Automated Insulin Delivery in Adults With Type 1 Diabetes and Suboptimal HbA1c During Prior Use of Insulin Pump and Continuous Glucose Monitoring: A Randomized Controlled Trial.

BACKGROUND: Automated insulin delivery (AID) systems offer promise in improving glycemic outcomes for individuals with type 1 diabetes. However, data on those who struggle with suboptimal glycemic levels despite insulin pump and continuous glucose monitoring (CGM) are limited. We conducted a randomized controlled trial to assess the effects of an AID system in this population. METHODS: Participants with hemoglobin A1c (HbA1c) ≥ 58 mmol/mol (7.5%) were allocated 1:1 to 14 weeks of treatment with the MiniMed 780G system (AID) or continuation of usual care (UC). The primary endpoint was change in time in range (TIR: 3·9-10·0 mmol/L) from baseline to week 14. After this trial period, the UC group switched to AID treatment while the AID group continued using the system. Both groups were monitored for a total of 28 weeks. RESULTS: Forty adults (mean ± SD: age 52 ± 11 years, HbA1c 67 ± 7 mmol/mol [8.3% ± 0.6%], diabetes duration 29 ±13 years) were included. After 14 weeks, TIR increased by 18.7% (95% confidence interval [CI] = 14.5, 22.9%) in the AID group and remained unchanged in the UC group (P < .0001). Hemoglobin A1c decreased by 10.0 mmol/mol (95% CI = 7.0, 13.0 mmol/mol) (0.9% [95% CI = 0.6%, 1.2%]) in the AID group but remained unchanged in the UC group (P < .0001). The glycemic benefits of AID treatment were reproduced after the 14-week extension phase. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study. CONCLUSIONS: For adults with type 1 diabetes not meeting glycemic targets despite use of insulin pump and CGM, transitioning to an AID system confers considerable glycemic benefits.

Sensor-Based Glucose Metrics during Different Diet Compositions in Type 1 Diabetes-A Randomized One-Week Crossover Trial.

By reducing carbohydrate intake, people with type 1 diabetes may reduce fluctuations in blood glucose, but the evidence in this area is sparse. The aim of this study was to investigate glucose metrics during a one-week low-carbohydrate-high-fat (HF) and a low-carbohydrate-high-protein (HP) diet compared with an isocaloric high-carbohydrate (HC) diet. In a randomized, three-period cross-over study, twelve adults with insulin-pump-treated type 1 diabetes followed an HC (energy provided by carbohydrate: 48%, fat: 33%, protein: 19%), HF (19%, 62%, 19%), and an HP (19%, 57%, 24%) diet for one week. Glucose values were obtained during intervention periods using a Dexcom G6 continuous glucose monitoring system. Participant characteristics were: 33% females, median (range) age 50 (22-70) years, diabetes duration 25 (11-52) years, HbA1c 7.3 (5.5-8.3)% (57 (37-67) mmol/mol), and BMI 27.3 (21.3-35.9) kg/m2. Glycemic variability was lower with HF (30.5 ± 6.2%) and HP (30.0 ± 5.5%) compared with HC (34.5 ± 4.1%) (PHF-HC = 0.009, PHP-HC = 0.003). There was no difference between groups in mean glucose (HF: 8.7 ± 1.1, HP: 8.2 ± 1.0, HC: 8.7 ± 1.0 mmol/L, POverall = 0.08). Time > 10.0 mmol/L was lower with HP (22.3 ± 11.8%) compared with HF (29.4 ± 12.1%) and HC (29.5 ± 13.4%) (PHF-HP = 0.037, PHC-HP = 0.037). In conclusion, a one-week HF and, specifically, an HP diet improved glucose metrics compared with an isocaloric HC diet.

Validation of the newly developed Sleep Screening Questionnaire Children and Adolescents (SSQ-CA) with objective sleep measures.

