Indices of glycemic variability as the basis for building a prognostic model for the development of diabetic complications

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Abstract

The use of mathematical indices of glycemic variability (IGV) opens up new possibilities in predicting diabetic complications, which allows more accurate correction of therapy and prevention of the development of acute and chronic conditions.

Aim. To analyze the predictive capabilities of glycemic variability indices to assess the development and progression of diabetic complications

Material and methods. The study included 307 patients with type 1 diabetes mellitus (DM1). In all patients, glycated hemoglobin (HbA1c) was assessed, as well as the main indicators of glycemic control and IGV, which were selected to predict the formation and progression of diabetic complications. Statistical analysis was carried out using the SPSS 23.0 program. To build a model for predicting diabetic complications, the logistic regression method was used.

Results. During the work, there was a decrease in HbA1c from 9.0 to 8.0% (p<0.005), as well as a change in the main parameters of carbohydrate metabolism and IGV. A predictive model for diabetic complications was built on the basis of HbA1c, mean glycemia and IGV - SD, CONGA, LI, LBGI, HBGI, MODD, MAGE, ADDR, MAG at the first study visit. The predictive model for the development of diabetic complications was considered significant at p<0.05. The resulting model showed a high sensitivity - 92% and a sufficient specificity of 85%. Not all parameters turned out to be statistically significant, however, with the exclusion of some, the sensitivity and specificity of the model decrease, which indicates the importance of each of the IGVs in predicting diabetic complications.

Concludion. By analyzing the IGV, and not just the standard methods for assessing carbohydrate metabolism, the doctor can more accurately judge the compensation for diabetes and give the patient individual recommendations for treatment. Evaluation of GV, in particular its mathematical indices, play a significant role in predicting the development and progression of diabetic complications in patients with DM1 in childhood and adolescence.

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About the authors

Marina V. Koshmeleva

Siberian State Medical University

Email: mvbulavko@mail.ru
ORCID iD: 0000-0001-8142-1226

MD, Associate Professor of the Department of Pediatrics with the course, Candidate of Medical Sciences

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Iuliia G. Samoilova

Siberian State Medical University

Author for correspondence.
Email: samoilova_y@inbox.ru
ORCID iD: 0000-0002-4377-7309

MD, Head of the Department of Pediatrics with the Course of Endocrinology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Svetlana V. Fomina

Siberian State Medical University

Email: statfom@mail.ru
ORCID iD: 0000-0001-7517-3393

MD, Head of the Department of Ultrasound Diagnostics, Candidate of Medical Sciences, assistant

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Ekatherina I. Trifonova

Siberian State Medical University

Email: trifonowa.18@yandex.ru
ORCID iD: 0000-0002-2825-5035

MD, Assistant of the Department of Pediatrics with the course of Endocrinology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Dmitrii A. Kachanov

Siberian State Medical University

Email: doctorssmupf@gmail.com
ORCID iD: 0000-0002-6519-8906

MD, Assistant of the Department of Pediatrics with the course of Endocrinology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Vera E. Yun

Siberian State Medical University

Email: verayun05@gmail.com
ORCID iD: 0000-0002-9127-8619

MD, Assistant of the Department of Pediatrics with the course of Endocrinology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Maria S. Gaun

Siberian State Medical University

Email: davidovamsergeevna@mail.ru
ORCID iD: 0000-0002-9770-3989

MD, Assistant of the Department of Pediatrics with the course of Endocrinology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Dmitry A. Kudlay

Lomonosov Moscow State University

Email: d624254@gmail.com
ORCID iD: 0000-0003-1878-4467

Professor of the Department of Pharmacognosy and Industrial Pharmacy, Faculty of Fundamental Medicine, Corresponding Member

Russian Federation, Leninskie Gory, 1, Moscow, 119991

Maria A. Koshkarova

Siberian State Medical University

Email: mariakoskarova0@gmail.com
ORCID iD: 0009-0002-8342-8872

student of the Faculty of Medicine and Biology

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Lilit A. Pogosyan

Siberian State Medical University

Email: lilit_pogosyan_01@mail.ru
ORCID iD: 0009-0008-9593-7299

student of the Faculty of Medicine and Biology

 

Russian Federation, Moskovsky tract, 2, Tomsk, 634050

Elena G. Novoselova

MGIMO University

Email: egnovoselova@gmail.com
ORCID iD: 0009-0005-7076-9143

Professor of the Department of Innovation Management

Russian Federation, Prospect Vernadskogo, 76, Moscow, 119454

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Supplementary files

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2. ROC curve illustrating the quality of the model (sensitivity - 92%; specificity - 85%)

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