Fetal programming of overweight through the microbiome: boys are disproportionately affected
Bibliographic record
Abstract
Maternal and childhood obesity in pregnancy are worrisome public health issues facing our world today. New gene sequencing methods have advanced our knowledge of the disruptive effect of birth interventions and postnatal exposures on the maturation of gut microbiota and immunity during infancy. Yet, little is known about the impact of maternal pregnancy overweight on gut microbes and related processes, and how this may affect overweight risk in offspring. To address this gap in knowledge, we surveyed human studies for evidence in children, infants and pregnant women to piece together the limited literature and generate hypotheses for future investigation. From this literature, we learned that higher Lactobacillus yet lower Bacteroides spp. colonization of gut microbiota within 3 months of birth predicted risk for infant and child overweight. The abundance of bifidobacteria and staphylococci also appeared to play a role in the association with overweight, as did infant fecal immunoglobulin A levels, glycoproteins of the gut immune system that are acquired from breast milk and produced by the infant. We proposed that pregnancy overweight influences the compositional structure of gut microbiota in infants through vertical transfer of microbiota and/or their metabolites during pregnancy, delivery and breastfeeding. Finally, we brought forward emerging evidence on sex dimorphism, as well as ethnic and geographic variation, in reported associations between maternal overweight-induced gut microbiota dysbiosis and overweight risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".