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Vitamin D deficiency and dysfunctional adipose tissue are involved in the development of cardiometabolic disturbances (eg, hypertension, insulin resistance, type 2 diabetes mellitus, obesity, and dyslipidemia). We evaluated the relation between vitamin D and adipocytokines derived from adipose tissue. We studied 50 obese individuals who were classified into different subgroups according to medians of observed anthropometric parameters (body mass index, body fat percentage, waist circumference, and trunk fat mass). There was a negative correlation between vitamin D level and leptin and resistin (r = -.61, P < .01), while a positive association with adiponectin concentrations was found (r = .7, P < .001). Trend estimation showed that increase in vitamin D level is accompanied by intensive increase in adiponectin concentrations (growth coefficient: 12.13). In conclusion, a positive trend was established between vitamin D and the protective adipocytokine adiponectin. The clinical relevance of this relationship needs to be investigated in larger studies.

The diagnosis of metabolic syndrome (MetS) has a leading role in the early prevention of chronic disease, such as cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease. It would be very greatful that MetS diagnosis can be predicted in everyday clinical practice. This paper presents artificial neural network (ANN) prediction of the diagnosis of MetS that includes solely non-invasive, low-cost and easily-obtained diagnostic methods. This solution can extract the risky persons and suggests complete tests only on them by saving money and time. ANN input vectors are very simple and contain solely non-invasive, low-cost and easily-obtained parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures. ANN output is M e t S-coefficient in true/false form, obtained from MetS definition of International Diabetes Federation (IDF). ANN training, validation and testing are conducted on the large dataset that includes 2928 persons. Feed-forward ANNs with 1-100 hidden neurons were considered and an optimal architecture were determinated. Comparison with other authors leads to the conclusion that our solution achieves the highest positive predictive value P P V = 0.8579. Further, obtained negative predictive value N P V = 0.8319 is also high and close to PPV, which means that our ANN solution is suitable both for positive and negative MetS prediction.

Vitamin D deficiency is associated with cardiometabolic risk factors (eg, hypertension, insulin resistance, type 2 diabetes mellitus, obesity, and dyslipidemia). We studied 50 obese patients (body mass index [BMI]: 43.5 ± 9.2 kg/m(2)) and 36 normal weight participants (BMI: 22.6 ± 1.9 kg/m(2)). The prevalence of vitamin D deficiency (25-hydroxyvitamin D, 25(OH)D < 50 nmol/L) was 88% among obese patients and 31% among nonobese individuals; 25(OH)D levels were lower in the obese group (27.3 ± 13.7 vs 64.6 ± 21.3 nmol/L; P < .001). There was a negative correlation between vitamin D level and anthropometric indicators of obesity: BMI (r = -0.64; P < .001), waist circumference (r = -0.59; P < .001), and body fat percentage (r = -0.64; P < .001) as well as with fasting plasma insulin (r = -0.35; P < .001) and homeostasis model assessment of insulin resistance (r = -0.35; P < .001). In conclusion, we observed a higher prevalence of vitamin D deficiency among obese participants and this was associated with a proatherogenic cardiometabolic risk profile.

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Knowledge-based economy has become a major trend in international society in the 21st century. However, today?s strategies place a greater emphasis on sustainability than in the past, while continuing to emphasize the importance of education and its connection with labour market. There has been a re-orientation, where resource, eco-efficiency and innovation have become major elements for achieving national objectives and a relevant level of competitiveness. This article deals with 30 indices, which define the competitiveness of a specific economy, and involve knowledge parameters. They are classified into four main categories and one special category. They are then analysed regarding the participation of Serbia and their availability. The main focus of this paper is to give detailed analyses of energy indices, as a special category of knowledge indexes. It has been shown that Serbia, in many cases, was not included in the study analysis or that there was insufficient information about Serbia?s position. This article shows that only a part of the presented indices includes Serbia. It is concluded that a new, revised model is needed that will include more exact indicators.