针对性泌尿代谢组与机器学习相结合,以识别IBD中枢碳代谢相关的生物标志物
Miao-Lin Lei1, Guan-Wei Bi1, Xiao-Lin Yin2,3
1Department of Gastroenterology, Qilu Hospital, Shandong University, Jinan, Shandong, China.
这项研究使用机器学习确定了用于诊断炎症性肠病 (IBD) 亚型,克罗恩病 (CD) 和性结肠炎 (UC) 的关键尿代谢物. 这些发现为非侵入性IBD诊断提供了潜力.
科学领域:
- 代谢学
- 胃肠病学
- 生物化学
背景情况:
- 炎症性肠病 (IBD),包括克罗恩病 (CD) 和性结肠炎 (UC),是一种慢性胃肠炎症.
- 目前IBD的诊断方法具有侵入性,昂贵和耗时性,需要开发非侵入性的替代方法.
研究的目的:
- 研究尿路代谢特征以区分IBD亚型 (CD和UC) 与健康对照.
- 通过代谢数据开发和验证用于非侵入性IBD诊断的机器学习模型.
主要方法:
- 从尿样中对中央碳代谢中的49种代谢物的向代谢分析.
- 使用六种机器学习算法和交叉验证的AUC构建和评估诊断模型.
- 应用SHAP (SHapley添加剂解释) 进行模型解释和确定关键的歧视性代谢物.
主要成果:
- 六种代谢物 (西洛斯,异酸,果糖,L- 糖,N- 乙- D- 葡萄糖胺,葡萄糖酸) 与对照物有区别.
- 三种代谢物 (糖,L-糖,酸) 与对照物区分了CD.
- 在最佳模型中,UC的AUC为0. 84,CD的AUC为0. 93,在疾病活性调整后保持准确度. 通过SHAP分析确定的主要代谢物.
结论:
- 中心碳代谢中的不同尿道代谢特征与IBD亚型相关.
- 代谢板和机器学习模型显示出与健康个体区分UC和CD的非侵入性工具的前景.
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