What this is
- This study investigates the association between long-term () and () in male railway workers in southwest China.
- It includes 11,023 participants aged over 40 with a history of for at least 10 years.
- The research examines the prevalence of and its components, such as hypertension and waist circumference.
Essence
- Long-term does not significantly increase the risk of among male railway workers, despite a higher prevalence of certain metabolic factors.
Key takeaways
- 43.2% of participants were diagnosed with , indicating a notable prevalence among long-term night shift workers.
- No significant association was found between long-term and (OR 1.03, 95% CI 0.94-1.12, p = 0.543).
- was associated with higher systolic blood pressure (SBP ≥ 130 mmHg, OR 1.11, 95% CI 1.02-1.21, p < 0.001) and increased waist circumference (≥ 90 cm, OR 1.11, 95% CI 1.02-1.21, p < 0.001).
Caveats
- The study's cross-sectional design limits causal inferences about the relationship between and .
- Data on physical activity, sleep quality, and nutritional status were not collected, potentially affecting the analysis.
- The study predominantly includes male participants, which may limit the generalizability of the findings to female workers.
Definitions
- Metabolic syndrome (MetS): A cluster of conditions including abdominal obesity, hypertension, hyperglycemia, high triglycerides, and low HDL, increasing cardiovascular disease risk.
- Night shift work (NSW): Work that occurs during evening and overnight hours, disrupting normal sleep patterns and potentially affecting health.
Simplified
Introduction
Metabolic syndrome (MetS) is a disease syndrome characterized by abdominal obesity, hypertension, hyperglycemia, high triglyceride (TG), and low high-density lipoprotein (HDL), which increased the risk of new diabetes and cardiovascular disease [1, 2]. With the development of the social economy and the improvement of per capita living standards, the incidence of metabolic syndrome is on the rise [3, 4]. Precious studies indicated that people with MetS were at increased risk of cardiovascular events [5], however, there may be gender differences [6, 7]. MetS evaluated BP, plasma glucose, apo B-containing lipoprotein, and inflammatory cytokines, which leads to atherosclerotic plaque development and rupture [8, 9].
Rotating NSW refers to work day and night because of the nature of the jobs, which can lead to sleep rhythm inversion and disturbance [10, 11]. The prevalence of NSW ranged from 20.88%to 45.5% according to different types of work and were higher in women [12–14]. Previous studies showed that occupations such as nursing may require long night shifts, which may associate with a statistically significant but small absolute increase in CHD risk [15, 16]. Other studies concentrated on the association between NSW and MetS, however, the results were inconsistent [6, 7, 12, 14]. Some studies have suggested that NSW and sleep quality may be associated with an increased risk of MetS [12, 14], while others have suggested the opposite, especially in male patients [6, 7]. NSW also influenced the hepatorenal function of night shift workers, studies on Japanese workers showed that shift work and awaking in the night increased the risk of chronic kidney diseases [17, 18], while a 4-year cohort study of 15,871 workers indicated that shift work is associated with hyperuricemia [19]. The previous study has shown that liver function is also affected by NSW [20]. However, previous systematic reviewed retrieved original reviews and found some methodological problems presented in these studies, such as waist circumference being replaced by body mass index [21]. On the other hand, study indicated that the short time of NSW do not increase the risk of CVD [15].
Since most of the previous studies focused on female nurses, there was no accurate description of the length of NSW. We therefor do the study aims to investigate the association between long-term NSW (more than 10 years) and MetS combine with its components in male railway workers in southwest China.
Methods
Study population
A cross-sectional survey was conducted in the Physical Examination Center of the Affiliated Hospital of Chengdu University from January 2020 to December 2020. Railway workers from Sichuan, Chongqing, and Guizhou provinces were enrolled in our study.
Patient and public involvement
All included participants voluntarily participated in this study and signed the informed consent form. This study was approved by the Affiliated Hospital of Chengdu University.
Inclusion and exclusion criteria
Inclusion criteria: People over 40 years of age and 10 working years were included in this study. Exclusion criteria are as follows: severe hepatic and renal insufficiency; malignant tumor; incomplete basic information or blood test data.
