Distinct maternal DNA methylation associations with gestational age at early and late-mid term pregnancy in a low- and middle-income country: evaluation of biological, genetic, and psychosocial contributors
Bibliographic record
Abstract
Abstract Mothers undergo physiological and molecular changes over the course of gestation. These modifications “get under the skin” and may be reflected in the maternal epigenome through processes such as DNA methylation. Such an epigenetic mark may offer insights into maternal responses to prenatal influences and biological cues from the developing fetus, thereby functioning as an indirect indicator of the conditions the fetus experiences in utero. We measured whole blood DNA methylation using the MethylationEPIC BeadChip Infinium microarray v1.0 in 22 pregnant women from Pakistan, a low- and middle-income country (LMIC), at two timepoints during their term pregnancies (early: 10–19 weeks and late-mid: 22–29 weeks). We used DNA methylation profiles to predict immune cell proportions and tested differences in these proportions and DNA methylation patterns between the two timepoints. Further, we evaluated DNA methylation associations with gestational age at each timepoint and examined the contribution of genetic, psychosocial, and biological factors. Our analyses documented changes in immune cell proportions and DNA methylation profiles over the course of gestation, albeit in a small percentage of the measured DNA methylome. We also observed timepoint-specific DNA methylation associations with gestational age, predominantly at early pregnancy, with predicted interleukin-6 level and socioeconomic status contributing to a few of these associations. On comparing to three external cohorts from different sociocultural contexts, we also noted these signatures to be unique to LMIC settings. Overall, these changes measured in term pregnancies may be used to assess both fluctuations in pregnancy and birth outcomes, particular in women from LMIC settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".