The Effects of Maternal and Postnatal Dietary Methyl Nutrients on Epigenetic Changes that Lead to Non-Communicable Diseases in Adulthood
Bibliographic record
Abstract
The risk for non-communicable diseases in adulthood can be programmed by early nutrition. This programming is mediated by changes in expression of key genes in various metabolic pathways during development, which persist into adulthood. These developmental modifications of genes are due to epigenetic alterations in DNA methylation patterns. Recent studies have demonstrated that DNA methylation can be affected by maternal or early postnatal diets. Because methyl groups for methylation reactions come from methionine cycle nutrients (i.e., methionine, choline, betaine, folate), deficiency or supplementation of these methyl nutrients can directly change epigenetic regulation of genes permanently. Although many studies have described the early programming of adult diseases by maternal and infant nutrition, this review discusses studies that have associated early dietary methyl nutrient manipulation with direct effects on epigenetic patterns that could lead to chronic diseases in adulthood. The maternal supply of methyl nutrients during gestation and lactation can alter epigenetics, but programming effects vary depending on the timing of dietary intervention, the type of methyl nutrient manipulated, and the tissue responsible for the phenotype. Moreover, the postnatal manipulation of methyl nutrients can program epigenetics, but more research is needed on whether this approach can rescue maternally programmed offspring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".