Long working hours at midlife and arterial stiffness at older age among white-collar workers followed over 24 years

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This study aims to examine whether long working hours, repeatedly assessed at midlife, is associated with increased arterial stiffness at older age in a 24-year prospective study of white-collar workers in Quebec City, Canada. Methods This study relied on a prospective cohort, initiated in 1991–1993 (T1) with two follow-ups after 8 years (T2, 1999–2000) and 24 years (T3, 2015–2018). Participants (N = 1,629) were randomly selected for arterial stiffness measurement at T3 using carotid-femoral pulse wave velocity (PWV). Long working hours (> 40 h/week) were assessed at baseline (T1) and at the first follow-up (T2). Mean differences in PWV were estimated using generalized linear models, accounting for sociodemographic factors, lifestyle-related risk factors, clinical factors and psychosocial stressors at work. Results Among participants who remained actively employed over the study period, baseline (+ 0.54 m/s, 95% CI: 0.05–1.02) and repeated (+ 1.54 m/s, 95% CI: 0.83–2.26) exposure to long working hours was associated with increased arterial stiffness. No association was observed among participants who retired between follow-ups. Conclusion The present study suggests that working long hours during midlife is associated with increased arterial stiffness, among aging workers. Workplace preventive strategies reducing long working hours may be effective to mitigate long-term arterial stiffening. Occupational Stress Work Environment Arterial Stiffness Cardiovascular Disease Background The cardiovascular continuum is defined as a sequence of events, initiated by exposure to risk factors, progressing to asymptomatic vascular damage and leading to cardiovascular diseases (CVD) ( 1 ). Arterial stiffness is an asymptomatic CVD marker, resulting from reduced ability of large proximal arteries to dilate and retract ( 2 ). Arterial stiffness is associated with increased CVD risk, independently of blood pressure (BP) ( 3 ). Modifiable lifestyle-related risk factors, such as smoking ( 4 ) and physical inactivity ( 5 ), were shown to be associated with higher arterial stiffness. Evidence suggest that tackling those upstream factors may alter the pathogenic process leading to CVD, by reducing arterial stiffness progression ( 6 , 7 ). Long working hours is a frequent and modifiable risk factor from the work environment. According to the World Health Organization and International Labor organization (WHO / ILO), the number of individuals working long hours is increasing worldwide ( 8 ). In 2023, about 20% of workers in the United States( 9 ) and 15% in Canada ( 10 ) were working more than 40 hours per week, exceeding the standard weekly hours in many industrialized ( 11 ). Long working hours were shown to increase the risk of CVD, including coronary heart disease and stroke ( 12 , 13 ). According to the WHO/ILO, long working hours were responsible for 745,000 deaths from ischemic heart diseases and stroke worldwide, in 2016 ( 8 ). There is sparse evidence about the effect of long working hours on arterial stiffness. In a previous cross-sectional study conducted in Japan, older men (≥ 50 years old) working overtime had higher arterial stiffness ( 14 ). In a recent prospective cohort study, workers exposed to long working hours at baseline had higher arterial stiffness progression over 5 years ( 15 ). Both studies have used different methods to assess arterial stiffness, and neither have used carotid-femoral pulse wave velocity (PWV), recognized as the reference assessment method ( 16 , 17 ). Moreover, none assessed repeated exposure over time. Prolonged exposure to long working hours through the working life could lead to increased damage to the arterial walls, leading to faster arterial stiffness progression. The objective of the present study was to examine the association between baseline and repeated exposure to long working hours at midlife and arterial stiffness, assessed at older age, using carotid-femoral pulse wave velocity. Methods Study design and population This study relied on the PROspective Québec (PROQ) Study on Work and Health, described elsewhere ( 18 ). The cohort was composed of white-collar workers from 19 public and semi-public organisations in Quebec City, Canada. Their professional activities encompassed the entire range of white-collar positions, including senior management, professional, technical and office workers. All workers were invited to participate. A total of 9,188 white-collar workers (participation rate: 75%) were recruited in 1991–1993 (T1). Follow-up participation included 8,120 workers (90% of those eligible) in 1999–2001 (T2) and 6,707 (81% of those eligible) in 2015–2018 (T3). A third of the population at baseline, in 1991–1993, was randomly selected from the entire PROQ cohort ( 19 ). A second random sampling was performed, to include 50% of those who were actively employed in 2015–2018 (T3). In total, 3,411 participants were randomly selected for arterial stiffness assessment. Participants with prevalent CVD (N = 170) and those working less than 21 hours per week (N = 259) at baseline or at the first follow-up were excluded. After applying these selection criteria, 2,982 participants were identified as eligible for the present study. A total of 309 participants were excluded at T2 (1999–2001) and 629 at T3 (2015–2018) because of losses to follow-up or deaths. Additionally, 368 participants were excluded because they refused arterial stiffness assessment or had invalid measures. Finally, participants with missing values on long working hours (N = 3) and covariates (N = 44) were also excluded. The final study sample therefore consisted of 1,629 participants. Data collection At all three data collection, workers completed a self-administered questionnaire on sociodemographic, lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work. Long Working Hours The primary independent variable was self-reported weekly worked hours in the respondents’ paid job. Long working hours were defined as hours of work more than usual full-time working hours, namely working more than 40 hours per week. At each time, working hours were grouped into the following categories: ≤40 hours (reference category) and > 40 hours per week. Repeated exposure was defined as working more than 40 hours per week at both T1 (1991–1993) and T2 (1999–2001). Never exposed participants and those exposed at a single time-point were combined in the reference category. Arterial stiffness Arterial stiffness was assessed at the last follow-up (T3). It was measured with carotid femoral pulse PWV according to recent recommendations ( 20 ), using the Complior Analyses Device ( 21 ). The velocity was measured from the carotid-femoral distance and the transit time between the carotid and femoral pulse, recorded simultaneously. In each participant, the carotid femoral PWV was measured twice, and if the difference in velocity between the two measurements was greater than 0.5 m/s, a third measurement was made. The average of all measurements was used in the analyses. Inter- and intra-observer reproducibility has been evaluated in previous studies and has been shown to be excellent ( 22 ). Covariates Sociodemographic variables included age (continuous), sex (men and women), and education (less than college, college completed, and university completed). Lifestyle-related risk factors included alcohol consumption, smoking status and physical activity. Alcohol consumption was categorized into three categories, based on the weekly frequency of intake: low consumption (fewer than 1 drink per week), moderate consumption (1–10 drinks per week for women and 1–15 for men), and high consumption (more than 10 drinks per week for women and more than 15 for men) ( 23 ). Smoking status was defined as the daily consumption of at least one cigarette per day (yes/no). Physical activity was assessed using a validated question on the duration and frequency of their physical activity: inactive (< 1 session per week), insufficiently active (1–2 sessions per week), and active (≥ 3 sessions per week) ( 24 ). Clinical risk factors included body mass index (BMI), BP and diabetes. BMI was assessed using body weight and height measured by a trained nurse and calculated as the ratio between weight in kilograms and the square of height in meters. BP was measured following the American Heart Association protocol ( 25 ). Resting BP was measured after they had been sitting for 5 min. The average of two BP measurements taken 1 to 2 min apart, was used. Antihypertensive medication and diabetes were self-reported. Psychosocial stressors at work from the demand-control model were also assessed, using the validated French version of the Job Content Questionnaire ( 26 , 27 ). This model includes psychological demands (9 items), which refer to the quantity of work, time constraints, interruptions, conflicting demands and the intensity of intellectual effort required. It also measures decision latitude (9 items), referring to opportunities for learning, autonomy and