Abstract
Introduction
D
Notwithstanding the pandemic, last year brought us important new data on the use of CGM in different populations, supporting its efficacy for the management of glycemia as well as its positive impact on the quality of life. Importantly, new data on the cost‐effectiveness of CGM are also available from large insurance databases (6), adding this crucial financial edge to ensure a broader access and reimbursement.
Our article this year reviews a handful of papers providing strong evidence in favor of the use of CGM for daily glucose management for all individuals with diabetes.
Key Articles Reviewed for the Article
Gerhardsson P, Schwandt A, Witsch M, Kordonouri O, Svensson J, Forsander G, Battelino T, Veeze H, Danne T
Nathanson D, Svensson AM, Miftaraj M, Franzén S, Bolinder J, Eeg‐Olofsson K
Strategies to Enhance New CGM Use in Early Childhood (SENCE) Study Group*
Addala A, Zaharieva D, Gu AJ, Prahalad P, Scheinker D, Buckingham B, Hood KK, Maahs DM
Visser MM, Charleer S, Fieuws S, De Block C, Hilbrands R, Van Huffel L, Maes T, Vanhaverbeke G, Dirinck E, Myngheer N, Vercammen C, Nobels F, Keymeulen B, Mathieu C, Gillard P
Henriksen MM, Andersen HU, Thorsteinsson B, Pedersen‐Bjergaard U
Martens T, 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, McGill JB, Aleppo G, Nguyen QT, Orozco I, Biggs W, Lucas KJ, Polonsky WH, Buse JB, Price D, Bergenstal RM, for the MOBILE Study Group
Galindo RJ, Migdal AL, Davis GM, Urrutia MA, Albury B, Zambrano C, Vellanki P, Pasquel FJ, Fayfman M, Peng L, Umpierrez GE
den Braber N, Vollenbroek‐Hutten MMR, Westerik KM, Bakker SJL, Navis G, van Beijnum BF, Laverman GD
Roussel R, Riveline JP, Vicaut E, de Pouvourville G, Detournay B, Emery C, Levrat‐Guillen F, Guerci B
The SWEET Project 10‐Year Benchmarking in 19 Countries Worldwide Is Associated with Improved HbA1c and Increased Use of Diabetes Technology in Youth with Type 1 Diabetes
Gerhardsson P1, Schwandt A2,3, Witsch M4, Kordonouri O5, Svensson J6,7, Forsander G8, Battelino T9, Veeze H10, Danne T5,11
1Department of Epidemiology, Institute of Applied Economics and Health Research, Copenhagen, Denmark; 2Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich‐Neuherberg, Germany; 4Department of Pediatrics DCCP, Center Hospitalier de Luxembourg, Luxembourg, Luxembourg; 5Children's Hospital AUF DER BULT, Hannover Medical School, Hannover, Germany; 6Department of Pediatrics and Adolescents, Copenhagen University Hospital, Herlev and Gentofte, Herlev, Denmark; 7Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 8Department of Pediatrics, Institute for Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Region Västra Götaland, Sahlgrenska University Hospital, Queen Silvia Children's Hospital, Gothenburg, Sweden; 9UMC‐University Children's Hospital and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia; 10Diabeter, Diabetes Center for Pediatric and Adolescent Diabetes Care and Research, Rotterdam, Netherlands; 11SWEET e.V., Hannoversche Kinderheilanstalt, Hannover, Germany
Objective
The international SWEET registry was initiated in 2008 to improve outcomes in pediatric diabetes. A 10‐year follow‐up allowed for studying time trends of key quality indicators in youth with type 1 diabetes (T1D) in 22 centers from Europe, Australia, Canada, and India.
Methods
Aggregated data per person with T1D <25 years of age were compared between 2008–2010 and 2016–2018. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age, and diabetes duration groups.
Results
The first and second time periods included 4930 versus 13,654 persons, 51% versus 52% male, median age 11.3 (Q1; Q3: 7.9; 14.5) versus 13.3 (9.7; 16.4) years, and T1D duration 2.9 (0.8; 6.4) versus 4.2 (1.4; 7.7) years. The adjusted hemoglobin A1C (HbA1c) improved from 68 (95% confidence interval [CI]: 66–70) to 63 (60; 65) mmol/mol (P < 0.0001) or 8.4 (95% CI: 8.2–8.6) to 7.9% (7.6; 8.1) (P < 0.0001). Across all age groups, HbA1c was significantly lower in pump and sensor users. Severe hypoglycemia declined from 3.8% (2.9; 5.0) to 2.4% (1.9; 3.1) (P < 0.0001), whereas diabetic ketoacidosis events increased significantly with injection therapy only. Body mass index–standard deviation score also showed significant improvements 0.55 (0.46; 0.64) versus 0.42 (0.33; 0.51) (P < 0.0001). Over time, the increase in pump use from 34% to 44% preceded the increase in HbA1c target achievement (<53 mmol/mol) from 21% to 34%.
