Cross-tissue meta-analysis of blood and brain epigenome-wide association studies in Alzheimer’s disease
Bibliographic record
Abstract
ABSTRACT We performed a meta-analysis of two large independent blood-based Alzheimer’s disease (AD) epigenome-wide association studies, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that were significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four additional methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. Our integrative analysis revealed expressions levels of 13 genes and 10 pathways were significantly associated with the AD-associated methylation differences in both brain and blood, many are involved in the immune responses in AD, such as the CD79A, LY86, SP100, CD163, CD200 , and MS4A1 genes and the neutrophil degranulation, antigen processing and presentation, interferon signaling pathways. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model included age, sex, cell types and methylation risk score based on prioritized CpGs from cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P- value = 2.78 × 10 −5 ). Our study provides a valuable resource for future mechanistic and biomarker studies in AD.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".