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Variability of glycemia in patients with type 1 diabetes mellitus: the relationship with cognitive dysfunction and the results of magnetic resonance imaging

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Abstract


Background. Variability of glycemia is an important problem in the control of diabetes mellitus. It can be assumed that cognitive impairment associated with this disease is due to the fact that variability of glycemia affects not only the structure, but also metabolism of the brain.

Objective — the study was aimed at assessing the values of glycemia variability, as well as their relationship with neuropsychological testing and magnetic resonance imaging data in patients with type 1 diabetes mellitus (DM1).

Material and methods. We carried out a one-stage observational study of sex and age matched patients with DM1 and individuals without diabetes. All participants underwent neuropsychological testing, magnetic resonance imaging (MRI), and proton magnetic resonance spectroscopy (PMRS) of the brain; fasting plasma glucose and glycated hemoglobin (HbA1c) levels were assessed. The results of continuous monitoring of glycemia were analyzed in DM1 patients followed by calculation of glycemic variability coefficients.

Results. DM1 58 patients demonstrated decrease in neuropsychological testing scores (p<0.05), decrease in the gray matter volume (p=0.004), and increase in the white matter volume (p=0.001), as well as impaired metabolism of the brain (p<0.05). Correlations between the total result of the MoCa test and LI (r=–0.34; p=0.008), MODD (r=–0.36; p=0.005), and ADRR (r=–0.28; p=0.032) were found. Negative relationship between the CONGA index and the volume of the left hippocampus (r=–0.27; p=0.044) was found. There were also some correlations between the glycemic variability indexes and the content of the main metabolites in different areas of the brain (p<0.05).

Conclusion. MD1 patients with cognitive dysfunction demonstrated anatomical and metabolic brain disorders associated with glycemic variability.


Iuliia G. Samoilova

Siberian State Medical University

Email: samoilova_y@inbox.ru
ORCID iD: 0000-0002-2667-4842
SPIN-code: 8644-8043

Russian Federation, 2, Moscowski Trakt, Tomsk, 634050

MD, PhD, Professor

Maria A. Rotkank

Siberian State Medical University

Author for correspondence.
Email: rotkank.mariya@mail.ru
ORCID iD: 0000-0002-6921-2859
SPIN-code: 1453-1060

Russian Federation, 2, Moscowski Trakt, Tomsk, 634050

MD

Natali G. Zhukova

Siberian State Medical University

Email: znatali@yandex.ru
ORCID iD: 0000-0001-6547-6622
SPIN-code: 6982-5313

Russian Federation, 2, Moscowski Trakt, Tomsk, 634050

MD, PhD, Professor

Mariia V. Matveeva

Siberian State Medical University

Email: matveeva.mariia@yandex.ru
ORCID iD: 0000-0001-9966-6686
SPIN-code: 3913-5419

Russian Federation, 2, Moscowski Trakt, Tomsk, 634050

MD, PhD

Ivan V. Tolmachev

Siberian State Medical University

Email: ivantolm@mail.ru
ORCID iD: 0000-0002-2888-5539
SPIN-code: 1074-1268

Russian Federation, 2, Moscowski Trakt, Tomsk, 634050

MD, PhD

Dmitrii A. Kudlai

Institute for Advanced Studies of the Federal Medical and Biological Agency

Email: d62@lenta.ru
ORCID iD: 0000-0002-4212-3848
SPIN-code: 4129-7880

Russian Federation, 91, Volokolamskoe sh., Moscow, 125371

MD, PhD

  1. Peyser TA, Balo AK, Buckingham BA, et al. Glycemic variability percentage: a novel method for assessing glycemic variability from continuous glucose monitor data. Diabetes Technol Ther. 2018;20(1):6-16. doi: https://doi.org/10.1089/dia.2017.0187
  2. Duarte JM. Metabolic alterations associated to brain dysfunction in diabetes. Aging Dis. 2015;6(5):304-321. doi: https://doi.org/10.14336/ad.2014.1104
  3. Ronnemaa E, Zethelius B, Sundelof J, et al. Impaired insulin secretion increases the risk of Alzheimer disease. Neurology. 2008;71(14):1065-1071. doi: https://doi.org/10.1089/dia.2017.0187
  4. Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015;3(1):75-89. doi: https://doi.org/10.1016/s2213-8587(14)70148-2
  5. Дедов И.И., Шестакова М.В., Галстян Г.Р. и др. Алгоритмы специализированной медицинской помощи больным сахарным диабетом. / Под ред. И.И. Дедова, М.В. Шестаковой. — 7-й выпуск. // Сахарный Диабет. — 2015. — Т. 18. — № 1s. — C. 1—112. [Dedov II, Shestakova MV, Galstyan GR, et al. / Dedov II, Shestakova MV, Editors. Standards of specialized diabetes care. 7th Edition. Diabetes Mellitus. 2015;18(1s):1-112. (In Russ.)]. doi: https://doi.org/10.14341/dm7078
  6. Дедов И.И., Шестакова М.В., Майоров А.Ю., и др. Алгоритмы специализированной медицинской помощи больным сахарным диабетом. / Под ред. Дедова И.И., Шестаковой М.В., Майорова А.Ю. — 8-й выпуск. // Сахарный Диабет. — 2017. — Т. 20. — №1s. — C. 1—121. [Dedov II, Shestakova MV, Mayorov AYu, et al. /Dedov II, Shestakova MV, Mayorov AY, et al. Standards of specialized diabetes care. 8th Edition. Diabetes Mellitus. 2017;20(1s):1-121. (In Russ.)]. doi: https://doi.org/10.14341/dm20171s8
  7. Holmes CS, Richman LC. Cognitive profiles of children with insulin-dependent diabetes. J Dev Behav Pediatr. 1985;6(6):323-326. doi: https://doi.org/10.1097/00004703-198512000-00001
  8. Ryan CM, Williams TM, Finegold DN, Orchard TJ. Cognitive dysfunction in adults with type 1 (insulin-dependent) diabetes mellitus of long duration: effects of recurrent hypoglycaemia and other chronic complications. Diabetologia. 1993;36(4):329-334. doi: https://doi.org/10.1007/bf00400236
  9. Ryan CM, Williams TM. Effects of insulin-dependent diabetes on learning and memory efficiency in adults. J Clin Exp Neuropsychol. 1993;15(5):685-700. doi: https://doi.org/10.1080/01688639308402589
  10. Brands AMA, Biessels GJ, De Haan EHF, et al. The effects of type 1 diabetes on cognitive performance: a metaanalysis. Diabetes Care. 2005;28(3):726-735. doi: https://doi.org/10.2337/diacare.28.3.726
  11. Rizzo MR, Marfella R, Barbieri M, et al. Relationships between daily acute glucose fluctuations and cognitive performance among aged type 2 diabetic patients. Diabetes Care. 2010;33(10):2169-2174. doi: https://doi.org/10.2337/dc10-0389
  12. Zhong Y, Zhang Xy, Miao Y, et al. The relationship between glucose excursion and cognitive function in aged type 2 diabetes patients. Biomed Environ Sci. 2012;25(1):1-7. doi: https://doi.org/10.3967/0895-3988.2012.01.001
  13. Wisse LE, De Bresser J, Geerlings MI, et al. Global brain atrophy but not hippocampal atrophy is related to type 2 diabetes. J Neurol Sci. 2014;344(1-2):32-36. doi: https://doi.org/10.1016/j.jns.2014.06.008
  14. Ларионова Е.В. Магнитно-резонансная спектроскопия в исследовании мнестической деятельности. // Вестник Московского университета. Серия 14: Психология. — 2013. — № 3. — С. 59—72. [Larionova EV. Magnetic resonance spectroscopy in the study of mnestic activity. Vestnik Moskovskogo Universiteta. Seriia Xiv, Psikhologiia. 2013;(3):59-72. (In Russ.)].

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Copyright (c) 2018 Samoilova I.G., Rotkank M.A., Zhukova N.G., Matveeva M.V., Tolmachev I.V., Kudlai D.A.

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