Population-specific association and risk discrimination utility of the cardiometabolic index for prevalent hypertension-diabetes comorbidity: insights from CHARLS and NHANES.
12 affiliations
- 1School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- 2The First Affiliated Hospital, Department of Emergency, Hengyang Medical School, University of South China, Hengyang, Hunan421001, China.
- 3Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510000, Guangdong, China.
- 4Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510000, Guangdong, China.
- 5The Second Affiliated Hospital, Department of Endocrinology, Hengyang Medical School, University of South China, Hengyang, 421001, China.
- 6Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510000, Guangdong, China. [email protected].
- 7Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510000, Guangdong, China. [email protected].
- 8Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510000, Guangdong, China. [email protected].
- 9Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510000, Guangdong, China. [email protected].
- 10School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China. [email protected].
- 11Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510000, Guangdong, China. [email protected].
- 12Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510000, Guangdong, China. [email protected].
Abstract
The comorbidity of cardiometabolic diseases, such as hypertension and diabetes, is increasingly prevalent worldwide, particularly among middle-aged and elderly populations. The cardiometabolic index (CMI), a novel composite biomarker integrating features of dyslipidemia and abdominal obesity, has emerged as a potential indicator. However, its association with prevalent hypertension-diabetes comorbidity and its risk discrimination utility across Chinese and American populations remain insufficiently investigated. This investigation utilized data from CHARLS and NHANES, encompassing 12,261 Chinese and 1,329 American participants. Multivariable logistic regression models and restricted cubic spline (RCS) analyses were employed to evaluate the association and dose-response relationship between CMI and prevalent hypertension-diabetes comorbidity. Machine learning models were constructed to evaluate risk discrimination performance, including LightGBM, GBM, and CatBoost. Mediation analysis was further conducted to assess the potential mediating role of body mass index (BMI) in the association between CMI and the outcome. Elevated CMI was significantly associated with an increased risk of hypertension-diabetes comorbidity (CHARLS: OR = 2.16, 95% CI: 2.00-2.34; NHANES: OR = 1.99, 95% CI: 1.48-2.69). RCS analysis revealed a marked nonlinear association within the Chinese cohort (threshold effect, P-nonlinear < 0.001), contrasting with a more linear relationship in the American population (P-nonlinear = 0.764). Among the machine learning models, LightGBM and GBM demonstrated superior risk discrimination performance (CHARLS AUC = 0.841; NHANES AUC = 0.948). Mediation analysis indicated that BMI exerted a significant partial mediating effect, accounting for 32.90% and 43.32% of the total association in the Chinese and American cohorts, respectively. CMI was independently associated with prevalent hypertension-diabetes comorbidity, with notable population-specific differences in association patterns. Machine learning models incorporating CMI showed favorable risk discrimination performance. The partial mediation effect of BMI underscores the critical importance of adiposity management in mitigating cardiometabolic risk. This study provides a novel theoretical framework and methodological support for cross-population cardiometabolic health assessment.
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BibTeX
@article{you2026populationspecific,
title = {{Population-specific association and risk discrimination utility of the cardiometabolic index for prevalent hypertension-diabetes comorbidity: insights from CHARLS and NHANES.}},
author = {Ting You and Jia Xu and Jukun Liu and Linhui Jiang and Zhihao Deng and Yinglan Liu and Hailong Qiu and Jia Zhou and Caixia Sun and Tianyu Chen and Wenkai Zhou and Dengfeng Zhang and Jian Zhuang},
journal = {Clinical and experimental medicine},
year = {2026},
month = jul,
doi = {10.1007/s10238-026-02248-7},
url = {https://europepmc.org/article/MED/42905363},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Population-specific association and risk discrimination utility of the cardiometabolic index for prevalent hypertension-diabetes comorbidity: insights from CHARLS and NHANES.
AU - Ting You
AU - Jia Xu
AU - Jukun Liu
AU - Linhui Jiang
AU - Zhihao Deng
AU - Yinglan Liu
AU - Hailong Qiu
AU - Jia Zhou
AU - Caixia Sun
AU - Tianyu Chen
AU - Wenkai Zhou
AU - Dengfeng Zhang
AU - Jian Zhuang
PY - 2026
DA - 2026/07/26
JO - Clinical and experimental medicine
DO - 10.1007/s10238-026-02248-7
UR - https://europepmc.org/article/MED/42905363
AB - The comorbidity of cardiometabolic diseases, such as hypertension and diabetes, is increasingly prevalent worldwide, particularly among middle-aged and elderly populations. The cardiometabolic index (CMI), a novel composite biomarker integrating features of dyslipidemia and abdominal obesity, has emerged as a potential indicator. However, its association with prevalent hypertension-diabetes comorbidity and its risk discrimination utility across Chinese and American populations remain insufficiently investigated. This investigation utilized data from CHARLS and NHANES, encompassing 12,261 Chinese and 1,329 American participants. Multivariable logistic regression models and restricted cubic spline (RCS) analyses were employed to evaluate the association and dose-response relationship between CMI and prevalent hypertension-diabetes comorbidity. Machine learning models were constructed to evaluate risk discrimination performance, including LightGBM, GBM, and CatBoost. Mediation analysis was further conducted to assess the potential mediating role of body mass index (BMI) in the association between CMI and the outcome. Elevated CMI was significantly associated with an increased risk of hypertension-diabetes comorbidity (CHARLS: OR = 2.16, 95% CI: 2.00-2.34; NHANES: OR = 1.99, 95% CI: 1.48-2.69). RCS analysis revealed a marked nonlinear association within the Chinese cohort (threshold effect, P-nonlinear < 0.001), contrasting with a more linear relationship in the American population (P-nonlinear = 0.764). Among the machine learning models, LightGBM and GBM demonstrated superior risk discrimination performance (CHARLS AUC = 0.841; NHANES AUC = 0.948). Mediation analysis indicated that BMI exerted a significant partial mediating effect, accounting for 32.90% and 43.32% of the total association in the Chinese and American cohorts, respectively. CMI was independently associated with prevalent hypertension-diabetes comorbidity, with notable population-specific differences in association patterns. Machine learning models incorporating CMI showed favorable risk discrimination performance. The partial mediation effect of BMI underscores the critical importance of adiposity management in mitigating cardiometabolic risk. This study provides a novel theoretical framework and methodological support for cross-population cardiometabolic health assessment.
SN - 1591-8890
SN - 1591-9528
ER -