participation in the decision-making process. Psychological demands and decision latitude were dichotomized at the median observed in a random sample of all Quebec workers ( 28 ). Job strain was defined as a combination of high psychological demands and low decision latitude. Analyses Generalized linear equations were used to examine the association between long working hours and PWV. PWV mean differences and 95% confidence intervals (CI) were computed. Models were adjusted for sociodemographic factors (Model 1), then additionally adjusted for lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work (Model 2). Analyses were conducted in the total sample and separately for actively employed and inactive participants at the time of PWV assessment (T3). Inactive participants were defined as being retired from the workforce or working less than 21 hours per week. The potential modifying effect of sex was examined using a multiplicative interaction term. This interaction term was not statistically significant in any model. Two post-hoc sensitivity analyses were conducted, replacing baseline systolic BP with 1- baseline mean arterial pressure and 2-systolic BP changes over the study period. The present study was approved by the ethics review board of CHU de Quebec- Université Laval. All patients provided informed consent. All analyses were performed with SAS v.9.4 software . Results Table 1 presents the baseline distribution of the population. The study population was composed of 835 women (51.3%) and 794 men (48.7%), resulting in a total of 1,629 participants. The average age of participants was 37 years at baseline (standard deviation [SD]: 6.4) and 46.5% had a university degree (Table 1 ). Regarding lifestyle-related risk factors, most participants were non-smokers (84.8%), and a majority were moderate drinkers (57.0%). According to physical activity, 41.5% were identified as insufficiently active, 37.9% were active, and 20.6% were inactive. Mean BMI was 24.1 kg/m 2 (SD: 3.6), while mean systolic and diastolic BP were 115.3 mmHg (SD: 13.2) and 72.8 mmHg (SD: 9.8), respectively. A small proportion of participants were taking antihypertensive medication (1.4%) or had diabetes (1.1%). According to occupational risk factors, 21.6% were exposed to job strain and 7.4% were exposed to long working hours at baseline. Participants who remained actively employed over the study period were younger at baseline (31.2 years vs 39.7 years). This subgroup also had a lower average systolic blood pressure, less alcohol consumption, and a lower prevalence of diabetes. Table 1 Description of the Study Population at Baseline (1991–1993) (n = 1,629) Total Workers Actively Employed in 1991–1993 Actively Employed Workers in 2015-18 Inactive Participants in 2015-18 N = 1,629 N = 533 (32.7%) N = 1,096 (67.3%) Sex Women 835 (51.3) 286 (53.7) 549 (50.1) Men 794 (48.7) 247 (46.3) 547 (49.9) Mean age (year, SD) 37.0 (6.4) 31.2 (5.0) 39.7 (5.1) Education Less than college 366 (22.5) 73 (13.7) 293 (26.7) College 505 (31.0) 194 (36.4) 311 (28.4) University 758 (46.5) 266 (49.9) 492 (44.9) Alcohol intake Low 650 (39.9) 238 (44.7) 412 (37.6) Moderate 929 (57.0) 290 (54.4) 639 (58.3) High 50 (3.1) 5 (0.9) 45 (4.1) Smoking 248 (15.2) 79 (14.8) 169 (15.4) Physical activity Inactive 336 (20.6) 196 (36.8) 421 (38.4) Insufficently active 676 (41.5) 230 (43.2) 446 (40.7) Active 617 (37.9) 107 (20.0) 229 (20.9) Mean BMI, (kg/m 2 , SD) 24.1 (3.6) 23.7 (3.5) 24.2 (3.6) Hypertension medication 23 (1.4) 4 (0.8) 19 (1.7) Mean Systolic BP, (mmHg, SD) 115.3 (13.2) 114.1 (12.3) 115.9 (13.6) Mean Diastolic BP, (mmHg, SD) 72.8 (9.8) 70.3 (9.2) 74 (9.8) Diabetes 18 (1.1) 5 (1.0) 13 (1.2) Job strain exposure 351 (21.6) 124 (23.3) 227 (20.7) Long working hours (> 40h/week) Baseline exposure 1 121 (7.4) 29 (5.4) 92 (8.4) Repeated exposure 2 46 (2.8) 13 (2.4) 33 (3.0) Note: SD = Standard Deviation; BP = Blood Pressure; BMI = Body Mass Index. 1 Baseline exposure at T1 (1991–1993) 2 Repeated exposure at both T1 (1991–1993) and T2 (1999–2001) Table 2 presents mean PWV (m/s) in 2015–2018 according to long working hours at baseline (1991–1993) among the total sample (n = 1,629), as well as stratified for actively employed workers (subsample n = 533) and inactive participants (subsample n = 1,096) in 2015–2018 (T3). In the crude models, arterial stiffness was slightly higher among those exposed to long working hours, in the total sample (+ 0.68 m/s [95% CI (0.37, 0.98]), as well as among actively employed workers (+ 0.90 m/s [95% CI (0.40, 1.41]) and inactive participants (+ 0.52 m/s [95% CI (0.15, 0.90]) at T3 (2015–2018). In the fully adjusted models, long working hours at baseline remained associated with arterial stiffness among actively employed workers (+ 0.54 m/s [95% CI (0.05, 1.02)]). Table 2 Pulse wave velocity (m/s) in 2015–2018 according to long working hours at baseline (T1: 1991–1993) Baseline exposure to long working hours (> 40 h/week) N Pulse Wave Velocity, m/s (95% CI) Crude Model Adjusted Model 1 Adjusted Model 2 Total (n = 1,629) Unexposed at T1 (ref) 1,508 8.06 8.10 8.10 Exposed at T1 121 + 0.68 (0.37, 0.98) + 0.14 (-0.15, + 0.42) + 0.12 (-0.16, + 0.40) Actively Employed Workers in 2015-18 (n = 533) Unexposed at T1 (ref) 504 7.63 7.65 7.65 Exposed at T1 29 + 0.90 (0.40, 1.41) + 0.54 (0.05, 1.03) + 0.54 (0.05, 1.02) Inactive Participants in 2015-18 (n = 1,096) Unexposed at T1 (ref) 1,004 8.27 8.32 8.32 Exposed at T1 92 + 0.52 (0.15, 0.90) -0.01 (-0.36, 0.34) -0.04 (-0.38, 0.31) Ref: Reference category, m/s: meter per second, CI: confidence intervals Adjusted model 1: Adjusted for age, sex, education Adjusted model 2: Model 1 plus alcohol intake, smoking, physical inactivity, body mass index, systolic blood pressure, diabetes diagnosis, hypertension medication, and job strain. Table 3 presents mean PWV (m/s) in 215–2018 according to repeated exposure to long working hours at baseline (T1: 1991–1993) and at first follow-up (T2: 1999–2001). In the crude model, mean PWV was higher among participants who were repeatedly exposed to long working hours when compared to all other workers (+ 0.93 m/s [95% CI (0.44, 1.41)]). Among actively employed workers in 2015–2018, there was a 2.09 m/s increase in PWV among participants repeatedly exposed to long working hours. The association remained statistically significant in the fully adjusted model (+ 1.54 m/s [95% CI (0.83, 2.26)]). Repeated exposure to long working hours remained associated with increased PWV and estimates were of comparable magnitude in post-hoc analyses, replacing baseline systolic BP with mean arterial pressure or systolic BP changes over the study period ( not shown ). Among inactive participants at T3, there was no association between repeated exposure to long working hours and PWV. Table 3 Pulse wave velocity (m/s) in 2015–2018 according to repeated exposure to long working hours at baseline (T1: 1991–1993) and at first follow-up (T2: 1999–2001) Repeated exposure to long working hours (> 40 h/week) N Pulse Wave Velocity, m/s (95% CI) Crude Model Adjusted Model 1 Adjusted Model 2 Total (n = 1,629) Unexposed at T1 and/or T2 (ref) 1583 8.08 m/s 8.10 m/s 8.10 m/s Exposed at both T1 and T2 46 + 0.93 (0.44, 1.41) + 0.27 (-0.18, + 0.72) + 0.24 (-0.20, + 0.69) Actively employed workers at T3 (2015-18) (n = 533) Unexposed at T1 and/or T2 (ref) 520 7.62 m/s 7.64 m/s 7.65 m/s Exposed at both T1 and T2 13 + 2.09 (1.36, 2.41) + 1.51 (0.79, 2.22) + 1.54 (0.83, 2.26) Inactive participants at T3 (2015-18) (n = 1,096) Unexposed at T1 and/or T2 (ref) 1063 8.31 m/s 8.32 m/s 8.32 m/s Exposed at both T1 and T2 33 + 0.43 (-0.18, 1.03) -0.22 (-0.79, 0.34) -0.27 (-0.82, 0.28) Ref: Reference category, m/s: meter per second, CI: confidence intervals Adjusted model 1: Adjusted for age, sex, education Adjusted model 2: Model 1 plus alcohol intake, smoking, physical inactivity, body mass index, systolic blood pressure, diabetes diagnosis, hypertension medication, and job strain. Discussion The present study relied on prospective cohort composed of men and women followed over 24 years. In this study, repeated exposure to long working hours at midlife was associated with increased arterial stiffness among participants who remained actively employed over the whole study period. This association was robust to adjustment for socio-demographics, lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work. The magnitude of this association (+ 1.54 m/s) is of clinical importance. For instance, a meta-analysis has reported a 15% increased CVD risk for each 1 m/s increase in PWV ( 2 ). To our knowledge, the current study is the first to examine the association between repeated exposure to long working hours and arterial stiffness. It is also the first to use carotid-femoral PWV, which is recognized as the reference standard measurement for arterial stiffness. One previous prospective study has reported an association between long working hours, as defined as working 55 hours or more per week, and arterial stiffness progression over 5 years (+ 0.32 m/s) ( 15 ). The present study showed association of comparable magnitude to that reported in this previous study, using baseline exposure (+ 0.54 m/s). The association was of higher magnitude using repeated exposure to long working hours. Therefore, results are consistent with the hypothesis of a potential underestimation of the association between long working hours and arterial stiffness using a single assessment. As a summary measure of vascular aging, arterial stiffness may capture vascular damage accumulated over the life course. Our results are consistent with this hypothesis and suggest that long working hours exert its effect on arterial stiffness over the working life and may be especially harmful among older workers exposed to long work hours over a prolonged period of time. Pathophysiological mechanisms could explain the adverse effect of long working hours on arterial stiffness. Repeated exposure to long working hours may lead to increased activity of the sympathetic nervous system (catecholamines) and the hypothalamic-pituitary-adrenal axis (glucocorticoids). Moreover, the sympathetic nervous system is one of the pathways activating the renin-angiotensin system. Therefore, in conjunction with other risk factors, exposure to long working hours can trigger vasoconstriction, endothelial dysfunction, cellular proliferation, and inflammation that promote arterial stiffness ( 29 – 31 ). Indirect mechanisms could also explain the observed association. First, evidence suggests that long working hours could be associated with sleep deprivation, which could in turn increase cardiovascular risk ( 32 , 33 ). Second, individuals working long hours might be more likely to adopt or maintain unhealthy behaviors ( 34 ). Third, long working hours could also be associated with prolonged exposure to psychosocial stressors at work ( 35 ). In a recent study conducted by our research team, job strain at midlife was associated with increased PWV among workers with high BP ( 36 ). In the present study, association between long working hours and arterial stiffness was observed following adjustment for lifestyle-related risk factors and psychosocial stressors at work from the job strain model. Therefore, lifestyle-related risk factors and psychosocial stressors at work unlikely explain the observed association. Our study has limitations. First, exposure assessment at baseline and follow-up were spaced by approximately 8 years, which could have led to non-differential misclassification of the exposure. However, most workers (82%) remained in the same occupation between T1 and T2, in favor of exposure stability ( 37 ). Second, 7.4% of the study population worked more than 40 hours per week and only 2.8% were repeatedly exposed at both times. Therefore, the association between long working hours and arterial stiffness could not be examined using of higher thresholds of exposure to long working hours and the investigation of a potential dose-response relationship. Third, arterial stiffness was available at the last follow-up only. Therefore longitudinal progression could not be assessed. Adjusting for BP changes over the study period led to similar estimates suggesting that the longitudinal progression of BP could not explain the observed association. Future studies using multiple assessment of PWV are needed to examine the adverse effect of long working hours on arterial stiffness longitudinal progression. Finally, the generalizability of our findings might be limited to white-collar workers. More specifically, results might not be generalizable to blue-collar workers, who are more frequently exposed to different working arrangements including shift work and night work. However, the restriction to a white-collar population minimized the possibility for a large amount of unmeasured worked hours that could be more frequent in populations with different occupational profile. Our study also has important strengths. First, carotid-femoral PWV was used, which is considered as the gold standard measurement for arterial stiffness. Moreover, the present study relied on a prospective cohort and long working hours were assessed at two different time points. The 24-year follow-up also allowed to examine the long-term association between long working hours at midlife and arterial stiffness at older age. The high participation proportion at each time, and the use of random sample for PWV assessment minimized the potential for selection bias. Finally, a large number of potential confounders were assessed and were adjusted for in the analyses. Conclusion The present study examined the association between long working hours and arterial stiffness. Results suggest that long working hours assessed at midlife are associated with increased arterial stiffness, among older workers. This association was of higher magnitude among participants repeatedly exposed to long working hours. Preventive workplace intervention aiming at reducing the prevalence of long working hours are needed to examine potential benefits on cardiovascular health, including the potential for such interventions to mitigate arterial stiffness progression. At the clinical level, the present study suggest that long working hours may be useful for the early identification of workers who may be at increased risk of developing asymptomatic vascular damage. Abbreviations CVD Cardiovascular Diseases BP Blood Pressure WHO/ILO World Health Organization and International Labor Organization PWV Pulse Wave Velocity PROQ Study PROspective Québec Study BMI Body Mass Index CI Confidence Intervals SD Standard Deviation Declarations Ethics approval and consent to participate The study was approved by the ethics review boards of the Centre hospitalier universitaire de Québec - Laval University. Informed consent was obtained from all participants involved in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant from the Canadian Institutes of Health Research (CIHR). Authors' contributions CBS wrote the manuscript and led the analysis. MGO and MLR participated in revising and editing the manuscript. CB was the cohort principal investigator, supervised the data collection and study design. AM supervised arterial stiffness assessment and clinical interpretation. XT supervised the methodological aspects, reviewed and edited the manuscript, and is responsible for the overall content. All authors contributed significantly to the work and approved the final manuscript for publication. Acknowledgements The authors wish to extend special thanks to Caty Blanchette for revising the statistical programs and providing essential support throughout the study. Authors' information Not applicable. References Dzau VJ, Antman EM, Black HR, Hayes DL, Manson JE, Plutzky J, et al. The cardiovascular disease continuum validated: clinical evidence of improved patient outcomes: part I: Pathophysiology and clinical trial evidence (risk factors through stable coronary artery disease). Circulation. 2006;114(25):2850-70. 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Brisson C, Blanchette C, Guimont C, Dion G, Moisan J, Vézina M. Reliability and validity of the French version of the 18-item Karasek Job Content Questionnaire. Work & Stress. 1998;12(4):322-36. Larocque B, Brisson C, Blanchette C. Cohérence interne, validité factorielle et validité discriminante de la traduction française des échelles de demande psychologique et de latitude décisionnelle du "Job Content Questionnaire" de Karasek. Rev Epidém et Santé Publ. 1998;46:371-81. Santé Québec. Enquête québécoise sur la santé cardiovasculaire [Quebec survey on cardiovascular health] 1990 , Rapport final. 1993. Fisher JP, Paton JF. The sympathetic nervous system and blood pressure in humans: implications for hypertension. J Hum Hypertens. 2012;26(8):463-75. Lu XT, Zhao YX, Zhang Y, Jiang F. Psychological stress, vascular inflammation, and atherogenesis: potential roles of circulating cytokines. J Cardiovasc Pharmacol. 2013;62(1):6-12. Groeschel M, Braam B. Connecting chronic and recurrent stress to vascular dysfunction: no relaxed role for the renin-angiotensin system. Am J Physiol Renal Physiol. 2011;300(1):F1-10. Nagai M, Hoshide S, Kario K. Sleep duration as a risk factor for cardiovascular disease- a review of the recent literature. Curr Cardiol Rev. 2010;6(1):54-61. Liu Y, Tanaka H, Fukuoka Heart Study G. Overtime work, insufficient sleep, and risk of non-fatal acute myocardial infarction in Japanese men. Occup Environ Med. 2002;59(7):447-51. Yang H, Schnall PL, Jauregui M, Su TC, Baker D. Work hours and self-reported hypertension among working people in California. Hypertension. 2006;48(4):744-50. Virtanen M, Heikkila K, Jokela M, Ferrie JE, Batty GD, Vahtera J, et al. Long working hours and coronary heart disease: a systematic review and meta-analysis. Am J Epidemiol. 2012;176(7):586-96. Massamba VK, Talbot D, Milot A, Trudel X, Dionne CE, Vezina M, et al. Association between psychosocial work-related factors at midlife and arterial stiffness at older age in a prospective cohort of 1736 white-collar workers. BMJ Open. 2023;13(9):e073649. Duchaine CS, Brisson C, Talbot D, Gilbert-Ouimet M, Trudel X, Vezina M, et al. Cumulative exposure to psychosocial stressors at work and global cognitive function: the PROspective Quebec Study on Work and Health. Occup Environ Med. 2021;78(12):884-92. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 May, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 08 Oct, 2024 Reviews received at journal 22 Sep, 2024 Reviewers agreed at journal 13 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers invited by journal 07 Sep, 2024 Editor assigned by journal 15 Aug, 2024 Submission checks completed at journal 15 Aug, 2024 First submitted to journal 15 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4920299","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351887753,"identity":"c3de41de-f990-4079-81c3-2db82711bb86","order_by":0,"name":"Carolina Braga Sisti","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"Braga","lastName":"Sisti","suffix":""},{"id":351887755,"identity":"ee9cc6a1-ae00-4e86-b659-4d2d38a3d5d8","order_by":1,"name":"Mahée Gilbert-Ouimet","email":"","orcid":"","institution":"Université du Québec à Rimouski","correspondingAuthor":false,"prefix":"","firstName":"Mahée","middleName":"","lastName":"Gilbert-Ouimet","suffix":""},{"id":351887756,"identity":"ab9a8456-7629-42fd-9ad6-f390347477cc","order_by":2,"name":"Mathilde Lavigne-Robichaud","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Lavigne-Robichaud","suffix":""},{"id":351887757,"identity":"e6e43003-125f-4e87-9177-7450cbc6c201","order_by":3,"name":"Chantal Brisson","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Chantal","middleName":"","lastName":"Brisson","suffix":""},{"id":351887758,"identity":"4344b797-a12a-4810-94fb-e7d993641226","order_by":4,"name":"Alain Milot","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Milot","suffix":""},{"id":351887759,"identity":"940b0908-e44d-477f-a0bf-a0edbc578c23","order_by":5,"name":"Xavier Trudel","email":"data:image/png;base64,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","orcid":"","institution":"Université Laval","correspondingAuthor":true,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Trudel","suffix":""}],"badges":[],"createdAt":"2024-08-15 15:41:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4920299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4920299/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-22954-3","type":"published","date":"2025-05-17T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067651,"identity":"1597cc8b-21b1-4be0-82e9-8150ff787aad","added_by":"auto","created_at":"2025-05-19 16:01:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":902282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4920299/v1/fbd8f03b-9b7d-4974-b9a6-bd95620c018b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long working hours at midlife and arterial stiffness at older age among white-collar workers followed over 24 years","fulltext":[{"header":"Background","content":"\u003cp\u003eThe cardiovascular continuum is defined as a sequence of events, initiated by exposure to risk factors, progressing to asymptomatic vascular damage and leading to cardiovascular diseases (CVD) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Arterial stiffness is an asymptomatic CVD marker, resulting from reduced ability of large proximal arteries to dilate and retract (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Arterial stiffness is associated with increased CVD risk, independently of blood pressure (BP) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Modifiable lifestyle-related risk factors, such as smoking (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and physical inactivity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), were shown to be associated with higher arterial stiffness. Evidence suggest that tackling those upstream factors may alter the pathogenic process leading to CVD, by reducing arterial stiffness progression (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLong working hours is a frequent and modifiable risk factor from the work environment. According to the World Health Organization and International Labor organization (WHO / ILO), the number of individuals working long hours is increasing worldwide (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In 2023, about 20% of workers in the United States(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and 15% in Canada (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) were working more than 40 hours per week, exceeding the standard weekly hours in many industrialized (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Long working hours were shown to increase the risk of CVD, including coronary heart disease and stroke (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). According to the WHO/ILO, long working hours were responsible for 745,000 deaths from ischemic heart diseases and stroke worldwide, in 2016 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThere is sparse evidence about the effect of long working hours on arterial stiffness. In a previous cross-sectional study conducted in Japan, older men (\u0026ge;\u0026thinsp;50 years old) working overtime had higher arterial stiffness (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In a recent prospective cohort study, workers exposed to long working hours at baseline had higher arterial stiffness progression over 5 years (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Both studies have used different methods to assess arterial stiffness, and neither have used carotid-femoral pulse wave velocity (PWV), recognized as the reference assessment method (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Moreover, none assessed repeated exposure over time. Prolonged exposure to long working hours through the working life could lead to increased damage to the arterial walls, leading to faster arterial stiffness progression.\u003c/p\u003e \u003cp\u003eThe objective of the present study was to examine the association between baseline and repeated exposure to long working hours at midlife and arterial stiffness, assessed at older age, using carotid-femoral pulse wave velocity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis study relied on the PROspective Qu\u0026eacute;bec (PROQ) Study on Work and Health, described elsewhere (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The cohort was composed of white-collar workers from 19 public and semi-public organisations in Quebec City, Canada. Their professional activities encompassed the entire range of white-collar positions, including senior management, professional, technical and office workers. All workers were invited to participate. A total of 9,188 white-collar workers (participation rate: 75%) were recruited in 1991\u0026ndash;1993 (T1). Follow-up participation included 8,120 workers (90% of those eligible) in 1999\u0026ndash;2001 (T2) and 6,707 (81% of those eligible) in 2015\u0026ndash;2018 (T3).\u003c/p\u003e \u003cp\u003eA third of the population at baseline, in 1991\u0026ndash;1993, was randomly selected from the entire PROQ cohort (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A second random sampling was performed, to include 50% of those who were actively employed in 2015\u0026ndash;2018 (T3). In total, 3,411 participants were randomly selected for arterial stiffness assessment.\u003c/p\u003e \u003cp\u003eParticipants with prevalent CVD (N\u0026thinsp;=\u0026thinsp;170) and those working less than 21 hours per week (N\u0026thinsp;=\u0026thinsp;259) at baseline or at the first follow-up were excluded. After applying these selection criteria, 2,982 participants were identified as eligible for the present study. A total of 309 participants were excluded at T2 (1999\u0026ndash;2001) and 629 at T3 (2015\u0026ndash;2018) because of losses to follow-up or deaths. Additionally, 368 participants were excluded because they refused arterial stiffness assessment or had invalid measures. Finally, participants with missing values on long working hours (N\u0026thinsp;=\u0026thinsp;3) and covariates (N\u0026thinsp;=\u0026thinsp;44) were also excluded. The final study sample therefore consisted of 1,629 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eAt all three data collection, workers completed a self-administered questionnaire on sociodemographic, lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work.\u003c/p\u003e \u003cp\u003eLong Working Hours\u003c/p\u003e \u003cp\u003eThe primary independent variable was self-reported weekly worked hours in the respondents\u0026rsquo; paid job. Long working hours were defined as hours of work more than usual full-time working hours, namely working more than 40 hours per week. At each time, working hours were grouped into the following categories: \u0026le;40 hours (reference category) and \u0026gt;\u0026thinsp;40 hours per week. Repeated exposure was defined as working more than 40 hours per week at both T1 (1991\u0026ndash;1993) and T2 (1999\u0026ndash;2001). Never exposed participants and those exposed at a single time-point were combined in the reference category.