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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.

Background: Safety and significant improvement in overall glycated hemoglobin (A1C) and percentage of time spent in (TIR), below (TBR), and above (TAR) glucose range were demonstrated in the pivotal trial of adolescents and adults using the MiniMed™ advanced hybrid closed-loop (AHCL) system with the adjunctive, calibration-required Guardian™ Sensor 3. The present study evaluated early outcomes of continued access study (CAS) participants who transitioned from the pivotal trial investigational system to the approved MiniMed™ 780G system with the non-adjunctive, calibration-free Guardian™ 4 Sensor (MM780G+G4S). Study data were presented alongside those of real-world MM780G+G4S users from Europe, the Middle East, and Africa. Methods: The CAS participants ( N  = 109, aged 7-17 years and N  = 67, aged >17 years) used the MM780G+G4S for 3 months and data of real-world MM780G+G4S system users ( N  = 10,204 aged ≤15 years and N  = 26,099 aged >15 years) were uploaded from September 22, 2021 to December 02, 2022. At least 10 days of real-world continuous glucose monitoring (CGM) data were required for analyses. Glycemic metrics, delivered insulin and system use/interactions underwent descriptive analyses. Results: Time in AHCL and CGM use were >90% for all groups. AHCL exits averaged 0.1/day and there were few blood glucose measurements (BGMs) (0.8/day-1.0/day). Adults in both cohorts met most consensus recommendations for glycemic targets. Pediatric groups met recommendations for %TIR and %TBR, although not those for mean glucose variability and %TAR, possibly due to low use of recommended glucose target (100 mg/dL) and active insulin time (2 h) settings (28.4% in the CAS cohort and 9.4% in the real-world cohort). The CAS pediatric and adult A1C were 7.2% ± 0.7% and 6.8% ± 0.7%, respectively, and there were no serious adverse events. Conclusions: Early clinical use of the MM780G+G4S was safe and involved minimal BGMs and AHCL exits. Consistent with real-world pediatric and adult use, outcomes were associated with achievement of recommended glycemic targets. Clinical Trial Registration number: NCT03959423.

Background: While we expect that patients who adjust their insulin delivery algorithms between clinic visits to have better glucose control compared to those who do not, this effect has not been quantified., Method: This is a single-center retrospective cohort study including pediatric and adult patients with type 1 diabetes evaluating insulin pump self-management behaviors. Basal insulin dose information was obtained from the Glooko-Diasend database, and used to quantify the frequency and magnitude of basal insulin daily dose adjustments within the 90-day window preceding HbA1c measurement. We use a linear mixed-effects model to analyze associations between frequency/magnitude of daily basal insulin changes and HbA1c., Results: We present data on 114 adult (44 ± 17 years, 60% female) and 212 pediatric (12 ± 4 years, 50% female) patients. Individuals changed their basal insulin dose on 72%-94% (interquartile range [IQR]) of observed days relative to the previous day. These changes varied 0.6%-2.4% IQR from the previous day's value. In pediatric patients, lower HbA1c was associated with more frequent daily profile adjustments, while controlling for rate of hypoglycemia (z = -3.2, P  = .001). In adults, there was no relationship between HbA1c and magnitude or frequency of basal profile adjustments., Conclusions: Pediatric patients who frequently modify their basal insulin exhibit somewhat better clinical outcomes, although the magnitude by which their basal amount is changed does not contribute to this effect.

