Search

ContextDiabetes or hyperglycemia at admission are established risk factors for adverse outcomes during hospitalization for COVID-19, but the impact of prior glycemic control is not clear.ObjectiveWe aimed to examine the associations between admission variables, including glycemic gap, and adverse clinical outcomes in patients hospitalized with COVID-19 infection.MethodsWe examined the relationship between clinical predictors, including acute and chronic glycemia, and clinical outcomes, including intensive care unit (ICU) admission, mechanical ventilation (MV), and mortality among 1786 individuals with diabetes or hyperglycemia (glucose > 10 mmol/L twice in 24 hours) who were admitted from March 2020 through February 2021 with COVID-19 infection at 5 university hospitals in the eastern United States.ResultsThe cohort was 51.3% male, 53.3% White, 18.8% Black, 29.0% Hispanic, with age = 65.6 ± 14.4 years, BMI = 31.5 ± 7.9 kg/m2, glucose = 12.0 ± 7.5 mmol/L [216 ± 135 mg/dL], and HbA1c = 8.07% ± 2.25%. During hospitalization, 38.9% were admitted to the ICU, 22.9% received MV, and 10.6% died. Age (P < 0.001) and admission glucose (P = 0.014) but not HbA1c were associated with increased risk of mortality. Glycemic gap, defined as admission glucose minus estimated average glucose based on HbA1c, was a stronger predictor of mortality than either admission glucose or HbA1c alone (OR = 1.040 [95% CI: 1.019, 1.061] per mmol/L, P < 0.001). In an adjusted multivariable model, glycemic gap, age, BMI, and diabetic ketoacidosis on admission were associated with increased mortality, while higher estimated glomerular filtration rate (eGFR) and use of any diabetes medication were associated with lower mortality (P < 0.001).ConclusionRelative hyperglycemia, as measured by the admission glycemic gap, is an important marker of mortality risk in COVID-19.

OBJECTIVE: To evaluate glycemic outcomes in the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) randomized clinical trial (RCT) participants during an observational extension phase. RESEARCH DESIGN AND METHODS: WISDM RCT was a 26-week RCT comparing continuous glucose monitoring (CGM) with blood glucose monitoring (BGM) in 203 adults aged ≥60 years with type 1 diabetes. Of the 198 participants who completed the RCT, 100 (98%) CGM group participants continued CGM (CGM-CGM cohort) and 94 (98%) BGM group participants initiated CGM (BGM-CGM cohort) for an additional 26 weeks. RESULTS: CGM was used a median of >90% of the time at 52 weeks in both cohorts. In the CGM-CGM cohort, median time

Type 1 diabetes (T1D) is a chronic condition characterized by glucose variability due to autoimmune destruction of insulin-secreting pancreatic beta-cells. Prior research (linking greater glucose variability to cognitive impairment and adverse health outcomes) and recent technological innovations (continuous glucose monitoring [CGM], cognitive ecological momentary assessment [EMA]) laid a foundation for the current study, which aimed to advance understanding of between- and within-person influences on physiological and cognitive processes relevant to health in T1D. We obtained intensive longitudinal, naturalistic measurements of cognition (EMA) and glucose (CGM) in N=199 adults with T1D. Given reduced cognitive performance during periods of low (hypo) and high (hyper) glycemia, our analyses focused on the strength of within-person quadratic associations between glucose and cognition—operationalized as the rate of change in the slope of the glucose-cognition relationship, hereafter, glucose acceleration. First, we used multilevel modeling to obtain group (i.e., aggregated across participants) and individual (i.e., specific to each participant) estimates of glucose acceleration. Next, we used machine learning to identify between-person predictors of individual estimates of glucose acceleration. Finally, we built personalized models to contextualize individual estimates of glucose acceleration in relation to time-varying processes. Group estimates of glucose acceleration were significant for processing speed but not sustained attention. Specifically, processing speed was fastest at glucose concentrations slightly above individuals’ means, regardless of absolute levels of the means. Between individuals, estimates of glucose acceleration varied in relation to several clinical and demographic variables, including age, microvascular complications, and percent time in hypoglycemia (< 70 mg/dL). Within individuals, fluctuations in glucose and processing speed were related to prior night’s sleep duration, self-reported assessment context (e.g., noise), and self-reported stress. Results generate testable insights about shared (across individuals) factors that impact cognitive fluctuations. They also highlight opportunities to support personalized medicine by increasing understanding about unique (person-specific) influences on cognitive fluctuations in T1D.

To investigate whether an increased bolus: basal insulin ratio (BBR) with liver-targeted bolus insulin (BoI) would increase BoI use and decrease hypoglycaemic events (HEv).We enrolled 52 persons (HbA1c 6.9% ± 0.12%, mean ± SEM) with type 1 diabetes using multiple daily injections. Hepatic-directed vesicle (HDV) was used to deliver 1% of peripheral injected BoI to the liver. A 90-day run-in period was used to introduce subjects to unblinded continuous glucose monitoring and optimize standard basal insulin (BaI) (degludec) and BoI (lispro) dosing. At 90 days, BoI was changed to HDV-insulin lispro and subjects were randomized to an immediate 10% or 40% decrease in BaI dose.At 90 days postrandomization, total insulin dosing was increased by ~7% in both cohorts. The -10% and -40% BaI cohorts were on 7.7% and 13% greater BoI with 6.9% and 30% (P = .02) increases in BBR, respectively. Compared with baseline at randomization, nocturnal level 2 HEv were reduced by 21% and 43%, with 54% and 59% reductions in patient-reported HEv in the -10% and -40% BaI cohorts, respectively.Our study shows that liver-targeted BoI safely decreases HEv and symptoms without compromising glucose control. We further show that with initiation of liver-targeted BoI, the BBR can be safely increased by significantly lowering BaI dosing, leading to greater BoI usage.

Objective We examined predictors of early and late loss of glycemic control in individuals with youth-onset type 2 diabetes, as well as predictors of short-term deterioration in youth from the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study. Methods Demographic, physical, and biochemical measures at baseline and 48 months, and change over time, were examined in 584 participants separated into those with loss of glycemic control (sustained HbA1c ≥ 8%) before 48 months or at 48 months or later, and those who remained in control until the end of the study (median 6.8 years). Univariate and multivariate models, and receiver operating characteristic curve analyses were performed. Results Approximately 45% of youth remained in control at 48 months; of these, 30% subsequently lost glycemic control prior to the end of follow-up. Predictors of early loss of glycemic control included baseline HbA1c, C-peptide index, oral disposition index, proinsulin, and proinsulin to insulin ratio. Predictors of late loss included baseline measures of insulin secretion and change in HbA1c and insulin processing at 48 months. A baseline HbA1c cutoff of ≥ 6.2% was optimally predictive of loss of glycemic control at any time, while an absolute rise in HbA1c > 0.5% related to loss of glycemic control within 3 to 6 months. Conclusion This analysis demonstrates that youth with type 2 diabetes at risk for loss of glycemic control, including impending rapid deterioration, can be identified using available clinical measures, allowing for closer monitoring of at-risk youth, and facilitating the design of research on better therapeutic options.

