Abstract
INTRODUCTION
It was a bit of work and a definite pleasure screening over 4,000 published articles on glucose monitoring over the past year, and after shortlisting 50 publications, this chapter highlights 13 manuscripts that bring new data on continuous glucose monitoring (CGM) in the management of diabetes and beyond. Importantly, CGM is now used in the broadest spectrum of populations: in people without diabetes, in those with prediabetes (both type 1 and type 2), in pregnancy complicated with dysglycemia, in individuals with advanced-stage kidney disease, in individuals with diabetes around physical activity and sport, and increasingly in critically ill patients. Reports on hospital-wide use of CGM for managing glycemia incorporated in the hospital electronic medical record (EMR) system demonstrate improvements in care and better utilization of human resources (1). Importantly, CGM is again demonstrated to be accurate and reliable in ICU settings also when used at an alternative infraclavicular site (2), and mortality in critically ill patients is associated with hyperglycemia >190 mg/dl (10.6 mmol/L) in a large observational study (3). The nuisance of CGM data allows for determining the hyperglycemic effect of statins (4), and enriching outcomes in several clinical trials evaluating novel medications for diabetes. The upcoming metric currently designated as Time in Tight Range (TITR—glucose 70–140 mg/dL or 3.9–7.8 mmol/L) is gaining momentum (5) and may bring additional benefits in routine diabetes management (6, 7); however, its implementation into clinical practice will be gradual and must be voluntary—just as one among the options (8).
Let us now immerse together into the chapter below: in addition to the existing data, it will suggest to us some insights and perspectives, which we may want to discuss together during the ATTD annual meeting and build the future of excellence in GCM-based diabetes diagnosis and management together.
Continuous Glucose Monitoring and Intrapersonal Variability in Fasting Glucose
Shilo S1,2,3,4, Keshet A1,2, Rossman H1,2,5, Godneva A1,2, Talmor-Barkan Y1,2,4,6, Aviv Y4,6, Segal E1,2
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel; 2Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; 3The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel; 4Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel; 5Pheno.AI, Tel-Aviv, Israel; 6Department of Cardiology, Rabin Medical Center, Petah Tikva, Israel
Nat Med 2024; 30: 1424–1431
Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40–70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dL−1, rising by 0.234 mg dL−1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dL−1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.
Despite its crucial role as a primary diagnostic criteria for diabetes classification (9), data regarding the day-to-day intraperson variability is limited. This study investigating fasting glucose (FG) levels using CGM in adults without diabetes provides a critical reevaluation of how glycemic status is currently assessed and diagnosed. By leveraging CGM technology, the study reveals significant intraperson variability in FG levels, challenging the reliability of single or even dual measurements for diabetes classification. Notably, 40% of individuals initially classified as having normal FG were later reclassified as prediabetes based on sequential measurements, with a smaller percentage (3%) being classified as having diabetes. These findings have profound implications for both clinical practice and public health, particularly in the context of early identification and intervention for individuals at risk of diabetes. The variability observed in FG measurements implies that many individuals may have fluctuating glucose levels that are not captured by traditional diagnostic approaches. As a result, a considerable proportion of individuals at risk for diabetes may go undetected until the disease has progressed further, potentially reducing the effectiveness of preventive measures.
Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women
Skroce K1,2, Zignoli A2,3, Fontana FY4, Maturana FM5, Lipman D2, Tryfonos A6,7, Riddell MC8, Zisser HC2
1Faculty of Medicine, University of Rijeka, Rijeka, Croatia; 2Supersapiens Inc., Atlanta, GA; 3Department of Industrial Engineering, University of Trento, Trento, Italy; 4Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Bern University Hospital, University of Bern, Bern, Switzerland; 5Sports Medicine Department, University Hospital of Tübingen, Tübingen, Germany; 6Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska Institute, Stockholm, Sweden; 7School of Science, Department of Life Science, European University Cyprus, Nicosia, Cyprus; 8School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
Sensors (Basel) 2024; 24: 744
The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep, and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, whereas the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70–140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (P < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.
The increasing use of CGM in individuals without diabetes provides an unprecedented opportunity to understand glycemic responses to various daily stimuli such as meals, sleep, and exercise (10). This large study involving over 12,500 physically active adults without diabetes who ranged from recreationally active to elite level athletes, evaluated various CGM metrics and their association with how everyday activities influence glucose levels in a healthy active population. One of the key findings was that the majority of participants maintained an overall 24-hour average glucose level between 90 and 110 mg/dL, which aligns with typical healthy glycemic ranges. As expected, meals and exercise were associated with transient glucose elevations, and sleep was characterized by lower glucose levels. This highlights the dynamic nature of glucose regulation in response to daily activities, even in individuals without diabetes. A notable aspect of this study is the sex-specific differences in glycemic responses. Men had slightly higher average glucose levels compared to women, both over a 24-hour period and during specific activities such as exercise and sleep, which could reflect differences in metabolism, hormonal influences, or variations in body composition between genders, such as estrogen's influence on insulin sensitivity and glucose metabolism in women. Understanding these subtle variations could help guide tailored nutrition and recovery strategies for male and female athletes alike. Exercise-induced glycemic fluctuations are particularly interesting. Participants spent about 10.3% of exercise time with glucose levels above 140 mg/dL and about 11.9% of the time below 70 mg/dL, indicating the body's dynamic glycemic adaptation to physical effort. Overall, this study adds valuable data to our understanding of how glycemic profiles vary in response to different stimuli in a healthy, physically active population. By capturing normative data, it provides a benchmark for future research into how exercise, sleep, and nutrition affect glucose regulation, especially as CGMs continue to grow in popularity among individuals without diabetes. Furthermore, it opens opportunities for personalized glucose monitoring, allowing athletes and active individuals to optimize their performance and recovery by better understanding their own glycemic responses (11).
Early Dysglycemia Is Detectable Using Continuous Glucose Monitoring in Very Young Children at Risk of Type 1 Diabetes
Haynes A1,2, Tully A1, Smith GJ1, Penno MAS3, Craig ME4,5, Wentworth JM6,7, Huynh T8,9, Colman PG6, Soldatos G10,11, Anderson AJ3, McGorm KJ3, Oakey H3, Couper JJ12, Davis EA1,13,14; on behalf of the ENDIA Study Group
1Children's Diabetes Centre, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia; 2Paediatrics, UWA Medical School, University of Western Australia, Nedlands, Western Australia, Australia; 3Faculty of Health and Medical Sciences and Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia; 4Faculty of Medicine, School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia; 5Institute of Endocrinology and Diabetes, Children's Hospital at Westmead, Sydney, New South Wales, Australia; 6Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Melbourne, Victoria, Australia; 7Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; 8Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, Queensland, Australia; 9Faculty of Medicine, Children's Health Research Centre, University of Queensland, South Brisbane, Queensland, Australia; 10Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; 11Diabetes and Vascular Medicine Unit, Monash Health, Melbourne, Victoria, Australia; 12Department of Diabetes and Endocrinology, Women's and Children's Hospital, Adelaide, South Australia, Australia; 13Department of Diabetes and Endocrinology, Perth Children's Hospital, Nedlands, Western Australia, Australia; 14School of Paediatrics, University of Western Australia, Nedlands, Western Australia, Australia
Diabetes Care 2024; 47: 1750–1756
Continuous glucose monitoring (CGM) can detect early dysglycemia in older children and adults with presymptomatic type 1 diabetes (T1D) and predict risk of progression to clinical onset. However, CGM data for very young children at greatest risk of disease progression are lacking. This study aimed to investigate the use of CGM data measured in children being longitudinally observed in the Australian Environmental Determinants of Islet Autoimmunity (ENDIA) study from birth to age 10 years.
