» Articles » PMID: 36493795

Continuous Glucose Monitoring and Metrics for Clinical Trials: an International Consensus Statement

Abstract

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA as the measure of average blood glucose levels for the 3 months preceding the HbA test date. The use of this measure highlights the long-established correlation between HbA 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 HbA findings, and further assess the effects of therapeutic interventions on HbA. 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 HbA 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 HbA for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Citing Articles

Trajectories of Sleep Duration, Sleep Onset Timing, and Continuous Glucose Monitoring in Adults.

Shen L, Li B, Gou W, Liang X, Zhong H, Xiao C JAMA Netw Open. 2025; 8(3):e250114.

PMID: 40042843 PMC: 11883496. DOI: 10.1001/jamanetworkopen.2025.0114.


Implications of the Glycemia Risk Index in Assessing Metabolic Control and its Correlation With Therapy-Related Quality of Life During-Ramadan Fasting in Adults With Type 2 Diabetes.

Al Hayek A, Al Zahrani W, Al Dawish M J Diabetes Sci Technol. 2025; :19322968251321860.

PMID: 40022432 PMC: 11871576. DOI: 10.1177/19322968251321860.


Cardiac arrhythmia and hypoglycaemia among individuals with and without diabetes receiving haemodialysis (the CADDY study): a Danish multicentre cohort study.

Kofod D, Diederichsen S, Bomholt T, Andersen M, Andersen A, Mannheimer E Diabetologia. 2025; .

PMID: 40019498 DOI: 10.1007/s00125-025-06388-5.


Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.

Mendez C, Kaykayoglu C, Bahler T, Kunzler J, Lizoain A, Rothenbuhler M J Diabetes Sci Technol. 2025; :19322968251319800.

PMID: 39996274 PMC: 11851596. DOI: 10.1177/19322968251319800.


A Processing Algorithm to Address Real-World Data Quality Issues With Continuous Glucose Monitoring Data.

Williamson W, Lee J, Gaynanova I J Diabetes Sci Technol. 2025; :19322968251319801.

PMID: 39980261 PMC: 11843558. DOI: 10.1177/19322968251319801.