DNA Methylation of the Aryl Hydrocarbon Receptor Repressor Associations With Cigarette Smoking and Subclinical Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: Tobacco smoke contains numerous agonists of the aryl hydrocarbon receptor (AhR) pathway, and activation of the AhR pathway was shown to promote atherosclerosis in mice. Intriguingly, cigarette smoking is most strongly and robustly associated with DNA modifications to an AhR pathway gene, the AhR repressor (AHRR). We hypothesized that altered AHRR methylation in monocytes, a cell type sensitive to cigarette smoking and involved in atherogenesis, may be a part of the biological link between cigarette smoking and atherosclerosis. METHODS AND RESULTS: DNA methylation profiles of AHRR in monocytes (542 CpG sites ± 150 kb of AHRR, using Illumina 450K array) were integrated with smoking habits and ultrasound-measured carotid plaque scores from 1256 participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Methylation of cg05575921 significantly associated (P=6.1 × 10(-134)) with smoking status (current versus never). Novel associations between cg05575921 methylation and carotid plaque scores (P=3.1 × 10(-10)) were identified, which remained significant in current and former smokers even after adjusting for self-reported smoking habits, urinary cotinine, and well-known cardiovascular disease risk factors. This association replicated in an independent cohort using hepatic DNA (n=141). Functionally, cg05575921 was located in a predicted gene expression regulatory element (enhancer) and had methylation correlated with AHRR mRNA profiles (P=1.4 × 10(-17)) obtained from RNA sequencing conducted on a subset (n=373) of the samples. CONCLUSIONS: These findings suggest that AHRR methylation may be functionally related to AHRR expression in monocytes and represents a potential biomarker of subclinical atherosclerosis in smokers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".