Protective association of <i>HLA‐DRB1</i>*04 subtypes in neurodegenerative diseases implicates acetylated tau PHF6 sequences
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic Lateral Sclerosis (ALS) and other neurodegenerative diseases are responsible for considerable morbidity and mortality. With incidence rising with aging, these also represent a growing societal challenge. Pathophysiology involves accumulation of tau (neurofibrillary tangles) and Amyloid‐β‐rich (amyloid plaques) aggregates in AD, α‐synuclein‐rich aggregates (Lewy bodies) in PD and TDP‐43 aggregates in ALS, although co‐presence of these aggregates may occur. Consensus is also growing that tau may also play a key role in PD and ALS. Method Using genome‐wide association data, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with PD or Alzheimer’s AD disease versus controls across ancestry groups. Result A shared genetic association was observed across diseases at rs601945 (PD: odds ratio (OR)=0.84; 95% confidence interval, [0.80; 0.88]; p=2.2x10‐13; AD: OR=0.91[0.89; 0.93]; p=1.8x10‐22), and with a protective HLA association recently reported in ALS. Hierarchical protective effects of HLA‐DRB1*04 subtypes best accounted for the association, strongest with HLA‐DRB1*04:04 and HLA‐DRB1*04:07, intermediary with HLA‐DRB1*04:01 and HLA‐DRB1*04:03, and absent for HLA‐DRB1*04:05. The same signal was associated with decreased neurofibrillary tangles (but not neuritic plaque density) in postmortem brains and was more strongly associated with tau levels than Aβ42 levels in the cerebrospinal fluid. Finally, protective HLA‐DRB1*04 subtypes strongly bound the aggregation‐prone tau PHF6 sequence, but only when acetylated at K311, a modification central to aggregation. Conclusion An HLA‐DRB1*04‐mediated adaptive immune response, potentially against tau, decreases PD, AD and ALS risk, offering the possibility of new therapeutic avenues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".