α‐synuclein genetic variability: A biomarker for dementia in Parkinson disease
Bibliographic record
Abstract
OBJECTIVE: The relationship between Parkinson disease (PD), PD with dementia (PDD), and dementia with Lewy bodies (DLB) has long been debated. Although PD is primarily considered a motor disorder, cognitive impairment is often present at diagnosis, and only ∼20% of patients remain cognitively intact in the long term. Alpha-synuclein (SNCA) was first implicated in the pathogenesis of the disease when point mutations and locus multiplications were identified in familial parkinsonism with dementia. In worldwide populations, SNCA genetic variability remains the most reproducible risk factor for idiopathic PD. However, few investigators have looked at SNCA variability in terms of cognitive outcomes. METHODS: We have used targeted high-throughput sequencing to characterize the 135kb SNCA locus in a large multinational cohort of patients with PD, PDD, and DLB and healthy controls. RESULTS: An analysis of 43 tagging single nucleotide polymorphisms across the SNCA locus shows 2 distinct association profiles for symptoms of parkinsonism and/or dementia, respectively, toward the 3' or the 5' of the SNCA gene. In addition, we define a specific haplotype in intron 4 that is directly associated with PDD. The PDD risk haplotype has been interrogated at single nucleotide resolution and is uniquely tagged by an expanded TTTCn repeat. INTERPRETATION: Our data show that PD, PDD, and DLB, rather than a disease continuum, have distinct genetic etiologies albeit within one genomic locus. Such results may serve as prognostic biomarkers to these disorders, to inform physicians and patients, and to assist in the design and stratification of clinical trials aimed at disease modification. Ann Neurol 2016;79:991-999.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".