Search

Introduction: We aimed to characterise and compare individuals diagnosed with type 1 diabetes (T1D), latent autoimmune diabetes in adults (LADA) and type 2 diabetes (T2D), in a real-world setting., Methods: Anthropometric and clinical data from 36 959 people with diabetes diagnosed at age 30-70 years enrolled in the prospective diabetes patients follow-up (DPV) registry from 1995 to 2022 were analysed cross-sectionally at diagnosis and follow-up (≥6 months after diagnosis). LADA was defined as clinical diagnosis of T2D, positivity of ≥1 islet autoantibody and an insulin-free interval of ≥6 months upon diabetes diagnosis., Results: At diagnosis, age, body mass index, waist circumference, C-peptide and HbA1c in people with LADA (n = 747) fell in between individuals with T1D (n = 940) and T2D (n = 35 272) (all p-values < 0.01). At follow-up, after adjusting for age, sex and diabetes duration, the prevalence of dyslipidemia and hypertension was the highest in people with LADA (90.6%, 77.7%) compared to people with T2D (81.8%, 60.4%) and T1D (75.7%, 39.7%) (p < 0.01). The prevalence of diabetic kidney disease (DKD) was higher in LADA (44.2%), than in T1D (19.9%) (p < 0.01). The prevalence of peripheral neuropathy was higher in individuals with LADA (55.1%) than in T2D (43.9%) and T1D (42.1%) (p < 0.05). Coverage of treatment for hypertension and dyslipidemia were 22.4% and 15.0% in T1D, 63.0% and 36.6% in LADA and 29.4% and 18.2% in T2D., Conclusion: People with LADA had a higher prevalence of cardiovascular risk factors (dyslipidemia, hypertension) and cardiovascular complications (DKD and peripheral neuropathy), suggesting that people with LADA are at need for improved recognition and care., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

With increasing prevalence of obesity and cardiovascular diseases, there is a growing interest in the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) as an adjunct therapy in type 1 diabetes (T1D). The GLP-1RAs are currently not approved by the US Food and Drug Administration for the treatment of T1D in the absence of randomized controlled trials documenting efficacy and safety of these agents in this population. The Diabetes Technology Society convened a series of three consensus meetings of clinicians and researchers with expertise in diabetes technology, GLP-1RA therapy, and T1D management. The project was aimed at synthesizing current literature and providing conclusions on the use of GLP-1RA therapy as an adjunct to automated insulin delivery (AID) systems in adults with T1D. The expert panel members met virtually three times on January 17, 2024, and April 24, 2024, and August 14, 2024, to discuss topics ranging from physiology and outcomes of GLP-1RAs in T1D to limitations of current sensors, algorithms, and insulin for AID systems. The panelists also identified research gaps and future directions for research. The panelists voted to in favor of 31 recommendations. This report presents the consensus opinions of the participants that, in adults with T1D using AID systems, GLP-1RAs have the potential to (1) provide effective adjunct therapy and (2) improve glycemic and metabolic outcomes without increasing the risk of severe hypoglycemia or diabetic ketoacidosis., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: VNS received research support from Novo Nordisk, Insulet, and Tandem Diabetes Care and received honoraria from LifeScan for advisory board attendance and from Dexcom, Embecta, and Insulet for speaking arrangements. ALP is on the advisory board of Medscape, Vertex, and Lilly; has received research support from Insulin and Abbott; and reports stock options from Omada Health. GEU has received research support (to Emory University) from Abbott, Bayer, Dexcom, and Sanofi. HKA received research support through University of Colorado from Dexcom, Tandem Diabetes, Senseonics, Medtronic, Eli Lilly, REMD Biotherapeutics, IM Therapeutics, and IAFMS and received honoraria through University of Colorado from Senseonics and Mannkind for advisory board attendance. GA has received research support to her institution Northwestern University from Fractyl Health, MannKind, Insulet, Tandem Diabetes, and Welldoc. GA has received consulting fees from Dexcom and Insulet. LB reports having participated in advisory boards of Eli Lilly, Novo Nordisk, Oviva, Roche Diabetes Care, Sanofi, and Ypsomed and received speaker fees from Dexcom, Ypsomed and Oviva. EC is an advisory board member and consultant for Novo Nordisk, Eli Lilly, Adocia, MannKind, Lexicon, Arecor, PortalInsulin, and Proventionbio. EC was also a speaker for Novo Nordisk. KD discloses research support from Dexcom, Sanofi, Viacyte, Abbott, and Insulet; consulting with Eli Lilly, Insulet, Oppenheimer, Dexcom; and honorarium from UptoDate, Medscape, Med Learning Group, Impact Education, and Elsevier. CF receives royalties from Dexcom and Novo Nordisk managed through her institution. PGJ reports receiving grants from the National Institutes of Health, The Leona M. and Harry B. Charitable Trust, the Juvenile Diabetes Research Foundation, Dexcom, and the Oregon Health & Science University Foundation; consultancy fees from CDISC; US patents 62/352,939, 63/269,094, 62/944,287, 8810388, 9,480,418, 8,317,700, 61/570382, 8,810,388, 7,976,466, and 6,558,321; and reports stock options from Pacific Diabetes Technologies, outside submitted work. RAL reports consulting fees from Abbott Diabetes Care, Biolinq, Capillary Biomedical, Deep Valley Labs, Gluroo, PhysioLogic Devices, ProventionBio, and Tidepool. JKM is a member of the advisory board of Abbott Diabetes Care, Becton-Dickinson, Boehringer Ingelheim, Eli Lilly, Embecta, Medtronic, Novo Nordisk A/S, Prediktor A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris and received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Menarini, MSD, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Servier, and Ypsomed. JLS has conducted clinical trials for Eli Lilly, Insulet, and Medtronic and has received in-kind support for research studies from Dexcom and Medtronic. She has consulted for Eli Lilly, Lexicon, Medtronic, and Sanofi. She has been a member of advisory boards for Bigfoot Biomedical, Cecelia Health, Eli Lilly, Insulet, the T1D Fund, and Vertex. DCK is a consultant for Afon, Better Therapeutics, Integrity, Lifecare, Nevro, Novo, Samsung, and Thirdwayv. AC, UM, PP, SS, EZ, CNH, ATA, TT, and REA have nothing to disclose.

Aims/hypothesis: Glycaemic control and clinical outcomes in diabetes are improved by continuous subcutaneous insulin infusion (CSII). Atmospheric pressure changes during flights may affect insulin delivery from pumps and cause unintended metabolic consequences, including hypoglycaemia, in people with type 1 diabetes. The present report evaluates both hypobaric flight simulation and real-world data in pilots using insulin pumps while flying., Methods: In the flight simulation part of this study, an in vitro study of insulin pumps was conducted in a hypobaric chamber, de-pressurised to 550 mmHg to mimic the atmospheric pressure changes in airliner cabins during commercial flights. Insulin delivery rates and bubble formation were recorded for standard flight protocol. Insulin infusion sets, without pumps, were tested in a simulated rapid decompression scenario. The real-world observational study was a 7.5-year retrospective cohort study in which pre- and in-flight self-monitored blood glucose (SMBG) values were monitored in pilots with insulin-treated diabetes. Commercial and private pilots granted a medical certificate to fly within the European Union Aviation Safety Agency approved protocol and receiving insulin either by pump or multiple daily injections (MDI) were included., Results: In the flight simulation study, full cartridges over-delivered 0.60 U of insulin during a 20 min ascent and under-delivered by 0.51 U during descent compared with ground-level performance. During emergency rapid decompression, 5.6 U of excess insulin was delivered. In the real-world study, seven pilots using CSII recorded 4656 SMBG values during 2345 h of flying across 1081 flights. Only 33 (0.7%) values were outside an acceptable safe range (5.0-15.0 mmol/l [90-270 mg/dl]). No clinically significant fall in the median SMBG concentration was observed after aircraft ascent and no in-flight SMBG values were within the hypoglycaemic range (<4.0 mmol/l [<72 mg/dl]). Compared with pilots receiving MDI therapy, pilots using CSII recorded more SMBG values within the acceptable range (99.3% vs 97.5%), fewer values in the low red range (0.02% vs 0.1%), fewer in-flight out-of-range values (0.2% vs 1.3%) and maintained stricter glycaemic control during flight., Conclusions/interpretation: Ambient pressure reduction during simulated flights results in bubble formation and expansion within insulin cartridges. This causes unintended delivery of small insulin doses independent of pre-determined delivery rates and represents the maximum amount of insulin that could be delivered and retracted. However, in vivo, pilots using CSII in-flight did not experience a fall in blood glucose or episodes of hypoglycaemia during these atmospheric pressure changes and the use of insulin pumps can be endorsed in view of their clinical benefits., (© 2024. The Author(s).)

