ArticleScientific reports2025
Development and validation of a predictive model for carotid atherosclerosis in postmenopausal women.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multimodal data analysis reveals asynchronous aging dynamics across female reproductive organs.Nature aging · 2026Article
- Development and Validation of a Predictive Model Using Logistic Regression and Machine Learning for Carotid Artery Atherosclerosis Risk in Postmenopausal Han Women from Northern China: A Retrospective Case-Control Study.International journal of women's health · 2026Article
- Prediction of carotid vulnerable plaques in patients with type 2 diabetes using interpretable machine learning models.Frontiers in endocrinology · 2026Article
- A novel nomogram for predicting carotid atherosclerosis risks in diabetic patients.BMC cardiovascular disorders · 2025Article
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Authors and funding
6 authors.
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Abstract
With the global aging of the population, menopausal women face higher cardiovascular disease (CVD) risks, with carotid atherosclerosis as the primary pathological basis. However, no effective tools exist for assessing carotid atherosclerosis risk, and this study fills the gap in predictive tools in this field. Using data from 4,446 menopausal women in Shenzhen, we developed and validated a Nomogram model for carotid atherosclerosis risk. The sample was divided into training (2,178), internal validation (934), and external validation (1,334) sets. Variables were selected using logistic regression and LASSO, including age, systolic blood pressure (SBP), lipoprotein a (LPa), non-HDL cholesterol (Non-HDL-C), TC/HDL-C ratio, glycosylated hemoglobin (HbA1c), and blood glucose (GLU). Random Forest validation confirmed the model's robustness. The Nomogram's C-index was 0.706 (training), 0.664 (internal), and 0.668 (external), with Random Forest results of 0.721, 0.662, and 0.661, respectively. Calibration and decision curve analyses demonstrated the model's accuracy and clinical utility. Additionally, a slight negative correlation between age and GLU (OR = 0.689, P = 0.068) suggested reduced glycemic risk with age. This model provides a scientific basis for early risk assessment and personalized interventions for menopausal women, guiding future research on related biological mechanisms.
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