Prevalence of Metabolic Syndrome and Population Attributable Risk For

Obesity Medicine 14 (2019) 100086

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Obesity Medicine

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Review Prevalence of metabolic syndrome and population attributable risk for cardiovascular, stroke, and coronary heart diseases as well as myocardial T infarction and all-cause mortality in middle-east: Systematic review & meta- analysis

∗ A. Ansari-Moghaddama, H.A. Adinehb, , I. Zarebanc, K.H. Kalan Farmanfarmaa a Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran b Department of Epidemiology and Biostatistics, Iranshahr University of Medical Sciences, Iranshahr, Iran c Health Education Department, Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

ARTICLE INFO ABSTRACT

Keywords: Metabolic syndrome (MS) is a cluster of risk factors which associated with cardiovascular disease, myocardial Metabolic syndrome infraction, stroke, and mortality due to these diseases. There is not a clear and comprehensive dataset of the MS Population attributable risk prevalence in middle-east countries whilst the frequency of disease is increasing in both developed and devel- Systematic review & meta-analysis oping countries. Then, the present study aimed to estimate the prevalence of the metabolic syndrome among all 15 countries located in middle-east region. A meta-analysis was conducted on a total of 61 eligible studies with a total of 120071 individuals which were selected and critically appraised through a systematic approach within each country. The pooled estimates of MS varied in different areas with a range of 23.6% in Kuwait to 40.1% in U.A Emirates. Overall, nearly one-third of population (around 80,000,000) suffering from MS in the Middle-East. Moreover, the west countries in this area have relatively higher prevalence than those located in the east. The highest Population Attributable Risk (PAR) of the diseases due to MS was related to the mortality because of cardiovascular disease (29.6 per cent) compared to the lowest one was obtained for coronary heart disease (17.2 per cent). The study also verified PAR for stroke, myocardial infraction, and all-cause mortality overall and by country. Present findings strongly support the fact that high prevalence of MS have notifiable portion in developing cardiovascular disease, stroke, myocardial infarction and mortality. Therefore, it should be regarded as a major health concern in the region.

1. Introduction world (Alberti and Zimmet, 2005; Sliem et al., 2012; Hu et al., 2004). The available evidence indicates that in most countries about quarter of The Metabolic Syndrome (MS) is a complex of metabolic derange- the general population can be identified as having the metabolic syn- ments defined by cluster of components, increased waist circumference, drome (Scuteri et al., 2015). In middle-east populations, the prevalence decreased serum high‐density lipoprotein, and increased serum trigly- is estimated to be about twenty five per cent and higher ceride levels, hypertension, and insulin resistance, that effect on en- (Ansarimoghaddam et al., 2018), it seemed to be differed by the age of docrine systems and other organs (Potenza and Mechanick, 2009). population, identification criteria, sex, and some other variables Currently, different criteria like the World Health Organization (WHO), (Scuteri et al., 2015). Moreover, lifestyle, socio-economic status, un- the National Cholesterol Education Program (NCEP), the Adult Treat- healthy diets and physical inactivity have been considered as effective ment Panel III (ATP III), and the International Diabetes Foundation factors on the prevalence of MS (Mehio Sibai et al., 2010). (IDF) are used for definition and diagnosis of MS (Huang, 2009; Mente On the other hand, the metabolic syndrome is related to cardio- et al., 2010). vascular disease and it increases the risk of cardiovascular disease and MS is regarded as one of the major public health challenge in the all-cause mortality 2-fold and 1.5-fold, respectively. Furthermore,

