一个新的初步代谢组小组用于IHD诊断和病变发生
S S Markin1, E A Ponomarenko2, Yu A Romashova2
1Institute of Biomedical Chemistry, Moscow, Russia, 119121. phospholipovit@ibmc.msk.ru.
Scientific reports
|February 1, 2024
概括
这项研究使用机器学习识别了用于诊断缺血性心脏病 (IHD) 的关键血液代谢物. 机器学习模型准确地预测了IHD,突出了潜在诊断用途的特定代谢物.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,缺血性心脏病 (IHD) 占了很大一部分.
- 准确和早期诊断IHD对于有效的患者管理和改善结果至关重要.
研究的目的:
- 调查针对性代谢分析的潜力,结合机器学习来诊断IHD.
- 为了确定与IHD相关的特定的血代谢物和代谢途径.
主要方法:
- 在112名受试者中对87种内源性血代谢物的定量分析 (76名IHD患者,36名对照).
- 开发一种新的代谢学数据的年龄调整校正方法.
- 应用各种机器学习算法 (逻辑回归,SVM,决策树,随机森林,梯度提升) 用于IHD诊断模型开发.
主要成果:
- 在IHD患者中鉴定出36种显著变化的代谢物,包括氨基酸,乙卡尼丁和托代谢途径的变化.
- 随机森林模型实现了最高的诊断准确性,曲线下的面积 (AUC) 为0.98.
- 关键的代谢物,如上腺素,桑氨酸,氨酸和氨被确定为IHD的显著预测因素.
结论:
- 有针对性的代谢分析与机器学习相结合,为IHD诊断提供了一个有前途的方法.
- 一组已识别的代谢物组可以作为IHD检测的新型初步生物标志物.
- 需要进一步的验证研究来证实这些发现的临床实用性.
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