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Review

Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence

by
Valeria D’Argenio
1,2,
Rossella Tomaiuolo
3,4,*,
Silvia Bargeri
5 and
Giulia Sancesario
6,7
1
Department of Human Sciences and Quality of Life Promotion, San Raffaele Open University, Via di Val Cannuta 247, 00166 Rome, Italy
2
CEINGE-Biotecnologie Avanzate Franco Salvatore, Via G. Salvatore 486, 80145 Naples, Italy
3
Università Vita-Salute San Raffaele, 20123 Milan, Italy
4
IRCCS Galeazzi Sant’Ambrogio, 20157 Milan, Italy
5
Unit of Clinical Epidemiology, IRCCS Istituto Ortopedico Galeazzi, 20157 Milan, Italy
6
Biobank and Clinical Neurochemistry, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
7
European Center for Brain Research, 00143 Rome, Italy
*
Author to whom correspondence should be addressed.
Genes 2026, 17(2), 233; https://doi.org/10.3390/genes17020233
Submission received: 12 December 2025 / Revised: 2 January 2026 / Accepted: 26 January 2026 / Published: 12 February 2026

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.
Keywords: Alzheimer’s Disease; neurogenomics; gender; artificial intelligence; digital health; GWAS; data treatment; ethics Alzheimer’s Disease; neurogenomics; gender; artificial intelligence; digital health; GWAS; data treatment; ethics

Share and Cite

MDPI and ACS Style

D’Argenio, V.; Tomaiuolo, R.; Bargeri, S.; Sancesario, G. Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence. Genes 2026, 17, 233. https://doi.org/10.3390/genes17020233

AMA Style

D’Argenio V, Tomaiuolo R, Bargeri S, Sancesario G. Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence. Genes. 2026; 17(2):233. https://doi.org/10.3390/genes17020233

Chicago/Turabian Style

D’Argenio, Valeria, Rossella Tomaiuolo, Silvia Bargeri, and Giulia Sancesario. 2026. "Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence" Genes 17, no. 2: 233. https://doi.org/10.3390/genes17020233

APA Style

D’Argenio, V., Tomaiuolo, R., Bargeri, S., & Sancesario, G. (2026). Alzheimer’s 2030: From Precision Genomics to Artificial Intelligence. Genes, 17(2), 233. https://doi.org/10.3390/genes17020233

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