Search

Disordered eating (DE) in type 1 diabetes (T1D) includes insulin restriction for weight loss with serious complications. Gut microbiota-derived short chain fatty acids (SCFA) may benefit host metabolism but are reduced in T1D. We evaluated the hypothesis that DE and insulin restriction were associated with reduced SCFA-producing gut microbes, SCFA, and intestinal microbial diversity in adults with T1D.We collected stool samples at four timepoints in a hypothesis-generating gut microbiome pilot study ancillary to a weight management pilot in young adults with T1D. 16S ribosomal RNA gene sequencing measured the normalized abundance of SCFA-producing intestinal microbes. Gas-chromatography mass-spectrometry measured SCFA (total, acetate, butyrate, and propionate). The Diabetes Eating Problem Survey-Revised (DEPS-R) assessed DE and insulin restriction. Covariate-adjusted and Bonferroni-corrected generalized estimating equations modeled the associations. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 data. Data were available for 45 participants at 109 visits, which included 42 participants at 65 visits pre-COVID-19. Participants reported restricting insulin "At least sometimes" at 53.3% of visits. Pre-COVID-19, each 5-point DEPS-R increase was associated with a -0.34 (95% CI -0.56, -0.13, p = 0.07) lower normalized abundance of genus Anaerostipes; and the normalized abundance of Lachnospira genus was -0.94 (95% CI -1.5, -0.42), p = 0.02 lower when insulin restriction was reported "At least sometimes" compared to "Rarely or Never".DE and insulin restriction were associated with a reduced abundance of SCFA-producing gut microbes pre-COVID-19. Additional studies are needed to confirm these associations to inform microbiota-based therapies in T1D.

Achieving glycemic targets in youth and young adults with type 1 diabetes (T1D) is challenging. Diabetes devices, including continuous glucose monitors (CGM) may impact glycemic control. We analyzed the proportion of CGM use in youth and young adults with T1D at nine U.S. T1D Exchange Quality Improvement (T1DX-QI) Collaborative centers and 402 European diabetes prospective follow-up registry (Diabetes-Patienten-Verlaufsdokumentation [DPV]) sites from 2017 to 2020 and examined the association of CGM use to glycemic control as measured by hemoglobin A1c (HbA1c). CGM use increased each year from 2017 to 2020 across all age ranges (6, 6-12, 12-18, 18-25 years) in both registries and lower mean HbA1c was observed in CGM users compared with nonusers regardless of insulin delivery method for all years analyzed. CGM use appeared to increase more so in the European DPV than the U.S. T1DX-QI, which may be due to transatlantic differences in health care systems, insurance coverage, and prescriber habits.

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland–Altman analyses and Lin’s concordance correlation coefficient (𝜌c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (𝜌c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (𝜌c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

The benefits of intensive diabetes management have been established by the Diabetes Control and Complications Trial. However, challenges with optimizing glycemic management in youth with type 1 diabetes (T1D) remain across pediatric clinics in the United States. This article will review our Teamwork, Targets, Technology, and Tight Control (4T) study that implements emerging diabetes technology into clinical practice with a team approach to sustain tight glycemic control from the onset of T1D and beyond to optimize clinical outcomes.During the 4T Pilot study and study 1, our team-based approach to intensive target setting, education, and remote data review has led to significant improvements in hemoglobin A1c throughout the first year of T1D diagnosis in youth, as well as family and provider satisfaction.The next steps include refinement of the current 4T study 1, developing a business case, and broader implementation of the 4T study. In study 2, we are including a more pragmatic cadence of remote data review and disseminating exercise education and activity tracking to both English- and Spanish-speaking families. The overall goal is to create and implement a translatable program that can facilitate better outcomes for pediatric clinics across the USA.

