Abnormal glycemic profiles by CGMS in obese first-degree relatives of type 2 diabetes mellitus patients

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.

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.

Detection and Intervention: Use of Continuous Glucose Monitoring in the Early Stages of Type 2 Diabetes

The term "prediabetes" has been used to identify the state of abnormal glucose homeostasis (dysglycemia) that often leads to the development of clinical type 2 diabetes. However, this term does not describe the cellular changes that are already taking place in individuals with elevated glucose levels. This article describes our approach to detecting early dysglycemia using continuous glucose monitoring and explains how this approach can be integrated into clinical practice settings.

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.

Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments

The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management.

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

Prevalence of diabetes related vascular complications in subjects with normal glucose tolerance, prediabetes, newly detected diabetes and known diabetes

AIMS

Varying prevalence of individual diabetes related vascular complications in prediabetes has been reported. However, very few studies have looked at both macrovascular and microvascular complications in prediabetes.

METHODS

Study subjects without any history of diabetes underwent oral glucose tolerance test (OGTT) and were classified as either normal glucose tolerance (NGT), prediabetes (PD), newly detected diabetes mellitus (NDDM) on the basis of American Diabetes Association (ADA) criteria. Age and sex matched known diabetes mellitus (KDM) patients were also recruited. All the participants were subsequently screened for both macrovascular (CAD, CVA,PVD) and microvascular (retinopathy, nephropathy and neuropathy)complications of diabetes.

RESULTS

Prevalence of vascular complications among prediabetes subjects was 11.1% as compared to 1.4% among NGT subjects, 13.9% among NDDM subjects and 23.8% among KDM subjects. There was no significant between complication rates in prediabetes and NDDM group (p = 0.060). The prevalence of macrovascular and microvascular complications among prediabetes subjects was 4.2% and 6.9% while the same in NDDM was 4.2% and 9.7%.

CONCLUSIONS

The proportion of subjects with prediabetes and vascular complications was about half of those with known diabetes and almost similar to those with newly detected diabetes mellitus.

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.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

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.

Higher blood glucose and larger fluctuations detected postoperatively using continuous glucose monitoring: a preliminary study following total knee or hip arthroplasty

BACKGROUND

The control of diabetes mellitus (DM) should help reduce the incidence of periprosthetic joint infection (PJI). Self-monitoring of blood glucose (SMBG) concentration is usually undertaken at fixed time-points. Therefore, the extent of postoperative blood glucose fluctuation might be underestimated. To provide a more comprehensive assessment, continuous glucose monitoring (CGM) is beginning to be used. However, no previous studies have evaluated blood glucose concentrations using CGM following orthopedic surgery. Therefore, the differences between the maximum blood glucose concentrations measured using SMBG and CGM, and the mean amplitude of the glycemic fluctuation in patients with frank diabetes mellitus (DM) or pre-diabetes were evaluated. Blood glucose was measured in 20 patients who had undergone total hip or total knee arthroplasty (12 patients with DM and eight with pre-diabetes). Patients were fitted with a CGM device in the operating room, which was worn for 6 days postoperatively, and used to evaluate blood glucose concentration continuously. SMBG was performed simultaneously for the same period.

RESULTS

The mean difference between the maximum blood glucose concentrations measured using SMBG and CGM was 25.0 ± 20.3 mg/dl (range, - 17 to 81 mg/dl), with the concentrations measured using CGM tending to be higher than those measured using SMBG (P = 0.04). Blood glucose concentrations measured using CGM tended to be higher than those measured using SMBG until postoperative day 2, and to decrease gradually after postoperative day 4. There were no significant differences in the standard deviation of the blood glucose concentrations between the two groups.

CONCLUSIONS

Blood glucose concentrations > 200 mg/dl and larger fluctuations were more frequently recorded using CGM than SMBG, especially until postoperative day 2. Thus, CGM is more useful for the identification of high blood glucose concentrations and larger fluctuations. However, this information was not provided in real time.

Evaluation of continuous glucose monitoring system for detection of alterations in glucose homeostasis in pediatric patients with β-thalassemia major

BACKGROUND

Disturbances of glucose metabolism are common in β-thalassemia major (β-TM).

AIM

This study was conducted to assess the pattern of glucose homeostasis in pediatric β-TM patients comparing oral glucose tolerance test (OGTT) and continuous glucose monitoring system (CGMS).

METHODS

Two-hundred β-TM patients were studied and those with random blood glucose (RBG) ≥7.8 mmol/L (140 mg/dL) were subjected to OGTT, insertion of CGMS and measurement of fasting C peptide, fasting insulin, and hemoglobin A1c (HbA1c).

RESULTS

Twenty patients (10%) had RBG ≥ 7.8 mmol/L. Using OGTT, 6 out of 20 patients (30%) had impaired glucose tolerance (IGT) while 7 (35%) patients were in the diabetic range. CGMS showed that 7/20 (35%) patients had IGT and 13 (65%) patients had diabetes mellitus (DM); 10 of the latter group had HbA1c readings within diabetic range. The percentage of diabetic patients diagnosed by CGMS was significantly higher than that with OGTT (P = 0.012). Serum ferritin was the only independent variable related to elevated RBG. All β-TM patients with DM were non-compliant to chelation therapy.

CONCLUSIONS

The use of CGMS in the diagnosis of early glycemic abnormalities among pediatric patients with β-TM appears to be superior to other known diagnostic modalities.

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.

High-normal serum thyrotropin levels and increased glycemic variability in type 2 diabetic patients

PURPOSE

High-normal thyrotropin (TSH) is related to reduced insulin sensitivity and may contribute to glycemic disorders in diabetes. We investigated the relationship between normal serum TSH levels and glycemic variability in euthyroid type 2 diabetic patients.

METHODS

A total of 432 newly diagnosed type 2 diabetic patients with euthyroid function and normal serum TSH levels were recruited between March 2013 and February 2017. Insulin sensitivity was evaluated by the Matsuda index (ISI_Matsuda) following a 75-g oral glucose tolerance test. Multiple glycemic variability indices, including the mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and standard deviation of glucose (SD), were calculated from glucose data obtained with a continuous glucose monitoring system. Average glucose accessed by 24-h mean glucose (24-h MG) was also calculated.

RESULTS

A normal serum TSH level was positively correlated with MAGE, MODD, SD, and 24-h MG (r = 0.206, 0.178, 0.186, and 0.132, respectively, p < 0.01). After adjusting for somatometric parameters, lipid profiles, ISI_Matsuda, and HbA1c via multiple linear regression analysis, mean differences [B(95% CI)] in MAGE, MODD, SD, and 24-h MG between the patients in the lowest and highest quartiles of TSH levels were 0.128(0.031, 0.226), 0.085(0.022, 0.148), 0.039(0.001, 0.078), and 0.002(-0.264, 0.267) mmol/L, respectively. High-normal TSH was independently associated with MAGE, MODD, and SD, but not 24-h MG.

CONCLUSIONS

High-normal serum TSH is a significant additional risk factor for increased glycemic variability in type 2 diabetic patients.

Controversies and Advances in Gestational Diabetes-An Update in the Era of Continuous Glucose Monitoring

Diabetes in pregnancy, both preexisting type 1 or type 2 and gestational diabetes, is a highly prevalent condition, which has a great impact on maternal and fetal health, with short and long-term implications. Gestational Diabetes Mellitus (GDM) is a condition triggered by metabolic adaptation, which occurs during the second half of pregnancy. There is still a lot of controversy about GDM, from classification and diagnosis to treatment. Recently, there have been some advances in the field as well as recommendations from international societies, such as how to distinguish previous diabetes, even if first recognized during pregnancy, and newer diagnostic criteria, based on pregnancy outcomes, instead of maternal risk of future diabetes. These new recommendations will lead to a higher prevalence of GDM, and important issues are yet to be resolved, such as the cost-utility of this increase in diagnoses as well as the determinants for poor outcomes. The aim of this review is to discuss the advances in diagnosis and classification of GDM, as well as their implications in the field, the issue of hyperglycemia in early pregnancy and the role of hemoglobin A1c (HbA1c) during pregnancy. We have looked into the determinants of the poor outcomes predicted by the diagnosis by way of oral glucose tolerance tests, highlighting the relevance of continuous glucose monitoring tools, as well as other possible pathogenetic factors related to poor pregnancy outcomes.

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.

