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A CVD Classification Model for Individuals With Obesity: Multi-Ethnic Validation Based on Multiple Metabolic
Runmin Cao1, Yurun Zhang2, Ling Cao3
1Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu, China.
Obesity (Silver Spring, Md.)
|July 21, 2026
Summary
This study presents a new nomogram to classify cardiovascular disease (CVD) in obese individuals. The validated model effectively identifies CVD risk in this high-risk population.
Area of Science:
- Cardiology
- Public Health
- Medical Informatics
Background:
- Obesity is a significant global health concern and a primary risk factor for cardiovascular disease (CVD).
- Accurate classification of prevalent CVD in individuals with obesity is crucial for timely intervention and management.
- Existing risk assessment tools may not be sufficiently optimized for the obese population.
Purpose of the Study:
- To develop and validate a predictive model for classifying prevalent cardiovascular disease (CVD) specifically in individuals with obesity.
- To construct a nomogram based on the validated model for practical clinical use.
- To provide a tool that aids in the risk stratification of CVD in the obese population.
Main Methods:
- A multivariate logistic regression model was developed using risk factors identified through univariate and LASSO regression in a training cohort.
- A nomogram was constructed based on the logistic model.
- Model performance was evaluated using Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA). Internal and external validation were performed, including analysis in the Korea cohort using a unified BMI standard.
- A web-based dynamic nomogram was implemented for accessibility.
Main Results:
- The final nomogram included age, hypertension, diabetes, metabolic score for visceral fat (METS-VF), serum creatinine, and blood urea nitrogen.
- Internal validation demonstrated an AUC of 0.799 (Hosmer-Lemeshow p=0.823).
- External validation yielded an AUC of 0.821 (Hosmer-Lemeshow p=0.083).
- The model exhibited good clinical utility in both internal and external validation sets.
Conclusions:
- A validated nomogram for classifying prevalent CVD in individuals with obesity has been successfully developed.
- The study provides an optimized and validated model tailored for the obese population.
- The developed tool can assist clinicians in assessing CVD risk among obese patients.
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