Multi-Omics, an Integrated Approach to Identify Novel Blood Biomarkers of Alzheimer's Disease

Abstract

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD/MCI pathogenesis is unclear. This study compared the metabolomic and proteomic signature of plasma from cognitively normal (CN) and dementia patients diagnosed with MCI or AD, to identify specific cellular pathways and new biomarkers altered with the progression of the disease. We analysed 80 plasma samples from individuals with MCI or AD, as well as age- and gender-matched CN individuals, by utilising mass spectrometry methods and data analyses that included combined pathway analysis and model predictions. Several proteins clearly identified AD from the MCI and CN groups and included plasma actins, mannan-binding lectin serine protease 1, serum amyloid A2, fibronectin and extracellular matrix protein 1 and Keratin 9. The integrated pathway analysis showed various metabolic pathways were affected in AD, such as the arginine, alanine, aspartate, glutamate and pyruvate metabolism pathways. Therefore, our multi-omics approach identified novel plasma biomarkers for the MCI and AD groups, identified changes in metabolic processes, and may form the basis of a biomarker panel for stratifying dementia participants in future clinical trials.

Keywords: Alzheimer’s disease; biomarkers; metabolomics; proteomics; systems biology.

Figure 1

Partial least squares-data analysis (PLS-DA) score scatter plots for the proteomics (A), metabolomics (B) and the lipidomics (C), discriminating the three groups. A legend for the colour is provided in each plot. Abbreviations: AD, Alzheimer’s disease; CN, cognitively normal; MCI, mild cognitive impairment.

Figure 2

The top 6 protein biomarkers that separated the MCI group from the CN group in Table 2. The CN group is shown in black while the MCI and AD groups are shown in blue and red, respectively. Abbreviations: AD, Alzheimer’s disease; CN, cognitively normal; EFEMP1, epidermal growth factor containing fibulin like extracellular matrix protein 1; MCI, mild cognitive impairment.

Figure 3

The top 6 protein biomarkers that separated the AD group from the CN group in Table 2. The CN group is shown in black while the MCI and AD groups are shown in blue and red, respectively. Abbreviations: AD, Alzheimer’s disease; CN, cognitively normal; MCI, mild cognitive impairment.

Figure 4

Lipid pathway enrichment analysis of plasma samples showing (A) the lipid classes that were detected in plasma regardless of the groups and (B) the lipids changing in the MCI and AD samples, representing the most impacted pathways. The green bars show the number of lipids that were measured in our sample and belonged to these pathways. Abbreviations: GPI, glycosylphosphatidylinositol; GPL, glycerophospholipid.

Figure 5

Significant pathways expressed in plasma from AD and MCI (compared with CN). The number of components of each pathway is presented as “Total metabolites” (grey bars) and the number of molecules matched to these pathways are presented as “Matched Metabolites” (blue bars). The “Impact” value represents the extent to which these pathways are impacted with the disease.

Figure 6

Heatmaps of metabolic pathways. Heatmaps showing metabolomic and proteomic expressions during (A) arginine metabolism, (B) alanine, aspartate, and glutamate metabolism, (C) pyruvate metabolism, (D) pyrimidine metabolism, and (E) purine metabolism in the plasma matrix of AD and MCI patients compared with CN individuals. The metabolites and proteins used in the multi-omics integration were selected based on the fold changes (Log₂FC ≥ 1.00 or Log₂FC ≤ −1.00) with statistically significant different false discovery rates (FDR ≤ 0.05)). The heatmaps were graphed by manually inputting the fold changes into GraphPad Prism 9.