Biljana Srdic

Body mass index, body fat mass and the occurrence of amenorrhea in ballet dancers

To evaluate the connection between menstrual disorders and body fat mass, we examined a group of 30 ballet dancers and a group of 30 non-athletic girls (controls). Body mass index (BMI) was calculated and percent body fat (FAT%) was measured using the bioelectrical impedance method. A questionnaire was used to obtain age at menarche and duration of menstrual cycles. Ballet dancers had significantly lower values of BMI (18.56 ± 1.53 vs. 19.96 ± 2.12 kg/m2) and FAT% (18.85 ± 4.50 vs. 23.41 ± 4.34%) compared with controls. According to BMI, 50.0% of ballet dancers and 23.3% of the control group were underweight. Of underweight ballet dancers, 66.7% had lower values of body fat, while most underweight girls from the control group had normal body fat. Normal-weight obesity was registered in 40.9% of the control group and in 6.7% of ballet dancers. Amenorrhea was found in 20.0% and oligomenorrhea in 10.0% of ballet dancers. Ballet dancers more frequently had later appearance of menarche and menstrual cycles of longer duration than did non-athletic girls. A significant negative correlation was found between menstrual cycle duration and FAT% among ballet dancers (r =–0.415). To prevent complications caused by changes of body fat mass, we conclude that body composition assessment in ballet dancers is very important.

Expression and subcellular localization of estrogen receptors α and β in human fetal brown adipose tissue.

CONTEXT Brown adipose tissue (BAT) has the unique ability of generating heat due to the expression of mitochondrial uncoupling protein 1 (UCP1). A recent discovery regarding functional BAT in adult humans has increased interest in the molecular pathways of BAT development and functionality. An important role for estrogen in white adipose tissue was shown, but the possible role of estrogen in human fetal BAT (fBAT) is unclear. OBJECTIVE The objective of this study was to determine whether human fBAT expresses estrogen receptor α (ERα) and ERβ. In addition, we examined their localization as well as their correlation with crucial proteins involved in BAT differentiation, proliferation, mitochondriogenesis and thermogenesis including peroxisome proliferator-activated receptor γ (PPARγ), proliferating cell nuclear antigen (PCNA), PPARγ-coactivator-1α (PGC-1α), and UCP1. DESIGN The fBAT was obtained from 4 human male fetuses aged 15, 17, 20, and 23 weeks gestation. ERα and ERβ expression was assessed using Western blotting, immunohistochemistry, and immunocytochemistry. Possible correlations with PPARγ, PCNA, PGC-1α, and UCP1 were examined by double immunofluorescence. RESULTS Both ERα and ERβ were expressed in human fBAT, with ERα being dominant. Unlike ERβ, which was present only in mature brown adipocytes, we detected ERα in mature adipocytes, preadipocytes, mesenchymal and endothelial cells. In addition, double immunofluorescence supported the notion that differentiation in fBAT probably involves ERα. Immunocytochemical analysis revealed mitochondrial localization of both receptors. CONCLUSION The expression of both ERα and ERβ in human fBAT suggests a role for estrogen in its development, primarily via ERα. In addition, our results indicate that fBAT mitochondria could be targeted by estrogens and pointed out the possible role of both ERs in mitochondriogenesis.

Relationship between body mass index and body fat in children—-Age and gender differences

SUMMARY When defining obesity body mass index (BMI) has been used as the main criterion. However it indicates only the nutritional status, whereas body fat demonstrates the real body composition picture. This study aimed at analyzing the relationship between nutritional status and adiposity in the population of 2284 Serbian children (1217 boys and 1067 girls). According to BMI subjects were divided into underweight, normal-weight, overweight and obese, and %BF values (based on skinfold thickness measurements) were analyzed with regard to BMI-category, age and gender. Girls showed stronger correlation between BMI and %BF comparing to boys (r = 0.834 vs. 0.577). Differences in %BF between underweight, normal weight and overweight children from different age groups were more obvious in girls, whereas in boys younger than 8 years overlapping in %BF values between different BMI-categories was registered. In normal weight children we found age-related oscillations in %BF values: 8- and 9-year-old boys had lower %BF comparing to 7-year-old boys, which was followed by %BF increasement in 10- and 11-year-old ones; in girls %BF values gradually increased with aging, with significant jumps in 9-, 10- and 11-year-old ones. Thus, adiposity rebound may appear somehow later in boys. In overweight and obese children of both genders %BF continually increased with aging, whereas in underweight children %BF values remained unchanged. Our results pointed to age- and gender-dependent variations of %BF in normal weight and overweight children. We also indicated inconsistency between %BF and BMI especially in boys, and the need for definition of references for %BF.:

Automated extraction of decision rules for leptin dynamics-A rough sets approach

A significant area in the field of medical informatics is concerned with the learning of medical models from low-level data. The goals of inducing models from data are twofold: analysis of the structure of the models so as to gain new insight into the unknown phenomena, and development of classifiers or outcome predictors for unseen cases. In this paper, we will employ approach based on the relation of indiscernibility and rough sets theory to study certain questions concerning the design of model based on if-then rules, from low-level data including 36 parameters, one of them leptin. To generate easy to read, interpret, and inspect model, we have used ROSETTA software system. The main goal of this work is to get new insight into phenomena of leptin levels while interplaying with other risk factors in obesity.

The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients

This paper aims to investigate possible application of the rough set approach to table-organized data in the medical domain, which reveals some relationships among sagittal abdominal diameter, anthropometric parameters and cardiovascular risk factors. When applied to table-organized data, the methodology based on the rough set theory is capable of producing decision rules in the form of If-Then rules. Such rules are suitable for inspection, examination and further analysis. By examination of the selected 30 decision rules, sagittal abdominal diameter could point out a group of obese and preobese patients with high content of visceral fat with different combination and composition of cardiovascular risk factors. These results suggest that sagittal abdominal diameter could be a clinically useful marker for identification of risk factors, combination and structure of total cardiovascular risk by applying different rules in obese and preobese persons.

Body composition analysis in female ballet dancers

Introduction The aim of this study was to evaluate and compare the nutritional status and body composition in female ballet dancers and a group of non-athletic female controls. Materials and methods The study group consisted of 30 female ballet dancers, aged 17.4±2.01, whereas the control group included 30 non-athletic female examinees, aged 18.00 years on average. Height and weight were measured and body mass index (BMI) was calculated in all subjects. Body composition was estimated using the bioelectrical impedance method. Results Body composition analysis of ballet dancers revealed significantly lower values of body fat mass compared to the control group (18.85±4.50% vs. 23.41±4.34). Most examinees in both groups were of normal weight. 50% of ballet dancers and 23.33% of examinees in the control group were underweight, while overweight subjects were registered only in the control group. Most underweight ballet dancers had lower body fat mass, whereas majority of underweight examinees in the control group presented with normal body fat mass. Normal-weight obesity was established in 40.91% candidates in the control and 6.67% in the study group. Conclusion Ballet dancers had significantly lower values of body mass and BMI, compared to the study group. In order to prevent very serious complications caused by changes in size and proportion of some body compartments, it is necessary to carry out assessment of body composition more often in high-risk groups, such as the study group of ballet dancers.

A primary estimation of the cardiometabolic risk by using artificial neural networks

Estimation of the cardiometabolic risk (CMR) has a leading role in the early prevention of atherosclerosis and cardiovascular diseases. The CMR estimation can be separated into two parts: primary estimation (PE-CMR) that includes easily-obtained, non-invasive and low-cost diagnostic methods and secondary estimation (SE-CMR) involving complex, invasive and/or expensive diagnostic methods. This paper presents a PE-CMR solution based on artificial neural networks (ANN) as it would be of great interest to develop a procedure for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete SE-CMR tests only on them. ANN inputs are values obtained by using PE-CMR methods, i.e. primary risk factors: gender, age, waist-to-height ratio, body mass index, systolic and diastolic blood pressures. ANN output is cmr-coefficient obtained from the number of disturbances in biochemical indicators, i.e. secondary risk factors: HDL-, LDL- and total cholesterol, triglycerides, glycemia, fibrinogen and uric acid. ANN training and testing are done by dataset that includes 1281 persons. The accuracy of our solution is 82.76%.

A Comparison of Rule Sets Generated from Databases by Indiscernibility Relation - A Rough Sets Approach

Rough sets theory (Pawlak 1980s) proved to be an excellent mathematical tool for the task of automated extraction of If-Then rule sets from table-organized data. In this paper, we employ an approach based on the relation of indiscernibility and rough sets theory in comparison to the method based on pure classification. We have used a well-known ROSETTA software system. The main goal of this work is to compare rule set generated by ROSETTA and rule set generated by method based on pure classification. Comparison is conducted on real-life database from domain of medicine including recently discovered protein hormone Leptin.

Automated Extraction of Decision Rules from Medical Databases - A Rough Sets Approach

One of the three goals of this work is to research automated knowledge acquisition process from table-organized data by using the Rough set theory. This theory proved to be an excellent mathematical tool for the task of automated extraction of rule sets from table-organized data. The second goal is to employ a system for automated theorem proving -LogPro, based on ordered linear resolution with marked literals. This approach has shown better performances that the one based on linear resolution with selection function for definite clauses and negation as failure, e.g. Prolog. The rule sets produced by rough sets technique, in form of Prolog-like clauses are generated from real life medical table-organized data including 36 parameters, one of them being protein hormone Leptin. The third goal of this work is to get new insight into phenomena of Leptin levels while interplaying with other risk factors in obesity, especially with Trunk Fat. This is done by employing generated rule sets in LogPro.

Sagittal Abdominal Diameter as the Anthropometric Measure of Cardiovascular Risk

Obesity has a profound impact on the cardiovascular disease development, and is associated with a reduced overall survival. There is a strong correlation between the central (abdominal) type of obesity and the cardiovascular and metabolic diseases. Among a variety of anthropometric measurements of the abdominal fat size, sagittal abdominal diameter has been proposed as the valid measurement of the visceral fat mass and cardiometabolic risk level. Many studies have analyzed the relationship between sagittal abdominal diameter (SAD), visceral fat area, and different markers of cardiometabolic disturbances with respect to age, gender and ethnicity. Some of them have offered the cut-off values that could be useful in clinical practice, in identifying individuals who are at higher risk of comorbidities of the obesity. Using the principles of rough set theory, based on producing If-Then rules, we have developed a model that allows better applicability of SAD in identifying patients at higher cardiovascular risk. In this chapter, we describe the basic principles of the proposed model. Furthermore, we give a broad overview of the main concerns regarding the significance of SAD and its use in diagnosing the abdominal obesity and predicting the adverse cardiometabolic outcomes.