METHODS: We randomized 108 overweight and obese patients with T2D (46 M/62F; age 60 ± 10 years; HbA1c 8.07 ± 1.05%; weight 101.4 ± 21.1 kg and BMI 35.2 ± 7.7 kg/m2) into three groups. Group A met with RDN to develop an individualized eating plan. Group B met with RDN and followed a structured meal plan. Group C did similar to group B and received weekly phone support by RDN.
RESULTS: After 16 weeks, all three groups had a significant reduction of their energy intake compared to baseline. HbA1c did not change from baseline in group A, but decreased significantly in groups B (- 0.66%, 95% CI -1.03 to - 0.30) and C (- 0.61%, 95% CI -1.0 to - 0.23) (p value for difference among groups over time
METHODS: Haemoglobin variants were identified by HbA1c analysis in 93 of 3522 samples sent to our laboratory in a period of 1 month. Haemoglobin analysis identified HbE trait in 81 of 93 samples. To determine the influence of HbE trait on HbA1c analysis by Variant II Tubo 2.0, boronate affinity high performance liquid chromatography (HPLC) method (Primus PDQ) was used as the comparison method. Two stage linear regression analysis, Bland Altman plot and Deming regression analysis were performed to analyse whether the presence of HbE trait produced a statistically significant difference in the results. The total allowable error for HbA1c by the Royal Australasian College of Pathologists (RCPA) external quality assurance is 5%. Hence clinically significant difference is more than 5% at the medical decision level of 6% and 9%.
RESULTS: Statistically and clinically significant higher results were observed in Variant II Turbo 2.0 due to the presence of HbE trait. A positive bias of ∼10% was observed at the medical decision levels.
CONCLUSION: Laboratories should be cautious when evaluating HbA1c results in the presence of haemoglobin variants.
METHODS: This prospective, randomized controlled, open-label trial evaluated 50 women with insulin-treated GDM randomized to either retrospective CGM (6-day sensor) at 28, 32 and 36 weeks' gestation (Group 1, CGM, n = 25) or usual antenatal care without CGM (Group 2, control, n = 25). All women performed seven-point capillary blood glucose (CBG) profiles at least 3 days per week and recorded hypoglycaemic events (symptomatic and asymptomatic CBG
METHODS: Demographic and clinical variables were assessed at baseline, after three and six months in 73 type 2 diabetes patients. Regression analysis, using SPSS, evaluated the concurrent and longitudinal association of medication adherence and glycemic control. Potential confounders of variables were identified using bi-variate correlation analyses.
RESULTS: Concurrent Medication adherence and HbA1c association were significant after adjusting for ethnicity (P = 0.005). For longitudinal observation at 3 months, the association was significant after adjusting for ethnicity (P = 0.016); however, it became non-significant when baseline glycemic control was included in the model (P = 0.28).
CONCLUSION: Easy to administer MALMAS significantly predicted concurrent glycemic control independent of potential confounders. This association persisted in longitudinal observation after 3 months when adjusted for confounders and became non-significant after adjusting for baseline glycemic control.
PARTICIPANTS: Between 2020 and 2021, 383 children and young people with T1D who were active in the A4D supported programmes were reviewed including information on health coverage, multidisciplinary team management, diabetic ketoacidosis (DKA) on admission and insulin regimen.
RESULTS: Mean HbA1c between 2020 and 2021 for patients in these LMICs arereported for the first time. The average glycaemic index in the five SEAcountries reviewed between 2020 and 2021 were high at 83 mmol/mol (9.7%).
CONCLUSIONS: Government partnership working with non-government organisationsto support T1D from diagnosis to adulthood are the first steps to closing thegaps in many LMICs. Further epidemiological studies are needed to identify the glycaemic outcomes and DKA rates on admission for many of these countries.
MATERIAL AND METHODS: This was a five-year retrospective open cohort study using secondary data from the National Diabetes Registry. The study setting was all public health clinics (n = 47) in the state of Negeri Sembilan, Malaysia. Time to treatment intensification was defined as the number of years from the index year until the addition of another oral antidiabetic drug or initiation of insulin. Life table survival analysis based on best-worst case scenarios was used to determine the time to treatment intensification. Discrete-time proportional hazards model was fitted for the factors associated with treatment intensification.
RESULTS: The mean follow-up duration was 2.6 (SD 1.1) years. Of 7,646 patients, the median time to treatment intensification was 1.29 years (15.5 months), 1.58 years (19.0 months) and 2.32 years (27.8 months) under the best-, average- and worst-case scenarios respectively. The proportion of patients with treatment intensification was 45.4% (95% CI: 44.2-46.5), of which 34.6% occurred only after one year. Younger adults, overweight, obesity, use of antiplatelet medications and poorer HbA1c were positively associated with treatment intensification. Patients treated with more oral antidiabetics were less likely to have treatment intensification.
CONCLUSION: Clinical inertia is present in the management of T2D patients in Malaysian public health clinics. We recommend further studies in lower- and middle-income countries to explore its causes so that targeted strategies can be developed to address this issue.