The infants’ gut microbiome: setting the stage for the early onset of obesity
Bibliographic record
Abstract
In the past three decades, dietary and lifestyle changes worldwide have resulted in a global increase in the prevalence of obesity in both adults and children. Known to be highly influenced by genetic, environmental and lifestyle factors, obesity is characterized by a low-grade chronic inflammation that contributes to the development of other metabolic diseases such as diabetes and cardiovascular disease. Recently, the gut microbiome has been added as a cause/contributor to the development of obesity. As differences in the microbiome between obese and normoweight individuals have been observed, we set out to determine whether infants harbor an obesogenic microbiome early on and whether the pre-pregnancy status of the mother (obese or normoweight) is correlated to their infant’s microbiome composition. Using shotgun sequencing, we analyzed stool samples throughout the first year of life from infants born to obese (n = 23 participants, m = 104 samples) and normoweight (n = 23 participants, m = 99 samples) mothers. We found that the infants’ microbiome diversity at taxonomic and functional levels was significantly influenced by time (ANOVA p < 0.001) but not by the mother’s pre-pregnancy status. Overall, no deterministic succession of taxa or functions was observed. However, infants born to obese mothers were found to have a significantly higher Bacillota/Bacteroidota ratio (p = 0.02) at six months, were significantly depleted from six months old of the well-established obesity biomarkers Akkermansia municiphila and Faecalibacterium prausnitzii (p < 0.01), and were at one week old, significantly enriched in pathways such as the UDP-N-acetyl-D-glucosamine biosynthesis II (p = 0.02) involved in leptin production, suggesting perhaps that there may exist some underlying mechanisms that dictate the development of an obesogenic microbiota early on.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".