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dc.contributor.authorPérez López, Paloma
dc.contributor.authorFernández Velasco, Pablo
dc.contributor.authorBahillo Curieses, María del Pilar 
dc.contributor.authorLuis Román, Daniel Antonio de 
dc.contributor.authorDíaz Soto, Gonzalo 
dc.date.accessioned2023-10-30T09:27:30Z
dc.date.available2023-10-30T09:27:30Z
dc.date.issued2023
dc.identifier.citationEndocrine, 2023.es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/62466
dc.descriptionProducción Científicaes
dc.description.abstractObjective 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 considerationes
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherSpringeres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.classificationGlucose variabilityes
dc.subject.classificationCoefficient of variationes
dc.subject.classificationglycemia risk index (GRI)es
dc.titleImpact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metricses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The Author(s)es
dc.identifier.doi10.1007/s12020-023-03511-7es
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s12020-023-03511-7es
dc.identifier.publicationtitleEndocrinees
dc.peerreviewedSIes
dc.description.projectPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLEes
dc.identifier.essn1559-0100es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco32 Ciencias Médicases


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