The effects of rotating and extended night shift work on the prevalence of metabolic syndrome and its components

tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Correlation analysis between occupational stress and metabolic syndrome in workers of a petrochemical enterprise: based on two assessment models of occupational stress

BACKGROUND

Occupational stress is becoming a common phenomenon around the world. Being in a high occupational stress state for a long time may destroy the metabolic balance of the body, thereby increasing the risk of metabolic diseases. There is limited evidence regarding the correlation between occupational stress and metabolic syndrome (MetS), particularly in the petrochemical workers.

METHODS

A total of 1683 workers of a petrochemical enterprise in China were included in the survey by cluster sampling method. The occupational stress assessment was carried out by the Job Content Questionnaire and the Effort-Reward Imbalance Questionnaire, and the general demographic characteristics, work characteristics, occupational hazards, lifestyle and health examination data of the participants were collected. Logistic regression and multiple linear regression were used to analyze the correlations and influencing factors between occupational stress and its dimensions with MetS and its components.

RESULTS

A total of 1683 questionnaires were sent out, and 1608 were effectively collected, with an effective recovery rate of 95.54%. The detection rates of occupational stress in Job Demand-Control (JDC) and Effort-Reward Imbalance (ERI) models were 28.4% and 27.2%, respectively. In this study, 257 participants (16.0%) were diagnosed with MetS. Compared with the non-MetS group, body mass index (BMI), waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG) and fasting blood-glucose (FBG) levels were significantly higher in the MetS group, and high density lipoprotein-cholesterol (HDL-C) levels were significantly lower (P < 0.001). The results of multiple linear regression showed that after adjusting for nation, marital status, education, work system, smoking and drinking, and further adjusting for occupational hazards, the D/C ratio was significantly negatively correlated with SBP in the JDC model. Social support was negatively correlated with WC. In the ERI model, there was a significant positive correlation between over-commitment and FBG.

CONCLUSIONS

The detection rates of occupational stress and MetS were high in workers of a petrochemical enterprise. In the JDC model, occupational stress was negatively correlated with SBP, and social support was negatively correlated with WC. In the ERI model, there was a significantly positive correlation between over-commitment and FBG.

tauFisher accurately predicts circadian time from a single sample of bulk and single-cell transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tau-Fisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher's out-standing performance in both bulk and single-cell transcriptomic data collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNA-seq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNA-seq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Consequences of Shift Work and Night Work: A Literature Review

Nonstandard work schedules such as shift work and night work tend to trigger problems for workers in different areas. To illustrate the diversity of areas affected and the relative interest of the scientific community, we conducted a literature review of the effects of shift work and night work on workers. In particular, we intended to identify the main variables addressed in the field of health, the family sphere, and the organizational context. The literature review was carried out using the Web of Science with the following terms: "shift work", "rotating shifts", and "night work". Inclusion criteria incorporated empirical studies and articles written in Portuguese or English published in 2019. We selected 129 of the 619 articles identified. Regarding the impacts of shift work and night work, there existed a high discrepancy of focus between the three defined areas: health, family life, and organizational context. Specifically, health-related variables were the most studied (83.4%), followed by organizational variables (9.2%), and, lastly, family variables (7.4%). Based on these results, it is essential to extend the study of the two underrepresented impacts to other crucial areas, not only for the worker but also for organizations.

Dietary Patterns, Occupational Stressors and Body Composition of Hospital Workers: A Longitudinal Study Comparing before and during the COVID-19 Pandemic

This longitudinal study aimed to evaluate the association between dietary patterns and the body composition of hospital workers subjected to occupational stressors before and during the COVID-19 pandemic. Data on sociodemographic, occupational, lifestyle, anthropometric, food consumption and occupational stress were collected before and during the COVID-19 pandemic. A total of 218 workers from a private hospital in Santo Antônio de Jesus, Bahia, Brazil were included in the study. After evaluating the normality of the data, parametric or non-parametric tests were used to characterize the sample. Dietary pattern was defined with Exploratory Factor Analysis and Structural Equation Modeling was used to test the desired association. During the pandemic, work per shift increased by 8.2% (p = 0.004) and working hours > 40 h/week increased by 9.2% (p = 0.006). Despite the higher prevalence of low occupational stress (85.8% vs. 72.1%), high stress increased by 13.7% from 2019 to 2020 (p < 0.001) and 30.3% reported a positive mediating effect on the variables of body composition, body mass index (b = 0.478; p < 0.001), waist circumference (b = 0.395; p = 0.001), fat-free mass (b = 0.440; p = 0.001) and fat mass (b = -0.104; p = 0.292). Therefore, a dietary pattern containing high-calorie foods was associated with changes in the body composition of hospital workers, including occupational stressors as mediators of this relationship.

