Diabetes technology & therapeutics最新文献

Objective: To assess the clinical impact of flash glucose monitoring (FGM) systems on fear of hypoglycemia (FoH) and quality of life in adults with type 1 diabetes mellitus (T1DM). Methods: Prospective quasi-experimental study with a 12-month follow-up. People with T1DM (18-80 years old) and self-monitoring by blood capillary glycemia controls were included. The FH15 questionnaire, a survey validated in Spanish in a comparable study population, was used to diagnose FoH with a cutoff point of 28 points. Results: A total of 181 participants were included, with a FoH prevalence of 69% (n = 123). A mean reduction in FH15 score of -4 points (95% confidence interval [-5.5 to -3]; P < 0.001) was observed, along with an improvement in quality of life (EsDQOL-test (Diabetes Quality of Life, Spanish version), -7 points [-10; -4], P < 0.001) and satisfaction with treatment (Diabetes Treatment Satisfaction questionnaire, self-reported version [DTSQ-s] test, +4.5 points [4; 5.5], P < 0.001). At the end of the follow-up, 64.2% of the participants saw an improved FoH intensity, compared to 35.8% who scored the same or higher. This improvement in FoH status was associated with a higher time-in-range at the end of the follow-up (P = 0.003), as well as a lower time spent in hyperglycemia (P = 0.005). In addition, it was linked to participants with a high baseline FoH levels (P < 0.001) and those who were university degree holders (P = 0.07). Conclusions: FGM is associated with an overall reduction of FoH in adults with T1DM and with an increase in their quality of life. Nevertheless, a significant percentage of patients may experience an increase of this phenomenon leading to clinical repercussions and a profound impact on quality of life.

Differences in the effectiveness of real-time continuous glucose monitoring (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM) in type 1 diabetes (T1D) are reported. The impact on percent time in range of switching from an isCGM with glucose threshold-based optional alerts only (FreeStyle Libre 2 [FSL2]) to an rtCGM (Dexcom G7) with an urgent low soon predictive alert was assessed, alongside other secondary outcomes including hemoglobin A1c (HbA1c) and other continuous glucose monitoring metrics. Adults with T1D using FSL2 were switched to Dexcom G7 for 12 weeks. HbA1c and continuous glucose data during FSL2 and Dexcom G7 use were compared. Data from 29 participants (aged 44.8 ± 16.5 years, 12 male and 17 female) were analyzed. After switching to rtCGM, participants spent less time in hypoglycemia below 3.9 mmol/L (70 mg/dL) (3.0% [1.0%, 5.0%] vs. 2.0% [1.0%, 3.0%], P = 0.006) and had higher percentage achievement of time below 3.9 mmol/L (70 mg/dL) of <4% (55.2% vs. 82.8%, P = 0.005). Coefficient of variation was lower (39.3 ± 6.6% vs. 37.2 ± 5.6%, P = 0.008). In conclusion, adults with T1D who switched from isCGM to rtCGM may benefit from reduced exposure to hypoglycemia and glycemic variability.

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Background: No published data are available on the use of the community-derived open-source Loop hybrid closed-loop controller ("Loop") by individuals with type 2 diabetes (T2D). Methods: Through social media postings, we invited individuals with T2D currently using the Loop system to join an observational study. Thirteen responded of whom seven were eligible for the study, were using the Loop algorithm, and provided data. Results: Mean (±standard deviation) age was 61 ± 13 years, and mean body mass index was 31 ± 5 kg/m². All but one participant were using noninsulin glucose-lowering medications. Self-reported mean hemoglobin A1c decreased from 7.3% ± 1.1% before starting Loop to 6.0% ± 0.5% on Loop. Time in range 70-180 mg/dL increased from 84% to 93%. The amount of time in hypoglycemia was extremely low before and with Loop (time <54 mg/dL was 0.04% ± 0.06% vs. 0.09% ± 0.07%, respectively). No severe hypoglycemia or diabetic ketoacidosis events were reported while using Loop. Conclusion: These data, though limited, suggest that the Loop system is likely to be effective when used by individuals with T2D and should be evaluated in large-scale studies. Clinical Trial Registration numbers: NCT05951569.

