Daily consumption of ultra-processed foods and cardiometabolic risk factors in children aged 7 to 10 years in Northeast Brazil

Background: Ultra-processed foods (UPFs) consumption is associated with pediatric overweight and obesity. Aim: To evaluate the UPFs consumption in children classified either as eutrophic or with excess weight (overweight and obesity). It was also described the fasting plasma glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) and the correlation between UPFs consumption and cardiometabolic risk factors. Methods: A total of 139 children aged 7-10years of both sexes, living in Northeast Brazil were classified as eutrophic (n = 65) or excess weight (n = 62). Waist circumference (WC), percentage of body fatness (% BF), fat-free-mass and fat mass were evaluated. Fasting blood sample were collected for biochemical analysis. Food consumption was classified according to the degree of processing. Results: Children with excess weight had a reduction in plasma HDL concentration (45.00; IQR:36.00-54.50 mg/dL vs. 40.00; IQR:35.75-45.25 mg/dL; p = 0.021) and an increase in blood glucose (82.00; IQR:79.00-86.00 mg/dL vs. 86.00; IQR:81.00-90.00 mg/dL; p < 0.001) and TG (64.00; IQR:45.00-92.50 mg/dL vs. 81.00; IQR:57.50-111.75 mg/dL; p < 0.021) when compared with the eutrophic children. UPFs accounted for 43.43% of the total calories consumed by children. Children with excess weight had higher total energy consumption resulting from consumption of UPFs (714.30 ± 26.32 kcal vs. 848.06 ± 349.46 kcal; p = 0.011). The absolute consumption of the UPFs showed a positive correlation with WC (r = 0.202; p = 0.023) and %BF (r = 0.198; p = 0.026). Conclusion: UPFs consumption was higher for children with excess weight and positively correlated with two cardiometabolic risk factors, suggesting the need for strengthening public policies that discourage the consumption of these foods.

Food consumption habits, gestational age and birth weight are predictive for children with excess weight: An analysis based on artificial neural network

The relationship between body weight gain and the onset of obesity is linked to environmental and behavioral factors, and may be dependent on biological predisposing. Artificial neural networks are useful predictive tools in the field of artificial intelligence, and can be used to identify risk factors related to obesity. The aim of this study is to establish, based on artificial neural networks, a predictive model for overweight/obesity in children based on the recognition and selection of patterns associated with birth weight, gestational age, height deficit, food consumption, and the physical activity level, TV time and family context. Sample consisted of 149 children (72 = eutrophic and 77 = overweight/obese). Collected data consisted of anthropometry and demographic characteristics, gestational age, birth weight, food consumption, physical activity level, TV time and family context. The gestational age, daily caloric intake and birth weight were the main determinants of the later appearance of overweight and obesity. In addition, the family context linked to socioeconomic factors, such as the number of residents in the household, had a great impact on excess weight. The physical activity level was the least important variable. Modifiable risk factors, such as the inadequate food consumption, and non-modifiable factors such as gestational age were the main determinants for overweight/obesity in children. Our data indicate that, combating excess weight should also be carried out from a social and preventive perspective during critical periods of development, such as pregnancy, lactation and early childhood, to reach a more effective strategy to combat obesity and its complications in childhood and adult life.