Novel <scp>CSF</scp> biomarkers in genetic frontotemporal dementia identified by proteomics
Bibliographic record
Abstract
Abstract Objective To identify novel CSF biomarkers in GRN ‐associated frontotemporal dementia ( FTD ) by proteomics using mass spectrometry ( MS ). Methods Unbiased MS was applied to CSF samples from 19 presymptomatic and 9 symptomatic GRN mutation carriers and 24 noncarriers. Protein abundances were compared between these groups. Proteins were then selected for validation if identified by ≥4 peptides and if fold change was ≤0.5 or ≥2.0. Validation and absolute quantification by parallel reaction monitoring ( PRM ), a high‐resolution targeted MS method, was performed on an international cohort ( n = 210) of presymptomatic and symptomatic GRN , C9orf72 and MAPT mutation carriers. Results Unbiased MS revealed 20 differentially abundant proteins between symptomatic mutation carriers and noncarriers and nine between symptomatic and presymptomatic carriers. Seven of these proteins fulfilled our criteria for validation. PRM analyses revealed that symptomatic GRN mutation carriers had significantly lower levels of neuronal pentraxin receptor ( NPTXR ), receptor‐type tyrosine‐protein phosphatase N2 ( PTPRN 2), neurosecretory protein VGF , chromogranin‐A ( CHGA ), and V‐set and transmembrane domain‐containing protein 2B ( VSTM 2B) than presymptomatic carriers and noncarriers. Symptomatic C9orf72 mutation carriers had lower levels of NPTXR , PTPRN 2, CHGA , and VSTM 2B than noncarriers, while symptomatic MAPT mutation carriers had lower levels of NPTXR and CHGA than noncarriers. Interpretation We identified and validated five novel CSF biomarkers in GRN ‐ associated FTD . Our results show that synaptic, secretory vesicle, and inflammatory proteins are dysregulated in the symptomatic stage and may provide new insights into the pathophysiology of genetic FTD. Further validation is needed to investigate their clinical applicability as diagnostic or monitoring biomarkers.
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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) | 0.000 | 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 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".