1. Background
The prevalence of noncommunicable diseases is increasing and it indicates the presence of epidemiological and nutritional transitions. Metabolic syndrome is an important noncommunicable disorder and its increasing rate has been shown in countries with low to middle social and economic strata (1). This syndrome is a cluster of conditions including excess body fat around the waist, a high blood glucose level, dyslipidemia, and high blood pressure. Metabolic syndrome increases the risk of diabetes type 2 and cardiovascular diseases (2, 3), two major noncommunicable diseases in the world.
There are some evidences that social and economic factors have influence on some chronic diseases like cardiovascular diseases and diabetes (4-6). Social factors are defined as social life and work conditions, these conditions could be affected by distribution of wealth and power, global and national income (7). A study in China showed the higher risk of metabolic syndrome in women with lower education and monthly income, but male subjects did not reveal this relationship (8). Moreover, some studies that were conducted about the relationship between cardiovascular diseases and socioeconomic status reported a higher prevalence of cardiovascular disease in developed countries (9-11). It seems that the study of the relationship between the socioeconomic factors and metabolic syndrome, cardiovascular disease or diabetes in developing countries has a great importance because these countries experience nutritional transition.
Studies on the relationship between ethnicity and metabolic syndrome reported that either all ethnic groups or individuals within the same ethnic group are not affected in the same way. One study in Malaysia showed a significant difference in the prevalence of the metabolic syndrome among different ethnics; ethnic Chinese had the lowest prevalence of the metabolic syndrome, while ethnic Indians had the highest (12). In Iran, researches indicated a high prevalence of the metabolic syndrome among women; this prevalence in both sexes was higher compared with some European countries such as France (13). The results of another study in Iran showed higher food insecurity among people with the metabolic syndrome and they reported some socioeconomic factors related to food insecurity, such as monthly income, family number and the economical level of family (14).
According to the increasing rate of the metabolic syndrome and lack of the studies about the relationship of socioeconomic factors and ethnicity with risk factors of the metabolic syndrome in Iran, this cross-sectional and field study was conducted on a sample of people residing in Ahvaz city, Iran, to investigate some risk factors of the metabolic syndrome (such as blood pressure, waist circumference (WC) and blood sugar) in different social and ethnic groups. The interesting point of this study was that healthy apparent people were studied and it was important because they did not refer to hospital or health services.
2. Objectives
Also, in this study, dietary intakes were studied as an important lifestyle factor related to the metabolic syndrome
3. Methods
This cross-sectional study was conducted on people residing in Ahvaz city, Iran, who participated in health exhibition and visited the nutrition station. This exhibition was held by corporation of cultural and social affairs of Ahvaz municipality and Red Crescent society for one week. The study protocol conforms to the ethical guidelines of the 2008 Declaration of Helsinki and an informed written consent was obtained from all the participants. The subjects were informed of the objectives of the study and were free to leave the study at any time. The inclusion criterion was age ranges between 20 and 70 years, and exclusion criterion was past medical history (including major illnesses, myocardial infraction, stroke, any previous surgery/operations, or other severe diseases). From people who visited the nutrition station, 211 subjects agreed to participate the study. All of them completed a consent form. The participants were asked to answer general questionnaire included information about age, gender, ethnicity, job, level of education, marital status, number of family members and spouse’s job and education. Also, familial history of chronic diseases and physical activity were asked. Anthropometric indices, including weight, height, body mass index (BMI), WC, hip circumference (HC) and WC to HC ratio (WHR) were also measured. All of measurements were done by one person to minimize the individual error. Height was measured when subjects’ shoes were off, facing directly ahead and arms by the sides. Also, subjects’ heels, buttocks and upper back were in contact with the wall when the height measurement was made. The weight was measured using Seca scale to the nearest 0.1 kg (it was calibrated before using). The body mass index was calculated using the following formula: weight in kilograms divided by height squared in meters. To measure the WC, a nonelastic tape (sensitivity 0.1 centimeter) was placed around the abdomen at the level of midway point between the uppermost border of the iliac crest and the lower border of the costal margin (rib cage) and a reading taken when the tape was snug but did not compress the skin. The hip circumference was measured when subjects had stood with feet fairly close together. Tape was placed in the position around the maximum circumference of the buttocks. To determine WHR, WC was divided by HC. Blood pressure was taken from the left arm, which was flexed at the elbow and supported at the heart level by an aneroid manometer in the sitting position on the chair.