Data collection
Basic data are collected by the health train, which passes along the railway and conducts health check-ups for employees. The investigators used questionnaires designed by professionals to collect the data, including basic demographic information (age, sex), health behaviors (alcohol consumption, smoking), and history of chronic diseases (hypertension and diabetes, cardiovascular diseases). The body mass index (BMI) was calculated as weight in kilograms (measured to the nearest 0.1 kg by a uniform scale)divided by the square of height in meters (measured to the nearest 0.1 cm by uniform height ruler). Waist circumference was measured to the nearest 0.1 cm at the end of normal expiration on bare skin, midway between the lower rib margin and iliac crest by a uniform flexible rule. Blood pressure readings were taken from the participants’ resting blood pressure in the morning. Blood samples were obtained in the morning, including blood lipid, creatinine (Cr), uric acid (UA), liver function, fasting blood glucose (FBG). The biochemical parameters were measured at the laboratory of the Affiliated Hospital of Chengdu University. All control values were consistent with the standards recommended by the medical laboratory of the China Center for Disease Control and Prevention. All laboratory technicians were trained in formal laboratory biosafety and biosecurity procedures.
Definitions
The definition of MetS has been updated over time [2], and we used the following definitions according to the American Heart Association [1] (at least 3 of the following 5 risk factors are present): abdominal obesity: waist circumference ≥ 90 cm for men and ≥ 80 cm for women; hypertension: systolic blood pressure (SBP) ≥ 130 mm Hg or diastolic blood pressure (DBP) ≥ 85 mm Hg or current use of antihypertensive medication; hyperglycemia: fasting plasma glucose (FBG) ≥ 100 mg/dl (5.6 mmol/l) or current use of antidiabetic medication; low HDL: HDL < 40 mg/dl (1.04 mmol/l) or current use medication treatment; and hypertriglyceridemia: triglyceride ≥ 150 mg/dl (1.7 mmol/l) or current use of medication treatment for elevated triglyceride. Overweight was defined as a BMI ≥ 24.0 kg/m2, according to the cut-off points for Chinese adults. Smoking status was categorized according to 1 year of smoking at least one cigarette per day. Alcohol consumption was considered in terms of whether the participant consumed alcohol at least 12 times in 1 year. Based on previous literature [10, 11], NSW was defined as working during the evening and overnight hours (6 P.M.–8 A.M). The working rhythms were provided by the Chengdu Railway Bureau, the working rhythms varies from different types of job.
Statistical analysis
All data were analyzed using SPSS statistical software (version 22.0). The means and standard deviations of the continuous variables were provided. Student’s t-test was used to test the differences in the means of the continuous variables, and the chi-square test was used to test the differences in the categorical variables. We did stratified analysis by age and working years. We applied univariate logistic regression models to assess the association of NSW and covariates with MetS, and their odds ratios (OR) and 95% confidence intervals (CI) were estimated. We employed multivariable logistic regression models (model 1) to analyze the association between the independent variable (NSW) and the dependent variable (MetS).Model 2 was adjusted for age, model 3 was adjusted for age and working years, model 4 was adjusted for age, working years, smoking status, alcohol consumption, and previous CAD. We also analyzed the association between NSW and the components of MetS. Bonferroni adjustment was used as post hoc comparisons to adjust for type I error, p value below 0.05 (p < 0.05) was considered as statistically significant.
