Brain DNA Methylation Patterns in <i>CLDN5</i> Associated With Cognitive Decline
Bibliographic record
Abstract
Abstract Objective Cognitive decline is a hallmark of dementia; however, the brain epigenetic signature of cognitive decline is unclear. We investigated the associations between brain tissue-based DNA methylation and cognitive trajectory. Methods We performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex (dPFC). To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits (GAMuT) test to analyze the five measured cognitive domains simultaneously. Results We found an epigenome-wide association for differential methylation of sites in the Claudin-5 ( CLDN5 ) locus and cognitive trajectory (p-value x 9.96 × 10 -7 ), which was robust to adjustment for cell type proportions (p-value = 8.52 x 10 -7 ). This association was primarily driven by association with declines in episodic (p-value = 4.65 x 10 -6 ) and working memory (p-value = 2.54 x 10 -7 ). This association between methylation in CLDN5 and cognitive decline was independent of beta-amyloid and neurofibrillary tangle pathology and present in participants with low levels of neuropathology. In addition, only 13-31% of the association between methylation and cognitive decline was mediated through levels of neuropathology, whereas the major part of the association was independent of it. Interpretation We identified methylation in CLDN5 as new epigenetic factor associated with cognitive trajectory. Higher levels of methylation in CLDN5 were associated with faster cognitive decline implicating the blood brain barrier in maintenance of cognitive trajectory.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".