原发性IgA肾病尿蛋白质组学数据库及无创诊断模型的建立

IgA肾病是世界范围内最常见的原发性肾小球疾病，尤其在亚洲人群，也是我国尿毒症人群最常见的原发病因。目前，对IgA肾病的诊断和病情评估仍依赖肾活检，不利于早期筛查及治疗。本研究拟利用解放军总医院作为全军肾病中心拥有大样本及多中心IgA肾病研究队列的优势，筛选和建立原发性IgA肾病患者的大样本随访人群队列，同时采集患者生物信息，开发建立尿液生物样本资源库及临床随访数据库；利用定量（比较）尿蛋白质组学技术，定期对随访队列人群进行尿液蛋白质组动态检测分析，建立尿蛋白质组动态纵向变化数据库；并通过与肾穿刺活检病理分析结果及临床数据进行相关分析，利用机器学习算法构建诊断决策模型，并通过交叉验证的方法对模型进行评估和检验，进一步建立原发性IgA肾病患者早期诊断、病情评估及预后判断的无创性诊断模型。这将有利于提高疾病的早期诊断及早期治疗率，并为寻找发现新的药物治疗靶点提供基础依据。

Primary Immunoglobulin A nephropathy (IgAN) is most common form of primary glomerulonephritis throughout the world, especially in Asia. IgA nephropathy also is the main cause of end-stage renal disease in patients with primary glomerular disease who require renal-replacement therapy in China. But now the diagnosis of IgAN can only rely on the kidney biopsy, lack of non-invasive diagnosis model for early diagnosis to patients. This study was to make use of the clinical advantage, the large sample of patients in PLA general hospital, to establish IgAN patients' large sample follow-up crowd, to collect biological information, and to develop a clinical follow-up database and the urine samples database. The work was to make use of quantitative (more) urinary proteomics technology to regularly analyze and detect the follow-up crowd's urinary proteomics longitudinal changes, and establish a follow-up crowd's urinary proteomics longitudinal changes database; Finally, the database, pathological analysis of biopsy results and clinical data innovatively related, we can use machine learning algorithms to build decision-making model and use cross-validation method to test and evaluate the model in order to establish the disease non-invasive diagnosis of disease model to screen high-risk patients with IgAN, diagnose early, evaluate and predict the outcome. This work would contribute to early diagnosis, early intervention of IgAN, and provide the basis for the discovery of new drugs based on therapeutic targets.