Plasma inflammatory cytokines are associated with cognitive impairment after acute minor ischemic stroke and transient ischemic attack
Bibliographic record
Abstract
Background: Inflammation plays a complex role in post-stroke cognitive impairment (PSCI). The purpose of our study was to explore the possible relationship between peripheral blood inflammatory biomarkers and PSCI. Methods: This prospective observational cohort study included patients with mild ischemic stroke and transient ischemic attack (TIA) who were hospitalized at the First Hospital of Jilin University between April 2019 and March 2022. Fasting plasma, collected on the second day after admission, was used to detect 10 inflammatory cytokines by ELISA and multifactorial assay. The Montreal Cognitive Assessment scale score < 22 was used as the diagnostic criterion for PSCI. We explored the associations between baseline plasma cytokines and PSCI at 0-30 months of follow-up using multivariable logistic regression and further applied structural equation modeling (SEM) to explore the potential pathways. Results: A total of 236 patients were included in the analysis. Interleukin-18 (IL-18), matrix metalloproteinase-9 (MMP-9), and macrophage inflammatory protein-1α (MIP-1α) were independently associated with PSCI at 3-30 months of follow-up, while IL-18, MMP-9, and IL-8 were independently associated with delayed-onset PSCI (>6 months of follow-up). Similar findings were observed in the sensitivity analysis after excluding the patients with ischemic symptoms with an onset of more than 1 week and those with TIA. The associations of IL-18, MMP-9, and MIP-1α with PSCI remained consistent across age and sex subgroups. In delayed-onset PSCI, IL-18, and MMP-9 showed stable associations, while IL-8 mainly affected patients younger than 65 years. Furthermore, SEM suggested that peripheral inflammation involving MMP-9 and IL-18 mediated the development of PSCI. Conclusions: The plasma inflammatory markers centered on neutrophils-MMP-9 and IL-18-interact with various risk factors (age, gender, DMTS, DWMH, cerebral atrophy) and contribute to the development of PSCI. Clinical Trial Registration: https://www.chictr.org.cn/, identifier ChiCTR1900022675.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".