Osteoarthritis affects different patient phenotypes with heterogeneous clinical presentation, rate of progression, and response to therapy, and thus appears as an optimal candidate for personalized medicine. |
The level of pain, functional limitation, and presence of coexistent chronic conditions including frailty status should be considered to guide treatment decisions. |
Magnetic resonance imaging-based diagnosis could be used in drug development and in clinical practice to identify patients more likely to benefit from treatment. |
Promising potential biomarkers (e.g., biochemical, genetic, epigenetic) currently under investigations could be used in the near future to guide clinical decision making. |
1 Introduction
2 Process and Outcomes
3 Risk Factors for Progression and Predictors of Response to Treatments
3.1 Risk Factors for Progression
3.2 Predictors of Response to Treatment
4 Imaging Markers
4.1 Extent of Cartilage Damage
4.2 Presence of Bone Marrow Lesions
4.3 Presence of Meniscal Extrusion
5 Clinical and Biochemical Biomarkers
5.1 Clinical Biomarkers
5.2 Biochemical Biomarkers
5.3 Genetic and Epigenetic Markers
5.4 Genetic Biomarkers
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FTO (fat mass and obesity-associated) signal is robustly associated with BMI, and showed evidence of association with OA underpinning the known epidemiological link between BMI and OA [42];
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The IL1RN (interleukin-1 receptor antagonist) C-T-A haplotype may have a role in severe knee OA which is consistent with the possible role of IL-1 as a regulator of cartilage degradation [43];
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COL11A1, which could play a role in erosion of joints cartilage in OA, is a strong candidate gene for OA [44];
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GDF5 (Growth and differentiation factor 5), to promote the development, maintenance, and repair of joint tissues is associated with OA of the knee with genome-wide statistical significance [45];
5.5 Epigenetic Biomarkers
6 Frailty and OA
6.1 The Frailty Phenotype
6.2 Association of OA with Frailty Status
6.3 Ongoing Projects with a Strong Potential for Synergies and Complementarities in the Fields of Frailty and OA
7 Conclusions
Identify published randomized clinical trials and observational cohorts assessing the efficacy of different class of interventions on clinically relevant outcomes, divided in structural and symptomatic outcomes |
Using above data, produce clinical prediction tools to quantify a patient’s risk of progression and good outcomes from treatment interventions |
With available data, identify phenotypes of patients according to their outcome. Panel of (bio)markers (clinical, biochemical, imaging) should be investigated, rather than individual items |
Assess the uniformity of data across clinical trials and cohorts |
Proceed to a validation step on a separate validation cohort |
Ensure that all new cohorts and trials use the same core dataset to allow easy integration into extant data |
Possible limitations: |
The availability of the data and of the biological specimens in cohorts |
The high heterogeneity in the assessments methods and reporting (e.g., multiple assessment tools for pain), which would require a hierarchical/standardization of criteria |
Age [67] |
BMI [10] |
Racial origin [69] |
Occupation [70] |
Comorbidities [71] |
Menopausal status [72] |
Presence of chondrocalcinosis [73] |
OA pain in other joints [10] |
X-ray grade of target joint [10] |
OA medication use (analgesia and disease modification) |
Previous joint surgery (in particular menisectomy) [75] |