A comparative study of the gut microbiota in immune-mediated inflammatory diseases—does a common dysbiosis exist?
Bibliographic record
Abstract
BACKGROUND: Immune-mediated inflammatory disease (IMID) represents a substantial health concern. It is widely recognized that IMID patients are at a higher risk for developing secondary inflammation-related conditions. While an ambiguous etiology is common to all IMIDs, in recent years, considerable knowledge has emerged regarding the plausible role of the gut microbiome in IMIDs. This study used 16S rRNA gene amplicon sequencing to compare the gut microbiota of patients with Crohn's disease (CD; N = 20), ulcerative colitis (UC; N = 19), multiple sclerosis (MS; N = 19), and rheumatoid arthritis (RA; N = 21) versus healthy controls (HC; N = 23). Biological replicates were collected from participants within a 2-month interval. This study aimed to identify common (or unique) taxonomic biomarkers of IMIDs using both differential abundance testing and a machine learning approach. RESULTS: Significant microbial community differences between cohorts were observed (pseudo F = 4.56; p = 0.01). Richness and diversity were significantly different between cohorts (pFDR < 0.001) and were lowest in CD while highest in HC. Abundances of Actinomyces, Eggerthella, Clostridium III, Faecalicoccus, and Streptococcus (pFDR < 0.001) were significantly higher in all disease cohorts relative to HC, whereas significantly lower abundances were observed for Gemmiger, Lachnospira, and Sporobacter (pFDR < 0.001). Several taxa were found to be differentially abundant in IMIDs versus HC including significantly higher abundances of Intestinibacter in CD, Bifidobacterium in UC, and unclassified Erysipelotrichaceae in MS and significantly lower abundances of Coprococcus in CD, Dialister in MS, and Roseburia in RA. A machine learning approach to classify disease versus HC was highest for CD (AUC = 0.93 and AUC = 0.95 for OTU and genus features, respectively) followed by MS, RA, and UC. Gemmiger and Faecalicoccus were identified as important features for classification of subjects to CD and HC. In general, features identified by differential abundance testing were consistent with machine learning feature importance. CONCLUSIONS: This study identified several gut microbial taxa with differential abundance patterns common to IMIDs. We also found differentially abundant taxa between IMIDs. These taxa may serve as biomarkers for the detection and diagnosis of IMIDs and suggest there may be a common component to IMID etiology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".