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Macronutrient intake and food sources in the very old: analysis of the Newcastle 85+ Study.

Nuno MendonçaTom R HillAntoneta GranicKaren DaviesJoanna CollertonJohn C MathersMario SiervoWendy L WriedenChris J SealThomas B L KirkwoodCarol JaggerAshley J Adamson
Published in: The British journal of nutrition (2016)
Food and nutrient intake data are scarce in very old adults (85 years and older) - one of the fastest growing age segments of Western societies, including the UK. Our primary objective was to assess energy and macronutrient intakes and respective food sources in 793 85-year-olds (302 men and 491 women) living in North-East England and participating in the Newcastle 85+ cohort Study. Dietary information was collected using a repeated multiple-pass recall (2×24 h recalls). Energy, macronutrient and NSP intakes were estimated, and the contribution (%) of food groups to nutrient intake was calculated. The median energy intake was 6·65 (interquartile ranges (IQR) 5·49-8·16) MJ/d - 46·8 % was from carbohydrates, 36·8 % from fats and 15·7 % from proteins. NSP intake was 10·2 g/d (IQR 7·3-13·7). NSP intake was higher in non-institutionalised, more educated, from higher social class and more physically active 85-year-olds. Cereals and cereal products were the top contributors to intakes of energy and most macronutrients (carbohydrates, non-milk extrinsic sugars, NSP and fat), followed by meat and meat products. The median intakes of energy and NSP were much lower than the estimated average requirement for energy (9·6 MJ/d for men and 7·7 MJ/d for women) and the dietary reference value (DRV) for NSP (≥18 g/d). The median SFA intake was higher than the DRV (≤11 % of dietary energy). This study highlights the paucity of data on dietary intake and the uncertainties about DRV for this age group.
Keyphrases
  • weight gain
  • electronic health record
  • human health
  • mental health
  • type diabetes
  • metabolic syndrome
  • risk assessment
  • big data
  • middle aged
  • climate change
  • insulin resistance
  • machine learning
  • disease virus