PDF(Pubmed)
Abstract：
Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.
摘要：
II型糖尿病（T2DM）是一个不断上升的全球健康负担，因为它在全球范围内的患病率迅速增加。并可能导致严重的并发症。因此,最重要的是尽早确定有风险的个体,以避免长期T2DM并发症.在这项研究中,我们开发了一个可解释的机器学习模型，利用氧化应激（OS）的生物标志物的基线水平，炎症,和线粒体功能障碍（MD）用于识别有发展为T2DM风险的个体。特别是,隔离森林（iForest）被用作异常检测算法来解决类不平衡问题。根据对照组数据对iForest进行了培训，以检测T2DM发展的高风险病例作为异常值。通过十倍交叉验证对两个iForest模型进行了训练和评估，第一个关于传统生物标志物(BMI，单独的血糖水平（BGL）和甘油三酯），第二个包括上述其他生物标志物。在所有评估指标中，第二个模型的性能优于第一个模型，特别是F1得分和回忆，分别从0.61±0.05增加到0.81±0.05和0.57±0.06增加到0.81±0.08。特征重要性评分确定了一种新的生物标志物组合，包括白细胞介素-10（IL-10），8-异前列腺素,humanin(HN),和氧化型谷胱甘肽(GSSG),在结果预测中，这些生物标志物比传统生物标志物更具影响力。这些结果揭示了一种同时预测和理解T2DM发展风险的有希望的方法，并建议可能的药物干预以解决疾病进展早期的炎症和OS。
