Analysis of socioeconomic disadvantage and pace of aging measured in saliva DNA methylation of children and adolescents
Bibliographic record
Abstract
Abstract Children who grow up in socioeconomically disadvantaged families face increased burden of disease and disability as they mature into adulthood. One hypothesized mechanism for this increased burden is that early-life disadvantage and its associated psychological stress accelerate biological processes of aging, increasing vulnerability to subsequent disease. In order to evaluate this hypothesis and the potential impact of preventive interventions, measures to quantify the early acceleration of biological aging in childhood are needed. Here, we evaluated a novel DNA-methylation measure of the pace of aging, DunedinPoAm, and compared DunedinPoAm results with results for several published epigenetic clocks. Data on saliva DNA-methylation and socioeconomic circumstances were collected from N = 600 children and adolescents aged 8- to 18-years-old (48% female) participating in the Texas Twin Project. Participants living in more disadvantaged families and neighborhoods exhibited faster pace of aging ( r = 0.18, p = 0.001 for both). Latinx-identifying children exhibited faster DunedinPoAm compared to both White- and Latinx-White-identifying children, consistent with higher levels of disadvantage in this group. Children with more advanced pubertal development and those with had higher body-mass index also exhibited faster DunedinPoAm, but these covariates did not account for the observed socioeconomic gradient in methylation pace of aging. In contrast to findings for DunedinPoAm, we did not detect associations of socioeconomic disadvantage with five published epigenetic clocks. Findings suggest that DNA-methylation pace-of-aging measures may prove more sensitive to health damaging effects of adversity, particularly when measurements are taken early in the life course, before substantial aging has occurred.
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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 | 0.001 | 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) | 0.001 | 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".