AI Insight
Researchers developed and tested six machine learning models to predict prediabetes risk in a cohort of 77,478 Chinese adults using eight routine clinical measurements including age, BMI, fasting glucose, and lipid markers. The gradient boosting machine (GBM) model demonstrated the best overall performance with a C-index of 0.751, showing superior calibration and clinical utility compared to deep learning and traditional Cox regression approaches. The model has been deployed as a web-based tool but requires external validation before clinical implementation.
Why it matters
This tool could enable earlier identification of individuals at high risk for developing diabetes using inexpensive, routinely collected clinical data, potentially allowing for timely preventive interventions. The web-based application makes risk assessment more accessible to healthcare providers, though its effectiveness in populations outside the Chinese cohort studied remains to be determined.
Understand the Science
by Zhengxin Jiang, Wen Qian, Haifang Yang, Yifang Luo, Wen Peng, Qiong Wu, Xiaoxing Wei
Background
Diabetes imposes a growing health burden among Chinese adults, necessitating scalable tools for early identification of high-risk individuals. We aimed to develop and internally validate survival machine-learning models for prediabetes risk stratification using routinely available clinical indicators.
Methods
This retrospective cohort study included 77,478 Chinese adults from the Dryad repository (2010–2016). Candidate predictors were selected via LASSO regression and random survival forests (RSF). Six survival models, Cox proportional hazards (Cox-PH), CoxBoost, DeepSurv, gradient boosting machine (GBM), random survival forests (RSF), and survival tree, were developed and evaluated using the concordance index (C-index), time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). The best-performing model was deployed as a web-based application.
Results
Eight predictors were consistently selected across methods: age, BMI, fasting plasma glucose, total cholesterol, triglycerides, HDL-C, LDL-C, and creatinine clearance rate. In the test set, DeepSurv and GBM achieved comparable discrimination (C-index: 0.756 vs. 0.751; adjusted P = 0.694). GBM was preferred due to better calibration and greater net benefit across clinically relevant thresholds, leading to its selection as the candidate model.
Conclusion
The GBM-based prediabetes risk model, built on eight inexpensive and routinely available clinical measures, showed promising internal discrimination and calibration in a large Chinese cohort. An interactive web tool was developed as a research prototype to facilitate future external validation and early risk identification. External validation in independent populations is warranted before clinical use.
Source: Development and internal validation of machine learning models for predicting prediabetes in China