The prevalence of metabolic syndrome among Iranian bakers

Abstract

BACKGROUND:

Metabolic syndrome (MetS) is a group of risk factors that are associated with increased risk for atherosclerotic cardiovascular disease.

OBJECTIVE:

The objective of this study was to determine the prevalence of metabolic syndrome and its individual components among Iranian bakery workers.

METHODS:

A cross-sectional study enrolling 163 randomly selected bakery workers from Semnan province (Northeast Iran), was conducted in 2017. A standard questionnaire was used for data collection. Analyses were performed using t-test, one-way ANOVA and Latent Class Analysis (LCA) in R software.

RESULTS:

The prevalence of MetS was 19.8% among bakery workers. More bakery workers with MetS had no regular exercise (p = 0.001) and worked more than eight hours a day (p = 0.001). In this study, the International Diabetes Federation (IDF) diagnostic criteria had more agreement with the American Heart Association (AHA) (49.1%) than the World Health Organization (WHO) and Adult Treatment Panel (ATP) criteria. Also, the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) had the highest sensitivity for diagnosing MetS. The sensitivity of the four diagnostic methods, WHO, IDF, ATP and AHA, were 45.2%, 61.7%, 67.9% and 64.8%; and their specificity were 98.2%, 100%, 100% and 100%, respectively.

CONCLUSION:

The prevalence of MetS is high among bakery workers in Iran. Increased physical activity and intake of fruit and vegetables are recommended for the prevention of MetS.

Keywords

Diagnostic screening programs cardiovascular disease occupational health sensitivity and specificity

1 Introduction

Metabolic syndrome (MetS) is a cluster of risk factors that is associated with increased risk of atherosclerotic cardiovascular disease (ASCVD) and Type 2 diabetes [1, 2]. This syndrome consists of five metabolic risk factors that together increase the risk for ASCVD; these include atherogenic dyslipidemia, elevated blood pressure, elevated glucose, a prothrombotic state, and a pro-inflammatory state [1, 3]. MetS, also called insulin resistance syndrome, has been described in many ways, in part due to the lack of a “gold standard” diagnostic test [4]. Several definitions have been proposed for MetS and the most commonly used are the Adult Treatment Panel III (ATP-III) criteria [5] by the National Cholesterol Education Program (NCEP), the International Diabetes Federation (IDF) [6] and the World Health Organization (WHO) criteria [7]. Table 1 provides definitions of Metabolic Syndrome.

Table 1
Definitions of Metabolic Syndrome

Modified WHO Modified NCEP ATP III [58 –60] IDF [3] AHA [38]

Absolutely required Hyperglycemia (upper quartile of the nondiabetic population) or fasting plasma glucose ≥110 mg/dL or Rx^* None Central obesity (waist circumference) ≥94 cm None

Criteria Absolutely required AND Plus 2 or more of the following: Any three of the five criteria below Obesity, plus two of the four criteria below Any three of the five criteria below

Obesity Waist/hip ratio: >0.90, or BMI > 30 kg/m² Waist circumference: >40 inches(102 cm) Central obesity already required Waist circumference: >40 inches(102 cm)

Hyperglycemia Already required Fasting glucose ≥100 mg/dl or Rx^* Fasting glucose ≥100 mg/dl or Rx Fasting glucose ≥100 mg/dl or Rx

Plasma triglycerides TG≥150 mg/dL or Rx TG≥150 mg/dl or Rx TG≥150 mg/dl or Rx TG≥150 mg/dL or Rx

HDL cholesterol <35 mg/dL or Rx <40 mg/dl or Rx <40 mg/dl or Rx <40 mg/dl or Rx

Hypertension ≥140 mm Hg systolic or ≥90 mm Hg diastolic or Rx >130 mmHg systolic or >85 mmHg diastolic or Rx >130 mmHg systolic or >85 mmHg diastolic or Rx >130 mmHg systolic or >85 mmHg diastolic or Rx