Between January 2021 and June 2023, 31 ENDIA children with persistent multiple islet autoimmunity (PM Ab+) and 24 age-matched controls underwent CGM assessment alongside standard clinical monitoring. The CGM metrics of glucose SD (SDSGL), coefficient of variation (CEV), mean sensor glucose (SGL), and percentage of time >7.8 mmol/L (140 mg/dL) were determined and examined for between-group differences.
The mean (SD) ages of PM Ab+ and Ab− children were 4.4 (1.8) and 4.7 (1.9) years, respectively. Eighty-six percent of eligible PM Ab+ children consented to CGM wear, achieving a median (quartile 1 [Q1], Q3) sensor wear period of 12.5 (9.0, 15.0) days. PM Ab+ children had higher median (Q1, Q3) SDSGL (1.1 [0.9, 1.3] vs. 0.9 [0.8, 1.0] mmol/L; P < 0.001) and CEV (17.3% [16.0, 20.9] vs. 14.7% [12.9, 16.6]; P < 0.001). Percentage of time >7.8 mmol/L was greater in PM Ab+ children (median [Q1, Q3] 8.0% [4.4, 13.0] compared with 3.3% [1.4, 5.3] in Ab- children; P = 0.005). Mean SGL did not differ significantly between groups (P = 0.10).
CGM is feasible and well tolerated in very young children at risk of T1D. Very young PM Ab+ children have increased SDSGL, CEV, and percentage of time >7.8 mmol/L, consistent with prior studies involving older participants.
The recent study from the Australian Environmental Determinants of Islet Autoimmunity (ENDIA) cohort offers crucial insights into the potential of CGM use to detect early dysglycemia in very young children at high risk for type 1 diabetes (T1D). This study fills a significant gap in our understanding of CGM-captured early glycemic fluctuations in children with persistent multiple islet autoimmunity (PM Ab+), providing data that should inform future approaches to type 1 diabetes prevention and early intervention (12).
The increased glycemic variability and time spent in hyperglycemic ranges among PM Ab+ children, even in the absence of elevated mean glucose levels, highlight the subtle early changes that precede the clinical onset of type 1 diabetes. These data suggest that traditional markers, such as fasting glucose or mean glucose levels, may not adequately capture early dysglycemia in this high-risk group. Instead, metrics of glucose variability (standard deviation of mean glucose and coefficient of variation) and time spent above certain glucose thresholds may provide a more sensitive and early indication of disease progression. As our understanding of early type 1 diabetes progresses, incorporating CGM data into clinical practice could play a crucial role in the timely identification and management of individuals at risk. Complementing data from previous trials and recently published consensus guidance (12–14), these insights could influence decision-making for diabetes classification and stratification for ongoing and future type 1 diabetes prevention trials to preserve beta cell insulin secretion (15–20).
Residual β-Cell Function Is Associated with Longer Time in Range in Individuals with Type 1 Diabetes
Fuhri Snethlage CM1, McDonald TJ2, Oram RD2, de Groen P1, Rampanelli E1, Schimmel AWM1, Holleman F1, Siegelaar S1, Hoekstra J1, Brouwer CB3, Knop FK4,5,6,7, Verchere CB8, van Raalte DH9, Roep BO10, Nieuwdorp M1, Hanssen NMJ1
1Department of Endocrinology and Metabolism, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands; 2Peninsula College of Medicine and Dentistry, Peninsula National Institute for Health and Care Research Clinical Research Facility, Exeter, Devon, UK; 3Onze Lieve Vrouwe Gasthuis, Oost, Amsterdam, the Netherlands; 4Center for Clinical Metabolic Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark; 5Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 6Steno Diabetes Center Copenhagen, Herlev, Denmark; 7Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 8BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada; 9Department of Endocrinology and Metabolism, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands; 10Internal Medicine, Leids Universitair Medisch Centrum, Leiden, the Netherlands
Diabetes Care 2024; 47: 1114–1121
Little is known about the influence of residual islet function on glycemic control in type 1 diabetes (T1D). We investigated the associations between residual β-cell function and metrics of continuous glucose monitoring (CGM) in individuals with T1D.
In this cross-sectional cohort comprising 489 individuals (64% female, age 41.0 ± 14.0 years), T1D duration was 15.0 (interquartile range [IQR] 6.0–29.0) years. Individuals had a time in range (TIR) of 66% (IQR 52–80%) and a urinary C-peptide-to-creatinine ratio (UCPCR) of 0.01 (IQR 0.00–0.41) nmol/mmol. To assess β-cell function, we measured UCPCR (detectable >0.01 nmol/mmol), and to assess α-cell function, fasting plasma glucagon/glucose ratios were measured. CGM was used to record TIR (3.9–10 mmol/L), time below range (TBR) (<3.9 mmol/L), time above range (TAR) (>10 mmol/L), and glucose coefficient of variance (CV). For CGM, 74.7% used FreeStyle Libre 2, 13.8% Medtronic Guardian, and 11.5% Dexcom G6 as their device.
The percentage of patients with T1D who had a detectable UCPCR was 49.4%. A higher UCPCR correlated with higher TIR (r = 0.330, P < 0.05), lower TBR (r = −0.237, P < 0.05), lower TAR (r = −0.302, P < 0.05), and lower glucose CV (r = −0.356, P < 0.05). A higher UCPCR correlated negatively with HbA1c levels (r = −0.183, P < 0.05) and total daily insulin dose (r = −0.183, P < 0.05). Glucagon/glucose ratios correlated with longer TIR (r = 0.234, P < 0.05).
Significantly longer TIR, shorter TBR and TAR, and lower CV were observed in individuals with greater UCPCR-assessed β-cell function. Therefore, better CGM-derived metrics in individuals with preserved β-cell function may be a contributor to a lower risk of developing long-term complications.
