Continuous and Intermittent Glucose Monitoring in 2022
2023; Mary Ann Liebert, Inc.; Volume: 25; Issue: S1 Linguagem: Inglês
10.1089/dia.2023.2502
ISSN1557-8593
AutoresKlemen Dovč, Bruce W. Bode, Tadej Battelino,
Tópico(s)Diabetes and associated disorders
ResumoDiabetes Technology & TherapeuticsVol. 25, No. S1 Original ArticlesFree AccessContinuous and Intermittent Glucose Monitoring in 2022Klemen Dovc, Bruce W. Bode, and Tadej BattelinoKlemen DovcUniversity Medical Center University Children's Hospital Ljubljana, Ljubljana, Slovenia.Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.Search for more papers by this author, Bruce W. BodeAtlanta Diabetes Associates and Emory University School of Medicine, Atlanta, GA, USA.Search for more papers by this author, and Tadej BattelinoUniversity Medical Center University Children's Hospital Ljubljana, Ljubljana, Slovenia.Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.Search for more papers by this authorPublished Online:20 Feb 2023https://doi.org/10.1089/dia.2023.2502AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionOur societies are slowly and cautiously returning to normality, leaving the pandemic turmoil behind us and affording us some more time to focus on the evaluation of the recent telemedicine experiences in diabetes. Many lessons were learned quicker than anticipated under normal circumstances; however, several questions remained unanswered, and some new uncertainties appeared. Continuous glucose monitoring (CGM) clearly took a central position in diabetes management, particularly in tertiary diabetes centers. Conversely, care at the primary level, where most individuals with type 2 diabetes get most of their care, largely remained unchanged, sticking to the old models of diabetes management (1).Additionally, significant disparities in routine diabetes care became more visible.Another unmet need became more apparent: after several years of data accumulation, CGM still lacks solid direct evidence for its relation to the chronic complications of diabetes (2). The question is whether we need large long-term randomized controlled trials, or should the data from large national or international registries and insurance databases provide satisfactory evidence? Regulatory agencies will also play an important role in this question: will they finally include CGM-derived data and outcomes into the labeling, or will they linger with the more than 3-decade-old paradigm of mean glucose levels assessed by using glycated hemoglobin levels?In our present article, we selected papers that provide novel evidence for a broad spectrum of diabetes care and perhaps open new areas where the use of CGM could further facilitate and improve routine care with a concomitant reduction in the daily disease burden.Key Articles ReviewedCGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) StudySteck AK, Dong F, Geno Rasmussen C, Bautista K, Sepulveda F, Baxter J, Yu L, Frohnert BI, Rewers MJ for the ASK Study GroupDiabetes Care 2022;45: 365–371Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG, Delfosse A, Martin M, Louis J, Gies I, den Brinker M, Seret N, Lebrethon MC, Mouraux T, Gatto L, Lysy PA on behalf of DIATAG Working GroupDiabetes Care 2022;45: 2360–2368Universal Subsidized Continuous Glucose Monitoring Funding for Young People with Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based StudyJohnson SR, Holmes-Walker DJ, Chee M, Earnest A, Jones TW on behalf of the CGM Advisory Committee and Working Party and the ADDN Study GroupDiabetes Care 2022;45: 391–397Improved CGM Glucometrics and More Visits in Pediatric Type 1 Diabetes Using Telemedicine During 1 Year of COVID-19Kaushal T, Tinsley LJ, Volkening LK, Turcotte C, Laffel LJ Clin Endocrinol Metab 2022;107: e4197–e4202Continuous Glucose Monitoring in Adults with Type 1 Diabetes with 35 Years Duration from the DCCT/EDIC StudyGubitosi-Klug RA, Braffett BH, Bebu I, Johnson ML, Farrell K, Kenny D, Trapani VR, Meadema-Mayer L, Soliman EZ, Pop-Busui R, Lachin JM, Bergenstal RM, Tamborlane WVDiabetes Care 2022;45: 659–665Addition of Intermittently Scanned Continuous Glucose