Wong Kenneth Chi-Yin, Leung Perry Bok-Man, Lee Benedict Ka-Wa, Zheng Zoe Zi-Yu, Tsang Emily Man-Wah, Liu Meng-Hui, Lee Kelly Wing-Kwan, Rao Shi-Tao, Sham Pak-Chung, Lui Simon Sai-Yu, So Hon-Cheong
School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Transl Psychiatry. 2025 Aug 19;15(1):295. doi: 10.1038/s41398-025-03499-w.
Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects like dyslipidemia and obesity. We conducted genome-wide association studies (GWASs) to identify genetic variants associated with SGA-induced lipid and BMI changes in Chinese SCZ patients. A longitudinal cohort of Chinese SCZ receiving SGAs was followed for up to 18.7 years (mean = 5.7 years, SD = 3.3 years). We analysed the patients' genotypes (N = 669), lipid profiles, and BMI using 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were employed to evaluate seven SGAs' random effects on metabolic changes for each patient, followed by GWAS and gene set analyses with Bonferroni and FDR correction. Five SNPs achieved p-value < 5 × 10 before multiple testing correction: rs6532055 (ABCG2) linked to olanzapine-induced LDL changes, rs2644520 (near SORCS1) linked to aripiprazole-induced triglyceride changes, rs115843863 (near UPP2) linked to clozapine-induced HDL changes, rs2514895 (near KIRREL3) linked to paliperidone-induced LDL changes, and rs188405603 (SLC2A9) linked to quetiapine-induced triglyceride changes. These five SNPs passed FDR correction at 0.2 but not Bonferroni-corrected genome-wide significance threshold (p-value < 3.125 × 10) for 160 GWAS analyses. Gene-based analysis revealed six genome-wide significant genes after Bonferroni correction (p-value < 2.73 × 10): ABCG2, APOA5, ZPR1, GCNT4, MAST2, and CRTAC1. Four gene sets were significantly associated with SGA-induced metabolic side effects. In summary, this pharmacogenetic GWAS identified several genetic variants potentially associated with SGA-induced metabolic side effects, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients. Given our limited sample size, further replications are required to confirm the findings.
第二代抗精神病药物（SGA）被广泛用于治疗精神分裂症（SCZ），但它们常常会引发代谢副作用，如血脂异常和肥胖。我们开展了全基因组关联研究（GWAS），以确定与中国SCZ患者中SGA诱发的血脂和体重指数变化相关的基因变异。对接受SGA治疗的中国SCZ患者纵向队列进行了长达18.7年的随访（平均 = 5.7年，标准差 = 3.3年）。我们利用19316份处方记录以及每个结局3917至7596次代谢测量数据，分析了患者的基因型（N = 669）、血脂谱和体重指数。采用线性混合模型评估七种SGA对每位患者代谢变化的随机效应，随后进行GWAS以及经Bonferroni和FDR校正的基因集分析。五个单核苷酸多态性（SNP）在多重检验校正前达到p值 < 5×10：与奥氮平诱发的低密度脂蛋白（LDL）变化相关的rs6532055（ABCG2）、与阿立哌唑诱发的甘油三酯变化相关的rs2644520（靠近SORCS1）、与氯氮平诱发的高密度脂蛋白（HDL）变化相关的rs115843863（靠近UPP2）、与帕利哌酮诱发的LDL变化相关的rs2514895（靠近KIRREL3）以及与喹硫平诱发的甘油三酯变化相关的rs188405603（SLC2A9）。这五个SNP在160次GWAS分析中通过了FDR校正（0.2），但未达到Bonferroni校正的全基因组显著性阈值（p值 < 3.125×10）。基于基因的分析在Bonferroni校正后（p值 < 2.73×10）揭示了六个全基因组显著基因：ABCG2、APOA5、ZPR1、GCNT4、MAST2和CRTAC1。四个基因集与SGA诱发的代谢副作用显著相关。总之，这项药物遗传学GWAS确定了几个可能与SGA诱发的代谢副作用相关的基因变异，可能为个性化治疗策略提供依据，以将SCZ患者中的代谢风险降至最低。鉴于我们的样本量有限，需要进一步重复研究以证实这些发现。
