Genome‐wide analysis of DNA methylation in relation to socioeconomic status during development and early adulthood
Bibliographic record
Abstract
OBJECTIVES: Socioeconomic status (SES) is a powerful determinant of health, but the underlying biological mechanisms are poorly understood. This study investigates whether levels of DNA methylation at CpG sites across the genome are associated with SES in a cohort of young adults in the Philippines. METHODS: DNA methylation was assayed with the Illumina HumanMethylation450 Bead Chip, in leukocytes from 489 participants in the Cebu Longitudinal Health and Nutrition Survey (mean age = 20.9 years). SES was measured in infancy/childhood and adulthood, and was based on composite measures of income, assets, and education. Genome-wide analysis of variable probes identified CpG sites significantly associated with SES after adjustment for multiple comparisons. Functional enrichment analysis was used to identify biological pathways associated with these sites. RESULTS: A total of 2,546 CpG sites, across 1,537 annotated genes, were differentially methylated in association with SES. In comparison with high SES, low SES was associated with increased methylation at 1,777 sites, and decreased methylation at 769 sites. Functional enrichment analysis identified over-representation of biological pathways related to immune function, skeletal development, and development of the nervous system. CONCLUSIONS: Socioeconomic status predicts DNA methylation at a large number of CpG sites across the genome. The scope of these associations is commensurate with the wide range of biological systems and health outcomes that are shaped by SES, and these findings suggest that DNA methylation may play an important role.
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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 | 0.001 |
| 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".