Motor and nonmotor features of p.A53T alpha-synuclein PD vs idiopathic PD: longitudinal data from the PPMI study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The phenotype of p.A53T-α-synuclein (SNCA) mutation carriers with Parkinson's disease (A53T-PD) appears more severe compared to idiopathic PD (iPD), however, information is limited. Here we conducted a comprehensive longitudinal study to investigate the progression of motor and nonmotor features of Α53Τ-PD compared to iPD. METHODS: Detailed longitudinal 3-year data, concerning both motor and non-motor features, of 16 p.A53T-PD and 48 iPD, matched for age (51-53 years) and disease duration (approximately 4 years) at baseline, were downloaded from the Parkinson's Progression Markers Initiative (PPMI) database and compared between the two groups. Additionally, a cognitive composite score was generated by five cognitive tests, focused more on executive/visuospatial function. RESULTS: At baseline, global cognitive function, as assessed by the Montreal Cognitive Assessment (MOCA), was not significantly different between the two groups, in contrast to tests evaluating executive/visuospatial function, including the composite score, which were worse in A53T-PD. There was a significant decline over time in all neuropsychological tests in A53T-PD, while iPD remained stable. A similar pattern was revealed for motor status and function, as well as autonomic function, which were similar between the two groups at baseline, but deteriorated significantly only in A53T-PD over time. DISCUSSION: A53T-PD patients present an accelerated decline in both motor and non-motor parameters, with an impairment in executive-visuospatial function occurring early in the disease process. Such data may set the stage for targeted disease-modifying therapies in this particular subtype, while generated data may be widely applicable to iPD, which is largely a sporadic synucleinopathy.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".