OBJECTIVES: Objectively validated pediatric sleep questionnaires covering a broader age range and different sleep disturbances are lacking, therefore we developed the Sleep Screening Questionnaire Children and Adolescents (SSQ-CA) and compared it with objective sleep parameters. METHODS: This child-reported questionnaire was developed by a multidisciplinary panel and face validated. In a cross-sectional prospective design, participants aged 6-17, answered the questionnaire twice with 21-28 days in between, wore actigraphy (AG) and kept a sleep diary for seven nights and home-polysomnography (PSG) for one of these nights. Exploratory factor analyses (EFA), reliability and validity assessments were performed. RESULTS: Of the 139 participants, 128 (F:47.7%, AG: n = 128, PSG: n = 59), were included in the analyses. Mean age: 11.3 years (SD: 2.9). EFA revealed 11 factors and 40 items loading above r = 0.4. Subscale internal consistency: 0.54-0.92. Subscale test-retest reliability: r = 0.71-0.87. Total sleep time (TST) from SSQ-CA on weekdays correlated with PSG (r = 0.48, p = 0.001) and with AG (r = 0.75, p < 0.001). The subscale total score for "Sleep duration and latency" correlated with TST from AG (r = -0.19, p = 0.03) and sleep latency (r = 0.31, p < 0.001), but not for PSG variables. The subscale "Awakenings" showed no correlation with objective measures whereas "Circadian rhythm" correlated to AG-derived mid-sleep time (r = 0.34, p < 0.001). CONCLUSIONS: The SSQ-CA shows adequate reliability for the 6-17-year-olds and acceptable criterion validity for two subscales. It appears to be a useful tool for screening for sleep disturbances in combination with objective tools as the subjective and objective parameters seem to uncover different aspects of sleep.

Glucose Monitoring Metrics in Individuals With Type 1 Diabetes Using Different Treatment Modalities: A Real-World Observational Study.

OBJECTIVE: This study aimed to investigate the association between continuous glucose monitoring (CGM)-derived glycemic metrics and different insulin treatment modalities using real-world data. RESEARCH DESIGN AND METHODS: A cross-sectional study at Steno Diabetes Center Copenhagen, Denmark, included individuals with type 1 diabetes using CGM. Data from September 2021 to August 2022 were analyzed if CGM was used for at least 20% of a 4-week period. Individuals were divided into four groups: multiple daily injection (MDI) therapy, insulin pumps with unintegrated CGM (SUP), sensor-augmented pumps with low glucose management (SAP), and automated insulin delivery (AID). The MDI and SUP groups were further subdivided based on CGM alarm features. The primary outcome was percentage of time in range (TIR: 3.9-10.0 mmol/L) for each treatment group. Secondary outcomes included other glucose metrics and HbA1c. RESULTS: Out of 6,314 attendees, 3,184 CGM users were included in the analysis. Among them, 1,622 used MDI, 504 used SUP, 354 used SAP, and 561 used AID. Median TIR was 54.0% for MDI, 54.9% for SUP, 62,9% for SAP, and 72,1% for AID users. The proportion of individuals achieving all recommended glycemic targets (TIR >70%, time above range <25%, and time below range <4%) was significantly higher in SAP (odds ratio [OR] 2.4 [95% CI 1.6-3.5]) and AID (OR 9.4 [95% CI 6.7-13.0]) compared with MDI without alarm features. CONCLUSIONS: AID appears superior to other insulin treatment modalities with CGM. Although bias may be present because of indications, AID should be considered the preferred choice for insulin pump therapy.

Effects of a Low-Carbohydrate-High-Protein Pre-Exercise Meal in Type 1 Diabetes-a Randomized Crossover Trial.

CONTEXT: Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise. OBJECTIVE: To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump-treated type 1 diabetes. METHODS: Fourteen adults (11 women, 3 men) with insulin pump-treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise. RESULTS: Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P = .001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ-1.49 ± 1.89 vs HCLP: Δ-3.78 ± 1.95 mmol/L, P = .001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P < .001). CONCLUSION: Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise.

Automated Insulin Delivery Around Exercise in Adults with Type 1 Diabetes: A Pilot Randomized Controlled Study.

