Early-life and concurrent predictors of the healthy adolescent microbiome in a cohort study
Bibliographic record
Abstract
BACKGROUND: The microbiome of adolescents is poorly understood, as are factors influencing its composition. We aimed to describe the healthy adolescent microbiome and identify early-life and concurrent predictors of its composition. METHODS: We performed metagenomic sequencing of 247 fecal specimens from 167 adolescents aged 11-14 years participating in the Health Outcomes and Measures of the Environment (HOME) Study, a longitudinal pregnancy and birth cohort (Cincinnati, OH). We described common features of the adolescent gut microbiome and applied self-organizing maps (SOMs)-a machine-learning approach-to identify distinct microbial profiles (n = 4). Using prospectively collected data on sociodemographic characteristics, lifestyle, diet, and sexual maturation, we identified early-life and concurrent factors associated with microbial diversity and phylum relative abundance with linear regression models and composition with Kruskal-Wallis and Fisher's exact tests. RESULTS: We found that household income and other sociodemographic factors were consistent predictors of the microbiome, with higher income associated with lower diversity and differential relative abundances of Firmicutes (increased) and Actinobacteria (decreased). Sexual maturation, distinct from chronological age, was related to higher diversity in females and differences in phylum relative abundances and compositional profiles in both males and females. CONCLUSIONS: Our study suggests that adolescence is a unique window for gut microbial composition and that it may be shaped by both early-life and concurrent exposures, highlighting its potential in future epidemiologic research.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".