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Ultra-processed food intake is associated with children and adolescents with congenital heart disease clustered by high cardiovascular risk factors.

Michele HonickySilvia Meyer CardosoFrancilene Gracieli Kunradi VieiraPatrícia de Fragas HinnigIsabela de Carlos Back GiulianoYara Maria Franco Moreno
Published in: The British journal of nutrition (2022)
The excessive intake of ultra-processed foods (UPF) is associated with an increase in cardiovascular risk. However, the effect of UPF intake on cardiovascular health in children and adolescents with congenital heart disease (CHD) is unknown. The aim of the present study was to describe UPF intake and evaluate associations with isolated cardiovascular risk factors and children and adolescents with CHD clustered by cardiovascular risk factors. A cross-sectional study was conducted involving 232 children and adolescents with CHD. Dietary intake was assessed using three 24-hour recalls. UPFs were categorized using the NOVA classification. The cardiovascular risk factors evaluated were central adiposity, elevated high-sensitivity C-reactive protein (hs-CRP) and subclinical atherosclerosis. The clustering of cardiovascular risk factors (waist circumference, hs-CRP and carotid intima-media thickness) was performed, allocating the participants to two groups (high versus low cardiovascular risk). UPFs contributed 40.69% (SD 6.21) to total energy intake. The main UPF groups were ready-to-eat and take-away/fast foods (22.2% energy from UPFs). The multivariable logistic regression revealed that an absolute increase of 10% in UPF intake (OR=1.90; 95% CI: 1.01;3.58) was associated with central adiposity. An absolute increase of 10% in UPF intake (OR=3.77; 95% CI: 1.80;7.87) was also associated with children and adolescents with CHD clustered by high cardiovascular risk after adjusting for confounding factors. Our findings demonstrate that UPF intake should be considered a modifiable risk factor for obesity and its cardiovascular consequences in children and adolescents with CHD.
Keyphrases
  • cardiovascular risk factors
  • weight gain
  • metabolic syndrome
  • cardiovascular disease
  • body mass index
  • insulin resistance
  • type diabetes
  • high resolution
  • machine learning
  • weight loss
  • physical activity
  • deep learning