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Research article
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Background
Alzheimer’s disease (AD) is the most common cause of dementia, accounting for an estimated 60 to 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.
Methods
In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2,080 metabolites and features in 48 post-mortem 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 non-specific mild cognitive impairment (MCI), and 12 subjects with AD.
Results
Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a 9-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.
Conclusions
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 multi-site 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.
Keywords
Alzheimer’s disease, biomarkers, mass spectrometry, metabolomics, pathogenesis
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 20 Nov, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Alzheimer’s disease (AD) is the most common cause of dementia, accounting for an estimated 60 to 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.
Methods
In this study, complementary gas chromatography- and liquid chromatography-MS platforms were used to detect and monitor 2,080 metabolites and features in 48 post-mortem 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 non-specific mild cognitive impairment (MCI), and 12 subjects with AD.
Results
Multivariate statistics informed the construction and cross-validation (p < 0.01) of partial least squares-discriminant analysis (PLS-DA) models defined by a 9-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.
Conclusions
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 multi-site 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This is a list of supplementary files associated with this preprint. Click to download.
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