Real-World Diabetes Technology: Overcoming Barriers and Disparities.

Abstract

Diabetes Technology & TherapeuticsVol. 25, No. S1 Original ArticlesFree AccessReal-World Diabetes Technology: Overcoming Barriers and DisparitiesLaurel H. Messer, Ananta Addala, and Stuart A. WeinzimerLaurel H. MesserBarbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA.Search for more papers by this author, Ananta AddalaDivision of Pediatric Endocrinology & Diabetes, Department of Pediatrics, Stanford University, Palo Alto, CA, USA.Search for more papers by this author, and Stuart A. WeinzimerDepartment of Pediatrics, Yale University School of Medicine, New Haven, CT, USA.Search for more papers by this authorPublished Online:20 Feb 2023https://doi.org/10.1089/dia.2023.2511AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail IntroductionIn recent years, the diabetes community has focused on documenting the presence of inequities in diabetes outcomes and management, focusing particularly in the diabetes technology space. These real-world data repeatedly indicate that glycemia improves with the addition of continuous glucose monitoring, insulin pumps, and hybrid closed-loop systems; these results also demonstrate the persistence of inequities. While these findings are consistent, it is important to acknowledge that published studies do not represent the majority of people living with diabetes, and that understanding the context of real-world evidence is nuanced. Last year's article on real-world data highlighted studies from the United States (1–3) and Europe (4) that shed light on the socioeconomic and racial/ethnic disparities in technology uptake (5). Identifying and describing these disparities is the first step in advocating for those who are “missing” from the diabetes technology landscape and for which areas need further study for comprehensive real-world evidence. Further elucidating patterns of disparities, delving into causes, and discussion of novel ways to measure disparities are the extension of this work.This year's article on real-world evidence takes new approaches to examining these contexts, progressing through three themes. First, it aims to broaden the representation of published studies to new countries and regions and spotlights the populations represented in large benchmarking studies. This is important because it is insufficient to assume that all real-world evidence is applicable or representative of all people with diabetes. Second, our article highlights new populations using diabetes technology, to help understand the broad utility of devices for global health. Finally, our article highlights the application of varied methodologies to address disparities in diabetes technology utilization. We highlight four methodological approaches utilized to address disparities with the goal of encouraging the diabetes community to move beyond only documenting disparities by race/ethnicity and socioeconomic status alone and lay a roadmap to action.This article includes original research articles retrieved from PubMed that were published between July 2021 and June 2022 and contain search terms related to diabetes technologies, including insulin pump, hybrid closed loop, HCL, continuous glucose monitor, CGM, intermittently scanned CGM, isCGM, real-time continuous glucose monitoring, and rtCGM. Important context terms included “disparities”, “real-world use,” “barriers,” “discontinuation,” “practical,” and “clinical care.” Over 300 article titles were reviewed for pertinence and possible inclusion in this article. Of these, 54 abstracts were reviewed in detail, and 17 were selected for inclusion in this article.Key Articles ReviewedAssociation of the Use of Diabetes Technology with HbA1c and BMI-SDS in an International Cohort of Children and Adolescents with Type 1 Diabetes: The SWEET Project ExperienceMarigliano M, Eckert AJ, Guness PK, Herbst A, Smart CE, Witsch M, Maffeis C, SWEET Study GroupPediatr Diabetes 2021;22: 1120–1128The 