Longitudinal cognitive decline characterizes the profile of non-PD-manifest GBA1 mutation carriers.
Bibliographic record
Abstract
With disease-modifying treatment for Parkinson's disease (PD) associated with variants in the glucocerebrosidase gene (GBA1) under way, the challenge to design clinical trials with non-PD-manifest GBA mutation carriers (GBA1NMC) comes within close reach. To delineate trajectories of motor and non-motor markers as well as serum neurofilament light (sNfL) levels and to evaluate clinical endpoints as outcomes for clinical trials in GBA1NMC, longitudinal data of 56 GBA1NMC carriers and 112 age- and sex-matched GBA1 wildtype participants (GBA1wildtype) with up to 9 years of follow-up was analyzed using linear mixed-effects models (LMEM) and Kaplan-Meier survival analysis of clinical endpoints for motor and cognitive function. GBA1NMC showed worse performance in Pegboard, 20 m fast walking, global cognition as well as in executive and memory function at baseline. Longitudinally, LMEM revealed a higher annual increase of the MDS-UPDRS III bradykinesia subscore in GBA1NMC compared to GBA1wildtype, but comparable trajectories of all other motor and non-motor markers as well as sNfL. Kaplan-Meier survival analysis showed a significantly earlier progression to clinical endpoints of cognitive decline in GBA1NMC. Incidence of PD was significantly higher in GBA1NMC. In conclusion, our study extends data on GBA1NMC indicating early cognitive decline as a potentially characteristic feature. Comprehensive longitudinal assessments of cognitive function are crucial to delineate the evolution of early changes in GBA1NMC enabling a more accurate stratification and allow for a more precise definition of trial design and sample size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".