Trans-omics analyses identify the biochemical network of LPCAT1 associated with coronary artery disease

This study used a trans-omics approach—combining genome-wide SNP analysis and metabolomics—to distinguish coronary artery disease (CAD) patients from high-risk and healthy individuals. It identified declining plasma phospholipids as potential biomarkers, linked key SNPs and genes (notably LPCAT1) to lipid changes, and developed a machine-learning model that accurately predicts CAD (AUC = 0.917). The results highlight the role of phospholipid metabolism and genetic variation in CAD progression. The genomic DNA was collected from peripheral white blood cells using the phenol/chloroform DNA extraction method after lysis of red blood cells. Each subject was genotyped using Axiom Genome-Wide TWB 2.0 array plates. SNPs were excluding with a minor allele frequency rate of 0 or SNPs with a missing rate of more than 10%.

本研究采用跨组学（trans-omics）策略——整合全基因组单核苷酸多态性（single nucleotide polymorphism, SNP）分析与代谢组学——区分冠状动脉粥样硬化性心脏病（coronary artery disease, CAD）患者、高危人群与健康个体。本研究鉴定出血浆磷脂水平降低可作为潜在生物标志物，将关键SNP及基因（尤其是LPCAT1）与脂质代谢改变建立关联，并构建了可精准预测CAD的机器学习模型，其受试者工作特征曲线下面积（Area Under the Curve, AUC）达0.917。研究结果揭示了磷脂代谢与遗传变异在CAD进展中的关键作用。本研究先对红细胞进行裂解，随后采用酚-氯仿DNA提取法从外周血白细胞中获取基因组DNA。所有受试者均通过Axiom全基因组TWB 2.0阵列板完成基因分型。筛选SNP时，排除次要等位基因频率为0，或缺失率超过10%的位点。