<i>F2RL3</i> Methylation as a Biomarker of Current and Lifetime Smoking Exposures
Bibliographic record
Abstract
BACKGROUND: Recent genome-wide DNA methylation studies have found a pronounced difference in methylation of the F2RL3 gene (also known as PAR-4) in blood DNA according to smoking exposure. Knowledge on the variation of F2RL3 methylation by various degrees of smoking exposure is still very sparse. OBJECTIVES: We aimed to assess dose-response relationships of current and lifetime active smoking exposure with F2RL3 methylation. METHODS: In a large population-based study, we quantified blood DNA methylation at F2RL3 for 3,588 participants using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Associations of smoking exposure with methylation intensity were examined by multiple linear regression, controlling for potential confounding factors and paying particular attention to dose-response patterns with respect to current and lifetime smoking exposure as well as time since cessation of smoking. RESULTS: F2RL3 methylation intensity showed a strong association with smoking status (p < 0.0001), which persisted after controlling for potential confounding factors. Clear inverse dose-response relationships with F2RL3 methylation intensity were seen for both current intensity and lifetime pack-years of smoking. Among former smokers, F2RL3 methylation intensity increased gradually from levels close to those of current smokers for recent quitters to levels close to never smokers for long-term (> 20 years) quitters. CONCLUSIONS: F2RL3 methylation is a promising biomarker for both current and long-term past tobacco exposure, and its predictive value for smoking-related diseases warrants further exploration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".