Diabetes technology & therapeutics最新文献

Introduction: Low- and very-low-carbohydrate eating patterns, including ketogenic eating, can reduce glycated hemoglobin (HbA1c) in people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) has also been shown to improve glycemic outcomes, such as time in range (TIR; % time with glucose 70-180 mg/dL), more than blood glucose monitoring (BGM). CGM-guided nutrition interventions are sparse. The primary objective of this study was to compare differences in change in TIR when people with T2D used either CGM or BGM to guide dietary intake and medication management during a medically supervised ketogenic diet program (MSKDP) delivered via continuous remote care. Methods: IGNITE (Impact of Glucose moNitoring and nutrItion on Time in rangE) study participants were randomized to use CGM (n = 81) or BGM (n = 82) as part of a MSKDP. Participants and their care team used CGM and BGM data to support dietary choices and medication management. Glycemia, medication use, ketones, dietary intake, and weight were assessed at baseline (Base), month 1 (M1), and month 3 (M3); differences between arms and timepoints were evaluated. Results: Adults (n = 163) with a mean (standard deviation) T2D duration of 9.7 (7.7) years and HbA1c of 8.1% (1.2%) participated. TIR improved from Base to M3, 61-89% for CGM and 63%-85% for BGM (P < 0.001), with no difference in change between arms (P = 0.26). Additional CGM metrics also improved by M1, and improvements were sustained through M3. HbA1c decreased by ≥1.5% from Base to M3 for both CGM and BGM arms (P < 0.001). Diabetes medications were de-intensified based on change in medication effect scores from Base to M3 (P < 0.001). Total energy and carbohydrate intake decreased (P < 0.001), and participants in both arms lost clinically significant weight (P < 0.001). Conclusion: Both the CGM and BGM arms saw similar and significant improvements in glycemia and other diabetes-related outcomes during this MSKDP. Additional CGM-guided nutrition intervention research is needed.

Objective: To compare the accuracy of commonly used continuous glucose monitoring (CGM) analysis programs with ambulatory glucose profile (AGP) and Dexcom Clarity (DC) in analyzing CGM metrics in patients with type 1 diabetes (T1D). Research Methods: CGM data up to 90 days from 152 adults using the same CGM and automated insulin delivery system with T1D were collected. Six of the 19 CGM analysis programs (CDGA, cgmanalysis, Glyculator, iglu, EasyGV, and GLU) were selected to compare with AGP and DC. Metrics were compared etween all tools with two one-sided t-tests equivalence testing. For the equivalence test, the acceptable range of deviation was set as ±2 mg/dL for mean glucose, ±2% for time in range (TIR), ±1% for time above range (TAR), time above range level 1 (TAR1), time above range level 2 (TAR2), and coefficient of variation (CV). Results: All packages were compared with each other for all CGM metrics, and most of them had statistically significant differences for at least some metrics. All tools were equivalent to AGP for mean glucose, TIR, TAR, TAR1, and TAR2 within ±2 mg/dL, ±2%, ±1%, ±1% and 1%, respectively. CDGA, Glyculator, cgmanalysis, and iglu were not equivalent to AGP for CV within ±1%. All tools were equivalent to DC for mean glucose, TIR, and TAR2 within ±2 mg/dL, ±2%, and ±1%, respectively. Glyculator was not equivalent for TAR1, TAR, and CV. CGDA, cgmanalysis, and iglu were not equivalent to DC for TAR1 and TAR. EasyGV and GLU were not equivalent for TAR within ±1%. Conclusions: CGM analysis programs reported CGM metrics statistically differently, but these differences may not be applicable in clinical practice. The equivalence test also confirmed that the differences are negligible for TIR and mean glucose, while they can be important for hyperglycemic ranges and CV. A standardization for CGM data handling and analysis is necessary for clinical studies reporting CGM-generated outcomes.

