Alzheimer's disease beyond the genomic era: nuclear magnetic resonance (NMR) spectroscopy-based metabolomics

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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¹H-MRS spectrum from a healthy volunteer obtained at 1.5 T from a 8 cm³ (2 × 2 × 2 cm) volume of interest within the brain with a PRESS sequence TR 1600 msec, TE 30 msec. Assignments are as follows: N-acetylaspartate, NAA; Creatine plus phosphocreatine, Cr; Choline, Cho; myo-inositol, mI; Glutamate plus glutamine, Glx.

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¹H-MAS spectra of mouse brain cortex obtained at 400 MHz (9.4 T). Each resonance corresponds to a chemical moiety within a particular metabolite, with the intensity being proportional to the concentration of that metabolite. Tentative assignments based only on a chemical shift are as follows: 1, methyl group of lipids; 2, lactate; 3, alanine; 4, Acetate/γ-amino butiric acid; 5, N-acetylaspartate; 6, Glutamate/Glutamine; 7, Glutamate; 8, Glutamine; 9, Creatine; 10, Choline; 11, Phosphocholine; 12, Glicerophosphocholine; 13, Taurine; 14, myo-inositol.

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Flow chart of the chemometric approach used to cluster NMR data. (A) Raw data are organized as a dataset that it is pruned, scaled and normalized. (B) Unsupervised classification methods (Principal component analysis) allow to see any major trends in the dataset variation and to detect any outlayers. (C) Supervised classification (Discriminant Analysis) allows detecting variation related to a variable of interest. (D) Cooman's plot shows the separation of two different groups. Axes represent a measure of the distance the data are from an ideal model. In this manner confidence intervals can be built. (E) Metabolic data can be derived for any classification models created, both supervised and unsupervised.