M172. POLYGENIC RISK SCORES ANALYSES IN ANTIPSYCHOTIC-INDUCED WEIGHT GAIN

BACKGROUND: Antipsychotic-induced weight gain (AIWG) is a common and serious side effect with antipsychotic medications, which frequently leads to obesity and metabolic disorders. Previous single-gene analyses have shown an overlap between AIWG and genes associated with obesity and energy homeostasis (e.g., MC4R). However, given the polygenic nature of AIWG, polygenic risk scores (PRS), which combine thousands of common variants weighted by their effect size, provide a novel opportunity to investigate the genetic liability for AIWG. Therefore, we analyzed whether PRSs based on large genome-wide association studies (GWAS) for schizophrenia (SCZ), body mass index (BMI), and diabetes (Type 1 & 2) were associated with AIWG. METHODS: We used a combined dataset (N=345) from two cohorts, prospectively assessed for AIWG: (1) a subset of the Clinical Antipsychotic Trials in Intervention Effectiveness cohort (CATIE; n=189, Brandl et al., 2016), and (2) the Toronto multi-study cohort (n=156, Brandl et al., 2014). The combined cohort was predominantly male (n=249, 72.2%) and on average 39.3±11.9 years old with a total of 196,787 genetic variants. Our phenotypes of interest included the percentage of BMI/weight change from baseline to end-of-treatment, as well as the presence/absence of significant weight gain (≥7% weight change). We investigated associations between PRSs of SCZ, BMI, and diabetes (Type 1 & 2) and AIWG using regression models, corrected for age, sex, study duration and presence of other risk medication for AIWG. We used the Psychiatric Genomics Consortium schizophrenia GWAS reports to calculate PRSs for SCZ. We used GWAS summary statistics from the GWAS Catalog of BMI and metabolic disorders. For BMI, we used one dataset for BMI (i.e., GCST006900: 2,336,269 variants across up to 700,000). For Type-1 diabetes (T1D), we used one dataset from the GWAS catalog (ID: GCST005536) which included 123,130 variants across 6,683 cases, 12,173 controls, 2,601 affected sibling-pair families, and 69 trios. Likewise, we used three datasets for T2D (i.e., GCST006801: 8,404,432 variants across 4,040 cases and 113,735 controls, GCST007517: 133,871 variants across up to 48,286 cases and up to 250,617 controls, and GCST007518: 133,586 variants across up to 48,286 cases and up to 250,617 controls). RESULTS: We observed significant associations with PRS for T1D and percentage BMI/weight change from baseline to the endpoint at P-value threshold=0.0022 (R2=0.02, p=0.03), as well as presence/absence of significant weight gain at PT=0.00015 (R2=0.02, p=0.047). In contrast, we observed no significant associations with PRS for SCZ, BMI, or T2D and AIWG (p>0.05). However, our findings with T1D would not remain significant after correction for multiple testing according to the Bonferroni method. DISCUSSION: To the best of our knowledge, this is the first study examining whether PRSs for various metabolic-related phenotypes are associated with AIWG in patients with SCZ. Our findings suggest a possible role for PRS of diabetes type 1 being associated with risk for AIWG. This observation would indicate that (auto)immune processes might be related to AIWG which has not previously been reported. Further studies with larger sample sizes and individuals of various ethnic ancestries are required.