Key result
Routine clinical indicator models identify arterial stiffness better than metabolic indices, achieving ~0.92 AUC.
Why the study?
Direct assessment of arterial stiffness is often impractical in resource-limited settings, prompting the need for diagnostic models using easily collectible clinical indicators.
Do diagnostic models using routine clinical indicators improve the identification of arterial stiffness compared to single metabolic indices in community-based populations?
Cross-Sectional (n=4,952)
Do diagnostic models using routine clinical indicators improve the identification of arterial stiffness compared to single metabolic indices in community-based populations?
Diagnostic models incorporating routine clinical indicators (age, sex, blood pressure, waist circumference, and lipids) provide excellent discrimination for arterial stiffness, significantly outperforming single metabolic indices and offering a practical screening tool for resource-limited settings.
Answers are grounded in this paper and cite their sources.
May support noninvasive arterial stiffness screening in resource-limited settings; leaves open prospective validation before clinical use.
Background: Arterial stiffness is a strong independent predictor of cardiovascular diseases, yet its direct assessment (e.g., pulse wave velocity) is often impractical in resource-limited settings. The present study aimed to develop and validate diagnostic models for arterial stiffness using easily collectible clinical indicators. Methods: A cross-sectional analysis was conducted using data from two community-based cardiovascular health surveys in Yinchuan City, Ningxia, China. The derivation cohort (January 2020 to January 2021, n=2,440) and a temporally independent validation cohort (January 2025 to January 2026, n=2,512) were included. Two logistic regression models were developed: Model 1 included sex, age, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP); Model 2 additionally incorporated triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C). Model performance was assessed using area under the receiver operating characteristic curve (AUC), calibration curves, bootstrap internal validation, and decision curve analysis (DCA). The models were compared against five metabolic indices: Lipid Accumulation Product (LAP), Weight-Adjusted Waist Index (WWI), Visceral Adiposity Index (VAI), triglyceride-glucose index (TyG), and TG/HDL-C ratio. Nomograms were constructed for clinical application. Calibration curves demonstrated good agreement between predicted and observed risks, and DCA confirmed positive net benefit across a wide range of threshold probabilities. Conclusions: The two models provide an accurate, easy-to-use tool for screening arterial stiffness, with temporal validation in an independent cohort from the same geographic region. Their excellent discriminative performance and favorable clinical utility make them particularly suitable for large-scale community-based screening or home-based self-management in resource-limited settings.
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Xy et al. (2026) conducted a cross-sectional in Arterial stiffness (n=4,952). Diagnostic models using routine clinical indicators (Model 1 and Model 2) vs. Established metabolic indices (WWI, LAP, VAI, TyG, TG/HDL-C) was evaluated on Area under the receiver operating characteristic curve (AUC) for identifying arterial stiffness in the validation cohort (p=<0.001). Diagnostic models incorporating routine clinical indicators accurately identified arterial stiffness with an AUC of 0.916 in the validation cohort, significantly outperforming established metabolic indices.
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