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

Characterisation of transfusion-dependent prediabetes using continuous glucose monitoring: The Haemoglycare study

AIMS

Continuous Glucose Monitoring (CGM) may help detect early dysglycemia in Transfusion-Dependent Thalassemia (TDT) patients, though previous reports suggest it may overestimate prediabetes prevalence. This study analyzed glucose-related metrics in TDT patients with negative diabetes screening tests, compared with healthy controls. A secondary objective was to assess the association between TAR140 > 6 % and clinical/laboratory characteristics of patients.

METHODS

Patients resulted negative to the screening tests for glucose disorders were compared to healthy controls using CGM system for 7 days.

RESULTS

This study involved 39 participants (19 patients, 20 controls). HbA1c was falsely elevated in patients, despite normal mean glucose and GMI. Standard deviations and coefficients of variation were higher in patients than controls. No healthy control but 7/19 (37 %) TDT patients presented the interval TAR140 > 6 %. Significant differences were observed between "really euglycaemic" TDT patients (TAR140 ≤ 6 %) and "hyperglycemic" ones (TAR140 > 6 %) in terms of GMI, mean glucose and TAR140%. Comparing the glucose metrics of TDT euglycaemic patients (TAR140 ≤ 6 %) and healthy controls, no significant difference was reported. No differences in iron overload indexes were found between the hyperglycemia and euglycemia groups.

CONCLUSIONS

CGM reliably detects prediabetes in 37 % of TDT patients. TAR140 > 6 % may serve as a diagnostic cutoff.

Glucose Abnormalities Detected by Continuous Glucose Monitoring in Very Old Adults With and Without Diabetes

OBJECTIVE

To characterize the prevalence of continuous glucose monitoring (CGM)-defined glucose abnormalities in a large, community-based population of very old adults (>75 years).

RESEARCH DESIGN AND METHODS

A cross-sectional analysis of 1,150 older adults with and without diabetes who attended the Atherosclerosis Risk in Communities Study (2021-2022). Diabetes was based on a self-reported diagnosis of diabetes by a health care provider, use of diabetes medication, or a hemoglobin A1c (HbA1c) ≥6.5%. Prediabetes was defined as an HbA1c of 5.7% to <6.5% and normoglycemia as an HbA1c of <5.7%. We analyzed CGM metrics, including mean glucose, measures of hyperglycemia, and the coefficient of variation, by diabetes status.

RESULTS

Of the 1,150 participants (mean age 83 years, 59% women, 26% who are Black), 35.1% had normoglycemia, 34.5% had prediabetes, and 30.4% had diabetes. The summary 24-h ambulatory glucose profile for participants with prediabetes was very similar to those with normoglycemia. No participants with normoglycemia or prediabetes had a CGM mean glucose >140 mg/dL, while 32.7% of participants with diabetes had a CGM mean glucose >140 mg/dL.

CONCLUSIONS

In very old adults with normal or prediabetes HbA1c, hyperglycemia detected by CGM was rare. This suggests that HbA1c adequately captures the burden of hyperglycemia for most people in this population.

Parameters of glycemic variability in continuous glucose monitoring as predictors of diabetes: a prospective evaluation in a non-diabetic general population

Objectives

To prospectively examine the ability of some glycemic variability metrics from continuous glucose monitoring (CGM) to predict the development of diabetes in a non-diabetic population.

Methods

A total of 497 non-diabetic patients from the AEGIS study were included. Participants used a CGM system (iPro2®) over a six-day period. The following parameters were analyzed: standard deviation (SD), coefficient of variation (CV) and mean amplitude of glucose excursion (MAGE). Six-years follow-up was performed. ROC curves were constructed to determine the predictive value of glycemic variability metrics. Sensitivity and specificity were calculated.

Results

Of the 497 participants, 16 women (4.9 %) and 9 men (5.2 %) developed diabetes. Initial HbA1c and fasting glucose levels were significantly higher in the participants who ultimately developed diabetes. Glycemic variability metrics were also significantly higher in these subjects (SD: 18 vs. 13 mg/dL; CV: 17 vs. 14 %; MAGE: 36 vs. 27 mg/dL; p<0.001 in all cases). SD showed the highest AUC (0.81), with a sensitivity of 80 % and a specificity of 72 % for a cut-off of 14.9 mg/dL. AUCs were higher in men for all metrics.

Conclusions

The metrics obtained by MCG, especially SD, are effective predictors of progression to type 2 diabetes in a non-diabetic population. These findings suggest that glycemic variability is useful for the early identification of subjects at a higher risk of developing diabetes.

