Alzheimer's disease pathology explains association between dementia with Lewy bodies and APOE‐ε4/TOMM40 long poly‐T repeat allele variants
Bibliographic record
Abstract
Abstract Introduction The role of TOMM40‐APOE 19q13.3 region variants is well documented in Alzheimer's disease (AD) but remains contentious in dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). Methods We dissected genetic profiles within the TOMM40‐APOE region in 451 individuals from four European brain banks, including DLB and PDD cases with/without neuropathological evidence of AD‐related pathology and healthy controls. Results TOMM 40 ‐L/ APOE ‐ε4 alleles were associated with DLB (OR TOMM40 ‐ L = 3.61; P value = 3.23 × 10 −9 ; OR APOE ‐ε4 = 3.75; P value = 4.90 × 10 −10 ) and earlier age at onset of DLB (HR TOMM40 ‐L = 1.33, P value = .031; HR APOE ‐ε4 = 1.46, P value = .004), but not with PDD. The TOMM40 ‐L/ APOE ‐ε4 effect was most pronounced in DLB individuals with concomitant AD pathology (OR TOMM40 ‐L = 4.40, P value = 1.15 × 10 −6 ; OR APOE ‐ ε 4 = 5.65, P value = 2.97 × 10 −8 ) but was not significant in DLB without AD. Meta‐analyses combining all APOE ‐ε4 data in DLB confirmed our findings (OR DLB = 2.93, P value = 3.78 × 10 −99 ; OR DLB+AD = 5.36, P value = 1.56 × 10 −47 ). Discussion APOE ‐ε4/ TOMM 40 ‐L alleles increase susceptibility and risk of earlier DLB onset, an effect explained by concomitant AD‐related pathology. These findings have important implications in future drug discovery and development efforts in DLB.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".