Proteomics in Patients with Fibromyalgia Syndrome: A Systematic Review of Observational Studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Fibromyalgia syndrome (FMS) is a disease of unknown pathophysiology, with the diagnosis being based on a set of clinical criteria. Proteomic analysis can provide significant biological information for the pathophysiology of the disease but may also reveal biomarkers for diagnosis or therapeutic targets. The present systematic review aims to synthesize the evidence regarding the proteome of adult patients with FMS using data from observational studies. RECENT FINDINGS: An extensive literature search was conducted in MEDLINE/PubMed, CENTRAL, and clinicaltrials.gov from inception until November 2022. The study protocol was published in OSF. Two independent reviewers evaluated the studies and extracted data. The quality of studies was assessed using the modified Newcastle-Ottawa scale adjusted for proteomic research. Ten studies fulfilled the protocol criteria, identifying 3328 proteins, 145 of which were differentially expressed among patients with FMS against controls. The proteins were identified in plasma, serum, cerebrospinal fluid, and saliva samples. The control groups included healthy individuals and patients with pain (inflammatory and non-inflammatory). The most important proteins identified involved transferrin, α-, β-, and γ-fibrinogen chains, profilin-1, transaldolase, PGAM1, apolipoprotein-C3, complement C4A and C1QC, immunoglobin parts, and acute phase reactants. Weak correlations were observed between proteins and pain sensation, or quality of life scales, apart from the association of transferrin and a2-macroglobulin with moderate-to-severe pain sensation. The quality of included studies was moderate-to-good. FMS appears to be related to protein dysregulation in the complement and coagulation cascades and the metabolism of iron. Several proteins may be dysregulated due to the excessive oxidative stress response.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".