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Consumption and Breakfast Patterns in Children and Adolescents with Congenital Heart Disease.

Joanna MaraschimMichele HonickyYara Maria Franco MorenoPatrícia de Fragas HinnigSilvia Meyer CardosoIsabela de Carlos BackFrancilene Gracieli Kunradi Vieira
Published in: International journal of environmental research and public health (2023)
Little is known about skipping breakfast and breakfast patterns (BP) and their evaluation according to sociodemographic, clinical, lifestyle, cardiometabolic and nutritional data in children and adolescents with congenital heart disease (CHD). This cross-sectional study with 232 children and adolescents with CHD identified the prevalence and patterns of the breakfast, described these according to sociodemographic, clinical and lifestyle characteristics, and assessed their association with cardiometabolic and nutritional markers. Breakfast patterns were identified by principal components, and bivariate and linear regression analysis were applied. Breakfast consumption was observed in 73% of participants. Four BP were identified: pattern 1 "milk, ultra-processed bread, and chocolate milk", pattern 2 "margarine and processed bread", pattern 3 "cold meats/sausages, cheeses and butter/cream" and pattern 4 "fruits/fruit juices, breakfast cereals, yogurts, and homemade cakes/pies and sweet snacks". Family history for obesity and acyanotic CHD were associated with breakfast skipping. Younger participants and greater maternal education were associated with greater adherence to pattern 1 and pattern 4. Older participants and longer post-operative time showed greater adherence to pattern 3. No association between skipping breakfast or BP and cardiometabolic and nutritional markers was observed. Nonetheless, the findings reinforce the need for nutritional guidance for healthy breakfast, aiming to reduce the consumption of ultra-processed foods and to prioritize fresh and minimally processed foods.
Keyphrases
  • metabolic syndrome
  • healthcare
  • physical activity
  • type diabetes
  • weight loss
  • cardiovascular disease
  • risk factors
  • machine learning
  • skeletal muscle
  • quality improvement
  • glycemic control
  • neural network