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Non-commercial insulin delivery closed-loop systems: results of comparative research with traditional methods of insulin therapy

https://doi.org/10.14341/DM13366

Abstract

BACKGROUND: The problem of achieving target glycemic control in patients with diabetes mellitus type 1 (T1DM) remains relevant. Currently, new, more technologically advanced methods of insulin therapy (IT) are being actively developed — one of them is the IT method of a closed loop (do-it-yourself closed loop system, DIY-CLS). This type of therapy is not registered in Russia, however, patients install these systems themselves, in connection with which it seems important to discuss the principle of DIY-CLS operation, the possibilities and prospects of their use.

OBJECTIVE: To evaluate the glycemic control indicators, the frequency of acute complications of diabetes in patients with type 1 diabetes on different types of therapy.

MATERIALS AND METHODS: We observed 98 patients who were divided into 3 groups: patients using MII (n=40), patients with CSII (n=40) and patients with DIY-CLS (n=18). All groups were comparable in age, sex and the duration of T1DM history.

RESULTS: The majority of patients were women (73.47%), the average age was 33.3±2.4 years, the duration of diabetes was 17.1±2.2 years. It was found that in patients from the DIY-CLS group, compared with the MII and CSII groups, according to continuous glucose monitoring, the time in range was significantly higher, the mean glucose, standard deviation, time above range 10.1–13.9 mmol/l and time above range >13.9 mmol/l were significantly lower. The number of hypoglycemic states and hyperglycemic events leading to the development of ketosis was comparable.

CONCLUSION: Glycemic control values were significantly better in patients using DIY-CLS. Patients in the DIY-CLS group more often achieved target time above range and coefficient of variation levels.

About the Authors

M. E. Chernaya
Pavlov First Saint Petersburg State Medical University
Russian Federation

Maria E. Chernaya - PhD, Assistant, Professor.

6-8 L’va Tolstogo street, 197022 Saint Petersburg


Competing Interests:

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



Y. S. Khalimov
Pavlov First Saint Petersburg State Medical University
Russian Federation

Yuriy S. Khalimov - MD, PhD, Professor; Scopus Author ID: 55531165300.

Saint Petersburg


Competing Interests:

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



A. R. Volkova
Pavlov First Saint Petersburg State Medical University
Russian Federation

Anna R. Volkova - MD, PhD, Professor; Scopus Author ID: 57200116986.

Saint Petersburg


Competing Interests:

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



A. V. Lisker
Pavlov First Saint Petersburg State Medical University
Russian Federation

Anna V. Lisker - PhD.

Saint Petersburg


Competing Interests:

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



A. A. Nersesyan
Pavlov First Saint Petersburg State Medical University
Russian Federation

Artem A. Nersesyan - clinical resident.

Saint Petersburg


Competing Interests:

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



E. V. Korotkova
Pavlov First Saint Petersburg State Medical University
Russian Federation

Elena V. Korotkova - student.

Saint Petersburg


Competing Interests:

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



Y. A. Obiedkova
Pavlov First Saint Petersburg State Medical University
Russian Federation

Yulia A. Obiedkova - student.

Saint Petersburg


Competing Interests:

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



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Supplementary files

1. Figure 1. Operating principles of the CRM (Control-to-Range Module) and CTM (Control-to-Target Module) strategies in the MDLAP algorithm.
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Type Исследовательские инструменты
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2. Figure 2. Principle of closed-loop therapy using algorithms with data transmission systems to the DIY-CLS cloud storage.
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Type Исследовательские инструменты
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3. Figure 3. Flowchart of participant enrollment.
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Type Исследовательские инструменты
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4. Figure 4. Number of patients achieving target glycated hemoglobin levels, target time in range (TIR), and coefficient of variation.
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Type Исследовательские инструменты
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5. Figure 5. Comparison of median rates of acute diabetes complications.
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Type Исследовательские инструменты
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Review

For citations:


Chernaya M.E., Khalimov Y.S., Volkova A.R., Lisker A.V., Nersesyan A.A., Korotkova E.V., Obiedkova Y.A. Non-commercial insulin delivery closed-loop systems: results of comparative research with traditional methods of insulin therapy. Diabetes mellitus. 2026;29(2):157-168. (In Russ.) https://doi.org/10.14341/DM13366

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