Neurofilament Light and GFAP as prognostic biomarkers of cognitive outcomes after stroke: a 5-year prospective multi-centre cohort study
Bibliographic record
Abstract
Introduction Cognitive impairment is a common consequence of stroke, yet early identification of patients at risk remains challenging. Blood-based biomarkers of neuroaxonal and astroglial injury, such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), may improve risk stratification, but evidence on their prognostic value for long-term cognitive outcomes is limited. Methods We conducted a prospective cohort study of dementia-free patients with ischaemic or haemorrhagic stroke admitted to six German tertiary stroke centres between May 2011 and January 2019 (DEMDAS; NCT01334749). Baseline blood samples were collected at a median of 3 days (IQR 2–5) post-stroke, and plasma NfL and GFAP were measured using the Simoa platform. Neuropsychological assessments were performed at 6, 12, 36, and 60 months. The primary outcome was post-stroke cognitive impairment (PSCI), defined as ≥1 domain <-1.5 SD. Secondary outcomes included global cognitive performance and domain-specific impairments. Associations of baseline biomarkers with outcomes over 5 years were estimated using generalized estimating equations, adjusted for demographics, stroke severity, acute-phase cognition, vascular risk factors, imaging parameters, and time to sampling. We also assessed model improvement and subgroup effects. Results Among 558 patients with baseline biomarker data, higher NfL was significantly associated with PSCI (OR=1.57, 95% CI 1.28–1.91, p<0.0001), while GFAP was not. NfL also predicted worse global cognitive performance (β=–0.11, 95% CI –0.20 to –0.03, p=0.007) and impairments in the attention (OR=1.90, 1.38–2.60) and executive (OR=1.30, 1.04–1.62) domains. NfL improved model fit for PSCI and cognitive performance beyond clinical predictors, although gains in explained variance and discrimination were modest. The prognostic value of NfL was particularly pronounced in patients with poorer performance on acute-phase Montreal Cognitive Assessment (MoCA). Conclusions Baseline plasma NfL was independently associated with cognitive outcomes over five years after stroke, especially in domains that are commonly affected by vascular pathology. Future studies should explore the benefits of longitudinal NfL monitoring for timely risk stratification and its potential to guide patient selection for clinical trials targeting post-stroke cognitive impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".