ASSESSMENT OF BODY COMPOSITION AND DIETARY INTAKE FOR PRIMARY SCHOOL BOYS IN MAKKAHAREA

Eslam A. Header 1,2 , Hassan M. Bukhari 1 , Nehal A. A. Alfky 1 , Mousa A. Abo Doraib 3 and Doaa R. Negm 1 . 1. Clinical Nutrition department, Faculty of Applied Medical Sciences, Umm al Qura University, Makkah, Saudi Arabia. 2. Department of Nutrition and Food Science, Faculty of Home Economics, Minufiya University, Egypt. 3. Clinical NutritionDepartment, Hera'a General Hospital,Makkah, Saudi Arabia. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History


646
were calculated separately for each of the two age groups (≤8, and >8years) according to dietary references intake (17).
Statistical analysis:-Statistical analysis was performed using the Statistical Package for Social Science (SPSS) version 20. Frequencies, relative and cumulative percentages, means, standard deviation (SD), and range were computed. Quantitative data are presented as mean±SD. Qualitative data were expressed as percentages. For the quantitative variables,compliance with the normal distribution was assessed using the Kolmogorov-Smirnoff test, as appropriate. For comparing the groups, the chi-square test or Fisher's exact test were used for qualitative variables as well as the t-test or Mann-Whitney U test for quantitative variables.P value of less than 0.05 was considered to indicate statistical significance.

Results:-Sample Disruption:-
The study sample comprised 100 boys, ages between from 6 to 12 years old (y-old). The majority of investigated children (60%) were in age group >8 y-old, the mean age was 10.7±1.18, range 8-12 years, age distribution and the mean age of studied sample of school children were shown in table 1and fig. 1.

Body compositionfor the children:-
The mean and SD for the body fat as % of total body weight, body fat in kg, lean muscle mass in kg, and total fluid in the body as % are shown in table (1). Analyses of all the children revealed that, mean body fat% for boys in age group≤8 y-old was significantly higher (P=0.039) when compared with boys in age group>8 y-old (30.52±7.75 vs 26.73±9.0resp.). While detailed evaluation of the parameters showed that, mean values of lean and dry lean body (kg) and boy fluid (liter) for boys in age group>8 y-old were significantly higher (p<0.01) compared with boys in another group.  The sample distribution according to the height for age is shown in table (3). Using the cutoff value of -2 Z-scores of height-for-age, the overall prevalence of stunting was 7.5% and 16.7% in both age groups ≤8 and >8 y-old resp., with mean height of 111±2.6 and 124.4±4.2cm (p=0.007), while the prevalence of linear growth deficit (-2 to -1 SD height of the median of the NCHS\WHO) in the same age group were 25.0% and 33.3% in both age groups. Height level increased gradually when height-for-age Z-score level increased, as the detected data shows for both age groups. Table (4) show that the frequency distribution of studied samples according to grades of BMI-for-age Z-score. The majority of students (50%) had normal BMI for age at the level (-2 to 1 SD), meanwhile mean values of BMI for boys in age group ≤8 y-old were significantly higher (P<0.001) than boys in age group >8 y-old (16.6±1.6 and 15.5±1.2 Kg/m 2 resp.). It is worth to mentioning that, about 20.0% vs 21.7% of boys in both age groups were obese, while 25.0% vs 21.7% resp. were overweight. Regarding boys in age group >8 y-old, results revealed that only 3.3% was severely thin (<-3SD). A curve up shows for BMI as levels of BMI-for-age Z-score for both age groups increased. Nutrient intake for boyscompared with adequate intake (AI):-Data of tables (5) shows the means and standard deviations of several nutrients intake for boys compared with the AI. It could be noticed that calories intake was lower than AI, tending to be lower for boys in age group >8 (y-old) than boys in age group ≤8 y-old (62% vs. 76% of AI resp.), (P=0.588). Total protein intake was highly increased for boys in age group ≤8 y-old (215.3%) of AI compared with boys in age group >8 (y-old) (108.5%) of AI, (P=0.720).

648
Concerning carbohydrate, fiber and total sugars there were insignificant differences between both groups (P=0.464, P=0.208 and P=0.206 resp.), meanwhile fiber intakefor boys in age group >8 (y-old) was severely decreased compared daily requirements, represent (39%) of AI. It can be notice that the difference in macro-nutrients intake were non-significant (p>0.05) between school children in both age groups. The mean and SD for protein, fat and carbohydrate as source of energy (energy ratios) for boys are shown in table (6). As regard to total calories derived from protein, It was found protein energy ratio (PER) of boys in age group ≤8 (y-old) were not different significantly (15.84±8.9% of total calories) compared with boys in age group >8 (y-old) (13.72±5.8 of total calories) (p= 0.477). With respect to total calories derived from fat, fat energy ratio (FER) it was almost the same (29.55±6.4 vs 31.46±8.5 of calories) for the two groups. Meanwhile, carbohydrate energy ratio (CER) values were quite equal (54.61±7.7 vs 54.82±8.9) for both groups respectively.  Table (7) showed the correlation coefficients between body composition and nutrients intake for boys. There was a highly significant positive correlation (P<0.01) between height and each of age, waist, hip, MAC, lean body weight,andHt for Age Zscor. Concerning to waist cir., a positive highly significant correlation (p<0.01) was reported between it and each of age, hip, MAC, body fat%, lean body weight, Ht for age Zscor and BMI for age Zscor. As for hip cir., a positive highly significant correlation at level (1%) was reported between it and each of MAC, body fat%, lean body weight,Ht for age Zscor and BMI for age Zscor. It is obvious that, there was a highly significant positive correlation (P<0.01) between MAC and each of age, body fat%, lean body weight, Ht for age Zscor and BMI for age Zscor. With regard to body fat%, a highly positive significant correlation at level (1%) was reported between it and each of and BMI for age Zscor, also there was a significant positive correlation (P<0.05) between it and carbohydrate intake. Lean body weight correlated significantly positive (p<0.01) with age, however it correlated significant with energy intake at level (5%).

Correlation coefficients between body composition and nutrient intake:-
Concerning to energy intake, A positive significant relationship (p<0.01) was existed between it and each of fat, carbohydrate, total sugars intakes, also there was a positive significant association at level (5%) between it and each of protein and BMI for age Zscor. There was a positive significant relationship between protein intake and fat intake (p<0.05), however it correlated significantly negative with age (p<0.05). In addition there was a positive significant correlation between fat intake and each of carbohydrate, and total sugars (p<0.01). There was also a positive significant relationship (p<0.01) between carbohydrate intake and total sugars intake.

Discussion:-Body composition and anthropometric measurements:-
Our results revealed that, stunting was prevalent in both age groups. Height level increased gradually when heightfor-age Z-score level increased, as the detected data show for both age groups. Childhood stunting has been associated with an increased risk of obesity in adulthood, but the causes are unclear (18). Stunting affected 35% of these physically active children. Using multiple linear regression analysis, greater lean body mass predicted higher resting and activity energy expenditure. Stature was not a significant predictor of resting energy expenditure. Alower height-for-age z-score, but not stunting as a categorical variable, significantly predicted lower activity energy expenditure (18 (25), and 16.5% in rural areas of southern Pakistan in 2005 (26). The prevalence of stunting only, and concurrent stunting and underweight, in Baghdad, Iraq 2009 were 18.7% and 13.5% respectively (27). Chronic undernutrition, as evidenced by the proportion of stunted children, was of mild prevalence in this school-aged cohort (18.7%) and the overall prevalence of linear growth deficit was 53.3%. Subtracting the normal baseline or expected prevalence of stunting of 2.3% of the normally distributed population (28), the remaining prevalence was 16.4%. This is belowthe prevalence of stunting (22.2%) reported in the Administrative Committee on Coordination/ Sub-Committee on Nutrition (ACC/SCN) 3 rd . report on the world nutrition situation for the Near East/North Africa region (29).

650
Stunting reduces total energy expenditure (resting+active) in children. Rather, children with shorter stature and less lean body mass have lower total energy expenditure. Complex interactions between body size, body composition, and metabolic activity appear to elevate the risk for later life obesity of children (18). In own study, there was a gradual increase of BMI, was observed as increase levels of BMI-for-age Z-score for both age groups. Concerns have been raised about childhood obesity and its long-term impact on the health of children (Hodgkin et al., 2010). The problem of childhood obesity is accelerating throughout the world. Obesity is the result of excess body fat (30).
Fisheret al., found that more than three quarters of boys (82%) and girls (77%) were of normal weight. Twice as many girls (6%) than boys (3%) were obese (31). Michelset al.,mentioned that the last decades, the prevalence of childhood obesity has increased (32).
The overweight and obesity percentages observed in the baseline children's body composition and stress survey are low compared to the most recent reference data available for Flemish children (33). Rural children had a significantly lower BMI, smaller waist circumferences and thinner skinfold measurements than urban children. The differences in skin fold thicknesses remained after controlling for ethnicity and socioeconomic status (13).
Usual school-meal participation was significantly related to children's BMI but in opposite directions-positively for breakfast and inversely for lunch (34). Walking or bicycling to school (ie, active commuting) has shown promise for improving physical activity and preventing obesity in youth . Active commuting was inversely associated with BMI z-score (β=-0.07, P=.046) and skinfolds (β=-0.06, P=.029), and positively associated with overall daily (β=0.12, P=.024) and before-and after-school (β=0.20, P<.001) moderate-to-vigorous physical activity (35). Another study curried out on healthy children (n = 245), aged 4-16 years, were included (124 girls and 121 boys). A gender analysis revealed that Amplitude-Dependent Speed Of Sound (Ad-SoS) and Broadband Ultrasound Attenuation (BUA) parameters increased significantly with age and that both positively correlated with age, weight, height, BMI, Fat percentage, fat mass, lean mass (FFM) and total body water (TBWater). For both genders, (Ad-SoS) showed significant and positive correlations with age, weight, height, BMI, FFM, BUA and TBWate (36).
There are essentially six relevant levels, which could be involved in prevention of child and adolescent obesity: family (child, parents, siblings, etc), schools, health professionals, government, industry and media. Evidence-based health promotion programs has to be given a high priority (30).

Nutrient intake:-
Our results revealed that,a positive significant relationship was detected between energy intake and each of fat, carbohydrate and total sugars intakes, also there was a positive significant association at level (5%) between it each of protein and BMI for age Zscor. It could be noticed that calories intake was lower than AI, tending to be lower of boys in age group >8 years than boys in age group ≤8 years, these results agreed withChitra, and Reddy (37) who reported that mean nutrient intakes calculated from 24-hour recalls revealed that the children's diets were inadequate compared with the recommended values for energy and protein.In disabled children, while the mean energy intake was more than 90% of the amount required, Despite absence of significant difference in energy and fat intake, the intakes of protein, calcium and riboflavin were significantly lower in girls than in boys (38).
Our results revealed that, total protein intake was highly increased for boys in age group ≤8 (y-old) of AI compared with boys in age group >8 (y-old). Concerning carbohydrate, fiber and total sugars there were insignificant difference between both groups, meanwhile fiber intake for boys in age group >8 (y-old) was severe decreased compared with daily requirements.
The carbohydrate and fat contents of the diet as a percent of energy did not differ comparing normal and overweight children, but the percentage of protein was significantly higher in overweight children. Intakes of energy, carbohydrates and fat were not significantly correlated with body mass index (BMI) standard deviation scores (SDS) after controlling for age, gender and total energy for carbohydrates and fat (39). Malnutrition (low weight and stunting) is quite prevalent among Iranian children with motor disabilities and it is more prevalent in girls than in boys. It seems that poor food composition is a more important contributing factor than total low calorie intake (38).
Our results found that, calories intake was lower than AI, in spite 21% for total samples were obese, these results may be due to bad lifestyle or disturbance of thyroid gland.In obese children, an increased free triiodothyronine(fT3) concentration is the most frequent thyroid function abnormality. Serum fT3 and TSH correlate with BMI. Moderate 651 weight loss frequently restores these abnormalities (40). It is important to know and to follow nutritional factors, energy intake and composition of the diet, nutrition and hormonal status, food preferences and behavior, and the influence of non-nutritional factors (30).
Aeberliet al., (39) reported that more hours spent on watching TV and playing computer games are associated with overweight in primary school-aged Swiss children. Multi-component model of nutrition and lifestyle education was successful in improving the nutrition-related knowledge, eating habits and lifestyle practices, and resulted in beneficial changes in anthropometric and biochemical profiles (41). Family dietary coaching improves nutritional intake in free-living children and parents, with beneficial effects on weight control in parents (42). Another study mentioned that, Prepubertal gymnasts have higher percentage of fat free mass and daily energy expenditure and dietary intakes, but lower percent body fat than age matched controls (43). In the study of 239 primary school children, there were no significant differences in daily activity levels, body composition, or estimated dietary energy intake between those who walk to school (Walk) and those who travel by car (CAR; p<.05). Walk children were more active between 8 a.m. and 9 a.m. and 3 p.m. and 4 p.m. than CAR children (p<0.05). In addition, there were no significant differences in the main analysis when participants were sub-grouped by gender and age (44).

Conclusion and Recommendation:-
Differences were found in body composition of boys in age group≤8 years being leaner than boys in age group>8 years. Obesity and stunting are prevalent and undernutrition, especially severe thinness, was uncommon in this target group. The most important factors founded in the current study was poor calories and fiber intake compared with AI.The results arrived at suggest inevitable increase of calories takinginto consideration variety and balance of diets and children must do regular exercise. Moreover nutrition education is recommended as one of the long-term approaches for improving nutrition and health status for boys and their parents. Future more researches are needed to evaluated the prevalence of thyroid function abnormalities with weight loss and obesity for boys.

Limitations:-
The limitation in this study may be related to the small sample size.