Sex-Specific Associations Between Dynapenia and Risk of Atherosclerotic Cardiovascular Disease: A Machine-Learning-Based Approach DOI Open Access
Gyumin Lee, Hye-Jin Kim,

Heeji Choi

et al.

Journal of Personalized Medicine, Journal Year: 2025, Volume and Issue: 15(3), P. 83 - 83

Published: Feb. 25, 2025

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

Language: Английский

Interaction effect between sleep duration and dynapenic abdominal obesity for predicting functional disability: A longitudinal study DOI Creative Commons
Wenjin Han,

Tianmeng Wang,

Zhiqiang He

et al.

The journal of nutrition health & aging, Journal Year: 2025, Volume and Issue: 29(4), P. 100510 - 100510

Published: Feb. 17, 2025

Language: Английский

Citations

0
Sex-Specific Associations Between Dynapenia and Risk of Atherosclerotic Cardiovascular Disease: A Machine-Learning-Based Approach DOI Open Access
Gyumin Lee, Hye-Jin Kim,

Heeji Choi

et al.

Journal of Personalized Medicine, Journal Year: 2025, Volume and Issue: 15(3), P. 83 - 83

Published: Feb. 25, 2025

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

Language: Английский

Citations

0