|Year : 2017 | Volume
| Issue : 4 | Page : 993-998
|Which factors predict metabolic syndrome? A cross sectional study in Kermanshah, Iran
Yahya Pasdar1, Lida Hagh Nazari2, Mansour Rezaei3, Akbar Barzegar4, Mitra Darbandi5, Parisa Niazi6
1 Research Centre for Environmental Determinacies of Health, School of Public Health, Kermanshah University of Medical Sciences; Nutritional Science Department, School of Nutritional Science and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
2 Department of Clinical Biochemistry, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
3 Department of Biostatistics, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
4 Department of Occupational Health Engineering, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
5 Research Centre for Environmental Determinacies of Health, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
6 Nutritional Science Department, School of Nutritional Science and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
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|Date of Web Publication||5-Oct-2017|
| Abstract|| |
Background: Lifestyle and food pattern play a key role in the creation and control of metabolic syndrome (MS). Objective: The present study was conducted to determine the role of nutritional and anthropometric factors as the predictors of MS in food production and distribution business owners. Materials and Methods: The present cross-sectional study was conducted on 112 food retailers selected in a random fashion. The study questionnaires included the demographic information questionnaire and food frequency questionnaire. The body composition was measured using the body analyzer. To examine lipid profiles and blood sugar, 5 ml fasting blood samples were taken from the participants. The data were analyzed using the logistic regression test. Results: The present study found high waist–hip ratio and body fat mass to be the strongest anthropometric predictors of MS. High lean body mass and total body water were negatively correlated with and played a protective role in MS. Consumption of dairy products as a food group was negatively correlated with MS, i.e., the higher the consumption, the lower the emergence rate of MS. The prevalence of MS was found to be 30.4% in the over 40 age group, 51.4% in 40–49 years old and 61.3% in those aged 50 and above, suggesting a positive correlation between the prevalence of MS and increasing age (P = 0.02). Conclusion: The present study found increased waist circumference and reduced dairy consumption to increase the risk of MS. Furthermore, weight reduction changes in lifestyle by increasing physical activities and observing proper diets were found to significantly decrease the risk of MS.
Keywords: Body composition, food suppliers, metabolic syndrome, nutrition
|How to cite this article:|
Pasdar Y, Nazari LH, Rezaei M, Barzegar A, Darbandi M, Niazi P. Which factors predict metabolic syndrome? A cross sectional study in Kermanshah, Iran. Ann Trop Med Public Health 2017;10:993-8
|How to cite this URL:|
Pasdar Y, Nazari LH, Rezaei M, Barzegar A, Darbandi M, Niazi P. Which factors predict metabolic syndrome? A cross sectional study in Kermanshah, Iran. Ann Trop Med Public Health [serial online] 2017 [cited 2021 Feb 28];10:993-8. Available from: https://www.atmph.org/text.asp?2017/10/4/993/215880
| Introduction|| |
Metabolic syndrome (MS) is a collection of metabolic disorders, risk factors for cardiovascular diseases (CVD), and type 2 diabetes, including hypertension, glucose and blood sugar metabolic disorders, abdominal obesity, and lipid disorders. Several risk factors including genetic and environmental factors such as lifestyle, regular exercise, diet, and smoking have a role in the incidence of MS. The precise etiology of MS is unknown, but it is thought to be caused by the interaction of genetic, metabolic, and environmental factors, among which diet and physical activity are more emphasized.
The prevalence of MS is increasing at an alarming rate. A number of recent studies have shown that the prevalence of MS is different in different occupational groups. The prevalence of MS is reported 15% in administrative employees of the oil industry, 17.5% in bank clerks, 24% in professional long-haul drivers, and 56.6% in firefighters.,,,
An interesting point raised in the recent studies conducted in western countries is easy access to prepared foods, which increases their consumption. Restaurant foods, including traditional and fast foods, often contain greater amounts of fat (for better appearance and taste). Thus, their consumption is associated with health risk factors, including increased risk of overweightness and obesity, diabetes, insulin resistance, poor nutritional quality, and MS. Consumption of foods containing high levels of trans fatty acid in the recent years has had detrimental health effects in people. The detrimental effects of this fat on plasma lipoproteins (LPs) lead to increased low-density LPs and reduced levels of LP and high-density LP (HDL). Due to easy and frequent access to high-calorie prepared foods, people working in food preparation and distribution are more prone to the above complications. Different studies have blamed many different factors for the incidence of MS. Thus, examining the role of factors predicting MS is particularly important. As stated earlier, due to their working conditions, food vendors are exposed to greater risks for certain diseases, especially for MS and its associated complications.
