Associations of gut microbiota features and circulating metabolites with systemic inflammation in children
Bibliographic record
Abstract
Objective Gut microbes and microbe-dependent metabolites (eg, tryptophan-kynurenine-serotonin pathway metabolites) have been linked to systemic inflammation, but the microbiota-metabolite-inflammation axis remains uncharacterised in children. Here we investigated whether gut microbiota features and circulating metabolites (both microbe-dependent and non-microbe-dependent metabolites) associated with circulating inflammation markers in children. Methods We studied children from the prospective Gen3G birth cohort who had data on untargeted plasma metabolome (n=321 children; Metabolon platform), gut microbiota (n=147; 16S rRNA sequencing), and inflammation markers (plasminogen activator inhibitor-1 (PAI-1), monocyte chemoattractant protein-1, and tumour necrosis factor-α) measured at 5–7 years. We examined associations of microbial taxa and metabolites—examining microbe-dependent and non-microbe-dependent metabolites separately—with each inflammatory marker and with an overall inflammation score (InfSc), adjusting for key confounders and correcting for multiple comparisons. We also compared the proportion of significantly associated microbe-dependent versus non-microbe-dependent metabolites, identified a priori (Human Microbial Metabolome Database), with each inflammation marker. Results Of 335 taxa tested, 149 were associated (q FDR <0.05) with at least one inflammatory marker; 10 of these were robust to pseudocount choice. Several bacterial taxa involved in tryptophan metabolism were associated with inflammation, including kynurenine-degrading Ruminococcus , which was inversely associated with all inflammation markers. Of 1037 metabolites tested, 315 were previously identified as microbe dependent and were more frequently associated with PAI-1 and the InfSc than non-microbe dependent metabolites. In total, 87 metabolites were associated (q FDR <0.05) with at least one inflammation marker, including kynurenine (positively), serotonin (positively), and tryptophan (inversely). Conclusion A distinct set of gut microbes and microbe-dependent metabolites, including those involved in the tryptophan-kynurenine-serotonin pathway, may be implicated in inflammatory pathways in childhood.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".