The coronavirus disease 2019 (COVID-19) pandemic necessitated using telehealth to bridge the clinical gap, but could increase health disparities. This article reports on a chart review of diabetes telehealth visits occurring before COVID-19, during shelter-in-place orders, and during the reopening period. Visits for children with public insurance and for those who were non-English speaking were identified. Telehealth visits for children with public insurance increased from 26.2% before COVID-19 to 37.3% during shelter-in-place orders and 34.3% during reopening. Telehealth visits for children who were non-English speaking increased from 3.5% before COVID-19 to 17.5% during shelter-in-place orders and remained at 15.0% during reopening. Pandemic-related telehealth expansion included optimization of workflows to include patients with public insurance and those who did not speak English. Increased participation by those groups persisted during the reopening phase, indicating that prioritizing inclusive telehealth workflows can reduce disparities in access to care.

Background Hybrid Closed Loop (HCL) systems aid individuals with type 1 diabetes in improving glycemic control, however, sustained use over time has not been consistent for all users. This study developed and validated prognostic models for successful 12-month use of the first commercial HCL system based on baseline and 1-month or 3-month data. Methods and materials Data from participants at the Barbara Davis Center (N=85) who began use of the MiniMed 670G HCL were used to develop prognostic models using logistic regression and Lasso model selection. Candidate factors included sex, age, duration of diabetes, baseline HbA1c, race, ethnicity, insurance status, history of insulin pump and continuous glucose monitor use, 1-month or 3-month Auto Mode use, boluses per day, and time in range (70-180 mg/dL; TIR), and scores on behavioral questionnaires. Successful use of HCL was predefined as Auto Mode use ≥60%. The 3-month model was then externally validated against a sample from Stanford University (N=55). Results Factors in the final model included baseline HbA1c, sex, ethnicity, 1-month or 3-month Auto Mode use, Boluses per Day, and TIR. The 1-month and 3-month prognostic models had very good predictive ability with area under the curve values of 0.894 and 0.900, respectively. External validity was acceptable with an area under the curve of 0.717. Conclusions Our prognostic models use clinically accessible baseline and early device-use factors to identify risk for failure to succeed with 670G HCL technology. These models may be useful to develop targeted interventions to promote success with new technologies.

BACKGROUND In the U.S., there are over 37 million people with diabetes (PwD), but only 8,000 endocrinologists. Therefore, many PwD receive care exclusively from primary care providers (PCPs). To democratize knowledge regarding insulin-requiring diabetes through tele-education, Stanford University and the University of Florida developed Project Extension for Community Healthcare Outcomes (ECHO) Diabetes. OBJECTIVE ECHO Diabetes uses a Hub and Spoke model connecting specialists (the “Hub”) with PCPs (the “Spokes”). One-hour, weekly sessions include Hub diabetes didactic presentations and Spoke de-identified case presentations. Lessons learned during these sessions target provider knowledge and confidence surrounding diabetes management and patient care. METHODS Spokes were asked to provide short descriptions of PwD whose diabetes management improved directly or indirectly from their providers’ participation and/or their involvement with a Diabetes Support Coach (DSC). We provide a case series to describe individuals and outcomes. Because this study was not a randomized controlled trial and was a prospective observation of patients with the intervention delivered to providers, the trial is not registered in a public trials registry. RESULTS A case series of 11 PwD was compiled from 10 PCPs and one DSC from California and Florida between 2021 and 2022. The principal impact of ECHO Diabetes is the education amplified from PCPs and DSCs to PwD. In all cases, PwD reported increased engagement and improved diabetes management. Several cases reflected increased access to diabetes technology, improvement in glycemic outcomes, and positive trends in mental health measures. CONCLUSIONS This case series elucidates the potential value of the ECHO Diabetes program to PwD who receive their diabetes care from PCPs. Those matched with a DSC saw clinically significant improvements in HbA1c and mental health outcomes. CLINICALTRIAL Not applicable - Because this study was not a randomized controlled trial and was a prospective observation of patients with the intervention delivered to providers, the trial is not registered in a public trials registry.

Co-management of weight and glycaemia is critical yet challenging in type 1 diabetes (T1D). We evaluated the effect of a hypocaloric low carbohydrate, hypocaloric moderate low fat, and Mediterranean diet without calorie restriction on weight and glycaemia in young adults with T1D and overweight or obesity.We implemented a 9-month Sequential, Multiple Assignment, Randomized Trial pilot among adults aged 19-30 years with T1D for ≥1 year and body mass index 27-39.9 kg/mAdjusted weight and HbA1c (n = 38) changed by -2.7 kg (95% CI -3.8, -1.5, P .0001) and -0.91 percentage points (95% CI -1.5, -0.30, P = .005), respectively, while adjusted body fat percentage remained stable, on average (P = .21). Hypoglycaemia indices remained unchanged following adjustment (n = 28, P .05). Variability in all outcomes, including weight change, was considerable (57.9% were re-randomized primarily due to loss of2% body weight). No outcomes varied by diet.Three months of a diet, irrespective of macronutrient distribution or caloric restriction, resulted in weight loss while improving or maintaining HbA1c levels without increasing hypoglycaemia in adults with T1D.

ContextEarly initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.ObjectiveOur aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.MethodsWe analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.ResultsYouth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; ConclusionAs TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM.

OBJECTIVE Disparities in type 1 diabetes related to use of technologies like continuous glucose monitors (CGMs) and utilization of diabetes care are pronounced based on socioeconomic status (SES), race, and ethnicity. However, systematic reports of perspectives from patients in vulnerable communities regarding barriers are limited. RESEARCH DESIGN AND METHODS To better understand barriers, focus groups were conducted in Florida and California with adults ≥18 years old with type 1 diabetes with selection criteria including hospitalization for diabetic ketoacidosis, HbA1c >9%, and/or receiving care at a Federally Qualified Health Center. Sixteen focus groups were conducted in English or Spanish with 86 adults (mean age 42 ± 16.2 years). Transcript themes and pre–focus group demographic survey data were analyzed. In order of frequency, barriers to diabetes technology and endocrinology care included 1) provider level (negative provider encounters), 2) system level (financial coverage), and 3) individual level (preferences). RESULTS Over 50% of participants had not seen an endocrinologist in the past year or were only seen once including during hospital visits. In Florida, there was less technology use overall (38% used CGMs in FL and 63% in CA; 43% used pumps in FL and 69% in CA) and significant differences in pump use by SES (P = 0.02 in FL; P = 0.08 in CA) and race/ethnicity (P = 0.01 in FL; P = 0.80 in CA). In California, there were significant differences in CGM use by race/ethnicity (P = 0.05 in CA; P = 0.56 in FL) and education level (P = 0.02 in CA; P = 0.90 in FL). CONCLUSIONS These findings provide novel insights into the experiences of vulnerable communities and demonstrate the need for multilevel interventions aimed at offsetting disparities in diabetes.

Background: Diabetes technology use is associated with favorable type 1 diabetes (T1D) outcomes. American youth with public insurance, a proxy for low socioeconomic status, use less diabetes technology than those with private insurance. We aimed to evaluate the role of insurance-mediated provider implicit bias, defined as the systematic discrimination of youth with public insurance, on diabetes technology recommendations for youth with T1D in the United States. Methods: Multi-disciplinary pediatric diabetes providers completed a bias assessment comprised of a clinical vignette and ranking exercises ( n = 39). Provider bias was defined as providers: (1) recommending more technology for those on private insurance versus public insurance or (2) ranking insurance in the top 2 of 7 reasons to offer technology. Bias and provider characteristics were analyzed with descriptive statistics, group comparisons, and multivariate logistic regression. Results: The majority of providers [44.1 ± 10.0 years old, 83% female, 79% non-Hispanic white, 49% physician, 12.2 ± 10.0 practice-years] demonstrated bias ( n = 33/39, 84.6%). Compared to the group without bias, the group with bias had practiced longer (13.4±10.4 years vs 5.7 ± 3.6 years, P = .003) but otherwise had similar characteristics including age (44.4 ± 10.2 vs 42.6 ± 10.1, p = 0.701). In the logistic regression, practice-years remained significant (OR = 1.47, 95% CI [1.02,2.13]; P = .007) when age, sex, race/ethnicity, provider role, percent public insurance served, and workplace location were included. Conclusions: Provider bias to recommend technology based on insurance was common in our cohort and increased with years in practice. There are likely many reasons for this finding, including healthcare system drivers, yet as gatekeepers to diabetes technology, providers may be contributing to inequities in pediatric T1D in the United States.

Aims: To investigate the prevalence of modifiable cardiovascular risk factors (CVRFs), including dyslipidaemia, obesity and high glycated haemoglobin (HbA1c) concentration, in patients with type 1 diabetes (T1D), and to evaluate their association with blood pressure (BP) categories. Methods: We analysed 21 634 children and adolescents with T1D from the SWEET international database with office BP values assessed at a three or more visits within a year from 2010 to 2021. Participants were classified into a normotensive group, a group with elevated BP (90 to 94th percentile) or a hypertensive group (≥95th percentile), based on the median BP for the visits within the last treatment year. The prevalences of dyslipidaemia [cholesterol ≥ 5.18 mmol/L (200 mg/dL) and/or HDL cholesterol ≤ 1.036 mmol/L (40 mg/dL) and/or LDL cholesterol ≥ 2.59 mmol/L (100 mg/dL)], obesity (body mass index ≥2 standard deviation score) and elevated HbA1c [≥ 75 mmol/mol (9%)] were evaluated in patients within each BP group. Results: Patients with hypertension/elevated BP had less favourable lipid profiles, and a higher prevalence of obesity and HbA1c ≥ 75 mmol/mol than normotensive patients. A total of 38.4% of hypertensive patients and 36.0% of those with elevated BP had one CVRF, 15.1% and 10.1%, respectively, had two CVRFs, and 2.3% and 0.8%, respectively, had three CVRFs. Patients with hypertension/elevated BP had a higher prevalence of one or more CVRFs versus normotensive patients (P < 0.001). Obesity was the CVRF most strongly related to hypertension. Girls had a higher prevalence of one or more CVRFs than boys. Similar results were found in patients aged ≥13 years with hypertension compared to those aged

The ACT1ON pilot study evaluated the feasibility of three dietary strategies to optimize weight and glycemic management among young adults with T1D and overweight or obesity. As a secondary measure, self-reported physical activity (PA) was collected at baseline, 3-, 6-, and 9-months from 68 young adults with T1D (age 25.5 ± 3.1 years, 72.1% female, HbA1c 7.9 ± 1.8%, BMI 30.4 (27.9 - 33.9)) . Using the Global Physical Activity Questionnaire (GPAQ, n=195) and Previous Day Physical Activity Recalls (PDPAR, n=123) , we estimated weekly minutes of moderate-to-vigorous physical activity (MVPA) . Following the COVID-19 outbreak, a subset of participants wore Garmin Vivosmart4® PA trackers for two weeks at each visit (44 measurements from 27 participants) . Mixed effects regression models assessed the relationship between weekly minutes of MVPA and HbA1c using each PA measure. Median weekly minutes of MVPA were 33% lower following the COVID-19 outbreak compared to pre-pandemic PA levels (p=0.02) per the GPAQ, but not PDPAR (-7.7%, p=0.34) . After adjusting for design, demographic, clinical, and dietary variables, a 1 standard deviation increase in weekly minutes of MVPA (GPAQ) was associated with an absolute increase of 0.27% HbA1c (p>0.001) . A small, statistically non-significant association was observed for PDPAR (β=0.13, p=0.19) ; however, we observed a borderline statistically significant association using the PA tracker data (β=0.231, p=0.08) , despite a smaller sample size (n=44) . These results suggest that among young adults with T1D and overweight and obesity, higher levels of PA may lead to challenges in achieving optimal glycemia. Future work is needed to determine how to best support young adults with T1D and overweight and obesity in attaining both their PA and glycemic management goals. Disclosure F. R. Muntis: None. R. E. Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck & Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. K. Corbin: None. E. J. Mayer-davis: None. D. P. Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D. Igudesman: None. A. Cristello: None. J. Thomas: None. N. Arrizon- ruiz: None. J. Hooper: None. A. Addala: None. J. Crandell: n/a. D. M. Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. Funding National Institutes of Health (1DP3DK113358-01)

We previously examined a type 1 diabetes care model based on the use of a remote patient monitoring (RPM) tool that analyzes CGM data. The care model was associated with improved glucose management and reduced provider effort. We compare the financial feasibility of this care model in value-based care (VBC) and fee-for-service (FFS) settings. We set parameters based on the current deployment of the care model. In the FFS setting, we estimated Medicare reimbursement rates and labor costs for in-person diabetes education. We compared these to costs in a VBC setting where providers make prospective payments for RPM services. We evaluated the financial impact of the care model across a variety of parameters (Fig) . In a FFS setting, we estimated that in-person care incurs net costs of $1per patient. Under a VBC contract, we estimated that prospective payments are offset by reduced labor costs, incurring net costs of $65 per patient (i.e., 49% savings) . These results are sensitive to input parameters (Fig) . We identified financially viable payment models for an algorithm-enabled telemedicine-based care model associated with improved T1D glucose management. The analysis may inform operational planning at the clinic level as well as reimbursement policy design. Disclosure R.Leonard pei: None. P.Dupenloup: None. P.Prahalad: None. R.Johari: None. A.Addala: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. D.Scheinker: None. Funding Helmsley Charitable Trust.ISPAD-JDRF Fellowship.R18DK122422.SDRC.LPCH Auxiliaries.The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.Stanford Maternal and Child Health Research Institute.

Background: Sequential Multiple Assignment Randomized Trials (SMARTs) efficiently address practical treatment comparison questions and adapt dynamically based on response. They may be useful for development of approaches to co-manage weight and glycemia T1D, which is critical yet challenging. Methods: Our SMART pilot with three diet periods enrolled young adults with T1D (BMI 27-39.9 kg/m²) . Participants were re-randomized after ∼3 months on the hypocaloric Look AHEAD (Low Fat) or Low Carbohydrate (Low Carb) ; or Mediterranean (Med, not calorie restricted) diet if Results: The proportion re-randomized was 57.9% and did not vary by diet. Weight was lost overall but insufficient weight loss was the most common reason for re-randomization for Low Fat and Med. An HbA1c increase ≥0.5% was most common on Med. Low diet acceptability was the most common reason for re-randomization on Low Carb. Conclusions: We achieved safe weight loss among young adults with T1D but observed heterogeneity in reasons for re-randomization by diet, although differences were not statistically significant. A fully-powered efficacy trial may confirm our findings. Disclosure D.Igudesman: None. R.E.Pratley: Other Relationship; Bayer AG, Corcept Therapeutics, Dexcom, Inc., Hanmi Pharm. Co., Ltd., Merck & Co., Inc., Metavention, Novo Nordisk, Pfizer Inc., Poxel SA, Sanofi, Scohia Pharma Inc., Sun Pharmaceutical Industries Ltd. M.R.Kosorok: n/a. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.J.Mayer-davis: None. J.Crandell: n/a. T.Pokaprakarn: None. K.Corbin: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. A.Addala: None. J.Thomas: None. A.Casu: None. M.Riddell: Advisory Panel; Zealand Pharma A/S, Zucara Therapeutics, Consultant; Eli Lilly and Company, Jaeb Center for Health Research, Speaker's Bureau; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. Funding National Institutes of Health 1DP3DK113358

Standardization of GFR by BSA can underestimate kidney function in overweight individuals. Standardization of GFR by ECV, rather than BSA, may be a way to avoid measurement bias. Our aim was to compare the distribution and agreement of GFR measures in people with type 1 diabetes at risk of kidney disease, and to determine associations with clinical hemodynamic measures. In this exploratory analysis of the PERL clinical trial of allopurinol, the distribution and agreement of 4 measurements for GFR were compared in 542 participants with eligible data. GFR was measured using gold-standard iohexol plasma clearance procedures, and ECV was calculated using the Jødal-Bröchner-Mortensen method, which estimates ECV using results from clearance studies. GFR was standardized using ECV corrected for 14 L of fluid volume rather than 1.73 m2 of BSA. Agreement between measurements was assessed by Bland-Altman and Spearman correlation analyses. The trial primary outcome was re-analyzed using ECV-adjusted GFR. Mean age and BMI were 51±years and 29.5±6.0 kg/m2. The distribution of normalized GFR measures overlapped and agreement was strong; GFR/ECV overestimated GFR/BSA by 2.9 numerical units (mean 70.5 ml/min/14 L vs. 67.6 ml/min/1.73 m2) . Heart rate was associated with unstandardized GFR (r=-0.10, p=0.023) . Associations between diastolic blood pressure and all measures of GFR were observed (r ranged from 0.17-0.18; p-values Disclosure L.Lovblom: None. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. S.Polsky: Advisory Panel; Medtronic, Other Relationship; diaTribe, Research Support; Dexcom, Inc., Eli Lilly and Company, Leona M. and Harry B. Helmsley Charitable Trust, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Sanofi-Aventis U.S. A.B.Karger: Consultant; Roche Diagnostics, Research Support; Kyowa Kirin Co., Ltd., Siemens. A.Galecki: None. B.A.Perkins: Advisory Panel; Abbott Diabetes, Insulet Corporation, Sanofi, Board Member; Novo Nordisk, Other Relationship; Abbott Diabetes, Insulet Corporation, Medtronic, Novo Nordisk, Research Support; BMO Bank of Montreal, Novo Nordisk. D.Cherney: Other Relationship; AbbVie Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Research & Development, LLC, Lilly, Maze, BMS, CSL-Behring, Merck, Otsuka, Novartis and Novo-Nordisk , Mitsubishi Tanabe Pharma Corporation, Sanofi, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca and Novo-Nordisk. S.O.Lanctot: Employee; Medtronic. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Bjornstad: Advisory Panel; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Horizon Therapeutics plc, LG Chem, Lilly, Novo Nordisk, Consultant; AstraZeneca, Bristol-Myers Squibb Company. M.Mauer: None. L.Caramori: Advisory Panel; Bayer AG, Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk, Research Support; Bayer AG, Novartis AG. S.Rosas: Advisory Panel; AstraZeneca, Teladoc Health, Other Relationship; Bayer AG, Research Support; AstraZeneca, Bayer AG. P.Rossing: Consultant; Astellas Pharma Inc., AstraZeneca, Bayer AG, Gilead Sciences, Inc., Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Speaker's Bureau; Eli Lilly and Company. K.R.Tuttle: Advisory Panel; Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., Consultant; AstraZeneca, Eli Lilly and Company, Research Support; Bayer AG, Goldfinch Bio, Inc., Novo Nordisk, Travere. Funding Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; PERL ClinicalTrials.gov number, NCT02017171

We previously showed that the use of a tool for remote analysis of continuous glucose monitor data, Timely Interventions for Diabetes Excellence (TIDE) , was associated with improved glucose management for patients with type 1 diabetes. To facilitate the deployment of TIDE at two partner institutions, we developed an operational planning tool. We designed an interactive dashboard to match capacity and demand with a variety of operational, population, and workforce parameters. To improve the dashboard’s accessibility, simplicity, and interpretability, we performed iterative design based on feedback from users and two partner institutions. We set parameter values based on the first deployment of TIDE and conducted sensitivity analyses of the capacity-demand match. The dashboard (Figure 1) contains seven modifiable parameters; generates a table illustrating the match between capacity and demand; displays a timeseries plot of capacity and demand; and calculates three alternative capacity plans that satisfy annual patient demand. It is available online (www.bit.ly/surf-tide) . Small absolute reductions in per-patient review time produced the most significant changes in clinic capacity. We developed an interactive dashboard that facilitates data-driven operational planning for clinics seeking to deploy algorithm-enabled telemedicine-based diabetes care. Figure 1. Capacity Planning Dashboard Disclosure A.Chang: None. M.Z.Gao: None. J.Ferstad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Prahalad: None. R.Johari: None. D.Scheinker: None. Funding NIH R18DK122422

The COVID-pandemic necessitated worldwide lockdowns in 2020, with restrictions on physical activity and changes in eating habits. We investigated temporal trends in BMI in three international T1D registries between 2018-2020. Data were extracted from DPV (Germany/Austria/Luxembourg/Switzerland) , T1D Exchange (T1DX, US) , and Australasian Diabetes Data Network (ADDN) . The period affected by the COVID-pandemic was defined as Mar to Dec 2020 and compared with the 9-mth periods in 2018 and 2019. Median age and median BMI were aggregated for each individual per year. Mean BMI was aggregated by year, sex, and age groups (stratified by 2 yearly intervals to age 20 years, and yearly until age 60. Study population: ADDN (n=29,875, mean age 21.9 years, 51% male) ; DPV (n=118,580, 21.2 years, 53% male) ; T1DX (n=47,122, 21.3 years, 51% male) . BMI stratified by age group and sex are shown in the figure. There were no apparent BMI differences across the three years. Females aged 30-40 years and 40-50 years in ADDN only demonstrated a slightly higher BMI in 2020. Conclusion: At a population level, mean BMI was not different before and during the pandemic in people with T1D. In some individuals, a healthier lifestyle or more regulated nutritional intake may have counteracted potential adipogenic effects of lockdown measures. Disclosure K.Narayan: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. R.W.Holl: None. M.E.Craig: None. M.Auzanneau: None. N.Noor: None. S.Rompicherla: None. O.Ebekozien: None. S.Glastras: None. C.E.Smart: None. S.Bachmann: None. R.Welp: None.

In the Pilot 4T study (n=135) , remote patient monitoring (RPM) was associated with improved time in range (TIR) and lower A1c. Measuring differences in how patients respond to messages from the care team is essential for the design of effective, personalized care models. We analyzed electronic health record (EHR) and continuous glucose monitor (CGM) data to estimate the week-over-week impact of RPM messages on patients’ TIR. We applied statistical clustering methods to divide patients who received messages into two groups based on their changes in TIR and compared the characteristics of these groups. Receiving a message was associated with a greater mean week-to-week improvement in TIR after a low-TIR week [4.9 percentage points (pp) after a message vs. 2.5pp without a message; p < 0.001]. A patient’s TIR improvement after receiving a message is greater on average if the same patient’s TIR improved following previous messages. We identified two groups of patients with significantly different responses to messages. The group with greater mean TIR improvement after messages contains a larger proportion of non-white, non-English speaking, and publicly insured patients. We found that messages were associated with different magnitudes of TIR improvement across two clusters of patients. Identifying patients who benefit more from RPM could facilitate the personalization of management strategies. Disclosure J.Ferstad: None. P.Prahalad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.Fox: None. R.Johari: None. D.Scheinker: None. Funding Helmsley Charitable Trust, ISPAD-JDRF Fellowship,NIH R18DK122422,Stanford Diabetes Research Center,LPCH Auxiliaries,Stanford Maternal and Child Health Research Institute, The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.

Poor outcomes for people with diabetes have necessitated targeted quality improvement (QI) projects to affect change. Project ECHO Diabetes is a tele-education and tele-mentoring program for community health clinics in California and Florida serving under-resourced patients with diabetes who lack routine access to specialty care. The model provided a unique opportunity to undertake a multi-site QI initiative, where 12 community clinics participated in measuring and improving quality of care. Using a modified-Delphi approach, clinic providers rated various QI measures on their validity and feasibility. A total of 18 measures were selected, and each clinic chose its own measures to track throughout the one-year initiative. The number of measures each clinic focused on ranged from 1 to 9, with a median of 3. Quarterly, clinics met remotely to report performance based on selected measures and discuss their efforts, successes, and setbacks. At the end of the QI year, clinics reported widespread improvements across structural, process, and outcome measures (Table) . Remarkable improvement ideas came from the sharing of best practices during the remote tele-ECHO clinic sessions and the changes made in the data systems used to track important measures. The Project ECHO Diabetes tele-education model has the potential to improve care for people living with diabetes in clinics serving under-resourced communities. Disclosure C.Wang: None. L.Figg: None. C.Anez-zabala: None. E.Sheehan: None. S.C.Westen: None. C.E.Deremer: Research Support; Pfizer-BMS Alliance, Stock/Shareholder; Portola Pharmaceuticals, Inc. A.Bernier: None. W.T.Donahoo: None. A.F.Walker: None. E.Lewit: n/a. C.Clark: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. M.J.Haller: Advisory Panel; SAB Biotherapeutics , Consultant; MannKind Corporation, Sanofi. A.Addala: None. R.Lal: Advisory Panel; Provention Bio, Inc., Consultant; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, Morgan Stanley, Tidepool. N.Cuttriss: Advisory Panel; American Diabetes Association, Board Member; American Youth Understanding Diabetes Abroad , Consultant; Cecelia Health, Leona M. and Harry B. Helmsley Charitable Trust, Med-IQ. L.G.Baer: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Background: Individuals underrecruited in diabetes studies include those not seen by an endocrinologist and those from rural, low socioeconomic, and racial/ethnic minority groups. Project ECHO Diabetes aimed to recruit historically excluded participants with insulin-requiring diabetes. Objective: The purpose of this descriptive analysis is to outline and evaluate our recruitment efforts at 9 sites affiliated with the University of Florida. Results: In total, 340 participants with type 1 diabetes and 1225 participants with type 2 diabetes were eligible and approached for recruitment. Local community health centers were funded to recruit for and participate in Project ECHO. Participants were contacted on average 2 times (range 1-13 times) to finalize a consent disposition: Yes, No, or No Response (Table 1) . For those individuals who we categorized as “No Response,” the most common reason was lack of reliable contact information. Conclusions: Recruiting historically underrepresented populations is feasible with repeated touch points. Funding the partnerships with community health centers, who have the trust of the local community and are equipped to address local needs, was key in our recruitment strategy. Language, a common exclusion criteria and barrier to diverse recruitment, was overcome through multi-lingual study staff. Disclosure A.Addala: None. X.Roque: None. L.Figg: None. C.Anez-zabala: None. C.Clark: None. R.Lal: Advisory Panel; Provention Bio, Inc., Consultant; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, Morgan Stanley, Tidepool. M.J.Haller: Advisory Panel; SAB Biotherapeutics , Consultant; MannKind Corporation, Sanofi. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. A.F.Walker: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Introduction: The multi-center T1D Exchange QI Collaborative (T1DX-QI) is a learning health network sharing de-identified electronic medical record data for type 1 diabetes (T1D) care improvement. Objectives: To investigate trends in racial and ethnic inequities in glycemic outcomes in children and adults with T1D from 2016 to 2021. Methods: Children and adults with T1D for at least 6 months in participating T1DX-QI centers with data available from 2016 to 2021 were included. Temporal trends by race and ethnicity in HbA1c were analyzed using regression models. Age, gender, duration of diabetes, insurance type, were adjusted as covariates. Results: Figure 1, among 36,390 children and adults with T1D, HbA1c outcomes for Non-Hispanic White (NHW) patients decreased from 8.5% in 2016 to 8.2% in 2021 (p-0.001) , remained stable across the six years for Hispanic patients (8.9%) and Asian (8.4%) but increased for Non-Hispanic Black (NHB) from 9.6% in 2016 to 9.9% in 2021 (p-0.001) . The use of Insulin Pumps and CGM increased significantly among all groups. Conclusions: Six years of longitudinal follow-up revealed lower HbA1c over time in NHW patients but higher HbA1c for NHB patients despite adjusting for multiple confounders. It is critical to address the root causes of inequities in NHB and patients of other racial minorities with T1D. Disclosure O.Ebekozien: None. A.Albanese-o'neill: Consultant; Association of Diabetes Care & Education Specialists, Provention Bio, Inc., Other Relationship; Insulet Corporation, Tandem Diabetes Care, Inc. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. M.A.Clements: Consultant; Glooko, Inc., Research Support; Abbott Diabetes, Dexcom, Inc. T1dx-qi collaborative: n/a. N.Noor: None. M.K.Kamboj: None. O.Odugbesan: n/a. S.Majidi: None. R.Hopkins: None. E.L.Dewit: None. R.Izquierdo: None. S.Agarwal: Advisory Panel; Medtronic, Consultant; Beta Bionics, Inc. Funding Helmsley Charitable Trust