Epigenome‐wide association study of cerebrospinal fluid–based biomarkers of Alzheimer's disease in cognitively normal individuals
Bibliographic record
Abstract
INTRODUCTION: Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are reliable predictors of future AD risk. We investigated whether pre-clinical changes in AD CSF biomarkers are reflected in blood DNA methylation (DNAm) levels in cognitively normal participants. METHODS: We profiled blood-based DNAm with the EPIC array in participants without a diagnosis of cognitive impairment in the Emory Healthy Brain Study (EHBS; N = 495), Alzheimer's Disease Neuroimaging Initiative (N = 122), and Parkinson's Progression Markers Initiative (N = 118) cohorts. Their CSF amyloid beta 42, total tau (t-tau), and phosphorylated tau181 levels were quantified using Elecsys immunoassays. We conducted epigenome-wide association studies to assess associations between DNAm and CSF biomarkers of AD. RESULTS: In EHBS, no loci were Bonferroni significant after adjusting for confounding factors. In the meta-analysis of all three cohorts, DNAm in cg22976567 (LMNA) was significantly associated with higher CSF t-tau levels. DISCUSSION: Our study showed little evidence of an association between differential blood-based DNAm and pre-clinical AD CSF biomarkers. HIGHLIGHTS: We conducted one of the largest (n = 735) blood DNA methylation (DNAm) studies of Alzheimer's disease cerebrospinal fluid (AD CSF) biomarkers. This is the first epigenome-wide association study in cognitively normal participants examining AD CSF biomarkers. Limited associations between blood DNAm and AD CSF biomarkers were identified.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".