title: Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  In this study, gender-specific prediction models for hyperuricemia for urban Han Chinese adults were developed and performed well.
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. caojin0806@163.com.
authors.forename: Jin
authors.initials: J
authors.lastname: Cao
authors.affiliation: Health Management Center, Affiliated Hospital of Jining Medical University, Jining 272000, Shandong, China. jyfy_tjzx@163.com.
authors.forename: Chunxia
authors.initials: C
authors.lastname: Wang
authors.affiliation: Health Management Center, Shandong Provincial Qianfoshan Hospital, Jinan 250014, Shandong, China. zhangguangsd@163.com.
authors.forename: Guang
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Geriatrics Qilu Hospital of Shandong University, Jinan 250012, Shandong, China. drjixiang@126.com.
authors.forename: Xiang
authors.initials: X
authors.lastname: Ji
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. liu-yx@sdu.edu.cn.
authors.forename: Yanxun
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. sunxiubin@sdu.edu.cn.
authors.forename: Xiubin
authors.initials: X
authors.lastname: Sun
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. yuanzhongshang@sdu.edu.cn.
authors.forename: Zhongshang
authors.initials: Z
authors.lastname: Yuan
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. lawjiang1@hotmail.com.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Jiang
authors.affiliation: Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China. xuefzh@sdu.edu.cn.
authors.forename: Fuzhong
authors.initials: F
authors.lastname: Xue
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers; D001794:Blood Pressure; D015992:Body Mass Index; D005260:Female; D006801:Humans; D033461:Hyperuricemia / Q000097:blood / Q000453:epidemiology*; D015994:Incidence; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012307:Risk Factors; D017678:Sex Distribution; D014280:Triglycerides / Q000097:blood; D014505:Urban Population; D014527:Uric Acid / Q000097:blood
chemical_list: D015415:Biomarkers; D014280:Triglycerides; D014527:Uric Acid
keywords: Cox model; cohort study; gender specific; hyperuricemia; predictive model