Lower Risks of New-Onset Hepatocellular Carcinoma in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors Versus DPP4 Inhibitors.

BACKGROUND: Type 2 diabetes mellitus (T2DM) may be a risk factor for development of hepatocellular carcinoma (HCC). The association between risk of developing HCC and treatment with sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) is currently unknown. This study aimed to compare the risk of new-onset HCC in patients treated with SGLT2i versus DPP4i. METHODS: This was a retrospective cohort study of patients with T2DM in Hong Kong receiving either SGLT2i or DPP4i between January 1, 2015, and December 31, 2020. Patients with concurrent DPP4i and SGLT2i use were excluded. Propensity score matching (1:1 ratio) was performed by using the nearest neighbor search. Multivariable Cox regression was applied to identify significant predictors. RESULTS: A total of 62,699 patients were included (SGLT2i, n=22,154; DPP4i, n=40,545). After matching (n=44,308), 166 patients (0.37%) developed HCC: 36 in the SGLT2i group and 130 in the DPP4i group over 240,269 person-years. Overall, SGLT2i use was associated with lower risks of HCC (hazard ratio [HR], 0.42; 95% CI, 0.28-0.79) compared with DPP4i after adjustments. The association between SGLT2i and HCC development remained significant in patients with cirrhosis or advanced fibrosis (HR, 0.12; 95% CI, 0.04-0.41), hepatitis B virus (HBV) infection (HR, 0.32; 95% CI, 0.17-0.59), or hepatitis C virus (HCV) infection (HR, 0.41; 95% CI, 0.22-0.80). The results were consistent in different risk models, propensity score approaches, and sensitivity analyses. CONCLUSIONS: SGLT2i use was associated with a lower risk of HCC compared with DPP4i use after adjustments, and in the context of cirrhosis, advanced fibrosis, HBV infection, and HCV infection.

Comparative effectiveness of sodium-glucose cotransporter-2 inhibitors for new-onset gastric cancer and gastric diseases in patients with type 2 diabetes mellitus: a population-based cohort study.

OBJECTIVE: To compare the risks of gastric cancer and other gastric diseases in patients with type-2 diabetes mellitus (T2DM) exposed to sodium-glucose cotransporter 2 inhibitors (SGLT2I), dipeptidyl peptidase-4 inhibitors (DPP4I) or glucagon-like peptide-1 receptor agonists (GLP1a). DESIGN: This was a population-based cohort study of prospectively collected data on patients with T2DM prescribed SGLT2I, DPP4I or GLP1a between January 1st 2015 and December 31st 2020 from Hong Kong. The outcomes were new-onset gastric cancer, peptic ulcer (PU), acute gastritis, non-acute gastritis, and gastroesophageal reflux disease (GERD). Propensity score matching (1:1) using the nearest neighbour search was performed, and multivariable Cox regression was applied. A three-arm comparison between SGLT2I, DPP4I and GLP1a was conducted using propensity scores with inverse probability of treatment weighting. RESULTS: A total of 62,858 patients (median age: 62.2 years old [SD: 12.8]; 55.93% males; SGLT2I: n = 23,442; DPP4I: n = 39,416) were included. In the matched cohort, the incidence of gastric cancer was lower in SGLT2I (Incidence rate per 1000 person-year, IR: 0.32; 95% confidence interval, CI 0.23-0.43) than in DPP4I (IR per 1000 person-year: 1.22; CI 1.03-1.42) users. Multivariable Cox regression found that SGLT2I use was associated with lower risks of gastric cancer (HR 0.30; 95% CI 0.19-0.48), PU, acute gastritis, non-acute gastritis, and GERD (p < 0.05) compared to DPP4I use. In the three-arm analysis, GLP1a use was associated with higher risks of gastric cancer and GERD compared to SGLT2I use. CONCLUSIONS: The use of SGLT2I was associated with lower risks of new-onset gastric cancer, PU, acute gastritis, non-acute gastritis, and GERD after matching and adjustments compared to DPP4I use. SGLT2I use was associated with lower risks of GERD and gastric cancer compared to GLP1a use.

Clinical Characteristics, Genetic Basis and Healthcare Resource Utilisation and Costs in Patients with Catecholaminergic Polymorphic Ventricular Tachycardia: A Retrospective Cohort Study.

Background: This study examined the clinical characteristics, genetic basis, healthcare utilisation and costs of catecholaminergic ventricular tachycardia (CPVT) patients from a Chinese city. Methods: This was a territory-wide retrospective cohort study of consecutive CPVT patients at public hospitals or clinics in Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and outpatient attendances were analysed over 19 years (2001-2019) followed by calculations of annualised costs (in USD). Results: Sixteen patients with a median presentation age (interquartile range (IQR) of 11 (9-14) years old) were included. Fifteen patients (93.8%) were initially symptomatic. Ten patients had both premature ventricular complexes (PVCs) and ventricular tachycardia/fibrillation (VT/VF). One patient had PVCs without VT/VF. Genetic tests were performed on 14 patients (87.5%). Eight (57.1%) tested positive for the ryanodine receptor 2 (RyR2) gene. Seven variants have been described elsewhere (c.14848G > A, c.12475C > A, c.7420A > G, c.11836G > A, c.14159T > C, c.10046C > T and c.7202G > A). c.14861C > G is a novel RyR2 variant not been reported outside this cohort. Patients were treated with beta-blockers (n = 16), amiodarone (n = 3) and verapamil (n = 2). Sympathectomy (n = 8) and implantable-cardioverter defibrillator implantation (n = 3) were performed. Over a median follow-up of 13.3 years (IQR: 8.4-18.1) years, six patients exhibited incident VT/VF. At the patient level, the median (IQR) annualised costs for A&E, inpatient and outpatient attendances were $ 66 (40-95), $ 10521 (5240-66887) and $ 791 (546-1105), respectively. Conclusions: All patients presented before the age of 19. The yield of genetic testing was 57%. The most expensive attendance type was inpatient stays, followed by outpatients and A&E attendances.

Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification.

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.

Precision medicine in catecholaminergic polymorphic ventricular tachycardia: Recent advances toward personalized care.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare inherited cardiac ion channelopathy where the initial disease presentation is during childhood or adolescent stages, leading to increased risks of sudden cardiac death. Despite advances in medical science and technology, several gaps remain in the understanding of the molecular mechanisms, risk prediction, and therapeutic management of patients with CPVT. Recent studies have identified and validated seven sets of genes responsible for various CPVT phenotypes, including RyR2, CASQ-2, TRDN, CALM1, 2, and 3, and TECRL, providing novel insights into the molecular mechanisms. However, more data on atypical CPVT genotypes are required to investigate the underlying mechanisms further. The complexities of the underlying genetics contribute to challenges in risk stratification as well as the uncertainty surrounding nongenetic modifiers. Therapeutically, although medical management involving beta-blockers and flecainide, or insertion of an implantable cardioverter defibrillator remains the mainstay of treatment, animal and stem cell studies on gene therapy for CPVT have shown promising results. However, its clinical applicability remains unclear. Current gene therapy studies have primarily focused on the RyR2 and CASQ-2 variants, which constitute 75% of all CPVT cases. Alternative approaches that target a broader population, such as CaMKII inhibition, could be more feasible for clinical implementation. Together, this review provides an update on recent research on CPVT, highlighting the need for further investigation of the molecular mechanisms, risk stratification, and therapeutic management of this potentially lethal condition.