The postnatal window is critical for the development of sex-specific metabolic and gut microbiota outcomes in offspring
Bibliographic record
Abstract
The Developmental Origins of Health and Disease (DOHaD) concept has been proposed to explain the influence of environmental conditions during critical developmental stages on the risk of diseases in adulthood. The aim of this study was to compare the impact of the prenatal vs. postnatal environment on the gut microbiota in dams during the preconception, gestation and lactation periods and their consequences on metabolic outcomes in offspring. Here we used the cross-fostering technique, e.g. the exchange of pups following birth to a foster dam, to decipher the metabolic effects of the intrauterine versus postnatal environmental exposures to a polyphenol-rich cranberry extract (CE). CE administration to high-fat high-sucrose (HFHS)-fed dams improved glucose homeostasis and reduced liver steatosis in association with a shift in the maternal gut microbiota composition. Unexpectedly, we observed that the postnatal environment contributed to metabolic outcomes in female offspring, as revealed by adverse effects on adiposity and glucose metabolism, while no effect was observed in male offspring. In addition to the strong sexual dimorphism, we found a significant influence of the nursing mother on the community structure of the gut microbiota based on α-diversity and β-diversity indices in offspring. Gut microbiota transplantation (GMT) experiments partly reproduced the observed phenotype in female offspring. Our data support the concept that the postnatal environment represents a critical window to influence future sex-dependent metabolic outcomes in offspring that are causally but partly linked with gut microbiome alterations.
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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 | 0.000 | 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 | 0.001 |
| 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".