Aim: To assess the effectiveness of an automated insulin delivery (AID) system around exercise in adults with type 1 diabetes (T1D). Methods: This was a three-period, randomized, crossover trial involving 10 adults with T1D (hemoglobin A1C; HbA1c: 8.3% ± 0.6% [67 ± 6 mmol/mol]) using an AID system (MiniMed 780G; Medtronic USA). Participants performed 45 min of moderate intensity continuous exercise 90 min after consuming a carbohydrate-based meal using three strategies: (1) a 100% dose of bolus insulin with exercise announcement immediately at exercise onset "spontaneous exercise" (SE) or a 25% reduced dose of bolus insulin with exercise announcement either (2) 90 min (AE90) or (3) 45 min (AE45) before exercise. Venous-derived plasma glucose (PG) taken in 5 and 15 min intervals over a 3 h collection period was stratified into the percentage of time spent below (TBR [<3.9 mmol/L]), time in range (TIR [3.9-10 mmol/L]), and time above range (TAR [ > 10 mmol/L]). In instances of hypoglycemia, PG data were carried forward for the remainder of the visit. Results: Overall, TBR was greatest during SE (SE: 22.9 ± 22.2, AE90: 1.1 ± 1.9, AE45: 7.8% ± 10.3%, P = 0.029). Hypoglycemia during exercise occurred in four participants in SE but one in both AE90 and AE45 (ꭓ2 [2] = 3.600, P = 0.165). In the 1 h postexercise period, AE90 was associated with higher TIR (SE: 43.8 ± 49.6, AE90: 97.9 ± 5.9, AE45: 66.7% ± 34.5%, P = 0.033), lower TBR (SE: 56.3 ± 49.6, AE90: 2.1 ± 5.9, AE45: 29.2% ± 36.5%, P = 0.041) with the greatest source of discrepancy observed relative to SE. Conclusion: In adults using an AID system and undertaking postprandial exercise, a strategy involving both bolus insulin dose reduction and exercise announcement 90 min before commencing the activity may be most effective in minimizing dysglycemia. The study was registered as a clinical trial (Clinical Trials Register; NCT05134025).

Pen-administered low-dose dasiglucagon vs usual care for prevention and treatment of non-severe hypoglycaemia in people with type 1 diabetes during free-living conditions: a Phase II, randomised, open-label, two-period crossover trial.

AIMS/HYPOTHESIS: Consumption of excess carbohydrates to manage hypoglycaemia can lead to rebound hyperglycaemia and promote weight gain. The objective of this trial was to evaluate the efficacy, safety and feasibility of pen-administered low-dose dasiglucagon for prevention and treatment of non-severe hypoglycaemia in people with type 1 diabetes during free-living conditions. METHODS: Twenty-four adults with insulin pump-treated type 1 diabetes (HbA1c ≤70 mmol/mol [8.5%]) completed a randomised, open-label, two-period crossover study with 2 week periods. During the usual care and dasiglucagon intervention (DASI) periods, participants managed impending and manifested episodes of hypoglycaemia with regular carbohydrate consumption or pen-administered low-dose (80 µg) s.c. dasiglucagon, respectively. Glycaemic control was evaluated using continuous glucose monitoring (Dexcom G6) and event registration of prevention and treatment episodes. RESULTS: Compared with usual care, the mean difference (95% CI) in the DASI period for time in (3.9-10.0 mmol/l) and below (<3.9 mmol/l) range was 2.4 %-points (-0.7, 5.5) and -0.5 %-points (-1.2, 0.2), respectively. In the DASI period, recovery rate (time from hypoglycaemia treatment to euglycaemia) was 44% (11, 87) faster while total daily carbohydrate intake was reduced by 11% (-18, -3). Dasiglucagon use was safe and well tolerated with mild nausea being the most frequent adverse effect. Among the participants, 96% (p<0.0001) were likely to include dasiglucagon in their future routine management of hypoglycaemia. CONCLUSIONS/INTERPRETATION: Use of low-dose dasiglucagon to prevent and treat non-severe hypoglycaemia during free-living conditions was safe, fast and efficacious while significantly reducing the total daily carbohydrate intake and yielding high treatment satisfaction. TRIAL REGISTRATION: ClinicalTrials.gov NCT04764968 FUNDING: The study was an investigator-initiated trial. Zealand Pharma supplied the investigational drug and device and provided financial support for the conduct of the trial.

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy.

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Performance of a dual-hormone closed-loop system versus insulin-only closed-loop system in adolescents with type 1 diabetes. A single-blind, randomized, controlled, crossover trial.

