A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Original paper
Researched by:
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Kimberly Eyer
ID
Kimberly Eyer
Kimberly Eyer, a Registered Nurse with 30 years of nursing experience across diverse settings, including Home Health, ICU, Operating Room Nursing, and Research. Her roles have encompassed Operating Room Nurse, RN First Assistant, and Acting Director of a Same Day Surgery Center. Her specialty areas include Adult Cardiac Surgery, Congenital Cardiac Surgery, Vascular Surgery, and Neurosurgery.
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Autoimmune Diseases
Autoimmune Diseases
Autoimmune disease is when the immune system mistakenly attacks the body's tissues, often linked to imbalances in the microbiome, which can disrupt immune regulation and contribute to disease development.
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Multiple Sclerosis (MS)
Multiple Sclerosis (MS)
OverviewIn the past decade, research has shown that the enormous community of microbes that live in the gut, known as the gut microbiota, are closely linked to human health and disease. This relationship is primarily due to the gut microbiota’s impact on systemic immune responses. There is growing evidence that these impacts on immune function are […]
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Kimberly Eyer
Researched by:
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Kimberly Eyer
ID
Kimberly Eyer
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass
What was studied?
This study examined whether a common gut microbiota dysbiosis exists across multiple immune-mediated inflammatory diseases (IMIDs), specifically Crohn’s disease (CD), ulcerative colitis (UC), multiple sclerosis (MS), and rheumatoid arthritis (RA). Researchers employed 16S rRNA gene sequencing of stool samples and machine learning techniques to identify both disease-specific and shared microbial signatures. This pilot investigation also explored the potential of taxonomic features to classify disease states using random forest classifiers.
Who was studied?
The study included 99 participants: 20 with CD, 19 with UC, 19 with MS, 21 with RA, and 23 healthy controls (HC). Patients were recruited from clinical centers in Winnipeg, Canada, and met disease-specific diagnostic criteria. Inclusion criteria mandated age above 18 and no antibiotic use in the preceding 8 weeks. Biological replicates were collected approximately two months apart to assess microbial stability over time.
What were the most important findings?
The study identified a shared gut microbiota dysbiosis signature across IMIDs, marked by reduced diversity and distinct taxonomic shifts compared to healthy controls. Alpha diversity was significantly lower in IMID groups, especially in CD. Key genera enriched across all disease groups included Actinomyces, Eggerthella, Clostridium III, Faecalicoccus, and Streptococcus—potential Major Microbial Associations (MMAs) due to their pro-inflammatory profiles and consistent presence in IMID cohorts. In contrast, Gemmiger, Lachnospira, and Roseburia were significantly depleted in IMIDs and are known to produce anti-inflammatory metabolites like butyrate. Machine learning classifiers distinguished disease from HC with high accuracy (AUC up to 0.95 for CD), confirming the reliability of these microbial features as diagnostic indicators. Disease-specific signatures were also detected: Bifidobacterium was elevated in UC, Intestinibacter in CD, and unclassified Erysipelotrichaceae in MS.
| Key Domain | Details |
|---|---|
| Conditions Studied | Crohn’s disease, ulcerative colitis, multiple sclerosis, rheumatoid arthritis |
| Shared Microbial Increases | Actinomyces, Eggerthella, Clostridium III, Faecalicoccus, Streptococcus |
| Shared Microbial Decreases | Gemmiger, Lachnospira, Roseburia |
| Disease-Specific Associations | Intestinibacter (CD), Bifidobacterium (UC), Erysipelotrichaceae (MS), Roseburia (↓ in RA) |
| Microbiome Metrics | Alpha diversity lowest in CD, highest in healthy controls; compositional shifts significant |
| Clinical Implications | Supports development of microbiome-targeted diagnostics and interventions |
| Diagnostic Performance | AUCs: CD vs HC = 0.95; classification robust for all IMIDs using Gram-positive taxa |
What are the greatest implications of this study?
This study provides compelling evidence for a partially conserved gut microbiota dysbiosis pattern in IMIDs, despite their diverse clinical presentations. The findings suggest that microbial taxa such as Streptococcus and Eggerthella may contribute to shared pathogenic mechanisms via modulation of host immunity, while depletion of butyrate-producing genera like Roseburia may reflect a breakdown in mucosal tolerance. These MMAs highlight targets for microbiome-modulating interventions and support their integration into risk stratification and personalized treatment strategies. Furthermore, the study underscores the diagnostic potential of microbiota-based machine learning tools, offering a route to non-invasive, microbiome-informed screening across inflammatory conditions.