A decrease in serum 1,5-anhydroglucitol levels is associated with the presence of a first-degree family history of diabetes in a Chinese population with normal glucose tolerance

AIM

This study aimed to investigate alterations in HbA_1c , glycated albumin (GA) and 1,5-anhydroglucitol (1,5-AG) in Chinese first-degree relatives of individuals with diabetes (FDR) in pursuit of an index for early screening of glucose metabolism disturbance.

METHODS

A total of 467 participants (age range: 20-78 years) with normal weight and normal glucose tolerance, as determined by a 75-g oral glucose tolerance test, were enrolled. HbA_1c was measured using high-performance liquid chromatography. Serum GA and 1,5-AG levels were determined by enzymatic methods. Serum insulin levels were measured using an electrochemiluminescence immunoassay.

RESULTS

The study population included 208 FDR and 259 non-FDR. Serum 1,5-AG levels were lower in FDR than that in non-FDR (20.4 ± 7.5 vs 23.8 ± 8.3 μg/ml, P < 0.001), but HbA_1c and GA levels did not differ between them (P = 0.835 and 0.469, respectively). Logistic regression analysis revealed an independent relationship between a first-degree family history of diabetes and reduced serum 1,5-AG levels (odds ratio = 0.944, P < 0.001). Multiple regression analysis showed that a first-degree family history of diabetes (β = -3.041, P < 0.001) and insulinogenic index (β = 0.081, P = 0.001) were independently associated with serum 1,5-AG levels.

CONCLUSION

In a Chinese population with normal glucose tolerance, serum 1,5-AG levels were lower among FDR, and serum 1,5-AG levels were independently associated with FDR status. For FDR, serum 1,5-AG levels were more sensitive than HbA_1c or GA levels to early-phase abnormality in glucose metabolism.

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.

Seventy two-hour glucose monitoring profiles in mild gestational diabetes mellitus: differences from healthy pregnancies and influence of diet counseling

OBJECTIVE

To study glucose profiles of gestational diabetes (GDM) patients with 72 h of continuous glucose monitoring (CGM) either before (GDM1) or after (GDM2) dietary counseling, comparing them with nondiabetic (NDM) controls.

DESIGN AND METHODS

We performed CGM on 22 GDM patients; 11 before and 11 after dietary counseling and compared them to 11 healthy controls. Several physiological and clinical characteristics of the glucose profiles were compared across the groups, including comparisons for pooled 24-h measures and hourly median values, summary measures representing glucose exposure (area under the median curves) and variability (amplitude, standard deviation, interquartile range), and time points related to meals.

RESULTS

Most women (81.8%) in the GDM groups had fasting glucose <95mg/dL, suggesting mild GDM. Variability, glucose levels 1 and 2h after breakfast and dinner, peak values after dinner and glucose levels between breakfast and lunch, were all significantly higher in GDM1 than NDM (P<0.05 for all comparisons). The GDM2 results were similar to NDM in all aforementioned comparisons (P>0.05). Both GDM groups spent more time with glucose levels above 140mg/dL when compared with the NDM group. No differences among the groups were found for: pooled measurements and hourly comparisons, exposure, nocturnal, fasting, between lunch and dinner and before meals, as well as after lunch (P>0.05 for all).

CONCLUSION

The main differences between the mild GDM1 group and healthy controls were related to glucose variability and excursions above 140mg/dL, while glucose exposure was similar. Glucose levels after breakfast and dinner also discerned the GDM1 group. Dietary counseling was able to keep glucose levels to those of healthy patients.

Effects of Acute Ingestion of Native Banana Starch on Glycemic Response Evaluated by Continuous Glucose Monitoring in Obese and Lean Subjects

An abnormal glycemic profile, including postprandial glycemia and acute glucose spikes, precedes the onset of overt diabetes in obese subjects. Previous studies have shown the beneficial effects of chronic native banana starch (NBS) supplementation. In this study, we examined the effects of acute ingestion of NBS on glycemic profiles by means of continuous glucose monitoring in obese and lean subjects. In a crossover study, obese and lean subjects consumed beverages containing either 38.3 g of NBS or 38.3 g of digestible corn starch (DCS) twice daily during 4 days. On day 5, a 3-h meal tolerance test (MTT) was performed to evaluate glucose and insulin responses. After 1 week of washout period, treatments were inverted. NBS supplementation reduced the 48-h glycemia AUC in lean, obese, and in the combined group of lean and obese subjects in comparison with DCS. Postprandial glucose and insulin responses at MTT were reduced after NBS in comparison with DCS in all groups. However, no changes were observed in glycemic variability (GV) indexes between groups. In conclusion, acute NBS supplementation improved postprandial glucose and insulin responses in obese and lean subjects during 48 h of everyday life and at MTT. Further research to elucidate the mechanism behind these changes is required.

