Prevalence of metabolic syndrome and its associated factors among Iranian steel workers

Abstract

BACKGROUND:

Metabolic syndrome (MetS) is composed of a collection of risk factors for heart diseases and diabetes. In recent decades, metabolic syndrome has been identified as one of the important risk factors leading to the development of work-related diseases.

OBJECTIVE:

Since few studies have been conducted on evaluating the prevalence of MetS among Iranian workers, this cross-sectional study aimed at assessing the prevalence of MetS and the factors affecting it among Iranian steel workers.

METHODS:

This study was carried out on 510 employees working in a large steel producing company. The data pertaining to blood pressure, triglycerides, cholesterol, glucose, and demographic information were collected and the Adult Treatment Panel (ATP III) criteria were implemented to diagnose MetS.

RESULTS:

The prevalence of Mets was obtained equal to 13% and a significant positive relationship was observed between age and the prevalence of metabolic syndrome. From among MetS elements, low HDL cholesterol and increased waist circumference were recognized as the most and the least frequently involved elements with 39.3% and 6.5% prevalence, respectively. Chi-square test was run and the results showed that the prevalence of MetS and some of its components rose at higher BMI values. It was also indicated that MetS and its components had no significant relationship with shift work.

CONCLUSION:

The current findings revealed that the prevalence of MetS increased with aging. Low HDL and high triglycerides levels were among the main risk factors for MetS. Therefore, considering these risk factors, it should be attempted to develop relevant strategies at workplace to encourage workers to go for a healthier lifestyle so that they can prevent the incidence of MetS.

Keywords

Shift work work-related diseases cardiovascular diseases steel workers

1. Introduction

Metabolic syndrome (MetS) entails a cluster of interrelated metabolic risk factors. The most widely-recognized set of these risk factors included the increased waist circumference (abdominal obesity), elevated blood pressure, elevated triglycerides, a growth in serum glucose, and a reduction in high-density lipoproteins [1]. Various definitions have been proposed for the diagnosis of MetS, among which the ones introduced by the International Diabetes Federation (IDF) and the Adult Treatment Panel (ATP III) are more widely implemented since they do not require the measurement of insulin resistance [2]. Based on the definition presented by ATP III, if a person exhibits three of the aforementioned factors, s/he is diagnosed with MetS [3].

MetS seems to directly promote the development of cardiovascular disease and diabetes [4]. Since cardiovascular diseases, diabetes, and obesity are considered the major health challenges in the 21st century [5] and their incidence has increased substantially [6], metabolic syndrome has received increasing attention in the past few years [4, 7]. Various factors affect MetS that age and body weight are the most important ones [8]. Studies in this area have demonstrated that a lack of physical activities, alcohol consumption, unhealthy food habits, and smoking increase the occurrence of MetS [9]. Gender, race, Body Mass Index (BMI), and prolonged sitting at work are among the other factors affecting MetS [10, 11].

The reported prevalence rates of MetS differ in various studies [6]. The global prevalence of MetS varies from about 10 to 50 percent. According to IDF, one-fourth of adults in the world suffer from MetS and the risk of death and chance of a stroke and heart attack are two and three times higher than other scenarios, respectively [12]. The estimated rate of MetS in Iran equals 34.4 percent [13], whereas 20 percent of the general population in the United States suffer from MetS according to American College of Emergency Physicians (ACEP) criteria [14]. The huge expenses and socioeconomic costs MetS imposes on the healthcare system have made it an important health challenge [3, 5]. Related estimates show that the average annual treatment costs per individual suffering MetS in the United States hit 4000 dollars [15].

Workers are an important pillar of any organization, and the workplace directly affects worker’s physical, mental, and social conditions; therefore, workplace is a very important environmental and social factor that influences workers’ health [3]. To date, only few studies have been done on the prevalence of MetS in industrial work environments in Iran, and most of them have been conducted on a small sample size [1 , 17]. The identification of the risk factors relating to MetS can play an important role in the workers’ health and, consequently, it may increase industrial productivity. Thus, it is necessary to carry out a thorough and comprehensive study on a large sample population of Iranian workers in order to identify and control this syndrome and its related risk factors. The present study aimed to investigate the prevalence of MetS and its associated factors in the Iranian worker population in order to develop relevant intervention strategies and prevention plans.