Conclusions
Twice yearly benchmarking within the SWEET registry was associated with significantly improved HbA1c on a background of increasing pump and sensor use for 10 years in young persons with T1D.
Comment
This 10‐year, follow‐up analysis report on SWEET came as a surprise after the bleak reports form the T1DEx database (7) and the large U.S. electronic medical record database (8). The SWEET project (Better Control in Pediatric and Adolescent Diabete
Effect of Flash Glucose Monitoring in Adults with Type 1 Diabetes: A Nationwide, Longitudinal Observational Study of 14,372 Flash Users Compared with 7691 Glucose Sensor Naive Controls
Nathanson D1, Svensson AM2,3, Miftaraj M3, Franzén S3,4, Bolinder J1, Eeg‐Olofsson K2,5
1Department of Medicine, Karolinska University Hospital Huddinge, Karolinska Institute, Stockholm, Sweden; 2Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 3Centre of Registers Västra Götaland, Gothenburg, Sweden; 4Health Metrics, Department of Public Health and Community Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 5Department of Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
Aims
This study aimed to evaluate changes in HbA1c and rates of severe hypoglycemia over a 2‐year period after beginning flash glucose monitoring (FM) in type 1 diabetes.
Methods
A total of 14,372 adults with type 1 diabetes and a new registration of FM during 2016–2017 and continued FM for two consecutive years thereafter, as well as 7691 control individuals using conventional self‐monitoring of blood glucose (SMBG) during the same observation period, were included in a cohort study of data from the Swedish National Diabetes Registry. Propensity scores and inverse probability of treatment weighting (IPTW) were used to balance FM users with SMBG users. Changes in HbA1c and events of severe hypoglycemia were compared.
Results
After the initiation of FM, the difference in IPTW change in HbA1c was slightly greater in FM users compared with the control group during the follow‐up period, with an estimated mean absolute difference of −1.2 mmol/mol (−0.11%) (95% CI −1.64 [−0.15], −0.75 [−0.07]; p<0.0001) after 15 to 24 months. The change in HbA1c was greatest in those with baseline HbA1c ≥70 mmol/mol (8.5%), with the estimated mean absolute difference being −2.5 mmol/mol (−0.23%) (95% CI −3.84 [−0.35], −1.18 [−0.11]; p=0.0002) 15 to 24 months post index. The change was also significant in the subgroups with initial HbA1c ≤52 mmol/mol (6.9%) and 53–69 mmol/mol (7.0–8.5%). Risk of severe hypoglycemic episodes was reduced by 21% for FM users compared with control individuals using SMBG (OR 0.79 [95% CI 0.69, 0.91]; p=0.0014).
Conclusions
The use of FM correlated with a small and sustained improvement in HbA1c, most evident in those with higher baseline HbA1c levels. In addition, FM users experienced lower rates of severe hypoglycemic events compared with control individuals using SMBG for self‐management of glucose control.
Comment
There are several ways to interpret the results of this largest prospective observational national‐registry‐based report. The most important question certainly is whether the small albeit significant reduction in HbA1c is clinically relevant. Formally, according to the clinical significance margins of the Food and Drug Administration (FDA) and European Medicines Agency (EMA) for HbA1c (usually between 0.3–0.4%), it is not clinically significant. Similarly, the 21% reduction in severe hypoglycemia (SH) in a population with low general incidence of SH does not seem exceptionally impressive. The efficacy of intermittently scanned CGM (isCGM) depends on the number of scans per day. Unfortunately, this information is not provided in the report and therefore we cannot evaluate the usage patterns for isCGM in this study; a better outcome with isCGM could only be expected if the number of scans was significantly higher than the number of SMBG measures per day. Therefore, perhaps the most important message from this report is that isCGM does not work on its own—its success still depends on the actions of the user. Consequently, the education and continuous encouragement from the healthcare provider (HCP) team is paramount, as is the mutual agreement on glycemic targets: isCGM users should agree with the HCP team on the time in range (TIR) targets and verify their TIR several times per day. Only when isCGM is used with clear and agreed‐upon TIR targets, that are verified as often as several times per day, can a clinically meaningful improvement in metabolic control can be expected.
A recent short report on 348 children from Western Australia (11) followed longitudinally with a mean duration pre‐CGM of 2.4 (0.5) years and post‐CGM of 1.2 (0.5) years, with a mean HbA1c at CGM start of 8.5% (1.5%) (69 mmol/mol), demonstrates CGM use being associated with a significant decrease of 0.39% (95% CI 0.50–0.28, P<0.001), which is well within the clinically significant margin. This population had considerably higher mean HbA1c at CGM onset compared to the Swedish registry cohort. Importantly, the trend of increasing HbA1c pre‐CGM is reverted to stable post‐CGM at the lower level. These data additionally support the notion that the outcomes with CGM or isCGM depend on the education and agreed‐upon TIR targets.
