Metabolic syndrome and its relationship with shift work in petrochemical workers

Abstract

BACKGROUND:

The metabolic syndrome is a complex of interdependent risk factors for cardiovascular disease and diabetes. Shift work might have an impact on metabolic variables, and be a risk factor for type 2 diabetes. 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.

OBJECTIVE:

The aim of this study was to evaluate the impact of shift work on prevalence of metabolic syndrome in one of the petrochemical companies in Iran.

METHODS:

This cross-sectional study was conducted among 692 male workers of a petrochemical company in south-west Iran. Metabolic syndrome was diagnosed according to criteria recommended by Adult Treatment Panel III. In order to determine correlation between MetS and its factors with shift work odds ratio (ORs) for the MetS, 95% confidence level (95% CL), chi-square test and logistic regression analysis were performed.

RESULTS:

Overall 15.1% of workers were diagnosed with metabolic syndrome and 80% of them were shift workers. A significant difference for prevalence of metabolic syndrome and mean values for body mass index, blood pressure, fast blood sugar, waist circumference among shift workers and non-shift workers were identified (p < 0.001). Compared with the day workers, shift workers had a significantly higher risk of MetS (odds ratio = 4.852; 95% CI 2.34–9.974).

CONCLUSIONS:

There is an association between metabolic syndrome and shift work in petrochemical workers. Promising intervention strategies are needed for prevention of metabolic disorders for shift workers.

Keywords

Metabolic syndrome shift work worker cardiovascular diseases circadian rhythm

1 Introduction

Metabolic syndrome (MetS) is defined as the co-occurrence of metabolic risk factors which are associated with prevalence of type II diabetes and cardiovascular disease [1, 2]. The prevalence of MetS is constantly increasing due to the socio-environmental factors and in recent decades MetS has become one of the major risk factors for public health. The National Cholesterol Education Program, Adult Treatment Panel III (NCEP ATP III) considers MetS a multidimensional risk factor for cardiovascular disease and recommend more clinical care. Abdominal obesity, hypertension, hyperglycemia, elevated serum triglycerides, and high-density lipoprotein (HDL) deficiency are the most recognized risk factors resulting in MetS [3 –5]. Among several diagnosis criteria for MetS, the methods presented by the International Diabetes Federation (IDF) and the Adult Treatment Panel (ATP III) are the most commonly used methods. According to ATP III criteria, presence of at least three of the aforementioned factors in a person is considered as MetS [6, 7]. The ATP III definition of MetS particularly considers waist circumference as the indicator for obesity [1, 8]. Previous studies confirm the relation between shiftwork and the risk of heart attack and coronary heart disease and also discuss that sleep disorder might result in insulin resistance and clinical sign of MetS [2 , 9–12]. Shift work is defined as a working schedule in hours rather than regular daytime [13, 14] which is increasing in modern societies sice it is a policy implemented by organizations in order to increase flexibility in planning. Shift work involves a timetable of unusual and irregular working hours such as working during the night or rotary shifts [15, 16]. Prolonged circadian rhythm disruption is likely to cause sleep disorder, obesity and hypertension which are risk factors of cardiovascular disease [17]. Shift work is accompanied by a greater prevalence of several health problems, such as, gastrointestinal, cardiovascular and metabolic disturbances. Studies have shown that night work may lead to changes in the concentrations of serum triglycerides in shift workers [11, 16]. The Iranian petrochemical industry is important for both economic and recruitment reasons. Iran is now the second-largest producer and exporter of petrochemicals in the Middle East, with more than 54 petrochemical complexes. The production of petrochemical products is such that usually one main unit is placed at the top of suppliers and produces the primary material such as types of polymeric materials and chemicals for other units. According to the Council of Labor Affairs’ 2010 annual report, the number of workers suffering injuries and illness was highest in the petrochemical industry [18]. 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. Most research carried out on MetS in Iran has been focused on specific age groups and patients and few studies have been conducted on the prevalence of MetS among worker populations, especially in Iran industrial units. 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 shift-work in petrochemical workers in order to develop relevant intervention strategies and prevention plans.

