Composition and Functions of the Gut Microbiome in Pediatric Obesity: Relationships with Markers of Insulin Resistance
Bibliographic record
Abstract
The gut microbiome is hypothesized to play a crucial role in the development of obesity and insulin resistance (IR); the pathways linking the microbiome to IR in pediatrics have yet to be precisely characterized. We aimed to determine the relationship between the gut microbiome composition and metabolic functions and IR in children with obesity. In a cross-sectional study, fecal samples from children with obesity (10-16 years old) were collected for taxonomical and functional analysis of the fecal microbiome using shotgun metagenomics. The homeostatic model assessment for insulin resistance (HOMA-IR) was determined using fasting glucose and insulin. Associations between HOMA-IR and α-diversity measures as well as metabolic pathways were evaluated using Spearman correlations; relationships between HOMA-IR and β-diversity were assessed by permutational multivariate analysis of variance. Twenty-one children (nine males; median: age = 12.0 years; BMI z-score = 2.9; HOMA-IR = 3.6) completed the study. HOMA-IR was significantly associated with measures of α-diversity but not with β-diversity. Children with higher HOMA-IR exhibited lower overall species richness, Firmicutes species richness, and overall Proteobacteria species Shannon diversity. Furthermore, HOMA-IR was inversely correlated with the abundance of pathways related to the biosynthesis of lipopolysaccharides, amino acids, and short-chain fatty acids, whereas positive correlations between HOMA-IR and the peptidoglycan biosynthesis pathways were observed. In conclusion, insulin resistance was associated with decreased microbial α-diversity measures and abundance of genes related to the metabolic pathways. Our study provides a framework for understanding the microbial alterations in pediatric obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".