Quantifying the phenome‐wide disease burden of obesity using electronic health records and genomics

Abstract

Objective: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome-and phenomewide approach.Methods: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank.Results: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux.Conclusions: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Associations between BMI, polygenic risk score for BMI, lifestyle and the risk of upper gastrointestinal cancer

“…This nding primarily support that BMI would deserve more attention than genetic predisposition (for BMI) in preventing UG cancer. Moreover, the BMI variance explained by the PRS in TRAILS adolescents was 6.47% [30], and the genome-wide polygenic risk score for BMI accounting for 7.5% of BMI variance based on a clinical cohort of 736,726 adults [31]; both of them were either lower than or similar to the heritability (7%) of BMI reported in this study. We also observed a relatively obvious separation between actual BMI and genetically predicted BMI (Figure The potential separation would not only provide valuable insights into both genetic architecture and potential intervention of complex diseases and traits, but also deserve more attention in preventing UG cancer.…”