2 Methods

The following cross-sectional study was conducted among 692 male workers of a petrochemical company in south-west Iran. Indeed, the production-line operators who supervised and controlled petrochemical materials 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. All participants had at least one-year work experience without any history of congenital disease, heart failure, kidney problem or hypertension. Among participants 232 were shift workers and 460 ones were day workers. The participants were asked whether they consume any medicine affecting the measuring factors of the study and if they do they were exempt from data collection. 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. Shift work status was categorized as below: daytime workers (8:00–15:00) 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. Weight, height, blood pressure and waist circumference were measured using a scale with 0.1 kg precision, height measurement scale with 1mm resolution, sphygmomanometer and a tape measure respectively. Waist circumference was measured according to WHO protocol, mid-point between the top of the iliac crest and the ribcage [19]. Blood pressure was collected twice on right forearm in seated position with five minutes interval and results were averaged. Participants were asked to visit a laboratory after a 12–14 hours fast, blood samples were taken and tests on glucose, high-density lipoprotein cholesterol (HDL-C) and triglyceride were performed. In this study MetS was defined according to the US national cholesterol education program adult treatment panel III (NCEP ATP III) criteria, accordingly, MetS is diagnosed by presence of at least three of following factors: Waist circumference more than 102 cm and 88 cm in men and women respectively, triglyceride level≥150 dl/mg, HDL-C≤40 dl/mg in men and≤50 dl/mg in women, systolic blood pressure≥130 mm Hg and diastolic blood pressure≥85 mm Hg, fasting plasma glucose≥110 dl/mg [3, 20]. Using SPSS version 24, descriptive statistics were analyzed. Mean, percentage, range and standard deviation were calculated. In order to determine correlation between MetS and its factors with shift work odds ratio (ORs) for the MetS, 95% confidence level (95% CL), chi-square test, Spearman correlation test and logistic regression analysis were performed. To assess the effect of shift work on NCEP ATP III factors and demographic factors, independent samples T- test and Cramer’s V were implemented. The level of statistical significance for p-value was set at < 0.05.

3 Results

The demographic characteristics and blood biomarkers of participants are presented in Table 1. The prevalence of MetS in shit workers was 39.4% while for the non-shift workers it was 4.3%.

Table 1
Demographic characteristics and blood biomarkers of participants

Variables N (%) Mean±SD Min-Max

Age 692 42.98±7.73 24–70

Weight (Kg) 692 83.60±14. 54–147

Height (m) 692 173.33±6.894 105–191

FBS (mg/dL) 692 98.34±49.55 40.00–543.00

Total chol (mg/dL) 692 190.81±40.98 12.00–367.00

LDL (mg/dL) 692 114.47±35.52 9.00–234.00

HDL (mg/dL) 692 42.472±4.38 35.00–754

TG (mg/dL) 692 169.61±88.25 35.00–754

BMI (kg/m²)

