Work

The multifactorial nature of Alzheimer’s disease: A review of diverse determinants and the essential role of multimodal machine learning

Year: 2025

Type: article

Abstract: Alzheimer’s disease (AD) is the most prevalent form of dementia, profoundly impacting individuals, families, and healthcare systems. Its multifactorial etiology arises from a complex interplay of determinants, including neurological and cognitive impairments, molecular and genetic markers, systemic comorbidities, and lifestyle factors. While traditional research has often focused on individual or narrow sets of determinants, recent advancements highlight the necessity of examining these diverse contributors in unison. Consequently, this review synthesizes the evidence on the broad spectrum of AD risk factors and mechanisms. Moreover, the surge in heterogeneous multimodal data necessitates sophisticated analytical frameworks. Hence, we examine the necessity and potential of multimodal machine learning techniques in integrating complex datasets, facilitating deeper exploration of AD complexity, and contributing to personalized therapeutic strategies for this disease. This narrative review qualitatively synthesizes 250 peer-reviewed studies published between 2010 and 2024.

Source: Archives of Gerontology and Geriatrics Plus

DOI: 10.1016/j.aggp.2025.100207

Authors: Hesameddin Mostaghimi, Daniel A. Cohen, Hamid. R. Okhravi, Bahar Niknejad, Michel A. Audette

Institution: Old Dominion University

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,150