Genetics, Environment and Weight Gain Posttransplant

描述（由申请人提供）：肥胖是美国日益增长的流行病，也是2010年健康人医疗保健的优先事项，是肾移植受者患2型糖尿病、心血管疾病和其他慢性疾病的危险因素。肾移植受者在移植后的第一年平均增重22磅，这明显高于美国成年人平均增重2磅。此外，非裔美国人（AA）女性体重增加和发病的风险最高。这种可预见的短时间内显著的体重增加，特别是在AAs中，以及它与发病率和死亡率的关系，使其成为一个高度优先关注的问题。本应用程序的目的是前瞻性地检查遗传（基因表达）和环境因素（食物摄入、身体活动、人口统计学、健康状况、社会心理）对移植时非肥胖受者肾移植后一年内肥胖的影响。长期目标包括预防和治疗接受者的肥胖。我们的假设是基因-环境的相互作用可以预测个体在移植后一年内是否会变得肥胖。具体来说，我们将(1)确定与移植后肥胖相关的多达10个环境因素，(2)确定与肥胖相关的多达10个基因表达，(3)使用贝叶斯分析来确定预测肥胖的基因-环境相互作用的组合。前瞻性设计将用于比较遗传和环境因素以及移植后基线、3、6、12和24个月的临床结果。利用微阵列分析和实时聚合酶链反应对脂肪组织进行基因表达谱分析，将用于确定在肥胖中起主要作用的关键调控元件。贝叶斯网络建模将用于调查因果关系。这项重要而创新的研究采用跨学科的方法，将新兴的基因组和生物信息学技术与传统方法相结合，以解释导致移植后肥胖的关键基因-环境相互作用。本研究的相关性是，研究结果将有助于医护人员照顾肾移植受者，使他们不会在肾移植后体重增加和肥胖。这将减少移植后的医疗保健问题。

DESCRIPTION (provided by applicant): Obesity, a growing epidemic in the US and a health care priority in Healthy People 2010, is a risk factor for type 2 diabetes mellitus, cardiovascular disease and other chronic diseases in renal transplant recipients. Renal transplant recipients gain an average of 22 Ibs during the first year following transplant, which is significantly more than the 2 Ibs average weight gain in US adults. In addition, African American (AA) females are most at risk for weight gain and morbidity. This predictable and significant weight gain within a short amount of time, particularly in AAs, and its association with morbidity and mortality makes this a high priority concern. The purpose of this application 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 who are non-obese at time of transplant. Long-term goals include prevention and treatment of obesity in recipients. Our hypothesis is that gene- environmental interactions can predict whether individuals will become obese at one year post transplant. Specifically we will (1) identify up to 10 environmental factors associated with post transplant obesity, (2) identify up to 10 gene expressions associated with obesity, (3) use Bayesian analysis to determine combinations of gene- environment interactions that predict obesity. A prospective design will be used to compare genetic and environmental factors and clinical outcomes at baseline, 3, 6, 12 and 24 months post transplant. Gene expression profiling using microarray analysis and real-time polymerase chain reaction on adipose tissue will be used to identify key regulatory elements that play a major role in obesity. Bayesian Network modeling will be 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.