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Aims/Hypothesis: In diabetes haptoglobin (Hp) 2 vs Hp 1 allelic product is associated with cardiac and renal complications. Few studies report both Hp phenotype and Hp levels. In a Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial substudy we evaluated the Hp phenotype, Hp levels, and fenofibrate effects. Materials and Methods: In 480 adults with type 2 diabetes (T2D) the Hp phenotype was assessed and the Hp level quantified (both using ELISAs assays) in plasma from baseline, after 6 weeks of fenofibrate, and (in n = 200) at 2 years post‐randomization to fenofibrate or placebo. Results: The Hp phenotypes 1‐1, 2‐1, and 2‐2 frequencies were 15%, 49%, and 36%, respectively. Baseline Hp levels differed by phenotype (P < 0.0001) and decreased (median 21%) after 6 weeks fenofibrate in all phenotypes (adjusted mean (95% CI): −0.27 (−0.32, −0.23) mg/mL in Hp 1‐1, −0.29 (−0.31, −0.27) mg/mL in Hp 2‐1 and −0.05 (−0.07, −0.02) mg/mL in Hp 2‐2 (P = 0.005 and P = 0.055 vs Hp 1‐1 and Hp 2‐1, respectively)). At 2 years post‐randomization the Hp levels in the placebo group had returned to baseline, whilst the fenofibrate‐group levels remained similar to the 6 week levels. Conclusions: In type 2 diabetes, Hp levels differ by Hp phenotype and are decreased by fenofibrate in all phenotypes, but the effect is diminished in Hp 2‐2. [ABSTRACT FROM AUTHOR]

Aims: Continuous glucose monitoring (CGM) systems are standard of care for youth with type 1 diabetes with the goal of spending >70% time in range (TIR; 70–180 mg/dL, 3.9–10 mmol/L). We aimed to understand paediatric CGM user experiences with TIR metrics considering recent discussion of shifting to time in tight range (TITR; >50% time between 70 and 140 mg/dL, 3.9 and 7.8 mmol/L). Methods: Semi‐structured interviews and focus groups with adolescents with type 1 diabetes and parents of youth with type 1 diabetes focused on experiences with TIR goals and reactions to TITR. Groups and interviews were audio‐recorded, transcribed and analysed using content analysis. Results: Thirty participants (N = 19 parents: age 43.6 ± 5.3 years, 79% female, 47% non‐Hispanic White, 20 ± 5 months since child's diagnosis; N = 11 adolescents: age 15.3 ± 2 years, 55% female, 55% non‐Hispanic White, 16 ± 3 months since diagnosis) attended. Participants had varying levels of understanding of TIR. Some developed personally preferred glucose ranges. Parents often aimed to surpass 70% TIR. Many described feelings of stress and disappointment when they did not meet a TIR goal. Concerns about TITR included increased stress and burden; risk of hypoglycaemia; and family conflict. Some participants said TITR would not change their daily lives; others said it would improve their diabetes management. Families requested care team support and a clear scientific rationale for TITR. Conclusions: The wealth of CGM data creates frequent opportunities for assessing diabetes management and carries implications for management burden. Input from people with type 1 diabetes and their families will be critical in considering a shift in glycaemic goals and targets. [ABSTRACT FROM AUTHOR]

Introduction: Algorithm‐enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole‐population RPM‐based care for T1D. Methods: Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. Results: The primary population‐level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic‐level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. Conclusion: We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM‐based care programs. [ABSTRACT FROM AUTHOR]

