The Role of Gut Microbiota in Pediatric Obesity: A Systematic Review and Meta Analysis of Microbiota Profiles in Obese versus Normal Weight Children
Bibliographic record
Abstract
Background: Pediatric obesity is increasingly acknowledged as a significant public health issue with the gut microbiome identified as a potential contributing factor. Increasing evidence indicated that the gut microbiome is integral to metabolic health and the etiology of obesity. Nonetheless, data pertaining specifically to pediatric populations is still limited and underexplored. This study compared the composition of gut microbiota between obese and normal-weight children and to identify microbial patterns associated with pediatric obesity. Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thorough literature search was performed across various databases. We looked at eligible studies and then rated their quality and analyzed them with Newcastle–Ottawa Scale (NOS) and Review Manager (RevMan) 5.4. Result: This systematic review and meta-analysis included ten studies involving 562 children, utilizing cross-sectional and case-control methodologies. The meta-analysis, which included two studies with 124 participants (64 obese and 60 normal-weight), showed that the Firmicutes to Bacteroidetes (F/B) ratio was much higher in obese children than in normal-weight (mean difference = 5.15; p < 0.00001). Taxonomic analysis showed obese children had more members of the phylum Firmicutes, such as Lactobacillus, Clostridium, and Megamonas. On the other hand, Bacteroidetes, especially Prevotella and Bacteroides, were usually less abundant. Conclusion: The results indicate that dysbiosis in gut microbiota may contribute to pediatric obesity. These results underscore the potential of gut microbiota modulation as a treatment for childhood obesity. Research is necessary to clarify causal mechanisms and investigate microbiota-based-interventions.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".