Parámetros de variabilidad glucémica de la monitorización continua de glucosa como predictores de diabetes: evaluación prospectiva en una población general sin diabetes

Objetivos

Evaluar prospectivamente la capacidad de distintas métricas de variabilidad glucémica obtenidas mediante monitorización continua de glucosa (MCG) para la predicción del desarrollo de diabetes en una población sin diabetes.

Métodos

Se incluyeron 497 participantes sin diabetes del estudio AEGIS. Los participantes utilizaron un sistema de MCG (iPro2®) durante seis días. Se evaluaron las siguientes métricas: desviación estándar (SD), coeficiente de variación (CV) y amplitud media de las excursiones glucémicas (MAGE). Los sujetos fueron seguidos durante una media de 6 años. Se utilizaron curvas ROC para determinar la capacidad predictiva de las métricas de variabilidad glucémica y se calcularon la sensibilidad y especificidad.

Resultados

De los 497 participantes, 16 mujeres (4,9 %) y 9 hombres (5,2 %) desarrollaron diabetes. Las concentraciones iniciales de HbA1c y glucosa en ayunas fueron significativamente más altos en aquellos que progresaron a diabetes. Las métricas de variabilidad glucémica también fueron significativamente mayores en estos individuos (SD: 18 vs. 13 mg/dL; CV: 17 % vs. 14 %; MAGE: 36 vs. 27 mg/dL; p<0,001 en todos los casos). La SD mostró la mayor AUC (0,81), con una sensibilidad del 80 % y una especificidad del 72 % para un punto de corte de 14,9 mg/dL. Las AUC fueron mayores en hombres para todas las métricas estudiadas.

Conclusiones

Las métricas obtenidas por MCG, especialmente la SD, son predictores efectivos de la progresión a diabetes tipo 2 en una población sin diabetes. Estos hallazgos sugieren la utilidad de la variabilidad glucémica en la identificación temprana de individuos en riesgo de desarrollar diabetes.

From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.

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.

Reframing prediabetes: A call for better risk stratification and intervention

Prediabetes is an intermediate state of glucose homeostasis whereby plasma glucose concentrations are above normal but below the threshold of diagnosis for diabetes. Over the last several decades, criteria for prediabetes have changed as the cut points for normal glucose concentration and diagnosis of diabetes have shifted. Global consensus does not exist for prediabetes criteria; as a result, the clinical course and risk for type 2 diabetes vary. At present, we can identify individuals with prediabetes based on three glycemic tests (hemoglobin A1c, fasting plasma glucose, and 2-h plasma glucose during an oral glucose tolerance test). The majority of individuals diagnosed with prediabetes meet only one of these criteria. Meeting one, two, or all glycemic criteria changes risk for type 2 diabetes, but this information is not widely known and does not currently guide intervention strategies for individuals with prediabetes. This review summarizes current epidemiology, prognosis, and intervention strategies for individuals diagnosed with prediabetes and suggests a call for more precise risk stratification of individuals with prediabetes as elevated (one prediabetes criterion), high risk (two prediabetes criteria), and very high risk (three prediabetes criteria). In addition, the roles of oral glucose tolerance testing and continuous glucose monitoring in the diagnostic criteria for prediabetes need reassessment. Finally, we must reframe our goals for prediabetes and prioritize intensive interventions for those at high and very high risk for type 2 diabetes.

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.

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.

Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?

BACKGROUND

Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly.

METHODS

PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use.

RESULTS

Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use.

CONCLUSIONS

Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.

Time above range for predicting the development of type 2 diabetes

Aim

To investigate the prognostic value of time range metrics, as measured by continuous glucose monitoring, with respect to the development of type 2 diabetes (T2D).

Research design and methods

A total of 499 persons without diabetes from the general population were followed-up for 5 years. Time range metrics were measured at the start and medical records were checked over the period study.

Results

Twenty-two subjects (8.3 per 1,000 person-years) developed T2D. After adjusting for age, gender, family history of diabetes, body mass index and glycated hemoglobin concentration, multivariate analysis revealed 'time above range' (TAR, i.e., with a plasma glucose concentration of >140 mg/dL) to be significantly associated with a greater risk (OR = 1.06, CI 1.01-1.11) of developing diabetes (AUC = 0.94, Brier = 0.035).

Conclusions

Time above range provides additional information to that offered by glycated hemoglobin to identify patients at a higher risk of developing type 2 diabetes in a population-based study.