The prevalence of overweight and obesity in young people with type 1 diabetes (T1D) now parallels that of the general population. Excess adiposity increases the risk of cardiovascular disease, which is already elevated up to 10-fold in T1D, underscoring a compelling need to address weight management as part of routine T1D care. Sustainable weight management requires both diet and physical activity (PA). Diet and PA approaches must be optimized towards the underlying metabolic and behavioral challenges unique to T1D to support glycemic control throughout the day. Diet strategies for people with T1D need to take into consideration glycemic management, metabolic status, clinical goals, personal preferences, and sociocultural considerations. A major barrier to weight management in this high-risk population is the challenge of integrating regular PA with day-to-day management of T1D. Specifically, exercise poses a substantial challenge due to the increased risk of hypoglycemia and/or hyperglycemia. Indeed, about two-thirds of individuals with T1D do not engage in the recommended amount of PA. Hypoglycemia presents a serious health risk, yet prevention and treatment often necessitates the consumption of additional calories, which may prohibit weight loss over time. Exercising safely is a concern and challenge with weight management and maintaining cardiometabolic health for individuals living with T1D and many healthcare professionals. Thus, a tremendous opportunity exists to improve exercise participation and cardiometabolic outcomes in this population. This article will review dietary strategies, the role of combined PA and diet for weight management, current resources for PA and glucose management, barriers to PA adherence in adults with T1D, as well as findings and lessons learned from the Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON). [ABSTRACT FROM AUTHOR]

Regular exercise is essential to overall cardiovascular health and well-being in people with type 1 diabetes, but exercise can also lead to increased glycemic disturbances. Automated insulin delivery (AID) technology has been shown to modestly improve glycemic time in range (TIR) in adults with type 1 diabetes and significantly improve TIR in youth with type 1 diabetes. Available AID systems still require some user-initiated changes to the settings and, in some cases, significant pre-planning for exercise. Many exercise recommendations for type 1 diabetes were developed initially for people using multiple daily insulin injections or insulin pump therapy. This article highlights recommendations and practical strategies for using AID around exercise in type 1 diabetes. [ABSTRACT FROM AUTHOR]

Aims: Co‐management of weight and glycaemia is critical yet challenging in type 1 diabetes (T1D). We evaluated the effect of a hypocaloric low carbohydrate, hypocaloric moderate low fat, and Mediterranean diet without calorie restriction on weight and glycaemia in young adults with T1D and overweight or obesity. Materials and Methods: We implemented a 9‐month Sequential, Multiple Assignment, Randomized Trial pilot among adults aged 19‐30 years with T1D for ≥1 year and body mass index 27‐39.9 kg/m2. Re‐randomization occurred at 3 and 6 months if the assigned diet was not acceptable or not effective. We report results from the initial 3‐month diet period and re‐randomization statistics before shutdowns due to COVID‐19 for primary [weight, haemoglobin A1c (HbA1c), percentage of time below range <70 mg/dl] and secondary outcomes [body fat percentage, percentage of time in range (70‐180 mg/dl), and percentage of time below range <54 mg/dl]. Models adjusted for design, demographic and clinical covariates tested changes in outcomes and diet differences. Results: Adjusted weight and HbA1c (n = 38) changed by −2.7 kg (95% CI −3.8, −1.5, P <.0001) and −0.91 percentage points (95% CI −1.5, −0.30, P =.005), respectively, while adjusted body fat percentage remained stable, on average (P =.21). Hypoglycaemia indices remained unchanged following adjustment (n = 28, P >.05). Variability in all outcomes, including weight change, was considerable (57.9% were re‐randomized primarily due to loss of <2% body weight). No outcomes varied by diet. Conclusions: Three months of a diet, irrespective of macronutrient distribution or caloric restriction, resulted in weight loss while improving or maintaining HbA1c levels without increasing hypoglycaemia in adults with T1D. [ABSTRACT FROM AUTHOR]

