Bifidobacterium deficit in United States infants drives prevalent gut dysbiosis
Bibliographic record
Abstract
The composition of the infant gut microbiome is critical to immune development and noncommunicable disease (NCD) trajectory. However, a comprehensive evaluation of the infant gut microbiome in the United States is lacking. The My Baby Biome study, designed to address this knowledge gap, evaluated the gut microbiomes of 412 infants (representative of U.S. demographic diversity) using metagenomics and metabolomics. Regardless of birth mode and/or feeding method, widespread Bifidobacterium deficit was observed, with approximately 25% of U.S. infants lacking detectable Bifidobacterium. Bifidobacterium-dominant microbiomes exhibit distinct features when compared to microbiomes with other dominant microbial compositions including reduced antimicrobial resistance and virulence factor genes, altered carbohydrate utilization pathways, and altered metabolic signatures. In C-section birth infants, Bifidobacterium tended to be replaced in the human milk oligosaccharide utilization niche with potentially pathogenic species. Longitudinal health outcomes from these infants suggest that the disappearance of key Bifidobacterium may contribute to the development of atopy. The My Baby Biome Study demonstrates that three gut microbiome community clusters exist in infants in the United States. These compositions vary on the prevalence and abundance of Bifidobacterium and impact risk for the development of atopic disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".