Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Journal: Frontiers in endocrinology
PMID: 38915893

Abstract

OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients.

Authors

  • Tian-Feng An
    Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
  • Zhi-Peng Zhang
    Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
  • Jun-Tang Xue
    Department of Surgery, Peking University Third Hospital, Beijing, China.
  • Wei-Ming Luo
    Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Zhong-Ze Fang
    Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
  • Guo-Wei Zong
    Department of Mathematics, School of Public Health, Tianjin Medical University, Tianjin, China.

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