Effort-reward imbalance at work, glycated hemoglobin and prediabetes prevalence in a prospective cohort

Context– Prospective studies and meta-analyses suggest that psychosocial stressors at work from the effort-reward imbalance model are associated with an increased risk of type 2 diabetes mellitus (T2DM). Prediabetes is an intermediate disorder on the glucose metabolism continuum. It increases the risk of developing T2DM, while also being separately associated with increased mortality. Evidence about the effect of effort-reward imbalance at work on prediabetes is scarce. Objective– The objective was to evaluate, in women and men, the association between effort- reward imbalance at work, glycated hemoglobin (HbA1c) concentration and the prevalence of prediabetes in a prospective cohort study. Methods– This study was conducted among 1,354 white-collar workers followed for an average of 16 years. Effort-reward imbalance at work (ERI) was measured at baseline (1999-2001) using a validated instrument. HbA1c was assessed at follow-up (2015-18). Several covariates were considered including sociodemographics, anthropometric, and lifestyle risk factors. Differences in mean HbA1c concentration were estimated with linear models. Prediabetes prevalence ratios (PRs) were computed using Poisson regressions models. Results– In women, those exposed to effort-reward imbalance at work had a higher prevalence of prediabetes (adjusted PR=1.52, 95% confidence interval: 1.01-2.29). There was no difference in HbA1c concentration among those exposed and those unexposed to an effort-reward imbalance at work. Conclusion– Among women, effort-reward imbalance at work was associated with the prevalence of prediabetes. Preventive workplace interventions aiming to reduce the prevalence of effort-reward imbalance at work may be effective to reduce the prevalence of prediabetes among women. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 3

Type II diabetes mellitus (T2DM) affects over 400 million individuals across the globe and is on the rise (1). T2DM increases the risk of renal, cardiovascular and neurocognitive pathologies, making it a leading cause of mortality worldwide (1). Prediabetes refers to high blood glucose that is not within diabetes range. Prediabetes is associated with a significant increase in morbidity and mortality whether individuals develop T2DM or not (2-5). A prospective study also showed a linear relationship between glycated hemoglobin (HbA1c), an indicator of glucose metabolism, and health complications including cardiovascular diseases and all-cause mortality among nondiabetic adults (6). Primary prevention strategies should therefore target modifiable risk factors associated with glucose metabolism imbalance and prediabetes, in order to delay or prevent progression to T2DM as well as adverse health outcomes associated with prediabetes itself. Previous evidence suggests that psychosocial stressors at work are associated with an increased risk T2DM (7, 8). Two well defined and empirically supported models have been used to assess the adverse effect of psychosocial stressors at work. The demand-control (i.e. job strain) model states that stress-related ill health risks increase from the combined effects of high psychological demand and low job control (9, 10). The effort-reward imbalance model (ERI) posits that failed reciprocity between efforts invested at work and rewards obtained in return (monetary, social and organizational) induces a high-stress situation (11). Little is known about the effect of psychosocial stressors at work on prediabetes. To our knowledge, only one prospective study, conducted among 7,503 workers in Brazil has examined this effect (12) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 4 showing sex-specific associations. Indeed, this previous study has reported that, among women, job strain is associated with a 32% increase in prediabetes incidence. However, exposure to an ERI at work showed no independent association with prediabetes. This result could be explained by the relatively low participation at baseline (30%) (13) which could have led to selection bias and effect underestimation (14, 15). Moreover, the 4-year follow-up might have been too short to capture the adverse effect of ERI exposure on prediabetes. Further studies are therefore required to determine whether ERI at work is associated with the prevalence of prediabetes. The objective of the present study was to examine the association between effort-reward imbalance at work, glycated hemoglobin (HbA1c) concentration and the prevalence of prediabetes in a prospective cohort study of women and men followed for 16 years.