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 DiabetesGerhardsson P, Schwandt A, Witsch M, Kordonouri O, Svensson J, Forsander G, Battelino T, Veeze H, Danne T on Behalf of the SWEET Study GroupDiabetes Technol Ther 2021;23: 491–499Temporal Trends for Diabetes Management and Glycemic Control Between 2010 and 2019 in Korean Children and Adolescents with Type 1 DiabetesChoe J, Won SH, Choe Y, Park SH, Lee YJ, Lee J, Lee YA, Lim HH, Yoo JH, Lee SY, Kim EY, Shin CH, Kim JHDiabetes Technol Ther 2022;24: 201–211Real-World Performance of the Minimed 780G System: First Report of Outcomes from 4120 UsersDe Silva JD, Lepore G, Battelino T, Arrieta A, Castañeda J, Grossman B, Shin J, Cohen ODiabetes Technol Ther 2022;24: 113–119Intermittently Scanned Continuous Glucose Monitoring Data of Polish Patients from Real-Life Conditions: More Scanning and Better Glycemic Control Compared to Worldwide DataHohendorff J, Gumprecht J, Mysliwiec M, Zozulinska-Ziolkiewicz D, Malecki MTDiabetes Technol Ther 2021;23: 577–585Use of Technology in Older Adults with Type 1 Diabetes: Clinical Characteristics and Glycemic MetricsMunshi M, Slyne C, Davis D, Michals A, Sifre K, Dewar R, Atakov-Castillo A, Toschi EDiabetes Technol Ther 2022;24: 1–9Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World DataToschi E, Atakov-Castillo A, Slyne C, Munshi MDiabetes Technol Ther 2022;24: 140–142Continuous Glucose Monitor Use Prevents Glycemic Deterioration in Insulin-Treated Patients with Type 2 DiabetesKarter AJ, Parker MM, Moffet HH, Gilliam LK, Dlott RDiabetes Technol Ther 2022;24: 332–337Flash Glucose Monitoring in Type 2 Diabetes Managed with Basal Insulin in the USA: A Retrospective Real-World Chart Review Study and Meta-analysisCarlson AL, Daniel TD, DeSantis A, Jabbour S, Karslioglu French E, Kruger D, Miller E, Ozer K, Elliott TBMJ Open Diabetes Res Care 2022;10: e002590Flash CGM Associated with Event Reduction in Nonintensive Diabetes TherapyMiller E, Kerr MSD, Roberts GJ, Nabutovsky Y, Wright EAm J Manag Care 2021;27: e372–e377Heterogeneity of Access to Diabetes Technology Depending on Area Deprivation and Demographics Between 2016 and 2019 in GermanyAuzanneau M, Rosenbauer J, Maier W, von Sengbusch S, Hamann J, Kapellen T, Freckmann G, Schmidt S, Lilienthal E, Holl RWJ Diabetes Sci Technol 2021;15: 1059–1068The Impact of Socio-Economic Deprivation on Access to Diabetes Technology in Adults with Type 1 DiabetesFallon C, Jones E, Oliver N, Reddy M, Avari PDiabet Med 2022;25: e14906Universal 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: 391–397Improving Equitable Access to Continuous Glucose Monitors for Alabama's Children with Type 1 Diabetes: A Quality Improvement ProjectSchmitt J, Fogle K, Scott ML, Iyer PDiabetes Technol Ther 2022;24: 481–491Implicit Racial-Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider CohortOdugbesan O, Addala A, Nelson G, Hopkins R, Cossen K, Schmitt J, Indyk J, Jones NY, Agarwal S, Rompicherla S, Ebekozien ODiabetes Technol Ther 2022;24: 619–627Hispanic Caregivers' Experience of Pediatric Type 1 Diabetes: A Qualitative StudyTremblay ES, Ruiz J, Dykeman B, Maldonado M, Garvey KPediatr Diabetes 2021;22: 1040–1050Technology Utilization in Black Adolescents with Type 1 Diabetes: Exploring the Decision-Making ProcessMencher SR, Weinzimer SA, Nally LM, Van Name M, Nunez-Smith M, Sadler LSDiabetes Technol Ther 2022;24: 249–257REAL-WORLD OUTCOMES ACROSS POPULATIONS AND REGIONAssociation of the Use of Diabetes Technology with HbA1c and BMI-SDS in an International Cohort of Children and Adolescents with Type 1 Diabetes: The SWEET Project ExperienceMarigliano M1, Eckert AJ2,3, Guness PK4, Herbst A5, Smart CE6, Witsch M7, Maffeis C1, SWEET Study Group1Regional Center for Pediatric Diabetes, University of Verona, University City Hospital, Verona, Italy; 2Institute of Epidemiology and MedicalBiometry, ZIBMT, University of Ulm, Ulm, Germany; 3German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; 4Nongovernment