Monitoring to Standard Care in a Cohort of Pregnant Women with Type 1 Diabetes: Effect on Glycaemic Control and Pregnancy OutcomesPerea V, Picón MJ, Megia A, Goya M, Wägner AM, Vega B, Seguí N, Montañez MD, Vinagre IDiabetologia 2022;65: 1302–1314Reduction of Clinically Important Low Glucose Excursions with a Long-Term Implantable Continuous Glucose Monitoring System in Adults with Type 1 Diabetes Prone to Hypoglycaemia: The France Adoption Randomized Clinical TrialRenard E, Riveline JP, Hanaire H, Guerci B on behalf of the investigators of France Adoption Clinical TrialDiabetes Obes Metab 2022;24: 859–867Dynamics of Hemoglobin A1c, Body Mass Index, and Rates of Severe Hypoglycemia in 4434 Adults with Type 1 or Type 2 Diabetes After Initiation of Continuous Glucose MonitoringLanzinger S, Best F, Bergmann T, Laimer M, Lipovsky B, Danne T, Zimny S, Bramlage P, Meyhöfer S, Holl RWDiabetes Technol Ther 2022;24: 763–769The Effect of Discontinuing Continuous Glucose Monitoring in Adults with Type 2 Diabetes Treated with Basal InsulinAleppo G, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Peters AL, Pop-Busui R, Philis-Tsimikas A, Bao S, Umpierrez G, Davis G, Kruger D, Bhargava A, Young L, Buse JB, McGill JB, Martens T, Nguyen QT, Orozco I, Biggs W, Lucas KJ, Polonsky WH, Price D, Bergenstal RM for the MOBILE Study GroupDiabetes Care 2021;44: 2729–2737Continuous Glucose Monitoring-Guided Insulin Administration in Hospitalized Patients with Diabetes: A Randomized Clinical TrialSpanakis EK, Urrutia A, Galindo RJ, Vellanki P, Migdal AL, Davis G, Idrees T, Pasquel FJ, Coronado WZ, Albury B, Moreno E, Singh LG, Marcano I, Lizama S, Gothong C, Munir K, Chesney C, Maguire R, Scott WH, Perez-Guzman MC, Cardona S, Peng L, Umpierrez GEDiabetes Care 2022: 2022;45: 2369–2375Efficacy of Once-Weekly Tirzepatide Versus Once-Daily Insulin Degludec on Glycaemic Control Measured by Continuous Glucose Monitoring in Adults with Type 2 Diabetes (SURPASS-3 CGM): A Substudy of the Randomised, Open-Label, Parallel-Group, Phase 3 SURPASS-3 TrialBattelino T, Bergenstal RM, Rodríguez A, Fernández Landó L, Bray R, Tong Z, Brown KLancet Diabetes Endocrinol 2022;10: 407–417CGM Metrics Predict Imminent Progression to Type 1 Diabetes: Autoimmunity Screening for Kids (ASK) StudySteck AK, Dong F, Geno Rasmussen C, Bautista K, Sepulveda F, Baxter J, Yu L, Frohnert BI, Rewers MJ for the ASK Study GroupBarbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CODiabetes Care 2022;45: 365–371BackgroundChildren who are identified through population screening to have multiple islet autoantibodies (stage 1) type 1 diabetes are at increased risk for progressing to clinical type 1 diabetes (stage 3) and require accurate monitoring. This study aimed to establish continuous glucose monitoring (CGM) metrics that could predict imminent progression to diabetes.MethodsIn the Autoimmunity Screening for Kids study (ASK), 91 children who were persistently positive for islet autoantibodies (median age 11.5 years; 48% non-Hispanic white; 57% female) with a baseline CGM were followed for the development of diabetes for a median of 6 (range 0.2–34) months. Of these, 16 (18%) progressed to clinical diabetes in a median of 4.5 (range 0.4–29) months.ResultsCompared with children who did not progress to clinical diabetes, those who did had significantly higher average sensor glucose levels (119 vs 105 mg/dL, P<.001) and increased metrics of glycemic variability (P<.001). For progressors, 21% of the time was spent with glucose levels >140 mg/dL (noted as TA140) and 8% of time >160 mg/dL, compared with 3% and 1%, respectively, for nonprogressors. In survival analyses, the risk of progression to diabetes in 1 year was 80% in those with TA140>10%; in contrast, it was only 5% in the other participants. Performance of prediction by receiver operating curve analyses showed an area under the curve of ≥0.89 for both individual and combined CGM metric models.ConclusionsFor autoantibody-positive children whose TA140>10%, there is a high risk of developing clinical diabetes within the next year. CGM should be used to monitor these children regularly and could possibly be a requirement for including participants in prevention trials.CommentsThis prospective study evaluated CGM-based glucose metrics as predictors of progression to clinical type 1 diabetes in autoantibody-positive children identified through general population screening (3). The Autoimmunity Screening for Kids study is a clinical research study in which children aged 1–17 years in Colorado, United States are screened for islet and celiac autoantibodies. The analysis of CGM data at baseline revealed that the children who progressed to clinical type 1 diabetes spent a significantly greater proportion (21% vs 3%) of their time above 140 mg/dL (7.8 mmol/L), compared to those who did not progress. Having a TA140 value greater than 10% was 88% sensitive and 91% specific for distinguishing between progressors and nonprogressors. Children who met this threshold had an 80% chance of progressing within the next year, whereas those who did not meet it had just a 5% chance. Importantly, both individual and combined CGM-based metrics can accurately predict progression to clinical (stage 3) type 1 diabetes within the next year. Therefore, TA140 >10% could be implemented as a new criterion for dysglycemia (stage 2 type 1 diabetes) with a high risk of progression to clinical type 1 diabetes (stage 3). It has been previously demonstrated that a period of dysglycemia, lasting for several months or even years, precedes type 1 diabetes onset and provides an opportunity to avoid diabetic ketoacidosis by implementing prevention or early treatment that could help preserve endogenous insulin secretion, especially as screening for type 1 diabetes is becoming increasingly considered for the general population (4–7). Moreover, CGM-based metrics could replace the oral glucose tolerance test (OGTT) currently required for establishing the diagnosis of early type 1 diabetes, particularly in prevention trials including the pediatric population, for whom OGTT is often difficult to perform.Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 DiabetesPollé OG1,2, Delfosse A1,2, Martin M3, Louis J4, Gies I5,6, den Brinker M7,8, Seret N9, Lebrethon MC10, Mouraux T11, Gatto L3, Lysy PA1,2 on behalf of DIATAG Working Group1Pôle de PEDI, Institut de Recherche Expérimentale et Clinique, UCLouvain, Brussels, Belgium; 2Specialized Pediatrics Service, Cliniques Universitaires Saint-Luc, Brussels, Belgium; 3Computational Biology and Bioinformatics Unit, de Duve Institute, UC Louvain, Brussels, Belgium; 4Division of Pediatric Endocrinology, Department of Pediatrics, Grand Hôpital de Charleroi, Charleroi, Belgium; 5Division of Pediatric Endocrinology, Department of Pediatrics, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium; 6Research Group GRON, Vrije Universiteit Brussel, Brussels, Belgium; 7Laboratory of Experimental Medicine and Pediatrics and member of the Infla-Med Centre of Excellence, University of Antwerp, Faculty of Medicine and Health Sciences, Antwerp, Belgium; 8Division of Pediatric Endocrinology, Department of Pediatrics, Antwerp University Hospital, Antwerp, Belgium; 9Division of Pediatric Endocrinology, Department of Pediatrics, Centre Hospitalier Chrétien MontLégia, Liège, Belgium; 10Division of Pediatric Endocrinology, Department of Pediatrics, CHU Liège, Liège, Belgium; 11Division of Pediatric Endocrinology, Department of Pediatrics, CHU Namur, Namur, BelgiumDiabetes Care 2022;45: 2360–2368BackgroundA comprehensive composite evaluation of clinical parameters and continuous glucose monitoring (CGM) data are required to decipher glucose homeostasis evolution during the first year after type 1 diabetes onset and provide clues to achieve outcome-focused individual stratification. This study evaluated whether indexes of glycemic variability may be more accurate than residual β-cell secretion estimates in the longitudinal evaluation of partial remission in a cohort of pediatric participants with new-onset type 1 diabetes.MethodsValues of residual β-cell secretion estimates, clinical parameters (e.g., HbA1c or insulin daily dose), and CGM data from 78 pediatric