Abstract
Background
Is there a difference in the prevalence of metabolic syndrome between employee service jobs and industrial jobs in Iran? In this study, we tried to answer this question. For this purpose, we compared the two populations of employees and workers. We compared the staff of the University of Medical Sciences as a service employees population (clinical and office work) to the industrial workers of a large automotive company (often with industrial occupations).
Method
In this cross-sectional study conducted in Tehran in 2020, the prevalence of metabolic syndrome among 4,372 people employed by the university and 3,899 automotive industry employees was examined and compared. The prevalence of metabolic syndrome was assessed based on two criteria, National Cholesterol Education Program Adult Treatment Panel III (ATP III) and International Diabetes Federation (IDF).
Results
The results showed that the prevalence of metabolic syndrome among university staff was higher than the automotive industrial workers. According to ATP III criteria, the former and latter showed the prevalence of metabolic syndrome of 13.1% among and 6.1%, respectively among. Also, based on IDF criteria, the prevalence of metabolic syndrome was 23.3% and 12.6% in two groups mentioned.
Conclusion
Based on the findings of this study, the prevalence of metabolic syndrome in university staff was almost double that in industry workers. At first glance, the physical activity of most automotive, industrial workers seems to be the main reason for this difference; however, a prevalence of about twice implies further factors. According to the authors, the legal implementation of monitoring, promotion, and surveillance programs of occupational health, in industrial environments can be a factor accounting for a significant difference in the prevalence of metabolic syndrome between the two populations observed. The authors suggest implementing similar programs for Iranian public sector employees to enhance their health status.
Keywords: Metabolic syndrome x, Prevalence, Sedentary lifestyle, Occupational groups, Occupational health
Introduction
Metabolic syndrome is a common cause of non-communicable diseases worldwide. Metabolic syndrome multiplies the risk of type 2 diabetes and cardiovascular disease [1]. It is estimated that about a quarter of the world's population has metabolic syndrome [2].
Among the most critical factors involved in the occurrence of metabolic syndrome are lifestyle and level of physical activity [3]. Working adults often spend much time in the workplace. In this study, we try to compare and describe the two populations: employees of the Tehran University of Medical Sciences and production workers of a large automotive industry, in terms of the prevalence of metabolic syndrome and its elements. Metabolic syndrome is defined according to various criteria. Past studies have examined the prevalence of metabolic syndrome in different populations based on existing definitions.
Metabolic syndrome is relatively common in the Middle East. A meta-analysis study in Middle Eastern countries showed the high accuracy of this estimation for the region countries [4]. Regarding the population of Iran, a 15-year meta-analysis reported a metabolic syndrome prevalence of 32% [5]. Other studies have also shown a similar prevalence in the Iranian population [6–8]. Some studies have also been conducted on the prevalence of metabolic syndrome among employees. Significantly, workers are often composed of adults with a better health status than ordinary people in the community. This population is known as the wealth-producing force in societies. Comparison of the prevalence of diseases between the general population and the workforce may make a well-known bias called the healthy worker effect [9]. The results of previous studies vary from community to community. In South Korea, the prevalence of metabolic syndrome in the working population was 23%. The prevalence among men was reported to be almost twice that of women [10]. In Taiwan's high-tech industries, the prevalence of metabolic syndrome was reported to be about 8%, with the onset being higher in men than women [11]. Also, in the Spanish working population, the total prevalence of metabolic syndrome was about 10%, which was significantly higher in men than women [12]. In a cross-sectional study in the United States, the prevalence of metabolic syndrome was reported to be 30% among the population of manufacturing workers [13]. Also, the prevalence of metabolic syndrome among Iranian healthcare workers was 22.4% [14]. According to the ATP III criteria, the prevalence of metabolic syndrome among Iranian military personnel was 11.1% [15].
Physical activity, education level, and job rank can affect the rate of metabolic syndrome [16]. Studies show that the prevalence of the metabolic syndrome is more common among office workers [17]. In contrast, a study in Turkey found that blue-collar workers were 2.6 times more likely than white-collar workers to develop metabolic syndrome. The prevalence was 29.6% in office workers and 45% in blue-collar workers [18].
Due to some inconsistencies in the results, we decided to compare the prevalence of metabolic syndrome among university employees (mostly office workers) and industrial workers populations with physical and manual activities.
Method
Participants
In this cross-sectional study, two different populations were studied. A: Employees of the University of Medical Sciences in Tehran and B: Workers of a large automotive industry in Tehran city. The study participants comprised 4372 Tehran University of Medical Sciences employees, including 1727 men and 2645 women, and 3899 men workers of a large automotive company. All participants voluntarily signed the informed consent form at the study centers. All examinations and data collection were performed in one working day at the designated center by trained personnel and according to the approved protocol of the study. All data collection steps were supervised by independent quality control and assurance team. All staff was allowed to enter the study voluntarily. Also, there were no exclusion criteria [19, 20].
Blood sample
All participants were asked to be fast for 12 h before attending the study. 10 mL of venous blood was collected from each participant using a sodium fluoride tube [16]. Plasma was separated by a 15-min centrifuge at 3000 rpm. Levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides were measured using the glycerol kinase oxidase/peroxidase method. The blood glucose was determined using the glucose oxidase/peroxidase method, and blood cholesterol was measured using the esterase oxidase/peroxidase method [21].
Blood pressure measurement
The participants' blood pressure values were measured three times, and the average values were reported with a precision of one mm Hg. The participants’ blood pressure was measured in a sitting position after a 15-min break. There was a thirty-minute interval between the first and second round measurements and a two-hour interval between the second and third rounds. Blood pressure was measured using a standard and calibrated clinical mercury manometer [22].
