Metabolic Profiling of Neocortical Tissue Discriminates Alzheimer's Disease from Mild Cognitive Impairment, High Pathology Controls, and Normal Controls

Abstract

Alzheimer's disease (AD) is the most common cause of dementia, accounting for an estimated 60-80% of cases, and is the sixth-leading cause of death in the United States. While considerable advancements have been made in the clinical care of AD, it remains a complicated disorder that can be difficult to identify definitively in its earliest stages. Recently, mass spectrometry (MS)-based metabolomics has shown significant potential for elucidation of disease mechanisms and identification of therapeutic targets as well diagnostic and prognostic markers that may be useful in resolving some of the difficulties affecting clinical AD studies, such as effective stratification. In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2080 metabolites and features in 48 postmortem tissue samples harvested from the superior frontal gyrus of male and female subjects. Samples were taken from four groups: 12 normal control (NC) patients, 12 cognitively normal subjects characterized as high pathology controls (HPC), 12 subjects with nonspecific mild cognitive impairment (MCI), and 12 subjects with AD. Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a nine-metabolite panel of disease markers (lauric acid, stearic acid, myristic acid, palmitic acid, palmitoleic acid, and four unidentified mass spectral features). Receiver operating characteristic analysis showed high predictive accuracy of the resulting PLS-DA models for discrimination of NC (97%), HPC (92%), MCI (∼96%), and AD (∼96%) groups. Pathway analysis revealed significant disturbances in lysine degradation, fatty acid metabolism, and the degradation of branched-chain amino acids. Network analysis showed significant enrichment of 11 enzymes, predominantly within the mitochondria. The results expand basic knowledge of the metabolome related to AD and reveal pathways that can be targeted therapeutically. This study also provides a promising basis for the development of larger multisite projects to validate these candidate markers in readily available biospecimens such as blood to enable the effective screening, rapid diagnosis, accurate surveillance, and therapeutic monitoring of AD. All raw mass spectrometry data have been deposited to MassIVE (data set identifier MSV000087165).

Keywords: Alzheimer’s disease; biomarkers; mass spectrometry; metabolomics; pathogenesis.

Figure 1.

Overview of the analytical workflow of the current study. Created with BioRender.com.

Figure 2.

PLS-DA and ROC analysis of case (MCI and AD) and control (NC and HPC) constructed using levels of lauric acid and myristic acid: (A) Score plot of the PLS-DA model (R²X = 0.593, R²Y = 0.814, R²Q = 0.701; 10-fold cross-validated Q² = −0.183) and (B) ROC analysis by 100-fold leave-one-out cross-validation (LOOCV) of model-implied values showing AUC = 0.95.

Figure 3.

Relative abundances of four metabolites found to be significant between groups (LSD p < 0.05) by MANOVA testing. Data were log₁₀-transformed and Pareto scaled prior to plotting.

Figure 4.

Top row: Relative abundances of myristic acid, palmitic acid, stearic acid, and palmitoleic acid with high predictive accuracy (AUC > 0.90) and significance (FDR q < 0.05) in univariate ROC analysis and t-testing between HPC and MCI groups. Bottom row: Relative abundances of four unidentified features from untargeted GC–MS analysis with good predictive accuracy (AUC > 0.80) and significance (FDR q < 0.05) in univariate ROC analysis and t-testing between MCI and AD groups.

Figure 5.

ROC analysis of classification performance by 100-fold LOOCV: (A) PLS-DA model constructed using levels of myristic acid, palmitic acid, stearic acid, and palmitoleic acid (observed p = 0.005) for classification of MCI and HPC samples (AUC = 0.966) and (B) PLS-DA model constructed using levels of four significant unidentified features (observed p = 0.022) for classification of MCI and AD samples (AUC = 0.917).

Figure 6.

(A) PLS-DA model constructed using levels of myristic acid, lauric acid, palmitic acid, and stearic acid for classification of AD and HPC groups (observed p = 0.007) and (B) ROC analysis of the PLS-DA model by 100-fold LOOCV showing AUC = 0.948.

Figure 7.

Correlation coefficients among the panel of candidate markers and clinical characteristics.

Figure 8.

Metabolome view of pathway analysis conducted using levels of all reliably detected metabolites showing significantly altered pathways (p < 0.05) and those with high impact (>0.50).

Figure 9.

Conceptual schema articulating observed changes in substrate utilization and energy production as a function of increasing AD pathogenesis. The darker red areas (to the right) are more increased with AD pathology, and greater enrichment is observed in those pathways. Results show reduced aerobic glycolysis and increased degradation of BCAA associated with HPC and MCI groups as compared to NC. Meanwhile, a preference for fatty acid substrates is seen in MCI and AD groups, with further reductions in aerobic glycolysis as compared to NC. ETC, electron transport chain; IMM, inner mitochondrial membrane; OMM, outer mitochondrial membrane; and OXPHOS, oxidative phosphorylation.