Impact of Maternal Obesity on Offspring microRNA Profiles: A Systematic Review of Experimental Models
Bibliographic record
Abstract
BACKGROUND: Maternal malnutrition, including obesity, can have long-term adverse effects on offspring health, potentially mediated by epigenetic mechanisms such as microRNAs (miRNAs). miRNAs play a critical role in regulating gene expression and may contribute to the developmental programming of offspring outcomes. This systematic review aimed to explore the association between maternal obesity during pregnancy and miRNA alterations in offspring, focusing on evidence from animal models. METHODS: A comprehensive search of Embase, PubMed, and Scopus databases identified 811 articles, of which 15 met the inclusion criteria. RESULTS: Our analysis highlighted a great variability of miRNAs and target tissues studied. Across the reviewed studies, 35 miRNAs were identified as differentially expressed in offspring exposed to maternal high-fat diets during pregnancy. These alterations were predominantly observed in the brain, liver, cardiac tissue, and adipose tissue, affecting processes related to insulin signaling, development and growth, immune response, and lipid metabolism. CONCLUSIONS: The miRNA alterations observed across studies support the hypothesis that a maternal high-fat diet may induce a programmed epigenetic signature in 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".