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Introduction: This trial assessed safety and effectiveness of an advanced hybrid closed-loop (AHCL) system with automated basal (Auto Basal) and automated bolus correction (Auto Correction) in adol...

Team-based models that use medical assistants (MAs) to provide self-management support for adults with type 2 diabetes (T2D) have not been pragmatically tested in diverse samples. This cluster-randomized controlled trial compares MA health coaching with usual care in adults with T2D and poor clinical control (“MAC Trial”). The purpose was to conduct a multi-method process evaluation of the MAC Trial using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework. Reach was assessed by calculating the proportion of enrolled participants out of the eligible pool and examining representativeness of those enrolled. Key informant interviews documented adoption by MA Health Coaches. We examined implementation from the research and patient perspectives by evaluating protocol adherence and the Patient Perceptions of Chronic Illness Care (PACIC-SF) measure, respectively. Findings indicate that the MAC Trial was efficient and effective in reaching patients who were representative of the target population. The acceptance rate among those approached for health coaching was high (87%). Both MA Health Coaches reported high satisfaction with the program and high levels of confidence in their role. The intervention was well-implemented, as evidenced by the protocol adherence rate of 79%; however, statistically significant changes in PACIC-SF scores were not observed. Overall, if found to be effective in improving clinical and patient-reported outcomes, the MAC model holds potential for wider-scale implementation given its successful adoption and implementation and demonstrated ability to reach patients with poorly controlled T2D who are at-risk for diabetes complications in diverse primary care settings.

OBJECTIVE To explore the effect of discontinuing continuous glucose monitoring (CGM) after 8 months of CGM use in adults with type 2 diabetes treated with basal without bolus insulin. RESEARCH DESIGN AND METHODS This multicenter trial had an initial randomization to either real-time CGM or blood glucose monitoring (BGM) for 8 months followed by 6 months in which the BGM group continued to use BGM (n = 57) and the CGM group was randomly reassigned either to continue CGM (n = 53) or discontinue CGM with resumption of BGM for glucose monitoring (n = 53). RESULTS In the group that discontinued CGM, mean time in range (TIR) 70–180 mg/dL, which improved from 38% before initiating CGM to 62% after 8 months of CGM, decreased after discontinuing CGM to 50% at 14 months (mean change from 8 to 14 months −12% [95% CI −21% to −3%], P = 0.01). In the group that continued CGM use, little change was found in TIR from 8 to 14 months (baseline 44%, 8 months 56%, 14 months 57%, mean change from 8 to 14 months 1% [95% CI −11% to 12%], P = 0.89). Comparing the two groups at 14 months, the adjusted treatment group difference in mean TIR was −6% (95% CI −16% to 4%, P = 0.20). CONCLUSIONS In adults with type 2 diabetes treated with basal insulin who had been using real-time CGM for 8 months, discontinuing CGM resulted in a loss of about one-half of the initial gain in TIR that had been achieved during CGM use.

OBJECTIVEThe current standard for hospital glucose management is point-of-care (POC) testing. We conducted a randomized controlled trial of real-time continuous glucose monitoring (RT-CGM) compared with POC in a non–intensive care unit (ICU) hospital setting.RESEARCH DESIGN AND METHODSA total of 110 adults with type 2 diabetes on a non-ICU floor received RT-CGM with Dexcom G6 versus usual care (UC). RT-CGM data were wirelessly transmitted from the bedside. Hospital telemetry monitored RT-CGM data and notified bedside nursing of glucose alerts and trends. Standardized protocols were used for interventions.RESULTSThe RT-CGM group demonstrated significantly lower mean glucose (M∆ = −18.5 mg/dL) and percentage of time in hyperglycemia >250 mg/dL (−11.41%) and higher time in range 70–250 mg/dL (+11.26%) compared with UC (P values CONCLUSIONSRT-CGM can be used successfully in community-based hospital non-ICU settings to improve glucose management. Continuously streaming glucose readings may truly be the fifth vital sign.

Aim To investigate the efficacy and safety of initiating insulin degludec/liraglutide (IDegLira) in patients with type 2 diabetes (T2D) who had discontinued pretrial sulphonylureas (SUs) or dipeptidyl peptidase‐4 inhibitors (DPP4is) versus patients not previously treated with these regimens. Materials and Methods In DUAL II, patients with T2D uncontrolled on basal insulin and metformin ± SU/glinides were randomized to insulin degludec or IDegLira (both capped at 50 U). In DUAL IX, patients were randomized to insulin glargine U100 (no maximum dose) or IDegLira, as add‐on to sodium‐glucose co‐transporter‐2 inhibitors ± oral antidiabetic drugs. In this post hoc analysis, patients were grouped according to pretrial use of SU (DUAL II) or DPP4i (DUAL IX). Results Regardless of pretrial SU/DPP4i use, IDegLira was favourable versus insulin comparators with respect to change in HbA1c and body weight. Lower hypoglycaemia rates and comparable end‐of‐trial daily insulin dose were achieved with IDegLira, regardless of pretrial regimen. There was no clinically relevant increase in mean self‐measured blood glucose in the early weeks after IDegLira initiation. There was no statistically significant interaction between the randomized treatments and previous SU/DPP4i use. Conclusions IDegLira was more favourable compared with degludec or glargine U100 in terms of change in HbA1c and body weight, regardless of antecedent treatment. Clinicians should be aware of a potential transient rise in self‐measured blood glucose when transitioning therapy in patients. This shows that SUs/DPP4is can be safely discontinued, without deterioration in glycaemic control when initiating IDegLira, allowing a simplified treatment regimen.

Aims/hypothesis A head-to-head randomised trial was conducted to evaluate hypoglycaemia safety with insulin degludec 200 U/ml (degludec U200) and insulin glargine 300 U/ml (glargine U300) in individuals with type 2 diabetes treated with basal insulin. Methods This randomised (1:1), open-label, treat-to-target, multinational trial included individuals with type 2 diabetes, aged ≥18 years with HbA1c ≤80 mmol/mol (9.5%) and BMI ≤45 kg/m2. Participants were previously treated with basal insulin with or without oral glucose-lowering drugs (excluding insulin secretagogues) and had to fulfil at least one predefined criterion for hypoglycaemia risk. Both degludec U200 and glargine U300 were similarly titrated to a fasting blood glucose target of 4.0–5.0 mmol/l. Endpoints were assessed during a 36 week maintenance period and a total treatment period up to 88 weeks. There were three hypoglycaemia endpoints: (1) overall symptomatic hypoglycaemia (either severe, an event requiring third-party assistance, or confirmed by blood glucose [ Results Of the 1609 randomised participants, 733 of 805 (91.1%) in the degludec U200 arm and 734 of 804 (91.3%) in the glargine U300 arm completed the trial (87.3% and 87.8% completed on treatment, respectively). Baseline characteristics were comparable between the two treatment arms. For the primary endpoint, the rate of overall symptomatic hypoglycaemia was not significantly lower with degludec U200 vs glargine U300 (rate ratio [RR] 0.88 [95% CI 0.73, 1.06]). As there was no significant difference between treatments for the primary endpoint, the confirmatory testing procedure for superiority was stopped. The pre-specified confirmatory secondary hypoglycaemia endpoints were analysed using pre-specified statistical models but were now considered exploratory. These endpoints showed a lower rate of nocturnal symptomatic hypoglycaemia (RR 0.63 [95% CI 0.48, 0.84]) and severe hypoglycaemia (RR 0.20 [95% CI 0.07, 0.57]) with degludec U200 vs glargine U300. Conclusions/interpretation There was no significant difference in the rate of overall symptomatic hypoglycaemia with degludec U200 vs glargine U300 in the maintenance period. The rates of nocturnal symptomatic and severe hypoglycaemia were nominally significantly lower with degludec U200 during the maintenance period compared with glargine U300. Trial registration ClinicalTrials.gov NCT03078478 Funding This trial was funded by Novo Nordisk (Bagsvaerd, Denmark)

Aim\ud \ud Treat‐to‐target, randomized controlled trials have confirmed lower rates of hypoglycaemia at equivalent glycaemic control with insulin degludec (degludec) versus insulin glargine 100 units/mL (glargine U100) in patients with type 1 (T1D) or type 2 diabetes (T2D). Treat‐to‐target trials are designed to enable comparisons of safety and tolerability at a similar HbA1c level. In this post hoc analysis of the SWITCH 1 and 2 trials, we utilised a patient‐level modelling approach to compare how glycaemic control might differ between basal insulins at a similar rate of hypoglycaemia.\ud \ud \ud \ud Materials and Methods\ud \ud Data for HbA1c and symptomatic hypoglycaemia from the SWITCH 1 and SWITCH 2 trials were analyzed separately for patients with type 1 diabetes and type 2 diabetes, respectively. The association between the individual patient‐level risk of hypoglycaemia and HbA1c was investigated using a Poisson regression model and used to estimate potential differences in glycaemic control with degludec versus glargine U100, at the same rate of hypoglycaemia.\ud \ud \ud \ud Results\ud \ud Improvements in glycaemic control increased the incidence of hypoglycaemia with both basal insulins across diabetes types. Our analysis suggests that patients could achieve a mean HbA1c reduction of 0.70 [0.05; 2.20]95% CI (for type 1 diabetes) or 0.96 [0.39; 1.99]95% CI (for type 2 diabetes) percentage points (8 [1; 24]95% CI or 10 [4; 22]95% CI mmol/mol, respectively) further with degludec than with glargine U100 before incurring an equivalent risk of hypoglycaemia.\ud \ud \ud \ud Conclusion\ud \ud Our findings suggest that patients in clinical practice may be able to achieve lower glycaemia targets with degludec versus glargine U100, before incurring an equivalent risk of hypoglycaemia.

