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Record W2623948558 · doi:10.1016/s1474-4422(17)30124-2

Neurofilament light protein in blood as a potential biomarker of neurodegeneration in Huntington's disease: a retrospective cohort analysis

2017· article· en· W2623948558 on OpenAlexaff
Lauren M. Byrne, Filipe B. Rodrigues, Kaj Blennow, Alexandra Dürr, Blair R. Leavitt, Raymund A.C. Roos, Rachael I. Scahill, Sarah J. Tabrizi, Henrik Zetterberg, Douglas R. Langbehn, Edward J. Wild

Bibliographic record

VenueThe Lancet Neurology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilVetenskapsrådetNational Institutes of HealthKnut och Alice Wallenbergs StiftelseResearch Councils UKNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
KeywordsNeurodegenerationHuntington's diseaseBiomarkerInternal medicineMedicineCohortOncologyRetrospective cohort studyDiseaseBiologyGenetics

Abstract

fetched live from OpenAlex

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 (also known as NF-L) in blood is a potential prognostic marker of neurodegeneration in patients with Huntington's disease. METHODS: We did a retrospective analysis of healthy controls and carriers of CAG expansion mutations in HTT participating in the 3-year international TRACK-HD study. We studied associations between NfL concentrations in plasma and clinical and MRI neuroimaging findings, namely cognitive function, motor function, and brain volume (global and regional). We used random effects models to analyse cross-sectional associations at each study visit and to assess changes from baseline, with and without adjustment for age and CAG repeat count. In an independent London-based cohort of 37 participants (23 HTT mutation carriers and 14 controls), we further assessed whether concentrations of NfL in plasma correlated with those in CSF. FINDINGS: Baseline and follow-up plasma samples were available from 97 controls and 201 individuals carrying HTT mutations. Mean concentrations of NfL in plasma at baseline were significantly higher in HTT mutation carriers than in controls (3·63 [SD 0·54] log pg/mL vs 2·68 [0·52] log pg/mL, p<0·0001) and the difference increased from one disease stage to the next. At any given timepoint, NfL concentrations in plasma correlated with clinical and MRI findings. In longitudinal analyses, baseline NfL concentration in plasma also correlated significantly with subsequent decline in cognition (symbol-digit modality test r=-0·374, p<0·0001; Stroop word reading r=-0·248, p=0·0033), total functional capacity (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 Stroop word reading and total functional capacity remained significant after adjustment for age and CAG repeat count. In 104 individuals with premanifest Huntington's disease, NfL concentration in plasma at baseline was associated with subsequent clinical onset during the 3-year follow-up period (hazard ratio 3·29 per log pg/mL, 95% CI 1·48-7·34, p=0·0036). Concentrations of NfL in CSF and plasma were correlated in mutation carriers (r=0·868, p<0·0001). INTERPRETATION: NfL in plasma shows promise as a potential prognostic blood biomarker of disease onset and progression in Huntington's disease. FUNDING: Medical Research Council, GlaxoSmithKline, CHDI Foundation, Swedish Research Council, European Research Council, Wallenberg Foundation, and Wolfson Foundation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.278
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations357
Published2017
Admission routes1
Has abstractyes

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