Organization, Quatres Bornes, Mauritius; 5Department of Pediatric and Adolescent Medicine, Hospital Leverkusen GmbH, Leverkusen, Germany; 6Department of Paediatric Endocrinology, John Hunter Children's Hospital, New Lambton Heights, Australia; 7Pediatric Diabetology, Centre Hospitalier de Luxembourg, Luxembourg, LuxembourgPediatr Diabetes 2021;22: 1120–1128ObjectiveTwo devices used in diabetes management are insulin pumps (for continuous subcutaneous insulin infusion [CSII]) and glucose sensors (for continuous glucose monitoring [CGM]). The aim of this study was to determine whether the use of at least one of these devices was associated with metabolic control (HbA1c) and body adiposity (body mass index standard deviation score [BMI-SDS]) in children and adolescents with type 1 diabetes who have never used these devices before.Subjects and MethodsA total of 4643 T1D patients (aged 2–18 years, T1D ≥1 year, without celiac disease, no CSII or CGM before 2016) participating in the SWEET prospective multicenter diabetes registry were enrolled. Data were collected at two points (2016 and 2019). Metabolic control was assessed by glycated hemoglobin (HbA1c) and body adiposity by BMI-SDS (according to WHO standards). Patients were categorized by treatment modality (multiple daily injections [MDIs] or CSII) and the use or not of CGM. Linear regression models, adjusted for age, gender, duration of diabetes and region, were applied to assess differences in HbA1c and BMI-SDS among patient groups.ResultsThe proportion of patients using MDI with CGM and CSII with CGM significantly increased from 2016 to 2019 (7.2% to 25.7%, 7.8% to 27.8% respectively; P<.001). Linear regression models showed a significantly lower HbA1c in groups that switched from MDIs to CSII with or without CGM (P<.001), but a higher BMI-SDS for those who switched from MDIs without CGM to CSII with CGM (P<.05) and those who switched from MDI without CGM to CSII without CGM (P<.01).ConclusionsSwitching from MDI to CSII was significantly associated with improvement in glycemic control but increased BMI-SDS over time. Diabetes technology may improve glucose control in youths with T1D, although further strategies to prevent excess fat accumulation are needed.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 DiabetesGerhardsson P1, Schwandt A2,3, Witsch M4, Kordonouri O5, Svensson J6,7, Forsander G8, Battelino T9, Veeze H10, Danne T5,11 on Behalf of the SWEET Study Group1Department 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; 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, GermanyDiabetes Technol Ther 2021;23: 491–499This manuscript is also discussed in DIA-2023-2508, page S-118.ObjectiveThe international SWEET registry (trial registration: NCT04427189) was initiated in 2008 to improve outcomes in pediatric diabetes. A 10-year follow-up allowed researchers to study time trends of key quality indicators in youth with type 1 diabetes (T1D) who were seen in at least one of 22 centers from Europe, Australia, Canada, and India.MethodsAggregated data per person with T1D <25 years of age were compared between the 2008–2010 and 2016–2018 time periods. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age, and diabetes duration groups.ResultsThe first and second time periods included 4930 and 13,654 persons, respectively (51% versus 52% male, median age 11.3 [IQR, 7.9–14.5] vs 13.3 [IQR, 9.7–16.4] years); median T1D duration was 2.9 (IQR, 0.8–6.4) years for the first time period and 4.2 (IQR, 1.4–7.7) years for the second period. The adjusted hemoglobin A1C (HbA1c) improved significantly (P<.0001) from 68 (95% CI, 66–70) mmol/mol to 63 (60–65) mmol/mol or 8.4% (95% CI, 8.2%–8.6%) to 7.9% (95% CI, 7.6%–8.1%). Across all age groups, HbA1c was significantly lower in pump and sensor users. Severe hypoglycemia declined from 3.8% (95% CI, 2.9%–5.0%) to 2.4% (95% CI, 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, from 0.55 (95% CI, 0.46–0.64) in the first period to 0.42 (95% CI, 0.33–0.51) in the second period (P<.