Type 1 diabetes is characterized by autoimmune destruction of pancreatic beta-cells. At clinical diagnosis most people have residual pancreatic β-cells which can continue to secrete insulin for several additional years. Loss of beta cells is gradual, with a substantial number remaining at clinical presentation and an ongoing decline after diagnosis. Previous studies of endogenous insulin production in people with type 1 diabetes assessed the variation in absolute levels of C-peptide both at diagnosis and in long-duration type 1 diabetes (21, 22). Importantly, there is evidence that the amount of persistent endogenous insulin is associated with favorable glycemic outcomes, risk of hypoglycemia and risk of long-term complications across the duration of diabetes. This study provides valuable insights into the relationship between residual β-cell function and glycemic outcomes, even years after type 1 diabetes diagnosis. Utilizing the urinary C-peptide-to-creatinine ratio (UCPCR) as a marker of residual β-cell function, this research highlights how even minimal endogenous insulin production can significantly impact key CGM-derived metrics. Nearly half of the almost 500 study participants had detectable levels of residual β-cell function, as measured by UCPCR. These individuals experienced more favorable glycemic outcomes, including higher TIR, reduced both TBR and TAR, and less glucose variability. A higher UCPCR was associated with lower HbA1c levels and importantly also with a reduced daily insulin dose. These findings emphasize the clinical importance of preserving residual β-cell function in individuals with type 1 diabetes, even years after diagnosis. The ability to maintain some level of endogenous insulin production appears to be associated with better overall glucose outcomes and potentially a reduced risk of long-term complications. This has significant implications for diabetes management, suggesting that strategies aimed at preserving β-cell function—such as immune modulation therapies or early intensive insulin therapy—could be beneficial in prolonging the diabetes remission and improving long-term outcomes (15, 19, 20, 23, 24). Additionally, the association between higher fasting glucagon/glucose ratios and improved glycemic outcomes raises intriguing questions about the role of glucagon in type 1 diabetes management. The study suggests that the interaction between residual β-cell function and glucagon could influence how well individuals can manage their blood glucose levels, particularly in avoiding hyperglycemia. This observation is particularly relevant in the context of developing dual-hormone closed-loop systems (e.g., bionic pancreas), which aim to use both insulin and glucagon to more closely mimic physiological glucose regulation (25, 26). However, the limited stability and availability of glucagon remain challenges for these systems. Finally, the study highlights the potential for UCPCR to serve as a practical and noninvasive biomarker for assessing residual β-cell function in routine clinical practice.
Equitable Implementation of a Precision Digital Health Program for Glucose Management in Individuals with Newly Diagnosed Type 1 Diabetes
Prahalad P1,2, Scheinker D1,2,3,4, Desai M5, Ding VY5, Bishop FK1,2, Lee MY1, Ferstad J3, Zaharieva DP1, Addala A1,2, Johari R2,3, Hood K1,2, Maahs DM1,2,6
1Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA; 2Stanford Diabetes Research Center, Stanford University, Stanford, CA; 3Department of Management Science and Engineering, Stanford University, Stanford, CA; 4Clinical Excellence Research Center, Stanford University, Stanford, CA; 5Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA; 6Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA
Nat Med 2024; 30: 2067–2075
This article is also discussed in chapter 5 page 152, Chapter 12 page 368
Few young people with type 1 diabetes (T1D) meet glucose targets. This study prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis), and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70–180 mg dL−1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases.
The 4T Study 1 introduces an innovative, technology-driven approach to managing type 1 diabetes in young people and focusing on equitable care, addressing a critical challenge: few youth with type 1 diabetes meet glucose targets (27). By integrating tighter glucose targets (HbA1c < 7%), early technology adoption within the first month of diagnosis, and remote monitoring, the study demonstrates how digital health and team-based care can significantly improve glycemic outcomes in this population. The cohort, with 36.8% Hispanic and 35.3% publicly insured participants, represents a diverse group often underserved in diabetes management. Despite these potential barriers, the program successfully maintained a mean HbA1c of 6.58% one year after diagnosis, with 64% of participants achieving HbA1c levels below 7%. This suggests that early technology integration and continuous monitoring can bridge health disparities, providing high-quality care across socioeconomic groups. Moreover, the core concepts of the 4T approach could be more broadly applicable to improving the management of individuals with other chronic diseases. The success of the 4T Study 1 is especially noteworthy given the broader context: many young people with type 1 diabetes struggle to meet glycemic targets, placing them at risk for complications later in life (28–32). By implementing CGM within the first month of diagnosis, the program empowers individuals with type 1 diabetes and their families to take early and active control of the disease. Additionally, with a mean time in range (70–180 mg/dL) of 68%, the study highlights the effectiveness of remote patient monitoring in sustaining tight glucose outcomes. Study results additionally suggest that broader access to and use of insulin pumps might further eliminate disparities and bridge the gap among youth with type 1 diabetes. Data modeling from the Epidemiology of Diabetes Interventions and Complications (EDIC) study cohort suggests that initiating intensive therapy earlier during type 1 diabetes provides greater benefits compared to starting it later. Earlier intensification was linked to a more significant reduction in the risks of kidney and cardiovascular complications, even though both early and late groups had the same average glycemic exposure over time (33). This emphasizes the importance of adopting therapies that enable tight glycemic outcomes as soon as possible after type 1 diabetes diagnosis. Recent studies have demonstrated that automated insulin delivery (AID) from the onset of type 1 diabetes in children and adolescents offer significant glycemic benefits that were sustained over a longer period of time and improve quality of life (23, 34). Given these findings, the early adoption of AID systems in youth with type 1 diabetes could offer significant benefits for long-term glucose outcomes, relieve the burden of diabetes, and reduce the risk of complications. Although more research is needed, incorporating AID systems from diagnosis may be a viable strategy to consider in optimizing care for this population (23, 34).
The Acute Effects of Real-World Physical Activity on Glycemia in Adolescents with Type 1 Diabetes: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study
Riddell MC1, Gal RL2, Bergford S2, Patton SR3, Clements MA4, Calhoun P2, Beaulieu LC2, Sherr JL5
1School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada; 2Jaeb Center for Health Research, Tampa, FL; 3Nemours Children's Health, Jacksonville, FL; 4Children's Mercy Hospital, Kansas City, MO; 5Yale School of Medicine, New Haven, CT
Diabetes Care 2024; 47: 132–139
Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes.
In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads.
Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified “noncompetitive” activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L), and time 70–180 mg/dL >70% in the 24 h before the activity (all P < 0.001).
Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease.
The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) is largest pediatric type 1 diabetes exercise study to date that evaluated person-specific factors (age, disease duration, glucose management strategies, etc.) and event-specific factors (type and intensity of activity, etc.) and expands upon a previously published data assessing the impact of exercise in adults with type 1 diabetes (T1DEXI) (35). By analyzing data from 251 adolescents, the study identifies both participant-level and event-level factors that influence glucose changes during activity and offers valuable insights into the glucose dynamics, having significant implications for personalized diabetes management in youths. One of the most remarkable results is the observed average drop in glucose levels during physical activity, from a preactivity glucose of 163 mg/dL to 148 mg/dL, regardless of insulin delivery modality. Adolescents with lower baseline HbA1c, shorter disease duration, lower BMI, and less fear of hypoglycemia experienced more significant drops in glucose. Additionally, event-specific factors like engaging in noncompetitive activities, having insulin on board, and starting with preactivity glucose levels above 150 mg/dL were associated with greater glucose reductions. The identification of predictors for glucose drops can lead to more effective decision support tools, allowing for real-time adjustments in insulin dosing and carbohydrate intake. For example, adolescents with lower HbA1c or those engaging in noncompetitive activities may need to monitor their glucose levels more closely or adjust their insulin dosing before exercising.
Focusing on a postexercise period (36), a significant finding was the increased incidence of nocturnal hypoglycemia following days with higher levels of physical activity. Specifically, hypoglycemia was more common on nights after exercise (14% of nights) compared to sedentary days (12% of nights), with even higher rates (17%) when total daily activity exceeded 60 minutes. Notably, the research found that larger drops in glucose during exercise were predictive of lower postexercise glucose levels, both immediately and up to 12–16 hours later, underscoring the prolonged impact of exercise on glycemia.
These findings underscore the importance of careful monitoring and management of glucose levels in the 24 hours following exercise. The increased risk of nocturnal hypoglycemia on days with extended or intense physical activity suggests that standard insulin dosing protocols may need to be adjusted to account for the prolonged effects of exercise. Future research should explore the integration of these insights into decision support tools and automated insulin delivery systems, which could help tailor insulin dosing in real-time based on individual activity levels. Importantly, both datasets (T1DEXI and T1DEXIP) were made publicly available and provide an important repository for further research to expand understanding of the relationship between exercise and type 1 diabetes in different age populations.
Demographic, Clinical, Management, and Outcome Characteristics of 8,004 Young Children with Type 1 Diabetes
Sandy JL1,2, Tittel SR3,4, Rompicherla S5, Karges B6, James S7, Rioles N5, Zimmerman AG8, Fröhlich-Reiterer E9, Maahs DM10, Lanzinger S3,4, Craig ME1,2,11,12, Ebekozien O5; on behalf of the Australasian Diabetes Data Network (ADDN); T1D Exchanged Quality Improvement Collaborative (T1DX-QI); Prospective Diabetes Follow-Up Registry Initiative (DPV)
1Sydney Children's Hospital Network, Westmead, NSW; Australia; 2Westmead Clinical School, Faculty of Medicine and Health, University of Sydney, Westmead, NSW, Australia; 3Institute for Epidemiology and Medical Biometry, Central Institute for Biomedical Technology, Ulm University, Ulm, Germany; 4German Centre for Diabetes Research, Munich-Neuherberg, Germany; 5T1D Exchange, Boston, MA; 6Division of Endocrinology and Diabetes, Medical Faculty, Rheinisch-Westfälische Technische Hochschule, Aachen University, Aachen, Germany; 7University of the Sunshine Coast, Petrie, Queensland, Australia; 8Lyell McEwin Hospital, Adelaide, South Australia, Australia; 9Division of General Paediatrics, Department of Paediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria; 10Division of Endocrinology, Department of Pediatrics, Stanford University, Stanford, CA; 11Discipline of Paediatrics and Child Health, School of Clinical Medicine, University of New South Wales Medicine Sydney, Sydney, NSW, Australia; 12Charles Perkins Centre, Westmead, NSW, Australia
Diabetes Care 2024; 47: 660–667
This article is also discussed in chapter 8 page 242
The objective of this study is to compare demographic, clinical, and therapeutic characteristics of children with type 1 diabetes age <6 years across three international registries: Diabetes Prospective Follow-Up Registry (DPV; Europe), T1D Exchange Quality Improvement Network (T1DX-QI; U.S.), and Australasian Diabetes Data Network (ADDN; Australasia).
An analysis was conducted comparing 2019–2021 prospective registry data from 8,004 children.
Mean ± SD ages at diabetes diagnosis were 3.2 ± 1.4 (DPV and ADDN) and 3.7 ± 1.8 years (T1DX-QI). Mean ± SD diabetes durations were 1.4 ± 1.3 (DPV), 1.4 ± 1.6 (T1DX-QI), and 1.5 ± 1.3 years (ADDN). BMI z scores were in the overweight range in 36.2% (DPV), 41.8% (T1DX-QI), and 50.0% (ADDN) of participants. Mean ± SD HbA1c varied among registries: DPV 7.3 ± 0.9% (56 ± 10 mmol/mol), T1DX-QI 8.0 ± 1.4% (64 ± 16 mmol/mol), and ADDN 7.7 ± 1.2% (61 ± 13 mmol/mol). Overall, 37.5% of children achieved the target HbA1c of <7.0% (53 mmol/mol): 43.6% in DPV, 25.5% in T1DX-QI, and 27.5% in ADDN. Use of diabetes technologies such as insulin pump (DPV 86.6%, T1DX 46.6%, and ADDN 39.2%) and continuous glucose monitoring (CGM; DPV 85.1%, T1DX-QI 57.6%, and ADDN 70.5%) varied among registries. Use of hybrid closed-loop (HCL) systems was uncommon (from 0.5% [ADDN] to 6.9% [DPV]).
Across three major registries, more than half of children age <6 years did not achieve the target HbA1c of <7.0% (53 mmol/mol). CGM was used by most participants, whereas insulin pump use varied across registries, and HCL system use was rare. The differences seen in glycemia and use of diabetes technologies among registries require further investigation to determine potential contributing factors and areas to target to improve the care of this vulnerable group.
Management of type 1 diabetes is challenging in very young children, owing to the high variability of insulin requirements, marked insulin sensitivity, and unpredictable eating and activity patterns (37–39). The recent study comparing demographic, clinical, and therapeutic characteristics of very young children (<6 years) with type 1 diabetes across three major international registries—Diabetes Prospective Follow-Up Registry (DPV), T1D Exchange Quality Improvement Network (T1DX-QI), and Australasian Diabetes Data Network (ADDN), highlights significant global variations in diabetes management and outcomes. Unfortunately, more than half of the children under six did not meet the recommended HbA1c target of <7.0% (53 mmol/mol), with even fewer achieving the stricter target of <6.5% (48 mmol/mol) advocated by ISPAD and other international guidelines (40). This is concerning given the established link between achieving tight glycemic outcomes in early childhood and reduced risk of long-term complications, including a greater risk for neurocognitive deficits (41). These differences underline the challenges in achieving optimal care for this vulnerable age group and raise important questions about how to standardize and improve type 1 diabetes care for young children worldwide.