Total 692 27.83±4.52 17.79–52.71

Normal 168(24.6%)^* 22.85±1.63 18.29–24.97

Overweight 327(47.8%) 27.36±1.35 25–29.91

Obese 189(27.6%) 33.18±4.07 30–52.71

BP

Total 692 Systolic 125.82±17.207 90–210

Diastolic 80.40±11.076 10–130

Normal 281(40.1%) Systolic 112.8±9.768 90–150

Diastolic 72.047±6.119 60–90

Prehypertension 278(40.5%) Systolic 127.050±8.278 110–190

Diastolic 82.086±5.303 70–100

Stage 1 hypertension 107(15.6%) Systolic 144.579±9.037 130–180

Diastolic 92.851±4.902 80–100

Stage 2 hypertension 26(3.8%) Systolic 173.077±16.191 150–210

Diastolic 99.192±26.626 100–130

Variables	N (%)	Mean±SD	Min-Max
Age	692	42.98±7.73	24–70
Weight (Kg)	692	83.60±14.	54–147
Height (m)	692	173.33±6.894	105–191
FBS (mg/dL)	692	98.34±49.55	40.00–543.00
Total chol (mg/dL)	692	190.81±40.98	12.00–367.00
LDL (mg/dL)	692	114.47±35.52	9.00–234.00
HDL (mg/dL)	692	42.472±4.38	35.00–754
TG (mg/dL)	692	169.61±88.25	35.00–754
BMI (kg/m²)
Total	692	27.83±4.52	17.79–52.71
Normal	168(24.6%)^*	22.85±1.63	18.29–24.97
Overweight	327(47.8%)	27.36±1.35	25–29.91
Obese	189(27.6%)	33.18±4.07	30–52.71
BP
Total	692	Systolic	125.82±17.207	90–210
		Diastolic	80.40±11.076	10–130
Normal	281(40.1%)	Systolic	112.8±9.768	90–150
		Diastolic	72.047±6.119	60–90
Prehypertension	278(40.5%)	Systolic	127.050±8.278	110–190
		Diastolic	82.086±5.303	70–100
Stage 1 hypertension	107(15.6%)	Systolic	144.579±9.037	130–180
		Diastolic	92.851±4.902	80–100
Stage 2 hypertension	26(3.8%)	Systolic	173.077±16.191	150–210
		Diastolic	99.192±26.626	100–130

BMI = Body mass index (Kg/m²); FBS = Fasting blood sugar (mg/dL); Total chol = Total cholesterol (mg/dL); LDL = Low-density lipoprotein (mg/dL); HDL = High-density lipoprotein (mg/dL); TG = Triglyceride (mg/dL); BP = Blood pressure (mmHg); ^* = there are 8 missing in BMI data.

Overall 100 workers were diagnosed with MetS which is 15.1 percent of study population and among them 80 (80%) were shit workers. Amongst five risk factors associated with MetS, the most common was HDL-C deficiency with 492 (71.6%), after that, elevated triglyceride by 340 (49.5%), then, high blood pressure with 234 (34.1%), followed by elevated waist circumference with 187 (27.3%) and lastly, elevated fasting plasma glucose by only 92 (13.4%). Among people diagnosed with MetS, 44.4% were overweight, while, amongst BMI categories, obese individuals were mostly prone to MetS with 17.1%. MetS was mostly observed in the age group of 40–49 years old, while, people in the age group of over 60 had the biggest rate of MetS compare to other age groups with 40%. Considering the blood pressure, stage1 hypertension was the most common group (38%), while MetS was mostly diagnosed in the group of stage 2 hypertension with 60.9%. Table 2 represents the results for correlation tests of MetS, Shift work, age, BMI and blood pressure. All factors found to be correlated to each other except BMI with age and BMI with shift work.

Table 2

Correlation analysis of MetS, shift work, age, BMI and blood pressure

Variables	Correlation matrix^a
	Age	BP	BMI	MetS	Shift work
Age	1
BP	0.232 (< 0.001^*)	1
BMI	–0.044 (0.247^*)	0.166 (< 0.001^*)	1
MetS	0.150 (< 0.001^*)	0.316 (< 0.001^*)	0.371 (< 0.001^*)	1
Shift work	–0.082(0.031^*)	0.115(0.003^*)	–0.051(0.182^*)	0.452(< 0.001^**)	1

BP = Blood pressure (have or have not abnormal values); BMI = Body mass index (Kg/m²); MetS = Metabolic syndrome (have or have not abnormal values); a: Severity (p-value); ^*P value for Spearman Correlation; ^**P value for χ² test.

Table 3 illustrates the results of regression analysis of MetS with blood biomarkers, demographic characteristics and shift work. Shift work (OR = 4.852, C.I. 95% = 2.345–9.974), waist circumference (OR = 3.653, C.I. 95% = 1.765–7.563) and blood pressure (OR = 3.12, C.I. 95% = 2.374–4.100) significantly increased the likelihood of MetS. Also age (OR = 1.059, C.I. 95% = 1.031–1.088), BMI (OR = 1.301, C.I. 95% = 1.224–1.382), fasting plasma glucose (OR = 1.015, C.I. 95% = 1.01–1.019) and triglyceride (OR = 1.009, C.I. 95% = 1.006–1.011) were associated with chance of MetS occurrence.