Anthropometry
The participants' weight in light clothing was measured by the SECA scales with an accuracy of 0.1 kg. Height was also measured using the SECA stadiometer to an accuracy of one millimeter. Based on height and weight data, the BMI index was calculated by dividing weight (kg) by height squared (m2). In addition, all circumferences were measured with an accuracy of one millimeter by a standard tape soft meter. In the standing position, waist circumferences were measured as the minimum abdominal circumference between the xiphoid process and the umbilicus [23].
Met-S and its components
In this study, metabolic syndrome is defined based on two essential criteria The National Cholesterol Education Program (NCEP), which developed its definition in 2001 known as Adult Treatment Panel III (ATP III) [24], and The International Diabetes Federation (IDF) [25]. In this study, definitions have been presented based on both criteria.
The present study defined metabolic syndrome according to National Cholesterol Education Program (NCEP) Adult Treatment Panel-III and International Diabetes Federation (IDF) criteria. The criteria for the diagnosis of metabolic syndrome based on APT III are 3 or more of the following:
Waist circumference greater than/equal to 102 cm for males and greater than 88 cm for females;
Blood triglycerides higher than/equal to 150 mg/dl or being on high triglyceride medication;
HDL cholesterol level of less than 40 mg/dl among men and less than 50 mg/dl among women;
Fasting blood glucose greater than/ equal to 100 mg/dl or being on medication for high blood sugar;
Systolic blood pressure (SBP) greater than 130 mm Hg or diastolic blood pressure (DBP) greater than/equal to 85 mm Hg or being on medication for hypertension.
Diagnostic criteria for metabolic syndrome based on IDF criteria include:
Obesity based on abdominal circumference greater than 94 cm among men and greater than 80 cm among women;
And having two of the following:
Blood triglyceride (TG) of more than 150 mg/dl, or being on high blood triglyceride treatment;
HDL cholesterol levels less than 40 mg/dl among men and less than 50 mg/dl among women;
Fasting blood glucose greater than 100 mg/dl or being on high blood sugar medication;
Systolic blood pressure (SBP) greater than 130 mmHg or diastolic blood pressure (DBP) greater than 85 mmHg or being on medication for hypertension.
Metabolic syndrome was assessed according to the above criteria.
Statistics
We used frequency and percentage to describe qualitative data; mean and standard deviation were used for quantitative data. We also tested all quantitative variables for normal distribution using the Kolmogorov–Smirnov test. For quantitative data with non-normal distribution, we described the variable with mean, standard deviation, median, and range.
The chi-square test for association between two qualitative variables was used for univariate analysis. Also, for the association between qualitative and quantitative variables, independent t-test, analysis of variance, Mann–Whitney U test, Wilcoxon, and Kruskal–Wallis were used based on the type and normality of the variable. We also used logistic regression analysis to determine the predictor variables of metabolic syndrome. Data analysis was performed using the Chicago SPSS-24 software. A P-value less than 0.05 was considered significant.
Ethical considerations
This study was carried out under the approval of the ethics committee of Tehran University of Medical Sciences with the ethics code IR.TUMS.FNM.REC.1399.228. All participants took part in this study voluntarily and completed informed consent forms in the research plan approved by the ethics committee before starting the data gathering process. Participants had the right to withdraw from the study at any stage. All the participants' identity information was registered in complete confidentiality according to the study protocol. The protocol involved providing researchers with codes to analyze the information anonymously. The authors confirm the accordance of all steps and methods to the approvals issued by the ethics committee of the Tehran University of Medical Sciences. The study was carried out based on the Helsinki Declaration Principles.
Results
The prevalence of metabolic syndrome (based on ATP criteria) was 13.1% and 6.1% among university employees and industrial workers, respectively. Noteworthy, all industrial workers were male, while 40% of university employees were male and 60% were women. A better comparison shows the 13.8% prevalence of metabolic syndrome among male university staff slightly more than twice that for male industrial workers. This difference was naturally observed according to the IDF criteria. The total prevalence of metabolic syndrome in the total population of 8271 people was equal to 18.3 percent, which was 23.3 percent among university employees and 12.6 percent among industrial workers. The reported prevalence for male university employees was 25.1%.
Here, we first explain the variables among the total volume of the sample under study. The mean age of the participants in this study was 39.1 years, with a standard deviation of 7.4 years. According to both ATP and IDF criteria, the mean age of participants with metabolic syndrome was about 43 years, and the mean age of participants without metabolic syndrome was about 38 years. Most individuals with Met-Syn were 30 to 49 years old, so the highest prevalence was in the same age group. Also, with increasing age, the prevalence of metabolic syndrome was increased.
In this study, 32% of the participants were female (only belonging to the university staff group), and 68% were male. The prevalence of the metabolic syndrome was 12.7% among female participants according to the ATP criteria and 8.5% among men. According to the IDF, prevalence was 22.2% for women and 16.4% for men; according to both criteria, the prevalence of metabolic syndrome was higher among women than men.
All participants' body mass index (BMI) was 27.0 kg/m2, with a standard deviation of 3.9 kg/m2. BMI index of people with metabolic syndrome (ATP criteria) was 31.2 kg/m2, the standard deviation was 4.1 kg/m2, and the BMI of people without metabolic syndrome (ATP criteria) was 26.5 kg/m2 with a standard deviation of 3.6 kg/m2. According to the IDF criterion, BMI was the same (BMI 30), and people without metabolic syndrome had a BMI index of 26.3. Based on both criteria, participants with metabolic syndrome showed a mean BMI of obesity, and the mean of participants without metabolic syndrome was in the overweight category. Among obese people, 60.2% had metabolic syndrome according to ATP III criteria, and 44.9% had metabolic syndrome according to IDF criteria.
