Work

Association of Insulin Resistance and Insulin Secretion Indices and Glucose Metrics From Continuous Glucose Monitoring in People With Obesity

Year: 2025

Type: article

Abstract: OBJECTIVE: To investigate the relationship between free-living glucose metrics obtained with continuous glucose monitoring (CGM) and validated indices of insulin resistance and insulin secretion in individuals with obesity: OB(+T2D) or OB(-T2D) for with and without type 2 diabetes, respectively. RESEARCH DESIGN AND METHODS: Thirty-seven individuals (17 OB(+T2D) and 20 OB(-T2D) wore a CGM device and had a 2-h oral glucose tolerance test (OGTT). Of these, 27 also underwent a two-step hyperglycemic-euglycemic clamp. CGM metrics calculated with EasyGV software were correlated with indices of insulin secretion and insulin resistance from OGTT and clamp. RESULTS: CGM metrics, such as mean and glycemic variability indices, were higher in OB(+T2D) than in OB(-T2D) (P < 0.001). CGM mean inversely correlated with insulin resistance indices (insulin sensitivity index from OGTT and glucose infusion rate [GIR] from the clamp) when analyzing all participants together (GIR: r = -0.82, P < 0.001) or separately (P < 0.01). CGM mean negatively correlated with insulin secretion indices only when the two groups were analyzed together (P < 0.05). When analyzing each group separately, CGM mean positively correlated with insulin levels during the OGTT and hyperglycemic step of the clamp in the OB(-T2D), but not the OB(+T2) group. CONCLUSIONS: Our findings suggest that CGM metrics, in particular, the simplest metric, CGM mean, was associated with the degree of insulin resistance. Further research is needed to determine whether CGM mean could be a tool to identify normal glucose-tolerant individuals with obesity potentially at risk for progressing to prediabetes and diabetes, and whether its convergence with increased glycemic variability can further contribute to risk prediction of glycemic disorders.

Source: Diabetes Care

DOI: 10.2337/dc25-1395

Authors: Renata Belfort-DeAguiar, Catherine W. Yeckel, Ahmed Elshafie, Ana Paula Comarella, Janice J. Hwang

Institutions: The University of Texas at San Antonio Health Science Center, Yale University, UConn Health, Universidade Federal do Espírito Santo, University of North Carolina at Chapel Hill

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences, Division of Diabetes, Endocrinology, and Metabolic Diseases