Decentralized clinical trials (DCTs), in which all or part of the trial activities are moved to the participants' immediate surroundings, promise to improve trial conduct. However, no evidence is available on what motivates people to participate in DCTs. Our aim was to determine the drivers and perceptions for participation in clinical trials with different decentralization levels in persons with type 2 diabetes mellitus. Five focus groups were conducted utilizing the nominal group technique in the Netherlands (n = 1), Germany (n = 1), and Austria (n = 3) with four to six participants per group. The focus groups were analyzed using thematic analysis. Of the 26 participants (10 females, median age: 66 years [IQR: 62-72]) 42% had previously participated in a trial, and almost all had internet access at home (96%). A total of seven main themes regarding participation in clinical trials (location, time investment, contact with healthcare professionals (HCPs), digital technologies, data collection, perceived risk, and motivation) were identified, of which a total of 20 drivers emerged. Perceptions regarding trial participation differed widely among participants, and individual preferences influenced which drivers were considered more important by participants. Flexibility of location and time spent on the trial were identified as the most motivating factors for participation in DCTs. Some drivers, such as digital infrastructure, digital literacy, home visits, personal interaction, and relationship with HCPs were perceived as both enablers and barriers, depending on personal preferences. However, most of the potential barriers regarding DCTs may be resolved by addressing them in the design of future DCTs., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background: Individuals with maturity-onset diabetes of the young (MODY) are often misdiagnosed as type 1 or type 2 diabetes and receive inappropriate care. We aimed to investigate the characteristics and treatment of all MODY types in a multicenter, real-world setting., Methods: Individuals with MODY from the diabetes prospective follow-up (DPV) registry were studied. We compared clinical parameters during the first year of diabetes and the most recent treatment year after MODY diagnosis., Results: A total of 1640 individuals were identified with GCK-MODY (n = 941) and HNF1A-MODY (n = 417) as the most frequent types. Among these, 912 individuals were available with information during the first and the most recent treatment year (median duration of follow-up: 4.2 years [2.6-6.6]). Positive beta cell autoantibodies were present in 20.6% (15.2% IAA). Median age at diagnosis ranged from 9.9 years in GCK-MODY (Q1-Q3: 6.2-13.1 years) and INS-MODY (2.7-13.7 years) to 14.3 years (5.0-17.1) in KCNJ11-MODY. Frequency of oral antidiabetic agents (OAD) use increased and insulin decreased in HNF4A-MODY (OAD: 18% to 39%, insulin: 34% to 23%) and in HNF1A-MODY (OAD: 18% to 31%, insulin: 35% to 25%). ABCC8-MODY was characterized by a decrement in nonpharmacological treatment (26% to 16%) and "insulin only" treatment (53% to 42%), while the proportion of individuals treated with OAD but no insulin increased from 0% to 21%., Conclusions: Our results indicate that some teams caring for individuals with MODY are hesitant with regard to current recommendations. Registries are an essential source of information and provide a basis for discussing treatment guidelines for MODY., (© 2024 The Author(s). Journal of Diabetes published by Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Background: Continuous glucose monitoring (CGM) can provide a comprehensive assessment of glycaemic control. This exploratory analysis of the ONWARDS 1 trial assessed CGM-based metrics and CGM-derived hypoglycaemia duration in insulin-naive individuals with type 2 diabetes treated with subcutaneous once-weekly insulin icodec (icodec) versus once-daily insulin glargine U100 (glargine U100)., Methods: ONWARDS 1 was a 78-week (52-week main treatment phase and a 26-week treatment extension phase plus a 5-week follow-up), randomised, open-label, treat-to-target, phase 3a trial done at 143 sites (outpatient clinics and hospital departments) across 12 countries. Adults (aged ≥18 years) with type 2 diabetes (HbA 1c : 7·0-11·0%) who had not previously received insulin were randomly assigned (1:1) via an interactive web-response system to once-weekly icodec or once-daily glargine U100. Double-masked CGM data were collected during treatment initiation (weeks 0-4), midtrial (weeks 22-26), end of main phase (weeks 48-52), end of extension phase (weeks 74-78), and follow-up (weeks 78-83). Secondary and exploratory outcomes were CGM-based metrics, including the mean percentages of time in glycaemic range (TIR; sensor glucose 3·9-10·0 mmol/L [70-180 mg/dL]), time in tight range (TITR; 3·9-7·8 mmol/L [70-140 mg/dL]), time above range (TAR; >10·0 mmol/L [>180 mg/dL]), and time below range (TBR; <3·9 mmol/L [<70 mg/dL] and <3·0 mmol/L [<54 mg/dL]), and CGM-derived hypoglycaemic episode durations (episodes defined by sensor glucose <3·9 mmol/L [<70 mg/dL for ≥15 consecutive minutes]). Analyses were done in the full analysis set (all randomly assigned participants). The ONWARDS 1 trial is registered with ClinicalTrials.gov, NCT04460885, and is complete., Findings: Participants were enrolled and randomly assigned in ONWARDS 1 between Nov 25, 2020, and Dec 1, 2022 (n=492 in each treatment group). During treatment initiation, we observed no statistically significant differences in the mean percentages of TIR, TITR, TAR, and TBR with icodec versus glargine U100. During the midtrial, end of main phase, and end of extension phase periods, the mean percentages of TIR and TITR were statistically significantly greater and the mean percentages of TAR statistically significantly lower with icodec versus glargine U100. The mean percentages of TIR met the internationally recommended CGM target (>70%) with icodec but not with glargine U100 during the three periods. TBR (<3·9 mmol/L [<70 mg/dL] and <3·0 mmol/L [<54 mg/dL]) was low and below recommended targets (<4% and <1%, respectively) across all study periods in both treatment groups, with no statistically significant differences between treatment groups for the lower threshold (<3·0 mmol/L [<54 mg/dL]). During the follow-up period, mean percentages of TIR, TITR, TAR, and TBR did not statistically significantly differ with icodec versus glargine U100. The duration of overall hypoglycaemic episodes was similar between treatment groups throughout the trial (median duration ≤35 min)., Interpretation: These CGM data support the long-term efficacy and safety of icodec versus glargine U100 during treatment and indicated no increase in the duration of individual hypoglycaemic episodes with icodec versus glargine U100 in insulin-naive individuals with type 2 diabetes., Funding: Novo Nordisk., Competing Interests: Declaration of interests RMB has received research support, consultant fees, or has served on advisory panels for Abbott Diabetes Care, Ascensia Diabetes Care, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Embecta, Hygieia, Insulet, Medtronic, Novo Nordisk, Onduo, Roche Diabetes Care, Sanofi, Tandem Diabetes Care, United Healthcare, Vertex Pharmaceuticals, and Zealand Pharma. RMB's employer, the nonprofit organisation HealthPartners Institute, contracts for his services and he receives no personal income from these activities. BÁ and SKW are employees of Novo Nordisk; TN is both an employee and shareholder of Novo Nordisk. IL has received research funding (paid to their institution) from Boehringer Ingelheim, Merck, Mylan, Novo Nordisk, Pfizer, and Sanofi; and has received advisory or consulting fees, travel support, or manuscript editorial support from AstraZeneca, Bayer, Boehringer Ingelheim, Carmot Therapeutics, Eli Lilly, Intercept Pharmaceuticals, Johnson & Johnson, Merck, Novo Nordisk, Pfizer, Sanofi, Shionogi, Structure Therapeutics, Target Pharma, Translational Medical Academy, Valeritas, WebMD, and Zealand Pharma. JKM has received speaker fees from Abbott Diabetes Care, A Menarini Diagnostics, BD and Embecta, Dexcom, Eli Lilly, Medtronic, Medtrust, Novo Nordisk, Roche Diabetes Care, Sanofi, Servier, Viatris, and Ypsomed; is a member of advisory boards for Abbott Diabetes Care, BD and Embecta, Boehringer Ingelheim, Eli Lilly, Medtronic, Novo Nordisk, Prediktor, Roche Diabetes Care, Sanofi, and Viatris; is a board member of the Austrian Diabetes Society and of the European Association for the Study of Diabetes; is a chair for the Postgraduate Education Committee of the European Association for the Study of Diabetes; has received materials provided by Menarini Diagnostics and Dexcom; is a shareholder of Decide Clinical Software and Elyte Diagnostics; and serves as the Chief Medical Officer at Elyte Diagnostics. JR reports clinical research grants from Applied Therapeutics, Biomea Fusion, Boehringer Ingelheim, Carmot, Corcept, Eli Lilly, Hanmi, Merck, Novartis, Novo Nordisk, Oramed, Regeneron, Pfizer, and Sanofi; has served on scientific advisory boards and received honorarium or consulting fees from Applied Therapeutics, Biomea Fusion, Boehringer Ingelheim, Eli Lilly, Hanmi, Novo Nordisk, Oramed, Regeneron, Roche, Sanofi, Structure Therapeutics, and Zealand Pharma; and has received honoraria for lectures from Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs)., Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy., Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose., Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D.C.K. is a consultant for Afon, Lifecare, Novo, Samsung, embecta, Glucotrack, SynchNeuro, and Thirdwayv. G.F. is CEO of IfDT. G.F./IfDT has/have received research support, speakers’ honoraria or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Glucoset, i-SENS, Lilly, Menarini, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, Ypsomed. S.P. is an employee of IfDT. B.P.K. declares patent royalties handled by UVA from: DexCom, J&J, Novo Nordisk, and Sanofi; and research support handled by UVA: Dexcom, Novo Nordisk, Tandem Diabetes Care. D.K. has received research support from Abbott Diabetes Care. M.S.D.A. is currently receiving in kind support (CGM devices) from Dexcom Inc for an investigator-initiated clinical study. K.D. receives research funding from Dexcom, Abbott, Sanofi, Viacyte, Insulet, consulting fees from Eli Lilly, Dexcom, Insulet, Oppenheimer, Elsevier, honoraria from Academy for Continued Healthcare Learning, Med Learning Group, Medscape, Impact Education, and royalties from UptoDate. S.M. is in receipt of an investigator-initiated grant from DexCom, has received speaker fees from Sanofi & Lilly, is funded by a Wellcome Trust (223024/Z/21/Z), and is supported by the NIHR Imperial Biomedical Research Centre. A.S. was supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). V.N.S. reports receiving personal fees from Sanofi, Embecta, NovoNordisk, Dexcom, Insulet, Tandem Diabetes Care, Ascensia Diabetes Care, Genomelink and LumosFit for consulting, advising or speaking. N.K.T. is a consultant for Roche Diagnostics / Roche Molecular Systems / Radiometer. UC Davis is a Roche Diagnostics Center of Excellence. N.K.T. has received speaking honoraria for Roche Diagnostics, Nova Biomedical, Thermo Fisher, and Radiometer. N.K.T. is also Chair-Elect for the Association for Diagnostic and Laboratory Medicine (ADLM) Critical and Point-of-Care Testing Division, Co-Founder of the Machine Intelligene Learning Optimizer (MILO), Inc, and a member of the International Federation for Clinical Chemistry (IFCC) continuous glucose monitoring workgroup. T.T. is a consultant for Clinical ink. R.E.A. is a consultant for Clinical ink. D.T.A. has received speaker’s honoraria from Abbott, Ascensia Diabetes Care, Insulet, Lilly Diabetes, Mannkind, Novo Nordisk, and Xeris Pharmaceuticals. D.T.A. has received consulting fees from Ascensia Diabetes Care, Lilly Diabetes, and Senseonics. H.K.A. received research grants through University of Colorado from Dexcom, Tandem Diabetes, Medtronic, Mannkind, IM Therapeutics, IAFMS, and received honorarium through University of Colorado for consulting from Dexcom, Tandem Diabetes, and Medtronic. M.E.A.