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.

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.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Hypopharyngeal multichannel intraluminal impedance-pH (HMII-pH) technology incorporating 2 trans-upper esophageal sphincter impedance channels has been developed to detect pharyngeal reflux. We used the HMII-pH technique to validate the candidate pharyngeal acid reflux (PAR) episodes based on the dual-pH tracings and determined the interobserver reproducibility. Methods: We conducted a cross-sectional study in tertiary centers in Taiwan. Ninety patients with suspected laryngopharyngeal reflux and 28 healthy volunteers underwent HMII-pH test when off acid suppressants. Candidate PAR episodes were characterized by pharyngeal pH drops of at least 2 units and reaching a nadir pH of 5 within 30 seconds during esophageal acidification. Two experts manually independently identified candidate PAR episodes based on the dual-pH tracings. By reviewing the HMII-pH tracings, HMII-pH-proven PAR episodes were subsequently confirmed. The consensus reviews of HMII-pH-proven PAR episodes were considered to be the reference standard diagnosis. The interobserver reproducibility was assessed. Results: A total of 105 candidate PAR episodes were identified. Among them 84 (80.0%; 95% CI, 71.0-87.0%) were HMII-pH-proven PAR episodes (82 in 16 patients and 2 in 1 healthy subject). Patients tended to have more HMII-pH-proven PAR episodes than healthy controls (median and percentile values [25th, 75th, and 95th percentiles]: 0 [0, 0, 3] vs 0 [0, 0, 0], P = 0.067). The concordance rate in diagnosing HMII-pH-proven PAR episodes between 2 independent observers was 92.2%. Conclusion Our preliminary data showed that 80.0% (71.0-87.0%) of the proposed candidate PAR episodes were HMII-pH-proven PAR episodes, among which the interobserver reproducibility was good.

Objective: aaWilson’s disease (WD) is a rare genetic disorder of copper metabolism, and longitudinal follow-up studies are limited. We performed a retrospective analysis to determine the clinical characteristics and long-term outcomes in a large WD cohort. Methods: aaMedical records of WD patients diagnosed from 2006–2021 at National Taiwan University Hospital were retrospectively evaluated for clinical presentations, neuroimages, genetic information, and follow-up outcomes. Results: aaThe present study enrolled 123 WD patients (mean follow-up: 11.12 ± 7.41 years), including 74 patients (60.2%) with hepatic features and 49 patients (39.8%) with predominantly neuropsychiatric symptoms. Compared to the hepatic group, the neuropsychiatric group exhibited more Kayser-Fleischer rings (77.6% vs. 41.9%, p < 0.01), lower serum ceruloplasmin levels (4.9 ± 3.9 vs. 6.3 ± 3.9 mg/dL, p < 0.01), smaller total brain and subcortical gray matter volumes (p < 0.0001), and worse functional outcomes during follow-up (p = 0.0003). Among patients with available DNA samples (n = 59), the most common mutations were p.R778L (allelic frequency of 22.03%) followed by p.P992L (11.86%) and p.T935M (9.32%). Patients with at least one allele of p.R778L had a younger onset age (p = 0.04), lower ceruloplasmin levels (p < 0.01), lower serum copper levels (p = 0.03), higher percentage of the hepatic form (p = 0.03), and a better functional outcome during follow-up (p = 0.0012) compared to patients with other genetic variations. Conclusion aaThe distinct clinical characteristics and long-term outcomes of patients in our cohort support the ethnic differences regarding the mutational spectrum and clinical presentations in WD.

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS) are evidenced-based treatments for patients with major depressive disorder (MDD) who fail to respond to standard first-line therapies. However, although various TMS protocols have been proven to be clinically effective, the response rate varies across clinical applications due to the heterogeneity of real-world psychiatric comorbidities, such as generalized anxiety disorder, posttraumatic stress disorder, panic disorder, or substance use disorder, which are often observed in patients with MDD. Therefore, individualized treatment approaches are important to increase treatment response by assigning a given patient to the most optimal TMS treatment protocol based on his or her individual profile. This literature review summarizes different rTMS or TBS protocols that have been applied in researches investigating MDD patients with certain psychiatric comorbidities and discusses biomarkers that may be used to predict rTMS treatment response. Furthermore, we highlight the need for the validation of neuroimaging and electrophysiological biomarkers associated with rTMS treatment responses. Finally, we discuss on which directions future efforts should focus for developing the personalization of the treatment of depression with rTMS or iTBS.

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS) are evidenced-based treatments for patients with major depressive disorder (MDD) who fail to respond to standard first-line therapies. However, although various TMS protocols have been proven to be clinically effective, the response rate varies across clinical applications due to the heterogeneity of real-world psychiatric comorbidities, such as generalized anxiety disorder, posttraumatic stress disorder, panic disorder, or substance use disorder, which are often observed in patients with MDD. Therefore, individualized treatment approaches are important to increase treatment response by assigning a given patient to the most optimal TMS treatment protocol based on his or her individual profile. This literature review summarizes different rTMS or TBS protocols that have been applied in researches investigating MDD patients with certain psychiatric comorbidities and discusses biomarkers that may be used to predict rTMS treatment response. Furthermore, we highlight the need for the validation of neuroimaging and electrophysiological biomarkers associated with rTMS treatment responses. Finally, we discuss on which directions future efforts should focus for developing the personalization of the treatment of depression with rTMS or iTBS.