Study Design and Population The study sample was derived from the PROspective Quebec (PROQ) cohort, which has been previously described (16). Briefly, PROQ is a prospective white-collar workers cohort initiated in 1991–1993 (T1). The 9,188 workers (48.5% women) were recruited from 19 public organizations in Quebec City, Canada. They were followed up 8 and 24 years later (T2 and T3), with good participation rates: 75% at baseline, 90% at the 8-year follow-up and 81% at the 24- year follow-up. This study was approved by the CHU de Quebec’s Research Center ethical review board. All participants, or their respondents in case of inability, provided their informed consent. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 5 At the 24-year follow-up (T3), a convenience sample of 2,318 participants were targeted for blood tests including HbA1c concentration. Of these, 17 died before the follow-up, 86 were lost to follow up, 344 refused to participate, 367 participated by mail only, and 41 did not get adequate blood samples. Furthermore, 77 individuals did not participate at T2 (45 refusals, 15 lost to follow-up and 17 inactive workers) and were excluded from the analysis. Finally, 32 participants had missing data on ERI exposure and were further excluded. A total of 1,354 participants (649 men and 656 women) were included in the analysis. Effort-reward imbalance at work Effort-reward imbalance was measured at T2 (1999-2001) using validated scales. Reward at work was measured by nine original questions from the French version (17-19) of the effort- reward imbalance scale (three of five questions from the esteem subscale, the four-item subscale of promotion prospects and salary, and the two-item subscale of job security). The questions were answered in two steps (19). The respondents were first asked to indicate whether they agreed or disagreed that the question content described an experience typical of their work situation. If they agreed, they were then asked to indicate to what extent they felt distressed by the experience with 1 = very distressed, 2 = distressed, 3 =somewhat distressed, 4 = not at all distressed (scores were inverted for positive items). The 5-point answer mode were recoded in 3 points with a range from 9 to 27, lower values indicating lower reward. Effort was measured by nine items from the validated French version of the psychological demand scale of the Job Content Questionnaire (20, 21). The 4-point answer mode were recoded in 3 points with a range from 9 to 27, with higher values indicating higher effort. The psychometric qualities of this version have been demonstrated (22). In line with previous literature, the ERI score was All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 6 calculated by dividing the effort score by the reward score and ERI was categorized in the following way: participants were considered exposed to an ERI if their effort-reward ratio was ≥ 1 and unexposed if their ratio was <1. HbA1c and prediabetes Non fasting blood samples were taken at T3 (2015-2018) by a trained nurse following a standardized protocol. EDTA whole-blood samples were analyzed at Biron Groupe Santé inc, a certified laboratory. HbA1c was measured using the immunochemical method on an Integra platform from Roche diagnostics (coefficient of variation of 1.6%). The American Diabetes Association guidelines were followed, which define prediabetes as an HbA1c between 5.7% and 6.49% (23). Workers with a HbA1c concentration under these thresholds were considered to have normal glucose metabolism. Participants with a HbA1c ≥ 6.5% were considered prevalent T2DM cases. Participants who reported a previous T2DM diagnosis were 1) excluded from the HbA1c analyses and 2) categorized as prevalent T2DM cases for the prediabetes analyses. These participants with a history of T2DM are likely to have a different HbA1c value than at time of diagnosis because of their treatment, which could diminish the strength of the association between ERI at work and HbA1c. Covariates The following risk factors for prediabetes were assessed at T2 (1999-2001) and retained as covariates. Age was divided into tertiles: <40 years old, 40–49 years old and 50 years old or All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 7 above. Education was defined as the highest degree obtained: less than college, college, or university. Smoking status was categorized following the WHO guidelines: nonsmokers, ex- smokers and current smokers (24). Physical activity was categorized as follows, based on participants’ answers to a validated question on the duration and frequency of their physical activity: sedentary (active once or less per week), insufficiently active (active 2–3 times per week), and active (>3 physical activity sessions per week) (25). Alcohol consumption was dichotomized based on a validated question on alcohol consumption (26): 1) nondrinkers: less than 1 alcoholic beverage per week and 2) drinkers: ≥ 1 alcoholic beverage per week. Long working hours were dichotomized based on a standard full-time employment schedule in Canada: 1) <41hrs/week and 2) ≥ 41hrs/week. Clinical characteristics (blood pressure [BP], weight, height) were measured by trained employees using validated protocols (26). BP was measured following the American Heart Association protocol (27). After 5 minutes of rest, two blood pressure measurements were taken 1–2 minutes apart and the average of the two measures was used in the analyses. BP was dichotomized according to the presence or absence of hypertension (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg). Body mass index (BMI) was obtained by dividing the weight in kilograms by the height in metres squared and was treated as a continuous variable. Hypercholesterolemia was self-reported. Statistical Analysis First, descriptive analyses were conducted. Comparisons between men and women were examined using Student’s t-tests for continuous variables and chi-square tests for categorical variables. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 8 Crude and adjusted HbA1c mean differences according to ERI exposure as well as its individual components (efforts and rewards) were computed using linear regression analyses. Prevalence and prevalence ratio (PR) of prediabetes with 95% confidence intervals were computed using generalized linear models with a log link and a Poisson working model (28). A robust variance was used to account for the larger variance of the Poisson distribution compared to the binomial distribution. Crude and adjusted PRs were estimated. Missing data on each variable was less than 1% except for family history of T2DM (7.6%). A dummy indicator was used to prevent the exclusion of participants with missing data on this variable. According to prior literature, the impact of psychosocial stressors at work on T2DM may be partially mediated by adoption of unhealthy lifestyle habits (29-32). Therefore, sequential adjustments were used. However, given that estimates with and without lifestyle risk factors yielded similar results, only fully adjusted models are presented. Prior studies found important sex differences in the association between psychosocial stressors at work and glucose metabolism imbalances (12, 33). Therefore, separate analyses were performed for men and women. Statistical analyses were conducted using the statistical software package SAS, version 9.4. Two tailed statistical tests with a significance level of 0.05 were used.

