Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Elife. 2024 Sep 20;13:RP98709. doi: 10.7554/eLife.98709.
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
识别有发生糖尿病风险的糖尿病前期个体，有助于实施精准干预。本研究旨在探讨基于磁共振（NMR）代谢组学特征预测糖尿病前期向糖尿病进展的作用。
本前瞻性研究纳入了 UK Biobank 中 13489 例糖尿病前期患者，检测其代谢组学数据。采用 NMR 光谱法定量检测循环代谢物。使用 Cox 比例风险（CPH）模型估计代谢物与糖尿病风险之间的关联。采用支持向量机、随机森林和极端梯度增强算法选择最优的代谢物组合进行预测。CPH 和随机生存森林（RSF）模型用于验证代谢物的预测能力。
中位随访 13.6 年期间，2525 例患者发生糖尿病。调整协变量后，168 种代谢物中有 94 种与进展为糖尿病的风险相关。三种机器学习算法均选择的 9 种代谢物组合，在 CPH 模型中可显著改善传统危险因素对糖尿病风险的预测（CPH 模型中，1 年时风险因素+代谢物的曲线下面积为 0.823，而风险因素为 0.759，5 年时分别为 0.830 和 0.798，10 年时分别为 0.801 和 0.776，均 P<0.05）。RSF 模型也得出了相似的结果。根据预测值阈值对患者进行分类，可观察到不同的糖尿病累积风险。
本研究支持使用代谢物标志物来帮助确定有进展为糖尿病风险的糖尿病前期患者，并为有针对性和高效的干预措施提供信息。
上海市卫生健康委员会（2022XD017）。上海市高校高水平地方高校创新团队建设计划（SHSMU-ZDCX20212501）。上海市人力资源和社会保障局（2020074）。上海市医院发展中心临床研究计划（SHDC2020CR4006）。上海市科学技术委员会（22015810500）。
