Abstract

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.

Highlights

  • Hyperuricemia (HUA) contributes to gout and many other diseases

  • The models can be used for primary physicians to identify risk groups for hyperuricemia models demonstrated good performance with high C statistics in both derivation and validation of the models

  • People with high risk factors could be motivated to pay attention to their current health status and perform some proper management to lower the probability of developing hyperuricemia and potentially reduce the adverse outcomes associated with hyperuricemia such as gout, metabolic syndrome, cardiovascular disease, etc

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Summary

Introduction

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. After 10-fold cross-validation, the C statistics were still steady, with 0.782 for males and. Hyperuricemia is a set of heterogeneity diseases caused by obstacles in purine metabolism and/or a decrease in the excretion of uric acid. National Health and Nutrition Examination Survey 2007–2008 study, the prevalence of hyperuricemia was over 21% in both males and females [1]. Public Health 2017, 14, 67; doi:10.3390/ijerph14010067 www.mdpi.com/journal/ijerph

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