Diet-induced changes in maternal gut microbiota and metabolomic profiles influence programming of offspring obesity risk in rats
Bibliographic record
Abstract
Maternal obesity and overnutrition during pregnancy and lactation can program an increased risk of obesity in offspring. In this context, improving maternal metabolism may help reduce the intergenerational transmission of obesity. Here we show that, in Sprague-Dawley rats, selectively altering obese maternal gut microbial composition with prebiotic treatment reduces maternal energy intake, decreases gestational weight gain, and prevents increased adiposity in dams and their offspring. Maternal serum metabolomics analysis, along with satiety hormone and gut microbiota analysis, identified maternal metabolic signatures that could be implicated in programming offspring obesity risk and highlighted the potential influence of maternal gut microbiota on maternal and offspring metabolism. In particular, the metabolomic signature of insulin resistance in obese rats normalized when dams consumed the prebiotic. In summary, prebiotic intake during pregnancy and lactation improves maternal metabolism in diet-induced obese rats in a manner that attenuates the detrimental nutritional programming of offspring associated with maternal obesity. Overall, these findings contribute to our understanding of the maternal mechanisms influencing the developmental programming of offspring obesity and provide compelling pre-clinical evidence for a potential strategy to improve maternal and offspring metabolic outcomes in human pregnancy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".