Alzheimer’s disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0–6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.

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Background: CSF from unique groups of Parkinson’s disease (PD) patients were biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis and novel biochemical biomarkers of the disease were characterized. Methods: Utilizing both 1H NMR and DI-LC-MS/MS we quantitatively profiled CSF from patients with sporadic PD (n=20), those who are genetically predisposed (LRRK2) to the disease (n=20) and compared them with age,- and gender-matched controls (n=20). Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD.Results: 1H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group’s previous findings identifying Bile Acid metabolism as one of the major aberrant biochemical pathways in PD patients.Conclusion: This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF based biomarkers in more accessible biomatrices. Further, it is essential that we determine if the biochemical pathways highlighted here are recapitulated in the brain of PD patients with the aim of identifying potential therapeutic targets.

Background: Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer’s disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. Objective: Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in serum can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. Methods: Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS). and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. Results: Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72–0.76, with the sensitivity and specificity values ranging from 0.75–0.85 and 0.69–0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. Conclusion: A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported in that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.