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. 2023 May 1;24(9):8117.
doi: 10.3390/ijms24098117.

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia

Hanan Ehtewish  1   2 , Areej Mesleh  1   2 , Georgios Ponirakis  3 , Alberto De la Fuente  4 , Aijaz Parray  5 , Ilham Bensmail  6 , Houari Abdesselem  6 , Marwan Ramadan  7 , Shafi Khan  7 , Mani Chandran  7 , Raheem Ayadathil  5 , Ahmed Elsotouhy  5   8 , Ahmed Own  5   9 , Hanadi Al Hamad  7 , Essam M Abdelalim  1   4 , Julie Decock  1   10 , Nehad M Alajez  1   10 , Omar Albagha  1 , Paul J Thornalley  1   4 , Abdelilah Arredouani  1   4 , Rayaz A Malik  3 , Omar M A El-Agnaf  1   2
Affiliations

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia

Hanan Ehtewish et al. Int J Mol Sci. .

Abstract

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Keywords: MCI; Olink assay; biomarkers; dementia; machine learning; plasma proteomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of this study’s experimental design: plasma from 22 dementia patients, 64 MCI patients, and 36 healthy subjects were isolated. proteomic analysis (PAE) was elaborated with bioinformatics to identify the potential biomarkers and their associated pathways.
Figure 2
Figure 2
Differential expression of plasma proteome (dementia vs. control): (A) Supervised cluster analysis across the control and dementia samples using the 61 significantly altered proteins in the dataset (p < 0.05); (B) volcano plot displaying the log2 fold change (x-axis) against the limma-derived −log10 statistical p-value (y-axis) for all proteins differentially expressed between control and dementia cases of the plasma proteome. Proteins with significantly decreased levels in dementia (p < 0.05) are shown in blue, while the proteins with significantly increased levels in disease are noted in red. Select proteins are labeled; (C) representative Gene Ontology (GO) terms associated with significantly altered proteins in dementia in the domains of biological process, molecular function, cellular component, and KEGG and REAC pathway are shown.
Figure 3
Figure 3
Differential expression of plasma proteome (dementia vs. MCl): (A) Supervised cluster analysis across the control and dementia samples using the 152 significantly altered proteins in the dataset (p < 0.05); (B) volcano plot displaying the log2 fold change (x-axis) against the limma-derived −log10 statistical p-value (y-axis) for all proteins differentially expressed between dementia and MCI cases of the plasma proteome. Proteins with significantly decreased levels in dementia (p < 0.05) are shown in blue, while the proteins with significantly increased levels in disease are noted in red. Selected proteins are labeled; (C) representative Gene Ontology (GO) terms associated with significantly altered proteins in dementia in the domains of biological process, molecular function, cellular component, and KEGG and REAC pathway are shown.
Figure 4
Figure 4
Machine learning model to discriminate dementia from controls: (A) Two-variable (feature) selection algorithms were used to select the most robust proteins to differentiate dementia from cognitively normal controls; MUVR (multivariate modeling with minimally biased variable selection) and Boruta (a wrapper algorithm for all relevant feature selection and feature importance with random selection runs); (B) the predictive variables selected using MUVR and Boruta (Panel A). A total of 17 plasma proteins, with MUVR rank, fold change, and p-value. Red and blue indicate up- and downregulated plasma proteins, respectively; those with * indicate the limma-identified significant proteins (p-value < 0.05); (C) the ROC curve of the model using the 17 candidates’ variables. SVM outcome shows the trade-off between the true-positive rate (sensitivity) and false-positive rate (1–pecificity) for different classification thresholds.
Figure 5
Figure 5
Machine learning model to discriminate dementia from MCI: (A) Two-variable (feature) selection algorithms were used to select the most robust proteins to differentiate dementia from MCI cases; MUVR (multivariate modeling with minimally biased variable selection) and Boruta (a wrapper algorithm for all relevant feature selection and feature importance with random selection runs); (B) the predictive variables selected using MUVR and Boruta (Panel B). A total of 8 plasma proteins, with MUVR rank, fold change, and p-value. Red and blue indicate up- and downregulated plasma proteins, respectively. Those with * indicate the limma-identified significant proteins (p-value < 0.05); (C) the ROC curve of the model using the 8 candidates’ variables. SVM outcome shows the trade-off between the true-positive rate (sensitivity) and false-positive rate (1–specificity) for different classification thresholds.
Figure 6
Figure 6
Correlations between the plasma levels of the proteins and cognitive decline are indicated by Montreal Cognitive Assessment (MoCA) scores. The top correlated proteins with Spearman’s correlation coefficients (r2) and the associated p-values are shown. Venn diagram shows the number of correlated proteins that overlapped with the selected variable of differentially expressed proteins using limma and machine learning (ML) algorithms in dementia compared with MCI.
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