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

The use of continuous glucose monitoring in people living with obesity, intermediate hyperglycemia or type 2 diabetes

A global trend towards increased obesity, intermediate hyperglycemia (previously termed prediabetes) and type 2 diabetes, has prompted a range of international initiatives to proactively raise awareness and provide action-driven recommendations to prevent and manage these linked disease states. One approach, that has shown success in managing people already diagnosed with type 2 diabetes mellitus, is to use continuous glucose monitoring (CGM) devices to help them manage their chronic condition through understanding and treating their daily glucose fluctuations, in assocation with glucose-lowering medications, including insulin. However, much of the burden of type 2 diabetes mellitus is founded in the delayed detection both of type 2 diabetes mellitus itself, and the intermediate hyperglycemia that precedes it. In this review, we provide evidence that using CGM technology in people at-risk of intermediate hyperglycemia or type 2 diabetes mellitus can significantly improve the rate and timing of detection of dysglycemia. Earlier detection allows intervention, including through continued use of CGM to guide changes to diet and lifestyle, that can delay or prevent harmful progression of early dysglycemia. Although further research is needed to fully understand the cost-effectiveness of this intervention in people at-risk or with early dysglycemia, the proposition for use of CGM technology is clear.

Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?

AIM

The present study comparatively evaluated glucose variability (GV) parameters derived from both continuous glucose monitoring (CGM) performed under standard conditions for a 24-h period and under usual everyday conditions for a 14-day period in a high-risk population without diabetes.

METHODS AND RESULTS

Seventy five subjects: 14 with normal glucose tolerance (NGT; mean age 43.6 ± 10.7 years; BMI 30.5 ± 6.9 kg/m2), 19 with high 1-h postload glucose > 8.6 mmol/l (1hrOGTT; mean age 45.6 ± 8.9 years; BMI 33.7 ± 6.9 kg/m2), and 42 with isolated impaired glucose tolerance (iIGT; mean age 47.6 ± 11.8 years; BMI 31.0 ± 6.5 kg/m2), were enrolled. An OGTT was performed. CGM was performed with blinded FreeStyleLibrePro for 24 h under standard conditions and for the rest of the 14-day period under usual everyday conditions. GV parameters derived from both periods were compared. There was a significant increase in GV with worsening of glucose tolerance from NGT, to 1hrOGTT and iIGT, independently of the conditions. Our findings showed moderate to strong correlations among GV indices between the studied periods in the cohort and in the 1hrOGTT and iIGT groups. However, a significant difference was found in some of the GV parameters between the analyzed periods.

CONCLUSION

The trend in GV is independent of the conditions, under which CGM is performed, in subjects at early stages of glucose intolerance. Although its measurements to some extend differ in standard and everyday conditions, there is no need of standardized conditions for correct interpretation of GV indices in this population.

Non-Invasive Continuous Glucose Monitoring in Patients Without Diabetes: Use in Cardiovascular Prevention-A Systematic Review

Continuous glucose monitoring (CGM) might provide immediate feedback regarding lifestyle choices such as diet and physical activity (PA). The impact of dietary habits and physical activity can be demonstrated in real time by providing continuous data on glucose levels and enhancing patient engagement and adherence to lifestyle modifications. Originally developed for diabetic patients, its use has recently been extended to a non-diabetic population to improve cardiovascular health. However, since data in this population are scarce, the effect on cardiovascular outcomes is unclear. CGM may offer potential benefits for cardiovascular prevention in healthy individuals without diabetes. The aim of this systematic review is to evaluate the use of CGM in healthy non-diabetic individuals, focusing on its potential to guide lifestyle interventions in the context of cardiovascular prevention, which may ultimately reduce cardiovascular risk.

Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?

BACKGROUND

Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes.

METHODS

We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies.

RESULTS

Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes.

CONCLUSIONS

Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.

Intra-pancreatic fat is associated with continuous glucose monitoring metrics

AIM

To investigate the relationship of fat in the pancreas with time spent in different glycaemic ranges.

METHODS

Abdominal magnetic resonance imaging at 3.0 Tesla was used to quantify fat in the pancreas as both continuous [i.e. intra-pancreatic fat deposition (IPFD)] and binary (i.e. fatty change of the pancreas vs. normal pancreas) variables. Dexcom G6 devices were used to collect continuous glucose monitoring data every 5 min over a continuous 7-day period. Time above range (TAR), time in range (TIR) and time below range were computed. Statistical models were built to adjust for age, sex, body composition, and other covariates in linear regression analysis and analysis of covariance.

RESULTS

In total, 38 individuals were studied. IPFD was significantly associated with TAR (p = .036) and TIR (p = .042) after adjustment for covariates. For every 1% increase in IPFD, there was a 0.3 unit increase in TAR and a decrease in TIR. Individuals with fatty change of the pancreas, when compared with those with normal pancreas, had significantly higher TAR (p = .034) and lower TIR (p = .047) after adjustment for covariates. Neither IPFD (p = .805) nor fatty change of the pancreas (p = .555) was associated with time below range after adjustment for covariates.

CONCLUSION

Increased fat in the pancreas is associated with excessive glycaemic variability. Fatty change of the pancreas may contribute to heightening the risk of cardiovascular diseases.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

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.

Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis

INTRODUCTION

Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

METHODS

Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed.

RESULTS

In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT).

CONCLUSION

Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project

BACKGROUND

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.

METHODS

We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.

FINDINGS

In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.

CONCLUSIONS

The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.

FUNDING

E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Gut Microbiota Composition and Functionality Are Associated With REM Sleep Duration and Continuous Glucose Levels

CONTEXT

Sleep disruption is associated with worse glucose metabolic control and altered gut microbiota in animal models.

OBJECTIVE

We aimed to evaluate the possible links among rapid eye movement (REM) sleep duration, continuous glucose levels, and gut microbiota composition.

METHODS

This observational, prospective, real-life, cross-sectional case-control study included 118 (60 with obesity), middle-aged (39.1-54.8 years) healthy volunteers recruited at a tertiary hospital. Glucose variability and REM sleep duration were assessed by 10-day continuous glucose monitoring (CGM) (Dexcom G6) and wrist actigraphy (Fitbit Charge 3), respectively. The coefficient of variation (CV), interquartile range (IQR), and SD of glucose variability was assessed and the percentage of time in range (% TIR), at 126-139 mg/dL (TIR2), and 140-199 mg/dL (TIR3) were calculated. Shotgun metagenomics sequencing was applied to study gut microbiota taxonomy and functionality.

RESULTS

Increased glycemic variability (SD, CV, and IQR) was observed among subjects with obesity in parallel to increased % TIR2 and % TIR3. REM sleep duration was independently associated with % TIR3 (β = -.339; P < .001) and glucose variability (SD, β = -.350; P < .001). Microbial taxa from the Christensenellaceae family (Firmicutes phylum) were positively associated with REM sleep and negatively with CGM levels, while bacteria from Enterobacteriacea family and bacterial functions involved in iron metabolism showed opposite associations.

CONCLUSION

Decreased REM sleep duration was independently associated with a worse glucose profile. The associations of species from Christensenellaceae and Enterobacteriaceae families with REM sleep duration and continuous glucose values suggest an integrated picture of metabolic health.

Michaelis-Menten kinetic modeling of hemoglobin A1c status facilitates personalized glycemic control

INTRODUCTION

Discrepancy between measured HbA1c and HbA1c calculated from plasma glucose is associated with higher risk for diabetic complications. However, quantification of this difference is inaccurate due to the imperfect linear conversion models. We propose to introduce a mathematical formula that correlates with the observational data and supports individualized glycemic control.

METHODS

We analysed 175,437 simultaneous plasma glucose and HbA1c records stored in our laboratory database. Employing the Michaelis-Menten (MM) equation, we compared the calculated HbA1c levels to the measured HbA1c levels. Data from patients with multiple records were used to establish the patients' glycemic status and to assess the predictive power of our MM model.

RESULTS

HbA1c levels calculated with the MM equation closely matched the population's average HbA1c levels. The Michaelis constant (Km) had a negative correlation with HbA1c (r2 = 0.403). Using personalized Km values in the MM equation, 85.1% of HbA1c predictions were within 20% error (ADAG calculation: 78.4%). MM prediction also performed better in predicting pathologic HbA1c levels (0.904 AUC vs. 0.849 AUC for ADAG).

CONCLUSION

MM equation is an improvement over linear models and could be readily employed in routine diabetes management. Km is a reliable and quantifiable marker to characterize variations in glucose tolerance.

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.

Hyperglycemia and Other Glycemic Measures Throughout Therapy for Pediatric Acute Lymphoblastic Leukemia and Lymphoma

Transient hyperglycemia during induction chemotherapy is associated with increased morbidity and mortality in patients with acute lymphoblastic leukemia (ALL). Treatment with glucocorticoids, asparaginase, and stress are the proposed causal factors. Although these risks are not exclusive to induction, glycemic control throughout the remainder of ALL/lymphoma (ALL/ALLy) therapy has not been described. Furthermore, prior research has been limited to transient hyperglycemia. This study aimed to characterize glycemic control throughout ALL/ALLy and to evaluate risk factors and outcomes associated with increased mean glucose and glucose coefficient of variation (glucose CV) during induction chemotherapy. The records for 220 pediatric/young adult patients, age 1 to 26 years, who underwent treatment for ALL/ALLy from 2010 to 2014 at Children's Hospital Colorado were retrospectively reviewed. Measures of glycemic control were calculated for each cycle. For the cycle with the highest mean glucose, induction (n=208), multivariable models were performed to identify potential risk factors and consequences of increased glucose. Highest mean glucose by cycle were induction 116 mg/dL, pretreatment 108 mg/dL, delayed intensification 96 mg/dL, and maintenance 93 mg/dL; these cycles also had the most glycemic variability. During induction, patients with Down syndrome, or who were ≥12 years and overweight/obese, had higher mean glucoses; age and overweight/obese status were each associated with increased glucose CV. In multivariable analysis, neither induction mean glucose nor glucose CV were associated with increased hazard of infection, relapse, or death.

Regimen comprising GLP-1 receptor agonist and basal insulin can decrease the effect of food on glycemic variability compared to a pre-mixed insulin regimen

BACKGROUND

Increasing evidence suggests that glucagon-like peptide 1 (GLP-1) receptor agonists (RA) can stabilize glycemic variability (GV) and interfere with eating behavior. This study compared the impact of insulin, GLP-1 RA, and dietary components on GV using professional continuous glucose monitoring (CGM).

METHODS

Patients with type 2 diabetes underwent CGM before and after switching from a twice-daily pre-mixed insulin treatment regimen to a GLP-1 RA (liraglutide) plus basal insulin regimen. The dietary components were recorded and analyzed by a certified dietitian. The interactions between the medical regimen, GV indices, and nutrient components were analyzed.

RESULTS

Sixteen patients with type 2 diabetes were enrolled in this study. No significant differences in the diet components and total calorie intake between the two regimens were found. Under the pre-mixed insulin regimen, for increase in carbohydrate intake ratio, mean amplitude of glucose excursion (MAGE) and standard deviation (SD) increased; in contrast, under the new regimen, for increase in fat intake ratio, MAGE and SD decreased, while when the protein intake ratio increased, the coefficient of variation (CV) decreased. The impact of the food intake ratio on GV indices disappeared under the GLP-1 RA regimen. After switching to the GLP-1 RA regimen, the median MAGE, SD, and CV values decreased significantly. However, the significant difference in GV between the two regimens decreased during the daytime.

CONCLUSION

A GLP-1 RA plus basal insulin regimen can stabilize GV better than a regimen of twice-daily pre-mixed insulin, especially in the daytime, and can diminish the effect of food components on GV.

Continuous glucose monitoring reveals similar glycemic variability in individuals with obesity despite increased HOMA-IR

Background/aims

Continuous glucose monitoring is a well-tolerated and versatile tool for management of diabetes and metabolic disease. While its use appears to be feasible to monitor glycemic profiles in diabetics, there is a paucity of data in individuals with obesity and normal glucose tolerance. The aim of this study is to investigate glucose fluctuations and insulin resistance patterns in normoglycemic participants with obesity vs. without obesity and contextualize these results against leading models for obesity.

Materials and methods

We designed a prospective, observational pilot study of two cohorts including 14 normoglycemic participants with obesity and 14 normoglycemic participants without obesity. Participants were monitored with continuous glucose monitoring (CGM) for five consecutive days. Insulin resistance levels were measured and glucometric data were extracted from CGM for all participants.

Results

Fasting serum insulin and homeostasis model assessment of insulin resistance (HOMA-IR) were significantly higher in the group with obesity (P < 0.05). While the group with obesity had a higher mean blood glucose (MBG), mean amplitude of glycemic excursions (MAGE), and continuous overall glycemic action-1 h (CONGA-1), these differences were not significant. On univariate linear regression, insulin resistance (HOMA-IR) was associated with body mass index (BMI), waist circumference (WC), cohort with obesity, cohort consuming a high glycemic diet, hemoglobin A1c (HbA1c), and fasting insulin levels. WC and fasting insulin levels remained predictors of HOMA-IR in our multivariable model.

Conclusion

While there is much excitement surrounding the use of commercial CGM products in obesity management, our results suggest that fasting insulin and HOMA-IR values may be more clinically useful than CGM data alone.

Continuous glucose monitoring metrics for earlier identification of pre-diabetes: protocol for a systematic review and meta-analysis

INTRODUCTION

Glycaemic variability and other metrics are not well characterised in subjects without diabetes. More comprehensive sampling as obtained with continuous glucose monitoring (CGM) may improve diagnostic accuracy of the transition from health to pre-diabetes. Our goal is to investigate the glycaemic system as it shifts from health to pre-disease in adult patients without diabetes using CGM metrics. New insights may offer therapeutic promise for reversing dysglycaemia more successfully with dietary, nutritional and lifestyle change before progression occurs to pre-diabetes and diabetes.

METHODS AND ANALYSIS

This systematic review will include comprehensive searches of the PubMed, Scopus, Cochrane Library and ClinicalTrials.gov databases, with restrictions set to studies published in the last 10 years in English and planned search date 10 March 2022. Reference lists of studies that meet eligibility criteria in the screening process will subsequently be screened for the potential inclusion of additional studies. We will include studies that examine CGM use and report diagnostic criteria such as fasting glucose and/or haemoglobin A1c such that we can assess correlation between CGM metrics and established diagnostic criteria and describe how CGM metrics are altered in the transition from health to pre-diabetes. The screening and data extraction will be conducted by two independent reviewers using Covidence. All included papers will also be evaluated for quality and publication bias using Cochrane Collaboration risk of bias tools. If there are two or more studies with quantitative estimates that can be combined, we will conduct a meta-analysis after assessing heterogeneity.

ETHICS AND DISSEMINATION

The systematic review methodology does not require formal ethical review due to the nature of the study design. Study findings will be publicly available and published in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42022308222.

Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes

We examined how dietary and physical activity behaviors influence fluctuations in blood glucose levels over a seven-day period in people at high risk for diabetes. Twenty-eight participants underwent a mixed meal tolerance test to assess glucose homeostasis at baseline. Subsequently, they wore an accelerometer to assess movement behaviors, recorded their dietary intakes through a mobile phone application, and wore a flash glucose monitoring device that measured glucose levels every 15 min for seven days. Generalized estimating equation models were used to assess the associations of metabolic and lifestyle risk factors with glycemic variability. Higher BMI, amount of body fat, and selected markers of hyperglycemia and insulin resistance from the meal tolerance test were associated with higher mean glucose levels during the seven days. Moderate- to vigorous-intensity physical activity and polyunsaturated fat intake were independently associated with less variation in glucose levels (CV%). Higher protein and polyunsaturated fatty acid intakes were associated with more time-in-range. In contrast, higher carbohydrate intake was associated with less time-in-range. Our findings suggest that dietary composition (a higher intake of polyunsaturated fat and protein and lower intake of carbohydrates) and moderate-to-vigorous physical activity may reduce fluctuations in glucose levels in persons at high risk of diabetes.

Cross-Sectional and Individual Relationships between Physical Activity and Glycemic Variability

Introduction/Purpose

Overweight or obese adults spend more time sedentary and less time performing physical activity (PA) and are at an increased risk for developing impaired glycemic health. Free-living environments may provide insight into glycemic health in addition to clinical assessments. The purpose of this study was to examine the relationship between PA and glycemic health assessed by continuous glucose monitoring (CGM).

Methods

Twenty-eight overweight or obese adults each wore an accelerometer and CGM over the same 7 consecutive days. Average daily time (minutes and metabolic-equivalent minutes (MET-minutes)) and associated energy expenditure performing light (LPA), moderate-to-vigorous (MVPA), total PA, and standard deviation (SD) across days were calculated. Average daily 24-h and waking glycemia, mean glucose concentration, glycemic variability measured as the continuous overlapping net glycemic action, mean amplitude of glycemic excursions, and mean of daily difference were assessed.

Results

LPA MET-minutes per day was positively associated with 24-h and waking glycemia time-in-range and negatively associated with 24-h and waking time in hyperglycemia. Total PA time and the SD of MVPA and total PA time were negatively associated with 24-h mean glucose concentration. Individual-level analysis identified that most participants (50%-71%) expressed negative associations between LPA and MVPA time with 24-h mean glucose concentration, mean amplitude of glycemic excursion, and 4-h continuous overlapping net glycemic action.

Conclusions

Expectedly, greater total PA time and intensity-specific PA time were associated with lower 24-h and waking mean glucose concentration, greater glycemia time-in-range, and less time in hyperglycemia. The relationship between glucose concentrations and PA time SD was unexpected, whereas most participants expressed hypothesized relationships, which necessitates further exploration.

Use of continuous glucose monitoring in obesity research: A scoping review

BACKGROUND

This scoping review provides a timely synthesis of the use of continuous glucose monitoring in obesity research with considerations to adherence to continuous glucose monitor devices and metrics most frequently reported.

METHODS

This scoping review was conducted adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. Eligible studies (n = 31) evaluated continuous glucose monitor use in research on participants, of all ages, with overweight or obesity.

RESULTS

Reviewed studies varied in duration from one to 84 days (mean: 8.74 d, SD 15.2, range 1-84 d) with 889 participants total (range: 11-118 participants). Across all studies, the mean percent continuous glucose monitor wear time (actual/intended wear time in days) was 92% (numerator - mean: 266.1 d, SD: 452, range: 9-1596 d/denominator - mean: 271.6 d, SD: 451.5, range: 9-1596 d). Continuous glucose monitoring was utilized to provide biofeedback (n = 2, 6%), monitor dietary adherence (n = 2, 6%), and assess glycemic variability (n = 29, 93%). The most common variability metrics reported were standard deviation (n = 19, 62%), area under the curve (n = 12, 39%), and glycemic range (n = 12, 39%).

CONCLUSIONS

Available evidence suggests that continuous glucose monitoring is a well-tolerated and versatile tool for obesity research in pediatric and adult patients. Future investigation is needed to substantiate the feasibility and utility of continuous glucose monitors in obesity research and maximize comparability across studies.

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

INTRODUCTION

Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.

RESEARCH DESIGN AND METHODS

We recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8-10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2-6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.

RESULTS

A total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor's importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).

CONCLUSIONS

This study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

The Beta-Cell Function and Glucose Profile of Newly Diagnosed Acromegalic Patients with Normal Glucose Tolerance

OBJECTIVES

Untreated acromegaly is a nature model for unveiling the diabetogenic effects of GH. CGMS can uncover more glucose profile of acromegaly. This study aimed to evaluate the insulin resistance (IR), β-cell function, and glycemic spectrum of patients with newly diagnosed acromegaly with normal glucose tolerance (NGT).

METHODS

This study was conducted in Huashan Hospital from January 2015 to February 2019. Eight newly diagnosed acromegalic patients without history of diabetes and eight age- and gender-matched healthy subjects were enrolled. All participants underwent oral glucose tolerance test (OGTT) and 72 h continuous glucose monitoring (CGM). Parameters on β-cell function and IR were calculated. Mean blood glucose (MBG) in 24 hours was adopted for the evaluation of the glycemic level, and standard deviation of blood glucose (SDBG) and mean amplitude of glycemic excursion (MAGE) were used for glucose fluctuation.

RESULTS

HbA1c in the acromegaly group was significantly higher than in the control. During OGTT, glucose peaked at 60 min in acromegaly and at 30 min in controls. After glucose load, the acromegaly group had significantly higher insulin levels than controls, especially in 120 min and 180 min. Both insulin sensitivity index and disposal index after glucose load of acromegaly were significantly lower than those of controls. Moreover, acromegalic subjects had significantly higher MBG than controls.

CONCLUSIONS

The newly diagnosed acromegalic patients with NGT were characterized by IR and impaired β-cell function after glucose load. CGM showed that MBG of NGT acromegaly patients was higher than that of normal people.

Pre-Stroke Glycemic Variability Estimated by Glycated Albumin Is Associated with Early Neurological Deterioration and Poor Functional Outcome in Prediabetic Patients with Acute Ischemic Stroke

<b><i>Introduction:</i></b> Whether glycemic variability prior to stroke increases the risk of stroke outcomes in prediabetic patients presenting with acute ischemic stroke is still unclear. We evaluated whether pre-stroke glycemic variability, estimated by glycated albumin (GA), increased early neurological deterioration (END) and functional outcomes in prediabetic patients with acute ischemic stroke. <b><i>Methods:</i></b> A total of 215 acute ischemic stroke patients with prediabetes were evaluated. The primary outcome was END, defined as an incremental increase in the National Institutes of Health Stroke Scale score by ≥1 point in motor power or ≥2 points in the total score within the 7 days after admission. The secondary outcome was poor functional status defined by a modified Rankin Scale at 3 months. Higher GA (≥16.0%) was determined to reflect glycemic fluctuation prior to ischemic stroke. <b><i>Results:</i></b> Of the 215 prediabetic patients, 77 (35.8%) were in the higher GA group. In prediabetic patients, END occurrence and poor functional status were higher in the higher GA group than in the lower GA group. The multivariate analysis showed that a higher GA was associated with an increased risk of END occurrence and poor functional outcomes at 3 months (adjusted odds ratio [95% confidence interval], 4.58 [1.64–12.81], <i>p</i> = 0.004 and 2.50 [1.19–5.25], <i>p</i> = 0.02, respectively). <b><i>Conclusion:</i></b> Pre-stroke glycemic variability estimated by GA was associated with END occurrence and poor functional outcome after ischemic stroke in patients with prediabetes.

Glycemic variability is associated with poor outcomes in pediatric hematopoietic stem cell transplant patients

BACKGROUND

Among pediatric hematopoietic stem cell transplant (HSCT) recipients, abnormal glycemic control is shown to be associated with increased risk of transplant-related mortality, death from any cause, risk of infection, increased hospitalized, and intensive care days. Independent effects of higher glycemic variability, a component of glycemic control, have not been described. This study aimed to characterize risk factors for, and consequences of, higher glycemic variability in HSCT patients.

PROCEDURE

Medical records for a cohort of 344 patients, age 0-30 years, who underwent first HSCT from 2007 to 2016 at Children's Hospital Colorado were retrospectively reviewed. Glucose coefficients of variation (CV) were analyzed for HSCT days -14 to 0 and 0-30, and patients were assessed for potential risk factors and outcomes.

RESULTS

Roughly one-third of patients had pre-HSCT and day 0-30 glucose CV above the reported healthy adult range. Independent of HSCT type, doubling of pre-HSCT glucose CV was associated with a 4.91-fold (95% confidence interval [CI], 1.40-17.24) increased hazard of infection, as well as increased risk for intensive care hospitalization for allogenic HSCT patients. Multivariable analysis demonstrated that allogeneic HSCT patients had a 1.40- and 1.38-fold (95% CI, 0.98-1.99 and 1.00-1.91) increased hazard of death for every doubling of pre-HSCT and day 0-30 glucose CV, respectively.

CONCLUSIONS

Just as with higher mean glucose, higher glycemic variability in the pediatric HSCT population is independently associated with significantly increased morbidity. Additional research is required to evaluate the utility of glucose control to mitigate these relationships and improve HSCT outcomes.

The dawn phenomenon across the glycemic continuum: Implications for defining dysglycemia

AIMS

To investigate the frequency of dawn phenomenon (DP) and its relationship with time in range (TIR) and glycemic variability (GV) using continuous glucose monitoring (CGM).

METHODS

781 subjects of a multicenter CGM study in China were included: those with normal glucose tolerance (NGT n = 360); impaired glucose regulation (IGR n = 173); newly diagnosed type 2 diabetes mellitus (T2D n = 248). Analysis of the magnitude of DP (ΔG) was conducted with the primary definition of 1.11 mmol/L and a secondary definition of 0.56 mmol/L.

RESULTS

The frequency of DP was 8.9%, 30.1% and 52.4% in NGT, IGR and T2D group, respectively, using the primary definition. In all three groups, TIR was lower (all P < 0.05), coefficient of variation (CV) was higher in DP subgroup (all P < 0.05). In DP subgroup of T2D, TIR was 7.0% (1.68 h) lower and CV was 3.0% higher, and HbA_1c was 0.6% (7 mmol/mol) higher using the primary definition (all P < 0.05).

CONCLUSIONS

DP was present in a high percent of subjects with NGT and IGR. In newly diagnosed T2D group, the presence of DP was associated with poorer overall glycemic control.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices

BACKGROUND

Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices.

METHODS

We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods.

RESULTS

The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%).

CONCLUSION

The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Association between visit-to-visit variability of HbA1c and cognitive decline: a pooled analysis of two prospective population-based cohorts

AIMS/HYPOTHESIS

The aim of this study was to investigate the association between visit-to-visit variability in HbA_1c and cognitive function decline in the elderly population.

METHODS

We performed a pooled analysis of two prospective population-based cohorts (the Health Retirement Study [HRS] and the English Longitudinal Study of Ageing [ELSA]). Cognitive function, including memory and executive function, were assessed at baseline and every 2 years, while HbA_1c levels were assessed at baseline and every 4 years. Visit-to-visit variability (VVV) in HbA_1c was calculated using the CV, SD and variation independent of the mean (VIM) during the follow-up period. Linear mixed models were used to evaluate the association between HbA_1c variability and cognitive function decline with adjustment for demographics, mean HbA_1c, education, smoking, alcohol consumption, BMI, baseline hypertension, baseline diabetes status and HDL-cholesterol.

RESULTS

The study enrolled 6237 participants (58.23% women, mean age 63.38 ± 8.62 years) with at least three measurements of HbA_1c. The median follow-up duration was 10.56 ± 1.86 years. In the overall sample, compared with the lowest quartile of HbA_1c variability, participants in the highest quartile of HbA_1c variability had a significantly worse memory decline rate (-0.094 SD/year, 95% CI -0.185, -0.003) and executive function decline rate (-0.083 SD/year, 95% CI -0.125, -0.041), irrespective of mean HbA_1c values over time. Among individuals without diabetes, each 1-SD increment in HbA_1c CV was associated with a significantly higher rate of memory z score decline (-0.029, 95% CI -0.052, -0.005) and executive function z score decline (-0.049, 95% CI -0.079, -0.018) in the fully adjusted model.

CONCLUSIONS/INTERPRETATION

We observed a significant association between long-term HbA_1c variability and cognitive decline among the non-diabetic population in this study. The effect of maintaining steady glucose control on the rate of cognitive decline merits further investigation.

Assessment of glucose variability in subjects with prediabetes

The aim of the study was to assess glucose variability in subjects with prediabetes by means of CGM.

MATERIAL AND METHODS

32 subjects with prediabetes - mean age 56.6 ± 9.6 years, mean BMI 30.3 ± 5.3 kg/m² and 18 subjects with normal glucose tolerance (NGT) - mean age 54.4 ± 9.9 years, mean BMI 24.8 ± 6.9 kg/m², were enrolled. Glucose tolerance was studied during OGTT. HbA1c was measured by NGSP certified method. CGM was performed with FreeStyle Libre Pro sensor.

RESULTS

The following indices of glucose variability were significantly higher in the prediabetes group - CV (p < 0.041), J-index (p < 0.014), CONGA (p < 0.047) and GRADE (p < 0.036). A significant increase in HbA1c (p < 0.036), mean interstitial glucose (p < 0.025), time above range (p < 0.018) and a significant decrease in time in range (p < 0.014) was found in prediabetes compared to NGT. Significant correlations between HbA1c and LBGI (r = -0.33, p = 0.02), HBGI (r = 0.31, p = 0.03), CONGA (r = 0.36, p = 0.01), J-index (r = 0.37, p = 0.01) and M-value (r = -0.34, p = 0.02) were established.

CONCLUSION

Glucose variability is significantly increased in prediabetes and is an additional parameter in the assessment of glucose homeostasis even at these early stages of glucose dysregulation.

Visit-to-Visit Glycemic Variability and Risks of Cardiovascular Events and All-Cause Mortality: The ALLHAT Study

OBJECTIVE

The prognostic value of long-term glycemic variability is incompletely understood. We evaluated the influence of visit-to-visit variability (VVV) of fasting blood glucose (FBG) on incident cardiovascular disease (CVD) and mortality.

RESEARCH DESIGN AND METHODS

We conducted a prospective cohort analysis including 4,982 participants in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) who attended the baseline, 24-month, and 48-month visits. VVV of FBG was defined as the SD or variability independent of the mean (VIM) across FBG measurements obtained at the three visits. Participants free of CVD during the first 48 months of the study were followed for incident CVD (coronary heart disease [CHD], stroke, and heart failure [HF]) and all-cause mortality.

RESULTS

Over a median follow-up of 5 years, there were 305 CVD events (189 CHD, 45 stroke, and 81 HF) and 154 deaths. The adjusted hazard ratio (HR) comparing participants in the highest versus lowest quartile of SD of FBG (≥26.4 vs. <5.5 mg/dL) was 1.43 (95% CI 0.93-2.19) for CVD and 2.22 (95% CI 1.22-4.04) for all-cause mortality. HR for VIM was 1.17 (95% CI 0.84-1.62) for CVD and 1.89 (95% CI 1.21-2.93) for all-cause mortality. Among individuals without diabetes, the highest quartile of SD of FBG (HR 2.67 [95% CI 0.14-6.25]) or VIM (HR 2.50 [95% CI 1.40-4.46]) conferred a higher risk of death.

CONCLUSIONS

Greater VVV of FBG is associated with increased mortality risk. Our data highlight the importance of achieving normal and consistent glycemic levels for improving clinical outcomes.

Continuous glucose monitoring is more sensitive than HbA1c and fasting glucose in detecting dysglycaemia in a Spanish population without diabetes

AIMS

To investigate whether continuous glucose monitoring (CGM) reveals patterns of glycaemic behaviour, the detection of which might improve early diagnosis of dysglycaemia.

METHODS

A total 1521 complete days of valid CGM data were recorded under real-life conditions from a healthy sample of a Spanish community, as were matching FPG and HbA1_C data. No participant was pregnant, had a history of kidney or liver disease, or was taking drugs known to affect glycaemia.

RESULTS

CGM and fingerstick measurements showed a mean relative absolute difference of 6.9 ± 2.2%. All subjects were normoglycaemic according to FPG and HbA_1C except 21% who were prediabetic. The normoglycaemic subjects had a 24-hour mean blood glucose concentration (MBG) of 5.7 ± 0.4 mmol/L, spending a median of 97% of their time within the target range (3.9-7.8 mmol/L). 73% of them experienced episodes with blood glucose levels above the threshold for impaired glucose tolerance, and 5% levels above the threshold for diabetes. These normoglycaemic participants with episodes of high glycaemia had glycaemic variabilities similar to those of prediabetic subjects with episodes of similar intensity or combined duration.

CONCLUSIONS

CGM is a better indicator of possible early dysglycaemia than either FPG or HbA1c.

Metformin add-on continuous subcutaneous insulin infusion on precise insulin doses in patients with type 2 diabetes

To investigate whether metformin add-on to the continuous subcutaneous insulin infusion (Met + CSII) therapy leads to a significant reduction in insulin doses required by type 2 diabetes (T2D) patients to maintain glycemic control, and an improvement in glycemic variation (GV) compared to CSII only therapy. We analyzed data from our two randomized, controlled open-label trials. Newly diagnoses T2D patients were randomized assigned to receive either CSII therapy or Met + CSII therapy for 4 weeks. Subjects were subjected to a 4-day continuous glucose monitoring (CGM) at the endpoint. Insulin doses and GV profiles were analyzed. The primary endpoint was differences in insulin doses and GV between the two groups. A total of 188 subjects were admitted as inpatients. Subjects in metformin add-on therapy required significantly lower total, basal and bolus insulin doses than those of control group. CGM data showed that patients in Met + CSII group exhibited significant reduction in the 24-hr mean amplitude of glycemic excursions (MAGE), the standard deviation, and the coefficient of variation compared to those of control group. Our data suggest that metformin add-on to CSII therapy leads to a significant reduction in insulin doses required by T2D patients to control glycemic variations.

Implementation of Low Glycemic Index Diet Together with Cornstarch in Post-Gastric Bypass Hypoglycemia: Two Case Reports

Post-bariatric hypoglycemia (PBH) is an increasingly recognized long-term complication of bariatric surgery. The nutritional treatment of PBH includes a high-fiber diet and the restriction of soluble and high-glycemic index carbohydrates; however, these measures are not always enough to prevent hypoglycemia. We evaluated the efficacy of uncooked cornstarch, a low-glycemic index carbohydrate characterized by slow intestinal degradation and absorption, in addition to a high-fiber diet, for the treatment of PBH. We report the cases of two young women suffering from severe postprandial and fasting hypoglycemia following Roux-en-Y gastric bypass (RYGB). The patients underwent Continuous Glucose Monitoring (CGM) before and 12–16 weeks after the administration of uncooked cornstarch (respectively 1.25 g/kg b.w. and 1.8 g/kg b.w.) in addition to a high-fiber diet. In both patients, CGM showed more stable glucose levels throughout monitoring, a remarkable reduction of the time spent in hypoglycemia (<55 mg/dL) both during the day (−11% for both patients) and the night (−22% and −32%), and the improvement of all glycemic variability indexes. Our report, within the limit of only two cases, suggests that the implementation of a dietary intervention through the addition of uncooked cornstarch reduces daily glycemic fluctuations and hypoglycemic episodes in patients with PBH.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

BACKGROUND

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

METHODS

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

RESULTS

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

CONCLUSIONS

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

The Comprehensive Glucose Pentagon: A Glucose-Centric Composite Metric for Assessing Glycemic Control in Persons With Diabetes

BACKGROUND

Composite metrics have the potential to provide more complete and clinically useful information about glycemic control than traditional individual metrics such as hemoglobin A1C, %/time/area under curve of hypoglycemia and hyperglycemia.

METHODS

Using five key metrics that are derived from continuous glucose monitoring, we developed a new, multicomponent composite metric, the Comprehensive Glucose Pentagon (CGP) that demonstrates glycemic control both numerically and visually. Two of its axes are composite metrics-the intensity of hypoglycemia and intensity of hyperglycemia. This approach eliminates the use of the surrogate marker, hemoglobin A1C (A1C), and replaces it with glucose-centric metrics.

RESULTS

We reanalyzed the data from two randomized control trials, the STAR 3 and ASPIRE In-Home studies using the CGP. It provided new insights into the effect of sensor-augmented pumping (SAP) in the STAR 3 trial and sensor-integrated pumping with low-glucose threshold suspend (SIP+TS) in the ASPIRE In-Home trial.

CONCLUSIONS

The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).

Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes

OBJECTIVE

To define the threshold for excess glucose variability (GV), one of the main features of dysglycemia in diabetes.

RESEARCH DESIGN AND METHODS

A total of 376 persons with diabetes investigated at the University Hospital of Montpellier (Montpellier, France) underwent continuous glucose monitoring. Participants with type 2 diabetes were divided into several groups-groups 1, 2a, 2b, and 3 (n = 82, 28, 65, and 79, respectively)-according to treatment: 1) diet and/or insulin sensitizers alone; 2) oral therapy including an insulinotropic agent, dipeptidyl peptidase 4 inhibitors (group 2a) or sulfonylureas (group 2b); or 3) insulin. Group 4 included 122 persons with type 1 diabetes. Percentage coefficient of variation for glucose (%CV = [(SD of glucose)/(mean glucose)] × 100) and frequencies of hypoglycemia (interstitial glucose <56 mg/dL [3.1 mmol/L]) were computed.

RESULTS

Percentages of CV (median [interquartile range]; %) increased significantly (P < 0.0001) from group 1 (18.1 [15.2-23.9]) to group 4 (37.2 [31.0-42.3]). In group 1, the upper limit of %CV, which served as reference for defining excess GV, was 36%. Percentages of patients with %CVs above this threshold in groups 2a, 2b, 3, and 4 were 0, 12.3, 19.0, and 55.7%, respectively. Hypoglycemia was more frequent in group 2b (P < 0.01) and groups 3 and 4 (P < 0.0001) when subjects with a %CV >36% were compared with those with %CV ≤36%.

CONCLUSIONS

A %CV of 36% appears to be a suitable threshold to distinguish between stable and unstable glycemia in diabetes because beyond this limit, the frequency of hypoglycemia is significantly increased, especially in insulin-treated subjects.

Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes

Continuous Glucose Monitoring (CGM) has been demonstrated to be clinically valuable, reducing risks of hypoglycemia and hyperglycemia, glycemic variability (GV), and improving patient quality of life for a wide range of patient populations and clinical indications. Use of CGM can help reduce HbA1c and mean glucose. One CGM device, with accuracy (%MARD) of approximately 10%, has recently been approved for self-adjustment of insulin dosages (nonadjuvant use) and approved for reimbursement for therapeutic use in the United States. CGM had previously been used off-label for that purpose. CGM has been demonstrated to be clinically useful in both type 1 and type 2 diabetes for patients receiving a wide variety of treatment regimens. CGM is beneficial for people using either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). CGM is used both in retrospective (professional, masked) and real-time (personal, unmasked) modes: both approaches can be beneficial. When CGM is used to suspend insulin infusion when hypoglycemia is detected until glucose returns to a safe level (low-glucose suspend), there are benefits beyond sensor-augmented pump (SAP), with greater reduction in the risk of hypoglycemia. Predictive low-glucose suspend provides greater benefits in this regard. Closed-loop control with insulin provides further improvement in quality of glycemic control. A hybrid closed-loop system has recently been approved by the U.S. FDA. Closed-loop control using both insulin and glucagon can reduce risk of hypoglycemia even more. CGM facilitates rigorous evaluation of new forms of therapy, characterizing pharmacodynamics, assessing frequency and severity of hypo- and hyperglycemia, and characterizing several aspects of GV.

Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA1c values

To define the features of glycemic variations in drug naïve type 2 diabetic (T2D) patients with different HbA_1c values using continuous glucose monitoring (CGM), a total of 195 drug naïve T2D patients were admitted. The subjects were divided into the following groups: lower HbA_1c values (≤8%), moderate HbA_1c values (>8% and ≤10%), and higher HbA_1c values (>10%). The patients underwent oral glucose tolerance tests and were then subjected to 3-day CGM. The primary endpoint was the differences in the 24-hr mean amplitude of glycemic excursions (MAGE) in patients with different HbA_1c values. Patients with higher HbA_1c values had larger MAGEs than those in the moderate and lower groups (7.44 ± 3.00 vs. 6.30 ± 2.38, P < 0.05, 7.44 ± 3.00 vs. 5.20 ± 2.35, P < 0.01, respectively). The 24-hr mean glucose concentrations increased incrementally in the patients with lower, moderate and higher HbA_1c values. Moreover, the patients with higher HbA_1c values exhibited higher peak glucose concentrations and prolongation in the time to peak glucose. Patients with higher HbA_1c values had larger MAGE compared with those with lower and moderate HbA_1c values. Our data indicated patients with higher HbA_1c values should receive special therapy aimed at reducing the larger glycemic variations.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AIM

Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.

METHODS

Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N = 1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) and were correlated with HbA1c change and hypoglycaemic events.

RESULTS

All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes, only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.

CONCLUSIONS

Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AIM

Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.

METHODS

Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N=1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) were correlated with HbA1c change and hypoglycaemic events.

RESULTS

All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.

CONCLUSIONS

Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.