Biological Markers of Musculoskeletal Pain: A Scoping Review
Bibliographic record
Abstract
Background: Musculoskeletal pain (MSP) is the leading contributor to disability, limiting mobility and dexterity. As research on the determinants of MSP is evolving, biomarkers can probably play a significant role in understanding its causes and improving its clinical management. This scoping review aimed to provide an overview of the associations between biomarkers and MSP. Methods: This study followed Arksey and O'Malley and PRISMA-ScR recommendations. Keywords related to biomarkers, association, and MSP were searched on PubMed, Embase, Cochrane, and Web of Science databases from inception to September 28th, 2023. Data were systematically retrieved from the retained articles. A narrative synthesis approach - but no quality assessment - was used to map the core themes of biological markers of MSP that emerged from this work. Results: In total, 81 out of 25,165 identified articles were included in this scoping review. These studies were heterogeneous in many aspects. Overall, vitamin D deficiency, dyslipidemia (or hypercholesterolemia), and cytokines (high levels) were the most studied biomarkers with regards to MSP and were most often reported to be associated with non-specific MSP. Cadmium, calcium, C-reactive protein, collagen, creatinine, hormones, omega-3 fatty acids, sodium, tumor necrosis factor-alpha, and vitamin C were also reported to be associated with MSP syndromes, but the evidence on these associations was sketchier. No conclusions could be drawn as to age and sex. Conclusions: Our findings suggest that some biomarkers are associated with specific MSP syndromes, while others would be associated with non-specific syndromes. Among all candidate markers, the evidence seems to be more consistent for vitamin D, cytokines and lipids (total cholesterol, triglycerides, low- and high-density lipoproteins). High-quality studies, stratified by age and sex, are needed to advance our understanding on biomarkers of MSP.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".