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使用机器学习集成的代谢和临床特征早期预测妊娠糖尿病

Qun Ji¹, Lan Gao¹, Haiwei Liu¹

¹Department of Endocrinology, Hainan Provincial People's Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.

Frontiers in endocrinology

|December 1, 2025

概括

这项研究使用第一季度代谢数据和临床因素开发了妊娠糖尿病 (GDM) 的早期预测模型. 该模型准确地识别高风险妊娠,使得在并发症出现之前及时介入.

科学领域:

生物化学生物化学
代谢学代谢学代谢学
怀孕健康怀孕健康怀孕健康

背景情况:

孕期糖尿病 (GDM) 是一种常见的妊娠并发症,通常是晚期诊断出来的.
目前针对GDM的干预措施经常被推迟到代谢并发症发展后.
早期检测至关重要,以防止对母亲和胎儿的不良后果.

研究的目的:

开发用于早期GDM检测的综合预测模型.
将第一季度的新陈代谢特征与临床风险因素结合起来.
为了在不可逆转的代谢损伤之前,在高风险怀孕中及时进行干预.

主要方法:

招募了89名孕妇 (45名GDM,44名正常葡萄糖耐受).
在血清和尿液样本上进行了非向的UPLC-MS/MS代谢分析.
使用拉索回归和机器学习 (ML) 模型进行预测,并使用SHAP分析进行解释.

主要成果:

确定了528种不同的代谢物和20种相关途径.
拉索回归选择了11种代谢物和3种临床特征.
一个多层感知子模型实现了高精度 (AUC 0.984),其中GlcCer (d18:1/16:0) 和甘油三作为关键预测因素.

结论:

在GDM和正常葡萄糖耐受性组之间存在显著的代谢特征差异.
开发的ML模型从第一季度的数据准确地预测了GDM风险.
代谢分析显示,在怀孕期间早期GDM预测具有前途.

关键词:

早期预测早期预测孕期糖尿病孕期糖尿病机器学习是机器学习.代谢产物的代谢产物代谢组概况分析

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