-S. has served on the advisory boards for Abbott, Medtronic, Insulet, VitalAire, Sanofi, Eli Lilly, and Dexcom; has received honoraria for speaking from Abbott, Eli Lilly, Medtronic, Novo Nordisk, Sanofi, VitalAire, and Eli Lilly; and received research support from Medtronic and Sanofi. T.S.B. has received research support from Abbott Rapid Diagnostics, Biolinq, Dexcom, Eli Lilly, Medtronic, Medtrum, Novo Nordisk, Sanofi, Senseonics, and vTv Therapeutics and consulting honoraria from Abbott Diabetes, Abbott Rapid Diagnostics, ACON, CeQur, HagarTech, Intuity Medical, Lifescan, Mannkind, Medtronic, Novo, Perspirion, Sanofi, Sequel Med Tech, and Ypsomed. M.B. currently participates on the Hospital Advisory board with Dexcom and has received research support from Dexcom. L.B. has received research/product support from Dexcom, Ypsomed, and Boehringer Ingelheim; speaker honoraria from Dexcom and Oviva; and participated in advisory boards of Dexcom, Novo Nordisk, Sanofi, Ypsomed, Oviva, and Roche Diabetes Care. Y.M.B. has received honoraria for lectures and scientific advisory from Roche and AstraZeneca in the past 12 months. J.E.B. is on the Speaker’s bureau for Insulet and on the Advisory Board for Cardinal Health and Lifescan. J.E.B. is a consultant for Embecta. J.E.B. receives research support from the Leona M. and Harry B. Helmsley Charitable Trust. E.C. is on the scientific advisory board for Novo Nordisk, Eli-Lilly, Adocia, Arecor, Proventionbio, Portal Insulin, and MannKind. D.C. was a Dexcom employee from August 2018 to May 2024. D.C. is a shareholder of Dexcom, Lilly, Abbott and Tandem. M.C. has received consulting fees from Glooko and research support from Dexcom and Abbott Diabetes Care. G.L.C. is a shareholder of BioTex, Inc., Basepair Biotechnologies Inc., Coordination Centric, Inc., and Shape Memory Medical, Inc. G.L.C. is supported by the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (#1648451). K.K.D. has received honoraria, travel, or fees for advisory boards from AstraZeneca, Novo Nordisk, Boehringer Ingelheim, Eli Lilly, Abbott Diabetes, Menarini, and Sanofi in the last 12 months. J.E. receives federal funding from FDA, NIMHD, and NCATS and is a consultant for Sanofi. C.F. receives royalties from Dexcom and Novo Nordisk managed through her institution. G.A.F. is an advisor for 180 Life Sciences, 89bio, Adocia, Abbott, AdipoPharma, Aerami, Amolyt, Carthera, Catalyst, Cohen Global, CMC Magnetics, Diasome, Eleos, Entera Bio, Enterin, Glyscend, Hagar, Hogan Lovells, IM Therapeutics, Innoneo, Intarcia, Levicure, Lilly Asia Ventures, Lumos, Mars Symbioscience, Melior, metaLead Therapeutics, Microbiotix, MMD, Modular Medical, New Amsterdam, Northwestern, NuSirt, NuVox, Oramed, Pano, Pasithea, PhylloPharma, Pleiogenix, Recordati, Regor, Remodeless, Renaissance, RenovoRx, Rivus, RxMP, Sera Biopharma, Seraxis, Serpin, SFC Fluidics, Skinject, SROne, Stalicla, Surf Bio, TIXiMED, Veroscience, Verthermia, WaveBreak, Zealand Pharma, and Zucara. M.A.L.G. is a consultant for Abbott, Medtronic, Novonordisk, and Roche. R.J.G. received research support from Novo Nordisk, Dexcom and Eli Lilly and consulting fees/advisory fees from Abbott Diabetes Care, Aztra Zeneca, Bayer, Boehringer, Dexcom, Eli Lilly, Novo Nordisk, and Medtronic, outside of this work. R.J.G. is partially supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Numbers P30DK111024, K23DK123384, R03DK138255, and U2CD137135. A.M.G.-M. reports speaker fees from Novo Nordisk, Sanofi, Elli Lilly, Boehringer Ingelheim, Abbott, and Medtronic. L.H. is a consultant for a number of companies that are developing innovative solutions for the treatment of people with diabetes. N.H. reports Advisory Board member fees from Abbott Diabetes Care and Insulet as well as honoraria for lectures from Berlin-Chemie AG, Becton Dickenson, Sanofi Germany, Roche Diabetes Care, and Dexcom Germany. T.H. serves on the advisory board for Acella and Horizons Therapeutics (no financial compensation). S.H. has served on the advisory board for Tandem, Dexcom, and Medtronic, and received honoraria for nonpromotional educational and/or consultancy work from Abbott, Insulet, Dexcom, Roche, and Sanofi. P.G.J. is a shareholder of Pacific Diabetes Technologies Inc. and serves on the advisory board for Eli Lilly. P.G.J. received grant support from SFC Fluidics and research support from Eli Lilly and Dexcom. J.J. has received fees from lectures and or advisory boards from Abbott, Astra Zeneca, Boeringer Ingelheim, Eli Lilly, Medtronic, Nordic Infucare, Novo Nordisk, and Sanofi. S.R.J is a consultant for USV, Marico, Glenmark, Franco Indian, Twin Health, Biocon, and Zydus Lifesciences. S.R.J. received speaking honoraria from Abbott, Novo Nordisk, MSD, Sanofi, Boehringer Ingelheim, AstraZeneca, Lupin, Bayer Zydus, USV, DRL, Meyer Organics, Servier, Natco. R.A.L. is a consultant for Abbott Diabetes Care, Biolinq, Capillary Biomedical, Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, and Tidepool. R.A.L. has served on advisory boards for. ProventionBio and Lilly. R.A.L. receives research support through his institution from Insulet, Medtronic, Tandem, and Sinocare. L.A.L. has received research funding from, has provided CME on behalf of, and/or has acted as an advisor to Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Lexicon, Merck, Novo Nordisk, Pfizer, and Sanofi. M.L. has been a consultant or received honoraria from Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Nordic InfuCare, and Novo Nordisk, and has received research grants from Eli Lilly and Novo Nordisk, all outside of the submitted work. J.K.M. is a member of advisory boards of Abbott Diabetes Care, Becton-Dickinson, Biomea Fusion, Eli Lilly, Embecta, Medtronic, NovoNordisk A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris and received speaker honoraria from A. Menarini Diagnostics, Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Medtrust, MSD, NovoNordisk A/S, Roche Diabetes Care, Sanofi, Viatris, and Ypsomed. J.K.M. is a shareholder of decide Clinical Software GmbH and elyte Diagnostics. A.M. reports speaker fees from Novo Nordisk. M.M. has an existing research & development relationship with Scientific Bioprocessing, Inc and has been a consultant for Abbott. S.-J.M. is a consultant for Abbott, Curestream, Daewoong, EOFlow, G2e, Huons, iSense, Medtronic, Novo Nordisk, and Sanofi. D.N.O. has served on advisory boards for Abbott Laboratories, Medtronic, Merck Sharp & Dohme, Novo Nordisk, Roche, and Sanofi; received research support from Medtronic, Novo Nordisk, Roche, Eli Lilly and Company, and Sanofi; and received travel support from Novo Nordisk and Merck Sharp & Dohme. F.J.P. has received research support (to Emory University) from Dexcom, Insulet, Novo Nordisk, Tandem, and Ideal Medical Technologies, and has received consulting fees from Dexcom. A.L.P. is on the advisory board of Medscape, Vertex, and Lilly; receives research support from Insulin and Abbott; and has stock options in Omada Health. R.P.-B. receives grant support from Novo Nordisk, Lexicon Pharmaceuticals, and Medtronic and received consulting fees from Bayer, Lexicon Pharmaceuticals, Novo Nordisk, and Roche. C.M.R. has received honoraria and/or grant support from AstraZeneca, Dexcom, Fresenius, and Vifor. D.B.S. is supported by the Intramural Research Program of the National Institutes of Health. S.S. was a Novo Nordisk employee from 2022-2023. S.S. has served as advisor for Novo Nordisk and has received speaker fees from Novo Nordisk and Nordic Infucare within the past three years. G.D.S’s employer, nonprofit International Diabetes Center, HealthPartners Institute, has received educational grant funds from Abbott Diabetes Care, Medscape, and Sanofi-Aventis Groupe. G.D.S receives no personal income/honorarium from these activities. K.S. received grant supports from Arkray, Terumo, and Dexcom. E.K.S. is partly supported by the VA Merit Award (1I01CX001825) from the US Department of Veterans Affairs Clinical Sciences Research and has received unrestricted research support from Dexcom and Tandem Diabetes (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. N.L.S. received funding from Novo Nordisk for an investigator-initiated research grant unrelated to the current project. G.E.U. is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). G.E.U. has received research support (to Emory University) from Abbott, Bayer, and Dexcom; and has participated in advisory boards for Dexcom and GlyCare. E.W. has received consulting fees from Abbott Diabetes Care, Ascensia, Bayer, Boehringer, Ingelheim, Embecta, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. has received honoraria from Abbott Diabetes Care, Bayer, Boehringer Ingelheim, GlaxoSmithKline, Lilly, Medtronic, Renalytix, and Sanofi. E.W. is on the Speakers’ Bureau from Abbott Diabetes Care, Bayer, Boehringer, Ingelheim, GlaxoSmithKline, Lilly, Renalytix, and Sanofi. M.Z. is a consultant for DexCom, Inc. M.A.K. is Chief Medical Officer of QuesGen, Inc. C.T., C.L., B.V.H., J.S.K., W.A.L., S.Y.R., J.J.S., K.W., C.W., K.R., C.N.H., A.T.A., A.A., R.R.B., E.M.B., J.G.C., K.Y.C., A.D., N.E., S.K.K., U.M., N.M., C.M., J.H.N., A.P., P.R., S.M.S., B.S., M.V., H.W.V., J.W., A.H.B.W., and S.Y. have nothing to disclose.

Hybrid closed-loop (HCL) systems remain underexplored within aviation, and as atmospheric pressure changes can independently affect insulin pumps and continuous glucose monitoring readings, this preliminary study assessed the feasibility of HCL safety evaluation, in both fasting and post-prandial states, by using hypobaric chamber to simulate flights. Participants with type 1 diabetes and on HCL were studied: Medtronic Guardian 4-Medtronic 780G-SmartGuard ( n = 4), Dexcom G6-Omnipod DASH-Android APS ( n = 1), and Dexcom G6-Ypsomed Pump-CamAPS ( n = 1). Flight cabin pressures of 550 mmHg and 750 mmHg were simulated in a hypobaric chamber. Seven-hundred-50 glucose measurements were taken, with glucose levels demonstrating a stable decline to 4 mmol/L during fasting. To maintain a tight fasting and post-prandial glucose range across the different pressure settings, the HCL administered insulin as expected. While not demonstrating any apparent issues, repeating flight simulation protocol with other systems, examining longer flights, and undertaking larger, powered randomised controlled trials can confirm their safety in aviation.

Objective: The objective was to evaluate the safety and efficacy of ultra-rapid-acting insulin with the Boost and Ease-off features of the Cambridge hybrid closed-loop system., Methods: A secondary analysis of Boost and Ease-off from two double-blind, randomized, crossover hybrid closed-loop studies comparing (1) Fiasp to insulin aspart (n = 25), and (2) Lyumjev to insulin lispro (n = 26) was carried out. Mean glucose on initialization of Boost and Ease-off, change in glucose 60 and 120 minutes after initialization, duration and frequency of use, mean glucose, and time in, above, and below target glucose range were calculated for periods of Boost use, Ease-off use, or neither., Results: Participants used Boost for longer with Fiasp than insulin aspart (median [interquartile range, IQR] = 75 [53-125] minutes vs 60 [49-75] minutes; P = .01). Mean glucose on Boost initialization with Fiasp was 238 ± 62 mg/dL compared with 218 ± 45 mg/dL with insulin aspart ( P = .08). Fiasp use resulted in a greater glucose reduction 120 minutes after Boost initialization [-59 ± 34 mg/dL vs -43 ± 31 mg/dL; P = .02]. There were no statistically significant differences in sensor glucose endpoints during Boost or Ease-off periods between Fiasp and aspart. There were no statistically significant differences during Boost or Ease-off periods when comparing Lyumjev with insulin lispro. There were no safety issues when using Boost and Ease-off with ultra-rapid insulins., Conclusions: The use of Fiasp and Lyumjev during Boost or Ease-off resulted in comparable safety and efficacy to using insulin aspart and lispro., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: CB is a consultant at CamDiab and has received speaker honoraria from Ypsomed. MN has received travel grant support from Sanofi, Janssen, and Eli Lilly and was previously chair of the Young Diabetologists’ and Endocrinologists’ Forum in the United Kingdom, which uses unrestricted sponsorship from industry partners to deliver educational programs for health care professionals. SH serves as a member of Medtronic advisory board, is a director of Ask Diabetes Ltd, providing training and research support in health care settings, and reports having received training honoraria from Medtronic and Sanofi and consulting fees for CamDiab. MEW is a consultant to CamDiab and reports patents related to closed loop. JW has received speaker honoraria from Ypsomed and Novo Nordisk. HT receives consulting fees and speaker honoraria from Eli Lilly and Dexcom Inc, reports having received research support from Dexcom Inc, and participated in advisory groups for Medtronic, Abbott Diabetes Care, and Roche Diabetes Care. JKM is a member in the advisory boards of Abbott Diabetes Care, Becton-Dickinson/Embecta, Biomea, Eli Lilly, Medtronic, Novo Nordisk, Pharmasens, Roche Diabetes Care, Sanofi, and Viatris; received speaker honoraria from Abbott Diabetes Care, A. Menarini Diagnostics, Becton-Dickinson/Embecta, Boehringer-Ingelheim, Eli Lilly, MedTrust, Novo Nordisk, Roche Diabetes Care, Sanofi, and Ypsomed; and is shareholder of decide Clinical Software GmbH and elyte Diagnostics. LB reports having participated in advisory boards of Eli Lilly, Novo Nordisk, Oviva, Roche Diabetes Care, Sanofi, and Ypsomed and received speaker fees from Dexcom, Ypsomed, and Oviva. LL has received research support from Abbott Diabetes Care and Dexcom, participated in advisory groups for Abbott Diabetes Care, Insulet, Dexcom, Medtronic, and Roche Diabetes, and received fees for speaking from Sanofi, Insulet, Medtronic, and Abbott Diabetes Care. MLE has been a member of advisory panels and/or received speaker fees from Novo Nordisk, Eli Lilly, Sanofi, Abbott Diabetes Care, Dexcom, Medtronic, Ypsomed, Pila Pharma, and Zucara. RH reports having received speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk; receiving license fees from B. Braun; patents related to closed-loop; being consultant to Abbott Diabetes Care; and being director at CamDiab. CR, RL, and JMA report no conflicts of interest related to the present paper.

Aims: This study assessed the achievement rates of metabolic risk factor targets and their association with clinical characteristics and comorbidities among individuals with type 2 diabetes (T2D) treated in the primary care in Austria., Materials and Methods: A countrywide cross-sectional study, the AUSTRO-PROFIT, was conducted in Austria from 2021 to 2023 on 635 individuals with T2D. Metabolic risk factor targets were defined as the percentage of people achieving low-density lipoprotein cholesterol (LDL-C) <70 mg/dL (or < 55 mg/dL if cardiovascular or microvascular disease was present), glycated haemoglobin (HbA1c) <7% (53 mmol/mol) and blood pressure < 140/90 mmHg., Results: The mean age of the participants was 65.7 ± 11.2 years; the median duration of T2D was 8 (4-14) years; and 58.7% of the participants were male. The percentages of participants achieving LDL-C, HbA1c, blood pressure and all targets were 44%, 53%, 57% and 13%, respectively. Older age, longer T2D duration, cardiovascular disease and microvascular complications were associated with suboptimal achievement of metabolic risk factor targets., Conclusions: The AUSTRO-PROFIT study revealed notable variations in metabolic targets achievement with respect to clinical characteristics and comorbidities. These findings underscore the importance of establishing national diabetes registries and implementing multifactorial targeted and individualized interventions to further improve the quality of T2D care in primary care settings in Austria., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Objective: Use of continuous glucose monitoring (CGM) has led to greater detection of hypoglycemia; the clinical significance of this is not fully understood. The Hypoglycaemia-Measurement, Thresholds and Impacts (Hypo-METRICS) study was designed to investigate the rates and duration of sensor-detected hypoglycemia (SDH) and their relationship with person-reported hypoglycemia (PRH) in people living with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D) with prior experience of hypoglycemia., Research Design and Methods: We recruited 276 participants with T1D and 321 with T2D who wore a blinded CGM and recorded PRH in the Hypo-METRICS app over 10 weeks. Rates of SDH <70 mg/dL, SDH <54 mg/dL, and PRH were expressed as median episodes per week. Episodes of SDH were matched to episodes of PRH that occurred within 1 h., Results: Median [interquartile range] rates of hypoglycemia were significantly higher in T1D versus T2D; for SDH <70 mg/dL (6.5 [3.8-10.4] vs. 2.1 [0.8-4.0]), SDH <54 mg/dL (1.2 [0.4-2.5] vs. 0.2 [0.0-0.5]), and PRH (3.9 [2.4-5.9] vs. 1.1 [0.5-2.0]). Overall, 65% of SDH <70 mg/dL was not associated with PRH, and 43% of PRH had no associated SDH. The median proportion of SDH associated with PRH in T1D was higher for SDH <70 mg/dL (40% vs. 22%) and SDH <54 mg/dL (47% vs. 25%) than in T2D., Conclusions: The novel findings are that at least half of CGM hypoglycemia is asymptomatic, even below 54 mg/dL, and many reported symptomatic hypoglycemia episodes happen above 70 mg/dL. In the clinical and research setting, these episodes cannot be used interchangeably, and both need to be recorded and addressed., (© 2024 by the American Diabetes Association.)

Aims/hypothesis: The aim of this work was to examine the impact of hypoglycaemia on daily functioning among adults with type 1 diabetes or insulin-treated type 2 diabetes, using the novel Hypo-METRICS app., Methods: For 70 consecutive days, 594 adults (type 1 diabetes, n=274; type 2 diabetes, n=320) completed brief morning and evening Hypo-METRICS 'check-ins' about their experienced hypoglycaemia and daily functioning. Participants wore a blinded glucose sensor (i.e. data unavailable to the participants) for the study duration. Days and nights with or without person-reported hypoglycaemia (PRH) and/or sensor-detected hypoglycaemia (SDH) were compared using multilevel regression models., Results: Participants submitted a mean ± SD of 86.3±12.5% morning and 90.8±10.7% evening check-ins. For both types of diabetes, SDH alone had no significant associations with the changes in daily functioning scores. However, daytime and night-time PRH (with or without SDH) were significantly associated with worsening of energy levels, mood, cognitive functioning, negative affect and fear of hypoglycaemia later that day or while asleep. In addition, night-time PRH (with or without SDH) was significantly associated with worsening of sleep quality (type 1 and type 2 diabetes) and memory (type 2 diabetes). Further, daytime PRH (with or without SDH), was associated with worsening of fear of hyperglycaemia while asleep (type 1 diabetes), memory (type 1 and type 2 diabetes) and social functioning (type 2 diabetes)., Conclusions/interpretation: This prospective, real-world study reveals impact on several domains of daily functioning following PRH but not following SDH alone. These data suggest that the observed negative impact is mainly driven by subjective awareness of hypoglycaemia (i.e. PRH), through either symptoms or sensor alerts/readings and/or the need to take action to prevent or treat episodes., (© 2024. The Author(s).)

Adolescence remains a crucial age associated with diabetes distress in individuals living with type 1 diabetes (T1D). The Austrian organization "Diabär" regularly hosts a one-week adventure camp for adolescents (12-18 years) living with T1D. The camp focuses on "fun activities" without a structured educational protocol in order to minimize diabetes distress and increase diabetes management skills. In contrast to educational camps, training is kept to a minimum. However, attendees analyze the glycemic data of the previous day with their medical supervisor once daily during the camp. All subjects used a standardized real-time continuous glucose monitoring (CGM) system (DexcomG7) throughout the whole study. Glycemic metrics were prospectively analyzed during three periods: week 1 = home phase, week 2 = adventure camp, and week 3 = after the camp. Safety (time below range 1 [TBR1], 69-54 mg/dL, and time below range 2 [TBR2], <54 mg/dL) and efficacy (time in range [TIR], 70-180 mg/dL) were assessed by comparing the CGM data during weeks 1-3. The CGM data of 14 participants were analyzed. The TIR was higher during the camp week versus week 1 (70.4 ± 11.1% vs. 53.1 ± 20.2%; p = 0.001). The TBR1 significantly increased during camp compared to week 1 (2.5 ±1.7% vs. 1.3 ± 1.2%; p = 0.009), whereas the TBR2 did not differ. No serious adverse events occurred. This adventure camp without a main focus on education showed feasibility and safety in adolescents with T1D.

Background: In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated., Methods: Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days. During four in-clinic visits, frequent comparison measurements with capillary blood glucose (BG) samples were performed. The primary endpoint was the 20/20 agreement rate (AR): the percentage of CGM readings within ±20 mg/dL (at BG values <100 mg/dL) or ±20% (at BG values ≥100 mg/dL) of the comparator. Further evaluations included mean absolute relative difference (MARD) and 20/20 AR in different BG ranges and across the wear time., Results: Data from 48 participants and 139 sensors were analyzed. During in-clinic sessions the 20/20 AR was 90.5% and the MARD was 9.2%. For BG ranges <70, 70-180, and >180 mg/dL, 20/20 AR was 94.3%, 89.0%, and 92.5%, respectively. At the beginning, middle, and end of sensor wear time, 20/20 AR was 92.8%, 91.5%, and 85.9%, respectively. The 14-day survival probability was 82.4%. Pain and bleeding after sensor insertion were within the expected range. Based on the study outcome, the use of the device is regarded as safe., Conclusions: The system showed a good performance compared to capillary BG measurements. This level of accuracy could be shown over the entire measurement range, especially in the low glycemic range, and the whole wear time of the sensors. The results of this study are supporting a non-adjunctive use of the device., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JKM is a member in the advisory boards of Abbott Diabetes Care, Becton-Dickinson/Embecta, Biomea, Eli Lilly, Medtronic, Novo Nordisk, PharmaSens, Roche Diabetes Care, Sanofi, and Viatris; received speaker honoraria from Abbott Diabetes Care, A. Menarini Diagnostics, Becton-Dickinson/Embecta, Boehringer-Ingelheim, Eli Lilly, MedTrust, Novo Nordisk, Roche Diabetes Care, Sanofi, Servier, and Ypsomed; and is shareholder of decide Clinical Software GmbH and elyte Diagnostics GmbH. GF is general manager and medical director of the IfDT (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. GF/IfDT have received speakers’ honoraria or consulting fees in the last three years from Abbott, Berlin Chemie, Boydsense, Dexcom, Lilly Deutschland, Novo Nordisk, Perfood, PharmaSens, Roche, Sinocare, Terumo, and Ypsomed. ME and DW are employees of IfDT. WM-H, KM, MA, GV, and CCR are employees of Roche Diabetes Care GmbH, Mannheim, Germany. TF is Chief Medical Officer and Chief Operations Officer of Clinical Research Services, Mannheim, Germany. TF received speaker honoraria or consulting fees in the last three years from Amarin, Astra Zeneca, Bayer, Boehringer-Ingelheim, Cipla, Daiichi-Sankyo, derCampus, Diabetes Academy Bad Mergentheim, Eli Lilly, Fortbildungskolleg, MSD, Novo Nordisk, Roche Diagnostics, Sanofi, Santis, and Sciarc. TF serves as associate editor for Endocrinology, Diabetes & Metabolism.

Diabetes is unique among chronic diseases because clinical outcomes are intimately tied to how the person living with diabetes reacts to and implements treatment recommendations. It is further characterised by widespread social stigma, judgement and paternalism. This physical, social and psychological burden collectively influences self-management behaviours. It is widely recognised that the individual's perspective about the impact of trying to manage the disease and the burden that self-management confers must be addressed to achieve optimal health outcomes. Standardised, rigorous assessment of mental and behavioural health status, in interaction with physical health outcomes is crucial to aid understanding of person-reported outcomes (PROs). Whilst tempting to conceptualise PROs as an issue of perceived quality of life (QoL), in fact health-related QoL is multi-dimensional and covers indicators of physical or functional health status, psychological and social well-being. This complexity is illuminated by the large number of person reported outcome measures (PROMs) that have been developed across multiple psychosocial domains. Often measures are used inappropriately or because they have been used in the scientific literature rather than based on methodological or outcome assessment rigour. Given the broad nature of psychosocial functioning/mental health, it is important to broadly define PROs that are evaluated in the context of therapeutic interventions, real-life and observational studies. This report summarises the central themes and lessons derived in the assessment and use of PROMs amongst adults with diabetes. Effective assessment of PROMs routinely in clinical research is crucial to understanding the true impact of any intervention. Selecting appropriate measures, relevant to the specific factors of PROs important in the research study will provide valuable data alongside physical health data., (© 2024 Diabetes UK.)