Throughout history, up to the early part of the 20th century, diabetes has been a devastating disorder, particularly when diagnosed in childhood when it was usually fatal. Consequently, the successful pancreatic extraction of insulin in 1921 was a miraculous, life-changing advance. In this review, the truly transformative effect that insulin has had on the lives of people with type 1 diabetes and on those with type 2 diabetes who are also dependent on insulin is described, from the time of its first successful use to the present day. We have highlighted in turn how each of the many facets of improvements over the last century, from advancements in the properties of insulin and its formulations to the evolution of different methods of delivery, have led to continued improvement in clinical outcomes, through the use of illustrative stories from history and from our own clinical experiences. This review concludes with a brief look at the current challenges and where the next century of technological innovation in insulin therapy may take us.

COVID-19 challenges organizations to modify approaches to optimize diabetes management while reducing Personal Protective Equipment (PPE) use and nurse time, and maintaining patient safety. In April 2020, Scripps Health, a five-hospital health system in southern California created a subcutaneous Diabetes Ketoacidosis (DKA) treatment option for use with adults, admitted with mild to moderate DKA. Primary aims were to achieve DKA resolution and glycemic targets similar to the intravenous (IV) insulin treatment option, and no hypoglycemia The new order set utilized weight-based dosing, with similar orders for labs, electrolyte and IV fluid replacement, and resolution criteria. Differences included automatic insulin dosing and basal/bolus insulin. Key changes were a reduced frequency of point of care testing (POCT) and ≤140 mg/dL hypoglycemia prevention orders. Post-implementation audits between April - July 2020 showed no hypoglycemia and resolution time similar to IV insulin. However, only 6% (N=17) of DKA cases were managed with subcutaneous, as compared to IV insulin. To help understand why there was such a limited uptake, a detailed retrospective chart review was undertaken on a subset of twenty-two DKA cases managed with IV insulin. Findings showed that 17 (77%) had type 2 diabetes, 5 (23%) had type 1 diabetes. 47% of cases had opportunities for subcutaneous management based on DKA classification and no contraindications for subcutaneous therapy. Findings indicate organizations should consider a DKA subcutaneous option. Lack of knowledge of DKA severity may be a factor in low utilization, therefore an EHR DKA Classification tool was implemented to support providers. Research is needed to determine if this increases utilization of the subcutaneous treatment option and what other barriers exist. Disclosure D. Meehan: None. L. Talavera: None. A. Philis-tsimikas: Advisory Panel; Self; Bayer Inc., Lilly Diabetes, Medtronic, Novo Nordisk, Consultant; Self; Sanofi-Aventis, Employee; Spouse/Partner; Ionis, Other Relationship; Self; Merck & Co., Inc., Research Support; Self; Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk, Sanofi-Aventis. A. L. Fortmann: None.

Aims: To compare derived time in range (dTIR) with insulin degludec/liraglutide (IDegLira) and insulin glargine 100 units/mL (IGlar U100) in patients with type 2 diabetes (T2D). Methods: Data from DUAL V, a trial of patients with T2D uncontrolled on IGlar U100 randomized to switch to IDegLira or up-titration of IGlar U100 (+metformin; n=557), and DUAL VIII, a durability trial of insulin-naive patients with T2D randomized to either IDegLira or IGlar U100 (+ oral antidiabetic drugs; n=1012), were evaluated post hoc. Self-monitored blood glucose profiles (weeks 0, 12, and 26 [DUAL V] or weeks 0, 26, and 104 [DUAL VIII]) were used to derive the proportion of available readings within (dTIR: 70-180 mg/dL [3.9-10 mmol/L]), below (dTBR: 180 mg/dL [>10 mmol/L]) target range. Results: Estimated treatment differences (IDegLira− IGlar U100) for change from baseline to end of treatment (EOT) in dTIR were significantly greater with IDegLira vs. IGlar U100 (Figure). The proportion of patients achieving ≥70% dTIR at EOT with IDegLira and IGlar U100, respectively, was 62% and 60% in DUAL V, and 50% and 26% in DUAL VIII; the proportion achieving a ≥5% increase in dTIR from baseline to EOT was 63% in both groups in DUAL V, and 44% and 25%, respectively, in DUAL VIII. Conclusion: IDegLira was associated with significantly greater dTIR vs. IGlar U100 in insulin-experienced and insulin-naive patients with T2D. Disclosure A. Philis-tsimikas: Advisory Panel; Self; Bayer Inc., Lilly Diabetes, Medtronic, Novo Nordisk, Consultant; Self; Sanofi-Aventis, Employee; Spouse/Partner; Ionis, Other Relationship; Self; Merck & Co., Inc., Research Support; Self; Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk, Sanofi-Aventis. V. R. Aroda: Consultant; Self; Applied Therapeutics, Duke, Novo Nordisk, Pfizer Inc., Sanofi, Employee; Spouse/Partner; Janssen , Merck , Research Support; Self; Applied Therapeutics, Eli Lilly and Company, Fractyl , Medpace, Medpace, Novo Nordisk, Premier , Sanofi, Stock/Shareholder; Spouse/Partner; Janssen, Merck. C. De block: Advisory Panel; Self; A. Menarini Diagnostics, Abbott Diagnostics, AstraZeneca, Boehringer Ingelheim , Eli Lilly and Company, MSD, Novartis AG, Novo Nordisk, Roche Diagnostics, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk, Speaker9s Bureau; Self; A. Menarini Diagnostics, Abbott Diagnostics, Boehringer Ingelheim , Eli Lilly and Company, Novo Nordisk, Sanofi. L. K. Billings: Advisory Panel; Self; Bayer Inc., Lilly Diabetes, Novo Nordisk, Sanofi. E. Hachmann-nielsen: Employee; Self; Novo Nordisk A/S. A. Liebl: Advisory Panel; Self; AstraZeneca, Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, MSD, Novo Nordisk, Roche, Speaker9s Bureau; Self; AstraZeneca, Bayer, Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Bristol Myers Squibb , Dexcom, Inc., Eli Lilly and Company, Medtronic, MSD, Novo Nordisk, OmniaMed, Roche, Sanofi. R. Sivarathinasami: Employee; Self; Novo Nordisk Service Centre India Private Ltd. J. M. D9cruz: Employee; Self; NovoNordisk GBS. I. Lingvay: Advisory Panel; Self; Bayer Healthcare Pharmaceuticals Inc., Lilly Diabetes, Consultant; Self; TARGET PharmaSolutions, Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk, Pfizer Inc., Zealand Pharma A/S, Research Support; Self; Mylan N. V., Sanofi. Funding Novo Nordisk A/S

Background: A head-to-head trial (NCT03078478) between insulin degludec and insulin glargine U300 with the primary objective of comparing the risk of hypoglycemia is being conducted. During trial conduct, safety concerns related to the glycemic data collection system led to a postinitiation protocol amendment, described here. Methods: This randomized (1:1), open-label, treat-to-target, multinational trial was initiated in March 2017 with a planned treatment period of 52 weeks (16 weeks titration + 36 weeks maintenance). Overall, ~1600 insulin-experienced patients at risk of developing hypoglycemia based on predefined risk factors were included. The protocol amendment implemented in February 2018 resulted in assuring patient safety and an extension of the total treatment period up to 88 weeks (16 weeks titration + variable maintenance 1 + 36 weeks maintenance 2). The original glycemic data collection system (MyGlucoHealth blood glucose meter + electronic diary) was discontinued because of safety concerns and replaced with an Abbott blood glucose meter and paper diary to collect self-measured blood glucose and hypoglycemic episodes. The primary endpoint of number of severe or blood-glucose confirmed symptomatic hypoglycemic episodes will be evaluated with the same analysis duration and statistical methods as the original protocol. Only relevant changes were implemented to maintain patient safety while permitting evaluation of the scientific objectives of the trial. Conclusions: These observations highlight the importance of safety surveillance during trial conduct despite the use of currently marketed glucose monitoring devices. The prompt protocol amendment and ensuing actions ensured that the scientific integrity of the trial was not compromised.

Introduction Insulin dosing based on carbohydrate counting is the gold standard for improving glycaemic control in type 1 diabetes (T1D). This post hoc analysis aimed to explore the efficacy and safety of fast-acting insulin aspart (faster aspart) according to bolus dose adjustment method in people with T1D. Methods Post hoc analysis of two 26-week, treat-to-target, randomised trials investigating treatment with double-blind mealtime faster aspart, insulin aspart (IAsp), or open-label post-meal faster aspart (onset 1, n = 1143; onset 8, n = 1025). Participants with previous experience continued carbohydrate counting (onset 1, n = 669 [58.5%]; onset 8, n = 428 [41.8%]), while remaining participants used a bolus algorithm. Results In onset 1, HbA1c reduction was statistically significantly in favour of mealtime faster aspart versus IAsp with carbohydrate counting (estimated treatment difference [ETD 95% CI] − 0.19% [− 0.30; − 0.09]; − 2.08 mmol/mol [− 3.23; − 0.93]). In onset 8, there was no statistically significant difference in HbA1c reduction with either dose adjustment method, although a trend towards improved HbA1c was observed for mealtime faster aspart with carbohydrate counting (ETD − 0.14% [− 0.28; 0.003]; − 1.53 mmol/mol [− 3.10; 0.04]). In both trials, bolus insulin doses and overall rates of severe or blood glucose-confirmed hypoglycaemia were similar between treatments across dose adjustment methods. Conclusion For people with T1D using carbohydrate counting, mealtime faster aspart may offer improved glycaemic control versus IAsp, with similar insulin dose and weight gain and no increased risk of hypoglycaemia. Trial Registration ClinicalTrials.gov: NCT01831765 (onset 1) and NCT02500706 (onset 8). Funding Novo Nordisk. Electronic supplementary material The online version of this article (10.1007/s13300-019-0608-4) contains supplementary material, which is available to authorized users.

Aims To investigate the glycemic efficacy, effects on cardiovascular risk factors, and safety of exenatide once weekly (QW) in patients with type 2 diabetes over 7 years in the DURATION-1 study. Methods Patients were initially randomized to exenatide QW 2 mg or exenatide twice daily for 30 weeks, after which they received open-label, open-ended treatment with exenatide QW 2 mg for up to 7 years. Efficacy analyses included changes from baseline in glycated hemoglobin (HbA1C) and cardiovascular risk factors. Results Of 295 patients in the intention-to-treat population, 122 (41%) completed 7 years of treatment. Patients in the 7-year completer population showed sustained glycemic improvements from baseline (7-year least-squares mean [LSM] change in HbA1C, −1.53%) and significant improvements in several cardiovascular risk factors, including body weight, diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Seven-year completers who received no additional glucose-lowering therapies (n = 65 [53%]) had similar improvements in HbA1C, and numerically greater reductions in body weight (7-year LSM change, −6.46 kg vs −3.87 kg), compared with the overall cohort. There were no unexpected safety findings. Conclusions Treatment with exenatide QW for 7 years was associated with sustained improvements in glycemic control and several cardiovascular risk factors.

Aims To confirm non-inferiority of insulin degludec/insulin aspart (IDegAsp) once-daily (OD) versus insulin glargine (IGlar) U100 OD + insulin aspart (IAsp) OD for HbA1c after 26 weeks, and compare efficacy and safety between groups at W26 + W38. Methods A 38-week, randomised, open-label, treat-to-target (HbA1c Results For W0–W26, mean percentage-change (standard deviation) HbA1c was: IDegAsp, −1.1 (0.9); IGlar U100 + IAsp, −1.1 (0.8); estimated treatment difference: 0.07% (95% confidence interval [CI]: −0.06; 0.21) confirmed non-inferiority. At W26 and W38, target HbA1c achievement, and mean fasting and postprandial glucose were similar across groups. At W38, more subjects achieved target HbA1c without hypoglycaemia with IDegAsp (22.5%) than with IGlar U100 + IAsp (21.1%), with significantly fewer nocturnal episodes (W0–W38, estimated rate ratio: 0.61 [95% CI: 0.40; 0.93]). Safety profiles were similar across treatment groups throughout. Conclusions IDegAsp OD/BID are effective treatment intensification options versus multiple injection basal–bolus therapies, achieving similar glycaemic control, with significantly less nocturnal hypoglycaemia.

IN BRIEF Hispanic patients with type 2 diabetes have poorer glycemic control and are at higher risk of severe diabetes complications and mortality than non-Hispanic white patients. This post hoc analysis investigated the safety and efficacy of insulin degludec versus insulin glargine 100 units/mL (glargine U100) in the Hispanic patient subpopulation from the SWITCH 2 trial. In Hispanic patients, hypoglycemia was consistently lower and nocturnal hypoglycemia was significantly lower with degludec versus glargine U100 at similar levels of glycemic control. Overall, results in Hispanic patients in SWITCH 2 were consistent with those in non-Hispanic patients.

Aims The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2‐year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. Materials and methods Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data‐driven machine‐learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data‐driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data‐driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset. Results Both the data‐driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time‐dependent area under the curve index (0.63 and 0.66, respectively) over a 2‐year time horizon. Conclusions Both the data‐driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care.

Background: Automated insulin delivery systems improve glycemic control including time spent in, below and above range (TIR, TBR and TAR, respectively) in people with T1D. A next generation system, the MiniMedTM AHCL system, that offers a target set point (SP) of 100 or 120mg/dL, autocorrects to 120mg/dL every 5 mins and has fewer Auto Mode exits, was evaluated. Methods: A 16-site, single-arm, in-home trial of the AHCL system in 39 adolescents (14-21yrs) and 118 adults (≥22yrs) with T1D was conducted. After a baseline period of sensor-integrated pump, HCL feature, or predictive low glucose feature use (~14 days), the AHCL feature was enabled with a 100 or 120mg/dL SP for ~45 days and then the other SP for ~45 days. Study endpoints included safety events, change in A1C, sensor glucose (SG), %TIR, %TBR and %TAR. Results: There were no DKA or severe hypoglycemia episodes during the study period. Auto Mode was used ≥95% of the time; autocorrection was 22% of daily bolus. The table shows outcomes (mean±SD) for the overall group and each age group at baseline and study period (100 and 120mg/dL SP), and at the 100mg/dL SP. Best glycemic results (SG of 141±8.8mg/dL, TIR of 78.8±5.5% and TBR of 2.6±2.0%; N=29) were seen with a 100mg/dL SP and active insulin time of 120 minutes. Conclusion: These pivotal trial data demonstrate that AHCL is safe and significantly improved A1C and SG in subjects aged ≥14 years with T1D. Disclosure A.L. Carlson: Consultant; Self; Medtronic. Research Support; Self; Abbott, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S, Sanofi, UnitedHealth Group. B.W. Bode: Advisory Panel; Self; Medtronic, Novo Nordisk A/S. Consultant; Self; Eli Lilly and Company, Medtronic, Novo Nordisk A/S. Research Support; Self; Abbott, Advance, Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Diasome Pharmaceuticals, Inc., Eli Lilly and Company, Insulet Corporation, Janssen Pharmaceuticals, Inc., MannKind Corporation, Medtronic, National Institutes of Health, Nova Biomedical, Novo Nordisk A/S, Provention Bio, Inc., Sanofi, Senseonics. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., MannKind Corporation, Medtronic, Novo Nordisk A/S, Sanofi, Senseonics. Stock/Shareholder; Self; Aseko. R.L. Brazg: None. M.P. Christiansen: Research Support; Self; Abbott, Ascensia Diabetes Care, Biolinq, Dexcom, Inc., Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novo Nordisk A/S, Sanofi-Aventis, Senseonics, ViroMed Laboratories, Xeris Pharmaceuticals, Inc. S.K. Garg: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diagnostics France. Research Support; Self; Dexcom, Inc., Eli Lilly and Company, Medtronic. K. Kaiserman: Advisory Panel; Self; Medtronic. Consultant; Self; Medtronic. Research Support; Self; Medtronic. Speaker’s Bureau; Self; Medtronic. M. Kipnes: None. D.R. Liljenquist: None. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. R. Pop-Busui: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., Novo Nordisk Inc. Research Support; Self; AstraZeneca. Other Relationship; Self; American Diabetes Association. J. Sherr: Advisory Panel; Self; Bigfoot Biomedical, Cecelia Health. Consultant; Self; Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Sanofi, T1D Exchange. D.I. Shulman: Advisory Panel; Self; Medtronic. K.K. Singh: None. R.H. Slover: None. J. Thrasher: Advisory Panel; Self; Lexicon Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi. Board Member; Self; Medtronic. Consultant; Self; Medtronic. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb, Eli Lilly and Company, Lexicon Pharmaceuticals, Inc., Medtronic, Mylan, Novo Nordisk Inc., Sanofi. Speaker’s Bureau; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Medtronic, Sanofi. X. Chen: Employee; Self; Medtronic. B. Grosman: Employee; Self; Medtronic. S.W. Lee: Employee; Self; Medtronic. L.J. Lintereur: Employee; Self; Medtronic. M. Liu: Employee; Self; Medtronic. N. Parikh: None. A.S. Rhinehart: Employee; Self; Glytec, Medtronic. Stock/Shareholder; Self; Dexcom, Inc., Glytec, Lexicon Pharmaceuticals, Inc., MannKind Corporation, Tandem Diabetes Care. F. Peng: None. A. Roy: Employee; Self; Medtronic. J. Shin: Employee; Self; Medtronic. D. Wu: None. R. Vigersky: Employee; Self; Medtronic.

Self-adjusting insulin is challenging for people with type 2 diabetes (PWT2D). Mobile health technologies (mHealth) can help PWT2D track blood glucose more easily, but it remains unclear whether an mHealth-enabled digital therapeutic tool can effectively support basal insulin (BI) titration in a large multispecialty health care setting. We randomized 242 PWT2D on BI (baseline HbA1c 7.5% - 12.5%) to use either an app-based self-titration tool [Mobile Insulin Dosing System (MIDS)] or an enhanced paper titration tool based on a stepped algorithm with diabetes educator support (control) for 16 weeks. Cohort characteristics for the intent to treat sample (MIDS n = 117; control n = 120) were: median age 61 years [IQR: 53 - 69], 41.7% female, 75.9% non-Hispanic White, 11.4% new to insulin, median duration of diabetes 11 years [IQR: 7 - 18], and median baseline HbA1c of 8.7% [IQR: 8.0 - 9.6]. Improvements in HbA1c (-1.3% [IQR: -2.2 - -.5] and -1.2% [IQR: -1.9 - -.5]; both Ps < .05) with increased median insulin dose (+8 IU [IQR: 2 - 22] and +10 IU [IQR: 2 - 25.5]; both Ps < .05) and no change in hypoglycemia were observed at 16 weeks in both the MIDS and control groups, respectively. No differences in HbA1c (P = .48) or insulin dose change (P = .34) were observed between the groups. Among SMBG readings recorded during the study, the mean proportion of readings between 70-180 mg/dL (76.8% ± 22.0; 70.0% ± 26.7; p250 mg/dL (4.9% ± 9.2; 9.2% ± 18.4; p Disclosure A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. A.L. Fortmann: None. A. Bastian: None. A. Kanchi: Employee; Self; Glooko, Inc. Stock/Shareholder; Self; Glooko, Inc. R. Abad: Employee; Self; Glooko, Inc. T. Sheng: Employee; Self; Glooko, Inc. L. Parks: Employee; Self; Glooko, Inc. M. Greenfield: Consultant; Self; Glooko, Inc. M.A. Clements: Consultant; Self; Glooko, Inc. Other Relationship; Self; Glooko, Inc.

Continuous glucose monitors (CGM) are an integral aspect in the glucose management of many individuals with type 1 diabetes, and a growing number of people with insulin-requiring type 2 diabetes (T2D). A 2019 meta-analysis of 7 randomized controlled trials that investigated CGMs for T2D reported that CGM users had significantly lower HbA1c and shorter time spent with hypoglycemia compared with self-monitoring blood glucose comparison groups. However, little is known about optimal, real world approaches for integrating CGM in the routine management of patients with T2D. A pragmatic, feasibility pilot (n=6) was conducted to document the preliminary effectiveness and acceptability of introducing CGM in the context of a 5-class diabetes self-management education (DSME) series at Scripps Health, a large, non-profit, private insurance-based health system in San Diego, CA. Prior to class 2, English- (n = 3) and Spanish-speaking (n = 3) adults with T2D self-inserted the CGM and received enough sensors to wear the CGM until 6 weeks post-DSME (10 weeks total). During classes 3-5, time was devoted to reviewing the past week’s CGM data report and participants were encouraged to make associations between glucose trends and eating and exercise patterns. CGM wear ranged from 5 to 10 weeks (M =7.8 ± 1.7 weeks). Time-in-range (70-180 mg/dL) and mean average glucose improved from 54% and 185.1 ± 67.9 mg/dL at baseline to 74% and 159.1 ± 50.5 mg/dL by the end of DSME, and to 84% and 142.7 ± 30.6 mg/dL at 4-weeks post-DSME. Self-report surveys indicated improvements in healthful eating and physical activity over this time, and participants reported very high satisfaction with the CGM; the majority expressed a desire to continue CGM use post-study. Findings highlight DSME as a promising environment for effectively integrating CGM in the management of T2D. Future research should pinpoint the duration and frequency of CGM use for optimal clinical benefit in the general T2D population. Disclosure A.L. Fortmann: None. A. Bastian: None. C.J. Lensing: Employee; Self; UnitedHealth Group. Employee; Spouse/Partner; UnitedHealth Group. S. Hoversten: Employee; Self; UnitedHealth Group. K. Luu: None. K. Barger: None. L. Talavera: None. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Funding UnitedHealth Group; Dexcom, Inc.

CONCLUDE was a randomized, open-label, treat-to-target trial in participants with T2D, who received basal insulin ± oral antihyperglycemic drugs (OADs) and had ≥1 baseline hypoglycemia risk factors, including moderate renal impairment. Participants were randomized to degludec U200 or glargine U300 (± OADs). CONCLUDE demonstrated no significant difference in the overall symptomatic hypoglycemia rate with degludec U200 vs. glargine U300 in the maintenance period (primary endpoint, tested for superiority). However, lower rates of nocturnal symptomatic and severe hypoglycemia in the maintenance period (exploratory secondary endpoints) were observed with degludec U200 vs. glargine U300. The current post-hoc analysis investigated these hypoglycemia endpoints and HbA1c stratified by baseline estimated glomerular filtration rate (eGFR) subgroups ( Disclosure T.R. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Novo Nordisk A/S. Speaker’s Bureau; Self; Novo Nordisk A/S, Roche Diagnostics K.K. H.S. Bajaj: Research Support; Self; Amgen, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Kowa Pharmaceuticals America, Inc., Novartis Pharmaceuticals Canada Inc., Novo Nordisk Inc., Sanofi. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., mdBriefCase, Medscape, Merck Sharp & Dohme Corp., Novo Nordisk Inc. S.R. Heller: Advisory Panel; Self; Eli Lilly and Company, Novo Nordisk A/S, Zealand Pharma A/S. Speaker’s Bureau; Self; AstraZeneca, Novo Nordisk A/S. Other Relationship; Self; MannKind Corporation. T. Jia: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. K. Khunti: Advisory Panel; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Board Member; Self; AstraZeneca, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi-Aventis. Consultant; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Sanofi-Aventis, Servier. Speaker’s Bureau; Self; Amgen, AstraZeneca, Bayer AG, Berlin-Chemie AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Menarini Group, Merck Sharp & Dohme Corp., Napp Pharmaceuticals, Novartis AG, Novo Nordisk A/S, Roche Pharma, Sanofi-Aventis, Servier. D.C. Klonoff: Consultant; Self; Abbott, Ascensia Diabetes Care, Dexcom, Inc., EOFlow, Fractyl Laboratories, Inc., Know Labs, LifeCare, Inc., Novo Nordisk Inc., Roche Diabetes Care. S. Ladelund: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. L.A. Leiter: Advisory Panel; Self; Abbott, Amgen, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. Research Support; Self; Amgen, AstraZeneca, Kowa Pharmaceuticals America, Inc., Medicines Company. Speaker’s Bureau; Self; Amgen, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Medscape, Merck & Co., Inc., Novo Nordisk Inc., Sanofi, Servier. M.V. Hansen: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Funding Novo Nordisk A/S

Continuous Glucose Monitors (CGM) automatically capture interstitial glucose levels every 5 minutes, 24 hours/day, allowing users to view their glucose levels in real-time and make informed decisions about how to manage diet, exercise, and medications. While CGMs are a central component in the glucose management of type 1 diabetes and insulin-dependent type 2 diabetes, less is known about the potential value of CGMs for diabetes prevention. The original Diabetes Prevention Program (DPP) demonstrated that 5-7% weight loss - an outcome reliant upon healthful eating and exercise - was more effective than medication in preventing or delaying the onset of T2D; however, subsequent DPP translations have not consistently achieved the same weight loss outcomes. CGM may represent a way to reinforce and incentivize hard-to-make health behavior changes in real-time that are crucial to the more distal (and often elusive) weight loss outcome. This small (n=7) feasibility pilot examined the preliminary effectiveness and acceptability of integrating CGM in a DPP at Scripps Health, a large, non-profit, private insurance-based health system in San Diego, CA. Following a brief introduction to the CGM and target glucose ranges, DPP participants self-inserted and wore the CGM for 2 weeks. Mean time-in-range (70-140 mg/dL) improved from 70.4% to 76.4%, and time > 140 mg/dL decreased from 29.4% to 23.1% during this period. Individual change in time-in-range ranged from -9.7% to + 38.8% (M ∆ = +6.0%). Participants reported high satisfaction with the CGM, but expressed a desire for more education on blood glucose management. Given these findings, a longer-term, randomized controlled trial is planned to investigate whether a CGM-enhanced DPP leads to greater weight loss than DPP alone. This research will also further our understanding of the potential benefit of improved glycemic control for diabetes prevention even among the subset of cases who do not ultimately achieve the targeted weight loss goal. Disclosure A.L. Fortmann: None. A. Bastian: None. S.R.S. Bagsic: None. I. Loupasi: None. K. Luu: None. A.R. Hottinger: None. K.A. Barry: None. M. Ruiz: None. C. Walker: None. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Funding Confidence Foundation; Dexcom, Inc.

Hispanics have a 66% higher risk of developing type 2 diabetes (T2D), and exhibit worse outcomes than non-Hispanic whites. Many Hispanics lack access to diabetes self-management education and support (DSME/S) due to practical, cultural, and other barriers. Mobile health (mHealth) technology has the potential to circumvent such barriers and expand the reach of DSME/S. A 3-group, randomized controlled trial is currently underway to evaluate the effectiveness of mHealth DSME/S approaches in improving diabetes outcomes among predominantly low income, Hispanic adults with T2D and HbA1c ≥ 8% at a Federally Qualified Health Center in Southern California. Dulce Digital-Me (DD-Me) is an adaptive mHealth intervention that integrates educational text messaging with personalized goal-setting and feedback. Over 6 months, participants receive 2-3 messages/day, and are provided with a glucometer and pillbox that wirelessly transmit real-time data to a cloud. Ecological momentary assessment (EMA) gauges progress in healthful eating, exercise, and emotional well-being. Participants’ progress in lifestyle changes, medication adherence, and BG monitoring frequency and control is compared to pre-defined targets. Real-time feedback is delivered via automated, algorithm-driven messaging or by medical assistant Health Coach (n = 104 in each group). The control group (n = 207) receives educational text messages alone. Preliminary analyses (N = 163) showed higher BG monitoring frequency in the DD-Me Health Coach group (M = 7.02 ± 0.45 checks/week) versus the DD-Me algorithm-driven (M = 4.80 ± 0.49) and control groups (M = 5.25 ± 0.65), p Disclosure A.L. Fortmann: None. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. S.R.S. Bagsic: None. D.G. Vital: None. J.A. Jones: None. H. Sandoval: None. K.L. Savin: None. T. Clark: None. K. Luu: None. J.G. Godino: None. L. Gallo: None. Funding National Institutes of Health (1R01DK112322)

Randomized controlled trials (RCTs) support use of a glucagon-like peptide-1 receptor agonist (GLP-1RA) combined with a sodium-glucose cotransporter-2 inhibitor (SGLT2i) in people with type 2 diabetes (T2D). As RCTs do not fully reflect the real world, there is a need to explore the real-world population on this combination and determine outcomes with this approach. As a first step, in this large-scale cross-sectional study, we identified adults with T2D, ≥2 prescriptions for GLP-1RA and/or SGLT2i and available data from electronic medical records in the U.S. IBM Explorys database. The first prescription of GLP-1RA and/or SGLT2i in 2018 set the index date, irrespective of prior prescriptions. Data 6 months pre-index were used for patient characteristics and up to 5 years pre-index for medical history. Of 41,421 patients, 12.8% had GLP-1RA+SGLT2i prescribed together, 46.0% GLP-1RA and 41.2% SGLT2i. Of those prescribed both drugs, female: 48.0%; BMI ≥35 kg/m2: 50.2%; mean age: 56.2 years; mean HbA1c 8.3%. In multinomial regression, there was little difference between those prescribed GLP-1RA+SGLT2i vs. either drug alone in terms of HbA1c or presence of comorbidities (data not shown). However, those prescribed both drugs vs. GLP-1RA alone were less likely to be ≥65 years (odds ratio 0.57 [95% CI 0.51;0.63]), have a history of chronic kidney disease (CKD; 0.56 [0.49;0.66]) and more likely to have prior prescription of metformin (1.56 [1.46;1.68]). Those prescribed both drugs vs. SGLT2i alone were less likely to be ≥65 years (0.60 [0.54;0.67]) and have prior prescription of dipeptidyl peptidase-4 inhibitor (0.57 [0.52;0.61]), but more likely to have BMI ≥35 kg/m2 (2.11 [1.75;2.54]) and prior prescription of insulin (1.97 [1.84;2.11]). Those prescribed GLP-1RA+SGLT2i vs. either drug in 2018 had many common characteristics. However, key differentiators (age, obesity, CKD history and prior glucose-lowering drugs) reflected considerations for each therapy based on data available then. Disclosure R.J. McCrimmon: Advisory Panel; Self; Novo Nordisk A/S, Sanofi-Aventis. H.N. Christensen: Employee; Self; AstraZeneca, Novo Nordisk A/S. I. Holst: Employee; Self; Novo Nordisk A/S. A. Lenart: Employee; Self; Novo Nordisk A/S. B. Ludvik: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; Bayer Healthcare Pharmaceuticals Inc., Eli Lilly and Company, Merck & Co., Inc., Novartis Pharmaceuticals Corporation. Speaker’s Bureau; Self; Amgen. R.S. Brandt: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. J.S. Piltoft: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. A. Philis-Tsimikas: Advisory Panel; Self; Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Sanofi. Funding Novo Nordisk A/S

In the US, nearly 11% of adults were living with diagnosed diabetes in 2017, and significant type 2 diabetes (T2D) disparities are experienced by socioeconomically disadvantaged, racial/ethnic minority populations, including Hispanics. The standard 15-minute primary care visit does not allow for the ongoing self-management support that is needed to meet the complex needs of individuals with diabetes. “Team-based” chronic care delivery is an alternative approach that supplements physician care with contact from allied health personnel in the primary care setting (e.g., medical assistants; MAs) who are specially trained to provide ongoing self-management support or “health coaching.” While rigorous trials have shown MA health coaching to improve diabetes outcomes, less is known about if and how such a model can be integrated within real world, primary care clinic workflows. Medical Assistant Health Coaching for Type 2 Diabetes in Diverse Primary Care Settings – A Pragmatic, Cluster-Randomized Controlled Trial will address this gap. Specifically, this study compares MA health coaching versus usual care in improving diabetes clinical control among N=600 at-risk adults with T2D, and is being conducted at four primary care clinics that are part of two health systems that serve large, ethnically/racially, and socioeconomically diverse populations in Southern California. Electronic medical records are used to identify eligible patients at both health systems, and to examine change in clinical control over one year in the overall sample. Changes in behavioral and psychosocial outcomes are being evaluated by telephone assessment in a subset (n=300) of participants, and rigorous process and cost evaluations will assess potential for sustainability and scalability. ClinicalTrials.gov: NCT02643797 (registered November 30, 2015: https://clinicaltrials.gov/ct2/show/NCT02643797?term=philis-tsimikas&rank=10) Trial registration: NCT02643797

Purpose The purpose of this study was to examine whether baseline levels of diabetes distress (DD) impacted clinical benefit from a mobile health (mHealth) diabetes self-management education and support (DSME/S) intervention (“Dulce Digital”). Methods This secondary analysis included the full sample of 126 Hispanic adults (mean age = 48.43 years, SD = 9.80) with type 2 diabetes and glycosylated hemoglobin A1C >7.5% enrolled from a Federally Qualified Health Center in a randomized, nonblinded clinical trial that compared Dulce Digital to usual care. Dulce Digital participants received educational/motivational, medication reminders, and blood glucose monitoring prompt text messages over 6 months. Results Baseline levels of DD prospectively moderated the effect of Dulce Digital (vs usual care) on glycemic control over 6 months, such that Dulce Digital participants with higher DD experienced relatively greater benefit from the intervention. The effect of the intervention on A1C change was 178% larger among individuals experiencing moderate/high versus no/low DD. Conclusions Although research has found DD to be associated with poorer self-management and clinical outcomes, individuals already distressed about their diabetes may benefit from a lower-burden mHealth DSME/S approach.

Aims To undertake a post‐hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. Materials and Methods The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all‐cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all‐cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all‐cause mortality were investigated. Results Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all‐cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all‐cause mortality). Conclusions The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all‐cause mortality and that, in this high‐risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events.

Background Multimorbidity affects four of ten US adults and eight of ten adults ages 65 years and older, and frequently includes both cardiometabolic conditions and behavioral health concerns. Hispanics/Latinos (hereafter, Latinos) and other ethnic minorities are more vulnerable to these conditions, and face structural, social, and cultural barriers to obtaining quality physical and behavioral healthcare. We report the protocol for a randomized controlled trial that will compare Mi Puente (My Bridge), a cost-efficient care transitions intervention conducted by a specially trained Behavioral Health Nurse and Volunteer Community Mentor team, to usual care or best-practice discharge approaches, in reducing hospital utilization and improving patient reported outcomes in Latino adults with multiple cardiometabolic conditions and behavioral health concerns. The study will examine the degree to which Mi Puente produces superior reductions in hospital utilization at 30 and 180 days (primary aim) and better patient-reported outcomes (quality of life/physical health; barriers to healthcare; engagement with outpatient care; patient activation; resources for chronic disease management), and will examine the cost effectiveness of the Mi Puente intervention relative to usual care. Methods Participants are enrolled as inpatients at a South San Diego safety net hospital, using information from electronic medical records and in-person screenings. After providing written informed consent and completing self-report assessments, participants randomized to usual care receive best-practice discharge processes, which include educational materials, assistance with outpatient appointments, referrals to community-based providers, and other assistance (e.g., with billing, insurance) as required. Those randomized to Mi Puente receive usual-care materials and processes, along with inpatient visits and up to 4 weeks of follow-up phone calls from the intervention team to address their integrated physical-behavioral health needs and support the transition to outpatient care. Discussion The Mi Puente Behavioral Health Nurse and Volunteer Community Mentor team intervention is proposed as a cost-effective and culturally appropriate care transitions intervention for Latinos with multimorbidity and behavioral health concerns. If shown to be effective, close linkages with outpatient healthcare and community organizations will help maximize uptake, dissemination, and scaling of the Mi Puente intervention. Trial registration ClinicalTrials.gov: NCT02723019. Registered on 30 March 2016.

Despite significant treatment advances, diabetes outcomes remain suboptimal and health care costs continue to rise. There are limited data on the feasibility and financial implications of integrating a diabetes-specific care team in the primary care setting (ie, where the majority of diabetes is treated). This pragmatic quality improvement project investigated whether a cardiometabolic care team intervention (CMC-TI) could achieve greater improvements in clinical, behavioral, and cost outcomes compared to usual diabetes care in a large primary care group in Southern California. Over 12 months, n = 236 CMC-TI and n = 239 usual care patients with type 1 or 2 diabetes were identified using the electronic medical record. In the CMC-TI group, a registered nurse (RN)/certified diabetes educator care manager, medical assistant health coach, and RN depression care manager utilized electronic medical record-based risk stratification reports, standardized decision-support tools, live and remote tailored treatments, and coaching to manage care. Results indicated that the CMC-TI group achieved greater improvements in glycemic and lipid control, diabetes self-management behaviors, and emotional distress over 1 year compared with the usual care group (all P

The incidence of diabetes continues to increase in the United States. Management of diabetes remains a significant challenge across the nation, particularly in underserved populations with low socioeconomic status (SES) from diverse racial and ethnic backgrounds that are disproportionately affected by this chronic disease. A large, multistate diabetes registry created from the electronic health records of three networks of safety net clinics that provide care to underserved populations recently demonstrated a 14.4% prevalence of diabetes, which is higher in this low-SES population than previously reported (1,2). Men had a higher prevalence than women (16.5 vs. 13.2%), and diabetes prevalence increased across age categories. White patients had the lowest prevalence (11.4%), and Hawaiian/Pacific Islanders had the highest (21.9%), with prevalence ranging from 15.2 to 16.5% for other races/ethnicities. Furthermore, comorbid conditions such as cardiovascular, neurological, renal, and retinal diseases occurred at higher rates in low-SES diverse communities, possibly due to inadequate medical and self-care, as well as inaccurate culture-bound beliefs. By 2060, the number of U.S. adults with diagnosed diabetes is projected to nearly triple, and the prevalence is expected to double (3). Diabetes imposes large health and …

IN BRIEF In the United States, Hispanics have a 66% greater risk of developing type 2 diabetes and, once diagnosed, exhibit worse outcomes than non-Hispanic whites. It is therefore imperative to ensure that interventions meet the specific needs of this at-risk group. This article provides a selective review of the evidence on innovative, real-world approaches (both live and technology-based) to improving behavioral, psychosocial, and clinical outcomes in underserved Hispanics with type 2 diabetes. Key aspects of successful live interventions have included multimodal delivery, greater dosage/attendance, and at least some in-person delivery; effective technology-based approaches involved frequent but intermittent communication, bi-directional messaging, tailored feedback, multimodal delivery, and some human interaction. Across modalities, cultural tailoring also improved outcomes. Additional research is needed to address methodological limitations of studies to date and pinpoint the most efficacious components and optimal duration of interventions. Future efforts should also attend to variability within the U.S. Hispanic population to ensure acceptability and sustainability of interventions in this diverse group.

