Taxonomic and functional characteristics of the gut microbiota in obesity: A systematic review
Bibliographic record
Abstract
Obesity is a growing public health problem. In recent decades, scientific evidence has linked gut microbiota to obesity. This systematic review summarizes current knowledge on the composition and functional differences in gut microbiota between individuals with obesity and those with normal weight. Following PRISMA 2020 recommendations, studies published in adult populations between January 2014 and May 2024 were reviewed. PubMed, Web of Science, and Scopus databases were searched for observational studies that had used advanced sequencing methods, such as 16S rRNA and shotgun metagenomics, to assess gut microbiota. The quality of these studies was also analyzed using the Newcastle-Ottawa scale. Our review of 16 studies shows a reduction in microbial diversity in individuals with obesity. In addition, a higher relative abundance of the phylum Firmicutes , the families Enterobacteriaceae , Gemellaceae , Prevotellaceae , Streptococcaceae and Veillonellaceae , as well as the genera Blautia , Butyricimonas , Collinsella , Megamonas , and Streptococcus , while beneficial bacteria such as the families Porphyromonadaceae and Rikenellaceae , and the genera Bifidobacterium spp . and Faecalibacterium prausnitzii , were depleted. Functional analysis showed a tendency to an increase in metabolic pathways associated with carbohydrate and lipid metabolism, with reduced pathways related to short-chain fatty acid production. Obesity is associated with altered gut microbiota composition and function. However, the variability across studies regarding population characteristics, dietary pattern, and sequencing techniques limits the comparability of findings. Future research should prioritize standardized methodologies and confounding factors to elucidate the role of the gut microbiome in 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".