To measure the randomized blood sugar, a blood glucometer (EasyGluco, South Korea) was used. Before using, it was calibrated by inserting a test strip. Following the manufacturer's instructions to prepare the lancing device, a drop of blood was gotten from the side of subjects’ fingertip. Then blood sugar was measured by touching and holding the test strip opening to the drop until it had absorbed enough to begin the test. The levels of randomized blood sugar for healthy, prediabetic and diabetic adults were considered 79 - 140 mg/dL, 140 - 200 mg/dL and more than 200 mg/dL, respectively (15).
A food frequency brief instrument was used to determine food intakes. This brief instrument included 25 kinds of foods that were classified in eight following groups according to food pyramid: bread and grain (bread, rice and pasta), meat and the alternatives (meat, chicken, fish, egg and legume), milk and dairy products (milk, yogurt, Iranian yogurt drink (doogh) and cheese), different kinds of vegetables, different kinds of fruits, cake and sweets, fast foods and drinks. The validity and reliability of this questionnaire were shown in some studies (16, 17). Subjects reported their serving intakes per day of each food during six months pass.
Data were analyzed using SPSS version 17.0. Chi-squared test was used to determine whether there was a significant difference between the expected frequencies and the observed frequencies in one or more categories. Quantitative variables were shown by mean ± standard deviation (SD). One-way AVOVA was used to compare the mean of each quantitative variable among different groups.
4. Results
This study was conducted on 211 subjects who was resident in Ahvaz city, 152 males (72%) and 52 females (28%). Males had more systolic blood pressure compared to females, (124.8 ± 11.1 and 116.9 ± 13, respectively; P < 0.001); also, this significant difference was seen for diastolic blood pressure (78.6 ± 6.1 and 74.5 ± 8.1, respectively; P < 0.001), but the means of blood pressure in both sexes were normal. There was no significant difference in blood sugar between males and females (117.1 ± 50.3 and 114.2 ± 58.8, respectively).
Anthropometric measurements were assayed in all participants. Table 1 shows the mean ± standard deviation (SD) of anthropometric measurements in different degrees of education, job and ethnicity. BMI had significant difference between different degrees of education. Weight, height, WC and WHR had significant differences in different jobs (Table 1). Self-employed subjects had more weight, and a Tukey post-hoc analysis showed this difference between the self-employed with jobless (P < 0.001), and employee (P = 0.026). The waist circumference was higher in self-employed compared to jobless (Tukey post-hoc analysis, P = 0.009). Higher WHR was seen in those who were worker and lower WHR in high-ranking employee, and the post- hoc analysis displayed the difference between jobless and worker (P = 0.009), jobless and employee (P = <0.001), and jobless and self-employed (P = 0.008).