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%.ConclusionsTwice yearly benchmarking within the SWEET registry was associated with significantly improved HbA1c and increased pump and sensor use over a 10-year period in young persons with T1D. Temporal Trends for Diabetes Management and Glycemic Control Between 2010 and 2019 in Korean Children and Adolescents with Type 1 DiabetesChoe J1, Won SH2, Choe Y3, Park SH3, Lee YJ3,4, Lee J5, Lee YA3,4, Lim HH6, Yoo JH7, Lee SY4,8, Kim EY9, Shin CH3,4, Kim JH1,41Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, South Korea; 2Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam, South Korea; 3Department of Pediatrics, Seoul National University Children's Hospital, Seoul, South Korea; 4Department of Pediatrics, Seoul National University College of Medicine, Seoul, South Korea; 5Department of Pediatrics, Inje University Ilsan Paik Hospital, Goyang, South Korea; 6Department of Pediatrics, Chungnam National University Hospital, Daejeon, South Korea; 7Department of Pediatrics, Dong-A University Hospital, Busan, South Korea; 8Department of Pediatrics, SMG-SNU Boramae Medical Center, Seoul, South Korea; 9Department of Pediatrics, Chosun University Hospital, Gwangju, South KoreaDiabetes Technol Ther 2022;24: 201–211PurposeThere is increasing use of modern devices in the management of patients with type 1 diabetes (T1D). We investigated temporal trends for diabetes management and outcomes in Korean pediatric T1D patients over 10 years.MethodsWe retrospectively collected the data from 752 participants (311 [41.4%] boys) diagnosed with T1D and aged ≤18 years, with ≥1 year of follow-up between 2010 and 2019 in any of the seven study hospitals in Korea.ResultsOver the 10-year study period, use of continuous glucose monitoring (CGM) increased from 1.4% to 39.3%. From 2010 to 2019, use of multiple daily insulin injections (MDIs) increased from 63.9% to 77.0%, and continuous subcutaneous insulin infusion (CSII) increased from 2.1% to 14.0%. However, during the same time period, conventional insulin therapy (CIT) decreased from 33.9% to 9.0%. Mean glycated hemoglobin (HbA1c) decreased from 8.56% to 8.01% (P<.001) and was lower in younger patients, boys, and CGM users (P<.001). MDI and CSII users had lower mean HbA1c levels than CIT users (P=0.003). Regarding the acute complications of T1D, CGM use was associated with lower incidences of diabetic ketoacidosis (P=.015), and CSII users were likely to experience less severe hypoglycemia (P=.008).ConclusionsThe use of CSII and CGM increased ∼7- and 30-fold, respectively, over the 10-year study period. The glycemic control of pediatric T1D patients in Korea improved from 2010 to 2019, probably because of increased use of T1D technologies.CommentsSeveral articles were published this year related to outcomes in large and small regional populations (6–11). The three highlighted here present benchmarking data, with the SWEET projects using registry data from 19 countries around the world (10,11) and a new report from Korea (9), a country often not represented in larger registry studies, prompting notable mention. All three articles report on an increase in CGM use and insulin pump use over time, as well as glycemic improvement in the respective cohorts over the same time period. In the SWEET registry, CGM use increased most dramatically over a decade, from negligible percentages to over 50% of the main cohort (11) and from 1.4% to 39.3% in the Korean cohort (9). Insulin pump use also increased across all studies, a finding that was associated with better glycemic outcomes and reduction in HbA1c.Marigliano and colleagues (10) uniquely report an increase in body adiposity in SWEET registry youth from 2016 to 2019 who switched from multiple daily injections to insulin pump with or without CGM use (P<.05 and P<.01, respectively). This effect was not seen in any other technology group. This