Background. Managing glycemia around exercise is challenging for individuals with type 1 diabetes (T1D) and their healthcare professionals (HCP). We investigated HCP knowledge and confidence around exercise counseling for youth with T1D worldwide. Objective. To assess HCP familiarity with ISPAD Clinical Practice Consensus Guidelines and confidence to deliver recommendations about T1D and exercise. Methods. A new online survey was developed on strategies and competencies about exercise for youth with T1D, comprising of 64 questions, divided into eight different categories, assessing HCPs perceived exercise knowledge, confidence, training, and barriers to exercise counseling. Results. A total of 125 HCPs mean ± SD age 42 ± 8.2 years (74% female, 73% physicians) completed the survey. The ISPAD exercise guidelines were considered familiar to 68/125 (54%) of responders. Overall, 91/125 (73%) felt confident with giving recommendations about exercise with 47/125 (38%) recommending 45–60 mins/day of physical activity, while 16/125 (13%) recommended >60 mins/day. Several topics related to self-management around exercise were covered by most, but not all responders, and differences were observed in exercise content "confidence" and/or "competence" based on geographic location (p = 0.048). No differences in exercise recommendation dose, confidence, or familiarity with ISPAD guidelines were observed for age, sex, type of HCP, years in practice, or healthcare type. Conclusions. Exercise counseling for youth with T1D remains a challenge in most healthcare settings, globally. In general, the number of physically active minutes per week is under-prescribed for youth with T1D and many HCPs in various settings around the world feel that more professional education is needed to boost confidence around the education of several exercise-related topics. [ABSTRACT FROM AUTHOR]

Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data. [ABSTRACT FROM AUTHOR]

The article presents the discussion on progress being made in the field of diabetes management and physical activity (PA). Topics include guideline incorporating a new theme focused on strategies for glucose management for athletes living with T1D based in part on a randomized controlled trial (RCT); and optimizing time in range and preventing hypoglycemia during and after exercise in all children and adolescents with diabetes.

The article reviews the Omnipod 5 automated insulin delivery system for diabetes management from Insulet Corp.

Introduction: Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data. Methods: Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line. Results: The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPMCPT codes and lead to margin expansion. Conclusion: We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithmenabled RPM to provide flexible, more timely interventions to its patients. [ABSTRACT FROM AUTHOR]

Aim: Initiating continuous glucose monitoring (CGM) shortly after Type 1 diabetes diagnosis has glycaemic and quality of life benefits for youth with Type 1 diabetes and their families. The SARS‐CoV‐2 pandemic led to a rapid shift to virtual delivery of CGM initiation visits. We aimed to understand parents' experiences receiving virtual care to initiate CGM within 30 days of diagnosis. Methods: We held focus groups and interviews using a semi‐structured interview guide with parents of youth who initiated CGM over telehealth within 30 days of diagnosis during the SARS‐CoV‐2 pandemic. Questions aimed to explore experiences of starting CGM virtually. Groups and interviews were audio‐recorded, transcribed and analysed using thematic analysis. Results: Participants were 16 English‐speaking parents (age 43 ± 6 years; 63% female) of 15 youth (age 9 ± 4 years; 47% female; 47% non‐Hispanic White, 20% Hispanic, 13% Asian, 7% Black, 13% other). They described multiple benefits of the virtual visit including convenient access to high‐quality care; integrating Type 1 diabetes care into daily life; and being in the comfort of home. A minority experienced challenges with virtual care delivery; most preferred the virtual format. Participants expressed that clinics should offer a choice of virtual or in‐person to families initiating CGM in the future. Conclusion: Most parents appreciated receiving CGM initiation education via telehealth and felt it should be an option offered to all families. Further efforts can continue to enhance CGM initiation teaching virtually to address identified barriers. [ABSTRACT FROM AUTHOR]

