Abstract P495: Gut Microbiome and Plasma Metabolome Signatures Are Associated With Systemic Inflammation in Children
Bibliographic record
Abstract
Introduction: Gut microbiota and microbial metabolites are hypothesized to influence health via systemic inflammation, but pediatric studies are lacking. We investigated associations of gut microbiota and plasma metabolome with inflammation in children aged 5-7. Hypothesis: Children with greater abundances of pro-inflammatory microbial taxa and metabolites have more systemic inflammation. Methods: We analyzed cross-sectional data from the community-based Gen3G cohort (Quebec), including children with 3 inflammation measures (IMs; plasminogen activator inhibitor 1, monocyte chemoattractant protein 1, tumor necrosis factor α), untargeted plasma metabolome (Metabolon platform) and gut microbiota (stool 16S rRNA sequencing) data at the 5-year visit. We log-transformed and standardized each IM, averaging them as an inflammation score (InfSc). We pareto-scaled log-transformed metabolites, imputing missing values as half the minimum detected. We selected 76 known bacterial metabolites a priori . We excluded metabolites detected in <50% of samples and microbial amplicon sequence variants (ASVs) in <10% of the samples or with <0.1% overall proportion. We examined associations of ASVs and metabolites with IMs and InfSc using ANCOM-BC2 and linear regression, respectively, after multivariable adjustment (see Figure legend). FDR p-values <0.05 were considered significant. Results: A total of 320 children (mean age 63±4 months, 47% female, 95% white) had IMs and metabolome data, and 146 had microbiota data. Of 335 ASVs tested, 59 were associated with InfSc and 154 with any IM (10 strongest associations in Figure A ). Of the 1038 total metabolites and 76 bacterial metabolites tested, 53 and 5 metabolites, respectively, were associated with InfSc and 80 and 10, respectively, with any IM (10 bacterial metabolites in Figure B ). Conclusions: Microbial ASVs and metabolites, including pro-inflammatory ones (e.g., P. succinatutens, lactate, kynurenine), were associated with systemic inflammation in 5yo children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".