Antibiotics taken within the first year of life are linked to infant gut microbiome disruption and elevated atopic dermatitis risk
Bibliographic record
Abstract
Background Atopic dermatitis (AD) is the most common chronic inflammatory skin disease in both pediatric and adult populations. The development of AD has been linked to antibiotic usage, which causes perturbation of the microbiome and has been associated with abnormal immune system function. However, imbalances in the gut microbiome itself associated with antibiotic usage have been inconsistently linked to AD. Objectives This study aimed to elucidate the timing and specific factors mediating the relationship between systemic (oral or intravenous) antibiotic usage and AD. Methods We used statistical modeling and differential analysis to link CHILD Cohort Study participants' history of antibiotic usage and early-life gut microbiome alterations to AD. Results Here we report that systemic antibiotics during the first year of life, as compared to later, are associated with AD risk (adjusted odds ratio [aOR] = 1.81; 95% CI: 1.28-2.57; P < .001), with an increased number of antibiotic courses corresponding to a dose response–like increased risk of AD risk (1 course: aOR: 1.67; 95% CI: 1.17-2.38; 2 or more courses: aOR: 2.16; 95% CI: 1.30-3.59). Further, we demonstrate that microbiome alterations associated with both AD and systemic antibiotic usage fully mediate the effect of antibiotic usage on the development of AD (β indirect = 0.072; P < .001). Alterations in the 1-year infant gut microbiome of participants who would later develop AD included increased Tyzzerella nexilis , increased monosaccharide utilization, and parallel decreased Bifidobacterium and Eubacterium spp, and fermentative pathways. Conclusions These findings indicate that early-life antibiotic usage, especially in the first year of life, modulates key gut microbiome components that may be used as markers to predict and possibly prevent the development of AD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".