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Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.
RESUMO
Background/Aims@#Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. @*Methods@#We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. @*Results@#The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. @*Conclusions@#Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
RESUMO
Hepatitis B virus (HBV) is responsible for more than 50% of hepatocellular carcinoma (HCC) in HBV hyperendemic areas, such as the Asia-Pacific region. Several hepatitis B viral factors are involved in HBV-related hepatocarcinogenesis. Hepatitis B viral load is the most important risk factor of HCC development. In addition, HBV integration, HBV genotype C, and core-promoter mutations are also associated with a risk of HCC development. For untreated chronic hepatitis B (CHB) patients, the estimated HCC incidence rates per 100 patient-years were 0.03–0.17 in inactive carriers, 0.07–0.42 in asymptomatic carriers, 0.12–0.49 in chronic hepatitis, and 2.03–3.37 in cirrhosis. Complementary to HBV DNA, serum levels of the hepatitis B surface antigen and hepatitis B core-related antigen (HBcrAg) can predict the occurrence of HCC for untreated patients with low and intermediate viral loads, respectively. For patients receiving antiviral therapy, the risks of HCC occurrence 40–60% lower than those for untreated patients. Patients treated with residual detectable HBV DNA or intrahepatic cccDNA still have a risk of HCC. Serum levels of HBcrAg, M2BPGi and fibrosis-4 are predictive of the risk of HCC development in treated patients. Several well-developed HCC risk scores can help clinicians identify high-risk CHB patients for HCC surveillance, regardless of treatment status. These strategies can help minimize the threat of HCC and prolong survival in CHB patients.
RESUMO
With recent advances in molecular and genomic investigations, the impact of hepatitis B viral and host factors on the progression of chronic HBV infection has been explored. For viral factors, hepatitis B viral load is a strong predictor for liver disease progression. Hepatitis B viral kinetics appear to be important for successful anti-viral therapy. Serum HBsAg level serves as a complementary marker to viral load for the prediction of HBV-related adverse outcomes in patients with low viral load. In those with low viral load, high serum HBsAg level is associated with higher risks of cirrhosis and HCC. Hepatitis B core-related antigen (HBcrAg) induces host immune responses, and the reduction of the HBcrAg level as well as the increment of total anti-HBc level are significantly associated with favorable outcomes. HBV genotypes (genotype C/D) and mutants (basal core promoter and deletion mutation in pre-S genes) are well known viral genetic markers to predict disease progression. For host factors, serum inflammatory biomarkers have been developed to evaluate the HBV-associated hepatic necroinflammation and fibrosis. Host single nucleotide polymorphism on sodium taurocholate cotransporting polypeptide (NTCP, an HBV entry receptor) may be associated with a decreased risk for cirrhosis and HCC. In conclusion, patients with chronic hepatitis B should be evaluated with relevant viral and host markers to identify those who are at a higher risk of liver disease progression and then receive timely antiviral therapy.