Long working hours as a risk factor for atrial fibrillation: A multi-cohort study

Aims Studies suggest that people who work long hours are at increased risk of stroke, but the association of long working hours with atrial fibrillation, the most common cardiac arrhythmia and a risk factor for stroke, is unknown. We examined the risk of atrial fibrillation in individuals working long hours (>55 per week) and those working standard 35-40 hours per week. Methods In this prospective multi-cohort study from the Individual-Participant-Data Meta-analysis in and results Working Populations (IPD-Work) Consortium, the study population was 85,494 working men and women (mean age 43.4 years) with no recorded atrial fibrillation. Working hours were assessed at study baseline (1991-2004). Mean follow-up for incident atrial fibrillation was 10 years and cases were defined using data on electrocardiograms, hospital records, drug reimbursement registers, and death certificates. We identified 1061 new cases of atrial fibrillation (10-year cumulative incidence 12.4 per 1000). After adjustment for age, sex and socioeconomic status, individuals working long hours had a 1.4-fold increased risk of atrial fibrillation compared to those working standard hours (hazard ratio=1.42, 95%CI=1.13-1.80, P=0.003). There was no significant heterogeneity between the cohortspecific effect estimates (I2=0%, P=0.66) and the finding remained after excluding participants with coronary heart disease or stroke at baseline or during the follow-up (N=2006, hazard ratio=1.36, 95%CI=1.05-1.76, P=0. 0180). Adjustment for potential confounding factors, such as obesity, risky alcohol use and high blood pressure, had little impact on this association. Conclusion Individuals who worked long hours were more likely to develop atrial fibrillation than those working standard hours

Association of adiposity measures with mental health functioning in UKHLS (waves 2–3, 5/2010-7/2013): Basic model^a.

<p>Association of adiposity measures with mental health functioning in UKHLS (waves 2–3, 5/2010-7/2013): Basic model<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148561#t002fn005" target="_blank">^a</a>.</p

Contribution of each physical health condition to the association between obesity and GHQ-12 at specific ages.

<p>Contribution of each physical health condition to the association between obesity and GHQ-12 at specific ages.</p

Association of continuous adiposity measures with mental health functioning by age: Basic model.

<p>The graphs present associations of adiposity measures with mental health functioning by age estimated by augmenting basic models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148561#pone.0148561.t002" target="_blank">Table 2</a>) with polynomial interactions of age with adiposity measures (linear and quadratic interactions in case of BMI; linear, quadratic and cubic interactions in case of WC). These associations were obtained by linear combinations of the main adiposity coefficients and the age-adiposity interactions across different ages (solid lines; shaded area: 95% confidence intervals).</p

Association of dichotomous obesity measures with mental health functioning by age: Basic model.

<p>The graphs present predicted differences in mental health functioning between obese/non-obese by age estimated by augmenting basic models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148561#pone.0148561.t002" target="_blank">Table 2</a>) with polynomial interactions of age with obesity measures (linear and quadratic interactions in case of BMI-obesity measures; linear, quadratic and cubic interactions in case of BF%-obesity and abdominal obesity). These results were obtained by linear combinations of the main obesity coefficients and the age-obesity interactions across different ages (solid lines; shaded area: 95% confidence intervals).</p

Contribution of each physical health condition to the association between continuous adiposity measures and GHQ-12 at specific ages.

<p>Contribution of each physical health condition to the association between continuous adiposity measures and GHQ-12 at specific ages.</p

Association of dichotomous obesity measures with GHQ-12 by age: the dominant role of physical health.

<p>The graphs present predicted differences in mental health functioning between obese/non-obese by age estimated when previous models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148561#pone.0148561.g002" target="_blank">Fig 2</a>) were further adjusted for (a) socio-economic characteristics, marital status and smoking (“without health”; solid lines with shaded area representing the 95%CI) and (b) for all the previous including the full set of physical health conditions (“full model”; dashed lines with spike plots representing the 95%CI). These associations were obtained by linear combinations of the main obesity coefficients and the age-obesity interactions (linear and quadratic interactions in case of BMI-obesity; linear, quadratic and cubic interactions in case of BF%-obesity and abdominal obesity) across different ages.</p

Participant characteristics for the analysis sample of UKHLS (waves 2–3, 5/2010-7/2013) by age group^a.

<p>Participant characteristics for the analysis sample of UKHLS (waves 2–3, 5/2010-7/2013) by age group<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148561#t001fn002" target="_blank">^a</a>.</p

Salivary cortisol levels at waking and 30-min-later by job-demand in women and men.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081020#pone-0081020-g002" target="_blank">Figure 2</a>. Salivary cortisol levels (adjusted means including 95% CI) at waking and 30-min later by job demand status in women and men, adjusted for age, gender, ethnicity, time of waking and time since waking. SD: standard deviation.</p

Participant characteristics with data available for work stress and cortisol secretion at Whitehall II Phase 7 (2002–2004) ^#.

<p>^#Within the 2,126 participants included in current analysis, 2,094 and 2,090 had complete data for job strain and ERI measures, respectively. CAR, cortisol awakening response; Slope, cortisol decline across the day.</p><p>^b Cortisol data adjusted for age, gender, ethnicity.</p><p>* P<0.05, ** P<0.01.</p