Table 1. Mean (SD) Values of Anthropometric Measurements in Different Degrees of Education, Job and Ethnicity
| Weight, kg | Height, cm | BMI, kg/m2 | WC, cm | HC, cm | WHR, Ratio | |
|---|---|---|---|---|---|---|
| Degree of education | ||||||
| Illiterate | 77.5 ± 15.2 | 167.9 ± 12.6 | 27.5 ± 4.3 | 91.7 ± 15.6 | 104.9 ± 14.7 | 0.88 ± 0.15 |
| Under diploma | 78.6 ± 16 | 170.1 ± 9.7 | 27.2 ± 5.6 | 91.4 ± 13.1 | 102.6 ± 14.9 | 0.89 ± 0.11 |
| Diploma | 81.3 ± 13.4 | 170.1 ± 8.5 | 28.1 ± 4.2 | 93.8 ± 10.1 | 105.8 ± 8.2 | 0.88 ± 0.07 |
| Associate and Bachelor | 76.4 ± 17.3 | 173.2 ± 8.25 | 25.4 ± 5.1 | 88.6 ± 1.4 | 101.9 ± 10.3 | 0.87 ± 0.08 |
| Master and above | 72.4 ± 15.6 | 169.2 ± 10.7 | 25.1 ± 4 | 86.5 ± 9.9 | 100.9 ± 8.6 | 0.85 ± 0.07 |
| P valuea | 0.469 | 0.108 | 0.044# | 0.177 | 0.492 | 0.426 |
| Job | ||||||
| Jobless | 72.9 ± 13.9 | 165.8 ± 11.6 | 26.7 ± 5.4 | 86.9 ± 13 | 104.1 ± 13.5 | 0.84 ± 0.10 |
| Worker | 76.9 ± 13.5 | 174.3 ± 7.4 | 25.2 ± 3.8 | 90.4 ± 7.4 | 96.6 ± 9.8 | 0.94 ± 0.08 |
| Employee | 77.0 ± 16.5 | 172.1 ± 7.7 | 26.0 ± 5 | 90.8 ± 11.1 | 101.1 ± 11.3 | 0.90 ± 0.09 |
| High-ranking employee | 74.0 ± 18.5 | 169.4 ± 9.6 | 25.4 ± 3.4 | 89.2 ± 8.3 | 108.2 ± 2.2 | 0.82 ± 0.06 |
| Self-employed | 85.0 ± 15.8 | 175.2 ± 5.5 | 27.7 ± 5.2 | 94.2 ± 12.6 | 105.1 ± 11.2 | 0.89 ± 0.08 |
| P valuea | 0.001b | 0.001b | 0.296 | 0.027b | 0.083 | 0.001b |
| Ethnicity | ||||||
| Lor, Lak, Bakhtiyari | 81.1 ± 16.8 | 172.5 ± 8.5 | 27.2 ± 5.4 | 91.8 ± 12.8 | 103.6 ± 12.2 | 0.89 ± 0.09 |
| Turk | 78.40 ± 14.7 | 172.5 ± 10 | 26.3 ± 4.3 | 90.4 ± 10.5 | 104.2 ± 11.2 | 0.87 ± 0.06 |
| Kurd | 68.3 ± 4.0 | 168.6 ± 5.8 | 24.1 ± 2.2 | 80.7 ± 11.2 | 90.4 ± 17.1 | 0.9 ± 0.12 |
| Arab | 78.3 ± 17.3 | 173.0 ± 8.8 | 26.2 ± 5.6 | 91.1 ± 12.3 | 102.7 ± 12.8 | 0.89 ± 0.1 |
| Persian | 73.5 ± 12.5 | 165.8 ± 9.9 | 26.8 ± 4.1 | 89.0 ± 10.7 | 104.1 ± 8.1 | 0.85 ± 0.08 |
| P valuea | 0.066 | 0.001b | 0.509 | 0.176 | 0.073 | 0.259 |
aChi-square statistical analysis.
bSignificance level < 0.05.
Among different anthropometric measurements, height showed significant differences in different ethnicities (Table 1). Statistical analysis confirmed the difference between Persian and Arab (P < 0.001), Persian and Lor, Lak and Bakhtiyari (P < 0.001).
Blood sugar, systolic and diastolic blood pressure had no significant differences in various degrees of education and ethnicities (Table 2). Systolic blood pressure showed a significant difference in different jobs and the Tukey analysis confirmed the difference between employee and jobless (P = 0.025).