	Modified WHO	Modified NCEP ATP III [58 –60]	IDF [3]	AHA [38]
Absolutely required	Hyperglycemia (upper quartile of the nondiabetic population) or fasting plasma glucose ≥110 mg/dL or Rx^*	None	Central obesity (waist circumference) ≥94 cm	None
Criteria	Absolutely required AND Plus 2 or more of the following:	Any three of the five criteria below	Obesity, plus two of the four criteria below	Any three of the five criteria below
Obesity	Waist/hip ratio: >0.90, or BMI > 30 kg/m²	Waist circumference: >40 inches(102 cm)	Central obesity already required	Waist circumference: >40 inches(102 cm)
Hyperglycemia	Already required	Fasting glucose ≥100 mg/dl or Rx^*	Fasting glucose ≥100 mg/dl or Rx	Fasting glucose ≥100 mg/dl or Rx
Plasma triglycerides	TG≥150 mg/dL or Rx	TG≥150 mg/dl or Rx	TG≥150 mg/dl or Rx	TG≥150 mg/dL or Rx
HDL cholesterol	<35 mg/dL or Rx	<40 mg/dl or Rx	<40 mg/dl or Rx	<40 mg/dl or Rx
Hypertension	≥140 mm Hg systolic or ≥90 mm Hg diastolic or Rx	>130 mmHg systolic or >85 mmHg diastolic or Rx	>130 mmHg systolic or >85 mmHg diastolic or Rx	>130 mmHg systolic or >85 mmHg diastolic or Rx

NCEP: ATP III: National Cholesterol Education Program’s Adult Treatment Panel III. IDF: International Diabetes Federation. AHA: American Heart Association. ^*Rx: pharmacologic treatment.

ATP III defines adult MetS as three or more of the following abnormalities: hypertriglyceridemia, low High-Density Lipoprotein (HDL), high fasting glucose, excessive waist circumference, and hypertension, based on associations with adverse cardiovascular outcomes reported in large epidemiologic studies [4].

Adults with MetS are at a greater risk for cardiovascular disease and diabetes mellitus [8]. MetS is an important metabolic disorder which imposes a noticeable burden on the national health systems [9].

Evidence suggests that all of the metabolic syndrome criteria are independently atherogenic. Moreover, individuals with MetS, particularly when associated with impaired fasting glucose (IFG), have a high likelihood of progression to Type 2 diabetes [1]. Researchers think that the increasing prevalence of obesity worldwide is mainly responsible for the increasing occurrence of metabolic syndrome [10, 11]. Other factors that can exacerbate MetS are physical inactivity, advanced age, endocrine dysfunction, and genetic susceptibility [1].

NCEP, ATP III [12] has proposed simple clinical criteria for the diagnosis of metabolic syndrome. Diagnosis is based on identification of at least three of five risk factors mentioned above [1].

The prevalence of MetS is increasing globally [4]. Based on the Third National Health and Nutritional Survey (NHANES III), Ford et al. estimated that the syndrome affected 25% of US adults [13]. The prevalence rate of MetS is high in middle eastern countries [9]. Moreover, in Western countries, 23% of the population suffers from this syndrome [14]. According to the IDF criteria, one in four adults in the world has MetS. The risk of death, stroke and heart attacks is two to three times more in subjects with MetS compared to individuals without this syndrome [3]. More importantly, the prevalence of this disorder is increasing in children and young adults worldwide [15, 16].

As mentioned above, MetS enhances the risk for various diseases such as diabetes, cardiovascular disease, fatty liver, asthma, ovarian cysts [17] and some cancers [18]. Studying the prevalence of this syndrome in different populations is essential for planning preventive strategies. Accordingly, several studies have investigated the prevalence of MetS in various provinces of Iran. This prevalence was reported 23.7% in Zanjan [19], 22.8% in Ahvaz [20], and 35.9% in Kashan [21] provinces and 12.14% in the adult population of Shahroud [22]. Moreover, the prevalence of MetS among Type 2 diabetic patients’ in Kerman was about 70.4% [23].

Working in the bakery is one of the hard jobs. There are about 90,000 bakeries in Iran and about 450–600 thousand workers are working in these bakeries and face several risk factors [24]. Some researchers have suggested that the prevalence of MetS should be determined separately in specific job groups [25]. However, there are not enough studies about this hard job. Therefore, the aim of the present study was to determine the prevalence of MetS among bakery workers in Semnan, Iran.

2 Material and method

This was a cross-sectional study conducted in Semnan province, Northeast of Iran in 2017. The study population were bakery workers. Among the 95 bakeries and 270 workers, 74 bakeries and 163 workers were selected randomly. At least one year of work experience in a bakery was required for inclusion in this study. Institutional Review Board approval was obtained prior to the start of this study.

2.1 Data collection

A standard demographic questionnaire was used for data collection in the first phase of this study. The questionnaire included demographic variables, which were age, marital status, number of people in the household, education, tobacco/cigarette use, regular weekly sports activities (at least three times per week), daily working hours, work experience and regular weekly consumption of vegetables and fruits (at least three times a week).

After seeking permission from the Bakeries Association, the researcher explained the objectives of the study to the participants and completed the questionnaire for them.

Then the participants were referred to a specific laboratory to do blood tests without charge.

