Genetic Insights and Predictive Models in Alzheimer’s Disease
DOI:
https://doi.org/10.62051/bsh2zw61Keywords:
Alzheimer’s Disease; Genetic Risk Factors; Polygenic Risk Score; Machine Learning Models.Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a significant genetic component, making the identification of genetic risk factors critical for predicting disease onset and progression. This review explores the genetic characteristics associated with AD, focusing on key genes such as APOE, APP, PSEN1, and PSEN2, and their roles in amyloid deposition, tau tangles, and overall disease pathology. By utilizing genetic databases like the Alzheimer’s Disease Neuroimaging Initiative (ADNI), AlzGene, and the National Alzheimer’s Coordinating Center (NACC), researchers can integrate genetic findings with neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) to better understand the interplay between genetic, environmental, and lifestyle factors in AD development. In addition, advanced machine learning models, including support vector machines (SVM), random forest (RF), convolutional neural networks (CNN), autoencoders (AE), and deep learning algorithms, combined with Polygenic Risk Scores (PRS), are increasingly used to integrate genetic, imaging, and clinical data for more precise AD risk prediction. While these advancements offer promising pathways for early diagnosis and intervention, significant challenges remain. These include difficulties in data integration across diverse sources, the development of robust and personalized predictive models, and addressing ethical concerns related to the use of genetic data. Future research will need to focus on overcoming these challenges to create more effective personalized prediction models that incorporate genetic, environmental, and lifestyle factors. Such developments hold the potential to revolutionize early detection, diagnosis, and treatment strategies for AD.
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