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Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Comprehensive Analysis of the Relationship Between Cardiometabolic Index and Coronary Heart Disease
Ruoling Guo1, Mingliang Sun1, Wenxin Lin1
1Graduate School, Chengde Medical University, Chengde, 067000, People's Republic of China.
Insights
The cardiometabolic index (CMI) is a significant predictor of coronary heart disease (CHD). Higher CMI values are strongly associated with an increased risk of developing CHD, indicating its clinical utility in risk assessment.
Area of Science:
- Cardiology
- Biomarkers
- Risk Prediction
Background:
- The cardiometabolic index (CMI) is increasingly recognized for its association with cardiovascular diseases.
- The specific relationship between CMI and coronary heart disease (CHD) requires further investigation.
Purpose of the Study:
- To investigate the association between CMI and CHD.
- To evaluate the predictive value of CMI for CHD in a clinical cohort.
Main Methods:
- Retrospective study of patients with suspected CHD undergoing coronary angiography.
- LASSO and logistic regression models used to assess risk factors including CMI.
- Restricted cubic spline (RCS) methodology to explore CMI-CHD association.
- Analysis of diagnostic efficacy using Area Under the Curve (AUC).
Main Results:
- CMI independently predicts CHD, with significantly elevated risk in the highest CMI quartile (Q4).
- A linear relationship was observed between CMI and CHD incidence.
- Model calibration and Decision Curve Analysis (DCA) confirmed clinical utility and predictive accuracy.
Conclusions:
- CMI demonstrates a positive association with CHD incidence.
- CMI functions as an independent risk factor with notable predictive power for CHD occurrence.
Objective:
Emerging evidence substantiates the cardiometabolic index (CMI) as a pivotal indicator demonstrating robust associations with an array of cardiovascular pathologies. However, its specific link to coronary heart disease (CHD) remains insufficiently explored. This study aimed to investigate both the association and the predictive value of CMI for CHD in a clinical cohort.
Methods:
This retrospective study included patients with suspected CHD who underwent coronary angiography at the Cardiology Department of Chengde Central Hospital between October 2023 and December 2024. Participants were stratified into CHD and non-CHD groups based on angiographic results. A LASSO regression and a logistic regression framework was implemented to examine the influence of age, sex, hypertension, diabetes, smoking, WBC, CK, CMI, and LDL-C on CHD. The association between CMI and CHD was explored using restricted cubic spline (RCS) methodology. The diagnostic efficacy of the model was scrutinized through the utilization of the area under the curve (AUC).
Results:
CMI exhibits an independent predictor for CHD, particularly in individuals with high CMI values (Q4 group), where the risk of CHD is markedly elevated. Furthermore, a linear relationship exists between CMI and CHD. Calibration curves demonstrate a strong alignment correlation linking predicted to observed probabilities. Decision curve analysis (DCA) reveals that the model provides substantial clinical benefit within a threshold probability range of 0.13 to 0.72. Receiver operating characteristic (ROC) curve analysis indicates that CMI possesses certain predictive merit for the occurrence of CHD.
Conclusion:
A positive association exists between CMI and incidence of CHD. Additionally, CMI serves as an independent risk factor, demonstrating certain predictive power in clinical settings, thereby effectively forecasting the risk of CHD occurrence.
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