Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c

Abstract

Background/Objective: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes."Methods: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). Results:The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose

Clinically relevant stratification of patients with type 2 diabetes by using continuous glucose monitoring data

“…In the stratification of T2DM, however, the use of CGM is quite limited. In adolescents with type 1 diabetes, Kahkoska et al 11 identified three dysglycaemia clusters based on CGM data, which displayed a significant difference in the progression of HbA1c over 18 months.…”

“…However, existing studies focused on patient clustering have primarily utilized conventional CGM metrics recommended by international consensus rather than delving into the raw data. [10][11][12] It is crucial to note that widely used CGM metrics, such as time in range (TIR) and coefficient of variation (CV), primarily capture statistical characteristics and overlook the time-series nature of CGM data. Effectively interpreting the extensive data generated by CGM remains a critical challenge.…”

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

“…Data collected with CGM have also provided an important tool for testing the feasibility and effectiveness of insulin delivery systems, insulin treatments, adjunctive diabetes medications, T1D screening, new glucose monitoring systems, algorithms for the promotion of improved glycemic control, sensor-augmented therapy algorithms, and closed-loop systems ; diabetes alert dogs [92]; education programs aimed at improving impaired hypoglycemia unawareness, daily therapy decisions, and cardiovascular health [93][94][95]; and use of glucose sharing data with others [96]. This technology has also been used to assess the relationship between continuous glucose measures and other diabetes-related outcomes, including long-term glycemic control, dysglycemia [97,98], future T1D diagnosis or dysglycemia in preclinical youth [99][100][101], HbA 1c [102][103][104][105], C-peptide [102], insulin sensitivity [106], severe hypoglycemia [107], time in target range [108][109][110], glucose variability [109][110][111][112][113], detection of hypo-or hyperglycemia [108][109][110][114][115][116], glycated albumin [104], fructosamine [104], and 1,5-anhydroglucitrol [104]. Furthermore, CGM has been used to determine the relationship between continuous glucose measures and other medical outcomes, including body composition [117], markers of inflammation [118], cardiovascular health …”

Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes

“… 12 13 By dividing the complete CGM curve into segments, Hall and colleagues 12 found three different fluctuation modes of CGM, and the frequencies of the three modes in individuals without diabetes, patients with pre-diabetes and patients with diabetes were finally found to be different. Kahkoska and colleagues 13 used eight CGM features to identify new subgroups of type 1 diabetes. Three subgroups showed significant differences in glycated hemoglobin A1c (HbA1c).…”

“…Three subgroups showed significant differences in glycated hemoglobin A1c (HbA1c). 13 The problem of using CGM indices to stratify patients with type 2 diabetes among different categories needs to be discussed further.…”