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eArterial stiffness\u003c/h2\u003e \u003cp\u003eArterial stiffness was assessed at the last follow-up (T3). It was measured with carotid femoral pulse PWV according to recent recommendations (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), using the \u003cem\u003eComplior Analyses Device\u003c/em\u003e (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The velocity was measured from the carotid-femoral distance and the transit time between the carotid and femoral pulse, recorded simultaneously. In each participant, the carotid femoral PWV was measured twice, and if the difference in velocity between the two measurements was greater than 0.5 m/s, a third measurement was made. The average of all measurements was used in the analyses. Inter- and intra-observer reproducibility has been evaluated in previous studies and has been shown to be excellent (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eSociodemographic variables included age (continuous), sex (men and women), and education (less than college, college completed, and university completed).\u003c/p\u003e \u003cp\u003eLifestyle-related risk factors included alcohol consumption, smoking status and physical activity. Alcohol consumption was categorized into three categories, based on the weekly frequency of intake: low consumption (fewer than 1 drink per week), moderate consumption (1\u0026ndash;10 drinks per week for women and 1\u0026ndash;15 for men), and high consumption (more than 10 drinks per week for women and more than 15 for men) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Smoking status was defined as the daily consumption of at least one cigarette per day (yes/no). Physical activity was assessed using a validated question on the duration and frequency of their physical activity: inactive (\u0026lt;\u0026thinsp;1 session per week), insufficiently active (1\u0026ndash;2 sessions per week), and active (\u0026ge;\u0026thinsp;3 sessions per week) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical risk factors included body mass index (BMI), BP and diabetes. BMI was assessed using body weight and height measured by a trained nurse and calculated as the ratio between weight in kilograms and the square of height in meters. BP was measured following the American Heart Association protocol (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Resting BP was measured after they had been sitting for 5 min. The average of two BP measurements taken 1 to 2 min apart, was used. Antihypertensive medication and diabetes were self-reported.\u003c/p\u003e \u003cp\u003ePsychosocial stressors at work from the demand-control model were also assessed, using the validated French version of the Job Content Questionnaire (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This model includes psychological demands (9 items), which refer to the quantity of work, time constraints, interruptions, conflicting demands and the intensity of intellectual effort required. It also measures decision latitude (9 items), referring to opportunities for learning, autonomy and participation in the decision-making process. Psychological demands and decision latitude were dichotomized at the median observed in a random sample of all Quebec workers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Job strain was defined as a combination of high psychological demands and low decision latitude.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses\u003c/h2\u003e \u003cp\u003eGeneralized linear equations were used to examine the association between long working hours and PWV. PWV mean differences and 95% confidence intervals (CI) were computed. Models were adjusted for sociodemographic factors (Model 1), then additionally adjusted for lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work (Model 2). Analyses were conducted in the total sample and separately for actively employed and inactive participants at the time of PWV assessment (T3). Inactive participants were defined as being retired from the workforce or working less than 21 hours per week. The potential modifying effect of sex was examined using a multiplicative interaction term. This interaction term was not statistically significant in any model. Two post-hoc sensitivity analyses were conducted, replacing baseline systolic BP with 1- baseline mean arterial pressure and 2-systolic BP changes over the study period. The present study was approved by the ethics review board of CHU de Quebec- Universit\u0026eacute; Laval. All patients provided informed consent. All analyses were performed with \u003cem\u003eSAS v.9.4 software\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline distribution of the population. The study population was composed of 835 women (51.3%) and 794 men (48.7%), resulting in a total of 1,629 participants. The average age of participants was 37 years at baseline (standard deviation [SD]: 6.4) and 46.5% had a university degree (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding lifestyle-related risk factors, most participants were non-smokers (84.8%), and a majority were moderate drinkers (57.0%). According to physical activity, 41.5% were identified as insufficiently active, 37.9% were active, and 20.6% were inactive. Mean BMI was 24.1 kg/m\u003csup\u003e2\u003c/sup\u003e (SD: 3.6), while mean systolic and diastolic BP were 115.3 mmHg (SD: 13.2) and 72.8 mmHg (SD: 9.8), respectively. A small proportion of participants were taking antihypertensive medication (1.4%) or had diabetes (1.1%). According to occupational risk factors, 21.6% were exposed to job strain and 7.4% were exposed to long working hours at baseline. Participants who remained actively employed over the study period were younger at baseline (31.2 years vs 39.7 years). This subgroup also had a lower average systolic blood pressure, less alcohol consumption, and a lower prevalence of diabetes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the Study Population at Baseline (1991\u0026ndash;1993) (n\u0026thinsp;=\u0026thinsp;1,629)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Workers Actively Employed in 1991\u0026ndash;1993\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActively Employed Workers\u003c/p\u003e \u003cp\u003ein 2015-18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInactive Participants\u003c/p\u003e \u003cp\u003ein 2015-18\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;533 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,096 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549 (50.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e794 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e547 (49.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean age (year, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.7 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293 (26.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e311 (28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e758 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e492 (44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e650 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412 (37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e929 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e639 (58.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e421 (38.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e676 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446 (40.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e617 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229 (20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean BMI, (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e, \u003cb\u003eSD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension medication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Systolic BP, (mmHg, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.3 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.1 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.9 (13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Diastolic BP, (mmHg, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.8 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.3 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob strain exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong working hours (\u0026gt;\u0026thinsp;40h/week)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline exposure \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated exposure \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: SD\u0026thinsp;=\u0026thinsp;Standard Deviation; BP\u0026thinsp;=\u0026thinsp;Blood Pressure; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index.\u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Baseline exposure at T1 (1991\u0026ndash;1993)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Repeated exposure at both T1 (1991\u0026ndash;1993) and T2 (1999\u0026ndash;2001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents mean PWV (m/s) in 2015\u0026ndash;2018 according to long working hours at baseline (1991\u0026ndash;1993) among the total sample (n\u0026thinsp;=\u0026thinsp;1,629), as well as stratified for actively employed workers (subsample n\u0026thinsp;=\u0026thinsp;533) and inactive participants (subsample n\u0026thinsp;=\u0026thinsp;1,096) in 2015\u0026ndash;2018 (T3). In the crude models, arterial stiffness was slightly higher among those exposed to long working hours, in the total sample (+\u0026thinsp;0.68 m/s [95% CI (0.37, 0.98]), as well as among actively employed workers (+\u0026thinsp;0.90 m/s [95% CI (0.40, 1.41]) and inactive participants (+\u0026thinsp;0.52 m/s [95% CI (0.15, 0.90]) at T3 (2015\u0026ndash;2018). In the fully adjusted models, long working hours at baseline remained associated with arterial stiffness among actively employed workers (+\u0026thinsp;0.54 m/s [95% CI (0.05, 1.02)]).