A Randomized Clinical Trial Assessing Continuous Glucose Monitoring (CGM) Use with Standardized Education with or Without a Family Behavioral Intervention Compared with Fingerstick Blood Glucose Monitoring in Very Young Children with Type 1 Diabetes
Strategies to Enhance New CGM Use in Early Childhood (SENCE) Study Group
Aims
This study aimed to determine the effects of CGM combined with family behavioral intervention (CGM1FBI) and CGM alone (standard‐CGM) on glycemic outcomes and parental quality of life compared with blood glucose monitoring (BGM) in children with T1D aged 2 to < 8 years.
Methods
This multicenter (N=14), 6‐month, randomized controlled trial included 143 youth ages 2 to < 8 years old with T1D. Primary analysis included treatment group comparisons of percent time in range (TIR) (70–180 mg/dL) across follow‐up visits.
Results
Approximately 90% of participants in the CGM groups used CGM > 6 days/week at 6 months. Between‐group TIR comparisons showed no significant changes: CGM1FBI vs BGM 3.2% (95% CI 20.5, 7.0), standard‐CGM vs BGM 0.5% (22.6 to 3.6), and CGM1FBI vs standard‐CGM 2.7% (20.6, 6.1). Mean time with glucose level <70 mg/dL was reduced from baseline to follow‐up in the CGM1FBI (from 5.2% to 2.6%) and standard‐CGM (5.8% to 2.5%) groups, compared with 5.4% to 5.8% with BGM (CGM1FBI vs BGM, P<0.001, and standard‐CGM vs BGM, P<0.001). No severe hypoglycemic events occurred in the CGM1FBI group, one occurred in the standard‐CGM group, and five occurred in the BGM group. CGM1FBI parents reported greater reductions in diabetes burden and fear of hypoglycemia compared with standard‐CGM (P<0.008 and 0.04) and BGM (P<0.02 and 0.002).
Conclusions
CGM used consistently in young children with type 1 diabetes over a 6‐month period did not improve TIR but did significantly reduce time in hypoglycemia. The FBI benefited parental well‐being.
Comment
Contrary to my comments on the previous observational studies, this unusual report demonstrates no effect of the specific education with the use of CGM. The primary end point of TIR improvement is not met with CGM either with or without additional education despite an excellent adherence to the CGM usage; however, the time below range (TBR < 70) is significantly reduced. This apparent discrepancy is of particular interest. A recent report from the SWEET registry confirmed previous notions of specific glycemic targets set by the HCP team and agreed upon by the individual with diabetes and/or her/his family are tightly related to the outcomes (12). It is perhaps fair to speculate that albeit the primary outcome in this study is TIR, the specific education and specific target setting was rather focused on hypoglycemia, hence the TBR secondary end point is significantly lower with CGM. Assuming our speculation holds true, this study teaches one step further than the above observational reports: teaching/educating only works if/when setting and agreeing upon specific targets focused on TIR as the primary outcome. With well‐established association between hyperglycemia (time above range, TAR) and damage to the developing brain (demonstrated both with cognitive measures as well as diagnostic imaging) (9, 13), the reduction of TAR and increase of TIR in preschool children with T1D emerges is an undisputed priority.
Clinically Serious Hypoglycemia Is Rare and Not Associated with Time‐in‐Range in Youth with New‐Onset Type 1 Diabetes
Addala A1, Zaharieva D1, Gu AJ1,2, Prahalad P1,3, Scheinker D1,2,3, Buckingham B1,3, Hood KK1,3, Maahs DM1,3
1Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA; 2Department of Management Science and Engineering, Stanford University, Stanford, CA; 3Stanford Diabetes Research Center, Stanford, CA
Aims
Early initiation of CGM is encouraged for youth with T1D. Data to guide CGM use on time‐in‐range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited. This study aimed to determine whether (1) an association between increased TIR and hypoglycemia exists, and (2) how time in hypoglycemia varies by PCR status.
Methods
The authors studied 80 youth who started on CGM shortly after T1D diagnosis. The participants were followed for up to 1 year postdiagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.
Results
Youth initiated CGM 8.0 (interquartile range, 6.0–13.0) days postdiagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6±16.7%, 2.4±2.4% hypoglycemia at 1 month postdiagnosis; lowest TIR 61.3±20.3%, 2.1±2.7% hypoglycemia at 12 months postdiagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare, and there was minimal difference for PCR vs non‐PCR youth (54–70 mg/dL: 1.8% vs 1.2%, P=0.04; < 54 mg/dL: 0.3% vs 0.3%, P=0.55). Approximately 50% of the time spent in hypoglycemia was in the 65 to 70 mg/dL range.
