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Background <p>The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitations of traditional obesity metrics. TyG–ABSI, an index combining Triglyceride-Glucose (TyG) with A Body Shape Index (ABSI), is a novel marker reflecting both functional insulin resistance and structural visceral adiposity. However, its predictive value for PAD remains unexplored in large prospective cohorts.</p> Methods <p>We included 390,274 adults from the UK Biobank. Baseline characteristics were analyzed across TyG–ABSI quartiles and PAD status. Associations between TyG-related indices and incident PAD were assessed using multivariable-adjusted Cox regression, Kaplan–Meier survival curves, and restricted cubic splines. Robustness was evaluated via Fine-Gray competing risk models, propensity score matching, subgroup analyses, and external validation in the NHANES database. Consensus k-means clustering, integrating biochemical and insulin resistance markers, identified metabolic phenotypes and stratified PAD risk. Feature selection (LASSO, Boruta, and Minimum Redundancy Maximum Relevance [mRMR]) guided the development of six machine learning models (logistic regression, GBM, XGBoost, AdaBoost, LightGBM, and neural network) for PAD prediction, with interpretability assessed via SHAP analysis.</p> Results <p>Higher TyG–ABSI and related indices were strongly associated with increased PAD incidence (cumulative incidence at 15&#xa0;years: 4.16% in the top quartile vs. 0.98% in the bottom quartile; fully-adjusted Hazard Ratio [HR] per 1-SD increase for TyG–ABSI: 1.22, 95% Confidence Interval [CI] 1.17–1.27), which were robust in the NHANES external validation cohort. Clustering analysis revealed four distinct metabolic subgroups, with the highest PAD risk in the insulin resistance/glucose dysfunction cluster (HR vs. healthy phenotype: 7.48, 95% CI 6.82–8.21). Feature selection identified 19 key predictors. Logistic regression provided the most stable and generalizable prediction (validation Area Under the Curve [AUC] = 0.788, 95% CI 0.778–0.798), demonstrating superior generalizability compared to complex ensemble methods. SHAP analysis demonstrated TyG–ABSI, age, and neutrophil count as leading predictors for incident PAD and confirmed the interpretability of the model.</p> Conclusion <p>TyG–ABSI is a robust, independent predictor of long-term PAD risk. Data-driven phenotyping and interpretable machine learning facilitate more precise risk stratification. Logistic regression offers optimal performance and interpretability, holding potential clinical utility for individualized PAD risk prediction.</p> Graphical abstract <p></p>

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Comprehensive evaluation of triglyceride glucose index–a body shape index (TyG–ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank

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  • 发表日期：

    2026年3月9日

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  • Wenxin Zhao,
  • Xiaolu Li,
  • Xihao Zhang,
  • Yaming Guo,
  • Bowen Zhang,
  • Yifan Cao,
  • Yongpeng Diao,
  • Zhiyuan Wu,
  • Chengran Lu,
  • Zuoguan Chen,
  • Yong Lan,
  • Yongjun Li

摘要

Background

The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitations of traditional obesity metrics. TyG–ABSI, an index combining Triglyceride-Glucose (TyG) with A Body Shape Index (ABSI), is a novel marker reflecting both functional insulin resistance and structural visceral adiposity. However, its predictive value for PAD remains unexplored in large prospective cohorts.

Methods

We included 390,274 adults from the UK Biobank. Baseline characteristics were analyzed across TyG–ABSI quartiles and PAD status. Associations between TyG-related indices and incident PAD were assessed using multivariable-adjusted Cox regression, Kaplan–Meier survival curves, and restricted cubic splines. Robustness was evaluated via Fine-Gray competing risk models, propensity score matching, subgroup analyses, and external validation in the NHANES database. Consensus k-means clustering, integrating biochemical and insulin resistance markers, identified metabolic phenotypes and stratified PAD risk. Feature selection (LASSO, Boruta, and Minimum Redundancy Maximum Relevance [mRMR]) guided the development of six machine learning models (logistic regression, GBM, XGBoost, AdaBoost, LightGBM, and neural network) for PAD prediction, with interpretability assessed via SHAP analysis.

Results

Higher TyG–ABSI and related indices were strongly associated with increased PAD incidence (cumulative incidence at 15 years: 4.16% in the top quartile vs. 0.98% in the bottom quartile; fully-adjusted Hazard Ratio [HR] per 1-SD increase for TyG–ABSI: 1.22, 95% Confidence Interval [CI] 1.17–1.27), which were robust in the NHANES external validation cohort. Clustering analysis revealed four distinct metabolic subgroups, with the highest PAD risk in the insulin resistance/glucose dysfunction cluster (HR vs. healthy phenotype: 7.48, 95% CI 6.82–8.21). Feature selection identified 19 key predictors. Logistic regression provided the most stable and generalizable prediction (validation Area Under the Curve [AUC] = 0.788, 95% CI 0.778–0.798), demonstrating superior generalizability compared to complex ensemble methods. SHAP analysis demonstrated TyG–ABSI, age, and neutrophil count as leading predictors for incident PAD and confirmed the interpretability of the model.

Conclusion

TyG–ABSI is a robust, independent predictor of long-term PAD risk. Data-driven phenotyping and interpretable machine learning facilitate more precise risk stratification. Logistic regression offers optimal performance and interpretability, holding potential clinical utility for individualized PAD risk prediction.

Graphical abstract