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePulse wave velocity (m/s) in 2015\u0026ndash;2018 according to long working hours at baseline (T1: 1991\u0026ndash;1993)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaseline exposure to long working hours (\u0026gt;\u0026thinsp;40 h/week)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePulse Wave Velocity, m/s (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted Model 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Model 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.68\u003c/p\u003e \u003cp\u003e(0.37, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e(-0.15, +\u0026thinsp;0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e(-0.16, +\u0026thinsp;0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActively Employed Workers in 2015-18\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.90\u003c/p\u003e \u003cp\u003e(0.40, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.54\u003c/p\u003e \u003cp\u003e(0.05, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.54\u003c/p\u003e \u003cp\u003e(0.05, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInactive Participants in 2015-18\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.52\u003c/p\u003e \u003cp\u003e(0.15, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.36, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003cp\u003e(-0.38, 0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eRef: Reference category, m/s: meter per second, CI: confidence intervals\u003c/p\u003e \u003cp\u003eAdjusted model 1: Adjusted for age, sex, education\u003c/p\u003e \u003cp\u003eAdjusted model 2: Model 1 plus alcohol intake, smoking, physical inactivity, body mass index, systolic blood pressure, diabetes diagnosis, hypertension medication, and job strain.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents mean PWV (m/s) in 215\u0026ndash;2018 according to repeated exposure to long working hours at baseline (T1: 1991\u0026ndash;1993) and at first follow-up (T2: 1999\u0026ndash;2001). In the crude model, mean PWV was higher among participants who were repeatedly exposed to long working hours when compared to all other workers (+\u0026thinsp;0.93 m/s [95% CI (0.44, 1.41)]). Among actively employed workers in 2015\u0026ndash;2018, there was a 2.09 m/s increase in PWV among participants repeatedly exposed to long working hours. The association remained statistically significant in the fully adjusted model (+\u0026thinsp;1.54 m/s [95% CI (0.83, 2.26)]). Repeated exposure to long working hours remained associated with increased PWV and estimates were of comparable magnitude in post-hoc analyses, replacing baseline systolic BP with mean arterial pressure or systolic BP changes over the study period (\u003cem\u003enot shown\u003c/em\u003e). Among inactive participants at T3, there was no association between repeated exposure to long working hours and PWV.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePulse wave velocity (m/s) in 2015\u0026ndash;2018 according to repeated exposure to long working hours at baseline (T1: 1991\u0026ndash;1993) and at first follow-up (T2: 1999\u0026ndash;2001)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRepeated exposure to long working hours (\u0026gt;\u0026thinsp;40 h/week)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePulse Wave Velocity, m/s (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted Model 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Model 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 and/or T2 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.08 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.10 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.10 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at both T1 and T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.93\u003c/p\u003e \u003cp\u003e(0.44, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.27\u003c/p\u003e \u003cp\u003e(-0.18, +\u0026thinsp;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e(-0.20, +\u0026thinsp;0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActively employed workers at T3 (2015-18)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;533)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 and/or T2 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.62 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.64 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.65 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at both T1 and T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2.09\u003c/p\u003e \u003cp\u003e(1.36, 2.41)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;1.51\u003c/p\u003e \u003cp\u003e(0.79, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;1.54\u003c/p\u003e \u003cp\u003e(0.83, 2.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInactive participants at T3 (2015-18)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed at T1 and/or T2 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.31 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.32 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.32 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed at both T1 and T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.43\u003c/p\u003e \u003cp\u003e(-0.18, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003cp\u003e(-0.79, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003cp\u003e(-0.82, 0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eRef: Reference category, m/s: meter per second, CI: confidence intervals\u003c/p\u003e \u003cp\u003eAdjusted model 1: Adjusted for age, sex, education\u003c/p\u003e \u003cp\u003eAdjusted model 2: Model 1 plus alcohol intake, smoking, physical inactivity, body mass index, systolic blood pressure, diabetes diagnosis, hypertension medication, and job strain.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e The present study relied on prospective cohort composed of men and women followed over 24 years. In this study, repeated exposure to long working hours at midlife was associated with increased arterial stiffness among participants who remained actively employed over the whole study period. This association was robust to adjustment for socio-demographics, lifestyle-related risk factors, clinical risk factors and psychosocial stressors at work. The magnitude of this association (+\u0026thinsp;1.54 m/s) is of clinical importance. For instance, a meta-analysis has reported a 15% increased CVD risk for each 1 m/s increase in PWV (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo our knowledge, the current study is the first to examine the association between repeated exposure to long working hours and arterial stiffness. It is also the first to use carotid-femoral PWV, which is recognized as the reference standard measurement for arterial stiffness. One previous prospective study has reported an association between long working hours, as defined as working 55 hours or more per week, and arterial stiffness progression over 5 years (+\u0026thinsp;0.32 m/s) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The present study showed association of comparable magnitude to that reported in this previous study, using baseline exposure (+\u0026thinsp;0.54 m/s). The association was of higher magnitude using repeated exposure to long working hours. Therefore, results are consistent with the hypothesis of a potential underestimation of the association between long working hours and arterial stiffness using a single assessment. As a summary measure of vascular aging, arterial stiffness may capture vascular damage accumulated over the life course. Our results are consistent with this hypothesis and suggest that long working hours exert its effect on arterial stiffness over the working life and may be especially harmful among older workers exposed to long work hours over a prolonged period of time.