Conclusion
TIR gradually decreased over 12 months postdiagnosis, and hypoglycemia was limited, with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. When starting CGM earlier, modifying CGM hypoglycemia education, including alarm settings, should be considered. These data support a trial in the year postdiagnosis to determine alarm thresholds for youth who use CGM.
Comment
This paper provides interesting new data on glycemia in the first year after diabetes onset. But the real importance is likely related to the very brave recommendation to focus more on time in range and less on hypoglycemia, suggesting a hypoglycemia alarm threshold of 65 mg/dL. Remembering less‐than‐perfect results from the previously discussed SENCE study and another recent RCT in adolescents (14), this may be a landmark proposal. Hypoglycemia‐focused diabetes care is preventing children and adolescents from achieving glycemic targets and unfortunately leaves them with hours of hyperglycemia and time above range, which is associated with lower cognitive outcomes, as measured by intellectual quotient, and reduced brain volume (13). Of note, even in this study by the end of the first year, the TIR was at 61.3% with TAR > 180 of 36.6%—still considerably above the recommended 25%. TBR < 70 stayed at 2.1%, which is half as low as recommended. There was no severe hypoglycemia. It really looks like what we need most is refocusing on time in range in pediatric diabetes care and substituting our deeply rooted instinctive fear of hypoglycemia with evidence‐based and data‐driven avoidance of excessive hyperglycemia. Recently presented data from the advanced hybrid closed‐loop systems clearly demonstrate that when artificial intelligence replaces human decision‐making, this is absolutely achievable (15).
Comparing Real‐Time and Intermittently Scanned Continuous Glucose Monitoring in Adults with Type 1 Diabetes (ALERTT1): A 6‐Month, Prospective, Multicentre, Randomised Controlled Trial
Visser MM1, Charleer S1, Fieuws S2, De Block C3, Hilbrands R4, Van Huffel L5, Maes T6, Vanhaverbeke G7, Dirinck E3, Myngheer N7, Vercammen C6, Nobels F5, Keymeulen B4, Mathieu C1, Gillard P1,4
1Department of Endocrinology, University Hospitals Leuven–KU Leuven, Leuven, Belgium; 2Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven and University of Hasselt, Leuven, Belgium; 3Department of Endocrinology‐Diabetology‐Metabolism, University Hospital Antwerp, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; 4Academic Hospital and Diabetes Research Centre, Vrije Universiteit Brussel, Brussels, Belgium; 5Department of Endocrinology, OLV Hospital Aalst, Aalst, Belgium; 6Department of Endocrinology, Imeldaziekenhuis Bonheiden, Bonheiden, Belgium; 7Department of Endocrinology, AZ Groeninge, Kortrijk, Belgium
Background
Patients with T1D can continuously monitor their glucose levels on demand (intermittently scanned continuous glucose monitoring [isCGM]) or in real time (real‐time continuous glucose monitoring [rtCGM]). Yet it is unclear whether switching from isCGM to rtCGM with alert functionality has additional benefits. Therefore, the authors did a trial comparing rtCGM and isCGM in adults with T1D (ALERTT1).
Methods
A prospective, double‐arm, parallel‐group, multicenter, randomized controlled trial took place in six hospitals in Belgium. Adults with T1D who previously used isCGM were randomly assigned (1:1) to rtCGM (intervention) or isCGM (control). Randomization was done centrally using minimization dependent on study center, age, gender, glycated hemoglobin (HbA1c), time in range (sensor glucose 3.9–10.0 mmol/L), insulin administration method, and hypoglycemia awareness. Participants, investigators, and study teams were not masked to group allocation. Primary end point was mean between‐group difference in time in range after 6 months assessed in the intention‐to‐treat sample.
Findings
Between January 29 and July 30, 2019, 269 participants were recruited, 254 of whom were randomly assigned to rtCGM (n=127) or isCGM (n=127); 124 and 122 participants completed the study, respectively. After 6 months, time in range was higher with rtCGM than with isCGM (59.6% vs 51.9%; mean difference 6.85 percentage points [95% CI 4.36–9.34]; p<0.0001). After 6 months, HbA1c was lower (7.1% vs 7.4%; p<0.0001), as was time < 3.0 mmol/L (0.47% vs 0.84%; p=0.0070) and Hypoglycaemia Fear Survey version II worry subscale score (15.4 vs 18.0; p=0.0071). Fewer participants on rtCGM experienced severe hypoglycemia (n=3 vs n=13; p=0.0082). Skin reaction was more often observed with isCGM, and bleeding after sensor insertion was more often reported by rtCGM users.
Conclusion
In an unselected adult T1D population, switching from isCGM to rtCGM significantly improved time in range after 6 months of treatment, implying that clinicians should consider rtCGM instead of isCGM to improve the health and quality of life of patients with T1D.
