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The plasma metabolome as a predictor of biological aging in humans

  • Lawrence C. Johnson
  • Keli Parker
  • Brandon F. Aguirre
  • Travis G. Nemkov
  • Angelo D’Alessandro
  • Sarah A. Johnson
  • Douglas R. Seals
  • Christopher R. MartensEmail author
Original Article

Abstract

Chronological age is an important predictor of morbidity and mortality; however, it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a “biological age” using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, we sought to develop a metabolomic signature of biological aging that could predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a cohort of healthy adults and was then applied to a group of healthy older adults who were tracked longitudinally over a 5–10-year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. Metabolites most associated with the rate of biological aging included amino acid, fatty acid, acylcarnitine, sphingolipid, and nucleotide metabolites. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

Keywords

Biological aging, Metabolomics, Healthspan, Precision medicine 

Notes

Author contributions

L.C.J. and C.R.M. conceived and developed the overall study design and collected data used in the biological aging model. K.P. established the mathematical models used to quantify biological age. K.P. and B.F.A. developed and packaged R scripts to execute the biological age algorithm. T.G.N. and A.D. conducted metabolomics analysis and contributed technical support. S.J. conducted dietary analyses. L.C.J. performed statistical analysis. L.C.J., C.R.M., and D.R.S. contributed critical input towards study design and manuscript development. All authors edited and approved the final version.

Funding information

This work was supported by National Institutes of Health awards T32 AG00027912 and Colorado CTSA UL1TR001082.

Compliance with ethical standards

All study procedures were reviewed and approved by the University of Colorado Boulder Institutional Review Board. Clinical and physiological measurements were performed at the University of Colorado Boulder Clinical Translational Research Center (CTRC). All study participants provided written informed consent after the nature, benefits, and risks of the study were explained.

Supplementary material

11357_2019_123_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1176 kb)
11357_2019_123_MOESM2_ESM.docx (15 kb)
ESM 2 (DOCX 15.4 kb)

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Copyright information

© American Aging Association 2019

Authors and Affiliations

  • Lawrence C. Johnson
    • 1
  • Keli Parker
    • 2
  • Brandon F. Aguirre
    • 1
  • Travis G. Nemkov
    • 3
  • Angelo D’Alessandro
    • 3
  • Sarah A. Johnson
    • 4
  • Douglas R. Seals
    • 1
  • Christopher R. Martens
    • 1
    • 5
    Email author
  1. 1.Department of Integrative PhysiologyUniversity of Colorado BoulderBoulderUSA
  2. 2.Department of MathematicsUniversity of Colorado BoulderBoulderUSA
  3. 3.Department of Biochemistry and Molecular Genetics, Anschutz Medical CampusUniversity of Colorado DenverAuroraUSA
  4. 4.Department of Food Science and Human Nutrition, College of Health and Human SciencesColorado State UniversityFort CollinsUSA
  5. 5.Department of Kinesiology and Applied PhysiologyUniversity of DelawareNewarkUSA

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