通过多种生物标志物在妊娠早期预测妊娠糖尿病
Meng-Nan Yang1,2, Lin Zhang3, Wen-Juan Wang1,4
1Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Department of Pediatrics, Xinhua Hospital, Early Life Health Institute, Shanghai Jiao-Tong University School of Medicine, Kong-Jiang Road, Shanghai, 200092, China.
BMC pregnancy and childbirth
|September 16, 2024
概括
在机器学习模型中,可以使用禁食血葡萄糖 (FPG) 和胰岛素类生长因子结合蛋白2 (IGFBP-2) 预测早期妊娠糖尿病 (GDM),提供一种简单但有效的查工具.
科学领域:
- 生殖医学 生殖医学
- 内分泌学 在内分泌学.
- 生物标志物和诊断方法
背景情况:
- 使用早期怀孕生物标志物的妊娠糖尿病 (GDM) 预测仍然具有挑战性.
- 识别可靠的早期标志物对于及时干预和改善妊娠结果至关重要.
研究的目的:
- 确定早期妊娠期生物标志物的最佳组合,以预测GDM.
- 评估各种机器学习模型在GDM预测中的性能.
主要方法:
- 一个嵌套的病例对照研究,涉及100个GDM和对照怀孕对.
- 测量各种血清生物标志物 (例如hs-CRP,SHBG,IGF-I,IGFBP-2,脂肪内素,纤维内素) 和常规的第一季度测试 (FPG,脂质,甲状腺激素) 在怀孕11-14周.
- 应用五种机器学习模型 (逻辑回归,LASSO,随机森林,SVM,k-NN) 进行GDM预测,数据分为训练和测试集.
主要成果:
- 在所有机器学习模型中,禁食血葡萄糖 (FPG) 和类似胰岛素的增长因子结合蛋白2 (IGFBP-2) 始终被确定为关键预测因子.
- 结合FPG和IGFBP-2的随机森林模型表现最好,AUC达到0.80.
- 包括额外的生物标志物并没有显著提高模型的预测歧视力.
结论:
- 在妊娠早期 (11-14周) 测量FPG和IGFBP-2的组合可以预测晚期GDM的发展,准确度中等.
- 这种简单的两种生物标志物模型显示了GDM查的潜力.
- 建议在独立队列中进一步验证,以确认其临床实用性.
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