Thinking bigger: How early‐life environmental exposures shape the gut microbiome and influence the development of asthma and allergic disease
Bibliographic record
Abstract
Imbalance, or dysbiosis, of the gut microbiome of infants has been linked to an increased risk of asthma and allergic diseases. Most studies to date have provided a wealth of data showing correlations between early-life risk factors for disease and changes in the structure of the gut microbiome that disrupt normal immunoregulation. These studies have typically focused on one specific risk factor, such as mode of delivery or early-life antibiotic use. Such "micro-level" exposures have a considerable impact on affected individuals but not necessarily the whole population. In this review, we place these mechanisms under a larger lens that takes into account the influence of upstream "macro-level" environmental factors such as air pollution and the built environment. While these exposures likely have a smaller impact on the microbiome at an individual level, their ubiquitous nature confers them with a large influence at the population level. We focus on features of the indoor and outdoor human-made environment, their microbiomes and the research challenges inherent in integrating the built environment microbiomes with the early-life gut microbiome. We argue that an exposome perspective integrating internal and external microbiomes with macro-level environmental factors can provide a more comprehensive framework to define how environmental exposures can shape the gut microbiome and influence the development of allergic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".