Objective: To evaluate postprandial glucose control when applying (1) faster-acting insulin aspart (Fiasp) compared to insulin aspart and (2) ultra-rapid insulin lispro (Lyumjev) compared to insulin lispro using the CamAPS FX hybrid closed-loop algorithm. Research Design and Methods: We undertook a secondary analysis of postprandial glucose excursions from two double-blind, randomized, crossover hybrid closed-loop studies contrasting Fiasp to standard insulin aspart, and Lyumjev to standard insulin lispro. Endpoints included incremental area under curve (iAUC)-2h, iAUC-4h, 4 h postprandial time in target range, time above range, and time below range. It was approved by independent research ethics committees. Results: Two trials with 8 weeks of data from 51 adults with type 1 diabetes were analyzed and 7137 eligible meals were included. During Lyumjev compared with insulin lispro, iAUC-2h and iAUC-4h were significantly decreased following breakfast (mean difference 92 mmol/L per 2 h (95% confidence interval [CI]: 56 to 127); P < 0.001 and 151 mmol/L per 4 h (95% CI: 74 to 229); P < 0.001, respectively) and the evening meal (P < 0.001 and P = 0.011, respectively). Mean time in target range (3.9-10.0 mmol/L) for 4 h postprandially significantly increased during Lyumjev with a mean difference of 6.7 percentage points (95% CI: 3.3 to 10) and 5.7 percentage points (95% CI: 1.4 to 9.9) for breakfast and evening meal, respectively. In contrast, there were no significant differences in iAUC-2h, iAUC-4h, and the other measures of postprandial glucose control between insulin aspart and Fiasp during breakfast, lunch, and evening meal (P > 0.05). Conclusion: The use of Lyumjev with CamAPS FX closed-loop system improved postprandial glucose excursions compared with insulin lispro, while the use of Fiasp did not provide any advantage compared with insulin aspart. Clinical Trial Registration numbers: NCT04055480, NCT05257460.

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

Objective: To evaluate the impact of missed or late meal boluses (MLBs) on glycemic outcomes in children and adolescents with type 1 diabetes using automated insulin delivery (AID) systems. Research Design and Methods: AID-treated (Tandem Control-IQ or Medtronic MiniMed 780G) children and adolescents (aged 6-21 years) from Stanford Medical Center and Steno Diabetes Center Copenhagen with ≥10 days of data were included in this two-center, binational, population-based, retrospective, 1-month cohort study. The primary outcome was the association between the number of algorithm-detected MLBs and time in target glucose range (TIR; 70-180 mg/dL). Results: The study included 189 children and adolescents (48% females with a mean ± standard deviation age of 13 ± 4 years). Overall, the mean number of MLBs per day in the cohort was 2.2 ± 0.9. For each additional MLB per day, TIR decreased by 9.7% points (95% confidence interval [CI] 11.3; 8.1), and compared with the quartile with fewest MLBs (Q₁), the quartile with most (Q₄) had 22.9% less TIR (95% CI: 27.2; 18.6). The age-, sex-, and treatment modality-adjusted probability of achieving a TIR of >70% in Q₄ was 1.4% compared with 74.8% in Q₁ (P < 0.001). Conclusions: MLBs significantly impacted glycemic outcomes in AID-treated children and adolescents. The results emphasize the importance of maintaining a focus on bolus behavior to achieve a higher TIR and support the need for further research in technological or behavioral support tools to handle MLBs.

Aims: The objective of this real-world, observational study was to evaluate change in continuing glucose monitoring (CGM) metrics for 1 year after CGM initiation in adults with noninsulin-treated type 2 diabetes (T2D). Methods: Data were analyzed from Dexcom G6 and G7 users who self-reported: T2D, ≥18 years, gender, no insulin use, and had a baseline percent time in range (TIR) 70-180 mg/dL of ≤70%. Outcomes were change in CGM metrics from baseline to 6 and 12 months overall and for younger (<65 years) and older (≥65 years) cohorts. Additional analyses explored the relationship between use of the high alert feature and change in TIR and time in tight range (TITR) 70-140 mg/dL. Results: CGM users (n = 3,840) were mean (SD) 52.5 (11.2) years, 47.9% female, mean TIR was 41.7% (21.4%), and 12.4% of participants were ≥65 years. Significant improvement in all CGM metrics not meeting target values at baseline was observed at 6 months, with continued improvement at 12 months. Mean baseline TIR increased by 17.3% (32.1%) from 41.7% (21.4%) to 59.0% (28.9%), and mean glucose management indicator decreased by 0.5% (1.2%) from 8.1% (0.9%) to 7.6% (1.1%) (both P < 0.001). Participants who maintained or customized the high alert default setting of 250 mg/dL had a greater increase in TIR and TITR compared with participants who disabled the alert. Days of CGM use over 12 months were high in 84.7% (15.9%). Conclusion: In this large, real-world study of adults with suboptimally controlled T2D not using insulin, Dexcom CGM use was associated with meaningful improvements in glycemic control over 12 months. Use of the high alert system feature was positively associated with glycemic outcomes. High use of CGM over 12 months suggests benefits related to consistent CGM use in this population.