\u003c/p\u003e \u003cp\u003ePathophysiological mechanisms could explain the adverse effect of long working hours on arterial stiffness. Repeated exposure to long working hours may lead to increased activity of the sympathetic nervous system (catecholamines) and the hypothalamic-pituitary-adrenal axis (glucocorticoids). Moreover, the sympathetic nervous system is one of the pathways activating the renin-angiotensin system. Therefore, in conjunction with other risk factors, exposure to long working hours can trigger vasoconstriction, endothelial dysfunction, cellular proliferation, and inflammation that promote arterial stiffness (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Indirect mechanisms could also explain the observed association. First, evidence suggests that long working hours could be associated with sleep deprivation, which could in turn increase cardiovascular risk (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Second, individuals working long hours might be more likely to adopt or maintain unhealthy behaviors (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Third, long working hours could also be associated with prolonged exposure to psychosocial stressors at work (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In a recent study conducted by our research team, job strain at midlife was associated with increased PWV among workers with high BP (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In the present study, association between long working hours and arterial stiffness was observed following adjustment for lifestyle-related risk factors and psychosocial stressors at work from the job strain model. Therefore, lifestyle-related risk factors and psychosocial stressors at work unlikely explain the observed association.\u003c/p\u003e \u003cp\u003eOur study has limitations. First, exposure assessment at baseline and follow-up were spaced by approximately 8 years, which could have led to non-differential misclassification of the exposure. However, most workers (82%) remained in the same occupation between T1 and T2, in favor of exposure stability (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Second, 7.4% of the study population worked more than 40 hours per week and only 2.8% were repeatedly exposed at both times. Therefore, the association between long working hours and arterial stiffness could not be examined using of higher thresholds of exposure to long working hours and the investigation of a potential dose-response relationship. Third, arterial stiffness was available at the last follow-up only. Therefore longitudinal progression could not be assessed. Adjusting for BP changes over the study period led to similar estimates suggesting that the longitudinal progression of BP could not explain the observed association. Future studies using multiple assessment of PWV are needed to examine the adverse effect of long working hours on arterial stiffness longitudinal progression. Finally, the generalizability of our findings might be limited to white-collar workers. More specifically, results might not be generalizable to blue-collar workers, who are more frequently exposed to different working arrangements including shift work and night work. However, the restriction to a white-collar population minimized the possibility for a large amount of unmeasured worked hours that could be more frequent in populations with different occupational profile.\u003c/p\u003e \u003cp\u003eOur study also has important strengths. First, carotid-femoral PWV was used, which is considered as the gold standard measurement for arterial stiffness. Moreover, the present study relied on a prospective cohort and long working hours were assessed at two different time points. The 24-year follow-up also allowed to examine the long-term association between long working hours at midlife and arterial stiffness at older age. The high participation proportion at each time, and the use of random sample for PWV assessment minimized the potential for selection bias. Finally, a large number of potential confounders were assessed and were adjusted for in the analyses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study examined the association between long working hours and arterial stiffness. Results suggest that long working hours assessed at midlife are associated with increased arterial stiffness, among older workers. This association was of higher magnitude among participants repeatedly exposed to long working hours. Preventive workplace intervention aiming at reducing the prevalence of long working hours are needed to examine potential benefits on cardiovascular health, including the potential for such interventions to mitigate arterial stiffness progression. At the clinical level, the present study suggest that long working hours may be useful for the early identification of workers who may be at increased risk of developing asymptomatic vascular damage.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO/ILO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization and International Labor Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePWV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulse Wave Velocity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePROQ Study\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePROspective Qu\u0026eacute;bec Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics review boards of the Centre hospitalier universitaire de Qu\u0026eacute;bec - Laval University. Informed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Canadian Institutes of Health Research (CIHR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCBS wrote the manuscript and led the analysis. MGO and MLR participated in revising and editing the manuscript. CB was the cohort principal investigator, supervised the data collection and study design. AM supervised arterial stiffness assessment and clinical interpretation. XT supervised the methodological aspects, reviewed and edited the manuscript, and is responsible for the overall content. All authors contributed significantly to the work and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to extend special thanks to Caty Blanchette for revising the statistical programs and providing essential support throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDzau VJ, Antman EM, Black HR, Hayes DL, Manson JE, Plutzky J, et al. The cardiovascular disease continuum validated: clinical evidence of improved patient outcomes: part I: Pathophysiology and clinical trial evidence (risk factors through stable coronary artery disease). Circulation. 2006;114(25):2850-70.\u003c/li\u003e\n\u003cli\u003eVlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness. A systematic review and meta-analysis. Journal of the American College of Cardiology. 2010;55:1318-27.\u003c/li\u003e\n\u003cli\u003eBen-Shlomo Y, Spears M, Boustred C, May M, Anderson SG, Benjamin EJ, et al. Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J Am Coll Cardiol. 2014;63(7):636-46.\u003c/li\u003e\n\u003cli\u003eMahmud A, Feely J. Effect of smoking on arterial stiffness and pulse pressure amplification. Hypertension. 2003;41(1):183-7.\u003c/li\u003e\n\u003cli\u003eAhmadi-Abhari S, Sabia S, Shipley MJ, Kivimaki M, Singh-Manoux A, Tabak A, et al. Physical Activity, Sedentary Behavior, and Long-Term Changes in Aortic Stiffness: The Whitehall II Study. J Am Heart Assoc. 2017;6(8).\u003c/li\u003e\n\u003cli\u003eSacre JW, Jennings GL, Kingwell BA. Exercise and dietary influences on arterial stiffness in cardiometabolic disease. Hypertension. 2014;63(5):888-93.\u003c/li\u003e\n\u003cli\u003eSaz-Lara A, Martinez-Vizcaino V, Sequi-Dominguez I, Alvarez-Bueno C, Notario-Pacheco B, Cavero-Redondo I. The effect of smoking and smoking cessation on arterial stiffness: a systematic review and meta-analysis. Eur J Cardiovasc Nurs. 2022;21(4):297-306.\u003c/li\u003e\n\u003cli\u003ePega F, Nafradi B, Momen NC, Ujita Y, Streicher KN, Pruss-Ustun AM, et al. Global, regional, and national burdens of ischemic heart disease and stroke attributable to exposure to long working hours for 194 countries, 2000-2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;154:106595.\u003c/li\u003e\n\u003cli\u003eLabor Force Statistics from the Current Population Survey: Bureau of Labor Statistics; 2024 [Available from: https://www.bls.gov/cps/cpsaat19.htm.\u003c/li\u003e\n\u003cli\u003eStatistics Canada: Usual hours worked by job type (main or all jobs), annual: Statistics Canada; 2024 [Available from: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1410003101\u0026amp;pickMembers%5B0%5D=1.1\u0026amp;pickMembers%5B1%5D=4.1\u0026amp;pickMembers%5B2%5D=5.1\u0026amp;cubeTime\u003cbr\u003eFrame.startYear=2019\u0026amp;cubeTimeFrame.endYear=2023\u0026amp;referencePeriods=20190101%2C20230101.\u003c/li\u003e\n\u003cli\u003eRoozedaal WL, Hoekstra RF. Working Hours and Overtime : Balancing Economic Interests and Fundamental Rights in a Globalized Economy. Edited by the International Labour and Employment Relations Association (ILERA). 2015;https://www.ilera2015.com/dynamic/full/IL186.pdf.\u003c/li\u003e\n\u003cli\u003eLi J, Pega F, Ujita Y, Brisson C, Clays E, Descatha A, et al. The effect of exposure to long working hours on ischaemic heart disease: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2020;142:105739.\u003c/li\u003e\n\u003cli\u003eDescatha A, Sembajwe G, Pega F, Ujita Y, Baer M, Boccuni F, et al. The effect of exposure to long working hours on stroke: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2020;142:105746.\u003c/li\u003e\n\u003cli\u003eHata K, Nakagawa T, Hasegawa M, Kitamura H, Hayashi T, Ogami A. Relationship between overtime work hours and cardio-ankle vascular index (CAVI): a cross-sectional study in Japan. J Occup Health. 2014;56(4):271-8.\u003c/li\u003e\n\u003cli\u003eRossnagel K, Jankowiak S, Liebers F, Schulz A, Wild P, Arnold N, et al. Long working hours and risk of cardiovascular outcomes and diabetes type II: five-year follow-up of the Gutenberg Health Study (GHS). Int Arch Occup Environ Health. 