Elaine Holmes

In this final chapter, the current state of the art in metabolic phenotyping is summarized. The challenges for future integration of the topic into mainstream clinical and health studies are delineated and discussed. These include the need for validation, standard protocols, and the problems associated with “big data.” The prospects for metabolic phenotyping in clinical and epidemiologic studies are described. Potential new phenotyping outputs and possible impacts of metabolic phenotyping on medicine are listed.

This chapter describes the concept of the patient journey, that is, the various interactions between a patient and a medical team as the patient first encounters the system, is diagnosed, treated, and followed up after whatever course of action was deemed appropriate. The various bottlenecks in the process are explained. As a new paradigm, the role of metabolic phenotyping (metabotyping) in monitoring the patient journey is discussed and examples are provided. The potential of such metabolic phenotyping in the clinic has implications in terms of stratified or personalized medicine, including adding information to aid diagnosis or to allow better prognosis, and these implications are listed. Finally, one example of the process, a dedicated phenome center, is illustrated.

This chapter contains sections titled:
Background
Cancer
Cardiovascular disease
Neurodegenerative disorders
The way forward
References

This chapter contains sections titled:
Introduction
Analytical Platforms Utilized for Metabonomics
Statistical Tools Applied to Model Metabonomic Data
Integrated Metabonomic and ‘Multi‐omic’ Studies
COMET: A Consortium Project Using NMR‐driven Metabonomics
The COMET‐2 Project
Prospects for the Future
Concluding Remarks
References

The determination of the human genome sequence has spurred a great deal of interest in using changes in levels of gene expression in individuals to discover the basis of disease and to identify new drug targets. While this process has been successful and there are indeed specific gene changes in certain diseases, it has also recently been shown that variation in life span and longevity in identical and nonidentical twins is mainly explained by environmental effects such as smoking and diet [1]. This new approach has been incorporated into drug discovery programs in pharmaceutical companies and although some significant advances have been made-notably in some aspects of understanding cancer susceptibility [2,3]—it often remains difficult to relate any changes seen to conventional end points used in disease diagnosis and to optimize efficacy and minimize toxicity in pharmaceutical development. For example, in toxicogenomics studies, candidate drugs can give rise to many gene expression changes that have no actual pathological consequences.