Table 3

Regression analysis of MetS with blood biomarkers, demographic characteristics and shift work

Variables	B	S.E.	p-value	OR	95% C.I.for OR
					Lower	Upper
BP	1.138	0.139	<0.001	3.12	2.374	4.100
Age
Total	0.057	0.014	< 0.001	1.059	1.031	1.088
30–39^a	–20.797	10377.78	0.998	—	—	—
40–49	–1.684	0.502	0.001	0.186	0.069	0.496
50–59	–1.308	0.485	0.007	0.27	0.105	0.700
≥60	–0.910	0.516	0.078	0.402	0.146	1.106
BMI
Total	0.263	0.031	< 0.001	1.301	1.224	1.382
Normal^a	—	—	—	—	—	—
Overweight	–3.026	0.479	< 0.001	0.049	0.019	0.124
Obese	–2.028	0.259	< 0.001	0.132	0.079	0.219
Shift work	2.661	0.270	< 0.001	4.853	2.345	9.974
FBS	0.015	0.002	< 0.001	1.015	1.010	1.019
WC	2.285	0.243	< 0.001	3.653	1.765	7.563
Total chol	0.005	0.003	0.067	1.005	1.000	1.010
HDL	0.013	0.025	0.599	1.013	0.965	1.064
TG	0.008	0.001	< 0.001	1.009	1.006	1.011

BP = Blood pressure; BMI = Body mass index (Kg/m²); FBS = Fasting blood sugar; Total chol = Total cholesterol; HDL = High-density lipoprotein; TG = Triglyceride.

Table 4 shows the relation between shift work and components of MetS and also demographic characteristics. The results indicate a significant difference between shift workers and day workers in waist circumference, fasting plasma glucose, blood pressure, HDL-C, triglyceride and MetS occurrence.

Table 4

Components of MetS and demographic characteristics, according to the shift schedule

Mean±SD or N(%)
Variables^b	Total	Day worker (n = 460)	Shift worker(n = 232)	P-value	Severity^a
Age	42.98±7.74	43.31±7.57	42.33±8.04	0.118^*	—
BMI	27.829±4.52	27.84±4.15	27.64±4.49	0.581^*	—
MetS	100(15.1%)	20(20%)	80(80%)	< 0.001^**	0.452
WC	187(27.3%)	93(49.7%)	94(50.3%)	< 0.001^**	0.227
TG	340(49.5%)	209(61.5%)	131(38.5%)	0.002^**	0.115
HDL	195(28.4%)	114(58.5%)	81(41.5%)	0.003^**	0.114
BP	234(34.1%)	130(55.6%)	104(44.4%)	< 0.001^**	0.174
FBS	92(13.4%)	46(50%)	46(50%)	< 0.001^**	0.142
Total chol	260(37.8%)	181(69.6%)	79(30.4%)	0.248	—

SD = Standard deviation; BMI = Body mass index (Kg/m²); MetS = Metabolic syndrome; WC = Waist circumference; FBS = Fasting blood sugar; Total chol = Total cholesterol; HDL = High-density lipoprotein; TG = Triglyceride; BP = Blood pressure; ^*P value for Independent Samples Test; ^**P value for χ² test; a: Cramer’s V; b:In all of the following variables except age and BMI, the rest are in terms of the number of people with abnormal values of these variables.

