Diabetic Complications and Genetic Variants in the Million Veterans Program

我们建议在百万退伍军人计划(MVP)内进行分析 评估糖尿病并发症与遗传因素相关性的数据库 变种。目标1涉及创建虚拟基线的数据库开发 检查将MVP数据与VAMC门诊就诊联系起来，并在适当的情况下将VAMC联系起来 住院治疗，使用电子健康记录。我们将进行尖端技术 普通型(CVA)和稀有型(RVA)的全基因组关联研究 变种。研究的结果是糖尿病的眼睛、肾脏和心血管疾病。 并发症和低血糖。目标2：糖尿病并发症分析 使用风险因素的时间综合纵向测量来考虑结果风险 (RFS)同时包括遗传变异和传统RFS，同时考虑单一和 用横断面和时期患病率对结果进行多项评估 估计。我们将按类型分析特定器官和代谢并发症 使用退伍军人事务部记录中的EHR数据显示1和2型糖尿病的状态。具体结果 调查对象：眼部(视网膜疾病、青光眼)、肾脏(微量和大量蛋白尿、 并降低EGFR[CKD分期和ESRD]，心血管(心力衰竭)，以及 低血糖(急诊科就诊和住院，根据HbA1c进行调整 和血糖水平，以及降血糖药物的使用[来自EHR])。目标3将 制定糖尿病并发症风险评分。既包括传统射频，也包括独立射频 对于每个结果，将使用Logistic回归对遗传预测模型进行比较。 模型将评估RFS的作用和对异常血液的药物治疗 血压、血脂和血糖。我们将使用特定于结果的基因分数作为 潜在的重新分类变量。

We propose to undertake analyses within the Million Veterans Program (MVP) database to Assess Associations between Diabetic Complications and Genetic Variants. Aim 1 involves Database Development that will create a virtual baseline examination linking MVP data to VAMC outpatient visits, and, when appropriate, VAMC hospitalizations, using the electronic health record. We will conduct cutting edge Genome Wide Association Studies (GWAS) of common (CVAS) and rare (RVAS) variants. The outcomes investigated are diabetic eye, kidney, and cardiovascular complications, and hypoglycemia. Aim 2 is Diabetes Complications Analyses concerning outcome risks using time-integrated longitudinal measures of risk factors (RFs) to include both genetic variants and traditional RFs considering both single and multiple assessments of outcomes with cross-sectional and period prevalence estimates. We will analyze organ-specific and metabolic complications according to type 1 and type 2 diabetes status using EHR data in VA records. Specific outcomes investigated: Ocular (retinal disease, glaucoma), Renal (micro- and macro-albuminuria, and decreased eGFR [CKD stages and ESRD), Cardiovascular (cardiac failure), and Hypoglycemia (emergency department visits and hospitalizations, adjusted for HbA1c and glucose levels, and use of glycemic medications [from the EHR]). Aim 3 will develop Diabetic Complications Risk Scores. Both traditional RF and separate genetic prediction models will be compared using logistic regression for each outcome. Models will evaluate the role of RFs and pharmacologic therapy for abnormal blood pressure, lipids, and glucose. We will use an outcome-specific genetic score as a potential reclassification variable.