Early-Life Adversity and Epigenetic Aging : Findings from a 17-Year Longitudinal Study
Bibliographic record
Abstract
Youth exposed to early-life adversity (ELA) are at greater risk for poorer physical and mental health outcomes in adolescence and adulthood. Although the biological mechanisms underlying these associations remain elusive, DNA methylation (DNAm) has emerged as a potential pathway. DNAm-based measures of epigenetic age have been associated with ELA, indicating accelerated aging. According to the stress sensitization hypothesis, prenatal adversity may further heighten sensitivity to subsequent stressors in childhood and adolescence. This study examined the associations between ELA and six epigenetic aging measures, considering both the timing of adversity and the participant’s sex. Data were drawn from the Quebec Longitudinal Study of Child Development, with two cumulative indices of ELA derived from prospectively collected data: the Perinatal Adversity and the Child and Adolescent Adversity indices. Higher Perinatal Adversity scores were associated with accelerated DunedinPACE scores. No significant associations were found between ELA and the other epigenetic clocks, nor did we find support for the stress sensitization hypothesis—though a sex-specific trend emerged among girls. The findings suggest that DunedinPACE may be more sensitive to variations in ELA than other clocks. Future research should systematically investigate sex-dimorphic associations between ELA and epigenetic aging, with particular attention to the impact of perinatal adversity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".