Objective: To assess the efficacy and safety of a dual-hormone (DH [insulin and glucagon]) closed-loop system compared to a single-hormone (SH [insulin only]) closed-loop system in adolescents with type 1 diabetes. Methods: This was a 26-hour, two-period, randomized, crossover, inpatient study involving 11 adolescents with type 1 diabetes (nine males [82%], mean ± SD age 14.8 ± 1.4 years, diabetes duration 5.7 ± 2.3 years). Except for the treatment configuration of the DiaCon Artificial Pancreas: DH or SH, experimental visits were identical consisting of: an overnight stay (10:00 pm until 7:30 am), several meals/snacks, and a 45-minute bout of moderate intensity continuous exercise. The primary endpoint was percentage of time spent with sensor glucose values below range (TBR [<3.9 mmol/L]) during closed-loop control over the 26-h period (5:00 pm, day 1 to 7:00 pm, day 2). Results: Overall, there were no differences between DH and SH for the following glycemic outcomes (median [IQR]): TBR 1.6 [0.0, 2.4] vs. 1.28 [0.16, 3.19]%, p=1.00; time in range (TIR [3.9-10.0 mmol/L]) 68.4 [48.7, 76.8] vs. 75.7 [69.8, 87.1]%, p=0.08; and time above range (TAR [>10.0 mmol/L]) 28.1 [18.1, 49.8] vs. 23.3 [12.3, 27.2]%, p=0.10. Mean ( ± SD) glucose was higher during DH than SH (8.7 ( ± 3.2) vs. 8.1 ( ± 3.0) mmol/L, p<0.001) but coefficient of variation was similar (34.8 ( ± 6.8) vs. 37.3 ( ± 8.6)%, p=0.20). The average amount of rescue carbohydrates was similar between DH and SH (6.8 ( ± 12.3) vs. 9.5 ( ± 15.4) grams/participant/visit, p=0.78). Overnight, TIR was higher, TAR was lower during the SH visit compared to DH. During and after exercise (4:30 pm until 7 pm) the SH configuration produced higher TIR, but similar TAR and TBR compared to the DH configuration. Conclusions: DH and SH performed similarly in adolescents with type 1 diabetes during a 26-hour inpatient monitoring period involving several metabolic challenges including feeding and exercise. However, during the night and around exercise, the SH configuration outperformed DH.

Modeling the effect of glucagon on endogenous glucose production in type 1 diabetes: On the role of glucagon receptor dynamics.

This paper validates a glucoregulatory model including glucagon receptors dynamics in the description of endogenous glucose production (EGP). A set of models from literature are selected for a head-to-head comparison in order to evaluate the role of glucagon receptors. Each EGP model is incorporated into an existing glucoregulatory model and validated using a set of clinical data, where both insulin and glucagon are administered. The parameters of each EGP model are identified in the same optimization problem, minimizing the root mean square error (RMSE) between the simulation and the clinical data. The results show that the RMSE for the proposed receptors-based EGP model was lower when compared to each of the considered models (Receptors approach: 7.13±1.71 mg/dl vs. 7.76±1.45 mg/dl (p=0.066), 8.45±1.38 mg/dl (p=0.011) and 8.99±1.62 mg/dl (p=0.007)). This raises the possibility of considering glucagon receptors dynamics in type 1 diabetes simulators.

Glycemia Around Exercise in Adults with Type 1 Diabetes Using Automated and Nonautomated Insulin Delivery Pumps: A Switch Pilot Trial.

In an in-patient switch study, 10 adults with type 1 diabetes (T1D) performed 45 min of moderate-intensity exercise on 2 occasions: (1) when using their usual insulin pump (UP) and (2) after transitioning to automated insulin delivery (AID) treatment (MiniMed™ 780G). Consensus glucose management guidelines for performing exercise were applied. Plasma glucose concentrations measured over a 3-h monitoring period were stratified into time below range (TBR, <3.9 mmol/L), time in range (TIR, 3.9-10.0 mmol/L), and time above range (TAR, >10.0 mmol/L). Overall, TBR (UP: 11 ± 21 vs. AID: 3% ± 10%, P = 0.413), TIR (UP: 53 ± 27 vs. AID: 66% ± 39%, P = 0.320), and TAR (UP: 37 ± 34 vs. AID: 31% ± 41%, P = 0.604) were similar between arms. A proportionately low number of people experienced exercise-induced hypoglycemia (UP: n = 2 vs. AID: n = 1, P = 1.00). In conclusion, switching to AID therapy did not alter patterns of glycemia around sustained moderate-intensity exercise in adults with T1D. Clinical Trial Registration number: NCT05133765.

