Affiliations: [a] Institute of Transfusion Medicine and Immunology, Heidelberg University, Medical Faculty Mannheim, German Red Cross Blood Service of Baden-Württemberg – Hessen gGmbH, Mannheim, Germany
| [b] Geiselgasteig Ambulance Gruenwald, Munich, Germany
| [c] Institute for Medical Engineering and Information Processing, University of Koblenz, Mainz, Germany
| [d] Multimodal Imaging Physics Group, University of Applied Sciences Koblenz, Koblenz, Germany
| [e] Department of Transfusion and Hemostaseology, University Hospital of Erlangen, Erlangen, Germany
| [f] Department of Geriatric Psychiatry, Central Institute for Mental Health, Mannheim, Germany
Correspondence:
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Correspondence to: Prof. Dr. Peter Bugert, Institute of Transfusion Medicine and Immunology, Friedrich-Ebert-Straße 107, D-68167 Mannheim, Germany. Tel.: +49 621 3706 9495; Fax: +49 621 3706 9496; E-mail: peter.bugert@medma.uni-heidelberg.de.
Note: [1] These authors contributed equally to this work.
Abstract: Background:Early diagnosis of Alzheimer’s disease (AD) is challenging, and easily accessible biomarkers are an unmet need. Blood platelets frequently serve as peripheral model for studying AD pathogenesis and might represent a reasonable biomarker source. Objective:In the present study, we investigated the potential to differentiate AD patients from healthy controls (HC) based on blood count, platelet morphology, and function as well as molecular markers at the time of first clinical diagnosis. Methods:Blood samples from 40 AD patients and 29 age-matched HC were included for determination of 78 parameter by blood counting, platelet morphometry, aggregometry, flow cytometry (CD62P, CD63, activated fibrinogen receptor), protein quantification of nicotinic acetylcholine receptor α7 (nAChRα7) and caveolin-1 (CAV-1), and miRNA quantification (miR-26b, miR-199a, miR-335). Group comparison between patients and controls was performed in univariate and multivariate statistical analyses. Results:AD patients showed significantly lower aggregation response to ADP and arachidonic acid and significantly decreased CD62P and CD63 surface expression induced by ADP and U46619 compared to HC. Relative nAChRα7 and CAV-1 expression was significantly higher AD platelets than in HC. Multivariate analysis of 63 parameter revealed significant differences between AD patients and healthy controls. The best performing feature model revealed a sensitivity of 96.6%, a specificity of 80.0%, and a positive predictive value of 89.3%. No grouping could be achieved by using single parameter groups. Conclusion:Significant differences between platelet characteristics from AD patients and HC at the time of first clinical diagnosis were observed. The best performing parameter can be used as a blood-based biomarker for AD diagnosis in a multivariate model in addition to the standardized mental tests.