Objective: This study examined acute effects of interrupting prolonged sitting with short activity breaks on postprandial glucose/insulin responses and estimations of insulin sensitivity in adults with type 1 diabetes (T1D). Method: In a randomized crossover trial, eight adults (age = 46 ± 14 years [mean ± SD], body mass index [BMI] = 27.2 ± 3.8 kg/m²) receiving continuous subcutaneous insulin infusion (CSII) therapy completed two 6-h conditions as follows: uninterrupted sitting (SIT) and sitting interrupted with 3-min bouts of simple resistance activities (SRAs) every 30 min. Basal and bolus insulin were standardized across conditions except in cases of hypoglycemia. Postprandial responses were assessed using incremental area-under-the-curve (iAUC) and total AUC (tAUC) from half-hourly venous sampling. Meal-based insulin sensitivity determined from glucose sensor and insulin pump (S_i^SP) was assessed from flash continuous glucose monitor and insulin pump data. Outcomes were analyzed using mixed models adjusted for sex, BMI, treatment order, and preprandial values. Results: Glucose iAUC did not differ by condition (SIT: 19.8 ± 3.0 [estimated marginal means ± standard error] vs. SRA: 14.4 ± 3.0 mmol.6 h.L^-1; P = 0.086). Despite CSII being standardized between conditions, insulin iAUC was higher in SRA compared to SIT (137.1 ± 22.7 vs. 170.9 ± 22.7 mU.6 h.L^-1; P < 0.001). This resulted in a lower glucose response relative to the change in plasma insulin in SRA (tAUCglu/tAUCins: 0.32 ± 0.02 vs. 0.40 ± 0.02 mmol.mU^-1; P = 0.03). Si^SP was also higher at dinner following the SRA condition, with no between-condition differences at breakfast or lunch. Conclusion: Regularly interrupting prolonged sitting in T1D may increase plasma insulin and improve insulin sensitivity when meals and CSII are standardized. Future studies should explore underlying mechanistic determinants and the applicability of findings to those on multiple daily injections. Trial Registration: Australian and New Zealand Clinical Trial Registry Identifier-ACTRN12618000126213 (www.anzctr.org.au).

Background: Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID. Method: Eleven adults with T1D participated in a supervised randomized-crossover trial assessing glycemic control during two 24-h sessions with identical meals and activity-with and without BPS. On the day in-between study sessions, participants underwent food and activity challenges to test BPS safety and robustness. Continuous glucose monitor (CGM) outcomes and total insulin doses were assessed overall and following meals with potential for BPS to dose additional insulin (CGM >90 mg/dL for 1 h prior). Results: Daytime CGM outcomes were similar with and without BPS: time-in-range (TIR) 70-180 mg/dL 70.6% [62.2-76.5] versus 65.7% [58.6%-80.6%]; time-below-range <70 mg/dL 0% [0-2.1] versus 0% [0-1.3]; respectively. Insulin delivery during 3 h postprandial was indistinguishable 33.5 U [26.4-47.0] versus 35.7 U [28.7-44.9]. Among 43 out of 66 meals with potential to trigger BPS (24/19 BPS/no-BPS), postprandial incremental area-under-the-curve (iAUC) was lower for BPS versus no-BPS (2530 ± 1934 versus 3228 ± 2029, P = 0.047), but CGM outcomes were inconclusive: 4-h-TIR 51.2% [19.8-83.3] versus 40.2% [20.8-56.3] (P = 0.24). There were no severe adverse events. Conclusion: While there was no difference in TIR, when BPS was active an improved postprandial AUC in FCL was obtained via earlier insulin injection.

Background: Scuba diving was previously excluded because of hypoglycemic risks for patients with type 1 diabetes mellitus(T1DM). Specific eligibility criteria and a safety protocol have been defined, whereas continuous glucose monitoring (CGM) systems have enhanced diabetes management. This study aims to assess the feasibility and accuracy of CGM Dexcom G7 and Free Style Libre 3 in a setting of repetitive scuba diving in T1DM, exploring the possibility of nonadjunctive use. Material and Methods: The study was conducted during an event of Diabete Sommerso^® association in 2023. Participants followed a safety protocol, with capillary glucose as reference standard (Beurer GL50Evo). Sensors' accuracy was evaluated through median and mean absolute relative difference (MeARD, MARD) and surveillance error grid (SEG). Data distribution and correlation were estimated by Spearman test and Bland-Altman plots. The ability of sensors to identify hypoglycemia was assessed by contingency tables. Results: Data from 202 dives of 13 patients were collected. The overall MARD was 31% (Dexcom G7) and 14.2% (Free Style Libre 3) and MeARD was 19.7% and 11.6%, respectively. Free Style Libre 3 exhibited better accuracy in normoglycemic and hyperglycemic ranges. SEG analysis showed 82.1% (Dexcom G7) and 97.4% (Free Style Libre 3) data on no-risk zone. Free Style Libre 3 better performed on hypoglycemia identification (diagnostic odds ratio of 254.10 vs. 58.95). Neither of the sensors reached the MARD for nonadjunctive use. Conclusions: The study reveals Free Style Libre 3 superior accuracy compared with Dexcom G7 in a setting of repetitive scuba diving in T1DM, except for hypoglycemic range. Both sensors fail to achieve accuracy for nonadjunctive use. Capillary tests remain crucial for safe dive planning, and sensor data should be interpreted cautiously. We suggest exploring additional factors potentially influencing sensor performance.

