以代谢学为指导的机器学习揭示了MASLD的CHB中的诊断和机械生物标志物
Chuyang Wang1,2, Yutao Chen3,4, Huanming Xiao5
1Biological Resource Center, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
慢性乙型肝炎 (CHB) 患者的代谢功能障碍相关的脂肪性肝病 (MASLD) 可以使用特定的代谢特征来识别. 整合这些代谢物的机器学习模型为早期诊断提供了一个有希望的方法.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 代谢学 代谢学 代谢学
- 机器学习 机器学习
背景情况:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 经常与慢性乙型肝炎 (CHB) 一起发生.
- 与CHB相关的MASLD的早期诊断具有挑战性,特别是在非肥胖个体或那些有微妙症状的人中.
- 这项研究旨在发现CHB相关MASLD的代谢指标,并开发一种预测工具.
研究的目的:
- 确定独特的代谢特征,使CHB-MASLD患者与健康对照患者有区别.
- 开发和验证基于机器学习的CHB-MASLD预测模型.
- 阐明CHB-MASLD的代谢基础.
主要方法:
- 在160名参与者 (80名CHB-MASLD,80名对照人) 上进行了非向血清代谢.
- 利用KEGG丰富分析和四种机器学习算法 (随机森林,XGBoost,SVM,LASSO) 来识别差异化代谢物.
- 评估了代谢物与临床相关性以及预测模型的诊断性能.
主要成果:
- 确定了924种不同的代谢物,富含TCA循环,脂代谢和氨基酸途径.
- 六个关键代谢物 (L-酸,酸,酸,酸,脂酸,单甲基酸,糖类甲) 有效地区分了CHB-MASLD患者 (AUC > 0.75).
- 结合新陈代谢和临床数据的综合模型实现了完美的诊断准确性 (AUC = 1.000).
结论:
- 与CHB相关的MASLD涉及全身代谢转变,包括线粒体功能障碍,氧化应激和改变的氨基酸代谢.
- 已识别的代谢物提供了机械洞察力,并有可能在CHB患者中进行非侵入性MASLD查.
- 多算法代谢学对早期诊断和复杂的肝脏并发症的个性化管理有显著的希望.
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