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Record W4417160052 · doi:10.1002/acn3.70272

Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease

2025· article· en· W4417160052 on OpenAlexaff
Daniel Chelsky, Cara Joyce, H. Jeremy Bockholt, Paul A. Rudnick, William Adams, Fiona E. McAllister, Justin Smock, Michael A. Newton, Jane S. Paulsen

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsDomtar (Canada)
FundersNational Institutes of Health
KeywordsDiseaseProteomicsMEDLINEDisease monitoringProteome

Abstract

fetched live from OpenAlex

OBJECTIVE: Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. METHODS: Cerebrospinal fluid was obtained from PREDICT-HD and ancillary studies. Cohorts included HD family members who were gene-tested and considered prodromal following neuroexam. An initial unbiased mass spectrometry proteomics analysis identified candidate disease biomarkers that were then added to a targeted mass spectrometry assay including 100+ proteins associated with other neurodegenerative diseases. This assay determined relative quantifications of proteins in a single analysis. Significant biomarkers were examined against genetic and clinical measures of disease onset and progression. RESULTS: Two overlapping targeted analyses using 180 samples from 125 participants (61% female, 89% White, average age of 42 ± 14) were performed; longitudinal duration was 1-4 years. Based on participants' clinical data, 25 proteins correlated significantly with CAG-age-product (CAP) score and Unified HD Rating Scale (UHDRS) motor and cognitive measures. While most proteins increase in abundance with disease progression, proenkephalin and prodynorphin were downregulated before CMD. Power was low for longitudinal analysis. However, the reliability of HD family normal controls indicates that each individual's proteome remains relatively stable over time. INTERPRETATION: Findings replicate and extend the verification of HD biomarkers. Monitoring proenkephalin and prodynorphin levels in persons with HD may facilitate early detection and disease-tracking. These disease-specific biomarkers may improve the rigor of therapeutic intervention before clinical motor diagnosis. Further studies emphasizing longitudinal changes are needed to assess disease-monitoring. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00051324.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.143
GPT teacher head0.424
Teacher spread0.281 · 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 designBench or experimental
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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Citations0
Published2025
Admission routes1
Has abstractyes

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