Early Exposures, Body Composition and the Gut Microbiome
Bibliographic record
Abstract
The gut microbiome is essential for immune system development and infant metabolic health. The first three years of life are critical for colonization and maturation of the infant gut microbiome. Our study involves 245 full-term, low-risk mother-infant pairs recruited by midwifery practices at McMaster University. It spanned from birth to three years, with data collected from birth records, study questionnaires, anthropometric measures, and stool samples processed via 16S rRNA sequencing. We investigated associations between the infant gut microbiome and [1] early-life exposures such as delivery place, maternal parity, and intrapartum antibiotic prophylaxis and [2] growth (BMIz) and body composition (FMI and LMI). We characterized the following aspects of the infant gut microbiome longitudinally over the first three years of life: (1) alpha diversity, within sample diversity, (2) beta diversity, among sample diversity, and (3) bacterial taxonomy. Our study reveals significant associations between early-life exposures and gut microbiome diversity during infancy. Infants delivered at home show elevated levels of Bacteroides at ten days postpartum. Parity is the strongest predictor of beta diversity variation, with infants showing higher levels of Bifidobacterium in their first year. Infants exposed to intrapartum antibiotics showed reduced Bifidobacterium and increased Streptococcus, which persisted for up to three years. Growth and body composition show minimal associations with gut microbiome diversity. BMIz, FMI and LMI are inversely related to alpha diversity at five months of age, but no associations existed at other timepoints. FMI and BMIz are associated with beta diversity at 12 weeks and 5 months, respectively. While the microbiota correlates with growth and body composition measures at five months, its predictive utility diminishes by three years, emphasizing the lasting association of early-life exposures on gut microbiome variation. Future work should focus on elucidating the underlying microbial mechanisms and developing microbiome-focused interventions aimed at improving infant health.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".