Assessment of the Light Exposures of Shift-working Nurses in London and Dortmund in Relation to Recommendations for Sleep and Circadian Health

Shift work causes disruption to circadian physiological processes in the human body, and desynchronization from the natural day-and-night rhythm. Circadian disruption is thought to explain the associations between shift work and various long-term diseases; light is an unrivalled synchronizer (or Zeitgeber) of circadian processes and inappropriate light exposure plausibly plays a critical role in the development of health impairments. As published measurement data on the actual light environments encountered by shift workers are sparse, nurses working in two hospitals in London (UK) and Dortmund (Germany) wore light-logging dosimetry devices to measure personal light exposures continuously over a week in three different seasons. The study identifies and quantifies several of the characteristics of light exposure related to different working patterns in winter, spring, and summer, and quantifies interindividual variations. These data enable informed design of light exposure interventions or changes to shifts to reduce unwanted effects of disruptive light exposure profiles.

Association between shift work and risk of metabolic syndrome: A systematic review and meta-analysis

AIMS

A comprehensive assessment of the association of shift work with risk of metabolic syndrome (MetS) through a systematic review and meta-analysis has not been reported. We aimed to evaluate the relationship from observational studies.

DATA SYNTHESIS

We searched PubMed, Embase, and Web of Science databases from inception to December 16, 2020. Articles were chosen according to established inclusion criteria. Studies with data on men and women and different types of shift work were treated as independent studies. Relative risks (RRs) and 95% confidence intervals (CIs) were pooled by using random-effects models with heterogeneity (I²) > 50%; otherwise, a fixed-effects model was used. A total of 7192 articles was searched from PubMed, Embase and Web of science. Finally, we included 23 articles (38 studies) in this meta-analysis. The pooled RRs and 95% CI of MetS risk with shift work, 1-shift work, 2-shift work, and 3-shift work versus non-shift work were 1.30 (95% CI 1.19-1.41), 0.95 (95% CI 0.82-1.11), 1.19 (95% CI 0.91-1.56) and 1.17 (95% CI 1.00-1.37), respectively. The results from subgroup analyses stratified by sex, age, and region supported our overall findings that shift work is a risk factor for MetS.

CONCLUSIONS

This meta-analysis suggests that shift work increases risk of MetS. Higher risk of MetS was found in the shift workers who were 2-shift or 3-shift or women or Asian workers.

Association between shift work or long working hours with metabolic syndrome: a systematic review and dose-response meta-analysis of observational studies

This meta-analysis investigates the relationship between both shift work and long working hours and risk of developing metabolic syndrome (MetS). PubMed, EMBASE, and Web of Science databases were searched from the outset to December 10, 2019. Two reviewers independently screened studies, extracted data, and assessed the risk of bias of included studies. A total of 36 (30 cross-sectional, 5 cohort, and a nested case-control) studies, involving 274,263 participants, were included. The pooled odds ratio of shift work and development of MetS was 1.35 (95% confidence interval: 1.24-1.48), and the pooled odds ratio of long working hours and development of MetS was 1.19 (95% confidence interval: 0.97-1.46). In the subgroup analysis stratified by gender, the pooled odds ratios for male and female shift workers were 1.25 (95% confidence interval: 1.14-1.37) and 1.47 (95% confidence interval: 1.18-1.82), respectively. The dose-response (number of years of shift work and development of MetS) analysis showed the pooled odds ratio for 5 years of shift work was 1.07 (95% confidence interval: 1.05-1.09) and for 10 years of shift work 1.11 (95% confidence interval: 1.06-1.15). Our meta-analysis confirmed shift work is significantly associated with risk of metabolic syndrome, but the relationship between long working hours and MetS was not substantiated. Additionally, there was a nonlinear dose-response relationship between the number of years of shift work and risk of MetS, showing positive relationship to about 20 years of shift work but not for longer than 20 years. Prospective cohort studies regarding specific shift work schedules are needed to confirm these results.

