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Aims: This study aimed to explore the level of and changes in the 1,5-anhydroglucitol × glycated hemoglobin A1c/100 (AH index, AHI) associated with different glucose metabolism statuses and to evaluate the islet function and insulin sensitivity of patients with type 2 diabetes (T2DM) with different AHI levels. Methods: Of the 3562 subjects enrolled in this study, 1697 had T2DM. The disposition index (DI) was the product of islet secretion function and insulin sensitivity-related indexes. Results: The mean AHI level was 1.0 (0.7–1.3) in the general population, while the mean AHI level in the T2DM group was 0.8 (0.5–1.2), which was significantly lower than that in the impaired glucose regulation and normal glucose tolerance group (both 1.2 (0.9–1.5), both P < 0.01). We further divided patients with T2DM into four subgroups according to the quartile of AHI. The results showed that with the increase in AHI level, the homeostasis model assessment of insulin resistance (HOMA-IR) decreased, while HOMA-β, insulin generation index, insulin sensitivity index, and DI increased (all Pfor trend < 0.01). Multivariate logistic regression showed that the odds ratios for a low DI for increasing levels of AHI were 1.00, 0.22 (0.16–0.29), 0.16 (0.11–0.22), and 0.09 (0.06–0.13), showing a decreasing trend (Pfor trend < 0.05). Conclusion: The AHI could reflect the variation in glycemic disorder and the function of islet β cells. The lower the AHI, the worse the glycemic disorder, as well as the islet β-cell function. [ABSTRACT FROM AUTHOR]

Background Recent studies have discussed the relationship between body mass index (BMI) and glycated albumin (GA) level. However, the extent of the influence of BMI on GA remains uncertain. We investigated the associations between BMI and GA, glycated hemoglobin A 1c (HbA 1c ) and GA/HbA 1c , and to analyze the influence of obesity on GA, HbA 1c , and GA/HbA 1c in both Chinese diabetic and non-diabetic populations. Methods A total of 2562 participants, including 1177 men and 1385 women (age 20–80 y), were enrolled. Each subject underwent a 75-g oral glucose tolerance test. Serum GA was detected using a liquid enzyme method, and HbA 1c was assayed using high-performance liquid chromatography. Results In the diabetic patients ( n  = 1223), the GA, HbA 1c, and GA/HbA 1c levels were 16.7 ± 3.0%, 6.6 ± .9% (49 ± 9 mmol/mol), and 2.51 ± .33, respectively. In the non-diabetic subjects ( n  = 1339), the GA, HbA 1c, and GA/HbA 1c concentrations were 13.8 ± 1.7%, 5.6 ± .4% (38 ± 4 mmol/mol), and 2.47 ± .31, respectively. Decreasing trends in the GA and GA/HbA 1c concentrations and an increasing trend in the HbA 1c concentration (all P for trend <.05) were found to accompany with the increase in BMI, regardless of diabetes status. Multiple regression analysis revealed that BMI was independently related to HbA 1c in the non-diabetic population (standardized β  = .158, P <  .001); however, the relationship disappeared in the diabetic population ( P >  .05). Moreover, in the diabetic and non-diabetic populations, BMI was negatively correlated with GA (standardized β  = −.167 and − .231, both P <  .001) and GA/HbA 1c (standardized β  = −.273 and − .310, both P <  .001). Further analysis showed that a 1 kg/m 2 increment in BMI was associated with a .13% decrease in the absolute value of GA. Conclusions In both diabetic and non-diabetic populations, GA and GA/HbA 1c levels are independently and negatively associated with BMI. For every 1 kg/m 2 increment in BMI, the absolute value of GA decreases approximately .13%. [ABSTRACT FROM AUTHOR]