In this study, participants were weighted without shoes and heavy clothing using a digital scale with 100 grams accuracy. Height was measured using a one-meter strip in standing position without shoes, while the scapulas were in normal position. Waist circumference was measured in its narrowest region when the person was at the end of his/her normal exhalation. Waist circumference was measured using a non-elastic tape without any compression on the body and with a precision of onemillimeter.

Blood pressure was measured two times by a standardized method by trained people about 15 minutes after the subject was sitting on a hard base. The mean of the two measurements was considered as the actual blood pressure for each person.

Participants were advised that from about half an hour before measuring blood pressure, they should not drink tea, and coffee, and should not use decongestants, or cigarettes (for smokers).

Fasting blood glucose was measured by the Helena machine made by Germany’s Ependorf Company, using German Merchant kits.

Serum triglyceride, HDL and cholesterol tests were performed with an auto-analyzer lysis machine made in Switzerland and using Mann Kit and Enzymatic Endpoint.

The devices were controlled for quality before and after the experiments were done. Experiments were performed with a repeatability of r = 0.995 and accuracy (CV) of <4% [8].

All experiments (measuring blood glucose, blood pressure, weight, height, and waist circumference) were performed after 12-hours fasting.

2.2 Ethics statement

This study was approved by the Ethics Committee of Shahroud University of Medical Sciences (Ethics Code No: 9529). The aims and scope of this study were explained to all participants. All participants consented to participate. Privacy and confidential issues were considered throughout the study.

2.3 Statistical analyses

Analyses were performed with SPSS for Windows (SPSS Inc, Chicago, IL) version 11. The significance level was 0.05. T-test and one way ANOVA were used to determine the relation between demographic variables and job characteristic with MetS. In order to determine the degree of consistency between the IDF definition with three other definitions and the degree of coordination of individuals with each of the definitions, the kappa coefficient was used. A coefficient above >= 0.81 is categorized as very good and excellent, 0.6 to 0.8 is good, 0.4 to 0.6 is moderate and under 0.4 is weak [26, 27].

Subsequently, sensitivity and specificity (with 95% confidence intervals) of each test was calculated in comparison to each of the other tests as there is, currently, no diagnosis gold standard as a reference [28].

In order to calculate the sensitivity and specificity of each of these four methods, latent class analysis (LCA) in R software was used. LCA is one of the statistical methods that can be used to check the sensitivity and specificity of diagnostic tests in the absence of a golden standard method [29]. In this analysis, the existence of the MetS, as a latent variable, was evaluated via each of the four diagnostic methods, as manifest variables. This analysis provides an estimate of the sensitivity and specificity of the diagnostic methods, without using a golden standard test [30].

3 Results

Table 2 shows the demographic variables of the participants. The majority of study participants with MetS were in the 29 to 45 age range, were married and had elementary school education. Smoking was more prevalent among metabolic syndrome patients than others (P = 0.001). Most patients had no regular exercise and (P = 0.001) and worked more than eight hours a day (P = 0.001).

Table 2
The prevalence of metabolic syndrome in different demographic subgroups

Variables WHO ATP IDF AHA

w/o MS with MS w/o MS with MS w/o MS with MS w/o MS with MS

Characteristics 146(89.6) 17(10.4) 141(86.5) 22(13.5) 143(87.7) 20(12.3) 140 (85.9) 23 (14.1)

<29 54 (90) 6 (10) 52 (86.7) 8 (13.3) 57 (95) 3 (5) 52 (86.7) 8 (13.3)

29–45 75 (88.2) 10 (11.8) 72 (84.7) 13 (15.3) 71 (83.5) 14 (16.5) 72 (84.7) 13 (15.3)

>45 17 (94.4) 1 (5.6) 17 (94.4) 1 (5.6) 15 (83.3) 3 (16.7) 16 (88.9) 2 (11.1)

P-Value 0.729 0.546 0.097 0.877

Marital status

Single 15 (88.2) 2 (11.8) 14 (82.4) 3 (17.6) 15 (88.2) 2 (11.8) 14 (82.4) 3 (17.6)

Married 131 (89.7) 15 (10.3) 127 (87) 19 (13) 128 (87.7) 18 (12.3) 126 (86.3) 20 (13.7)

P-Value 0.554 0.410 0.653 0.442

Educational Level

Elementary 112 (91.8) 10 (8.2) 108 (88.5) 14 (11.5) 106 (86.9) 16 (13.1) 107 (87.7) 15 (12.3)

Diploma 27 (87.1) 4 (12.9) 26 (83.9) 5 (16.1) 29 (93.5) 2 (6.5) 26 (83.9) 5 (16.1)

