[Neural networks in the predictive diagnosis of cognitive impairment in type 1 and type 2 diabetes mellitus].

BACKGROUND: Cognitive dysfunction, including mild cognitive impairment and dementia, is increasingly recognized as a serious complication of diabetes mellitus (DM) that affects patient well-being and disease management. Magnetic resonance imaging (MRI)-studies have shown varying degrees of cortical atrophy, cerebral infarcts, and deep white matter lesions. To explain the relationship between DM and cognitive decline, several hypotheses have been proposed, based on the variability of glycemia leading to morphometric changes in the brain. The ability to predict cognitive decline even before its clinical development will allow the early prevention of this pathology, as well as to predict the course of the existing pathology and to adjust medication regimens. AIM: To create a computer neural network model for predicting the development of cognitive impairment in DM on the basis of brain neuroimaging techniques. MATERIALS AND METHODS: The study was performed in accordance with the standards of good clinical practice; the protocol was approved by the Ethics Committee. The study included 85 patients with type 1 diabetes and 95 patients with type 2 diabetes, who were divided into a group of patients with normal cognitive function and a group with cognitive impairment. The patient groups were comparable in age and duration of disease. Cognitive impairment was screened using the Montreal Cognitive Assessment Scale. Data for glycemic variability were obtained using continuous glucose monitoring (iPro2, Libre). A standard MRI scan of the brain was performed axially, sagittally, and coronally on a Signa Creator E, GE Healthcare, 1.5 Tesla, China. For MRI data processing we used Free Surfer program (USA) for analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies, and for segmentation we used Recon-all batch program directly. All statistical analyses and data processing were performed using Statistica Statsofi software (version 10) on Windows 7/XP Pro operating systems. The IBM WATSON cognitive system was used to build a neural network model. RESULTS: As a result of the study, cognitive impairment in DM type 1was predominantly of mild degree 36.9% (n=24) and moderate degree 30.76% (n=20), and in DM type 2 mild degree 37% (n=30), moderate degree 49.4% (n=40) and severe degree 13.6% (n=11). Cognitive functions in DM type 1 were impaired in memory and attention, whereas in DM type 2 they were also impaired in tasks of visual-constructive skills, fluency, and abstraction (p0.001). The analysis revealed differences in glycemic variability indices in patients with type 1 and type 2 DM and cognitive impairment. Standard MRI of the brain recorded the presence of white and gray matter changes (gliosis and leukoareosis). General and regional cerebral atrophy is characteristic of type 1 and type 2 DM, which is associated with dysglycemia. When building neural network models for type 1 diabetes, the parameters of decreased volumes of the brain regions determine the development of cognitive impairment by 93.5%, whereas additionally, the coefficients of glycemic variability by 98.5%. The same peculiarity was revealed in type 2 DM 95.3% and 97.9%, respectively. CONCLUSION: In DM type 1 and type 2 with cognitive impairment, elevated coefficients of glycemic variability are more frequently recorded. This publication describes laboratory and instrumental parameters as potential diagnostic options for effective management of DM and prevention of cognitive impairment. Neural network models using glycemic variability coefficients and MR morphometry allow for predictive diagnosis of cognitive disorders in both types of diabetes.

[Cerebral perfusion and tractography in obese children]. / Rezul'taty izucheniya tserebral'noi perfuzii i traktografii u detei s ozhireniem.

OBJECTIVE: To study the relationship of the structure of the white matter of the brain, neurovascularization and cognitive functions in obese children and adolescents. MATERIAL AND METHODS: The study included 64 obese patients, aged 12-17 years, and 54 children without excess body weight. A general clinical examination, neuropsychological testing (the Raven's test with the calculation of IQ, MoCA, the Rey 15-Item Memory Test (RMT), 1 and 2), magnetic resonance imaging (MR) tractography and contrast-free perfusion of the brain were conducted. RESULTS: Obese children and adolescents had both a decrease in scores on MoCA and the Raven's test, and in terms of IQ, while according to RMT-1, there were significant differences in the two groups, and in RMT-2 the results were comparable. Perfusion analysis showed a decrease in vascularization in the white matter area of the occipital lobe on the left and its increase in the temporal lobe area also on the left. When assessing the white matter according to MR tractography, a decrease in fractional anisotropy was noted in the area of the hook-shaped beam on the right and left, anterior and posterior commissural tracts. These changes were correlated with neuropsychological results. CONCLUSION: In obese children and adolescents, there was a destruction of the integrity of the white matter and neurovascularization of the brain associated with a deficit of cognitive functions.

