<i>HLA</i> in isolated REM sleep behavior disorder and Lewy body dementia
Bibliographic record
Abstract
Abstract Background and Objectives Isolated/idiopathic REM sleep behavior disorder (iRBD) and Lewy body dementia (LBD) are synucleinopathies that have partial genetic overlap with Parkinson’s disease (PD). Previous studies have shown that neuroinflammation plays a substantial role in these disorders. In PD, specific residues of the human leukocyte antigen ( HLA ) were suggested to be associated with a protective effect. This study examined whether the HLA locus plays a similar role in iRBD, LBD and PD. Methods We performed HLA imputation on iRBD genotyping data (1,072 patients and 9,505 controls) and LBD whole-genome sequencing (2,604 patients and 4,032 controls) using the multi-ethnic HLA reference panel v2 from the Michigan Imputation Server. Using logistic regression, we tested the association of HLA alleles, amino acids and haplotypes with disease susceptibility. We included age, sex and the top 10 principal components as covariates. We also performed an omnibus test to examine which HLA residue positions explain the most variance. Results In iRBD, HLA-DRB1 *11:01 was the only allele passing FDR correction (OR=1.57, 95% CI=1.27-1.93, p =2.70e-05). We also discovered associations between iRBD and HLA-DRB1 70D (OR=1.26, 95%CI=1.12-1.41, p =8.76e-05), 70Q (OR=0.81, 95% CI=0.72-0.91, p =3.65e-04) and 71R (OR=1.21, 95% CI=1.08-1.35, p =1.35e-03). In HLA-DRB1 , position 71 ( p omnibus =0.00102) and 70 ( p omnibus =0.00125) were associated with iRBD. We found no association in LBD. Discussion This study identified an association between HLA-DRB1 11:01 and iRBD, distinct from the previously reported association in PD. Therefore, the HLA locus may play different roles across synucleinopathies. Additional studies are required better to understand HLA’s role in iRBD and LBD.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".