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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.

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 p0.05. 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.

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.

Introduction.The quality of rehabilitation measures used during early functional recovery can be assessed by registering somatosensory evoked potentials (SSEP). In many patients, SSEP are either not recorded, or the results are poorly reproducible. To overcome these difficulties, we proposed to modify the method of recording SSEP in patients post ischaemic stroke. Theaimof the study was to evaluate changes in SSEP after patients with ischaemic stroke underwent motor rehabilitation in the early recovery period. Materials and methods.We examined 36 patients with acute ischaemic stroke in the middle cerebral artery territory. The severity of neurological deficits and the functional state of the nervous system were assessed using international clinical scales, based on electrophysiological and neuroimaging studies. The motor rehabilitation consisted of 10 sessions. SSEP were measured before and after the full motor rehabilitation course. We calculated the standard values for SSEP. Results.Before rehabilitation, SSEP were not detected in the ipsilateral hemisphere in 40% of patients. After a course of rehabilitation, SSEP were detected in the majority (83%) of patients, but the values showed significant inter-individual variation, and in such patients, SSEP cannot be used as an indicator of rehabilitation effectiveness. In the group of patients whose SSEP could be reliably recorded and the main components P and N were measurable, we found that the average component latency in the ipsilateral hemisphere was N = 48 15 msec and P = 55 16 msec. These values are significantly higher than in the healthy population. The amplitude parameters corresponded to the published normal values. No statistically significant changes in the latency of components N and P were observed after the course of rehabilitation. Conclusion.Using a method for measuring SSEP with spatiotemporal separation will significantly expand the range of patients whose condition, as well as the effectiveness of the rehabilitation procedures aimed at restoring lost motor function caused by ischaemic brain damage, can be monitored over time.

Background: brain-derived neurotrophic factor (BDNF) may play a role during neurorehabilitation following ischemic stroke. This study aimed to elucidate the possible role of BDNF during early recovery from ischemic stroke assisted by motor training. Methods: fifty patients were included after acute recovery from ischemic stroke: 21 first received classical rehabilitation followed by ‘motor rehabilitation using motion sensors and augmented reality’ (AR-rehabilitation), 14 only received AR-rehabilitation, and 15 were only observed. Serum BDNF levels were measured on the first day of stroke, on the 14th day, before AR-based rehabilitation (median, 45th day), and after the AR-based rehabilitation (median, 82nd day). Motor impairment was quantified clinically using the Fugl–Meyer scale (FMA); functional disability and activities of daily living (ADL) were measured using the Modified Rankin Scale (mRS). For comparison, serum BDNF was measured in 50 healthy individuals. Results: BDNF levels were found to significantly increase during the phase with AR-based rehabilitation. The pattern of the sequentially measured BDNF levels was similar in the treated patients. Untreated patients had significantly lower BDNF levels at the endpoint. Conclusions: the fluctuations of BDNF levels are not consistently related to motor improvement but seem to react to active treatment. Without active rehabilitation treatment, BDNF tends to decrease.

In this study, we aim to investigate associations between body fat parameters, glucose and lipid profiles, thyroid-stimulating hormone (TSH), and thyroid hormones (THs) levels in Tomsk-region schizophrenia patients depending upon the presence or absence of metabolic syndrome (MetS). A total of 156 psychiatric inpatients with schizophrenia who had been treated with antipsychotics for at least six months before entry were studied: 56 with and 100 without MetS. Reference groups consisted of general hospital inpatients with MetS and without schizophrenia (n = 35) and healthy individuals (n = 35). Statistical analyses were performed using the Mann–Whitney U-test, chi-square test, Spearman’s rank correlation coefficient, multiple regression analyses, and descriptive statistics. Patients with schizophrenia and MetS had significantly higher levels of free triiodothyronine (FT3) and thyroxine (FT4) compared to schizophrenia patients without MetS (3.68 [3.25; 5.50] vs. 3.24 [2.81; 3.66], p = 0.0001, and 12.68 [10.73; 15.54] vs. 10.81 [9.76; 12.3], p = 0.0001, in pmol/L, respectively). FT3 maintained an association with MetS (p = 0.0001), sex (p = 0.0001), age (p = 0.022), and high-density lipoproteins (p = 0.033). FT4 maintained an association with MetS (p = 0.0001), sex (p = 0.001), age (p = 0.014), and glucose (p = 0.009). The data obtained showed body fat parameters, glucose and lipid profiles, and THs levels in Western-Siberian schizophrenia patients depending on MetS presence or absence.