Waist circumference showed a significant difference between participants with and without metabolic syndrome based on both criteria (ATP, IDF). There was also a significant difference between men's and women’s waist circumferences. Among the other constituent elements of the metabolic syndrome, HDL, FBS, triglycerides, and systolic and diastolic blood pressure were the most common causes of metabolic syndrome based on both criteria, with an HDL deficiency of more than 50% prevalence. The prevalence of increased abdominal circumference that exceeded the ATP criteria (≥ 88 for females, ≥ 102 for males) was 17.7% in men and 30.8% in women.
The prevalence of elevated triglyceride in the total population was about 31%. About 8% of the study population showed FBS above 100, and more than 10% of participants showed systolic and diastolic blood pressures of more than 130- and 85-mm Hg, respectively. It is important to note that a significant difference was observed in the univariate analysis of the comparison between all groups in Table 1.
Table 1.
Prevalence of metabolic syndrome based on IDF and ATP III criteria and its components in both working and employee populations
| University employees n = 4372 mean ± SD | Automotive workers n = 3899 mean ± SD | P value | |
|---|---|---|---|
| Age (year) | 40.8 ± 8.6 | 37.3 ± 4.9 | < 0.001 |
| Age group ≤ 30 n (%) | 538 (12.3) | 247 (6.3) | < 0.001 |
| 31–40 n (%) | 1707 (39.0) | 2861 (73.4) | |
| 41–50 n (%) | 1491 (34.1) | 735 (18.9) | |
| 51–60 n (%) | 594 (13.6) | 55 (1.4) | |
| ≥ 61 n (%) | 42 (1.0) | 1 (0.0) | |
| BMI (kg/m2) | 27.0 ± 4.4 | 27.0 ± 3.4 | 0.489 |
| Waist Circumference (cm) | 88.1 ± 11.5 | 93.0 ± 9.2 | < 0.001 |
| Fasting blood sugar (mg/dL) | 86.0 ± 19.5 | 84.0 ± 17.1 | < 0.001 |
| High-density lipoprotein (mg/dL) | 46.9 ± 10.8 | 43.2 ± 10.6 | < 0.001 |
| Low-density lipoprotein (mg/dL) | 104.6 ± 26.6 | 104.5 ± 35.5 | 0.854 |
| Cholesterol (mg/dL) | 180.7 ± 39.7 | 177.3 ± 36.0 | < 0.001 |
| Triglyceride (mg/dL) | 123.1 ± 71.3 | 149.7 ± 84.3 | < 0.001 |
| Systolic blood pressure (mmHg) | 113.1 ± 14.8 | 114.0 ± 10.0 | 0.002 |
| Diastolic blood pressure (mmHg) | 74.8 ± 9.3 | 74.9 ± 7.0 | 0.97 |
| Mean arterial blood pressure (mmHg) | 87.6 ± 10.2 | 87.9 ± 7.4 | 0.13 |
| Metabolic syndrome ATP III criteria n (%) | 573 ± 13.1 | 239 ± 6.1 | < 0.001 |
| Metabolic syndrome IDF criteria n (%) | 1020 ± 23.3 | 491 ± 12.6 | < 0.001 |
| Risk factors A* n (%) | |||
| One risk factor | 1258 (28.8) | 1504 (38.6) | < 0.001 |
| Two risk factor | 385 (8.8) | 337 (8.6) | |
| Three risk factor | 64 (1.5) | 23 (0.6) | |
| Risk factors B** n (%) | |||
| One risk factor | 1955 (44.7) | 1666 (42.7) | < 0.001 |
| Two risk factor | 697 (15.9) | 380 (9.7) | |
| Three risk factor | 106 (2.4) | 25 (0.6) | |
* hypertension + high glucose + high triglyceride prevalence. **: hypertension + high glucose + high waist circumference prevalence. Both risk factors based on IDF criteria
Now, after describing the total population, we return to the study's main purpose, which is to compare the two populations working in universities and workers in the automotive industry in terms of the prevalence of metabolic syndrome and its components based on ATP and IDF criteria.
As mentioned in the previous section, the prevalence of metabolic syndrome among university staff, regardless or by considering gender, was twice the syndrome prevalence among industrial workers. The first hypothesis outlined is that university participants were about 3.5 years older than the industrial workers. The mean age of university staff is 40.8 years with a standard deviation of 8.6, and the mean age of industrial workers is 37.3 years with a standard deviation of 4.9 years. Based on the modified values for age, there is still a significant difference in the prevalence of metabolic syndrome between the university staff and industrial workers. Based on the result of regression analysis, the effect of sex and age were adjusted for both populations, and again a significant difference in prevalence was observed between them, and again a significant difference in prevalence was observed between them. The difference between BMI in the two populations is very close, and no statistically significant difference was observed between them. The mean BMI of university staff is 27.0 with a standard deviation of 4.4 and for industrial workers is 27.0 with a standard deviation of 3.4, which falls into the overweight category in both groups. Gender, age, and even BMI were not influential in the initial difference between the two populations. Probably metabolic issues and the effect of physical activity between the two populations are the likely culprits of this difference.
The difference between the mean abdominal circumference between the two populations is significant so that the mean abdominal circumference of university employees is 88.1 cm with a standard deviation of 11.5 cm and industry workers 93 cm with a standard deviation of 9.2 cm, which is also higher in industrial workers. This might be confounded by including women in the university employees only, while industrial workers were only men. So we calculated the mean abdominal circumference for men in both study groups and found that The means abdominal circumference of male university employees was greater than the abdominal circumference of industrial workers.
Despite this, it is observed that the prevalence of the metabolic syndrome is higher in university staff. The triglyceride of university personnel is 123.1 mg/dl with a standard deviation of 73.1 mg/dl and industry personnel 150.0 mg/dl with a standard deviation of 84.3 mg/dl, which also shows a statistically significant difference, so the mean of TG of industry personnel is higher than university staff.
