Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study

Abstract

AbstractSecond-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects, including dyslipidemia and obesity, posing significant clinical challenges. While genetic factors are believed to contribute to the variability of these side effects, pharmacogenetic studies remain limited. This study aimed to identify genetic variants associated with SGA-induced lipid and BMI changes in a Chinese SCZ cohort using genome-wide association studies (GWASs). A naturalistic longitudinal cohort of Chinese SCZ patients receiving SGAs was followed for up to 18.7 years. We analyzed the patients’ genotypes (N=669), lipid profiles and BMI, utilizing 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were used to estimate the random effects of SGAs on lipid profiles and BMI changes for each patient. GWAS and gene set analyses were conducted with false discovery rate (FDR) correction. Two genome-wide significant SNPs were identified under an additive genetic model: rs6532055 inABCG2(olanzapine-induced LDL changes) and rs2644520 nearSORCS1(aripiprazole-induced triglyceride changes). Three additional SNPs achieved genome-wide significance under non-additive models: rs115843863 nearUPP2(clozapine-induced HDL changes), rs2514895 nearKIRREL3(paliperidone-induced LDL changes), and rs188405603 inSLC2A9(quetiapine-induced triglyceride changes). Gene-based analysis revealed six genome-wide significant (p<2.73e-06, Bonferroni correction) genes:ABCG2,APOA5,ZPR1,GCNT4,MAST2, andCRTAC1. Four gene sets were significantly associated with SGA-induced metabolic side effects. This pharmacogenetic GWAS identified several genetic variants associated with metabolic side effects of seven SGAs, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients.