METHODS: The PURE study is a prospective cohort study of 127 594 adults aged 35-70 years from 20 high-income, middle-income, and low-income countries. Diet was assessed at baseline using country-specific validated food frequency questionnaires. The glycaemic index and the glycaemic load were estimated on the basis of the intake of seven categories of carbohydrate-containing foods. Participants were categorised into quintiles of glycaemic index and glycaemic load. The primary outcome was incident type 2 diabetes. Multivariable Cox Frailty models with random intercepts for study centre were used to calculate hazard ratios (HRs).
FINDINGS: During a median follow-up of 11·8 years (IQR 9·0-13·0), 7326 (5·7%) incident cases of type 2 diabetes occurred. In multivariable adjusted analyses, a diet with a higher glycaemic index was significantly associated with a higher risk of diabetes (quintile 5 vs quintile 1; HR 1·15 [95% CI 1·03-1·29]). Participants in the highest quintile of the glycaemic load had a higher risk of incident type 2 diabetes compared with those in the lowest quintile (HR 1·21, 95% CI 1·06-1·37). The glycaemic index was more strongly associated with diabetes among individuals with a higher BMI (quintile 5 vs quintile 1; HR 1·23 [95% CI 1·08-1·41]) than those with a lower BMI (quintile 5 vs quintile 1; 1·10 [0·87-1·39]; p interaction=0·030).
INTERPRETATION: Diets with a high glycaemic index and a high glycaemic load were associated with a higher risk of incident type 2 diabetes in a multinational cohort spanning five continents. Our findings suggest that consuming low glycaemic index and low glycaemic load diets might prevent the development of type 2 diabetes.
FUNDING: Full funding sources are listed at the end of the Article.
AIMS: The aims of this study were to explore the usability and internet data analytics of the HelloType1 online educational platform within each country.
METHODS: The data analytics were extracted Google analytics that tracks data from the website hellotype1.com and Facebook analytics associated with the website.
RESULTS: There was a 147% increase in the number of HelloType1 users between the first 6 months versus the latter 6 months in 2022 and a 15% increase in the number of pages visited were noted. The majority of traffic source were coming from organic searches with a significant increase of 80% growth in 2022.
CONCLUSIONS: The results of the analytics provide important insights on how an innovative diabetes digital educational resource in local languages may be optimally delivered in low-middle income countries with limited resources.
MATERIALS AND METHODS: A cross-sectional study was conducted in Luyang Health Clinic from 1st June 2020 to 3rd September 2020. A self-interviewed questionnaire comprises of three sections; sociodemographic, Wake Forest Physician Trust Scale (WFS) and Adherence to Refills and Medications Scale (ARMS) was completed by 281 respondents. Glycaemic control is based on the latest Hba1c profile of the respondents. Descriptive and nonparametric bivariate analysis were performed using IBM SPSS version 26.
RESULTS: The median (IQR) level of trust in physician was 43(8) out of a possible score range of 10 to 50. Trust in physician was correlated with treatment adherence (r=-0.12, p=0.048). There was no significant association between trust in physician with sociodemographic factors, which include age (p=0.33), gender (p=0.46), ethnicity (p=0.70), education level (p=0.50), and household income (p=0.37). Similarly, there was no significant association between the level of trust in physician with glycaemic control (p=0.709).
CONCLUSION: In conclusion, trust in physician was associated with treatment adherence but not with glycaemic control. In our local context, the glycaemic control could be due to other factors. Further studies should include a multicentre population to assess other potential factors that could contribute to glycaemic control.
METHODS: This was a cross-sectional, single-center study involving adults with established COPD (n = 186) divided into those with or without hospital admissions for acute exacerbation. Oral glucose tolerance test (OGTT) was performed in patients with no known history of dysglycemia.
RESULTS: There were 16 patients who had overt diabetes, and 32 had prediabetes following the OGTT. Forty percent had histories of hospital admissions for COPD exacerbations. Both groups demonstrated similar 2-h post prandial glucose, glycated hemoglobin (HbA1c) and fasting blood glucose. The incidences of newly diagnosed dysglycemia were higher in both groups (40.8% vs 34.6%, p = 0.57). Cumulative days of admission (≥6 days/year) and weight (≥65 kg) were identified as predictors for dysglycemia within the study population.
DISCUSSION: This study demonstrated a high number of overt and newly diagnosed dysglycemia among COPD patients who had no previous history of abnormal glucose. Recent acute exacerbations of COPD could have a negative impact on glycemia, although the results did not attain statistical significance. However, there is a need for adequate screening for dysglycemia, particularly among those with frequent acute exacerbations of their condition.
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.