Adipokines as Biomarkers of Cachexia and High-Risk Rheumatoid Arthritis

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Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease associated with arthritis and significant disability. The disease is also associated with a greater risk of early death. Active RA is also associated with greater use of energy, which results in unhealthy weight loss and muscle loss, and likely contributes to the substantial risk of disability and death. This study focuses on markers that can be measured in the blood that may identify these processes more clearly and identify those at greatest risk of these adverse outcomes. Adipokines, or fat-secreted cytokines, are important regulators of energy usage in the body. For example, adiponectin, aptly named the “starvation signal”, is thought to boost appetite and alter energy usage in an effort to maintain adequate energy availability in lean times. Therefore, high adiponectin levels are likely to be observed in patients who have experienced low energy availability as a result of their disease. High levels of adiponectin may help identify individuals at high risk. High adiponectin levels have been associated with greater mortality in chronic inflammatory conditions such as congestive heart failure and renal disease, and correlated with evidence of muscle loss. While similar studies have not been performed in RA, high adiponectin levels are associated with other adverse outcomes including joint damage progression. While observations in RA have led to speculation that adiponectin may play a causal role in the disease, we instead hypothesize that high serum adiponectin levels are in fact a marker of low energy availability in RA and therefore predictive of adverse outcomes. We previously demonstrated that weight loss in RA is associated with a higher risk of death. Accessible measures that are able to identify at-risk individuals would improve identification of high-risk disease to help focus therapy. This is an issue of precision medicine in the VA, since therapies for RA are expensive and likely over-utilized. Results of this study will affect how researchers consider adipokines and their role in the disease process. Aim 1 will leverage the VA Rheumatoid Arthritis (VARA) registry and National Data Bank (NDB). Each include an extensive DNA and serum repository among patients with RA and linkages to reliable and extensive clinical data. Aim 2 leverages a landmark clinical trial to evaluate prediction of outcomes in two common treatment strategies. Aim 3 is mechanistic and ancillary to Dr. Baker's existing VA- funded cohort with comprehensive longitudinal assessments of muscle and fat mass. Dr. Baker's cohort will be augmented through collaboration with two RA investigators to compile the largest-ever longitudinal RA cohort with muscle and fat assessments. The overall goal is to gain insight into the relationship between adiponectin and the disease, weight, obesity, muscle loss, disability and risk of early death. Aim 1 will determine if circulating adiponectin and variants in the adiponectin gene are associated with sustained remission, progressive disability, osteoporotic fractures, and mortality. We hypothesize that higher circulating adiponectin (but not gene variation) will be associated with greater long-term risks- an effect partly attenuated with adjustment for weight loss and low BMI. Aim 2 will evaluate adiponectin as a prognostic and predictive biomarker for attainment of low disease activity and radiographic progression in the RA: Comparison of Therapies Clinical Trial. We hypothesize that high adiponectin is associated with refractory disease and greater benefit for the biologic therapy arm. Aim 3 is more mechanistic and will determine if progression of muscle loss and altered fat distribution is associated with higher and increasing adiponectin in a longitudinal cohort. We hypothesize that greater increases in adiponectin will be observed among individuals with loss of muscle mass. These independent aims will provide information to guide the interpretation of adipocytokines in chronic inflammatory diseases and will lead to risk calculators that can be incorporated automatically into clinical care. Accessible clinical biomarkers would focus expensive treatments towards individuals at greatest long-term risk and identify individuals who are likely to benefit from interventions specific to their individual risks.