# Sex-Specific Associations Between Dynapenia and Risk of Atherosclerotic Cardiovascular Disease: A Machine-Learning-Based Approach

## TL;DR

This study finds that dynapenia, or loss of muscle strength, is a significant risk factor for heart disease in women but not in men, using machine learning to highlight sex-specific patterns.

## Contribution

The study introduces a machine-learning-based approach to uncover sex-specific associations between dynapenia and atherosclerotic cardiovascular disease risk.

## Key findings

- Dynapenia is significantly associated with high ASCVD risk in women but not in men.
- Machine learning models, especially XGBoost, show high accuracy in predicting ASCVD risk.
- SHAP analysis reveals dynapenia as a critical risk factor for women, while BMI and socioeconomic factors are important for both sexes.

## Abstract

Background/Objectives: Dynapenia, age-associated loss in muscle strength, is an emerging risk factor for atherosclerotic cardiovascular disease (ASCVD), which may have different effects depending on sex. This study aims to investigate the association between dynapenia and ASCVD risk, evaluate its predictive significance among traditional factors, and explore sex-specific patterns through machine learning models. Methods: This retrospective case–control study uses data from 19,582 participants aged 40–79 from the Korean National Health and Nutrition Examination Survey (KNHANES). ASCVD risk is assessed using the American College of Cardiology/American Heart Association 10-year risk algorithm, with dynapenia defined based on hand grip strength. Multivariable logistic regression and ML algorithms, including light gradient boosting (LGB) and XGBoost (XGB), are applied to examine predictive factors. Model performance is evaluated via the area under the receiver operating characteristic curve (AUROC), and Shapley additive explanation (SHAP) analysis highlights variable importance. Results: Dynapenia prevalence is higher in women (33.4%) than men (13.9%) at high ASCVD risk. Logistic regression shows dynapenia is significantly associated with high ASCVD risk in women (odds ratio, 1.47; 95% confidence interval, 1.20–1.81) but not in men. Machine learning models demonstrate excellent predictive performance, with XGB achieving the highest AUROC (0.950 in men and 0.963 in women). The SHAP analysis identifies dynapenia as a critical risk factor in women, while body mass index, educational status, and household income are influential in both sexes. Conclusions: Dynapenia is a significant ASCVD risk factor in women, emphasizing sex-specific prevention strategies. Machine learning enhances risk assessment precision, underscoring muscle health’s role in cardiovascular care.