Table 2. Mean (SD) Values of Blood Sugar and Blood Pressure in Different Degrees of Education, Job and Ethnicity
| Blood Sugar, mg/dL | Systolic Blood Pressure | Diastolic Blood Pressure | |
|---|---|---|---|
| Degree of education | |||
| Illiterate | 150.9 ± 108.6 | 127.3 ± 13.7 | 80.7 ± 4.6 |
| Under diploma | 119.6 ± 65.9 | 121.6 ± 11.9 | 77.3 ± 6.6 |
| Diploma | 108.1 ± 36.75 | 123.6 ± 11.2 | 78.4 ± 5.9 |
| Associate and Bachelor | 112.6 ± 25.2 | 122.4 ± 11.7 | 76.7 ± 7.3 |
| Master and above | 101.3 ± 18.9 | 119.4 ± 19.1 | 75.7 ± 11.5 |
| P valuea | 0.066 | 0.481 | 0.214 |
| Job | |||
| Jobless | 115.9 ± 57.5 | 119.8 ± 11.8 | 76.5 ± 7.7 |
| Worker | 124.6 ± 79.7 | 120.4 ± 12.1 | 76.4 ± 8.1 |
| Employee | 120.5 ± 58.2 | 125.3 ± 13.2 | 77.3 ± 6.9 |
| High-ranking employee | 104.0 ± 18.0 | 120.0 ± 8.4 | 72.0 ± 8.4 |
| Self-employed | 107.5 ± 32.3 | 124.1 ± 10.2 | 79.3 ± 5.3 |
| P valuea | 0.802 | 0.008b | 0.073 |
| Ethnicity | |||
| Lor, Lak, Bakhtiyari | 109.1 ± 18.4 | 122.6 ± 12.8 | 76.6 ± 7.8 |
| Turk | 123.5 ± 61.5 | 126.8 ± 16.2 | 78.2 ± 6.0 |
| Kurd | 132.9 ± 49.2 | 120.0 ± 11.5 | 75.7 ± 4.5 |
| Arab | 119.8 ± 62.2 | 122.8 ± 11.2 | 78.6 ± 6.2 |
| Persian | 117.5 ± 68.4 | 121.7 ± 12.2 | 76.9 ± 7.2 |
| P valuea | 0.632 | 0.475 | 0.969 |
aOne-way ANOVA.
bSignificance level < 0.05.
Food intakes in different degrees of education have been shown in Table 3. Only vegetable intake had a significant difference. Interestingly, the post- hoc analysis declared this difference between illiterate with under diploma (P = 0.019), diploma (P < 0.001), associate and bachelor (P < 0.001), Master of sciences and above (P = 0.05).
Table 3. Mean (SD) Values of Food Intake (Serving/day) in Different Degrees of Education
| foods Erving/Day | Illiterate | Under Diploma | Diploma | Associate And Bachelor | Master and Above | P Valuea |
|---|---|---|---|---|---|---|
| Rice | 5.9 ± 4.3 | 6.2 ± 3.6 | 7.2 ± 4.3 | 6.6 ± 4.3 | 5.1 ± 2.5 | 0.592 |
| Pasta | 0.8 ± 1 | 1.0 ± 1.1 | 1.4 ± 2.1 | 1.3 ± 1.5 | 1.1 ± 1.6 | 0.572 |
| Bread | 6.6 ± 4.0 | 7.9 ± 7.4 | 9.1 ± 7.5 | 7.8 ± 6.5 | 4.3 ± 2.4 | 0.380 |