Importance: Continuous glucose monitoring (CGM) has been shown to be beneficial for adults with type 2 diabetes using intensive insulin therapy, but its use in type 2 diabetes treated with basal insulin without prandial insulin has not been well studied., Objective: To determine the effectiveness of CGM in adults with type 2 diabetes treated with basal insulin without prandial insulin in primary care practices., Design, Setting, and Participants: This randomized clinical trial was conducted at 15 centers in the US (enrollment from July 30, 2018, to October 30, 2019; follow-up completed July 7, 2020) and included adults with type 2 diabetes receiving their diabetes care from a primary care clinician and treated with 1 or 2 daily injections of long- or intermediate-acting basal insulin without prandial insulin, with or without noninsulin glucose-lowering medications., Interventions: Random assignment 2:1 to CGM (n = 116) or traditional blood glucose meter (BGM) monitoring (n = 59)., Main Outcomes and Measures: The primary outcome was hemoglobin A1c (HbA1c) level at 8 months. Key secondary outcomes were CGM-measured time in target glucose range of 70 to 180 mg/dL, time with glucose level at greater than 250 mg/dL, and mean glucose level at 8 months., Results: Among 175 randomized participants (mean [SD] age, 57 [9] years; 88 women [50%]; 92 racial/ethnic minority individuals [53%]; mean [SD] baseline HbA1c level, 9.1% [0.9%]), 165 (94%) completed the trial. Mean HbA1c level decreased from 9.1% at baseline to 8.0% at 8 months in the CGM group and from 9.0% to 8.4% in the BGM group (adjusted difference, -0.4% [95% CI, -0.8% to -0.1%]; P = .02). In the CGM group, compared with the BGM group, the mean percentage of CGM-measured time in the target glucose range of 70 to 180 mg/dL was 59% vs 43% (adjusted difference, 15% [95% CI, 8% to 23%]; P < .001), the mean percentage of time at greater than 250 mg/dL was 11% vs 27% (adjusted difference, -16% [95% CI, -21% to -11%]; P < .001), and the means of the mean glucose values were 179 mg/dL vs 206 mg/dL (adjusted difference, -26 mg/dL [95% CI, -41 to -12]; P < .001). Severe hypoglycemic events occurred in 1 participant (1%) in the CGM group and in 1 (2%) in the BGM group., Conclusions and Relevance: Among adults with poorly controlled type 2 diabetes treated with basal insulin without prandial insulin, continuous glucose monitoring, as compared with blood glucose meter monitoring, resulted in significantly lower HbA1c levels at 8 months., Trial Registration: ClinicalTrials.gov Identifier: NCT03566693.

Importance: Continuous glucose monitoring (CGM) provides real-time assessment of glucose levels and may be beneficial in reducing hypoglycemia in older adults with type 1 diabetes., Objective: To determine whether CGM is effective in reducing hypoglycemia compared with standard blood glucose monitoring (BGM) in older adults with type 1 diabetes., Design, Setting, and Participants: Randomized clinical trial conducted at 22 endocrinology practices in the United States among 203 adults at least 60 years of age with type 1 diabetes., Interventions: Participants were randomly assigned in a 1:1 ratio to use CGM (n = 103) or standard BGM (n = 100)., Main Outcomes and Measures: The primary outcome was CGM-measured percentage of time that sensor glucose values were less than 70 mg/dL during 6 months of follow-up. There were 31 prespecified secondary outcomes, including additional CGM metrics for hypoglycemia, hyperglycemia, and glucose control; hemoglobin A1c (HbA1c); and cognition and patient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate., Results: Of the 203 participants (median age, 68 [interquartile range {IQR}, 65-71] years; median type 1 diabetes duration, 36 [IQR, 25-48] years; 52% female; 53% insulin pump use; mean HbA1c, 7.5% [SD, 0.9%]), 83% used CGM at least 6 days per week during month 6. Median time with glucose levels less than 70 mg/dL was 5.1% (73 minutes per day) at baseline and 2.7% (39 minutes per day) during follow-up in the CGM group vs 4.7% (68 minutes per day) and 4.9% (70 minutes per day), respectively, in the standard BGM group (adjusted treatment difference, -1.9% (-27 minutes per day); 95% CI, -2.8% to -1.1% [-40 to -16 minutes per day]; P <.001). Of the 31 prespecified secondary end points, there were statistically significant differences for all 9 CGM metrics, 6 of 7 HbA1c outcomes, and none of the 15 cognitive and patient-reported outcomes. Mean HbA1c decreased in the CGM group compared with the standard BGM group (adjusted group difference, -0.3%; 95% CI, -0.4% to -0.1%; P <.001). The most commonly reported adverse events using CGM and standard BGM, respectively, were severe hypoglycemia (1 and 10), fractures (5 and 1), falls (4 and 3), and emergency department visits (6 and 8)., Conclusions and Relevance: Among adults aged 60 years or older with type 1 diabetes, continuous glucose monitoring compared with standard blood glucose monitoring resulted in a small but statistically significant improvement in hypoglycemia over 6 months. Further research is needed to understand the long-term clinical benefit., Trial Registration: ClinicalTrials.gov Identifier: NCT03240432.

Context: Evidence suggests that heart rate (HR) is a prognostic factor for cardiovascular disease (CVD), for which persons with diabetes are at increased risk., Objective: The objective of this article is to determine the association between HR and glycemic status in a nationally representative sample of US adults, and, among adults with diagnosed diabetes, the association between HR and hemoglobin A1c (HbA1c) level., Design: A cross-sectional study was conducted., Setting: The setting of this study is the National Health and Nutrition Examination Surveys, 2011 to 2016., Participants: US general adult (age ≥ 20 years) population who had information on glycemic status based on self-report, HbA1c, and fasting plasma glucose (N = 8562)., Intervention: There was no intervention., Main Outcome Measure: The main outcome measure of this study was mean HR (beats per minute)., Results: After adjustment for examination time, age, other demographic characteristics, health insurance, health behaviors, body mass index, CVD and kidney disease, and taking antihypertensive medications, mean HR was significantly higher for those with diagnosed (75 bpm), undiagnosed diabetes (75 bpm), and prediabetes (73 bpm) compared to those with normoglycemia (71 bpm, P < .05 for all); this association was robust both for men and women. Mean HR increased with increasing HbA1c level among individuals with diagnosed diabetes independent of other risk factors (HbA1c < 7.0% [< 53 mmol/mol], 73 bpm vs A1c ≥ 11.0% [≥ 97mmol/mol], 79 bpm, P < .001); this association was most pronounced for women., Conclusions: Adjusted mean HR was higher among individuals with diabetes and increased glycemia, which may reflect underlying autonomic and/or myocardial dysfunction among those with diabetes., (Published by Oxford University Press on behalf of the Endocrine Society 2020.)

Aim: To assess the relationship between glucose variability (GV) and non-dipping of blood pressure (BP) as a marker of cardiovascular autonomic neuropathy (CAN) among patients with type 1 diabetes (T1D)., Methods: Forty-one subjects with T1D (age 34 ± 13 years, duration 13 ± 6 years, HbA1c 8 ± 1.2%) without cardiovascular disease, dyslipidemia, or hypertension at baseline were enrolled in a 3-year observational cohort study. Subjects were phenotyped for CAN with heart rate variability, cardiovascular autonomic reflex tests, and 24-h BP profiles at baseline and during follow-up. Non-dipping was defined as nocturnal systolic and diastolic BP fall of ≤10%. Reverse dipping BP was defined as a <0% change in the day to night for systolic and diastolic BP. Indices of GV were derived from 5-day continuous glucose monitoring obtained at 3-month intervals, and serum inflammatory biomarkers in all subjects., Results: At baseline 10% of the T1D subjects were non-dippers. The dippers and non-dippers were similar in age, diabetes duration, glucose control, traditional cardiovascular risk factors, GV and inflammatory markers. No significant correlations were found at baseline between non-dipping nocturnal blood pressure and measures of GV. At 3 years there were no differences in risk factor profile of subjects who were non-dippers over time (progressors) and those who were dippers (non-progressors)., Conclusion: In a cohort of contemporary patients with T1D following the current standard of care in diabetes, the prevalence of non-dipping is relatively low. There were no clear phenotypes that explained the difference in the risk for non-dipping, including GV. Ambulatory blood pressure monitoring could be used as a tool for improved CVD risk stratification and development of therapeutic interventions in these patients., (Published by Elsevier Inc.)

