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

Kenneth Chi-Yin Wong, Perry Bok-Man Leung, Benedict Ka-Wa Lee, Zoe Zi-Yu Zheng, Emily Man-Wah Tsang, Meng-Hui Liu, Kelly Wing-Kwan Lee, Shi-Tao Rao, Pak-Chung Sham, Simon Sai-Yu Lui, Hon-Cheong So
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Abstract

Second-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 in ABCG2 (olanzapine-induced LDL changes) and rs2644520 near SORCS1 (aripiprazole-induced triglyceride changes). Three additional SNPs achieved genome-wide significance under non-additive models: rs115843863 near UPP2 (clozapine-induced HDL changes), rs2514895 near KIRREL3 (paliperidone-induced LDL changes), and rs188405603 in SLC2A9 (quetiapine-induced triglyceride changes). Gene-based analysis revealed six genome-wide significant (p<2.73e-06, Bonferroni correction) genes: ABCG2, APOA5, ZPR1, GCNT4, MAST2, and CRTAC1. 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.
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中国精神分裂症患者抗精神病药物诱发血脂和体重指数变化的药物遗传学研究:全基因组关联研究
第二代抗精神病药物(SGAs)被广泛用于治疗精神分裂症(SCZ),但它们经常会引起代谢副作用,包括血脂异常和肥胖,给临床带来了巨大挑战。虽然遗传因素被认为会导致这些副作用的变化,但药物遗传学研究仍然有限。本研究旨在利用全基因组关联研究(GWAS)在中国 SCZ 队列中鉴定与 SGA 引起的血脂和体重指数变化相关的遗传变异。我们对接受 SGAs 治疗的中国 SCZ 患者进行了长达 18.7 年的自然纵向队列随访。我们利用 19 316 份处方记录和 3 917 至 7 596 项代谢测量结果,分析了患者的基因型(N=669)、血脂概况和体重指数。线性混合模型用于估算 SGA 对每位患者血脂状况和 BMI 变化的随机效应。在进行GWAS和基因组分析时,对错误发现率(FDR)进行了校正。在加性遗传模型下,确定了两个全基因组意义重大的 SNP:ABCG2 中的 rs6532055(奥氮平诱导的低密度脂蛋白变化)和 SORCS1 附近的 rs2644520(阿立哌唑诱导的甘油三酯变化)。另外三个 SNP 在非加成模型下具有全基因组显著性:UPP2 附近的 rs115843863(氯氮平诱导的高密度脂蛋白变化)、KIRREL3 附近的 rs2514895(帕利哌酮诱导的低密度脂蛋白变化)和 SLC2A9 中的 rs188405603(喹硫平诱导的甘油三酯变化)。基于基因的分析发现了六个全基因组显著基因(p<2.73e-06,Bonferroni 校正):ABCG2、APOA5、ZPR1、GCNT4、MAST2 和 CRTAC1。有四个基因组与 SGA 引起的代谢副作用有明显关联。这项药物基因遗传学全球基因组研究发现了与七种SGA代谢副作用相关的多个基因变异,有可能为个性化治疗策略提供信息,以最大限度地降低SCZ患者的代谢风险。
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