Gut microbiome and serum metabolome alterations in isolated dystonia
Bibliographic record
Abstract
Abstract Background Dystonia is a complex neurological movement disorder characterised by involuntary muscle contractions. The relationship between the gut microbiota and isolated dystonia remains poorly explored. Methods We collected faeces and blood samples to study the microbiome and the serum metabolome from a cohort of 57 drug-naïve isolated dystonia patients and 27 age- and environment-matched healthy individuals. We first sequenced the V4 regions of the 16S rDNA gene from all faeces samples. Further, we performed metagenomic sequencing of gut microbiome and non-targeted metabolomics profiling of serum from dystonia patients with significant dysbiosis. Results Gut microbial β-diversity was significantly different, with a more heterogeneous community structure among dystonia individuals than healthy controls, while no difference in α-diversity was found. Gut microbiota in dystonia patients was enriched with Blautia obeum, Dorea longicatena and Eubacterium hallii, but depleted with Bacteroides vulgatus and Bacteroides plebeius. Metagenomic sequencing revealed that genes related to the citrate cycle, vitamin B6 and glycan metabolism were less abundant in dystonia, while genes linked to purine and tryptophan biosynthesis were more abundant. Serum metabolome analysis revealed altered levels of tyrosine and glutamate. The integrative analysis of the gut microbiome and serum metabolomics identified dystonia-associated gut microbial species linked to changes in serum metabolites, reflecting the effect of the gut microbiome on metabolic activity in isolated dystonia. Conclusion This study is the first to reveal gut microbial dysbiosis in dystonia patients. Our findings identified previously unknown links between intestinal microbiota alterations, circulating amino acids and dystonia, providing new insight into the pathogenesis of isolated dystonia.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".