Distinct proteomic CSF profiles in genetic frontotemporal lobar degeneration
Bibliographic record
Abstract
Fluid biomarkers to diagnose frontotemporal lobar degeneration (FTLD) are currently lacking. In this study, we aimed to identify proteomic changes in CSF associated with FTLD pathogenesis, focusing on signatures unique to different genetic groups. Additionally, we sought proteins distinguishing FTLD-spectrum disorders from controls. To this end, we measured a comprehensive library of over 2900 proteins in CSF using proximity extension assay technology in two well-characterized FTLD cohorts. The discovery cohort, selected from the GENFI cohort, included 47 symptomatic pathogenic variant carriers (22 C9orf72, 14 GRN, 10 MAPT and 1 TARDBP), 124 presymptomatic pathogenic variant carriers (55 C9orf72, 44 GRN, 24 MAPT and 1 TARDBP) and 57 healthy non-carriers. The validation cohort comprised individuals clinically diagnosed with an FTLD-spectrum disorder (n = 132) and cognitively intact controls (n = 32). We assessed differentially abundant proteins using linear regression, adjusting for age and sex. Over-representation analysis was conducted for the three genetic groups using Gene Ontology Biological Processes as the ontology source. To develop diagnostic tools, we applied a LASSO regression, establishing two types of panels: one to distinguish individuals with an FTLD-spectrum disorder from controls (FTLD panel) and another to differentiate individuals with underlying TDP pathology from controls (TDP panel). We observed 23 dysregulated proteins in symptomatic carriers. Of these, four were also significantly dysregulated (NEFL, TPM3, MSLN and DNM3) in the validation cohort. When focusing on genetic subgroups, 63 upregulated proteins were observed in symptomatic MAPT carriers, with enriched biological pathways linked to immune function. In symptomatic C9orf72 carriers, four proteins-related to energy metabolism-were upregulated. When limiting symptomatic carriers to GRN, six proteins were dysregulated, with enriched pathways involved in neuronal development and projection. Notably, NEFL and TPM3 were consistently significant in all comparisons across both cohorts. We developed two diagnostic panels: one for FTLD and one for FTLD-TDP. The FTLD panel consisted of six proteins (NEFL, RBFOX3, NPTX1, TFF1, ENTPD5 and CNP). The TDP panel was made up of seven proteins (NEFL, RBFOX3, CBLN4, ENTPD5, CCL25, CNP and MMP1). Both panels were successfully replicated in the validation cohort (AUC of 0.94 and 0.96, respectively). This study highlights distinct proteomic signatures across FTLD genetic subgroups and their associated pathologies using a targeted proteomic approach. Additionally, we present two diagnostic panels-comprising both established and novel proteins-that effectively differentiate individuals with FTLD-spectrum disorders from healthy controls, offering promising avenues for improved clinical diagnosis.
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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 | 0.001 | 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 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".