The consumption of ultra-processed foods plays an important role in the development of obesity and hypertension. The present study investigated the association between consumption of food according to the degree of processing and anthropometric indicators of obesity and blood pressure in children. This is a cross-sectional study with 164 children aged 7–10 years. The body mass index (BMI) for age, waist circumference (WC), and waist-to-height ratio (WHtR) was evaluated. Food consumption was analyzed by three 24-h dietary recalls, and classified as: G1—unprocessed or minimally processed; G2—culinary ingredients and processed food; and G3—ultra-processed food. Linear regression analyses were used to investigate the associations among variables. The average energy consumption was 1762.76 kcal/day, split into 45.42%, 10.88%, and 43.70%, provided by G1, G2, and G3, respectively. Adjusted linear regression analyses identified that the caloric contribution of G1 was inversely associated with DBP, showing that for each 10% increase in the energy intake of minimally processed foods, there was a reduction of 0.96 mmHg in the DBP (β:−0.10; 95% CI:−0.19 to −0.01; r² = 0.20). There was no association between the caloric contribution of food groups and BMI, WC, WHtR, and SBP. Increasing consumption of G1 could be a strategy for the prevention and treatment of hypertension in schoolchildren.

(1) Background: Dysregulated serum amino acids (AA) are known to be associated with obesity and risk of Type 2 Diabetes (T2D) in adults, and recent studies support the same notion in the pubertal age. It is, however, unknown whether childhood overweight may already display alterations of circulating AA. (2) Methods: We used liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)—targeted metabolomics to determine plasma concentrations of AA and AA-related molecules in 36 children aged 7–12 years with normal weight or overweight. Clinical and anthropometric parameters were measured. (3) Results: Overweight in children is associated with an altered AA profile, with increased branched-chain amino acids (BCAA) and decreased glycine levels, with no clinically manifested metabolic conditions. Moreover, z-BMI was positively and negatively correlated with BCAA and glycine levels, respectively, even after adjustment for age and gender. We also found a correlation between the AA profile and clinical parameters such as lipids profile and glycemia. (4) Conclusions: A pattern of low glycine, and increased BCAA is correlated to z-BMI, total cholesterol, and triglycerides in overweight but otherwise healthy children. Our data suggest that, in childhood overweight, AA disturbances may precede other clinical parameters, thus providing an early indicator for the later development of metabolic disease.

Background: Food addiction (FA) is a condition in which ultra-processed foods (UPFs) activate the brain's reward pathways, leading to binge eating, loss of control, and continued consumption despite negative consequences. It can appear early in childhood and is linked to behavioral, sociodemographic, and metabolic factors. This study assessed the contribution of FA, its structure, and connectivity in relation to sociodemographic, nutritional status, and metabolic variables in network analysis. Methods: A cross-sectional study was conducted with 93 children (7–11 years old) living in Vitória de Santo Antão, Brazil. FA was assessed using the Yale Food Addiction Scale for Children, which was translated and validated for the Brazilian child population. Sociodemographic (age, sex, race, socioeconomic class), anthropometric (body weight, height, waist circumference, BMI, BMI-for-age, body fat percentage, lean mass, and fat mass), and metabolic (blood pressure, total cholesterol, triglycerides, HDL, LDL, and fasting glucose) factors were analyzed. For network analysis, the degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), and eigenvector centrality (EC) were evaluated. Results: FA exhibited moderate centrality in sociodemographic and metabolic networks, acting as a connector between key variables such as age and socioeconomic class (BC = 0.071–0.500; EC = 0.301–0.500; CC = 0.636–0.667). These metrics indicate that FA, while not dominant, maintains access to influential nodes and participates in relevant information pathways. In contrast, within the anthropometric network, FA showed a peripheral role, with fewer direct links (DC = 0.222–0.285) and limited intermediation (BC = 0.111). Conclusion: Variation in centrality across domains underscores the selective integration of FA, suggesting that its impact is context dependent.