Unveiling the Role of Gut Microbiota and Metabolites in Autoimmune Thyroid Diseases: Emerging Perspectives
Bibliographic record
Abstract
Autoimmune thyroid diseases (AITDs) are among the most prevalent organ-specific autoimmune disorders, with thyroid hormones playing a pivotal role in the gastrointestinal system's structure and function. Emerging evidence suggests a link between AITDs and the gut microbiome, which is a diverse community of organisms that are essential for digestion, absorption, intestinal homeostasis, and immune defense. Recent studies using 16S rRNA and metagenomic sequencing of fecal samples from AITD patients have revealed a significant correlation between a gut microbiota imbalance and the severity of AITDs. Progress in animal models of autoimmune diseases has shown that intervention in the gut microbiota can significantly alter the disease severity. The gut microbiota influences T cell subgroup differentiation and modulates the pathological immune response to AITDs through mechanisms involving short-chain fatty acids (SCFAs), lipopolysaccharides (LPSs), and mucosal immunity. Conversely, thyroid hormones also influence gut function and microbiota composition. Thus, there is a bidirectional relationship between the thyroid and the gut ecosystem. This review explores the pathogenic mechanisms of the gut microbiota and its metabolites in AITDs, characterizes the gut microbiota in Graves' disease (GD) and Hashimoto's thyroiditis (HT), and examines the interactions between the gut microbiota, thyroid hormones, T cell differentiation, and trace elements. The review aims to enhance understanding of the gut microbiota-thyroid axis and proposes novel approaches to mitigate AITD severity through gut microbiota modulation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".