Claire J Hoogendoorn, Raymond Hernandez, Stefan Schneider, Mark Harmel, Loree T Pham, Gladys Crespo-Ramos, Shivani Agarwal, Jill Crandall, Anne L Peters, Donna Spruijt-Metz, Jeffrey S Gonzalez, Elizabeth A Pyatak
{"title":"Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes.","authors":"Claire J Hoogendoorn, Raymond Hernandez, Stefan Schneider, Mark Harmel, Loree T Pham, Gladys Crespo-Ramos, Shivani Agarwal, Jill Crandall, Anne L Peters, Donna Spruijt-Metz, Jeffrey S Gonzalez, Elizabeth A Pyatak","doi":"10.1177/19322968231164151","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones (<i>P</i> 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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418469/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/19322968231164151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/30 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.