Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes

Author:

Hoogendoorn Claire J.12ORCID,Hernandez Raymond34,Schneider Stefan45,Harmel Mark6,Pham Loree T.3,Crespo-Ramos Gladys2,Agarwal Shivani27,Crandall Jill27,Peters Anne L.6,Spruijt-Metz Donna456,Gonzalez Jeffrey S.127,Pyatak Elizabeth A.3

Affiliation:

1. Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, USA

2. Division of Endocrinology, Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

3. Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA

4. Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA

5. Department of Psychology, University of Southern California, Los Angeles, CA, USA

6. Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

7. New York Regional Center for Diabetes Translation Research, Albert Einstein College of Medicine, Bronx, NY, USA

Abstract

Background: The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes. Methods: A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia. Results: Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones ( P values < .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score ≥84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores ≥58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months. Conclusions: Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

Abbott Laboratories

Publisher

SAGE Publications

Subject

Biomedical Engineering,Bioengineering,Endocrinology, Diabetes and Metabolism,Internal Medicine

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