Asymptomatic carriers of the p.A53T SNCA mutation: data from the PPMI study
Bibliographic record
Abstract
Abstract Introduction There has been great interest in the prodromal phase of Parkinson’s disease (PD), especially in subjects who are asymptomatic carriers of genetic mutations leading to PD because of the high risk to convert to PD. The objective of the present study was to assess non motor characteristics of asymptomatic p.A53T mutation carriers (A53T-AC) compared with healthy controls (HC). Methods We compared 12 A53T-AC with 36 matched HC enrolled into in the Parkinson’s Progression Markers Initiative (PPMI) study. Baseline data extracted from the PPMI database, contained demographics and non-motor symptoms (e.g. the Montreal Cognitive Assessment (MOCA) for cognition, the University of Pennsylvania Smell Identification Test (UPSIT) for olfaction, MDS-UPDRS I etc.) Results The mean UPSIT score was lower in A53T-AC vs HC (p =0.000). MoCA test showed a trend towards lower scores in A53T AC. We found a significant positive correlation between UPSIT score and MOCA in A53T-AC (r s = 0,68, p=0,021) but not in HC. Total scores for MDS-UPDRS I did not differ between the groups but the subscore of anxiety was more prevalent in A53T-AC. Conclusion The more affected olfaction in A53T-AC may indicate that olfactory function is affected quite early in A53T carriers. The strong positive correlation between UPSIT and MOCA in the A53T-AC group may indicate that cognitive dysfunction and olfactory impairment progress alongside, prior to nigrostriatal degeneration. Anxiety was also more prevalent in A53T-AC and may represent an additional prodromal feature in this group of subjects.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".