Comment
After the first data published on this interesting topic from the somewhat smaller CORRIDA trial (16), this study clearly demonstrates the superiority of continuous CGM data, possibly along with trend indications and alarms. It actually should not come as a surprise to any of us: more information on glycemia is usually associated with better glycemic outcomes, even in the self‐blood glucose monitoring (SBGM) era. This notion reiterates the importance of “using” CGM data for the routine management of diabetes; each individual person with diabetes much reach a delicate balance between sufficient information for quality decision‐making and avoidance of diabetes management fatigue. Perhaps decision support systems using artificial intelligence that digests the sometimes complex CGM information and provides specific advice on dosing will become a standard companion to every real‐time CGM device (17), likely embedded in a cloud‐based connected care environment (18).
Effects of Continuous Glucose Monitor‐Recorded Nocturnal Hypoglycaemia on Quality of Life and Mood During Daily Life in Type 1 Diabetes
Henriksen MM1, Andersen HU2, Thorsteinsson B1,3, Pedersen‐Bjergaard U1,3
1Department of Endocrinology and Nephrology, Nordsjællands Hospital, Hillerød, Denmark; 2Steno Diabetes Center Copenhagen, Gentofte, Denmark; 3Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Aims
This study aimed to determine how quality of life and mood is affected in T1D patients in the days following spontaneous nocturnal hypoglycemia.
Methods
A total of 153 people with T1D participated in 6 days of blinded continuous glucose monitoring while documenting hypoglycemic symptoms—quality of life and mood—daily. Hypoglycemia was defined by interstitial glucose ≤ 3.9 mmol/l (IG3.9) and ≤ 3.0 mmol/l (IG3.0) for ≥ 15 min and was classified as asymptomatic if no hypoglycemic symptoms were reported.
Results
Self‐estimated quality of life, which was assessed by the EQ‐5D VAS (but not by the WHO Well‐Being Index), was higher the day after asymptomatic (but not after symptomatic) hypoglycemic nights, as compared with nonhypoglycemic nights (IG3.9, p=0.021; IG3.0, p=0.048). The effect increased with lower glucose nadir and longer duration of nocturnal hypoglycemia (IG3.9, p=0.03). The finding was confined to participants with impaired hypoglycemia awareness. There was no effect of nocturnal hypoglycemia on mood or self‐estimated effectiveness at work the following day.
Conclusions
Individuals with T1D and impaired hypoglycemia awareness reported higher quality of life on days preceded by asymptomatic (but not symptomatic) nights of hypoglycemia. The effect was amplified by lower glucose nadir and longer duration of episodes and may help explain resistance to implementation of interventions to reduce hypoglycemia in many people with impaired hypoglycemia awareness.
Comment
These data seem intuitively controversial: asymptomatic hypoglycemia in individuals with impaired awareness of hypoglycemia improves quality of life. If we try to take a broader view, however, this is only logical: we tend to like what we are used to. And, as unfortunately happens, things we are used to are often not helpful for us. On the other side of the coin, increased TIR is associated with improved diabetes management satisfaction (19). It is therefore conceivable to expect that by organizing diabetes management around increasing TIR and decreasing TBR, we replace one quality‐of‐life modality with another and perhaps overcome the traditionally strong influence of our habits. Furthermore, parental quality of life increased with the CGM data sharing (20). In any instance, the clinical importance of the quality of life is still underrated, and we need more data to better understand how we can increase factors improving this outcome so crucial for individuals with diabetes.
Effect of Continuous Glucose Monitoring on Glycemic Control in Patients with Type 2 Diabetes Treated with Basal Insulin: A Randomized Clinical Trial
Martens T1, Beck RW2, Bailey R2, Ruedy KJ2, Calhoun P2, Peters AL3, Pop‐Busui R4, Philis‐Tsimikas A5, Bao S6, Umpierrez G7, Davis G7, Kruger D8, Bhargava A9, Young L10, McGill JB11, Aleppo G12, Nguyen QT13, Orozco I14, Biggs W15, Lucas KJ16, Polonsky WH17, Buse JB10, Price D18, Bergenstal RM1, for the MOBILE Study Group
1International Diabetes Center, Park Nicollet Internal Medicine, Minneapolis, MN; 2Jaeb Center for Health Research, Tampa, FL; 3Keck School of Medicine of the University of Southern California, Los Angeles, CA; 4University of Michigan, Ann Arbor, MI; 5Scripps Whittier Diabetes Institute, San Diego, CA; 6Vanderbilt University Medical Center, Nashville, TN; 7Emory University School of Medicine, Atlanta, GA; 8Henry Ford Health System, Detroit, MI; 9Iowa Diabetes Research, West Des Moines, IA; 10University of North Carolina School of Medicine, Chapel Hill, NC; 11Washington University School of Medicine, St Louis, MO; 12Feinberg School of Medicine, Northwestern University, Chicago, IL; 13Las Vegas Endocrinology, Henderson, NV; 14Carteret Medical Group, Morehead City, NC; 15Amarillo Medical Specialists, Amarillo, TX; 16Diabetes & Endocrinology Consultants PC, Morehead City, NC; 17Behavioral Diabetes Institute, San Diego, CA; 18Dexcom Inc., San Diego, CA
This manuscript is also discussed in article on Primary Care and Diabetes Technologies and Treatments, page XX
Aims
CGM has been shown to be beneficial for adults with type 2 diabetes (T2D) using intensive insulin therapy, but its use in T2D treated with basal insulin without prandial insulin has not been well studied. The aim of this study was to determine the effectiveness of CGM in adults with T2D treated with basal insulin without prandial insulin in primary care practices.
Methods
This randomized clinical trial, conducted at 15 centers in the United States (enrollment from July 30, 2018, to October 30, 2019; follow‐up completed July 7, 2020), included adults with T2D receiving their diabetes care from a primary care clinician and treated with one or two daily injections of long‐ or intermediate‐acting basal insulin without prandial insulin, with or without noninsulin glucose‐lowering medications. Random assignment 2:1 to CGM (n=116) or traditional blood glucose meter (BGM) monitoring (n=59).
Results
Among 175 randomized participants (mean [SD] age, 57 [9] years; 88 women [50%]; 92 racial/ethnic minority individuals [53%]; mean [SD] baseline HbA1c level, 9.1% [0.9%]), 165 (94%) completed the trial. Mean HbA1c level decreased from 9.1% at baseline to 8.0% at 8 months in the CGM group and from 9.0% to 8.4% in the BGM group (adjusted difference, −0.4% [95% CI, −0.8% to −0.1%]; P=0.02). In the CGM group, compared with the BGM group, the mean percentage of CGM‐measured time in the target glucose range of 70 to 180 mg/dL was 59% vs 43% (adjusted difference, 15% [95% CI, 8% to 23%]; P<0.001), the mean percentage of time at greater than 250 mg/dL was 11% vs 27% (adjusted difference, −16% [95% CI, −21% to −11%]; P<0.001), and the means of the mean glucose values were 179 mg/dL vs 206 mg/dL (adjusted difference, −26 mg/dL [95% CI, −41 to −12]; P<0.001). Severe hypoglycemic events occurred in 1 participant (1%) in the CGM group and in 1 (2%) in the BGM group.
Conclusions
Among adults with poorly controlled T2D treated with basal insulin without prandial insulin, continuous glucose monitoring resulted in significantly lower HbA1c levels at 8 months versus blood glucose meter monitoring.
Comment
This MOBILE trial extends evidence for CGM efficacy into a large population of individuals with diabetes using basal insulin only in addition to other noninsulin medications. The primary outcome is difference in HbA1c, which looks somehow old‐fashioned, but the achieved difference is clinically meaningful. All secondary CGM‐derived metrics are also significantly better in the CGM group, with the mean difference in TIR of 15%. These important results of reduction in HbA1c and improvement in TIR come at no cost from hypoglycemia. In the follow‐up of this study, when some returned to the use of SBGM all improvements disappear (21), confirming the well‐accepted concept of CGM being a behavior‐modification tool. More importantly, those that continued with the use of CGM, an additional increase in TIR is observed at month 14, confirming the success of CGM beyond 1 year in this population. Again, to avoid long‐term fatigue from CGM usage, artificial intelligence decision support systems will likely be integrated in all advanced CGM devices. Clinical trials with decision support system–enabled CGM devices in T2D are warranted.
Comparison of the FreeStyle Libre Pro Flash Continuous Glucose Monitoring (CGM) System and Point‐of‐Care Capillary Glucose Testing in Hospitalized Patients with Type 2 Diabetes Treated with Basal‐Bolus Insulin Regimen
Galindo RJ1, Migdal AL1, Davis GM1, Urrutia MA1, Albury B1, Zambrano C1, Vellanki P1, Pasquel FJ1, Fayfman M1, Peng L2, Umpierrez GE1
1Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, GA; 2Rollins School of Public Health, Emory University, Atlanta, GA
Objective
This study aimed to compare the FreeStyle Libre Pro CGM and POC capillary glucose testing among insulin‐treated hospitalized patients with T2D.
Methods
This prospective study assessed adult patients with T2D who were admitted to general medicine and surgery wards. Patients were monitored with POC before meals and bedtime and with CGM during the hospital stay. Study end points included differences between POC and CGM in mean daily blood glucose (BG), hypoglycemia <70 and <54 mg/dL, and nocturnal hypoglycemia. The study also determined the mean absolute relative difference (MARD), ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL, and error grid analysis between matched glucose pairs.
