Decision trees for the analysis of genes involved in Alzheimer's disease pathology

Alzheimer's disease (AD) is characterized by a gradual loss of memory, orientation, judgement and language. There is still no cure for this disorder. AD pathogenesis remains fairly unknown and its underlying molecular mechanisms are not yet fully understood. Several studies have shown that the abnormal accumulation of beta-amyloid and tau proteins occurs 10 to 20 years before the onset of symptoms of the disease, so it is extremely important to identify changes in the brain before the first symptoms. We used decision trees to classify 31 individuals (9 healthy controls and 22 AD patients in three different stages of disease) according to the expression of 69 genes previously reported in a meta-analysis, plus the expression levels of APP, APOE, BACE1, NCSTN, PSEN1, PSEN2 and MAPT. We also included in our analysis the MMSE (Mini-Mental State Examination) scores and number of NFT (neurofibrillary tangles). Results allowed us to generate a model of classification values for different AD stages of severity, according to MMSE scores, and achieve the identification of the expression level of protein tau that may possibly determine the onset (incipient stage) of AD. We used decision trees to model the different stages of AD (severe, moderate, incipient and control) based on the meta-analysis of gene expression levels plus MMSE and NFT scores. Both classifiers reported the variable MMSE as most informative, however it we were found that the protein tau also an important role in the onset of AD.