The association between long working hours and metabolic syndrome remains elusive
Bibliographic record
Abstract
In the SUN longitudinal study with 6845 university graduates followed-up for 8.3 years, we found that long working hours did not exhibit any association with an increased risk of MetS. 1 Recently, a cross-sectional study including 466 female employees from two hospitals in Ontario, Canada, reported that full-time work status, extended shift length, and working ≥ 35 paid overtime hours/year were associated with higher risk of MetS. 2 Another cross-sectional study with 4,456 employees in Korea reported no significant differences in the prevalence of MetS according to weekly working hours. 3 These studies had a cross-sectional design, which does not guarantee the temporal sequence for this association. We appreciate the comments on our article by Dr. Kawada. However, it should be consider that (1) Though MetS increases the risk for cardiovascular disease (CVD), not all subjects with MetS will eventually develop cardiovascular events; they are two different entities; (2) No study evaluating the association between long working hours and MetS was quoted by Kawada. Indeed, an important meta-analysis concluded that employees who work for long hours (≥ 55 h/week) have a higher risk of stroke than those working standard hours (< 35 h/week), but the association with coronary heart disease (CHD) was weaker. 4 Moreover, when the analysis was stratified by the occupational socioeconomic status, long working hours increased the risk of CHD only in participants in the low socioeconomic status group; (3) Other labour exposure factors were used to justify the association between long working hours and CVD such as job strain, shift work and sleep restriction. We believe that, beyond the quantification of working hours, further characteristics of the work should also be considered because some of these conditions might be strongly associated with MetS or CVD. As previously cited, Kivimäki et al.4 reported that workers in low socioeconomic status had a higher risk of CHD. Canuto et al.5 reported that higher educational level was protective against MetS in 902 fixed-shift Brazilian workers. It has been proposed that ‘blue collar workers’ have a greater risk of negative health events than ‘white collar workers’. Occupations which require more challenging and mentally active work may have a protective effect against negative health events, because the professional is less affected by job strain. Thus, the lack of association between long working hours and MetS in our study could be explained because the SUN cohort included only highly educated individuals that, generally, are white collar workers; (4) It was suggested that our results could be explained by lack of the adjustment for sleep parameters and other potential confounders. Additionally, we adjusted our results for sleep duration. Long working hours were not significantly associated with MetS; (5) Finally, we used the same cut-off points proposed by Kiwimäki et al.4 in their study to categorize working hours. Once again, long working hours were not related to MetS after multivariate adjustment. Thus, we reinforce our conclusion that long working hours did not increase the risk of MetS development in highly educated subjects with white collar jobs. Spanish Government (Grants PI01/0619, PI030678, PI040233, PI042241, PI050976, PI070240, PI070312, PI081943, PI080819, PI1002658, PI1002293, PND2010/87, RD06/0045and G03/140), the Navarra Regional Government (36/2001, 43/2002, 41/2005, 36/2008 and 45/2011) and the University of Navarra. Conflicts of interest : None declared. Key point Long working hours did not increase the risk of MetS development in highly educated subjects with white collar jobs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".