The average HDL in industry workers was 43.2 mg/dl with a standard deviation of 10.6 mg/dl, and the average HDL in university staff was 46.9 mg/dl with a standard deviation of 10.7 mg/dl. Mean fasting blood sugar or FBS concentration in the two populations is close. However, a statistically significant difference is observed. The mean fasting blood sugar among university staff was 86 mg/dL, and the mean for industry personnel was 84 mg/dL. Obviously, the mean of university staff FBS is higher than industry staff. Also, systolic and diastolic blood pressure in both populations are very close to each other, and only we observed a significant difference in systolic blood pressure between the two populations.
In Table 1, two groups of risk factors were reported under the title of risk factors A and B. Risk factors A included high blood pressure, high fasting blood sugar, and high triglycerides, presented as at least one, two risk, and three risk factors.
Risk factor B included the same risk factor as A; only triglycerides' level was replaced by waist circumference. As can be seen, waist circumference is one of the critical risk factors among university employees.
One of the main factors resulting in varied prevalence of metabolic syndrome between two populations is the history of diseases and the use of drugs, including hypertension, diabetes, and hyperlipidemia. Here we examine these records in two populations. First, the history of type 2 diabetes mellitus in two populations is examined. About 8% of the university staff in the study had a history of diabetes or were taking blood sugar-lowering and regulating drugs, compared with 3.8% for industrial workers. Also, 14.6% of the university staff had a history of hypertension or were taking blood pressure medications, which was 10.6% among industrial workers. About 5% of the university staff had a history of taking lipid-lowering and regulating drugs, which was about 3% for the industry staff. Table 1 shows the two understudy populations in terms of the prevalence of metabolic syndrome, its components, and the means of variables. based on two criteria of ATP III and IDF, Table 2 compares both populations statistically regarding the prevalence of metabolic syndrome and its constituent elements.
Table 2.
Comparison of metabolic syndrome prevalence in both university staff and automotive workers populations based on two criteria: ATP III and IDF
| Variables | Subgroup | Based on ATP III criteria |
P–v based on ATP |
Based IDF criteria |
P–v based on IDF |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nonindustrial employees university staff n = 4372 |
Industrial employees automotive workers n = 3899 |
Nonindustrial employees university staff n = 4372 |
Industrial employees automotive workers n = 3899 |
||||||||
| Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | ||||||||
| Yes (13.1%) |
No (86.9%) |
Yes (6.1%) |
No (93.9%) |
Yes (23.3%) |
No (76.7%) |
Yes (12.6%) |
No (87.4%) |
||||
| Age (year) | Total mean ± SD | 45.3 ± 8.4 | 40.1 ± 8.5 | 38.4 ± 5.3 | 37.2 ± 4.9 | < 0.001 | 44.7 ± 8.4 | 39.6 ± 8.4 | 38.4 ± 5.4 | 37.1 ± 4.8 | < 0.001 |
| Age Group | ≤ 30 n(%) | 25 (4.4) | 513 (13.5) | 10 (4.2) | 237 (6.5) | < 0.001 | 47 (4.6) | 491 (14.6) | 21 (4.3) | 226 (6.6) | < 0.001 |
| 31–40 n(%) | 142 (24.8) | 1565 (41.2) | 165 (5.8) | 2696 (73.7) | 283 (27.7) | 1424 (42.5) | 338 (11.8) | 2523 (74.0) | |||
| 41–50 n(%) | 242 (42.2) | 1241 (32.9) | 58 (24.3) | 677 (18.5) | 432 (42.4) | 1059 (31.6) | 119 (24.2) | 616 (18.1) | |||
| 51–60 n(%) | 151 (26.4) | 443 (11.7) | 6 (2.5) | 49 (1.3) | 238 (23.3) | 356 (10.6) | 12 (2.4) | 43 (1.3) | |||
| ≥ 61 n(%) | 13 (2.3) | 29 (0.8) | 0 | 1 (0.0) | 20 (2.0) | 22 (0.7) | 1 (0.2) | 0 | |||
| Sex (M/F) | Male n (%) | 238 (41.5) | 1489 (39.2) | 239 (100) | 3660 (100) | 0.285 | 433 (42.5) | 1295 (38.6) | 491 (100) | 3408 (100) | 0.028 |
| Female n (%) | 335 (58.5) | 2310 (60.8) | 0 | 0 | 587 (57.5) | 2058 (61.4) | 0 | 0 | |||
| BMI kg/m2 | Total mean ± SD | 31.4 ± 4.3 | 26.3 ± 3.9 | 30.6 ± 3.6 | 26.8 ± 3.3 | < 0.001 | 30.2 ± 4.1 | 26.0 ± 4.0 | 29.7 ± 3.1 | 26.6 ± 3.3 | < 0.001 |