The differences in HbA1c levels among the registries, with the DPV registry showing better overall outcomes compared to T1DX-QI and ADDN, suggest that regional practices, healthcare policies, and perhaps differing healthcare access levels play significant roles in determining outcomes. Noteworthy, disparities were observed in the adoption of diabetes technologies, the principal modifiable factor associated with glycemic outcomes (42). The DPV cohort demonstrated higher use of both insulin pumps and CGM compared to T1DX-QI and ADDN. Notably, the uptake of automated insulin delivery was very low (between 0.5% and 6.9%) as these devices were not yet commercially available in this age group during the study period. This lag is particularly critical for very young children, where maintaining (near)normoglycemia without glucose-responsive insulin delivery is challenging due to greater variability in insulin requirements (37). The clear benefits of automated insulin delivery, including improved glycemic outcomes and quality of life (43, 44), should drive efforts to accelerate the broader adoption of the latest advanced technologies through regulatory approval and clinician education, making these tools more universally accessible, especially in this vulnerable population (45).
Improved Glycemic Outcomes with Diabetes Technology Use Independent of Socioeconomic Status in Youth with Type 1 Diabetes
Lomax KE1,2, Taplin CE1,2,3, Abraham MB1,2,4, Smith GJ2, Haynes A2, Zomer E5, Ellis KL1, Clapin H2, Zoungas S5, Jenkins AJ6,7, Harrington J8,9, de Bock MI10, Jones TW1,2,4, Davis EA1,2,4; on behalf on the Australasian Diabetes Data Network (ADDN) Study Group
1Department of Endocrinology and Diabetes, Perth Children's Hospital, Nedlands, Western Australia, Australia; 2Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia; 3Centre for Child Health Research, The University of Western Australia, Perth, Western Australia, Australia; 4Division of Paediatrics Within the Medical School, The University of Western Australia, Perth, Western Australia, Australia; 5School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; 6Diabetes and Vascular Medicine, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; 7NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia; 8Division of Endocrinology, Women's and Children's Health Network, North Adelaide, South Australia, Australia; 9Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia; 10Department of Paediatrics, University of Otago, Christchurch, New Zealand
Diabetes Care 2024; 47: 707–711
This article is also discussed in chapter 8 page 249
Technology use in type 1 diabetes (T1D) is impacted by socioeconomic status (SES). This analysis explored relationships between SES, glycemic outcomes, and technology use.
A cross-sectional analysis of HbA1c data from 2,822 Australian youth with T1D was undertaken. Residential postcodes were used to assign SES based on the Index of Relative Socio-Economic Disadvantage (IRSD). Linear regression models were used to evaluate associations among IRSD quintile, HbA1c, and management regimen.
Insulin pump therapy, continuous glucose monitoring, and their concurrent use were associated with lower mean HbA1c across all IRSD quintiles (P < 0.001). There was no interaction between technology use and IRSD quintile on HbA1c (P = 0.624), reflecting a similar association of lower HbA1c with technology use across all IRSD quintiles.
Technology use was associated with lower HbA1c across all socioeconomic backgrounds. Socioeconomic disadvantage does not preclude glycemic benefits of diabetes technologies, highlighting the need to remove barriers to technology access.
The issue of barriers to accessing diabetes technology for children with type 1 diabetes remains a global concern, possibly widening disparities in glycemic outcomes. Particularly, individuals with diabetes from lower socioeconomic status (SES), those living in rural areas, those who have public health insurance, and those from historically minoritized racial/ethnic groups trailed in general use of CGM technology (46–48). In this recent cross-sectional analysis involving almost 3,000 youths with type 1 diabetes, ADDN study group investigated how SES interconnects with the use of diabetes management technologies and glycemic outcomes. Across all SES quintiles, the use of insulin pumps, CGM, or both was consistently associated with lower HbA1c levels, demonstrating that the benefits of these technologies are not confined to those of higher socioeconomic backgrounds. This is a critical insight, as it challenges the assumption that lower SES might diminish the effectiveness of advanced diabetes management technologies. The lack of interaction between technology use and SES on HbA1c suggests that the potential for improved glycemic outcomes through these technologies could be applied uniformly across different socioeconomic groups.
The implications of these findings are profound for policymakers, healthcare providers, and patient advocates. If lower SES does not preclude the effectiveness of technology, then the primary barrier for disadvantaged groups may well be the availability and affordability of these technologies rather than their utility. The clear clinical takeaway is that efforts should be made, in addition to evolving diabetes technology, also towards broader coverage ensuring early, equitable and sustainable access across all SES (48, 49). Only with this we could ensure that the glycemic benefits observed in this study are realized by all youths with type 1 diabetes, regardless of their socioeconomic background (30, 48, 50).
Time in Range Is Associated with Incident Diabetic Retinopathy in Adults with Type 1 Diabetes: A Longitudinal Study
Shah VN1, Kanapka LG2, Akturk HK1, Polsky S1, Forlenza GP1, Kollman C2, Beck RW2, Snell-Bergeon JK1
1Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO; 2Jaeb Center for Health Research, Tampa, FL
Diabetes Technol Ther 2024; 26: 246–251
The objective of this study is to evaluate the association between continuous glucose monitoring (CGM)-based time in various ranges and the subsequent development of diabetic retinopathy (incident DR) in adults with type 1 diabetes.
Between June 2018 and March 2022, adults with type 1 diabetes with incident DR or no retinopathy (control) were identified. CGM data were collected retrospectively for up to 7 years before the date of eye examination defining incident DR or control. Associations between incident DR and CGM metrics were evaluated using logistic regression models.
This analysis included 71 adults with incident DR (mean age 27 years, 52% females, and mean diabetes duration 15 years) and 92 adults without DR (mean age 38 years, 48% females, and mean diabetes duration 20 years). Adjusting for age, diabetes duration, and CGM type, each 0.5% increase in glycated hemoglobin (HbA1c), 10 mg/dL increase in mean glucose, 5% decrease in time in target range 70–180 mg/dL (TIR), 5% decrease in time in tight target range 70–140 mg/dL (TITR), and 5% increase in time above 180 mg/dL (TAR) were associated with 24%, 22%, 18%, 28%, and 20% increase in odds of incident DR, respectively. Spearman correlations of TIR, TITR, TAR, and mean glucose with each other were all ≥0.97.
Similar to HbA1c, TIR, TITR, TAR, and mean glucose were associated with increased risk for incident DR in adults with type 1 diabetes. These CGM metrics are highly correlated indicating that they provide similar information on glycemic control and diabetic retinopathy risk.
