The role of blood-based biomarkers in Parkinsonian disorders, Alzheimer's disease and frontotemporal dementia
Bibliographic record
Abstract
The complexity of neurodegenerative disorders necessitates an integrative approach that incorporates morphological, functional, and molecular biomarkers. The advent of highly sensitive single-molecule array (Simoa®) assays has significantly enhanced the accuracy of blood-based biomarker quantification, including glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau181 (p-tau181). This study evaluates the diagnostic utility of these biomarkers in neurodegenerative diseases. We analyzed data from 279 individuals from the PADUA-CESNE cohort: 120 with Parkinson's disease (PD), 88 with Alzheimer's disease (AD), 16 with frontotemporal dementia (FTD), 11 with multiple system atrophy (MSA), 14 with progressive supranuclear palsy (PSP), and 30 cognitively unimpaired controls. NfL levels were significantly lower in PD and AD compared to atypical parkinsonisms and FTD, effectively distinguishing MSA and PSP from controls. NfL also negatively correlated with Montreal Cognitive Assessment (MoCA) scores in AD and PD, indicating its association with cognitive decline. Elevated GFAP levels were observed in both PD and AD and inversely correlated with global cognition. Combining GFAP and p-tau181 improved AD differentiation from PD and other parkinsonian disorders, while the integration of all three biomarkers facilitated the distinction between AD and FTD. Notably, lower NfL levels (<20 ng/L) in conjunction with elevated p-tau181 were indicative of AD, whereas NfL levels below 40 ng/L were suggestive of PD. In conclusion, NfL serves as a sensitive indicator of neurodegeneration, albeit with limited specificity. However, by establishing biomarker concentration thresholds and integrating complementary biomarkers, blood-based assays may enhance the differential diagnosis of neurodegenerative diseases, providing valuable clinical insights.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".