2. Methods

2.1 Study population

The number of 510 male workers in one of the steel Producing companies in Ahvaz, Iran participated in this cross-sectional study. Indeed, the production-line workers who supervised and controlled steel production processes constituted the research participants. The production line workers settled in control rooms spend most of their working time on handling and monitoring the automatic production process. In the present study, the data belonging to the workers in charge of office work, engineering sections, management and/or the ones having any history or manifestation of acute or chronic inflammatory diseases, allergic diseases or cancer in the records of their health checkups were excluded from the final analysis.

Stratified random sampling was employed in order to select the participants. Then, the procedure and objectives of the study were explained to the participants and they were assured that their personal information would be kept confidential. Moreover, all participants signed an informed written consent form prior to their participation. The study framework was approved by the Ethics Review Committee of Jundishapur University of Medical Sciences, Ahvaz, Iran.

2.2. Measures

Standard questionnaires were used to collect data on participants’ demographics, occupational history, self-reported medical history, and lifestyles. Information on shift work history was collected via questionnaires. Shift work was defined as any work schedule involving unusual or irregular working hours as opposed to a normal daytime work schedule. hift work status was categorized as: daytime workers (8:00–17:00) and shift workers working rotating shifts including morning (7:00–15:00), afternoon (15:00–23:00), and night (23:00–7:00) shifts.

The first part of the questionnaire was filled out by the participants; then, their anthropomorphic data and blood pressure were collected and measured by the trained personnel of the study. Thereafter, they were asked to attend the laboratory to have their blood samples taken after fasting for 12–14 hours. Three markers from the blood sample test, namely fasting blood sugar, triglycerides, and high-density lipoprotein cholesterol (HDL-C) were recorded. After the blood samples were taken, the relevant tests were carried out to measure fasting plasma glucose and triglycerides levels using the enzymatic colorimetric method and employing Pars Azmoon standard kits. HDL cholesterol tests were performed by means of the antibody-enzyme method and Pars Azmoon standard kits.

Based on World Health Organization criteria, the BMI values were classified into four categories, i.e. less than 18.5 (underweight), 18.5–24.9 (normal range), 25–29.9 (overweight), and higher than 30 (obese) [18]. Since only few participants had BMI values smaller than 18.5, the BMI values of underweight participants and the BMI values in the normal range were categorized into one group. Suitable armbands for each participant’s arm circumference and a standard mercury manometer were used to measure his blood pressure. Each participant’s blood pressure was measured twice in a sitting position on the right arm with a 5-minute rest interval between the two measurement rounds. In fact, they were requested to sit down for at least five minutes before measuring their blood pressure.

2.3. Ascertainment of metabolic syndrome

Adult Treatment Panel III (ATP III) method was employed to diagnose MetS among the study population. MetS diagnosis was fulfilled based on abdominal obesity, increased blood pressure, increased triglycerides, low HDL cholesterol level, and increased fasting blood sugar. Based on ATP III method, the co-presence of at least three indicators of the following ones is considered the condition for MetS diagnosis: waist circumference ≥102 cm in men and ≥88 cm in women, triglycerides level ≥150 mg/dl, HDL level ≤40 mg/dl in men and ≤50 mg/dl in women, systolic blood pressure ≥130.85 mm Hg, diastolic pressure 85 mm ≥Hg and fasting blood sugar ≥100 mg/dl [19].

2.4. Data analysis

After the questionnaires were responded to and the tests were performed, the statistical analysis was run using SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA). Descriptive tests and inferential tests, including Chi-square and Logistic regression were run to analyze the data (P < 0.05).

3. Results

Table 1 presents the demographic characteristics of the participants in the study. All the participants were male with an average of 33.12 years of age. The majority of participants were married (79%), 31.4% of had a university degree, and only 22% were smoking at that time.

Table 1
Demographic characteristics of the subjects (N = 510)

Variable Mean±SD or N (%)

Age 33.12±5.56

Marital status

Married 403(79)

Single 107(21)

Education

High school diploma 350(68.6)

University education 160(31.4)

Smoking 122(22)

Variable	Mean±SD or N (%)
Age	33.12±5.56
Marital status
Married	403(79)
Single	107(21)
Education
High school diploma	350(68.6)
University education	160(31.4)
Smoking	122(22)

As it is shown in Table 2, MetS prevalence was 13.9 percent based on the National Cholesterol Education Expert Panel (Adult Treatment Panel III). The most frequently observed components of MetS are low HDL-C level (39.3%), high triglycerides level (30.6%), high blood pressure level (25.3%), high glucose level (25%), and elevated waist circumference (6.5%), respectively.

