School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, No. 6 Ankang Avenue, Gui'an New District, Guiyang, Guizhou Province, 561113, China.
Moutai Institute, Renhuai, 564507, China.
Sci Rep. 2024 Oct 28;14(1):25755. doi: 10.1038/s41598-024-77352-3.
Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.
心血管疾病 (CVD) 是导致死亡、残疾和医疗保健费用的主要原因，对老年人有重大影响，并导致各年龄段（包括 70 岁以下人群）的过早死亡。尽管几十年来有了变革性的发现和临床努力，但 CVD 的诊断、预防和治疗仍然面临巨大挑战。本研究旨在揭示潜在的 CVD 相关生物标志物，并建立 CVD 风险评估的机器学习模型。对 57 名受试者的空腹静脉血标本进行了非靶向代谢分析和超高效液相色谱-串联质谱分析以及常规临床生化测试。确定了四个相关的临床特征和 164 种与 CVD 相关的代谢物，特别是与甘油磷脂代谢和不饱和脂肪酸生物合成相关的代谢物。从棕榈酰胺、油酸、138-pos（正离子模式下检测到的第 138 个代谢组学特征）、磷脂酰胆碱、亚油酸、年龄、直接胆红素和无机磷的综合生物标志物面板中获得的机器学习模型，能够提高 CVD 风险评估的准确性，达到高达 0.91 的高满意度值。研究结果表明，生物膜和能量代谢过程的紊乱与 CVD 患者血管损伤风险增加显著相关。通过机器学习方法，关键代谢物和临床生物标志物为 CVD 的有效风险评估和诊断提供了有希望的潜力。
