The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and nonlinear associations. Additionally, the modeling strategy carried out allowed us to compare the stability of model performance and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modeling strategies and the value of interpretable machine learning.

中文翻译：

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机器学习揭示了对血糖调节具有复杂影响的时变微生物预测因子

全球 2 型糖尿病 (T2D) 的发病率一直在增加，越来越多的证据表明 2 型糖尿病与微生物群组成的改变有关。2 型糖尿病发生之前会经历长期的糖尿病前期状态，其特征是各种代谢参数的变化。我们测试了肠道微生物组是否对健康和糖尿病前期疾病阶段的 T2D 发展具有预测潜力。我们使用从基于人群的男性代谢综合症 (METSIM) 研究中收集的 608 名表型良好的芬兰男性的前瞻性数据来构建机器学习模型，用于预测较短（1.5 年）和较长（4 年）内的连续血糖和胰岛素测量值时期。我们的结果表明，纳入肠道微生物组可以提高对 T2D 相关参数（如糖化血红蛋白和胰岛素测量值）进行建模的预测准确性。我们识别了新的微生物生物标志物，并使用可解释的机器学习技术描述了它们对预测的影响，这揭示了复杂的线性和非线性关联。此外，所执行的建模策略使我们能够比较模型性能和生物标志物选择的稳定性，也揭示了短期和长期预测的差异。确定的微生物组生物标志物为与 T2D 相关的各种代谢特征提供了预测指标，从而为个人风险评估提供了额外的参数。我们的工作还强调了对稳健建模策略的需求以及可解释机器学习的价值。