METHODS: The index prescriptions were those that when the annual blood tests were reviewed. Prescriptions of medication were verified, compared to the preceding prescriptions and classified as 1) no change, 2) stepping up and 3) stepping down. The treatment targets were HbA1c
OBJECTIVE: This study aims to evaluate the effects of remote telemonitoring with team-based management on people with uncontrolled type 2 diabetes.
DESIGN: This was a pragmatic 52-week cluster-randomized controlled study among 11 primary care government practices in Malaysia.
PARTICIPANTS: People with type 2 diabetes aged 18 and above, who had hemoglobin A1c ≥ 7.5% but less than 11.0% within the past 3 months and resided in the state of Selangor.
INTERVENTION: The intervention group received home gluco-telemonitors and transmitted glucose data to a care team who could adjust therapy accordingly. The team also facilitated self-management by supporting participants to improve medication adherence, and encourage healthier lifestyle and use of resources to reduce risk factors. Usual care group received routine healthcare service.
MAIN MEASURE: The primary outcome was the change in HbA1c at 24 weeks and 52 weeks. Secondary outcomes included change in fasting plasma glucose, blood pressure, lipid levels, health-related quality of life, and diabetes self-efficacy.
RESULTS: A total of 240 participants were recruited in this study. The telemonitoring group reported larger improvements in glycemic control compared with control at the end of study (week 24, - 0.05%; 95% CI - 0.10 to 0.00%) and at follow-up (week 52, - 0.03%; - 0.07 to 0.02%, p = 0.226). Similarly, no differences in other secondary outcomes were observed, including the number of adverse events and health-related quality of life.
CONCLUSION: This study indicates that there is limited benefit of replacing telemedicine with the current practice of self-monitoring of blood glucose. Further innovative methods to improve patient engagement in diabetes care are needed.
TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02466880.
METHODS: Five fresh-pooled blood samples were sent to participating laboratories twice each year. The results were evaluated against target values assigned by the National Glycohemoglobin Standardization Program network laboratories; a passing criterion of +/-7% of the target value was used. Measurement uncertainty at Hb A(1c) concentrations of 7.0% and 8.0% were determined.
RESULTS: A total of 276 laboratories from 11 countries took part in the Hb A(1c) survey. At the Hb A(1c) concentrations tested method-specific interlaboratory imprecision (CVs) were 1.1%-13.9% in 2005, 1.3%-10.1% in 2006, 1.2%-8.2% in 2007, and 1.1%-6.1% in 2008. Differences between target values and median values from the commonly used methods ranged from -0.24% to 0.22% Hb A(1c) in 2008. In 2005 83% of laboratories passed the survey, and in 2008 93% passed. At 7.0% Hb A(1c), measurement uncertainty was on average 0.49% Hb A(1c).
CONCLUSIONS: The use of accuracy-based proficiency testing with stringent quality criteria has improved the performance of Hb A(1c) testing in the Asian and Pacific laboratories during the 4 years of assessment.
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: 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.
METHODS: In this cross-sectional study, interviews and a standardised questionnaire comparing lifestyle changes before and during the lockdown were performed in follow-up clinic visits after the lockdown. Anthropometry measurements and glycated haemoglobin (HbA1c) values were compared 3 months prior and after the lockdown.
RESULTS: Participants were 93 patients with T1DM (11.08 ± 3.47 years) and 30 patients with T2DM (13.81 ± 2.03 years). Male gender, T2DM and pubertal adolescents were found to have a significant deterioration in glycaemic control. A significant increment of HbA1c was observed in patients with T2DM (8.5 ± 0.40 vs 9.9 ± 0.46%), but not in patients with T1DM (8.6 ± 0.28 vs 8.7 ± 0.33%). Contrarily, there was an improved glycaemic control in pre-pubertal T1DM children likely due to parental supervision during home confinement. Weight and BMI SDS increased in T1DM patients but surprisingly reduced in T2DM patients possibly due to worsening diabetes control. Reduced meal frequency mainly due to skipping breakfast, reduced physical activity level scores, increased screen time and sleep duration were observed in both groups.
CONCLUSIONS: Adverse impact on glycaemic control and lifestyle were seen mostly in patients with T2DM and pubertal adolescent boys.
METHODS: A randomized, double-blind, placebo-controlled trial was performed in a university hospital. Women with GDMA1 were recruited at 16-30 weeks of pregnancy and randomized to oral metformin 500 mg twice daily or identical placebo tablets to delivery. Level of HbA1c was taken at recruitment and at 36 weeks of pregnancy. The primary outcome was the change in level of HbA1c at recruitment and 36 weeks of pregnancy.
RESULTS: Data from 106 participants were analyzed. The level of HbA1c during pregnancy increased significantly with a mean increase of 0.20% ± 0.31% (P
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.
OBJECTIVE: To investigate clinical laboratory markers of SARS-CoV-2 and PASC.
DESIGN: Propensity score-weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024).
SETTING: 83 enrolling sites.
PARTICIPANTS: RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection.
MEASUREMENTS: Participants completed questionnaires and standard clinical laboratory tests.
RESULTS: Among 10 094 participants, 8746 had prior SARS-CoV-2 infection, 1348 were uninfected, 1880 had a PASC index of 12 or higher, and 3351 had a PASC index of zero. After propensity score adjustment, participants with prior infection had a lower mean platelet count (265.9 × 109 cells/L [95% CI, 264.5 to 267.4 × 109 cells/L]) than participants without known prior infection (275.2 × 109 cells/L [CI, 268.5 to 282.0 × 109 cells/L]), as well as higher mean hemoglobin A1c (HbA1c) level (5.58% [CI, 5.56% to 5.60%] vs. 5.46% [CI, 5.40% to 5.51%]) and urinary albumin-creatinine ratio (81.9 mg/g [CI, 67.5 to 96.2 mg/g] vs. 43.0 mg/g [CI, 25.4 to 60.6 mg/g]), although differences were of modest clinical significance. The difference in HbA1c levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero.
LIMITATION: Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined.
CONCLUSION: Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC.
PRIMARY FUNDING SOURCE: National Institutes of Health.
METHODS: A total of 1065 patients aged ≥18 years with T2DM initiating insulin therapy in normal clinical course were enrolled from Hong Kong, Malaysia, Philippines, Taiwan and Thailand. Participants' data was recorded by the treating physicians. Patient-reported outcomes (PROs) were assessed using questionnaires completed by participants.
RESULTS: The mean age of patients was 57.2 years with mean glycosylated hemoglobin (HbA1c) of 10.0%. About 66% of patients had an HbA1c ≥9.0% at insulin initiation despite 74% of them being on two or more oral antidiabetic agents at the time of insulin initiation. Basal insulin was initiated in 72% and premixed insulin in 27% of patients. Changes in insulin therapy was observed in 63% of patients and, by the end of study, 28% achieved HbA1c levels of <7.5%. The proportion of patients completely satisfied with their insulin treatment increased over the study course and the quality of life (QoL) score increased from baseline to the study end.
CONCLUSION: As high HbA1C levels indicate a delayed start of insulin therapy, timely initiation and early intensification of insulin therapy is necessary in the region to achieve adequate glycemic control in time and prevent diabetes complications. Data from PROs suggests that the insulin treatment improves QoL in most patients.