Results
Baseline characteristics of the study population
Flow chart of sample inclusion and exclusion
| Characteristics | Night shift workers (n = 3008) | Day workers (n = 8015) | Overall | valuep |
|---|---|---|---|---|
| Age (mean ± SD, y) | 47.5 ± 3.5 | 49.4 ± 4.9 | 48.9 ± 4.7 | < 0.001 |
| Working years(mean ± SD, y) | 28.6 ± 4.2 | 30.5 ± 5.7 | 30.0 ± 5.4 | < 0.001 |
| BMI (mean ± SD, kg/m)2 | 25.2 ± 3.1 | 24.8 ± 3.1 | 24.9 ± 3.1 | < 0.001 |
| Hip circumference (mean ± SD, cm) | 96.1 ± 6.3 | 95.6 ± 6.1 | 95.7 ± 6.1 | 0.024 |
| Smoking status (n, %) | 0.599 | |||
| Current smoker | 1973 (65.6) | 5250 (65.5) | 7223 (65.5) | |
| Former smoker | 263 (8.7) | 659 (8.2) | 922 (8.4) | |
| Never-smoker | 772 (25.7) | 2106 (26.3) | 2878 (26.1) | |
| Alcohol consumption (n, %) | < 0.001 | |||
| Current drinker | 542 (18.0) | 1980 (24.7) | 2522 (22.9) | |
| Former drinker | 46 (1.5) | 191 (2.4) | 237 (2.2) | |
| Never-drinker | 2420 (80.5) | 5844 (72.9) | 8264 (75.0) | |
| HBP (n, %) | 579 (19.2) | 1728 (21.6) | 2307 (20.9) | 0.008 |
| DM (n, %) | 205 (6.8) | 710 (8.9) | 915 (8.3) | 0.001 |
| CAD (n, %) | 12 (0.4) | 98 (1.2) | 110 (1.0) | < 0.001 |
| SBP (mmHg) | 126.7 ± 15.0 | 127.7 ± 16.5 | 127.4 ± 16.1 | < 0.001 |
| DBP (mmHg) | 85.1 ± 11.9 | 83.8 ± 8.3 | 84.7 ± 11.7 | < 0.001 |
| TC (mmol/L) | 5.1 ± 0.9 | 5.1 ± 0.9 | 5.1 ± 1.0 | 0.051 |
| TG (mmol/L) | 2.3 ± 2.1 | 2.3 ± 2.0 | 2.3 ± 2.1 | 0.693 |
| LDL (mmol/L) | 3.1 ± 0.7 | 3.1 ± 0.7 | 3.1 ± 0.7 | 0.478 |
| HDL (mmol/L) | 1.2 ± 0.3 | 1.3 ± 0.3 | 1.3 ± 0.3 | < 0.001 |
| hsCRP (mg/l) | 2.4 ± 3.4 | 2.6 ± 4.3 | 2.5 ± 4.1 | 0.095 |
| FBG (mmol/L) | 5.9 ± 1.7 | 6.0 ± 1.0 | 6.0 ± 1.9 | < 0.001 |
| ALT (U/L) | 31.1 ± 17.1 | 30.2 ± 17.4 | 30.5 ± 17.4 | 0.019 |
| Cr (μmol/L) | 75.0 ± 13.2 | 73.8 ± 13.6 | 74.1 ± 13.5 | < 0.001 |
| UA (mmol/L) | 394.8 ± 81.1 | 391.0 ± 83.5 | 392.0 ± 82.9 | 0.032 |
Mean value and prevalence of each criterion for MetS
| Characteristics | Nitht shift workers (n = 3008) | Day workers (n = 8015) | Overall (n = 11,023) | valuep |
|---|---|---|---|---|
| Waist circumference (≥ 90 cm for men) | 1304 (43.4) | 3313 (41.3) | 4617 (41.9) | 0.056 |
| Blood pressure (≥ 130/85 mm Hg or under medications) | 1763 (58.6) | 4963 (61.9) | 6726 (61.0) | 0.001 |
| Fasting blood sugar (≥ 5.6 mmol/L or under medications) | 1228 (40.8) | 3646 (45.5) | 4874 (44.2) | < 0.001 |
| Triglycerides (≥ 1.7 mmol/L or under medications) | 1612 (53.6) | 4173 (52.1) | 5785 (52.5) | 0.153 |
| High-density lipoprotein (< 1.04 mmol/L) | 748 (24.9) | 1813 (22.6) | 2561 (23.2) | 0.013 |
| Metabolic syndrome (≥ 3 factors) | 1282 (42.6) | 3477 (43.4) | 4759 (43.2) | 0.472 |
Age and working years stratified analysis of MetS
Due to the difference in age and length of service between night shift workers and day workers, we conducted a subgroup analysis by age and length of service stratification. The resulted indicated that the overall prevalence of MetS in 40–45, 45–50, 50–55 and ≥ 55 years old were 40.5%, 41.3%, 45.2%, 47.5%. The overall prevalence of MetS in 10–20, 20–25, 25–30, 30–35, and ≥ 35working years were 43.1%, 40.1%, 41.6%, 43.3% and 47.6%. However, no significant difference was found between night shift workers and day workers in all age groups and all working year subgroups (p ˃0.05, Additional file 1: Fig. S1).