[Brain tractography in type 1 and 2 diabetes and cognitive impairment]. / Traktografiya golovnogo mozga pri sakharnom diabete i kognitivnykh narusheniyakh.

OBJECTIVE: To study conductive white matter pathways in patients with type 1 and type 2 diabetes with- and without cognitive impairment. MATERIALS AND METHODS: The study included 85 patients with type 1 and 95 patients with type 2 diabetes who were divided into those who had normal cognitive functions and those with cognitive impairment. The groups were comparable in age and duration of the disease. Screening of cognitive functions was performed using the Montreal Scale for the Evaluation of Cognitive Function (MoCA-test). Brain MRI was performed on 1.5 Tesla system. All statistical analyses and data processing were performed using Statistica (Statsoft) software (version 10) on Windows 7/XP Pro operating systems. RESULTS: The study revealed the prevalence of mild and moderate cognitive impairment in type 1 diabetes, medium and severe in type 2 diabetes, which were mainly manifested by memory, attention and optical-spatial disorders. Intergroup analysis of the brain tractography did not show any difference in the integrity of tracts in type 1 and type 2 diabetes, but the most significant risk factors of pathway impairment were identified. They include arterial hypertension (H=6.602833, p=0.0368), degree of polyneuropathy (H=15.30420, p=0.0005), degree of nephropathy (H=9.993923, p=0.0068), degree of retinopathy (H=8.445891, p=0.0376) for type 1 diabetes and age (H=7.381742, p=0.0607), (H=8.359127, p=0.0391) for type 2 diabetes. Cholesterol level contributes to the risk in both types (H=4.009380, p=0.0452; H=4.057357, p=0.0440; H=6.454558, p=0.0111). The corticospinal and commissural tracts are most susceptible to damage. CONCLUSIONS: There are no significant differences in axial cerebral tract diffusion in patients with type 1 and type 2 diabetes with- and without cognitive impairment. However, the most important risk factors for white matter structure damage, namely, arterial hypertension, diabetic complications, cholesterol levels and age, are verified.

[Role of magnetic resonance imaging in the early diagnosis of cognitive impairments in patients with type 1 diabetes mellitus].

OBJECTIVE: To assess the role of brain magnetic resonance imaging (MRI) in patients with type 1 diabetes mellitus (DMI) in relation to clinical, metabolic, and psychoneurological disorders. MATERIAL AND METHODS: Fifty-eight patients aged 16 to 30 years with DM1 were examined; a control group consisted of 29 healthy young people matched by gender and age. Their examination involved clinical, metabolic, and psychological testing. The quality of life was assessed using the general Medical Outcomes Study Short Form (MOS SF-36) and the specific Audit-Dependent Quality of Life (AdDQoL). The Montreal Cognitive Assessment (MoCa test) was employed to screen for cognitive impairments. All the patients were advised by a neurologist. Brain MRI using a 1.0 T Siemens Magnetom scanner was carried out to evaluate structural changes in the central nervous system. RESULTS: The examination of the patients with DM1 revealed the signs of grey matter atrophy, enlarged Virchow-Robin spaces, white matter injury, which correlated with the presence of chronic hyperglycemia, cognitive impairments, and microvascular complications. CONCLUSIONS: Routine brain MRI is best carried out in patients with DMI and poor disease control to timely implement therapeutic-and-prophylactic measures for preventing cognitive impairments and improving the quality of life.