Clinical and Genetic Findings in a Chinese Cohort of Dentatorubral–Pallidoluysian Atrophy Patients
Bibliographic record
Abstract
OBJECTIVE: Dentatorubral-pallidoluysian atrophy (DRPLA) is a rare, inherited neurodegenerative disorder caused by the expansion of cytosine-adenine-guanine repeats in ATN1. Most studies on DRPLA to date are limited to case reports. We aimed to provide a comprehensive summary of the clinical, genetic, biological, and magnetic resonance imaging characteristics of DRPLA using cross-sectional baseline data. METHODS: This is a cross-sectional observational cohort study. We used an extensive battery of assessments, included clinical phenotypes, genotypes, cognitive performance, biological markers, and magnetic resonance imaging characteristics. RESULTS: We enrolled 116 DRPLA patients, including 96 manifest patients and 20 prodromal patients. We identified a previously unreported ATN1 haplotype consisting of 8 single-nucleotide polymorphisms. Cognitive assessments revealed that 51 manifest patients (96%) and 4 prodromal patients (29%) scored <26 on the Montreal Cognitive Assessment. Manifest patients showed impairments across all cognitive domains, whereas prodromal patients showed deficits only in phonemic fluency. Biological analyses showed significantly elevated plasma neurofilament light levels in manifest patients compared with prodromal patients (P < 0.001) and healthy controls (P < 0.001). Magnetic resonance imaging findings revealed widespread gray matter loss across the whole brain in manifest patients, whereas prodromal patients showed gray matter loss localized to the bilateral cerebellar hemispheres. INTERPRETATION: This is the first DRPLA cohort study to comprehensively report clinical, genetic, cognitive, imaging, and plasma neurofilament light data. This study provides robust data to enhance our understanding of the overall features of DRPLA. We also propose clear definitions for the preclinical stage of DRPLA, and demonstrate the high diagnostic utility of plasma neurofilament light as a biomarker. ANN NEUROL 2025;98:750-761.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".