University degree 7 (4.3) 3 (1.8) 7 (70) 3 (30) 8 (80) 2 (20) 7 (70) 3 (30)

P-Value 0.084 0.230 0.447 0.284

Household size

1 30 (85.7) 5 (14.3) 30 (85.7) 5 (14.3) 32 (91.4) 3 (8.6) 30 (85.7) 5 (14.3)

2–4 110 (90.2) 12 (9.8) 105 (86.1) 17 (13.9) 106 (86.9) 16 (13.1) 104 (85.2) 18 (14.8)

≥5 6 (100) 0 6 (100) 0 5 (83.3) 1 (16.7) 6 (100) 0

P-Value 0.522 0.614 0.729 0.598

Smoking

Yes 9 (40.9) 13 (59.1) 5 (22.7) 17 (77.3) 7 (31.8) 15 (68.2) 5 (22.7) 17 (77.3)

No 137 (97.2) 4 (2.8) 136 (96.5) 5 (3.5) 136 (96.5) 5 (3.5) 135 (95.7) 6 (4.3)

P-Value 0.000 0.000 0.000 0.000

Regular Exercise

Yes 99 (91.7) 9 (8.3) 101 (93.5) 7 (6.5) 106 (98.1) 2 (1.9) 101 (93.5) 7 (6.5)

No 47 (85.5) 8 (14.5) 40 (72.7) 15 (27.3) 37 (67.3) 18 (32.7) 39 (70.9) 16 (29.1)

P-Value 0.169 0.000 0.000 0.000

Job History (yr)

<5 37 (84.1) 7 (15.9) 33 (75) 11 (25) 37 (84.1) 7 (15.9) 33 (75) 11 (25)

5–15 46 (92) 4 (8) 46 (92) 4 (8) 46 (92) 4 (8) 46 (92) 4 (8)

>15 63 (91.3) 6 (8.7) 62 (89.9) 7 (10.1) 60 (87) 9 (13) 61 (88.4) 8 (11.6)

P-Value 0.377 0.459 0.490 0.451

Working hours (Daily

≤8 42 (95.5) 2 (4.5) 43 (97.7) 1 (2.3) 44 (100) 0 43 (97.7) 1 (2.3)

>8 104 (87.4) 15 (12.6) 98 (82.4) 21 (17.6) 99 (83.2) 20 (16.8) 97 (81.5) 22 (18.5)

P-Value 0.110 0.006 0.001 0.008

Regular use of fruit &vegetables

Yes 129 (99.2) 1 (0.8) 127 (97.7) 3 (2.3) 120 (92.3) 10 (7.7) 126 (96.9) 4 (3.1)

No 17 (51.5) 16 (48.5) 14 (42.4) 19 (57.6) 23 (69.7) 10 (30.3) 14 (42.4) 19 (57.6)

