الفهرس 
Body compositionOnly 14 pages are availabe for public view
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Abstract
Obesity was defined differently in the literature. According to National Institutes of Health Obesity refers to an excess amount of body fat. The American Obesity Association defines obesity as ‘a complex, multifactorial chronic disease involving environmental (social and cultural), genetic, physiologic, metabolic, behavioral, and psychological components. However, the most commonly used definitions of obesity are based on BMI, which is defined as weight in kilograms divided by height in meters squared. In adults, obesity is generally defined as a BMI of 30.0 or greater (Kamel et al., 2016).
People who have obesity, compared to those with a normal or healthy weight, are at increased risk for many serious diseases and health conditions, including: - Impact on physical health suvh as all-causes of death (mortality), high blood pressure (Hypertension), high LDL cholesterol, low HDL cholesterol, or high levels of triglycerides (dyslipidemia), type 2 diabetes, coronary heart disease, stroke, gallbladder disease, osteoarthritis (a breakdown of cartilage and bone within a joint), sleep apnea and breathing problems, some cancers. It also includes impact on mental, social and spiritual health (Djalalinia et al., 2015).
Body composition and growth are key components of health in both individuals and populations. The ongoing epidemic of obesity in children and adults has highlighted the importance of body fat for short term and long term health (Reilly et al., 2003).
The primary goal of assessing body composition is to determine the proportion of fat mass. Several techniques are available, varying in complexity and ease of use, and each making assumptions that may affect its suitability for different conditions. A single technique is unlikely to be optimal in all circumstances (Wells and Fewtrell, 2006).
There is no doubt that the first-line treatment of obesity is dietary management combined with behavior modification, and secondarily, increased physical activity. Weight loss medication and bariatric surgery are further recommended for specific subgroups of obese patients (Koliaki et al., 2018).
This is a community based study involving 300 ever married females from two rural areas of El-Qalyubia governorate and aims to, identify some of obesity risk factors, revealing different pattern of body composition, prediction of some NCD related to obesity and find the correlation between BMI AND BF% for assessment of obesity.
A questionnaire was used after testing during structured interview with women during the pilot study for data collection. The variables in questionnaire included personal data and medical history. Anthropometric measurements for weight , height and waist circumference and body composition according to bioelectrical impedance analysis using a portable scale for body composition (beurer scale) was taken for every participant.
The mean ages of the studied sample was 36.56± 8.28(SD). The results also revealed that 88.3% of studied group are overweight and obese and 11.7% according to BMI while 85.7%of studied females are obese according to FMI. It also reveals that 17.3% of studied group have hypertension, 19.3 are diabetic, 2.7% are cardiac and 13.7% have arthritis, more than half (67%) of studied group are free of studied NCD and nearly the third (33%) with one or more condition
ROC curve analysis for prediction of hypertension shows that there is a significant prediction of hypertension with WC, W/h ratio, BMI, BF% and FMI with the area under the curve is nearly equal (0.85) for the WC, BMI.FMI and 0.8 for BF% while it is the least for W/H ratio (0.66) . The cutoff point of WC, W/h ratio, BMI, BF% and FMI is 99.5 cm, 0.86, 33.7 kg/m2, 38.9% and 13.1 kg/m2 respectively.
ROC curve analysis for prediction of diabetes illustrates that there is a significant prediction of diabetes with WC, W/h ratio, BMI, BF% and FMI with the area under the curve is highest for BMI (0.84) while it is the least for W/H ratio (0.6). The cutoff point for WC, W/h ratio, BMI, BF% and FMI is (99.5 cm,0.85, 32.4 kg/ m2, 37.5% and 13 kg/m2) respectively.
ROC curve analysis for prediction of cardiac diseases shows that there is a significant prediction of cardiac diseases with WC, W/h ratio, BMI, BF% and FMI with the area under the curve is the highest for BMI and FMI (0.97 and 0.95) but it is the least for W/H ratio (0.58). The cutoff point of WC, W/h ratio, BMI, BF% and FMI is 106.5 cm,0.82, 42 kg/m2, 40.5% and 17.5kg/m2 respectively.
ROC curve analysis for prediction of arthritis illustrates that there is a significant prediction of arthritis with WC, W/h ratio, BMI, BF% and FMI with The area under the curve is the highest for BMI an FMI (0.98 and 0.96) but it is the least for W/H ratio. The cut off points for WC, W/h ratio, BMI, BF% and FMI is the cutoff point 100.5cm, 0.86, 39.3 kg/m2, 40 % and 13.9 kg/m2 respectively.
Bioelectrical impedance analysis was effective in predicting cut off points for the body fat percent associated with four types of NCDs (hypertension, diabetes, cardiovascular and joint disease). Therefore, it is recommended to be used in combination with BMI and WC to predict NCDs risk of these diseases. The use of BIA for women before age of 20 years could guide strategies for reducing body fat and its risk of NCD.