Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.

Journal: Annals of medicine
Published Date: Jun 22, 2025

Abstract

BACKGROUND: Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application.

Authors

  • Tao Huang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Yuanyuan Li
    Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Simin Wang
    Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou 510006, China.
  • Shijie Qiao
    College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Xiujuan Zheng
    Department of Automation, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, China.
  • Wenhui Xiong
    College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Menghan Yang
    College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Xirui Huang
    College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Bizhen Gao
    College of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.

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