Shah et al. evaluated the relationship between CGM-based glycemic metrics and the development of diabetic retinopathy (DR), a leading complication of diabetes. The findings provide critical evidence that CGM metrics—specifically TIR, time in tight target range (TITR), and TAR, are strongly associated with the risk of incident DR. The study demonstrates that each incremental decline in glycemic outcomes raises the likelihood of developing DR. For instance, a 5% reduction in TIR (70–180 mg/dL) or TITR (70–140 mg/dL) was associated with an 18% and 28% increase in the odds of incident DR, respectively. Similarly, a 5% increase in TAR (>180 mg/dL) corresponded to a 20% increase in DR risk. These associations suggest that CGM metrics provide predictive power for DR development that is comparable to or even superior to traditional HbA1c values. The study found that TIR, TITR, TAR, and mean glucose were highly correlated. Although this high degree of correlation underscores the reliability of CGM as a tool for assessing glycemic status, it also suggests that no single CGM metric is uniquely predictive of DR. Instead, a comprehensive view of glycemic patterns, incorporating multiple CGM parameters, is essential for assessing risk. This study highlights the potential utility of targeting tighter glycemic ranges (such as the 70–140 mg/dL; TITR) to mitigate DR risk (51). Furthermore, a recent study demonstrated that the use of CGM was associated with lower odds of developing DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes, even when adjusting for HbA1c levels (52). CGM use, both alone and in combination with insulin pumps, was linked to reduced risk, emphasizing the importance of tight glycemic outcomes beyond HbA1c. These findings highlight the potential of CGM as a valuable tool in mitigating the risk of long-term diabetes complications like DR and PDR, underscoring the need for broader adoption of CGM in diabetes management to improve diabetes outcomes.
Continuous Glucose Monitoring Profiles in Pregnancies with and Without Gestational Diabetes Mellitus
Durnwald C1, Beck RW2, Li Z2, Norton E1, Bergenstal RM3, Johnson M3, Dunnigan S3, Banfield M3, Krumwiede K3, Sibayan J2, Calhoun P2, Carlson AL3
1Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 2Jaeb Center for Health Research, Tampa, FL; 3International Diabetes Center, HealthPartners Institute, St. Louis Park, MN
Diabetes Care 2024; 47: 1333–1341
The objective of this study is to determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24–28 weeks' gestation compared with those without GDM.
The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics.
Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63–120 mg/dL (3.5–6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13–14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT.
Prior to OGTT at 24–34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM.
The study conducted by Durnwald et al. investigated the potential of CGM to characterize glycemic patterns in pregnancies complicated by gestational diabetes mellitus (GDM). A total of 768 participants were enrolled before 17 weeks of gestation and were monitored throughout their pregnancies using blinded CGMs. Results reveal distinct glycemic differences between those who developed GDM, diagnosed with the standard oral glucose tolerance test (OGTT) between 24- and 28-week gestation, and those who did not. Notably, the participants who developed GDM had consistently higher mean glucose levels, greater glucose variability, and more time spent in hyperglycemic ranges compared to those without GDM. These differences were evident as early as 13–14 weeks of gestation, hence before the conventional OGTT is routinely performed, indicating a persistent hyperglycemic trend throughout early pregnancy. Additionally, CGM can measure fetal exposure to maternal glucose in daily life, and it is likely to provide a reliable estimate of fetal exposure to maternal glucose than the, compared to current OGTT screening test.
This study provides compelling evidence that with CGM, early glycemic abnormalities in pregnancies that eventually develop GDM can be detected and presents important findings that could significantly impact prenatal care (53, 54). These findings are particularly noteworthy for several reasons. First, they suggest that CGM could serve as an early detection/screening tool GDM, identifying at-risk pregnancies much earlier than the standard OGTT. Early identification could lead to timely interventions, potentially improving outcomes for both mothers and their babies. Second, these observations challenge the current standard practice of diagnosing GDM only in the mid-second trimester, proposing instead that monitoring glucose levels continuously from early pregnancy could provide a more precise understanding of glycemic health in pregnant individuals.
Although findings also prompt further questions and areas for future research, implications of these observations could be profound for clinical practice. If CGM can reliably predict the development of GDM before the traditional testing window and this technology is getting more accessible and affordable, it could modernize the approach to managing at-risk pregnancies. Early detection would allow for earlier lifestyle modifications, closer monitoring, and potentially even early therapeutic or lifestyle interventions to mitigate hyperglycemia exposure, all of which could contribute to better maternal and fetal outcomes (55).
Comparing Continuous Glucose Monitoring and Blood Glucose Monitoring in Adults with Inadequately Controlled, Insulin-Treated Type 2 Diabetes (Steno2tech Study): A 12-Month, Single-Center, Randomized Controlled Trial
Lind N1,2, Christensen MB1, Hansen DL1, Nørgaard K1,2
1Department of Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark; 2Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Diabetes Care 2024; 47: 881–889
This article is also discussed in chapter 10 page 310
The objective of this study is to compare the 12-month effects of continuous glucose monitoring (CGM) versus blood glucose monitoring (BGM) in adults with insulin-treated type 2 diabetes.
This is a single-center, parallel, open-label, randomized controlled trial including adults with inadequately controlled, insulin-treated type 2 diabetes from the outpatient clinic at Steno Diabetes Center Copenhagen, Denmark. Inclusion criteria were ≥18 years of age, insulin-treated type 2 diabetes, and HbA1c ≥7.5% (58 mmol/mol). Participants were randomly assigned (1:1) to 12 months of either CGM or BGM. All participants received a diabetes self-management education course and were followed by their usual health care providers. Primary outcome was between-group differences in change in time in range (TIR) 3.9–10.0 mmol/L, assessed at baseline, after 6 and 12 months by blinded CGM. The prespecified secondary outcomes were differences in change in several other glycemic, metabolic, and participant-reported outcomes.
The 76 participants had a median baseline HbA1c of 8.3 (7.8, 9.1)% (67 [62–76] mmol/mol), and 61.8% were male. Compared with BGM, CGM usage was associated with significantly greater improvements in TIR (between-group difference 15.2%, 95% CI 4.6; 25.9), HbA1c (−0.9%, −1.4; −0.3 [−9.4 mmol/mol, −15.2; −3.5]), total daily insulin dose (−10.6 units/day, −19.9; −1.3), weight (−3.3 kg, −5.5; −1.1), and BMI (−1.1 kg/m2, −1.8; −0.3) and greater self-rated diabetes-related health, well-being, satisfaction, and health behavior.
In adults with inadequately controlled insulin-treated type 2 diabetes, the 12-month impact of CGM was superior to BGM in improving glucose control and other crucial health parameters. The findings support the use of CGM in the insulin-treated subgroup of type 2 diabetes.
Early intensive glucose management in people with type 2 diabetes has been shown to significantly reduce microvascular complications and lower the long-term risk of myocardial infarction and all-cause mortality. However, despite these clear benefits, many individuals struggle to achieve recommended HbA1c targets worldwide, particularly those on insulin therapy. Moreover, delaying treatment intensification by one year for those with HbA1c levels above 7.5% was associated increased the risk of MI by 67%, stroke by 51%, and heart failure by 64% (56).
