Association between gestational diabetes mellitus, maternal health and diet, and gut microbiota in mother-infant dyads
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) increasingly affects women and predisposes both mothers and their infants to short- and long-term health consequences. Emerging research links GDM to maternal gut microbiota dysbiosis. However, the impact of GDM on the infant gut microbiota remains unclear. This cross-sectional study aims to explore potential associations between GDM and the gut microbiota in mothers and their infants, as well as correlations between maternal diet, cardiometabolic profile, and gut microbiota composition. METHODS: Gut microbiota taxonomic composition was characterized by 16S rRNA gene sequencing on fecal samples collected at 2 months postpartum from 28 mothers, including 17 with (GDM+) and 11 without (GDM-) GDM, as well as 30 infants, 17 GDM + and 13 GDM-. Variations in overall composition and specific taxa between GDM + and GDM- were assessed. Correlations between maternal cardiometabolic profile, dietary intakes, and taxa were performed. RESULTS: GDM was associated with the overall composition of gut microbiota between GDM + and GDM- in the maternal group, but not in infants. No statistically significant difference in alpha diversity between groups was found in either mothers or infants. However, 14 taxa showed significantly different abundance between GDM + and GDM- mothers, and 4 taxa differed in infants. Specific taxa at the family rank were correlated with maternal dietary and cardiometabolic variables in both mothers and infants. CONCLUSIONS: GDM exposition was associated with gut microbiota composition in both mothers and infants at two months postpartum. This study enhances our understanding of how maternal health could be linked with the gut microbiota of mothers and their infants. TRIAL REGISTRATION: NCT02872402 (2016-08-04, https://clinicaltrials.gov/study/NCT02872402?term=NCT02872402&rank=1 ) and NCT04263675 (2020-02-07, https://clinicaltrials.gov/study/NCT04263675?term=NCT04263675&rank=1 ).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".