Novel metabolic classification for extrahepatic complication of metabolic associated fatty liver disease: A data-driven cluster analysis with international validation

Abstract

BackgroundTraditional classification systems of metabolic-associated fatty liver disease (MAFLD) do not account for the high rate of extrahepatic complications. To create a new classification of MAFLD using metabolic parameters to identify risks of complications more accurately. MethodsThe retrospective study included MAFLD patients from the First Affiliated Hospital of Sun Yat-sen University for model development, and the model was validated respectively using Chinese cohort and UK Biobank database. Cluster analysis with k-means cluster was built using age, body mass index (BMI), glycosylated hemoglobin (HbA1c), total cholesterol/high density lipoprotein cholesterol (HDL-C) ratio, triglyceride, and lipoprotein(a) [Lp(a)] levels. Cox regression models were used to compare the risk of type 2 diabetes (T2DM), chronic heart disease (CHD), stroke and mortality between the clusters. Results1038 MAFLD patients from cross-sectional population were recruited for the model derivation, with 10,451 cases (33.4 % of MAFLD) from Chinese cohort and 304,141 cases (34.9 % of MAFLD, 1010 cases with magnetic resonance imaging proton density fat fraction measurement [MRI-PDFF]) from the international cohort validated. Five replicable clusters of MAFLD patients were identified: Cluster 1(mild obesity and dyslipidemia-related), Cluster 2 (age related), Cluster 3 (severe insulin resistance-related), Cluster 4[high Lp(a)-related], and Cluster 5 (severe mixed hyperlipidemia-related). Patients in different clusters exhibited differences in the development of T2DM, CHD, stroke and all-causes mortality. Patients in Cluster 3 had significantly worst survival outcomes and higher risks of T2DM and CVD than those in other clusters. ConclusionThe novel classification offers improved discrimination of new-onset MAFLD patients with different metabolic complications.