Advancing the Phenotyping of Acute Kidney Injury for the Million Veterans Program

急性肾损伤（阿基）是一种复杂而致命的疾病，与进行性肾功能丧失密切相关 肾功能、心血管疾病、生活质量差和死亡。最严重的阿基类型 实质损伤，表现为肾功能的持续丧失。这种情况，称为内在 阿基（iAKI）具有最高的死亡率和长期肾功能丧失的风险。由于年龄较大， 和高患病率的危险因素，退伍军人是在经历iAKI的风险特别高，相比之下， 一般人口。尽管投资了几十年，但还没有成功的治疗方法从 临床前研究纳入常规临床实践。后者导致人们呼吁更好地了解 负责定义人类iAKI风险的机制。 越来越多的研究领域正在了解iAKI易感性的遗传基础。早期研究 由于样本量小，缺乏无偏倚的方法（如全基因组关联）， （GWA））和预测的基因表达研究，最重要的是， 区分阿基的原因。由于iAKI是一种异质性疾病，这种严重的缺陷可能会稀释 大规模研究中的生物信号和治疗效果。最后，很少有阿基研究探索了 鉴定iAKI的新表型或内表型，其已显示出改善iAKI的前景。 了解其他复杂和异质条件。本提案的总体目标是 a）推进最常见和严重形式的内源性阿基（iAKI）的临床表型分型，和B） 利用这些表型来鉴定与iAKI相关的遗传变异。 在目标1中，我们将把数据驱动的深度学习算法应用于密集的结构化数据和叙述性文本， 发现数据模式，这些数据模式可能代表先前识别的和未识别的 阿基的内表型。在目标2中，我们将通过生成概率表型来补充这一策略 使用人工图表审查的算法，在3种iAKI临床环境中识别传统iAKI表型， 常见的有：心血管手术、心导管插入术和败血症。在目标3中，我们将执行一系列 在这些背景下进行的GWA研究比较了Aim 2中通过我们的iAKI表型分型算法识别的病例， 百万退伍军人计划中无阿基的患者。我们还将使用 Aim 1数据驱动的内表型。我们将使用以下方法评估最相关的区域： PrediXcan检查肾组织中预测的基因表达。 拟议研究将在VA百万退伍军人伽马计划内由 阿基表型、基于信息学的表型开发人员和遗传学方面的多学科专家团队 流行病学家该提案的可交付成果将推进iAKI的计算表型分析， 扩大iAKI大规模基因型-表型研究的严谨性和规模，并提供重要的 关于iAKI疾病临床机制的信息。

Acute kidney injury (AKI) is a complex and deadly disease that is strongly associated with progressive loss of kidney function, cardiovascular disease, poor quality of life, and death. The most severe forms of AKI cause parenchymal damage, which manifests as a persistent loss of kidney function. This condition, termed intrinsic AKI (iAKI), carries the highest mortality and risk for long-term loss of kidney function. Due to their older age and high prevalence of risk factors, Veterans are at especially high risk for experiencing iAKI compared to the general population. Despite decades of investment, no successful treatments have been translated from preclinical studies into routine clinical practice. The latter has led to calls for greater understanding of the mechanisms responsible for defining risk in human iAKI. A growing area of investigation is understanding the genetic basis for susceptibility to iAKI. Early studies have been limited by small sample sizes, a lack of unbiased approaches (e.g. genome wide association (GWA)) and predicted gene expression studies, and most importantly, superficial phenotyping that does not distinguish between causes of AKI. As iAKI is a heterogeneous condition, this critical deficiency can dilute biological signals and treatment effects in large-scale studies. Lastly, few studies in AKI have explored identifying novel phenotypes, or endophenotypes of iAKI, which have shown promise for improving understanding of other complex and heterogeneous conditions. The overarching goals of this proposal are to a) advance the clinical phenotyping of the most common and severe forms of intrinsic AKI (iAKI), and b) leverage these phenotypes to identify genetic variants associated with iAKI. In Aim 1, we will apply a data-driven deep learning algorithm to dense structured data and narrative text to discover data patterns that will likely represent a mixture of previously recognized and unrecognized endophenotypes of AKI. In Aim 2, we will complement this strategy by generating probabilistic phenotype algorithms that use manual chart review to identify traditional iAKI phenotypes in 3 clinical settings where iAKI is common: cardiovascular surgery, cardiac catheterization, and sepsis. In Aim 3, we will perform a series of GWA studies within these settings comparing cases identified by our iAKI phenotyping algorithms in Aim 2 to patients without AKI within the Million Veteran Program. We will also conduct the same analyses using the most promising Aim 1 data-driven endophenotypes. We will evaluate the top associated regions using PrediXcan to examine predicted gene expression in kidney tissues. The proposed studies will be performed within the VA Million Veterans Gamma Program by a multidisciplinary team of experts in AKI phenotyping, informatics-based phenotype developers, and genetic epidemiologists. The deliverables from this proposal will advance the computational phenotyping of iAKI, expand the rigor and scale of large-scale genotype-phenotype studies in iAKI, and provide important information regarding clinical iAKI disease mechanisms.