Table 2

Prevalence and components of MetS

MetS components	N (%)
Elevated waist circumference	33(6.5)
Elevated triglycerides	156(30.6)
Low HDL-C	200(39.3)
Elevated BP	129(25.3)
Elevated fasting glucose	127(25)
MetS	71(13.9)

Table 3 presents the changes in various MetS factors among different groups. Based on the results of Chi-square test, there was a significant relationship between MetS prevalence and BMI where MetS prevalence rose with an increase in BMI values (P-value = 0.001). The examination of the relationship between BMI and MetS components showed that the prevalence of low HDL-C significantly increased with the increase of BMI (P-value = 0.001). Moreover, the prevalence of high triglycerides level and elevated waist circumference also significantly increased with the increase of BMI (P-value = 0.001). Univariate logistic regression analysis indicated that the chance of MetS occurrence in people with BMI≥30 were 8 times higher than that in people with BMI > 25 (CI 95% : 3.47–18.71) and also the odds of MetS occurrence in people with MBI values of 25–30 were three times greater than that in people with BMI≤25 (CI 95% : 1.4–6.85). In the same way, the relationship between age and MetS (Table 3) was assessed and it was revealed that MetS prevalence varied in different age groups; indeed, there was a significant relationship between MetS prevalence and aging (P value = 0.001). Among the components of MetS, a significant relationship existed between aging and increased waist circumference, and also between high blood pressure and decreased HDL-C. Univariate regression analysis demonstrated that the chance of MetS occurrence in people over 40 years of age were 5.8 times greater than that in those the participants younger than 30 years (95% CI: 2.38–14.2). Employment history was also investigated and the results indicated that MetS prevalence significantly rose with an increase in employment history (P value = 0.001; Table 3). The results of Chi-square test revealed that employment history had a significant relationship with MetS and its components (P value < 0.05). Based on Chi-square test, no significant relationship was observed between smoking and MetS and its components (P value > 0.05). Table 4 presents the relationship between shift work and components of MetS and also demographic characteristics. results of this study also demonstrated that there was not any relationship between MetS and components of MetS and shift work.

Table 3

Prevalence of MetS and its components based on effective variables in the subjects

Characteristics	Low HDL-C	Elevated fasting glucose	Elevated triglycerides	Elevated BP	Elevated waist circumference	MetS
	N%	N%	N%	N%	N%	N%
BMI
<25	45(28)	39(24.2)	27(16.8)	33(20.5)	0(0)	8(5)
25–30	109(42.2)	62(24)	94(36.4)	70(27.1)	4(1.6)	36(14)
≤30	46(50.5)	26(28.6)	35(38.5)	26(28.6)	29(31.9)	27(29.7)
p_value	0.001	0.67	0.001	0.23	0.001	0.001
AGE
<30	30 (26.8)	26(23.2)	26(23.2)	25(22.3)	3 (2.7)	10 (8.9)
30–40	149(42.2)	77(21.8)	113 (32)	84(23.8)	21 (5.9)	45(12.7)
>40	21 (47.7)	24(54.5)	17(38.6)	20(45.5)	9(20.5)	16(36.4)
p_value	0.007	0.001	0.1	0.006	0.001	0.001
Experience
1–7	110(36.9)	68(22.8)	83(27.9)	70(23.5)	15 (5)	27 (9.1)
8–14	69 (39.4)	45(25.7)	58(33.1)	42 (24)	13 (7.4)	29(16.6)
15–20	21 (60)	14 (40)	15(42.9)	17(48.6)	5 (14.3)	15(42.9)
p_value	0.03	0.08	0.13	0.005	0.09	0.001
Smoking
Yes	44(39.3)	26(23.2)	34 (30.4)	29(25.9)	10 (8.9)	17(15.2)
No	164(39)	103(24.5)	117 (27.9)	101(24)	23 (5.5)	54(12.9)
p_value	0.96	0.77	0.60	0.68	0.17	0.52

