A 14-year longitudinal study of neurofilament light chain dynamics in premanifest and transitional Huntington’s disease
Bibliographic record
Abstract
Abstract Background Growing evidence supports the value of neurofilament light (NfL) as a prognostic biomarker in premanifest Huntington’s disease (HD). To date, however, there has been no longitudinal study exceeding 3 years examining either its serial dynamics or predictive power in HD. We aimed to conduct the first such study. Methods Serum NfL was sampled using ultrasensitive immunoassay at four timepoints across a 14-year period in a cohort of HD gene carriers ( n = 21) and controls ( n = 14). Gene carriers were premanifest at baseline. Clinical features of HD were evaluated by Unified Huntington’s Disease Rating Scale (UHDRS TMS), Montreal Cognitive Assessment (MoCA), Trail A/B task, Symbol Digit Modalities Task and semantic/phonemic fluency tasks. Results 14/21 HD gene carriers converted to prodromal or manifest disease by the final timepoint (“converters”). At baseline and each subsequent timepoint, NfL levels were higher in converters than in non-converters and controls ( p = < 0.001–0.03, η p 2 = 0.25–0.66). The estimated rate of change in NfL was higher in converters than in non-converters ( p = 0.03) and controls ( p = 0.001). Baseline NfL was able to discriminate converters from non-converters (area under curve = 1.000, p = 0.003). A higher rate of change in NfL was predictive of more severe motor (UHDRS-TMS p = 0.007, β = 0.711, R 2 = 0.468) and cognitive deficits (MoCA p = 0.007, β = − 0.798, R 2 = 0.604; Trail B, p = 0.007, β = 0.772, R 2 = 0.567; phonemic fluency p = 0.035, β = − 0.632, R 2 = 0.345). Conclusions Our data suggest that (1) NfL longitudinal dynamics in premanifest/transitional HD are non-constant; rising faster in those closer to disease onset, and (2) NfL can identify individuals at risk of conversion to manifest disease and predict clinical trajectory, > 10 years from disease onset.
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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".