2022;95(1):303-12.\u003c/li\u003e\n\u003cli\u003eBoutouyrie P, Chowienczyk P, Humphrey JD, Mitchell GF. Arterial Stiffness and Cardiovascular Risk in Hypertension. Circ Res. 2021;128(7):864-86.\u003c/li\u003e\n\u003cli\u003eMancia G, Kreutz R, Brunstrom M, Burnier M, Grassi G, Januszewicz A, et al. 2023 ESH Guidelines for the management of arterial hypertension The Task Force for the management of arterial hypertension of the European Society of Hypertension: Endorsed by the International Society of Hypertension (ISH) and the European Renal Association (ERA). J Hypertens. 2023;41(12):1874-2071.\u003c/li\u003e\n\u003cli\u003eTrudel X, Gilbert-Ouimet M, Milot A, Duchaine CS, Vezina M, Laurin D, et al. Cohort Profile: The PROspective Quebec (PROQ) Study on Work and Health. Int J Epidemiol. 2018;47(3):693a-i.\u003c/li\u003e\n\u003cli\u003eDuchaine CS, Brisson C, Talbot D, Gilbert-Ouimet M, Trudel X, Vezina M, et al. Psychosocial stressors at work and inflammatory biomarkers: PROspective Quebec Study on Work and Health. Psychoneuroendocrinology. 2021;133:105400.\u003c/li\u003e\n\u003cli\u003eLaurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27(21):2588-605.\u003c/li\u003e\n\u003cli\u003eAsmar R, Topouchian J, Pannier B, Benetos A, Safar M, Scientific QCC, et al. Pulse wave velocity as endpoint in large-scale intervention trial. The Complior study. Scientific, Quality Control, Coordination and Investigation Committees of the Complior Study. J Hypertens. 2001;19(4):813-8.\u003c/li\u003e\n\u003cli\u003eDi Iorio BR, Cucciniello E, Alinei P, Torraca S. Reproducibility of regional pulse-wave velocity in uremic subjects. Hemodialysis international International Symposium on Home Hemodialysis. 2010;14(4):441-6.\u003c/li\u003e\n\u003cli\u003eCanadian Centre on Substance Use and Addiction. Canada\u0026apos;s low-risk alcohol drinking guidelines. Canadian Centre on Substance Abuse (CCSA) Ottawa; 2013.\u003c/li\u003e\n\u003cli\u003eGionet NJ, Godin G. Self-reported exercise behavior of employees: a validity study. J Occup Med. 1989;31(12):969-73.\u003c/li\u003e\n\u003cli\u003eFrohlich ED, Grim C, Labarthe DR, Maxwell MH, Perloff D, Weidman WH. Recommendations for human blood pressure determination by sphygmomanometers. Hypertension. 1988;11:209a-22a.\u003c/li\u003e\n\u003cli\u003eBrisson C, Blanchette C, Guimont C, Dion G, Moisan J, V\u0026eacute;zina M. Reliability and validity of the French version of the 18-item Karasek Job Content Questionnaire. Work \u0026amp; Stress. 1998;12(4):322-36.\u003c/li\u003e\n\u003cli\u003eLarocque B, Brisson C, Blanchette C. Coh\u0026eacute;rence interne, validit\u0026eacute; factorielle et validit\u0026eacute; discriminante de la traduction fran\u0026ccedil;aise des \u0026eacute;chelles de demande psychologique et de latitude d\u0026eacute;cisionnelle du \u0026quot;Job Content Questionnaire\u0026quot; de Karasek. Rev Epid\u0026eacute;m et Sant\u0026eacute; Publ. 1998;46:371-81.\u003c/li\u003e\n\u003cli\u003eSant\u0026eacute; Qu\u0026eacute;bec. Enqu\u0026ecirc;te qu\u0026eacute;b\u0026eacute;coise sur la sant\u0026eacute; cardiovasculaire [Quebec survey on cardiovascular health] 1990 , Rapport final. 1993.\u003c/li\u003e\n\u003cli\u003eFisher JP, Paton JF. The sympathetic nervous system and blood pressure in humans: implications for hypertension. J Hum Hypertens. 2012;26(8):463-75.\u003c/li\u003e\n\u003cli\u003eLu XT, Zhao YX, Zhang Y, Jiang F. Psychological stress, vascular inflammation, and atherogenesis: potential roles of circulating cytokines. J Cardiovasc Pharmacol. 2013;62(1):6-12.\u003c/li\u003e\n\u003cli\u003eGroeschel M, Braam B. Connecting chronic and recurrent stress to vascular dysfunction: no relaxed role for the renin-angiotensin system. Am J Physiol Renal Physiol. 2011;300(1):F1-10.\u003c/li\u003e\n\u003cli\u003eNagai M, Hoshide S, Kario K. Sleep duration as a risk factor for cardiovascular disease- a review of the recent literature. Curr Cardiol Rev. 2010;6(1):54-61.\u003c/li\u003e\n\u003cli\u003eLiu Y, Tanaka H, Fukuoka Heart Study G. Overtime work, insufficient sleep, and risk of non-fatal acute myocardial infarction in Japanese men. Occup Environ Med. 2002;59(7):447-51.\u003c/li\u003e\n\u003cli\u003eYang H, Schnall PL, Jauregui M, Su TC, Baker D. Work hours and self-reported hypertension among working people in California. Hypertension. 2006;48(4):744-50.\u003c/li\u003e\n\u003cli\u003eVirtanen M, Heikkila K, Jokela M, Ferrie JE, Batty GD, Vahtera J, et al. Long working hours and coronary heart disease: a systematic review and meta-analysis. Am J Epidemiol. 2012;176(7):586-96.\u003c/li\u003e\n\u003cli\u003eMassamba VK, Talbot D, Milot A, Trudel X, Dionne CE, Vezina M, et al. Association between psychosocial work-related factors at midlife and arterial stiffness at older age in a prospective cohort of 1736 white-collar workers. BMJ Open. 2023;13(9):e073649.\u003c/li\u003e\n\u003cli\u003eDuchaine CS, Brisson C, Talbot D, Gilbert-Ouimet M, Trudel X, Vezina M, et al. Cumulative exposure to psychosocial stressors at work and global cognitive function: the PROspective Quebec Study on Work and Health. Occup Environ Med. 2021;78(12):884-92.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Occupational Stress, Work Environment, Arterial Stiffness, Cardiovascular Disease","lastPublishedDoi":"10.21203/rs.3.rs-4920299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4920299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite the well-documented link between long working hours and increased cardiovascular disease risk, the specific impact of prolonged exposure to long working hours on arterial stiffness, an early marker of vascular damage, remains underexplored. This study aims to examine whether long working hours, repeatedly assessed at midlife, is associated with increased arterial stiffness at older age in a 24-year prospective study of white-collar workers in Quebec City, Canada.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study relied on a prospective cohort, initiated in 1991\u0026ndash;1993 (T1) with two follow-ups after 8 years (T2, 1999\u0026ndash;2000) and 24 years (T3, 2015\u0026ndash;2018). Participants (N\u0026thinsp;=\u0026thinsp;1,629) were randomly selected for arterial stiffness measurement at T3 using carotid-femoral pulse wave velocity (PWV). Long working hours (\u0026gt;\u0026thinsp;40 h/week) were assessed at baseline (T1) and at the first follow-up (T2). Mean differences in PWV were estimated using generalized linear models, accounting for sociodemographic factors, lifestyle-related risk factors, clinical factors and psychosocial stressors at work.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong participants who remained actively employed over the study period, baseline (+\u0026thinsp;0.54 m/s, 95% CI: 0.05\u0026ndash;1.02) and repeated (+\u0026thinsp;1.54 m/s, 95% CI: 0.83\u0026ndash;2.26) exposure to long working hours was associated with increased arterial stiffness. No association was observed among participants who retired between follow-ups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study suggests that working long hours during midlife is associated with increased arterial stiffness, among aging workers. Workplace preventive strategies reducing long working hours may be effective to mitigate long-term arterial stiffening.\u003c/p\u003e","manuscriptTitle":"Long working hours at midlife and arterial stiffness at older age among white-collar workers followed over 24 years","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 03:59:03","doi":"10.21203/rs.3.rs-4920299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-10T07:10:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-08T10:55:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-22T06:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331527628794773293693435838850020464885","date":"2024-09-13T05:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248657246124297477116530670591499674368","date":"2024-09-10T07:04:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332196585040233094696237048866891013403","date":"2024-09-08T12:51:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-07T16:17:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-16T03:59:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-16T03:51:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-08-15T15:40:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4ea6cbe-97c3-4cd9-8151-fad03ae59b92","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T15:58:33+00:00","versionOfRecord":{"articleIdentity":"rs-4920299","link":"https://doi.org/10.1186/s12889-025-22954-3","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-05-17 15:56:51","publishedOnDateReadable":"May 17th, 2025"},"versionCreatedAt":"2024-09-12 03:59:03","video":"","vorDoi":"10.1186/s12889-025-22954-3","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22954-3","workflowStages":[]},"version":"v1","identity":"rs-4920299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4920299","identity":"rs-4920299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}