Application of a metabolomic multiplatform to investigate Alzheimer's disease pathogenesis

Alzheimer’s disease (AD) is the most common neurodegenerative disorder among older people, but nowadays there is no cure mainly because its etiology is still unclear and existing diagnostic tests show great limitations, including low sensitivity and specificity, as well as the impossibility to detect characteristic symptoms at early stages of disease. Thus, the objective of this work was the optimization of metabolomics approaches based on mass spectrometry in order to investigate AD pathogenesis and discover potential biomarkers for diagnosis. With the aim to get a comprehensive metabolome coverage, multiple analytical platforms were developed, including screening procedures based on direct mass spectrometry analysis and hyphenated approaches with orthogonal separation mechanisms such as liquid chromatography, gas chromatography and capillary electrophoresis. The application of these techniques to serum samples from patients suffering from Alzheimer’s disease and mild cognitive impairment enabled the identification of numerous metabolic alterations linked to pathogenesis of this disorder and its progression from pre-clinical stages, including abnormalities in the composition of membrane lipids, deficits in energy metabolism and neurotransmission, and oxidative stress, among others. In turn, these metabolomics perturbations were also observed in multiple biological compartments from the APP/PS1 model, including serum, brain, liver, kidney, spleen and thymus, thus demonstrating the utility of these transgenic mice to model Alzheimer’s disease. The comparison of different brain regions evidenced that the most affected areas are hippocampus and cortex, but other regions were also significantly perturbed to a lesser extent, such as striatum, cerebellum and olfactory bulbs. Furthermore, alterations detected in peripheral organs confirm the systemic nature of this neurodegenerative disorder. Accordingly, it could be concluded that the combination of complementary metabolomics platforms allows studying etiology associated with Alzheimer’s disease in a deeper manner.