Table 1 presents baseline characteristics of our study sample, by sex. Compared to men, women were younger (38.9 years old, SD=4.5 vs 40.0 years old, SD=5.2). Women were less educated; 43.2% of them held a university diploma compared to 65.2% of men. More men lived with a spouse (83.6%) than women (77.3%). Women were healthier (lower prevalence of overweight individuals, hypertension and high cholesterol) and only 61.7% of them were alcohol All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 9 drinkers, compared to 76.3% of men. However, more women were current smokers (17.3%) than men (12.2%). Only 2.8% of women reported long working hours compared to 9.0% of men. Lastly, there was no difference in ERI prevalence between men and women, but more men reported high efforts at work (44.1%) more frequently when compared to women (37.4%). Table 2 presents mean HbA1c concentration and mean differences according to ERI, effort and reward exposures, by sex. There was no association between ERI exposure and mean HbA1c concentration in either men or women. Also, no association was observed when efforts and reward were considered separately. Table 3 presents the prevalence and prevalence ratio (PR) of prediabetes according to ERI, efforts and reward exposures, by sex. In women, the prevalence of prediabetes was higher among those exposed to ERI compared to unexposed women, after adjusting for potential confounders (PR=1.52, 95% CI: 1.01-2.29). In men, the results did not suggest an association between ERI exposure and prediabetes prevalence. The results did not suggest an association between efforts and prediabetes prevalence in either men or women. Lastly, in women, low rewards were associated with an increased prediabetes prevalence (PR=1.67, 95% CI: 1.02-2.72), although statistical significance was not reached in the adjusted model (PR=1.57, 95% CI: 0.94- 2.60). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 10 Table 1. Characteristics of the study sample at T2 (1999-2001) MEN WOMEN TOTAL Age (years), mean (SD) 40.0 (5.2)* 38.9 (4.5)* 39.4 (4.9) Education, n (%) Less than college 44 (6.3)* 141 (20.5)* 185 (13.4) College 198 (28.5)* 249 (36.2)* 447 (32.3) University 454 (65.2)* 297 (43.2)* 751 (54.3) Family history of T2DM, n (%) b 129 (20.2) 153 (23.9) 282 (22.1) Living with a spouse, n (%) 581 (83.6)* 530 (77.3)* 1111 (80.5) Overweight (BMI ≥ 25.0 kg/m2), n (%) 405 (59)* 215 (31.7)* 620 (45.4) Hypertension, n (%) a 106 (15.6)* 18 (2.7)* 124 (9.3) High cholesterol, n (%) 189 (27.2)* 96 (13.9)* 285 (20.6) Smoking status, n (%) Nonsmoker Ex-smoker Current smoker 437 (62.8)* 174 (25.0)* 85 (12.2)* 352 (51.2)* 217 (31.5)* 119 (17.3)* 789 (57.0) 391 (28.3) 204 (14.7) Alcohol drinker, n (%) c 530 (76.3)* 424 (61.7)* 954 (69.0) Physical activity, n (%) d Active Insufficiently active Sedentary 181 (26.0) 284 (40.9) 230 (33.1) 145 (21.1) 288 (41.9) 254 (37.0) 326 (23.6) 572 (41.4) 484 (35.0) Long working hour (≥ 41h/wk), n (%) 62 (9.0)* 19 (2.8)* 81 (5.9) Effort-reward ratio ≥ 1, n (%) 183 (26.8) 167 (24.8) 350 (25.9) Efforts, n (%) Low Medium High 152 (22.3)* 229 (33.6)* 301 (44.1)* 214 (31.9)* 207 (30.8)* 251 (37.4)* 366 (27.0) 436 (32.2) 552 (40.8) Rewards, n (%) High Medium Low 211 (39.0) 253 (37.1) 218 (32.0) 203 (30.2) 231 (34.4) 238 (35.4) 414 (30.6) 484 (35.8) 456 (33.7) aDefined as a systolic blood pressure ≥140 mmHg or a diastolic blood pressure ≥90 mmHg bMeasured at T3 cDrinker: ≥1 drink/week dOne session is defined as being physically active for at least 20 minutes. Active: ≥3 sessions/week, insufficiently active: 1–2 sessions/week, sedentary: <1 session/week *Statistically significant difference between men and women (p <0.05) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 11 Table 2. Mean HbA1c and mean difference (MD) according to ERI, effort and reward exposures, by sex (a) Adjusted for age, marital status, education, hypertension, working hours, family history of T2DM, total cholesterol, body mass index, smoking, alcohol intake and physical inactivity Crude Adjusted (a) ERI N Mean (SD) Mean difference 95% CI Mean difference 95% CI Women (n=656) ERI ratio <1 480 5.34 (0.33) 0.00 Ref. 0.00 Ref. ERI ratio ≥ 1 160 5.37 (0.36) 0.02 (-0.04-0.08) 0.02 (-0.04-0.09) Men (n=649) ERI ratio <1 471 5.41 (0.43) 0.00 Ref. 0.00 Ref. ERI ratio ≥ 1 164 5.42 (0.32) 0.01 (-0.06-0.09) -0.01 (-0.08-0.07) Efforts N Mean (SD) Mean difference 95% CI Mean difference 95% CI Women (n=656) Low 205 5.36 (0.40) 0.00 - 0.00 - Medium 198 5.33 (0.31) -0.03 (-0.09-0.04) -0.02 (-0.09-0.05) High 237 5.36 (0.31) -0.00 (-0.07-0.06) 0.01 (-0.06-0.07) Men (n=649) Low 144 5.42 (0.59) 0.00 - 0.00 - Medium 218 5.41 (0.36) -0.01 (-0.09- 0.08) -0.02 (-0.10-0.07) High 273 5.40 (0.31) -0.02 (-0.10-0.06) -0.05 (-0.13-0.04) Rewards N Mean (SD) Mean difference 95% CI Mean difference 95% CI Women (n=656) Low 196 5.33 (0.27) 0.00 - 0.00 - Medium 218 5.34 (0.38) 0.01 (-0.05-0.08) 0.02 (-0.05-0.08) High 226 5.37 (0.38) 0.04 (-0.02-0.11) 0.03 (-0.04-0.09) Men (n=649) Low 198 5.42 (0.35) 0.00 - 0.00 - Medium 239 5.39 (0.33) -0.03 (-0.11- 0.05) -0.03 (-0.11-0.05) High 198 5.43 (0.52) 0.01 (-0.07-0.09) 0.01 (-0.07-0.09) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 12 Table 3. Prevalence of prediabetes and prevalence ratio (PR) according to ERI, effort and reward exposures, by sex. (a) Adjusted for age, marital status, education, hypertension, working hours, family history of T2DM, total cholesterol, body mass index, smoking, alcohol intake and physical inactivity *Statistically significant (p <0.05). Crude Adjusted (a) ERI ratio N Prediabetes N (%) Prevalence ratio 95 % CI Prevalence ratio 95 % CI Women (N=672) ERI ratio <1 505 60 (11.9 %) 1.00 - 1.00 - ERI ratio ≥ 1 167 30 (18.0 %) 1.51* (1.01-2.26) 1.52* (1.01-2.29) Men (N=682) ERI ratio <1 499 98 (19.6 %) 1.00 - 1.00 - ERI ratio ≥ 1 183 33 (18.0 %) 0.92 (0.64-1.31) 0.91 (0.63-1.32) Efforts N Prediabetes N (%) Prevalence ratio 95 % CI Prevalence ratio 95 % CI Women (N=672) Low 214 32 (15.0%) 1.00 - 1.00 - Medium 207 25 (12.1%) 0.81 (0.50-1.31) 0.80 (0.47-1.34) High 251 33 (13.2%) 0.88 (0.56-1.38) 0.88 (0.55-1.40) Men (N=682) Low 152 28 (18.4%) 1.00 - 1.00 - Medium 229 56 (24.5%) 1.33 (0.89-1.99) 1.29 (0.87-1.92) High 301 47 (15.6%) 0.84 (0.55-1.30) 0.76 (0.49-1.19) Rewards N Prediabetes N (%) Prevalence ratio 95% CI Prevalence ratio 95% CI Women (N=672) Low 203 21 (10.3%) 1.00 - 1.00 - Medium 231 28 (12.1%) 1.17 (0.69-2.00) 1.15 (0.66-2.01) High 238 41 (17.2%) 1.67 (1.02-2.72) 1.57 (0.94-2.60) Men (N=682) Low 211 46 (21.8%) 1.00 - 1.00 - Medium 253 48 (19.0%) 0.87 (0.61-1.25) 0.86 (0.59-1.23) High 218 37 (17.0%) 0.78 (0.53-1.15) 0.81 (0.55-1.20) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 13

The aim of the present study was to evaluate the association between effort-reward imbalance at work, HbA1c concentration and prediabetes prevalence in a prospective cohort study. Results showed that the prevalence of prediabetes was higher in women exposed to ERI at work, compared to unexposed women. This association was robust to adjustment for sociodemographics, anthropometrics and lifestyle risk factors. Low rewards at work, considered separately, was also associated with an increased prevalence of prediabetes among women. Evidence about the adverse effect of psychosocial stressors at work on prediabetes is scarce. Previous cross-sectional studies on ERI and prediabetes reported conflicting results, which could be attributable to small sample sizes (34, 35). The sole prospective cohort study on ERI and prediabetes reported no association (12). The relatively low participation rate (30%) at baseline in this cohort (13) could have led to a healthy worker effect and could partly explain this null finding. Indeed, the healthy worker effect arises when less healthy workers are less likely to participate when compared to healthier workers (36). This selection bias can result in an underestimation of the true effect. Furthermore, the 4-year follow-up might not have been the optimal temporal window to capture the adverse effect of ERI on prediabetes. Indeed, previous studies have reported that an induction period of several years can be required for psychosocial stressors at work to exert their effect on cardiometabolic outcomes (37, 38). Inadequate period at risk can also leads to an underestimation of the effect. The present study is consistent with a long-term effect of psychosocial stressors at work on the prevalence of prediabetes, assessed 16 years later, among women. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 14

of the present study are in line with previous literature, which reported a stronger and more consistent association between psychosocial stressors at work and T2DM among women (7, 12). A recent systematic review and meta-analysis evaluated the effect of ERI on T2DM incidence. In this review, T2DM incidence was higher in workers exposed to a high ERI (RR=1.24, 95 % CI: 1.08-1.42). Given the small number of individual studies on ERI and T2DM (n=4), results were not stratified by sex. However, this previous meta-analysis also examined the effect of job strain, another work stressor, and reported a stronger and more consistent effect among women. Several hypotheses are put forward to explain the possibility for a sex-specific effect of psychosocial stressors at work on glucose metabolism imbalances. Previous evidence observed that both work-family conflict and total workload (i.e. paid and unpaid work) are