使用隔离森林和新生物标志物进行II型糖尿病风险预测
Hibba Yousef1, Samuel F Feng2,3, Herbert F Jelinek4,5
1Biotechnology Research Center, Technology Innovation Institute, Masdar City, P. O. Box 9639, Abu Dhabi, United Arab Emirates. Hibba.Yousef@tii.ae.
Scientific reports
|June 22, 2024
概括
早期发现II型糖尿病 (T2DM) 风险至关重要. 使用氧化应激,炎症和线粒体功能障碍生物标志物的机器学习模型有效预测T2DM风险,优于传统方法.
科学领域:
- 生物医学科学 生物医学科学
- 计算生物学 计算生物学
- 内分泌学 在内分泌学.
背景情况:
- 二型糖尿病 (T2DM) 是一个不断增长的全球健康挑战,具有显著的长期并发症.
- 早期识别高风险T2DM个体对于及时干预和预防并发症至关重要.
研究的目的:
- 开发一个可解释的机器学习模型来预测T2DM风险.
- 在T2DM风险预测中评估与氧化应激 (OS),炎症和线粒体功能障碍 (MD) 相关的生物标志物的疗效.
主要方法:
- 使用隔离森林 (iForest) 异常检测算法来处理T2DM预测中的类失衡.
- 开发和验证了两种iForest模型:一种使用传统的生物标志物 (BMI,BGL,甘油三) 和另一种结合OS,炎症和MD生物标志物.
- 使用十倍交叉验证来评估模型性能.
主要成果:
- 纳入OS,炎症和MD生物标志物的模型显著优于仅使用传统生物标志物的模型,F1得分 (0.61到0.81) 和回忆 (0.57到0.81) 显著改善.
- 确定了关键的有影响力的生物标志物包括互白素-10 (IL-10),8-异素,人氨酸 (HN) 和氧化谷氨 (GSSG).
- 这些新型生物标志物比传统指标对T2DM风险的预测能力更强.
结论:
- 开发的可解释机器学习模型为同时预测和理解T2DM开发风险提供了一个有希望的方法.
- 这些发现表明,在T2DM进展的早期阶段,潜在的药理干预措施可以针对炎症和氧化应激.
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