結合臨床與個人基因特徵預測24個月後之簡易心智量表（MMSE）變化

Abstract

The dramatic rise in life expectancy in the past few decades has resulted in a huge number of individuals achieving the age at which neurodegenerative disorders become common. Alzheimer's disease (AD) is one of the most common neurodegenerative disease discovered more than one century ago and also one of the most common elderly diseases in the world. Slowly but surely, AD patients will lose their memory and their cognitive abilities, and even their personalities may change dramatically. These changes are due to the progressive dysfunction and death of nerve cells that are responsible for the storage and processing of information. Currently, AD affects about 24 to 35 million people around the world. Combined with an aging population, prevalence is expected to increase to 1 in 85 people by 2050. In order to deal with the massive growth of the AD patients, it is important to find the mechanism of Alzheimer’s disease development. Alzheimer’s disease is known as a genetically complex and heterogeneous disorder disease. The late-onset Alzheimer’s disease is modulated by genetic variants with relatively low penetrance but high prevalence. Based on previous studies, the only firmly established genetic susceptibility factor for Alzheimer’s disease is the e-4 allele of APOE. Beyond this, hundreds of other putative risk alleles in other genes were reported. But the relationships between these published alleles and the Alzheimer’s disease still remain unclear. Both of the clinical and genetic data we used in this study were provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). To tackle the complex genetic variations of AD, this study aims to link not only the genetic (Genome-wide association study, GWAS) but also clinical data to the change of the 24 month follow-up cognitive scores (measured in the end of the 24-th month after initial assessment) by the machine learning algorithm, SVR (Support Vector Machine Regression, SVR). We retrieved 39 SNPs (Single Nucleotide Polymorphism, SNP) from 1.5 million SNPs that were shown to be highly correlated to the degeneration of Alzheimer’s disease. We built the predictive model using both clinical and genetic data, and the resultant Pearson correlation coefficient between the measured and the predicted scores is about 0.5 on one training data set and are 0.43 and 0.35 on two independent test data sets. With a relaxed threshold, we extracted 866 SNPs from 1.5 million SNPs in 120 genes that were shown to be highly correlated to the degeneration of Alzheimer’s disease. The constructed model not only can help to predict cognitive trajectory and provide new approaches for early identification of AD, but also provide an efficient solution to select the samples for clinical trials for earlier disease treatment.