Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal Biomechanics

骨关节炎（OA）是一种导致关节变性的多因素疾病，影响着2700万美国人

Osteoarthritis (OA), a multifactorial disease that causes joint degeneration, affects 27 million U.S. adults, and often leads to severe disability. The prevalence of OA is 33.6% in adults older than 65 years. Despite the fact that OA is a widespread and debilitating disease, treatment options are currently extremely limited, and established disease-modifying therapies do not exist. The solution generally offered to treat late stage, symptomatic knee OA is joint replacement (total knee/hip arthroplasty, TKA/THA), and while this surgically invasive and expensive remedy offers temporary relief, replacements often fail after 10-15 years, with shorter life spans in obese individuals. In order to reduce the number of TKA/THA procedures, preventive efforts and interventions targeting early stage OA are essential – the first step would be identifying subjects at high risk for disease development, at a stage when tissue is not yet lost, and cartilage matrix abnormalities are potentially reversible. Understanding the complex pathophysiology of joint degeneration, knee and hip joint interactions, impact of gait biomechanics, are all critical to determine the mechanistic basis of hip OA. Hip OA progression marked by changes in cartilage biochemistry has complex multi-joint interactions with several mechanistic factors, including morphological features, gait biomechanics and demographics. However, studies to date have mostly focused on the relationship between single mechanistic factors and semi-quantitative/ quantitative imaging measures, gait biomechanics, or non-objective symptomatic evidence of hip OA. The overall goal of this competitive renewal is to extend our longitudinal work to determine the structure-function-connectivity and mechanistic factors that mediate biochemical degeneration of hip cartilage associated with OA progression over the mid-term (6-8-year) period. In this proposal, subjects will be recruited for a longitudinal study (covering 5-8 year follow up) from our existing cohort (n=184 hips, 92 subjects), with the availability of existing 3-year follow-up data including bi- lateral hip radiographs and MR images, gait biomechanics and patient reported outcomes. We will use novel, fully automated and translational methods to measure MRI-based T1ρ and T2 relaxation time on a voxel basis, which will provide precise and localized information on cartilage proteoglycan and collagen integrity and water content. Automatic segmentation techniques combined with machine learning algorithms will be used to analyze cartilage T1ρ/T2 in a well-characterized dataset with quantifiable T1ρ/T2 values on a voxel-level, and to automatically detect the patterns of focal T1ρ/T2 elevations that lead to later stage OA and drive progression of degenerative disease (over 8 years). Detailed analysis of gait biomechanics using functional principal component analysis will utilize the rich skeletal biomechanics data and imaging (over 8 years). Imaging the knee and hip longitudinally will provide a multi-joint analysis of OA progression (over 4 years) not done previously and will provide unique insights in the mechanisms in hip OA, connections with knee changes.