease associations were supported by the mendelian randomisation analyses. 30 distinct disease associations were supported by the mendelian randomisation analyses. In inverse-variance weighted mendelian randomisation, genetically determined BMI was associated with endocrine disorders (odds ratio per one SD or 4·1 kg/m2 higher BMI 2·72, 95% CI 2·33-3·29 for type 2 diabetes; 2·11, 1·62-2·76 for type 1 diabetes; and 1·46, 1·25-1·70 for hypothyroidism), circulatory diseases (1·96, 1·53-2·51 for phlebitis and thrombophlebitis; 1·89, 1·39-2·57 for cardiomegaly; 1·68, 1·35-2·09 for congestive heart failure; 1·55, 1·37-1·76 for hypertension; 1·31, 1·13-1·52 for ischaemic heart disease; and 1·25, 1·14-1·37 for cardiac dysrhythmias), and inflammatory or dermatological conditions (2·00, 1·72-2·23 for superficial cellulitis and abscess; 3·37, 2·17-5·25 for chronic ulcers of leg and foot; 4·99, 2·54-9·82 for gangrene; and 2·24, 1·53-3·28 for atopy). Mendelian randomisation analyses provided further support for a causal effect of BMI on renal failure, osteoarthrosis, neurological (insomnia and peripheral nerve disorders) and respiratory diseases (asthma and chronic bronchitis), structural problems (hernias and knee derangement), and chemotherapy treatment. Mendelian randomisation with Egger regression produced consistently wider CIs compared with those of other methods. 26 of 72 distinct diseases detected under false discovery rate correction produced consistent estimates across at least four mendelian randomisation methods, and consistent evidence across all five approaches was obtained for 14 diseases.


Our data-driven approach identified a range of diseases as possibly affected by high BMI. This population-level screening approximated the accumulated consequences of high BMI, whereas the true effects might be more complex and vary by life stage. Our results highlight the importance of obesity prevention and effective management of obesity-related comorbidities.

  • Listing ID: 9125
  • Author/s: Hyppönen E, Mulugeta A, Zhou A, Santhanakrishnan VK.
  • Publication: Lancet Digital Health
  • Year: 2019
  • Volume: 1
  • Issue: July
  • Start Page: e116