Background Comanagement of glycemia and adiposity is the cornerstone of cardiometabolic risk reduction in type 1 diabetes (T1D), but targets are often not met. The intestinal microbiota and microbiota-derived short-chain fatty acids (SCFAs) influence glycemia and adiposity but have not been sufficiently investigated in longstanding T1D. Objectives We evaluated the hypothesis that an increased abundance of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity were associated with improved glycemia but increased adiposity in young adults with longstanding T1D. Methods Participants provided stool samples at ≤4 time points (NCT03651622: https://clinicaltrials.gov/ct2/show/NCT03651622). Sequencing of the 16S ribosomal RNA gene measured abundances of SCFA-producing intestinal microbes. GC-MS measured total and specific SCFAs (acetate, butyrate, propionate). DXA (body fat percentage and percentage lean mass) and anthropometrics (BMI) measured adiposity. Continuous glucose monitoring [percentage of time in range (70–180 mg/dL), above range (>180 mg/dL), and below range (54–69 mg/dL)] and glycated hemoglobin (i.e. HbA1c) assessed glycemia. Adjusted and Bonferroni-corrected generalized estimating equations modeled the associations of SCFA-producing gut microbes, fecal SCFAs, and intestinal microbial diversity with glycemia and adiposity. COVID-19 interrupted data collection, so models were repeated restricted to pre-COVID-19 visits. Results Data were available for ≤45 participants at 101 visits (including 40 participants at 54 visits pre-COVID-19). Abundance of Eubacterium hallii was associated inversely with BMI (all data). Pre-COVID-19, increased fecal propionate was associated with increased percentage of time above range and reduced percentage of time in target and below range; and abundances of 3 SCFA-producing taxa (Ruminococcus gnavus , Eubacterium ventriosum , and Lachnospira) were associated inversely with body fat percentage, of which two microbes were positively associated with percentage lean mass. Abundance of Anaerostipes was associated with reduced percentage of time in range (all data) and with increased body fat percentage and reduced percentage lean mass (pre-COVID-19). Conclusions Unexpectedly, fecal propionate was associated with detriment to glycemia, whereas most SCFA-producing intestinal microbes were associated with benefit to adiposity. Future studies should confirm these associations and determine their potential causal linkages in T1D. This study is registered at clinical.trials.gov (NCT03651622; https://clinicaltrials.gov/ct2/show/NCT03651622). [ABSTRACT FROM AUTHOR]

Objective: To assess the relationship between mindfulness and glycemia among adolescents with type 1 diabetes (T1D) with suboptimal glycemia, and evaluate the potential mediation by ingestive behaviors, including disordered eating, and impulsivity. Research Design and Methods: We used linear mixed models for hemoglobin A1c (HbA1c) and linear regression for continuous glucose monitoring (CGM) to study the relationship of mindfulness [Child and Adolescent Mindfulness Measure (CAMM)] and glycemia in adolescents with T1D from the 18‐month Flexible Lifestyles Empowering Change (FLEX) trial. We tested for mediation of the mindfulness‐glycemia relationship by ingestive behaviors, including disordered eating (Diabetes Eating Problem Survey—Revised), restrained eating, and emotional eating (Dutch Eating Behavior Questionnaire); and impulsivity (total, attentional, and motor, Barrett Impulsiveness Scale). Results: At baseline, participants (n = 152) had a mean age of 14.9 ± 1.1 years and HbA1c of 9.4 ± 1.2% [79 ± 13 mmol/mol]. The majority of adolescents were non‐Hispanic white (83.6%), 50.7% were female, and 73.0% used insulin pumps. From adjusted mixed models, a 5‐point increase in mindfulness scores was associated with a −0.19% (95%CI −0.29, −0.08, p = 0.0006) reduction in HbA1c. We did not find statistically significant associations between mindfulness and CGM metrics. Mediation of the relationship between mindfulness and HbA1c by ingestive behaviors and impulsivity was not found to be statistically significant. Conclusions: Among adolescents with T1D and suboptimal glycemia, increased mindfulness was associated with lower HbA1c levels. Future studies may consider mindfulness‐based interventions as a component of treatment for improving glycemia among adolescents with T1D, though more data are needed to assess feasibility and efficacy. [ABSTRACT FROM AUTHOR]

Context: Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets. Objective: This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. Methods: HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children’s Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c. Results: HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (–0.54% to –0.52%, and –0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (–0.14%, –0.18% to –0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P < .001). Conclusion: A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis. [ABSTRACT FROM AUTHOR]

