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首页> 外文期刊>International Journal of Environmental Research and Public Health >Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study
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Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study

机译：城市汉族成年人高尿酸血症的发病率和简单预测模型：一项前瞻性队列研究

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Background: Hyperuricemia (HUA) contributes to gout and many other diseases. Many hyperuricemia-related risk factors have been discovered, which provided the possibility for building the hyperuricemia prediction model. In this study we aimed to explore the incidence of hyperuricemia and develop hyperuricemia prediction models based on the routine biomarkers for both males and females in urban Han Chinese adults. Methods: A cohort of 58,542 members of the urban population (34,980 males and 23,562 females) aged 20–80 years old, free of hyperuricemia at baseline examination, was followed up for a median 2.5 years. The Cox proportional hazards regression model was used to develop gender-specific prediction models. Harrell’s C-statistics was used to evaluate the discrimination ability of the models, and the 10-fold cross-validation was used to validate the models. Results: In 7139 subjects (5585 males and 1554 females), hyperuricemia occurred during a median of 2.5 years of follow-up, leading to a total incidence density of 49.63/1000 person years (64.62/1000 person years for males and 27.12/1000 person years for females). The predictors of hyperuricemia were age, body mass index (BMI) systolic blood pressure, serum uric acid for males, and BMI, systolic blood pressure, serum uric acid, triglycerides for females. The models’ C statistics were 0.783 (95% confidence interval (CI), 0.779–0.786) for males and 0.784 (95% CI, 0.778–0.789) for females. After 10-fold cross-validation, the C statistics were still steady, with 0.782 for males and 0.783 for females. Conclusions: In this study, gender-specific prediction models for hyperuricemia for urban Han Chinese adults were developed and performed well.
机译：背景：高尿酸血症（HUA）导致痛风和许多其他疾病。已经发现了许多与高尿酸血症相关的危险因素，这为建立高尿酸血症预测模型提供了可能性。在这项研究中，我们旨在探讨高尿酸血症的发生率，并根据城市汉族成年人中男性和女性的常规生物标记物开发高尿酸血症预测模型。方法：对年龄为20-80岁的城市人口中的58542名成员（男性34980例，女性23562例）进行了队列研究，基线检查时无高尿酸血症，随访时间中位数为2.5年。使用Cox比例风险回归模型开发针对性别的预测模型。使用Harrell的C统计量评估模型的辨别能力，并使用10倍交叉验证法对模型进行验证。结果：在7139名受试者中（男性5585例，女性1554例），在中位2.5年的随访期间发生了高尿酸血症，导致总发病密度为49.63 / 1000人年（男性为64.62 / 1000人年，而27.12 / 1000年）女性的人年）。高尿酸血症的预测因素是年龄，男性的体重指数（BMI）收缩压，男性的血清尿酸，以及女性的BMI，收缩压，血清尿酸，甘油三酸酯。男性的模型的C统计量为0.783（95％置信区间（CI），0.779-0.786），女性为0.784（95％的置信区间，0.778-0.789）。经过10倍交叉验证后，C统计量仍然稳定，男性为0.782，女性为0.783。结论：在这项研究中，针对中国城市汉族成年人的高尿酸血症按性别分类的预测模型得到了开发和执行。

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