Authors
Yafei Chang, Luming Zhang, Shafiu Adam UmarShinge, Rui Gu, Xingchao Zhou, Qingzhou Zhang, Xinyi Zhang, Binbin Zhang, Jiawei Liu, Yanqi Yang
Published in
Frontiers in endocrinology. Volume 17. Pages 1872088. Epub Jun 24, 2026.
Abstract
The aim of this study was to evaluate and compare the predictive value of four insulin resistance (IR) surrogate indices for incident prediabetes and examine sex-specific disparities in their predictive performance.
This multicenter retrospective cohort study enrolled 63,795 adults aged 18-45 years (51.2% male) with normoglycemia at baseline from the Rich Healthcare Group Database (2010-2016). Participants were followed for a median of 2.97 years. Cox proportional hazards regression models, restricted cubic spline analyses, and receiver operating characteristic (ROC) curve analysis were employed to assess associations and discriminative performance. Statistical analyses were conducted using R software and EmpowerStats.
During follow-up, 5,304 (8.31%) participants developed prediabetes, with a significantly higher incidence in men than in women (10.76% vs. 5.74%; p< 0.001). After multivariable adjustment, all four IR indices were independently associated with incident prediabetes. Sex-stratified analyses demonstrated markedly stronger associations in women, particularly for metabolic score for insulin resistance (METS-IR) [fully adjusted hazard ratio (HR): 7.82, 95% confidence interval (CI): 5.81-10.51 vs. 1.45, 95% CI: 1.18-1.77 in men]. Triglyceride-glucose-body mass index (TyG-BMI) demonstrated the highest predictive accuracy in the overall population [area under the curve (AUC) = 0.6497], and women exhibited superior discriminative performance across all indices compared to men.
IR surrogate indices demonstrate significantly greater predictive value for prediabetes in young Chinese women than in men, with METS-IR exhibiting the most pronounced sex disparity. These findings support sex-specific risk stratification using routine IR surrogate indices in prediabetes screening.
PMID:
42422420
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.
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