Table 4

The relationship of shift work with MetS components and demographic characteristics

Variables ^b	Mean±SD or N (%)			P-value
	Total	Day worker (n = 87)	Shift worker (n = 423)
Age	33±7.74	34.43±5.61	32.8±5.32	0.12
BMI	24.7±6.12	24.64±5.15	24.81±5.78	0.23
MetS	% 14	15(% 17)	60(% 13)	0.32
WC	187(% 27)	28(% 32)	127(% 30.3)	0.25
TG	340(% 49)	8(% 9.2)	25(% 6)	0.72
HDL	195(% 28)	28(% 32)	170(% 40)	0.12
BP	234(% 34)	22(% 25)	106(% 25)	0.99
FBS	92(% 13)	25(% 29)	101(% 24)	0.36

BMI = Body mass index (Kg/m²); MetS = Metabolic syndrome; WC = Waist circumference; FBS = Fasting blood sugar; HDL = High-density lipoprotein; TG = Triglyceride; BP = Blood pressure; *P value for independent samples test; **P value for χ² test.

4. Discussion

MetS is an important risk factor in the development of cardiovascular diseases. Therefore, the diagnosis, prevention, and treatment of the risk factors affecting MetS occurrence must be taken into consideration in order to reduce the development of cardiovascular diseases among the individuals of any community [20]. Most research carried out on MetS in Iran has been focused on specific age groups and patients [16 , 22] and few studies have been conducted on the prevalence of MetS among worker populations, especially in Iran industrial units. This is the first study which is being conducted on a large population of workers in a heavy metal industry in Iran.

In this study, the mean value of the workers’ age was 33.12±5.56, which represents the young age of the worker population. Based on ATP III criteria, MetS prevalence was obtained equal to 13.9 percent; however, it is lower than the reported result for the Iranian general population [23].

Lee et al. reported a 17.9-percent prevalence of metabolic syndrome among steel rolling factory workers with the age range of 41.2±7.9 [24], which was higher than that of the present study. Considering the lower mean value of the workers’ age in the present study and the increased MetS prevalence as a result of aging [25], this difference may have resulted from the selection of workers of different age groups as participants in the studies. In comparison to other occupations, MetS prevalence was 11.67 percent among male nurses with the mean value of 35.75 years of age in an Iranian hospital, which was close to that of the present research considering the age and gender of the nurses [13]. The main reason for this discrepancy can be explained by the fact that the phenotype of MetS results from multiple basic mechanisms and the interaction of genotype with the environmental and behavioral factors.

In this study, the low values of HDL-C and increased waist circumference had the highest and lowest frequencies among MetS criteria, respectively. Park et al. also reported that the decreased HDL-C was the most frequent criterion among males [26]. This reduction can be attributed to the industrialization of the country, lifestyle changes, unhealthy diets, decreased physical activity, and obesity [27]. Different rates of MetS prevalence have been reported for various occupations, which can be due to the variation in obesity rates among the studied groups [28]. Obesity is among the common diseases and health problems in modern communities and its prevalence has increased globally [29]. The results of the study carried out by Framingham demonstrated that each 2.25-kilogram increase in body weight increased the risk of MetS occurrence by 21–45 percent [8]. Since obesity is an important risk factor for increased MetS rates, it is believed that prevention of obesity is the best solution for reducing MetS prevalence [29]. Obesity is rapidly growing among Asian workers and has become a common health problem in the working environments of Asian countries [30]. Moreover, many workers spend more than half of their working time in a sitting position [11]. Based on some research findings, excessive weight increase pertains to a higher occurrence of cardiovascular problems, hypertension, blood lipid disorders, and diabetes type 2 [31].

The results of the present study suggest that MetS prevalence rises with an increase in BMI.

The higher BMI was significantly related to a higher probability of MetS, which is consistent with the findings of previous studies [32]. For example, Gonzalez-Zapata showed that MetS prevalence was higher in overweight people [33]. Park et al. also found that the high BMI value was a risk factor for MetS and was significantly related to MetS occurrence [34]. Thus, the findings suggest that weight control should be included in the development of workstation-based interventions to reduce MetS rates among this population.

The results of Chi-square analysis revealed that three MetS components (HDL, triglycerides, and large waist circumference) increased significantly with the increase of BMI values in workers. Khosravi et al. (2006) reported that the levels of triglycerides, total cholesterol, and waist circumference significantly rose with the increase of BMI, and HDL was the only factor that was not affected by the increase of BMI does [31]. Based on the research findings reported by Yanovski, the increased levels of total cholesterol and triglycerides and the reductions in HDL cholesterol levels in overweight and obese people were significantly different from those in people with normal body weights [35]. Kissebah et al. reported a the availability of a strong correlation between elevated waist circumference and BMI values of healthy Koreans [36].

Previous research indicated that MetS prevalence rose with aging [37]. The present study also showed that higher employment history and age led to an increase in MetS prevalence in such a way that MetS was more prevalent among workers over 40 years of age compared to the ones younger than 40 years of age.

The current findings demonstrated that the risk factors of MetS were not influenced by smoking. Other research findings also revealed that smoking was not related to diabetes mellitus, serum total cholesterol, and LDL cholesterol [38]. However, some studies have indicated that smoking affects dyslipidemia and may reduce total cholesterol, LDL cholesterol, and triglycerides and increase HDL [39, 40]. This inconsistency between the results of those studies and that of the current study can be accounted for by the varying age ranges of the studied populations.

To date, various research findings have shown that shift work has a negative effect on the health status and, in particular, contributes to the incidence of cardiovascular and coronary heart disease, myocardial ischemia, and ischemic stroke [41 –43]. Furthermore, same research findings in developed countries have reported the increased probability of MetS in shift workers [44 –46].

The results of this study demonstrated that, among the five components of MetS, HDL and triglyceride levels were the most and the least common factors among shift workers, respectively. Similarly, the HDL level was higher in shift workers than that in day workers. The absence of a significant relationship between the shift work and cholesterol level was reported by Dochi et al. They found that shift work resulted in a 20%-to-45% increase in the total cholesterol level in Japanese steel company workers [35].

The results of this study also showed that there was no significant difference in blood pressure between shift workers and day workers. According to the literature review, there was insufficient evidence for shift work to be considered as a predictor for hypertension [47]. Although some studies have shown a strong association between shift work and metabolic syndrome, the results of this study showed that MetS and its components did not have any significant relationship with shift work in such a way that no meaningful difference was observed between shift workers and non-shift workers in terms of the factors leading to MetS occurrence. A recent review on shift work and MetS led to the conclusion that no association was observed between shift work and MetS when confounders are taken into account [48]. Canuto et al. believe that the varying criteria used for classifying both MetS and shift works as well as the misclassification errors of these concepts make it difficult to compare the findings of studies. For example, different data collection approaches and the different definitions used for shift work and schedules may increase the risk of misclassifications in the assessment of work schedules [49]. In addition to work schedules, other factors can affect tolerance to shift work and its consequences for workers’ health, such as individual characteristics, economic conditions, lifestyle (food intake habits and physical activity), working conditions, and the occurrence of MetS, which should be considered in future studies.

4.1. Limitations

One of the limitations of the present study was that all participants were male; therefore, the results cannot be generalized to the whole population. Other factors, such as dietary patterns, and details of work schedule, including the number of off days per month, long working hours, and income levels were not taken into consideration in the current research because of the limited financial resources. These influencing MetS factors can be studied in future studies.

5. Conclusion

The results of the present study suggested that the MetS prevalence in steel industrial workers equaled 13.9 percent, which is lower than those reported in studies conducted in similar industries in other countries. There were significant relationships between MetS and the variables of age and BMI; accordingly, these two factors were identified as risk factors for MetS occurrence. The results of this study also demonstrated that there was not any relationship between MetS and components of MetS and shift work. Furthermore, low HDL and high triglycerides levels were recognized as important risk factors for the development of MetS in workers with higher BMI values, and these people are likely to be more susceptible to the risk of MetS. Therefore, encouraging workers to do physical activity more and eating healthy diets can be regarded as primary steps for reducing MetS prevalence and, consequently, decreasing the risks of cardiovascular diseases and diabetes type 2 among workers.

Conflict of interest

None to report.

Footnotes

Acknowledgments

This study was a part of MSc thesis. The authors are grateful to the Ahvaz Jundishapur University of Medical Sciences for funding this research with project no. U-95013.

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