Discovery of urinary biomarkers of whole grain rye intake in free-living subjects using nontargeted LC-MS metabolite profiling

Abstract

Scope: Whole grain (WG) intake is associated with decreased risk of developing colorectal cancer, type 2 diabetes, and cardiovascular disease and its comorbidities. However, the role of specific grains is unclear. Moreover, intake of specific WG is challenging to measure accurately with traditional dietary assessment methods. Our aim was to use nontargeted metabolite profiling to discover specific urinary biomarkers for WG rye to objectively reflect intake under free-living conditions.Methods and results: WG rye intake was estimated by weighed food records, and 24 h urine collections were analyzed by LC-MS. Multivariate modeling was undertaken by repeated double cross-validated partial least squares regression against reported WG rye intake, which correlated well with multivariate prediction estimates (r = 0.67-0.80, p < 0.001), but not with intakes of WG wheat or oats. Hydroxyhydroxyphenyl acetamide sulfate, 3,5-dihydroxyphenylpropionic acid sulfate, caffeic acid sulfate, and hydroxyphenyl acetamide sulfate were among the 20 features that had the greatest potential as intake biomarkers of WG. In addition, three compounds exhibited MS/MS fragmentation of carnitine structures.Conclusion: With this nontargeted approach, we confirmed the specificity of alkylresorcinol metabolites as biomarkers for WG rye intake, but also discovered other compounds that should be evaluated as putative biomarkers in future studies.

Keywords

Biomarker; Metabolite profiling; Metabolomics; Rye; Whole grain

Published in

Molecular Nutrition and Food Research
2015, volume: 59, number: 11, pages: 2315-2325
Publisher: WILEY-BLACKWELL

SLU Authors

• Brunius, Carl

  • Department of Molecular Sciences, Swedish University of Agricultural Sciences
    

• Marklund, Matti

  • Uppsala University

• Landberg, Rikard

  • Department of Molecular Sciences, Swedish University of Agricultural Sciences
    
  • Karolinska Institute

Global goals (SDG)

SDG3 Good health and well-being

UKÄ Subject classification

Public Health, Global Health and Social Medicine
Bioinformatics and Computational Biology (Methods development to be 10203)
Public Health, Global Health, Social Medicine and Epidemiology
Epidemiology

Publication identifier

• DOI: https://doi.org/10.1002/mnfr.201500423

Permanent link to this page (URI)

https://res.slu.se/id/publ/71824