| Meat | 0.4 ± 0.4 | 1.0 ± 1 | 1.1 ± 1 | 1.2 ± 1.2 | 1.2 ± 0.5 | 0.067 |
| Chicken | 1.0 ± 1 | 1.4 ± 1.5 | 1.3 ± 1.2 | 1.6 ± 1.5 | 1.6 ± 1.2 | 0.495 |
| Fish | 0.6 ± 0.6 | 0.8 ± 0.9 | 0.4 ± 0.5 | 0.7 ± 0.9 | 0.7 ± 0.4 | 0.31 |
| Egg | 0.5 ± 0.5 | 0.4 ± 0.4 | 0.6 ± 0.7 | 0.5 ± 0.6 | 0.6 ± 0.6 | 0.594 |
| Legume | 0.5 ± 0.9 | 0.7 ± 0.6 | 0.7 ± 0.8 | 0.8 ± 1.1 | 0.5 ± 0.3 | 0.792 |
| Milk | 0.24 ± 0.5 | 0.47 ± 0.5 | 0.56 ± 0.7 | 0.46 ± 0.6 | 0.6 ± 0.5 | 0.488 |
| Yogurt | 0.36 ± 0.4 | 0.58 ± 0.7 | 0.4 ± 0.4 | 0.59 ± 0.5 | 0.68 ± 0.6 | 0.271 |
| Doogh | 0.43 ± 0.6 | 0.32 ± 0.4 | 0.46 ± 0.8 | 0.51 ± 0.7 | 0.28 ± 0.2 | 0.421 |
| Ice cream | 0.19 ± 0.4 | 0.16 ± 0.2 | 0.21 ± 0.3 | 0.29 ± 0.5 | 0.06 ± 0.1 | 0.142 |
| Cheese | 0.78 ± 0.7 | 0.76 ± 0.8 | 0.91 ± 1.1 | 1.0 ± 1.2 | 0.82 ± 0.7 | 0.701 |
| Fruits | 2.2 ± 3.1 | 0.8 ± 1.5 | 1.1 ± 1.2 | 0.8 ± 1.2 | 0.7 ± 1.1 | 0.22 |
| Vegetables | 1.5 ± 1.7 | 0.4 ± 0.8 | 0.3 ± 0.6 | 0.4 ± 0.6 | 0.6 ± 0.9 | 0.001b |
| Pizza | 0.1 ± 0.3 | 0.2 ± 0.3 | 0.3 ± 0.9 | 0.4 ± 1.1 | 0.2 ± 0.4 | 0.369 |
| Sausage | 0.1 ± 0.3 | 0.3 ± 0.5 | 0.2 ± 0.3 | 0.3 ± 0.6 | 0.1 ± 0.2 | 0.378 |
| Falafel | 0.3 ± 0.5 | 0.6 ± 0.9 | 0.5 ± 0.8 | 0.6 ± 1 | 0.2 ± 0.2 | 0.728 |
| Soda | 0.3 ± 0.4 | 0.4 ± 0.9 | 0.7 ± 1.8 | 0.9 ± 1.7 | 0.3 ± 0.4 | 0.237 |
| Cake and sweet | 0.6 ± 0.5 | 0.8 ± 1 | 0.7 ± 1.3 | 0.8 ± 1 | 0.5 ± 0.3 | 0.840 |
| Chocolate and candy | 0.3 ± 0.8 | 0.8 ± 1.4 | 0.8 ± 1.9 | 1.1 ± 1.9 | 0.4 ± 0.4 | 0.528 |
aOne- way ANOVA.
bSignificance level < 0.05.
Food intakes were studied in different jobs too (Table 4). Among different kinds of food groups, only intakes of cake and sweets were different; the Tukey analysis confirmed this difference between worker and jobless.
Table 4. Mean (SD) Values of Food Intake (Serving/day) in Different Jobs
| Foods Serving/Day | Jobless | Worker | Employee | High-Ranking Employee | Self-Employed | P Valuea |
|---|---|---|---|---|---|---|
| Rice | 6 ± 4.3 | 5.3 ± 2.8 | 6.7 ± 4 | 5.8 ± 2.8 | 7 ± 3.9 | 0.550 |
| Pasta | 1.2 ± 1.3 | 1.8 ± 1.3 | 1.0 ± 1.2 | 1.0 ± 1.4 | 1.2 ± 2.0 | 0.558 |