Objective: To compare glucose control with hybrid closed-loop (HCL) when challenged by high intensity exercise (HIE), moderate intensity exercise (MIE), and resistance exercise (RE) while profiling counterregulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes.Research Design and Methods: This study was an open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic MiniMed 670G) with a temporary target set 2 h prior to and during exercise and 15 g carbohydrates if pre-exercise glucose was <126 mg/dL to prevent hypoglycemia. Primary outcome was median (interquartile range) continuous glucose monitoring time-in-range (TIR; 70-180 mg/dL) for 14 h post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counterregulatory hormones were measured for 280 min post-exercise commencement.Results: Median TIR was 81% (67, 93%), 91% (80, 94%), and 80% (73, 89%) for 0-14 h post-exercise commencement for HIE, MIE, and RE, respectively (n = 30), with no difference between exercise types (MIE vs. HIE; P = 0.11, MIE vs. RE, P = 0.11; and HIE vs. RE, P = 0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases, respectively, in noradrenaline (P = 0.01 and P = 0.004), cortisol (P < 0.001 and P = 0.001), lactate (P ≤ 0.001 and P ≤ 0.001), and heart rate (P = 0.007 and P = 0.015). During HIE compared with MIE, there were greater increases in growth hormone (P = 0.024).Conclusions: Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counterregulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery. [ABSTRACT FROM AUTHOR]

Context: Early initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.Objective: Our aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.Methods: We analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.Results: Youth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; < 54mg/dL: 0.3% vs 0.3%, P = .55). Approximately 50% of the time spent in hypoglycemia was in the 65 to 70 mg/dL range.Conclusion: As TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM. [ABSTRACT FROM AUTHOR]

Objective: To evaluate glucose control using fast-acting insulin aspart (faster aspart) compared with insulin aspart (IAsp) delivered by the MiniMed Advanced Hybrid Closed-Loop (AHCL) system in adults with type 1 diabetes.Research Design and Methods: In this randomized, open-label, crossover study, participants were assigned to receive faster aspart or IAsp in random order. Stages 1 and 2 comprised of 6 weeks in closed loop, preceded by 2 weeks in open loop. This was followed by stage 3, whereby participants changed directly back to the insulin formulation used in stage 1 for 1 week in closed loop. Participants chose their own meals except for two standardized meal tests, a missed meal bolus and late meal bolus. The primary outcome was the percentage of time sensor glucose values were from 70 to 180 mg/dL (time in range; [TIR]).Results: Twenty-five adults (52% male) were recruited; the median (interquartile range) age was 48 (37, 57) years, and the median HbA1c was 7.0% (6.6, 7.2) (53 [49, 55] mmol/mol). Faster aspart demonstrated greater overall TIR compared with IAsp (82.3% [78.5, 83.7] vs. 79.6% [77.0, 83.4], respectively; mean difference 1.9% [0.5, 3.3]; P = 0.007). Four-hour postprandial glucose TIR was higher using faster aspart compared with IAsp for all meals combined (73.6% [69.4, 80.2] vs. 72.1% [64.5, 78.5], respectively; median difference 3.5% [1.0, 7.3]; P = 0.003). There was no ketoacidosis or severe hypoglycemia.Conclusions: Faster aspart safely improved glucose control compared with IAsp in a group of adults with well-controlled type 1 diabetes using AHCL. The modest improvement was mainly related to mealtime glycemia. While the primary outcome demonstrated statistical significance, the clinical impact may be small, given an overall difference in TIR of 1.9%. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to capture the experience of parents of youth with recent onset Type 1 diabetes who initiated use of continuous glucose monitoring (CGM) technology soon after diagnosis, which is a new practice. Methods: Focus groups and individual interviews were conducted with parents of youth with Type 1 diabetes who had early initiation of CGM as part of a new clinical protocol. Interviewers used a semi‐structured interview guide to elicit feedback and experiences with starting CGM within 30 days of diagnosis, and the benefits and barriers they experienced when adjusting to this technology. Groups and interviews were audio recorded, transcribed and analysed using content analysis. Results: Participants were 16 parents (age 44.13 ± 8.43 years; 75% female; 56.25% non‐Hispanic White) of youth (age 12.38 ± 4.15 years; 50% female; 50% non‐Hispanic White; diabetes duration 10.35 ± 3.89 months) who initiated CGM 11.31 ± 7.33 days after diabetes diagnosis. Overall, parents reported high levels of satisfaction with starting CGM within a month of diagnosis and described a high level of reliance on the technology to help manage their child's diabetes. All participants recommended early CGM initiation for future families and were committed to continue using the technology for the foreseeable future, provided that insurance covered it. Conclusion: Parents experienced CGM initiation shortly after their child's Type 1 diabetes diagnosis as a highly beneficial and essential part of adjusting to living with diabetes. [ABSTRACT FROM AUTHOR]

