Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Paloma Pérez-López; Pablo Férnandez-Velasco; Pilar Bahillo-Curieses; Daniel de Luis; Gonzalo Díaz-Soto

doi:10.1007/s12020-023-03511-7

Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics

Endocrine. 2023 Dec;82(3):560-568. doi: 10.1007/s12020-023-03511-7. Epub 2023 Sep 11.

Authors

Paloma Pérez-López^#^{1

2}, Pablo Férnandez-Velasco^{1

2}, Pilar Bahillo-Curieses³, Daniel de Luis^{1

2}, Gonzalo Díaz-Soto^#^{4

5}

Affiliations

¹ Endocrinology and Nutrition Department, Hospital Clínico Universitario Valladolid, Avenida Ramón y Cajal 3; CP, 47005, Valladolid, Spain.
² Centro de Investigación en Endocrinología y Nutrición Clínica (CIENC), Universidad de Valladolid, Avenida Ramón y Cajal 3; CP, 47005, Valladolid, Spain.
³ Pediatrics Department, Hospital Clínico Universitario Valladolid, Avenida Ramón y Cajal 3; CP, 47005, Valladolid, Spain.
⁴ Endocrinology and Nutrition Department, Hospital Clínico Universitario Valladolid, Avenida Ramón y Cajal 3; CP, 47005, Valladolid, Spain. gonzalo.diaz.soto@uva.es.
⁵ Centro de Investigación en Endocrinología y Nutrición Clínica (CIENC), Universidad de Valladolid, Avenida Ramón y Cajal 3; CP, 47005, Valladolid, Spain. gonzalo.diaz.soto@uva.es.

^# Contributed equally.

Abstract

Objective: To evaluate the impact of glucose variability on the relationship between the GRI and other glycemic metrics in a cohort of pediatric and adult patients with type 1 diabetes (T1D) using intermittent scanning continuous glucose monitoring (isCGM).

Methods: We performed a cross-sectional study of 202 patients with T1D under intensive insulin treatment (25.2% CSII) using isCGM. Clinical, metabolic, and glycemic metrics were collected, and the GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. The correlation between the GRI and other classical glycometrics in relation to the coefficient of variation (CV) was evaluated.

Results: A total of 202 patients were included (53% male; 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution (TIR 59.0 ± 17.0%; CV 39.8 ± 8.0%; GMI 7.3 ± 1.1%). The mean GRI was 54.0 ± 23.3 with a CHypo and CHyper component of 5.7 ± 4.8 and 23.4 ± 14.3, respectively. A strong negative correlation was observed between the GRI and TIR (R = -0.917; R² = 0.840; p < 0.001), showing differences when dividing patients with low glycemic variability (CV < 36%) (R = -0.974; R² = 0.948; p < 0.001) compared to those with greater CV instability (≥36%) (R = -0.885; R² = 0.784; p < 0.001). The relationship of GRI with its two components was strongly positive with CHyper (R = 0.801; R² = 0.641; p < 0.001) and moderately positive with CHypo (R = 0.398; R² = 0.158; p < 0.001). When the GRI was evaluated with the rest of the classic glycemic metrics, a strong positive correlation was observed with HbA1c (R = 0.617; R² = 0.380; p < 0.001), mean glucose (R = 0.677; R² = 0.458; p < 0.001), glucose standard deviation (R = 0.778; R² = 0.605; p < 0.001), TAR > 250 (R = 0.801; R² = 0.641; p < 0.001), and TBR < 54 (R = 0.481; R² = 0.231; p < 0.001).

Conclusions: The GRI correlated significantly with all the glycemic metrics analyzed, especially with the TIR. Glycemic variability (GV) significantly affected the correlation of the GRI with other parameters and should be taken into consideration.

Keywords: Coefficient of variation; GMI; GRI; Glucose variability; TIR.

MeSH terms

Adolescent
Adult
Blood Glucose / metabolism
Blood Glucose Self-Monitoring
Child
Cross-Sectional Studies
Diabetes Mellitus, Type 1* / drug therapy
Female
Glucose
Humans
Hypoglycemia*
Male
Young Adult

Substances

Blood Glucose
Glucose