Gut microbiota promotes pain in fibromyalgia
Bibliographic record
Abstract
Abstract Fibromyalgia is a chronic syndrome characterized by widespread pain in the absence of evident tissue injury or pathology, making it one of the most mysterious chronic pain conditions. Despite affecting 2–4% of the population, primarily women 1 , the cause and underlying mechanisms of fibromyalgia remain elusive, and effective targeted treatments are currently unavailable. The gut microbiota of women with fibromyalgia differs from healthy controls 2,3 . However, it is unknown whether changes in gut microbiota have a causal role in mediating pain and other symptoms of fibromyalgia. Here, we show that fecal microbiota transplantation (FMT) from individuals with fibromyalgia, but not from healthy controls, into germ-free mice induces persistent pain hypersensitivity. FMT from fibromyalgia patients led to a reduction in intraepidermal nerve fiber density and alterations in the peripheral immune profile, and induced activation of spinal microglia, which contributed to the development of pain in mice. Notably, the pain hypersensitivity in mice that were administered microbiota from fibromyalgia patients resolved after FMT from healthy controls. Consistent with these findings, an open-label pilot study showed that transplanting microbiota from healthy individuals to humans with fibromyalgia alleviated pain and reduced overall symptom severity. Thus, altered gut microbiota has a causal role in fibromyalgia pain, highlighting it as a promising target for therapeutic interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".