P-Value 0.000 0.000 0.001 0.000

Variables	WHO	ATP	IDF	AHA
Characteristics	146(89.6)	17(10.4)	141(86.5)	22(13.5)	143(87.7)	20(12.3)	140 (85.9)	23 (14.1)
<29	54 (90)	6 (10)	52 (86.7)	8 (13.3)	57 (95)	3 (5)	52 (86.7)	8 (13.3)
29–45	75 (88.2)	10 (11.8)	72 (84.7)	13 (15.3)	71 (83.5)	14 (16.5)	72 (84.7)	13 (15.3)
>45	17 (94.4)	1 (5.6)	17 (94.4)	1 (5.6)	15 (83.3)	3 (16.7)	16 (88.9)	2 (11.1)
P-Value	0.729	0.546	0.097	0.877
Marital status
Single	15 (88.2)	2 (11.8)	14 (82.4)	3 (17.6)	15 (88.2)	2 (11.8)	14 (82.4)	3 (17.6)
Married	131 (89.7)	15 (10.3)	127 (87)	19 (13)	128 (87.7)	18 (12.3)	126 (86.3)	20 (13.7)
P-Value	0.554	0.410	0.653	0.442
Educational Level
Elementary	112 (91.8)	10 (8.2)	108 (88.5)	14 (11.5)	106 (86.9)	16 (13.1)	107 (87.7)	15 (12.3)
Diploma	27 (87.1)	4 (12.9)	26 (83.9)	5 (16.1)	29 (93.5)	2 (6.5)	26 (83.9)	5 (16.1)
University degree	7 (4.3)	3 (1.8)	7 (70)	3 (30)	8 (80)	2 (20)	7 (70)	3 (30)
P-Value	0.084	0.230	0.447	0.284
Household size
1	30 (85.7)	5 (14.3)	30 (85.7)	5 (14.3)	32 (91.4)	3 (8.6)	30 (85.7)	5 (14.3)
2–4	110 (90.2)	12 (9.8)	105 (86.1)	17 (13.9)	106 (86.9)	16 (13.1)	104 (85.2)	18 (14.8)
≥5	6 (100)	0	6 (100)	0	5 (83.3)	1 (16.7)	6 (100)	0
P-Value	0.522	0.614	0.729	0.598
Smoking
Yes	9 (40.9)	13 (59.1)	5 (22.7)	17 (77.3)	7 (31.8)	15 (68.2)	5 (22.7)	17 (77.3)
No	137 (97.2)	4 (2.8)	136 (96.5)	5 (3.5)	136 (96.5)	5 (3.5)	135 (95.7)	6 (4.3)
P-Value	0.000	0.000	0.000	0.000
Regular Exercise
Yes	99 (91.7)	9 (8.3)	101 (93.5)	7 (6.5)	106 (98.1)	2 (1.9)	101 (93.5)	7 (6.5)
No	47 (85.5)	8 (14.5)	40 (72.7)	15 (27.3)	37 (67.3)	18 (32.7)	39 (70.9)	16 (29.1)
P-Value	0.169	0.000	0.000	0.000
Job History (yr)
<5	37 (84.1)	7 (15.9)	33 (75)	11 (25)	37 (84.1)	7 (15.9)	33 (75)	11 (25)
5–15	46 (92)	4 (8)	46 (92)	4 (8)	46 (92)	4 (8)	46 (92)	4 (8)
>15	63 (91.3)	6 (8.7)	62 (89.9)	7 (10.1)	60 (87)	9 (13)	61 (88.4)	8 (11.6)
P-Value	0.377	0.459	0.490	0.451
Working hours (Daily
≤8	42 (95.5)	2 (4.5)	43 (97.7)	1 (2.3)	44 (100)	0	43 (97.7)	1 (2.3)
>8	104 (87.4)	15 (12.6)	98 (82.4)	21 (17.6)	99 (83.2)	20 (16.8)	97 (81.5)	22 (18.5)
P-Value	0.110	0.006	0.001	0.008
Regular use of fruit &vegetables
Yes	129 (99.2)	1 (0.8)	127 (97.7)	3 (2.3)	120 (92.3)	10 (7.7)	126 (96.9)	4 (3.1)
No	17 (51.5)	16 (48.5)	14 (42.4)	19 (57.6)	23 (69.7)	10 (30.3)	14 (42.4)	19 (57.6)
P-Value	0.000	0.000	0.001	0.000

According to Table 3, IDF had more agreement with the AHA definition (49.1%) than other definitions.

Table 3

The agreement between the IDF definition and other metabolic syndrome definitions

		IDF definition
		Without MetS(%)	With MetS (%)	Kappa	P-value
ATP III	Without MetS (%)	132 (93.6)	9 (6.4)	45.4%	0.001
	With MetS (%)	11 (50)	11 (50)
WHO	Without MetS (%)	133 (91.1)	13 (8.9)	29.9%	0.001
	With MetS (%)	10 (58.8)	7 (41.2)
AHA	Without MetS (%)	132 (94.3)	8 (5.7)	49.1%	0.001
	With MetS (%)	11 (47.8)	12 (52.2)

