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ORIGINAL RESEARCH article

Front. Public Health

Sec. Aging and Public Health

Global patterns and risk factors of early and late-onset ischemic stroke: a population-based analysis across 204 countries and territories

Provisionally accepted
  • 1Shanxi Provincial People's Hospital, Taiyuan, China
  • 2Xuanwu Hospital of Capital Medical University, Beijing, China
  • 3Taiyuan City Central Hospital, The Ninth Clinical Medical College of Shanxi Medical University, Taiyuan, China
  • 4The First People's Hospital of Jinzhong, Jinzhong, China
  • 5Fifth Hospital of Shanxi Medical University, Taiyuan, China
  • 6Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Tongji Shanxi Hospital, Taiyuan, China

The final, formatted version of the article will be published soon.

Background and purpose Globally, ischemic stroke remains a major cause of mortality and loss of disability-adjusted life years (DALYs). Notably, the incidence of ischemic stroke is decreasing among older adults but rising in younger populations. Historically, most studies have analyzed early-onset and late-onset ischemic stroke as separate entities, with limited attention to their potential overlap or shared spatial patterns. To address this research gap, we investigated the global co-occurrence patterns of these two stroke subtypes, mapped their spatial distributions, and identified the risk factors associated with each. Methods We analyzed data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, extracting incidence rates of early-and late-onset ischemic stroke, as well as exposure levels of 26 risk factors, across 204 countries and territories. We stratified the incidence distributions of the two stroke subtypes into quartiles to define their co-occurrence patterns, classifying regions into three categories: consistent incidence (of both subtypes), early-onset dominant, and late-onset dominant. To identify associations between risk factors and stroke subtypes, we used ensemble learning and negative binomial regression. We also estimated population attributable fractions (PAFs) to quantify the proportion of stroke burden attributable to each risk factor. Results Of the 204 regions evaluated, 104 had concordant incidence rates for the two stroke subtypes (i.e., similar quartile rankings for early-and late-onset stroke), while 52 were classified as early-onset dominant and 48 as late-onset dominant. For early-onset ischemic stroke, significant associated risk factors included elevated systolic blood pressure, active tobacco smoking, impaired kidney function, and high ambient temperature. For late-onset ischemic stroke, the key associated risk factors were elevated systolic blood pressure and secondhand smoke exposure. Collectively, these risk factors contributed to 18.26% of the global burden of early-onset ischemic stroke (PAF: 18.26%) and 16.99% of the global burden of late-onset ischemic stroke (PAF: 16.99%). Conclusions This study is the first to integrate analyses of global co-occurrence patterns of early-and late-onset ischemic stroke with their risk factors. The findings highlight the need for more rigorous research to validate causal links between these factors and each stroke subtype, thereby informing targeted prevention strategies across age groups.

Keywords: Global Burden of Diseases, Population attributable fractions, Early-onset ischemic stroke, Late-onset ischemic stroke, Risk factors, Spatial Epidemiology, machine learning

Received: 19 Jul 2025; Accepted: 30 Oct 2025.

Copyright: © 2025 Li, Liu, Zhang, Fan and Hou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Li Li, lily911188@163.com

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