Alzheimer's 2030: From Precision Genomics to Artificial Intelligence

Area of Science:

• Neuroscience
• Genetics
• Public Health

Background:

• Alzheimer's disease (AD) poses a growing global health challenge, with prevalence and costs projected to double.
• While lifestyle factors are important, genetics significantly contribute to late-onset AD (40-80% heritability).
• Sex and gender are critical risk determinants, with women disproportionately affected due to biological and sociocultural influences.

Purpose of the Study:

• To review the impact of genetic and gender-related factors on Alzheimer's disease risk.
• To explore the utility of genome-wide association studies (GWASs) and genetic risk scores (GRS) for precision genomics in AD.
• To examine the role of Artificial Intelligence (AI) and digital health in personalized AD prevention.

Main Methods:

• Review of large-scale genome-wide association studies (GWASs).
• Analysis of genetic risk scores (GRS) for precision genomics.
• Exploration of Artificial Intelligence (AI) applications in multimodal big data analysis.

Main Results:

• Genetic factors significantly contribute to Alzheimer's disease heritability.
• Genome-wide association studies (GWASs) are advancing genetic risk prediction.
• Sex and gender significantly modulate AD risk, necessitating tailored approaches.

Conclusions:

• A precision medicine approach for Alzheimer's disease must incorporate genetic, sex, and gender information.
• Integrating AI and digital health tools can enhance personalized prevention strategies.
• Ethical considerations regarding data privacy are paramount in developing new AD interventions.