Table 4

The prevalence of the metabolic syndrome criteria among the participants

	N of patients with criteria	% (CI% 95)	Mean±SD
WHO
Blood sugar (≥110 mg/dL)	28	17.2 (12.3–22.7)	128.9±37.5
BMI (>30 kg/m²)	22	13.5 (8–18.4)	33.5±2.2
Abdominal obesity (WHR > 0.90)	88	54(47.4–60.7)	0.9±0.03
Triglyceride (≥150 mg/dL)	69	42.3 (33.8–50.9)	218.1±107.5
HDL-C (<35 mg/dL)	21	12.9 (7.4–19)	32.8±2.1
Blood pressure (mm Hg)
SBP≥140	30	18.4 (13.2–23.9)	140.3±1.8
DBP≥90	17	10.4 (6.1–14.7)	90.5±0. 2
ATP III
Blood sugar (≥100)	28	17.2 (11.7–23.3)	128.9±37.5
Triglyceride (≥150 mg/dL)	69	42.3 (33.2–50.2)	218.1±107.5
HDL-C (<40 mg/dL)	43	26.4 (18.5–32.5)	35.1±2.8
Abdominal obesity (Waist circumference >102 cm)	28	17.2 (12.3–22.7)	110.7±4.0
Blood pressure (mm Hg)
SBP > 130	30	18.4 (13.5–24.5)	140.3±1.8
DBP > 85	17	10.4 (5.5–15.3)	90.5±0. 2
IDF
Blood sugar (≥100)	28	17.2 (10.4–23.2)	128.92±37.5
Triglyceride (≥150 mg/dL)	69	42.3 (34.4–50.9)	218.18±107.5
HDL-C (<40 mg/dL)	43	26.4 (19.1–33.7)	35.18±2.8
Abdominal obesity (Waist circumference ≥94 cm)	62	38 (30.7–44.8)	103.37±7.4
Blood pressure (mm Hg)
SBP > 130	30	18.4 (12.3–23.9)	140.33±1.8
DBP > 85	17	10.4 (6.1–15.3)	90.5±0. 2
AHA
Blood sugar (≥100)	28	17.2 (11.7–24.5)	128.9±37.5
Triglyceride (≥150 mg/dL)	69	42.3 (34.4–50.3)	218.1±107.5
HDL-C (<40 mg/dL)	43	26.4 (191.–33.6)	35.1±2.8
Abdominal obesity (Waist circumference ≥102 cm)	29	17.8 (11.1–23.3)	110.4±4.3
Blood pressure (mm Hg)
SBP > 130	30	18.4 (12.9–24.5)	140.3±1.8
DBP > 85	17	10.4 (6.1–15.9)	90.5±0. 2

LCA estimated that the prevalence of MetS was 19.9 (95% CI: 13.8% –25.6%) among the bakery workers. And therefore, the sensitivity of the four diagnostic methods, WHO, IDF, ATP and AHA, were 45.2%, 61.7%, 67.9% and 64.8% respectively. Also, the specificity of the four methods was 98.2%, 100%, 100% and 100%, respectively.

Based on Table 5, NCEP ATPIII had the highest sensitivity and after that AHA, IDF and WHO had the highest, respectively. Specificity in NCEP ATPIII, AHA and IDF were 100%.

Table 5

Sensitivity and specificity estimates based on Latent Class Analysis for NCEP ATPIII, IDF, AHA and WHO definitions

	% Sensitivity (SE)	95% CI	% Specificity (SE)	95% CI
IDF	61.7 (0.09)	61.5% –61.8%	100.0 (0.09)	99.8% –100%
NCEP ATPIII	67.9 (0.08)	67.7% –68.0%	100.0 (0.01)	99.9% –100%
WHO	45.2 (0.09)	45.0% –45.3%	98.2 (0.01)	98.1% –98.2%
AHA	64.8(0.09)	64.6% –64.9%	100.0 (0.0)	100 % –100%

In Table 6, the sensitivity and specificity of each diagnostic test has been estimated assuming another test as the gold standard.

Table 6

The sensitivity and specificity of each diagnostic test assuming another test as the gold standard

Reference/test	Sensitivity	Specificity	Area (CI)
IDF/NCEP-ATP	0.550	0.923	0.737 (0.598; 0.875)
NCEP-ATP/IDF	0.500	0.936	0.718 (0.583; 0.854)
IDF/AHA	0.600	0.923	0.762 (0.627; 0.896)
AHA/IDF	0.522	0.943	0.732 (0.601; 0.864)
IDF/WHO	0.350	0.930	0.640 (0.494; 0.786)
WHO/IDF	0.412	0.911	0.661 (0.505; 0.817)
NCEP-ATP/AHA	1.000	0.993	0.996 (0.988; 1.000)
AHA/NCEP-ATP	0.957	1.000	0.978 (0.930; 1.000)
NCEP-ATP/WHO	0.545	0.965	0.755 (0.622; 0.888)
WHO/NCEP-ATP	0.706	0.932	0.819 (0.687; 0.950)
AHA/WHO	0.522	0.964	0.743 (0.611; 0.875)
WHO/AHA	0.706	0.925	0.815 (0.684; 0.947)

4 Discussion

MetS or insulin resistance syndrome is a coincidence of risk factors that can cause heart diseases [16]. According to the results of this study, about 19.9% of the bakery workers had MetS. This disease is an important metabolic disorder, which imposes a noticeable burden on the health care system [9, 31]. As shown in previous studies, the prevalence rate of MetS is high in Middle Eastern countries, especially in Iran [9 , 32]. Mohammadbeigi et al. [33] found that the prevalence of MetS among men in Qom (central Iran) was higher than reported in other countries. In this regard, Mokhayeri et al. [34] conducted a meta-analysis and reported that the prevalence of MetS was 28%, 23%, and 33% in the total, male, and female population of Iran according to ATP III, respectively.

In a meta-analysis, the national prevalence of MetS was about 25%, [32] and in another meta-analysis was 31% based on ATP III [10]. This prevalence was 26% among faculty members ≥35 years in Tehran [35] and 22.0% in male police officers [36]. Noshad et al. [37], found that although one-third of the Iranian adult population was diagnosed with MetS; during 2007–11, the prevalence of MetS has recently decreased slightly in Iran. Other authors have mentioned that although the prevalence of MetS is high in Iran, the prevalence of MetS has been declining steady in the last 10 years [34].

Cardiovascular disease (CVD) is the primary clinical outcome of MetS. Also the risk of Type 2 diabetes is higher in MetS; and MetS with diabetes is a major risk factor for CVD [38]. Ischemic stroke is also more common among MetS patients [39]. Therefore, it is necessary to pay more attention to risk factors causing MetS.

Our results show that MetS is more common among married people. Hosseinpour-Niazi et al. [40] confirms this finding and has stated that the risk of MetS is higher in married than single women. They found that marital status may affect the risk of MetS differently in two genders. Another study also showed that single African Americans people were less likely to have MetS than married persons [41]. Troxel et al. [42] found that people in high-quality marriages are at lower risk of developing the MetS. Other study results, contrary to the findings of the present study, showed that divorced men were more likely to have MetS compared to married men and this finding may be explained by their unhealthy lifestyle [43]. More studies are needed to determine the relation between marriage and MetS.

Based on our results, working long hours a day is a risk factor for MetS in bakery workers. Excessive work may cause people to be more vulnerable. Similar to his, Mehrdad et al. [44] found that the prevalence of MetS in workers with physical exertion was higher than office workers. Therefore, it is suggested that people work according to standard working hours.

Our findings support other authors’ findings that smoking is a risk factor for MetS and this was confirmed by all four criteria. It also supports previous reports of an increased risk of MetS among current smokers [45]. Moreover, Balhara et al. [46] stated that tobacco use is associated with various core components of the MetS and it plays a causal role in various pathways leading to the development of MetS. Other authors [47] have found that active smoking is associated with the development of MetS and quitting smoking can reduce the risk of MetS [48]. Therefore, tobacco control strategies, such as preventing smoking are necessary to reduce the burden of MetS in Iran.

Based on our results, the three methods (IDF, ATP and AHA) confirmed that not having regular exercise is a main risk factor for MetS. Findings of another study showed that adequate physical activity was significantly associated with reduced MetS risk among adults in China [49].

According to our results, not consuming fruit and vegetables regularly is associated with MetS. Ali et al. found that encapsulated fruit and vegetable juice powder concentrates did not alter insulin or glucose measures of adults with MetS [50]. Also Shin et al. [51] indicated that fruit and vegetable consumption could only decrease diastolic blood pressure in MetS patients. But, the last meta-analysis study in this field, conducted by Tian et al. [52] in 2018 showed that fruit or/and vegetable consumption may be inversely associated with the risk of MetS. There are other studies that confirm these findings as well [49, 53]. Therefore, it is suggested that consuming fruits and vegetables daily can decrease the risk of MetS.

This study suggests that, the NCEP ATPIII had the highest sensitivity for diagnosing MetS in Iran. In this regards, a study suggested the ATP III criteria provide a practical tool to identify patients at increased risk for CVD; and the WHO and AACE criteria can do the same [38]. Other authors have reported that the AHA definition has a good sensitivity but low specificity to diagnose insulin resistance in hypertensive patients. They state that the IDF definition has less false positives, but it was still not specific enough to identify insulin resistance in hypertensive patients [54]. Liao et al. [55] and other investigators [56] reported that diagnosing MetS by the ATP III criteria had low sensitivity (around 50%) and high specificity [>90 %) for the diagnosis of insulin resistance.

A limitation of this cross-sectional study is that it cannot show a cause and effect relation [57]. Furthermore, as our sample consisted of only bakery workers, we only can generalize the results of the study to bakery workers.

5 Conclusion

The prevalence of MetS is high among bakery workers in Iran. More attention and screening are needed to decrease the health system and household cost for MetS and its complications in Iran. Increased physical activity and more fruit and vegetable consumption is recommended for the prevention of MetS among this occupational group.

Conflict of interest

None to report.

Ethics

All Ethical issues have been observed by all authors. This study was approved by the Ethics Committee of Shahroud University of Medical Sciences (Ethics Code No: IR.SHMU.REC.1395.152).

Footnotes

Acknowledgment

This study was supported by Grant No 9529 from Shahroud University of Medical Sciences.

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Variables	WHO		ATP		IDF		AHA
	w/o MS	with MS	w/o MS	with MS	w/o MS	with MS	w/o MS	with MS
Characteristics	146(89.6)	17(10.4)	141(86.5)	22(13.5)	143(87.7)	20(12.3)	140 (85.9)	23 (14.1)
<29	54 (90)	6 (10)	52 (86.7)	8 (13.3)	57 (95)	3 (5)	52 (86.7)	8 (13.3)
29–45	75 (88.2)	10 (11.8)	72 (84.7)	13 (15.3)	71 (83.5)	14 (16.5)	72 (84.7)	13 (15.3)
>45	17 (94.4)	1 (5.6)	17 (94.4)	1 (5.6)	15 (83.3)	3 (16.7)	16 (88.9)	2 (11.1)
P-Value	0.729		0.546		0.097		0.877
Marital status
Single	15 (88.2)	2 (11.8)	14 (82.4)	3 (17.6)	15 (88.2)	2 (11.8)	14 (82.4)	3 (17.6)
Married	131 (89.7)	15 (10.3)	127 (87)	19 (13)	128 (87.7)	18 (12.3)	126 (86.3)	20 (13.7)
P-Value	0.554		0.410		0.653		0.442
Educational Level
Elementary	112 (91.8)	10 (8.2)	108 (88.5)	14 (11.5)	106 (86.9)	16 (13.1)	107 (87.7)	15 (12.3)
Diploma	27 (87.1)	4 (12.9)	26 (83.9)	5 (16.1)	29 (93.5)	2 (6.5)	26 (83.9)	5 (16.1)
University degree	7 (4.3)	3 (1.8)	7 (70)	3 (30)	8 (80)	2 (20)	7 (70)	3 (30)
P-Value	0.084		0.230		0.447		0.284
Household size
1	30 (85.7)	5 (14.3)	30 (85.7)	5 (14.3)	32 (91.4)	3 (8.6)	30 (85.7)	5 (14.3)
2–4	110 (90.2)	12 (9.8)	105 (86.1)	17 (13.9)	106 (86.9)	16 (13.1)	104 (85.2)	18 (14.8)
≥5	6 (100)	0	6 (100)	0	5 (83.3)	1 (16.7)	6 (100)	0
P-Value	0.522		0.614		0.729		0.598
Smoking
Yes	9 (40.9)	13 (59.1)	5 (22.7)	17 (77.3)	7 (31.8)	15 (68.2)	5 (22.7)	17 (77.3)
No	137 (97.2)	4 (2.8)	136 (96.5)	5 (3.5)	136 (96.5)	5 (3.5)	135 (95.7)	6 (4.3)
P-Value	0.000		0.000		0.000		0.000
Regular Exercise
Yes	99 (91.7)	9 (8.3)	101 (93.5)	7 (6.5)	106 (98.1)	2 (1.9)	101 (93.5)	7 (6.5)
No	47 (85.5)	8 (14.5)	40 (72.7)	15 (27.3)	37 (67.3)	18 (32.7)	39 (70.9)	16 (29.1)
P-Value	0.169		0.000		0.000		0.000
Job History (yr)
<5	37 (84.1)	7 (15.9)	33 (75)	11 (25)	37 (84.1)	7 (15.9)	33 (75)	11 (25)
5–15	46 (92)	4 (8)	46 (92)	4 (8)	46 (92)	4 (8)	46 (92)	4 (8)
>15	63 (91.3)	6 (8.7)	62 (89.9)	7 (10.1)	60 (87)	9 (13)	61 (88.4)	8 (11.6)
P-Value	0.377		0.459		0.490		0.451
Working hours (Daily
≤8	42 (95.5)	2 (4.5)	43 (97.7)	1 (2.3)	44 (100)	0	43 (97.7)	1 (2.3)
>8	104 (87.4)	15 (12.6)	98 (82.4)	21 (17.6)	99 (83.2)	20 (16.8)	97 (81.5)	22 (18.5)
P-Value	0.110		0.006		0.001		0.008
Regular use of fruit &vegetables
Yes	129 (99.2)	1 (0.8)	127 (97.7)	3 (2.3)	120 (92.3)	10 (7.7)	126 (96.9)	4 (3.1)
No	17 (51.5)	16 (48.5)	14 (42.4)	19 (57.6)	23 (69.7)	10 (30.3)	14 (42.4)	19 (57.6)
P-Value	0.000		0.000		0.001		0.000