Biomarkers to Predict Patient Outcomes and Guide Therapies

肥胖是美国日益严重的流行病，也是2015年健康人的医疗保健重点，它是2型糖尿病和心血管疾病的风险因素。在最近的工作中，我们发现，在我们的100名肾移植受者中，超过一半(56%)的人体重增加了，平均体重增加了9公斤，明显高于美国成年人平均1公斤的体重增加。这种可预测的、在短时间内显著增加的体重，及其与发病率和死亡率的关联，使这一问题成为高度优先关注的问题。 这项研究的目的是前瞻性地检查基因(基因表达)和环境因素(食物摄入量、体力活动、人口统计、健康状况、心理社会)在受者肾移植后一年内导致肥胖的原因。长期目标包括预防和治疗受者肥胖。我们的假设是，基因-环境相互作用可以预测个体在移植后一年是否会增加体重/变得肥胖。具体地说，我们将(1)确定与移植后体重增加相关的环境因素，(2)确定与体重增加相关的基因表达，(3)使用贝叶斯分析来确定预测体重增加和肥胖的基因-环境相互作用的组合。采用前瞻性设计，比较移植后3个月、6个月和12个月的遗传和环境因素以及临床结果。使用微阵列分析和脂肪组织的实时聚合酶链式反应进行基因表达谱分析，以确定在肥胖中起主要作用的关键调控元件。贝叶斯网络模型被用来研究因果关系。这项重要而创新的研究结合了一种跨学科的方法，将新兴的基因组和生物信息学技术与传统方法相结合，以阐明导致移植后肥胖的关键基因-环境相互作用。这项研究的相关性在于，研究结果将有助于卫生保健从业者照顾肾移植受者，使他们在肾移植后不会增加体重和变得肥胖。这将减少移植后的医疗保健问题。 我们最近的研究和出版物已经报道了这些结果，包括1。)强调脂肪组织中选定的基因表达水平与体重增加的特定途径之间的关联，并为潜在的潜在机制提供线索；贝叶斯网络模型确定了(在移植时)体重增加(在移植后一年)的四个显著预测因素：较年轻的年龄、较高的碳水化合物摄入量、较高的躯干脂肪百分比以及较高的心理健康生活质量。在移植后期间，没有发现体力活动增加；移植后第一年内食物供应对体重指数变化的影响。未来的工作将探索由脂肪和血液生物标记物组成的信号(即生物标记物)识别体重增加风险的能力，并将进一步探索潜在的生物机制。

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.