Given the risks that threaten these strata and lack of relevant information across the country, the present study was conducted to determine the role of nutritional and anthropometric factors predicting the incidence MS in food vendors in Kermanshah.
| Materials and Methods|| |
This cross-sectional study was conducted in 2015 on 112 all male workers in 30–65 years age range working in patisseries, sandwich shops, restaurants, pizza and doughnut outlets, lamb liver kebab shops, and lamb head and offal cookeries. Participants were randomly selected from among those with more than 3 years' experience in their current jobs.
Questionnaires used in the present study included a demographic questionnaire and food frequency questionnaire (FFQ). The demographic questionnaire contained questions on age, education, work history, and smoking. Assessment of people's usual food intake was carried out using FFQ, with confirmed validity and reliability in Iran.
Participants' normal food intake was assessed using FFQ, whose validity and reliability had been confirmed in some local studies. FFQ contains a list of 168 food items and standard portion size. Amounts of foods, as recommended portion size, were converted into daily units. According to food guide pyramid recommended by the Ministry of Health, recommended number of units per day for each food group was as follows: bread and cereals 6–11 units, fruits 2–4, vegetables 3 and 5, meats and pulses 2–3, milk and dairy products 2–3, and miscellaneous little.
Participants' body composition was measured using Avis 333 body analyzer system in terms of weight, height, mass of body fat (MBF), percentage body fat (PBF), soft lean mass, total body water (TBW), body mass index (BMI), body impedance, body protein, minerals, lean body mass (LBM), and waist–hip ratio (WHR). Height was measured using tape measure in standing position by the wall, without shoes and with shoulders heels and buttocks touching the wall at 1 cm precision. WHR for normal and obese upper body are defined by WHO (WHR >0.9 for men). According to WHO criteria, BMI ≥30 is considered obese and <25 BMI <29.9 overweight.
MS was defined according to the criteria set in the third report of the National Cholesterol Education Program/Adult Treatment Program 2005. Data were analyzed in SPSS-16 (SPSS, Inc, Chicago, Delaware) using logistic regression at significance level P < 0.05.
| Results|| |
The present study recruited 112 male food vendors, with mean age 43.4 ± 9.1 years, of whom, only 16.1% (18 people) had university education, and the rest had high school diploma or lower. Work history of more than 10 years in food vending was found in 69 people (61.6%).
Mean weight and BMI in participating food vendors was 80.8 ± 13.5 kg and 27.1 ± 3.9 kg/m 2, respectively. Mean protein and minerals were 11.9 ± 1.6 and 4.6 ± 0.7 kg, respectively. Mean systolic and diastolic blood pressure was 125.81 mmHg. Mean triglycerides and total cholesterol were 177 ± 6.2 and 196.1 ± 35.9 mg/dl, and mean fasting blood sugar was 81.2 ± 15.7 mg/dl [Table 1].
|Table 1: Mean anthropometric indices in food preparation and distribution workers|
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Mean intake of bread and cereals was 7.47 ± 3.61 units/day, meat and proteins 4.97 ± 1.4, dairy products 4.07 ± 2.93, fruits 2.91 ± 1.30, and vegetables 2.46 ± 1.15 units/day. Mean intake of meat and other proteins and dairy products was higher than the recommended amount, but mean consumption of vegetables was less than the recommended amount.
The prevalence of MS among participants was 45.5% (51 people). Among factors causing MS, the most common was waist–hip ratio (WHR) >0.9, which was observed in eighty participants (72.7%). High triglyceride (55.5%), low HDL (55.5%), hypertension (38.4%), and high blood sugar (7.3%) were also observed. The prevalence of MS was 30.4% in age group <40 years, 51.4% in 40–49 years age group, and 61.3% in age group ≥50 years, with a significant difference among these groups (P = 0.02).
Among anthropometric indices, high WHR and MBF were the strongest predictors of incidence of MS. The LBM and TBW, as protective factors, were inversely related to the incidence of MS. Among food groups, dairy products have an inverse relationship with the incidence of MS, and their high consumption has a protective effect against MS [Table 2] such that for every unit increase in consumption of dairy products, risk of MS is reduced by 0.82 times. The most important variables in the model were WHR followed by dairy products. [Figure 1] and [Figure 2] show an increase in a number of criteria for MS with increasing WHR and PBF. [Figure 3] shows the protective role of dairy products against MS and its components. Receiver operating characteristic curves in [Figure 4] and [Figure 5] show the effects of WHR and dairy products on the incidence of MS.
|Table 2: The role of anthropometric indices and food groups in the incidence of metabolic syndrome|
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|Figure 1: The relationship between percentage body fat and incidence metabolic syndrome|
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|Figure 2: The relationship between waist–hip ratio and incidence metabolic syndrome|
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|Figure 3: The relationship between consumption of dairy products and incidence metabolic syndrome|
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|Figure 4: Receiver operating characteristic curve showing the effect of consumption of dairy products on metabolic syndrome|
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|Figure 5: Receiver operating characteristic curve showing the effect of waist–hip ratio on metabolic syndrome|
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| Discussion|| |
The strongest predictors for the incidence of MS among food vendors were high WHR and MBF in the present study. LBM and TBW inversely affected MS and had a protective role. Among food groups, consumption of dairy products had an inverse effect on the incidence of MS such that its increased consumption was associated with reduced risk of MS.
