Development and Validation of a Predictive Model for Carotid Atherosclerosis in Postmenopausal Women

Overview

Journal Sci Rep

Specialty Science

Date 2025 Feb 11

PMID 39934244

Authors

Jing Liu

Xiaoyun Zeng

Jie Ruan

Yingnan Kang

Yao Lu

Siyi Li

Affiliations

Soon will be listed here.

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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