Associations with early-life socio-economic position in adult DNA methylation
Bibliographic record
Abstract
BACKGROUND: Disadvantaged socio-economic position (SEP) in childhood is associated with increased adult mortality and morbidity. We aimed to establish whether childhood SEP was associated with differential methylation of adult DNA. METHODS: Forty adult males from the 1958 British Birth Cohort Study were selected from SEP extremes in both early childhood and mid-adulthood. We performed genome-wide methylation analysis on blood DNA taken at 45 years using MeDIP (methylated DNA immunoprecipitation). We mapped in triplicate the methylation state of promoters of approximately 20,000 genes and 400 microRNAs. Probe methylation scores were averaged across triplicates and differential methylation between groups of individuals was determined. Differentially methylated promoter sites of selected genes were validated using pyrosequencing of bisulfite-converted DNA. RESULTS: Variably methylated probes (9112 from n = 223,359 on the microarray) corresponded to 6176 gene promoters with at least one variable probe. Unsupervised hierarchical clustering of probes obtained from the 500 most variable promoters revealed a cluster enriched with high SEP individuals confirming that SEP differences contribute to overall epigenetic variation. Methylation levels for 1252 gene promoters were associated with childhood SEP vs 545 promoters for adulthood SEP. Functionally, associations with childhood SEP appear in promoters of genes enriched in key cell signalling pathways. The differentially methylated promoters associated with SEP cluster in megabase-sized regions of the genome. CONCLUSIONS: Adult blood DNA methylation profiles show more associations with childhood SEP than adult SEP. Organization of these associations across the genome suggests a well-defined epigenetic pattern linked to early socio-economic environment.
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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) | 0.002 | 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".