Fibromyalgia patients have altered lipid concentrations associated with disease symptom severity and anti-satellite glial cell IgG antibodies
Bibliographic record
Abstract
Abstract Autoimmunity and immunoglobulin G (IgG) autoantibodies may contribute to pain in a subset of fibromyalgia (FM) patients. Previously, we saw that IgG from FM patients induces pain-like behavior in mice and binds to satellite glial cells (anti-SGC IgG). The anti-SGC IgG levels were also associated with more severe symptomatology. Lipid metabolism in FM subjects is altered with lysophosphatidylcholines (LPCs) acting as pain mediators. The relationship between autoantibodies, lipid metabolism, and FM symptomatology remains unclear. We analyzed serum lipidomics with liquid chromatography mass spectrometry, anti-SGC IgG levels, and clinical measures in 35 female FM subjects and 33 age- and body mass index-balanced healthy controls (HC). Fibromyalgia subjects with higher anti-SGC IgG levels experienced more intense pain than those with lower levels. Sixty-three lipids were significantly altered between FM subjects and HC or between FM subjects with severe (FM severe) and mild symptoms (FM mild). Compared to HC, FM subjects had lower concentrations of lipid species belonging to the classes LPC (n = 10), lysophosphatidylethanolamine (n = 7), phosphatidylcholine (n = 4), and triglyceride (n = 5), but higher concentrations of diglyceride (n = 3). Additionally, FM severe had higher LPC 19:0, 22:0, and 24:1 and lower sphingomyelin (n = 9) concentrations compared to FM mild. A positive association was seen for LPC 22:0 and 24:1 with pain intensity and anti-SGC IgG levels in FM subjects. Taken together, our results suggest an association between altered lipid metabolism and autoimmune mechanisms in FM. Perspective Our results suggest an association between the postulated autoimmunity in FM and lipids that can act as pain mediators.
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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 | 0.001 | 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".