Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Omics technologies and the study of human ageing

Nature Reviews Genetics volume 14, pages 601–607 (2013)Cite this article

Subjects

Abstract

Normal ageing is associated with diverse physiological changes in all organ systems but the rate and extent of these changes vary markedly among individuals. One aspect of ageing research focuses on the molecular profiling of the changes that occur with increasing age in humans. Such profiling has implications for disease prevention and treatment. New high-throughput 'omics' technologies (such as genomics, metabolomics, metagenomics and transcriptomics) are enabling detailed studies of these molecular changes and are thus revealing information about the biological pathways that change with age.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

References

  1. Fontana, L., Partridge, L. & Longo, V. D. Extending healthy life span – from yeast to humans. Science 328, 321–326 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Deelen, J., Beekman, M., Capri, M., Franceschi, C. & Slagboom, P. E. Identifying the genomic determinants of aging and longevity in human population studies: progress and challenges. Bioessays 35, 386–396 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kennedy, S. R., Loeb, L. A. & Herr, A. J. Somatic mutations in aging, cancer and neurodegeneration. Mech. Ageing Dev. 133, 118–126 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Laurie, C. C. et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nature Genet. 44, 642–650 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Jacobs, K. B. et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nature Genet. 44, 651–658 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Sondheimer, N. et al. Neutral mitochondrial heteroplasmy and the influence of aging. Hum. Mol. Genet. 20, 1653–1659 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Avital, G. et al. Mitochondrial DNA heteroplasmy in diabetes and normal adults: role of acquired and inherited mutational patterns in twins. Hum. Mol. Genet. 21, 4214–4224 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Andrew, T. et al. A twin study of mitochondrial DNA polymorphisms shows that heteroplasmy at multiple sites is associated with mtDNA variant 16093 but not with zygosity. PLoS ONE 6, e22332 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Payne, B. A. et al. Mitochondrial aging is accelerated by anti-retroviral therapy through the clonal expansion of mtDNA mutations. Nature Genet. 43, 806–810 (2011).

    Article  CAS  PubMed  Google Scholar 

  10. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nature Genet. 44, 1084–1089 (2012).

    Article  CAS  PubMed  Google Scholar 

  11. Kyng, K. J., May, A., Kolvraa, S. & Bohr, V. A. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc. Natl Acad. Sci. USA 100, 12259–12264 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. de Magalhaes, J. P., Curado, J. & Church, G. M. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 25, 875–881 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hong, M. G., Myers, A. J., Magnusson, P. K. & Prince, J. A. Transcriptome-wide assessment of human brain and lymphocyte senescence. PLoS ONE 3, e3024 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Passtoors, W. M. et al. Gene expression analysis of mTOR pathway: association with human longevity. Aging Cell 12, 24–31 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Berchtold, N. C. et al. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer's disease. Neurobiol. Aging 34, 1653–1661 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Zahn, J. M. et al. Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet. 2, e115 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  17. ElSharawy, A. et al. Genome-wide miRNA signatures of human longevity. Aging Cell 11, 607–616 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Wood, S. H., Craig, T., Li, Y., Merry, B. & de Magalhaes, J. P. Whole transcriptome sequencing of the aging rat brain reveals dynamic RNA changes in the dark matter of the genome. Age 35, 763–776 (2013).

    Article  CAS  PubMed  Google Scholar 

  19. Mazin, P. et al. Widespread splicing changes in human brain development and aging. Mol. Syst. Biol. 9, 633 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Laird, P. W. Principles and challenges of genomewide DNA methylation analysis. Nature Rev. Genet. 11, 191–203 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Horvath, S. et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 13, R97 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Heyn, H. et al. Distinct DNA methylomes of newborns and centenarians. Proc. Natl Acad. Sci. USA 109, 10522–10527 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Alisch, R. S. et al. Age-associated DNA methylation in pediatric populations. Genome Res. 22, 623–632 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kirkwood, T. B. & Melov, S. On the programmed/non-programmed nature of ageing within the life history. Curr. Biol. 21, R701–R707 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. Talens, R. P. et al. Epigenetic variation during the adult lifespan: cross-sectional and longitudinal data on monozygotic twin pairs. Aging Cell 11, 694–703 (2012).

    Article  CAS  PubMed  Google Scholar 

  26. Rakyan, V. K. et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 20, 434–439 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. West, J., Beck, S., Wang, X. & Teschendorff, A. E. An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways. Sci. Rep. 3, 1630 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Bell, J. T. et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy aging population. PLoS Genet. 8, e1002629 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mootha, V. K. & Hirschhorn, J. N. Inborn variation in metabolism. Nature Genet. 42, 97–98 (2010).