OBJECTIVETo assess the prevalence of high diabetes distress and associated factors in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY2) study cohort of young adults with youth-onset type 2 diabetes.RESEARCH DESIGN AND METHODSParticipants completed the Diabetes Distress Scale (DDS) at end-of-study visits. Factors examined for association with high distress were demographic (sex, race/ethnicity, age, education, income), medical (HbA1c, BMI, complications), psychological (depressive and anxiety symptoms), and social (number in household, offspring, health care coverage, established with diabetes care provider). Univariate logistic regression identified factors associated with high distress that were controlled for in multivariate logistic regressions.RESULTSOf 438 participants, 66% were female (mean age 26.8 years, 18% non-Hispanic White, 37% non-Hispanic Black, 38% Hispanic). High distress (DDS ≥2) was reported by 105 (24%) participants. Subscales identified 40% with high regimen distress and 29.7% with high emotional burden. A greater percentage of those with high distress were female (P = 0.002), diagnosed with hypertension (P = 0.037) and retinopathy (P = 0.005), treated with insulin, had higher HbA1c, and had moderate to severe depressive and anxiety symptoms (all P < 0.001). In multivariate analyses, female sex (P < 0.001), HbA1c (P < 0.001), anxiety symptoms (P = 0.036), and lack of health care coverage (P = 0.019) were associated with high distress, after controlling for potential confounders. Moderate to severe depressive symptoms were associated with high regimen distress (P = 0.018) and emotional burden (P < 0.001); insulin treatment was associated with high emotional burden (P = 0.027).CONCLUSIONSFuture research should identify modifiable factors associated with high diabetes distress in young adults with youth-onset type 2 diabetes that may inform distress interventions with this medically vulnerable group.

Background The prevalence of depression amongst adolescents with type 1 diabetes is estimated to be 2-3 times higher than in the general population. In adults with type 1 diabetes and depression short-term outcomes are worse compared to individuals just diagnosed with type 1 diabetes. This study aims to determine if depressive symptom endorsement is associated with glycemic outcomes and short-term complications in adolescents with type 1 diabetes. Methods Analysis was conducted using electronic medical records from the T1D Exchange Quality Improvement Collaborative. Adolescents with type 1 diabetes, aged 12-18, receiving treatment in a diabetes clinic who had been screened for depression with the PHQ-9 between 2016-2018 were eligible for inclusion. Individuals must have also had HbA1c data available from the day of depression screening and from 10-24 weeks after screening; the final sample size was 1,714. Results Almost 30% of adolescents endorsed mild or greater (PHQ-9 ≥ 5) depressive symptoms. Endorsement of mild or greater depressive symptoms was associated with an 18% increased risk of an HbA1c ≥7.5% and a 42% increased risk of an HbA1c ≥9.0% on the day of screener administration. Depressive symptom endorsement was also associated with an 82% increased risk for DKA. Conclusions This study suggests that depression symptoms are associated with an increased risk for elevated HbA1c and short-term complications. With the rising incidence of type 1 diabetes in youth, routine screening, and appropriate management of depression is needed. This article is protected by copyright. All rights reserved.

The American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) convened a writing group to develop a consensus statement on the management of type 1 diabetes in adults. The writing group has considered the rapid development of new treatments and technologies and addressed the following topics: diagnosis, aims of management, schedule of care, diabetes self-management education and support, glucose monitoring, insulin therapy, hypoglycaemia, behavioural considerations, psychosocial care, diabetic ketoacidosis, pancreas and islet transplantation, adjunctive therapies, special populations, inpatient management and future perspectives. Although we discuss the schedule for follow-up examinations and testing, we have not included the evaluation and treatment of the chronic microvascular and macrovascular complications of diabetes as these are well-reviewed and discussed elsewhere. The writing group was aware of both national and international guidance on type 1 diabetes and did not seek to replicate this but rather aimed to highlight the major areas that healthcare professionals should consider when managing adults with type 1 diabetes. Though evidence-based where possible, the recommendations in the report represent the consensus opinion of the authors. [Figure not available: see fulltext.]

This article describes how the T1D Exchange Quality Improvement Collaborative leverages an innovative web platform, the QI Portal, to gather and store electronic medical record (EMR) data to promote benchmarking and population health improvement in a type 1 diabetes learning health system. The authors explain the value of the QI Portal, the process for mapping center-level data from EMRs using standardized data specifications, and the QI Portal’s unique features for advancing population health.

To assess associations of psychosocial factors with medication adherence in young adults with youth-onset type 2 diabetes in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort.Participants (mean age 26 years) completed validated psychosocial measures. Adherence to oral hypoglycemia agents (OHAs) was assessed with 3-monthly unannounced phone pill counts; insulin adherence by self-report. Logistic and linear regressions identified factors associated with "low-adherence" (80% of pills/insulin) controlling for confounders.Of 212 participants taking OHAs (67% female, 39% Hispanic, 36% non-Hispanic Black), 69.8% were low-adherent. After adjustment, beliefs that medicines are necessary was associated with lower odds of low-adherence (p = 0.040, dichotomous). Less self-management support (p = 0.008), no healthcare coverage (p = 0.001), ≥1 (p = 0.008)/≥2 (p = 0.045) need insecurities were associated with higher odds of low-adherence. Factors associated with lower % adherence (continuous) were beliefs that medicines are harmful (p 0.001)/overused (p = 0.007)/less necessary (p = 0.022), low self-management support (p = 0.003), food insecurity (p = 0.036), no healthcare coverage (p 0.001), ≥1 (p = 0.003)/≥2 (p = 0.018) need insecurities. Of 192 taking insulin (69% female, 36% Hispanic, 41% non-Hispanic Black, 16% non-Hispanic white), 37.0% were low-adherent. Beliefs that medicines are overused (p = 0.009), that diabetes is not serious (p = 0.010), low diabetes self-efficacy (p = 0.035), high distress (p = 0.027), low self-management support (p = 0.001), food insecurity (p = 0.020), ≥1 (p = 0.011)/≥2 (p = 0.015) insecurities increased odds of insulin low-adherence.Poor medication adherence, common in young adults with youth-onset type 2 diabetes, is associated with interfering beliefs, diabetes distress and social factors. We must address these factors to develop tailored interventions for this vulnerable group.

