Alterations of the microbiome across body sites in systemic lupus erythematosus: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with unclear etiology. Growing evidence suggests the microbiome plays a role in SLE pathogenesis. However, findings are inconsistent across studies due to factors like small sample sizes and geographical variations. A comprehensive meta-analysis is needed to elucidate microbiome alterations in SLE. OBJECTIVE: This study aimed to provide a systematic overview of microbiota dysbiosis across body sites in SLE through a meta-analysis of alpha diversity indices, beta diversity indices, and abundance taxa of microbiome. METHODS: A literature search was conducted across four databases to identify relevant studies comparing SLE patients and healthy controls. Extracted data encompassed alpha and beta diversity metrics, as well as bacterial, fungal, and viral abundance across gut, oral, skin, and other microbiota. Study quality was assessed using the Newcastle-Ottawa Scale. Standardized mean differences and pooled effect sizes were calculated through meta-analytical methods. RESULTS: The analysis showed reduced alpha diversity and distinct beta diversity in SLE, particularly in the gut microbiota. Taxonomic analysis revealed compositional variations in bacteria from the gut and oral cavity. However, results for fungi, viruses, and bacteria from other sites were inconsistent due to limited studies. CONCLUSIONS: This meta-analysis offers a comprehensive perspective on microbiome dysbiosis in SLE patients across diverse body sites and taxa. The observed variations underscore the microbiome's potential role in SLE pathogenesis. Future research should address geographical variations, employ longitudinal designs, and integrate multi-omics approaches.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".