Results
Mean daily glucose was significantly higher by POC (188.9±37.3 vs 176.1±46.9 mg/dL), with an estimated mean difference of 12.8 mg/dL (95% CI 8.3–17.2 mg/dL), and proportions of patients with glucose readings <70 mg/dL (14% vs 56%) and <54 mg/dL (4.1% vs 36%) detected by POC BG were significantly lower compared with CGM (all P < 0.001). Nocturnal and prolonged CGM hypoglycemia <54 mg/dL were 26% and 12%, respectively. The overall MARD was 14.8%, ranging between 11.4% and 16.7% for glucose values between 70 and 250 mg/dL and higher for 51–69 mg/dL (MARD 28.0%). The percentages of glucose readings within ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL were 62%, 76%, and 91%, respectively. Error grid analysis showed 98.8% of glucose pairs within zones A and B.
Conclusions
Compared with POC, FreeStyle Libre CGM showed lower mean daily glucose and higher detection of hypoglycemic events, particularly nocturnal and prolonged hypoglycemia in hospitalized patients with T2D. CGM's accuracy was lower in the hypoglycemic range.
Comment
After reading this report, one is amazed that point‐of‐care (POC) management of insulin‐treated diabetes is still acceptable for in‐patient care. Particularly during the COVID‐19 pandemic, meticulous glucose management very significantly improves morbidity and mortality (22), and the use of CGM for in‐hospital management of any form, type, or stage of diabetes should be the standard of care. Why is this not the case? If we, diabetologists, are not sufficiently strong to push the necessary upgrades of diabetes care through hospital administration apparatus, should perhaps the legal teams be alerted? Individuals with diabetes have the right for better glycemia management while in hospital, and consequently for better morbidity and mortality outcomes; this is not only an ethical and professional imperative, it finally also saves resources.
Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated with Insulin: Real‐World Evidence from the DIALECT‐2 Cohort
den Braber N1,2, Vollenbroek‐Hutten MMR1,2, Westerik KM1, Bakker SJL3, Navis G3, van Beijnum BF2, Laverman GD1
1Division of Nephrology, Department of Internal Medicine, Ziekenhuisgroep Twente, Almelo and Hengelo, the Netherlands; 2Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands; 3Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
Objective
The aim of this study was to assess glucose variations associated with HbA1c in insulin‐treated patients with T2D.
Methods
Participants of Diabetes and Lifestyle Cohort Twente (DIALECT)‐2 (n=79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54–62, and ≥63 mmol/mol or ≤7, 7.1–7.8, and ≥7.9%, respectively). Blood glucose TIR, TBR, TAR, glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by CGM using the FreeStyle Libre sensor and compared between HbA1c categories.
Results
CGM was initiated for a median (interquartile range) of 10 (7–12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3–88.2] vs 76.0% [72.5.0–80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4–19.1] vs 0.7% [0.3–6.1] and 8.2% [5.7–17.6] vs 20.4% [11.6–27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9–67.3]) and higher TAR (44.1% [27.8–57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06±0.06/h vs 0.03±0.03/h; P=0.002) and longer (88.0 [45.0–195.5] vs 53.4 [34.4–82.8] minutes; P < 0.001) TBR episodes during the night than during the day.
Conclusions
A high HbA1c did not decrease the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR in this study. Optimal personalization of glycemic control requires the use of newer tools, including CGM‐derived parameters.
Comment
Several aspects are of interest in this study on a small population of individuals with T2D treated with insulin. Perhaps the most important is the notion that lower HbA1c is not achieved through higher TIR but with increased TBR when the disease management is guided by SBGM. This is perhaps the strongest message in support of CGM‐guided diabetes management, where the goal is “more TIR with less TBR.” Secondly, high HbA1c > 7.9% is not associated with less nocturnal hypoglycemia, confirming the now well‐established clinical fact that by increasing the mean glucose we cannot prevent hypoglycemia when the glucose variability remains unchanged. Finally, time in hypoglycemia was longest during the night, defined as midnight till 6 a.m. in all groups; this may be related to preferential use of basal insulin guided by the fasting blood glucose only. As confirmed in a recent analysis of different glycemic markers from several published trials (23), we need at least one marker describing mean glucose and one describing hypoglycemia—HbA1c is simply not enough anymore.
Important Drop in Rate of Acute Diabetes Complications in People with Type 1 or Type 2 Diabetes After Initiation of Flash Glucose Monitoring in France: The RELIEF Study
Roussel R1,2,3, Riveline JP2,3,4, Vicaut E5, de Pouvourville G6, Detournay B7, Emery C7, Levrat‐Guillen F8, Guerci B9
1Department of Diabetology, Endocrinology, and Nutrition, Bichat‐Claude Bernard Hospital, Paris, France; 2Unité INSERM U1138 Immunity and Metabolism in Diabetes, ImMeDiab Team, Centre de Recherches des Cordeliers, Paris, France; 3Université de Paris, Paris, France; 4Department of Diabetology and Endocrinology, Lariboisiere Hospital, Paris, France; 5Clinical Research Unit, Fernand Vidal Hospital, Paris, France; 6Department of Economics, ESSEC Business School, Cergy‐Pontoise, France; 7CEMKA, Bourg‐la‐Reine, France; 8Abbott Laboratories, Maidenhead, Berkshire, UK; 9Department of Endocrinology, Diabetology, and Nutrition, Brabois Adult Hospital, University of Lorraine, Vandoeuvre‐les‐Nancy, France
Objective
The RELIEF study examined rates of hospitalization in France for acute diabetes complications before and after initiation of the FreeStyle Libre system.
