High-fat diet intake modulates maternal intestinal adaptations to pregnancy, and results in placental hypoxia and impaired fetal gut development
Bibliographic record
Abstract
Shifts in maternal intestinal microbiota have been implicated in metabolic adaptations to pregnancy. In this study we investigated how high-fat diet intake impacts the maternal gut microbiota, intestinal inflammation and gut barrier integrity, placental inflammation, and fetal intestinal development at E18.5. High-fat diet (HFD) was associated with decreased relative abundancesof SCFA producing genera during pregnancy. These diet-induced shifts paralleled decreased maternal intestinal mRNA levels of SCFA receptor Gpr41 , modestly decreased cecal butyrate, and altered mRNA levels of inflammatory cytokines and immune cell markers in the maternal intestine. Maternal HFD resulted inimpaired gut barrier integrity, with corresponding increases in circulating maternal levels of LPS and TNF.Placentafromhigh-fat fed damsdemonstrated blood vessel immatu-rityand hypoxia, decreased freecarnitine, acylcarnitine derivatives, TMAO, as well as altered mRNA levels of inflammation, autophagy and ER stress markers. HFD exposed fetuses had increased activation of NF-κB and inhibition of the unfolded protein response in the developing intestine. Together, these data suggest that high-fat diet intake prior to and during pregnancy shifts the composition of the maternal gut microbiota and impairs gut barrier integrity, resulting in increased maternal circulating LPS, which may ultimate contribute to changes in placental vasculariza-tion and fetal gut development. Funding information Farncombe Family Digestive Health Research Institute (KMK); Canadian Institute of Health Research (CJB); Canada Research Chairs Program (MGS, DMS); Natural Sciences and Engineering Research Council of Canada, Genome Canada (PBM).
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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) | 0.001 | 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".