Objective: Exercising while fasted with type 1 diabetes facilitates weight loss; however, the best strategy to maintain glucose stability remains unclear.Research Design and Methods: Fifteen adults on continuous subcutaneous insulin infusion completed three sessions of fasted walking (120 min at 45% VO2max) in a randomized crossover design: 50% basal rate reduction, set 90 min pre-exercise (-90min50%BRR); usual basal rate with carbohydrate intake of 0.3 g/kg/h (CHO-only); and combined 50% basal rate reduction set at exercise onset with carbohydrate intake of 0.3 g/kg/h (Combo).Results: Combo had a smaller change in glucose (5 ± 47 mg/dL) versus CHO-only (-49 ± 61 mg/dL, P = 0.03) or -90min50%BRR (-34 ± 45 mg/dL). The -90min50%BRR strategy produced higher β-hydroxybutyrate levels (0.4 ± 0.3 vs. 0.1 ± 0.1 mmol/L) and greater fat oxidation (0.51 ± 0.2 vs. 0.39 ± 0.1 g/min) than CHO-only (both P < 0.05).Conclusions: All strategies examined produced stable glycemia for fasted exercise, but a 50% basal rate reduction, set 90 min pre-exercise, eliminates carbohydrate needs and enhances fat oxidation better than carbohydrate feeding with or without a basal rate reduction set at exercise onset. [ABSTRACT FROM AUTHOR]

Objective: To investigate physiological responses to cardiopulmonary exercise (CPX) testing in adults with type 1 diabetes compared with age-, sex-, and BMI-matched control participants without type 1 diabetes.Research Design and Methods: We compared results from CPX tests on a cycle ergometer in individuals with type 1 diabetes and control participants without type 1 diabetes. Parameters were peak and threshold variables of VO2, heart rate, and power output. Differences between groups were investigated through restricted maximum likelihood modeling and post hoc tests. Differences between groups were explained by stepwise linear regressions (P < 0.05).Results: Among 303 individuals with type 1 diabetes (age 33 [interquartile range 22; 43] years, 93 females, BMI 23.6 [22; 26] kg/m2, HbA1c 6.9% [6.2; 7.7%] [52 (44; 61) mmol/mol]), VO2peak (32.55 [26.49; 38.72] vs. 42.67 ± 10.44 mL/kg/min), peak heart rate (179 [170; 187] vs. 184 [175; 191] beats/min), and peak power (216 [171; 253] vs. 245 [200; 300] W) were lower compared with 308 control participants without type 1 diabetes (all P < 0.001). Individuals with type 1 diabetes displayed an impaired degree and direction of the heart rate-to-performance curve compared with control participants without type 1 diabetes (0.07 [-0.75; 1.09] vs. 0.66 [-0.28; 1.45]; P < 0.001). None of the exercise physiological responses were associated with HbA1c in individuals with type 1 diabetes.Conclusions: Individuals with type 1 diabetes show altered responses to CPX testing, which cannot be explained by HbA1c. Intriguingly, the participants in our cohort were people with recent-onset type 1 diabetes; heart rate dynamics were altered during CPX testing. [ABSTRACT FROM AUTHOR]

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin‐dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Regular exercise is important for health, fitness and longevity in people living with type 1 diabetes, and many individuals seek to train and compete while living with the condition. Muscle, liver and glycogen metabolism can be normal in athletes with diabetes with good overall glucose management, and exercise performance can be facilitated by modifications to insulin dose and nutrition. However, maintaining normal glucose levels during training, travel and competition can be a major challenge for athletes living with type 1 diabetes. Some athletes have low-to-moderate levels of carbohydrate intake during training and rest days but tend to benefit, from both a glucose and performance perspective, from high rates of carbohydrate feeding during long-distance events. This review highlights the unique metabolic responses to various types of exercise in athletes living with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach—teamwork, targets, technology, and tight control—to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life. [ABSTRACT FROM AUTHOR]

