Plasma glial fibrillary acidic protein and neurofilament light chain in behavioural variant frontotemporal dementia and primary psychiatric disorders
Bibliographic record
Abstract
ABSTRACT Objective Timely, accurate distinction between behavioural variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD) is a clinical challenge. Blood biomarkers such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) have shown promise. Prior work has shown NfL helps distinguish FTD from PPD. Few studies have assessed NfL together with GFAP. Methods We investigated plasma GFAP and NfL levels in participants with bvFTD, bipolar affective disorder (BPAD), major depressive disorder (MDD), treatment-resistant schizophrenia (TRS), healthy controls (HC), adjusting for age and sex. We compared ability of GFAP and NfL to distinguish bvFTD from PPD. Results Plasma GFAP levels were significantly (all p<0.001) elevated in bvFTD (n=22, mean (M)=273pg/mL) compared to BPAD (n=121, M=96pg/mL), MDD (n=42, M=105pg/mL), TRS (n=82, M=67.9pg/mL), and HC (n=120, M=76.8pg/mL). GFAP distinguished bvFTD from all PPD with an area under the curve (AUC) of 0.85, 95% confidence interval [0.76, 0.95]. The optimal cut-off of 105pg/mL was associated with 73% specificity and 86% sensitivity. NfL had AUC 0.95 [0.91, 0.99], 13.3pg/mL cut-off, 88% specificity, 86% sensitivity, and was superior to GFAP (p=0.02863) and combination of GFAP and NfL (p=0.04726). Conclusions This study found elevated GFAP levels in bvFTD compared to a large cohort of PPD, but NfL levels exhibited better performance in this distinction. These findings extend the literature on GFAP in bvFTD and build evidence for plasma NfL as a useful biomarker to assist with distinguishing bvFTD from PPD. Utilisation of NfL may improve timely and accurate diagnosis of bvFTD.
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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 | 0.001 | 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".