A systematic review and meta-analysis of the association between shift work and metabolic syndrome: The roles of sleep, gender, and type of shift work

This study was conducted to investigate the association between shift work and metabolic syndrome (MetS) and quantify the roles of sleep, gender, and type of shift work. We searched online databases, including PubMed, Scopus, and Web of Science on November 17, 2019. Of the 821 articles identified, 38 observational studies (27 cross-sectional, 10 cohorts, and one nested case-control), conducted on 128,416 participants, met our eligibility criteria. The pooled Odds ratio (OR) and 95% Confidence interval (CI) of MetS in shift-versus day-workers were estimated as 1.14 (1.07, 1.21) and 1.11 (1.06, 1.17) for the unadjusted and adjusted models. This association remained significant only for the studies with a cross-sectional design. There was a significantly higher odds of MetS in the studies conducted only on females (1.13 [1.06, 1.20]) or males (1.12 [1.02, 1.21]). The pooled adjusted OR (95% CI) for the studies without and with sleep adjustment was calculated as 1.14 (1.08, 1.21) and 1.29 (1.06, 1.52). We observed that rotating shift workers had stronger odds of MetS than the other shift workers. In conclusion, our findings revealed the significant odds of an association between shift work and MetS and different effects for sleep, gender, and type of shift work.

Association between Dietary Habits, Shift Work, and the Metabolic Syndrome: The Korea Nurses' Health Study

Metabolic syndrome (MetS) is an important public health problem, and unhealthy dietary habits and shift work are considered major factors that increase the prevalence of MetS. The purpose of this study was to examine whether dietary habits, alcohol drinking, and shift-working were associated with development of MetS in shift-working female nurses. This study analyzed cross-sectional survey data from the Korea Nurses’ Health Study (KNHS). Of the 1638 nurses, 403 participants were selected based on the propensity score matching method (PSM). These participants had either no or more than three MetS determinant factors. Analysis was conducted by using multivariable logistic regression to confirm the factors influencing MetS. The prevalence of MetS in this group (1638 participants) was 5.6% (92 participants). Consumption of over 50% of daily calorie intake after 7 p.m., consumption of carbonated drinks, family history of diabetes, and non-shift work were significant factors influencing MetS. Nurses are one of the at-risk groups for unhealthy dietary habits due to the nature of their work. Therefore, nurse managers should include regular dietary education for nurses and continue their policy efforts to resolve health problems that may arise in connection with nurses’ work.

The Association of Occupational Noises and the Prevalence of Metabolic Syndrome

Objectives

Previous studies have shown the association of exposure to noise with cardiovascular diseases such as hypertension, however, it is not well known whether the exposure has any effect on metabolic syndrome (MetS). This study aimed to quantify and clarify the association between noise exposure and the prevalence of MetS.

Methods

This cross-sectional study was conducted in 2017 among 518 workers in a thermal power plant industry. According to types of work and 8-h equivalent A-weighted sound pressure level (8-h LAeq), the participants were divided into the following groups: office workers and line-production workers exposed to &lt; 85, 90 to &lt;95, 95 to &lt;100, and ≥100 dBA. We used the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria to identify subjects with MetS. The logistic regression was used to determine the odds of MetS among study groups.

Results

We observed the significant differences in the number (%) of subjects with high blood pressure in line-production workers who exposed to noise ≥100 (12 [19.7%]) versus &lt;85 dBA (7 [7.1%]) and office (10 [4.7%]) groups. For the waist circumference (&gt;102 cm), there was a significant difference in the ≥100 dBA group (12 [19.7%]) compared with office group (21 [9.8%]). Obtained results indicated only the significant difference in the prevalence of MetS in ≥100 versus &lt;85 dBA groups (10 [16.4%] versus 6 [6.1%]). The unadjusted and adjusted odds ratios and 95% confidence intervals of MetS in ≥100 versus &lt;85 dBA groups were estimated 3.01 (1.03, 8.75) and 3.24 (1.01, 10.42), respectively.

Conclusions

This study indicated the significant association between noise exposure and MetS in line-production workers. However, more studies are needed to confirm our results.