The prevalence of obesity or metabolic syndrome is increasing worldwide (globally metabodemic). Approximately 25% of the adult general population is suffering from nonalcoholic fatty liver disease (NAFLD), which has become a serious health problem. In 2020, global experts suggested that the nomenclature of NAFLD should be updated to metabolic-dysfunction-associated fatty liver disease (MAFLD). Hepatic fibrosis is the most significant determinant of all cause- and liver -related mortality in MAFLD. The non-invasive test (NIT) is urgently required to evaluate hepatic fibrosis in MAFLD. The fibrosis-4 (FIB-4) index is the first triaging tool for excluding advanced fibrosis because of its accuracy, simplicity, and cheapness, especially for general physicians or endocrinologists, although the FIB-4 index has several drawbacks. Accumulating evidence has suggested that vibration-controlled transient elastography (VCTE) and the enhanced liver fibrosis (ELF) test may become useful as the second step after triaging by the FIB-4 index. The leading cause of mortality in MAFLD is cardiovascular disease (CVD), extrahepatic malignancy, and liver-related diseases. MAFLD often complicates chronic kidney disease (CKD), resulting in increased simultaneous liver kidney transplantation. The FIB-4 index could be a predictor of not only liver-related mortality and incident hepatocellular carcinoma, but also prevalent and incident CKD, CVD, and extrahepatic malignancy. Although NITs as milestones for evaluating treatment efficacy have never been established, the FIB-4 index is expected to reflect histological hepatic fibrosis after treatment in several longitudinal studies. We here review the role of the FIB-4 index in the management of MAFLD.

Liver-related diseases are the third-leading causes (9.3%) of mortality in type 2 diabetes (T2D) in Japan. T2D is closely associated with nonalcoholic fatty liver disease (NAFLD), which is the most prevalent chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH), a severe form of NAFLD, can lead to hepatocellular carcinoma (HCC) and hepatic failure. No pharmacotherapies are established for NASH patients with T2D. Though vitamin E is established as a first-line agent for NASH without T2D, its efficacy for NASH with T2D recently failed to be proven. The effects of pioglitazone on NASH histology with T2D have extensively been established, but several concerns exist, such as body weight gain, fluid retention, cancer incidence, and bone fracture. Glucagon-like peptide 1 (GLP-1) receptor agonists and sodium-glucose cotransporter 2 (SGLT2) inhibitors are expected to ameliorate NASH and NAFLD (LEAN study, LEAD trial, and E-LIFT study). Among a variety of SGLT2 inhibitors, dapagliflozin has already entered the phase 3 trial (DEAN study). A key clinical need is to determine the kinds of antidiabetic drugs that are the most appropriate for the treatment of NASH to prevent the progression of hepatic fibrosis, resulting in HCC or liver-related mortality without increasing the risk of cardiovascular or renal events. Combination therapies, such as glucagon receptor agonist/GLP-1 or gastrointestinal peptide/GLP-1, are under development. This review focused on antidiabetic agents and future perspectives on the view of the treatment of NAFLD with T2D.

Background and Aims: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide images of biopsy-proven steatotic liver disease. Approach and Results: We included 639 patients who did not develop HCC for ≥7 years after biopsy (non-HCC class) and 46 patients who developed HCC <7 years after biopsy (HCC class). Paired cases of the HCC and non-HCC classes matched by biopsy date and institution were used for training, and the remaining nonpaired cases were used for validation. The DL model was trained using deep convolutional neural networks with 28,000 image tiles cropped from whole-slide images of the paired cases, with an accuracy of 81.0% and an AUC of 0.80 for predicting HCC development. Validation using the nonpaired cases also demonstrated a good accuracy of 82.3% and an AUC of 0.84. These results were comparable to the predictive ability of logistic regression model using fibrosis stage. Notably, the DL model also detected the cases of HCC development in patients with mild fibrosis. The saliency maps generated by the DL model highlighted various pathological features associated with HCC development, including nuclear atypia, hepatocytes with a high nuclear-cytoplasmic ratio, immune cell infiltration, fibrosis, and a lack of large fat droplets. Conclusions: The ability of the DL model to capture subtle pathological features beyond fibrosis suggests its potential for identifying early signs of hepatocarcinogenesis in patients with steatotic liver disease. Export