Introduction: This study examined associations between hypoglycemia awareness status and hypoglycemia symptoms reported in real-time using the novel Hypoglycaemia-MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone application (app) among adults with insulin-treated type 1 (T1D) or type 2 diabetes (T2D). Methods: Adults who experienced at least one hypoglycemic episode in the previous 3 months were recruited to the Hypo-METRICS study. They prospectively reported hypoglycemia episodes using the app for 10 weeks. Any of eight hypoglycemia symptoms were considered present if intensity was rated between "A little bit" to "Very much" and absent if rated "Not at all." Associations between hypoglycemia awareness (as defined by Gold score) and hypoglycemia symptoms were modeled using mixed-effects binary logistic regression, adjusting for glucose monitoring method and diabetes duration. Results: Of 531 participants (48% T1D, 52% T2D), 45% were women, 91% white, and 59% used Flash or continuous glucose monitoring. Impaired awareness of hypoglycemia (IAH) was associated with lower odds of reporting autonomic symptoms than normal awareness of hypoglycemia (NAH) (T1D odds ratio [OR] 0.43 [95% confidence interval {CI} 0.25-0.73], P  = 0.002); T2D OR 0.51 [95% CI 0.26-0.99], P  = 0.048), with no differences in neuroglycopenic symptoms. In T1D, relative to NAH, IAH was associated with higher odds of reporting autonomic symptoms at a glucose concentration <54 than >70 mg/dL (OR 2.18 [95% CI 1.21-3.94], P  = 0.010). Conclusion: The Hypo-METRICS app is sensitive to differences in hypoglycemia symptoms according to hypoglycemia awareness in both diabetes types. Given its high ecological validity and low recall bias, the app may be a useful tool in research and clinical settings. The clinical trial registration number is NCT04304963.

Introduction: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1 and insulin-treated type 2 diabetes in real-time using ecological momentary assessment (EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion., Methods: Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics., Results: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66) years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR) overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and 94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion., Discussion: High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time., (© 2024 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic., Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown., Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR [70-180mg/dL] ) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR [<70mg/dL] ) and the time above range (TAR [>180mg/dL] ), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR [70-180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR [<70mg/dL] and the TAR [>180mg/dL] underscored the data in the main analysis., Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P  < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P  < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Objective: To evaluate postprandial glucose control when applying (1) faster-acting insulin aspart (Fiasp) compared to insulin aspart and (2) ultra-rapid insulin lispro (Lyumjev) compared to insulin lispro using the CamAPS FX hybrid closed-loop algorithm. Research Design and Methods: We undertook a secondary analysis of postprandial glucose excursions from two double-blind, randomized, crossover hybrid closed-loop studies contrasting Fiasp to standard insulin aspart, and Lyumjev to standard insulin lispro. Endpoints included incremental area under curve (iAUC)-2h, iAUC-4h, 4 h postprandial time in target range, time above range, and time below range. It was approved by independent research ethics committees. Results: Two trials with 8 weeks of data from 51 adults with type 1 diabetes were analyzed and 7137 eligible meals were included. During Lyumjev compared with insulin lispro, iAUC-2h and iAUC-4h were significantly decreased following breakfast (mean difference 92 mmol/L per 2 h (95% confidence interval [CI]: 56 to 127); P  < 0.001 and 151 mmol/L per 4 h (95% CI: 74 to 229); P  < 0.001, respectively) and the evening meal ( P  < 0.001 and P  = 0.011, respectively). Mean time in target range (3.9-10.0 mmol/L) for 4 h postprandially significantly increased during Lyumjev with a mean difference of 6.7 percentage points (95% CI: 3.3 to 10) and 5.7 percentage points (95% CI: 1.4 to 9.9) for breakfast and evening meal, respectively. In contrast, there were no significant differences in iAUC-2h, iAUC-4h, and the other measures of postprandial glucose control between insulin aspart and Fiasp during breakfast, lunch, and evening meal ( P  > 0.05). Conclusion: The use of Lyumjev with CamAPS FX closed-loop system improved postprandial glucose excursions compared with insulin lispro, while the use of Fiasp did not provide any advantage compared with insulin aspart. Clinical Trial Registration numbers: NCT04055480, NCT05257460.

Objective: Many hybrid closed-loop (HCL) systems struggle to manage unusually high glucose levels as experienced with intercurrent illness or pre-menstrually. Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode ("Boost"), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of "Boost" mode., Methods: We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (≥24 years, n = 13), and older adults (≥60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with ≥30 minutes of "Boost" use versus days with no "Boost" use. Participants with <10 "Boost" days were excluded. The main outcome was time spent in hypoglycemia <70 and <54 mg/dL., Results: Eight weeks of data for 76 participants were analyzed. There was no difference in time spent <70 and <54 mg/dL between "Boost" days and "non-Boost" days; mean difference: -0.10% (95% confidence interval [CI] -0.28 to 0.07; P = .249) time <70 mg/dL, and 0.03 (-0.04 to 0.09; P = .416) time < 54 mg/dL. Time in significant hyperglycemia >300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on "Boost" days, with higher mean glucose and lower time in target range ( P < .001)., Conclusions: Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on "Boost" days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RH reports having received speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk; receiving license fees from BBraun; patents related to closed-loop; and being director at CamDiab. JW reports receiving speaker honoraria from Ypsomed. YR is a consultant at CamDiab. MEW is a consultant at CamDiab and reports patents related to closed-loop. CKB reports receiving consultancy fees from CamDiab and speaker honoraria from Ypsomed. SH reports speaker and advisory board fees from Dexcom, Medtronic, Sanofi, and Ypsomed; being director at ASK Diabetes Ltd; and receiving consulting/training fees from CamDiab. LB reports receiving research support from Dexcom and CamDiab. REJB reports receiving speaking honoraria from Eli Lilly and Springer Healthcare, and sitting as a voluntary unpaid member of the NovoNordisk UK Foundation Research Selection committee. FMC reports receiving speaker honoraria from Eli Lilly, Dexcom, and Novo Nordisk and Insulet, and consultancy fees from Abbott Diabetes Care. EF-R reports having received speaker honoraria from Eli Lilly and Novo Nordisk, serving on advisory boards for Eli Lilly and Sanofi. MLE is a clinical triallist with or has served on advisory boards or received speakers or writers fees from Medtronic, Dexcom, Abbott Diabetes Care, Roche, AstraZeneca, Novo Nordisk, Eli Lilly, Zucara, Pila Pharma, and Imcyse Pharma. SEH has received speaker honoraria by Eli Lilly, Vertex, Minimed Medtronic, Insulet, Ypsomed, and Sanofi. TMK reports having received speaker honoraria from Eli Lilly and Novo. LL has received personal fees from Abbott Diabetes Care, Dexcom, Insulet, Medtronic, Novo Nordisk, Sanofi, and Diabetes Care. JKM is a member on the advisory board of Boehringer Ingelheim, Becton-Dickinson, Eli Lilly, Medtronic, Prediktor A/S, Roche Diabetes Care, and Sanofi-Aventis, and received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Becton-Dickinson, Dexcom, Eli Lilly, Mercke Sharp & Dohme, NovoNordisk, Roche Diabetes Care, Sanofi, Servier, and Ypsomed. TR reports receiving speaker honoraria from Novo Nordisk and consultancy fees from Abbott Diabetes Care. HT reports receiving research support from Dexcom and speaker honoraria from Eli Lilly. MT reports having received speaker honoraria from Eli Lilly, Novo Nordisk, and Medtronic and advisory board fees from Abbott Diabetes Care. BR-M has received speaker honoraria from Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi, and Menarini and has been on the advisory boards of Eli Lilly, Roche Diabetes Care, and Abbott Diabetes Care. CdB has received speaker honoraria from Minimed Medtronic, and has been member of their European Psychology and e-learning Advisory Board. JMA reports training fees from CamDiab. DE, WMM, PN, TP, AG, AT, KD, and NT have no disclosures.

People living with diabetes mellitus can be supported in the daily management by diabetes technology with automated insulin delivery (AID) systems to reduce the risk of hypoglycemia and improve glycemic control as well as the quality of life. Due to barriers in the availability of AID-systems, the use and development of open-source AID-systems have internationally increased. This technology provides a necessary alternative to commercially available products, especially when approved systems are inaccessible or insufficiently adapted to the specific needs of the users. Open-source technology is characterized by worldwide free availability of codes on the internet, is not officially approved and therefore the use is on the individual's own responsibility. In the clinical practice a lack of expertise with open-source AID technology and concerns about legal consequences, lead to conflict situations for health-care professionals (HCP), sometimes resulting in the refusal of care of people living with diabetes mellitus. This position paper provides an overview of the available evidence and practical guidance for HCP to minimize uncertainties and barriers. People living with diabetes mellitus must continue to be supported in education and diabetes management, independent of the chosen diabetes technology including open-source technology. Check-ups of the metabolic control, acute and chronic complications and screening for diabetes-related diseases are necessary and should be regularly carried out, regardless of the chosen AID-system and by a multidisciplinary team with appropriate expertise., (© 2024. The Author(s).)

Objective: To evaluate the association of insulin injection adherence, smart insulin pen engagement, and glycemic control using real-world data from 16 countries from adults self-administering basal insulin degludec and bolus insulin with a smart insulin pen (NovoPen 6 or NovoPen Echo Plus) alongside continuous glucose monitoring (CGM)., Research Design and Methods: Data were aggregated over 14-day periods. Treatment adherence was defined according to the number of missed basal and missed bolus insulin doses and smart pen engagement according to the number of days with data uploads., Results: Data from 3,945 adults, including 25,157 14-day periods with ≥70% CGM coverage, were analyzed. On average, 0.2 basal and 6.0 bolus insulin doses were missed over 14 days. The estimated probability of missing at least one basal insulin dose over a 14-day period was 17.6% (95% CI 16.5, 18.7). Missing one basal or bolus insulin dose per 14 days was associated with a significant decrease in percentage of time with glucose levels in range (TIR) (3.9-10.0 mmol/L), of -2.8% (95% CI -3.7, -1.8) and -1.7% (-1.8, -1.6), respectively; therefore, missing two basal or four bolus doses would decrease TIR by >5%. Smart pen engagement was associated positively with glycemic outcomes., Conclusions: This combined analysis of real-world smart pen and CGM data showed that missing two basal or four bolus insulin doses over a 14-day period would be associated with a clinically relevant decrease in TIR. Smart insulin pens provide valuable insights into treatment injection behaviors., (© 2024 by the American Diabetes Association.)

Background: CamAPS FX is a hybrid closed-loop smartphone app used to manage type one diabetes. The closed-loop algorithm has a default target glucose of 5.8 mmol/L (104.5 mg/dL), but users can select personal glucose targets (adjustable between 4.4 mmol/L and 11.0 mmol/L [79 mg/dL and 198 mg/dL, respectively])., Method: In this post-hoc analysis, we evaluated the impact of personal glucose targets on glycemic control using data from participants in five randomized controlled trials., Results: Personal glucose targets were widely used, with 20.3% of all days in the data set having a target outside the default target bin (5.5-6.0 mmol/L [99-108 mg/dL]). Personal glucose targets >6.5 mmol/L (117 mg/dL) were associated with significantly less time in target range (3.9-10.0 mmol/L [70-180 mg/dL]; 6.5-7.0 mmol/L [117-126 mg/dL]: mean difference = -3.2 percentage points [95% CI: -5.3 to -1.2; P < .001]; 7.0-7.5 mmol/L [126-135 mg/dL]: -10.8 percentage points [95% CI: -14.1 to -7.6; P < .001]). Personal targets >6.5 mmol/L (117 mg/dL) were associated with significantly lower time (<3.9 mmol/L [<70 mg/dL]; 6.5-7.0 mmol/L [117-126 mg/dL]: -1.85 percentage points [95% CI: -2.37 to -1.34; P < .001]; 7.0-7.5 mmol/L [126-135 mg/dL]: -2.68 percentage points [95% CI: -3.49 to -1.86; P < .001])., Conclusions: Discrete study populations showed differences in glucose control when applying similar personal targets., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: C.K.B. has received consulting fees from CamDiab and speaker honoraria from Ypsomed. J.W. has received speaker honoraria from Ypsomed. J.M.A. has received consulting fees from CamDiab. S.H. serves as a member of Sigma (Dexcom) and Medtronic advisory boards; is a consultant for CamDiab and a director of Ask Diabetes Ltd., providing training and research support in healthcare settings; and reports having received training honoraria from Medtronic, Sanofi, and Ypsomed. M.E.W. reports receiving license fees from B. Braun, patents related to closed-loop systems, and being a consultant at CamDiab. C.d.B. reports having received speaker honoraria from Medtronic and has served on the EU psychology e-learning board of Medtronic. E.F.-R. reports having received speaker honoraria from Medtronic, Eli Lilly and Company, Novo Nordisk, and Sanofi and serving on the advisory board for Eli Lilly and Company. S.E.H. has received speaker honoraria from Medtronic, Eli Lilly, Ypsomed, and Insulet. T.M.K. reports having received speaker honoraria from Eli Lilly, Merck Serono, and Novo Nordisk. B.R.-M. has received speaker honoraria from Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi, and Menarini and has been on the advisory boards of Eli Lilly, Roche Diabetes Care, and Abbott Diabetes Care. T.L.R. has received consultancy fees from Abbott Diabetes Care and speaker honoraria from Novo Nordisk. R.E.J.B. reports having received speaking honoraria from Eli Lilly and Springer Healthcare and sits as an unpaid member of the Novo Nordisk UK Research Foundation grant and selection committee. L.D. has received honoraria for taking part in an Interactive Advisory and Advocacy Forum on CGM Use in Pediatric Clinical Practice from Dexcom and has received conference fees from Novo Nordisk. H.T. reports having received research support from Dexcom and speaker honoraria from Eli Lilly and Dexcom. L.L. reports having received speaker honoraria from Animas, Abbott, Insulet, Medtronic, Novo Nordisk, Roche, and Sanofi; was on advisory panels for Animas, Abbott, Novo Nordisk, Dexcom, Medtronic, Sanofi, and Roche; and received research support from Novo Nordisk and Dexcom. M.L.E. reports having received speaker honoraria from Eli Lilly and Company, Novo Nordisk, Abbott Diabetes Care, Medtronic, AstraZeneca, and Ypsomed and acting on advisory boards for Medtronic, Novo Nordisk, Zucara Therapeutics, Pila Pharma, and Abbott Diabetes Care. J.K.M. is a member of the advisory boards of Abbott Diabetes Care, BD, Boehringer Ingelheim, Eli Lilly and Company, Medtronic, Novo Nordisk AS, Prediktor A/S, Roche Diabetes Care, and Sanofi; received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Dexcom, Eli Lilly and Company, Menarini Diagnostics, Novo Nordisk A/S, Roche Diabetes Care, Servier, and Ypsomed; and is a cofounder and shareholder of decide Clinical Software Ltd. R.H. reports having received speaker honoraria from Eli Lilly and Company, Dexcom, and Novo Nordisk; receiving license fees from Medtronic; receiving patents related to closed-loop systems; and being the director at CamDiab. M.F., A.T., F.M.C., A.G., D.E., N.T., N.D., and L.B. have no conflict of interest to disclose.

Background: Lipohypertrophy is a common complication in patients with diabetes receiving insulin therapy. There is a lack of consensus regarding how much lipohypertrophy affects diabetes management. Our study aimed to assess the potential correlation between lipohypertrophy and glycemic control, as well as insulin dosing in patients with diabetes. Methods: We performed a systematic review followed by a meta-analysis to collect data about glycemic control and insulin dosing in diabetic patients with and without lipohypertrophy. To identify relevant studies published in English, we searched medical databases (MEDLINE/PubMed, Embase, and CENTRAL) from 1990 to January 20, 2023. An additional hand-search of references was performed to retrieve publications not indexed in medical databases. Results of meta-analyses were presented either as prevalence odds ratios (pORs) or mean differences (MDs) with 95% confidence intervals (95% CIs). This study was registered on PROSPERO (CRD42023393103). Results: Of the 5540 records and 240 full-text articles screened, 37 studies fulfilled the prespecified inclusion criteria. Performed meta-analyses showed that patients with lipohypertrophy compared with those without lipohypertrophy were more likely to experience unexplained hypoglycemia (pOR [95% CI] = 6.98 [3.30-14.77]), overall hypoglycemia (pOR [95% CI] = 6.65 [1.37-32.36]), and glycemic variability (pOR [95% CI] = 5.24 [2.68-10.23]). Patients with lipohypertrophy also had higher HbA1c (MD [95% CI] = 0.55 [0.23-0.87] %), and increased daily insulin consumption (MD [95% CI] = 7.68 IU [5.31-10.06]). Conclusions: These results suggest that overall glycemic control is worse in patients with lipohypertrophy than in those without this condition.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Background: The aim of this study was to compare patient-reported outcomes (PROs) in people with type 1 diabetes using either continuous subcutaneous insulin infusion (CSII) with two different insulin patch pumps or multiple daily injections (MDIs)., Materials and Methods: In this randomized three-arm study, people with type 1 diabetes on MDI therapy were included and used either MDI, the Accu-Chek Solo micropump system (Solo) or Omnipod for 26 weeks. From weeks 26 to 39, all participants used CSII with Solo. Patient-reported outcomes were assessed using the diabetes technology questionnaire (DTQ); in addition, HbA 1c values were measured., Results: Overall, 181 participants were randomized (61 MDI arm, 62 Solo arm, 58 Omnipod arm) and 142 completed the study. After 26 weeks in the study, the DTQ "change" score in the Solo group (105.9 [100.6-111.2]; baseline-adjusted mean [95% confidence interval]) was significantly higher than in the MDI group (94.8 [89.6-100.0]) ( P = .001). The comparison between the Solo group (105.1 [99.1-111.1]) and the Omnipod group (108.7 [103.1-114.4]) showed no significant differences ( P = .382). HbA 1c increased by 0.2% ± 0.7% in the MDI group and decreased in both pump groups (Solo group -0.2% ± 0.8% and Omnipod group -0.1% ± 0.8%). Differences in HbA 1c between the Solo group and the MDI group were significant ( P = .009), but not between the Solo group and the Omnipod group ( P = .896)., Conclusions: This study showed that switching from MDI to CSII improves both psychosocial well-being and physiological outcomes. Furthermore, there were no substantial differences between the established and the recently released patch pump. Trial registration at www.clinicaltrials.gov is NCT03478969., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KBK has declared no conflict of interest. F.T. has declared no conflict of interest. J.K.M. is a member of the advisory board of Abbott Diabetes Care, Becton-Dickinson, Boehringer Ingelheim, Eli Lilly, Embecta, Medtronic, Novo Nordisk A/S, Prediktor A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris and received speaker honoraria from Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Menarini, MSD, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Servier, and Ypsomed. N.O. has participated in advisory boards for Medtronic Diabetes, Roche Diabetes Care, and Dexcom, has received speaker fees from Dexcom, and has received research funding from Medtronic Diabetes, Roche Diabetes Care, and Dexcom. E.F. has declared no conflict of interest. N.D., T.E., T.K., G.V., and I.V. are employees of Roche Diabetes Care GmbH.

Background: Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems., Materials and Methods: A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed., Results: The PWT1D using OS-AID systems spent more time in range (TIR) 70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR) 181-250 mg/dL (11.1% vs 19.6%), less TAR >250 mg/dL (2.5% vs 4.3%), and more time below range (TBR) 54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR <54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%)., Conclusion: In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: ISF received speaker honoraria from Dexcom, Medtronic, Abbott, and Roche Diabetes Care. BRM reports speaker’s honoraria/advisory board activity from Dexcom, Abbott, Insulet, Medtronic, Eli Lilly, Novo Nordisk, Sanofi, and Ypsomed. JKM is a member of the advisory boards of Abbott Diabetes Care, Becton-Dickinson, Boehringer Ingelheim, Eli Lilly, Embecta, Medtronic, NovoNordisk A/S, Roche Diabetes Care, Sanofi-Aventis, and Viatris, and received speaker honoraria from A.Menarini Diagnostics, Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Medtrust, MSD, NovoNordisk A/S, Roche Diabetes Care, Sanofi, Servier, and Ypsomed; furthermore, she is shareholder of decide Clinical Software GmbH and elyte Diagnostics where she also serves as chief marketing officer (CMO). The other authors declare no conflict of interest.

People living with diabetes have many medical devices available to assist with disease management. A critical aspect that must be considered is how systems for continuous glucose monitoring and insulin pumps communicate with each other and how the data generated by these devices can be downloaded, integrated, presented and used. Not only is interoperability associated with practical challenges, but also devices must adhere to all aspects of regulatory and legal frameworks. Key issues around interoperability in terms of data ownership, privacy and the limitations of interoperability include where the responsibility/liability for device and data interoperability lies and the need for standard data-sharing protocols to allow the seamless integration of data from different sources. There is a need for standardised protocols for the open and transparent handling of data and secure integration of data into electronic health records. Here, we discuss the current status of interoperability in medical devices and data used in diabetes therapy, as well as regulatory and legal issues surrounding both device and data interoperability, focusing on Europe (including the UK) and the USA. We also discuss a potential future landscape in which a clear and transparent framework for interoperability and data handling also fulfils the needs of people living with diabetes and healthcare professionals., (© 2023. The Author(s).)

The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MS and AM are full-time employees of PharmaSens AG; JKM is a member of advisory boards of Abbott Diabetes Care, Becton-Dickinson, Boehringer Ingelheim, Eli Lilly, Embecta, Medtronic, NovoNordisk A/S, Roche Diabetes Care, Sanofi-Aventis, Viatris and received speaker honoraria from A. Menarini Diagnostics, Abbott Diabetes Care, AstraZeneca, Boehringer Ingelheim, Dexcom, Eli Lilly, Medtrust, MSD, NovoNordisk A/S, Roche Diabetes Care, Sanofi, Servier, and Ypsomed; she is shareholder of decide Clinical Software GmbH and elyte Diagnostics where she also serves as CMO; GF has received speakers honoraria or consulting fees from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Lilly, Metronom, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, and Terumo; P-YB has received speaker honoraria from Abbott, Eli Lilly, Novo Nordisk, and Sanofi, is chief medical officer for Diabeloop, and served on advisory board panels for Abbott, Dexcom, Insulet, LifeScan, Eli Lilly, Pharmasens, Novo Nordisk, and Sanofi; Peter Diem is member of the PharmaSens AG Board of Directors; Lutz Heinemann is a consultant to several diagnostic and therapeutic companies in the development of new products, he is a shareholder in the Profil Institute for Metabolic Research in Neuss, Germany.

Background: The Hypoglycaemia - MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone app was developed to investigate the impact of hypoglycemia on daily functioning in adults with type 1 diabetes mellitus or insulin-treated type 2 diabetes mellitus. The app uses ecological momentary assessments, thereby minimizing recall bias and maximizing ecological validity. It was used in the Hypo-METRICS study, a European multicenter observational study wherein participants wore a blinded continuous glucose monitoring device and completed the app assessments 3 times daily for 70 days., Objective: The 3 aims of the study were to explore the content validity of the app, the acceptability and feasibility of using the app for the duration of the Hypo-METRICS study, and suggestions for future versions of the app., Methods: Participants who had completed the 70-day Hypo-METRICS study in the United Kingdom were invited to participate in a brief web-based survey and an interview (approximately 1h) to explore their experiences with the app during the Hypo-METRICS study. Thematic analysis of the qualitative data was conducted using both deductive and inductive methods., Results: A total of 18 adults with diabetes (type 1 diabetes: n=10, 56%; 5/10, 50% female; mean age 47, SD 16 years; type 2 diabetes: n=8, 44%; 2/8, 25% female; mean age 61, SD 9 years) filled out the survey and were interviewed. In exploring content validity, participants overall described the Hypo-METRICS app as relevant, understandable, and comprehensive. In total, 3 themes were derived: hypoglycemia symptoms and experiences are idiosyncratic; it was easy to select ratings on the app, but day-to-day changes were perceived as minimal; and instructions could be improved. Participants offered suggestions for changes or additional questions and functions that could increase engagement and improve content (such as providing more examples with the questions). In exploring acceptability and feasibility, 5 themes were derived: helping science and people with diabetes; easy to fit in, but more flexibility wanted; hypoglycemia delaying responses and increasing completion time; design, functionality, and customizability of the app; and limited change in awareness of symptoms and impact. Participants described using the app as a positive experience overall and as having a possible, although limited, intervention effect in terms of both hypoglycemia awareness and personal impact., Conclusions: The Hypo-METRICS app shows promise as a new research tool to assess the impact of hypoglycemia on an individual's daily functioning. Despite suggested improvements, participants' responses indicated that the app has satisfactory content validity, overall fits in with everyday life, and is suitable for a 10-week research study. Although developed for research purposes, real-time assessments may have clinical value for monitoring and reviewing hypoglycemia symptom awareness and personal impact., (©Uffe Søholm, Natalie Zaremba, Melanie Broadley, Johanne Lundager Axelsen, Patrick Divilly, Gilberte Martine-Edith, Stephanie A Amiel, Julia K Mader, Ulrik Pedersen-Bjergaard, Rory J McCrimmon, Eric Renard, Mark Evans, Bastiaan de Galan, Simon Heller, Christel Hendrieckx, Pratik Choudhary, Jane Speight, Frans Pouwer, Hypo-RESOLVE consortium Hypo-RESOLVE consortium. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 29.09.2023.)

Aims: Use of the CamAPS FX hybrid closed loop (CL) system is associated with improved time in range and glycated haemoglobin A1c across the age span, but little is known about its effects on patient-reported outcomes (PROs)., Methods: This open-label, randomized, multi-site study compared CamAPS FX to sensor-augmented pump (SAP) in a sample of older adults (≥60 years) with type 1 diabetes (T1D). Thirty-five older adults completed PROs surveys at the start of the study and after each period of 16 weeks using either CL or SAP. At the end of the study, 19 participated in interviews about their experiences with CL., Results: Results examining the 16 weeks of CL use showed that the overall Diabetes Distress Scale score and two subscales (powerlessness and physician distress) improved significantly along with trust on the Glucose Monitoring Satisfaction Survey. User experience interview responses were consistent in noting benefits of 'improved glycaemic control' and 'worrying less about diabetes'., Conclusion: In this sample of older adults with T1D who have previously shown glycaemic benefit, there are indicators of improved PROs and subjective user experience benefits., (© 2023 Diabetes UK.)

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data., Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation., Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals., Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Background and Aims: A preliminary study compared the use of continuous glucose monitoring (CGM) with the use of self-monitored blood glucose (SMBG) by aircraft pilots with insulin-treated diabetes in the United Kingdom, Ireland, and Austria, certified to fly commercial aircraft within the European Aviation Safety Agency ARA.MED.330 protocol. Methods: SMBG and simultaneous interstitial glucose measurements using CGM (Dexcom G6 ® ) were recorded during pre- and in-flight periods. Results: Eight male pilots (seven with type 1 diabetes and one with type 3c diabetes), median age of 48.5 years and median diabetes duration of 11.5 years, participated. The correlation coefficient ( R ) between 874 contemporaneously recorded SMBG and CGM values was 0.843, P  < 0.001. The mean glucose concentration was 8.78 mmol/L (standard deviation [SD] 0.67) using SMBG compared with 8.71 mmol/L (SD 0.85) recorded using CGM. The mean absolute relative difference was 9.39% (SD 3.12). Conclusions: CGM using Dexcom G6 systems is a credible alternative to SMBG for monitoring glucose levels when insulin-treated pilots fly commercial aircraft. The study was registered with Clinical Trials.gov NCT04395378.