<i>GBA</i> variants in REM sleep behavior disorder: a multicenter study
Bibliographic record
Abstract
Abstract Objective To study the role of GBA variants in the risk for isolated rapid-eye-movement (REM)-sleep behavior disorder (iRBD) and conversion to overt neurodegeneration. Methods A total of 4,147 individuals were included: 1,061 iRBD patients and 3,086 controls. GBA was fully sequenced using molecular inversion probes and Sanger sequencing. We analyzed the effects of GBA variants on the risk for iRBD, age at onset (AAO) and conversion rates. Results GBA variants were found in 9.5% of iRBD patients compared to 4.1% in controls (odds ratio [OR]=2.45, 95% CI=1.87–3.22, p =1×10 −10 ). The estimated OR for mild p.N370S variant carriers was 3.69, 95% CI=1.90–7.14, p =3.5×10 −5 , while for severe variant carriers it was 17.55, 95% CI=2.11–145.9, p =0.0015. Carriers of severe GBA variants had an average AAO of 52.8 years, 7-8 years earlier than those with mild variants or non-carriers ( p =0.029). Of the GBA variant carriers with available data, 52.5% had converted, compared to 35.6% in non-carriers ( p =0.011), with a trend for faster conversion among severe GBA variant carriers. However, the results on AAO and conversion were based on small numbers and should be taken with caution. Conclusions GBA variants robustly and differentially increase the risk of iRBD. The rate of conversion to neurodegeneration is also increased and may be faster among severe GBA variant carriers, although confirmation will be required in larger samples. Screening for RBD in healthy carriers of GBA variants should be studied as a potential way to identify GBA variant carriers who will develop a synucleinopathy in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".