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Updated: Aug 29, 2025

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
Published on: March 24, 2023
Heterogeneity of non-alcoholic fatty liver disease (NAFLD): Implication for cardiovascular risk stratification
Francesco Baratta1, Laura D'Erasmo2, Simone Bini2
1Department of Clinical Internal, Anaesthesiological and Cardiovascular Sciences, Sapienza University of Rome, 00161, Rome, Italy.
Abstract:
NAFLD is currently considered the most common liver disease worldwide and mounting data support its strong link with atherosclerotic cardiovascular disease (ASCVD). This association is important as cardiovascular disease (CVD) is generally recognized as the leading cause of death in individuals with NAFLD. However, NAFLD represents a heterogeneous condition showing a wide spectrum of clinical and pathophysiological sub-phenotypes with different adverse outcomes ranging from ASCVD to liver damage progression. The contribution to NAFLD pathogenesis of different environmental, metabolic, and genetic factors underlies this heterogeneity. The more frequent phenotype of NAFLD patients is associated with metabolic dysfunctions such as obesity and insulin-resistant syndrome and this has been recently named as Metabolic Associated Fatty Liver disease (MAFLD). However, NAFLD is encountered also in subjects without insulin resistance and metabolic alterations and in whom genetic factors play a major role. It has been suggested that these individuals are at risk of liver disease progression but not of cardiovascular complications. Separating metabolic from genetic factors could be useful in disentangling the intricate relationship between NAFLD and atherosclerosis. In the present review, we aim to address the epidemic of NAFLD, its epidemiologically association with ASCVD complications and the overall mechanisms involved in the pathophysiology of atherosclerotic vascular damage in NAFLD patients. Finally, we will revise the potential role of genetics in identifying disease subtyping and predicting individualised CVD risk.
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