Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence

TL;DR

This paper reviews how precision genomics and AI can improve Alzheimer's prevention, focusing on genetic and gender factors.

Contribution

The paper emphasizes integrating genetic risk scores and AI with sex and gender considerations for precision medicine in Alzheimer's.

Findings

Genetic factors contribute significantly to sporadic late-onset Alzheimer's disease.

Women are disproportionately affected due to biological and sociocultural factors.

AI and genetic risk scores offer potential for personalized prevention strategies.

Abstract

Alzheimer’s disease (AD) represents a critical global health challenge, with its prevalence and associated costs expected to double significantly by 2030 and 2050. While lifestyle interventions are crucial, sporadic late-onset AD has a substantial genetic component (40–80% heritability), though known variants limit the scope of traditional precision medicine. Crucially, sex and gender are significant risk determinants, with women accounting for two-thirds of cases due to a complex interplay of biological and sociocultural factors. This review focuses on the influence of genetic and gender-related factors, examining large-scale genome-wide association studies (GWASs) and their role in developing advanced genetic risk scores (GRS) for precision genomics. We also explore the potential of Artificial Intelligence (AI) for multimodal big data analysis and digital health tools to promote personalized prevention and emerging concerns about ethics, privacy and data treatment. The convergence of these findings underscores the urgent need for a genetic-, sex- and gender-informed precision-medicine approach to AD.

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