| Bread | 6.9 ± 5.0 | 6.4 ± 3.7 | 7.8 ± 7.2 | 5.6 ± 4.6 | 9.3 ± 8.1 | 0.314 |
| Meat | 1.0 ± 1.1 | 0.7 ± 0.5 | 1.0 ± 0.9 | 1.0 ± 0.8 | 1.2 ± 1.3 | 0.586 |
| Chicken | 1.5 ± 1.6 | 1.5 ± 1 | 1.5 ± 1.3 | 1.6 ± 1.9 | 1.3 ± 1.3 | 0.949 |
| Fish | 0.5 ± 0.5 | 0.6 ± 0.7 | 0.8 ± 0.9 | 0.4 ± 0.2 | 0.7 ± 0.9 | 0.245 |
| Egg | 0.5 ± 0.5 | 0.4 ± 0.3 | 0.5 ± 0.5 | 0.7 ± 0.8 | 0.5 ± 0.6 | 0.820 |
| Legume | 0.6 ± 0.8 | 0.8 ± 0.7 | 0.8 ± 0.1 | 0.5 ± 0.4 | 0.6 ± 0.7 | 0.705 |
| Milk | 0.48 ± 0.6 | 0.3 ± 0.3 | 0.43 ± 0.5 | 0.52 ± 0.3 | 0.53 ± 0.7 | 0.767 |
| Yogurt | 0.55 ± 0.7 | 0.4 ± 0.3 | 0.58 ± 0.5 | 0.67 ± 0.3 | 0.5 ± 0.3 | 0.795 |
| Doogh | 0.4 ± 0.5 | 0.44 ± 0.4 | 0.45 ± 0.8 | 0.35 ± 0.4 | 0.44 ± 0.5 | 0.984 |
| Ice cream | 0.29 ± 0.5 | 0.2 ± 0.2 | 0.2 ± 0.3 | 0.08 ± 0.1 | 0.2 ± 0.2 | 0.504 |
| Cheese | 0.9 ± 0.9 | 1.1 ± 1.2 | 0.9 ± 0.9 | 0.7 ± 0.3 | 0.8 ± 1.2 | 0.801 |
| Fruits | 1.1 ± 2.0 | 0.3 ± 0.5 | 0.7 ± 1.3 | 1.0 ± 1.2 | 1.1 ± 1.8 | 0.336 |
| Vegetables | 0.5 ± 0.9 | 0.2 ± 0.3 | 0.4 ± 0.8 | 0.8 ± 0.8 | 0.6 ± 0.8 | 0.423 |
| Pizza | 0.14 ± 0.2 | 0.1 ± 0.2 | 0.2 ± 0.7 | 0.4 ± 0.4 | 0.6 ± 1.3 | 0.056 |
| Sausage | 0.3 ± 0.7 | 0.3 ± 0.3 | 0.2 ± 0.4 | 0.1 ± 0.1 | 0.3 ± 0.4 | 0.816 |
| Falafel | 0.7 ± 0.9 | 0.7 ± 1.5 | 0.4 ± 0.8 | 0.2 ± 0.2 | 0.5 ± 0.8 | 0.363 |
| Soda | 0.7 ± 1.5 | 0.6 ± 1.8 | 0.5 ± 0.9 | 0.3 ± 0.4 | 0.8 ± 1.7 | 0.630 |
| Cake and sweet | 0.6 ± 0.7 | 1.4 ± 1.7 | 0.7 ± 0.9 | 0.3 ± 0.2 | 1.0 ± 1.2 | 0.021b |
| Chocolate and candy | 1.0 ± 1.8 | 1.0 ± 2.0 | 0.6 ± 1.2 | 0.6 ± 0.4 | 1.0 ± 2.0 | 0.673 |
aOne-way ANOVA.
bSignificance level < 0.05.
Different ethnicities had various intakes of some foods. Significant differences of food intakes were shown for bread, fish, vegetables, soda, chocolate and candy (Table 5). The Tukey analysis displayed different intakes of bread between Arab with Persian (P < 0.001), and Arab with Lor, Lak and Bakhtiyari (P < 0.001). The results showed different intakes of vegetables between Persian and Lor, Lak and Bakhtiyari (P = 0.024). Arab ethnicity had more intake of fish compared to Persian (P = 0.019) and Lor, Lak and Bakhtiyari (P = 0.016).
Table 5. Mean (SD) Values of Food Intake in Different Ethnicities
| Foods Serving/Day | Lor, Lak, Bakhtiyari | Turk | Kurd | Arab | Persian | P Valuea |
|---|---|---|---|---|---|---|
| Rice | 7.1 ± 4.3 | 5.7 ± 4.4 | 4.4 ± 1.6 | 6.4 ± 3.9 | 6.0 ± 3.5 | 0.318 |
| Pasta | 1.4 ± 1.7 | 1.2 ± 1.6 | 0.7 ± 0.6 | 1.1 ± 1.6 | 0.8 ± 0.9 | 0.295 |
| Bread | 6.1 ± 4.1 | 8.2 ± 5.6 | 10.1 ± 15.1 | 10.8 ± 8.1 | 5.0 ± 3.1 | 0.001b |
| Meat | 1.2 ± 1.2 | 1.4 ± 1.0 | 1.7 ± 1.5 | 0.9 ± 0.7 | 0.9 ± 1.2 | 0.118 |
| Chicken | 1.5 ± 1.2 | 1.9 ± 1.6 | 1.9 ± 2.3 | 1.5 ± 1.6 | 1.1 ± 1.0 | 0.235 |
| Fish | 0.5 ± 0.6 | 0.7 ± 1.1 | 0.3 ± 0.1 | 1.0 ± 1.0 | 0.5 ± 0.5 | 0.005b |
| Egg | 0.4 ± 0.4 | 0.7 ± 0.5 | 0.4 ± 0.3 | 0.6 ± 0.6 | 0.4 ± 0.5 | 0.296 |
| Legume | 0.8 ± 1.0 | 0.9 ± 0.9 | 0.3 ± 0.2 | 0.7 ± 0.9 | 0.5 ± 0.6 | 0.173 |
| Milk | 0.4 ± 0.4 | 0.5 ± 0.4 | 0.3 ± 0.4 | 0.5 ± 0.7 | 0.5 ± 0.6 | 0.677 |
| Yogurt | 0.5 ± 0.4 | 0.5 ± 0.6 | 0.4 ± 0.5 | 0.5 ± 0.4 | 0.7 ± 0.8 | 0.4 |
| Doogh | 0.46 ± 0.6 | 0.7 ± 0.7 | 0.2 ± 0.3 | 0.4 ± 0.6 | 0.4 ± 0.7 | 0.521 |
| Ice cream | 0.23 ± 0.4 | 0.4 ± 0.5 | 0.2 ± 0.3 | 0.2 ± 0.3 | 0.2 ± 0.3 | 0.648 |
| Cheese | 0.97 ± 1.2 | 1.2 ± 1.2 | 1.1 ± 0.7 | 0.8 ± 0.8 | 0.8 ± 1 | 0.488 |
| Fruits | 0.9 ± 1.4 | 1.8 ± 3.5 | 0.7 ± 1.0 | 0.8 ± 1.6 | 1.0 ± 1.5 | 0.463 |
| Vegetables | 0.3 ± 0.5 | 0.6 ± 0.7 | 0.3 ± 0.4 | 0.5 ± 0.8 | 0.8 ± 1.2 | 0.042 |
| Pizza | 0.2 ± 0.3 | 0.2 ± 0.4 | 0.1 ± 0.07 | 0.4 ± 1.2 | 0.3 ± 0.9 | 0.470 |
| Sausage | 0.2 ± 0.5 | 0.4 ± 0.4 | 0.2 ± 0.1 | 0.3 ± 0.5 | 0.2 ± 0.5 | 0.764 |
| Falafel | 0.4 ± 0.6 | 0.3 ± 0.3 | 0.6 ± 0.6 | 0.7 ± 1.0 | 0.6 ± 1.0 | 0.386 |
| Soda | 0.5 ± 0.8 | 0.4 ± 0.6 | 2.3 ± 4.4 | 0.5 ± 1.0 | 1.0 ± 1.8 | 0.006b |
| Cake and sweet | 0.6 ± 0.7 | 1.4 ± 1.9 | 0.3 ± 0.2 | 0.8 ± 1.0 | 0.8 ± 1.2 | 0.09 |
| Chocolate and candy | 0.6 ± 1.0 | 0.5 ± 0.6 | 2.3 ± 4.4 | 0.7 ± 1.4 | 1.4 ± 2.1 | 0.012b |
aOne-way ANOVA.
bSignificance level < 0.05.
5. Discussion
This study was conducted on subjects who residing in Ahvaz, with different ethnicities and socioeconomic status (SES). Weight, WC and systolic blood pressure showed significant differences in different jobs. Also, the results confirmed that workers had more consumption of cake and sweet. In different levels of education, blood sugar was higher in a trend of significance in subjects who were illiterate. Some studies showed that SES, (such as kind of job and level of education) was associated with a higher risk of the metabolic syndrome. In Portugal, Santos et al. (18) showed different frequencies of WC, blood pressure and high fasting blood glucose in different groups of job. Another study in China showed the effect of sex on the relationship between SES and the metabolic syndrome; the authors confirmed that the levels of household income per month were associated with a higher risk of the metabolic syndrome among women not in men (8). Also, a research that was conducted on Korean adults confirmed the relationship of a lower education level and personal income with a higher risk of the metabolic syndrome in women (19). Moreover, Mangat et al. (20) reported a significant correlation between higher SES and sedentary jobs and metabolic syndrome in North India. The results of other population-based cross-sectional study on males in British towns confirmed that adult and childhood social classes were both inversely related to the metabolic syndrome (21). In our study, females’ participation was less than males; so, SES was studied overall in both sexes. In the present study, the effect of job on the metabolic syndrome risk factors such as weight, WC and systolic blood pressure were more than the education level. Although education is a good indicator of social class, it seems that the effect of education is appeared in occupation and income levels. So possibly, the indirect effect of education was the reason for its less association with risk factors of the metabolic syndrome in the present study. In many developing countries, socioeconomic determinants of the metabolic syndrome are not considered as a preventative healthcare system. According to the importance of these parameters, it is suggested that the healthcare system should consider these factors in the preventive programs.
In the present study, there was no significant difference between the metabolic syndrome risk factors and ethnicity, but the interesting finding was difference in the intake of some foods among Iranian ethnicities. In Asia, a study that was conducted on Malaysians adults revealed that the prevalence of the metabolic syndrome was higher among Indians compared to Indigenous Sarawakians, Malays and Chinese (12). A study in the United States on adolescent students showed Hispanics had high abdominal adiposity and high triglycerides (22). Also, other study in this country confirmed that Hispanics and blacks had higher WC than whites (23). Different rates of the metabolic syndrome among various ethnicities maybe accounted as a result of different lifestyles and food intakes. Data from the national health and nutrition examination survey, 2009 to 2010, showed that non-Hispanic black adults consume significantly less dietary fiber compared with other ethnic groups (24). When the diets of African American and Hispanic families in the Special Supplemental Nutrition Program for women, infants, and children (WIC) were compared, diets of Hispanic mothers and children were lower in percentage of calories from fat, added sugars, sodium, and sweetened beverages, but higher in fruit, total dairy, and whole grains compared to African American (25). Sluyter et al. (26) compared food intakes of European, Asian, Pacific Island and Maori adolescents and resulted Europeans ate the fewest eggs, and Asians and Pacific Islanders ate more servings of chicken and fish, and fewer servings of cereal and milk than Europeans. Differences of dietary intakes among various ethnicities could be accounted as important indices that have a significant effect on some metabolic syndrome parameters, as a study confirmed that the intake of sweetened drinks was lower in subjects who had no risk factors than in subjects who had 1 - 2 risk factors among whites but not among African Americans (27). Although there was no significant relationship between ethnicity and the metabolic syndrome risk factors in this study, more intakes of some foods such as soda, cake, sweet and chocolate among some ethnicities showed the importance of cultural and lifestyle parameters. So, it is better these differences be considered when the metabolic syndrome is assessed among different ethnicities.
Lipid profile was not measured in this study because it was a field study; so, it can be considered as a limitation of the study. Although blood sugar was assessed in this study, fasting blood sugar is more valuable. The importance of this research was the study of metabolic syndrome risk factors and food intakes among different Iranian ethnicities.
5.1. Conclusion
Different ethnicities have various cultures. According to the results, it is better to mention socioeconomic status and cultural varieties in health programs to manage better lifestyle and reduce the risk factors of the metabolic syndrome.