Metabolic and physiological responses to graded exercise testing in individuals with type 1 diabetes using insulin pump therapy.

AIMS: To profile acute glycaemic dynamics during graded exercise testing (GXT) and explore the influence of glycaemic indicators on the physiological responses to GXT in adults with type 1 diabetes using insulin pump therapy. METHODS: This was a retrospective analysis of pooled data from four clinical trials with identical GXT protocols. Data were obtained from 45 adults with type 1 diabetes using insulin pumps [(30 females); haemoglobin A1c 59.5 ± 0.5 mmol/mol (7.6 ± 1.0%); age 49.7 ± 13.0 years; diabetes duration 31.2 ± 13.5 years; V̇O2peak 29.5 ± 8.0 ml/min/kg]. Integrated cardiopulmonary variables were collected continuously via spiroergometry. Plasma glucose was obtained every 3 min during GXT as well as the point of volitional exhaustion. Data were assessed via general linear modelling techniques with age and gender adjustment. Significance was accepted at p ≤ .05. RESULTS: Despite increasing duration and intensity, plasma glucose concentrations remained similar to rest values (8.8 ± 2.3 mmol/L) throughout exercise (p = .419) with an overall change of +0.3 ± 1.1 mmol/L. Starting glycaemia bore no influence on subsequent GXT responses. Per 1% increment in haemoglobin A1c there was an associated decrease in V̇O2peak of 3.8 ml/min/kg (p < .001) and powerpeak of 0.33 W/kg (p < .001) concomitant with attenuations in indices of peripheral oxygen extraction [(O2 pulse) -1.2 ml/beat, p = .023]. CONCLUSION: In adults with long-standing type 1 diabetes using insulin pump therapy, circulating glucose remains stable during a graded incremental cycle test to volitional exhaustion. Glycaemic indicators are inversely associated with aerobic rate, oxygen economy and mechanical output across the exercise intensity spectrum. An appreciation of these nexuses may help guide appropriate decision making for optimal exercise management strategies.

Cardiovascular Autonomic Neuropathy Is Associated With Increased Glucose Variability in People With Type 1 Diabetes.

OBJECTIVE: We investigated the association between the cardiovascular autonomic neuropathy (CAN) diagnosis and glucose variability (GV) in type 1 diabetes (T1D), as autonomic dysfunction previously has been associated with increased GV. RESEARCH DESIGN AND METHODS: CAN was assessed by three recommended cardiovascular reflex tests (CARTs). Glucose metrics were obtained from 10-day blinded continuous glucose monitoring (CGM). Between-group differences in GV indices were assessed by regression analyses in 24 participants with T1D with CAN and 24 matched control subjects without CAN. RESULTS: The CAN diagnosis was associated with 4.9% (95% CI 1.0, 8.7) higher coefficient of variation (CV) (P = 0.014), 0.7 mmol/L (0.3, 1.1) higher SD (P = 0.002) of glucose, and 1.4 mmol/mol (0.0, 2.7) higher mean amplitude of glycemic excursions (P = 0.047). Lower measures of CARTs were associated with higher CV, SD, and time above range values. CONCLUSIONS: The CAN diagnosis associates with a significantly higher GV in T1D, despite a high prevalence of routine CGM use.

A collaborative comparison of international pediatric diabetes registries.

