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。在之前的工作中，我们发现在我们的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。在移植后的第一年内，这明显超过了美国成年人平均体重增加1 kg。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。 使用NIH症状科学模型指导我们，我们对从肾移植受者前瞻性获得的样本进行了研究。我们研究了基因组学、蛋白质组学和环境因素（食物摄入、体力活动、人口统计学、健康状况、心理社会）对肾移植受者术后1年肥胖的影响。长期目标包括预防和治疗接受者的肥胖症。我们的假设是，基因与环境的相互作用可以预测个体在移植后一年是否会体重增加/肥胖。具体而言，我们将使用新兴的组学技术/方法来（1）鉴定与移植后体重增加相关的环境因素，（2）鉴定与体重增加相关的基因表达，（3）使用新兴的分析方法来确定预测体重增加和肥胖的基因-环境相互作用的组合。最初采用前瞻性设计比较遗传和环境因素以及基线、移植后3个月、6个月和12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，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一些发现，包括血浆脑源性神经营养因子（BDNF）浓度对肾移植后体重增加的影响，我们正在发表关于外泌体蛋白质谱的表征及其对肾移植后体重变化的影响。 与其他人群中新兴方法相关的出版物包括一篇关于重度再生障碍性贫血患者微生物组的出版物。 其他出版物包括评论使用基因组学和其他组学技术，探讨护理研究问题有关的症状科学。 今年我的国家卫生研究院实验室将关闭。 不过，我将继续与其他NIH项目合作。

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 previous work we showed that in our cohort of 100 kidney transplant recipients, over half (56%) gained weight with the average amount of 9 kg. within the first year following transplantation, 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. Using the NIH-Symptom Science Model to guide us, we have conducted studies on samples prospectively obtained from kidney transplant recipients. We examined genomic, proteomic, 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 use emerging omic technologies/methodologies to (1) identify environmental factors associated with post-transplant weight gain, (2) identify gene expressions associated with weight gain, (3) use emerging analysis methodologies to determine combinations of gene- environment interactions that predict weight gain and obesity. Originally 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 findings including the impact of plasma brain-derived neurotrophic factor (BDNF) concentration on weight gain after kidney transplantation and we are in the process of publishing on the characterization of exosomal protein profiles and their impact on weight changes after kidney transplantation. Publications related to emerging methods in other populations included a publication on the microbiome in patients with severe aplastic anemia. Other publications included commentaries on using genomics and other omic techniques to explore nursing research questions relevant to symptom science. This year my NIH lab will be closing. However, I will continue collaborative work with other NIH projects.