Aging of glucose profiles in an adult population without diabetes

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Intermittent Use of Continuous Glucose Monitoring: Expanding the Clinical Value of CGM

In addition to the continuous use, the intermittent use of continuous glucose monitoring (CGM) is an application of CGM, expanding the typical medical use cases. There are a variety of reasons and occasions that speak in favor of using CGM only for a limited time. To date, these circumstances have not been sufficiently discussed. In this article, we define discontinuous or intermittent CGM use, provide reasons for using it, and expand on the benefits and possibilities of using CGM on a temporary basis. We aim to draw attention to this important topic in the discussion of CGM use and give examples for a different method of CGM use. As well, we would like to foster the allocation of CGM to the right patient groups and indications, especially in cases of limited resources. From a global point of view, intermittent CGM use is more likely to occur than continuous use, primarily for economic reasons.

Effect of Exercise and Meals on Continuous Glucose Monitor Data in Healthy Individuals Without Diabetes

OBJECTIVE

The aim of these analyses was to characterize the effect of exercise and meals on glucose concentrations in healthy individuals without diabetes.

METHODS

Healthy individuals without diabetes (age ≥6 years) with nonobese body mass index were enrolled at 12 centers within the T1D Exchange Clinic Network. Participants wore a blinded Dexcom G6 for up to ten days. Throughout this sensor wear, participants completed a daily log indicating times and type of any exercise and start times of meals and snacks.

RESULTS

A total of 153 participants (age 7-80 years) were included in the analyses. Exercise induced a mean change of -15 ± 18 mg/dL from baseline to nadir sensor glucose level. Mean nadir glucose concentration during nights following exercise days was 82 ± 11 mg/dL compared with 85 ± 11 mg/dL during nights following nonexercise days (P = .05). Mean change from baseline to nadir sensor glucose level during aerobic exercise was -15 ± 18 and -9 ± 12 mg/dL for resistance exercise (P = .25). Overnight nadir glucose during nights following aerobic and resistance exercise was 83 ± 12 and 76 ± 14 mg/dL, respectively (P = .25). Overall mean peak postprandial glucose per participant increased from 93 ± 10 mg/dL premeal to 130 ± 13 mg/dL with an average time to peak glucose per participant of 97 ± 31 minutes. Consumption of alcohol on the day prior did not impact overnight mean or nadir glucose.

CONCLUSION

The present analysis provides important data characterizing the effect of exercise and meals on glucose in healthy individuals without diabetes. These data provide a repository to which future therapies, whether pharmacologic or technologic, can be compared.

Glycemic control and complications in glycogen storage disease type I: Results from the Swiss registry

BACKGROUND

Regular carbohydrate intake to avoid hypoglycemia is the mainstay of dietary treatment in glycogen storage disease type I (GSDI). The aim of this study was to evaluate the quality of dietary treatment and glycemic control in a cohort of GSDI patients, in relation to the presence of typical long-term complications.

METHODS

Data of 25 patients (22 GSD subtype Ia and 3 GSDIb, median age 20y) from the Swiss hepatic glycogen storage disease registry was analyzed cross-sectionally. Frequency and type of hypoglycemia symptoms were assessed prospectively using a structured questionnaire. Diagnostic continuous glucose monitoring (CGM) was performed as part of usual clinical care to assess glycemic control in 14 patients, usually once per year with a mean duration of 6.2 ± 1.1 consecutive days per patient per measurement.

RESULTS

Although maintenance of euglycemia is the primary goal of dietary treatment, few patients (n = 3, 13%) performed capillary blood glucose measurements regularly. Symptoms possibly associated with hypoglycemia were present in 13 patients (57%), but CGM revealed periods of low glucose (<4 mmol/l) in all patients, irrespective of the presence of symptoms. GSDIa patients with liver adenomas (n = 9, 41%) showed a higher frequency and area under the curve (AUC) of low blood glucose than patients without adenomas (frequency 2.7 ± 0.8 vs. 1.5 ± 0.7 per day, AUC 0.11 ± 0.08 vs. 0.03 ± 0.02 mmol/l/d; p < 0.05). Similarly, the presence of microalbuminuria was also associated with the frequency of low blood glucose. Z-Scores of bone density correlated negatively with lactate levels.

CONCLUSION

The quality of glucose control is related to the presence of typical long-term complications in GSDI. Many patients experience episodes of asymptomatic low blood glucose. Regular assessment of glucose control is an essential element to evaluate the quality of treatment, and increasing the frequency of glucose self-monitoring remains an important goal of patient education and motivation. CGM devices may support patients to optimize dietary therapy in everyday life.