An analysis of factors predicting memory loss in Alzheimer's disease prevention

In recent years, data-driven approaches have been developed to predict Alzheimer's Disease. The goal of our report is to identify the factors that are most associated with memory loss. Data from ADNI (Alzheimer's Disease Neuro-imaging Initiative) with 8 variables from socioeconomic factors and biomarkers are analyzed, and will be used to propose a predictive model for the onset of AD. On the mathematical side, we propose to evaluate the data and the results for stability and reproducibility. Principal component analysis was first adopted to directly visualize the data structure and gain intuitive understanding. PCA shows a clustering of biomarkers TAUAB42, APOE4 and the current diagnosis DXCURREN, another clustering of characteristic variables including sex, age and education level, and the response AVLTot by itself. Furthermore, in the second principal component, which signifies a contrast between the characteristic descriptors and the biomarkers APOE4 and TAUAB42. The sex of the respondents played a significant effect: the contrast is larger for males and smaller for females. Linear regression model using backwards elimination further reached the result that the degree of memory loss indicated by AVLTot0 is positively related to education, but negatively related to gender, age, APOE4 and TAUAB420. In addition, higher level of TAUAB420 is related to lower memory function, and this decline effect is more dramatic for people who are younger.