Gut microbiota in patients with sarcopenia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Intestinal dysbiosis was considered a pivotal pathological mechanism underlying sarcopenia. Despite the fervor surrounding research in this domain, substantial controversy persists regarding the obtained outcomes. Objective To systematically summarized the disparities in gut microbiota composition between the group afflicted by sarcopenia and non-sarcopenia controls. Methods PubMed, Medline, CINAHL, EMBASE, Scopus, Web of Science and Google Scholer, Cochrane Library and gray literature sources were systematically searched for in randomized controlled trials. Meta-analysis and random-effects meta-regression were conducted using Rev. Man 5.3. Overall effect was measured using Hedges’s g and determined using Z-statistics. Cochran’s Q test and I 2 were used to investigate heterogeneity. The Newcastle-Ottawa Scale was used to assess overall quality of evidence. Results Ten studies, including 421 cases of sarcopenia and 1,642 cases of controls, were included in the meta-analysis. Patients with sarcopenia showed significantly reduced gut microbiota in α diversity, and β diversity was significantly different in 8/9 of included studies. We also found more abundance of phylum Proteobacteria and genus Escherichia-Shigella , and less abundance of phylum Firmicutes and genus Faecalibacterium , Prevotella 9 , Blautia in the sarcopenia group. Conclusion The gut microbiota composition in patients with sarcopenia has undergone alterations, serving as a fundamental reference for further investigation into the potential pathogenic mechanisms and treatment strategies for sarcopenia.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".