D08 Neurofilament light protein in blood as a potential biomarker of neurodegeneration in hungtington’s disease: a retrospective cohort analysis
Bibliographic record
Abstract
Background Blood biomarkers of neuronal damage could facilitate clinical management of and therapeutic development for Huntington’s disease. We investigated whether neurofilament light protein (NfL) in blood is a potential prognostic marker of neurodegeneration in patients with Huntington’s disease. Methods In the 3 year, 298-participant TRACK-HD cohort, we did a retrospective analysis of the relationship between plasma NfL and clinical and neuroimaging measures previously identified as being the strongest predictors of HD progression. Cross-sectional and longitudinal relationships were analysed using random effect models of within-subject correlation. In a separate 37-participant cohort we quantified NfL in cerebrospinal fluid (CSF) and plasma. Findings Mean concentrations of plasma NfL at baseline were significantly higher in HTT mutation carriers than in controls and the difference increased with disease stage. At any given timepoint, plasma NfL correlated with clinical and MRI findings. In longitudinal analyses, baseline plasma NfL correlated significantly with subsequent decline in cognition (SDMT r=–0.374, p<0.0001; SWR r=–0.248, p=0.0033), TFC (r=–0.289, p=0.0264), and brain atrophy (caudate r=0.178, p=0.0087; whole-brain r=0.602, p<0.0001; grey matter r=0.518, p<0.0001; white matter r=0.588, p<0.0001; and ventricular expansion r=–0.589, p<0.0001). All changes except SWR and TFC remained significant after adjustment for age and CAG repeat. In premanifest HD individuals, plasma NfL at baseline was associated with subsequent clinical onset during the 3 year follow-up period. Concentrations of NfL in CSF and plasma were correlated in mutation carriers. Interpretation NfL in plasma shows promise as a potential prognostic blood biomarker of disease onset and progression in Huntington’s 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".