finding is important because excess body weight is directly associated with cardiovascular risk for people with diabetes (12,13). While associations between technology use and glycemic outcomes are important contributions to our body of knowledge, this report reminds us that clinical context is important, and uncovering the effect of technology on other diabetes risk factors presents a more holistic picture of long-term health. This should be a topic of further study and a model for how to use expanded clinical characteristics in benchmarking analyses.Real-World Performance of the Minimed 780G System: First Report of Outcomes from 4120 UsersDa Silva JD1, Lepore G2, Battelino T3, Arrieta A4, Castañeda J4, Grossman B5, Shin J5, Cohen O11Medtronic International Trading Sàrl, Tolochenaz, Switzerland; 2Unit of Endocrine Diseases and Diabetology, ASST Papa Giovanni XXIII, Bergamo, Italy; 3University Children's Hospital, University Medical Centre Ljubljana, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia; 4Medtronic Bakken Research Center, Maastricht, The Netherlands; 5Medtronic, Northridge, CADiabetes Technol Ther 2022;24: 113–119BackgroundThe MiniMed 780G system includes an advanced hybrid closed-loop (AHCL) algorithm that provides both automated basal and correction bolus insulin delivery. The preliminary performance of the system in real-world settings was evaluated.MethodsData uploaded from August 2020 to March 2021 by individuals living in Belgium, Finland, Italy, the Netherlands, Qatar, South Africa, Sweden, Switzerland, and the United Kingdom were aggregated and retrospectively analyzed to determine the mean glucose management indicator (GMI); percentage of time spent within (TIR; 70–180 mg/dL), below (TBR; <70 mg/dL), and above (TAR; >180 mg/dL) target glycemic ranges; system use; and insulin consumption in users having ≥10 days of sensor glucose (SG) data after initiating AHCL. The impact of initiating AHCL was evaluated in a subgroup of users also having ≥10 days of SG data before AHCL initiation.ResultsUsers (N=4120) were observed for a mean of 54±32 days. During this time, they spent a mean of 94.1%±11.4% of the time in AHCL and achieved a mean GMI of 6.8%±0.3%, TIR of 76.2%±9.1%, TBR of 2.5%±2.1%, and TAR of 21.3%±9.4%, after initiating AHCL. There were 77.3% and 79.0% of users who achieved a TIR >70% and a GMI of <7.0%, respectively. Users for whom comparison with pre-AHCL was possible (N=812) reduced their GMI by 0.4%±0.4% (P=.005) and increased their TIR by 12.1%±10.5% (P<.0001), post-AHCL initiation. More users achieved the glycemic treatment goals of GMI <7.0% (37.6% vs 75.2%, P<.0001) and TIR >70% (34.6% vs 74.9%, P<.0001) than they did before AHCL initiation.ConclusionMost MiniMed 780G system users achieved TIR >70% and GMI <7% while minimizing hypoglycemia in a real-world condition.Intermittently Scanned Continuous Glucose Monitoring Data of Polish Patients from Real-Life Conditions: More Scanning and Better Glycemic Control Compared to Worldwide DataHohendorff J1, Gumprecht J2, Mysliwiec M3, Zozulinska-Ziolkiewicz D4, Malecki MT11Department of Metabolic Diseases, Jagiellonian University Medical College, Krakow, Poland; 2Department of Internal Medicine, Diabetology and Nephrology, Medical University of Silesia, Katowice, Poland; 3Department of Pediatrics, Diabetology and Endocrinology, Medical University of Gdansk, Poland; 4Department of Internal Medicine and Diabetology, Poznan University of Medical Science, PolandDiabetes Technol Ther 2021;23: 577–585BackgroundRandomized trials and observational studies have shown that the use of FreeStyle Libre intermittently scanned continuous glucose monitoring system (isCGMS) is associated with improved glycemic indices and quality of life.Materials and MethodsIn this retrospective, real-world data analysis, we described country-specific glucometrics among isCGMS users from Poland and compared them with international data. The analyzed time period for the Polish