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

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 (HbA_1c) 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.

diabetes, MRI, PMRS, cognitive dysfunction, glycemia variability

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