    Article  CAS  PubMed  Google Scholar 

  31. Adamski, J. & Suhre, K. Metabolomics platforms for genome wide association studies-linking the genome to the metabolome. Curr. Opin. Biotechnol. 24, 39–47 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Lawton, K. A. et al. Analysis of the adult human plasma metabolome. Pharmacogenomics 9, 383–397 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Yu, Z. et al. Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Menni, C. et al. Metabolomic markers reveal novel pathways of aging and early development in human populations. Int. J. Epid. http://dx.doi.org/10.1093/ije/dyt094 (2013).

  35. Hanson, M. & Gluckman, P. Developmental origins of noncommunicable disease: population and public health implications. Am. J. Clin. Nutr. 94, 1754S–1758S (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Gonzalez-Covarrubias, V. et al. Lipidomics of familial longevity. Aging Cell 12, 426–434 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Biagi, E. et al. Through aging, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. McFall-Ngai, M. J., Heath-Heckman, E. A. C., Gillette, A. A., Peyer, S. M. & Harvie, E. A. The secret languages of coevolved symbioses: insights from the Euprymna scolopes-Vibrio fischeri symbiosis. Semin. Immunol. 24, 3–8 (2012).

    Article  PubMed  Google Scholar 

  41. Pucic, M. et al. High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations. Mol Cell Proteomics 10, M111.010090 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vanhooren, V. et al. Serum N-glycan profile shift during human aging. Exp. Gerontol. 45, 738–743 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Ruhaak, L. R. et al. Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health. J. Proteome Res. 10, 1667–1674 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Rodwell, G. E. et al. A transcriptional profile of aging in the human kidney. PLoS Biol. 2, e427 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Swindell, W. R. et al. Meta-profiles of gene expression during aging: limited similarities between mouse and human and an unexpectedly decreased inflammatory signature. PLoS ONE 7, e33204 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Glass, D. et al. Gene expression changes with age in skin, adipose, blood and brain. Genome Biol. 14, R75 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the European Union Framework Programme 7 small-scale focused research collaborative projects EurHEALTHAging 277849 and EuroBATS. The United Kingdom Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St Thomas' NHS Foundation Trust in partnership with King's College London (to TwinsUK); ERC Advanced Principal Investigator award to T.D.S.; Medical Research Council fellowship for D.G.

Author information

Authors and Affiliations

  1. Department of Twin Research and Genetic Epidemiology, Kings College London, London, SE1 7EH, UK

    Ana M. Valdes, Daniel Glass & Tim D. Spector

  2. Academic Rheumatology, University of Nottingham, Nottingham, NG5 1PB, UK

    Ana M. Valdes

  3. Department of Dermatology, North West London Hospitals NHS Trust, London, HA1 3UJ, UK

    Daniel Glass

Authors
  1. Ana M. Valdes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Glass
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tim D. Spector
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana M. Valdes.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

FURTHER INFORMATION

EuroBATS

EurHealth Ageing

TwinsUK

PowerPoint slides

Glossary

β-oxidation

The process of breaking down fatty acids to form acetyl-CoA. Occurs in mitochondria.

Biocrates

One of the metabolomics companies using a mass-spectrometry-based technology platform, mostly based on lipids.

Bisulphite sequencing

The treatment of DNA with bisulphite chemically converts unmethylated cytosines to uracil. As methylated cytosines are unaffected, the location of methylation can be identified by sequencing the bisulphite-treated DNA.

Differentially methylated regions

Genomic regions that exhibit significant differences in DNA methylation between sample groups.

Infinium BeadChip arrays

Manufactured by Illumina, these arrays allow the simultaneous testing of methylation levels at several thousands of CpG sites (methylation probes).

Metabolomics

The directed use of quantitative analytical methods for analysing the entire metabolic content of a cell or organism (that is, the metabolome) at a given time.

Metabolon

One of the metabolomics companies using a mass-spectrometry-based technology platform, which covers known and unknown metabolites.

Metagenomics

Describe the techniques that characterize the genomes of whole communities of organisms rather than individual species.

N-glycans

Glycans are oligosaccharides linked to a protein. N-linked glycans are attached in the endoplasmic reticulum to the nitrogen in the side chain of asparagine in an Asn-X-Ser or Asn-X-Thr sequence, in which X is any amino acid except proline.

Progeria syndromes

Rare inherited diseases that cause rapid ageing and shortened lifespans, such as Hutchinson–Gilford progeria and Werner's syndrome.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Valdes, A., Glass, D. & Spector, T. Omics technologies and the study of human ageing. Nat Rev Genet 14, 601–607 (2013). https://doi.org/10.1038/nrg3553

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3553

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research