Bidirectional Causality Between Graves’ Disease and the Gut Microbiome Revealed by Mendelian Randomization Original paper
Researched by:
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Karen Pendergrass
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Karen Pendergrass
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.
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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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Karen Pendergrass
Researched by:
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Karen Pendergrass
ID
Karen Pendergrass
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 investigated the causal relationship between Graves’ disease (GD) and the gut microbiome using a bidirectional two-sample Mendelian randomization (MR) approach. Researchers utilized genome-wide association study (GWAS) summary statistics to examine whether changes in the gut microbiome contribute to the development of GD and whether GD, in turn, alters the gut microbiome. The study was grounded in the concept of the thyroid–gut axis (TGA), which posits bidirectional regulation between thyroid function and gut microbial composition. By leveraging MR methods, including inverse-variance weighting (IVW), weighted median, weighted mode, MR-Egger, and simple mode, the study aimed to infer causality while controlling for confounding and reverse causation.
Who was studied?
The analysis was conducted using GWAS data from two large cohorts. The gut microbiome dataset (n = 18,340) was derived from 24 international cohorts spanning European, Middle Eastern, East Asian, Hispanic/Latin American, and African American populations, as part of the MiBioGen consortium. The Graves’ disease dataset (n = 212,453; including 2,176 GD cases and 210,277 controls) came from BioBank Japan, representing individuals of Asian descent. Taxonomic profiling in the microbiome dataset used 16S rRNA sequencing, capturing 211 taxa after quality control and rarefaction.
Most important findings
The study identified several taxa with significant causal effects in both directions:
Gut Microbiome → GD:
| Taxon | Direction | OR | Method |
|---|---|---|---|
| Deltaproteobacteria (Class) | ↑ Risk | 3.603 | MR-Egger |
| Mollicutes (Class) | ↑ Risk | 2.354 | Simple Mode |
| Ruminococcus torques group (Genus) | ↑ Risk | 1.445 | IVW |
| Oxalobacter (Genus) | ↑ Risk | 2.395 | Wald Ratio |
| Ruminococcaceae UCG 011 (Genus) | ↑ Risk | 1.379 | Weighted Median |
| Peptococcaceae (Family) | ↓ Risk | 0.536 | IVW |
| Anaerostipes (Genus) | ↓ Risk | 0.489 | Weighted Median |
GD → Gut Microbiome:
| Taxon | Direction | OR | Method |
|---|---|---|---|
| Anaerofilum (Genus) | ↑ Post-GD | 1.586 | MR-Egger |
| Oxalobacteraceae (Family) | ↑ Post-GD | 1.085 | IVW |
| Intestinimonas, Peptococcus, etc. | ↑ Post-GD | ~1.04–1.13 | IVW/Median |
| Clostridium innocuum group (Genus) | ↓ Post-GD | 0.918 | IVW |
| Sutterella (Genus) | ↓ Post-GD | 0.953 | IVW |
No evidence of heterogeneity, horizontal pleiotropy, or weak instrument bias was detected, strengthening the causal inference.
Key implications
This study provides robust evidence of a bidirectional causal relationship between Graves’ disease and specific gut microbial taxa, thereby supporting the existence of a thyroid–gut axis (TGA). The identification of microbiota such as Deltaproteobacteria and Anaerostipes as risk and protective factors, respectively, highlights candidate biomarkers and potential therapeutic targets. Moreover, the findings suggest that GD not only results from microbiome alterations but can itself induce compositional changes, potentially exacerbating autoimmune dysregulation. These results offer a strong foundation for incorporating Graves’ disease gut microbiome causality into both diagnostic algorithms and microbiome-targeted intervention (MBTI) frameworks.
Microbiome-Targeted Interventions (MBTIs)
Microbiome Targeted Interventions (MBTIs) are cutting-edge treatments that utilize information from Microbiome Signatures to modulate the microbiome, revolutionizing medicine with unparalleled precision and impact.