Background In subjects who present a first fasting plasma glucose (FPG 1 ) ≥ 7.0 mmol/l without classic symptoms of diabetes, diagnosis of diabetes will likely be missed without an additional oral glucose tolerance test (OGTT) in the Chinese population. Recent studies have shown that glycated albumin (GA) has advantages in reflecting postprandial hyperglycemia. Therefore, the present study evaluated whether additional measurement of GA could reduce the rate of missed diagnosis of diabetes. Methods A total of 1287 participants (711 men, 576 women) with a FPG 1 ≥ 7.0 mmol/l without classic symptoms of diabetes were enrolled and underwent a 75-g OGTT. Serum GA was measured by a liquid enzyme method. Diabetes was diagnosed based on the 2010 American Diabetes Association (ADA) criteria. Results A total of 992 (77.08%) participants were diagnosed diabetes by OGTT and glycated hemoglobin A 1c (HbA 1c ). The diagnostic validity of 2-h postload plasma glucose (2hPG) was superior to other glycemic index (the diagnostic sensitivity of 2hPG, HbA 1c , the second FPG (FPG 2 ) was 87.50%, 73.99%, 63.21%, respectively). Without 2hPG after OGTT, repeat testing of FPG 2 alone would result in missed diagnosis of 36.79% of diabetic participants, whereas testing FPG 2 with HbA 1c was associated with a missed diagnosis rate of 14.31%. While using the combined criteria of FPG 2 ≥ 7.0 mmol/l and/or HbA 1c ≥ 6.5% and/or GA ≥ 17.1%, the rate of missed diagnosis was merely 9.48%. That is, the rate of missed diagnosis was reduced by 33.75% with the addition of GA measurement. The k value reflecting the consistency of diagnosis between the FPG 2 and/or HbA 1c and/or GA criteria and the 2010 ADA criteria was 0.788. Conclusions For subjects with FPG 1 ≥ 7.0 mmol/l without classic symptoms of diabetes, additional measurement of GA can help prevent missed diagnosis of diabetes in Chinese population. [ABSTRACT FROM AUTHOR]

Background. Our previous study indicated that serum 1,5-anhydroglucitol (1,5-AG) levels slightly increased after a glucose load; therefore, this study was conducted to explore short-term changes in 1,5-AG levels after a steamed bread meal test (SBMT) and compare the agreement of 1,5-AG, glycated albumin (GA), and glycated hemoglobin A1c (HbA1c) levels between fasting and postprandial states after an SBMT. Methods. 104 participants were recruited and underwent a 100 g SBMT. Fasting, 30 min, and 120 min of 1,5-AG, GA, and HbA1c were measured. Results. Levels of 1,5-AG slightly increased from 30 to 120 min after an SBMT (P<0.01), and HbA1c and GA levels showed stability at 30 and 120 min. The Passing-Bablok regression linear equation showed that postprandial 1,5-AG, GA, and HbA1c levels were well fitted (all P>0.05), and Bland-Altman difference plot showed that 100% of data points for HbA1c30 and HbA1c120 fell within the limits of agreement; 94.2%, 96.2%, 95.2%, and 95.2% of data points for 1,5-AG30, 1,5-AG120, GA30, and GA120 fell within the limits of agreement, respectively. Conclusion. Agreement analyses indicated good stability of 1,5-AG, GA, and HbA1c levels after the SBMT. HbA1c had an optimal stability, which was superior to that of GA or 1,5-AG. [ABSTRACT FROM AUTHOR]

Background: In recent years, the rapid development of mobile medical technology has provided multiple ways for the long-term management of chronic diseases, especially diabetes. As a new type of management model, smartphone apps are global, convenient, cheap, and interactive. Although apps were proved to be more effective at glycemic control, compared with traditional computer- and Web-based telemedicine technologies, how to gain a further and sustained improvement is still being explored.Objective: The objective of this study was to investigate the effectiveness of an app-based interactive management model by a professional health care team on glycemic control in Chinese patients with poorly controlled diabetes.Methods: This study was a 6-month long, single-center, prospective randomized controlled trial. A total of 276 type 1 or type 2 diabetes patients were enrolled and randomized to the control group (group A), app self-management group (group B), and app interactive management group (group C) in a 1:1:1 ratio. The primary outcome was the change in glycated hemoglobin (HbA1c) level. Missing data were handled by multiple imputation.Results: At months 3 and 6, all 3 groups showed significant decreases in HbA1c levels (all P<.05). Patients in the app interactive management group had a significantly lower HbA1clevel than those in the app self-management group at 6 months (P=.04). The average HbA1c reduction in the app interactive management group was larger than that in the app self-management and control groups at both months 3 and 6 (all P<.05). However, no differences in HbA1c reduction were observed between the app self-management and control groups at both months 3 and 6 (both P>.05). Multivariate line regression analyses also showed that the app interactive management group was associated with the larger reduction of HbA1c compared with groups A and B at both months 3 and 6 (all P>.05). In addition, the app interactive management group had better control of triglyceride and high-density lipoprotein cholesterol levels at both months 3 and 6 compared with baseline (both P<.05).Conclusions: In Chinese patients with poorly controlled diabetes, it was difficult to achieve long-term effective glucose improvement by using app self-management alone, but combining it with interactive management can help achieve rapid and sustained glycemic control.Trial Registration: ClinicalTrials.gov NCT02589730; https://clinicaltrials.gov/ct2/show/NCT02589730. [ABSTRACT FROM AUTHOR]