Value of Biomarkers in Osteoarthritis
Value of Biomarkers in Osteoarthritis
None of the current biomarkers is sufficiently discriminating to aid the diagnosis of osteoarthritis or aid the prognosis of individuals with or without the disease or performs so consistently that it could function as a surrogate outcome in clinical trials as a secondary or supportive endpoint. The ESCEO group identified a number of possible avenues for future research in the field (Box 2), some of which are necessary before biomarkers can enter routine clinical use for monitoring disease activity or in drug development for the measurement of response to treatment in osteoarthritis. The arrival of new DMOADs may help better define biomarkers in osteoarthritis.
Despite this, there are a few very promising candidates. The most promising are biomarkers of cartilage metabolism, although biomarkers of synovial tissue degradation may also prove to be useful in future. In this context, studies of the synovial fluid proteome have explored the contribution of innate immunity and the complement cascade. The results suggest a major contribution of synovial gene products to the osteoarthritis synovial proteome. A systematic review applying the BIPED classification indicated that urinary CTX-II and serum COMP appear to have the best performance of all commercially available biomarkers. On the other hand, the same review also highlighted a general lack of consistent evidence, differences between the populations studied (clinical trial populations vs population-based cohorts), differences in sample collection and possibly publication bias. Thus, future research efforts should be made to validate the existing markers and identify new candidates (Box 2). Such research should involve further exploration of the underlying mechanisms of disease. The ongoing head-to-head comparison of a large panel of biomarkers (a dozen biochemical biomarkers and multiple sensitive imaging biomarkers) through the OARSI/Foundation for NIH study promises to provide much needed vital information for determining some of the best candidates for further qualification in clinical trial scenarios.
Interestingly, the biomarkers with the most consistent evidence appear to be those at the end of the pathways of tissue destruction, which may be more specific for a particular tissue (eg, CTX-II). In this context, there is evidence that there may be differences in some biomarkers between different joints or tissues, and there are indications of differences in relation to distance from the surface of the joint.
The use of biomarkers in drug development in osteoarthritis remains a subject of research. The ESCEO group noted the differences with the field of osteoporosis, for which the use of biomarkers is much better established. This is principally due to the magnitude of clinical advances in osteoporosis in the last 20 years, for which there is now a range of disease-modifying treatments, in comparison with osteoarthritis, for which DMOADs are only just beginning to appear with the need to sufficiently justify their efficacy and safety to use them in biomarker development or research. Another point is that the amount of bone mass involved in bone turnover of a joint is much larger than the small amount of cartilage available in the joints; this suggests that the systemic biomarker contribution due to localised high bone turnover at a specific osteoarthritic joint might be more readily detected than the contribution due to high cartilage turnover of the joint. However, in all cases, the biomarker contributions from local disease need to be distinguished from the contributions of normal bone and cartilage turnover; this is currently a primary limitation for all systemic (serum and urine) biomarker measures. For use in Phase II or dose-finding studies, biomarkers should be able to predict structural change. In Phase III, they should predict more concrete endpoints, such as surgery, or indeed play an explanatory role in terms of mechanism of action. In this context, there is still much discussion in the osteoarthritis field regarding hard endpoints, since joint replacement is affected by a number of factors unrelated to efficacy of treatment (eg, patient preference, access to healthcare, individual variations in pain and symptoms). Surrogate endpoints for joint replacement have been developed, such as virtual joint replacement, although consensus has not been reached in the field and the potential role of biomarkers remains to be defined. Moreover, there is currently no hard evidence that improving structure (JSN or cartilage volume loss) improves symptoms (pain and function) in osteoarthritis.
One theoretical advantage of biomarkers in all fields of medicine is the detection of the early phases of disease. The ability to detect early stage osteoarthritis would not only improve the management of patients via preventative measures and lifestyle changes, but may also facilitate efforts in drug development by improving the design of randomised clinical trials. Many biological markers appear to be sufficiently characterised for the study of progressive osteoarthritis, but few have been identified for the diagnosis of the early stage of the disease. Moreover, early osteoarthritis has not been clearly defined. This is an important avenue for future research (Box 2).
