Genome‐wide associations with metabolic syndrome among UK Biobank participants reporting use of second‐generation antipsychotics

Abstract Objectives Second‐generation antipsychotic (SGA) medications are frequently prescribed for mental health conditions; however, they are associated with an increased risk of metabolic syndrome (MetS). We aimed to identify genetic associations of SGA‐associated MetS (SGA–MetS) using genome‐wide approaches within the UK Biobank. We also set out to evaluate if genetically predicted obesity is associated with an increased risk of SGA–MetS. Methods We defined SGA–MetS based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria using cross‐sectional data from 1318 UK Biobank participants who reported being on an SGA medication. An SGA–MetS case was defined as meeting three or more of the five NCEP‐ATP III criteria. We performed a genome‐wide association study (GWAS) and gene‐based analysis to identify significant variants and gene associations. We computed the polygenic risk score (PGS) for body mass index (BMI) using 2,100,302 variants validated for obesity and metabolic traits from imputed single‐nucleotide polymorphism (SNP) data. We tested the association of PGS–BMI with SGA–MetS using logistic regression. Results GWAS identified suggestive associations ( p < 1 × 10 −5 ) on chromosome 15. The variant rs12914956 in CHD2 was associated with increased risk of SGA (odds ratio (OR) = 1.73, 95% confidence interval (CI) = 1.4–2.4, p = 3.6 × 10 −7 ). The gene‐based analysis identified significant gene associations with RBFOX1 ( p = 4.85 × 10 −7 ), PTPRD ( p = 7.6 × 10 −7 ), CSMD1 ( p = 2.2 × 10 −6 ), and CHD2 ( p = 1.3 × 10 −6 ). The PGS–BMI ( β = 0.23, p = 6.8 × 10 −5 ), was associated with increased MetS in a model adjusted for age, sex, physical activity, alcohol consumption, antidepressant medications, schizophrenia diagnosis, and principal components of ancestry. Conclusion Using a gene‐based analysis, we identified significant gene associations with SGA–MetS that have been previously associated with obesity and metabolic traits. The PGS–BMI was associated with MetS, suggesting that a genetic predisposition to a higher BMI may increase the risk of SGA–MetS. Future research should replicate the findings in a larger dataset with more diverse populations.