Plasma glial fibrillary acidic protein as a biomarker of disease progression in Parkinson’s disease: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Reactive astrogliosis has been demonstrated to have a role in Parkinson's disease (PD); however, astrocyte-specific plasma glial fibrillary acidic protein (GFAP)'s correlation with PD progression remains unknown. We aimed to determine whether plasma GFAP can monitor and predict PD progression. METHODS: A total of 184 patients with PD and 95 healthy controls (HCs) were included in this prospective cohort study and followed-up for 5 years. Plasma GFAP, amyloid-beta (Aβ), p-tau181, and neurofilament light chain (NfL) were measured at baseline and at 1- and 2-year follow-ups. Motor and non-motor symptoms, activities of daily living, global cognitive function, executive function, and disease stage were evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS) part III, UPDRS-I, UPDRS-II, Montreal Cognitive Assessment (MoCA), Frontal Assessment Battery (FAB), and Hoehn and Yahr (H&Y) scales at each visit, respectively. RESULTS: Plasma GFAP levels were higher in patients with PD (mean [SD]: 69.80 [36.18], pg/mL) compared to HCs (mean [SD]: 57.89 [23.54], pg/mL). Higher levels of GFAP were observed in female and older PD patients. The adjusted linear mixed-effects models showed that plasma GFAP levels were significantly associated with UPDRS-I scores (β: 0.006, 95% CI [0.001-0.011], p = 0.027). Higher baseline plasma GFAP correlated with faster increase in UPDRS-I (β: 0.237, 95% CI [0.055-0.419], p = 0.011) and UPDRS-III (β: 0.676, 95% CI [0.023-1.330], p = 0.043) scores and H&Y stage (β: 0.098, 95% CI [0.047-0.149], p < 0.001) and faster decrease in MoCA (β: - 0.501, 95% CI [- 0.768 to - 0.234], p < 0.001) and FAB scores (β: - 0.358, 95% CI [- 0.587 to - 0.129], p = 0.002). Higher baseline plasma GFAP predicted a more rapid progression to postural instability (hazard ratio: 1.009, 95% CI [1.001-1.017], p = 0.033). CONCLUSIONS: Plasma GFAP might be a potential biomarker for monitoring and predicting disease progression in PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".