Journal of Diabetes Science and Technology最新文献

Batteries are an essential component of many medical products used for diabetes therapy. The increased use of such products comes along with millions of batteries that are disposed of every year. The design of these products should enable the recycling of batteries as they contain a significant number of valuable resources. Regulations in the United States and the European Union concerning batteries used in medical products are changing toward requiring and supporting establishing recycling procedures. Currently, respective programs are active only in some countries. A greener diabetes therapy would include more attention to reducing usage and disposing of batteries.

Background: This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA_1c) according to sensor mean glucose and HbA_1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes.

Methods: A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA_1c.

Results: The mean HbA_1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA_1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA_1c when HbA_1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA_1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA_1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA_1c of 9.0% to 9.9% and higher by 0.5% in HbA_1c of 5.0% to 5.9% (all P < .001).

Conclusions: The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA_1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA_1c levels or higher HbA_1c levels.

Background: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D).

Method: A study of 118 participants with non-insulin-treated T2D (HbA_1c ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA_1c of 7.46% (0.93) used the DTx for three months to evaluate clinical endpoints, such as HbA_1c, body weight, quality of life and app usage, for a pre-post comparison. The study also included an assessment of initial long-term data from a second use of the DTx.

Results: After three months of using the DTx, there was an improvement of 0.67% HbA_1c in the complete cohort and -1.08% HbA_1c in patients with poorly controlled diabetes (BL-HbA_1c ≥ 7.0%) compared with standard of care (P < .001). The number of patients within the therapeutic target range (< 7.0%) increased from 38% to 60%, and 33% were on the way to remission (< 6.5%). Patients who used the DTx a second time experienced a reduction of -0.76% in their HbA_1c levels and a mean weight loss of -6.84 kg after six months (P < .001) compared with BL.

Conclusions: These results indicate that the DTx has clinically relevant effects on glycemic control and weight reduction for patients with both well and poorly controlled diabetes, whether through single or repeated usage. It is a noteworthy improvement in T2D management, offering a non-pharmacological, fully digital solution that integrates biofeedback through CGM and an advanced AI algorithm.

Background: The glycemia risk index (GRI) is a new composite continuous glucose monitoring (CGM) metric for weighted hypoglycemia and hyperglycemia. We evaluated the association between the GRI and cardiovascular autonomic neuropathy (CAN) and compared the effects of the GRI and conventional CGM metrics on CAN.

Methods: For this cross-sectional study, three-month CGM data were retrospectively analyzed before autonomic function tests were performed in 165 patients with type 1 diabetes. CAN was defined as at least two abnormal results of parasympathetic tests according to an age-specific reference.

Results: The overall prevalence of CAN was 17.1%. Patients with CAN had significantly higher GRI scores, target above range (TAR), coefficient of variation (CV), and standard deviation (SD) but significantly lower time in range (TIR) than those without CAN. The prevalence of CAN increased across higher GRI zones (P for trend <.001). A multivariate logistic regression analysis, adjusted for covariates such as HbA1c, demonstrated that the odds ratio (OR) of CAN was 9.05 (95% confidence interval [CI]: 2.21-36.96, P = .002) per 1-SD increase in the GRI. TIR and CV were also significantly associated with CAN in the multivariate model. The area under the curve of GRI for the prediction of CAN (0.85, 95% CI: 0.76-0.94) was superior to that of TIR (0.80, 95% CI: 0.71-0.89, P for comparison = .046) or CV (0.71, 95% CI: 0.57-0.84, P for comparison = .049).

Conclusions: The GRI is significantly associated with CAN in patients with type 1 diabetes and may be a better CGM metric than TIR for predicting CAN.

Pregnancy in people with type 1 diabetes mellitus (T1D) is well-known to be linked to adverse maternal and neonatal outcomes. Although advancements in diabetes technology, especially hybrid closed-loop (HCL) and advanced hybrid closed-loop (AHCL) systems, have greatly enhanced management for nonpregnant individuals with T1D, pregnant patients still represent a high-risk group that requires further research. Existing trials have shown mixed data in terms of clinically meaningful benefits in glycemic control, but these may be specific to the closed-loop system. Currently, there is one AHCL system approved and available for use in pregnancies complicated by diabetes in the United Kingdom, Europe, and Australia. However, there are no Food and Drug Administration (FDA)-approved closed-loop systems for use during pregnancy in the United States. Existing HCL/AHCL system use is off-label for pregnancy in the United States and often requires assistive techniques to target the tighter glucose levels needed during pregnancy. For patients struggling on multiple daily injections (MDIs) or sensor-augmented pump therapy (SAPT), studies have shown that HCL/AHCLs can reduce the burden of care and enable some people to achieve tighter glucose levels. This review aims to provide an overview of the existing evidence of closed-loop systems in pregnancies complicated by T1D and to discuss their implications and considerations with system use.

Background: Hybrid closed loop (HCL) technology is now standard of care for women with type 1 diabetes in pregnancy in the United Kingdom, but there is minimal evidence to guide HCL use in the preconception period, peripartum, and postnatally. We used real-world data to assess whether HCL use offered benefits upon glycemia in the preconception, peripartum, and postnatal periods.

Methods: This single-center retrospective observational study assesses the effect of HCL use upon HbA1c and continuous glucose monitoring (CGM) metrics, including time-in-range (TIR; 3.9-10.0 mmol/L; 72-180 mg/dL) or pregnancy time-in-range (TIRp; 3.5-7.8 mmol/L; 63-140 mg/dL) before (n = 46), during (n = 21), and after (n = 25) pregnancy. Data (mean (SD)) were analyzed using paired t tests (limit P < .05).

Results: Preconception initiation of HCL was associated with a reduction of HbA1c from 62.4 (14.0) to 54.2 (7.7) mmol/mol at three to six months (7.9 (1.3) to 7.1 (0.7) %; P < .0001). The TIR increased from 49% at baseline to 65% at one week (P < .001) and 72% at six months (P < .001) after initiation. Time-below-range (TBR) fell from 3.2% at baseline to 2.1% at one week (P = .006) and 2.1% at three months (P = .042). Pregnancy initiation of HCL was associated with a reduction of HbA1c from 61.2 (14.6) to 48.1 (8.6) mmol/mol at three months (n = 36; P < .0001) and increased TIRp (37% baseline to 57% after one week; P < .0001). Patients using HCL postnatally at one month had TIR 70% and TBR 1.8%.

Conclusions: When started preconception or in pregnancy, HCL significantly reduces HbA1c at three months and improves TIR by 15% to 20% within one week.

Automated insulin delivery (AID) systems enhance glucose management by lowering mean glucose level, reducing hyperglycemia, and minimizing hypoglycemia. One feature of most AID systems is that they allow the user to view "insulin on board" (IOB) to help confirm a recent bolus and limit insulin stacking. This metric, along with viewing glucose concentrations from a continuous glucose monitoring system, helps the user understand bolus insulin action and the future "threat" of hypoglycemia. However, the current presentation of IOB in AID systems can be misleading, as it does not reflect true insulin action or automatic, dynamic insulin adjustments. This commentary examines the evolution of IOB from a bolus-specific metric to its contemporary use in AID systems, highlighting its limitations in capturing real-time insulin modulation during varying physiological states.

The Diabetes Research Hub (DRH) is a centralized data management system and repository that will revolutionize how diabetes data are gathered, stored, analyzed, and utilized for research. By harnessing advanced analytics for large datasets, the DRH will support a nuanced understanding of physiological patterns and treatment effectiveness, ultimately advancing diabetes management and patient outcomes. This is an opportune time for researchers who are collecting continuous glucose data and related physiological data sources, to leverage the capabilities of the DRH to enhance the value of their data.

Aims: To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH).

Methods: Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH.

Results: When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, T_Hypo(L1) 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; P = .335, T_Hypo(L2) 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; P = .146, HIIT + CGM versus CGM, respectively, rate (EventPerWeek_Hypo 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, P = .340) or duration (Duration_Hypo 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; P = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, T_Hypo(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; P = .076; T_Hypo(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; P = .006, HIIT + CGM versus CGM, respectively.

Conclusions/interpretation: Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.