Trajectories of a Composite Lipid, Glucose, and Refined Obesity Index Predict Cardiovascular Disease
- 1Department of Cardiology The Second Affiliated Hospital of Xi′an Jiaotong University Xi′an Shaanxi China
- 2Shaanxi International Travel Healthcare Center (Xi′an Customs Port Outpatient Department) Xi′an Shaanxi China
- 3Department of Epidemiology Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment School of Public Health Fourth Military Medical University Xi′an Shaanxi China fmmu.edu.cn
Abstract
Background: Insulin resistance and central adiposity are key drivers of cardiovascular disease (CVD). Although the CHG index and obesity‐related indicators can separately predict metabolic risks, this study explored the predictive value of their composite indices and longitudinal trajectories for incident CVD.
Methods: Based on 4164 middle‐aged and elderly participants from a national prospective cohort, we analyzed baseline levels, cumulative exposures and K‐means‐defined longitudinal trajectories of CHG‐related composite indices. Cox regression, ROC analysis, and weighted quantile sum regression were adopted to explore CVD associations and predictive efficiency.
Results: During an 8‐year median follow‐up, 22.14% of participants developed CVD. Elevated baseline values, increased cumulative exposure, and unfavorable trajectories of composite indices were linked to higher CVD risk. Per 1‐SD rise in baseline CHG‐WC and CHG‐CVAI corresponded to 19% and 17% higher CVD risk, respectively. Their addition produced statistically significant improvements in predictive performance and risk stratifying capacity of the Framingham Risk Score, as further supported by NRI and IDI analyses.
Conclusion: Longitudinal trajectories and cumulative exposures of CHG‐obesity composite indices serve as reliable independent predictors of CVD. Dynamic monitoring of these indicators facilitates early CVD risk stratification.
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BibTeX
@article{xia2026trajectories,
title = {{Trajectories of a Composite Lipid, Glucose, and Refined Obesity Index Predict Cardiovascular Disease}},
author = {Linying Xia and Tengfei Ji and Yuan Wang and Danni Li and Yuyu Sun and Wenwen Wang and Jie Deng},
journal = {Cardiovascular Therapeutics},
year = {2026},
month = nov,
doi = {10.1155/cdr/1092234},
url = {https://doi.org/10.1155/cdr/1092234},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Trajectories of a Composite Lipid, Glucose, and Refined Obesity Index Predict Cardiovascular Disease
AU - Linying Xia
AU - Tengfei Ji
AU - Yuan Wang
AU - Danni Li
AU - Yuyu Sun
AU - Wenwen Wang
AU - Jie Deng
PY - 2026
DA - 2026/11/04
JO - Cardiovascular Therapeutics
DO - 10.1155/cdr/1092234
UR - https://doi.org/10.1155/cdr/1092234
AB - Background: Insulin resistance and central adiposity are key drivers of cardiovascular disease (CVD). Although the CHG index and obesity‐related indicators can separately predict metabolic risks, this study explored the predictive value of their composite indices and longitudinal trajectories for incident CVD. Methods: Based on 4164 middle‐aged and elderly participants from a national prospective cohort, we analyzed baseline levels, cumulative exposures and K‐means‐defined longitudinal trajectories of CHG‐related composite indices. Cox regression, ROC analysis, and weighted quantile sum regression were adopted to explore CVD associations and predictive efficiency. Results: During an 8‐year median follow‐up, 22.14% of participants developed CVD. Elevated baseline values, increased cumulative exposure, and unfavorable trajectories of composite indices were linked to higher CVD risk. Per 1‐SD rise in baseline CHG‐WC and CHG‐CVAI corresponded to 19% and 17% higher CVD risk, respectively. Their addition produced statistically significant improvements in predictive performance and risk stratifying capacity of the Framingham Risk Score, as further supported by NRI and IDI analyses. Conclusion: Longitudinal trajectories and cumulative exposures of CHG‐obesity composite indices serve as reliable independent predictors of CVD. Dynamic monitoring of these indicators facilitates early CVD risk stratification.
SN - 1755-5914
SN - 1755-5922
ER -