母亲肠道微生物组作为妊娠糖尿病中胰岛素治疗需求的预测因素
Polina V Popova1, Alexander A Loboda1, Aleh Liaudanski2
1Almazov National Medical Research Centre, Saint Petersburg, Russia Federation.
Journal of diabetes science and technology
|March 4, 2026
概括
结合临床和肠道微生物组数据,可以更好地预测妊娠糖尿病 (GDM) 中的胰岛素治疗需求. 机器学习模型准确地预测了GDM患者对胰岛素治疗的需求.
科学领域:
- 生殖医学 生殖医学
- 微生物组研究 微生物组研究
- 计算生物学 计算生物学
背景情况:
- 孕期糖尿病 (GDM) 往往需要药理干预,而不仅仅是改变生活方式,有效控制血糖.
- 在GDM中预测胰岛素治疗 (IT) 的需要对于及时和适当的患者管理至关重要.
- 机器学习提供了一种有前途的方法来整合复杂的数据集,以提高预测准确度.
研究的目的:
- 开发和验证集临床和肠道微生物组数据的机器学习模型,以预测GDM妇女的胰岛素治疗 (IT) 需求.
- 评估结合微生物组数据的模型的预测性能,与单独的临床数据相比.
- 确定影响GDM中胰岛素需求的关键临床和微生物特征.
主要方法:
- 利用了205名患有GDM的孕妇的数据,包括临床参数,生活方式,血糖记录和肠道微生物组概况 (16S rRNA测序).
- 训练渐变增强模型来预测胰岛素治疗 (IT),基础胰岛素 (BI) 和食用胰岛素 (PI) 的需求.
- 通过交叉验证的AUC-ROC评估模型歧视,并通过SHAP和排列分析评估特征重要性.
主要成果:
- 需要胰岛素的女性年龄较大,妊娠前BMI较高,血糖标志物升高 (禁食葡萄糖,OGTT,HbA1c).
- 整合肠道微生物组数据提高了IT,BI和PI的预测准确性 (AUC-ROC).
- 关键预测因素包括血糖标记,BMI和特定的微生物种群 (例如,Phascolarctobacterium faecium*,Alistipes ihumii*) 和代谢途径.
结论:
- 将肠道微生物组数据与临床信息相结合,可显著改善对GDM患者胰岛素治疗需求的预测.
- 结合微生物组数据的机器学习模型显示出增强的预测能力,特别是在基础胰岛素启动方面.
- 这些发现突显了多组方法在GDM个性化管理方面的潜力.
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