Objective: To reduce exercise-associated hypoglycemia, individuals with type 1 diabetes on continuous subcutaneous insulin infusion typically perform basal rate reductions (BRRs) and/or carbohydrate feeding, although the timing and amount of BRRs necessary to prevent hypoglycemia are unclear. The goal of this study was to determine if BRRs set 90 min pre-exercise better attenuate hypoglycemia versus pump suspension (PS) at exercise onset.Research Design and Methods: Seventeen individuals completed three 60-min treadmill exercise (∼50% of VO2peak) visits in a randomized crossover design. The insulin strategies included 1) PS at exercise onset, 2) 80% BRR set 90 min pre-exercise, and 3) 50% BRR set 90 min pre-exercise.Results: Blood glucose level at exercise onset was higher with 50% BRR (191 ± 49 mg/dL) vs. 80% BRR (164 ± 41 mg/dL; P < 0.001) and PS (164 ± 45 mg/dL; P < 0.001). By exercise end, 80% BRR showed the smallest drop (-31 ± 58 mg/dL) vs. 50% BRR (-47 ± 50 mg/dL; P = 0.04) and PS (-67 ± 41 mg/dL; P < 0.001). With PS, 7 out of 17 participants developed hypoglycemia versus 1 out of 17 in both BRR conditions (P < 0.05). Following a standardized meal postexercise, glucose rose with PS and 50% BRR (both P < 0.05), but failed to rise with 80% BRR (P = 0.16). Based on interstitial glucose, overnight mean percent time in range was 83%, 83%, and 78%, and time in hypoglycemia was 2%, 1%, and 5% with 80% BRR, 50% BRR, and PS, respectively (all P > 0.05).Conclusions: Overall, a 50-80% BRR set 90 min pre-exercise improves glucose control and decreases hypoglycemia risk during exercise better than PS at exercise onset, while not compromising the postexercise meal glucose control. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the pattern of change in blood glucose concentrations and hypoglycemia risk in response to prolonged aerobic exercise in adolescents with type 1 diabetes (T1D) that had a wide range in pre‐exercise blood glucose concentrations. Methods: Individual blood glucose responses to prolonged (~60 minutes) moderate‐intensity exercise were profiled in 120 youth with T1D. Results: The mean pre‐exercise blood glucose concentration was 178 ± 66 mg/dL, ranging from 69 to 396 mg/dL, while the mean change in glucose during exercise was −76 ± 55 mg/dL (mean ± SD), ranging from +83 to −257 mg/dL. Only 4 of 120 youth (3%) had stable glucose levels during exercise (ie, ± ≤10 mg/dL), while 4 (3%) had a rise in glucose >10 mg/dL, and the remaining (93%) had a clinically significant drop (ie, >10 mg/dL). A total of 53 youth (44%) developed hypoglycemia (≤70 mg/dL) during exercise. The change in glucose was negatively correlated with the pre‐exercise glucose concentration (R2 = 0.44, P < 0.001), and tended to be greater in those on multiple daily insulin injections (MDI) vs continuous subcutaneous insulin infusion (CSII) (−98 ± 15 vs −65 ± 7 mg/dL, P = 0.05). No other collected variables appeared to predict the change in glucose including age, weight, height, body mass index, disease duration, daily insulin dose, HbA1c, or sex. Conclusion: Youth with T1D have variable glycemic responses to prolonged aerobic exercise, but this variability is partially explained by their pre‐exercise blood glucose levels. When no implementation strategies are in place to limit the drop in glycemia, the incidence of exercise‐associated hypoglycemia is ~44% and having a high pre‐exercise blood glucose concentration is only marginally protective. [ABSTRACT FROM AUTHOR]