Given the proven benefits of early glycemic stabilization and the progressive nature of type 2 diabetes, CGM technology plays a crucial role in helping individuals actively manage their diabetes daily. It also allows health care providers to adjust/intensify treatment promptly to reach recommended glycemic outcomes. Increasing evidence, from both RCTs and large diabetes registries, supports the routine use of CGM in the care of people with type 2 diabetes, particularly those on basal insulin or other medication regimens (57–60). The Steno2tech Study examined the 12-month effects of continuous glucose monitoring (CGM) compared to traditional blood glucose monitoring (BGM) in adults with insulin-treated type 2 diabetes in addition to other glucose-lowering therapies (treatment with insulin injections at least once daily for more than 1 year), who had not achieved recommended glycemic targets. Conducted as a single-center, open-label, randomized controlled trial at the Steno Diabetes Center Copenhagen, the study enrolled 76 participants with baseline HbA1c levels of at least 7.5%. Results showed that participants using CGM had significantly better glycemic outcomes than those using BGM. Specifically, CGM users had a 15.2% higher time in range (TIR) after 12 months (primary endpoint) and a 12.4% higher TIR after 6 months. Importantly, most of his improvement was driven by reduction of time above 13.9 mmol/L (11.8% lower in CGM group). Additionally, CGM users saw a greater reduction in HbA1c (−0.9%), required lower daily insulin doses, and achieved notable reductions in body weight and BMI over both 6 and 12 months. Participants using CGM also reported higher satisfaction with their health, better diabetes-related well-being, and more positive health behaviors. CGM adherence was high, with wear time exceeding 95% in both study groups. It can be speculated that CGM offers personalized insights and immediate decision-making feedback on glucose trends, which may have contributed to the observed positive behavioral changes and sustained improvements in health behaviors, including a significant impact on self-reported medication taking. These findings indicate that CGM offers substantial clinical benefits over a one-year period and adds to the evidence supporting the incorporation of CGM technology as the standard of care option for managing insulin-treated type 2 diabetes (57–59, 61), improving both glycemic outcomes and overall health and well-being.
Continuous Glucose Monitoring-Based Metrics and Hypoglycemia Duration in Insulin-Experienced Individuals with Long-Standing Type 2 Diabetes Switched from a Daily Basal Insulin to Once-Weekly Insulin Icodec: Post Hoc Analysis of ONWARDS 2 and ONWARDS 4
Bajaj HS1, Ásbjörnsdóttir B2, Carstensen L2, Laugesen C2, Mathieu C3, Philis-Tsimikas A4, Battelino T5,6
1LMC Diabetes and Endocrinology, Brampton, Ontario, Canada; 2Novo Nordisk A/S, Søborg, Denmark; 3Clinical and Experimental Endocrinology, University of Leuven, Leuven, Belgium; 4Scripps Whittier Diabetes Institute, San Diego, CA; 5University Medical Centre Ljubljana, Ljubljana, Slovenia; 6Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
Diabetes Care 2024; 47: 729–738
This article is also discussed in chapter 4 page 91
This post hoc analysis assessed continuous glucose monitoring (CGM)-based metrics and hypoglycemia duration with once-weekly insulin icodec versus once-daily basal insulin analogs in insulin-experienced individuals with long-standing type 2 diabetes from two 26-week phase 3a trials (ONWARDS 2 and ONWARDS 4).
Time in range (TIR) (3.9–10.0 mmol/L), time above range (TAR) (>10.0 mmol/L), and time below range (TBR) (<3.9 mmol/L and <3.0 mmol/L) were assessed during three CGM time periods (switch [weeks 0–4], end of treatment [weeks 22–26], and follow-up [weeks 27–31]) for icodec versus comparators (ONWARDS 2, insulin degludec [basal regimen]; ONWARDS 4, insulin glargine U100 [basal-bolus regimen]) using double-blind CGM data. CGM-derived hypoglycemic episode duration (<3.9 mmol/L) was assessed.
In both trials, there were no statistically significant differences in TIR, TAR, or TBR (<3.0 mmol/L) for icodec versus comparators across all time periods. In the end-of-treatment period, mean TIR was 63.1% (icodec) vs. 59.5% (degludec) in ONWARDS 2 and 66.9% (icodec) vs. 66.4% (glargine U100) in ONWARDS 4. Mean TBR <3.9 mmol/L and <3.0 mmol/L remained within recommended targets (<4% and <1%, respectively) across time periods and treatment arms. Hypoglycemic episode duration (<3.9 mmol/L) was comparable across time periods and treatment arms (median duration ≤40 min).
In insulin-experienced participants with long-standing type 2 diabetes, CGM-based TIR, TAR, and CGM-derived hypoglycemia duration (<3.9 mmol/L) were comparable for icodec and once-daily basal insulin analogs during all time periods. TBR remained within recommended targets.
ONWARDS 2 and ONWARDS 4 were randomized, open-label, treat-to-target, multicenter, phase 3a trials designed to evaluate the efficacy and safety of basal insulin analogue suitable for one-weekly dosing (Icodec) in adults with type 2 diabetes (62, 63). Each trial comprised a 2-week screening period, a 26-week treatment period, and a 5-week follow-up period. This study conducted a post hoc analysis of the blinded CGM data, collected during three time periods of both studies, between once-weekly insulin icodec and once-daily basal insulin analogs (degludec and glargine) that provide detailed clinically relevant information regarding glycemic metrics and hypoglycemia duration.
The results showed no significant differences in TIR, TAR, or TBR between insulin icodec and the control during any time period. By the end of treatment, TIR was 63.1% for icodec versus 59.5% for degludec in ONWARDS 2, and 66.9% for icodec versus 66.4% for glargine U100 in ONWARDS 4. TBR remained within recommended safety targets (<4% for <3.9 mmol/L and <1% for <3.0 mmol/L). The duration of hypoglycemic episodes (<3.9 mmol/L) was similar across treatments, with a median duration of 40 minutes or less.
As previously demonstrated, RCTs evaluating new pharmaceutical treatments for diabetes management can gain valuable insights by incorporating CGM devices. CGM not only allows for comparative monitoring of interventions but also serves as a clinically relevant outcome measure that complements traditional metrics, as recommended in an international consensus statement on CGM and its use in clinical trials (64). Although CGM metrics, including TIR, TAR, and hypoglycemia duration, were comparable between once-weekly insulin icodec and daily basal insulin analogs in individuals with long-standing type 2 diabetes, this was complemented by reduced injection burden and greater treatment satisfaction that could contribute to improved adoption of basal insulin treatment (62).
Additionally, in the post hoc analysis of the ONWARDS 2 and ONWARDS 4 trials published by Bajaj and colleagues, CGM-based TIR, TAR, and CGM-derived hypoglycemia duration (<3.9 mmol/L) were comparable for icodec and once-daily basal insulin analogs during all time periods (65). CGM-derived metrics allowed for a much more accurate and extensive analysis of TBR and related hypoglycemia throughout the two clinical trials, substantiating the predefined trial outcomes and expanding the data on safety of this novel weekly insulin.
