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Objective To explore the correlation between the different glycemic variability (GV) indices calculated by self-monitoring of blood glucose (SMBG) and mean amplitude of glycemic excursion (MAGE) obtained from the continuous glucose monitor (CGM) system in patients with type 2 diabetes mellitus (T2DM). Methods This was a retrospective study. We analyzed the data of T2DM patients who received 48-72 h CGM and 7-point SMBG (pre and post-breakfast, lunch, dinner and prior to bedtime) simultaneously in Department of Endocrinology and Metabolic Disease, the Third Affiliated Hospital of Sun-Yat-sen University from January 2018 to October 2019. The GV indices calculated from the 7-point SMBG data included the standard deviation (SDBG) of the 7-point glucose profiles, the largest amplitude of glycemic excursions (LAGE) and the postprandial glucose excursion (PPGE), coefficient of variation of blood glucose (CV) and mean amplitude of glucose excursion (MAGE', calculated by SMBG profile). Spearman's correlation analysis, simple linear regression and multiple stepwise regression analysis were used to analyze the relationship between the different GV indices calculated from 7-point SMBG and MAGE obtained by CGM, and the receiver operator characteristic (ROC) curve was drawn to evaluate the ability of the former to predict the latter. Results Among 105 patients with T2DM, the SDBG, PPGE, LAGE and CV calculated by 7-point glucose profiles of SMBG were (2.02±0.77) mmol/L, (2.75±1.13) mmol/L, (5.62±2.13) mmol/L and (25.92±0.77)%, respectively, while the level of MAGE' and median MAGE were 4.11 (2.84, 5.92) mmol/L and 4.00 (2.65, 5.00) mmol/L, respectively. SDBG, PPGE, LAGE, CV and MAGE' were significantly correlative with MAGE (r=0.614, 0.499, 0.588, 0.533 and 0.473, respectively, all P<0.01). Multiple stepwise regression analysis was performed with MAGE as dependent variable and SDBG, PPGE, LAGE, CV and MAGE' as independent variables, and only SDBG entered the equation (P<0.01). The areas under ROC curve for SDBG [0.795, 95% confidence interval (CI) was 0.708-0.882], LAGE (0.782, 95%CI was 0.692-0.872) and CV (0.769, 95%CI was 0.677-0.846) were larger than those for PPGE (0.718, 95%CI was 0.620-0.816) and MAGE' (0.704, 95%CI was 0.607-0.789, all P<0.01) in reflecting the unqualified MAGE. Conclusions GV indices calculated from 7-point SMBG data including SDBG, LAGE, PPGE, CV and MAGE' are positively correlated with MAGE obtained from CGM. And SDBG has higher accuracy than the other four parameters. [ABSTRACT FROM AUTHOR]

Objective To investigate the relationship between self-management and dietary pattern among adults patients with type 1 diabetes mellitus (T1DM) by a Chinese version of the Summary of Diabetes Self-Care Activities Measure scale (C-SDSCA) and a short food frequency questionnaire (FFQ). Methods C-SDSCA scale and FFQ were performed on 100 adult T1DM patients recruited from Guangdong T1DM Translational Medicine Study from April to October in 2018. According to the score of C-SDSCA scale, patients were divided into three groups by self-management: excellent, moderate and poor behaviors. Dietary pattern of the participants was classified as appropriate/inappropriate based on the results of FFQ. Differences between groups were compared using t-test, ANOVA test or chi-square test, when applicable. The effect of self-management behavior on dietary pattern was analyzed using multivariate logistic regression model. Results In total, 100 patients responded and 93 provided eligible responses. The average score of C-SDSCA scale was 59%, while only 25.8% (24/93) of the subjects had appropriate dietary patterns. Compared with patients with appropriate dietary patterns, those with in appropriate ones had significantly elevated level of HbA1c [(7.4±1.1)% vs (6.9±1.0)%, t=-2.08, P=0.04]. The scores of C-SDSCA were higher in the group of patients with appropriate diet patterns comparing to those with inappropriate ones (45.63±11.71 vs 35.03±11.73, t=3.81, P<0.01). In the group of excellent self-management behaviors, 55.6% (10/18) patients had appropriate dietary patterns, but the ratio were only 22.0% (13/59) and 6.2% (1/16) in the groups with moderate and poor self-management behaviors, respectively. Results from logistic regression shows that deterioration in self-management level by one category was associated with a 4.78-fold increase in the risk of inappropriate dietary pattern (OR=4.78, 95%CI 1.71-13.34, P<0.01). Conclusions Current situation of self-managing behaviors and dietary patterns of adult patients with T1DM were not ideal. It was found that appropriate dietary pattern was associated with better glycemic control and better self-managing behaviors, indicating that more attention should be paid to diet education in patients who have poorer self-management behaviors. [ABSTRACT FROM AUTHOR]