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Associations of cardiometabolic index with stroke outcomes in European and American cohorts.

Sep 2026 · Journal of Stroke & Cerebrovascular Diseases · Vol 35, pp. 108748 · 0 citations
Medicine

TL;DR

This study suggests that CMI may serve as a potential biomarker associated with stroke incidence and mortality, and indicates that CMI could have utility in clinical settings for the identification of high-risk populations, although further validation is needed.

Abstract

Background

While the cardiometabolic index (CMI) has emerged as a potential stroke predictor, its utility across diverse populations and specific clinical contexts remains underexplored. This study aims to bridge these methodological and clinical knowledge gaps using large-scale, multi-ethnic cohorts.

Methods

Leveraging data from the UK Biobank (n=413,686) and NHANES (n=40,607), we employed multivariate Cox proportional hazards regression to evaluate the associations between CMI and incident ischemic/hemorrhagic stroke, as well as cerebrovascular mortality. Restricted cubic splines (RCS) were utilized to characterize potential non-linear dose-response relationships.

Results

RCS analysis demonstrated a non-linear association between CMI and both ischemic and hemorrhagic stroke. In fully adjusted models, each unit increase in CMI was associated with elevated stroke and mortality risk: 24% higher risk of ischemic stroke (HR = 1.24, 95% CI: 1.21-1.28) and a 11% increased risk of cerebrovascular mortality (HR = 1.11, 95% CI: 1.01-1.21). Notably, interaction analyses confirmed joint modifying effects, indicating that genetic susceptibility and metabolic dysfunction collectively amplify stroke risk. Subgroup analyses revealed that the association between CMI and stroke was stronger in younger individuals and females.

Conclusions

Our study suggests that CMI may serve as a potential biomarker associated with stroke incidence and mortality. These observations indicate that CMI could have utility in clinical settings for the identification of high-risk populations, although further validation is needed. To better understand the clinical applicability of these findings, future research should focus on the development of integrated clinical prediction tools and comprehensive health economic evaluations to assess the feasibility of CMI-based screening strategies.

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