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

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.

Methods

We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D p

SUBMITTER: An TF 

PROVIDER: S-EPMC11194401 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature