Project Summary
Osteoarthritis (OA) is a degenerative joint disease which affects more than 27 million people in the US and is
the single most common cause of disability in older adults. The number of people affected with symptomatic OA
will increase due to the aging of the population and the growing number of people with obesity, which represents
an established risk factor for OA. Obesity has become a US “epidemic,” and projections have suggested that
86.3% of adults in the United States will be overweight or obese by 2030. Obesity is a modifiable risk factor for
OA with weight loss offering a potential non-invasive therapy for disease prevention in obese and overweight
individuals. Research has shown that weight loss slows OA progression and weight gain exacerbates
progression. However, these studies did not specifically assess factors or pathways which would be responsible
for improved or worse outcomes, such as associated inflammation, local body composition and sarcopenia.
In the current proposal, we will comprehensibly examine the mechanisms associated with mechanical
loading (weight loss and gain) that are responsible for driving knee joint degenerative changes including
cartilage loss, namely concurrent changes in inflammation, local body composition (periarticular fat),
and muscle morphology and strength. Identifying which mechanism(s) are most beneficial for slowing
OA progression during weight loss will lead to targeted therapies for effective and optimized treatment
of OA at early stages of disease during which progression may be prevented. Through pathway analysis,
mediation analysis, and machine learning, we will identify potential preventive measures (such as muscle
strengthening and anti-inflammatory measures) that could amplify the positive effects of reduced mechanical
loading on OA. Thus, the clinical impact of our project is development of a subject-specific prediction model for
clinicians to individually-tailor treatment plans that slow joint breakdown, and decrease probability for invasive
and costly surgeries such as total knee arthroplasty.
Three specific aims are proposed:
Specific Aim 1: We will characterize the associations between changes in weight with changes in knee joint
inflammation, local body composition, muscle cross-sectional area (CSA), fat infiltration and muscle strength,
and investigate the associations between these parameters and progression of knee degenerative changes.
Specific Aim 2: Using a path analysis we will explore the mechanisms by which weight change impacts OA
progression, and quantify the degree to which these factors mediate this relationship.
Specific Aim 3: Finally, we will develop a prediction model using machine learning to determine which
demographic, clinical, and MRI features (including changes in local body composition, inflammation, muscle)
can predict progression of OA over 8 years.
