代谢亚型和基于机器学习的诊断揭示了化病的临床异质性
Jia Si1,2,3, Hangju Zhu4, Xinyu Ji1,2,3
1Department of Science and Technology Innovation, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China.
Metabolites
|January 27, 2026
概括
菌体表现出代谢差异,已识别出两种亚型. 一个亚型具有更高的感染风险和炎症,而另一个亚型显示纤维化标志物. 一个双代谢物面板有助于检测.
科学领域:
- 职业健康 职业健康 职业健康
- 代谢学 代谢学 代谢学
- 肺部医学 肺部医学
背景情况:
- 病是一种全球性的职业危险,疾病进展各异.
- 症异质性的代谢基础尚未得到充分理解.
研究的目的:
- 为了研究推动化异质性的代谢机制.
- 为了确定潜在的生物标志物用于病检测和亚型.
主要方法:
- 与156名病患者和132名对照进行的病例对照研究.
- 使用液态染色体质谱法 (LC-MS/MS) 的非向性血代谢组.
- 使用了非负矩阵因子化 (NMF) 集群,WGCNA和机器学习.
主要成果:
- 已确定860种不同丰富的代谢物,包括与病原体相关的化合物.
- 发现了两种不同的代谢亚型 (NMF1,NMF2),NMF2显示出更高的肺部感染风险.
- NMF2与炎症和脂质过氧化有关;NMF1与纤维化标志物相关.
- 一个双代谢物小组 (tyrosocholic acid,PI) 在病检测和亚型化方面实现了AUC>0.85.
结论:
- 菌体表现出显著的代谢异质性.
- 已识别的亚型为改善患者分层和有针对性的干预提供了潜力.
- 代谢洞察力为提升早期检测和治疗策略铺平了道路.
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