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.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis

BACKGROUND

Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.

MATERIALS AND METHODS

A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.

RESULTS

SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.

CONCLUSIONS

The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Glycemic variability in normal glucose regulation subjects with elevated 1-h postload plasma glucose levels

Subjects with normal glucose regulation (NGR), whose 1-h postload plasma glucose is ≥8.6 mmol/L (155 mg/dL, NGR 1 h ≥ 8.6) during 75-g oral glucose tolerance test (OGTT), have an increased risk of type 2 diabetes and subclinical organ damage. And, the deficiency in islet β cell function is responsible for glycemic disorders. The purpose of this study is to investigate glycemic variability in NGR subjects with elevated 1-h postload plasma glucose levels and its association with islet β cell function. The 29 NGR subjects with 1-h postload plasma glucose ≥8.6 mmol/L (NGR 1 h ≥ 8.6) and 29 age- and sex-matched NGR subjects with 1-h postload plasma glucose <8.6 mmol/L (NGR 1 h < 8.6) were recruited in the study. Insulin sensitivity (Matsuda index, ISI), insulin secretion (insulinogenic index ΔI30/ΔG30), and integrated β cell function measured by the oral disposition index (ΔI30/ΔG30 multiplied by the ISI) were derived from OGTT. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 h. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SDBG), mean blood glucose (MBG), mean of daily differences (MODD), and mean amplitude of glycemic excursions (MAGE). MAGE is considered as a gold standard of glycemic variability. Glycemic variability parameters SDBG, MBG, MODD, and MAGE in NGR 1 h ≥ 8.6 group were higher than those in NGR 1 h < 8.6 group (p < 0.05), and oral disposition index in NGR 1 h ≥ 8.6 group was lower than that in NGR 1 h < 8.6 group (p < 0.05). SDBG, MBG, MODD, MAGE, and 1-h postload plasma glucose all negatively associated with oral disposition index in the separate group (p < 0.05) and in the whole subjects (p < 0.05). After multivariate regression analysis, oral disposition index was the strongest independent contributor to MAGE and 1-h postload plasma glucose in the separate group (p < 0.05) and in the whole subjects (p < 0.05). It is concluded that NGR 1 h ≥ 8.6 group had higher glycemic variability and lower oral disposition index, compared with NGR 1 h < 8.6 group. Increased glycemic variability parameters and elevated 1-h postload plasma glucose consistently associated with declined oral disposition index in subjects from NGR 1 h < 8.6 to NGR 1 h ≥ 8.6 group.

Continuous glucose monitoring system and new era of early diagnosis of diabetes in high risk groups

Continuous glucose monitoring (CGM) systems are an emerging technology that allows frequent glucose measurements to monitor glucose trends in real time. Their use as a diagnostic tool is still developing and appears to be promising. Combining intermittent glucose self-monitoring (SGM) and CGM combines the benefits of both. Significant improvement in the treatment modalities that may prevent the progress of prediabetes to diabetes have been achieved recently and dictates screening of high risk patients for early diagnosis and management of glycemic abnormalities. The use of CGMS in the diagnosis of early dysglycemia (prediabetes) especially in high risk patients appears to be an attractive approach. In this review we searched the literature to investigate the value of using CGMS as a diagnostic tool compared to other known tools, namely oral glucose tolerance test (OGTT) and measurement of glycated hemoglobin (HbA1C) in high risk groups. Those categories of patients include adolescents and adults with obesity especially those with family history of type 2 diabetes mellitus, polycystic ovary syndrome (PCO), gestational diabetes, cystic fibrosis, thalassemia major, acute coronary syndrome (ACS), and after renal transplantation. It appears that the ability of the CGMS for frequently monitoring (every 5 min) glucose changes during real-life settings for 3 to 5 days stretches the chance to detect more glycemic abnormalities during basal and postprandial conditions compared to other short-timed methods.