Body composition analysis to determine gender specific physical fitness equations in a cohort of Saudi population

To determine an association between body composition analysis and physical fitness in the Saudi population and derive gender specific physical fitness equations. A total of 530 healthy Saudi adults aged 15-72 years [mean 37.16 +/- 14.12 years] were enrolled in this study. Body composition analysis was assessed by bioelectrical impedance analysis [BIA], with a commercially available body analyzer according to standard protocols. Different body composition parameters, such as age, height, BSA [body surface area], obesity degree, body mass index [BMI], body fat mass [BFM] and percent body fat [% BF] contents were significantly different in males and females except weight which was non-significant [p=0.649]. There was significant positive or negative correlation among different body composition parameters except weight with age in males and weight with age, height and BSA in females. In males, all the body composition characteristics contributed to the fitness score except BMI and BFM, while in females, the most significant effect was contributed by weight and BFM. Female body composition characteristics were strongly related to fitness score compared to males [R2 = 93.8% vs R2 = 78.5%]. Different body composition parameters like BFM and%BF played an important role in determining physical fitness of healthy male individuals instead of BMI, weight and BSA, while in females weight was the best predictor of physical fitness

Assessment of adipokines relationships with cardiovascular risk markers in relation to body indices in normoglycemic males

To evaluate the phenotypic relationship between obesity indices, resistin, adiponectin and cardiovascular risk markers in normoglycemic healthy individuals. This cross-sectional study was conducted in the Department of Physiology College of Medicine, King Saud University, Riyadh. A total of 120 male subjects were selected for the study. All subjects underwent analysis of body composition, glucose, glycosylated hemoglobin [HbA1c], lipids, adiponectin, resistin, lipoprotein[a] and high sensitivity C reactive protein [hsCRP]. Body mass index [BMI] [r=0.326, p < 0.001], body fat mass [BFM] [r=0.377, p < 0.001], body fat percentage [BF%] [r=0.326, p < 0.001], waist hip ratio [WHR] [r=0.402, p < 0.001] and basal Insulin levels [r=0.217, p=0.018] were positively correlated with hsCRP. However, serum adiponectin levels [r=0.189, p=0.0391] were negatively correlated with hsCRP. Adiponectin levels were significantly lower in obese compared to non obese subjects [p=0.0551]. Keeping hsCRP as dependant variable we observed that WHR, BFM, BF%, BMI and adiponectin were significant predictors in univariate analysis. In multiple regression analysis WHR and adiponectin were independent predictors of hsCRP. Obese individuals have significantly higher levels of hsCRP levels and lower adiponectin levels than non obese subjects. Serum adiponectin levels and WHR are independant predictors of hsCRP levels in normoglycemic subjects