Immune epigenetic age in pregnancy and 1 year after birth: Associations with weight change
Bibliographic record
Abstract
PROBLEM: Epigenetic age indices are markers of biological aging determined from DNA methylation patterns. Accelerated epigenetic age predicts morbidity and mortality. Women tend to demonstrate slower blood epigenetic aging compared to men, possibly due to female-specific hormones and reproductive milestones. Pregnancy and the post-partum period are critical reproductive periods that have not been studied yet with respect to epigenetic aging. The purpose of this paper was to examine whether pregnancy itself and an important pregnancy-related variable, changes in body mass index (BMI) between pregnancy and the post-partum period, are associated with epigenetic aging. METHOD OF STUDY: A pilot sample of 35 women was recruited as part of the Healthy Babies Before Birth (HB3) project. Whole blood samples were collected at mid-pregnancy and 1 year post-partum. DNA methylation at both time points was assayed using Infinium 450K and EPIC chips. Epigenetic age indices were calculated using an online calculator. RESULTS: Paired-sample t-tests were used to test differences in epigenetic age indices from pregnancy to 1 year after birth. Over this critical time span, women became younger with respect to phenotypic epigenetic age, GrimAge, DNAm PAI-1, and epigenetic age indices linked to aging-related shifts in immune cell populations, known as extrinsic epigenetic age. Post-partum BMI retention, but not prenatal BMI increases, predicted accelerated epigenetic aging. CONCLUSION: Women appear to become younger from pregnancy to the post-partum period based on specific epigenetic age indices. Further, BMI at 1 year after birth that reflects weight retention predicted greater epigenetic aging during this period.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".