Martins Lucas G, Manzini Bruna M, Montalvão Silmara, Honorato Millene A, Colella Marina P, Hayakawa Gabriela G Y, de Paula Erich V, Orsi Fernanda A, Braga Erik S, Avramović Nataša, Omage Folurunsho Bright, Tasic Ljubica, Annichino-Bizzacchi Joyce M
Laboratory of Biological Chemistry, Department of Organic Chemistry, Institute of Chemistry, Universidade Estadual de Campinas, Campinas 13083-862, SP, Brazil.
Laboratory of Hemostasis, Hemocentro-Unicamp, Universidade Estadual de Campinas, Campinas 13083-878, SP, Brazil.
Molecules. 2024 Dec 13;29(24):5895. doi: 10.3390/molecules29245895.
Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolism and antiphospholipid syndrome (APS). H-NMR-based metabonomics data of the serum samples of patients with arterial or venous thromboembolism (VTE) without APS (n = 32), thrombotic primary APS patients (APS, n = 32), and healthy controls (HCs) (n = 32) were investigated. Unique metabolic profiles between VTE and HCs, APS and HCs, and between VTE and triple-positive APS groups were indicative of the significant alterations in the metabolic pathways of glycolysis, the TCA cycle, lipid metabolism, and branched-chain amino acid (BCAA) metabolism, and pointed to the complex pathogenesis mechanisms of APS and VTE. Histidine, 3-hydroxybutyrate, and threonine were shown to be the top three metabolites with the most substantial impact on model predictions, suggesting that these metabolites play a pivotal role in distinguishing among APS, VTE, and HCs. These metabolites might be potential biomarkers to differentiate APS and VTE patients.
使用机器学习和人工智能工具来研究血清代谢物对血栓栓塞和抗磷脂综合征（APS）的鉴别潜力。对无APS的动脉或静脉血栓栓塞（VTE）患者（n = 32）、血栓形成性原发性APS患者（APS，n = 32）和健康对照者（HCs）（n = 32）的血清样本进行了基于氢核磁共振（H-NMR）的代谢组学数据研究。VTE与HCs、APS与HCs以及VTE与三阳性APS组之间独特的代谢谱表明糖酵解、三羧酸循环、脂质代谢和支链氨基酸（BCAA）代谢途径存在显著改变，并指出了APS和VTE复杂的发病机制。组氨酸、3-羟基丁酸和苏氨酸被证明是对模型预测影响最大的前三种代谢物，表明这些代谢物在区分APS、VTE和HCs中起关键作用。这些代谢物可能是区分APS和VTE患者的潜在生物标志物。
