A Sjogrens Syndrome Diagnostic

干燥综合症的诊断

• 批准号: 8593453
• 负责人: Michael B Centola
• 金额: $ 10.5万
• 依托单位: HAUS BIOCEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8593453
• 关键词: Sjogrens; Syndrome; Diagnostic

DESCRIPTION (provided by applicant): Sj"gren's Syndrome (SS) is autoinflammatory condition, characterized by a plethora of symptoms, including dry eyes and mouth. Despite the fact that the condition is relatively common, with up to 3.1 million persons affected in the US, accurate diagnosis has been complex and largely ineffective. If left undiagnosed, and thus untreated, SS can progress to significant morbidity and increase the risk of certain forms of cancer up to 100 fold. The identification of SS patients is, therefore, a pressing unmet need. Current SS diagnostic tools have been limited, in part because of the wide range of symptoms and frequent overlap with other autoinflammatory disorders. In this study, we will use genome-wide expression profiling of peripheral blood leukocytes coupled with multivariate analysis to identify SS diagnostic biomarkers of disease. Our preliminary data support the hypothesis that such biomarkers can be identified that correctly classify patients and provide a powerful adjunct to current SS diagnostic methods. Our preliminary data demonstrate that a multi-variate gene expression-based algorithm has superior performance to distinguish among SS, rheumatoid arthritis (RA), and unaffected controls than current diagnostic laboratory tests. In this proposal, an algorithm will be developed utilizing a broader set of samples from additional common rheumatologic diseases within a standard SS differential. Multi-variate algorithm development will be done using state-of-the-art multi-variate statistics, utilizing adequately powered cohorts, and an iterative process of biomarker identification and verification though serial associative analyses in independent cohorts. Our group has utilized this general methodology to create multivariate biomarker algorithms highly correlated to clinical outcome, which have been translated into clinical laboratory tests.

描述（由申请人提供）：干燥综合征（SS）是自身炎性病症，其特征在于包括眼干和口干的大量症状。尽管这种情况相对常见，在美国有多达310万人受到影响，但准确的诊断一直很复杂，而且基本上无效。如果不及时诊断，因此不进行治疗，SS可以发展为显著的发病率，并增加某些形式的癌症的风险高达100倍。因此，SS患者的识别是一个迫切的未满足的需求。目前的SS诊断工具是有限的，部分原因是广泛的症状和频繁的重叠与其他自身炎症性疾病。在这项研究中，我们将使用外周血白细胞的全基因组表达谱结合多变量分析来确定疾病的SS诊断生物标志物。我们的初步数据支持这样的假设，即可以识别出正确分类患者的生物标志物，并为目前的SS诊断方法提供有力的辅助手段。我们的初步数据表明，多变量基因表达为基础的算法有上级性能区分SS，类风湿性关节炎（RA），和未受影响的控制比目前的诊断实验室测试。在本提案中，将开发一种算法，该算法将利用标准SS鉴别诊断中来自其他常见风湿病的更广泛样本集。将使用最先进的多变量统计学、充分把握度的队列以及通过独立队列中的系列关联分析进行的生物标志物识别和验证的迭代过程来完成多变量算法开发。我们的团队利用这种通用方法来创建与临床结果高度相关的多变量生物标志物算法，这些算法已被转化为临床实验室测试。