Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study.

ABSTRACT: In view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) model

SUBMITTER: Wu R 

PROVIDER: S-EPMC9741614 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature