Establishment of normal reference ranges for glycemic variability in Chinese subjects using continuous glucose monitoring

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs. This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine. Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation). A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively. The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

Two novel GCK mutations in Chinese patients with maturity-onset diabetes of the young

PURPOSE

Heterozygous inactivating mutations in the glucokinase (GCK) gene result in the asymptomatic fasting hyperglycemia named as GCK-MODY or MODY2. The genetic testing can effectively avoid the misdiagnosis and inappropriate treatment for GCK-MODY.

METHODS

A total of 25 unrelated families with MODY were screened for mutations in coding region of GCK by using direct sequencing. Three different bioinformatics tools such as PolyPhen2, Mutation Taster and PROVEAN were performed to predict the function of mutant proteins. The glucose profile was recorded by continuous glucose monitoring system (CGMS) to evaluate the glycemic variability for the GCK-MODY patient.

RESULTS

Our study identified five GCK mutations in 24% of the families (6/25): two novel mutations (I126fs and G385A) and three already described mutations (G44S, H50fs and S383L). In silico analyses predicted that these mutations altered structural conformational changes. The values of mean amplitude of glycemic excursions (MAGE), an important index of blood glucose fluctuation in CGMS system, were 0.81 in the first 24 h and 1.61 in the second 24 h record in the patient with GCK-MODY (F3), suggesting little glucose fluctuation.

CONCLUSION

The genetic testing is suggested to be important to differentiate GCK-MODY from other types of diabetes. CGMS might be used to screen GCK-MODY cases prior to genetic testing.

Dual-basal-insulin regimen for the management of dawn phenomenon in children with type 1 diabetes: a retrospective cohort study

Background

Handling of the dawn phenomenon (DP) with multiple daily insulin injection (MDII) regimen is a real challenge.

Objective

We aimed to demonstrate the effectiveness of a dual-basal-insulin (a long-acting glargine and an intermediate-acting neutral protamine Hagedorn (NPH)) regimen for the management of DP in children with type 1 diabetes mellitus (T1DM). The primary efficacy outcome was to overcome morning hyperglycemia without causing hypoglycemia during the non-DP period of the night.

Design

Retrospective cohort study.

Method

Charts of 28 children with T1DM (12 female; 42.8%, mean age 13.7 ± 2.1 years) treated with MDII were retrospectively reviewed. The median duration of diabetes was 4.5 years (range 2-13.5 years). DP was diagnosed using a threshold difference of 20 mg/dL (0.1 mmol/L) between fasting capillary blood glucose at 3 a.m. and prebreakfast. NPH was administered at midnight in addition to daily bedtime (08.00-09.00 p.m.) glargine (dual-basal-insulin regimen). Midnight, 03:00 a.m., prebreakfast and postprandial capillary blood glucose readings, insulin-carbohydrate ratios, and basal-bolus insulin doses were recorded the day before the dual-basal-insulin regimen was started and the day after the titration of the insulin doses was complete. Body mass index standard deviation scores (BMI SDS) at the onset-3rd-12th month of treatment were noted.

Results

Before using dual basal insulin, prebreakfast capillary blood glucose levels were greater than those at midnight and at 03:00 a.m. (F = 64.985, p < 0.01). After titration of the dual-basal-insulin doses, there were significant improvements such that there were no statistically significant differences in the capillary blood glucose measurements at the three crucial time points (midnight, 03.00 a.m., and prebreakfast; F = 1.827, p = 0.172). No instances of hypoglycemia were reported, and the total daily insulin per kilogram of body weight did not change. The BMI SDS remained steady over the course of the 1-year follow-up.

Conclusion

In this retrospective cohort study, the dual-basal-insulin regimen, using a long-acting glargine and an intermediate-acting NPH, was effective in overcoming early morning hyperglycemia due to insulin resistance in the DP. However, the effectiveness of the dual-basal-insulin regimen needs to be verified by prospective controlled studies using continuous glucose monitoring metrics or frequent blood glucose monitoring.

Effects of mulberry twig alkaloids(Sangzhi alkaloids) and metformin on blood glucose fluctuations in combination with premixed insulin-treated patients with type 2 diabetes

Introduction

We aimed to evaluated the effect of premixed insulin (Ins), premixed insulin combined with metformin (Ins+Met) or mulberry twig alkaloids(Ins+SZ-A) on blood glucose fluctuations in patients with type 2 diabetes (T2DM) using continuous glucose monitors (CGM).

Methods

Thirty patients with T2DM and poor blood glucose control using drugs were evaluated for eligibility during the screening period. Subsequently, their original hypoglycemic drugs were discontinued during the lead-in period, and after receiving Ins intensive treatment for 2 weeks, they were randomly assigned to receive either Ins, Ins+Met, or Ins+SZ-A treatment for the following 12 weeks. The main efficacy endpoint comprised changes in their CGM indicators changes (mean blood glucose level [MBG], standard deviation of blood glucose [SDBG], mean amplitude of glycemic excursions [MAGE], postprandial glucose excursions [PPGE], the largest amplitude of glycemic excursions [LAGE], mean of daily difference [MODD], time in range between 3.9-10.0 mmol/L [TIR] and area under the curve for each meal [AUCpp]) during the screening, lead-in, and after 12-week treatment period. Changes in glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), 1-h postprandial blood glucose (1h-PBG), 2-h postprandial blood glucose (2h-PBG), fasting blood lipids and postprandial blood lipids were also measured at baseline and after 12 weeks of treatment.

Results

The CGM indicators of the three groups during the lead-in period all showed significant improvements compared to the screening period (P<0.05). Compared with those in the lead-in period, all of the CGM indicators improved in the the Ins+Met and Ins+SZ-A groups after 12 weeks of treatment (P<0.05), except for MODD. After 12-week treatment, compared with the Ins group, Ins+Met and Ins+SZ-A groups showed improved MBG, SDBG, TIR, breakfast AUCpp,lunch AUCpp, HbA1c, FBG, 1h-PBG, fasting blood lipid and postprandial blood lipid indicators (P<0.05). Further, the LAGE, PPGE, MAGE, dinner AUCpp and 2h-PBG levels of the Ins+SZ-A group were significantly lower than those of the Ins+Met and Ins groups (P<0.05).

Conclusion

Our findings highlight the efficacy of combination therapy (Ins+SZ-A or Ins+Met) in improving blood glucose fluctuations, as well as blood glucose and lipid levels. Ins+SZ-A reduces postprandial blood glucose fluctuations more than Ins+Met and Ins groups.

Trial registration number

ISRCTN20835488.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

CGMap: Characterizing continuous glucose monitor data in thousands of non-diabetic individuals

Despite its rising prevalence, diabetes diagnosis still relies on measures from blood tests. Technological advances in continuous glucose monitoring (CGM) devices introduce a potential tool to expand our understanding of glucose control and variability in people with and without diabetes. Yet CGM data have not been characterized in large-scale healthy cohorts, creating a lack of reference for CGM data research. Here we present CGMap, a characterization of CGM data collected from over 7,000 non-diabetic individuals, aged 40-70 years, between 2019 and 2022. We provide reference values of key CGM-derived clinical measures that can serve as a tool for future CGM research. We further explored the relationship between CGM-derived measures and diabetes-related clinical parameters, uncovering several significant relationships, including associations of mean blood glucose with measures from fundus imaging and sleep monitoring. These findings offer novel research directions for understanding the influence of glucose levels on various aspects of human health.

Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders

OBJECTIVES

Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability.

METHODS

Participants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability.

RESULTS

Individuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2  = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not.

DISCUSSION

Glucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study.

PUBLIC SIGNIFICANCE

This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

Clinical application guidelines for blood glucose monitoring in China (2022 edition)

Glucose monitoring is an important component of diabetes management. The Chinese Diabetes Society (CDS) has been producing evidence-based guidelines on the optimal use of glucose monitoring since 2011. In recent years, new technologies in glucose monitoring and more clinical evidence, especially those derived from Chinese populations, have emerged. In this context, the CDS organised experts to revise the Clinical application guidelines for blood glucose monitoring in China in 2021. In this guideline, we focus on four methods of glucose monitoring that are commonly used in clinical practice, including capillary glucose monitoring, glycated haemoglobin A1c, glycated albumin, and continuous glucose monitoring. We describe the definitions and technical characteristics of these methods, the factor that may interfere with the measurement, the advantages and caveats in clinical practice, the interpretation of glucose metrics, and the relevant supporting evidence. The recommendations for the use of these methods are also provided. The various methods of glucose monitoring have their strengths and limitations and cannot be replaced by one another. We hope that these guidelines could aid in the optimal application of common methods of glucose monitoring in clinical practice for better diabetes care.

Continuous glucose monitoring for detection of glycemic variability, hypoglycemia, and hyperglycemia in women with eating disorders

Background

The aim of this study was to investigate the relationships between hypoglycemia, hyperglycemia, glycemic variability (GV), and eating behavior by measuring daily glucose levels through an intermittently scanned continuous glucose monitoring (isCGM) system in outpatients classified according to eating disorder subtypes.

Methods

We analyzed data for 18 patients (four ANR, nine ANBP, and five BN cases). A FreeStyle Libre Pro® device was attached to the posterior aspect of the upper arm for glucose monitoring. This device conducted measurements every 15 min for five consecutive days. We estimated the mean amplitude of glycemic excursions (MAGE), hypoglycemia, and hyperglycemia.

Results

The mean glucose levels were 91.1 ± 2.2 mg/dL in the ANR group, 94.8 ± 7.5 mg/dL in the ANBP group, and 87.1 ± 8.0 mg/dL in the BN group (P = 0.174). The overall mean MAGE index was 52.8 ± 20.5 mg/dL. The mean MAGE values according to the subtypes were 42.2 ± 5.6 mg/dL in the ANR group, 57.4 ± 23.7 mg/dL in the ANBP group, and 53.0 ± 21.8 mg/dL in the BN group (P = 0.496). Over the course of five days, the frequency of hypoglycemia was as follows: three occurrences in the ANBP group, five occurrences in the BN group, and no occurrences in the ANR group (P = 0.016). Moreover, the occurrence of hypoglycemia was statistically significantly higher in the BN group than in the ANR group (P = 0.013). In the BN group, the frequency of hypoglycemia was highest between 2 and 6 AM, while hypoglycemia was observed throughout the day in the ANBP group. The frequency of hyperglycemia was one occurrence in the ANR group, one occurrence in the BN group, and zero occurrences in the ANBP group (P = 0.641).

Conclusions

Varying GV, hypoglycemia, and hyperglycemia were observed in all subtypes of eating disorders. Our findings suggest that eating behaviors such as binge eating and purging are associated with GV and hypoglycemia. We showed the importance of developing different nutritional approaches tailored to the subtype of eating disorder to prevent hypoglycemia. Additional studies are needed to explore the relationship between glucose levels and eating behaviors in patients with eating disorders.

Single-port laparoscopic appendectomy using a needle-type grasping forceps compared with conventional three-port laparoscopic appendectomy for patients with acute uncomplicated appendicitis: a single-center retrospective study

Objective

To compare the clinical outcomes between single-port laparoscopic appendectomy using a needle-type grasping forceps (SLAN) and conventional three-port laparoscopic appendectomy (CLA) for patients with uncomplicated appendicitis.

Methods

We retrospectively collected clinical data of patients with uncomplicated appendicitis who underwent SLAN or CLA from May 2019 to May 2021 in our center. The patients’ baseline characteristics, perioperative outcomes, and follow-up data were compared between the two groups. Additionally, baseline characteristics were compared with postoperative outcomes in the SLAN group.

Results

Ninety-six patients were enrolled (SLAN group, n = 32; CLA group, n = 64). The SLAN group had a shorter hospital stay, lower 24-hour postoperative visual analogue scale scores, shorter postoperative fasting time, lower frequency of antibiotic administration, and longer operative time than the CLA group. In the SLAN group, younger patients had a longer appendix and male patients had a thicker appendix; additionally, patients with an appendiceal diameter of 0.6 to 1.0 cm had a longer postoperative hospital stay and higher frequency of antibiotic administration.

Conclusions

Compared with CLA, SLAN may be less invasive, provide faster postoperative recovery, and result in better cosmesis for patients with uncomplicated appendicitis. Further research should be performed to evaluate the long-term outcomes.

Aging of glucose profiles in an adult population without diabetes

AIMS

This study aimed to determine the effect of aging on glucose profiles in a population without diabetes.

METHODS

We investigated the evolution of glucose profiles in an adult population without diabetes using continuous glucose monitoring (CGM) in two periods separated by 5 years. Anthropometrics, laboratory tests (HbA1c, fasting blood glucose) and CGM data (mean glycemia level, coefficient of variation, time in range) were measured in both periods to study the change in values over time.

RESULTS

125 participants (68% women) mean age 43.1 ± 12.4 years and classified as normoglycemic at baseline were included. Of the total population 15.2% had worsened glycemic status after 5 years, age and baseline glucose values (HbA1c and percentage of values above 175 mg/dL) were the variables related with this change. Related to CGM, we found that after 5 years there was a decrease in the percentage of values between 70 and 99 mg/dl (45.0% to 38.7%, p = 0.002) and an increase in the 100-139 mg/dL range (52.9% to 57.5% p = 0.016).

CONCLUSIONS

Our results indicate that in an adult population without diabetes there are changes in glucose profiles with aging highlighting the reduction of blood glucose values below 100 mg/dL.

Evaluation of Reference Metrics for Continuous Glucose Monitoring in Persons Without Diabetes and Prediabetes

BACKGROUND

Recent guidelines have been developed for continuous glucose monitoring (CGM) metrics in persons with diabetes. To understand what glucose profiles should be judged as normal in clinical practice and glucose-lowering trials, we examined the glucose profile of healthy individuals using CGM.

METHODS

Persons without diabetes or prediabetes were included after passing a normal oral glucose tolerance test, two-hour value <8.9 mmol/L (160 mg/dL), fasting glucose <6.1 mmol/L (110 mg/dL), and HbA1c <6.0% (<42 mmol/mol). CGM metrics were evaluated using the Dexcom G4 Platinum.

RESULTS

In total, 60 persons were included, mean age was 43.0 years, 70.0% were women, mean HbA1c was 5.3% (34 mmol/mol), and mean body mass index was 25.7 kg/m². Median and mean percent times in hypoglycemia <3.9 mmol/L (70 mg/dL) were 1.6% (IQR 0.6-3.2), and 3.2% (95% CI 2.0; 4.3), respectively. For glucose levels <3.0 mmol/L (54 mg/dL), the corresponding estimates were 0.0% (IQR 0.0-0.4) and 0.5% (95% CI 0.2; 0.8). Median and mean time-in-range (3.9-10.0 mmol/L [70-180 mg/dL]) was 97.3% (IQR 95.4-98.7) and 95.4% (95% CI 94.0; 96.8), respectively. Median and mean standard deviations were 1.04 mmol/L (IQR 0.92-1.29) and 1.15 mmol/L (95% CI 1.05; 1.24), respectively. Measures of glycemic variability (standard deviation, coefficient of variation, mean amplitude of glycemic excursions) were significantly greater during daytime compared with nighttime, whereas others did not differ.

CONCLUSIONS

People without prediabetes or diabetes show a non-negligible % time in hypoglycemia, median 1.6% and mean 3.2%, which needs to be accounted for in clinical practice and glucose-lowering trials. Glycemic variability measures differ day and night in this population.

Largest Amplitude of Glycemic Excursion Calculating from Self-Monitoring Blood Glucose Predicted the Episodes of Nocturnal Asymptomatic Hypoglycemia Detecting by Continuous Glucose Monitoring in Outpatients with Type 2 Diabetes

AIMS

Nocturnal asymptomatic hypoglycemia (NAH) is a serious complication of diabetes, but it is difficult to be detected clinically. This study was conducted to determine the largest amplitude of glycemic excursion (LAGE) to predict the episodes of NAH in outpatients with type 2 diabetes.

METHODS

Data were obtained from 313 outpatients with type 2 diabetes. All subjects received continuous glucose monitoring (CGM) for consecutive 72 hours. The episodes of NAH and glycemic variability indices (glucose standard deviation [SD], mean amplitude of plasma glucose excursion [MAGE], mean blood glucose [MBG]) were accessed via CGM. LAGE was calculated from self-monitoring blood glucose (SMBG).

RESULTS

A total of 76 people (24.3%) had NAH. Compared to patients without NAH, patients with NAH showed higher levels of glucose SD (2.4 ± 0.9 mmol/L vs 1.7 ± 0.9 mmol/L, p <0.001), MAGE (5.2 ± 2.1 mmol/L vs 3.7 ± 2.0, p<0.001) and LAGE (4.6 ± 2.3 mmol/L vs 3.8 ± 1.9 mmol/L, p=0.007), and lower level of MBG (7.5 ± 1.5 mmol/L vs 8.4 ± 2.2 mmol/L, p=0.002). LAGE was significantly associated with the incidence of NAH and time below rang (TBR) in model 1 [NAH: 1.189 (1.027-1.378), p=0.021; TBR: 0.008 (0.002-0.014), p=0.013] with adjustment for age, BMI, sex, work, hyperlipidemia, complication and medication, and in model 2 [NAH: 1.177 (1.013-1.367), p=0.033; TBR: 0.008 (0.002-0.014), p=0.012] after adjusting for diabetes duration based on model 1, as well as in model 3 [NAH: 1.244 (1.057-1.464), p=0.009; TBR: 0.009 (0.002-0.016), p=0.007] with further adjustment for HbA1c based on model 2. In addition, no significant interactions were found between LAGE and sex, age, HbA1c, duration of diabetes, BMI and insulin therapy on the risk of NAH. The receiver operator characteristic (ROC) curve shows the ideal cutoff value of LAGE for the prediction of NAH was 3.48 mmol/L with 66.7% sensitivity, 50% specificity and 0.587 (95% CI: 0.509-0.665) of area under the ROC curve.

CONCLUSIONS

High glycemic variability is strongly associated with the risk of NAH. The LAGE based on SMBG could be an independent predictor of NAH for outpatients with type 2 diabetes, and LAGE greater than 3.48 mmol/L could act as a warning alarm for high risk of NAH in daily life.

Glucose Fluctuations Are Linked to Disrupted Brain Functional Architecture and Cognitive Impairment

BACKGROUND

Type 2 diabetes mellitus (T2DM) accelerates cognitive decline, which is believed to be triggered by aberrant neural activity.

OBJECTIVE

To explore how glucose fluctuations impact brain functional architecture and cognition in T2DM patients.

METHODS

T2DM patients were divided according to glycemic variability, forming two categories: patients with fluctuating glucose levels and patients with stable glucose levels. Degree centrality (DC) was calculated within the cerebral gray matter of each participant and was compared among the two patient groups and a healthy control group. The relationships between glucose fluctuations and aberrant DC and cognitive performance, as well as the relationship between aberrant DC and cognitive performance, were further explored.

RESULTS

Compared with T2DM patients with stable glucose levels, T2DM patients with fluctuating glucose levels exhibited significantly worse performance on the Montreal Cognitive Assessment, Trail Making Test-B (TMT-B), and verbal fluency test (VFT), as well as significant decreases in DC in certain regions, most of which were within the default mode network. In the combined T2DM group, the mean amplitude of glycemic excursions (MAGE) was positively correlated with TMT-B scores and negatively correlated with VFT scores. Moreover, the MAGE was negatively correlated with DC in the left medial prefrontal cortex (mPFC). In addition, TMT-B scores were negatively correlated with reduced DC in the left mPFC.

CONCLUSION

These findings further contribute to the mounting evidence of the effects of glycemic variability on the diabetic brain. Tightened control of glucose fluctuations might prevent cognitive decline and changes in brain functional architecture in T2DM individuals.

Acute Effects of Sleeve Gastrectomy on Glucose Variability, Glucose Metabolism, and Ghrelin Response

PURPOSE

This study aims to examine the changes of glucose metabolism, glucose variability (GV), and ghrelin secretion within 1 week following SG in Chinese patients with obesity.

MATERIALS AND METHODS

Forty-nine patients with obesity (15 with type 2 diabetes) were enrolled to undergo SG. Within 1 week before and after surgery, liquid meal tests were performed in all subjects, and continuous glucose monitoring (CGM) was performed in diabetic patients. Blood samples were collected at 0, 15, 30, 45, 60, 120, and 180 min for glucose, C-peptide, insulin, and ghrelin analysis in liquid meal test. Mean amplitude of glucose excursions (MAGE), standard deviations (SD), and percent time-in-range (%TIR) determined by CGM were analyzed.

RESULTS

Both in diabetic and non-diabetic groups, significant decrease was observed in glucose, insulin, C-peptide, and ghrelin. Homeostasis model assessment-insulin resistance and liver fat content was decreased. In diabetic group, MAGE and SD were decreased significantly, and the percent time-in-range was higher. The decrease in blood glucose was positively correlated with the decrease in ghrelin concentration in non-diabetic group.

CONCLUSION

Within 1 week after SG, both glucose metabolism and glucose variability were improved significantly. Suppression of ghrelin secretion postoperatively might be a driver of this early improved glycemia homeostasis.

Defining the target value of the coefficient of variation by continuous glucose monitoring in Chinese people with diabetes

AIMS/INTRODUCTION

To define the target value for the percentage coefficient of variation for glucose (%CV) as a measure of glycemic variability (GV) in Chinese diabetes patients.

MATERIALS AND METHODS

This retrospective study included 3,007 diabetes patients who underwent continuous glucose monitoring for 3 days. Type 2 diabetes was divided into groups according to the received treatment: group 1, non-insulinotropic agent (n = 138); group 2, insulinotropic agent (n = 761); group 3, basal insulin therapy (n = 100); group 4, premixed insulin (n = 784); and group 5, intensive insulin therapy (n = 612). Type 1 diabetes patients were included as group 6 (n = 612). %CV and percentage of time per day within, below (3.9mmol/L; TBR3.9 ) and above (10.0 mmol/L) the target glucose range (3.9-10.0 mmol/L) were computed. TBR3.9 ≥4% was defined as excessive hypoglycemia.

RESULTS

Type 2 diabetes with a premixed or intensive insulin regimen had an increased %CV compared with those receiving oral therapy or basal insulin. The upper limit of %CV in group 1 was 33%, which was adopted as the threshold to define excessive GV. For each treatment group, the percentage of people with TBR3.9 ≥4% was significantly greater in the subgroup with %CV >33% than ≤33% (P < 0.001). In participants who achieved TBR3.9 <4%, the time per day spent within the target glucose range of 3.9-10.0 mmol/L > 70% and time per day above 10.0 mmol/L <25%, the 95th percentile of %CV was 32.70%. Further receiver operating characteristic curve analysis showed that the cut-off values of %CV for predicting TBR3.9 ≥4% varied by the type of diabetes and glycated hemoglobin categories.

CONCLUSIONS

A %CV of 33% was set as the threshold for excess glucose variability in Chinese diabetes patients. Meanwhile, glycated hemoglobin and the type of diabetes should be considered for the goal-setting of %CV.

Glycemic deviation index: a novel method of integrating glycemic numerical value and variability

BACKGROUND

There are many continuous blood glucose monitoring (CGM) data-based indicators, and most of these focus on a single characteristic of abnormal blood glucose. An ideal index that integrates and evaluates multiple characteristics of blood glucose has not yet been established.

METHODS

In this study, we proposed the glycemic deviation index (GDI) as a novel integrating characteristic, which mainly incorporates the assessment of the glycemic numerical value and variability. To verify its effectiveness, GDI was applied to the simulated 24 h glycemic profiles and the CGM data of type 2 diabetes (T2D) patients (n = 30).

RESULTS

Evaluation of the GDI of the 24 h simulated glycemic profiles showed that the occurrence of hypoglycemia was numerically the same as hyperglycemia in increasing GDI. Meanwhile, glycemic variability was added as an independent factor. One-way ANOVA results showed that the application of GDI showed statistically significant differences in clinical glycemic parameters, average glycemic parameters, and glycemic variability parameters among the T2D groups with different glycemic levels.

CONCLUSIONS

In conclusion, GDI integrates the characteristics of the numerical value and the variability in blood glucose levels and may be beneficial for the glycemic management of diabetic patients undergoing CGM treatment.

Fasting Glucose of 6.1 mmol/L as a Possible Optimal Target for Type 2 Diabetic Patients with Insulin Glargine: A Randomized Clinical Trial

To observe whether different insulin glargine titration algorithms based on fasting blood glucose (FBG) levels lead to different glycaemic variations (GVs) in type 2 diabetes (T2D) patients, a prospective, randomized, single-centre, comparative, three-arm parallel-group, open-label, treat-to-target, 24-week study was performed. A total of 71 uncontrolled T2D patients were recruited and randomized 1 : 3 : 3 into Groups 1, 2, and 3 (insulin titration goals of FBG ≤ 5.6, ≤6.1, and ≤7.0) for this study. The initiated insulin glargine dose was recommended at 0.2 U/kg/day and was then titrated following the FBG target. Patients were subjected to two 3-day continuous glucose monitoring (CGM) at baseline and the endpoint, wherein the CGM data were analysed, and the study's primary endpoint was the difference in 24 hrs mean amplitude of glycaemic excursion (MAGE) among the three groups. We observed that patients in the three groups had similar MAGE levels at the endpoint; however, Group 2 achieved a significant decrease in the MAGE level from baseline to the endpoint as compared to Groups 1 and 3 (all p < 0.05). We also observed that these patients had significant glycated haemoglobin A1c (HbA1c) value improvements as compared to the other two groups (all p < 0.05). Therefore, choosing an FBG level of 6.1 mmol/L as an insulin titration target provided significant GVs and HbA1c value improvements in T2D patients. Moreover, our data indicated that an FBG of 6.1 mmol/L could possibly be an insulin glargine titration target in T2D patients.

Blood glucose fluctuations detected by continuous glucose monitoring system in gout patients with normal glucose tolerance and the effect of urate-lowering therapy

AIM

The aim of this study was to investigate whether there are blood glucose fluctuations in gout patients with hyperuricemia and normal glucose tolerance, and the effect of urate-lowering therapy on blood glucose fluctuations.

METHODS

Thirty patients with newly diagnosed gout, hyperuricemia and normal glucose tolerance were enrolled in our study. Continuous glucose monitoring system (CGMS) was used to detect the blood glucose fluctuations of these gout patients. Changes in blood glucose fluctuations after allopurinol therapy were also evaluated.

RESULTS

Compared with the reference values of blood glucose fluctuation parameters in China, gout patients had greater glycemic fluctuations including higher mean amplitude of glucose excursions (MAGE) (4.65 vs 1.94 mmol/L, P < .001), higher largest amplitude of blood glucose excursions (LAGE) (4.99 vs 3.72 mmol/L, P < .001) and higher standard deviations of blood glucose (SDBG) (1.36 vs 0.79 mmol/L, P < .001). MAGE was significantly correlated with uric acid (β = .007, P = .024) and HOMA-insulin resistance (IR) (β = .508, P = .03). Allopurinol treatment significantly reduced MAGE (4.16 vs 4.65 mmol/L, P < .001), SDBG (0.99 vs 1.36 mmol/L, P < .001) and HOMA-IR (2.26 vs 3.01, P < .001) in gout patients.

CONCLUSION

Blood glucose fluctuation increased even in the stage of normal glucose tolerance among gout patients. Blood glucose fluctuations in gout patients were associated with the level of serum uric acid and allopurinol could decrease blood glucose fluctuation as well as IR.

Glucose Variability Assessed with Continuous Glucose Monitoring: Reliability, Reference Values, and Correlations with Established Glycemic Indices-The Maastricht Study

Background: Glucose variability (GV) measured by continuous glucose monitoring (CGM) has become an accepted marker of glycemic control. Nevertheless, several methodological aspects of GV assessment require further study. We, therefore, investigated the minimum number of days needed to reliably measure GV, assessed GV reference values, and studied the correlation of GV with established glycemic indices (i.e., HbA_1c, seven-point oral glucose tolerance test [OGTT]-derived indices). Methods: We used cross-sectional data from The Maastricht Study, an observational population-based cohort enriched with type 2 diabetes. Participants with more than 48 h of CGM (iPro2; Medtronic) were included for analysis (n = 851; age: 60 ± 9years; 49% women; 23% type 2 diabetes). We used mean sensor glucose (MSG), standard deviation (SD), and coefficient of variation (CV) as CGM-derived indices (the latter two for GV quantification). We calculated reliability using the Spearman-Brown prophecy formula, established reference values by calculating 2.5th-97.5th percentiles, and studied correlations using Spearman's rho. Results: Sufficient reliability (R > 0.80) was achieved with two (MSG and SD), or three monitoring days (CV). The reference ranges, assessed in individuals with normal glucose metabolism (n = 470), were 90.5-120.6 mg/dL (MSG), 7.9-24.8 mg/dL (SD), and 7.74%-22.45% (CV). For MSG, the strongest correlation was found with fasting plasma glucose (rho = 0.65 [0.61; 0.69]); for SD, with the 1-h OGTT value (rho = 0.61 [0.56; 0.65]); and for CV, with both the incremental glucose peak (IGP) during the OGTT (rho = 0.50 [0.45; 0.55]) and the 1-h OGTT value (rho = 0.50 [0.45; 0.55]). Conclusions: The reliability findings and reference values are relevant for studies that aim to investigate CGM-measured GV. One-hour OGTT and IGP values can be used as GV indices when CGM is unavailable.

Association Between Inflammatory Biomarkers and Cognitive Dysfunction Analyzed by MRI in Diabetes Patients

AIM

To explore the relationship between inflammatory biomarkers and cognitive dysfunction in patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM).

METHODS

T1DM patients (n=32), T2DM patients (n=90) and age-matched controls (n=36 and 81, respectively) were included. The 72-hour dynamic blood glucose test and cognitive function, including visuoconstructive function, executive function, learning and memory, attention, language expression ability, and orientation, were analyzed. The head, body and tail grey matter of the hippocampus were analyzed by magnetic resonance spectroscopy. In addition, serum HMGB1, IL-1β, IL-6, and TNF-α concentrations were examined.

RESULTS

HbA1C, MAGE and MODD were higher in T1DM patients than in T2DM patients (p<0.05). MoCA scores and IL-1β and IL-6 levels in patients with T2DM were higher than T1DM patients. NAA/Cr and Cho/Cr of the hippocampus were higher in patients with T1DM than in those with T2DM. Levels of inflammatory factors in T1DM and T2DM patients were higher than in nondiabetic subjects (p<0.05). Regression analysis showed that cognition was associated with MAGE, MODD, NAA/Cr of the left hippocampus and HMGB1 in T1DM patients, after adjustment for age, sex, BMI and other co-variables. In T2DM patients, cognitive impairment was associated with MAGE, NAA/Cr of the left hippocampus, HMGB1 and IL-6, after adjustment for co-variables such as sex, age and BMI.

CONCLUSION

T2DM patients have more cognitive impairment than T1DM patients. Changes in brain function connections and metabolites may be the structural basis of the differences in cognitive functional impairment. Inflammation is related to cognitive impairment in diabetes patients, especially in T2DM patients.

Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

INTRODUCTION

The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM).

METHODS

Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed.

RESULTS

Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption.

CONCLUSIONS

Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Chinese clinical guidelines for continuous glucose monitoring (2018 edition)

Blood glucose monitoring is an important part of diabetes management. Continuous glucose monitoring (CGM) technology has become an effective complement to conventional blood glucose monitoring methods and has been widely applied in clinical practice. The indications for its use, the accuracy of the generated data, the interpretation of the CGM results, and the application of the results must be standardized. In December 2009, the Chinese Diabetes Society (CDS) drafted and published the first Chinese Clinical Guideline for Continuous Glucose Monitoring (2009 edition), providing a basis for the standardization of CGM in clinical application. Based on the updates of international guidelines and the increasing evidence of domestic studies, it is necessary to revise the latest CGM guidelines in China so that the recent clinical evidence can be effectively translated into clinical benefit for diabetic patients. To this end, the CDS revised the Chinese Clinical Guideline for Continuous Glucose Monitoring (2012 Edition) based on the most recent evidence from international and domestic studies.

Standards of medical care for type 2 diabetes in China 2019

The prevalence of diabetes in China has increased rapidly from 0.67% in 1980 to 10.4% in 2013, with the aging of the population and westernization of lifestyle. Since its foundation in 1991, the Chinese Diabetes Society (CDS) has been dedicated to improving academic exchange and the academic level of diabetes research in China. From 2003 to 2014, four versions of Chinese diabetes care guidelines have been published. The guidelines have played an important role in standardizing clinical practice and improving the status quo of diabetes prevention and control in China. Since September 2016, the CDS has invited experts in cardiovascular diseases, psychiatric diseases, nutrition, and traditional Chinese medicine to work with endocrinologists from the CDS to review the new clinical research evidence related to diabetes over the previous 4 years. Over a year of careful revision, this has resulted in the present, new version of guidelines for prevention and care of type 2 diabetes in China. The main contents include epidemiology of type 2 diabetes in China; diagnosis and classification of diabetes; primary, secondary, and tertiary diabetes prevention; diabetes education and management support; blood glucose monitoring; integrated control targets for type 2 diabetes and treatments for hyperglycaemia; medical nutrition therapy; exercise therapy for type 2 diabetes; smoking cessation; pharmacologic therapy for hyperglycaemia; metabolic surgery for type 2 diabetes; prevention and treatment of cardiovascular and cerebrovascular diseases in patients with type 2 diabetes; hypoglycaemia; chronic diabetic complications; special types of diabetes; metabolic syndrome; and diabetes and traditional Chinese medicine.

Glycemic variation in uncontrolled Graves' disease patients with normal glucose metabolism: Assessment by continuous glucose monitoring

PURPOSE

To observe the glycemic variation (GV) in uncontrolled Graves' disease (GD) patients with normal glucose metabolism measured by continuous glucose monitoring (CGM).

METHODS

This was a single-center, open-label, observational study. From January 2017 to October 2017, 20 GD patients with normal glucose metabolism and 24 healthy control subjects were recruited. Serum samples were obtained at 0, 30, and 120 min after oral glucose loading for glucose, insulin, and C-peptide level measurements. Fasting plasma fasting free triiodothyronine (FT3), free thyroxin (FT4), and thyroid stimulating hormone concentrations were also detected. All participants were subjected to a 3-day CGM after baseline data were collected. The primary endpoint was the difference in the mean amplitude of the glycemic excursions between the two groups.

RESULTS

Compared with the healthy subjects, the GD patients had higher mean amplitude of glycemic excursions (MAGE) (P < 0.01). Multiple linear stepwise regression analysis showed that FT4 level was an independent factor for the MAGE. Interestingly, the GD patients had a significant prolongation in the time to peak glucose, especially after breakfast (P < 0.01), and the elevation in the incremental area under the curve of glucose after breakfast till 4 hours later.

CONCLUSIONS

Uncontrolled GD patients with normal glucose metabolism had a greater GV, and the FT4 level may contributed to the increased GV.

Association of high circulating testosterone with increased glycaemic variability in type 2 diabetes: A cross-sectional study in China

BACKGROUND

Testosterone affects insulin resistance, but the effect of testosterone treatment on type 2 diabetes (T2D) remains controversial. We aimed to investigate the association between circulating total testosterone (TT) and glycaemic variability using continuous glucose monitoring (CGM) in patients with T2D.

METHODS

A total of 248 men with T2D were enrolled in the study. Clinical characteristics and plasma for glycated haemoglobin (HbA1c) and C-peptide assessment were collected. TT was measured using a chemiluminescent immunometric assay. All patients were subjected to a 3-day CGM before making adjustments for hypoglycaemic therapy.

RESULTS

TT positively correlated with the standard deviation of mean blood glucose (SDBG) (P < 0.05), especially in older patients. Linear regression analysis showed that SDBG was associated with HbA1c (β = 0.354, P < 0.001) and TT (β = 0.164, P = 0.008) after adjusting for age, duration of diabetes, body mass index, fasting/postprandial C-peptide, and use of different hypoglycaemic drugs. The cut-off value of TT for predicting glycaemic variability was 14.76 mmol/L according to receiver operating characteristic (ROC) analysis. SDBG, the coefficient of variation, the incremental area under the curve of glucose (AUC) > 10 mmol/L, and AUC night were increased in the group with TT > 14.76 nmol/L (P < 0.01 for all variables). Body mass index and fasting/postprandial C-peptide were lower in the group with TT > 14.76 nmol/L than in the group with TT ≤ 14.76 nmol/L (P < 0.05).

CONCLUSIONS

Circulating TT levels should be assessed in patients with T2D in addition to HbA1c for predicting glycaemic variability. More frequent blood glucose monitoring or CGM is suggested for patients with T2D and high testosterone levels.

CLINICAL TRIALS REGISTRATION

NCT03519529, ClinicalTrials.gov.

Association of Higher Circulating Insulin Antibody with Increased Mean Amplitude Glycemic Excursion in Patients with Type 2 Diabetes Mellitus: A Cross-Sectional, Retrospective Case-Control Study

Insulin antibody (IA) may potentially affect a patient's glycemic control due to its variability in both binding and/or releasing insulin. However, the association between IA titer and daily glycemic variability (GV) is still unknown. We thus performed this cross-sectional, retrospective case-control study to assess the relationship between IA titer and mean amplitude glycemic excursion (MAGE) in type 2 diabetes mellitus (T2DM) patients using a continuous glucose monitoring (CGM) system. We recruited 100 eligible patients (IA > 5%, IA positive) and divided them into two groups-a low (L) group and a high (H) group-based on their IA titer. The control (C) group consisted of 47 patients (IA ≤ 5%, IA negative) matched for age, BMI, gender, and glycosylated hemoglobin A1c (HbA1c). The CGM determined the GV of enrolled patients. The primary outcome was the relationship between the IA titer and the MAGE, and the secondary outcome was the differences of GV among the three groups. We found that patients in the H group had higher levels of blood glucose fluctuation parameters than those in the L and C groups. The Ln(IA) was positively correlated with Ln(MAGE) even after adjusting for age, gender, BMI, HbA1c, and fasting and postprandial C-peptide(r = 0.423, p < 0.001). Multiple linear stepwise regression analysis revealed that Ln(IA) was an independent factor of Ln(MAGE) (beta = 0.405, p < 0.001). In conclusion, the higher circulating IA titer was associated with increased MAGE in T2DM patients, indicating that those patients with elevated IA titer should receive GV assessment and individualized treatment.

Continuous glucose monitoring can disclose glucose fluctuation in advanced Parkinsonian syndromes

Continuous glucose monitoring (CGM) is a method to examine glucose concentration in subcutaneous interstitial fluid sequentially. CGM can disclose glucose fluctuation (GF), which can be unrecognized in routine blood tests. A limited number of studies suggest advanced Parkinsonian syndromes (PS) is at risk of GF, however, the report of CGM in PS is scarce. We performed CGM for 72 h in 11 nondiabetic patients with advanced PS. The etiology was Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, or dementia with Lewy bodies. All participants were bedridden, elderly (≥65 year-old), and receiving enteral nutrition. The retrospective data was obtained after the removal of CGM device. In the glucose concentration, 9 (81.8%) participants showed nocturnal decline (≤70 mg/dL; 4 of them reached recordable limit of 40 mg/dL), and 6 (54.5%) participants showed remarkable elevation (≥200 mg/dL) postprandially. In the majority, these abnormalities were difficult to predict from routine blood tests. Standard deviation and mean of sequential glucose concentration were higher than those in precedent reports of young or middle-aged healthy controls. CGM in nondiabetic and elderly patients with advanced PS can disclose GF, with features of nocturnal decline and/or postprandial remarkable elevation of glucose concentration. Owing to limitations such as small sample size, heterogeneity of etiology, and retrospectivity of CGM data, further investigations are required.

Lower mean blood glucose during short-term intensive insulin therapy is associated with long-term glycemic remission in patients with newly diagnosed type 2 diabetes: Evidence-based recommendations for standardization

AIMS/INTRODUCTION

Optimal glycemic targets during short-term intensive insulin therapy in patients with newly diagnosed type 2 diabetes are not standardized. The present study was carried out to determine the optimal glycemic targets during therapy by analyzing the impacts of glucose levels on therapeutic outcomes.

MATERIALS AND METHODS

A total of 95 individuals with newly diagnosed type 2 diabetes were enrolled. Short-term intensive insulin therapy was carried out using an insulin pump to achieve and maintain glycemic targets (fasting blood glucose ≤6.0 mmol/L, 2-h postprandial blood glucose ≤7.8 mmol/L) for 14 days, with daily eight-point capillary blood glucose profiles recorded. Patients were followed up for 1 year after discharge.

RESULTS

In most participants, the mean blood glucose and glycemic excursion parameters during the therapy were controlled within the normal range. Mean blood glucose was independently associated with amelioration of acute insulin response (r = -0.25, P = 0.015) and 1-year remission (odds ratio 0.12, 95% confidence interval 0.034-0.426), but negatively associated with more level 1 hypoglycemia (r = -0.34, P = 0.001), although major hypoglycemia was rare. Among mean blood glucose tertiles, patients in the middle (68.7%) and lower (75.0%) tertiles had a higher 1-year remission rate compared with the upper tertile (32.3%, both P < 0.001), whereas only the middle tertile did not have increased hypoglycemia compared with the upper tertile (8.1 ± 5.4 vs 7.2 ± 3.9 events/person, P = 0.48).

CONCLUSIONS

Stricter glycemic control during short-term intensive insulin therapy produced more remission despite self-manageable hypoglycemia. Based on glycemic parameters in the middle mean tertile, we propose new glycemic targets that are approximately 0.4 mmol/L lower than current the targets, as long-term benefit outweighs short-term risks.

Characterization of edible bird's nest of different production, species and geographical origins using nutritional composition, physicochemical properties and antioxidant activities

Edible bird's nest (EBN) is a precious food made from the solidified saliva of swiftlets. EBN from three types of origin, namely production, swiftlet species and geographical were characterised based on its nutritional composition, physicochemical properties and antioxidant properties. Proximate composition, total phenolic content (TPC) and antioxidant activities were determined following official methods, while mineral and heavy metal contents were obtained by respective atomic adsorption spectrometry (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS). Amino acids profile and sialic acid were determined using high performance liquid chromatography (HPLC). Calcium and sodium were the major elements in EBN samples at averages of 17,267 mg/kg and 13,681 mg/kg, respectively. Despite protein contents were not significantly different; interestingly the total amino acids in A. fuciphagus EBN, 64.57 g/100 g was found to be 23% higher than in A. maximus EBN. EBN from house, A. fuciphagus and Peninsular Malaysia had greater antioxidant activities, 2.33-3.49 mg AAE/g and higher sialic acid, 13.57 g/100 g while those from cave, A. maximus and East Malaysia contained more minerals like calcium and magnesium. The 1, 1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging activity and ferric ion reducing antioxidant power (FRAP) of house, A. fuciphagus and Peninsular Malaysia EBNs were approximately 2 times greater than the others. All samples were complied with the Malaysian Standard MS 2334:2011, except for mercury and nitrite. The overall findings suggest that the quality of EBN was varied following the production, species and geographical origins.

Tracking the Sugar Rush: Incorporating Continuous Glucose Monitoring Into Multisite Early Clinical Research With Type 2 Diabetes Subjects

Continuous glucose monitoring (CGM) systems allow patients with diabetes mellitus to closely track glucose concentrations over several days, identify trends in glucose levels, and avoid glucose excursions. This technology has not only advanced diabetes mellitus management but has increased patient safety through greater glycemic awareness. Due to these attributes, CGM is now being applied in therapeutic research as a pharmacodynamic tool to support early clinical drug development programs. However, to date only a handful of studies have utilized CGM in type 2 diabetes mellitus (T2DM) drug development. A potential barrier from fostering greater use of CGM in clinical development may be related to concerns over subject variability. Therefore, we investigated a key consideration when implementing CGM into early clinical research studies: daily variation within patients with T2DM from multiple clinical research units. From 24 patients with T2DM, we observed strong daily reproducibility (Pearson R = 0.86, P < .0001) in CGM results and found that this technique is practical for multisite studies. Altogether, with low daily variability, CGM is a powerful pharmacodynamic tool for drug efficacy and safety monitoring.

Continuous Glucose Monitoring in Healthy Children Aged 2-8 Years

BACKGROUND

The purpose of this study was to add the missing information on glycemic levels and patterns as measured by continuous glucose monitoring (CGM) in the daily life of healthy children aged 2-8 years. These data are needed when studying glycemic patterns and treatment outcome in children aged 2-8 years with diabetes.

METHODS

Each of the 15 healthy children aged 2-7.99 years used a CGM device (Dexcom G4 Platinum) for 7 days.

RESULTS

A total of 15 children (10 girls) aged 5.4 ± 1.6 years registered a mean of 1976 ± 15 counts. Mean sensor glucose was 5.3 ± 1.0 mmol/L (95 ± 18 mg/dL) and 89% of values were in the range 4-7.8 mmol/L (72-140 mg/dL), 9% of sensor glucose values were <4.0 mmol/L (72 mg/dL), and 2% of sensor glucose values were >7.8 mmol/L (140 mg/dL).

CONCLUSION

We present glycemic data as measured by CGM in healthy children aged 2-8 years.

Distinct impacts of sleep-disordered breathing on glycemic variability in patients with and without diabetes mellitus

BACKGROUND

Sleep-disordered breathing (SDB) is highly prevalent in patients with diabetes mellitus (DM) and heart failure (HF) and contributes to poor cardiovascular outcomes. Enlarged glycemic variability (GV) is a risk factor of cardiac events independently of average blood glucose level, but the influence of SDB on GV is uncertain. In this study, we examined whether the impact of SDB on GV is modified by the presence of DM with or without HF.

METHODS AND RESULTS

Two hundred three patients (67.5±14.1 [SD] years old, 132 males) who were admitted to our institute for examination or treatment of DM and/or HF underwent continuous glucose monitoring and polysomnography. Both HbA1c (8.0±2.0 vs. 5.7±0.4%) and mean amplitude of glycemic excursion (MAGE, median: 95.5 vs. 63.5 mg/dl) were significantly higher in a DM group (n = 100) than in a non-DM group (n = 103), but apnea-hypopnea index (AHI: 29.0±22.7 vs. 29.3±21.5) was similar in the two groups. AHI was correlated with log MAGE in the non-DM group but not in the DM group, and multivariate regression analysis revealed that AHI was an independent variable for log MAGE in the non-DM group but not in the DM group. We then divided the non-DM patients into two subgroups according to BNP level (100 pg/ml). AHI was positively correlated with log MAGE (r = 0.74, p<0.001) in the non-DM low-BNP subgroup, but such a correlation was not found in the non-DM high-BNP subgroup. Continuous positive airway pressure (CPAP) reduced MAGE from 75.3 to 53.0 mg/dl in the non-DM group but did not reduce MAGE in the DM group.

CONCLUSION

Severity of SDB was associated with higher GV, but DM as well as HF diminished the contribution of SDB to GV. Treatment with CPAP was effective for reduction of GV only in patients without DM.

Glycemic Variability and Its Association With Demographics and Lifestyles in a General Adult Population

OBJECTIVE

The objective was to investigate glycemic variability indices in relation to demographic factors and common environmental lifestyles in a general adult population.

METHODS

The A Estrada Glycation and Inflammation Study is a cross-sectional study covering 1516 participants selected by sampling of the population aged 18 years and over. A subsample of 622 individuals participated in the Glycation project, which included continuous glucose monitoring procedures. Five glycemic variability indices were analyzed, that is, SD, MAGE, MAG, CONGA1, and MODD.

RESULTS

Participants had a mean age of 48 years, 62% were females, and 12% had been previously diagnosed with diabetes. In the population without diabetes, index distributions were not normal but skewed to the right. Distributional regression models that adjusted for age, gender, BMI, alcohol intake, smoking status, and physical activity confirmed that all indices were positively and independently associated with fasting glucose levels and negatively with heavy drinking. SD, MAGE, and CONGA1 were positively associated with aging, and MAG was negatively associated with BMI. None of the GVI studied were influenced by physical activity. Age-group-specific reference values are given for the indices.

CONCLUSIONS

This study yielded age-specific reference values for glucose variability indices in a general adult population. Significant increases were observed with aging. Heavy drinking of more than 140 g/week was associated with significant decreases in variability indices. No differences were found between males and females. These normative ranges provide a guide for clinical care, and may offer an alternative treatment target among persons with diabetes.

Glucose metabolism before and after radioiodine therapy of a patient with Graves' disease: Assessment by continuous glucose monitoring

Hyperthyroidism causes impaired glucose tolerance, insulin resistance (IR) and insulin secretion. However, the glucose variability affected by thyroid dysfunction remains unclear. Glucose variability was assessed by continuous glucose monitoring (CGM) in a non-diabetic patient with Graves' disease (GD), to the best of our knowledge, for the first time. A 28-year-old man with GD, who had been taking methimazole for 4 years, was treated with radioiodine on August 17th 2016. Although the patient exhibited normal glycated hemoglobin (HbA1c; 5.3%) and blood glucose values during the oral glucose tolerance test (OGTT; fasting and 120 min blood glucose were 5.38 and 6.39 mmol/l, respectively) before radioiodine therapy, CGM exhibited high 24 h mean glucose and nocturnal hyperglycemia. An increased fasting insulin level, suppressed levels of blood glucagon and high homeostatic model assessment of IR were also observed. The disordered glucose metabolism improved as soon as the patient's thyroid function turned to hypothyroidism 4 months after radioiodine therapy. The glucose intolerance in patients with hyperthyroidism, missed by the OGTT and HbA1c tests, may be more common than anticipated.

Effects of the Mean Amplitude of Glycemic Excursions and Vascular Endothelial Dysfunction on Cardiovascular Events in Nondiabetic Patients With Coronary Artery Disease

Background

Mean amplitude of glycemic excursion (MAGE) is commonly used to gauge the degree of glucose level fluctuations. MAGE plays a significant role in vascular endothelial dysfunction and cardiovascular events in patients with diabetes mellitus (DM), but its significance is not clear in non‐DM patients. Thus, we examined the impact of MAGE and vascular endothelial dysfunction on clinical outcomes in non‐DM patients with coronary artery disease.

Methods and Results

We followed non‐DM patients (n=65) for 12 months who underwent percutaneous coronary intervention and assessed the relationship among MAGE, reactive hyperemia index (RHI) measured by reactive hyperemia peripheral arterial tonometry as endothelial function, and cardiovascular events. Cardiovascular events analyzed were cardiovascular death, myocardial infarction, unstable angina, and revascularizations. Compared with patients with MAGE <65 mg/dL (normal glycemic excursions), the group with MAGE ≥65 mg/dL (high glycemic excursions) had significantly higher high‐sensitivity C‐reactive protein (0.10±0.11 mg/dL versus 0.18±0.13 mg/dL, P=0.006) and lower RHI (0.64±0.21 versus 0.51±0.22, P=0.035). The multivariable analysis identified high MAGE and low RHI (≤0.56) as risk factors associated with cardiovascular events (hazard ratio, 5.6; 95% RI, 1.72–18.4 [P=0.004] versus hazard ratio, 4.5; 95% RI, 1.37–14.9 [P=0.013]). When the prognosis was classified by combination with MAGE and RHI, the incidence of cardiovascular events was 46.7% (high MAGE+low RHI), 26.7% (high MAGE+high RHI), 20.0% (low MAGE+low RHI), and 6.6% (low MAGE+high RHI) in descending order (P=0.014). Receiver operating characteristic curve analysis revealed that MAGE, RHI, and MAGE+RHI were each associated with cardiovascular events (area under the curve 0.780, 0.727, and 0.796, respectively).

Conclusions

MAGE was associated with cardiovascular events in non‐DM patients with coronary artery disease. Furthermore, the combination with MAGE and RHI was useful for further subdivision of the risk of cardiovascular events.

