Abstract A014: Gastrointestinal Microbiome and Stroke Outcomes Network: Initial Results

Background: The gut microbiome is increasingly implicated in neurological disease, yet its relationship with stroke severity and recovery in humans remains unclear. We investigated the associations between gut microbial profiles and clinical outcomes in patients with acute ischemic stroke. Methods: This was a two-center cohort of 204 adult acute ischemic stroke patients. Stool samples were collected at baseline (n = 144) and 3 months (n = 65) and profiled via 16S rRNA gene sequencing. Stroke severity and disability were assessed using the NIH Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) at both time points. Global associations between community composition and outcomes were tested with PERMANOVA. Taxon-level associations used multivariable regression and mixed-effect models, which adjusted for age, sequencing batch, and premorbid mRS. α was set to 0.05 for the global/community tests, and at FDR-adjusted <0.1 for the taxon tests. Change in NIHSS was defined as the 3-month score minus the baseline score. Results: Alpha-diversity (richness, evenness, Shannon) was not associated with any outcome. Baseline gut microbiome composition was associated with baseline NIHSS (p = 0.00019) and premorbid mRS (p = 0.000016), as well as with the change in NIHSS over time (p = 0.019). These findings remained significant after adjusting for age and sequencing batch, and the change in NIHSS association remained significant with additional adjustment for premorbid mRS (p = 0.020). No significant associations were observed between the baseline microbiota and 90-day outcomes, likely due to the smaller follow-up sample size (n = 65). At the taxonomic level, several baseline taxa were associated with baseline severity and longitudinal change; notably, higher abundance of Lachnospiraceae-ND3007 was associated with greater improvement in NIHSS (FDR < 0.10). Longitudinal mixed-effects models also identified multiple taxa correlated with NIHSS and mRS measures over time. Conclusion: Baseline microbiome composition at stroke onset was associated with premorbid disability, initial stroke severity, and change in NIHSS over time. Specific taxa (e.g., Lachnospiraceae-ND3007 ) may serve as biomarkers of recovery trajectories. These findings support a potential role for the microbiome in modulating stroke outcomes and warrant further study of microbiota-targeted interventions.

Authors

Institutions

Publication Details

Journal
Stroke
Published
2026-01-29
DOI
https://doi.org/10.1161/str.57.suppl_1.a014
Primary Topic
Gut microbiota and health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract A014: Gastrointestinal Microbiome and Stroke Outcomes Network: Initial Results

Sonya A. Gunter, Daniel N. Frank, Karlie Smith, Michele M. Sale et al.
Stroke
Gut microbiota and health
article

Abstract A014: Gastrointestinal Microbiome and Stroke Outcomes Network: Initial Results

Sonya A. Gunter, Daniel N. Frank, Karlie Smith, Michele M. Sale, Nina Gu, Rahim A. Burdette, Saqib Chaudhry, Brian Donahue, Jennifer Kofonow, Miah Perch, Rachel Williams, Bradford Worrall
article en

Abstract

Background: The gut microbiome is increasingly implicated in neurological disease, yet its relationship with stroke severity and recovery in humans remains unclear. We investigated the associations between gut microbial profiles and clinical outcomes in patients with acute ischemic stroke. Methods: This was a two-center cohort of 204 adult acute ischemic stroke patients. Stool samples were collected at baseline (n = 144) and 3 months (n = 65) and profiled via 16S rRNA gene sequencing. Stroke severity and disability were assessed using the NIH Stroke Scale (NIHSS) and the modified Rankin Scale (mRS) at both time points. Global associations between community composition and outcomes were tested with PERMANOVA. Taxon-level associations used multivariable regression and mixed-effect models, which adjusted for age, sequencing batch, and premorbid mRS. α was set to 0.05 for the global/community tests, and at FDR-adjusted <0.1 for the taxon tests. Change in NIHSS was defined as the 3-month score minus the baseline score. Results: Alpha-diversity (richness, evenness, Shannon) was not associated with any outcome. Baseline gut microbiome composition was associated with baseline NIHSS (p = 0.00019) and premorbid mRS (p = 0.000016), as well as with the change in NIHSS over time (p = 0.019). These findings remained significant after adjusting for age and sequencing batch, and the change in NIHSS association remained significant with additional adjustment for premorbid mRS (p = 0.020). No significant associations were observed between the baseline microbiota and 90-day outcomes, likely due to the smaller follow-up sample size (n = 65). At the taxonomic level, several baseline taxa were associated with baseline severity and longitudinal change; notably, higher abundance of Lachnospiraceae-ND3007 was associated with greater improvement in NIHSS (FDR < 0.10). Longitudinal mixed-effects models also identified multiple taxa correlated with NIHSS and mRS measures over time. Conclusion: Baseline microbiome composition at stroke onset was associated with premorbid disability, initial stroke severity, and change in NIHSS over time. Specific taxa (e.g., Lachnospiraceae-ND3007 ) may serve as biomarkers of recovery trajectories. These findings support a potential role for the microbiome in modulating stroke outcomes and warrant further study of microbiota-targeted interventions.

StrokeVol. 57(Suppl_1)
Eastern Virginia Medical School (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), University of Colorado Denver (US)
Good health and well-being
Openalex Percentile: Top 74%
Gut microbiota and health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.