Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study

Abstract

BackgroundThe metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression.Methods and findingsQuantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and “asymptomatic Alzheimer’s disease” (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes—sphingolipids and glycerophospholipids—that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer’s Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer’s Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703–11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516–7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373–9.122, p = 0.009), and SM C18:1 (HR = 2....

Peripheral Serum Metabolomic Profiles Inform Antecedent Central Cognitive Impairment in Older Adults

“…To this end, we provide two predictive models: one based on differentially expressed metabolites and the other on identified metabolic pathways. Varma et al utilized machine learning approaches to identify potential metabolites related to AD pathology and progression 17 . We took their work one step further by constructing machine learning models to discriminant the final-state healthy controls vs. MCI/AD patients.…”

“…This profiling technology has already been used to identify differential metabolites that can distinguish mild cognitive impairment (MCI) subjects who will develop AD from stable MCI 9 . Mounting evidence suggests that AD is closely accompanied with the abnormal bile acid (BA) metabolism [10][11][12][13] , free fatty acid (FFA) metabolism 14,15,26 , lipid metabolism 16,17 and neurotransmitter metabolism 18 . BAs are increasingly recognized as important metabolic signaling molecules that modulate lipid, glucose, and energy metabolism 19 .…”

Emerging Applications of Metabolomics in Clinical Pharmacology

“…Recently, a quantitative and targeted metabolomics method revealed potential biomarkers for Alzheimer's disease (AD) and cardiovascular diseases. Varma, et al . discriminated AD patients from healthy controls using a panel of 26 metabolites from sphingolipids and glycerophospholipids metabolism.…”

“…48 Acute Recently, a quantitative and targeted metabolomics method revealed potential biomarkers for Alzheimer's disease (AD) and cardiovascular diseases. Varma, et al 52 discriminated AD patients from healthy controls using a panel of 26 metabolites from sphingolipids and glycerophospholipids metabolism. Some sphingolipids were associated with the pathology and progression of AD and were proposed to be biomarkers for early AD diagnosis.…”

Wuzang Wenyang Huayu decoction regulates differentially expressed transcripts in the rats' hippocampus after cerebral hypoperfusion

“…GO annotation classification statistics of differentially expressed genes in VD group vs VD-P group (A) and VD group vs VD-WY group(B). Results are summarized in three main GO categories: biological process (green), cellular component (red) and molecular function (blue) ischaemia have been shown to be strongly correlated with neuronal plasticity in the hippocampus, and the pathological changes through several biologically plausible pathways in increasing axon, myelin density, white matter bundles, oligodendrocyte and oligodendrocyte progenitor cell number are mainly associated with RNA sequencing operated proteins 29. Thus, in this study, the global RNA transcripts profile of hippocampal cells was investigated using label-free quantitative transcriptomics to explore the molecular events associated with cerebral hypoperfusion and the modulation of the concoction of Wuzang Wenyang.…”