Association of lower body mass index with increased glycemic variability in patients with newly diagnosed type 2 diabetes: a cross-sectional study in China

Previous studies have indicated that the pathogenesis of diabetes differs between obese and lean patients. We investigated whether newly diagnosed Chinese diabetic patients with different body mass indices (BMIs) have different glycemic variability, and we assessed the relationship between BMI and glycemic variability. This was a cross-sectional study that included 169 newly diagnosed and drug-naïve type 2 diabetic patients (mean age, 51.33 ± 9.83 years; 110 men). The clinical factors and results of the 75-g oral glucose tolerance test were all recorded. Glycemic variability was assessed using continuous glucose monitoring. Compared with overweight or obese patients (BMI ≥ 24 kg/m2), underweight or normal-weight patients (BMI < 24 kg/m2) had higher levels of blood glucose fluctuation parameters, particularly in terms of mean amplitude of glycemic excursion (MAGE 6.64 ± 2.38 vs. 5.67 ± 2.05; P = 0.007) and postprandial glucose excursions (PPGEs) (PPGE at breakfast, 7.72 ± 2.79 vs. 6.79 ± 2.40, P = 0.028; PPGE at lunch, 5.53 ± 2.70 vs. 5.07 ± 2.40, P = 0.285; PPGE at dinner, 5.96 ± 2.24 vs. 4.87 ± 2.50, P = 0.008). BMI was negatively correlated with glycemic variability (r = −0.243, P = 0.002). On multiple linear regression analyses, BMI (β = −0.231, P = 0.013) and Insulin Secretion Sensitivity Index-2 (β = −0.204, P = 0.048) were two independent predictors of glycemic variability. In conclusion, lower BMI was associated with increased glycemic variability, characterized by elevated PPGEs, in newly diagnosed Chinese type 2 diabetic patients.

Hyperglycemia and glycemic variability are associated with the severity of sepsis in nondiabetic subjects

PURPOSE

The purpose was to compare glucose variability (GV) obtained via continuous glucose monitoring between nondiabetic sepsis patients and healthy subjects and to seek associations between GV and sepsis severity in nondiabetic sepsis patients.

METHODS

Nondiabetic sepsis inpatients and healthy controls received a 72-hour continuous glucose monitoring (iPro2, Medtronic) postadmission and post-oral glucose tolerance test, respectively. The mean glucose level (MGL) along with GV represented by standard deviation (SD) and the mean amplitude of glycemic excursion (MAGE) were calculated at 24 and 72 hours. Sepsis severity was evaluated with the Sepsis-related Organ Failure Assessment Score (SOFA). MGL and GV in patients with SOFA ≥9 and <9 were compared.

RESULTS

Thirty nondiabetic sepsis and 10 healthy subjects were recruited. No differences were found between groups except for higher patient age in sepsis patients. The MGL and MAGE_72h of sepsis patients were significantly higher than those of healthy subjects. MGL and GV_24h were higher in patients with SOFA ≥9 than in patients with SOFA <9 (MGL_24h 195±17 vs 139±27, P<.001; SD_24h 32 [28, 36] vs 19 [5, 58], P=.02; and MAGE_24h 94 [58, 153] vs 54 [16, 179], P=.01).

CONCLUSION

Nondiabetic sepsis patients had higher MGL and GV values than healthy subjects. MGL and GV_24h were associated with sepsis severity.

Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring

OBJECTIVE

To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia.

METHODS

Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated.

RESULTS

Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADE_hypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADE_hypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use.

CONCLUSIONS

CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADE_hypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

Glycemic variability is an important risk factor for cardiovascular autonomic neuropathy in newly diagnosed type 2 diabetic patients

BACKGROUND

The relationship between glycemic variability, another component of glycemic disorders as well as chronic sustained hyperglycemia, and cardiovascular autonomic neuropathy (CAN) has not been clarified. Our aim is to investigate the association between glycemic variability and CAN in newly diagnosed type 2 diabetic patients.

METHODS

Ewing tests were performed in 90 newly diagnosed type 2 diabetic patients and 37 participants with normal glucose tolerance as control from May 1, 2009, through September 30, 2010. According to the scores from Ewing tests, diabetic patients were divided into two groups: without CAN (CAN-) and with CAN (CAN+). All participants underwent a 48-h to 72-h continuous glucose monitoring (CGM). Coefficient of variability of glycemia (%CV), mean amplitude of glycemic excursions (MAGE) and means of daily differences (MODD) were calculated with the CGM data.

RESULTS

The prevalence of CAN in patients with newly diagnosed type 2 diabetes was 22.2%. An increasing trend of glycemic variability was found from control group, CAN- group to CAN+ group. MAGE in CAN+ group was significantly higher than that in CAN- group (5.27±1.99mmol/L vs. 4.04±1.39mmol/L, P=0.001). In the Logistic regression analysis, a significant relationship was shown between MAGE and CAN [odds ratio (OR): 1.73, 95% confidence interval (CI): 1.01-2.73, P=0.018)]. The area under the receiver-operating characteristic curve for MAGE was superior to those for other dysglycemic indices in detecting CAN.

CONCLUSIONS

Glycemic variability is associated with CAN in patients with newly diagnosed type 2 diabetes. Among the glycemic variability indices, MAGE is a significant indicator for detecting CAN.

Continuous glucose monitoring in China: Then, now and in the future

Glucose monitoring is a key component in assessing glucose metabolic disturbance, evaluating therapeutic outcomes and guiding treatment regimens. For decades, continuous glucose monitoring (CGM) was one of the dreams of patients with diabetes and diabetologists. In this article, the research progress, opportunities and challenges of the use of CGM technology are reviewed.

Patterns of type 2 diabetes monitoring in rural towns: How does frequency of HbA1c and lipid testing compare with existing guidelines?

OBJECTIVE

To indicate levels of monitoring of type 2 diabetes in rural and regional Australia by examining patterns of glycated haemoglobin (HbA1c) and blood lipid testing.

DESIGN AND SETTING

Retrospective analysis of pathology services data from twenty regional and rural towns in eastern Australia over 24 months.

PARTICIPANTS

Of 13 105 individuals who had either a single HbA1c result ≥7.0% (53 mmol mol^-1 ); or two or more HbA1c tests within the study period.

MAIN OUTCOME MEASURES

Frequency of testing of HbA1c and blood lipids (cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides) were compared with guideline recommendations.

RESULTS

About 58.3% of patients did not have the recommended 6-monthly HbA1c tests and 30.6% did not have annual lipid testing. For those who did not receive tests at the recommended interval, the mean between-test interval was 10.5 months (95% CI = 7.5-13.5) rather than 6 months for HbA1c testing; and 15.7 (95% CI = 13.3-18.1) months rather than annually for blood lipids. For those with at least one out-of-range test result, 77% of patients failed to receive a follow-up HbA1c test and 86.5% failed to receive a follow-up blood lipid test within the recommended 3 months. Patients less than 50 years of age, living in a more remote area and with poor diabetes control were less likely to have testing at the recommended intervals (P < 0.0001).

CONCLUSIONS

Although poor diabetes testing is not limited to rural areas, more intensive diabetes monitoring is likely to be needed for patients living in non-metropolitan areas, particularly for some subgroups.

Effect of glucose variability on pathways associated with glucotoxicity in diabetes: Evaluation of a novel in vitro experimental approach

AIMS

Glycaemic variability (GV) has been hypothesized to increase the risk of diabetes complications; however, results of clinical studies are contradictory. The effect of GV on cell phenotypes has been investigated in vitro showing that GV may have more deleterious effect on cells that high glucose itself. However, methodology used to study GV in vitro differs significantly between studies and does not reflect in vivo situation. Therefore we aimed to establish clinically relevant an in vitro experimental approach for the study of GV that reflects intra-day glucose fluctuations of subjects with type 1 diabetes mellitus (T1DM) and of healthy subjects and to test how low and high GV affect expression of genes that protects cells from hyperglycaemia-induced damage.

METHODS

Human umbilical vein endothelial cells (HUVEC) were cultured 24h in medium with different glucose profiles: high GV, low GV and GV of healthy subjects-profiles created according to CGM of T1DM patients and healthy subjects. These profiles were compared to commonly used 5.5 and 25mmol/l glucose concentrations. Gene expression was determined using quantitative PCR.

RESULTS

Our results showed general down-regulation of enzymes that are involved in the protection against hyperglycaemia-induced intracellular changes in both low and high GV compared to normal glycaemia similarly to the decrease induced by continuous hyperglycaemia. Gene expressions did not differ between high and low GV.

CONCLUSION

Our data indicate that GV may have similar or even greater effect than continuous hyperglycaemia on the expression of several genes relevant to pathogenesis of diabetes microvascular complications.

