Changes in the gut microbiota of osteoporosis patients based on 16S rRNA gene sequencing: a systematic review and meta-analysis
Bibliographic record
Abstract
Osteoporosis (OP) has become a major public health issue, threatening the bone health of middle-aged and elderly people from all around the world. Changes in the gut microbiota (GM) are correlated with the maintenance of bone mass and bone quality. However, research results in this field remain highly controversial, and no systematic review or meta-analysis of the relationship between GM and OP has been conducted. This paper addresses this shortcoming, focusing on the difference in the GM abundance between OP patients and healthy controls based on previous 16S ribosomal RNA (rRNA) gene sequencing results, in order to provide new clinical reference information for future customized prevention and treatment options of OP. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we comprehensively searched the databases of PubMed, Web of Science, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI). In addition, we applied the R programming language version 4.0.3 and Stata 15.1 software for data analysis. We also implemented the Newcastle-Ottawa Scale (NOS), funnel plot analysis, sensitivity analysis, Egger’s test, and Begg’s test to assess the risk of bias. This research ultimately considered 12 studies, which included the fecal GM data of 2033 people (604 with OP and 1429 healthy controls). In the included research papers, it was observed that the relative abundance of Lactobacillus and Ruminococcus increased in the OP group, while the relative abundance for Bacteroides of Bacteroidetes increased (except for Ireland). Meanwhile, Firmicutes, Blautia, Alistipes, Megamonas , and Anaerostipes showed reduced relative abundance in Chinese studies. In the linear discriminant analysis Effect Size (LEfSe) analysis, certain bacteria showed statistically significant results consistently across different studies. This observational meta-analysis revealed that changes in the GM were correlated with OP, and variations in some advantageous GM might involve regional differences.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".