Introduction and Objective: HGI quantifies individual variation in HbA1c level not due to mean blood glucose (MBG) and is strongly associated with complications risk. Continuous Glucose Monitoring provides a predicted HbA1c as the GMI, enabling easy HGI calculation. Using repeated measures assessment, we evaluated whether mean HGI (mHGI) is a stable patient phenotype both before and after start of automated insulin delivery (AID) as well as the effect of ethnicity, MBG and sex. Methods: HbA1c and GMI data (90 days) were obtained before (1-2 measures) and after (2-3 measures) the start of AID in 145 youth with type 1 diabetes (age 12.4±3.6 y; duration 5.9±3.8 y). HGI was calculated as HbA1c-GMI. Patients were ranked by their mHGI from lowest to highest and divided into tertiles. Mixed-effects models accounted for missing data, quantified sources of HGI variability, and estimated reliability of mHGI tertile classification. Results: Based on 435 measures, 40 % of HGI variation was attributable to individual factors. Overall, mean mHGI was -0.14±0.38 (range -1.12 to 0.85). Both HbA1c and MBG declined after AID initiation, while mHGI remained stable. mHGI was higher in African heritage compared with White and South Asian groups (p<0.001), and slightly decreased with age (p=0.0114). mHGI slightly decreased after AID in African heritage (p = 0.0472), but not in White or South Asian groups. mHGI was not associated with MBG or sex. Reliability for classification into Low or High mHGI phenotypes increased with repeated measures: 0.74 with 4, 0.78 with 5, and 0.81 with 6 repeats. Conclusion: GMI enables easy calculation of HGI. Over time mHGI represents a stable phenotype independent of MBG and therapy. Four or more HGI measurements have high reliability for classification of individuals into low or high mHGI risk phenotypes, This data supports the use of GMI derived mHGI as an individualized characteristic for diabetes assessment and management. Disclosure J. Pemberton: Consultant; Current; Roche Diabetes Care, Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.E. Krone: None. S. Uday: None. Z. Fang: None. S. Chalew: None.

OBJECTIVES:Automated insulin delivery (AID) improves glycaemic outcomes in children and young people (CYP) with type 1 diabetes (T1D), but effects on body weight are less clear. We evaluated longitudinal changes in BMI z-score following AID initiation and examined predictors using mixed-effects modelling. METHODS:We retrospectively studied 170 CYP with T1D starting AID at a tertiary centre. BMI z-score was assessed at baseline, six, and twelve months. Predictors of six-month BMI change (ΔBMI) included demographic, socioeconomic, and glycaemic variables with sensor-weighted glucose metrics. RESULTS:Baseline BMI z-score was 0.75 (SD 1.27), rising to 0.90 at six months (p<0.001) and stable at twelve (p=0.851 vs. six). Increases occurred in underweight (+1.12, 95 % CI 0.61-1.62, p<0.001) and below-average healthy weight (+0.38, 95 % CI 0.20-0.57, p<0.001), with minimal change in overweight or obese groups. Mixed-effects modelling identified baseline BMI z-score (B = -0.14, p<0.001), baseline mean blood glucose (MBG; B = +0.07, p<0.001), and their interaction (B = -0.04, p=0.025) as independent predictors. Age, sex, socioeconomic status, ethnicity, therapy type, HbA1c, and sensor wear were not significant. Across groups, AID increased time in range by 15-17 % and reduced HbA1c by 6 mmol/mol without increasing hypoglycaemia. CONCLUSIONS:AID supports weight restoration in underweight CYP while maintaining stability in overweight or obese groups. BMI change is determined by baseline BMI and MBG, independent of demographic or treatment factors.

BackgroundThe UK National Paediatric Diabetes Audit (NPDA) data reports disparities in Haemoglobin A1c (HbA1c) levels among children and young people (CYP) with Type 1 Diabetes (T1D), with higher levels in those of Black ethnic background and lower socioeconomic status who have less access to technology. We investigate HbA1c differences in a T1D cohort with higher than national average technology uptake where > 60% come from an ethnic minority and/or socioeconomically deprived population.Design & methodsRetrospective cross-sectional study investigating the influence of demographic factors, technology use, and socioeconomic status (SES) on glycaemic outcomes. The study population was 222 CYP with T1D who attended the diabetes clinic in 2022 at a single tertiary paediatric diabetes centre.ResultsAmong 222 CYP, 60% were of ethnic minority (Asian, Black, Mixed and Other were 32%, 12%, 6% and 10% respectively) and 40% of white heritage. 94% used Continuous Glucose Monitoring (CGM) and 60% used Continuous Subcutaneous Insulin Infusion (CSII) via open or closed loop. 6% used Self-Monitoring of Blood Glucose (SMBG) and Multiple Daily Injections (MDI), 34% used CGM and MDI, 38% used CGM and CSII and 22% used Hybrid Closed-Loop (HCL) systems. Significant differences in HbA1c across therapy groups (p < 0.001) was noted with lowest HbA1c in HCL group (55 mmol/mol; p <0.001). Despite adjusting for therapy type, the Black group had higher HbA1c than their white and Asian counterparts (p<0.001). CYP from the most deprived tertile had significantly higher HbA1c levels (p < 0.001) but the difference was not sustained after adjusting for therapy type.ConclusionAdvanced diabetes technologies improve glycaemic control. Whilst equalising technology access mitigates socioeconomic disparities in HbA1c, CYP from Black ethnic background continue to display a higher HbA1c. The study underscores the necessity of fair technology distribution and further research into elevated HbA1c levels among Black CYP using advanced diabetes technology.

IntroductionThe UK national pediatric diabetes audit reports higher HbA1c for children and young people (CYP) with type 1 diabetes (T1D) of Black ethnicity compared with White counterparts. This is presumably related to higher mean blood glucose (MBG) due to lower socioeconomic status (SES) and less access to technology. We aimed to determine if HbA1c ethnic disparity persists after accounting for the above variables.Research design and methodsA retrospective analysis of participants who received structured education in continuous glucose monitoring (CGM) use was conducted at a tertiary center. HbA1c was paired with glucose metrics from 90-day CGM data. The influence of ethnicity, SES determined by Index of Multiple Deprivation (IMD), MBG and other covariates on HbA1c was evaluated using multiple variable regression analysis. Occurrence of hypoglycemia was evaluated.ResultsA total of 168 (79 White, 61 South Asian, 28 Black) CYP with T1D were included. There were no differences between groups for age, MBG, time in range (3.9–10.0 mmol/L), diabetes duration, gender, insulin delivery method (multiple daily injections vs continuous subcutaneous insulin infusion), or percent sensor use (PSU). In multiple variable analysis, MBG (p<0.0001), ethnicity (p<0.0001), age (p<0.001), duration of diabetes (p<0.01) and PSU (p<0.05) accounted for 81% of the variability in HbA1c. Adjusted HbA1c in the Black group (67 mmol/mol) was higher than both South Asian (63 mmol/mol) and White groups (62 mmol/mol) (p<0.001). Despite significant IMD differences between groups, it did not influence HbA1c. Multiple variable analysis showed that the Black group experienced more hypoglycemia than South Asian and White groups (<3.9 and <3.0 mmol/L, p<0.05).ConclusionsCYP from Black ethnic backgrounds have a higher HbA1c compared with their South Asian and White counterparts which is clinically significant and independent of MBG, potentially contributing to increased complications risk. Additionally, the Black group experienced a higher incidence of hypoglycemia, possibly due to a treat-to-HbA1c target approach.

Diabetic MedicineEarly View e15305 INVITED EDITORIAL Rethinking the safety and efficacy assessment of (Hybrid) Closed Loop systems: Should we promote the need for a minimum of exercise data within the regulatory approval? Othmar Moser, Corresponding Author Othmar Moser [email protected] Department of Exercise Physiology and Metabolism, University of Bayreuth, Bayreuth, Germany Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria Correspondence Othmar Moser, Department of Exercise Physiology and Metabolism, University of Bayreuth, Bayreuth, Germany. Email: [email protected]Search for more papers by this authorJohn S. Pemberton, John S. Pemberton Department of Endocrinology and Diabetes, Birmingham Children's Hospital, Birmingham Women's, and Children's NHS Foundation Trust, Birmingham, UKSearch for more papers by this author Othmar Moser, Corresponding Author Othmar Moser [email protected] Department of Exercise Physiology and Metabolism, University of Bayreuth, Bayreuth, Germany Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria Correspondence Othmar Moser, Department of Exercise Physiology and Metabolism, University of Bayreuth, Bayreuth, Germany. Email: [email protected]Search for more papers by this authorJohn S. Pemberton, John S. Pemberton Department of Endocrinology and Diabetes, Birmingham Children's Hospital, Birmingham Women's, and Children's NHS Foundation Trust, Birmingham, UKSearch for more papers by this author First published: 08 February 2024 https://doi.org/10.1111/dme.15305Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1Pemberton JS, Wilmot EG, Barnard-Kelly K, et al. CGM accuracy: contrasting CE marking with the governmental controls of the USA (FDA) and Australia (TGA): a narrative review. Diabetes Obes Metab. 2023; 25(4): 916-939. doi:10.1111/DOM.14962 10.1111/dom.14962 PubMedGoogle Scholar 2Klonoff DC, James Petisce FR, Bailey TS, et al. Performance Metrics for Continuous Interstitial Glucose Monitoring Suggested Citation. Published online 2020. Accessed February 3, 2024 www.clsi.org Google Scholar 3 CFR - Code of Federal Regulations Title 21. Accessed February 3, 2024. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?fr=862.1355 Google Scholar 4Alva S, Brazg R, Castorino K, Kipnes M, Liljenquist DR, Liu H. Accuracy of the third generation of a 14-day continuous glucose monitoring system. Diabetes Ther. 2023; 14(4): 767-776. doi:10.1007/S13300-023-01385-6 10.1007/s13300-023-01385-6 CASPubMedGoogle Scholar 5Freckmann G, Eichenlaub M, Waldenmaier D, et al. Clinical performance evaluation of continuous glucose monitoring systems: a scoping review and recommendations for reporting. J Diabetes Sci Technol. 2023; 17(6): 1506-1526. doi:10.1177/19322968231190941 10.1177/19322968231190941 PubMedGoogle Scholar 6Eichenlaub M, Pleus S, Rothenbühler M, et al. Comparator data characteristics and testing procedures for the clinical performance evaluation of continuous glucose monitoring systems. Diabetes Technol Ther. Published online February 2, 2024. doi:10.1089/DIA.2023.0465 10.1089/DIA.2023.0465 Google Scholar 7Cuerda del Pino A, Martín-San Agustín R, Laguna Sanz AJ, et al. Accuracy of two continuous glucose monitoring devices during aerobic and high-intensity interval training in individuals with type 1 diabetes. Diabetes Technol Ther. Published online January 12, 2024. doi:10.1089/DIA.2023.0535 10.1089/DIA.2023.0535 Google Scholar 8Zaharieva DP, Turksoy K, McGaugh SM, et al. Lag time remains with newer real-time continuous glucose monitoring technology during aerobic exercise in adults living with type 1 diabetes. Diabetes Technol Ther. 2019; 21(6): 313-321. doi:10.1089/dia.2018.0364 10.1089/dia.2018.0364 CASPubMedWeb of Science®Google Scholar 9Bergford S, Riddell MC, Jacobs PG, et al. The type 1 diabetes and EXercise initiative: predicting hypoglycemia risk during exercise for participants with type 1 diabetes using repeated measures random forest. Diabetes Technol Ther. 2023; 25(9): 602-611. doi:10.1089/DIA.2023.0140 10.1089/dia.2023.0140 CASPubMedGoogle Scholar 10Svehlikova E, Ashcroft NL, Gatschelhofer C, et al. Pharmacokinetics and pharmacodynamics of a novel U500 insulin aspart formulation: a randomized, double-blind, crossover study in people with type 1 diabetes. Diabetes Care. 2023; 46(4): 757-764. doi:10.2337/DC22-1054 10.2337/dc22-1054 CASPubMedGoogle Scholar 11Riddell MC, Gallen IW, Smart CE, et al. 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Objectives. Investigate the effect of using short bursts of moderate-intensity activity between meals to lower hyperglycaemia on glucose metrics. Design and Methods. Children and young people with type 1 diabetes (CYPD) attending continuous glucose monitoring education were taught to use moderate-intensity activity to lower high glucose levels (to <10.0 mmol/L using 10–15 minlowers ∼2.0 mmol/L) between meals. Retrospective cross-sectional data analysis of CYPD at a single tertiary centre between 2019 and 2022. Data were collected on demographics and glucose metrics (HbA1c, time in range (TIR, 3.9–10.0 mmol/L), time above range (TAR, >10.0 mmol/L), time below range (TBR, <3.9 mmol/L)). Minutes of activity usually performed to lower a glucose level of 14.0 mmol/L trending steady at 6 months grouped the CYPD into low (<5 min), mild (5–10 min), or moderate (11–20 min) activity groups. Results. 125 (n = 53, 40% male) CYPD with a mean (standard deviations) age of 12.3 (±3.7) years and diabetes duration of 7.0 ± 3.7 years were included. HbA1c improved from 58.5 (±8.6) mmol/mol at baseline to 54.9 (±7.2) mmol/mol at 6 months ( p < 0.001 ). Low, mild, and moderate activity was reported by 30% (n = 37), 34% (n = 43), and 36% (n = 45), respectively. At 6 months, HbA1c (52.0 vs. 54.3 vs. 59.4 mmol/mol, p < 0.001 ), TIR (68.0% vs. 59.71 vs. 51.1%, p < 0.001 ) and TAR (29.9% vs. 38.3% vs. 45.3%, p < 0.001 ) were significantly different across the moderate, mild, and low activity groups, respectively. No association was found for TBR (2.16% vs. 2.32% vs. 2.58%, p = 0.408 ) across groups. Conclusion. Increasing the use of moderate-intensity activity to lower hyperglycaemia between meals is associated with improved glucose control without increasing hypoglycaemia for CYPD.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)