Developing novel biomarkers of frontotemporal dementia
Bibliographic record
Abstract
Clinical trials for genetic frontotemporal dementia (FTD) are ongoing, yet many early phase studies have failed due to ineffective pre-selected clinical or surrogate endpoints. Unlike other neurodegenerative diseases, FTD lacks well-established biomarkers for diagnosis, staging, and disease progression, partly due to its heterogeneous nature. FTD can be sporadic or genetic, involve multiple proteinopathies, and present with varied clinical phenotypes, including motor symptoms, language problems, and personality changes. This thesis focuses on exploring dynamic fluid biomarkers in samples from the Genetic Frontotemporal Initiative (GENFI), the largest natural history study of genetic FTD in Europe and Canada. The GENFI Biobank, which I helped establish, forms centralised storage of biosamples collected using standardised GENFI protocols. These samples were used to explore biomarkers at baseline visits, but also investigate longitudinal changes in glial fibrillary acidic protein (GFAP) and neurofilament light chain protein (NfL). My research has helped to identify NfL as a disease staging marker, and GFAP as a promising biomarker to track disease progression, specifically in GRN-associated FTD. Therefore, these biomarkers will be useful in disease-modifying therapeutic trials, to identify the right time to administer treatment, track efficacy, and serve as surrogate clinical endpoints. Additionally, I will also explore novel biomarkers of FTD: cytokines from the vascular endothelial growth factor family in the cerebrospinal fluid of both sporadic and genetic FTD participants. Finally, I will examine the neurotrophin receptor p75 extracellular domain in a more distant sample type, urine, to see whether these samples collected as part of GENFI may be useful matrices to identify novel biomarkers, that have yet to be explored in FTD. These findings aim to impact future clinical trials to expedite access to therapeutic intervention for patients suffering from this devastating disease.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".