participants with new-onset type 1 diabetes were longitudinally collected for 1 year and cross-sectionally compared. Circadian patterns of CGM metrics were characterized and correlated to remission status using an adjusted mixed-effects model. Participants were clustered based on 46 CGM metrics and clinical parameters and compared using nonparametric ANOVA.ResultsStudy participants had a mean (±SD) age of 10.4 (±3.6) years at diabetes onset, and 65% underwent partial remission at 3 months. β-Cell residual secretion estimates demonstrated weak-to-moderate correlations with clinical parameters and CGM metrics (r2=0.05–0.25; P < .05). However, CGM metrics strongly correlated with clinical parameters (r2<0.52; P < .05) and were sufficient to distinguish those in remission from those who were not. Also, CGM metrics from those in remission displayed specific early morning circadian patterns characterized by better glycemic stability across days (within 63–140 mg/dL range) and lower rate of grade II hypoglycemia (P < .0001) compared with those not in remission. Thorough CGM analysis allowed the identification of four novel glucotypes (P < .001) that segregate individuals into subgroups and mirror the evolution of remission after diabetes onset.ConclusionsIn this study including children during the first year of type 1 diabetes, using a combination of CGM metrics and clinical parameters allowed for identifying four categories of patient groups that experienced varying degrees of glucose homeostasis and elucidating key clinical milestones of remission status.CommentsEarly and accurate identification of individuals who will experience a significant partial remission period is key in developing secondary type 1 diabetes prevention strategies (8). Residual β-cell secretion is commonly used as the primary endpoint of β-cell mass prevention trials. In individuals with clinical type 1 diabetes (stage 3), the evaluation of insulin secretion through standard oral tolerance testing poorly represents glucose homeostasis since it does not integrate key aspects of insulin sensitivity and since C-peptide assays lack the power to discriminate residual β-cell mass from β-cell function and glucose responsiveness of β-cells might only be observed in individuals with high levels of peak C-peptide (9). Additionally, the mitigated response of individuals with new-onset type 1 diabetes to a diverse portfolio of pharmacological protocols supports the heterogeneity of type 1 diabetes disease progression, possibly driven by underlying differences in pathophysiology or endotypes. Consequently, there is a need for individual stratification of subgroups of individuals who require different intervention or treatment approaches (8,10,11). Integrating various CGM metrics as endpoints in residual β-cell function protection trials, complementing stimulated C-peptide level, might provide clinically relevant and precise clues to evaluate individual responses to treatment protocols, hence enabling person-tailored intervention therapy. Additionally, wearing a modern CGM device may be considerably less burdensome to individuals with diabetes compared to repetitive C-peptide or OGTT testing.Universal Subsidized Continuous Glucose Monitoring Funding for Young People with Type 1 Diabetes: Uptake and Outcomes Over 2 Years, a Population-Based StudyJohnson SR1,2, Holmes-Walker DJ3,4, Chee M5, Earnest A6, Jones TW7,8 on behalf of the CGM Advisory Committee and Working Party and the ADDN Study Group1Department of Endocrinology and Diabetes, Queensland Children's Hospital, Brisbane, Queensland, Australia; 2Faculty of Medicine, University of Queensland, Herston, Queensland, Australia; 3Department of Diabetes and Endocrinology, Westmead Hospital, Sydney, New South Wales, Australia; 4Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; 5JDRF Australia, St Leonard's, New South Wales, Australia; 6Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; 7Perth Children's Hospital, Nedlands, Western Australia, Australia; 8Telethon Kids Institute, Nedlands, Western Australia, AustraliaDiabetes Care 2022;45: 391–397This manuscript is