Gut microbiota-derived tryptophan metabolites and inflammatory factors enhance prediction of early neurological deterioration after intravenous thrombolysis in stroke patients: a comparative model study
- 1Medical Imaging Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University
- 2Department of Neurology, Second People’s Hospital of Yibin City-West China Yibin Hospital, Sichuan University
Abstract
Background and purpose: Early neurological deterioration (END) after stroke thrombolysis predicts poor outcomes. This study developed predictive nomograms that incorporate gut-derived tryptophan metabolites and inflammatory markers to predict END.
Methods: We evaluated demographic, clinical, and laboratory variables, including gut-derived tryptophan metabolites and inflammatory markers. A multivariable logistic regression model was developed to predict END and presented as a nomogram. Model performance was assessed using variance inflation factors, ROC analysis, calibration curves, and decision curve analysis. Net Reclassification Improvement and Integrated Discrimination Improvement were used to compare models.
Results: In this study, END occurred in 11.26% of stroke patients and was independently associated with systolic blood pressure (SBP), fasting blood glucose, and serum levels of indole-3-propionic acid (IPA), indole-3-lactic acid (ILA), and interferon γ (IFN γ). Two nomogram models were developed to predict END: one based on conventional risk factors (SBP and glucose) and another incorporating emerging biomarkers (SBP, glucose, ILA, IPA, and IFN γ). The models achieved AUC scores of 0.719 (95% CI 0.658–0.780) and 0.909 (95% CI 0.879–0.939), respectively. Comparing the models showed a categorical Net Reclassification Improvement of 0.601 ( p = 0.000) and an Integrated Discrimination Improvement of 0.197 ( p = 0.000).
Conclusion: Incorporating gut microbiota-derived tryptophan metabolites and inflammatory factors improves prediction of END among thrombolyzed stroke patients.
Related
- Dynamic Changes in Serum Glial Fibrillary Acidic Protein Levels Predicts Early Neurological Deterioration After Intravenous Thrombolysis in Minor Stroke.
- Correlation of TyG and NLR with early neurological deterioration after thrombolysis in patients with acute ischemic stroke
- Clinical severity predominates over routine inflammatory and metabolic biomarkers in predicting hemorrhagic transformation after intravenous thrombolysis: internal temporal validation of incremental biomarker value
- Extending the Treatment Window for Intravenous Thrombolysis in Acute Ischemic Stroke
- Post-thrombolysis computed tomography angiography and perfusion features associated with an unfavorable early neurological response: a single-center retrospective study in a clinically selected anterior-circulation cohort
BibTeX
@article{li2026microbiotaderived,
title = {{Gut microbiota-derived tryptophan metabolites and inflammatory factors enhance prediction of early neurological deterioration after intravenous thrombolysis in stroke patients: a comparative model study}},
author = {Wei Li and Zhiyan Feng and Chunrong Zeng and Zhimei Cao and Yunfei Bai and Honglei Wang and Jiagui Huang},
journal = {Frontiers in Neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1934189},
url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1934189/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Gut microbiota-derived tryptophan metabolites and inflammatory factors enhance prediction of early neurological deterioration after intravenous thrombolysis in stroke patients: a comparative model study
AU - Wei Li
AU - Zhiyan Feng
AU - Chunrong Zeng
AU - Zhimei Cao
AU - Yunfei Bai
AU - Honglei Wang
AU - Jiagui Huang
PY - 2026
DA - 2026/10/20
JO - Frontiers in Neurology
DO - 10.3389/fneur.2026.1934189
UR - https://www.frontiersin.org/articles/10.3389/fneur.2026.1934189/full
AB - Background and purpose: Early neurological deterioration (END) after stroke thrombolysis predicts poor outcomes. This study developed predictive nomograms that incorporate gut-derived tryptophan metabolites and inflammatory markers to predict END. Methods: We evaluated demographic, clinical, and laboratory variables, including gut-derived tryptophan metabolites and inflammatory markers. A multivariable logistic regression model was developed to predict END and presented as a nomogram. Model performance was assessed using variance inflation factors, ROC analysis, calibration curves, and decision curve analysis. Net Reclassification Improvement and Integrated Discrimination Improvement were used to compare models. Results: In this study, END occurred in 11.26% of stroke patients and was independently associated with systolic blood pressure (SBP), fasting blood glucose, and serum levels of indole-3-propionic acid (IPA), indole-3-lactic acid (ILA), and interferon γ (IFN γ). Two nomogram models were developed to predict END: one based on conventional risk factors (SBP and glucose) and another incorporating emerging biomarkers (SBP, glucose, ILA, IPA, and IFN γ). The models achieved AUC scores of 0.719 (95% CI 0.658–0.780) and 0.909 (95% CI 0.879–0.939), respectively. Comparing the models showed a categorical Net Reclassification Improvement of 0.601 ( p = 0.000) and an Integrated Discrimination Improvement of 0.197 ( p = 0.000). Conclusion: Incorporating gut microbiota-derived tryptophan metabolites and inflammatory factors improves prediction of END among thrombolyzed stroke patients.
SN - 1664-2295
ER -