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Objective: This prospective study aimed to describe the clinical course in terms of glycemic outcomes, body weight, and adverse events during the first 12 weeks following a switch from glucagon-like peptide-1 receptor agonists (GLP-1 RAs) directly to tirzepatide 5 mg., Methods: Participants were ≥18 years with type 2 diabetes (T2D), glycated hemoglobin (HbA1c) ≥6.5% to ≤9.0%, body mass index ≥25 kg/m 2 and were on a stable treatment dose of GLP-1 RAs (liraglutide every day [1.2, 1.8 mg], semaglutide once-weekly [0.5, 1.0, 2.0 mg], or dulaglutide once-weekly [0.75, 1.5, 3.0, and 4.5 mg]) for ≥3 months at baseline. The primary end point was HbA1c change from baseline at week 12. Secondary end points included change from baseline in fasting serum glucose, body weight, and glucose assessed by continuous glucose monitoring. Safety was also assessed., Results: Participants were 58.3 years on average, with baseline HbA1c 7.39%, body mass index 35.18 kg/m 2 , T2D duration around 12.4 years, and included 55% females. Semaglutide (55%) and dulaglutide (42%) were the most commonly used GLP-1 RAs at baseline with semaglutide 1.0 mg and dulaglutide 1.5 mg being the most common treatment doses. At week 12, mean HbA1c changed from baseline by -0.43%, fasting serum glucose by -7.83 mg/dL, and body weight by -2.15 kg (all P < .01). Glycemic outcomes and body weight improved in participants in all baseline GLP-1 RA subgroups. Twenty participants (13.2%) developed gastrointestinal events. Three (2%) participants discontinued tirzepatide due to adverse events. There were no severe hypoglycemic events or deaths., Conclusion: In this prospective study, when people with T2D on stable GLP-1 RA treatment were switched directly to tirzepatide 5 mg, they experienced improved glycemic outcomes and additional weight reduction with an acceptable risk of adverse gastrointestinal events over 12 weeks., Competing Interests: Disclosure S.J. reports consultancy fees from Eli Lilly and Company and Sanofi. G.A. received consulting fees from Dexcom, Insulet, and Medscape. J.S.P., P.S., E.G.V., and B.D.B. are employees and shareholders of Eli Lilly and Company., (Copyright © 2024 AACE. Published by Elsevier Inc. All rights reserved.)

Background: The aim of this study was to develop a predictive model to classify people with type 2 diabetes (T2D) into expected levels of success upon bolus insulin initiation., Methods: Machine learning methods were applied to a large nationally representative insurance claims database from the United States (dNHI database; data from 2007 to 2017). We trained boosted decision tree ensembles (XGBoost) to assign people into Class 0 (never meeting HbA1c goal), Class 1 (meeting but not maintaining HbA1c goal), or Class 2 (meeting and maintaining HbA1c goal) based on the demographic and clinical data available prior to initiating bolus insulin. The primary objective of the study was to develop a model capable of determining at an individual level, whether people with T2D are likely to achieve and maintain HbA1c goals. HbA1c goal was defined at <8.0% or reduction of baseline HbA1c by >1.0%., Results: Of 15 331 people with T2D (mean age, 53.0 years; SD, 8.7), 7800 (50.9%) people met HbA1c goal but failed to maintain that goal (Class 1), 4510 (29.4%) never attained this goal (Class 0), and 3021 (19.7%) people met and maintained this goal (Class 2). Overall, the model's receiver operating characteristic (ROC) was 0.79 with greater performance on predicting those in Class 2 (ROC = 0.92) than those in Classes 0 and 1 (ROC = 0.71 and 0.62, respectively). The model achieved high area under the precision-recall curves for the individual classes (Class 0, 0.46; Class 1, 0.58; Class 2, 0.71)., Conclusions: Predictive modeling using routine health care data reasonably accurately classified patients initiating bolus insulin who would achieve and maintain HbA1c goals, but less so for differentiation between patients who never met and who did not maintain goals. Prior HbA1c was a major contributing parameter for the predictions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: NRK, JKH, MCB, CS, and RHP were employees of Optum Insight at the time this work was done and are currently employees of Optum Labs and stockholders of UnitedHealth Group. ELE, BDB, and DEF are employees and stockholders of Eli Lilly and Company; ESM was employee and stockholder of Eli Lilly and Company at the time when this study was conducte; ELE, ESM, BDB, DEF, was employed at Eli Lilly and Company when the study was conducted.

Introduction: Effectively engaging people with type 2 diabetes (T2D) earlier in their health journeys is critical to prevent downstream complications. Digitally based diabetes programs are a growing component of care delivery that have the potential to engage individuals outside of traditional clinic-based settings and use personalized data to pair people to tailored diabetes self-management interventions. Knowing an individuals' diabetes empowerment and health-related motivation can help drive appropriate recommendations for personalized interventions. We aimed to characterize diabetes empowerment and motivation towards changing health behaviors among participants in Level2, a T2D specialty care organization in the USA that combines wearable technology with personalized clinical support., Methods: A cross-sectional online survey was conducted among people enrolled in Level2 (February-March 2021). Distributions of respondent-reported diabetes empowerment and health motivation were analyzed using Motivation and Attitudes Toward Changing Health (MATCH) and Diabetes Empowerment Scale Short Form (DES-SF) scales, respectively. Associations between MATCH and DES-SF scores with Level2 engagement measures and glycemic control were analyzed., Results: The final analysis included 1258 respondents with T2D (mean age 55.7 ± 8.4 years). Respondents had high average MATCH (4.19/5) and DES-SF (4.02/5) scores. The average MATCH subscores for willingness (4.43/5) and worthwhileness (4.39/5) were higher than the average ability subscore (3.73/5). Both MATCH and DES-SF scores showed very weak correlations with Level2 engagement measures and glycemic control (ρ = - 0.18-0.19)., Conclusions: Level2 survey respondents had high average motivation and diabetes empowerment scores. Further research is needed to validate sensitivity of these scales to detect changes in motivation and empowerment over time and to determine whether differences in scores can be used to pair people to personalized interventions., (© 2023. The Author(s).)

Background: Does initiation of a continuous glucose monitor (CGM) or insulin pump lower health care utilization and/or costs?, Methods: Distinct cohorts of people with type 1 diabetes (T1D) or type 2 diabetes (T2D) using a blood glucose monitor (BGM), CGM, pump, or CGM with pump were identified from a large claims database. Patients ≥40 years old with 12 months of continuous enrollment before and after the device start date qualified for the study. Outcomes included one-year medical utilization and costs (minus device) for events such as hospitalizations and office visits. Generalized linear models were fitted, controlling for numerous baseline covariates. The Holm method corrected for the multiplicity of hypotheses tested., Results: Of the 8235 total patients, the BGM control group was the largest, had the lowest percentage of patients with T1D, and was significantly different from the device groups in most baseline categories. Formally, only two comparisons were statistically significant: Compared with BGM, the pump cohort had greater adjusted first-year total medical and office visit costs. Other secondary outcomes such as days hospitalized, emergency department visits and labs, favored pump. Most endpoints were favorable for CGM. Results for CGM with pump generally were intermediate between CGM and pump alone., Conclusions: During a one-year follow-up, unadjusted medical costs of both CGM and pump appear lower than BGM, but multivariable modeling yielded adjusted savings only for CGM use. Economic benefits might be observable sooner for CGMs than for pumps. Generalized linear models fitted to health care utilization event rates produced favorable results for both CGM and pump.

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)