BACKGROUND: Biomarkers of cartilage metabolism have prognostic potential. OBJECTIVE: To examine whether serum cartilage biomarkers, cartilage oligomeric matrix protein (COMP), N-propeptide of type IIA procollagen (PIIANP), type II collagen breakdown product (collagen type-II cleavage (C2C)) predict cartilage volume loss and knee joint replacement. METHODS: 117 subjects with knee osteoarthritis (OA) had MRI at baseline and 2 years. Cartilage biomarkers were measured at baseline. Change in knee cartilage volume over 2 years and knee joint replacement over 4 years was determined. The population was divided into subgroups with high or low cartilage biomarkers (based on biomarker levels greater than or equal to, or less than, the mean, respectively). The relationships between biomarkers and outcome measures were examined in the whole population, and separately in marker subgroups. RESULTS: The relationship between cartilage biomarkers and cartilage volume loss was not linear across the whole population. In the low (regression coefficient B=-9.7, 95% CI -0.01 to 0.003, p=0.01), but not high (B=-0.46, 95% CI -8.9 to 8.0, p=0.92) COMP subgroup, COMP was significantly associated with a reduced rate of medial cartilage volume loss (p for difference between groups=0.05). Similarly, in the low (B=-8.2, 95% CI -12.9 to -3.5, p=0.001) but not high (B=2.6, 95% CI -3.3 to 8.5, p=0.38) PIIANP subgroup, PIIANP was associated with a significantly reduced rate of medial volume cartilage loss (p for difference=0.003). C2C was not significantly associated with rate of cartilage volume loss. PIIANP was associated with a reduced risk of joint replacement (odds ratio (OR)=0.28, 95% CI 0.10 to 0.93, p=0.04). CONCLUSION: Cartilage biomarkers may be used to identify subgroups among those with clinical knee OA in whom disease progresses at different rates. This may facilitate our understanding of the pathogenesis of disease and allow us to differentiate phenotypes of disease within a heterogeneous knee OA population.
BACKGROUND: Biomarkers of cartilage metabolism have prognostic potential. OBJECTIVE: To examine whether serum cartilage biomarkers, cartilage oligomeric matrix protein (COMP), N-propeptide of type IIA procollagen (PIIANP), type II collagen breakdown product (collagen type-II cleavage (C2C)) predict cartilage volume loss and knee joint replacement. METHODS: 117 subjects with knee osteoarthritis (OA) had MRI at baseline and 2 years. Cartilage biomarkers were measured at baseline. Change in knee cartilage volume over 2 years and knee joint replacement over 4 years was determined. The population was divided into subgroups with high or low cartilage biomarkers (based on biomarker levels greater than or equal to, or less than, the mean, respectively). The relationships between biomarkers and outcome measures were examined in the whole population, and separately in marker subgroups. RESULTS: The relationship between cartilage biomarkers and cartilage volume loss was not linear across the whole population. In the low (regression coefficient B=-9.7, 95% CI -0.01 to 0.003, p=0.01), but not high (B=-0.46, 95% CI -8.9 to 8.0, p=0.92) COMP subgroup, COMP was significantly associated with a reduced rate of medial cartilage volume loss (p for difference between groups=0.05). Similarly, in the low (B=-8.2, 95% CI -12.9 to -3.5, p=0.001) but not high (B=2.6, 95% CI -3.3 to 8.5, p=0.38) PIIANP subgroup, PIIANP was associated with a significantly reduced rate of medial volume cartilage loss (p for difference=0.003). C2C was not significantly associated with rate of cartilage volume loss. PIIANP was associated with a reduced risk of joint replacement (odds ratio (OR)=0.28, 95% CI 0.10 to 0.93, p=0.04). CONCLUSION: Cartilage biomarkers may be used to identify subgroups among those with clinical knee OA in whom disease progresses at different rates. This may facilitate our understanding of the pathogenesis of disease and allow us to differentiate phenotypes of disease within a heterogeneous knee OA population.
Authors: I Gusti Ngurah Yudhi Setiawan; I Ketut Suyasa; Putu Astawa; I Wayan Suryanto Dusak; I Ketut Siki Kawiyana; I Gusti Ngurah Wien Aryana Journal: J Orthop Date: 2019-03-02
Authors: Matthew S Harkey; J Troy Blackburn; Anthony C Hackney; Michael D Lewek; Randy J Schmitz; Brian Pietrosimone Journal: Cartilage Date: 2020-09-19 Impact factor: 3.117
Authors: Uwe H W Schütz; Arno Schmidt-Trucksäss; Beat Knechtle; Jürgen Machann; Heike Wiedelbach; Martin Ehrhardt; Wolfgang Freund; Stefan Gröninger; Horst Brunner; Ingo Schulze; Hans-Jürgen Brambs; Christian Billich Journal: BMC Med Date: 2012-07-19 Impact factor: 8.775
Authors: M Lotz; J Martel-Pelletier; C Christiansen; M-L Brandi; O Bruyère; R Chapurlat; J Collette; C Cooper; G Giacovelli; J A Kanis; M A Karsdal; V Kraus; W F Lems; I Meulenbelt; J-P Pelletier; J-P Raynauld; S Reiter-Niesert; R Rizzoli; L J Sandell; W E Van Spil; J-Y Reginster Journal: Postgrad Med J Date: 2014-03 Impact factor: 2.401
Authors: M Lotz; J Martel-Pelletier; C Christiansen; M-L Brandi; O Bruyère; R Chapurlat; J Collette; C Cooper; G Giacovelli; J A Kanis; M A Karsdal; V Kraus; W F Lems; I Meulenbelt; J-P Pelletier; J-P Raynauld; S Reiter-Niesert; R Rizzoli; L J Sandell; W E Van Spil; J-Y Reginster Journal: Ann Rheum Dis Date: 2013-07-29 Impact factor: 19.103
Authors: Stefan Kluzek; Anne-Christine Bay-Jensen; Andrew Judge; Morten A Karsdal; Matthew Shorthose; Tim Spector; Deborah Hart; Julia L Newton; Nigel K Arden Journal: Biomarkers Date: 2016-02-05 Impact factor: 2.658