Ullah Ehsan, El-Menyar Ayman, Kunji Khalid, Elsousy Reem, Mokhtar Haira R B, Ahmad Eiman, Al-Nesf Maryam, Beotra Alka, Al-Maadheed Mohammed, Mohamed-Ali Vidya, Saad Mohamad, Al Suwaidi Jassim
Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha P.O. Box 5825, Qatar.
Clinical Research, Trauma & Vascular Surgery, Hamad Medical Corporation, Doha P.O. Box 3050, Qatar.
Metabolites. 2022 Jun 3;12(6):517. doi: 10.3390/metabo12060517.
Coronary heart disease (CHD) is a major cause of death in Middle Eastern (ME) populations, with current studies of the metabolic fingerprints of CHD lacking in diversity. Identification of specific biomarkers to uncover potential mechanisms for developing predictive models and targeted therapies for CHD is urgently needed for the least-studied ME populations. A case-control study was carried out in a cohort of 1001 CHD patients and 2999 controls. Untargeted metabolomics was used, generating 1159 metabolites. Univariate and pathway enrichment analyses were performed to understand functional changes in CHD. A metabolite risk score (MRS) was developed to assess the predictive performance of CHD using multivariate analysis and machine learning. A total of 511 metabolites were significantly different between the CHD patients and the controls (FDR p < 0.05). The enriched pathways (FDR p < 10−300) included D-arginine and D-ornithine metabolism, glycolysis, oxidation and degradation of branched chain fatty acids, and sphingolipid metabolism. MRS showed good discriminative power between the CHD cases and the controls (AUC = 0.99). In this first study in the Middle East, known and novel circulating metabolites and metabolic pathways associated with CHD were identified. A small panel of metabolites can efficiently discriminate CHD cases and controls and therefore can be used as a diagnostic/predictive tool.
冠心病（CHD）是中东（ME）人群的主要死因，目前关于冠心病代谢指纹的研究缺乏多样性。对于研究最少的中东人群而言，迫切需要鉴定特定生物标志物以揭示冠心病预测模型和靶向治疗的潜在机制。在一个包含1001名冠心病患者和2999名对照的队列中开展了一项病例对照研究。采用非靶向代谢组学方法，生成了1159种代谢物。进行单变量和通路富集分析以了解冠心病中的功能变化。开发了一种代谢物风险评分（MRS），使用多变量分析和机器学习来评估冠心病的预测性能。冠心病患者和对照组之间共有511种代谢物存在显著差异（FDR p < 0.05）。富集的通路（FDR p < 10−300）包括D-精氨酸和D-鸟氨酸代谢、糖酵解、支链脂肪酸的氧化和降解以及鞘脂代谢。MRS在冠心病病例和对照之间显示出良好的判别能力（AUC = 0.99）。在中东地区的这项首次研究中，鉴定出了与冠心病相关的已知和新型循环代谢物及代谢通路。一小部分代谢物能够有效区分冠心病病例和对照，因此可作为一种诊断/预测工具。
