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<p>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 &lt; 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.</p>

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Population-specific association and risk discrimination utility of the cardiometabolic index for prevalent hypertension–diabetes comorbidity: insights from CHARLS and NHANES

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月26日

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  • Ting You,
  • Jia Xu,
  • Jukun Liu,
  • Linhui Jiang,
  • Zhihao Deng,
  • Yinglan Liu,
  • Hailong Qiu,
  • Jia Zhou,
  • Caixia Sun,
  • Tianyu Chen,
  • Wenkai Zhou,
  • Dengfeng Zhang,
  • Jian Zhuang

摘要

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.