Literature DB >> 28268812

Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease.

E Jammeh, P Zhao, C Carroll, S Pearson, E Ifeachor.   

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%.

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Year:  2016        PMID: 28268812     DOI: 10.1109/EMBC.2016.7591217

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Authors:  Honghuang Lin; Jayandra J Himali; Claudia L Satizabal; Alexa S Beiser; Daniel Levy; Emelia J Benjamin; Mitzi M Gonzales; Saptaparni Ghosh; Ramachandran S Vasan; Sudha Seshadri; Emer R McGrath
Journal:  Cells       Date:  2022-04-30       Impact factor: 7.666

Review 2.  Blood-based molecular biomarkers for Alzheimer's disease.

Authors:  Henrik Zetterberg; Samantha C Burnham
Journal:  Mol Brain       Date:  2019-03-28       Impact factor: 4.041

3.  Diagnostic Accuracy of Blood-Based Biomarker Panels: A Systematic Review.

Authors:  Anette Hardy-Sosa; Karen León-Arcia; Jorge J Llibre-Guerra; Jorge Berlanga-Acosta; Saiyet de la C Baez; Gerardo Guillen-Nieto; Pedro A Valdes-Sosa
Journal:  Front Aging Neurosci       Date:  2022-03-11       Impact factor: 5.750

Review 4.  Recent Evidence in Epigenomics and Proteomics Biomarkers for Early and Minimally Invasive Diagnosis of Alzheimer's and Parkinson's Diseases.

Authors:  Sonia Mayo; Julián Benito-León; Carmen Peña-Bautista; Miguel Baquero; Consuelo Cháfer-Pericás
Journal:  Curr Neuropharmacol       Date:  2021       Impact factor: 7.363

  4 in total

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