Effects of Diet on the Microbiome and Serum Metabolome of South Asian Infants at 1 Year
Bibliographic record
Abstract
Abstract Diet is known to affect the gut microbiome and metabolome composition in adults, but this has not been fully explored in infants. Dietary patterns from 1 year-old infants (n=182) from the South Asian Birth Cohort (START) study were compared to gut microbiome alpha and beta diversity and to taxa abundance differences. Diet – serum metabolite associations were identified using multivariate analysis (partial least squares-discriminant analysis, PLS-DA) and univariate analysis (T-Test). Dietary biomarkers identified from START were also examined in a separate cohort of white Caucasian infants ( CHILD Cohort Study , n=82). Lastly, the association of diet with gut microbiome and serum biomarkers, considering maternal, perinatal and infant characteristics was investigated using multivariate forward stepwise regression. A dietary pattern characterized by breastfeeding, supplemented by formula and dairy was the strongest predictor of the gut microbiome that also differentiated the serum metabolome of infants. The formula and dairy dietary pattern was associated with a panel of circulating metabolites in both cohorts, including: S -methylcysteine, branched-chain/aromatic amino acids, lysine, dimethylglycine, and methionine. Breastfeeding status, the prominent feature of the dietary pattern, was also associated with a sub-set of serum metabolites in both cohorts. In START, this diet pattern was associated with the metabolites tryptophan betaine, 2-hydroxybutyric acid, tyrosine, phenylalanine, and trimethyl- N -oxide. In the CHILD Cohort Study(CHILD), breastfeeding status was associated with the metabolites aminooctanoic acid, 3-hydroxybutyric acid, and methyl-proline. The results of our study suggest that breastfeeding has the largest effect on the composition of the gut microbiome and the serum metabolome at 1 year, even when solid food diet and other covariates are considered.
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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 | 0.000 | 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".