DNA methylation mediates the association between breastfeeding and early-life growth trajectories
Bibliographic record
Abstract
BACKGROUND: The role of breastfeeding in modulating epigenetic factors has been suggested as a possible mechanism conferring its benefits on child development but it lacks evidence. Using extensive DNA methylation data from the ALSPAC child cohort, we characterized the genome-wide landscape of DNA methylation variations associated with the duration of exclusive breastfeeding and assessed whether these variations mediate the association between exclusive breastfeeding and BMI over different epochs of child growth. RESULTS: Exclusive breastfeeding elicits more substantial DNA methylation variations during infancy than at other periods of child growth. At the genome-wide level, 13 CpG sites in girls (miR-21, SNAPC3, ATP6V0A1, DHX15/PPARGC1A, LINC00398/ALOX5AP, FAM238C, NATP/NAT2, CUX1, TRAPPC9, OSBPL1A, ZNF185, FAM84A, PDPK1) and 2 CpG sites in boys (IL16 and NREP), mediate the association between exclusive breastfeeding and longitudinal BMI. We found enrichment of CpG sites located within miRNAs and key pathways (AMPK signaling pathway, insulin signaling pathway, endocytosis). Overall DNA methylation variation corresponding to 3 to 5 months of exclusive breastfeeding was associated with slower BMI growth the first 6 years of life compared to no breastfeeding and in a dose-response manner with exclusive breastfeeding duration. CONCLUSIONS: Our study confirmed the early postnatal period as a critical developmental period associated with substantial DNA methylation variations, which in turn could mitigate the development of overweight and obesity from infancy to early childhood. Since an accelerated growth during these developmental periods has been linked to the development of sustained obesity later in life, exclusive breastfeeding could have a major role in preventing the risks of overweight/obesity and children and adults through DNA methylation mechanisms occurring early in life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".