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Enregistrement W3192415723 · doi:10.4103/ijmr.ijmr_436_20

Cartilage-derived biomarkers in osteoarthritis

2021· letter· en· W3192415723 sur OpenAlexaboutno aff
Francesca Oliviero, Roberta Ramonda

Notice bibliographique

RevueThe Indian Journal of Medical Research · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueOsteoarthritis Treatment and Mechanisms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOsteoarthritisCartilageMedicineBiomarkerPathologyBiologyAnatomyBiochemistry

Résumé

récupéré en direct d'OpenAlex

Osteoarthritis (OA) is a chronic joint disease and a leading cause of disability worldwide. It is characterized by a metabolic imbalance between anabolic and catabolic factors produced by chondrocytes, which leads to cartilage degradation and destruction. A large body of evidence supports the role of inflammation in OA and the involvement of the synovium, subchondral bone and other joint structures in the disease1. There is no effective pharmacological treatment for OA despite its high impact and the growing global burden. While non-steroidal anti-inflammatory drugs aim to reduce symptoms, symptomatic slow-acting drugs for OA are recommended to delay structural joint changes. However, no drugs have shown to stop the progression of the disease so far, and prevention remains the best strategy to hinder OA development. The lack of validated biomarkers that can accurately predict the evolution of the disease makes the identification of new pharmacological targets more difficult2. This is further aggravated by the presence of multiple OA phenotypes which may potentially be treated and targeted differently3. In the last decades, there has been a progressive growing interest in identifying diagnostic and prognostic biomarkers in OA. With the development and the expansion of new proteomic methodology mainly focused on extracellular matrix (ECM) components4, a large number of molecules have been proposed as useful biomarkers. However, given the heterogeneous nature of the disease, the lack of clinical validation and the lack of reproducible and internationally validated techniques, none of these have, until now, found a routine application in identifying disease state or to orient to a specific cure in OA. ECM components and structural proteins of the cartilage are among the most investigated and studied molecules associated with OA progression. Their concentrations are linked to tissue metabolism and, therefore, can directly reflect the degree of tissue damage. Type II collagen epitopes, such as urinary C-terminal telopeptide of type II collagen (uCTX-II) and collagen degradation products, serum cartilage oligomeric matrix protein (COMP), hyaluronic acid (HA) and proteoglicans (PGs), are some of the cartilage-derived biomarkers which have demonstrated potential applications in assessing disease severity2. Small leucine-rich proteoglycans (SLRPs), in particular, have been shown to be involved in OA pathogenesis5. These have been demonstrated to act as endogenous danger signal thus activating innate immune receptors and trigger catabolic responses in chondrocytes6. Moreover, SLRPs possess different regulatory functions such as complement activation7, which has been shown to be abnormally high in human osteoarthritic joints and supposed to have a role in OA pathogenesis8. Among SLRPs, decorin consists of a protein core containing leucine repeats with a glycosaminoglycan chain which binds to collagen fibrils and plays a role in matrix assembly. Released from cartilage matrix as a result of tissue injury, decorin has attracted potential interest as OA biomarker. The study by Ozler et al9 published in this issue investigated the levels of decorin in serum and synovial fluid (SF) samples collected from patients with knee OA and their relationship with clinical indices. The authors found significantly higher levels of decorin in serum of OA patients with respect to a control group. Although higher in OA, SF decorin did not show a significant difference with respect to controls. Comparing the concentration of the proteoglycan in the two biological fluids, OA patients showed significantly higher decorin levels in serum with respect to SF9. Investigating the association between decorin and clinical indices, a positive correlation was found between serum decorin levels and the WOMAC (Western Ontario and McMaster University) score, a composite index of pain, structural and functional features, which defines disease severity in OA10. Cut-off levels of serum decorin and WOMAC score for the prediction of OA evaluated with receiver operating characteristic analysis were also reported by the authors. Finally, a multivariate logistic regression analysis showed that increased decorin was an independent risk factor for OA. Overall, the results reported by Ozler et al9 support a role for decorin as a marker of disease state in OA. The association of this molecule with early tissue damage may warrant of further investigation involving a larger number of patients. Although in this study neither SF decorin level nor its SF/serum ratio was associated with the disease, SF represents a valuable biological substrate in the search for biomarkers in both OA and other arthropathies, in particular regarding cartilage-derived components. Indeed, SF composition reflects the metabolic status of synovial tissue and, with respect to serum and urine, offers a unique opportunity to study specific molecular patterns at the site of inflammation. The identification of calcium crystals in SF, for instance, allows to classify a subset of OA patients with higher WOMAC, algo-functional and severity indexes11. Studies investigating the ability of SF biomarkers in predicting radiographic progression in knee OA showed a role for aggrecan fragments12 and pro-inflammatory cytokines13. By contrast, a more recent study found an association between SF cartilage-derived biomarkers and joint injury but failed to demonstrate their useful prognostic role14. A drawback concerning SF pertains the presence of a joint effusion as well as the discomfort that arthrocentesis might cause to some patients. A routine SF sampling might therefore, be useful for diagnostic reasons but not feasible in terms of the evaluation of disease progression due to the necessity of multiple injections over the course of the disease. Owing to its intimate relationship with all joint tissues, SF has been largely utilized in proteomic studies on OA pathogenesis and biomarker search. In particular, the metabolomic profiling of the SF in OA has identified some metabolites as potential biomarkers of OA and has confirmed the existence of metabolically different OA phenotypes15. As far as serum is concerned, blood is frequently sampled in patients affected with rheumatic diseases, principally due to the need of controlling systemic inflammatory indices and lipoprotein and lipid profiles linked to the increased risk of these patients to develop cardiovascular comorbidities. As a consequence, serum represents the most used biological fluid for biomarker research in OA. The most common molecules investigated in serum are those indicative of collagen II degradation (e.g., CTX-II Coll2-1, C2C and C2M) and synthesis (e.g., PIIANP, PIIBNP and CPII). Others include COMP and proteoglycan epitopes16. The expression pattern of new molecules such as miRNA has shown to change in the serum of OA patients through upregulation or downregulation17. Some of these have demonstrated to be independent risk factors for OA, while others to have a diagnostic value. Contrary to serum and SF, urine can be obtained through a non-invasive method. Urine is widely used in OA biomarker research mainly to investigate the significance of collagen II fragments such as the c-telopeptide of type II collagen (uCTX-II)18. Cleaved by metalloproteinases, collagen fragment uCTX-II has been demonstrated to be higher at baseline in patients developing OA with respect to non-progressors19. It has also been suggested to be independently associated with the incidence and progression of OA, in particular, when associated to imaging markers of the disease20. Identifying early articular degradation is the most important challenge in OA research and clinical care. Currently, OA is diagnosed by radiographs when clinical signs of pain and loss of function have already appeared. At this point, intervening successfully on disease progression is much difficult. The challenge is to detect those molecular and metabolic changes which precede by many years the onset of structural alterations, hence the early disease detection. Another challenge is to identify markers capable of predicting patient's long-term progression. This might allow to tailor a more aggressive treatment to each patient. In conclusion, cartilage-derived components and metabolites have demonstrated promising application as diagnostic and prognostic markers in OA. A better definition of their role in predicting disease severity and progression will allow to improve patients’ management and to find new targets in this disease.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,433
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,010
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 Opus0,057
Tête enseignante GPT0,360
Écart entre enseignants0,303 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations9
Publié2021
Routes d'admission1
Résumé présentoui

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