Variations in Brain Volume and Growth in Young Children With Type 1 Diabetes

Early-onset type 1 diabetes may affect the developing brain during a critical window of rapid brain maturation. Structural MRI was performed on 141 children with diabetes (4-10 years of age at study entry) and 69 age-matched control subjects at two time points spaced 18 months apart. For the children with diabetes, the mean (±SD) HbA1c level was 7.9 ± 0.9% (63 ± 9.8 mmol/mol) at both time points. Relative to control subjects, children with diabetes had significantly less growth of cortical gray matter volume and cortical surface area and significantly less growth of white matter volume throughout the cortex and cerebellum. For the population with diabetes, the change in the blood glucose level at the time of scan across longitudinal time points was negatively correlated with the change in gray and white matter volumes, suggesting that fluctuating glucose levels in children with diabetes may be associated with corresponding fluctuations in brain volume. In addition, measures of hyperglycemia and glycemic variation were significantly negatively correlated with the development of surface curvature. These results demonstrate that early-onset type 1 diabetes has widespread effects on the growth of gray and white matter in children whose blood glucose levels are well within the current treatment guidelines for the management of diabetes.

"Dual-remission" after Roux-en-Y gastric bypass surgery: Glycemic variability cannot always be improved in Chinese obese patients with type 2 diabetes

BACKGROUND

Glycemic variability after Roux-en-Y Gastric Bypass (RYGB) has not been adequately examined in Chinese obese patients with type 2 diabetes (T2D).

OBJECTIVE

We aimed to evaluate glucose variability after RYGB by continuous glucose monitoring (CGM) and then evaluate the remission rate based on the complete diabetes remission criteria combined with normal ranges of CGM for the Chinese population, which we defined as "dual-remission."

SETTING

The study was done at our academic university-affiliated hospital.

METHODS

Over a 3-day period, CGM was performed on 43 Chinese obese T2D patients combined with a mixed-meal test before and 1 year after RYGB. Mean amplitude of glucose excursions (MAGE), standard deviations (SD), and the time that patients' blood glucose levels were≥7.0 mmol/L,≥7.8 mmol/L,≥11.1 mmol/L, and≤3.9 mmol/L within 24 hours was analyzed. Multiple logistic regression analyses were used to identify predictors of "dual-remission."

RESULTS

Complete diabetes remission was achieved in 27 patients (62.8%) 1 year after RYGB. However, MAGE didn't change in the group, and only 18.6% patients met "dual-remission." Compared with patients in the complete remission group, patients in the dual-remission group had a shorter duration of diabetes, younger age, lower glycated hemoglobin (HbA1c) level, and no insulin usage at baseline. Correlation analysis showed MAGE after RYGB was positively correlated with diabetes duration (r = .43, P<.01). Multiple logistic regressions indicated a shorter duration was associated with a higher possibility to achieve dual-remission after adjusting for age, gender, HbA1c, and insulin therapy.

CONCLUSION

Glucose variability can't be effectively improved in most Chinese obese diabetic patients after RYGB. Shorter diabetes duration was associated with higher possibility to achieve "dual-remission."

Glucose-lowering effect and glycaemic variability of insulin glargine, insulin detemir and insulin lispro protamine in people with type 1 diabetes

AIM

To compare, using a continuous glucose monitoring (CGM) system, the effect on glycaemic variability of insulin glargine, detemir and lispro protamine.

METHODS

A total of 49 white people with type 1 diabetes, not well controlled by three times daily insulin lispro, taken for at least 2 months before study and on a stable dose, were enrolled. The study participants were randomized to add insulin glargine, detemir or lispro protamine, once daily, in the evening. We used a CGM system, the iPro Digital Recorder (Medtronic MiniMed, Northridge, CA, USA) for 1 week. Glycaemic control was assessed according to mean blood glucose values, the area under the glucose curve above 3.9 mmol/l (AUC(>3.9)) or above 10.0 mmol/l (AUC(>10.0)), and the percentage of time spent with glucose values >3.9 or >10.0 mmol/l. Intraday glycaemic variability was assessed using standard deviation (s.d.) values, the mean amplitude of glycaemic excursions and continuous overlapping of net glycaemic action. Day-to-day glycaemic variability was assessed using the mean of daily differences.

RESULTS

The s.d. was found to be significantly lower with insulin lispro protamine and glargine compared with insulin detemir. AUC(>3.9) was higher and AUC(>10.0) was lower with insulin lispro protamine and glargine compared with detemir. The mean amplitude of glycaemic excursions and continuous overlapping net glycaemic action values were lower with insulin lispro protamine and glargine compared with detemir. In addition, the mean of daily differences was significantly lower with insulin lispro protamine and glargine compared with detemir. Fewer hypoglycaemic events were recorded during the night-time with insulin lispro protamine compared with glargine and detemir.

CONCLUSIONS

The results suggest that insulin lispro protamine and glargine are more effective than detemir in reducing glycaemic variability and improving glycaemic control in people with type 1 diabetes. Insulin lispro protamine seems to lead to fewer hypoglycaemic events than other insulin regimens.

Glycemic variability in normal glucose tolerance women with the previous gestational diabetes mellitus

BACKGROUND

Women with previous gestational diabetes mellitus (pGDM) and postpartum normal glucose tolerance (NGT) may carry impaired islet β cell secretion, insulin resistance and subsequent altered glucose homeostasis. And certain normoglycemic groups at risks of diabetes were presented with elevated glycemic variability. The aim of study was to investigate the glycemic variability in NGT women with pGDM.

METHODS

Total 48 NGT women with pGDM (pGDM group) and 48 age- and BMI-matched NGT women without pGDM (control group) were recruited in the study. Integrated β cell function was assessed with the Insulin Secretion-Sensitivity Index-2 (ISSI-2) derived from oral glucose tolerance test. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 h. The multiple parameters of glycemic variability included the mean blood glucose (MBG), standard deviation of blood glucose (SDBG), mean of daily differences (MODD), mean amplitude of glycemic excursions (MAGE) and the incremental areas above preprandial glucose values (AUCpp).

RESULTS

The pGDM group had a higher MBG (6.5 ± 0.9 vs. 5.9 ± 0.8 mmol/L, p < 0.05), SDBG (1.3 ± 0.3 vs. 0.9 ± 0.2 mmol/L, p < 0.05), MODD (1.4 ± 0.3 vs. 1.1 ± 0.2 mmol/L, p < 0.05), MAGE (2.7 ± 0.4 vs. 1.8 ± 0.5 mmol/L, p < 0.05), and AUCpp (26.8 ± 3.4 vs. 19.2 ± 3.2 mmol/L·h, p < 0.05), when compared to the control group, and the differences remained significant after adjusting for anthropometric indices and metabolic risk factors. Islet β cell function index ISSI-2 in the pGDM group was lower than in the control group (p < 0.05). MBG, SDBG, MODD, MAGE and AUCpp were all negatively associated with ISSI-2 in the pGDM group (r = -0.31, -0.30, -0.34, -0.48 and -0.54, respectively, p < 0.05), and the correlations remained significant after adjusting for anthropometric indices and metabolic risk factors.

CONCLUSIONS

Normal glucose tolerance women with pGDM were presented with elevated glycemic variability, which may be associated with impaired islet β cell function.

Glycemic variability in nondiabetic morbidly obese persons: results of an observational study and review of the literature

Glycemic variability (GV) is correlated with oxidative stress which may lead to increased cardiovascular risk and poor clinical outcomes in people with prediabetes and diabetes. We sought to understand whether morbidly obese persons without diabetes by standard criteria have dysglycemia as measured by GV. We performed an observational study of GV metrics and carotid intima media thickness (CIMT) in 21 morbidly obese normoglycemic and 15 morbidly obese prediabetic applicants to The Biggest Loser television show. The results were compared to previously published studies in normoglycemic nonobese and obese individuals. Glucose was measured with a masked continuous glucose monitor (CGM) over 3 to 8 days and carotid intima media thickness (CIMT) was determined by ultrasound. CGM-derived GV metrics for GV were coefficient of variation (CV), standard deviation (SD), mean amplitude of glycemic excursions (MAGE), continuous overall net glycemic action-1 hour (CONGA1), and mean of daily differences (MODD). We found that morbidly obese subjects (n = 21) who were normoglycemic by standard criteria had higher GV (CV = 22%, SD = 24.2 mg/dl and MAGE = 48.6 mg/dl) than previous reports of normoglycemic, nonobese individuals (CV = 12-18%, SD = 11.5-15.0 mg/dl, and MAGE = 26.3-28.3 mg/dl). Morbidly obese prediabetic subjects (n = 15) had GV metrics indistinguishable from those morbidly obese subjects who were normoglycemic. CIMT was higher in both morbidly obese groups compared with historical age- and sex-matched controls. Normoglycemic and prediabetic morbidly obese individuals have higher GV compared with normal weight, nondiabetic individuals. We speculate that this may increase the risk for macrovascular disease through excessive oxidative stress.