The ability to predict the onset or progression of disease is an important feature of biomarker research since it will have implications in clinical medicine. In this context, clinicians need reassurance that the biomarkers truly represent clinical outcomes (ie, structural damage). Indeed, predictive value is essential to determine which patients are likely to progress rapidly and, ideally, which patients are likely to respond to treatment. A biomarker that characterises risk for generalised disease should have broad utility. A related issue is the role of biomarkers in the context of risk factors and their assessment.
There is increasing evidence that the best solution may be a panel of biomarkers, covering the range of physiological effects, or indeed a combination of tissue biomarkers with other parameters (eg, radiographic or MRI) into single diagnostic tests. An analysis of a set of 14 biomarkers in a cohort of 1002 individuals with early osteoarthritis (Cohort Hip and Cohort Knee) suggested the possibility of clusters of biomarkers related to specific pathogenic processes, for example, cartilage synthesis, synovium or inflammation. Recognising such underlying patterns may increase prognostic accuracy and predictive power, thus enabling better identification of at-risk patients; further research is essential (Box 2).
A well-selected panel of biomarkers may also assist in identifying the patients who are most likely to respond (personalised medicine). In theory, targeting responders would reduce the cost of treatment on the population level by eliminating redundant prescriptions and improving benefits for patients. The ideal panel should include, for example, biomarkers of turnover, synthesis and degradation and tissue-specific biomarkers (cartilage, subchondral bone and inflammation). Ratios of specific biomarkers may also prove to be important.
There are a number of major research limitations that need to be addressed in the field of biomarkers. One of these is the rarity of DMOADs, which has hindered research to find a valid biochemical marker. There are many potential errors in biomarker analysis related to sampling, biological variations (eg, seasonal, diurnal and food intake), analyte features, assay format and parameters, which can confound results. These should be resolved by further research in assays and technological development, as well as standardisation and calibration of known biomarkers and optimisation of data collection (Box 2). Another limitation is the non-linear relationship between biomarkers and some structural parameters, which will complicate the development of prognostic biomarkers. In addition, the identification of patients is complicated since the pathways to a failed joint may involve many different mechanisms (eg, bone-active treatments work in some patients and anti-inflammatories in others). Another limitation is the relationship between osteoarthritis biomarkers and 'normal' age-related processes independent of the osteoarthritis process, which is not well defined.
Discussion
None of the current biomarkers is sufficiently discriminating to aid the diagnosis of osteoarthritis or aid the prognosis of individuals with or without the disease or performs so consistently that it could function as a surrogate outcome in clinical trials as a secondary or supportive endpoint. The ESCEO group identified a number of possible avenues for future research in the field (Box 2), some of which are necessary before biomarkers can enter routine clinical use for monitoring disease activity or in drug development for the measurement of response to treatment in osteoarthritis. The arrival of new DMOADs may help better define biomarkers in osteoarthritis.
Despite this, there are a few very promising candidates. The most promising are biomarkers of cartilage metabolism, although biomarkers of synovial tissue degradation may also prove to be useful in future. In this context, studies of the synovial fluid proteome have explored the contribution of innate immunity and the complement cascade. The results suggest a major contribution of synovial gene products to the osteoarthritis synovial proteome. A systematic review applying the BIPED classification indicated that urinary CTX-II and serum COMP appear to have the best performance of all commercially available biomarkers. On the other hand, the same review also highlighted a general lack of consistent evidence, differences between the populations studied (clinical trial populations vs population-based cohorts), differences in sample collection and possibly publication bias. Thus, future research efforts should be made to validate the existing markers and identify new candidates (Box 2). Such research should involve further exploration of the underlying mechanisms of disease. The ongoing head-to-head comparison of a large panel of biomarkers (a dozen biochemical biomarkers and multiple sensitive imaging biomarkers) through the OARSI/Foundation for NIH study promises to provide much needed vital information for determining some of the best candidates for further qualification in clinical trial scenarios.
Interestingly, the biomarkers with the most consistent evidence appear to be those at the end of the pathways of tissue destruction, which may be more specific for a particular tissue (eg, CTX-II). In this context, there is evidence that there may be differences in some biomarkers between different joints or tissues, and there are indications of differences in relation to distance from the surface of the joint.
