Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2017 Mar;3(6):294-305.
doi: 10.1016/j.nhtm.2017.06.001.

Metabolomics for the masses: The future of metabolomics in a personalized world

Drupad K Trivedi  1 , Katherine A Hollywood  1 , Royston Goodacre  1
Affiliations
Review

Metabolomics for the masses: The future of metabolomics in a personalized world

Drupad K Trivedi et al. New Horiz Transl Med. 2017 Mar.

Abstract

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Figure illustrating disease progression (left hand side) along with the role of biomarkers on disease (right hand side) and how these may inform a range of personalised interventions.
Fig. 2
Fig. 2
Schematic representation of the major steps for metabolomics biomarker discover. This initially starts out with a “Discovery” phase which involves in depth metabolomics assessment in (for example) case-control for disease stratification; this tends to be done on relatively small cohorts (n = 100 s). Following this a “Pre-validation” phase then repeats this untargeted metabolomics assessment in a different patient-control cohort (also of n = 100 s and preferably from a geographically distinct area from the first discovery phase). Following this there is an analytical “Development” phase for the assessment of the biomarker(s) discovered using lower cost technologies: this represents a shift from mass spectrometry or NMR spectroscopy to targeted chromatography or direct measurements using (for example) lateral flow devices. Finally using this faster and cheaper technology there is a “Validation” phase in large patient cohorts (n = 10,000/100,000 s) to assess the robustness of the biomarker(s) discovered.
Fig. 3
Fig. 3
Flow diagram illustrating personalised medicine and highlighting the differences between Evidence-based versus Precision medicine-based approaches to disease treatment. As is clear the evidence-based approach is imprecise as it relies on the patient reporting progress to therapy. By contrast, precision medicine necessitates analytical measurements on the patient – typically from genetics (viz. SNPs) and metabolomics–and then using these to direct therapy.
Fig. 4
Fig. 4
The future cycle of metabolomics precision medicine-based research and healthcare where academia, industrial partners, corporate data analytics work with patients’ wearable data collection devices to provide health monitoring solutions.
Fig. 5
Fig. 5
A potential future where the patient is at the centre of their own health care. Where research/omics data and clinical data (right sides) are combined with novel future wearable and at home testing to generate more precise and thus precision medicine-based diagnostics. Thus, bucketing patients with similar health profiles would aid clinics to differentiate those that need urgent medical intervention from those that will benefit more from change in lifestyle choices and non-medical aid. This approach can thus help identify subgroup(s) of patients with similar drug responses or disease profiles, enabling affordable care as proposed by the Obama Care Bill without excluding those with pre-existing health conditions (that are not deemed life threatening but manageable) or comorbidities.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Oliver S.G. Systematic functional analysis of the yeast genome. Trends Biotechnol. 1998;16(9):373–378. - PubMed
    1. Kell D.B., Oliver S.G. The metabolome 18 years on: a concept comes of age. Metabolomics. 2016;12(9):148. - PMC - PubMed
    1. Strimbu K., Tavel J.A. What are biomarkers? Curr. Opin. HIV AIDS. 2010;5(6):463–466. - PMC - PubMed
    1. Broadhurst D.I., Kell D.B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics. 2006;2(4):171–196.
    1. Poste G. Bring on the biomarkers. Nature. 2011;469(7329):156–157. - PubMed

LinkOut - more resources