Altered serum bile acid profile in fibromyalgia is associated with specific gut microbiome changes and symptom severity
Bibliographic record
Abstract
ABSTRACT: Alterations in the composition and function of the gut microbiome in women with fibromyalgia have recently been demonstrated, including changes in the relative abundance of certain bile acid-metabolizing bacteria. Bile acids can affect multiple physiological processes, including visceral pain, but have yet to be explored for association to the fibromyalgia gut microbiome. In this study, 16S rRNA sequencing and targeted metabolomic approaches were used to characterize the gut microbiome and circulating bile acids in a cohort of 42 women with fibromyalgia and 42 healthy controls. Alterations in the relative abundance of several bacterial species known to metabolize bile acids were observed in women with fibromyalgia, accompanied by significant alterations in the serum concentration of secondary bile acids, including a marked depletion of α-muricholic acid. Statistical learning algorithms could accurately detect individuals with fibromyalgia using the concentration of these serum bile acids. Serum α-muricholic acid was highly correlated with symptom severity, including pain intensity and fatigue. Taken together, these findings suggest serum bile acid alterations are implicated in nociplastic pain. The changes observed in the composition of the gut microbiota and the concentration of circulating secondary bile acids seem congruent with the phenotype of increased nociception and are quantitatively correlated with symptom severity. This is a first demonstration of circulating bile acid alteration in individuals with fibromyalgia, potentially secondary to upstream gut microbiome alterations. If corroborated in independent studies, these observations may allow for the development of molecular diagnostic aids for fibromyalgia as well as mechanistic insights into the syndrome.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".