Allostatic Load as a mediator of the association between psychosocial risk factors and cardiovascular diseases. Recent evidence and indications for prevention
Abstract
Obiettivi. Alcune categorie di lavoratori sono maggiormente vulnerabili agli effetti negativi dello stress lavoro-correlato sul rischio cardiovascolare. Vogliamo indagare l'accumulo di carico allostatico come possibile meccanismo d'azione.
Metodi. Lo studio comprende lavoratori salariati di età 25-64 anni reclutati in 3 coorti di popolazione. Abbiamo definito il carico allostatico (CA) come somma di z-score di 9 biomarcatori; le classi occupazionali (CO) secondo lo schema Erikson-Goldthorpe-Portocarero; ed il job strain (JS) con modello di Karasek. Tramite decomposizione di OaxacaBlinder, il gradiente occupazionale in CA è stato decomposto nella somma di due componenti: differential exposure (dovuta a diversa prevalenza di JS nelle CO) e differential vulnerability (diverso effetto di JS sul CA nelle diverse classi). Risultati. Tra i 2010 lavoratori (62% uomini, 34% manuali), le classi occupazionali, ma non le categorie di JS, risultano 334 associate con il CA, indipendentemente da età e sesso (p-value: 0.02). Nel campione complessivo, JS non ha alcun effetto sul gradiente occupazionale nel CA. Tuttavia, nel sotto-gruppo di lavoratori con disturbi del sonno, disturbi depressivi e che non praticano attività fisica, la componente di differential vulnerability legata al JS è positiva (0.63; C195%: 0.05-1.21).
Conclusioni. I lavoratori manuali con ridotta capacità di risposta sono più vulnerabili ad accumulo di carico allostatico legato a job strain.
References
- 1) Fishta A, Backé EM. Psychosocial stress at work and cardiovascular diseases: an overview of systematic reviews. Int Arch Occup Environ Health 2015 Nov; 88(8): 997-1014. DOI: https://doi.org/10.1007/s00420-015-1019-0
- 2) Ferrario MM, Veronesi G, Bertù L, et al. Job strain and the incidence of coronary heart diseases: does the association differ among occupational classes? A contribution from a pooled analysis of Northern Italian cohorts. BMJ Open 2017 Jan 24; 7(1). DOI: https://doi.org/10.1136/bmjopen-2016-014119
- 3) Ferrario MM, Veronesi G, Roncaioli M, et al. Cohorts Collaborative Study in Northern Italy (CCSNI Research Group. Exploring the interplay between job strain and different domains of physical activity on the incidence of coronary heart disease in adult men. Eur J Prev Cardiol 2019 May 20: 2047487319852186. DOI: https://doi.org/10.1177/2047487319852186
- 4) Li J, Atasoy S, Fang X, et al. Combined effect of work stress and impaired sleep on coronary and cardiovascular mortality in hypertensive workers: The MONICA/KORA cohort study. Eur J Prev Cardiol 2019 Apr 27: 2047487319839183. DOI: https://doi.org/10.1177/2047487319839183
- 5) Ferrario MM, Landsbergis P, Tsutsumi A, et al. ICOH Scientific Committee on Cardiology in Occupational Health. Work environment: An opportunity for ground-breaking collaborations in cardiovascular disease prevention. Eur J Prev Cardiol 2017; 24(2 suppl): 4-6. 6) Johnson SC, Cavallaro FL, Leon DA. A systematic review of allostatic load in relation to socioeconomic position: Poor fidelity and major inconsistencies in biomarkers employed. Soc Sci Med 2017 Nov; 192: 66-73. DOI: https://doi.org/10.1016/j.socscimed.2017.09.025
- 7) Chandola T, Britton A, Brunner E, et al. Work stress and coronary heart disease: what are the mechanisms? Eur Heart J 2008 Mar; 29(5): 640-8. DOI: https://doi.org/10.1093/eurheartj/ehm584
- 8) McEwen BS, Stellar E. Stress and the individual. Mechanisms leading to disease. Arch Intern Med 1993 Sep 27; 153(18): 2093-101. 9) Mauss D, Li J, Schmidt B, et al. Measuring allostatic load in the workforce: a systematic review. Ind Health 2015; 53(1): 5-20. 10) Veronesi G, Tunstall-Pedoe H, Ferrario MM, et al. MORGAM Project. Combined effect of educational status and cardiovascular risk factors on the incidence of coronary heart disease and stroke in European cohorts: Implications for prevention. Eur J Prev Cardiol 2017; 24(4): 437-445. DOI: https://doi.org/10.1177/2047487316679521
- 11) Diderichsen F, Hallqvist J, Whitehead M. Differential vulnerability and susceptibility: how to make use of recent development in our understanding of mediation and interaction to tackle health inequalities. Int J Epidemiol 2019; 48(1): 268-274. DOI: https://doi.org/10.1093/ije/dyy167
- 12) Sen B. Using the Oaxaca-Blinder Decomposition as an empirical tool to analyze racial disparities in obesity. Obesity 2014; 22: 1750-55. 13) Inker LA, Schmid CH, Tighiouart H, et al. CKD-EPT Investigators. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012; 367(1): 20-9. doi: 10.1056/NEJMoal114248. DOI: https://doi.org/10.1002/oby.20755
- 14) Howard JT, Sparks PJ. Does allostatic load calculation method matter? Evaluation of different methods and individual biomarkers functioning by race/ethnicity and educational level. Am J Hum Biol 2016; 28(5): 627-35. DOI: https://doi.org/10.1002/ajhb.22843
- 15) Gianfagna F, Veronesi G, Bertù L, Cesana G, Grassi G, Stranges S, Callegari C, Ferrario MM. Influence of sleep disturbances on age at onset and long-term incidence of major cardiovascular events: the MONICABrianza and PAMELA cohort studies. Sleep Med 2016; 21: 126-32. 16) Jann B. The Blinder-Oaxaca decomposition for linear regression models. The Stata Journal 2008; 8: 453-79. DOI: https://doi.org/10.1016/j.sleep.2016.01.007
Metrics
Citations and online attention from Dimensions, Altmetric and PlumX.
License
Copyright (c) 2019 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.