Journal of Diabetes Science and Technology最新文献

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.

Subcutaneous insulin administration has come a long way; pens that are connected to smartphones/cloud enable data transfer about insulin dosing. The usage of detailed dosing information in a smart way can support the optimization of insulin therapy in many ways. This review discusses terminology aspects that are relevant to the optimal usage of this novel option for insulin administration. Taking such aspects into account might also be crucial to improving the uptake of these medical products. In contrast to systems for automated insulin delivery, people with diabetes have to administer the insulin dose themselves; the technology can only support them. Combining smart pens with systems for continuous glucose monitoring provides solutions that are close to an automated solution, but are more discrete and associated with lower costs.