Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".