Using epigenetic clocks in environmental epigenetics: recommendations for estimating biological aging in perinatal and pediatric samples
Bibliographic record
Abstract
Following a variety of early environmental experiences and exposures, epigenetic modifications such as DNA methylation are proposed as candidate mechanisms that contribute to health and disease across the lifespan. Epigenetic clocks are a type of aging biomarker that can offer insight into age-related changes associated with early environmental exposures. This review provides a brief overview of epigenetic clocks that are readily available for use with perinatal and/or pediatric samples, as well as highlights some recent research that has studied the associations between early environmental chemical exposures and epigenetic aging rates. Broadly, the easily accessible epigenetic clocks can be categorized as chronological age estimators and gestational age estimators, but some clocks were developed for use with specific tissues and/or age groups. Previous environmental epidemiology studies have shown that early environmental exposures such as air pollutants and endocrine-disrupting chemicals are associated with altered epigenetic aging rates in perinatal and pediatric populations. However, more research is needed that examines how factors such as exposure level, timing of exposure, and sex may affect the direction and magnitude of associations. This review concludes with some recommendations and future directions for the use of epigenetic clocks in environmental epigenetics. Overall, epigenetic clocks are promising, non-causal biomarkers of early exposures that can be examined in relation to environmental chemicals, health and disease outcomes, and as biological mediators. Future research could help determine whether these clocks hold promise as informative biomarkers that reflect developmental epigenotoxicity following early exposure to environmental chemicals.
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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 | 0.069 | 0.151 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".