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Mech Ageing Dev. 2019 Jan;177:135-143. doi: 10.1016/j.mad.2018.04.007. Epub 2018 Apr 30.

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

Author information

1
National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: liset.rietman@rivm.nl.
2
National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
3
Molecular Toxicology Group, Department of Biology, University of Konstanz, Konstanz, Germany.
4
CIG-Interdepartmental Center "L. Galvani", Alma Mater Studiorum, University of Bologna, Bologna, Italy.
5
Research Institute for Biomedical Aging Research, University of Innsbruck, Innsbruck, Austria.
6
BioTeSys GmbH, Esslingen, Germany.
7
Department of Molecular Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands.
8
URBC-NARILIS, University of Namur, Namur, Belgium.
9
Laboratory of the Molecular Bases of Ageing, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.
10
National Hellenic Research Foundation, Institute of Biology, Medicinal Chemistry and Biotechnology, Athens, Greece.
11
Department of Applied Nutritional Science/Dietetics, Institute of Nutritional Medicine, University of Hohenheim, Stuttgart, Germany.
12
Institute of Biological Chemistry and Nutrition, University of Hohenheim, Stuttgart, Germany.
13
Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.
14
Translational Research Center of Nutrition and Ageing, IRCCS-INRCA, Ancona, Italy.
15
Nestlé Institute of Health Sciences SA, Lausanne, Switzerland.
16
National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
17
National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.

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.

KEYWORDS:

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

PMID:
29719199
DOI:
10.1016/j.mad.2018.04.007

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