Antioxidants linked with physical, cognitive and psychological frailty: Analysis of candidate biomarkers and markers derived from the MARK-AGE study

M. Liset Rietman*, Annemieke M.W. Spijkerman, Albert Wong, Harry van Steeg, Alexander Bürkle, María Moreno-Villanueva, Thilo Sindlinger, Claudio Franceschi, Beatrix Grubeck-Loebenstein, Jürgen Bernhardt, P. Eline Slagboom, Olivier Toussaint, Florence Debacq-Chainiaux, Ewa Sikora, Efstathios S. Gonos, Nicolle Breusing, Wolfgang Stuetz, Daniela Weber, Tilman Grune, Andrea BassoFrancesco Piacenza, Marco Malavolta, Sebastiano Collino, Eugene H.J.M. Jansen, W. M.Monique Verschuren, Martijn E.T. Dollé

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Frailty among elderly people leads to an increased risk for negative health outcomes. To prevent frailty, we need a better understanding of the underlying mechanisms and early detection of individuals at risk. Both may be served by identifying candidate (bio)markers, i.e. biomarkers and markers, for the physical, cognitive, and psychological frailty domains. We used univariate (Rank-ANOVA) and multivariate (elastic net) approaches on the RASIG study population (age range: 35–74 years, n = 2220) of the MARK-AGE study to study up to 331 (bio)markers between individuals with and without frailty for each domain. Biomarkers and markers identified by both approaches were studied further regarding their association with frailty using logistic regression. Univariately, we found lower levels of antioxidants, including β-cryptoxanthin and zeaxanthin, in those who were physically, cognitively or psychologically frail. Additionally, self-reported health was worse in these three frail groups. Multivariately, we observed lower levels of β-cryptoxanthin and zeaxanthin in the cognitively frail. Levels of these carotenoids were inversely associated with the risk of being cognitively frail after adjusting for confounders. Antioxidants and self-reported health are potential (bio)markers to detect persons at risk of becoming frail. The biomarkers identified may indicate the involvement of inflammation in frailty, especially for physical and cognitive frailty.

Original languageEnglish
Pages (from-to)135-143
Number of pages9
JournalMechanisms of Ageing and Development
Volume177
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Ageing
  • Elastic net
  • Frailty
  • Machine learning
  • Multidimensional
  • Multivariate
  • Univariate

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