使用机器学习研究空腹血糖的预测因子：昼夜节律时间与年龄交互作用的见解
Using Machine Learning to Investigate Predictors of Fasting Blood Glucose: Insights into Circadian Timing and Age Interactions
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中文总结 AI 辅助
本研究利用NHANES数据构建可解释机器学习模型预测空腹血糖，发现糖化血红蛋白等为主要预测因子，并揭示睡眠中点与年龄的交互作用，为代谢健康研究提供新见解。
中文摘要 AI 辅助
血糖调节受损是代谢功能障碍和2型糖尿病的主要诱因。本研究开发了一个可解释的机器学习框架，利用美国国家健康与营养调查（NHANES）2017-2020年疫情前数据集中的代谢、激素、生活方式、人口统计学、营养和昼夜节律变量，预测对数转换后的空腹血糖。在合并多个NHANES子数据集后，数据处理采用了防泄漏流水线，其中插补、缩放和独热编码仅在数据集分割后且在训练折内进行。使用94个候选预测因子和工程化的昼夜节律交互项，对弹性网络（Elastic Net）、最小绝对收缩和选择算子（LASSO）以及极限梯度提升（XGBoost）模型进行了评估。性能评估采用平均绝对误差、均方根误差、决定系数、校准以及沙普利加性解释（SHAP）。最终的交互增强型XGBoost模型在独立测试集上取得了强劲性能，使用10个预测因子，平均绝对误差为0.0804，均方根误差为0.1148，决定系数为0.7761。糖化血红蛋白是主导预测因子，其次是胰岛素、糖尿病诊断、γ-谷氨酰转移酶、年龄、种族和性别。在工程化的交互项中，睡眠中点乘以年龄在重复随机分割分析中持续被保留，尽管其贡献相对于主导的血糖预测因子仍然较小。这些发现支持在代谢健康中进一步研究昼夜节律-年龄交互作用。
英文摘要
Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning framework to predict log-transformed fasting blood glucose using metabolic, hormonal, lifestyle, demographic, nutritional, and circadian variables from the National Health and Nutrition Examination Survey 2017--2020 pre-pandemic dataset. After merging multiple NHANES sub-datasets, data processing used a leakage-resistant pipeline in which imputation, scaling, and one-hot encoding were performed only after dataset splitting and within training folds. Elastic Net, LASSO, and XGBoost models were evaluated using 94 candidate predictors and engineered circadian interaction terms. Performance was assessed using mean absolute error, root mean squared error, coefficient of determination, calibration, and Shapley Additive Explanations. The final interaction-augmented XGBoost model achieved strong performance on the independent test set, with a mean absolute error of 0.0804, a root mean squared error of 0.1148, and a coefficient of determination of 0.7761, using 10 predictors. Glycohemoglobin was the dominant predictor, followed by insulin, diabetes diagnosis, gamma-glutamyl transferase, age, race, and gender. Among the engineered interaction terms, sleep midpoint multiplied by age was consistently retained in repeated random-split analyses, although its contribution remained modest relative to dominant glycaemic predictors. These findings support further investigation of circadian-age interactions in metabolic health.
