The Role of the Microbiota in Graves’ Disease and Graves’ Orbitopathy
Bibliographic record
Abstract
Graves‘ disease (GD) is a clinical syndrome with an enlarged and overactive thyroid gland, an accelerated heart rate, Graves’ orbitopathy (GO), and pretibial myxedema (PTM). GO is the most common extrathyroidal complication of GD. GD/GO has a significant negative impact on the quality of life. GD is the most common systemic autoimmune disorder, mediated by autoantibodies to the thyroid-stimulating hormone receptor (TSHR). It is generally accepted that GD/GO results from complex interactions between genetic and environmental factors that lead to the loss of immune tolerance to thyroid antigens. However, the exact mechanism is still elusive. Systematic investigations into GD/GO animal models and clinical patients have provided important new insight into these disorders during the past 4 years. These studies suggested that gut microbiota may play an essential role in the pathogenesis of GD/GO. Antibiotic vancomycin can reduce disease severity, but fecal material transfer (FMT) from GD/GO patients exaggerates the disease in GD/GO mouse models. There are significant differences in microbiota composition between GD/GO patients and healthy controls.Lactobacillus,Prevotella, andVeillonellaoften increase in GD patients. The commonly used therapeutic agents for GD/GO can also affect the gut microbiota. Antigenic mimicry and the imbalance of T helper 17 cells (Th17)/regulatory T cells (Tregs) are the primary mechanisms proposed for dysbiosis in GD/GO. Interventions including antibiotics, probiotics, and diet modification that modulate the gut microbiota have been actively investigated in preclinical models and, to some extent, in clinical settings, such as probiotics (Bifidobacterium longum) and selenium supplements. Future studies will reveal molecular pathways linking gut and thyroid functions and how they impact orbital autoimmunity. Microbiota-targeting therapeutics will likely be an essential strategy in managing GD/GO in the coming years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".