A Study on Predicting Impaired Fasting Glucose Risk in Chinese Adults Based on Individual Characteristics

YIJUN LIN¹Wenxu Wu^2,3Xiaoyan Liang¹Liping Zhou¹Gan Li¹Cuiling Kang¹Wuzhen Li⁴Chunyi Huang^1*Feng Tian^1*

• ¹Shunde Hospital, Southern Medical University, Foshan, China
• ²First People's Hospital of Nanning, Nanning, Guangxi Zhuang Region, China
• ³Fifth Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
• ⁴School of Politics and Public Administration, South China Normal University, Guangzhou, China

Introduction: This study aimed to develop a nomogram for early detection of impaired fasting glucose (IFG), predicting the 5-year risk in Chinese adults due to its link to various diseases.Materials and Methods: This retrospective cohort study included 28,875 participants without IFG at baseline, randomly divided them to a training set and a validation set. We developed four predictive models—LASSO, full, stepwise, and MFP—ultimately selecting the LASSO model for nomogram development due to its simplicity and predictive performance. Four prediction model performance was assessed through ROC analysis, calibration curves, and decision curve analysis, with external validation using Shunde Hospital (n=18,618) and NHANES (n=2,038) dataset.Results: We developed a nomogram to predict the risk of IFG by incorporating parameters including age, BMI, SBP, FPG, and TG, which demonstrated performance with AUCs of 0.8167 and 0.8155 in the training and validation set, respectively. External validation achieved AUC 0.9665 (Shunde Hospital dataset) and 0.9171 (NHANES).Conclusions: Our nomogram provides a personalized, validated approach for assessing 5-year IFG risk in Chinese adults, offering a practical screening tool for primary healthcare and resource-constrained environments.