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Author Notes:

Correspondence Daniel Promislow, Department of Pathology and Department of Biology, University of Washington, Box 357705, 1959 NE Pacific Street, Seattle, WA 98195, USA. Tel.: +206 616 6994; fax: +206 616 8271; e-mail: promislo@uw.edu

DJ and DP designed the study.

DP and AS performed all fly experiments.

QS carried out the metabolomics assays.

JH, SL, DP, and QS analyzed the data and created the figures.

DJ, JH, DP, and QS interpreted the results.

DJ, JH, DP, and QS wrote the manuscript, and all authors commented on the manuscript.

We are grateful for helpful comments from Yousin Suh and members of the ISEM (Institut des Sciences de l’Evolution—Montpellier).

The authors declare no conflicts of interest.


Research Funding:

This work was supported by grants NIH T32 GM007103 (JH), NIH R01AG038756 (DJ and DP), NSF 1021720 (DP), and an AFAR/EMF Breakthroughs In Gerontology grant (DP).

No funding information provided.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Cell Biology
  • Geriatrics & Gerontology
  • age
  • aging
  • Drosophila melanogaster
  • genetic variation
  • genotype
  • heritability
  • metabolomics
  • sex
  • systems biology
  • MICE

Journal Title:

Aging Cell


Volume 13, Number 4


, Pages 596-604

Type of Work:

Article | Final Publisher PDF


Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.

Copyright information:

© 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/).

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