Associations between night shift workers and metabolic syndrome
| Factors | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | valuep | OR (95% CI) | valuep | OR (95% CI) | valuep | OR (95% CI) | valuep | |
| SBP ≥ 130 mmHg | 0.97 (0.89, 1.05) | 0.446 | 1.08 (0.99, 1.18) | 0.082 | 1.08 (0.99, 1.18) | 0.082 | 1.11 (1.02, 1.21) | 0.022 |
| DBP ≥ 85 mmHg | 0.88 (0.81, 0.96) | 0.004 | 0.93 (0.85, 1.01) | 0.078 | 0.93 (0.85, 1.01) | 0.078 | 0.95 (0.87, 1.04) | 0.262 |
| SBP ≥ 130 mmHg and DBP ≥ 85 mmHg | 0.98 (0.89, 1.07) | 0.577 | 1.06 (0.97, 1.16) | 0.192 | 1.06 (0.97, 1.16) | 0.193 | 1.09 (1.00, 1.20) | 0.05 |
| Waist circumference ≥ 90 cm | 1.09 (1.00, 1.18) | 0.056 | 1.10 (1.01, 1.20) | 0.037 | 1.10 (1.01, 1,19) | 0.038 | 1.11 (1.02, 1.21) | 0.017 |
| TG ≥ 1.7 mmol/L | 1.06 (0.98, 1.16) | 0.153 | 1.02 (0.94, 1.11) | 0.682 | 1.02 (0.93, 1.11) | 0.694 | 1.02 (0.94, 1.11) | 0.653 |
| HDL-C < 1.04 mmol/L | 1.13 (1.03, 1.25) | 0.013 | 1.09 (1.00, 1.21) | 0.084 | 1.09 (0.99, 1.21) | 0.084 | 1.06 (0.96, 1.18) | 0.227 |
| DM or FBG > 5.6 mmol/L | 0.83 (0.76, 0.90) | < 0.001 | 0.94 (0.86, 1.02) | 0.136 | 0.94 (0.86, 1.02) | 0.135 | 0.96 (0.88, 1.04) | 0.304 |
| METS | 0.97 (0.89, 1.06) | 0.472 | 1.01 (0.93, 1.10) | 0.826 | 1.01 (0.93, 1.10) | 0.833 | 1.03 (0.94, 1.12) | 0.543 |
Discussion
This cross-sectional study examined the association between long-term NSW and metabolic syndrome and found that long-term NSW was not associated with an significantly increased risk of MetS. Night shift workers tended to have a higher proportion of low HDL when compared with day workers. No significant difference was found between night shift workers and day workers in all age groups and all working year subgroups.
Pervious study concentrated on nurses indicated that a longer duration of rotating NSW was associated with an increased risk in CHD [15], our study focuses on another special character, train drivers, who work the same round-the-clock shifts. Our present study showed the overall crude prevalence of metabolic syndrome was 43.2%, which was higher than previous studies [7, 22]. This result may be related to the high incidence of hypertension and diabetes in China, which is currently in the third to the fourth stage of epidemiology [23, 24]. Our study suggested that shift workers had a higher level of BMI and hip circumference, which was consistent with previous studies [25, 26]. However, night shift workers had a lower proportion of history of chorionic diseases such as hypertension, DM, and coronary heart diseases. So the influence of NSW on MetS could be quite important.
