Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal: Journal of health, population, and nutrition
Published Date:

Abstract

BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM in middle-aged and elderly patients is relatively high. The current research lacks an exploration into the impact of social and environmental determinants of health on depression in CKM patients.

Authors

  • Xinyi Xu
    School of Micro-Nano Electronics, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310027, China.
  • Xinru Li
    College of Health Management, China Medical University, Shenyang, Liaoning Province, China.
  • Xiyan Li
    Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province for Collaborative Innovation of Health Management Policy and Precision Health Service, Guangzhou, China.
  • Benli Xue
    School of Public Health, Southern Medical University, Guangzhou, China.
  • Xiao Zheng
    School of Computer, National University of Defense Technology, Deya Road, Changsha 410073, China.
  • Shujuan Xiao
    School of Public Health, Southern Medical University, Guangzhou, China.
  • Lingli Yang
    Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province for Collaborative Innovation of Health Management Policy and Precision Health Service, Guangzhou, China.
  • Xinyi Zhang
    Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
  • Chengyu Chen
    Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province for Collaborative Innovation of Health Management Policy and Precision Health Service, Guangzhou, China.
  • Ting Zheng
    Department of Medical Oncology, The First People's Hospital of Linping District, Hangzhou, 311100, Zhejiang Province, China. zz_tt666@163.com.
  • Yuyang Li
    Department of Oral and Maxillofacial Surgery Hospital of Stomatology Jilin University Changchun China.
  • Yanan Wang
    Vasculocardiology Department, The Third People's Hospital of Datong, Datong, Shanxi, China.
  • Jianan Han
    School of Health Management, Southern Medical University, Guangzhou, China.
  • Haoran Wu
    Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province for Collaborative Innovation of Health Management Policy and Precision Health Service, Guangzhou, China.
  • Mengjie Zhang
    Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand.
  • Yanming Liao
    School of Health Management, Southern Medical University, Guangzhou, China.
  • Siyi Bai
    College of Humanities and Social Sciences, Guangxi Medical University, Nanning, China.
  • Nan Zeng
    School of Public Health, Southern Medical University, Guangzhou, China. znshuyu@126.com.
  • Chichen Zhang
    Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.