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), using competing risk analyses. Results: We included 1,068 patients, of whom 162 (15.2%) were determined to be in the severe autoimmune diabetes subgroup, 266 (24.9%) had severe insulin-deficient diabetes, 95 (8.9%) had severe insulin-resistant diabetes (SIRD), 359 (33.6%) had mild obesity-related diabetes, and 186 (17.4%) were in the mild age-related diabetes subgroup. In multivariable analysis, patients in the SIRD cluster and those with excessive alcohol consumption at baseline had the highest risk for liver-related events. The SIRD cluster, excessive alcohol consumption, and hypertension were independently associated with clinically significant fibrosis, evaluated by liver biopsy or transient elastography. Using a simplified classification, patients assigned to the severe and mild insulin-resistant groups had a three- and twofold greater risk, respectively, of developing significant fibrosis compared with those in the insulin-deficient group. Conclusions: A novel clustering classification adequately stratifies the risk of liver-related events in a population with diabetes. Our results also underline the impact of the severity of insulin resistance and alcohol consumption as key prognostic risk factors for liver-related complications. Impact and implications: Diabetes represents a major risk factor for NAFLD development and progression. This study examined the ability of a novel machine-learning approach to identify at-risk diabetes subtypes for liver-related complications. Our results suggest that patients that had severe insulin resistance had the highest risk of liver-related outcomes and fibrosis progression. Moreover, excessive alcohol consumption at the diagnosis of diabetes was the strongest risk factor for developing liver-related events.

Keywords

• Liver fibrosis
• Diabetes
• Insulin-resistant
• Clustering