痛风严重影响老年健康且近年来发病呈上升趋势，高尿酸血症（HUA）是痛风最重要的危险因素。课题组前期研究发现，我国60岁及以上城市人群HUA患病率逾20%。约20%的HUA患者会进展为痛风，原因尚不明确，可能与尿酸转运基因有关，但国内外相关研究较少且缺乏基因环境交互作用的证据。本研究基于2016年建立的老年HUA高危人群队列，采用病例队列设计，选取基线调查时没有痛风的老年HUA患者作为对照组，以队列随访期间全部新发痛风患者作为病例组，拟：1.利用边际结构模型在控制依时混杂偏倚上的独特优势，构建风险预测模型，明确老年HUA患者进展为痛风的关键影响因素；2.应用乘法和加法模型，分析ABCG2和SLC2A9基因及其与环境因素的交互作用对HUA进展为痛风的影响；3.对模型的预测效能进行稳定性、区分度和一致性的评价。本研究可明确并量化老年HUA患者进展为痛风的关键因素，有助于开展针对性的健康管理。

Gout is a serious disease among aging population and its incidence is growing in recent years. Hyperuricemia (HUA) is one of the most important risk factors of gout. Our previous study revealed that the prevalence of HUA was over 20% among urban people aged 60 and above. About 20% of HUA patients will develop into gout and the reason is not clear. Uric acid transporter gene may influence the risk of gout, but the evidence is sparse and there is lack of estimation of gene-environmental interaction effect. Therefore, based on a large elderly HUA patient cohort established at 2016, we plan to design a case-cohort study, in which HUA patients without gout at baseline will be selected as the control arm, and all the new diagnosed gout cases during the follow up will be set as the case arm. In this study we aim to: 1. Use marginal structure model, which has unique technical strength dealing with time-varied confounding bias, to establish the risk predictive model to understand the key influence factors for the development of gout from elderly HUA patients; 2. Use addictive and multiplicative models, to investigate the gene-environmental interaction effect between ABCG2 gene, SLC2A9 gene and environmental risk factors toward the risk of gout among HUA elderly patients and 3. Assess the model in respects of stability, discrimination and calibration. The quantitative measurement of the risk for HUA patients developing into gout will enable health practitioners to initiate more targeted health management.