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Record W2313402955 · doi:10.5435/jaaos-21-10-644

Serum Biomarkers as Predictors of Stage of Work-related Musculoskeletal Disorders

2013· review· en· W2313402955 on OpenAlexaff
Mary F. Barbe, Sean Gallagher, Steven N. Popoff

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2013
Typereview
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineMusculoskeletal disorderInflammationWork-related musculoskeletal disordersFibromyalgiaPhysical medicine and rehabilitationPhysical therapyInternal medicinePoison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Musculoskeletal disorders (MSDs) are a leading worldwide cause of long-term pain and physical disability,1 with diagnoses including tendinopathies, nerve compression syndromes, and muscular and joint disorders.2,3 Studies in people with upper extremity work-related MSDs (WMSDs) find evidence of inflammation, fibrosis, and degeneration in serum and musculotendinous tissues, although the timing of each is unknown.4-7 Serum biomarkers that might aid in pinpointing the stage of these disorders are being investigated. Several risk factors have been identified, including forceful exertions, repetitive motion, and nonneutral body postures. A recent systematic review showed a consistent pattern of force-repetition interaction for musculoskeletal disorder risk, with low-force repetitive tasks demonstrating a modest increase in MSD risk, whereas high-force repetitive tasks result in rapid escalation in MSD risk, which is indicative of tissue fatigue failure.8 We have developed a rat model of voluntary reaching and handlepulling for food reward,9 in which reach rates and force levels were determined from epidemiologic studies. One goal of our laboratory is to identify biomarkers for monitoring disease progression of WMSDs and appropriate targeting of treatments. We recently examined whether serum inflammatory cytokines exhibit force × repetition interaction responses using this rat model.10 Serum tumor necrosis factor-α (TNF-α) shows a significant force × repetition interaction (P = 0.0003) with task performance (Figure 1, A). No increases in TNF-α were seen in low-force groups, but high increases were observed in rats performing high-repetition, high-force tasks.11-13 These results indicate that serum TNF-α follows the fatigue-failure theory during acute phases of ≤3 months. TNF-α and related cytokines may provide the best gauge of overall acute tissue damage resulting from repetitive, forceful exertions and may be the best biomarkers of this phase of WMSDs.Figure 1: Graph of serum biomarkers in a rat model of upper extremity work-related musculoskeletal disorders. A, Serum levels of tumor necrosis factor (TNF)-α in rats performing one of four tasks for 12 weeks. B, Schematic illustration showing temporal changes in serum levels of TNF-α and interleukin-10 (IL-10) (left axis) and connective tissue growth factor (CTGF) (right axis) in rats performing high-repetition, low-force (HRLF) tasks for 24 weeks. HRHF = high-repetition, high-force; LRHF = low-repetition, high-force; LRLF = low-repetition, low-forceThe timing of the inflammatory versus fibrotic responses with WMSDs is also of clinical interest. Studies have detected serum biomarkers of inflammation in patients with upper extremity WMSDs of short duration (≤3 months), including TNF-α,4-6 again suggesting a role for inflammatory cytokines early in the course of upper extremity WMSDs. However, studies examining tissues from patients with upper extremity WMSDs during surgical intervention show increased tissue fibrogenic proteins and fibrotic histopathology, which is indicative of deranged extracellular matrix production and degeneration in tissues by this time point.7 Therefore, we extended our rat studies to examine the effects of performing a high-repetition, low-force task for 24 weeks. Serum TNF-α levels increased early after training but declined by week 18 (Figure 1, B). In contrast, serum interleukin-10 (IL-10), an anti-inflammatory cytokine, increased steadily until week 18. This may be one reason for the decreasing TNF-α because IL-10 plays a key role in limiting immune responses. We next examined serum for connective tissue growth factor (CTGF), an important mediator of fibrosis. Studies have shown that CTGF increases in tissues under conditions of overload or injury and that it is a key player in the pathogenesis of fibrosis.14 We observed increased serum and tissue CTGF by week 24, compared with controls (P = 0.03) (Figure 1, B). It is known that transforming growth factor-β1 induces CTGF expression, leading to fibroblast proliferation and collagen deposition.14 However, CTGF production is also regulated by TNF-α,15 which is an interesting fact considering their temporal relationship (Figure 1, B). Importantly, because the CTGF level is normally low in sera of healthy individuals, it may serve as a predictive biomarker for patients in the fibrotic stage of WMSDs. Our results indicate that most serum inflammatory cytokines, including TNF-α, demonstrate force-repetition interactions. These findings support the use of key pro-inflammatory cytokines as biomarkers of acute tissue damage and the fatigue failure hypothesis as a mechanism underlying WMSDs. We have also observed significantly increased serum and tissue CTGF levels that correlated with tissue fibrosis. Additional studies are underway to determine whether elevated CTGF is an essential mediator of fibrotic events. If so, one could envision CTGF as a potential therapeutic target to prevent fibrosis and reduced function and as a serum biomarker of tissue fibrogenic changes occurring with WMSDs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.025
GPT teacher head0.340
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Citations29
Published2013
Admission routes1
Has abstractyes

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Same venueJournal of the American Academy of Orthopaedic SurgeonsSame topicFibromyalgia and Chronic Fatigue Syndrome ResearchFrench-language works237,207