Gut microbiota and microbial metabolites are associated with body composition in 5‐year‐old children: A cross‐sectional study in the <scp>Gen3G</scp> cohort
Bibliographic record
Abstract
OBJECTIVE: To examine gut microbiota diversity, composition and metabolites in relation to overall mass (OM), fat mass (FM) and lean soft tissue mass (LSTM) measured by dual x-ray absorptiometry (DXA) in 5-year-old children. METHODS: Mothers of the Gen3G cohort were enrolled prenatally in 2010-2013 in Quebec, Canada; 153 children from the cohort had data on gut microbiota and DXA scans at 5-6.4 years of age, and 140 also had plasma metabolite data. We characterized gut microbiota by 16S rRNA Illumina sequencing and metabolites by untargeted multiplatform mass spectrometry. We examined associations of microbial alpha diversity, beta diversity, composition (amplicon sequence variants; ASVs) and metabolites (microbial metabolites) with DXA measures, adjusting for age, sex, diet and drinking water. RESULTS: Of the 153 children, 43.1% were female, and 96.1% self-identified as white. The median BMI was the 52nd percentile. Microbial richness (alpha diversity) was positively associated with OM, FM and LSTM. Of the 542 ASVs tested, 7 were associated with OM, 5 with FM and 4 with LSTM. One Veillonella ASV and two Blautia ASVs were significantly associated with all outcomes. Among 278 microbial metabolites, no metabolites were associated with FM, while glycoursodeoxycholate was associated with OM, and glycoursodeoxycholate, 3-hydroxybutyrate and gamma-glutamylalanine were associated with LSTM. CONCLUSIONS: In 5-year-old children, gut microbiota alpha diversity, richness and specific gut microbes were associated with OM, FM and LSTM. Many of the associations followed a similar pattern for FM and LSTM, suggesting they may not be specific to adiposity but rather reflect overall growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".