BACKGROUND: An estimated 1.1 million children and adolescents aged under 20 years have type 1 diabetes worldwide. Principal investigators from seven well-established longitudinal pediatric diabetes registries and the SWEET initiative have come together to provide an international collaborative perspective and comparison of the registries. WORK FLOW: Information and data including registry characteristics, pediatric participant clinical characteristics, data availability and data completeness from the Australasian Diabetes Data Network (ADDN), Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids), Diabetes prospective follow-up registry (DPV), Norwegian Childhood Diabetes Registry (NCDR), National Paediatric Diabetes Audit (NPDA), Swedish Childhood Diabetes Registry (Swediabkids), T1D Exchange Quality Improvement Collaborative (T1DX-QI), and the SWEET initiative was extracted up until 31 December 2020. REGISTRY OBJECTIVES AND OUTCOMES: The seven diabetes registries and the SWEET initiative collectively show data of more than 900 centers and around 100,000 pediatric patients, the majority with type 1 diabetes. All share the common objectives of monitoring treatment and longitudinal outcomes, promoting quality improvement and equality in diabetes care and enabling clinical research. All generate regular benchmark reports. Main differences were observed in the definition of the pediatric population, the inclusion of adults, documentation of CGM metrics and collection of raw data files as well as linkage to other data sources. The open benchmarking and access to regularly updated data may prove to be the most important contribution from registries. This study describes aspects of the registries to enable future collaborations and to encourage the development of new registries where they do not exist.

Low-Dose Dasiglucagon Versus Oral Glucose for Prevention of Insulin-Induced Hypoglycemia in People With Type 1 Diabetes: A Phase 2, Randomized, Three-Arm Crossover Study.

OBJECTIVE: To compare the efficacy of low-dose subcutaneous dasiglucagon with oral glucose for prevention of insulin-induced hypoglycemia in people with type 1 diabetes. RESEARCH DESIGN AND METHODS: Twenty adults with type 1 diabetes using multiple daily injection or insulin pump therapy completed a phase 2, randomized, three-arm crossover study. On each study visit, an individualized subcutaneous insulin bolus was administered aiming for a plasma glucose (PG) concentration of 3.0 mmol/L (54 mg/dL). When a PG concentration of 4.5 mmol/L (81 mg/dL) was reached, 15 g oral glucose (CHO) from dextrose tablets, 80 µg dasiglucagon (D80), or 120 µg dasiglucagon (D120) was administered. PG was measured frequently for the following 180 min. RESULTS: Hypoglycemia (<3.9 mmol/L [70 mg/dL]) occurred in 10 participants after CHO, in 5 after D80, and in 4 after D120 (CHO vs. D80, P = 0.096; CHO vs. D120, P = 0.034). Time spent in hypoglycemia (<3.9 mmol/L [70 mg/dL]) was 14%, 7%, and 6% for CHO, D80, and D120, respectively (P = 0.273). The median time (95% CI) from intervention to first increase in PG of 1.1 mmol/L (20 mg/dL) was 30 (25-50), 15 (15-20), and 15 (15-20) minutes for CHO, D80, and D120, respectively (CHO vs. D80, P = 0.006; CHO vs. D120, P = 0.003). Episodes of nausea were numerically, but not significantly, higher after dasiglucagon administration. No significant differences in visual analog scale-assessed adverse effects were observed between interventions. CONCLUSIONS: Low-dose dasiglucagon safely and effectively prevented insulin-induced hypoglycemia with a faster glucose-elevating profile than oral glucose.

Glycemic Effects and Predictors of Increased Time-in-Range After Initiating MiniMed 670G: A 12-Month Observational Study.

We aimed to evaluate the glycemic effect and detect any predictors of improved time-in-range (TIR) in persons with type 1 diabetes after initiating hybrid closed-loop (HCL) treatment with MiniMed 670G in a 12-month retrospective observational study. Before starting HCL treatment, the 62 participants followed a Steno-developed training program; 7 participants (6.5%) discontinued the HCL therapy; the remaining 55 (58% female) had an age (mean ± standard deviation) of 45.6 ± 12.6 years and diabetes duration of 28.2 ± 10.9 years. After 12 months' HCL therapy, glycated hemoglobin A1c decreased from 7.4% +0.7% to 7.1% +0.5%, TIR increased from 59.3% ± 13.5% to 72% ± 9.3%, time in 54-70 mg/dL (3.0-3.9 mM) decreased from 2.4% ± 2.0% to 1.4% ± 1.0%, and time in 180-250 mg/dL (10.0-13.9 mM) decreased from 26.4% ± 8.3% to 20.8% ± 5.5%, all P < 0.001. Improvement in TIR was significantly associated with lower total daily insulin dose, higher amount of total carbohydrate, and more time spent in Auto Mode. Our findings support the promising results on glycemic outcomes seen with HCL treatment.

