Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.

Abstract

Authors

• ChuXia Tan
  Health Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
• Yuan Liu
  Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
• Lijun Li
  Department of Orthopedics, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, PR China; Orthopedics Research Institute of Zhejiang University, Hangzhou, 310000, PR China; Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou City, Zhejiang Province, PR China. Electronic address: lilijun@zju.edu.cn.
• Ying Li
  School of Information Engineering, Chang'an University, Xi'an 710010, China.
• Pingting Yang
  Health Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
• Yinglong Duan
  Nursing department, the Third Xiangya Hospital, Central South University, Changsha, 410013, China.
• Xingxing Wang
  Department of Pathology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai 200032, China.
• Huiyi Zhang
  Xiangya Nursing School of Central South University, Changsha, 410013, China.
• Jingying Wang
  Social and Public Administration School, East China University of Science and Technology, Shanghai, China.
• Honglian Zhang
  State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.

Keywords

External Resources