Gut Microbiota Profile in Patients with Type 1 Diabetes Based on 16S rRNA Gene Sequencing: A Systematic Review
Bibliographic record
Abstract
The gut microbiota has been presumed to have a role in the pathogenesis of type 1 diabetes (T1D). Significant changes in the microbial composition of T1D patients have been reported in several case-control studies. This study is aimed at systematically reviewing the existing literature, which has investigated the alterations of the intestinal microbiome in T1D patients compared with healthy controls (HCs) using 16S ribosomal RNA-targeted sequencing. The databases of MEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched until April 2019 for case-control studies comparing the composition of the intestinal microbiome in T1D patients and HCs based on 16S rRNA gene sequencing techniques. The Newcastle-Ottawa Scale was used to assess the methodological quality. Ten articles involving 260 patients with T1D and 276 HCs were included in this systematic review. The quality scores of all included studies were 6–8 points. In summary, a decreased microbiota diversity and a significantly distinct pattern of clustering with regard to β-diversity were observed in T1D patients when compared with HCs. At the phylum level, T1D was characterised by a reduced ratio of Firmicutes/Bacteroidetes in the structure of the gut community, although no consistent conclusion was reached. At the genus or species level, T1D patients had a reduced abundance of Clostridium and Prevotella compared with HCs, whereas Bacteroides and Ruminococcus were found to be more enriched in T1D patients. This systematic review identified that there is a close association between the gut microbiota and development of T1D. Moreover, gut dysbiosis might be involved in the pathogenesis of T1D, although the causative role of gut microbiota remains to be established. Further well-controlled prospective studies are needed to better understand the role of the intestinal microbiome in the pathogenesis of T1D, which may help explore novel microbiota-based strategies to prevent and treat T1D.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".