Hemoglobin A1c and Fructosamine Evaluated in Patients with Type 2 Diabetes Receiving Peritoneal Dialysis Using Long-Term Continuous Glucose Monitoring.

INTRODUCTION: Shortened erythrocyte life span and erythropoietin-stimulating agents may affect hemoglobin A1c (HbA1c) levels in patients receiving peritoneal dialysis (PD). We compared HbA1c with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with type 2 diabetes receiving PD. METHODS: Fourteen days of CGM (Ipro2, Medtronic) were performed in 23 patients with type 2 diabetes receiving PD and in 23 controls with type 2 diabetes and an estimated glomerular filtration rate over 60 mL/min/1.73 m2. Patients were matched on gender and age (±5 years). HbA1c (mmol/mol), its derived estimate of mean plasma glucose (eMPGA1c) (mmol/L), and fructosamine (µmol/L) were measured at the end of the CGM period and compared with the mean sensor glucose (mmol/L) from CGM. RESULTS: In the PD group, mean sensor glucose was 0.98 (95% con-fidence interval (CI): 0.43-1.54) mmol/L higher than the eMPGA1c compared with the control group (p = 0.002) where glucose levels were nearly identical (-0.05 (95% CI: -0.35-0.25) mmol/L). A significant association was found between fructosamine and mean sensor glucose using linear regression with no difference between slopes (p = 0.89) or y-intercepts (p = 0.28). DISCUSSION/CONCLUSION: HbA1c underestimates mean plasma glucose levels in patients with type 2 diabetes receiving PD. However, the clinical significance of this finding is undetermined. Fructosamine seems to more accurately reflect glycemic status. CGM or fructosamine could complement HbA1c to increase the accuracy of glycemic monitoring in the PD population.

Changes in the lipidome in type 1 diabetes following low carbohydrate diet: Post-hoc analysis of a randomized crossover trial.

Aims: Lipid metabolism might be compromised in type 1 diabetes, and the understanding of lipid physiology is critically important. This study aimed to compare the change in plasma lipid concentrations during carbohydrate dietary changes in individuals with type 1 diabetes and identify links to early-stage dyslipidaemia. We hypothesized that (1) the lipidomic profiles after ingesting low or high carbohydrate diet for 12 weeks would be different; and (2) specific annotated lipid species could have significant associations with metabolic outcomes. Methods: Ten adults with type 1 diabetes (mean ± SD: age 43.6 ± 13.8 years, diabetes duration 24.5 ± 13.4 years, BMI 24.9 ± 2.1 kg/m2, HbA1c 57.6 ± 2.6 mmol/mol) using insulin pumps participated in a randomized 2-period crossover study with a 12-week intervention period of low carbohydrate diet (< 100 g carbohydrates/day) or high carbohydrate diet (> 250 g carbohydrates/day), respectively, separated by a 12-week washout period. A large-scale non-targeted lipidomics was performed with mass spectrometry in fasting plasma samples obtained before and after each diet intervention. Longitudinal lipid levels were analysed using linear mixed-effects models. Results: In total, 289 lipid species were identified from 14 major lipid classes. Comparing the two diets, 11 lipid species belonging to sphingomyelins, phosphatidylcholines and LPC(O-16:0) were changed. All the 11 lipid species were significantly elevated during low carbohydrate diet. Two lipid species were most differentiated between diets, namely SM(d36:1) (ß ± SE: 1.44 ± 0.28, FDR = 0.010) and PC(P-36:4)/PC(O-36:5) (ß ± SE: 1.34 ± 0.25, FDR = 0.009) species. Polyunsaturated PC(35:4) was inversely associated with BMI and positively associated with HDL cholesterol (p < .001). Conclusion: Lipidome-wide outcome analysis of a randomized crossover trial of individuals with type 1 diabetes following a low carbohydrate diet showed an increase in sphingomyelins and phosphatidylcholines which are thought to reduce dyslipidaemia. The polyunsaturated phosphatidylcholine 35:4 was inversely associated with BMI and positively associated with HDL cholesterol (p < .001). Results from this study warrant for more investigation on the long-term effect of single lipid species in type 1 diabetes.

