title: Identification of a blood-based biomarker panel for classification of Alzheimer's disease.
abstract: An ideal diagnostic test for Alzheimer's disease (AD) should be non-invasive and easily applicable. Thus, there is a clear need to search for biomarkers in blood. In the present study, we have used multivariate data analysis [support vector machine (SVM)] to investigate whether a blood-based biomarker panel allows discrimination between AD patients and healthy controls at the individual level. We collected a total of 155 serum samples from individuals with early AD and age-matched healthy controls and measured serum levels of 24 markers involved in several biological pathways by ELISA. The dataset was randomly split into a training set for predictor discovery and classification training and a test set for class prediction of blinded samples (3:1 ratio) to evaluate the chosen predictors and parameters. After selection of a feature group of the three most discriminative parameters (cortisol, von Willebrand factor, oxidized LDL antibodies) in the training set, the application of SVM to the training/independent test dataset resulted in an 81.7%/87.1% correct classification for AD and control subjects. In conclusion, we identified a panel of three blood markers, which allowed SVM-based distinguishing of AD patients from healthy controls on a single-subject classification level with clinically relevant accuracy and validity. Blood-based biomarkers might have utility in AD diagnostics as screening tool before further classification with CSF biomarkers and imaging. Future studies should examine whether blood-based biomarkers may also be useful to differentiate AD patients from other dementias.
authors.affiliation: Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany. christoph.laske@med.uni-tuebingen.de
authors.forename: Christoph
authors.initials: C
authors.lastname: Laske
authors.forename: Thomas
authors.initials: T
authors.lastname: Leyhe
authors.forename: Elke
authors.initials: E
authors.lastname: Stransky
authors.forename: Nadine
authors.initials: N
authors.lastname: Hoffmann
authors.forename: Andreas J
authors.initials: AJ
authors.lastname: Fallgatter
authors.forename: Janko
authors.initials: J
authors.lastname: Dietzsch
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000097:blood* / Q000175:diagnosis* / Q000276:immunology; D001323:Autoantibodies / Q000032:analysis; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D004797:Enzyme-Linked Immunosorbent Assay; D005260:Female; D006801:Humans; D006854:Hydrocortisone / Q000097:blood; D008077:Lipoproteins, LDL / Q000037:antagonists & inhibitors; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014841:von Willebrand Factor / Q000032:analysis
chemical_list: D001323:Autoantibodies; D015415:Biomarkers; D008077:Lipoproteins, LDL; C099252:oxidized low density lipoprotein; D014841:von Willebrand Factor; D006854:Hydrocortisone