Among the criteria for MS were WHR >0.9, triglyceride >150 mg/dl, HDL <40 mg/dl, blood pressure >130/85, and blood sugar >100 mg/dl, respectively. In their study conducted in Tehran, Mirmiran et al. reported hypertension, hypertriglyceridemia, low HDL, and high WHR as the strongest predictors for MS. In a study by Berzin et al., BMI and WHR were equally strong in predicting MS, and their increase was associated with higher prevalence of MS. Various studies have highlighted the relationship between diet and the incidence of MS. The inverse relationship between consumption of fruits and vegetables and risk of MS has also been previously reported., The population in the present study consisted of food vendors that were found obese and overweight due to their easy and frequent access to high-calorie foods and also low physical activity, and as a result, their high WHR and MBF were reported as the main predictors for MS. TBW was also found a protective factor against MS. It is therefore recommended that all people take sufficient amounts of water during the day. High water intake and thus high TBW will lead to higher and faster excretion of toxins from the body and hence protection against many diseases.
It has been shown in more extensive studies on the relationship between dietary pattern and MS, including a study on Korean adults that consumption of dairy products and fruits leads to reduced risk of MS. A study by NJ Bodor conducted in New Orleans showed greater risk of obesity in people with easy access to restaurant and fast foods, especially among food vendors. In studies conducted by Fraser et al. in England and Hanibuchi in Japan, high BMI and dietary pattern relationships with neighborhood restaurant and fast foods and easy access to ready-made foods., A longitudinal study reported that consumption of restaurant foods at least once a week significantly increases the likelihood of overweightness compared to people that do not use these foods, and also weekly purchase of restaurant foods for family dinner led to increased mean percentage of body fat and incidence of CVD. A study conducted by Duffey et al. showed that consumption of home delivery foods for a week was associated with anthropometric changes and had a significant relationship with weight and waistline and could result in the incidence of MS. The agreement of the results of the present and other studies indicate the undeniable effects of frequent restaurant and fast food consumption in increased BMI, body fat, and incidence of chronic diseases.
The odds of the incidence of MS increased with aging and peaked at above 50 years of age. The significant increase in the incidence of MS in the fourth and fifth decades of life is associated with obesity and overweightness, caused by key factors of increased visceral fat, insulin resistance, dyslipidemia, hypertension, and impaired glucose metabolism. Furthermore, aging per se is associated with increased insulin resistance and visceral fat, which are both important factors in the incidence of MS.
The strong relationship between WHR (or obesity in general) and incidence of MS in food vendors can be due to their forced inactivity, high-calorie intake, lack of knowledge, and lack of attention to health checks. A healthy life plus a balanced diet, greater use of fruits and vegetables, sufficient physical activity, regular aerobic exercises, maintaining the right weight, and weight loss are the best strategies for preventing obesity and MS, which should be observed by everybody.
| Conclusion|| |
In the present study, obesity, especially high WHR, and absence of a balanced diet were proposed as the strong predictors for MS such that weight gain and low consumption of dairy products were associated with increased risk of MS. Given the high prevalence of overweightness and obesity and hyperlipidemia and also the high prevalence of MS among this occupational group, interventional strategies for changing nutritional and behavioral habits appear essential in this group. Physical activity for maintaining a balanced weight is another preventive strategy for reducing risk of MS.
We thank colleagues at the Center for Research into Environmental Factors Affecting Health of the School of Health for review and judgment of the project, and the Research Deputy of Kermanshah University of Medical Sciences for approval (code: 92097) and funding the project.
Financial support and sponsorship
This study was financially supported by Research Deputy of University of Medical Sciences, Kermanshah, Iran.
Conflicts of interest
There are no conflicts of interest.
| References|| |
International Diabetes Federation. Worldwide definition for use in clinical practice. In: Alberti G, Zimmet P, editors. The IDF Consensus Worldwide Definition of the Metabolic Syndrome. Brussels: IDF Communications; 2006. http://www.idf.org/webdata/docs/IDF_Meta_def_final.pdf
. [Last accessed on 2016 Jul 25].
Smith CJ, Ryckman KK. Epigenetic and developmental influences on the risk of obesity, diabetes, and metabolic syndrome. Diabetes Metab Syndr Obes 2015;8:295-302.
