Are blood-based protein biomarkers for Alzheimer's disease also involved in other brain disorders? A systematic review.

BACKGROUND: Alzheimer's disease (AD) biomarkers are urgently needed for both early and accurate diagnosis and prediction of disease progression. Past research has studied blood-based proteins as potential AD biomarkers, revealing many candidate proteins. To date only limited effort has been made to investigate the disease specificity of AD candidate proteins and whether these proteins are also involved in other neurodegenerative or psychiatric conditions. OBJECTIVE: This review seeks to determine if blood-based AD candidate protein biomarkers are disease specific. METHODS: A two-stage systematic literature search was conducted. Firstly, the most consistently identified AD protein biomarkers in blood were determined from a list of published discovery or panel-based (>100 proteins) blood proteomics studies in AD. Secondly, an online database search was conducted using the 10 most consistently identified proteins to determine if they were involved in other brain disorders, namely frontotemporal lobe dementia, vascular dementia, Lewy body disease, Parkinson's disease, schizophrenia, depression, and autism. RESULTS: Among the reviewed candidate proteins, plasma protease C1 inhibitor, pancreatic prohormone, and fibrinogen γ chain were found to have the least evidence for non-specificity to AD. All other candidates were found to be affected by other brain disorders. CONCLUSION: Since we found evidence that the majority of AD candidate proteins might also be involved in other brain disorders, more research into the disease specificity of AD protein biomarkers is required.

No Evidence to Suggest that the Use of Acetylcholinesterase Inhibitors Confounds the Results of Two Blood-Based Biomarker Studies in Alzheimer's Disease.

BACKGROUND: There is an urgent need to discover Alzheimer's disease (AD) biomarkers that are both easily measured and reliable. Research into blood-based biomarkers for AD using transcriptomics and proteomics has been an attractive and promising area of research. However, to date researchers have not looked into the possibility of AD medication being a confounding factor in these studies. OBJECTIVE: This study explored whether acetylcholinesterase inhibitors (AChEIs), the main class of AD medication, are a confounding factor in AD blood biomarker studies. METHODS: The most promising blood transcriptomic and proteomic biomarkers from two recent studies were analyzed to determine if they were differentially expressed between AD subjects on AChEIs and subjects that were not. RESULTS: None of the gene or protein biomarkers analyzed were found to be significantly altered between subjects in either group. CONCLUSION: This study found no evidence that AChEIs are a confounding factor in these published AD blood biomarker studies. Further work is needed to confirm that this is also the case for other proposed biomarkers.

Plasma based markers of [11C] PiB-PET brain amyloid burden.

Changes in brain amyloid burden have been shown to relate to Alzheimer's disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimer's disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimer's Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [(11)C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden--c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E--were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE [Symbol: see text] 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.