The association between NSW and MetS can be explained by the following reasons. First, it is reported that NSW leads to sleep loss and increases the risk of obesity and diabetes [27]. On the other hand, irregular food intake influence energy balance and weight regulation, which lead to metabolic disturbance [28, 29]. Second, circadian disruption and eating meals irregularly disturb the natural rhythmicity of insulin action and lead to insulin resistance and obesity [30–32]. Third, it is reported that the gene REV-ERBα, which regulates circadian rhythms, is associated with liver lipid metabolism, which influence metabolic disturbance [33, 34]. Therefore, NSW are key regulators of metabolic disturbance, which can affect metabolic syndrome.
NSW may result in a higher risk of liver disfunction and non-alcoholic fatty liver disease [20, 35], our present study consisted of the previous results. Shift work also has a confluence on renal function according to our study, shift workers showed a higher level of creatinine and uric acid. Animal study suggest that circadian rhythm reversals may affect renal metabolic rhythms, which may result in impaired kidney function [36]. However, no significant differences in TC, TG, and LDL-C were found between groups. Previous studies found that NSW may lead to higher risk of dyslipidemia and related to a higher risk of cardiovascular diseases [37, 38]. The association between shift work and lipid may need further studies.
Our study indicated that railway workers had a high prevalence of waist circumference increase, hyperglycemia, and high triglycerides over 40%, while the prevalence of hypertension was 60%. The railway workers are in poor health. Night shift workers had a higher prevalence of waist circumference increase and low LDL-C when compared with day workers, which was consistent with the previous study [15]. Hypertension and hyperglycemia were more common among day workers, which may be related to different lifestyles. However, the overall prevalence of metabolic syndrome of night shift workers and day workers showed no significant difference.
As for the difference of age and working years between shift workers and day workers, we did a subgroup analysis of the study. The finding suggested that no significant difference was found between night shift workers and day workers in all age groups and all working year subgroups. The prevalence of metabolic syndrome was increased by age and working years, which was consistent with previous studies [6, 39]. We did the analysis between night shift workers and metabolic syndrome and its components, the result insisted that NSW was not associated with a higher risk of metabolic syndrome. We reviewed the studies and found that the previous results were inconsistent. Some studies have suggested that shift work and sleep quality may be associated with an increased risk of metabolic syndrome [6, 12, 15], while others were not [14, 40]. The results of the study by Vetter C et al. suggested that recent night shift work may be more related to the onset of cardiovascular diseases [15]. It is not clear whether the results were affected by years of work, our study indicated that long-term shift work was not associated with the increased risk of metabolic syndrome. Further study may concentrate on recent shift work. After adjusting for age, working years, smoking status, alcohol consumption, and history of chronic diseases, our analysis showed that NSW was associated with elevated systolic blood pressure, and waist circumference increase. The treatment of metabolic syndrome was based on the diseases, studies showed exercise training, mediterranean diets, and supervised lifestyle intervention may improve outcomes and reduce individual risk factors of metabolic syndrome [41–43]. Our study insisted that railway workers need long-term and effective interventions to reduce the incidence of metabolic syndrome.
Strengths and limitations
This study investigates the relationship between long-term night shift workers and MetS and has some limitations. First, since there are more men working along railway lines, our study lacks data on women. Secondly, the data on physical activity, sleep quality and duration, nutritional status, and exposure to noise were absent, which may have effect on the results of analysis. Third, because we are an observational study and based on physical examination data, we lack data on the treatment of the disease. However, despite the above limitations, we still believe that this study can reflect the association between long-term night shift workers and metabolic syndrome. Although there is no significant difference between shift work and the incidence of metabolic syndrome, the prevention and treatment of metabolic syndrome and its factors still need to be carried out in all railway workers.
Conclusion
Long-term night shift workers had a higher prevalence of MetS. However, long-term NSW is not associated with a significantly increased risk of metabolic syndrome in male railway workers in southwest China. Long-term NSW is associated with elevated SBP, and waist circumference increase. All the railway workers need long-term and effective interventions to reduce the incidence of MetS.
Supplementary Information
Additional file 1:Fig. S1. Age and working years stratified analysis of MetS.