Objective: To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. Research Methods: In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. Results: The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. Conclusion: CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24-34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT.

Background: There is a need for accurate glycemic control metrics in patients with diabetes and end-stage kidney disease (ESKD). Hence, we assessed the relationship of continuous glucose monitoring (CGM) metrics and laboratory-measured hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2D) treated by hemodialysis. Methods: This prospective observational study included adults (age 18-80 years) with T2D (HbA1c 5%-12%), treated by hemodialysis (for at least 90 days). Participants used a Dexcom G6 Pro CGM for 10 days. Correlation analyses between CGM metrics [mean glucose, glucose management indicator (GMI), and time-in-range (TIR 70-180 mg/dL)] and HbA1c were performed. Results: Among 59 participants (mean age was 57.7 ± 9.3 years, 58% were female, 86% were non-Hispanic blacks), the CGM mean glucose level was 188.9 ± 45 mg/dL (95% CI: 177.2, 200.7), the mean HbA1c and GMI were 7.1% ± 1.3% and 7.8% ± 1.1%, respectively (difference 0.74% ± 0.95). GMI had a strong negative correlation with TIR 70-180 mg/dL (r = -0.96). The correlation between GMI and HbA1c (r = 0.68) was moderate. Up to 29% of participants had a discordance between HbA1c and GMI of <0.5%, with 22% having a discordance between 0.5% and 1%, and 49% having a discordance of >1%. Conclusions: In patients with diabetes and ESKD treated by hemodialysis, the GMI has a strong correlation with TIR, while HbA1c underestimated the average glucose and GMI. Given the limitations of HbA1c in this population, GMI or mean glucose and TIR may be considered as more appropriate glucose control markers.

Background: Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics combined into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. Methods: An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single report containing clinically useful information. An extensive literature review and identification of examples of current connected insulin pen reports were performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. Results: The Ambulatory Glucose Profile (AGP) Report: Connected Insulin Pen brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the AGP curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio, average bolus insulin dose, and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. Conclusion: The AGP Report: Connected Insulin Pen is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.

Aim: To compare the safety in terms of hypoglycemic events and continuous glucose monitoring (CGM) metrics during aerobic exercise (AE) of using temporary target (TT) versus suspension of insulin infusion (SII) in adults with type 1 diabetes (T1D) using advanced hybrid closed-loop systems. Methods: This was a randomized crossover clinical trial. Two moderate-intensity AE sessions were performed, one with TT and one with SII. Hypoglycemic events and CGM metrics were analyzed during the immediate (baseline to 59 min), early (60 min to 6 h), and late (6 to 36 h) post-exercise phases. Results: In total, 33 patients were analyzed (44.6 ± 13.8 years), basal time in range (%TIR 70-180 mg/dL) was 79.4 ± 12%, and time below range (%TBR) <70 mg/dL was 1.8 ± 1.7% and %TBR <54 mg/dL was 0.5 ± 0.9%. No difference was found in the number of hypoglycemic events, %TBR <70 mg/dL and %TBR <54 mg/dL between TT and SII. Differences were found in the early phase, with better values when using TT for %TIR 70-180 mg/dL (83.0 vs. 65.3, P = 0.005), time in tight range (%TITR 70-140 mg/dL) (56.3 vs. 41.5, P = 0.04), and time above range (%TAR >180 mg/dL) (15.3 vs. 31.8, P = 0.01). In the diurnal period, again %TIR was better for TT use (82.1 vs. 73.1, P = 0.02) and %TAR (15.0 vs. 22.96, P = 0.04). No significant differences were found in the CGM metrics during the different phases of AE. Conclusion: Our data appear to show that the use of TT compared with SII is equally safe in all phases of AE. However, the use of TT allows for a better glycemic profile in the early phase of exercise.