| Low weight n(%) | 0 | 44 (1.2) | 0 | 12 (0.3) | < 0.001 | 0 | 44 (1.3) | 0 | 12 (0.4) | < 0.001 | |
| Normal n(%) | 23 (4.0) | 1452 (38.2) | 20 (8.4) | 1007 (27.5) | 67 (6.6) | 1408 (42.0) | 24 (4.9) | 1003 (29.4) | |||
| Overweight n(%) | 195 (34.0) | 1716 (45.2) | 84 (35.1) | 2089 (57.1) | 483 (47.4) | 1428 (42.6) | 259 (52.7) | 1914 (56.2) | |||
| Obese n(%) | 354 (61.8) | 585 (15.4) | 135 (56.5) | 552 (15.1) | 470 (46.1) | 469 (14.0) | 208 (42.4) | 479 (14.1) | |||
| Waist Circumference cm | Total mean ± SD | 99.5 ± 9.8 | 86.4 ± 10.8 | 103.9 ± 10.3 | 92.2 ± 8.6 | < 0.001 | 96.7 ± 9.7 | 85.5 ± 10.7 | 101.3 ± 7.6 | 91.8 ± 8.8 | < 0.001 |
| Waist Circum ATP criteria | Male ≥ 102 cm n(%) | 168 (70.6) | 208 (14.0) | 163 (68.2) | 454 (12.4) | < 0.001 | 194 (44.8) | 182 (14.1) | 197 (40.1) | 420 (12.3) | < 0.001 |
| Female ≥ 94 cm n(%) | 302 (90.1) | 513 (22.2) | 0 | 0 | - | 374 (63.7) | 441 (21.4) | 0 | 0 | - | |
| Waist Circum IDF criteria | Male ≥ 88 cm n(%) | 212 (89.1) | 727 (48.8) | 199 (83.3) | 1559 (42.6) | < 0.001 | 431 (99.5) | 508 (39.3) | 481 (98.0) | 1277 (37.5) | < 0.001 |
| Female ≥ 80 cm n(%) | 326 (97.3) | 1244 (53.9) | 0 | 0 | - | 585 (99.7) | 985 (47.9) | 0 | 0 | - | |
| TG mg/dL | Total mean ± SD | 183.1 ± 95.8 | 114.1 ± 62.0 | 233.5 ± 128.5 | 144.2 ± 77.5 | < 0.001 | 169.0 ± 88.3 | 109.1 ± 58.5 | 225.4 ± 114.4 | 138.8 ± 72.8 | < 0.001 |
| < 150 mg/dl n(%) | 186 (32.5) | 3124 (82.2) | 18 (7.5) | 2377 (64.9) | < 0.001 | 445 (43.6) | 2865 (85.5) | 60 (12.2) | 2355 (68.5) | < 0.001 | |
| ≥ 150 mg/dl n(%) | 387 (67.5) | 675 (17.8) | 221 (92.5) | 1283 (35.1) | 575 (56.4) | 487 (14.5) | 431 (87.7) | 1073 (31.5) | |||
| HDL mg/dL | Total mean ± SD | 41.5 ± 8.2 | 47.7 ± 10.9 | 34.1 ± 8.6) | 43.8 ± 10.5 | < 0.001 | 43.1 ± 11.6 | 48.0 ± 10.2 | 35.0 ± 9.2 | 44.4 ± 10.3 | < 0.001 |
| HDL for Male | < 40 mg/dl n(%) | 174 (73.1) | 443 (29.8) | 190 (79.5) | 1074 (29.3) | < 0.001 | 265 (61.2) | 352 (27.2) | 357 (72.8) | 907 (26.6) | < 0.001 |
| ≥ 40 mg/dl n(%) | 64 (26.9) | 1046 (70.2) | 49 (20.5) | 2586 (70.7) | 168 (38.8) | 942 (72.8) | 134 (22.3) | 2501 (73.4) | |||
| HDL for Female | < 50 mg/dl n(%) | 282 (84.2) | 1184 (51.3) | 0 | 0 | < 0.001 | 456 (77.7) | 1010 (49.1) | 0 | 0 | < 0.001 |
| ≥ 50 mg/dl n(%) | 53 (15.8) | 1126 (48.7) | 0 | 0 | 131 (22.3) | 1048 (50.9) | 0 | 0 | |||
| FBS mg/dL | Total mean ± SD | 99.1 ± 35.1 | 84.0 ± 14.9 | 98.5 ± 43.9 | 83.1 ± 13.0 | < 0.001 | 95.3 ± 31.7 | 83.1 ± 12.5 | 90.6 ± 26.9 | 83.1 ± 14.9 | < 0.001 |
| FBS ATP criteria | ≥ 110 mg/dl n(%) | 111 (19.4) | 462 (80.6) | 48 (20.1) | 64 (1.7) | < 0.001 | 136 (13.3) | 40 (1.2) | 49 (10.0) | 63 (1.8) | < 0.001 |
| < 110 mg/dl n(%) | 65 (1.7) | 3734 (98.3) | 191 (79.9) | 3596 (98.3) | 884 (86.7) | 3312 (98.8) | 442 (90.0) | 3345 (98.2) | |||
| FBS IDF criteria | ≥ 100 mg/dl n(%) | 165 (28.8) | 193 (5.1) | 61 (25.5) | 227 (6.2) | < 0.001 | 248 (24.3) | 110 (3.3) | 106 (21.6) | 182 (5.3) | < 0.001 |
| < 100 mg/dl n(%) | 408 (71.2) | 3606 (94.9) | 178 (74.5) | 3433 (93.8) | 772 (75.7) | 3242 (96.7) | 385 (78.4) | 3226 (94.7) | |||
| Systolic blood pressure mmHg | Total mean ± SD | 125.5 ± 15.6 | 111.2 ± 13.7) | 124.5 ± 11.4 | 113.3 ± 9.5 | < 0.001 | 121.7 ± 16.1 | 110.4 ± 13.3 | 120.5 ± 12.1 | 113.0 ± 9.3 | < 0.001 |
| ≥ 130 mm Hg n(%) | 260 (45.4) | 314 (8.3) | 96 (40.2) | 202 (5.5) | < 0.001 | 337 (33.0) | 237 (7.1) | 132 (26.9) | 166 (4.9) | < 0.001 | |
| < 130 mm Hg n(%) | 313 (54.6) | 3485 (91.7) | 143 (59.8) | 3458 (94.5) | 683 (67.0) | 3115 (92.9) | 359 (73.1) | 3242 (95.1) | |||
| Diastolic blood pressure mmHg | Total mean ± SD | 81.1 ± 9.4 | 73.9 ± 8.9 | 81.6 ± 7.7 | 74.4 ± 6.6 | < 0.001 | 79.5 ± 9.9 | 73.4 ± 8.6 | 78.8 ± 8.2 | 74.3 ± 6.5 | < 0.001 |
| ≥ 85 mm Hg n(%) | 230 (40.1) | 298 (7.8) | 99 (41.4) | 247 (6.7) | < 0.001 | 318 (31.2) | 210 (6.3) | 132 (26.9) | 214 (6.3) | < 0.001 | |
| < 85 mm Hg n(%) | 343 (59.9) | 3501 (92.2) | 140 (58.6) | 3413 (93.3) | 702 (68.8) | 3142 (93.7) | 359 (73.1) | 3194 (93.7) | |||
In order to remove the gender selection bias (because the industry workers were only men) we presented Table 2 as Table 3. In Table 3, only men working in university were compared with male workers in industry.