Ebarahimi H, Esmaeilzadeh A, Rajaei F, Hasanzadeh A, Kafeshani O. Association of dietary pattern and metabolic syndrome in 15- to 49-Years-old women. J Isfahan Med Sch 2015;33:70-81.
Cavagioni LC, Bensenr IM, Halpern A, Pierin AMG. Metabolic syndrome in professional truck drivers who work on Highways BR-116 within the area of Sao Paulo City - Regis Bittencourt. Arq Bras Endocrinol Metabol 2008;52:15-23.
Ebrahimi-Mameghani M, Farsad Naimi AR, Nourmohammadi M, Azabdaftari N. Prevalence of metabolic syndrome in Tabriz city firefighters. J Kermanshah Med Sci 2011;15:296-302.
Felipe-de-Melo ER, da Silva Rde C, Assis AM, Pinto Ede J. Factors associated with metabolic syndrome in administrative workers in the oil industry. Cien Saude Colet 2011;16:3443-52.
Salaroli LB, Saliba RA, Zandonade E, Molina Mdel C, Bissoli NS. Prevalence of metabolic syndrome and related factors in bank employees according to different defining criteria, Vitória/ES, Brazil. Clinics (Sao Paulo) 2013;68:69-74.
Chiang PH, Wahlqvist ML, Lee MS, Huang LY, Chen HH, Huang ST. Fast-food outlets and walkability in school neighbourhoods predict fatness in boys and height in girls: A Taiwanese population study. Public Health Nutr 2011;14:1601-9.
Bahadoran Z, Mirmiran P, Azizi F. Fast food pattern and cardiometabolic disorders: A review of current studies. Health Promot Perspect 2015;5:231-40.
Chiang PH, Wahlqvist ML, Lee MS, Huang LY, Chen HH, Huang ST, et al.
Fast-food outlets and walkability in school neighbourhoods predict fatness in boys and height in girls: A Taiwanese population study. Public Health Nutr 2011;14:1601-9.
Mirmiran P, Hosseini Esfahani F, Azizi F. Relative validity and reliability of the food frequency questionnaire used to assess nutrient intake: Tehran Lipid and Glucose Study. Iran J Diabetes Lipid Disord 2009;9:185-97.
Kahn R, Buse J, Ferrannini E, Stern M; American Diabetes Association; European Association for the Study of Diabetes. The metabolic syndrome: Time for a critical appraisal: Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 2005;28:2289-304.
Mirmiran P, Noori N, Amirshekari G, Azizi F. Nutritional and anthropometrical predictors of the incidence of metabolic syndrome in adults. Iran Endocrinol Metab 2007;9:19-28.
Berzin M, Hossein Panahi F, Fekri S, Arjan S, Azizi F. Predictive value of BMI and waist circumference and metabolic syndrome in children 6 to 12 years in Tehran. J Diabetes Metab 2012;10:670-77.
Esmaillzadeh A, Azadbakht L. The effect of fruit andvegetable intakes on C-reactive protein and the metabolic syndrome among women. IJDLD 2007;6:271-84.
Wannamethee SG, Lowe GD, Rumley A, Bruckdorfer KR, Whincup PH. Associations of vitamin C status, fruit and vegetable intakes, and markers of inflammation and hemostasis. Am J Clin Nutr 2006;83:567-74.
Hong S, Song Y, Lee KH, Lee HS, Lee M, Jee SH, et al.
A fruit and dairy dietary pattern is associated with a reduced risk of metabolic syndrome. Metabolism 2012;61:883-90.
Bodor JN, Rice JC, Farley TA, Swalm CM, Rose D. The association between obesity and urban food environments. J Urban Health 2010;87:771-81.
Fraser LK, Clarke GP, Cade JE, Edwards KL. Fast food and obesity: A spatial analysis in a large United Kingdom population of children aged 13-15. Am J Prev Med 2012;42:e77-85.
Hanibuchi T, Kondo K, Nakaya T, Nakade M, Ojima T, Hirai H, et al.
Neighborhood food environment and body mass index among Japanese older adults: Results from the Aichi Gerontological Evaluation Study (AGES). Int J Health Geogr 2011;10:43.
Fulkerson JA, Farbakhsh K, Lytle L, Hearst MO, Dengel DR, Pasch KE, et al.
Away-from-home family dinner sources and associations with weight status, body composition, and related biomarkers of chronic disease among adolescents and their parents. J Am Diet Assoc 2011;111:1892-7.
Duffey KJ, Gordon-Larsen P, Steffen LM, Jacobs DR Jr., Popkin BM. Regular consumption from fast food establishments relative to other restaurants is differentially associated with metabolic outcomes in young adults. J Nutr 2009;139:2113-8.
Lida Hagh Nazari
Department of Biochemistry, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]
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