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Record W4402076201 · doi:10.1136/bmjgast-2024-001470

Associations of gut microbiota features and circulating metabolites with systemic inflammation in children

2024· article· en· W4402076201 on OpenAlexafffund
Bruno Bohn, Curtis Tilves, Yingan Chen, Myriam Doyon, Luigi Bouchard, Patrice Perron, Renée Guérin, Éric Massé, Marie‐France Hivert, Noel T. Mueller

Bibliographic record

VenueBMJ Open Gastroenterology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
FundersCanadian Institutes of Health ResearchUniversité de SherbrookeNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteDiabète Québec
KeywordsInflammationSystemic inflammationGut floraMedicineImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.299
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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