Table 3.
Comparison of metabolic syndrome prevalence in both university male staff and automotive workers populations based on two criteria: ATP III and IDF
| Variables | Subgroup | Based on ATP III criteria | P–v based on ATP |
Based IDF criteria | P–v based on IDF |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nonindustrial employees university Male staff n = 1727 |
Industrial employees automotive workers n = 3899 |
Nonindustrial employees university Male staff n = 1727 |
Industrial employees automotive workers n = 3899 |
||||||||
| Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | ||||||||
| Yes (13.8%) |
No (86.2%) |
Yes (6.1%) |
No (93.9%) |
Yes (25.1%) |
No (74.9%) |
Yes (12.6%) |
No (87.4%) |
||||
| Age (year) | Total mean ± SD | 45.0 ± 8.9 | 41.2 ± 8.9 | 38.4 ± 5.3 | 37.2 ± 4.9 | < 0.001 | 44.7 ± 8.8 | 40.8 ± 8.8 | 38.4 ± 5.4 | 37.1 ± 4.8 | < 0.001 |
| Age Group | ≤ 30 n(%) | 11(4.6) | 181(12.2) | 10 (4.2) | 237 (6.5) | < 0.001 | 20 (4.6) | 172 (13.3) | 21 (4.3) | 226 (6.6) | < 0.001 |
| 31–40 n(%) | 66(27.7) | 568(38.1) | 165 (5.8) | 2696 (73.7) | 129 (29.8) | 505 (39.0) | 338 (11.8) | 2523 (74.0) | |||
| 41–50 n(%) | 93(39.1) | 492(33.0) | 58 (24.3) | 677 (18.5) | 166 (38.3) | 419 (32.4) | 119 (24.2) | 616 (18.1) | |||
| 51–60 n(%) | 61(25.6) | 225(15.1) | 6 (2.5) | 49 (1.3) | 106 (24.5) | 180 (13.9) | 12 (2.4) | 43 (1.3) | |||
| ≥ 61 n(%) | 7(2.9) | 23(1.5) | 0 | 1 (0.0) | 12 (2.8) | 18 (1.4) | 1 (0.2) | 0 | |||
| BMI kg/m2 | Total mean ± SD | 31.2 ± 4.1 | 27.0 ± 3.6 | 30.6 ± 3.6 | 26.8 ± 3.3 | 0.296 | 30.2 ± 3.6 | 26.7 ± 3.6 | 29.7 ± 3.1 | 26.6 ± 3.3 | 0.196 |
| Low weight n(%) | 0 | 11(0.7) | 0 | 12 (0.3) | 0.107 | 0 | 11 (0.9) | 0 | 12 (0.4) | 0.110 | |
| Normal n(%) | 9(3.8) | 382(25.7) | 20 (8.4) | 1007 (27.5) | 10 (2.3) | 381 (29.4) | 24 (4.9) | 1003 (29.4) | |||
| Overweight n(%) | 85(35.7) | 853(57.3) | 84 (35.1) | 2089 (57.1) | 230 (53.1) | 708 (54.7) | 259 (52.7) | 1914 (56.2) | |||
| Obese n(%) | 144(60.5) | 242(16.3) | 135 (56.5) | 552 (15.1) | 193 (44.6) | 193 (14.9) | 208 (42.4) | 479 (14.1) | |||
| WC cm * | mean ± SD | 104.6 ± 9.3 | 93.6 ± 8.6 | 103.9 ± 10.3 | 92.2 ± 8.6 | 0.224 | 102.7 ± 7.1 | 92.6 ± 8.8 | 101.3 ± 7.6 | 91.8 ± 8.8 | < 0.001 |
| WC ATP criteria | ≥ 102 cm n(%) | 168 (70.6) | 208 (14.0) | 163 (68.2) | 454 (12.4) | 0.572 | 194 (44.8) | 182 (14.1) | 197 (40.1) | 420 (12.3) | 0.151 |
| WC IDF criteria | ≥ 88 cm n(%) | 212 (89.1) | 727 (48.8) | 199 (83.3) | 1559 (42.6) | 0.066 | 431 (99.5) | 508 (39.3) | 481 (98.0) | 1277 (37.5) | 0.035 |
| TG mg/dL | Total mean ± SD | 210.0 ± 120.2 | 139.0 ± 75.3 | 233.5 ± 128.5 | 144.2 ± 77.5 | 0.001 | 193.4 ± 107.0 | 133.8 ± 72.5 | 225.4 ± 114.4 | 138.8 ± 72.8 | < 0.001 |
| < 150 mg/dl n(%) | 53 (22.2) | 1032 (69.3) | 18 (7.5) | 2377 (64.9) | < 0.001 | 139 (32.1) | 946 (73.1) | 60 (12.2) | 2355 (68.5) | < 0.001 | |
| ≥ 150 mg/dl n(%) | 185 (77.7) | 457 (30.7) | 221 (92.5) | 1283 (35.1) | 294 (67.9) | 348 (26.9) | 431 (87.7) | 1073 (31.5) | |||
| HDL mg/dL | Total mean ± SD | 38.5 ± 7.5 | 44.4 ± 8.7 | 34.1 ± 8.6) | 43.8 ± 10.5 | < 0.001 | 40.2 ± 9.5 | 44.8 ± 8.2 | 35.0 ± 9.2 | 44.4 ± 10.3 | < 0.001 |
| HDL group | < 40 mg/dl n(%) | 64 (26.9) | 1046 (70.2) | 190 (79.5) | 1074 (29.3) | 0.101 | 265 (61.2) | 352 (27.2) | 357 (72.8) | 907 (26.6) | < 0.001 |
| ≥ 40 mg/dl n(%) | 174 (73.1) | 443 (29.8) | 49 (20.5) | 2586 (70.7) | 168 (38.8) | 942 (72.8) | 134 (22.3) | 2501 (73.4) | |||
| FBS mg/dL | Total mean ± SD | 101.0 ± 35.6 | 87.0 ± 20.4 | 98.5 ± 43.9 | 83.1 ± 13.0 | < 0.001 | 98.1 ± 35.21 | 85.8 ± 17.1 | 90.6 ± 26.9 | 83.1 ± 14.9 | < 0.001 |
| FBS ATP criteria | ≥ 110 mg/dl n(%) | 184 (77.3) | 45 (3.0) | 48 (20.1) | 64 (1.7) | 0.488 | 365 (84.3) | 1263 (97.6) | 49 (10.0) | 63 (1.8) | 0.009 |
