Social and physical environments early in development predict DNA methylation of inflammatory genes in young adulthood
Bibliographic record
Abstract
Chronic inflammation contributes to a wide range of human diseases, and environments in infancy and childhood are important determinants of inflammatory phenotypes. The underlying biological mechanisms connecting early environments with the regulation of inflammation in adulthood are not known, but epigenetic processes are plausible candidates. We tested the hypothesis that patterns of DNA methylation (DNAm) in inflammatory genes in young adulthood would be predicted by early life nutritional, microbial, and psychosocial exposures previously associated with levels of inflammation. Data come from a population-based longitudinal birth cohort study in metropolitan Cebu, the Philippines, and DNAm was characterized in whole blood samples from 494 participants (age 20-22 y). Analyses focused on probes in 114 target genes involved in the regulation of inflammation, and we identified 10 sites across nine genes where the level of DNAm was significantly predicted by the following variables: household socioeconomic status in childhood, extended absence of a parent in childhood, exposure to animal feces in infancy, birth in the dry season, or duration of exclusive breastfeeding. To evaluate the biological significance of these sites, we tested for associations with a panel of inflammatory biomarkers measured in plasma obtained at the same age as DNAm assessment. Three sites predicted elevated inflammation, and one site predicted lower inflammation, consistent with the interpretation that levels of DNAm at these sites are functionally relevant. This pattern of results points toward DNAm as a potentially important biological mechanism through which developmental environments shape inflammatory phenotypes across the life course.
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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 | 0.000 | 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) | 0.001 | 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".