应用非向性血代谢学和机器学习来构建超缩性心肌病的诊断模型:一个病例控制研究
Ruoxuan Li1, Bo Wang1, Bo Shan1
1Department of Ultrasound Medicine, Xijing Hospital, The Fourth Military Medical University, Xi'an, 710032, Shaanxi, China.
概括
这项研究开发了一种使用代谢学和机器学习的血代谢物诊断模型,以准确地选超性心肌病 (HCM). 该模型确定了关键代谢物和潜在的致病途径,提高了这种遗传性心血管疾病的诊断准确度.
科学领域:
- 心血管遗传学 心血管遗传学
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 超性心肌病 (HCM) 是最常见的遗传性心血管疾病.
- 代谢学为HCM病原和诊断策略提供了新的见解.
研究的目的:
- 分析HCM患者的血代谢变化.
- 开发一种使用非定位代谢学和机器学习 (ML) 的HCM诊断模型.
- 为了确定潜在的致病途径,并提高对HCM的查准确性.
主要方法:
- 招募了76名HCM患者和35名对照.
- 使用非目标代谢学和ML算法 (SVM,RF).
- 使用渐进多变量线性回归和KEGG路径分析.
主要成果:
- 在HCM患者和对照人群之间确定了240个显著差异化代谢物.
- 开发了高精度 (96.1-100%) 的SVM和RF模型,用于使用五种关键代谢物进行HCM分化:7-keto-8-aminopelargonic acid (KAPA),γ-linolenoyl ethanolamid,酸,D-quinovose和N-acetyl-l-aspartic acid (NAA).
- 揭示了HCM中中央碳代谢和蛋白质消化/吸收途径的上调,由氨酸,酸盐和谷氨酸代谢联系在一起,NAA与心脏结构相关.
结论:
- 血代谢物小组 (KAPA,γ-linolenoyl ethanolamid,酸,D-quinovose,NAA) 有效地选HCM.
- 代谢学与ML算法相结合,突出显示了氨酸,酸盐和谷氨酸代谢作为HCM潜在的致病途径.
- NAA成为HCM病原发生的潜在中心标.
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