MATERIALS AND METHODS: This longitudinal study included 2198 participants with mean age 43.4 ± 7.7 years, who underwent dental examinations in Yokohama, Japan, at two time points, 2003-2004 and 2008-2009, at an interval of 5 years. Periodontal condition was assessed by the mean value of probing pocket depth (PPD) and clinical attachment level (CAL). Glycaemic status was assessed by fasting glucose and glycated haemoglobin (HbA1c).
RESULTS: The cross-lagged panel models showed the effect of HbA1c at baseline on mean PPD at follow-up (β = 0.044, p = .039). There was a marginal effect of fasting glucose on the mean PPD (β = 0.037, p = .059). It was similar to the effect of fasting glucose or HbAlc on mean CAL. However, in the opposite direction, no effect of mean PPD or CAL at baseline on fasting glucose or HbAlc at follow-up was identified.
CONCLUSIONS: This study demonstrated a unidirectional relationship between glycaemic status and periodontal condition. The study population, however, had mostly mild periodontitis. Future studies are needed to investigate the effect of periodontal condition on glycaemic status in patients with severe periodontitis.
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.
METHODS: A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks.
RESULTS: SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes.
CONCLUSIONS: Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.
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: 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.