A proposal of new biomarkers for Alzheimer’s Disease non-invasive diagnosis through gene expression and image processing
Keywords:
Alzheimer, GWAS, Gene Expression, Image Processing, MRI, Software developmentAbstract
Alzheimer's disease (AD) is a progressive neurological disorder that causes brain atrophy. The current diagnosis is based on cognitive tests. Nevertheless, these techniques are not conclusive, and the disease can be diagnosed indubitably postmortem. For this reason, we proposed a set of new biomarkers to improve Alzheimer's non-invasive diagnosis based on three major factors: First, the analysis of specific expression genes in blood. Second, patient data of their medical history. And third, asking for an MRI image to be analyzed. That is why we performed a gene expression analysis and a genome-wide association study from free datasets studies on AD in blood samples in R to find biomarkers that were later used in a Multilayer Perceptron to diagnose patients. Subsequently, we tested different physiological parameters such as sex, age, or level of education to prove its prediction significance through a logistic regression model with data from the National Alzheimer's Coordinating Center. Finally, we processed magnetic resonance images made by the Austrian Science Fund and German Research Foundation. As a result, a set of 55 genes directly related to AD were identified. The logistic regression model showed that the significant variables correspond to age, the presence of other cognitive diseases and the existence of mutations in the APOE gene. And a decrease in intracranial volume of white matter in hippocampus was detected in patients with the disease.
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