[Structural and functional characteristics of the brain and their role in the development of eating behaviour in obesity: A review].

Obesity is a major public health problem that requires new approaches. Despite all interventions, the behavioural and therapeutic interventions developed have demonstrated limited effectiveness in curbing the obesity epidemic. Findings from imaging studies of the brain suggest the existence of neural vulnerabilities and structural changes that are associated with the development of obesity and eating disorders. This review highlights the clinical relevance of brain neuroimaging research in obese individuals to prevent risky behaviour, early diagnosis, and the development of new safer and more effective treatments.

[Body composition in sarcopenia in middle-aged individuals].

Sarcopenia is characterized by a progressive loss of muscle mass, strength, and function, leading to poor outcomes and reduced quality of life. In middle age, the decrease in muscle mass begins to be progressive. Bioimpedancemetry allows diagnosing this condition before the onset of clinical symptoms. THE PURPOSE OF THE STUDY: to evaluate the parameters of body composition in the early diagnosis of sarcopenia in middle-aged people. MATERIALS AND METHODS: The participants were divided into two groups - the main one with sarcopenia - 146 people and the control group - 75 people. The complex of examinations included: neuropsychological testing (Hospital Anxiety and Depression Scale (HADS), quality of life questionnaire for patients with sarcopenia (SarQoL), short health assessment form (SF-36)), 4-meter walking speed test, dynamometry and bioimpedancemetry. The results of neuropsychological examination did not differ in the main and control groups. Patients with sarcopenia showed a decrease in muscle strength according to dynamometry. The scores of the walking speed assessment test in the study group were significantly higher than in the control group. The main and control groups had excessive body weight. According to the results of bioimpedanceometry, the main group had increased fat mass, percentage of fat mass, visceral fat area, and fat mass index compared with the control group. Skeletal muscle mass was less in the main group, probable sarcopenia was confirmed by decreased appendicular mass, decreased protein and mineral content was also recorded. There was a more pronounced decrease in cell mass in the main group. In patients with sarcopenia the volume of intracellular and extracellular fluid was less than in the control group. Significant differences were considered at p<0.05. CONCLUSIONS: the introduction of bioimpedancemetry and dynamometry into early screening for muscle mass reduction will allow timely start of therapeutic and preventive measures even in middle age, which will lead to a decrease in the progression of sarcopenia in the elderly, as well as improve the quality of life.

[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.

[A prognostic model of cognitive impairment in patients with type 1 diabetes mellitus]. / Prognosticheskaya model' razvitiya kognitivnykh narushenii u patsientov s sakharnym diabetom 1-go tipa.

OBJECTIVE: To develop a model for the prognosis of cognitive impairment in patients with type 1 diabetes mellitus based on data from proton magnetic resonance spectroscopy. MATERIALS AND METHODS: Patients with type 1 diabetes mellitus and individuals without diabetes were examined (control group). All participants were evaluated for carbohydrate metabolism, underwent neuropsychological testing (MoCa test), proton magnetic resonance spectroscopy of the brain. Statistical processing of the results was performed using the IBM SPSS Statistics 20.0 program. The predictive model is calculated using discriminant analysis. RESULTS: Based on the data of proton magnetic resonance spectroscopy, a predictive model for the development of cognitive impairment in patients with type 1 diabetes mellitus was obtained using discriminant analysis. CONCLUSIONS: The method for the early diagnosis of cognitive impairment allows predicting the development of cognitive dysfunction in patients with type 1 diabetes in the early stages and can be used in clinical practice to assess the effectiveness of preventive therapy for cognitive impairment.

[Markers for cognitive impairments and variability of glycaemia in patients with type 1 diabetes mellitus]. / Markery kognitivnykh narushenii i variabel'nost' glikemii u patsientov s sakharnym diabetom 1-go tipa.

AIM: To analyze the relationship between the markers of cognitive impairment and the variability of glycaemia in patients with DM type 1. MATERIAL AND METHODS: Patients with DM type 1 and people without DM (the control group) were examined. Neuropsychological testing (MoCA-test), brain MRI and proton magnetic resonance spectroscopy of the brain, as well as parameters of carbohydrate metabolism (fasting blood glucose, glycated hemoglobin and glycemic variability coefficients) were used. RESULTS AND CONCLUSION: Data on the decrease in the overall performance of the MoCA-test (in particular, on assignments to memory and attention domains), atrophic changes in the cerebral cortex and violations of the content of the main metabolites of brain cells in patients with DM type 1 in comparison with the control group were obtained. A number of positive and negative correlations between these disorders and coefficients of glycemic variability were found in patients with DM type 1. The results suggest a significant negative effect of high levels of glycaemia variability on cognitive functions in patients with DM type 1.

A Method for Quantitative Evaluation of the Results of Postural Tests.

A method for quantitative evaluation of the results of postural tests is proposed. The method is based on contact-free measurements of 3D coordinates of body point movements. The result can serve as an integral test based on the Mahalanobis distance.