∗ Corresponding author. E-mail address: [email protected] (H.A. Adineh). https://doi.org/10.1016/j.obmed.2019.100086 Received 31 December 2018; Received in revised form 26 March 2019; Accepted 29 March 2019 2451-8476/ © 2019 Elsevier Ltd. All rights reserved. A. Ansari-Moghaddam, et al. Obesity Medicine 14 (2019) 100086 reviewed studies suggest that metabolic syndrome might be an im- effect size. Additionally, Non-parametric tests like Kruskal Wallis used portant risk factor of stroke and Myocardial Infraction (MI) (Yki- to compare the prevalence between countries. Järvinen, 2014; Mottillo et al., 2010; Li et al., 2017; Mente et al., 2010). An index which shows the attributed fraction of mentioned diseases due 4. Geographic Information System (GIS) to MS in inhabitants is population attributable risk. Population Attri- butable Risk (PAR) is defined as fraction of all cases of a disease in an To map the distribution of metabolic syndrome in Middle - East exposed population to a risk factor which can be prevented by elim- countries, the shape file format of the countries in the region was inating that risk factor (Leviton, 1973). prepared. Then GIS software (10.2.2) used to display geospatial of Nevertheless, there is lack of comprehensive information on the MS prevalence data in the region. Then, image was processed and produced prevalence in countries located in middle-east region. Additionally, no map according to the frequency of metabolic syndrome in defined re- data are available to show the portion of metabolic syndrome in de- gion. veloping cardiovascular stroke and myocardial infarction. Therefore, the study aimed to estimate combined prevalence of MS for each 5. population attributable risk country and overall in the region. Moreover, it identified the burden of mentioned chronic disease attributed to the MS. The Population Attributable Risk indicates that if a related risk factor with a disease would be eliminated (e.g. syndrome metabolic risk 2. Method and material factor for stroke), how many number or proportion of disease (e.g. stroke) can be prevented in that specified population. Population at- 2.1. Literature review tributable risk was estimated for cardiovascular disease (CVD), Cardiovascular mortality, stroke, myocardial infarction, and for all-

This study systematically reviewed prevalence of metabolic syn- cause mortality using fallowing formula; PAR = Pe (RRe-1)/[1 + Pe drome in middle-east countries. The English-language articles were (RRe-1)]. Pe was considered the pooled prevalence of metabolic syn- searched systematically from 2001 to 2018 in Google Scholar, PubMed, drome which was estimated for each country. Relative Risk (RR) for Web of science, ISI and Scopus. Search strategies included combined regarding exposure (metabolic syndrome) was extracted from the sys- terms of Prevalence, metabolic syndrome, name of country, population tematically reviewed published studies (Rockhill et al., 1998). The re- based study, and National study. Inclusion criteria were national or lative risk (RR) for coronary heart disease, myocardial infraction, and population based study which had done by cross-sectional design or mortality due to all-causes were 1.52, 1.99, and 1.58, respectively. baseline data of cohort study, English language study, studies measured There were two studies reported relative risk of metabolic syndrome prevalence of metabolic syndrome in a defined geographical place with CVD and CVD mortality as well as Stroke. Therefore, these com- (middle-east countries). There were no any limitation on diagnostic bined relative risks (RR) were used to estimate PAR (1.93, 2.05, and criteria of metabolic syndrome and it included studies measured fre- 1.99, respectively) (Mottillo et al., 2010; Galassi et al., 2006). quency of metabolic syndrome according to IDF, ATP III, WHO, and other local criteria. Articles were excluded if they measured prevalence 6. Results in specific age groups (adolescents), in men or women only, and/or in patients of a hospital or population. Studies were also excluded if they 6.1. Descriptive information of studies used a selective sample and studies that carried out on medical records (e.g. case-control studies). Searching and reviewing of articles were From the total of 521 originally - research articles that seems to be done independently by two authors (A.H.A & A.M.A). In case of dis- eligible for the objectives of this study, according to the appraisal of agreement, the third researcher (Z.I) made final decision. title and abstract; 129 population-based articles were reported prevalence of metabolic syndrome. Of these, 68 articles did not meet in- 2.2. Study selection and data extraction clusion criteria and were excluded because of being carried out on selective groups (women or men), adolescents, and using interventional At first, all searched studies were checked for duplicate records or case-control study design. The 61 remaining studies were retained between different bibliographic databases and deleted duplicate articles for meta-analysis and estimation of pooled prevalence. and then the remaining abstracts and full text were independently re- Generally, 61 studies included with a total of 120071 individuals viewed and appraised by two authors. In cases of studies that had been older than 18 years from 2001 to 2018 (thirty studies in 2001–2010 and measured frequency of metabolic syndrome by different diagnostic thirty one studies in 2011–2018) within 15 middle-east countries. criteria, the prevalence resulted from each definition were included as Remained studies were cross-sectional study or cross-sectional based independent study. Multiple publications from the same authors in data from cohort studies. According to the diagnostic criteria for different time were seemed as a different study. Authors used pre- Metabolic Syndrome (MS), 29 of 63 articles were used ATP III definition defined data-collection form to extract and record study's information. and 22 articles used IDF. In other studies, different criteria have been applied (i.e. WHO, OF5, National criteria, NCPE/ATP III, JIS and ADA) 3. Statistical analysis for diagnosis of MS (Table 1).

Extracted data from individual studies were entered in Microsoft 7. