Shah, Viral N.

Introduction: The objective of the study was to evaluate the prevalence of cardiovascular, renal, and hepatic biomarkers that fall outside guideline-based thresholds in adults with type 1 diabetes (T1D), and to identify demographic and clinical factors associated with these biomarkers. Methods: This observational study linked data on 2,923 adults with T1D from the T1D Exchange Registry and NorstellaLinQ’s laboratory data (2022–2023). Prevalence of biomarkers outside guideline-recommended thresholds was calculated. Generalized estimating equations (GEE) were used to model associations with age, sex, race/ethnicity, insurance, T1D duration, body mass index (BMI), and hemoglobin A1c (HbA1c), while accounting for within-subject correlation due to repeated measures. Results: Elevated low-density lipoprotein (LDL) cholesterol (≥70 mg/dL, above the recommended goal for individuals with diabetes at high risk for atherosclerotic cardiovascular disease) was common (80.2%), as were overweight (33.8%) and obesity (30.3%). Additional findings included high total cholesterol (≥200 mg/dL, 22.6%), low high-density lipoprotein (HDL) cholesterol (<40 mg/dL males, <50 mg/dL females, 13.4%), elevated urinary albumin-to-creatinine ratio (≥30 mg/g, 13.0%), reduced estimated glomerular filtration rate (<60 mL/min/1.73 m 2 , 6.2%), and elevated fibrosis-4 index (FIB-4) (≥1.3, 14.4%). In adjusted GEE models, higher HbA1c was significantly associated with increased odds of abnormal cardiovascular and renal biomarkers across most measures. Male sex and BMI were linked to lower HDL cholesterol, while older age was strongly associated with elevated FIB-4. Conclusion: Obesity and elevated LDL cholesterol are highly prevalent in adults with T1D, indicating substantial cardiometabolic risk. The lack of outcome trials in this population underscores the need for studies to evaluate strategies to improve weight and lipid management and thereby reduce cardiovascular and renal risk in adults with T1D.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Objective: To evaluate glycemic outcomes in the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) randomized clinical trial (RCT) participants during an observational extension phase. Research Design and Methods: WISDM RCT was a 26-week RCT comparing continuous glucose monitoring (CGM) with blood glucose monitoring (BGM) in 203 adults aged ≥60 years with type 1 diabetes. Of the 198 participants who completed the RCT, 100 (98%) CGM group participants continued CGM (CGM-CGM cohort) and 94 (98%) BGM group participants initiated CGM (BGM-CGM cohort) for an additional 26 weeks. Results: CGM was used a median of >90% of the time at 52 weeks in both cohorts. In the CGM-CGM cohort, median time <70 mg/dL decreased from 5.0% at baseline to 2.6% at 26 weeks and remained stable with a median of 2.8% at 52 weeks ( P < 0.001 baseline to 52 weeks). Participants spent more time in range 70–180 mg/dL (TIR) (mean 56% vs. 64%; P < 0.001) and had lower hemoglobin A1c (HbA1c) (mean 7.6% [59 mmol/mol] vs. 7.4% [57 mmol/mol]; P = 0.01) from baseline to 52 weeks. In BGM-CGM, from 26 to 52 weeks median time <70 mg/dL decreased from 3.9% to 1.9% ( P < 0.001), TIR increased from 56% to 60% ( P = 0.006) and HbA1c decreased from 7.5% (58 mmol/mol) to 7.3% (57 mmol/mol) ( P = 0.025). In BGM-CGM, a severe hypoglycemic event was reported for nine participants while using BGM during the RCT and for two participants during the extension phase with CGM ( P = 0.02). Conclusions: CGM use reduced hypoglycemia without increasing hyperglycemia in older adults with type 1 diabetes. These data provide further evidence for fully integrating CGM into clinical practice. Clinicaltrials.gov (NCT03240432)

Background: Knowledge regarding the burden and predictors of hypoglycemia among older adults with type 1 diabetes (T1D) is limited. Methods: We analyzed baseline data from the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, which enrolled participants at 22 sites in the United States. Eligibility included clinical diagnosis of T1D, age ≥60 years, no real-time continuous glucose monitoring (CGM) use in prior three months, and HbA1c <10.0%. Blinded CGM data from 203 participants with at least 240 hours were included in the analyses. Results: Median age of the cohort was 68 years (52% female, 93% non-Hispanic white, and 53% used insulin pumps). Mean HbA1c was 7.5%. Median time spent in the glucose range <70 mg/dL was 5.0% (72 min/day) and <54 mg/dL was 1.6% (24 min/day). Among all factors analyzed, only reduced hypoglycemia awareness was associated with greater time spent <54 mg/dL (median time of 2.7% vs 1.3% [39 vs 19 minutes per day] for reduced awareness vs aware/uncertain, respectively, P = .03). Participants spent a mean 56% of total time in target glucose range of 70-180 mg/dL and 37% of time above 180 mg/dL. Conclusions: Over half of older T1D participants spent at least an hour a day with glucose levels <70 mg/dL. Those with reduced hypoglycemia awareness spent over twice as much time than those without in a serious hypoglycemia range (glucose levels <54 mg/dL). Interventions to reduce exposure to clinically significant hypoglycemia and increase time in range are urgently needed in this age group.

ABSTRACT Background: Cannabis is widely used among patients with type 1 diabetes (T1D) in Colorado. Despite increasing use of cannabis among patients with T1D, the frequency and characteristics of unhealthy cannabis use are unknown. We investigated the differences in unhealthy cannabis use in recreational and medical cannabis users with T1D. Methods: Adult cannabis users with T1D completed an in-person questionnaire regarding their cannabis use patterns. They further divided into 2 categories; recreational and medical users. Cannabis Use Disorder Identification Test-Revised (CUDIT-R) was used to identify unhealthy cannabis use (CUDIT-R ≥ 8). Characteristics of cannabis users and unhealthy cannabis use between recreational and medical cannabis users were compared using unpaired t test. Results: Out of 117 patients, 85 patients reported recreational and 32 patients reported medical cannabis use. Based on CUDIT-R scores, 37.6% of adults with T1D had unhealthy cannabis use. Frequency of cannabis use ≥4 times a week was higher among medical users compared to recreational users (59.4% vs 28.2%, P < 0.01). There was no difference between the groups in duration of cannabis use. There was no difference in mean overall CUDIT-R score (7.14 vs 7.5, P = 0.76) between recreational and medical cannabis users. There were also no significant differences in mean CUDIT-R score between recreational and medical users within the nonhazardous use category (3.70 vs 4.4, P = 0.15) and unhealthy cannabis use category (12.84 vs 12.67, P = 0.92). Conclusions: Our study showed no difference in unhealthy cannabis use among recreational and medical cannabis users with T1D. Unhealthy cannabis use should be considered among users with T1D regardless of the reason for its use. Contexte: Le cannabis est largement utilisé chez les patients atteints de diabète de type 1 (DT1) au Colorado. Malgré l’augmentation de la consommation de cannabis chez les patients atteints de DT1, la fréquence et les caractéristiques d’une consommation de cannabis malsaine sont inconnues. Nous avons étudié les différences entre la consommation de cannabis malsaine chez les consommateurs de cannabis récréatifs et médicaux atteints de DT1. Méthodes: Les consommateurs de cannabis adultes atteints de DT1 ont rempli un questionnaire personnel sur leurs habitudes de consommation de cannabis. Ils ont été ensuite divisés en deux catégories; utilisateurs récréatifs et médicaux. Le Test d’identification des troubles liés à l’utilisation du cannabis révisé (CUDIT-R) a été utilisé pour identifier une utilisation malsaine du cannabis (CUDIT-R ≥8). Les caractéristiques des consommateurs de cannabis, ainsi que ceux d’une consommation de cannabis malsaine, entre les consommateurs de cannabis récréatifs et médicaux ont été comparées en utilisant un test t non apparié. Résultats: Sur 117 patients, 85 patients ont signalé des activités récréatives et 32 patients ont déclaré avoir utilisé du cannabis à des fins médicales. D’après les scores CUDIT-R, 37,6% des adultes atteints de DT1 avaient une consommation de cannabis malsaine. La fréquence de consommation de cannabis ≥ 4 fois par semaine était plus élevée chez les utilisateurs de médicaments que chez les utilisateurs récréatifs (59,4% contre 28,2%, p < 0,01). Il n’y avait pas de différence entre les groupes quant à la durée de consommation de cannabis. Il n’y avait pas de différence dans le score CUDIT-R global moyen (7,14 vs 7,5, p = 0,76) entre les utilisateurs de cannabis à des fins récréatives et médicales. Il n’existait pas non plus de différence significative entre le score CUDIT-R moyen entre les utilisateurs récréatifs et les utilisateurs à des fins médicales dans la catégorie d’usages non dangereux (3,70 contre 4,4, p = 0,15) et dans la catégorie d’abus de cannabis (12,84 contre 12,67, p = 0,92). Conclusions: Notre étude n’a montré aucune différence dans la consommation de cannabis malsaine chez les consommateurs de cannabis récréatifs et médicaux atteints de DT1. Une consommation de cannabis malsaine doit être envisagée chez les utilisateurs atteints de DT1, quelle que soit la raison de sa consommation.