A strategy focused on MAPT, APP, NCSTN and BACE1 to build blood classifiers for Alzheimer׳s disease

Abstract Background Although Alzheimer׳s disease (AD) is a brain disorder, a number of peripheral alterations have been found in these patients, including differences in leukocyte gene expression; however, the key genes involved in plaque and tangle formation have shown a relatively small potential as diagnostic markers. We focused on MAPT, APP, NCSTN and BACE1 as the basis to build and compare blood classifiers for AD. Methods We used a combined model to build disease classifiers, using measures of blood pressure and serum glucose, cholesterol and triglyceride levels as well as RT-PCR expression levels of APP, NCSTN and BACE1 in peripheral blood mononuclear cells (PBMCs) from an independent cohort of 36 individuals of cognitively-normal controls, AD and other neuropathologies. Also, a set of genes was carefully selected by molecular interactions with MAPT, APP, NCSTN and BACE1 to test an expression-based classifier in a public microarray dataset of 40 samples (AD and controls). A series of discriminant analyses and classification and regression trees (C&RTs) were used to perform classification tasks. Results Using C&RTs, the combined model showed potential to differentially diagnose AD with up to 94.4% accuracy and 100% specificity for our independent sample. Furthermore, a subset of 16 genes showed the best diagnostic potential using a minimum number of expression variables, correctly classifying up to 100% of samples in the public dataset. Conclusions Our unique method of variable selection proves that even elements showing no significant differences between controls and AD, but that have somehow been linked to AD or AD-related elements, still hold a potential to be used in its diagnosis. Sample size and inherent methodological limitations of this study need to be kept in mind. Our classifiers require careful further testing in larger cohorts. Nonetheless, we believe these results provide evidence for the utility of our innovative method, which contributes a different approach to generate promising diagnostic tools for neuropsychiatric disorders.