Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

Journal: Frontiers in endocrinology
PMID: 37711898

Abstract

BACKGROUND: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.

Authors

  • Xiujuan Deng
    Department of Clinical Nutrition, The Third Hospital of Changsha, Changsha, China.
  • Lin Qiu
    School of Water conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450011, PR China. Electronic address: qiulin@ncwu.edu.cn.
  • Xin Sun
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA.
  • Hui Li
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zejiao Chen
    Department of Clinical Nutrition, The Third Hospital of Changsha, Changsha, China.
  • Min Huang
    Department of Physiology, School of Basic Medicine, Chengdu Medical College, Sichuan, China.
  • Fangxing Hu
    Department of Clinical Nutrition, The Third Hospital of Changsha, Changsha, China.
  • Zhenyi Zhang
    Department of Clinical Nutrition, The Third Hospital of Changsha, Changsha, China.

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