Frontiers in endocrinology · 2026
Association between glycometabolic indicators and risk of early vascular aging and their predictive value.
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Wang Jinjin, Yang Yujiao, Teng Ruoling, Ding Yi, Liu Fenfen
Аннотация
BACKGROUND: Early vascular aging (EVA) elevates cardiovascular risk, and glycometabolic dysfunction is closely associated with arterial stiffness. Seven markers including TyG, CHG and HOMA-series indices were compared to explore their independent correlations and discriminative value for prevalent EVA among physical examinees.
METHODS: This retrospective cross-sectional study enrolled 555 participants who underwent baPWV testing, identified from routine health-check records. Baseline data, biochemical indicators and seven glycometabolic indices were collected. Logistic regression, Spearman correlation and ROC analyses were performed to assess associations and diagnostic performance.
RESULTS: Totally 156 participants had prevalent EVA. All seven glycometabolic markers were independently associated with prevalent EVA after confounding adjustment. The CHG index showed strong correlation with mean baPWV (r=0.402, P<0.001). ROC analysis indicated modest discriminative performance for prevalent EVA across all markers; HbA1c yielded the highest observed AUC (0.690), while the TyG index achieved the highest observed discriminative sensitivity (70.51%).
CONCLUSIONS: In this retrospective cross-sectional study, all seven glycometabolic indicators were independently associated with prevalent EVA and showed modest discriminative capacity for prevalent EVA within the study sample. The TyG index exhibited the highest observed discriminative sensitivity, while HbA1c showed the highest observed AUC. These two indices may be considered potential low-cost auxiliary markers for identifying prevalent EVA in health-check settings; bootstrap internal validation and prospective external validation are required before broad clinical application.
METHODS: This retrospective cross-sectional study enrolled 555 participants who underwent baPWV testing, identified from routine health-check records. Baseline data, biochemical indicators and seven glycometabolic indices were collected. Logistic regression, Spearman correlation and ROC analyses were performed to assess associations and diagnostic performance.
RESULTS: Totally 156 participants had prevalent EVA. All seven glycometabolic markers were independently associated with prevalent EVA after confounding adjustment. The CHG index showed strong correlation with mean baPWV (r=0.402, P<0.001). ROC analysis indicated modest discriminative performance for prevalent EVA across all markers; HbA1c yielded the highest observed AUC (0.690), while the TyG index achieved the highest observed discriminative sensitivity (70.51%).
CONCLUSIONS: In this retrospective cross-sectional study, all seven glycometabolic indicators were independently associated with prevalent EVA and showed modest discriminative capacity for prevalent EVA within the study sample. The TyG index exhibited the highest observed discriminative sensitivity, while HbA1c showed the highest observed AUC. These two indices may be considered potential low-cost auxiliary markers for identifying prevalent EVA in health-check settings; bootstrap internal validation and prospective external validation are required before broad clinical application.