Biomarkers to Predict Patient Outcomes and Guide Therapies

肥胖是美国日益增长的流行病，也是2015年健康人群的医疗保健重点，是2型糖尿病和心血管疾病的危险因素。 在最近的工作中，我们已经表明，在我们的100名肾移植受者队列中，超过一半（56%）的体重增加，平均体重增加9公斤，这明显高于美国成年人平均体重增加1公斤。这种可预测的和显着的体重增加在短时间内，其与发病率和死亡率的关联，使之成为一个高度优先关注。 这项研究计划的目的是前瞻性地检查遗传（基因表达）和环境因素（食物摄入量，体力活动，人口统计学，健康状况，心理社会）在肾移植后一年内导致肥胖。长期目标包括预防和治疗接受者的肥胖症。我们的假设是，基因-环境相互作用可以预测个体是否会在移植后一年增加体重/变得肥胖。具体来说，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了基线、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局。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，以确定在肥胖中起主要作用的关键调控元件。贝叶斯网络模型用于调查因果关系。这项重要的创新研究采用了跨学科的方法，将新兴的基因组和生物信息学技术与传统方法联合收割机相结合，以阐明导致移植后肥胖的关键基因-环境相互作用。这项研究的相关性是，研究结果将有助于医疗保健从业人员照顾肾移植受者，使他们不会增加体重，并成为肾移植后肥胖。这将减少移植后的医疗问题。 我们最近的研究和出版物报道了这些发现，包括1。强调了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了线索; 2.）贝叶斯网络模型确定了体重增加（移植后1年）的四个重要预测因素（移植时）：年龄更小，碳水化合物消耗量更高，躯干脂肪百分比更高，以及对心理健康生活质量的感知更高。在移植后期间没有发现身体活动增加;和3.）移植后第一年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志物组成的签名（即生物标志物）识别体重增加风险的能力，并将进一步探索潜在的生物学机制。

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.