Tracing Alzheimer's Genetic Footprints: A Pioneering Longitudinal Study Using Artificial Intelligence to Unravel Mutation-Driven Risks and Progression in Virtual Patients; Part 1 The APOE genotypes

Abstract

Alzheimers Disease (AD), the most common neurodegenerative disorder, presents a significant challenge for early detection and intervention due to its complex etiology involving genetic, environmental, and lifestyle factors. This study harnesses the innovative potential of artificial intelligence (AI) through the aiHumanoid platform to simulate AD progression. In this study, we focus on the impact of two APOE genotypes on disease development and progression. Our longitudinal virtual subject simulations, grounded in extensive medical literature and genetic information, explore the nuanced interplay between specific genetic variants, APOE {varepsilon}3/4 and {varepsilon}4/4, and their role in ADs heterogeneity. Despite the potential limitations associated with emerging technologies, including the translatability of AI simulations to real-world scenarios and the scope of genetic variants, this research provides key insights into early biomarkers and the progression patterns of AD. Future segments of this study (Part 2 and Part 3) will broaden the analysis to encompass a wider array of genetic factors and their interactions, enhancing the understanding of AD and paving the way for personalized intervention strategies. Ethical considerations surrounding the use of AI in medical research are acknowledged, emphasizing the need for responsible integration of technology in healthcare. Our findings underscore the transformative potential of AI in advancing AD research, offering a foundation for future studies aimed at refining diagnostic and therapeutic approaches through enhanced realism in simulations and a comprehensive exploration of genetic and environmental factors.