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Does Aging Affect Vitamin C Status Relative to Intake? Findings from NHANES 2017-2018.

Anitra C. CarrJens Lykkesfeldt
Published in: Nutrients (2023)
The aging population is growing and fueling a global increase in chronic diseases and healthcare expenditure. In this study, we examine vitamin C dose-concentration relationships based on data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 to identify a possible age-dependent change in intake vs. concentration relationship among non-supplemented individuals ( n = 2828). The vitamin C intake was similar between the younger (18-36 years), middle (37-58 years) and older (59-80+ years) age groups; however, circulating vitamin C concentrations were significantly lower in the middle and older age groups ( p < 0.001). For intakes above 75 mg/day, no significant difference in the intake vs. serum concentration relationship was identified between younger and older individuals. However, for intakes below 75 mg/day, we found significantly lower serum concentrations relative to intake for the older compared to younger individuals, despite smoking being more prevalent in the younger compared to older adults ( p < 0.001). This effect persisted among non-smokers and was further exacerbated by smoking in older people. Collectively, the present study suggests that healthy aging in non-institutionalized individuals does not increase requirements for vitamin C. In contrast, the lower serum concentrations relative to intake observed in older individuals at intakes < 75 mg/day may suggest that older individuals are more sensitive to a low vitamin C intake, perhaps due to the increased impact of long-term smoking and increased chronic disease prevalence in older adults. This finding may have implications for future intake guidelines in countries with low RDAs and for WHO/FAO, but requires further investigation.
Keyphrases
  • physical activity
  • community dwelling
  • middle aged
  • healthcare
  • weight gain
  • smoking cessation
  • magnetic resonance imaging
  • computed tomography
  • risk factors
  • machine learning