4 Discussion

The present study was conducted to evaluate the relationship of demographic characteristics and shift work with prevalence rate of MetS among petrochemical workers. In the present study the prevalence rate of MetS was 15.1% which is close to figures reported by Power et al. in Britain (18.26%), Pattaro et al. in Italy (17.69%) and Hwang et al. (12.09%) while it is much higher than the prevalence rate measured by Inouye et al. in Finland (4.65%) and Wichmann et al. in Germany (8.2%) [21 –25]. Similar studies among Iranian found the prevalence of MetS 22.4% and 34.7% [26, 27] which is more than our finding. The reason for this discrepancy is that phenotype of MetS is the result of multiple basic mechanism and interaction of genotype with the environmental and behavioral factors. Results from this study shows that among five components of MetS, elevated triglyceride level and fast blood sugar were the most and the least common factors with 340(49.5%) and 92(13.4%) respectively. This is not in accordance with the study conducted by Mini et al. in 2018 among industrial workers in India which found HDL-C deficiency the most common and elevated triglyceride the least abundant factors [28]. The frequency of MetS found to be higher in shift workers, which has been confirmed by previous studies [28 –32]. One of the reasons accounting for the increase of MetS in shift workers is the reduction in the night sleep duration which leads to metabolic disorders that promote MetS [10]. Results from this study show that there is a relation between components of MetS and demographic characteristics with shift work so that a meaningful difference between shift workers and non-shift workers in factors resulting in MetS has been identified (P < 0.001). Dochi and colleagues found an overall significant effect of shift work resulting in 20% –45% increases in total cholesterol in Japanese steel company workers [33]. Guo et al. in a study on Chinese workers concluded that the mean number of MetS components such as triglyceride, HDL-C and total cholesterol were higher in shift workers in comparison with non-shift workers [34]. Karlsson et al. reported that the incidence of obesity, elevated triglyceride and HDL-C deficiency were higher among shift workers [35], while Sookoain et al. claimed that shift work affects BMI, waist circumference, blood pressure and triglyceride level [36]. Our findings show that shift work mostly affect MetS prevalence (S = 0.452) and least of all HDL-C level (S = 0.114), and according to the ORs (odds ratio), with increase in BMI the probability of MetS goes up and its maximum figure is among obese individuals with (17.1%). These findings confirm the results from a cohort research conducted in 2017 which studied healthy metabolic obesity and its relation with hypertension and type II diabetes in over 20 years old individuals in Iran. In this cohort study prevalence of MetS found to be related with BMI and its maximum was 37.5% among obese people [37]. Our findings show that MetS was most frequently happened among the age group of over sixty years old people with 40% and it is in accordance with previous studies [34, 37]. Considering odds ratios, increase in blood pressure was found affecting occurrence of MetS. In general stage two hypertension was most frequent with 60.9% and among people diagnosed with MetS stage one hypertension was the most prevalent with 38% which confirms the results from the study directed by Guo et al. [34]. Our finding shows that MetS has a meaningful relation with age, blood pressure, BMI and shift work but the extent of this relation varies. The link between MetS with age is weak (S = 0.15) with blood pressure and BMI is moderate (S = 0.316 and 0.371 respectively) and with shift work almost strong (S = 0.452). Meaningful discrepancies in MetS prevalence, blood pressure, waist circumference, triglyceride, and fast blood sugar between shift workers and non-shift workers have been found which is in agreement with the results of the study conducted in 2007 in Argentina [36]. Computed odd ratios in this study show that each of the factors: blood pressure, BMI, age, shift work, fast blood sugar, waist circumference, and triglyceride affect the probability of MetS and this is similar to findings from other research [29 , 38] and in particular by the results from the study of Guo et al. which reported the following ORs in regard to the MetS occurrence chance: blood pressure OR = 1.07 (C.I. = 1.01–1.13), waist circumference OR = 1.10 (C.I. = 1.01–1.20), fast blood sugar OR = 1.09 (C.I. = 1.04–1.15) [34] and also the odds ratio found by Ryu et al. in 2016 for the effect of BMI on MetS probability (OR = 1.42 (C.I. = 1.30–1.57) [39]. Circadian rhythm disruption is considered to be one of the main reasons why shift workers are more prone to MetS. In fact, a group of metabolic disorders such as sleep disorder, obesity, hypertension and impaired glucose tolerance are stem from sleep disorder which encourage the prevalence of MetS and cardiovascular disease [40]. Despite that our findings do not show any significant connection between age and BMI with MetS, computed odds ratios indicate that probability of MetS occurrence rises with increases in age and BMI as it has been reported in previous studies [39, 41].

5 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.

6 Conclusion

The aim of the present study was to examine the association between shift work and the metabolic syndrome (MetS) using a large sample size. Overall 15.1% of workers were diagnosed with metabolic syndrome and 80% of them were shift workers. Our results demonstrate that shift work was strongly associated with MetS. Therefore, the study suggests appropriate dietary habits and physical activity more as a basis for managing the MetS risk of shift workers.

Footnotes

Acknowledgments

The authors would like to thank participants for their cooperation. This study was approved by the Ahvaz Jundishapur University of Medical Sciences Ethics Committee (reference number IR.AJUMS.REC.1398.743).

Conflicts of interest

The authors declare no conflicts of interest.

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