A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease

Abstract

A specific, sensitive and essentially non-invasive assay to diagnose and monitor Alzheimer's disease (AD) would be valuable to both clinicians and medical researchers. The aim of this study was to perform a metabonomic statistical analysis on plasma fingerprints. Objectives were to investigate novel biomarkers indicative of AD, to consider the role of bile acids as AD biomarkers and to consider whether mild cognitive impairment (MCI) is a separate disease from AD. Samples were analysed by ultraperformance liquid chromatography-MS and resulting data sets were interpreted using soft-independent modelling of class analogy statistical analysis methods. PCA models did not show any grouping of subjects by disease state. Partial least-squares discriminant analysis (PLS-DS) models yielded class separation for AD. However, as with earlier studies, model validation revealed a predictive power of Q(2)<0.5 and indicating their unsuitability for predicting disease state. Three bile acids were extracted from the data and quantified, up-regulation was observed for MCI and AD patients. PLS-DA did not support MCI being considered as a separate disease from AD with MCI patient metabolic profiles being significantly closer to AD patients than controls. This study suggested that further investigation into the lipid fraction of the metabolome may yield useful biomarkers for AD and metabolomic profiles could be used to predict disease state in a clinical setting.

Using direct infusion mass spectrometry for serum metabolomics in Alzheimer’s disease

“…These facts agree with our experimental findings that show low levels of PUFAs-containing phospholipids and plasmalogens in AD; and high concentrations of degradation products as GPCh, Ch, and FFA, as well as SFA-containing phospholipids. Therefore, the potential of blood serum samples have been demonstrated to investigate these cerebral biochemical changes since, to date, there are only a few studies of these metabolites in this matrix [30,56,57].…”

Metabolomics of Neurodegenerative Diseases

“…There are no effective treatments for AD and only very recently have biomarkers begun to show promise in predicting AD before symptoms appear (Andreasson, Vanmechelen, Shaw, Zetterberg, Vanderstichel, 2012;Mishur & Rea, 2012). Such predictive biomarkers are desperately needed as current clinical diagnosis of AD is not always accurate and is only possible well after the pathological process has begun (Graham et al, 2013;Greenberg, Grassano, Thambisetty, Lovestone, & Legido-Quigley, 2009;Hampel, Lista, & Khachaturian, 2012;Li et al, 2010). Further, genetic, epigenetic, and environmental roles in development of AD must all be considered toward a comprehensive, clinically valuable biomarker (Elias-Sonnenschein, Bertram, & Visser, 2012).…”

Small molecule biomarkers in Alzheimer’s disease

“…Other plasma lipids classes reported to have close relationships with AD included bile acids (Greenberg et al, 2009), LysoPCs and (Trushina et al, 2013b) AD(n = 3) FTD(n = 4) LBD(n = 3) CN(n = 9) Serum CE-MS ↑b-alanine, creatinine, hydroxyproline, glutamine, isocitrate, cytidine (Tsuruoka et al, 2013) AD(n = 12) CN(n = 5) Whole brain MRS ↓N-acetyl-aspartate, g-aminobutyric acid; ↑ glutamate (Klunk et al, 1992) AD ( Changes in levels of uracil, xanthine, uridine, tyrosyl-serine, methylsalsolinol, nonanoylglycine, dopamine-quinone, caproic acid, vanylglycol, histidine, pipecolic acid, hydroxyphophinylpiruvate, creatinine, taurine, C16-sphingosine-1-phosphate, tryptophan, 5 0 -methylthioadenosine (Ibanez et al, 2013) phytosphingosine (sphingolipid precursors) (Li et al, 2010b), among others (Mapstone et al, 2014). Utilizing in-vial dual extraction protocol Godzien et al, 2013), abnormal levels of long chain cholesteryl esters/triglycerides (Proitsi et al, 2015;Proitsi et al, 2017) and omega-3 containing PC molecules (Whiley et al, 2014;Kim et al, 2016) have been shown in two separate study cohorts.…”