METHODS: We conducted a cross-sectional analysis of data from adults with T2D from 11 Asian countries/regions with structured assessment enrolled in the prospective Joint Asia Diabetes Evaluation (JADE) register between November 2007 and December 2019. Patients receiving insulin and/or injectable glucagon-like peptide-1 receptor agonists were excluded.
RESULTS: Amongst 62 512 patients (mean ± standard deviation age: 57.3 ± 11.8 years; 53.6% men), 54 783 (87.6%) were treated with OGLDs at enrolment. Most received one (37.5%) or two (44.2%) OGLDs. In the entire cohort, 59.4% of treated patients received SU-based therapy with variations amongst countries/regions. Overall, 79.5% of SU regimens were based on SUs plus metformin, and 22.1% on SUs plus dipeptidyl peptidase-4 inhibitors. Among SU users, gliclazide was most commonly prescribed (46.7%), followed by glimepiride (40.0%) and glibenclamide (8.1%). More gliclazide users entered the cohort with glycated haemoglobin levels <53 mmol/mol (7%) than non-gliclazide SU users (odds ratio [OR] 1.09, 95% CI 1.02-1.17), with less frequent self-reported hypoglycaemia in the 3 months before registration (OR 0.81, 95% CI 0.72-0.92; adjusted for sociodemographic factors, cardiometabolic risk factors, complications, use of other OGLDs, country/region and year of registration).
CONCLUSION: In Asia, SUs are a popular OGLD class, often combined with metformin. Good glycaemic control and safety profiles associated with the use of SUs, including gliclazide, support their position as a key treatment option in patients with T2D.
AIMS: We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients.
METHODS: This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [n = 276] and Hong Kong and Wenzhou as validation cohort [n = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select 'highly important' predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.
RESULTS: MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800-898] and 0.823 [0.760-0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603-0.765], 0.663 [0.588-0.738]), Fibrosis-4 index (0.793 [0.735-0.854], 0.737 [0.660-0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731-0.844], 0.750 [0.674-0.827]) (DeLong's test p