Biomarkers to Predict Patient Outcomes and Guide Therapies

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Obesity, a growing epidemic in the US and a health care priority in Healthy People 2015, is a risk factor for type 2 diabetes and cardiovascular disease. In recent work we have shown that in our cohort of 100 kidney transplant recipients, over half (56%) gained weight with the average amount of 9 kg., which is significantly more than the 1 kg average weight gain in US adults. This predictable and significant weight gain within a short amount of time, and its association with morbidity and mortality, makes this a high priority concern. The purpose of this program of research is to prospectively examine genetic (gene expression) and environmental factors (food intake, physical activity, demographic, health status, psychosocial) contributing to obesity at one year following renal transplantation in recipients. Long-term goals include prevention and treatment of obesity in recipients. Our hypothesis is that gene- environmental interactions can predict whether individuals will gain weight/become obese at one year post-transplant. Specifically we will (1) identify environmental factors associated with post-transplant weight gain, (2) identify gene expressions associated with weight gain, (3) use Bayesian analysis to determine combinations of gene- environment interactions that predict weight gain and obesity. A prospective design was used to compare genetic and environmental factors and clinical outcomes at baseline, 3, 6, and 12 months post-transplant. Gene expression profiling using microarray analysis and real-time polymerase chain reaction on adipose tissue was used to identify key regulatory elements that play a major role in obesity. Bayesian Network modeling was used to investigate causal relationships. This significant and innovative study incorporates an interdisciplinary approach to combine emerging genomic and bioinformatic technologies with traditional methodologies to explicate key gene-environment interactions responsible for post-transplant obesity. The relevance of this study is that findings will assist health care practitioners in caring for renal transplant recipients so that they do not gain weight and become obese following renal transplantation. This will result in fewer health care problems following transplantation. Our recent studies and publications have reported on the findings including 1.) emphasized the association of selected gene expression levels in adipose tissue and specific pathways to weight gain and provided clues to potential underlying mechanisms; 2.) Bayesian network modeling identified four significant predictors (at time of transplantation) for weight gain (at 1-year post-transplant): younger age, higher carbohydrate consumption, higher trunk fat percentage, and higher perception of mental health quality of life. Physical activity was not found to increase during the post-transplant period; and 3.) effects of food availability on body mass index change during the first year post-transplant. Future work will explore the ability of a signature (ie, biomarker) composed of adipose and blood biomarkers to identify those at risk for weight gain and will further explore the underlying biologic mechanisms.