data ranged between August 2016 and August 2020, and the analyzed time period for the international data ranged from September 2014 to August 2020.ResultsData from the Polish population were collected from 10,679 readers and 92,627 sensors with 113 million automatically recorded glucose readings. The worldwide database included information from 981,876 readers and 11,179,229 sensors with 13.1 billion glucose readings. On average, isCGMS users of from Poland performed substantially more scans/day (21.2±14.2 vs 13.2±10.7), achieved lower eHbA1c (7.0%±1.2% vs 7.5%±1.5%), and spent more time-in-range (TIR) (64.2%±17.3% vs 58.1%±20.3%) and less time-above-range (TAR) (29.7%±18.0% vs 36.6%±21.3%) (P<.0001 for all comparisons). Moreover, they were more likely to achieve TIR >70% (36.3% vs 28.8%), but spent more time-below-range (TBR) (4.7% vs 3.6%). Our results confirmed that analyzed glucometrics improve as the scan rate frequency increases. However, at a similar scanning frequency to the comparative group, users from Poland achieved lower eHbA1c, higher TIR, lower TAR, but higher TBR.ConclusionsWe report more scanning and better glycemic control in isCGMS users in Poland than in users worldwide. The cause of this observation remains unknown. Our data also show that in real-life practice, a large number of patients may be willing to perform scanning more frequently than is usually assumed.CommentsThese two articles analyze de-identified, large datasets obtained from Minimed 780G hybrid closed-loop downloads (14) and Libre isCGM. The strength of these types of analyses is that device settings and user behavior (15) can be analyzed. The high TIR results with 780G reported by DeSilva and colleagues were impressive considering the majority of users did not tune the device with optimal settings: only 50% of the users had the algorithm target set at the recommended 100 mg/dL (16). In the paper by Hohendorff et al., higher TIR correlated to more frequent isCGM scans, both in the Polish cohort and in the international cohort.It is important, however, to pay attention to the inherent limitations to de-identified dataset analyses. While these results illuminate ideal behaviors and tuning for diabetes devices, they do not give us information on who might expect similar results, especially in light of socioeconomic and racial/ethnic disparities in access and differences in clinical populations of device users by country or region. The user age, duration of diabetes, and gender are all unknown. Hohendorff and colleagues make the point that the Polish cohort seemed to achieve better glycemia than the international cohort with the same number of isCGM scans; however, without understanding the clinical characteristics of the cohort, this cannot be meaningfully interpreted. It is thus important that we extrapolate the correct conclusions from de-identified datasets; it is not that these glycemic outcomes are achievable by everyone, or that one country is better at diabetes care than another, but rather that behavior and device tuning are what matter. We can all learn best practices with diabetes devices to achieve optimal outcomes, and likely these lessons can be translated across all populations.EXPANDING DIABETES TECHNOLOGY TO NOVEL POPULATIONSUse of Technology in Older Adults with Type 1 Diabetes: Clinical Characteristics and Glycemic MetricsMunshi M1,2,3, Slyne C1, Davis D1, Michals A1, Sifre K1, Dewar R1, Atakov-Castillo A1, Toschi E1,2,31Joslin Diabetes Center, Clinical Research, Boston, MA; 2Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA; 3Harvard Medical School, Boston, MADiabetes Technol Ther 2022;24: 1–9BackgroundThe use of diabetes-related technology, both for insulin administration and glucose monitoring, has shown benefits in older adults with type 1 diabetes (T1D). However, the characteristics of older adults with T1D and their use of technology in real-world situations are not well documented.MethodsOlder adults (aged ≥65 