title: Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease.
abstract: Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%.
authors.forename: E
authors.initials: E
authors.lastname: Jammeh
authors.forename: P
authors.initials: P
authors.lastname: Zhao
authors.forename: C
authors.initials: C
authors.lastname: Carroll
authors.forename: S
authors.initials: S
authors.lastname: Pearson
authors.forename: E
authors.initials: E
authors.lastname: Ifeachor
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000097:blood* / Q000175:diagnosis*; D001057:Apolipoproteins E / Q000378:metabolism; D015415:Biomarkers / Q000097:blood*; D001921:Brain / Q000000981:diagnostic imaging; D003199:Computer Systems; D030562:Databases, Protein; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D059906:Neuroimaging / Q000379:methods; D019095:Point-of-Care Systems; D040901:Proteomics; D012372:ROC Curve; D012680:Sensitivity and Specificity; D000511:alpha-Macroglobulins / Q000378:metabolism
chemical_list: D001057:Apolipoproteins E; D015415:Biomarkers; D000511:alpha-Macroglobulins