4.7 Article

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

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 20, Issue 9, Pages 4303-4317

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.1c00290

Keywords

Alzheimer's disease; biomarkers; mass spectrometry; metabolomics; pathogenesis

Funding

  1. College of Health Solutions (Jumpstart) at Arizona State University
  2. Arizona Alzheimer's Consortium - Arizona Department of Health Services [CTR040636]
  3. Midwestern University
  4. NIH [1R21AG07256101]
  5. BBDP
  6. National Institute of Neurological Disorders and Stroke National Brain and Tissue Resource for Parkinson's Disease and Related Disorders [U24 NS072026]
  7. National Institute on Aging, Arizona Alzheimer's Disease Core Center [P30 AG19610]
  8. Arizona Department of Health Services, Arizona Alzheimer's Consortium
  9. Arizona Biomedical Research Commission, Arizona Parkinson's Disease Consortium
  10. Michael J. Fox Foundation for Parkinson's Research

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This study conducted metabolomics analysis on brain tissues of dementia patients, identifying metabolite markers that can distinguish different cases, providing important clues for understanding disease mechanisms and identifying therapeutic targets, laying the foundation for future research on the diagnosis and treatment of Alzheimer's disease.
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 (similar to 96%), and AD (similar to 96%) groups. Pathway analysis revealed significant disturbances in lysine degradation, fatty acid metabolism, and the degradation of branchedchain 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).

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