Basal Insulin Degludec and Glycemic Control Compared to Aspart Via Insulin Pump in Type 1 Diabetes (BIGLEAP): A Single-Center, Open-Label, Randomized, Crossover Trial

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Are nocturnal hypoglycemia prevention strategies influenced by diabetes technology usage? A BETTER registry analysis

AIM

To assess the association of nocturnal hypoglycemia prevention strategies (NH-PS) and diabetes technology usage (insulin pump and/or continuous glucose monitors [CGM]) in people with type 1 diabetes (PWT1D).

METHODS

Logistic regression models were used to describe associations between self-reported NH-PS and diabetes technology (pump with intermittently-scanned or real-time CGM (isCGM or rtCGM), or automated insulin delivery (AID)), hypoglycemia history, and fear of hypoglycemia (FOH).

RESULTS

Among 831 adults (65 % female, aged 44 ± 15 years, T1D duration 26 ± 15 years), 32 % reported HbA1c ≤ 7.0 %, 88 % used ≥ 1 diabetes technology, 66 % reported ≥ 1 symptomatic NH in the past month, and 64 % used ≥ 2 NH-PS. Compared to multiple daily injections (MDI) + capillary blood glucose (CBG), bedtime snack consumption was less likely among pump + isCGM (OR [95 %CI]: 0.55 [0.31, 0.98]), pump + rtCGM (0.40 [0.20, 0.81]), and AID (0.34 [0.17, 0.66]) users, while evening insulin basal reduction was associated with CSII + CBG (3.15 [1.25, 7.99]), pump + isCGM 4.00 [1.99, 8.01]), and pump + rtCGM 2.89 [1.28, 6.50] use. Elevated FOH was associated with snack consumption (1.37 [1.00, 1.89]), evening bolus insulin avoidance (1.77 [1.11, 2.83]), limiting exercise (2.50 [1.30, 4.82]), and limiting alcohol consumption (2.33 [1.15, 4.70]) as NH-PS.

CONCLUSION

Technology use and elevated FOH might influence PWT1D' choice of NH-PS.