higher among women than their male counterparts (39). Moreover, evidence suggests that combining psychosocial stressors at work and high family responsibilities leads to an increase in blood pressure (40). Biological responses to chronic stressors might also differ across sexes. Previous studies showed that women exposed to such stressful situations had steeper psychoneuroendocrine activation (41), including higher cortisol hormone secretion(42) than men. However, further examinations of sex (biological) and gender (socio-cultural) differences are required. Overall, results did not suggest an association between ERI at work and mean HbA1c concentration. To our knowledge, there was no previous prospective study that has examined the effect of ERI at work on mean HbA1c concentration. However, previous cross-sectional studies found significant associations (33, 35, 43). These conflicting results highlight the need for further All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 15 quality prospective research on the association between ERI exposure and this important biomarker of glucose metabolism.

should be acknowledged. First, the present study relied on a convenience sample comprising of workers who were either still active or recently retired at the last follow- up. A comparison analysis showed that the study sample was younger and healthier, on average, than the rest of the cohort (not shown). Therefore, the possibility for a healthy worker effect and effect underestimation cannot be ruled out. Second, HbA1c and prediabetes were assessed at the last follow-up only. Therefore, causal inferences cannot be made. However, previous studies have reported the adverse effect of ERI on T2DM incidence and do not suggest a strong possibility for reverse causation to explain the observed associations. Moreover, the outcome of interest was measured 16 years after assessing ERI exposure, which makes reverse causality unlikely. In the present study, diet was assessed only at the last follow-up, using the alternative healthy eating index (aHEI) among participants who agreed to answer this questionnaire (71.4%). In a sensitivity analysis, we have restricted the study sample to those with available information on diet. Estimates with and without adjustment for diet were identical, minimizing the possibility for residual confounding (not shown). This result should however be interpreted with caution given that the temporal precedence of diet on prediabetes assessment was not respected. Lastly, the cohort involves white-collar workers, which limits generalization of the findings to workers with similar conditions. However, the participants in the present study held a diversity of white-collar occupations such as office workers, technicians, professionals and managers. Moreover, a majority of workers in Organisation for Economic Co-operation and Development (OECD) countries hold white-collar occupations, supporting generalization to a considerable section of the workforce (44). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 16 The present study also had important strengths. First, the association between ERI at work, HbA1c concentration and prediabetes prevalence was examined in a long-term prospective study composed of women and men. Second, a large number of potential confounders were considered, and selected according to a direct acyclic graph (Annex A), in line with recent epidemiological recommendations (45). Third, a sequential adjustment was used to account for the possible role of lifestyle habits as a mediating factor. The estimates did not change after adjusting for lifestyle habits, suggesting that their role as mediators was minor. Lastly, ERI at work, HbA1c and other clinical measurements were performed using validated questionnaires and protocols. The results of the present study suggest that, among women, ERI at work is associated with a higher prevalence of prediabetes. Prediabetes is on the continuum between normal glucose metabolism and T2DM. Since it is a reversible condition (46), early interventions have the potential to delay and prevent T2DM (47). Screening for psychosocial stressors at work may be relevant to identify patients at risk of developing prediabetes. Physicians and occupational health specialists could help to identify psychosocial stressors at work and cooperate with employers to implement targeted preventive interventions aiming at decreasing these adverse exposures at work. In recent years, non-pharmacological treatment options are considered as the standard of care in prediabetes as well as mild T2DM cases (48). Psychosocial stressors at work are promising targets to intensify non-pharmacological, population-based preventive interventions.

