Studying Alzheimer's disease through an integrative serum metabolomic and lipoproteomic approach

Background Alzheimer's disease (AD) is the most frequent neurodegenerative disorder worldwide. The great variability in disease evolution and the incomplete understanding of the molecular mechanisms underlying AD make it difficult to predict when a patient will convert from prodromal stage to dementia. We hypothesize that metabolic alterations present at the level of the brain could be reflected at a systemic level in blood serum of patients, and that these alterations could be used as prognostic biomarkers. Methods This pilot study proposes a serum investigation via nuclear magnetic resonance (NMR) spectroscopy in a consecutive series of AD patients including 57 patients affected by Alzheimer's disease at dementia stage (AD-dem) and 45 patients with mild cognitive impairment (MCI) due to AD (MCI-AD). As control group, we considered 31 subjects with mild cognitive impairment in whom AD and other neurodegenerative disorders were excluded (MCI). A panel of 26 metabolites and 112 lipoprotein-related parameters was quantified and the logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites-lipoproteins and their ratios to discriminate the groups of interest. Results In the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. These results were reproduced in the validation set (accuracy 75.0%). Evolution of MCI-AD patients was evaluated over time. Patients who displayed a decrease in MMSE < 1.5 point per year were considered at lower progression rate: we obtained a division in 18 MCI-AD at lower progression rate (MCI-AD LR) and 27 at higher progression rate (MCI-AD HR). The model calculated using 4 metabolic features identified MCI-AD LR and MCI-AD HR with an accuracy of 73.3%. Conclusions The identification of potential novel peripheral biomarkers of Alzheimer's disease, as proposed in this study, opens a new prospect for an innovative and minimally invasive method to identify AD in its very early stages. We proposed a novel approach able to sub-stratify MCI-AD patients identifying those associated with a faster rate of clinical progression.