| < 110 mg/dl n(%) | 54 (22.7) | 1444 (97.0) | 191 (79.9) | 3596 (98.3) | 68 (15.7) | 31 (2.4) | 442 (90.0) | 3345 (98.2) | |||
| FBS IDF criteria | ≥ 100 mg/dl n(%) | 158 (66.4) | 120 (8.1) | 61 (25.5) | 227 (6.2) | 0.053 | 316 (73.0) | 83 (6.4) | 106 (21.6) | 182 (5.3) | 0.054 |
| < 100 mg/dl n(%) | 80 (33.6) | 1369 (91.9) | 178 (74.5) | 3433 (93.8) | 117 (27.0) | 1211 (93.6) | 385 (78.4) | 3226 (94.7) | |||
| Systolic blood pressure mmHg | Total mean ± SD | 128.7 ± 14.7 | 116.7 ± 13.6 | 124.5 ± 11.4 | 113.3 ± 9.5 | < 0.001 | 124.9 ± 16.0 | 116.1 ± 13.1 | 120.5 ± 12.1 | 113.0 ± 9.3 | < 0.001 |
| ≥ 130 mm Hg n(%) | 125 (52.5) | 208 (14.0) | 96 (40.2) | 202 (5.5) | 0.007 | 266 (61.4) | 166 (12.8) | 132 (26.9) | 166 (4.9) | < 0.001 | |
| < 130 mm Hg n(%) | 113 (47.5) | 1281 (86.0) | 143 (59.8) | 3458 (94.5) | 167 (38.6) | 1128 (87.2) | 359 (73.1) | 3242 (95.1) | |||
| Diastolic blood pressure mmHg | Total mean ± SD | 82.8 ± 9.0 | 76.8 ± 8.7 | 81.6 ± 7.7 | 74.4 ± 6.6 | 0.043 | 81.4 ± 10.1 | 76.3 ± 8.2 | 78.8 ± 8.2 | 74.3 ± 6.5 | < 0.001 |
| ≥ 85 mm Hg n(%) | 107 (45.0) | 182 (12.2) | 99 (41.4) | 247 (6.7) | 0.436 | 276 (63.7) | 132 (10.2) | 132 (26.9) | 214 (6.3) | 0.002 | |
| < 85 mm Hg n(%) | 131 (55.0) | 1307 (87.8) | 140 (58.6) | 3413 (93.3) | 157 (36.3) | 1162 (89.8) | 359 (73.1) | 3194 (93.7) | |||
*Waist Circumference cm
According to Table 3, according to the comparison of patients with metabolic syndrome based on the ATP III criteria, significant differences were observed between the two groups of university employees and industrial workers in the mentioned variables. A significant difference in age and age groups was observed, and the age of university participants was significantly higher than the age of industrial workers. For this purpose, we corrected the age factor later in the regression analysis. Also, two groups of university employees and industrial workers had significant differences in blood triglyceride concentration. The blood triglyceride levels of industrial workers with metabolic syndrome were higher than the blood triglyceride levels of university workers with metabolic syndrome. Also, a significant difference was observed in the level of HDL, the level of HDL in industrial workers was lower than that of university employees. The level of FBS showed a significant difference between the two groups, which showed that the average blood sugar of those with metabolic syndrome was higher than that of industrial workers. The blood pressure of university employees suffering from metabolic syndrome was higher than that of industrial workers. These cases are also presented in Table 3 based on the IDF standard of metabolic syndrome.
Also, in Tables 4 and 5, the association between metabolic syndrome and work type was investigated based on IDF and ATP III criteria, respectively. The relationship was analysed with the logistic regression method. The regression analysis results showed the association between the type of work and the prevalence of metabolic syndrome adjusted for the variables of age, sex, and body mass index; also, having nonindustrial work could be correlated to the development of the metabolic syndrome.
Table 4.
Logistic regression for the association between Metabolic Syndrome and work type adjusted for age, sex, and BMI (based on IDF criteria)
| Variable | B | Odds ratio | P-value | 95% CI for odds ratio | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Age (year) | 0.058 | 1.059 | < 0.001 | 1.051 | 1.069 |
| Sex (F/M) | -0.034 | 0.967 | 0.68 | 0.824 | 1.134 |
| BMI (kg/m2) | 0.237 | 1.268 | < 0.001 | 1.247 | 1.289 |
| Work (N/I)* | -0.491 | 0.612 | < 0.001 | 0.520 | 0.720 |
| Constant | -9.759 | < 0.001 | < 0.001 | ||
R Square = 0.254, * Nonindustrial employees/ Industrial employees
Table 5.
Logistic regression for the association between Metabolic Syndrome and work type adjusted for age, sex, and BMI (based on ATP-III criteria)
| Variable | B | Odds ratio | P-value | 95% CI for odds ratio | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Age (year) | 0.058 | 1.059 | < 0.001 | 1.048 | 1.071 |
| Sex (F/M) | -0.030 | 0.971 | 0.771 | 0.794 | 1.186 |
| BMI (kg/m2) | 0.267 | 1.306 | < 0.001 | 1.280 | 1.333 |
| Work (N/I)* | -0.473 | 0.623 | < 0.001 | 0.503 | 0.772 |
| Constant | -11.537 | < 0.001 | < 0.001 | ||
R Square = 0.267 * Nonindustrial employees/ Industrial employees
Discussion
In the present study, we compared the prevalence of metabolic syndrome between two populations of employees of the University of Medical Sciences and workers employed in the automotive industry in Tehran. Clearly, university staff as white-collar workers have less physical activity in their workplace than industrial workers as blue-collar workers. Automotive workers often have manual jobs. Metabolic syndrome is presumed to be more prevalent among university employees than industrial workers. However, this simple yet controversial assumption of previous studies has been inconsistent with some studies. In some studies, it was observed that the prevalence of metabolic syndrome in blue-collar personnel was higher than in white-collar personnel [18, 26–29].
On the other hand, there was an issue casting doubt on the first premise, posing the main question of this study. The point was that the University of Medical Sciences staff had a higher level of education and better access to health services than the industrial workers. Even many of the participants in the university staff were nurses and doctors. For these reasons, it seemed that the health status of university staff could be better than that of industrial workers. University participants could easily access free para-clinical and clinical services. Contrary to the first hypothesis, this could lead to the assumption that the prevalence of metabolic syndrome among university staff is lower than that among automotive workers. However, the results of the present study showed that the prevalence of metabolic syndrome among university staff is almost double compared to industrial workers despite better access to para-clinical facilities and a higher level of education. Past studies have shown a higher prevalence of metabolic syndrome among white-collar personnel than blue-collar personnel in some cases [30]. Of course, as we have already mentioned, this issue has been controversial, and sometimes contradictory results have been observed.
In the present study, despite the difference in age and sex, it was still observed that the prevalence of metabolic syndrome is higher among university staff than among automotive workers. According to ATP III and IDF criteria, the prevalence of metabolic syndrome in university staff has been twice as high as in the automotive industry, suggesting the physical activity and the type of job tasks as its main reasons.
Accordingly, sedentary work and sitting tasks are more common among university staff than industrial workers.
One of the biases in occupational studies is the healthy worker effect. This effect occurs when comparing employees to the general population. On the other hand, this effect involves employing healthy people without the desired outcome (metabolic syndrome), and the outcome's development will probably lead to the firing of the worker. Of course, metabolic syndrome is not one of these outcomes, meaning no one is fired just for having metabolic syndrome. In this study, both groups of the study population are employed. Therefore, in the present study, the possibility of this bias is negligible.
Another issue that, according to the authors, can be an influential factor is the occupational health system in industrial sectors and the lack of occupational health programs in government agencies, including universities (it is noteworthy that in Iran, medical universities are run under the supervision of the Ministry of Health, and they are responsible for implementing health programs throughout the country). This issue has arisen due to the lack of laws in Iran. In general, in Iran, employees are divided into two groups workers who are subject to the labor law and employees of the public and governmental sectors who are subject to other specific laws. Chapter 4 of the 1990 Iranian Labor Law explicitly discusses the need to maintain and promote workers 'occupational health, and employers are directly responsible for maintaining and promoting workers' health. The two Ministries of Health and the Ministry of Labor are also responsible by the legislature for overseeing the proper implementation of this law. In recent years, the government has more or less performed these governance duties and monitored the proper implementation of occupational health services in the industries. Almost all small and large industries subject to labor law have codified programs to protect and promote workers' health. Including periodic screening examinations that all workers subject to the labor law are required to perform at least once a year, a health record is formed and followed up for them. Some of the cases checked annually in workers are the lipid profile, fasting blood sugar, blood pressure measurement, and BMI. If there is any discrepancy between the measured values of these workers and the normal values, the health system will warn them. Accordingly, follow-ups can promote health and prevent the progression of the disorder.
On the other hand, there are almost no occupational health promotion programs for government employees, and this is due to the legal lack, which has been neglected. It is worth mentioning that some government centers in Iran have defined programs to improve the health of personnel within the organization. However, most government agencies are deprived of these programs.
It may be suggested that this issue can be remedied by formulating and implementing a comprehensive plan for occupational health and safety in Iran that includes all employees, both governmental and non-governmental.
Conclusion
At first glance, it seems that the health status of automotive, industrial workers is better than that of university employees in terms of the prevalence of metabolic syndrome. However, the double prevalence of metabolic syndrome among university staff is a considerable difference with possible reasons other than different levels of physical activity. We believe that one of the reasons for this difference lies in providing better occupational health services to workers.
Limitations
One of the limitations of this study was the non-inclusion of women in study in the automotive, industrial environments due to single-sex employment policies (only men).
Declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Hamidreza Pouragha, Email: pouragha_h@razi.tums.ac.ir.
Gholamreza Pouryaghoub, Email: Pourya@tums.ac.ir.
Mahsa Naserpour, Email: Naserpour2011@gmail.com.
Ramin Mehrdad, Email: mehrdadr@tums.ac.ir.
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