MétaCan
Menu
Retour à la cohorte
Enregistrement W2313402955 · doi:10.5435/jaaos-21-10-644

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

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

Notice bibliographique

RevueJournal of the American Academy of Orthopaedic Surgeons · 2013
Typereview
Langueen
DomaineMedicine
ThématiqueFibromyalgia and Chronic Fatigue Syndrome Research
Établissements canadiensObject Research Systems (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineMusculoskeletal disorderInflammationWork-related musculoskeletal disordersFibromyalgiaPhysical medicine and rehabilitationPhysical therapyInternal medicinePoison controlHuman factors and ergonomics

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,340
Écart entre enseignants0,314 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations29
Publié2013
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of the American Academy of Orthopaedic SurgeonsMême sujetFibromyalgia and Chronic Fatigue Syndrome ResearchTravaux en français237 207