Plasma Glial Fibrillary Acidic Protein Correlates With Brain Metal Burden in Wilson's Disease
Bibliographic record
Abstract
OBJECTIVE: Neuroinflammation driven by extracellular copper contributes to neuronal damage in Wilson's disease (WD). This study investigated the relationship between brain metal burden and peripheral neuroinflammation markers in WD. METHODS: We conducted a cross-sectional study involving 89 participants, including patients with WD (n = 63), asymptomatic ATP7B heterozygous carriers (n = 12), and age/sex-matched controls (n = 14). Brain metal burden was assessed using quantitative susceptibility mapping (QSM) MRI. Plasma glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) levels were measured. Clinical severity was evaluated using the mini-Unified Wilson's Disease Rating Scale (UWDRS) and Montreal Cognitive Assessment (MoCA). The influence of copper chelation treatment on biomarker correlations was also examined. RESULTS: Patients with WD had a significantly higher level of GFAP (p = 0.02) and brain metal burden (p < 0.01) than the control group. Plasma NfL levels were marginally elevated in the WD group (p = 0.07). Notably, elevated plasma GFAP levels were significantly associated with higher UWDRS scores (r = 0.35, p < 0.01) and lower MoCA scores (r = -0.36, p < 0.01). The NfL levels were also correlated with UWDRS (r = 0.58, p < 0.01) and marginally correlated with MoCA (r = -0.32, p = 0.02). The brain magnetic susceptibility value was positively correlated with increased plasma GFAP level, particularly in the putamen (p < 0.001), which was more prominent in WD patients without chelating agent treatment (r = 0.58, p < 0.001). INTERPRETATIONS: Plasma GFAP levels reflect both clinical severity and brain metal accumulation in WD, with this association influenced by copper chelation therapy.
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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 | 0.000 | 0.001 |
| 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 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".