Association of α-, β-, and γ-Synuclein With Diffuse Lewy Body Disease
Bibliographic record
Abstract
OBJECTIVE: To determine the association of the genes that encode alpha-, beta-, and gamma-synuclein (SNCA, SNCB, and SNCG, respectively) with diffuse Lewy body disease (DLBD). DESIGN: Case-control study. Subjects A total of 172 patients with DLBD consistent with a clinical diagnosis of Parkinson disease dementia/dementia with Lewy bodies and 350 clinically and 97 pathologically normal controls. INTERVENTIONS: Sequencing of SNCA, SNCB, and SNCG and genotyping of single-nucleotide polymorphisms performed on an Applied Biosystems capillary sequencer and a Sequenom MassArray pLEX platform, respectively. Associations were determined using chi(2) or Fisher exact tests. RESULTS: Initial sequencing studies of the coding regions of each gene in 89 patients with DLBD did not detect any pathogenic substitutions. Nevertheless, genotyping of known polymorphic variability in sequence-conserved regions detected several single-nucleotide polymorphisms in the SNCA and SNCG genes that were significantly associated with disease (P = .05 to <.001). Significant association was also observed for 3 single-nucleotide polymorphisms located in SNCB when comparing DLBD cases and pathologically confirmed normal controls (P = .03-.01); however, this association was not significant for the clinical controls alone or the combined clinical and pathological controls (P > .05). After correction for multiple testing, only 1 single-nucleotide polymorphism in SNCG (rs3750823) remained significant in all of the analyses (P = .05-.009). CONCLUSION: These findings suggest that variants in all 3 members of the synuclein gene family, particularly SNCA and SNCG, affect the risk of developing DLBD and warrant further investigation in larger, pathologically defined data sets as well as clinically diagnosed Parkinson disease/dementia with Lewy bodies case-control series.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".