Exposome influences: a multi-omics perspective on the combined toxic effects of pharmaceuticals and personal care products in Alzheimer's disease.

Authors

I İpek Boşgelmez, Beyza Mertaş, Hayrunnisa Deliser

Published in

Frontiers in toxicology. Volume 8. Pages 1871830. Epub Aug 24, 2026.

Abstract

According to WHO data, approximately 57 million people worldwide were affected by dementia in 2021, with prevalence projected to rise. Alzheimer's disease (AD), responsible for 60%-80% of dementia cases, continues to be a leading cause of mortality, with current treatments offering limited efficacy and disease-modifying therapies lacking widespread adoption or conclusive safety evidence, shifting the focus toward prevention and risk modification. Risk factors for AD include both non-modifiable elements, such as age, genetics, and gender, and modifiable factors, like environmental pollution, health status, and diet. While age remains the primary non-modifiable risk factor, early-onset dementia represents only up to 9% of cases. Addressing modifiable factors is essential, as it could prevent or delay almost half of dementia cases, with interventions-such as increased physical activity, smoking cessation, alcohol limitation, and overall health management-being significantly associated with a reduced risk. In this context, the exposome approach offers a comprehensive, integrative framework in which both modifiable and non-modifiable risk factors interact to influence individual susceptibility. Within the neural exposome, chronic low-dose exposure to xenobiotics-such as industrial chemicals, pesticides, metals, pharmaceuticals and personal care products (PPCPs), and air pollutants-may induce neurodegeneration via mechanisms including oxidative stress, neuroinflammation, proteinopathies, and epigenetic modifications, although establishing causality remains challenging. Integration of genomics, transcriptomics, proteomics, metabolomics, and lipidomics, combined with artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL), provides promising avenues for biomarker discovery, enhanced preventive strategies, early non-invasive diagnosis, and therapeutic target identification by integrating multi-layered biological data with exposure profiles. This review highlights emerging AD risk factors-including PPCPs-underscoring complex, multifactorial nature of AD and exposome, and the requirement for an interdisciplinary research approach, while also addressing several critical research gaps and methodological limitations.

PMID:
42703487
Bibliographic data and abstract were imported from PubMed on 07 Sep 2026.

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