OBJECTIVE Advances in diabetes technology have transformed the treatment paradigm for type 1 diabetes, yet the burden of disease is significant. We report on a pivotal safety study of the first tubeless, on-body automated insulin delivery system with customizable glycemic targets. RESEARCH DESIGN AND METHODS This single-arm, multicenter, prospective study enrolled 112 children (age 6–13.9 years) and 129 adults (age 14–70 years). A 2-week standard therapy phase (usual insulin regimen) was followed by 3 months of automated insulin delivery. Primary safety outcomes were incidence of severe hypoglycemia and diabetic ketoacidosis. Primary effectiveness outcomes were change in HbA1c and percent time in sensor glucose range 70–180 mg/dL (“time in range”). RESULTS A total of 235 participants (98% of enrolled, including 111 children and 124 adults) completed the study. HbA1c was significantly reduced in children by 0.71% (7.8 mmol/mol) (mean ± SD: 7.67 ± 0.95% to 6.99 ± 0.63% [60 ± 10.4 mmol/mol to 53 ± 6.9 mmol/mol], P < 0.0001) and in adults by 0.38% (4.2 mmol/mol) (7.16 ± 0.86% to 6.78 ± 0.68% [55 ± 9.4 mmol/mol to 51 ± 7.4 mmol/mol], P < 0.0001). Time in range was improved from standard therapy by 15.6 ± 11.5% or 3.7 h/day in children and 9.3 ± 11.8% or 2.2 h/day in adults (both P < 0.0001). This was accomplished with a reduction in time in hypoglycemia CONCLUSIONS This tubeless automated insulin delivery system was safe and allowed participants to significantly improve HbA1c levels and time in target glucose range with a very low occurrence of hypoglycemia.

The Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) trial examined the effects of three treatment arms in a group of racially and ethnically diverse adolescents and youth with type 2 diabetes mellitus. TODAY2 was an observational follow-up study reporting outcomes and complications in these participants after having diabetes for approximately 13 years. Participant retention was essential to fulfill this objective. This report describes motivations and problems participants self-reported related to continuing in this study.The TODAY2 retention survey was administered to participants, mean age 27 years, 36% non-Hispanic Black, 18% non-Hispanic white, 39% Hispanic, 52% public and 35% private healthcare coverage, who completed the last study visit (63.8% of original TODAY cohort). The survey listed potential benefits and barriers to staying in the trial. Participants indicated agreement or disagreement with each statement using a four point Likert-type scale.More than 93% of survey responders agreed with benefits listed for staying in TODAY2. The most cited reason for staying in the study was related to the strong relationship that participants had with study staff. The common barriers to attending trial visits were: tending to other medical problems, fear of disappointing study staff, and school/work scheduling conflicts. Participants with public healthcare coverage were more likely to endorse benefits related to diabetes care (e.g. getting latest test results, staying motivated to care for my diabetes) and monetary compensation, whereas participants with poor glycemic control cited that a barrier to attending study visits was "fear of disappointing" study staff.In a racially and ethnically diverse population of youth-onset type 2 diabetes, benefits and barriers associated with long-term retention are described. These findings can be used to help inform future retention strategies for young adults in clinical trials.

There is a growing population of adults over the age of 65 years with T1DM, but their lived experiences have not been well-characterized, especially in the context of evolving advances in self-management technology. We conducted focus groups with adults 65+ yrs with T1DM to characterize their perspectives surrounding T1DM and its management, focusing on CGM experiences. Participants were recruited from a US academic medical center and encouraged to bring a caregiver. Focus groups were stratified by CGM use, facilitated by standardized questions, audiotaped and transcribed. A team-based, inductive coding approach using ATLAS.ti identified themes. There were 33 adults (mean age 73.3±4.3 years [range 66-85]; 55% women, 82% non-Hispanic white, 12% non-CGM users, 83% pump users, 12% caregivers) . Five CGM-related themes were identified from all participants, including adoption challenges and complex clinical and psychosocial effects (Table) . Caregiver participants emphasized increased security with CGM use. Patient participants expressed a prevalent “survivor” mentality and a desire for increased peer support and strategies to minimize intrusiveness of T1DM management. Findings reinforce that older adults’ experiences with T1DM and CGM necessitate individualized psychosocial and tactical support for management and adoption of new technology. Disclosure A.Kahkoska: None. L.A.Young: Research Support; Bayer AG, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, vTv Therapeutics. K.Hassmiller lich: None. A.Cristello: None. C.Smith: None. S.R.Thomas: None. J.Batsis: None. E.J.Mayer-davis: None. M.R.Kosorok: n/a. 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. R.S.Weinstock: Research Support; Boehringer Ingelheim International GmbH, Dexcom, Inc., Diasome, Eli Lilly and Company, Insulet Corporation, Kowa Pharmaceuticals America, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care, Inc., Tolerion, Inc. Funding ARK is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490. The project was supported by UL1TR002489 from the Clinical and Translational Science Award program of the Division of Research Resources, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Aim: To identify psychosocial factors in young adults (mean age 26 yrs) that predict medication adherence 1 year later in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY2) observational study. Methods: Validated psychosocial measures (attitudes, beliefs, self-efficacy, distress, depression, anxiety, self-management support, needs insecurities) were completed at baseline (T1) . Medication adherence was assessed at T1 and 1 year later (T2) . Adherence to oral hypoglycemia agents (OHA) was assessed with 3 monthly unannounced phone pill counts. Insulin adherence was self-report. Adherence was defined categorically (≥ 80% pills/insulin=high adherent; < 80% = low adherent) and continuously (% adherence) . Logistic and linear regressions assessed psychosocial measures as predictors of adherence group (high vs. low) and % adherence, controlling for potential confounders. Results: OHA adherence: Mean scores declined from T1 to T2 (57.9% vs. 51.4%, p=0.004; N=171) . In adjusted multivariable analyses, more concerns about diabetes medications (e.g. long-term effects) (p=0.035) and lack of healthcare coverage (p=0.043) predicted higher odds of being low adherent. Beliefs that medications are harmful (p=0.004) or overused (p=0.002) , lack of healthcare coverage (p Conclusions: Negative beliefs about medicines and unmet material needs predicted poorer OHA adherence. Negative beliefs, low support and distress predicted lower insulin adherence. These factors may be targets for future interventions. Disclosure P.M.Trief: None. S.Kalichman: None. D.Uschner: None. L.M.Fette: None. H.Wen: None. K.Drews: None. B.Anderson: None. J.D.Bulger: None. R.S.Weinstock: Research Support; Boehringer Ingelheim International GmbH, Dexcom, Inc., Diasome, Eli Lilly and Company, Insulet Corporation, Kowa Pharmaceuticals America, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care, Inc., Tolerion, Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (RO1DK110456, U01-DK61212, U01-DK61230, U01-DK61239, U01-DK61242, and U01-DK61254)

While diabetes and admission blood glucose (BG) are established risk factors for adverse outcomes during hospitalization for COVID-19, reports on the impact of prior glycemic control have been variable. We examined the relationship between acute and chronic glycemia on risk of ICU admission, mechanical ventilation (MV) , and mortality among 1,786 patients with diabetes or hyperglycemia (BG > 180 mg/dl twice during any 24-hr period during hospitalization) admitted from March 2020 to February 2021 with COVID-at 5 large university hospitals in the eastern U.S. The cohort was 51.3% male, 53.3% White, 18.8% Black, 29.3% Hispanic, with age = 64.8 ± 13.8 y, BMI = 31.5 ± 7.9 kg/m2, admission BG = 216 ± 134 mg/dl, and HbA1c = 8.1 ± 2.2%. During hospitalization, 38.9% were admitted to the ICU, 22.9% received MV, and 10.6% died. In multivariate regression analysis, among demographic factors, age was the strongest risk factor for in-hospital mortality (OR = 1.per year [95% CI: 1.04, 1.06]) , and Hispanic ethnicity was the greatest risk factor for ICU admission (OR = 1.45 [95% CI: 1.16, 1.80]) and intubation (OR = 1.64 [95% CI: 1.28, 2.10]) . Higher BMI (p = 0.005) and admission BG (p = 0.014) were associated with increased risk of mortality, but HbA1c was not. The glycemic gap (GG) , defined as admission BG minus estimated average BG based on HbA1c, was a stronger predictor of mortality than either admission BG or HbA1c alone. Mortality rate was 5.7% for GG < -20 mg/dl; 12.2% for GG = -20 to < 20 mg/dl; 12.4% for GG = 20 to < 100 mg/dl; and 16.1% for GG ≥ 100 mg/dl (p for trend < 0.001) . Conclusion: Among patients with diabetes or hyperglycemia admitted for COVID-19, in addition to previously established risk factors for poor outcomes (age, Hispanic ethnicity, and BMI) , we found that GG is a stronger predictor of in-hospital mortality than blood glucose alone. This suggests that relative hyperglycemia, as measured by the admission GG, is an important marker of disease severity in COVID-and potentially other serious illnesses. Disclosure M.E.Mcdonnell: Advisory Panel; Everlywell, Inc., Research Support; Lilly, Stock/Shareholder; Abbott Diabetes. G.P.Westcott: None. D.C.Simonson: Stock/Shareholder; GI Windows, Phase V Technologies, Inc. G.Gopalakrishnan: Research Support; Eli Lilly and Company, Spruce Biosciences. R.Garg: None. J.Mitri: Consultant; dairy management, Lnutra. R.S.Weinstock: Research Support; Boehringer Ingelheim International GmbH, Dexcom, Inc., Diasome, Eli Lilly and Company, Insulet Corporation, Kowa Pharmaceuticals America, Inc., Medtronic, Novo Nordisk, Tandem Diabetes Care, Inc., Tolerion, Inc. M.Greenfield: None. N.E.Palermo: Research Support; Dexcom, Inc. R.Radhakrishnan: None. Funding Brigham-TechFoundation, Cambridge, MA 2021

Glycemic outcomes of adults with type 1 diabetes may be affected by depression. Our aim was to compare outcomes of “depressed” (Patient Health Questionnaire-9 ⩾ 10, N = 83) to “not-depressed” matched control (Patient Health Questionnaire-2 < 3, N = 166) adults with type 1 diabetes with objective measures. The depressed group had poorer blood glucose control and, for those with glucose meter downloads, fewer glucose tests/day. The groups did not differ on glucose variability or episodes of hypoglycemia. Depression in adults with type 1 diabetes is associated with poorer glycemic control and less blood glucose monitoring. Future research should examine whether treatment of depression results in better self-care and glycemic outcomes.

Project ECHO® is a telementoring workforce development model that targets under-resourced communities lacking access to specialty care. The model builds virtual communities of practice, including specialists and community primary care professionals (PCPs) to combat clinical inertia and health disparities. While the ECHO model has gained global recognition, implementation of the model related to diabetes is lagging compared to other specialty conditions. This review highlights diabetes-endocrine (ENDO)-focused ECHOs using data reported in the ECHO Institute’s centralized data repository (iECHO) and the learning collaborative for diabetes ECHOs. It also describes the implementation of diabetes ECHOs and their evaluation. Learner and patient-centered outcomes related to diabetes ECHOs are reviewed. Program implementation and evaluations have demonstrated utility of the ECHO model for diabetes programs to (1) address unmet needs of diabetes care in the primary care setting, (2) improve knowledge and confidence in managing complex diabetes and change provider prescribing habits, (3) improve patient outcomes, and (4) address diabetes quality improvement practices in primary care. More studies with broader collaboration among sites are needed to evaluate the model related to diabetes, especially applied to addressing therapeutic inertia, adoption of diabetes technology, and reducing health disparities.

Objective: To report prevalence of depression and eating disorder symptoms, and impaired health-related quality of life (HRQOL) and examine their longitudinal associations with glycemia and diabetes complications in young adults with youth-onset type 2 diabetes. Research Design and Methods: Participants recruited over a 4-year period were enrolled at 15 clinical diabetes centers in the follow-up observational Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) study. From 2014-2020, prevalence of symptoms of depression, eating disorders and HRQOL by sex, race-ethnicity and baseline family income were assessed annually. Longitudinal relationships between assessments of glycemia and complications with psychiatric symptoms and HRQOL were evaluated in adjusted models. Results: Participants (N=514) were 21.7+2.5 years old with a diabetes duration of 8.6+1.5 years in year 1 of TODAY 2 (2014). Symptoms of depression and impaired HRQOL were common and increased significantly over 6 years (14.0% to 19.2%, P=0.003; and 13.1% to 16.7%, P=0.009, respectively). Depression and impaired HRQOL were more common in women and those with lower baseline family income but did not differ by race/ethnicity. Rates of binge eating were stable over time; self-reported purging increased. Over time, symptoms of depression were associated with higher HbA1c, hypertension and retinopathy progression; impaired HRQOL was associated with higher BMI, systolic blood pressure, hypertension, and retinopathy progression; symptoms of eating disorders were associated with higher BMI. Conclusions: Significant psychiatric symptoms and impaired HRQOL are common among emerging adults with youth-onset type 2 diabetes and are positively associated with glycemia, hypertension and retinopathy progression in this group that is at ongoing risk for medical morbidity.

The introduction of home blood glucose monitoring (BGM) 40 years ago revolutionized diabetes self-management, providing valuable glucose data that have helped many people with diabetes (PWD) improve their glycemic management. In the early 2000s, personal continuous glucose monitoring (CGM) systems also became available, raising questions regarding the future role of BGM. However, for some PWD, particularly many with type 2 diabetes who do not take medications associated with increased hypoglycemia risk, BGM remains more easily accessible and more affordable than CGM and can adequately meet their needs. In addition, PWD who use CGM still need to periodically use BGM. This publication reviews key issues related to the optimal and most cost-effective use of BGM. The authors address the accuracy of modern glucose meters and the recommended frequency of monitoring for people with different types of diabetes and therapeutic regimens. They suggest strategies for using glycemic data to inform therapy adjustments, as well as ways to overcome common barriers to BGM use. They then review the use of BGM in patients who also use CGM and describe the latest related technological innovations, including Cloud-based data management, mobile applications, insulin calculators, and remote and automated counseling systems. The authors conclude that BGM remains an important tool in diabetes management, even as diabetes management technologies continue to evolve.

AIMS: To test whether a long-acting GLP-1 receptor agonist would improve glucose control in patients with type 1 diabetes (T1D) and to determine whether the presence of residual beta cell function would affect the response. In addition, we sought to determine whether the drug would affect beta cell function. METHODS: We performed a randomized placebo-controlled trial of exenatide extended release (ER) in participants with T1D with and without detectable levels of C-peptide. Seventy-nine participants were randomized to exenatide ER 2 mcg weekly, or placebo, stratified by the presence or absence of detectable C-peptide levels. The primary outcome was the difference in glycated haemoglobin (HbA1c) levels at 24 weeks. Participants were followed for another 6 months off study drug. RESULTS: At week 24, the time of the primary outcome, the least squares (LS) mean HbA1c level was 7.76% (95% confidence interval [CI] 7.42, 8.10) in the exenatide ER group versus 8.0% (95% CI 7.64, 8.35) in the placebo group (P = 0.08). At week 12 the LS mean HbA1c levels were 7.71% (95% CI 7.37, 8.05) in the exenatide ER group versus 8.05% (95% CI 7.7, 8.4) in the placebo group (P = 0.01). The improvement at week 12 was driven mainly by those with detectable levels of C-peptide. Those treated with exenatide ER lost weight at 12 and 24 weeks compared to those treated with placebo (P

BACKGROUND: Higher serum urate levels are associated with an increased risk of diabetic kidney disease. Lowering of the serum urate level with allopurinol may slow the decrease in the glomerular filtration rate (GFR) in persons with type 1 diabetes and early-to-moderate diabetic kidney disease. METHODS: In a double-blind trial, we randomly assigned participants with type 1 diabetes, a serum urate level of at least 4.5 mg per deciliter, an estimated GFR of 40.0 to 99.9 ml per minute per 1.73 m(2) of body-surface area, and evidence of diabetic kidney disease to receive allopurinol or placebo. The primary outcome was the baseline-adjusted GFR, as measured with iohexol, after 3 years plus a 2-month washout period. Secondary outcomes included the decrease in the iohexol-based GFR per year and the urinary albumin excretion rate after washout. Safety was also assessed. RESULTS: A total of 267 patients were assigned to receive allopurinol and 263 to receive placebo. The mean age was 51.1 years, the mean duration of diabetes 34.6 years, and the mean glycated hemoglobin level 8.2%. The mean baseline iohexol-based GFR was 68.7 ml per minute per 1.73 m(2) in the allopurinol group and 67.3 ml per minute per 1.73 m(2) in the placebo group. During the intervention period, the mean serum urate level decreased from 6.1 to 3.9 mg per deciliter with allopurinol and remained at 6.1 mg per deciliter with placebo. After washout, the between-group difference in the mean iohexol-based GFR was 0.001 ml per minute per 1.73 m(2) (95% confidence interval [CI], −1.9 to 1.9; P= 0.99). The mean decrease in the iohexol-based GFR was −3.0 ml per minute per 1.73 m(2) per year with allopurinol and −2.5 ml per minute per 1.73 m(2) per year with placebo (between-group difference, −0.6 ml per minute per 1.73 m(2) per year; 95% CI, −1.5 to 0.4). The mean urinary albumin excretion rate after washout was 40% (95% CI, 0 to 80) higher with allopurinol than with placebo. The frequency of serious adverse events was similar in the two groups. CONCLUSIONS: We found no evidence of clinically meaningful benefits of serum urate reduction with allopurinol on kidney outcomes among patients with type 1 diabetes and early-to-moderate diabetic kidney disease. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; PERL ClinicalTrials.gov number, NCT02017171.)

AIM To examine the control of cardiovascular risk factors in type 1 diabetes (T1D) registries from the United States and Germany/Austria. MATERIALS AND METHODS Data on individuals aged ≥12 years with T1D for ≥1 year, from the T1D Exchange Clinic Network (T1DX, United States) and the Prospective Diabetes Follow-up Registry (DPV, Germany/Austria) from 1 January 2016 to 31 March 2018 were analysed. Linear and logistic regression models adjusted for age groups, sex, duration of diabetes and minority status were used to compare clinical characteristics and achievement of diabetes management targets between registries. RESULTS The cohort consisted of 47 936 patients (T1DX, n = 19 442; DPV, n = 28 494). Achievement of HbA1c goals (

To evaluate psychosocial outcomes for adults with type 1 diabetes (T1D) using the tubeless Omnipod® 5 Automated Insulin Delivery (AID) System.A single-arm, multicenter (across the United States), prospective safety and efficacy study of the tubeless AID system included 115 adults with T1D. Participants aged 18-70 years completed questionnaires assessing psychosocial outcomes - diabetes distress (T1-DDS), hypoglycemic confidence (HCS), well-being (WHO-5), sleep quality (PSQI), insulin delivery satisfaction (IDSS), diabetes treatment satisfaction (DTSQ), and system usability (SUS) - before and after 3 months of AID use. Associations among participant characteristics, psychosocial measures and glycemic outcomes were evaluated using linear regression analyses.Adults using the tubeless AID system demonstrated improvements in diabetes-specific psychosocial measures, including diabetes distress, hypoglycemic confidence, insulin delivery satisfaction, diabetes treatment satisfaction, and system usability after 3 months (all P 0.001). No changes in general well-being or sleep quality were observed. The psychosocial outcomes assessed were not consistently associated with baseline participant characteristics (i.e., age, sex, diabetes duration, glycemic outcomes including percent time in range 70-180 mg/dL, percent time below range 70 mg/dL, hemoglobin A1c, or insulin regimen).Use of the Omnipod 5 AID system was associated with significant improvements in diabetes-related psychosocial outcomes for adults with T1D.NCT04196140.

