Sandra Herranz-Antolín, Clara Coton-Batres, María Covadonga López-Virgos, Verónica Esteban-Monge, Visitación Álvarez-de Frutos, Leonel Pekarek, Miguel Torralba
{"title":"按变异系数分层的 1 型糖尿病患者队列中的血糖风险指数(GRI)。真实生活研究。","authors":"Sandra Herranz-Antolín, Clara Coton-Batres, María Covadonga López-Virgos, Verónica Esteban-Monge, Visitación Álvarez-de Frutos, Leonel Pekarek, Miguel Torralba","doi":"10.1089/dia.2024.0181","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Objective</i></b>: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). <b><i>Patients and Methods</i></b>: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. <b><i>Results:</i></b> Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; <i>P</i> < 0.001), CHypo (2.9% [SD 0.3]; <i>P</i> < 0.001), CHyper (6.3% [SD 1.4]; <i>P</i> < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; <i>P</i> < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; <i>P</i> < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; <i>P</i> < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; <i>P</i> = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; <i>P</i> = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; <i>P</i> < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; <i>P</i> < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; <i>P</i> = 0.004). <b><i>Conclusions</i></b>: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study.\",\"authors\":\"Sandra Herranz-Antolín, Clara Coton-Batres, María Covadonga López-Virgos, Verónica Esteban-Monge, Visitación Álvarez-de Frutos, Leonel Pekarek, Miguel Torralba\",\"doi\":\"10.1089/dia.2024.0181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Objective</i></b>: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). <b><i>Patients and Methods</i></b>: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. <b><i>Results:</i></b> Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; <i>P</i> < 0.001), CHypo (2.9% [SD 0.3]; <i>P</i> < 0.001), CHyper (6.3% [SD 1.4]; <i>P</i> < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; <i>P</i> < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; <i>P</i> < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; <i>P</i> < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; <i>P</i> = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; <i>P</i> = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; <i>P</i> < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; <i>P</i> < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; <i>P</i> = 0.004). <b><i>Conclusions</i></b>: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.</p>\",\"PeriodicalId\":11159,\"journal\":{\"name\":\"Diabetes technology & therapeutics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes technology & therapeutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/dia.2024.0181\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes technology & therapeutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/dia.2024.0181","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
摘要
目的:分析 GRI,并根据变异系数 (CV) 评估使用间歇扫描连续血糖监测 (isCGM) 的真实 1 型糖尿病 (DM) 患者队列中可能存在的差异。患者:横断面研究。纳入了 447 名使用 isCGM 且依从性≥ 70% 的成人用户。计算 GRI 及其低血糖 (CHypo) 和高血糖 (CHyper) 组成部分。结果:平均年龄 44.6 岁(SD 13.7),57.7% 为男性;DM 发病年龄 24.5 岁(SD 14.3),演变时间 20.6 年(SD 12.3)。在 CV > 36% (52.8%) 与 CV ≤ 36% (47.2%) 的患者中,GRI [18.8% (SD 1.9);p < 0.001]、CHypo [2.9% (SD 0.3);p < 0.001]、CHyper [6.3% (SD 1.4);p < 0.001]和所有经典血糖参数(1 级以上时间除外)均存在差异。在 CV > 36% 的患者中,与 GRI 独立相关的变量是范围内时间 (TIR) (β = -1.49; CI 95% -1.63 to -1.37; p < 0.001)、血糖管理指标 (GMI) (β = -7.22; CI 95% -9.53 to -4.91; p < 0.001) 和 CV (β = 0.85; CI 95% 0.69 to 1.02; p < 0.001)。然而,在 CV ≤ 36% 的患者中,年龄 (β = 0.15; CI 95% 0.03 to 0.28; p = 0.019)、发病年龄 (β = -0.15; CI 95% -0.28 to -0.02; p = 0.023)、TIR (β = -1.35; CI 95% -1.46 to -1.23; p < 0.001)、GMI (β = -6.结论:在这项研究中,根据 GRI 与代谢控制独立相关的因素被血糖变异性所改变。
Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study.
Objective: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). Patients and Methods: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Results: Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; P < 0.001), CHypo (2.9% [SD 0.3]; P < 0.001), CHyper (6.3% [SD 1.4]; P < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; P < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; P < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; P < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; P = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; P = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; P < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; P < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; P = 0.004). Conclusions: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.
期刊介绍:
Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.