Search

Introduction: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo‐METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1 and insulin‐treated type 2 diabetes in real‐time using ecological momentary assessment (EMA). To help assess its utility, we aimed to determine Hypo‐METRICS app completion rates and factors associated with completion. Methods: Adults with diabetes recruited into the Hypo‐METRICS study were given validated patient‐reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo‐METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics. Results: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45–66) years; diabetes duration 19 (11–27) years; HbA1c 57 (51–65) mmol/mol), median(IQR) overall app completion rate was 91 (84–96)%, ranging from 90 (81–96)%, 89 (80–94)% and 94(87–97)% for morning, afternoon and evening check‐ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion. Discussion: High app completion across all app domains and participant characteristics indicates the Hypo‐METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real‐time. [ABSTRACT FROM AUTHOR]

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR[70–180mg/dL]) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR[<70mg/dL]) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70–180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[<70mg/dL] and the TAR[>180mg/dL] underscored the data in the main analysis. Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Use of continuous glucose monitoring (CGM) has led to greater detection of hypoglycemia; the clinical significance of this is not fully understood. The Hypoglycaemia–Measurement, Thresholds and Impacts (Hypo-METRICS) study was designed to investigate the rates and duration of sensor-detected hypoglycemia (SDH) and their relationship with person-reported hypoglycemia (PRH) in people living with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D) with prior experience of hypoglycemia. RESEARCH DESIGN AND METHODS: We recruited 276 participants with T1D and 321 with T2D who wore a blinded CGM and recorded PRH in the Hypo-METRICS app over 10 weeks. Rates of SDH <70 mg/dL, SDH <54 mg/dL, and PRH were expressed as median episodes per week. Episodes of SDH were matched to episodes of PRH that occurred within 1 h. RESULTS: Median [interquartile range] rates of hypoglycemia were significantly higher in T1D versus T2D; for SDH <70 mg/dL (6.5 [3.8–10.4] vs. 2.1 [0.8–4.0]), SDH <54 mg/dL (1.2 [0.4–2.5] vs. 0.2 [0.0–0.5]), and PRH (3.9 [2.4–5.9] vs. 1.1 [0.5–2.0]). Overall, 65% of SDH <70 mg/dL was not associated with PRH, and 43% of PRH had no associated SDH. The median proportion of SDH associated with PRH in T1D was higher for SDH <70 mg/dL (40% vs. 22%) and SDH <54 mg/dL (47% vs. 25%) than in T2D. CONCLUSIONS: The novel findings are that at least half of CGM hypoglycemia is asymptomatic, even below 54 mg/dL, and many reported symptomatic hypoglycemia episodes happen above 70 mg/dL. In the clinical and research setting, these episodes cannot be used interchangeably, and both need to be recorded and addressed. [ABSTRACT FROM AUTHOR]

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin‐treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under‐dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in‐development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near‐infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle‐type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management. [ABSTRACT FROM AUTHOR]

To design a clinical trial it is important to know for how long CGM data should be collected to accurately assess time in different glucose ranges (time in ranges). Several studies approached this problem through the computation of the correlation coefficient (R2) between a metric computed in a month-long trial and in several shorter windows of increasing duration. The minimal duration (MD) granting R2>threshold (e.g., 0.9) is then used to estimate the long-term metric. Here, we focus on time below range (TBR), defined as time <70 mg/dl [%]. We first implemented the R2-based analysis on trials of different duration: A1, A2, A3, lasting 100, 200, 300 days (d), respectively, obtained selecting portions of the same Study A (n=45, duration=360 d, sensor=Abbott Freestyle Libre). Table 1 shows that the longer the trial duration, the larger the resulting MD. Notably, all the obtained trial duration fractions are similar. Then, the analysis was repeated for other two trials of equal duration: Study B (n=85, duration=240 d, sensor=Dexcom G5) and A4, obtained selecting 240 d from Study A. Although B and A4 refer to different T1D populations and different sensors, the resulting trial duration fraction is the same. In conclusion, the R2-based analysis yields different results based on the duration of the considered dataset, and seems to identify only the fraction of data needed to match the reference TBR. Disclosure: N. Camerlingo: None. M. Vettoretti: None. M. Cigler: None. A. Facchinetti: None. G. Sparacino: None. J.K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Medtronic, Prediktor Medical, Sanofi-Aventis. Speaker's Bureau; Self; Abbott, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. P. Choudhary: Advisory Panel; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Medtronic. Research Support; Self; European Union, JDRF. Speaker's Bureau; Self; Dexcom, Inc., Novartis AG, Novo Nordisk A/S, Sanofi-Aventis. S. Del Favero: Research Support; Self; Dexcom, Inc. Funding: Innovative Medicines Initiative 2 Joint Undertaking (777460); European Union; European Federation of Pharmaceutical Industries and Associations; T1D Exchange; JDRF; International Diabetes Federation; The Leona M. and Harry B. Helmsley Charitable Trust [ABSTRACT FROM AUTHOR]

Studies on CGM assume equal glycemic control between MDI and CSII. However, observation periods are often short and the impact of hypoglycemia on glycemic control and variability is unclear. In this study, we analyzed 12846 days (35 years; CSII 2232 days [6 years]; MDI 10614 days [29 years]) of CGM readings obtained from 99 patients with T1D. In the overall analysis mean glucose was 169.6 ± 18.9mg/dl for CSII and 175.0 ± 29.5 mg/dl for MDI. Estimated A1c was 7.54 ± 0.66% (CSII) and 7.7 ± 1.0% (MDI). Percentage of readings in target range (70-180mg/dL) was 54.0 ± 15.5% for CSII and 57.4 ± 11.4% for MDI. In total, 3.1/2.2% of readings were < 54 mg/dL and 14.9/12.4% were > 250mg/dL for CSII and MDI, respectively. In total, 464 hypoglycemic events (< 54mg/dL) occurred in CSII (day: 305 [64%]; night: 170 [36%]), corresponding to 0.21 hypoglycemic events daily. In MDI, 2297 hypoglycemic events occurred (day: 1507/66%; night: 783/34%), corresponding to 0.22 hypoglycemic events daily. Mean duration of hypoglycemia was 146.5 ± 65.9 minutes (CSII) and 132.8 ± 42.8 minutes (MDI); 233 (CSII) vs. 982 (MDI) events were prolonged (>120 minutes). CV was 41.1 ± 6.7% for CSII and 40.8 ± 3.8% for MDI. In the 24 hours prior to hypoglycemia, mean glucose was higher (182.9 ± 30.5 vs. 166.9 ± 26.6mg/dl; p<0.001 for CSII and 167.2 ± 14.7 vs. 152.7 ± 19.1mg/dl; p=0.013 for MDI) and CV was lower (p<0.001 for both treatments) compared to the subsequent 24 hours. Age correlated to CV (r=-.254; p=0.028 for MDI), HbA1c to eA1c (r=.611; p=0.009 for CSII; r=.482; p=0.028 for MDI), and C-peptide to CGM-readings in target range (r=.246; p=0.042 for MDI), which negatively correlated to HbA1c (r=-.484; p<0.001 for MDI). Glycemic control was comparable between MDI and CSII in T1D. Glycemic variability was lower prior to a hypoglycemic event and in older patients, while remaining insulin secretion was associated with longer time in target range. Disclosure: A. Melmer: None. T. Züger: None. T. Pöttler: None. H. Kojzar: None. M. Cigler: None. F. Aberer: None. M. Laimer: None. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH. [ABSTRACT FROM AUTHOR]