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Background <p>The impact of estimated pulse wave velocity (ePWV) on the prognosis of cardiovascular-kidney-metabolic (CKM) syndrome has not been explored. This study investigated the association between ePWV and mortality and its predictive performance in individuals with CKM syndrome.</p> Methods <p>This population-based prospective study of 9,416 American participants used ordinal logistic regression to assess the association between ePWV and CKM syndrome severity. Cox regression was applied to evaluate the relationship between ePWV and all-cause and cardiovascular mortality at different CKM stages, as well as their interaction effects on mortality. To evaluate the predictive performance, the area under the curve (AUC) was calculated, and the predictive capabilities of the combined model incorporating both CKM syndrome and ePWV were compared with CKM syndrome alone. Furthermore, various machine learning models were developed for prediction.</p> Results <p>ePWV was found to be significantly associated with the severity of CKM syndrome, with a common odds ratio (cOR) of 1.73 [95% confidence interval (CI): 1.68–1.77] per 1&#xa0;m/s increase in ePWV. Moreover, ePWV demonstrated a significant association with both all-cause and cardiovascular mortality. Specifically, the hazard ratios (HR) per 1&#xa0;m/s increase in ePWV were: 1.60 [95% CI: 1.51–1.69] for all-cause mortality in participants with early-stage CKM, 1.29 [95% CI: 1.23–1.35] for all-cause mortality in participants with advanced-stage CKM, 1.84 [95% CI: 1.63–2.08] for cardiovascular mortality in participants with early-stage CKM, and 1.23 [95% CI: 1.13–1.34] for cardiovascular mortality in participants with advanced-stage CKM. A notable interaction effect between ePWV and CKM syndrome was observed for both all-cause and cardiovascular mortality (P for interaction &lt; 0.001). ePWV significantly enhanced the predictive performance of CKM syndrome for all-cause and cardiovascular mortality [the net reclassification improvement (NRI) across different time points ranged from 0.25 to 0.382 for all-cause mortality and from 0.005 to 0.431 for cardiovascular mortality]. Furthermore, the combination of ePWV and CKM syndrome demonstrated strong predictive power in most models (time-dependent AUC &gt; 0.7).</p> Conclusion <p>ePWV serves as a critical indicator for both the severity and the mortality risk among individuals with CKM syndrome. ePWV significantly enhanced the accuracy of mortality risk prediction of CKM syndrome.</p> Graphical Abstract <p></p>

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Prognostic value of estimated pulse wave velocity for all-cause and cardiovascular mortality in individuals with cardiovascular–kidney–metabolic (CKM) syndrome: analyses of NHANES 2007–2018

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Zhongxing Zhou,
  • Ruming Shen,
  • Jiaxing Ke,
  • Shuaijie Chen,
  • Longqing Chen,
  • Hailin Zhang,
  • Xiaoyan Lin,
  • Jinxiu Lin,
  • Dajun Chai

摘要

Background

The impact of estimated pulse wave velocity (ePWV) on the prognosis of cardiovascular-kidney-metabolic (CKM) syndrome has not been explored. This study investigated the association between ePWV and mortality and its predictive performance in individuals with CKM syndrome.

Methods

This population-based prospective study of 9,416 American participants used ordinal logistic regression to assess the association between ePWV and CKM syndrome severity. Cox regression was applied to evaluate the relationship between ePWV and all-cause and cardiovascular mortality at different CKM stages, as well as their interaction effects on mortality. To evaluate the predictive performance, the area under the curve (AUC) was calculated, and the predictive capabilities of the combined model incorporating both CKM syndrome and ePWV were compared with CKM syndrome alone. Furthermore, various machine learning models were developed for prediction.

Results

ePWV was found to be significantly associated with the severity of CKM syndrome, with a common odds ratio (cOR) of 1.73 [95% confidence interval (CI): 1.68–1.77] per 1 m/s increase in ePWV. Moreover, ePWV demonstrated a significant association with both all-cause and cardiovascular mortality. Specifically, the hazard ratios (HR) per 1 m/s increase in ePWV were: 1.60 [95% CI: 1.51–1.69] for all-cause mortality in participants with early-stage CKM, 1.29 [95% CI: 1.23–1.35] for all-cause mortality in participants with advanced-stage CKM, 1.84 [95% CI: 1.63–2.08] for cardiovascular mortality in participants with early-stage CKM, and 1.23 [95% CI: 1.13–1.34] for cardiovascular mortality in participants with advanced-stage CKM. A notable interaction effect between ePWV and CKM syndrome was observed for both all-cause and cardiovascular mortality (P for interaction < 0.001). ePWV significantly enhanced the predictive performance of CKM syndrome for all-cause and cardiovascular mortality [the net reclassification improvement (NRI) across different time points ranged from 0.25 to 0.382 for all-cause mortality and from 0.005 to 0.431 for cardiovascular mortality]. Furthermore, the combination of ePWV and CKM syndrome demonstrated strong predictive power in most models (time-dependent AUC > 0.7).

Conclusion

ePWV serves as a critical indicator for both the severity and the mortality risk among individuals with CKM syndrome. ePWV significantly enhanced the accuracy of mortality risk prediction of CKM syndrome.

Graphical Abstract