Key result
TyG-WHR-based nomogram identifies prevalent carotid plaque with an AUROC of 0.85.
Why the study?
Existing ASCVD screening tools fail to adequately capture the combined impact of insulin resistance and central obesity for identifying individuals with carotid plaque in the general population.
Does a nomogram incorporating TyG-WHR improve the identification of prevalent carotid plaque in a general health examination population?
Cross-Sectional (n=8,804)
Does a nomogram incorporating TyG-WHR improve the identification of prevalent carotid plaque in a general health examination population?
A novel cross-sectional nomogram incorporating the TyG-WHR index demonstrates high discriminatory performance (AUROC 0.85) for identifying prevalent subclinical carotid plaque during routine health examinations.
May refine CP risk stratification in primary prevention using routine data; leaves open prospective validation before clinical adoption.
Introduction: Primary prevention of atherosclerotic cardiovascular disease (ASCVD) requires the identification of high-risk individuals in the general population who may currently harbor carotid plaque (CP). Existing tools fail to adequately capture the combined impact of insulin resistance (IR) and central obesity. This study aimed to develop a cross-sectional risk stratification model for identifying prevalent carotid plaque using routine health examination data. Methods: A total of 4,992 participants from a health examination cohort constituted the development set and were randomly divided into a training set and an internal validation set (7:3 ratio). An additional temporal validation set comprising 3,812 participants was included. All variables and the outcome (presence or absence of carotid plaque) were synchronously collected within a single health examination cycle. The triglyceride-glucose-waist-to-hip ratio (TyG-WHR) was calculated. Recursive feature elimination (RFE) was employed for variable selection, and a logistic regression model was constructed in the form of a nomogram. Model performance in discriminating prevalent carotid plaque was evaluated in the internal validation set and the temporal validation set. Results: TyG-WHR was independently associated with prevalent CP, and its discriminatory performance (area under the curve [AUC]=0.689) was superior to that of the conventional TyG index (AUC=0.642). Furthermore, its incremental value over a base model composed of traditional clinical variables was significant in the temporal validation set (net reclassification improvement [NRI]=0.472, P < 0.001). The logistic regression nomogram constructed using eight routine health examination indices achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 in the temporal validation set, with good calibration. In the absence of external validation, these results are intended for research purposes only and should not be directly applied in clinical practice. Discussion: This cross-sectional nomogram, developed from routine health examination data, provides a convenient auxiliary screening tool for identifying individuals in the general population who may currently harbor carotid plaque, thereby supporting risk stratification in large-scale health check-ups. It must be emphasized that this model is designed to identify individuals who may currently have subclinical carotid plaque, rather than to predict future incident events.
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Zhang et al. (2026) conducted a cross-sectional in Carotid plaque (n=8,804). Triglyceride-glucose-waist-to-hip ratio (TyG-WHR) and clinical nomogram vs. Conventional TyG index and traditional clinical variables was evaluated on Presence of carotid plaque (Nomogram AUROC 0.85; TyG-WHR AUC 0.689 vs TyG index AUC 0.642, p=<0.001). A cross-sectional nomogram incorporating the triglyceride-glucose-waist-to-hip ratio achieved an AUROC of 0.85 for identifying prevalent carotid plaque.
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