Early Life Exposure to Human Milk Oligosaccharides Reduces Allergic Response in a Murine Asthma Model
Bibliographic record
Abstract
Background. Studies suggest that early-life gut microbiota composition and intestinal short-chain fatty acids (SCFAs) are linked to future asthma susceptibility. Furthermore, infancy offers a critical time window to modulate the microbiota and associated metabolites through diet-microbe interactions to promote infant health. Human milk oligosaccharides (HMOs), nondigestible carbohydrates abundant in breast milk, are prebiotics selectively metabolized by gut microbiota that consequently modify microbiome composition and SCFA production. Methods. Using a house dust mite mouse model of allergy, we investigated the impacts of early oral treatment of pups with biologically relevant doses of 2′-fucosyllactose (2′-FL) and 6′-sialyllactose (6′-SL), two of the most abundant HMOs in human milk, in amelioration of allergic airway disease severity. Results. We found that administration of 2′-FL and 6′-SL during early life reduced lung histopathology scores, circulating IgE, cytokine levels, and inflammatory cell infiltration, all hallmark symptoms of allergic asthma. HMO supplementation also increased the relative abundance of intestinal Bacteroidetes and Clostridia, known SCFA producers within the gut. Indeed, we detected increased SCFA concentrations in both the intestine and blood of adult mice who received HMOs prior to weaning. Conclusion. We propose a model in which orally administered HMOs delivered during early life shift the microbiota toward increased production of SCFAs, which dampens the allergic immune responses behind allergy and asthma. Overall, these data suggest the potential for HMO supplementation to protect infants against asthma development later in life, with possible benefits against additional atopic diseases such as eczema and food allergies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| 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".