Aims/hypothesis: Chronic hyperglycaemia, as measured by HbA 1c levels, is a major risk factor for atherosclerosis and cardiovascular disease (CVD) in type 1 diabetes. Our aim was to describe the degree to which the effect of HbA 1c on the risk of CVD is mediated by its effect on traditional risk factors over time, and how these mediation pathways change over time., Methods: The DCCT and its observational follow-up study, the Epidemiology of Diabetes Interventions and Complications (EDIC), followed 1441 participants for a mean of 27 years, with periodic measurement of HbA 1c and risk factors over time. We assessed the proportion of the HbA 1c effect on risk of CVD that was mediated through its effects on systolic BP (SBP), pulse rate, triacylglycerols and LDL-cholesterol (LDLc) levels, and how the proportion mediated changed over time., Results: The association of HbA 1c with CVD outcomes was stable over time, while that of traditional risk factors (SBP, pulse rate, triacylglycerols and LDLc) increased. At 10 years of follow-up, the effect of HbA 1c on 10 year CVD risk was minimally mediated by SBP (2.7%), increasing to 26% at 20 years. Likewise, from 10 year follow-up to 20 year follow-up, the proportion of HbA 1c effect mediated through pulse rate increased from 6.3% to 29.3%, through triacylglycerols from 2.2% to 22.4%, and through LDLc from 9.2% to 30.7%., Conclusions/interpretation: As participants age, the predictive association of mean HbA 1c on subsequent CVD events is increasingly mediated by its effect on standard risk factors. Thus, management of traditional non-glycaemic CVD risk factors may have increasing benefits in an ageing type 1 diabetes population with longstanding hyperglycaemia., Trial Registration: ClinicalTrials.gov NCT00360893 and NCT00360815.

Objective: The Diabetes Control and Complications Trial (DCCT) demonstrated the beneficial effects of intensive versus conventional therapy on the development and progression of microvascular complications of type 1 diabetes. These beneficial effects were almost completely explained by the difference between groups in the levels of HbA 1c , which in turn were associated with the risk of these complications. We assessed the association of glucose variability within and between quarterly 7-point glucose profiles with the development and progression of retinopathy, nephropathy, and cardiovascular autonomic neuropathy during the DCCT., Research Design and Methods: Measures of variability included the within-day and updated mean (over time) of the SD, mean amplitude of glycemic excursions (MAGE), and M-value, and the longitudinal within-day, between-day, and total variances. Imputation methods filled in the 16.3% of expected glucose values that were missing., Results: Cox proportional hazards models assessed the association of each measure of glycemic variation, as a time-dependent covariate, with the risk of retinopathy and nephropathy, and a longitudinal logistic regression model did likewise for cardiovascular autonomic neuropathy. Adjusted for mean blood glucose, no measure of within-day variability was associated with any outcome. Only the longitudinal mean M-value (over time) was significantly associated with microalbuminuria when adjusted for the longitudinal mean blood glucose and corrected for multiple tests using the Holm procedure., Conclusions: Overall, within-day glycemic variability, as determined from quarterly glucose profiles, does not play an apparent role in the development of microvascular complications beyond the influence of the mean glucose., (© 2017 by the American Diabetes Association.)

Objective: To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D)., Research Design and Methods: A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA 1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only ( n = 149) or CGM+BGM ( n = 77) group. The primary outcome was time in range (70-180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%., Results: CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively ( P < 0.001). Mean time in 70-180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI -2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group., Conclusions: Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia., (© 2017 by the American Diabetes Association.)

PurposeTo quantify early neuroretinal alterations in patients with type 1 diabetes mellitus (T1DM) and to assess whether glycemic variability contributes to alterations in neuroretinal structure or function.MethodsThirty patients with T1DM and 51 controls underwent comprehensive ophthalmic examination and assessment of retinal function or structure with frequency doubling perimetry (FDP), contrast sensitivity, dark adaptation, fundus photography, and optical coherence tomography (OCT). Diabetic participants wore a subcutaneous continuous glucose monitor for 5 days, from which makers of glycemic variability including the low blood glucose index (LGBI) and area under the curve (AUC) for hypoglycemia were derived.ResultsSixteen patients had no diabetic retinopathy (DR), and 14 had mild or moderate DR. Log contrast sensitivity for the DM group was significantly reduced (mean±SD=1.63±0.06) compared with controls (1.77±0.13, P<0.001). OCT analysis revealed that the inner temporal inner nuclear layer (INL) was thinner in patients with T1DM (34.9±2.8 μm) compared with controls (36.5±2.9 μm) (P=0.023), although this effect lost statistical significance after application of the Bonferroni correction for multiple comparisons. Both markers of glycemic variability, the AUC for hypoglycemia (R=-0.458, P=0.006) and LGBI (R=-0.473, P=0.004), were negatively correlated with inner temporal INL thickness.ConclusionsPatients with T1DM and no to moderate DR exhibit alterations in inner retinal structure and function. Increased glycemic variability correlates with retinal thinning on OCT imaging, suggesting that fluctuations in blood glucose may contribute to neurodegeneration.

Diabetic peripheral and autonomic neuropathies are common complications of diabetes with broad spectrums of clinical manifestations and high morbidity. Studies using various agents to target the pathways implicated in the development and progression of diabetic neuropathy were promising in animal models. In humans, however, randomized controlled studies have failed to show efficacy on objective measures of neuropathy. The complex anatomy of the peripheral and autonomic nervous systems, the multitude of pathogenic mechanisms involved, and the lack of uniformity of neuropathy measures have likely contributed to these failures. To date, tight glycemic control is the only strategy convincingly shown to prevent or delay the development of neuropathy in patients with type 1 diabetes and to slow the progression of neuropathy in some patients with type 2 diabetes. Lessons learned about the role of glycemic control on distal symmetrical polyneuropathy and cardiovascular autonomic neuropathy are discussed in this review., Competing Interests: Lynn Ang, Mamta Jaiswal, Catherine Martin, and Rodica Pop-Busui declare that they have no conflict of interest.

Aims: The Michigan Neuropathy Screening Instrument (MNSI) is used to assess distal symmetrical peripheral neuropathy in diabetes. It includes two separate assessments: a 15-item self-administered questionnaire and a lower extremity examination that includes inspection and assessment of vibratory sensation and ankle reflexes. The purpose of this study was to evaluate the performance of the MNSI in detecting distal symmetrical peripheral neuropathy in patients with Type 1 diabetes and to develop new scoring algorithms., Methods: The MNSI was performed by trained personnel at each of the 28 Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications clinical sites. Neurologic examinations and nerve conduction studies were performed during the same year. Confirmed clinical neuropathy was defined by symptoms and signs of distal symmetrical peripheral neuropathy based on the examination of a neurologist and abnormal nerve conduction findings in ≥ 2 anatomically distinct nerves among the sural, peroneal and median nerves., Results: We studied 1184 subjects with Type 1 diabetes. Mean age was 47 years and duration of diabetes was 26 years. Thirty per cent of participants had confirmed clinical neuropathy, 18% had ≥ 4 and 5% had ≥ 7 abnormal responses on the MNSI questionnaire, and 33% had abnormal scores (≥ 2.5) on the MNSI examination. New scoring algorithms were developed and cut points defined to improve the performance of the MNSI questionnaire, examination and the combination of the two., Conclusions: Altering the cut point to define an abnormal test from ≥ 7 abnormal to ≥ 4 abnormal items improves the performance of the MNSI questionnaire. The MNSI is a simple, non-invasive and valid measure of distal symmetrical peripheral neuropathy in Type 1 diabetes., (Published 2012. This article is a US Government work and is in the public domain in the USA.)

Background: Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation., Methods: We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper- and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). In addition, we introduce and demonstrate a novel transition density profile that emphasizes the dynamics of transitions between defined glucose states., Results: Our combined dashboard of numerical statistics and graphical plots support the task of providing an integrated approach to describing glycemic variability. We integrated existing metrics, such as SD, area under the curve, and mean amplitude of glycemic excursion, with novel metrics such as the slopes across critical transitions and the transition density profile to assess the severity and frequency of glucose transitions per day as they move between critical glycemic zones., Conclusions: By presenting the above-mentioned metrics and graphics in a concise aggregate format, CGM-GUIDE provides an easy to use tool to compare quantitative measures of glucose variability. This tool can be used by researchers and clinicians to develop new algorithms of insulin delivery for patients with diabetes and to better explore the link between glucose variability and chronic diabetes complications.

Background: Hyperglycaemia is associated with increased risk of cardiovascular complications in people with type 2 diabetes. We investigated whether reduction of blood glucose concentration decreases the rate of microvascular complications in people with type 2 diabetes., Methods: ACCORD was a parallel-group, randomised trial done in 77 clinical sites in North America. People with diabetes, high HbA(1c) concentrations (>7.5%), and cardiovascular disease (or >or=2 cardiovascular risk factors) were randomly assigned by central randomisation to intensive (target haemoglobin A(1c) [HbA(1c)] of <6.0%) or standard (7.0-7.9%) glycaemic therapy. In this analysis, the prespecified composite outcomes were: dialysis or renal transplantation, high serum creatinine (>291.7 micromol/L), or retinal photocoagulation or vitrectomy (first composite outcome); or peripheral neuropathy plus the first composite outcome (second composite outcome). 13 prespecified secondary measures of kidney, eye, and peripheral nerve function were also assessed. Investigators and participants were aware of treatment group assignment. Analysis was done for all patients who were assessed for microvascular outcomes, on the basis of treatment assignment, irrespective of treatments received or compliance to therapies. ACCORD is registered with ClinicalTrials.gov, number NCT00000620., Findings: 10 251 patients were randomly assigned, 5128 to the intensive glycaemia control group and 5123 to standard group. Intensive therapy was stopped before study end because of higher mortality in that group, and patients were transitioned to standard therapy. At transition, the first composite outcome was recorded in 443 of 5107 patients in the intensive group versus 444 of 5108 in the standard group (HR 1.00, 95% CI 0.88-1.14; p=1.00), and the second composite outcome was noted in 1591 of 5107 versus 1659 of 5108 (0.96, 0.89-1.02; p=0.19). Results were similar at study end (first composite outcome 556 of 5119 vs 586 of 5115 [HR 0.95, 95% CI 0.85-1.07, p=0.42]; and second 1956 of 5119 vs 2046 of 5115, respectively [0.95, 0.89-1.01, p=0.12]). Intensive therapy did not reduce the risk of advanced measures of microvascular outcomes, but delayed the onset of albuminuria and some measures of eye complications and neuropathy. Seven secondary measures at study end favoured intensive therapy (p<0.05)., Interpretation: Microvascular benefits of intensive therapy should be weighed against the increase in total and cardiovascular disease-related mortality, increased weight gain, and high risk for severe hypoglycaemia., Funding: US National Institutes of Health; National Heart, Lung, and Blood Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; National Eye Institute; Centers for Disease Control and Prevention; and General Clinical Research Centers., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Objective: To determine if baseline subgroups in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial can be identified for whom intensive compared with standard glycemia treatment had different effects on all-cause mortality., Research Design and Methods: Exploratory post hoc intention-to-treat comparisons were made between intensive and standard glycemia groups on all-cause mortality by subgroups defined by baseline characteristics., Results: There were few significant interactions between baseline characteristics and effects of intensive versus standard glycemia treatment on mortality: self-reported history of neuropathy (hazard ratio [HR] 1.95, 95% CI 1.41-2.69) versus no history of neuropathy (0.99, 0.79-1.26; P value for interaction 0.0008), higher A1C (A1C >8.5%: HR 1.64, 95% CI 1.22-2.22; A1C 7.5-8.4%: 1.00, 0.75-1.34; A1C <7.5%: 1.00, 0.67-1.50; P value for interaction 0.04), and aspirin use (HR 1.45, 95% CI 1.13-1.85, compared with 0.96, 0.72-1.27, in nonusers; P value for interaction 0.03)., Conclusions: We found a remarkable similarity of effect from intensive compared with standard glycemia treatment on mortality across most baseline subgroups. No differential effect was found in subgroups defined by variables anticipated to have an interaction: age, duration of diabetes, and previous history of cardiovascular disease. The three baseline characteristics that defined subgroups for which there was a differential effect on mortality may help identify patients with type 2 diabetes at higher risk of mortality from intensive regimens for glycemic control. Further research is warranted.