Evidence map›Paper›PMID 41646810›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Stroke Subtypes Drive Distinct Neurological, Cognitive, and Mortality Outcomes in UK Biobank Cohort.

Ambika Grover, Venkatesh L Murthy, Chirag J Patel

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ambika GroverDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA. 02215.
Venkatesh L MurthyDepartment of Internal Medicine and Frankel Cardiovascular Center, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-7901-1321
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA. 02215.ORCID 0000-0002-8756-8525

Funding

NEXUS: Network for Exposomics in the U.S.U24ES036819 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Rima Habre, GARY W MILLER · 2024 to 2026
$4.6M
Data science tools to identify robust exposure-phenotype associations for precision medicineR01ES032470 · NIEHS · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR, PATEL, CHIRAG J. · 2021 to 2025
$3.5M
Cataloging multi-ancestry 'omic readouts of the environmental and genetic determinants of type 2 diabetesR01DK137993 · NIDDK · HARVARD MEDICAL SCHOOL · PI ARJUN KUMAR MANRAI, Josep Maria Mercader · 2024 to 2026
$2.0M
NIDDK NIH HHS R01 DK137993NIEHS NIH HHS R01 ES032470NIEHS NIH HHS U24 ES036819
6 · The paper itself

Abstract

Introduction: Pathophysiological distinctions among stroke subtypes-acute ischemic stroke (AIS), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), and other nontraumatic intracranial hemorrhage (ONIH)-are well described, but their associations with long-term mortality, cognitive outcomes, and dementia risk remain incompletely characterized. We examined whether stroke subtypes differ in post-stroke survival, cognitive performance, dementia risk, and neuroimaging phenotypes. Methods: Using data from the UK Biobank, we evaluated subtype-specific associations with all-cause mortality, incident all-cause dementia, post-stroke cognitive performance, and white matter hyperintensity (WMH) burden. We assessed time to mortality and all-cause dementia using multivariable Cox proportional hazards models. Cognitive outcomes were compared cross-sectionally between stroke subtypes and stroke-free participants using covariate-adjusted linear models with ANOVA and Tukey post hoc tests. Neuroimaging analyses assessed associations between stroke subtype and WMH volume. Results: Mortality risk varied substantially by stroke subtype. Of UKB participants who had a stroke during surveillance (n=14,806), we found that 69.24% of strokes were ischemic, 9.51% subarachnoid, 9.78% ONIH, and 11.78% ICH. Compared with stroke-free person-time, intracerebral hemorrhage was associated with the highest post-stroke hazard of death (HR 7.62, 95% CI 7.10-8.18), followed by other nontraumatic intracranial hemorrhage (HR 5.41, 95% CI 4.89-5.98), subarachnoid hemorrhage (HR 3.89, 95% CI 3.52-4.31), and ischemic stroke (HR 3.99, 95% CI 3.82-4.16). At 1 year after stroke, absolute mortality risk was highest following ICH (36.4%), followed by ONIH (31.9%) and SAH (26.0%), while ischemic stroke was associated with substantially lower risk (11.7%); by 5 years, corresponding risks increased to 43.5%, 39.8%, 30.0%, and 20.0%, respectively. Dementia risk also differed by subtype, with the highest risk observed following ICH. Post-stroke cognitive performance varied across domains, with slower reaction times observed across multiple stroke subtypes and lower fluid intelligence scores among AIS and ICH patients. Reaction time and fluid intelligence assessments were completed a median of approximately 6-9 years after stroke. WMH burden was higher in ICH and AIS compared with stroke-free participants. Conclusions: Stroke subtype is associated with distinct patterns of mortality, dementia risk, cognitive performance, and brain imaging phenotypes. These findings highlight the heterogeneity of long-term outcomes following stroke and support the importance of subtype-aware post-stroke monitoring and prognostication.

Indexed as

cognitive outcomefluid intelligenceheterogeneityintracerebralischemicother nontraumatic hemorrhagereaction speedstroke subtypesubarachnoidUK Biobank

Identifiers

PMID41646810
PMCPMC12870562

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.