Potential role of blood biomarkers in patients with fibromyalgia: a systematic review with meta-analysis
Bibliographic record
Abstract
ABSTRACT: Fibromyalgia (FM) is a complex chronic pain condition. Its symptoms are nonspecific, and to date, no objective test exists to confirm FM diagnosis. Potential objective measures include the circulating levels of blood biomarkers. This systematic review and meta-analysis aim to review studies assessing blood biomarkers' levels in patients with FM compared with healthy controls. We systematically searched the PubMed, MEDLINE, EMBASE, and PsycINFO databases. Fifty-four studies reporting the levels of biomarkers in blood in patients with FM were included. Data were extracted, and the methodological quality was assessed independently by 2 authors. The methodological quality of 9 studies (17%) was low. The results of most studies were not directly comparable given differences in methods and investigated target immune mediators. Thus, data from 40 studies only were meta-analyzed using a random-effects model. The meta-analysis showed that patients with FM had significantly lower levels of interleukin-1 β and higher levels of IL-6, IL-8, tumor necrosis factor-alpha, interferon gamma, C-reactive protein, and brain-derived neurotrophic factor compared with healthy controls. Nevertheless, this systematic literature review and meta-analysis could not support the notion that these blood biomarkers are specific biomarkers of FM. Our literature review, however, revealed that these same individual biomarkers may have the potential role of identifying underlying pathologies or other conditions that often coexist with FM. Future research is needed to evaluate the potential clinical value for these biomarkers while controlling for the various confounding variables.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".