The use of biomarkers in drug development in osteoarthritis remains a subject of research. The ESCEO group noted the differences with the field of osteoporosis, for which the use of biomarkers is much better established. This is principally due to the magnitude of clinical advances in osteoporosis in the last 20 years, for which there is now a range of disease-modifying treatments, in comparison with osteoarthritis, for which DMOADs are only just beginning to appear with the need to sufficiently justify their efficacy and safety to use them in biomarker development or research. Another point is that the amount of bone mass involved in bone turnover of a joint is much larger than the small amount of cartilage available in the joints; this suggests that the systemic biomarker contribution due to localised high bone turnover at a specific osteoarthritic joint might be more readily detected than the contribution due to high cartilage turnover of the joint. However, in all cases, the biomarker contributions from local disease need to be distinguished from the contributions of normal bone and cartilage turnover; this is currently a primary limitation for all systemic (serum and urine) biomarker measures. For use in Phase II or dose-finding studies, biomarkers should be able to predict structural change. In Phase III, they should predict more concrete endpoints, such as surgery, or indeed play an explanatory role in terms of mechanism of action. In this context, there is still much discussion in the osteoarthritis field regarding hard endpoints, since joint replacement is affected by a number of factors unrelated to efficacy of treatment (eg, patient preference, access to healthcare, individual variations in pain and symptoms). Surrogate endpoints for joint replacement have been developed, such as virtual joint replacement, although consensus has not been reached in the field and the potential role of biomarkers remains to be defined. Moreover, there is currently no hard evidence that improving structure (JSN or cartilage volume loss) improves symptoms (pain and function) in osteoarthritis.
One theoretical advantage of biomarkers in all fields of medicine is the detection of the early phases of disease. The ability to detect early stage osteoarthritis would not only improve the management of patients via preventative measures and lifestyle changes, but may also facilitate efforts in drug development by improving the design of randomised clinical trials. Many biological markers appear to be sufficiently characterised for the study of progressive osteoarthritis, but few have been identified for the diagnosis of the early stage of the disease. Moreover, early osteoarthritis has not been clearly defined. This is an important avenue for future research (Box 2).
The ability to predict the onset or progression of disease is an important feature of biomarker research since it will have implications in clinical medicine. In this context, clinicians need reassurance that the biomarkers truly represent clinical outcomes (ie, structural damage). Indeed, predictive value is essential to determine which patients are likely to progress rapidly and, ideally, which patients are likely to respond to treatment. A biomarker that characterises risk for generalised disease should have broad utility. A related issue is the role of biomarkers in the context of risk factors and their assessment.
There is increasing evidence that the best solution may be a panel of biomarkers, covering the range of physiological effects, or indeed a combination of tissue biomarkers with other parameters (eg, radiographic or MRI) into single diagnostic tests. An analysis of a set of 14 biomarkers in a cohort of 1002 individuals with early osteoarthritis (Cohort Hip and Cohort Knee) suggested the possibility of clusters of biomarkers related to specific pathogenic processes, for example, cartilage synthesis, synovium or inflammation. Recognising such underlying patterns may increase prognostic accuracy and predictive power, thus enabling better identification of at-risk patients; further research is essential (Box 2).
A well-selected panel of biomarkers may also assist in identifying the patients who are most likely to respond (personalised medicine). In theory, targeting responders would reduce the cost of treatment on the population level by eliminating redundant prescriptions and improving benefits for patients. The ideal panel should include, for example, biomarkers of turnover, synthesis and degradation and tissue-specific biomarkers (cartilage, subchondral bone and inflammation). Ratios of specific biomarkers may also prove to be important.
There are a number of major research limitations that need to be addressed in the field of biomarkers. One of these is the rarity of DMOADs, which has hindered research to find a valid biochemical marker. There are many potential errors in biomarker analysis related to sampling, biological variations (eg, seasonal, diurnal and food intake), analyte features, assay format and parameters, which can confound results. These should be resolved by further research in assays and technological development, as well as standardisation and calibration of known biomarkers and optimisation of data collection (Box 2). Another limitation is the non-linear relationship between biomarkers and some structural parameters, which will complicate the development of prognostic biomarkers. In addition, the identification of patients is complicated since the pathways to a failed joint may involve many different mechanisms (eg, bone-active treatments work in some patients and anti-inflammatories in others). Another limitation is the relationship between osteoarthritis biomarkers and 'normal' age-related processes independent of the osteoarthritis process, which is not well defined.
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