Obesity (Silver Spring, Md.)
July 21, 2026 · via Europe PMC
A CVD Classification Model for Individuals With Obesity: Multi-Ethnic Validation Based on Multiple Metabolic Indicators.
Cao R, Zhang Y, Cao L, Jiang H.
AI summary — for doctors, generated from the abstract only
A nomogram using age, hypertension, diabetes, visceral fat score, creatinine, and BUN classified prevalent cardiovascular disease in people with obesity with good accuracy across ethnicities.
What was studied
A model development and multi-ethnic validation study using LASSO and logistic regression to classify prevalent cardiovascular disease in individuals with obesity, with a Korean external cohort and a web calculator.
Key findings
- Final model included age, hypertension, diabetes, metabolic score for visceral fat, creatinine, and blood urea nitrogen
- Internal validation AUC 0.799 (Hosmer-Lemeshow p = 0.823)
- External validation AUC 0.821 (p = 0.083)
- Good clinical utility by decision curve analysis in both sets
Clinical relevance
Classifies existing rather than future cardiovascular disease, so its role is risk assessment support rather than prediction.
Who should care
Limitations
Cross-sectional classification of prevalent disease; cohort sizes are not stated in the abstract.
A simple nomogram identified existing cardiovascular disease in people with obesity.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objective
The global rise in obesity, a major risk factor for cardiovascular disease (CVD), contributes substantially to the disease burden. We developed and validated a model optimized for classifying prevalent CVD in individuals with obesity.
Methods
In the training cohort, risk factors were screened by univariate and LASSO regression and then incorporated into a multivariate logistic model to establish the classifier. Based on this model, a nomogram was constructed. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate model performance. Sensitivity was analyzed using a unified BMI standard in the Korea cohort. A web-based dynamic nomogram was also implemented (https://min115.shinyapps.io/CVDrisk/).
Results
The final nomogram incorporated age, hypertension, diabetes, metabolic score for visceral Fat (METS-VF), serum creatinine, and blood urea nitrogen. In internal validation, the model achieved an AUC of 0.799, with a Hosmer-Lemeshow p value of 0.823. In external validation, the AUC was 0.821, with a Hosmer-Lemeshow p value of 0.083. In both the internal and external validation sets, the model demonstrated good clinical utility.
Conclusions
This study developed and validated a nomogram for the classification of prevalent CVD in individuals with obesity, providing a validated model optimized for the population with obesity.
DOI
10.1002/oby.70265
PMID
42480980
PMCID
PMC13511318
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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