ArticleFrontiers in nutrition2026
Metabolic characteristics and factors associated with prediabetes in Chinese adults based on real-world health examination data: a cross-sectional study.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Prediabetes is a common intermediate state of abnormal glucose metabolism, but its metabolic characteristics and associated factors in health examination populations remain insufficiently defined. Based on a health examination population of Chinese adults, this study aimed to characterize the metabolic profile of prediabetes and identify factors associated with prediabetes using real-world health examination data, thereby providing a foundation for future multicenter, multiregional, and broader population-based validation and implementation studies. Methods: This cross-sectional study included adults undergoing routine health examinations between January 1, 2018 and December 31, 2024. Metabolic characteristics were compared between normoglycemic individuals and those with prediabetes. Univariable and multivariable logistic regression analyses were performed to identify factors associated with prediabetes. XGBoost combined with SHAP was further used to assess the relative importance of clinical and metabolic indicators. Results: A total of 20,271 participants were included in the main analysis, including 8,457 with prediabetes and 11,814 with normoglycemia. Compared with the normoglycemia group, the prediabetes group was older (58.48 ± 13.10 vs. 48.10 ± 12.90, Conclusion: In this health examination population, prediabetes was associated with age, hypertension, BMI, fatty liver, lipid profiles, and altered hepatorenal function-related indicators. Among these factors, BMI and fatty liver showed relatively stronger associations, and machine-learning analysis further highlighted BMI, age, TG, fatty liver, and TC as particularly informative variables for the early identification of prediabetes.
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