8287546 Occupational exposome analyses: an example based on lung function decline in the ECRHS study

<h3>Objective</h3> Occupational exposures have been linked to accelerated decline in lung function. We used an exposome approach to explore the complex associations reflecting the multifactorial aetiology of longitudinal changes in respiratory function. <h3>Material and Methods</h3> We analysed data from the European Community Respiratory Health Survey (ECRHS) (n=5,306), a prospective multicentre cohort with over 20 years of follow-up, to explore associations between occupational exposures and lung function decline. Participants’ job records were linked to multiple job-exposure matrices, covering 49 exposures classified into 11 groups. We applied the Super Learner algorithm to model the multipollutant relationships and to identify key exposure groups. G-computation was used to estimate the average causal effects (ACE). Models accounted for centre, age, sex, smoking, education, early-life factors, home environment, and urban exposures. <h3>Results</h3> Strong correlations were observed between exposures, particularly within specific sectors. Results from the Super Learner identified ‘Ergonomic Stressors’, ‘Physical Stressors’, ‘Gaseous Substances and Fumes’, and ‘Particulates and Fibrous Dusts’ as potentially important predictors of excess forced expiratory volume in 1s (FEV1 decline, but not of decline in forced vital capacity (FVC) or the FEV1/FVC ratio. Estimated ACEs suggested potential exposure-dependent associations between these groups (excluding ‘Ergonomic Stressors’) and accelerated FEV1 decline, although confidence intervals overlapped the null. Associations were weaker for FVC and FEV1/FVC ratio. <h3>Conclusion</h3> We identified several exposure groups as potential candidates for accelerated longitudinal lung function decline, highlighting the value of advanced exposome analytics in uncovering complex occupational determinants of respiratory health.

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Published
2025-10-01
DOI
https://doi.org/10.1136/oemed-2025-epicohabstracts.114
Primary Topic
Health, Environment, Cognitive Aging
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article
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article

8287546 Occupational exposome analyses: an example based on lung function decline in the ECRHS study

Þórarinn Gíslason, Wenxin Wan, Cecilie Svanes, Jenny Selander et al.
Health, Environment, Cognitive Aging
article

8287546 Occupational exposome analyses: an example based on lung function decline in the ECRHS study

Þórarinn Gíslason, Wenxin Wan, Cecilie Svanes, Jenny Selander, Theodore Lytras, Jesús Martínez‐Moratalla Rovira, Leopoldo Palacios Gómez, Isabel Urrutia Landa, Susan E. Peters, Pascal Demoly, Lützen Portengen, Roel C.H. Vermeulen, Deborah Jarvis, Judith García‐Aymerich, Lídia Casas, Christer Janson, Anjoeka Pronk, Anne Mette Lund Würtz, Hayat Bentouhami, Ioannis Basinas, Jan‐Paul Zock, María Albin, Lars Modig, Vivi Schlünssen, Caroline Lodge, Geza Benke, Jorunn Kirkeleit, Mathias Holm, Bénédicte Leynaert, Hans Kromhout, Valérie Siroux, Sheikh Mohammad Alif, Victoria Alcaraz-Serrano, Henrik Albert Kolstad, Theresa Møller Kynde
article en

Abstract

<h3>Objective</h3> Occupational exposures have been linked to accelerated decline in lung function. We used an exposome approach to explore the complex associations reflecting the multifactorial aetiology of longitudinal changes in respiratory function. <h3>Material and Methods</h3> We analysed data from the European Community Respiratory Health Survey (ECRHS) (n=5,306), a prospective multicentre cohort with over 20 years of follow-up, to explore associations between occupational exposures and lung function decline. Participants’ job records were linked to multiple job-exposure matrices, covering 49 exposures classified into 11 groups. We applied the Super Learner algorithm to model the multipollutant relationships and to identify key exposure groups. G-computation was used to estimate the average causal effects (ACE). Models accounted for centre, age, sex, smoking, education, early-life factors, home environment, and urban exposures. <h3>Results</h3> Strong correlations were observed between exposures, particularly within specific sectors. Results from the Super Learner identified ‘Ergonomic Stressors’, ‘Physical Stressors’, ‘Gaseous Substances and Fumes’, and ‘Particulates and Fibrous Dusts’ as potentially important predictors of excess forced expiratory volume in 1s (FEV1 decline, but not of decline in forced vital capacity (FVC) or the FEV1/FVC ratio. Estimated ACEs suggested potential exposure-dependent associations between these groups (excluding ‘Ergonomic Stressors’) and accelerated FEV1 decline, although confidence intervals overlapped the null. Associations were weaker for FVC and FEV1/FVC ratio. <h3>Conclusion</h3> We identified several exposure groups as potential candidates for accelerated longitudinal lung function decline, highlighting the value of advanced exposome analytics in uncovering complex occupational determinants of respiratory health.

RMIT University (AU)
Openalex Percentile: Top 64%
Health, Environment, Cognitive Aging
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