Predictive ability of visit-to-visit glucose variability on diabetes complications.

ABSTRACT:

Background

Identification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.

Methods

A retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hos

SUBMITTER: Teh XR 

PROVIDER: S-EPMC11917057 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature