Tirzepatide-Induced Rapid Weight Loss-Related Thyrotoxicosis.

This Teachable Moment describes a 62-year-old man with thyrotoxicosis due to excess levothyroxine exposure during tirzepatide treatment that induced rapid weight loss.

A Randomized Comparison of Postprandial Glucose Excursion Using Inhaled Insulin Versus Rapid-Acting Analog Insulin in Adults With Type 1 Diabetes Using Multiple Daily Injections of Insulin or Automated Insulin Delivery.

OBJECTIVE: To compare postprandial glucose excursions following a bolus with inhaled technosphere insulin (TI) or subcutaneous rapid-acting analog (RAA) insulin. RESEARCH DESIGN AND METHODS: A meal challenge was completed by 122 adults with type 1 diabetes who were using multiple daily injections (MDI), a nonautomated pump, or automated insulin delivery (AID) and who were randomized to bolus with their usual RAA insulin (n = 61) or TI (n = 61). RESULTS: The primary outcome, the treatment group difference in area under the curve for glucose >180 mg/dL over 2 h, was less with TI versus RAA (adjusted difference -12 mg/dL, 95% CI -22 to -2, P = 0.02). With TI, the glucose excursion was smaller (P = 0.01), peak glucose lower (P = 0.01), and time to peak glucose shorter (P = 0.006). Blood glucose <70 mg/dL occurred in one participant in each group. CONCLUSIONS: Postmeal glucose excursion was smaller with TI than with RAA insulin in a cohort that included both AID and MDI users.

Changes in Basal and Bolus Insulin Requirements with Tirzepatide as an Adjunctive Therapy in Adults with Type 1 Diabetes Using Tandem Control-IQ.

INTRODUCTION: This study was aimed at investigating changes in insulin requirements and glycemic outcomes in adults with type 1 diabetes (T1D) using Control IQ (Tandem Diabetes) automated insulin delivery system (AID) over 8 months of tirzepatide treatment. METHODS: In this single-center, observational study, we collected demographic, A1c, weight, sensor glucose, and insulin dose data for adults with T1D who were using AID and initiated tirzepatide adjunct therapy for clinical indications (n = 11, median age 37, 64% female and mean body mass index of 39.6 kg/m2). Data were compared from baseline and over 8 months. RESULTS: Within 2 months of tirzepatide treatment, there were significant reductions in total daily insulin [median (IQR) 73.9 (47.6-95.8) to 51.7 (46.7-66.8) units/day, p < 0.001], basal insulin [47 (28.2-51.8) to 32.4 (25.5-46.3) units/day, p < 0.001], and bolus insulin [31.4 (19.9-38.3) to 17.9 (14.9-22.2) units/day, p < 0.001] requirements. Insulin dose reduction from 2 to 8 months was modest. The frequency of user-initiated boluses did not differ throughout the study. Despite reductions in total insulin requirement, time in range (70-180 mg/dl) increased by 7%, A1c reduced by 0.5%, weight reduced by 9%, without increase in time below 70 mg/dl. CONCLUSIONS: This pilot study provides clinical guidance on insulin titration for adults with T1D who may initiate tirzepatide therapy. Based on the findings of this study, we recommend reducing 25% of total daily insulin dose at tirzepatide initiation in adults with T1D using AID with baseline A1c of less than 7.5%. Higher doses of tirzepatide were associated with greater weight loss, however, the reduction in insulin requirement was minimal.

Diabetes Technology Meeting 2023.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Efficacy and Safety of Tirzepatide in Overweight and Obese Adult Patients with Type 1 Diabetes.

Introduction and Objective: Most patients with type 1 diabetes (T1D) in the United States are overweight (OW) or obese (OB), contributing to insulin resistance and suboptimal glucose control. The primary Food and Drug Administration-approved treatment for T1D is insulin, which may adversely affect weight. Tirzepatide is approved for managing type 2 diabetes, improves glucose control, facilitates weight loss, and improves cardiovascular disease outcomes. We assessed the use of tirzepatide in OW/OB subjects with T1D. Methods: This was a retrospective single-center real-world study in 62 OW/OB adult patients with T1D who were prescribed tirzepatide (treated group) and followed for 1 year. At least 3 months of use of tirzepatide was one of the inclusion criteria. Based on the inclusion criteria, this study represents 62 patients out of 184 prescribed tirzepatide. The control group included 37 OW/OB patients with T1D (computer frequency matched by age, duration of diabetes, gender, body mass index (BMI), and glucose control) who were not using any other weight-loss medications during the same period. The mean (±standard deviation [SD]) dose of weekly tirzepatide at 3 months was 5.6 ± 1.9 mg that increased to 9.7 ± 3.3 mg at 1 year. Results: The gender, mean baseline age, duration of diabetes, and glycosylated hemoglobin (HbA1c) were similar in the two groups, whereas BMI and weight were higher in the treated group. There were significantly larger declines in BMI and weight in the treated group than in controls across all time points among those in whom data were available. HbA1c decreased in the treated group as early as 3 months and was sustained through a 1-year follow-up (-0.67% at 1 year). As expected, insulin dose decreased at 3 months and throughout the study period. There were no reported hospitalizations from severe hypoglycemia or diabetic ketoacidosis. The mean glucose, time-in-range, time-above-range, SD, and coefficient of variation (continuous glucose monitoring metrics) significantly improved in the treated group. Conclusions: In this pilot (off label) study, we conclude that tirzepatide facilitated an average 18.5% weight loss (>46 pounds) and improved glucose control in OW/OB patients with T1D at 1 year. For safe use of tirzepatide in patients with T1D, we strongly recommend a large prospective randomized control trial in OW/OB patients with T1D.

Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial.

BACKGROUND: Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study. METHODS: Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose. RESULTS: All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (-0.82%, P < .001). CONCLUSIONS: Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.

Future of Time-in-Range Goals in the Era of Advanced Hybrid Closed-Loop Automated Insulin Delivery Systems.

The concept of maintaining blood glucose levels within the 70-180 mg/dL range, known as time-in-range, has raised questions regarding its representation of true physiological euglycemia. Some have speculated that focusing on the time spent within the 70-140 mg/dL range, introduced as time in tight range (TITR) through the International Consensus statement, could serve as a more precise metric for assessing normoglycemia in individuals with type 1 diabetes. This article delves into the current status of TITR as an emerging marker and explores how advanced hybrid closed-loop systems may offer a promising avenue for achieving this higher level of glycemic control.

Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology.

BACKGROUND: Optimization of automated insulin delivery (AID) settings is required to achieve desirable glycemic outcomes. We evaluated safety and efficacy of a computerized system to initialize and adjust insulin delivery settings for the t:slim X2 insulin pump with Control-IQ technology in adults with type 1 diabetes (T1D). METHODS: After a 2-week continuous glucose monitoring (CGM) run-in period, adults with T1D using multiple daily injections (MDI) (N = 33, mean age 36.1 years, 57.6% female, diabetes duration 19.7 years) were transitioned to 13 weeks of Control-IQ technology usage. A computerized algorithm generated recommendations for initial pump settings (basal rate, insulin-to-carbohydrate ratio, and correction factor) and weekly follow-up settings to optimize glycemic outcomes. Physicians could override the automated settings changes for safety concerns. RESULTS: Time in range 70 to 180 mg/dL improved from 45.7% during run-in to 69.1% during the last 30 days of Control-IQ use, a median improvement of 18.8% (95% confidence interval [CI]: 13.6-23.9, P < .001). This improvement was evident early in the study and was sustained over 13 weeks. Time <70 mg/dL showed a gradual decreasing trend over time. Percentage of participants achieving HbA1c <7% went from zero at baseline to 55% at study end (P < .001). Only six of the 318 automated settings adaptations (1.9%) were overridden by study investigators. CONCLUSIONS: Computerized initiation and adaptation of Control-IQ technology settings from baseline MDI therapy was safe in adults with T1D. The use of this simplified system for onboarding and optimizing Control-IQ technology may be useful to increase uptake of AID and reduce staff and patient burden in clinical care.

Insulin Pump Utilization in 2017-2021 for More Than 22,000 Children and Adults With Type 1 Diabetes: A Multicenter Observational Study.

This large type 1 diabetes cohort study showed that insulin pump utilization has increased over time and that use differs by sex, insurance type, and race/ethnicity. Insulin pump use was associated with more optimal A1C, increased use of continuous glucose monitoring (CGM), and lower rates of diabetic ketoacidosis and severe hypoglycemia. People who used an insulin pump with CGM had lower rates of acute events than their counterparts who used an insulin pump without CGM. These findings highlight the need to improve access of diabetes technology through provider engagement, multidisciplinary approaches, and efforts to address health inequities.

Time in Range Is Associated with Incident Diabetic Retinopathy in Adults with Type 1 Diabetes: A Longitudinal Study.

Abstract Objective: To evaluate the association between continuous glucose monitoring (CGM)-based time in various ranges and the subsequent development of diabetic retinopathy (incident DR) in adults with type 1 diabetes. Methods: Between June 2018 and March 2022, adults with type 1 diabetes with incident DR or no retinopathy (control) were identified. CGM data were collected retrospectively for up to 7 years before the date of eye examination defining incident DR or control. Associations between incident DR and CGM metrics were evaluated using logistic regression models. Results: This analysis included 71 adults with incident DR (mean age 27 years, 52% females, and mean diabetes duration 15 years) and 92 adults without DR (mean age 38 years, 48% females, and mean diabetes duration 20 years). Adjusting for age, diabetes duration, and CGM type, each 0.5% increase in glycated hemoglobin (HbA1c), 10 mg/dL increase in mean glucose, 5% decrease in time in target range 70-180 mg/dL (TIR), 5% decrease in time in tight target range 70-140 mg/dL (TITR), and 5% increase in time above 180 mg/dL (TAR) were associated with 24%, 22%, 18%, 28%, and 20% increase in odds of incident DR, respectively. Spearman correlations of TIR, TITR, TAR, and mean glucose with each other were all ≥0.97. Conclusion: Similar to HbA1c, TIR, TITR, TAR, and mean glucose were associated with increased risk for incident DR in adults with type 1 diabetes. These CGM metrics are highly correlated indicating that they provide similar information on glycemic control and diabetic retinopathy risk.

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use.

BACKGROUND: Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days. METHODS: Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2). RESULTS: For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use. CONCLUSION: We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

Longitudinal Trends in Glycemic Outcomes and Technology Use for Over 48,000 People with Type 1 Diabetes (2016-2022) from the T1D Exchange Quality Improvement Collaborative.

Objective: Previous studies revealed that hemoglobin A1c (HbA1c) increased overall in the United States in the past decade. In addition, health inequities in type 1 diabetes (T1D) outcomes by race/ethnicity and insurance type persist. This study examines the trends in HbA1c from 2016 to 2022 stratified by race/ethnicity and insurance in a large multicenter national database. Research Design and Methods: We analyzed glycemic outcomes and diabetes device use trends for >48,000 people living with type 1 diabetes (PwT1D) from 3 adult and 12 pediatric centers in the T1D Exchange Quality Improvement Collaborative (T1DX-QI), comparing data from 2016 to 2017 with data from 2021 to 2022. Results: The mean HbA1c in 2021-2022 was lower at 8.4% compared with the mean HbA1c in 2016-2017 of 8.7% (0.3% improvement; P < 0.01). Over the same period, the percentage of PwT1D using a continuous glucose monitor (CGM), insulin pump, or hybrid closed-loop system increased (45%, 12%, and 33%, respectively). However, these improvements were not equitably demonstrated across racial/ethnic groups or insurance types. Racial/ethnic and insurance-based inequities persisted over all 7 years across all outcomes; comparing non-Hispanic White and non-Hispanic Black PwT1D, disparate gaps in HbA1c (1.2%-1.6%), CGM (30%), pump (25%-35%), and hybrid-closed loop system (up to 20%) are illuminated. Conclusion: Population-level data on outcomes, including HbA1c, can provide trends and insights into strategies to improve health for PwT1D. The T1DX-QI cohort showed a significant improvement in HbA1c from 2016 to 2022. Improvements in diabetes device use are also demonstrated. However, these increases were inconsistent across all racial/ethnic groups or insurance types, an important focus for future T1D population health improvement work.

Increasing Use of Diabetes Devices: What Do Health Care Professionals Need?

Despite evidence of improved diabetes outcomes with diabetes technology such as continuous glucose monitoring (CGM) systems, insulin pumps, and hybrid closed-loop (HCL) insulin delivery systems, these devices are underutilized in clinical practice for the management of insulin-requiring diabetes. This low uptake may be the result of health care providers' (HCPs') lack of confidence or time to prescribe and manage devices for people with diabetes. We administered a survey to HCPs in primary care, pediatric endocrinology, and adult endocrinology practices in the United States. Responding HCPs expressed a need for device-related insurance coverage tools and online data platforms with integration to electronic health record systems to improve diabetes technology uptake in these practice settings across the United States.

Association Between Diabetes Technology Use and Glycemic Outcomes in Adults With Type 1 Diabetes Over a Decade.

OBJECTIVE: To evaluate change in mean clinic HbA1c from 2014 to 2021 with diabetes technology use in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS: In this single-center study, we analyzed diabetes technology use and mean clinic HbA1c among unique adults (age ≥18 years) with type 1 diabetes (last visit of the year per patient) between 1 January 2014 and 31 December 2021 from the electronic medical record. Diabetes technology use was defined as the use of continuous glucose monitors (CGMs) without an automated insulin delivery (AID) system or an AID system. Diabetes technology use and HbA1c over time were analyzed using mixed models adjusted for age, sex, and visit year. RESULTS: A total of 15,903 clinic visits over 8 years (mean 1,988 patients per year, 4,174 unique patients, 52.7% female, 80.0% Non-Hispanic White) showed significant increases in CGM and AID use (P < 0.001 for both), resulting in an increase of diabetes technology use from 26.9% in 2014 to 82.7% in 2021. These increases were associated with a lower mean clinic HbA1c (7.7-7.5%, P < 0.001) and a higher percentage of adults achieving an HbA1c <7.0% (32.3-41.7%, P < 0.001) from 2014 to 2021. The HbA1c difference between technology users and nonusers increased over time from 0.36% (95% CI 0.26-0.47%, P < 0.001) in 2014 to 0.93% (95% CI 0.80-1.06%, P < 0.001) in 2021. CONCLUSIONS: Adopting diabetes technology in adults with type 1 diabetes decreased HbA1c and increased the number of people achieving an HbA1c <7.0%, supporting the current international recommendation to offer AID systems to most individuals with type 1 diabetes.

Assessing the relationship between low-calorie sweetener use and quality of life measures in adults with type 1 diabetes.

BACKGROUND: To evaluate use of low-calorie sweeteners (LCS) among adults with type 1 diabetes (T1D) and its impact on quality of life (QOL). METHODS: In this single center, cross-sectional survey study with 532 adults with T1D, Food related QOL (FRQOL), LCS specific questionnaire (LCSSQ), Diabetes Self-Management Questionnaire (DSMQ), Food Frequency Questionnaire (FFQ), Audit of Diabetes-Dependent QOL (AddQOL), Type 1 Diabetes and Life (T1DAL) questionnaires were administered through RedCAP, a secure, HIPAA-compliant web-based application. Demographics and scores of adults who used LCS in last month (recent users) and others (non-users) were compared. Results were adjusted for age, sex, diabetes duration and other parameters. RESULTS: Of 532 participants (mean age 36 ± 13, 69% female), 99% heard LCS before, 68% used them in the last month, 73% reported better glucose control with LCS use and 63% reported no health concerns about LCS use. Recent LCS users were older and had a longer diabetes duration and more complications (hypertension, or any complication) than non-users. However, A1c, AddQOL, T1DAL, FRQOL scores did not differ significantly between recent LCS users and non-users. DSMQ scores, DSMQ management, diet, health care scores did not differ between two groups; however, recent LCS users had lower physical activity score than non-users (p = 0.001). CONCLUSIONS: Most of the adults with T1D have used LCS and perceived that LCS use improved their QOL and glycemic control; however, these were not verified with questionnaires. There was no difference in QOL questionnaires except DSMQ physical activity between recent LCS users and not users with T1D. However, more patients in need to increase their QOL may be using LCS; therefore, associations between the exposure and outcome can be bi-directional.

Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes.

AIM: To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D). MATERIALS AND METHODS: Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100). RESULTS: Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01). CONCLUSIONS: GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.

Relationship Between Daytime Versus Nighttime Continuous Glucose Monitoring Metrics with A1C in Adults with Type 1 Diabetes.

Objective: To evaluate influence of daytime versus nighttime continuous glucose monitoring (CGM)-based metrics on A1C in adults with type 1 diabetes (T1D). Research Design and Methods: CGM data from 407 adults with T1D (age 39 ± 15 years, diabetes duration 20 ± 12 years, A1C 7.3% ± 1.4% and 53% female) from two studies were included in this analysis. The association between daytime (6 AM-10.59 PM) and nighttime (11 PM-5.59 AM) CGM variables such as mean glucose, time in range (TIR; 70-180 mg/dL), time in tight target range (TTIR; 70-140 mg/dL), and time above range (TAR >180 mg/dL) was examined within five A1C categories (<7%, 7%-7.9%, 8%-8.9%, 9%-9.9%, and ≥10%). Results: Although mean glucose was increasing with higher A1C, there was no statistical difference in mean glucose between daytime versus nighttime within five A1C groups (143.2 ± 22.7 vs. 143.6 ± 25.0 for A1C <7%, 171.4 ± 17.3 vs. 175.3 ± 28.8 for A1C 7.0%-7.9%, 193.4 ± 19.4 vs. 195.3 ± 29.5 for A1C 8.0%-8.9%, 214.9 ± 28.8 vs. 219.7 ± 36.1 for A1C 9.0%-9.9% and 244.0 ± 39.0 vs. 239.9 ± 50.9 for A1C ≥10%, P > 0.05). Similarly, there was no difference between various CGM metrics by daytime versus nighttime within five A1C groups. Differences between five A1C groups' daytime versus nighttime mean glucose, TIR, TTIR, and TAR were also not statistically significant (all P > 0.05) Conclusion: Daytime versus nighttime glycemic control has similar influence on A1C in adults with T1D.

Retained Diabetes Devices-A Literature Review.

BACKGROUND: Diabetes management and treatment requires the use of many devices that frequently must puncture the skin, creating a risk of unintentional retention in the body as a retained diabetes device. In this article, we reviewed case studies about retained diabetes devices and presented analyses of the success rate of current imaging techniques in identifying retained devices and the success rate of device removal. METHODS: PubMed and Google Scholar were searched for articles about retained diabetes devices. Relevant articles that included sufficient details about discovery and removal of the device were included. The success rate of identification and the success rate of removal of retained devices were both calculated as percentages. RESULTS: Sixteen case studies of retained diabetes devices were identified. These devices included parts of continuous glucose monitors and infusion sets, a lancet, and various types of needles for insulin injection. Each case is presented with details about the year of publication, the retained diabetes device, the company that produced the device, the age and gender of the patient, the type of diabetes that the patient had, the location of the device, the reason for initial discovery of the retained device, the process of locating the device, the success rate for removal of the device, and the removal procedure of the device. Analysis revealed a 100% success rate for the use of imaging technology including X-rays and computed tomography to identify a retained diabetes device. The patients with retained diabetes devices had a 62.5% success rate for eventual removal of the device. CONCLUSIONS: With the increasing use of injected, inserted, and implanted diabetes wearables for digital health, it is likely that some of the devices will detach, break apart, or otherwise become retained in the body. It is important to be aware of available technologies to identify retained diabetes devices so that it will be possible in most cases to surgically remove these devices if they detach or become retained.