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Assessment of dietary intake of bioactive food compounds according to income level in the Brazilian population.

Renata A CarnaubaFlavia M SartiNeuza Mariko Aymoto HassimottoFranco M Lajolo
Published in: The British journal of nutrition (2021)
There is an inverse association between bioactive compounds intake and disease risk. The knowledge of its consumption according to socio-economic strata is important, which allows identification of potential intervention targets. Thus, we aimed to investigate bioactive compounds intake according to income level in Brazilian population. Data were obtained from the Brazilian Household Budget Survey, a cross-sectional survey which included data on individual food intake of 34,003 subjects aged 10 years and over collected using two 24-h dietary records. Polyphenol and carotenoid content of foods was identified using published databases. Total polyphenol and carotenoid intake were determined according to per capita income, as well as main food sources. Total polyphenols and flavonoids intake increased with income level, and subjects with lower income showed higher phenolic acids intake than individuals in highest income (p = 0.0001). Total carotenoids and classes intake (with exception to β-cryptoxanthin and zeaxanthin) were higher among subjects in highest income quartile, compared to the lowest quartile (p = 0.0001). Coffee was major source to total polyphenols and phenolic acids intake, and orange juice was main flavonoid provider in individuals from all income levels. In the upper income quartile, total carotenoid was supplied mainly by tomato and kale, and fruits had important contribution to carotenoid intake in the lowest income quartile. There is important influence of income level on diet quality regarding intake of foods with bioactive compounds, and individuals with lower income may experience lower quality diets due to less availability of foods with bioactive compounds.
Keyphrases
  • physical activity
  • mental health
  • weight gain
  • healthcare
  • randomized controlled trial
  • systematic review
  • machine learning
  • body mass index
  • weight loss
  • risk assessment
  • climate change
  • deep learning
  • quality improvement