years) with T1D who were using insulin pump or multiple daily injections (MDIs) for insulin administration and continuous glucose monitoring (CGM) or glucometer (blood glucose monitoring [BGM]) for glucose monitoring were evaluated. Participants wore CGM devices for 2 weeks, completed surveys, and underwent laboratory evaluation.ResultsWe evaluated 165 older adults with T1D (mean age 70±10 years, diabetes duration 40±17 years, and A1c 7.4%±0.9% [57±10 mmol/mol]). For insulin administration, 63 (38%) were using MDIs, while 102 (62%) were using pump. Compared to MDI users, pump users were less likely to have cognitive dysfunction (49% vs 65%, P=.04) and had lower scores on the hypoglycemia fear survey (P=.03). For glucose monitoring, 95 (58%) used CGM, while 70 (42%) used BGM. Compared to BGM users, CGM users were more likely to report impaired awareness of hypoglycemia (IAH) (P=.01), and had lower A1c (P=.02). Participants who used any technology (pump or CGM) had lower A1c (pump, P=0.04; CGM, P=.006), less hypoglycemia ≤54 mg/dL (pump, P=.0006; CGM, P<.0001) and <70 mg/dL (pump, P=.0002; CGM, P=.0001), and lower glycemic variability (pump, P=0.0001; CGM, P<.0001), while reporting more IAH (pump, P=.04; CGM, P=.006) and diabetes distress (pump, P=0.02; CGM, P<.004).ConclusionOlder adults with T1D who use newer diabetes-related technology had better glycemic control, lower hypoglycemia risk, and fewer glycemic excursions. However, they were more likely to report IAH and diabetes-related distress.Closed-Loop Insulin Therapy in Older Adults with Type 1 Diabetes: Real-World DataToschi E1,2, Atakov-Castillo A1, Slyne C1, Munshi M1,2,31Joslin Diabetes Center, Boston, MA; 2Harvard Medical School, Boston, MA; 3Beth Israel Deaconess Medical Center Boston, MADiabetes Technol Ther 2022;24: 140–142ObjectiveTo assess the impact of initiating closed-loop control (CLC) on glycemic metrics in older adults with type 1 diabetes (T1D) in the real world.MethodsRetrospective analysis of electronic health records from a single tertiary diabetes center of older adults prescribed CLC between January 2020 and December 2020.ResultsA total of 48 patients (mean age 70±4 years, T1D duration 42±14 years) were prescribed CLC, and 39/48 started on the CLC. Among the CLC starters, 97.5% and 95% were prior pump and continuous glucose monitoring (CGM) users, respectively. CGM metrics showed an increase in time-in-range (mean±SD 62±14% to 76±9%; P<.001) and a reduction in both time spent at <70 mg/dL (median[IQR] 2[1-3]% to 1[1-2]%; P=0.03), and time spent >180 mg/dL (mean±SD 30±11% to 20±9%; P<0.001) at 3 months.ConclusionIn these real-world data, most of the older patients with T1D initiating CLC were prior pump or CGM users. Initiation of CLC improved glycemic control and reduced time spent in hypoglycemia with respect to the levels during prior therapy.CommentsWith the rising incidence of type 1 diabetes across multiple age groups and improvements in medical care that result in better long-term outcomes, there are more older adults living with type 1 diabetes than ever before. Data from the Type 1 Diabetes Exchange and the Diabetes Patienten Verlaufsdokumentation (DPV) registries indicate the majority of older adults with type 1 diabetes are not meeting recommended A1c targets, even with use of newer diabetes technologies (17–19). Increased susceptibility to hypoglycemia, complicated medical diagnoses, visual and hearing impairments, and cognitive changes all present potentially significant obstacles to the attainment and maintenance of optimal diabetes management. These two articles (21,22) report the use of insulin pumps, glucose sensors, and automated insulin delivery systems in this important but understudied population.In a cohort of 165 adults over 60 years old (mean age 70 years) with type 1 diabetes, Munshi and colleagues (20) found that use of diabetes technology, most notably CGM (more than pumps), was associated with lower average blood glucose leve