Methods
A total of 74,011 patients with T1D or T2D who initiated the FreeStyle Libre system were identified from the French national claims database with use of ICD‐10 codes, from hospitalizations with diabetes as a contributing diagnosis, or from insulin prescriptions. Patients were subclassified based on SMBG strip acquisition prior to starting FreeStyle Libre. Hospitalizations for diabetic ketoacidosis (DKA), severe hypoglycemia, diabetes‐related coma, and hyperglycemia were recorded for 12 months before and after initiation.
Results
Hospitalizations for acute diabetes complications decreased in T1D (−49.0%) and T2D (−39.4%) following the use of FreeStyle Libre. DKA decreased in T1D (−56.2%) and in T2D (−52.1%), as did diabetes‐related comas in T1D (−39.6%) and T2D (−31.9%). Hospitalizations for hypoglycemia and hyperglycemia decreased in T2D (−10.8% and −26.5%, respectively). Before initiation, hospitalizations were most marked for people noncompliant with SMBG and for those with highest acquisition of SMBG, which fell by 54.0% and 51.2%, respectively, following FreeStyle Libre initiation. Persistence with FreeStyle Libre at 12 months was at 98.1%.
Conclusions
This large retrospective study on hospitalizations for acute diabetes complications shows that a significantly lower incidence of admissions for DKA and for diabetes‐related coma is associated with use of flash glucose monitoring. This study has significant implications for patient‐centered diabetes care and potentially for long‐term health economic outcomes.
Comment
It started with the report from the Northern California integrated Kaiser Permanente healthcare delivery system (24), where out of 36,080 insulin‐treated individuals with T2D, 344 began using CGM. Mean weighted and adjusted difference in event rates (emergency department visit or hospitalization) for hypoglycemia was (95% CI) −4.0 (−7.8 to −0.2) p<0.04, with a decrease in HbA1c (95% CI) of −0.56% (−0.72 to −0.41) p<0.001 favoring CGM use. Assuming that the association was causal, the number needed to treat to avoid one hypoglycemic event was 25 (95% CI, 13–476), and to achieve 1 more person with HbA1c lower than 8% was 6 (95% CI, 4–10). This strong initial message that CGM usage is cost saving also in T2D was soon confirmed with the data analysis from the French national insurance database, including 74,011 individuals with type 1 or type 2 diabetes treated with insulin who initiated isCGM. Hospitalizations for acute diabetes complications fell for −49.0% in type 1 diabetes and for −39.4% in type 2 diabetes following isCGM initiation, with the isCGM usage persistence of 98.1% after 1 year. An almost half overall reduction in hospitalizations for acute events in this large national cohort bring immediate substantial cost savings, in addition to all obvious benefits to the individuals with diabetes, their families, and the healthcare providers. Unfortunately, the number of dollars or euros saved is not provided in the publications and will hopefully emerge in more economic outcomes–focused analyses.
Conclusion
With the accumulating evidence from all angles of CGM use in clinical research and routine practice (25, 26), as well as evidence on cost savings related to reduced hospitalization rate with the use of CGM, the remaining barrier remains accessibility. It is unfortunately not enough to adopt guidelines and standards of care as clear and unequivocal as they may be, when the access to high‐quality diabetes technology remains limited. A comparison between a mostly private insurance‐driven healthcare system and a publicly funded social‐care system demonstrated disparities in access to the technology and related diabetes outcomes (27), particularly pronounced in the private insurance environment where highest socioeconomic status had four‐times higher access to CGM and roughly 1% difference in HbA1c favoring the wealthy. Ethnic disparities are also reported (28), likely linked to socioeconomic status. Finally, barriers related to socioeconomic status may also include general access to specialist care (29). Regulatory agencies, national social security administrations, and finally governments may need to intervene to ensure a more universal access to diabetes technology and better care for all individuals with diabetes.
Footnotes
Author Disclosure Statement
KD declares he has no conflicts of interest. 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 boards of Sanofi, Eli Lilly, Novo Nordisk Indigo, and Medtronic. TB's employer University of Ljubljana and UMC Ljubljana received research grant support, with receipt of travel and accommodation expenses in some cases, from Abbott, Medtronic, Novo Nordisk, Sanofi, Sandoz, Novartis, and Zealand. TB received honoraria for participating on the speaker's bureau of Eli Lilly, Astra Zeneca, Novo Nordisk, Medtronic, Sanofi, and Roche. TB owns stocks of DreaMed Diabetes.