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Background <p>Cardiovascular-Kidney-Metabolic (CKM) syndrome&#xa0;has arisen as a public health burden due to the complex interplay between metabolic dysfunction, kidney disease, and cardiovascular disease (CVD). The modified cardiometabolic index (MCMI), derived from lipid profiles and anthropometric measurements, has recently garnered attention as a potential indicator for metabolic risk assessment. Nonetheless, its prognostic value for CKM syndrome progression and related mortality risk remains ambiguous.</p> Methods <p>Clinical data of 9630 patients with CKM syndrome from the National Health and Nutrition Examination Survey (NHANES) were analyzed. Meanwhile, participants were stratified into two distinct groups based on the severity of their CKM syndrome: early-stage CKM syndrome (including stages 0–2) and those with advanced CKM stages (including stages 3 and 4). The MCMI was evaluated both as a continuous variable and by tertiles. Logistic regression was employed to examine the association between MCMI and advanced CKM stages, and multivariable Cox proportional hazards models evaluated the risk of all-cause and CVD mortality. Restricted cubic spline (RCS) analyses were performed to explore dose–response relationships, and subgroup analysis was conducted to explore population heterogeneity. In addition, eight machine learning (ML) algorithms were applied to validate the predictive performance of MCMI for advanced CKM stages.</p> Results <p>Elevated MCMI was significantly associated with advanced stages of CKM. In fully adjusted models, participants in the highest tertile had a 2.49-fold higher risk of advanced CKM stages (95% CI 2.07–2.99, P &lt; 0.001) relative to those in the lowest tertile. Each unit increase in MCMI was consistently linked to higher odds of CKM progression (OR 1.77, 95% CI 1.63–1.93, P &lt; 0.001). While its association with all-cause mortality lost statistical significance after full adjustment, Cox regression showed that increased MCMI levels remained a modest but independent predictor of CVD mortality (HR 1.30, 95% CI 1.09–1.55). RCS analyses indicated a positive, approximately linear dose–response relationship. Subgroup analysis confirmed the robustness of these findings. ML models further supported the predictive role of MCMI, and ROC analysis demonstrated that MCMI displayed comparable diagnostic performance with CMI, TyG, and TyG-BMI for advanced CKM stages, all-cause mortality, and CVD mortality.</p> Conclusions <p>The MCMI is independently correlated with advanced CKM stages and serves as a modest prognostic indicator for CVD mortality. This straightforward and practical index may serve as a valuable tool for risk stratification and early intervention in cardiometabolic care.</p>

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The modified cardiometabolic index and cardiovascular–kidney–metabolic syndrome: insights from NHANES

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Jiahua Xu,
  • Caiying Ke,
  • Yanxia Qiu,
  • Shiyun Chen,
  • Xin Chen,
  • Changlong Bi,
  • Huanji Zhang

摘要

Background

Cardiovascular-Kidney-Metabolic (CKM) syndrome has arisen as a public health burden due to the complex interplay between metabolic dysfunction, kidney disease, and cardiovascular disease (CVD). The modified cardiometabolic index (MCMI), derived from lipid profiles and anthropometric measurements, has recently garnered attention as a potential indicator for metabolic risk assessment. Nonetheless, its prognostic value for CKM syndrome progression and related mortality risk remains ambiguous.

Methods

Clinical data of 9630 patients with CKM syndrome from the National Health and Nutrition Examination Survey (NHANES) were analyzed. Meanwhile, participants were stratified into two distinct groups based on the severity of their CKM syndrome: early-stage CKM syndrome (including stages 0–2) and those with advanced CKM stages (including stages 3 and 4). The MCMI was evaluated both as a continuous variable and by tertiles. Logistic regression was employed to examine the association between MCMI and advanced CKM stages, and multivariable Cox proportional hazards models evaluated the risk of all-cause and CVD mortality. Restricted cubic spline (RCS) analyses were performed to explore dose–response relationships, and subgroup analysis was conducted to explore population heterogeneity. In addition, eight machine learning (ML) algorithms were applied to validate the predictive performance of MCMI for advanced CKM stages.

Results

Elevated MCMI was significantly associated with advanced stages of CKM. In fully adjusted models, participants in the highest tertile had a 2.49-fold higher risk of advanced CKM stages (95% CI 2.07–2.99, P < 0.001) relative to those in the lowest tertile. Each unit increase in MCMI was consistently linked to higher odds of CKM progression (OR 1.77, 95% CI 1.63–1.93, P < 0.001). While its association with all-cause mortality lost statistical significance after full adjustment, Cox regression showed that increased MCMI levels remained a modest but independent predictor of CVD mortality (HR 1.30, 95% CI 1.09–1.55). RCS analyses indicated a positive, approximately linear dose–response relationship. Subgroup analysis confirmed the robustness of these findings. ML models further supported the predictive role of MCMI, and ROC analysis demonstrated that MCMI displayed comparable diagnostic performance with CMI, TyG, and TyG-BMI for advanced CKM stages, all-cause mortality, and CVD mortality.

Conclusions

The MCMI is independently correlated with advanced CKM stages and serves as a modest prognostic indicator for CVD mortality. This straightforward and practical index may serve as a valuable tool for risk stratification and early intervention in cardiometabolic care.