Risks for obesity development, features of food behavior and bio-impedansemetric parameters in adolescents

Background. The problem of obesity in adolescents has gained its medical and social relevance since the incidence of the disease is steadily increasing. The purpose of the study is to improve the effectiveness of early diagnosis and prevention of obesity in adolescents. Materials and methods. To study the risk factors for obesity, a comparative assessment of lifestyle among adolescents aged 15–17 years with obesity, overweight and physiological body weight was conducted. In adolescents of the same age with comorbid obesity (basic group), overweight and physiological body weight (two comparison groups), the types of food behavior were determined using the Dutch eating behavior questionnaire (DEBQ). Bioelectrical indicators of bio-impedancemetric analysis were studied on a Tanita apparatus (Japan). The probability of differences in mean values was determined using Student’s t-test. Results. It was found that the frequency of imbalance of the food pyramid, movement disorders, sleep and rest, nervous tension, burdened heredity increased significantly among obese adolescents. Obesity was found to dominate by external type of eating behavior (57.2 %); at the excess weight — restrictive type (60 %); at physiological body weight — emotional type (40 %). Bio-impedancemetry showed that the reliable bioelectrical parameters of obesity were total fat (35.5 ± 4.5 % and 29 ± 2 % vs. 17.5 ± 2.5 %); visceral fat (17 ± 2 % and 12 ± 3 % vs. 8.5 ± 1.5 %); and metabolic age (33 ± 8 and 21.5 ± 1.5 years versus 16 ± 1 years). Conclusions. Risk factors for obesity include the imbalance of the food pyramid, movement disorders, sleep and rest, nervous tension, bad habits, burdened here­dity. The predominance of external type of eating behavior in obesity, restrictive — in overweight and emotional type in adolescents with physiological body weight should be taken into account when prescribing personalized lifestyle adjustments. The most informative bioelectrical parameters of bio-impedancemetry were total fat, visceral fat, and metabolic age. Bio-impedancemetry as a non-invasive predictive method of diagnosing obesity can be re­commended for widespread implementation in pediatric practice.

MODEL OF HUMAN METABOLIC AGE

With aging, regular changes develop in metabolism, first of all, these are changes in lipid and carbohydrate metabolism. With accelerated aging, metabolic disorders are more expressed, which leads to the development of metabolic syndrome. The purpose of the work was to develop a method for calculating metabolic age using available clinical tests and to assess the rate of metabolic aging in people with metabolic syndrome. Materials and methods. The study involved 283 apparently healthy people aged 20 to 80 years and 82 people with metabolic syndrome. Anthropometric parameters and biochemical tests were measured for all people included in the study. The formula for calculating metabolic age was obtained by the method of stepwise multiple regression.Results. The calculation of the metabolic age in healthy people according to the formula we obtained showed that the average absolute error is 6.01 years. In 20.5% of people with metabolic syndrome, metabolic age exceeds chronological age by more than 10 years. At the same time, in the group of healthy people, the share of such people was only 4.2%. Conclusions. The method we have developed for assessing the rate of metabolic aging has a sufficiently high accuracy and can be used to assess the risk of developing metabolic syndrome and other agerelated pathology.

Estimation of human metabolic age using regression and neural network analysis

The aim is to develop the methods for assessing the rate of human aging by metabolic parameters (metabolic age). Materials and methods. The study examined 120 subjects aged 40–80 years. All the people included in the study underwent the determination of plasma glucose concentration, lipid profile – total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein, very low-density lipoprotein cholesterol and creatinine as well as the standard glucose tolerance test. Validation of the panel of indicators was carried out using regression and neural network analysis. Results. According to the study results, the standard error in determining the metabolic age using the multiple regression equation was 9.31 years, and using the neural network – 3.18 years. Conclusions. The methods that we have developed for assessing the rate of metabolic aging showed sufficient (regression analysis) and high (neural network analysis) accuracy and can be used to assess the risk of metabolic syndrome, cardiovascular disease, and type II diabetes. The implementation of the proposed methods would not only identify people at risk for pathology, but also assess the effectiveness of treatment, prevention and rehabilitation measures.

Differences in body composition between three groups of students

The purpose of this paper was to confirm whether there is any difference in body composition between the three groups: Group1 – females (N=25) aged 19–21, Group2 – females aged 22–24, (N=27), and Group3 – males aged 19–24 years (N=22). Respectively to determine whether gender and calendar age has an impact on the body composition of women and men. The parameters of the body composition applied in this research were: height of the body (height), body weight (weight), body fat (body fat), muscle mass (muscle), daily calorie intake (kcal), metabolic age (metabolic age), total body water (water), visceral fat (visceral fat) and body mass index (BMI). Measurements were performed at the Faculty of Education in Pristina. The basic statistical and distribution parameters that have been applied in this paper are Minimum, Maximum, Mean, Std. Deviation, Skewness, Kurtosis, max-D, and Kolmogorov-Smirnov test (K-S). To confirm the differences between the arithmetic mean of the groups the LSD-post hoc test was applied. The results obtained showed that differences were obtained in the arithmetic averages in all parameters of body composition between Group1 and Group2. But a statistically significant difference between these two groups has been gained in the metabolic age variable which shows that from the age of 22 in women begins a decrease in daily physical movements. Also, after processing the results by ANOVA analysis, significant statistical differences were obtained between the groups of females and the group of males, in the arithmetic mean in all variables of body composition p<0.01 (except for the variable of body composition Daily Calorie Intake (Kcal) p>0.38).