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Automated analysis of retinal microcirculation in type 1 diabetes mellitus

https://doi.org/10.14341/DM12931

Abstract

BACKGROUND: The paper is dedicated to the assessment of the retinal microvasculature in patients with type 1 diabetes mellitus (DM) with various features of the clinical course and different stages of diabetic retinopathy (DR). Automatic analysis of optical coherence tomogram angiograms (OCT-A) was carried out with specially developed software that provides the ability to estimate quantitative vascular parameters.

AIM: The purpose of the study was to assess diagnostic accuracy of clinical parameters and imaging biomarkers in type 1 diabetes using a new algorithm for OCT-A analysis.

MATERIALS AND METHODS: The study involved 186 people (365 eyes) with type 1 diabetes. The analysis of the OCT-A parameters was performed with a specially developed software. The range of studied parameters included: foveal avascular zone (FAZ), vessel area density (VAD), skeletonized vessel density (VSD), vessel diameter index (VDI), vascular curvature index (VCI) at the level of superficial (SCP) and deep (DCP) retinal capillary plexuses in the macular region. A correlation between the involvement of OCT-A biomarkers and age, degree of DM, increased glycated hemoglobin (HbA1c) level, stage of DR, and maximally corrected visual acuity (BCVA) was analysed.

RESULTS: A significant dependence of all quantitative OCT-A parameters on the age of and duration of diabetes (p<0.05) was revealed. An increase in FAZ SCP (K=0.788, p=0) and DCP (K=0.764, p=0.03); decrease in VAD SCP (K=-0.476, p=0) and DCP (K=-0.485, p=0); VSD SCP (K=0.692, p=0) and DCP (K=0.713, p=0); an increase in VDI SCP (K=0.698, p=0) and DCP (K=787, p<0.01), as well as an increase in the VCI SCP (K=0.735, p=0) and DCP (K=0.694, p p=0). An inverse relationship was found between HbA1c level and VAD SCP (K=-0.636, p=0) and DCP (K=-0.619, p=0.05) were identified as well as a direct relationship with VDI DCP (K=0.717, p<0.05). The influence of the HbA1c level on other parameters was not confirmed (p>0.05). The presence of correlation between BCVA and FAZ DCP (K=-0.728, p=0), as well as VSD DCP (K=-0.754, p=0) was proved.

CONCLUSION: As a result of a comprehensive analysis of clinical data and imaging biomarkers, a number of patterns that have diagnostic value in diabetic retinopathy were identified.

About the Authors

Yu. N. Yusef
Research institute of eye diseases
Russian Federation

Yusef N. Yusef - MD, PhD.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



M. H. Durzhinskaya
Research institute of eye diseases
Russian Federation

Madina H. Durzhinskaya - PhD; ResearcherID: D-3729-2018; Scopus Author ID: 57218617872.

11A Rossolimo street, 119021 Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



V. G. Pavlov
Research institute of eye diseases
Russian Federation

Vladislav G. Pavlov.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



D. V. Petrachkov
Research institute of eye diseases
Russian Federation

Denis V. Petrachkov - PhD.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



I. B. Gurevich
Federal Research Center Computer Science and Control
Russian Federation

Igor B. Gurevich - PhD in Physics and Mathematics.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



V. V. Yashina
Federal Research Center Computer Science and Control
Russian Federation

Vera V. Yashina - PhD in Physics and Mathematics.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



A. T. Tleubaev
Federal Research Center Computer Science and Control
Russian Federation

Adil T. Tleubaev - master.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



V. V. Fadeyev
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Valentin V. Fadeyev - MD, PhD, Зrofessor.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



I. V. Poluboyarinova
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Irina V. Poluboyarinova - PhD.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



A. E. Goldsmid
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Anna E. Goldsmid

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



R. А. Karamullina
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation

Regina A. Karamullina

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



D. V. Lipatov
Endocrinology research centre
Russian Federation

Dmitry V. Lipatov - MD, PhD, Professor.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



M. V. Budzinskaya
Research institute of eye diseases
Russian Federation

Maria V. Budzinskaya - MD, PhD.

Moscow


Competing Interests:

Авторы декларируют отсутствие явных и потенциальных конфликтов интересов, связанных с содержанием настоящей статьи



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Supplementary files

1. Figure 1. Algorithmic scheme for the formation of features with subsequent quantitative analysis of optical coherence tomograms-angiograms.
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Type Исследовательские инструменты
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Yusef Yu.N., Durzhinskaya M.H., Pavlov V.G., Petrachkov D.V., Gurevich I.B., Yashina V.V., Tleubaev A.T., Fadeyev V.V., Poluboyarinova I.V., Goldsmid A.E., Karamullina R.А., Lipatov D.V., Budzinskaya M.V. Automated analysis of retinal microcirculation in type 1 diabetes mellitus. Diabetes mellitus. 2024;27(1):41-49. (In Russ.) https://doi.org/10.14341/DM12931

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ISSN 2072-0351 (Print)
ISSN 2072-0378 (Online)