METHODS: Data of 328 eligible housewives who participated in the MyBFF@Home study was used. Intervention group of 169 subjects were provided with an intervention package which includes physical activity (brisk walking, dumbbell exercise, physical activity diary, group exercise) and 159 subjects in control group received various health seminars. Physical activity level was assessed using short-International Physical Activity Questionnaire. The physical activity level was then re-categorized into 4 categories (active intervention, inactive intervention, active control and inactive control). Physical activity, blood glucose and lipid profile were measured at baseline, 3rd month and 6th month of the study. General Linear Model was used to determine the effect of physical activity on glucose and lipid profile.
RESULTS: At the 6th month, there were 99 subjects in the intervention and 79 control group who had complete data for physical activity. There was no difference on the effect of physical activity on the glucose level and lipid profile except for the Triglycerides level. Both intervention and control groups showed reduction of physical activity level over time.
CONCLUSION: The effect of physical activity on blood glucose and lipid profile could not be demonstrated possibly due to physical activity in both intervention and control groups showed decreasing trend over time.
METHODOLOGY: ARISE, an open-label, multicenter, non-interventional, prospective study was conducted between August 2019 and December 2020. Adult Malaysian patients with T2DM who were enrolled from 14 sites received IDegAsp as per the local label for 26 weeks. The primary endpoint was change in glycated hemoglobin (HbA1c) levels from baseline to end of study (EOS).
RESULTS: Of the 182 patients included in the full analysis set, 159 (87.4%) completed the study. From baseline to EOS, HbA1c (estimated difference [ED]: -1.3% [95% CI: -1.61 to -0.90]) and fasting plasma glucose levels (ED: -1.8 mmol/L [95% CI: -2.49 to -1.13]) were significantly reduced (p<0.0001). The patient-reported reduced hypoglycemic episodes (overall and nocturnal) during treatment. Overall, 37 adverse events were observed in 23 (12.6%) patients.
CONCLUSION: Switching or initiating IDegAsp treatment resulted in significant improvements in glycemic control and a reduction in hypoglycemic episodes.
OBJECTIVES: This study aimed to examine associations between SSB intake and cardiometabolic risks among Malaysian adolescents.
METHODS: Anthropometric data, blood pressure (BP), fasting blood glucose (FBG), lipid profiles and insulin levels measured involved 873 adolescents (aged 13 years). SSB intake, dietary patterns and physical activity level (PAL) were self-reported.
RESULTS: Mean SSB consumption was 177.5 mL day-1 with significant differences among ethnicities (Malay, Chinese, Indians and Others) (p
Methods: A cross-sectional study was conducted at the Universiti Kebangsaan Malaysia Medical Centre (UKMMC) using outpatient population diabetic patients. Demographic data on social and clinical characteristics were collected from participants. Several questionnaires were administered, including the Beck Depression Inventory-II (BDI-II) to measure depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) to assess anxiety symptoms, the Big Five Inventory (BFI) to evaluate personality traits, and the WHO Quality of Life-BREF (WHOQOL-BREF) to assess QOL. Multivariate binary logistic regression was performed to determine the predictors of poor glycaemic control.
Results: 300 patients with diabetes mellitus were recruited, with the majority (90%) having type 2 diabetes. In this population, the prevalence of poor glycaemic control (HbA1C ≥ 7.0%) was 69%, with a median HbA1C of 7.6% (IQR = 2.7). Longer duration of diabetes mellitus and a greater number of days of missed medications predicted poor glycaemic control, while older age and overall self-perception of QOL protected against poor glycaemic control. No psychological factors were associated with poor glycaemic control.
Conclusion: This study emphasizes the importance of considering the various factors that contribute to poor glycaemic control, such as duration of diabetes, medication adherence, age, and QOL. These findings should be used by clinicians, particularly when planning a multidisciplinary approach to the management of diabetes.
METHODS: A retrospective observational study of 60 type 1 and 100 type 2 diabetes subjects. All underwent professional continuous glucose monitoring (CGM) for 3-6 days and recorded self-monitored blood glucose (SMBG). Indices were calculated from both CGM and SMBG. Statistical analyses included regression and area under receiver operator curve (AUC) analyses.
RESULTS: Hypoglycemia frequency (53.3% vs. 24%, P Blood Glucose Index (LBGI)CGM, Glycemic Risk Assessment Diabetes Equation (GRADE)HypoglycemiaCGM, and Hypoglycemia IndexCGM predicted hypoglycemia well. %CVCGM and %CVSMBG consistently remained a robust discriminator of hypoglycemia in type 1 diabetes (AUC 0.88). In type 2 diabetes, a combination of HbA1c and %CVSMBG or LBGISMBG could help discriminate hypoglycemia.
CONCLUSION: Assessment of glycemia should go beyond HbA1c and incorporate measures of GV and glycemic indices. %CVSMBG in type 1 diabetes and LBGISMBG or a combination of HbA1c and %CVSMBG in type 2 diabetes discriminated hypoglycemia well. In defining hypoglycemia risk using GV and glycemic indices, diabetes subtypes and data source (CGM vs. SMBG) must be considered.