Abstract
Alzheimer's disease (AD) is a complex disease, with no definitive biomarkers available that allow clinical diagnosis; this represents a major problem for the advance of efficient drug discovery programs. A successful approach towards the understanding and treatment of AD should take into consideration this complex nature. In this sense, metabolic networks are subject to severe stoichiometric restrictions. Metabolomics amplifies changes both in the proteome and the genome, and represents a more accurate approximation to the phenotype of an organism in health and disease. In this article, we will examine the current rationale for metabolomics in AD, its basic methodology and the available data in animal models and human studies. The discussed topics will highlight the importance of being able to use the metabolomic information in order to understand disease mechanisms from a systems biology perspective as a non-invasive approach to diagnose and grade AD. This could allow the assessment of new therapies during clinical trials, the identification of patients at risk to develop adverse effects during treatment and the final implementation of new tools towards a more personalized medicine.
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