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Get Free AccessOccupation-related stress and work characteristics are possible determinants of social inequalities in epigenetic aging but have been little investigated. Here, we investigate the association of several work characteristics with epigenetic age acceleration (AA) biomarkers. The study population included employed and unemployed men and women (n = 631) from the UK Understanding Society study. We evaluated the association of employment and work characteristics related to job type, job stability; job schedule; autonomy and influence at work; occupational physical activity; and feelings regarding the job with four epigenetic age acceleration biomarkers (Hannum, Horvath, PhenoAge, GrimAge) and pace of aging (DunedinPoAm, DunedinPACE). We fitted linear regression models, unadjusted and adjusted for established risk factors, and found the following associations for unemployment (years of acceleration): HorvathAA (1.51, 95% CI 0.08, 2.95), GrimAgeAA (1.53, 95% CI 0.16, 2.90) and 3.21 years for PhenoAA (95% CI 0.89, 5.33). Job insecurity increased PhenoAA (1.83, 95% CI 0.003, 3.67), while working at night was associated with an increase of 2.12 years in GrimAgeAA (95% CI 0.69, 3.55). We found effects of unemployment to be stronger in men and effects of night shift work to be stronger in women. These results provide evidence of associations between unemployment with accelerated ageing and suggest that insecure employment and night work may also increase age acceleration. Our findings have implications for policies relating to current changes in working conditions and highlight the utility of biological age biomarkers in studies in younger populations without long-term health information.
Anna Freni Sterrantino, Giovanni Fiorito, Angelo d’Errico, Marianna Virtanen, Leena Ala‐Mursula, Paul M Ridker, Paolo Vineis, Oliver Robinson (2022). Association between work characteristics and epigenetic age acceleration: cross-sectional results from UK – Understanding Society study. , 14(19), DOI: https://doi.org/10.18632/aging.204327.
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Type
Article
Year
2022
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.18632/aging.204327
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