from the present study suggest that workplace interventions tackling ERI at work may reduce the prevalence of prediabetes among women. Previous studies have shown that psychosocial stressors at work can be reduced through organizational interventions (49) and can All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 17 provide benefits on workers’ cardiovascular health (50). However, such evidence is scarce and further intervention studies are needed to examine whether reducing psychosocial stressors at work could lead to beneficial effects on prediabetes and T2DM.

This study examined the effect of exposure to effort-reward imbalance on HbA1c concentration and on the prevalence of prediabetes in a prospective cohort. In women, those exposed to ERI at work had an increased prevalence of prediabetes, when compared to unexposed women. Addressing these frequent and modifiable occupational factors could improve the health of workers. Moreover, establishing workplace interventions aiming at decreasing psychosocial stressors at work may be considered as a promising avenue to reduce the prevalence of prediabetes, among women.

This research was supported by a grant from the Canadian Institutes of Health Research (grant 57,750). CB was a Canadian Institutes of Health Research Investigator at the time this cohort was initiated, and CR was supported by a Canadian Institutes of Health Research training award when this work was conducted. CR was also supported by training awards from the Fond de Recherche du Quebec en Santé (FRQS) and by VITAM, a research center in sustainable health affiliated with the Centre Intégré Universitaire de Santé et Services Sociaux Capitale- Nationale. CONFLICT OF INTEREST The author declares that they have no conflict of interest. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 18 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 1 9 Annex A: Direct Acyclic Graph (DAG) 9 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 20

1. Organization WH. Global report on diabetes: World Health Organization; 2016. 2. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J, et al. Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. Bmj. 2020;370:m2297. 3. Raghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, et al. Diabetes Mellitus–Related All‐Cause and Cardiovascular Mortality in a National Cohort of Adults. Journal of the American Heart Association. 2019;8(4):e011295. 4. Echouffo-Tcheugui JB, Narayan KM, Weisman D, Golden SH, Jaar BG. Association between prediabetes and risk of chronic kidney disease: a systematic review and meta-analysis. Diabet Med. 2016;33(12):1615-24. 5. Huang Y, Cai X, Qiu M, Chen P, Tang H, Hu Y, et al. Prediabetes and the risk of cancer: a meta-analysis. Diabetologia. 2014;57(11):2261-9. 6. Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, et al. Glycated Hemoglobin, Diabetes, and Cardiovascular Risk in Nondiabetic Adults. New England Journal of Medicine. 2010;362(9):800-11. 7. Pena-Gralle APB, Talbot D, Duchaine CS, Lavigne-Robichaud M, Trudel X, Aube K, et al. Job strain and effort-reward imbalance as risk factors for type 2 diabetes mellitus: A systematic review and meta-analysis of prospective studies. Scand J Work Environ Health. 2022;48(1):5-20. 8. Nyberg ST, Fransson EI, Heikkila K, Ahola K, Alfredsson L, Bjorner JB, et al. Job strain as a risk factor for type 2 diabetes: a pooled analysis of 124,808 men and women. Diabetes Care. 2014;37(8):2268-75. 9. Karasek RA, Theorell T. Healthy work: stress, productivity and the reconstruction of working life. New-York: Basic Books; 1990. 10. Karasek R. Job demands, job decision latitude, and mental strain: implications for job redesign. Adm Sci Quaterly. 1979;24:285-308. 11. Siegrist J. Adverse health effects of high-effort/low-reward conditions. J Occup Health Psychol. 1996;1(1):27-41. 12. de Souza Santos R, Härter Griep R, Mendes da Fonseca MJ, Chor D, Santos IS, Melo ECP. Combined Use of Job Stress Models and the Incidence of Glycemic Alterations (Prediabetes and Diabetes): Results from ELSA-Brasil Study. Int J Environ Res Public Health. 