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

Objective: The safety and impact of the advanced hybrid closed-loop (AHCL) system on glycemic outcome in 2- to 6-year-old children with type 1 diabetes and the diabetes distress of caregivers were evaluated. Research Design and Methods: This was an open-label prospective study (n = 35) with historical controls matched by treatment unit, diabetes duration, age, gender, and baseline treatment modality. The inclusion criteria were (1) type 1 diabetes diagnosis >6 months, (2) total daily dose of insulin ≥8 U/day, (3) HbA1c <10% (85 mmol/mol), and (4) capability to use insulin pump and continuous glucose monitoring. The MiniMed 780G™ AHCL in SmartGuard™ Mode was used for 12 weeks. Parental diabetes distress was evaluated with a validated Problem Areas In Diabetes-Parent, revised (PAID-PR) survey. Results: No events of diabetic ketoacidosis or severe hypoglycemia occurred. Between 0 and 12 weeks, HbA1c (mean change = -2.7 mmol/mol [standard deviation 5.7], P = 0.010), mean sensor glucose value (SG) (-0.8 mmol/L [1.0], P < 0.001), and time above range (TAR) (-8.6% [9.5], P < 0.001) decreased and time in range (TIR) (8.3% [9.3], P < 0.001) increased significantly, whereas no significant change in time below range (TBR) was observed. At the same time, PAID-PR score decreased from 37.5 (18.2) to 27.5 (14.8) (P = 0.006). Conclusions: MiniMed 780G™ AHCL is a safe system and 12-week use was associated with improvements in glycemic control in 2- to 6-year-old children with type 1 diabetes. In addition, AHCL is associated with a reduction in parental diabetes distress after 12-week use. ClinicalTrials.gov registration number: NCT04949022.

MiniMed 780G™ in 2- to 6-Year-Old Children: Safety and Clinical Outcomes After the First 12 Weeks.

The renin-angiotensin system (RAS) is one of the most complex hormonal regulatory systems, involving several organs that interact to regulate multiple body functions. The study of this system initially focused on investigating its role in the regulation of both cardiovascular function and related pathologies. From this approach, pharmacological strategies were developed for the treatment of cardiovascular diseases. However, new findings in recent decades have suggested that the RAS is much more complex and comprises two subsystems, the classic RAS and an alternative RAS, with antagonistic effects that are usually in equilibrium. The classic system is involved in pathologies where inflammatory, hypertrophic and fibrotic phenomena are common and is related to the development of chronic diseases that affect various body systems. This understanding has been reinforced by the evidence that local renin-angiotensin systems exist in many tissue types and by the role of the RAS in the spread and severity of COVID-19 infection, where it was discovered that viral entry into cells of the respiratory system is accomplished through binding to angiotensin-converting enzyme 2, which is present in the alveolar epithelium and is overexpressed in patients with chronic cardiometabolic diseases. In this narrative review, preclinical and clinical aspects of the RAS are presented and topics for future research are discussed some aspects are raised that should be clarified in the future and that call for further investigation of this system. 

Renin–angiotensin system: Basic and clinical aspects—A general perspective

BACKGROUND
Automated insulin delivery is an efficient treatment for patients with type 1 diabetes. Little is known on its impact on patients with excessive time in hypoglycaemia.


METHODS
We performed a post hoc analysis of three randomized control trials that used the DBLG1 (Diabeloop Generation 1) hybrid closed-loop solution. Patients whose time below 70 mg/dL during baseline, open-loop phase exceeded 5% were selected. The outcomes were the differences between the closed-loop and the open-loop phases in time in various ranges and Glycemia Risk Index (GRI).


RESULTS
We identified 45 patients exhibiting ≥5% of time below 70 mg/dL during the open-loop phase. Under closed-loop, the time in hypoglycaemia (54 to <70 mg/dL) dropped from 7.9% (SD 2.4) to 3.2% (SD 1.6) (difference -4.7% [-5.3; -4.1], P < 10-4). The time below 54 mg/dL decreased from 1.9% (SD 1.3) to 0.8% (SD 0.7) (difference -0.9% [-1.4; -0.8], P < 10-4). The time in range (TIR 70-180 mg/dL) improved from 63.3 (SD 9.5) to 68.2% (SD 8.2) (difference 5.1% [2.9; 7.0], P < 10-4). The GRI improved from 51.2 (SD 12.4) to 38.0 (SD 10.9) (difference 13.2 [10.4; 16.0], P < 10-4).


CONCLUSION
DBLG1 decreased time in hypoglycaemia by more than 50% even in patients with excessive time in hypoglycaemia at baseline, while also improving both TIR and GRI, under real-life conditions. The improvement in GRI (13.2%) exceeded that of the improvement in TIR (5.1%) indicating that in this data set, GRI was more sensitive than TIR to the improvement in glycaemia achieved with closed-loop. These results support the safety and efficacy of this treatment.

Efficacy of a Hybrid Closed-Loop Solution in Patients With Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes. 

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials

BACKGROUND
A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.


METHODS
We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.


RESULTS
The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.


CONCLUSION
The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

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A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.