Time spent in hypoglycemia is comparable when the same amount of exercise is performed 5 or 2 days weekly: a randomized crossover study in people with type 1 diabetes.

INTRODUCTION: People with type 1 diabetes are recommended to exercise regularly. However, limited evidence exists on how frequency and duration of exercise affect the risk of hypoglycemia. The study aimed to compare the percentage of time spent in hypoglycemia between two 5-day periods with different frequency and duration of physical activity. RESEARCH DESIGN AND METHODS: In this outpatient randomized crossover study, 26 participants aged 18-65 years with type 1 diabetes for ≥2 years and insulin pump use for ≥1 year were included. After a 7-day observation period, participants completed two 5-day intervention periods separated by a washout period of at least 14 days. One period included five exercise sessions on 5 consecutive days (5S), each consisting of 4 min of resistance training and 30 min of aerobic exercise. Another period included two exercise sessions on 2 days with at least 2 days in between (2S), each consisting of 10 min of resistance training and 75 min of aerobic exercise. During each period, participants performed in total 150 min of aerobic exercise and 20 min of resistance training and wore continuous glucose monitors (Dexcom G6) and accelerometers (ActiGraph wGT3X-BT). RESULTS: Twenty insulin pump-treated adults (10 women) with type 1 diabetes completed the study. The baseline median (range) age was 48 (24-64) years, glycated hemoglobin 55 (44-66) mmol/mol, diabetes duration 24 (8-57) years, and body mass index 28.4 (22.3-35.8) kg/m2. No differences were observed between 5S and 2S in the percentage (mean±SD) of time spent below 3.9 mmol/L (3.5%±2.8% vs 4.5%±4.2%, p=0.28), time spent in 3.9-10.0 mmol/L (65.3%±15.0% vs 68.5%±13.6%, p=0.31), time spent above 10.0 mmol/L (31.2%±16.4% vs 27.3%±14.5%, p=0.15), mean glucose (8.7±1.3 mmol/L vs 8.5±1.2 mmol/L, p=0.33) and glycemic variability (35.8%±5.3% vs 35.8%±6.6%, p=0.97). CONCLUSIONS: Time spent in hypoglycemia was comparable between the two 5-day periods with different duration and frequency of physical activity. TRIAL REGISTRATION NUMBER: NCT04089462.

The Use of HbA1c, Glycated Albumin and Continuous Glucose Monitoring to Assess Glucose Control in the Chronic Kidney Disease Population Including Dialysis.

BACKGROUND: Glycated haemoglobin A1c (HbA1c) has limitations as a glycemic marker for patients with diabetes and CKD and for those receiving dialysis. Glycated albumin is an alternative glycemic marker, and some studies have found that glycated albumin more accurately reflects glycemic control than HbA1c in these groups. However, several factors are known to influence the value of glycated albumin including proteinuria. Continuous glucose monitoring (CGM) is another alternative to HbA1c. CGM allows one to assess mean glucose, glucose variability, and the time spent in hypo-, normo-, and hyperglycemia. Currently, several different CGM models are approved for use in patients receiving dialysis; CKD (not on dialysis) is not a contraindication in any of these models. Some devices are for blind recording, while others provide real-time data to patients. Small studies suggest that CGM could improve glycemic control in hemodialysis patients, but this has not been studied for individual CKD stages. SUMMARY: Glycated albumin and CGM avoid the pitfalls of HbA1c in CKD and dialysis populations. However, the value of glycated albumin may be affected by several factors. CGM provides a precise estimation of the mean glucose. Here, we discuss the strengths and limitations for using HbA1c, glycated albumin, or CGM in CKD and dialysis population. Key Messages: Glycated albumin is an alternative glycemic marker but is affected by proteinuria. CGM provides a precise estimation of mean glucose and glucose variability. It remains unclear if CGM improves glycemic control in the CKD and dialysis populations.