Differential effects of severe vs mild <i>GBA</i> mutations on Parkinson disease
Bibliographic record
Abstract
OBJECTIVE: To better define the genotype-phenotype correlations between the type of GBA (glucosidase, beta, acid) mutation, severe or mild, and the risk and age at onset (AAO), and potential mechanism of Parkinson disease (PD). METHODS: We analyzed 1,000 patients of Ashkenazi-Jewish descent with PD for 7 founder GBA mutations, and conducted a meta-analysis of risk and AAO according to GBA genotype (severe or mild mutation). The meta-analysis included 11,453 patients with PD and 14,565 controls from worldwide populations. The statistical analysis was done with and without continuity correction (constant or empirical), considering biases that could potentially affect the results. RESULTS: Among Ashkenazi-Jewish patients with PD, the odds ratios for PD were 2.2 and 10.3 for mild and severe GBA mutation carriers, respectively. The observed frequency of severe GBA mutation carriers among patients with PD was more than 4-fold than expected (4.4% vs 0.9%, respectively, p < 0.0001, Fisher exact test). In the different models of the meta-analysis, the odds ratios for PD ranged between 2.84 and 4.94 for mild GBA mutation carriers and 9.92 and 21.29 for severe GBA mutation carriers (p < 1 × 10(-6) for all analyses). Pooled analysis demonstrated AAO of 53.1 (±11.2) and 58.1 (±10.6) years for severe and mild GBA mutation carriers, respectively (p = 4.3 × 10(-5)). CONCLUSIONS: These data demonstrate that mild and severe heterozygous GBA mutations differentially affect the risk and the AAO of PD. Our results have important implications for genetic counseling and clinical follow-up.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".