AIMS: To compare the safety and efficacy of fast-acting insulin aspart (faster aspart) with conventional insulin aspart (IAsp) in adults with type 1 diabetes (T1D). MATERIALS AND METHODS: onset 1 was a randomized, multicentre, treat-to-target, phase III, 52-week (initial 26 weeks + additional 26 weeks) trial conducted at 165 sites across 9 countries. Adults with T1D were randomly allocated to double-blind mealtime faster aspart or IAsp, each with once- or twice-daily insulin detemir. The primary endpoint, change in glycated haemoglobin (HbA1c) from baseline after the initial 26 weeks, has been reported previously. In the present paper, we report data from the full 52-week study period. RESULTS: Between August 2013 and June 2015, 381 participants were assigned to double-blind faster aspart and 380 participants to IAsp. After 52 weeks, estimated mean changes from baseline in HbA1c levels were -0.08% (faster aspart) and +0.01% (IAsp); estimated treatment difference significantly favoured faster aspart (-0.10% [95% confidence interval {CI} -0.19;-0.00]; P = .0424). Changes from baseline in 1-hour postprandial plasma glucose (PPG) increment (meal test; faster aspart -1.05 mmol/L; IAsp -0.14 mmol/L) also significantly favoured faster aspart (estimated treatment difference -0.91 mmol/L [95% CI -1.40;-0.43]; -16.48 mg/dL [95% CI -25.17;-7.80]; P = .0002). There was no difference in overall severe or blood glucose-confirmed hypoglycaemic episodes or treatment-emergent adverse events between treatments. CONCLUSIONS: At 52 weeks, overall glycaemic control had significantly improved with faster aspart vs IAsp, consistent with the 26-week study findings. Achieving an insulin profile closer to physiological insulin secretion with faster aspart translates into lower PPG and HbA1c levels compared with those achieved with IAsp in people with T1D. ispartof: Diabetes, Obesity & Metabolism vol:20 issue:5 pages:1148-1155 ispartof: location:England status: published

IMPORTANCE: Continuous glucose monitoring (CGM) has been shown to be beneficial for adults with type 2 diabetes using intensive insulin therapy, but its use in type 2 diabetes treated with basal insulin without prandial insulin has not been well studied. OBJECTIVE: To determine the effectiveness of CGM in adults with type 2 diabetes treated with basal insulin without prandial insulin in primary care practices. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted at 15 centers in the US (enrollment from July 30, 2018, to October 30, 2019; follow-up completed July 7, 2020) and included adults with type 2 diabetes receiving their diabetes care from a primary care clinician and treated with 1 or 2 daily injections of long- or intermediate-acting basal insulin without prandial insulin, with or without noninsulin glucose-lowering medications. INTERVENTIONS: Random assignment 2:1 to CGM (n = 116) or traditional blood glucose meter (BGM) monitoring (n = 59). MAIN OUTCOMES AND MEASURES: The primary outcome was hemoglobin A(1c) (HbA(1c)) level at 8 months. Key secondary outcomes were CGM-measured time in target glucose range of 70 to 180 mg/dL, time with glucose level at greater than 250 mg/dL, and mean glucose level at 8 months. RESULTS: Among 175 randomized participants (mean [SD] age, 57 [9] years; 88 women [50%]; 92 racial/ethnic minority individuals [53%]; mean [SD] baseline HbA(1c) level, 9.1% [0.9%]), 165 (94%) completed the trial. Mean HbA(1c) level decreased from 9.1% at baseline to 8.0% at 8 months in the CGM group and from 9.0% to 8.4% in the BGM group (adjusted difference, −0.4% [95% CI, −0.8% to −0.1%]; P = .02). In the CGM group, compared with the BGM group, the mean percentage of CGM-measured time in the target glucose range of 70 to 180 mg/dL was 59% vs 43% (adjusted difference, 15% [95% CI, 8% to 23%]; P

OBJECTIVE Type 2 diabetes is growing in epidemic proportions and disproportionately affects lower-income, diverse communities. Text messaging may provide one of the most rapid methods to overcome the “digital divide” to improve care. RESEARCH DESIGN AND METHODS A randomized, nonblinded, parallel-groups clinical trial design allocated N = 126 low-income, Hispanic participants with poorly controlled type 2 diabetes to receive the Dulce Digital intervention or usual care (UC). Dulce Digital participants received up to three motivational, educational, and/or call-to-action text messages per day over 6 months. The primary outcome was HbA1c; lipids, blood pressure, and BMI were secondary outcomes. Satisfaction and acceptability were evaluated via focus groups and self-report survey items. RESULTS The majority of patients were middle-aged (mean age 48.43 years, SD 9.80), female (75%), born in Mexico (91%), and uninsured (75%) and reported less than a ninth-grade education level (73%) and mean baseline HbA1c 9.5% (80 mmol/mol), SD 1.3, and fasting plasma glucose 187.17 mg/dL, SD 64.75. A statistically significant time-by-group interaction effect indicated that the Dulce Digital group achieved a significantly greater reduction in HbA1c over time compared with UC (P = 0.03). No statistically significant effects were observed for secondary clinical indicators. The number of blood glucose values texted in by participants was a statistically significant predictor of month 6 HbA1c (P < 0.05). Satisfaction and acceptability ratings for the Dulce Digital intervention were high. CONCLUSIONS Use of a simple, low-cost text messaging program was found to be highly acceptable in this sample of high-risk, Hispanic individuals with type 2 diabetes and resulted in greater improvement in glycemic control compared with UC.

OBJECTIVE This multicenter, treat-to-target, phase 3 trial evaluated the efficacy and safety of fast-acting insulin aspart (faster aspart) versus conventional insulin aspart (IAsp) in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS The primary end point was change from baseline in HbA1c after 26 weeks. After an 8-week run-in, subjects were randomized (1:1:1) to double-blind mealtime faster aspart (n = 381), IAsp (n = 380), or open-label postmeal faster aspart (n = 382)—each with insulin detemir. RESULTS HbA1c was reduced in both treatment groups, and noninferiority to IAsp was confirmed for both mealtime and postmeal faster aspart (estimated treatment difference [ETD] faster aspart–IAsp, mealtime, –0.15% [95% CI –0.23; –0.07], and postmeal, 0.04% [–0.04; 0.12]); mealtime faster aspart statistically significantly reduced HbA1c versus IAsp (P = 0.0003). Postprandial plasma glucose (PPG) increments were statistically significantly lower with mealtime faster aspart at 1 h (ETD –1.18 mmol/L [95% CI –1.65; –0.71], –21.21 mg/dL [–29.65; –12.77]; P < 0.0001) and 2 h (–0.67 mmol/L [–1.29; –0.04], –12.01 mg/dL [–23.33; –0.70]; P = 0.0375) after the meal test; superiority to IAsp for the 2-h PPG increment was confirmed. The overall rate of severe or blood glucose–confirmed (plasma glucose CONCLUSIONS Faster aspart effectively improved HbA1c, and noninferiority to IAsp was confirmed, with superior PPG control for mealtime faster aspart versus IAsp. Subjects randomized to postmeal faster aspart for all meals maintained HbA1c noninferior to that obtained with mealtime IAsp.

OBJECTIVE To determine whether the use of continuous glucose monitoring (CGM) without confirmatory blood glucose monitoring (BGM) measurements is as safe and effective as using CGM adjunctive to BGM in adults with well-controlled type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS A randomized noninferiority clinical trial was conducted at 14 sites in the T1D Exchange Clinic Network. Participants were ≥18 years of age (mean 44 ± 14 years), had T1D for ≥1 year (mean duration 24 ± 12 years), used an insulin pump, and had an HbA1c ≤9.0% (≤75 mmol/mL) (mean 7.0 ± 0.7% [53 ± 7.7 mmol/mol]); prestudy, 47% were CGM users. Participants were randomly assigned 2:1 to the CGM-only (n = 149) or CGM+BGM (n = 77) group. The primary outcome was time in range (70–180 mg/dL) over the 26-week trial, with a prespecified noninferiority limit of 7.5%. RESULTS CGM use averaged 6.7 ± 0.5 and 6.8 ± 0.4 days/week in the CGM-only and CGM+BGM groups, respectively, over the 26-week trial. BGM tests per day (including the two required daily for CGM calibration) averaged 2.8 ± 0.9 and 5.4 ± 1.4 in the two groups, respectively (P < 0.001). Mean time in 70–180 mg/dL was 63 ± 13% at both baseline and 26 weeks in the CGM-only group and 65 ± 13% and 65 ± 11% in the CGM+BGM group (adjusted difference 0%; one-sided 95% CI −2%). No severe hypoglycemic events occurred in the CGM-only group, and one occurred in the CGM+BGM group. CONCLUSIONS Use of CGM without regular use of confirmatory BGM is as safe and effective as using CGM with BGM in adults with well-controlled T1D at low risk for severe hypoglycemia.

The enhanced recovery after surgery (ERAS) protocol is a perioperative care pathway adopted in health systems across the nation as part of quality improvement efforts. A bundle of 20 elements demonstrated improved clinical and financial outcomes. A key element promotes carbohydrate loading a few hours before surgery using beverages such as Boost or ClearFast. There is limited data on pre-DM and DM patients and no clear guidelines on use in these populations. Scripps Health, a large health system in Southern California, adopted the ERAS protocol as a systemwide approach for care improvement in the perioperative process in 2018. Pre-DM and DM patients were represented more commonly in the bariatric surgery group thus providing an opportunity to compare outcomes in these patients receiving a carbohydrate loaded beverage pre and post implementation of the standardized ERAS protocol. We compared N=97 bariatric pre-DM and DM patients who did not receive Clearfast from Jan-Sept 2017 to N=64 bariatric pre-DM and DM patients who received Clearfast from Jan-Sept 2018. See Table 1. Interestingly, in patients with pre-DM and DM, there was a substantial increase in pre-op hyperglycemia but a post-op decrease was noted when a carb-loaded beverage-Clearfast was given to patients prior to bariatric surgery when compared to the previous year when ERAS and carb loading were not part of the perioperative preparation process. Disclosure K.L. Barthelmess: None. L. Talavera: None. A. Philis-Tsimikas: Advisory Panel; Self; AstraZeneca, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc., Glooko, Inc., National Institute of Diabetes and Digestive and Kidney Diseases. Stock/Shareholder; Spouse/Partner; Ionis Pharmaceuticals, Inc., Novo Nordisk Inc.

Weight loss can significantly reduce an individual’s risk for T2D. However, it can be challenging to engage participants in the DPP long enough to lose weight. This analysis examines sociodemographic characteristics and health behaviors as predictors of participant engagement and weight loss/gain. Participants were recruited into the DPP through a referral from a provider, family/friend, or other. Classes were conducted through local ambulatory clinics and community settings in San Diego, CA. Baseline self-report surveys were administered prior to program start, and weight was assessed at every session. Participants (N=290) were mostly female (81%) with a mean age of 57 (±14), diverse [51% Hispanic, 9% Asian/Native Hawaiian or Pacific Islander (ANHPI), 4% Other], and reported household incomes Disclosure M. AuYoung: None. K. Luu: None. M. Ruiz: None. C. Walker: None. A. Philis-Tsimikas: Advisory Panel; Self; AstraZeneca, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc., Glooko, Inc., National Institute of Diabetes and Digestive and Kidney Diseases. Stock/Shareholder; Spouse/Partner; Ionis Pharmaceuticals, Inc., Novo Nordisk Inc. A.L. Fortmann: None.

Background: Lorcaserin is a serotonin 2C receptor agonist approved in the U.S. as adjunct to a reduced-calorie diet and increased physical activity for chronic weight management in patients with a BMI ≥ 30 or ≥ 27 with at least one weight-related comorbidity. To date, there is limited data on the real-world use of lorcaserin for weight management, including in patients diagnosed with T2DM, prediabetes, and metabolic syndrome. Methods: A retrospective chart review was performed at the Scripps Clinic Weight Management Center. Patients were identified if they had at least one prescription for lorcaserin and were ≥ 18 years old. Weight change at 90 days and reason for discontinuation were assessed for patients with at least one day on pharmacotherapy. Patients with weight loss of ≥ 5% at 90 days were defined as medication “responders.” Results: A total of 118 overweight and obese patients filled a prescription for lorcaserin. Of these, 69 (58%) remained on lorcaserin ≥ 90 days (median follow-up was 306 days). Mean age was 53 years old, 73% were female, and mean baseline weight was 102 kg (BMI= 36). For patients who had follow-up weight information available (N=65), overall mean weight loss at 90 days was -3.5 kg (-4%) from baseline, responders had a decrease -7 kg (-7%, n=24) versus -1 kg (-2%) for non-responders (n=41). For patients diagnosed with metabolic syndrome (n=25), mean weight loss for responders (n=10, 40%) was -6.7 kg (-7%) versus -2.1 kg (-2%) for non-responders (n=15). Similar weight loss was observed in patients with T2DM and prediabetes. Conclusion: These "real world" results represent a group of commercially insured overweight and obese patients and demonstrate that weight loss programs can use lorcaserin and expect to get significant weight loss when used according to the label. Disclosure S.R. Harris: Consultant; Self; Sanofi. Research Support; Self; Eisai Inc. R.L. Knoth: Employee; Self; Eisai Inc. X. Li: Employee; Self; Eisai Inc. I. Khilfeh: Employee; Self; Eisai Inc. J. Choi: Employee; Self; Eisai Inc. Stock/Shareholder; Self; Johnson & Johnson. A. Philis-Tsimikas: Advisory Panel; Self; AstraZeneca, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc., Glooko, Inc., National Institute of Diabetes and Digestive and Kidney Diseases. Stock/Shareholder; Spouse/Partner; Ionis Pharmaceuticals, Inc., Novo Nordisk Inc. K. Fujioka: Consultant; Self; Eisai Inc. Funding Eisai Inc.

Hispanics are disproportionately affected by chronic cardiometabolic conditions (e.g., obesity, diabetes). Unmet behavioral health needs (e.g., mental health problems, stress, lack of health care) contribute to these disparities. Sustainably reducing Hispanic health disparities demands innovative, cost-effective interventions that address both behavioral and physical health. A randomized controlled trial is underway to evaluate Mi Puente - i.e., a Behavioral Health Nurse (BHN) + volunteer Community Mentor (CM) intervention designed to reduce readmissions in N = 560 Hispanics with ≥ 2 cardiometabolic conditions and ≥ 1 behavioral health concern(s) hospitalized at a safety-net hospital near the U.S./Mexico border in San Diego, CA. Qualifying patients at high-risk for readmission are identified via automated electronic medical record reports. The BHN triages patients’ needs, provides education, and conducts tailored coaching and goal-setting. Post-discharge, the BHN conducts telephone follow-up at 1-week, and CMs provide referrals to community resources and transition support via phone for up to 1-month. Prevalent behavioral health concerns were: chronic stress (52.5%), health-related distress (37.3%), and lack of routine medical care (19.8%). Preliminary process data indicated that the BHN met with 82.5% of participants prior to discharge; challenges related to medication adherence (28.7%) and access to outpatient care (41.1%) were common. Approximately 80% received ≥1 CM phone call (M = 1.7± 1.1); the transition home and follow-up appointment(s) were frequently addressed (both 97.9%). Findings reveal high levels of unmet behavioral health needs among hospitalized Hispanics, and the feasibility of a sustainable, low-cost, team model that builds upon personnel commonly found in hospital settings - i.e., nurses and volunteers. Important “lessons learned” will be presented to inform future care transitions programs in high-risk, underserved groups. Disclosure J.I. Bravin: None. M.L. Anderson: None. T. Clark: None. K.L. Savin: None. D. Lara Ledesma: None. J.A. Euyoque: None. A.L. Fortmann: None. A. Philis-Tsimikas: Advisory Panel; Self; AstraZeneca, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc., Glooko, Inc., National Institute of Diabetes and Digestive and Kidney Diseases. Stock/Shareholder; Spouse/Partner; Ionis Pharmaceuticals, Inc., Novo Nordisk Inc. L. Gallo: None. Funding National Institutes of Health

The SUSTAIN clinical trials demonstrated the efficacy and safety of once-weekly subcutaneous semaglutide, a glucagon-like peptide 1 analog for the treatment of type 2 diabetes (T2D). SUSTAIN 9 investigated semaglutide 1.0 mg vs. placebo as add-on to a stable dose of sodium-glucose co transporter-2 inhibitor (SGLT2i) therapy, with or without metformin or a sulfonylurea. The primary and secondary endpoints were, respectively, change from baseline in HbA1c and body weight at week 30. In this post hoc analysis, SUSTAIN 9 data were analyzed by background SGLT2i (empagliflozin, canagliflozin, dapagliflozin or other [ipragliflozin, luseogliflozin and tofogliflozin; drugs available only in Japan]). In total, 302 subjects were randomized to semaglutide or placebo. Reductions in HbA1c and body weight were greater with semaglutide vs. placebo. There was no significant interaction between background SGLT2i and treatment effect (interaction p-value >0.05 for both endpoints), with a smaller observed weight reduction in the ‘Other’ group (Table). No safety concerns were identified when adding semaglutide to SGLT2i therapy. No diabetic ketoacidosis or lower limb amputation events occurred. In conclusion, in subjects with T2D already receiving an SGLT2i, semaglutide generally resulted in superior HbA1c and body weight reductions vs. placebo, regardless of background SGLT2i therapy. Disclosure B. Zinman: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc. Consultant; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc. V. Bhosekar: None. R.S. Busch: Advisory Panel; Self; Janssen Pharmaceuticals, Inc. Research Support; Self; Bayer AG. Speaker's Bureau; Self; Abbott, Akcea Therapeutics, Amarin Corporation, Amgen Inc., AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Stock/Shareholder; Self; Amarin Corporation. I. Holst: Employee; Self; Novo Nordisk A/S. B. Ludvik: Advisory Panel; Self; Amgen Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Sanofi, Takeda Development Centre Europe Ltd. Research Support; Self; Akebia Therapeutics, Eli Lilly and Company, Novartis AG, Novo Nordisk A/S. Speaker's Bureau; Self; Merck Sharp & Dohme Corp. J. Thrasher: Advisory Panel; Self; Lexicon Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi, Senseonics. Consultant; Self; Medtronic MiniMed, Inc. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb Company, Lexicon Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi. Speaker's Bureau; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Sanofi. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc. V.C. Woo: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Novo Nordisk Inc. A. Philis-Tsimikas: Advisory Panel; Self; AstraZeneca, Lilly Diabetes, Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. Research Support; Self; Dexcom, Inc., Glooko, Inc., National Institute of Diabetes and Digestive and Kidney Diseases. Stock/Shareholder; Spouse/Partner; Ionis Pharmaceuticals, Inc., Novo Nordisk Inc. Funding Novo Nordisk A/S

Aim To investigate the efficacy and safety of insulin degludec/liraglutide (IDegLira) versus insulin glargine 100 units/mL (IGlar U100) as add‐on to sodium‐glucose co‐transporter‐2 (SGLT2) inhibitor therapy. Materials and methods In this 26‐week, phase IIIb, open‐label, parallel‐group, treat‐to‐target trial, conducted at 74 sites in 11 countries, insulin‐naïve people aged ≥18 years with glycated haemoglobin (HbA1c) 53–97 mmol/mol (7.0–11.0%), body mass index 20–40 kg/m2 and inadequately controlled type 2 diabetes (T2D) on SGLT2 inhibitor ± oral antidiabetic drugs were randomized 1:1 to once‐daily IDegLira or IGlar U100, both as add‐on to existing therapy. The primary endpoint was change in HbA1c from baseline to week 26. Results A total of 210 participants were randomized to each treatment arm. Mean HbA1c reductions were 21 mmol/mol (1.9%‐points) with IDegLira and 18 mmol/mol (1.7%‐points) with IGlar U100; confirming non‐inferiority (P

Summary Background Semaglutide is a once-weekly glucagon-like peptide-1 (GLP-1) analogue for type 2 diabetes. Few clinical trials have reported on the concomitant use of GLP-1 receptor agonists with sodium-glucose cotransporter-2 (SGLT-2) inhibitors. We aimed to investigate the efficacy and safety of semaglutide when added to SGLT-2 inhibitor therapy in patients with inadequately controlled type 2 diabetes. Methods The SUSTAIN 9 double-blind, parallel-group trial was done at 61 centres in six countries (Austria, Canada, Japan, Norway, Russia, and the USA). Adults with type 2 diabetes and HbA1c 7·0–10·0% (53–86 mmol/mol), despite at least 90 days of treatment with an SGLT-2 inhibitor, were randomly assigned (1:1) to receive subcutaneous semaglutide 1·0 mg or volume-matched placebo once weekly for 30 weeks, after a dose-escalation schedule of 4 weeks of 0·25 mg semaglutide or placebo and 4 weeks of 0·5 mg semaglutide or placebo. Existing antidiabetic medications, including SGLT-2 inhibitor treatment, were continued for the duration of the trial. Rescue medication, defined as intensification of background antidiabetic treatment or the initiation of new glucose-lowering medications, could be given to patients meeting specific criteria at the discretion of the investigator. The primary outcome was change in HbA1c from baseline at week 30, assessed in the full analysis set (all patients randomly allocated to treatment) using on-treatment data collected before rescue medication was started. The confirmatory secondary outcome was change in bodyweight from baseline to week 30. Safety was also assessed in the safety analysis set (all patients who received at least one dose of treatment). The trial was registered with ClinicalTrials.gov ( NCT03086330 ). Findings Between March 15, and Dec 4, 2017, 302 patients were enrolled and randomly assigned to receive semaglutide 1·0 mg or placebo (full analysis set), of whom 301 received at least one dose of treatment (safety analysis set). One patient was assigned to semaglutide but was not treated (reason unknown). 294 (97·4%) patients completed the trial and 267 (88·4%) completed treatment. Baseline characteristics were generally comparable between groups. In addition to randomised medication and SGLT-2 inhibitor, 216 (71·5%) patients were taking metformin and 39 (12·9%) were taking sulphonylurea. Patients given semaglutide had greater reductions in HbA1c (estimated treatment difference −1·42% [95% CI −1·61 to −1·24]; −15·55 mmol/mol [–17·54 to −13·56]) and bodyweight (−3·81 kg [–4·70 to −2·93]) versus those randomised to placebo (both p Interpretation Adding semaglutide to SGLT-2 inhibitor therapy significantly improves glycaemic control and reduces bodyweight in patients with inadequately controlled type 2 diabetes, and is generally well tolerated. Funding Novo Nordisk.

Aim To evaluate the efficacy and safety of adding insulin degludec (IDeg) to treatment in patients with type 2 diabetes receiving liraglutide and metformin and qualifying for treatment intensification because of inadequate glycaemic control. Methods In this 26-week, double-blind trial, patients who still had inadequate glycaemic control after a 15-week run-in period with initiation and dose escalation of liraglutide to 1.8 mg in combination with metformin (≥1500 mg) were randomized to addition of once-daily IDeg (‘IDeg add-on to liraglutide’ arm; n = 174) or placebo (‘placebo add-on to liraglutide’ arm; n = 172), with dosing of both IDeg and placebo based on titration guidelines. Results At 26 weeks, the mean change in glycated haemoglobin level was greater in the IDeg add-on to liraglutide arm (−1.04%) than in the placebo add-on to liraglutide arm (−0.16%; p

Background: The global epidemic of diabetes calls for innovative interventions. This study evaluated the effectiveness of the Project Dulce model, with and without wireless technology, on glycemic control and other clinical and self-reported outcomes in patients with poorly controlled type 2 diabetes in Mexico. Subjects and Methods: Adults with type 2 diabetes and a glycated hemoglobin A1c (HbA1c) level of ≥8% were recruited from Family Medical Unit #27 of the Instituto Mexicano del Seguro Social (IMSS) in Tijuana, México, and randomly assigned to one of three groups: Project Dulce–only (PD); Project Dulce technology-enhanced with mobile tools (PD-TE); or IMSS standard of care/control group (CG). Clinical and self-reported outcomes were assessed at baseline, Month 4, and Month 10. Time-by-group interactions and within-group changes were analyzed. Results: HbA1c reductions from baseline to Month 10 were significantly greater in PD-TE (−3.0% [−33 mmol/mol]) and PD (−2.6% [−28.7 mmol/mol]) compared with CG (−1.3% [−14.2 mmol/mol]) (P = 0.009 and 0.001, respectively). PD-TE and PD also exhibited significant improvement in diabetes knowledge when compared with CG (P

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

This study investigated the safety and efficacy of IDegLira as add-on to SGLT2i. In this 26-week, phase 3b, open-label trial, 420 patients with T2D uncontrolled on SGLT2i ± OADs were randomized 1:1 to receive add-on therapy of IDegLira or IGlar U100 (100 units [U]/mL). Starting doses were 10 U in both treatment arms. Doses were titrated twice-weekly to a fasting glucose target of 72 to 90 mg/dL; only IDegLira had a maximum dose (50 U). Mean A1C decreased from 8.2% at baseline to 6.3% at week 26 for IDegLira and from 8.4 to 6.7% for IGlar U100; IDegLira superiority confirmed (p In conclusion, superiority of IDegLira vs. IGlar U100 as add-on to SGLT2i was confirmed for glycemic control, body weight, hypoglycemia rate and total daily insulin dose. Disclosure A. Philis-Tsimikas: Research Support; Self; Dexcom, Inc.. Advisory Panel; Self; Dexcom, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; Novo Nordisk A/S, Sanofi, Mylan. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc.. Stock/Shareholder; Spouse/Partner; Esperion Therapeutics, Ionis Pharmaceuticals, Inc.. Advisory Panel; Self; AstraZeneca. L. Billings: Advisory Panel; Self; Novo Nordisk A/S, Sanofi. Consultant; Self; Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S, Dexcom, Inc. R.S. Busch: Speaker's Bureau; Self; AbbVie Inc., AstraZeneca. Research Support; Self; Bayer AG, Intarcia Therapeutics, Inc.. Advisory Panel; Self; Janssen Pharmaceuticals, Inc.. Speaker's Bureau; Self; Amgen Inc.. Research Support; Self; Sanofi US. Speaker's Bureau; Self; Eli Lilly and Company, Boehringer Ingelheim Pharmaceuticals, Inc.. Research Support; Self; Amarin Corporation. C. Morales Portillo: Advisory Panel; Self; Novo Nordisk A/S, Sanofi, Eli Lilly and Company, Boehringer Ingelheim GmbH, Merck Sharp & Dohme Corp., Janssen Pharmaceuticals, Inc., AstraZeneca, Abbott. Research Support; Self; Novo Nordisk A/S, Sanofi, Eli Lilly and Company, Boehringer Ingelheim GmbH, Merck Sharp & Dohme Corp., Janssen Pharmaceuticals, Inc., AstraZeneca, Theracos, Inc., Lexicon Pharmaceuticals, Inc.. Speaker's Bureau; Self; Novo Nordisk A/S, Sanofi, Eli Lilly and Company, Boehringer Ingelheim GmbH, Merck Sharp & Dohme Corp., Janssen Pharmaceuticals, Inc., AstraZeneca, Abbott. R. Sahay: Advisory Panel; Self; Boehringer Ingelheim, Sanofi, Eli Lilly and Company, Novo Nordisk A/S. Advisory Panel; Spouse/Partner; Intas Pharmaceuticals Ltd., Pfizer Inc.. Speaker's Bureau; Self; Sanofi, Eli Lilly and Company, Boehringer Ingelheim, Dr. Reddys Laboratories, USV Private Limited, Glenmark. Speaker's Bureau; Spouse/Partner; Boehringer Ingelheim. N. Halladin: Employee; Self; Novo Nordisk A/S. R. Juhl: Employee; Self; Novo Nordisk A/S. S. Harris: Advisory Panel; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Amgen Inc., Lilly/Boehringer Ingelheim, Abbott, Janssen. Consultant; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Lilly/Boehringer Ingelheim, Abbott, Janssen. Research Support; Self; Novo Nordisk A/S, Sanofi, Merck, Abbott, AstraZeneca, Janssen. Other Relationship; Self; CIHR, CDA, The Lawson Foundation.