also discussed in DIA-2023-2508, page S-118 and DIA-2023-2511, page S-176.BackgroundDifferent funding models exist for continuous glucose monitoring (CGM), a method used to monitor type 1 diabetes. The new national health policy subsidizes CGM for people younger than 21 years who have type 1 diabetes. This study aimed to assess changes in CGM use and glycemic outcomes after the policy took effect.MethodsLongitudinal data from 12 months before the subsidy until 24 months after were analyzed. Measures and outcomes included age, diabetes duration, HbA1c, episodes of diabetic ketoacidosis and severe hypoglycemia, insulin regimen, CGM uptake, and percentage CGM use. Two data sources were used: the Australasian Diabetes Database Network (ADDN) registry (a prospective diabetes database) and the National Diabetes Service Scheme (NDSS) registry that includes almost all individuals with type 1 diabetes nationally.ResultsCGM uptake increased from 5% before the subsidy to 79% after 2 years. After CGM introduction, the odds ratio (OR) of achieving the HbA1c target of <7.0% improved at 12 months (OR 2.5, P<.001) and was maintained at 24 months (OR 2.3, P<.001). The OR for suboptimal glycemic control (HbA1c ≥9.0%) decreased to 0.34 (P<.001) at 24 months. Of CGM users, 65% used CGM >75% of time, and had a lower HbA1c at 24 months compared with those whose usage was <25% (7.8±1.3% vs 8.6±1.8%, respectively; P<0.001). Diabetic ketoacidosis was also reduced in this group (incidence rate ratio 0.49; 95% CI, 0.33–0.74; P<0.001).ConclusionsAfter the national subsidy took effect, CGM use greatly increased and was associated with sustained improvement in glycemic control. These results can be used in economic analyses and to support similar government initiatives in the future. In addition, the methods in this study can be used to evaluate other diabetes technologies.CommentsAdoption of continuous glucose monitoring (CGM), either as a stand-alone approach or used in conjunction with insulin pumps, has increased rapidly worldwide over the past decade in different age groups; this change is based on data demonstrating safety and efficacy in clinical trials, improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement (12–18). These results are, especially in adults, further supported by findings from large international registries (12,19–23). Access to CGM, however, is restricted by its cost, as varying degrees of subsidy are available to some countries.This study reports improved glycemic control and higher prevalence of CGM use following the introduction of universal subsidized CGM funding for youth with type 1 diabetes aged <21 years in Australia (24). Prior to the subsidy, less than 5% of youth <21 years with type 1 diabetes were using CGM, but the usage rate in this group increased to 79% nationwide. The likelihood of achieving the target HbA1c of <7% increased more than twofold from the presubsidy period, and the mean HbA1c was reduced by 0.3% to 0.5% overall. Additionally, parents and children reported improvements in psychosocial outcomes (fear of hypoglycemia, improved diabetes treatment satisfaction) after commencement of CGM following the national subsidy, emphasizing the potential value of CGM in the management of type 1 diabetes beyond glycemic control (25). A nation-wide unrestricted reimbursement of CGM in adults with type 1 diabetes resulted in higher treatment satisfaction, less severe hypoglycemia, and less work absenteeism, while maintaining quality of life and HbA1c (26). Importantly, a recent cost-effectiveness analysis demonstrated that universal use of CGM or isCGM is predicted to reduce diabetes-related complications and mortality over a 20-year time horizon, being a cost-effective modality for glucose monitoring in adults living with type 1 diabetes (27). These results may inform clinical practice and reimbursement decisions.Improved CGM Glucometrics and More Visits in Pediatric Type 1 Diabetes Using Telemedicine During 1 Year of COVID-19Kaushal T, Tinsley LJ, Volkening LK, Turcotte C, Laffel LJoslin Diabetes Center, Boston, MAJ Clin Endocrinol Metab 2022;107: e4197–e4202BackgroundIn response to the COVID-19 pandemic, telemedicine was quickly adopted to provide care for children and adolescents who have type 1 diabetes. To evaluate the effectiveness of virtual care for youth with type 1 diabetes, we compared glucometrics of a subset of these patients who used continuous glucose monitoring (CGM) both before and during the pandemic.MethodsChildren and adolescents aged 1 to 17 years with type 1 diabetes duration ≥1 year if ≥6 years old or ≥6 months if <6 years old, with ≥1 visit with recorded CGM data both before the pandemic (April 1, 2019 through March 15, 2020) and during it (April 1, 2020 through March 15, 2021) were included. Data were extracted from the electronic health record.ResultsThe study sample comprised 555 young people (46% male, 87% White, 79% pump-treated) with these characteristics: mean age 12.3±3.4 years; type 1 diabetes duration, 5.9±3.5 years; baseline glycated hemoglobin, and A1c 8.0±1.0% (64±10.9 mmol/mol). Diabetes visit frequency increased from 3.8±1.7 visits during the prepandemic period to 4.3±2.2 visits during pandemic period (P<.001); during pandemic period, 92% of visits were virtual. Glucose management indicator (GMI) values improved slightly from 7.9% (63 mmol/mol) before the pandemic to 7.8% (62 mmol/mol) during the pandemic (P<.001). When participants were divided based on visit frequency categories, those with equal or greater visit frequency (n=437 [79% of sample]) had significant improvement in GMI (8.0% to 7.8% [64 to 62 mmol/mol], P 70% TIR and only 28% achieved <1% of observations of <54 mg/ dL. Indeed, participants with the highest percentage of hypoglycemia had the lowest HbA1c levels. However, patients using insulin pumps and CGM spent a lower percentage of time at <54 mg/dL.ConclusionsIn adults with long-standing type 1 diabetes, short-term blinded CGM profiles revealed frequent clinically significant hypoglycemia ( 70% of time in range; 28% of participants achieved the recommended <1% of time below 54 mg/dL, and 22% achieved the recommended 180 mg/dL). Importantly, the use of advanced technologies was associated with reduced proportions of time below and above range. The proportion of participants achieving less than 1% of time at <54 mg/dL was 26% among those who did not use advanced technologies, 36% among those who used CGM and insulin pumps without insulin-suspend features, and 47% among those who used insulin-suspend pumps. A similar pattern was observed for hyperglycemia. Importantly, only approximately 25% used personal CGM routinely, which is in line with a recent study reporting a decrease in the probability of CGM use with increasing age in older adulthood. As CGM use was associated with clinically significant improvement in glycemic control both in real-world and clinical studies, this underscores the need to identify and overcome age-related barriers to CGM use (17,34,35). Importantly, barriers preventing broader CGM use may also be on the side of health-care providers caring for these populations at increased risk, which is of particular concern and must be improved.Addition of Intermittently Scanned Continuous Glucose Monitoring to Standard Care in a Cohort of Pregnant Women with Type 1 Diabetes: Effect on Glycaemic Control and Pregnancy OutcomesPerea V1, Picón MJ2,3, Megia A4,5, Goya M6, Wägner AM7,8, Vega B8,9, Seguí N10, Montañez MD11, Vinagre I101Endocrinology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain; 2Endocrinology Department, Hospital Universitario Virgen de la Victoria, IBIMA, Málaga, Spain; 3Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; 4Endocrinology Department, Hospital Universitari Joan XXIII, IISPV, Universitat Rovira i Virgili, Tarragona, Spain; 5Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; 6Obstetrics and Gynaecology Department, Hospital Universitari Vall d’ Hebrón, Barcelona, Spain; 7Endocrinology Department, Complejo Hospitalario Universitario Insular Materno Infantil de Canarias, Las Palmas de Gran Canaria, Spain; 8Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de l
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