2020;17(5). 13. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, et al. Cohort Profile: Longitudinal Study of Adult Health (ELSA-Brasil). International journal of epidemiology. 2015;44(1):68-75. 14. Choi B, Schnall P, Landsbergis P, Dobson M, Ko S, Gómez-Ortiz V, et al. Recommendations for individual participant data meta-analyses on work stressors and health outcomes: comments on IPD-Work Consortium papers. Scandinavian journal of work, environment & health. 2015;41(3):299-311. 15. Taouk Y, Spittal MJ, LaMontagne AD, Milner AJ. Psychosocial work stressors and risk of all-cause and coronary heart disease mortality: a systematic review and meta-analysis. Scandinavian journal of work, environment & health. 2020;46(1):19-31. 16. Trudel X, Gilbert-Ouimet M, Milot A, Duchaine CS, Vézina M, Laurin D, et al. Cohort Profile: The PROspective Québec (PROQ) Study on Work and Health. International journal of epidemiology. 2018;47(3):693-i. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 21 17. Niedhammer I, Siegrist J, Landre M-F, Goldberg M, Leclerc A. Étude des qualités psychométriques de la version française du modèle du Déséquilibre Efforts/Récompenses. Revue d'épidémiologie et de santé publique. 2000;48(5):419-38. 18. Siegrist J. Adverse health effects of high-effort/low-reward conditions. Journal of occupational health psychology. 1996;1(1):27. 19. Siegrist J, Starke D, Chandola T, Godin I, Marmot M, Niedhammer I, et al. The measurement of effort-reward imbalance at work: European comparisons. Soc Sci Med. 2004;58(8):1483-99. 20. Karasek R. Job Content Questionnaire user's guide. Department of Work Environemnt. 1985. 21. Brisson C, Blanchette C, Guimont C, Dion G, Moisan J, Vézina M, et al. Reliability and validity of the French version of the 18-item Karasek Job Content Questionnaire. Work & Stress. 1998;12(4):322-36. 22. Aboa-Eboule C, Brisson C, Blanchette C, Maunsell E, Bourbonnais R, Abdous B, et al. Effort-reward imbalance at work and psychological distress: a validation study of post- myocardial infarction patients. Psychosom Med. 2011;73(6):448-55. 23. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33 Suppl 1(Suppl 1):S62-9. 24. Weitkunat R, Coggins CRE, Sponsiello-Wang Z, Kallischnigg G, Dempsey R. Assessment of Cigarette Smoking in Epidemiologic Studies. Beiträge zur Tabakforschung International/Contributions to Tobacco Research. 2013;25(7):638-48. 25. Gionet NJ, Godin G. Self-reported exercise behavior of employees: a validity study. J Occup Med. 1989;31(12):969-73. 26. Santé Québec, Daveluy, C., Chenard, L., Levasseur, M. et Emond, A. (sous la direction de) Et votre cœur, ça va? Enquête québécoise sur la santé cardiovasculaire 1990, Montréal, ministère de la santé et des services sociaux, gouvernement du Québec, 1994. 27. Frohlich ED. Recommendations for human blood pressure determination by sphygmomanometers. Circulation. 1988;77(2):501A-14A. 28. Lumley T, Kronmal R, Ma S. Relative risk regression in medical research: models, contrasts, estimators, and algorithms. 2006. 29. Heikkilä K, Fransson EI, Nyberg ST, Zins M, Westerlund H, Westerholm P, et al. Job strain and health-related lifestyle: findings from an individual-participant meta-analysis of 118 000 working adults. 2013;103(11):2090-7. 30. Heikkilä K, Nyberg ST, Fransson EI, Alfredsson L, De Bacquer D, Bjorner JB, et al. Job strain and tobacco smoking: an individual-participant data meta-analysis of 166,130 adults in 15 European studies. PloS one. 2012;7(7):e35463. 31. Heikkilä K, Nyberg ST, Fransson EI, Alfredsson L, De Bacquer D, Bjorner JB, et al. Job strain and alcohol intake: a collaborative meta-analysis of individual-participant data from 140,000 men and women. PloS one. 2012;7(7):e40101. 32. Fransson EI, Heikkilä K, Nyberg ST, Zins M, Westerlund H, Westerholm P, et al. Job strain as a risk factor for leisure-time physical inactivity: an individual-participant meta-analysis of up to 170,000 men and women: the IPD-Work Consortium. American journal of epidemiology. 2012;176(12):1078-89. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 22 33. Xu W, Hang J, Gao W, Zhao Y, Li W, Wang X, et al. Association between effort-reward imbalance and glycosylated hemoglobin (HbA1c) among Chinese workers: results from SHISO study. International archives of occupational and environmental health. 2012;85(2):215-20. 34. Reynolds RY. Prevalence and characteristics of prediabetes in workers in industry. J Adv Nurs. 2020;76(3):803-13. 35. Li J, Jarczok MN, Loerbroks A, Schöllgen I, Siegrist J, Bosch JA, et al. Work stress is associated with diabetes and prediabetes: cross-sectional results from the MIPH Industrial Cohort Studies. Int J Behav Med. 2013;20(4):495-503. 36. Kachi Y, Inoue A, Eguchi H, Kawakami N, Shimazu A, Tsutsumi A. Occupational stress and the risk of turnover: a large prospective cohort study of employees in Japan. BMC Public Health. 2020;20(1):174. 37. Li 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. 38. Trudel X, Brisson C, Talbot D, Gilbert-Ouimet M, Milot A. Long Working Hours and Risk of Recurrent Coronary Events. J Am Coll Cardiol. 2021;77(13):1616-25. 39. Griep RH, Toivanen S, van Diepen C, Guimarães JMN, Camelo LV, Juvanhol LL, et al. Work-Family Conflict and Self-Rated Health: the Role of Gender and Educational Level. Baseline Data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Int J Behav Med. 2016;23(3):372-82. 40. Fadel M, Li J, Sembajwe G, Gagliardi D, Pico F, Ozguler A, et al. Cumulative Exposure to Long Working Hours and Occurrence of Ischemic Heart Disease: Evidence From the CONSTANCES Cohort at Inception. J Am Heart Assoc. 2020:e015753. 41. Rohleder N, Kudielka BM, Hellhammer DH, Wolf JM, Kirschbaum C. Age and sex steroid- related changes in glucocorticoid sensitivity of pro-inflammatory cytokine production after psychosocial stress. Journal of neuroimmunology. 2002;126(1-2):69-77. 42. Maina G, Palmas A, Bovenzi M, Filon FL. Salivary cortisol and psychosocial hazards at work. American journal of industrial medicine. 2009;52(3):251-60. 43. Jarczok MN, Koenig J, Li J, Mauss D, Hoffmann K, Schmidt B, et al. The Association of Work Stress and Glycemic Status Is Partially Mediated by Autonomic Nervous System Function: Cross-Sectional Results from the Mannheim Industrial Cohort Study (MICS). PloS one. 2016;11(8):e0160743. 44. Chen W-H, Mehdi T. Assessing job quality in Canada: A multidimensional approach. Canadian Public Policy. 2019;45(2):173-91. 45. Rothman KJ, Lash TL, VanderWeele TJ, Haneuse S. Modern epidemiology. Philadelphia: Wolters Kluwer / Lippincott Williams & Wilkins ;; 2021. Available from: https://ovidsp.ovid.com/ovidweb.cgi?T=JS&NEWS=n&CSC=Y&PAGE=booktext&D=books&AN=0 2211188$&XPATH=/PG(0)&EPUB=Y https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3 241828 . 46. Morris DH, Khunti K, Achana F, Srinivasan B, Gray LJ, Davies MJ, et al. Progression rates from HbA1c 6.0-6.4% and other prediabetes definitions to type 2 diabetes: a meta-analysis. Diabetologia. 2013;56(7):1489-93. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint 23 47. Association of Faculties of Medicine of Canada, Donovan D, McDowell I, Hunter D. AFMC Primer on Population Health: A Virtual Textbook on Public Health Concepts for Clinicians. AFMC Association of Faculties of Medicine of Canada; 2013. 48. Galaviz KI, Weber MB, Suvada KB, Gujral UP, Wei J, Merchant R, et al. Interventions for Reversing Prediabetes: A Systematic Review and Meta-Analysis. Am J Prev Med. 2022;62(4):614-25. 49. Letellier MC, Duchaine CS, Aube K, Talbot D, Mantha-Belisle MM, Sultan-Taieb H, et al. Evaluation of the Quebec Healthy Enterprise Standard: Effect on Adverse Psychosocial Work Factors and Psychological Distress. Int J Environ Res Public Health. 2018;15(3). 50. Trudel X, Gilbert-Ouimet M, Vezina M, Talbot D, Masse B, Milot A, et al. Effectiveness of a workplace intervention reducing psychosocial stressors at work on blood pressure and hypertension. Occup Environ Med. 2021;78(10):738-44. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted January 12, 2023. ; https://doi.org/10.1101/2023.01.07.23284201doi: medRxiv preprint