A machine learning-based classification of adult-onset diabetes identifies patients at risk of liver-related complications.

ABSTRACT:

Background & aims

Diabetes mellitus is a major risk factor for fatty liver disease development and progression. A novel machine learning method identified five clusters of patients with diabetes, with different characteristics and risk of diabetic complications using six clinical and biological variables. We evaluated whether this new classification could identify individuals with an increased risk of liver-related complications.

Methods

We used a prospective cohort of patients with a diagnosis of type 1 or type 2 diabetes without evidence of advanced fibrosis at baseline recruited between 2000 and 2020. We assessed the risk of each diabetic cluster of developing liver-related complications (i.e. ascites, encephalopathy, variceal haemorrhage, hepatocellular carcinoma)

SUBMITTER: Otero Sanchez L 

PROVIDER: S-EPMC10339249 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature