Food patterns in relation to weight status and body composition of female adolescents in Tshwane

Abstract

Introduction: In South Africa there is a high prevalence of overweight/ obesity among adolescence, which in turn indicates an increased risk of non-communicable diseases (NCDs) in adulthood (DoH, 2016, WHO, 2018). Obesity/overweight is linked to a diet high in saturated fat foods (e.g. fast foods), snacks and/or sugar added foods and beverages (Popkin et al., 2012, Romieu et al., 2017). Aims: This cross-sectional study aimed to determine the relationship between food patterns, and weight status (BMI-for-age) and body composition (BC), respectively, of female adolescents (N=91) aged 13 – 19 years attending two private schools in the City of Tshwane. Methods: Convenience sampling was used to recruit participants. Weight and height were measured with the Seca mBCA 514 and stadiometer 274, respectively, and used to calculate the body mass index (BMI). World Health Organization reference standards were used to obtain BMI-for-age z-score. Bioelectrical impedance analysis with the Seca mBCA 514 was used to obtain frequency outputs for the calculation of fat mass (FM) and fat free mass (FFM). The rapid eating assessment for patients (REAP) questionnaire was used to assess food patterns and obtain a diet quality score, in total and per food group. The Spearman correlation test was performed to determine the relationship between REAP scores and: FM, FFM, fat mass index (FMI), fat free mass index (FFMI) and BMI-for-age z-score, respectively. Results: The mean BMI-for-age z-score was 0.62 (0.37; 0.87), FM was 23.50kg (21.77; 26.32), FFM was 39.03 kg (38.07; 39.98), FMI was 8.95 kg/m2 (8.26; 10.03), FFMI was 14.79 kg/m2 and total REAP score was 52.95 (51.62; 54.27). A total of 29.2% was overweight and 8.99% obese. A moderate, statistically significant correlation was found in the overweight category between the REAP score for whole grains/starch consumption and FFM (r=0.51, p=0.01) and FFMI (r=0.47, p=0.02), respectively, and between high sodium consumption and FM (r=0.42, p=0.04). A weak, negative, yet statistically significant correlation was found between the dairy consumption and FFM (r= -0.30, p=0.04) of the black African subgroup. Lastly, a moderate, negative, yet statistically significant correlation was found between the dairy consumption and FFM (r=-0.40, p=0.05) of the overweight category. Conclusion: The present findings indicate that participants have a poor-quality diet including a high consumption of fats and oils, sugar, sodium and high fat meat, and a low consumption of fruits and vegetables and whole grains. The BMI-for-age z-scores, FM and FMI indicate that a high percentage of participants were overweight with an increased risk of developing NCDs later in life. The correlation analysis could not be used to draw a meaningful conclusion about the relationship between dietary intake and BMI-for-age z-score, FM, FMI, FFM and FFMI. Future research may need to include a more detailed analysis of dietary intake