Assessment of Glycemic Control by Continuous Glucose Monitoring, Hemoglobin A1c, Fructosamine, and Glycated Albumin in Patients with End-Stage Kidney Disease and Burnt-Out Diabetes
Kaminski CY1, Galindo RJ2, Navarrete JE3, Zabala Z2, Moazzami B2, Gerges A2, McCoy RG4,5, Fayfman M2, Vellanki P2, Idrees T2, Peng L6, Umpierrez GE2
1Emory University School of Medicine, Atlanta, GA; 2Division of Endocrinology, Department of Medicine, Emory University, Atlanta, GA; 3Division of Nephrology, Department of Medicine, Emory University, Atlanta, GA; 4Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD; 5University of Maryland Institute for Health Computing, Bethesda, MD; 6Emory University Rollins School of Public Health, Atlanta, GA
Diabetes Care 2024; 47: 267–271
Patients with diabetes and end-stage kidney disease (ESKD) may experience “burnt-out diabetes,” defined as having an HbA1c value <6.5% without antidiabetic therapy for >6 months. We aim to assess glycemic control by continuous glucose monitoring (Dexcom G6 CGM) metrics and glycemic markers in ESKD patients on hemodialysis with burnt-out diabetes.
In this pilot prospective study, glycemic control was assessed by continuous glucose monitoring (CGM), HbA1c measures, and glycated albumin and fructosamine measurements in patients with burnt-out diabetes (n = 20) and without a history of diabetes (n = 20).
Patients with burnt-out diabetes had higher CGM-measured daily glucose levels, lower percent time in the range 70–180 mg/dL, higher percent time above range (>250 mg/dL), and longer duration of hyperglycemia >180 mg/dL (hours/day) compared with patients without diabetes (all P < 0.01). HbA1c and fructosamine levels were similar; however, patients with burnt-out diabetes had higher levels of glycated albumin than did patients without diabetes.
The use of CGM demonstrated that patients with burnt-out diabetes have significant undiagnosed hyperglycemia. CGM and glycated albumin provide better assessment of glycemic control than do values of HbA1c and fructosamine in patients with ESKD.
With the increasing prevalence of type 2 diabetes, diabetes has become the leading cause of CKD and is responsible for approximately half of all cases of end-stage kidney disease (ESKD) worldwide (66). The phenomenon of “burnt-out diabetes,” where individuals with diabetes and end-stage kidney disease (ESKD) previously treated with insulin or other antihyperglycemic agents, exhibit seemingly “normal” HbA1c levels (<6.5%) without antidiabetic therapy, presents significant challenge in the accurate assessment of glycemic outcomes. Whether individuals with burnt-out diabetes, which is present in approximately every one out of five individuals with diabetes and ESKD, are truly euglycemic or they continue to have hyperglycemia but are misdiagnosed due to limitations of HbA1c is unknown. Markers like HbA1c may be misleading in these individuals due to altered red blood cell turnover and anemia, the use of erythropoietin-stimulating agents, and the influence of hemodialysis, necessitating alternative methods for evaluating glycemia (66). In this pilot study including 40 individuals with ESKD with (n = 20) or without (n = 20) diabetes, the use of CGM (Dexcom G6) in individuals with burnt-out diabetes revealed a considerable degree of undiagnosed hyperglycemia compared to those without diabetes. The CGM-based metrics, including almost 14% lower TIR (driven mainly by higher TAR with no difference in TBR), 16 mg/dL higher mean glucose and longer hyperglycemic episodes indicated that people with burnt-out diabetes experience significant glycemic excursions that are not captured by HbA1c (mean HbA1c was 5.5% vs. 5.3%, P = 0.26). This reinforces the notion that HbA1c may not be a reliable indicator of glycemic status in ESKD. Interestingly, although both groups (burnt-out diabetes and nondiabetic) had similar HbA1c and fructosamine levels, individuals with burnt-out diabetes showed significantly higher glycated albumin levels. Glycated albumin, which reflects shorter-term glycemic outcomes (2–3 weeks), is less disturbed by the altered metabolism seen in ESKD, making it a more reliable marker in this context. The similar fructosamine levels between groups may also be influenced by factors such as protein turnover and inflammation, further supporting the superiority of glycated albumin in these individuals. As CGM offers real-time insights into glucose fluctuations and hyperglycemic episodes that would otherwise go undetected, we should consider integrating CGM into the routine management of ESKD to better screen, monitor, and tailor therapeutic strategies and prevent the complications of undiagnosed hyperglycemia.
Conclusions
At the end of our joint yearly CGM journey, we can allow us to dream a bit about the near future (67). As the screening for stage 2 type 1 diabetes is gradually developing into a reality (3), CGM will likely become the diagnostic tool for dysglycemia. We will perhaps see novel CGM-derived diagnostic markers based on “glucose disorder” (58), and artificial intelligence-enabled tools will play an increasing role in all aspects of type 1 diabetes prediction, diagnosis, and management (68, 69).
Furthermore, CGM is entering noninsulin-treated type 2 diabetes management, with several systematic meta-analyses confirming its efficacy and user satisfaction in this vast population of individuals with diabetes (70–72). Additionally, using connected pens for all medications and presented in a standardized report form will likely further improve management (73). The use of novel disease-modifying medications guided by advanced technologies has the potential to reduce disparities and improve long-term outcomes (74, 75).
Finally, CGM will likely become the diagnostic tool also in pre-type 2 diabetes and early dysglycemia (76). As the data on CGM in different populations without diabetes are accumulating, and the CGM being available »over-the-counter« in the USA, we may envisage a very broad use of CGM. We diabetologist as well as general physicians on the primary care level should better get ready for this exponential use of CGM with a clear clinical guidance, hopefully assisted by powerful artificial intelligence tools.
Author Disclosure Statement
KD has received honoraria for speaking engagements from Abbott, Eli Lilly, Medtronic, Novo Nordisk, and Pfizer, and is on an advisory board for Medtronic and Novo Nordisk. BB received consultancy and speaker fees from Adocia, Astra Zeneca, Bayer, Diasome, Intarcia, Janssen, Mannkind, Medtronic, Novo Nordisk, and Sanofi. BB’s employer, Atlanta Diabetes Associates, has received research and grant support from Abbott, Becton Dickson, Boehringer Ingelheim, Diasome, Dexcom, Janssen, Lilly, Mannkind, Medtronic, Novo Nordisk, Sanofi, and Senseonics. TB served on advisory panels of Novo Nordisk, Sanofi, Eli Lilly, Boehringer, Medtronic, Abbott, and Indigo Diabetes. TB received honoraria for participating on the speaker’s bureaux of Eli Lilly, Novo Nordisk, Medtronic, Abbott, Sanofi, Dexcom, Aventis, Astra Zeneca, and Roche. TB’s Institution received research grant support from Abbott, Medtronic, Novo Nordisk, Sanofi, Novartis, Sandoz, and Zealand Pharma, Slovenian Research Innovation Agency, the National Institutes of Health, and the European Union.
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