title: Differences in abundances of cell-signalling proteins in blood reveal novel biomarkers for early detection of clinical Alzheimer's disease.
abstract: BACKGROUND
  In November 2007 a study published in Nature Medicine proposed a simple test based on the abundance of 18 proteins in blood to predict the onset of clinical symptoms of Alzheimer's Disease (AD) two to six years before these symptoms manifest. Later, another study, published in PLoS ONE, showed that only five proteins (IL-1, IL-3, EGF, TNF- and G-CSF) have overall better prediction accuracy. These classifiers are based on the abundance of 120 proteins. Such values were standardised by a Z-score transformation, which means that their values are relative to the average of all others.
  
  
  METHODS
  The original datasets from the Nature Medicine paper are further studied using methods from combinatorial optimisation and Information Theory. We expand the original dataset by also including all pair-wise differences of z-score values of the original dataset ("metafeatures"). Using an exact algorithm to solve the resulting Feature Set problem, used to tackle the feature selection problem, we found signatures that contain either only features, metafeatures or both, and evaluated their predictive performance on the independent test set.
  
  
  CONCLUSIONS
  It was possible to show that a specific pattern of cell signalling imbalance in blood plasma has valuable information to distinguish between NDC and AD samples. The obtained signatures were able to predict AD in patients that already had a Mild Cognitive Impairment (MCI) with up to 84% of sensitivity, while maintaining also a strong prediction accuracy of 90% on a independent dataset with Non Demented Controls (NDC) and AD samples. The novel biomarkers uncovered with this method now confirms ANG-2, IL-11, PDGF-BB, CCL15/MIP-1; and supports the joint measurement of other signalling proteins not previously discussed: GM-CSF, NT-3, IGFBP-2 and VEGF-B.
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, Australia.
authors.forename: Mateus
authors.initials: M
authors.lastname: Rocha de Paula
authors.forename: Martín
authors.initials: M
authors.lastname: Gómez Ravetti
authors.forename: Regina
authors.initials: R
authors.lastname: Berretta
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D000544:Alzheimer Disease / Q000097:blood* / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D015415:Biomarkers / Q000097:blood; D001798:Blood Proteins; D030562:Databases, Protein; D042241:Early Diagnosis; D006801:Humans; D015398:Signal Transduction
chemical_list: D015415:Biomarkers; D001798:Blood Proteins