Gender Difference in Liver Enzymes in Newly Defined Subgroups of Diabetes Revealed by a Data-Driven Cluster Analysis.

Abstract

Recently, a newly proposed data-driven approach for classifying diabetes has challenged the status quo of the classification of adult-onset patients with diabetes. This study investigated the association between liver injury and diabetes, classified by data-driven cluster analysis, as liver injury is a significant risk factor for diabetes. We enrolled 822 adult patients with newly diagnosed diabetes. Two-step cluster analysis was performed using six parameters, including age at diagnosis, body mass index, hemoglobin A1C, homoeostatic assessment model 2 estimates about insulin resistance (HOAM2-IR) and beta-cell function (HOMA2-B), and glutamic acid decarboxylase antibodies (GADA) positivity. Patients were allocated into five clusters. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity were compared as indicators of liver injury among clusters. Serum ALT and AST activities were significantly different among clusters (P=0.002), even among those without GADA positivity (P=0.004). Patients with severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) had a more severe liver injury. Gender dimorphism was also found for serum ALT and AST activities among subgroups. Female patients had better liver function than males with SIRD and MOD. We verified the feasibility of a newly proposed diabetes classification system and found robust and significant relationship and gender differences between serum ALT and AST activities and diabetes in some specific subgroups. Our findings indicate that more attention should be paid to diabetes subgroups when studying risk factors, indicators, or treatment in diabetic research.