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During recent years, we are moving away from the 'one exposure, one disease'-approach in occupational settings and towards a more comprehensive approach, taking into account the totality of exposures during a life course by using an exposome approach. Taking an exposome approach however is accompanied by many challenges, one of which, for example, relates to the collection of biological samples. Methods used for sample collection in occupational exposome studies should ideally be minimally invasive, while at the same time sensitive, and enable meaningful repeated sampling in a large population and over a longer time period. This might be hampered in specific situations e.g., people working in remote areas, during pandemics or with flexible work hours. In these situations, using self-sampling techniques might offer a solution. Therefore, our aim was to identify existing self-sampling techniques and to evaluate the applicability of these techniques in an occupational exposome context by conducting a literature review. We here present an overview of current self-sampling methodologies used to characterize the internal exposome. In addition, the use of different biological matrices was evaluated and subdivided based on their level of invasiveness and applicability in an occupational exposome context. In conclusion, this review and the overview of self-sampling techniques presented herein can serve as a guide in the design of future (occupational) exposome studies while circumventing sample collection challenges associated with exposome studies.
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http://dx.doi.org/10.1016/j.envres.2023.117001 | DOI Listing |
Int J Hyg Environ Health
September 2025
CHU Rennes, University of Rennes, Inserm, EHESP, IRSET (Institut de Recherche en Santé, Environnement et Travail), UMR_S 1085, F-35000, Rennes, France.
Objective: To use data-driven approaches to investigate maternal multi-occupational exposures during pregnancy and their effects on intrauterine growth.
Methods: Maternal occupational exposure to 47 factors during pregnancy was evaluated with job-exposure matrices in the French ELFE cohort. The outcomes of interest were birthweight (BW), small for gestational age (SGA) and head circumference (HC).
Toxics
July 2025
Institute of Physics Belgrade, a National Institute of the Republic of Serbia, Pregrevica 118, 11080 Belgrade, Serbia.
Human milk has been used for over 70 years to monitor pollutants such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Despite the growing body of data, our understanding of the pollutant exposome, particularly co-exposure patterns and their interactions, remains limited. Artificial intelligence (AI) offers considerable potential to enhance biomonitoring efforts through advanced data modelling, yet its application to pollutant dynamics in complex biological matrices such as human milk remains underutilized.
View Article and Find Full Text PDFSci Rep
August 2025
Postgraduate Program in Experimental Pathology, State University of Londrina, Londrina-Paraná, Brazil.
Rural women are at a higher risk of developing breast cancer, but the molecular mechanisms involved remain poorly understood. This study investigates the impact of chronic pesticide exposure on systemic and tissue-level protein expressions in women diagnosed with breast cancer. Plasma and tumor samples were collected from 215 women diagnosed with breast cancer; among them, 128 patients were occupationally exposed (n = 128) and 87 patients were not occupationally exposed (87) were analyzed for further studies.
View Article and Find Full Text PDFCancers (Basel)
July 2025
SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield 0002, South Africa.
Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), (), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis.
View Article and Find Full Text PDFBMC Public Health
August 2025
Graduate School of Biomedical Sciences, Department of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: The New York City (NYC) Pregnant Workers Fairness Act (PWFA) went into effect in January 2014 to provide greater flexibility for pregnant workers' accommodations, yet no studies to date have evaluated its effectiveness and utilization. We examined factors associated with pregnant workers' PWFA awareness, understanding of PWFA, and receipt of accommodations in a lower socioeconomic status population in NYC.
Methods: Participants included 481 pregnant workers who attended prenatal visits at Mount Sinai Hospital Obstetrics and Gynecology Clinic in NYC in 2017.