To understand the relationship of obesity and 27 circulating inflammatory biomarkers to the prevalence of non-proliferative diabetic retinopathy (NPDR) in youth with type 2 diabetes.Youth with type 2 diabetes who participated in the TODAY (Treatment Options for Type 2 Diabetes in Adolescents and Youth) study were followed for 2-6.5 years. Digital fundus photographs were obtained in the last year of the study. Blood samples during the study were processed for inflammatory biomarkers, and these were correlated with obesity tertiles and presence of retinopathy.Higher BMI was associated with an increase in circulating levels of metabolic biomarkers including high sensitivity C-reactive protein (hsCRP), plasminogen activator inhibitor 1 (PAI-1), fibrinogen, LDL-cholesterol (LDL-C) and Apolipoprotein B (ApoB), tumor necrosis factor receptors 1 and 2 (TNFR-1 and -2), interleukin 6 (IL-6), E-selectin, and homocysteine, as well as a decrease in the metabolic risk markers HDL-cholesterol (HDLC), and insulin-like growth factor binding protein 1 (IGFBP-1). Although NPDR risk decreased with increasing obesity, it was not associated with any of the measured biomarkers.Circulating levels of measured biomarkers did not elucidate the "obesity paradox" of decreased NPDR in the most obese participants in the TODAY study.clinicaltrials.govNCT00081328.

Objectives: To assess prevalence of high diabetes distress and associated factors in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort of young adults with youth-onset type 2 diabetes. Methods: Participants completed the Diabetes Distress Scale (DDS) at end-of-study visits. Factors examined for association with high distress were demographic (gender, race/ethnicity, age, education, income), medical (HbA1c, BMI, complications), psychological (depressive and anxiety symptoms), and social (number in household, have offspring, healthcare coverage, established with diabetes care provider). Univariate logistic regressions identified factors associated with high distress that were controlled for in multivariate logistic regressions. Results: Of 438 participants, 66% were female, mean age=26.8 years, 18% non-Hispanic white, 37% non-Hispanic Black, 38% Hispanic. High distress (DDS ≥2) was reported by 105 (24%) participants. Subscales identified 40% with high “Regimen Distress,” 29.7% with high “Emotional Burden.” A greater percentage of those with high distress were female (p=0.002), diagnosed with hypertension (p=0.037) and retinopathy (p=0.005), insulin treated, had higher HbA1c, and moderate-to-severe depressive and anxiety symptoms (all p’s In multivariate analyses, female gender, HbA1c (p Conclusion: Future research should identify modifiable factors associated with high diabetes distress in those with youth-onset type 2 diabetes that may inform distress interventions with this medically vulnerable group.

Continuous glucose monitoring (CGM) can reduce hypoglycemia in older adults with type 1 diabetes (T1D). We aimed to characterize factors that influence effective use in this age group.Older adults with type T1D (age ≥65) and their caregivers participated in one of a series of parallel group model building workshops, a participatory approach to system dynamics involving drawing and scripted group activities. Data were synthesized in a qualitative model of the hypothesized system of factors producing distinct patterns of CGM use in older adults. The model was validated through virtual follow-up interviews.Data were collected from 33 participants (four patient-caregiver dyads, mean age 73.8±4.4 years [range 66-85 years]; 16% non-CGM users, 79% pump users). The system model delineates drivers of CGM uptake, drivers of ongoing CGM use, and feedback loops that either reinforce or counteract future CGM use. Participants emphasized the importance of different sets of feedback loops at different points in the duration of CGM use.The holistic system model underscores that factors and feedback loops driving effective CGM use in older adults are both individualized and dynamic (e.g., changing over time), suggesting opportunities for staged and tailored age-specific education and support. Short title: CGM Use in Older Adults with Type 1 Diabetes.

Background: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time Method: The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (Results: The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use. Conclusions: The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Background Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes. Objective We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3. Methods Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia ( Results A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t17=1.16; P=.26; Cohen dz=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day. Conclusions The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.

Background: The benefits of Continuous Glucose Monitoring (CGM) on glycemic management have been demonstrated in numerous studies; however, widespread uptake remians limited. The aim of this study was to provide real-world evidence of patient attributes and clinical outcomes associated with CGM use across clinics in the U.S. based T1D Exchange Quality Improvement (T1DX-QI) Collaborative. Method: We examined electronic Health Record data from eight endocrinology clinics participating in the T1DX-QI Collaborative during the years 2017-2019. Results: Among 11,469 type 1 diabetes patients, 48% were CGM users. CGM use varied by race/ethnicity with Non-Hispanic Whites having higher rates of CGM use (50%) compared to Non-Hispanic Blacks (18%) or Hispanics (38%). Patients with private insurance were more likely to use CGM (57.2%) than those with public insurance (33.3%) including Medicaid or Medicare. CGM users had lower median HbA1c (7.7%) compared to nonusers (8.4%). Rates of diabetic ketoacidosis (DKA) and severe hypoglycemia were significantly higher in nonusers compared to CGM users. Conclusion: In this real-world study of patients in the T1DX-QI Collaborative, CGM users had better glycemic control and lower rates of DKA and severe hypoglycemia (SH) events, compared to nonusers; however, there were significant sociodemographic disparities in CGM use. Quality improvement and advocacy measures to promote widespread and equitable CGM uptake have the potential to improve clinical outcomes.

People with diabetes (DM) hospitalized with COVID-19 infection have a 2-3 fold higher risk of death compared with those without DM, and mechanical ventilation (MV) has been identified as a major risk factor for death. While stress hyperglycemia (StH) has been established as a risk factor for death in some critically ill cohorts, it is not a well-established risk factor for MV in COVID-19. The COVIDEastDM consortium pooled data from 5 academic hospitals on the East Coast of the US to study the relationship between hyperglycemia and COVID-19 outcomes. Data were obtained retrospectively from electronic records of adults with COVID-19 and either DM or StH (defined in this cohort as day-1 admission blood glucose >180 mg/dl and A1c Disclosure M. E. Mcdonnell: Stock/Shareholder; Spouse/Partner; Abbott Diabetes. N. E. Palermo: None. R. Radhakrishnan: None. G. P. Westcott: None. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. R. Garg: None. D. C. Simonson: Stock/Shareholder; Spouse/Partner; Phase V Technologies, Inc. G. Cromwell: None. G. Gopalakrishnan: None. M. Greenfield: None. M. Johnson: None. S. R. Katta: None. J. Lebastchi: None. J. Mitri: Consultant; Self; L-Nutra. Funding TechFoundation; Brigham Education Institute

Aims: Diabetes distress (DD) is associated with poor health outcomes. Little is known about level of DD in young adults (YAs) with youth-onset type 2 diabetes (T2D) or individual factors associated with DD. Aims were to assess level of DD, and factors associated with high DD, in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Methods: YAs completed the Diabetes Distress Scale (DDS), PHQ-9 (depressive symptoms) and GAD7 (anxiety), at the end-of-study visit. Factors that may relate to DD were demographics (sex, race/ethnicity, age, education, income), medical history (HbA1c, BMI, complications), and social/economic factors (# persons in household, parent status, healthcare coverage, access to diabetes care provider). Univariate logistic regressions found factors associated with high DD. Multivariate regression controlled for sex, HbA1c, healthcare coverage, anxiety, depressive symptoms and retinopathy. Results: Of 457 YAs, mean age=26.7 yrs, 65% were female, 20% non-Hispanic white, 38% non-Hispanic Black, 20% Hispanic, 62% Conclusion: In young adults with youth-onset T2D, high DD was significantly more likely in females, and those with anxiety, high HbA1c, and without healthcare coverage. Disclosure P. M. Trief: None. M. S. Rayas: Research Support; Self; National Center for Advancing Translational Sciences. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. D. Uschner: None. M. Tung: None. L. Chao: None. R. M. Farrell: Stock/Shareholder; Self; Tandem Diabetes Care. J. Keady: None. S. A. Macleish: Advisory Panel; Self; Insulet Corporation. M. D. Marcus: Advisory Panel; Self; Weight Watchers International, Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK61212, U01DK61230, U01DK61239, U01DK61242, U01DK61254)

Background: Medication adherence is poor in young adults (YAs) with youth-onset type 2 diabetes (T2D). Little is known about intrapersonal factors that may relate to adherence. Aims: To assess associations of intrapersonal factors to medication adherence in Treatment Options for Adolescents and Youth (TODAY2) YAs. Methods: Validated measures (attitudes, beliefs, self-efficacy, distress, depression, anxiety) and insulin use surveys, were completed at a TODAY2 visit. Adherence to oral hypoglycemia agents (OHAs) was assessed with 3 monthly unannounced phone pill counts. Those taking ≥ 80% of pills/insulin classified “high-adherent;” < 80% of pills/insulin, “low-adherent.” If taking none, reasons were queried and adjudicated (no need vs. low-adherent). Logistic regressions assessed factors associated with OHA/insulin adherence controlling for gender, race/ethnicity, education, duration, healthcare coverage, and HbA1c. Results: Of 381 YAs in iCount (mean age 26 yrs, 68% female, 20% non-Hispanic white, 38% non-Hispanic Black, 36% Hispanic, 64.9% Conclusion: Poor medication adherence is common in YAs with youth-onset T2D. More needs to be learned about factors associated with adherence to develop tailored, effective interventions. Disclosure P. M. Trief: None. D. Uschner: None. M. Tung: None. K. Drews: None. S. Kalichman: None. B. Anderson: None. J. D. Bulger: None. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1RO1DK110456-01A1; U01DK61230)

Background: Satisfaction with televisits across age groups has not been well studied. Aim: To evaluate patient satisfaction with remote diabetes self-management education and support (DSMES) throughout adulthood. Methods: Surveys were administered electronically to patients with type 1 (T1D) or type 2 diabetes who had completed at least one remote education visit. Televisits were conducted without charge by a CDCES who had no prior relationship with the patient. If there was no response to the emailed survey within one week or an email address was unavailable the survey was conducted by phone. Survey questions used a 5-point Likert scale (1-strongly disagree to 5-strongly agree). Data were analyzed using t-test to assess differences in means, and Pearson correlation coefficients to assess relationship between Likert scaled items and age. SPSS v. 27 was used. Results: N=200 surveys were completed (45 by email, 155 by phone). Response rate was 37.9%. Mean age 52.5 years (range 21-82), 56% female, 78.5% T1D, 45.5% private insurance, 36% Medicare, 18% Medicaid, mean HbA1c 8.0% (SD 1.6). Correlation between mean score and age are shown in the table. Medicaid recipients were more likely to agree with “In the past, cost was a barrier to receiving diabetes education” compared to those with private insurance (p=0.005). Conclusions: Satisfaction with remote DSMES was less in older adults. Studies of remote DSMES tailored to older adults are needed. Disclosure C. Feuerstein-simon: None. M. Greenfield: None. P. Saha: None. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. Funding The Leona M. and Harry B. Helmsley Charitable Trust; T1D Exchange QI Learning Collaborative

Aim: To understand how remote Certified Diabetes Care and Education Specialists (CDCES) helped adults with type 1 diabetes (T1D) transition from provider in-person to tele-visits. Methods: Adults with T1D were referred to remote CDCES to fulfill needs from 4/16/2020 - 8/14/2020. Pertinent information from the EMR was provided to the CDCES who were not previously known to the patients. A summary of the remote CDCES encounter was communicated to the provider. Data analysis used t-tests to assess differences in means and Pearson correlation coefficients to assess relationship between the number of encounters and insurance and age. SPSS v. 27 was used. Results: Adults (n=1089) with T1D [mean age 46 (range 18-90 yrs); 50% female; 20% Medicaid, 23% Medicare, 54% private coverage; mean A1c 8.4% (range 4.6 to >14.0%); 52% pump and 61% CGM users], had 1662 encounters with remote CDCES. Of those referred, 232 (21%) could not be reached or did not receive education/support. Older age was associated with more encounters (r=0.126, p Conclusions: Most remote CDCES encounters in adults with T1D were successfully completed, assisting the majority with CGM education/data review and nearly half with device download training in preparation for their provider tele-visit. Older adults and those with Medicare required more encounters to fulfill needs. Future study could assess whether remote encounters result in improved glycemic control or quality of life. Disclosure M. Greenfield: None. D. L. Stuber: None. D. Stegman-barber: None. K. Kemmis: None. B. A. Wells: None. T. S. Mcarthur: None. B. Matthews: None. C. P. Morley: None. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. Funding The Leona M. and Harry B. Helmsley Charitable Trust; T1D Exchange QI Learning Collaborative

Safe and effective use of the Omnipod 5 System was demonstrated in adults and children with type 1 diabetes (T1D) during a 3-month pivotal study. Longer studies are needed to evaluate durability of glycemic benefit. Those completing the pivotal study were invited to continue system use for an additional 12 months, with 95% enrolling in the extension. We present results from the first 3-month follow-up. Participants aged 6-70y with T1D≥6 months and A1C Disclosure A. L. Carlson: Board Member; Self; JDRF, Other Relationship; Self; Medtronic, Research Support; Self; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Omnipod, Sanofi, UnitedHealth Group. J. Sherr: Advisory Panel; Self; Cecelia Health, Insulet Corporation, Medtronic, Consultant; Self; Insulet Corporation, Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Research Support; Self; Dexcom, Inc., Insulet Corporation, Medtronic, Speaker’s Bureau; Self; Lilly Diabetes. S. N. Mehta: None. L. M. Laffel: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompe, Insulogic LLC, Janssen Pharmaceuticals, Inc., Laxmi Therapeutic Devices, LifeScan, Lilly Diabetes, Medtronic, Provention Bio, Inc. V. Shah: Advisory Panel; Self; Medscape, Sanofi, Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Sanofi, vTv Therapeutics. A. Bhargava: Research Support; Self; Abbott, AbbVie Inc., Amgen Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Boston Therapeutics, Inc., Covance Inc., Dexcom, Inc., Eli Lilly and Company, Gan & Lee Pharmaceuticals, Insulet Corporation, Janssen Research & Development, LLC, Kowa Pharmaceuticals America, Inc., Madrigal Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Mylan N. V., Novo Nordisk, Poxel SA, Quintiles, Sanofi, Senseonics, Tolerion, Inc., Viking Therapeutics. R. S. Weinstock: Research Support; Self; Boehringer Ingelheim International GmbH, Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Kowa Research Institute, Inc., Medtronic, Tolerion, Inc. S. A. Macleish: Advisory Panel; Self; Insulet Corporation. D. Desalvo: Consultant; Self; Insulet Corporation, Research Support; Self; Insulet Corporation, Speaker’s Bureau; Self; Dexcom, Inc. T. C. Jones: None. G. Aleppo: Consultant; Self; Dexcom, Inc., Insulet Corporation, Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Novo Nordisk. A. B. Criego: Consultant; Self; Bigfoot Biomedical, Inc., Other Relationship; Self; Sanofi, Research Support; Self; Abbott Diabetes, Eli Lilly and Company, Insulet Corporation, Medtronic. B. A. Buckingham: Advisory Panel; Self; Medtronic, Tolerion, Inc., Research Support; Self; Beta Bionics, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. T. T. Ly: Employee; Self; Insulet Corporation. Omnipod 5 research group: n/a. G. P. Forlenza: Advisory Panel; Self; Medtronic, Consultant; Self; Beta Bionics, Inc., Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Research Support; Self; Abbott Diabetes, Dexcom, Inc., Insulet Corporation, Medtronic, Tandem Diabetes Care. B. W. Bode: Advisory Panel; Self; Eli Lilly and Company, Consultant; Self; Bigfoot Biomedical, Inc., Companion Medical, Lexicon Pharmaceuticals, Inc., Medtronic, Novo Nordisk Inc., Zealand Pharma A/S, Research Support; Self; Abvance Therapeutics, Dexcom, Inc., Diasome Pharmaceuticals, Inc., Dompe, Eli Lilly and Company, Eyenuk, Inc., Insulet Corporation, Jaeb Center for Health Research, Medtro Funding Insulet Corporation

OBJECTIVE Higher serum uric acid (SUA) is associated with diabetic kidney disease (DKD). Preventing Early Renal Loss in Diabetes (PERL) evaluates whether lowering SUA with allopurinol slows glomerular filtration rate (GFR) loss in people with type 1 diabetes (T1D) and mild to moderate DKD. We present the PERL rationale, design, and baseline characteristics. RESEARCH DESIGN AND METHODS This double-blind, placebo-controlled, multicenter trial randomized 530 participants with T1D, estimated GFR (eGFR) of 40–99.9 mL/min/1.73 m2, SUA ≥4.5 m/dL, and micro- to macroalbuminuric DKD or normoalbuminuria with declining kidney function (NDKF) (defined as historical eGFR decline ≥3 mL/min/1.73 m2/year) to allopurinol or placebo. The primary outcome is baseline-adjusted iohexol GFR (iGFR) after 3 years of treatment plus a 2-month washout period. RESULTS Participants are 66% male and 84% white. At baseline, median age was 52 years and diabetes duration was 35 years, 93% of participants had hypertension, and 90% were treated with renin-angiotensin system inhibitors (median blood pressure 127/71 mmHg). Median HbA1c was 8%, SUA 5.9 mg/dL, iGFR 68 mL/min/1.73 m2, and historical eGFR slope −3.5 mL/min/1.73 m2/year. Compared with participants with albuminuria (n = 419), those with NDKF (n = 94) were significantly older (56 vs. 52 years), had lower HbA1c (7.7 vs. 8.1%) and SUA (5.4 vs. 6.0 mg/dL), and had higher eGFR (82 vs. 74 mL/min/1.73 m2) and historical eGFR loss (−4.7 vs. −2.5 mL/min/1.73 m2/year). These differences persisted when comparing groups with similar rates of historical eGFR loss. CONCLUSIONS PERL will determine the effect of allopurinol on mild to moderate DKD in T1D, with or without albuminuria. Participants with normoalbuminuria and rapid GFR loss manifested many DKD risk factors of those with albuminuria, but with less severity.

Background: Barriers to mealtime insulin include complexity, fear of injections, and lifestyle interference. This multicenter, randomized controlled trial evaluated efficacy, safety, and self-reported outcomes in adults with type 2 diabetes, inadequately controlled on basal insulin, initiating and managing mealtime insulin with a wearable patch versus an insulin pen. Methods: Adults with type 2 diabetes (n = 278, age: 59.2 ± 8.9 years), were randomized to patch (n = 139) versus pen (n = 139) for 48 weeks, with crossover at week 44. Baseline insulin was divided 1:1 basal: bolus. Using a pattern-control logbook, subjects adjusted basal and bolus insulin weekly using fasting and premeal glucose targets. Results: Glycated hemoglobin (HbA1c) change (least squares mean ± standard error) from baseline to week 24 (primary endpoint) improved (P

Context Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking. Objective To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor. Design Multicenter, prospective study. Setting Twelve centers within the T1D Exchange Clinic Network. Patients or Participants Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index. Intervention Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days. Main Outcome Measures CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. Results A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels Conclusion By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.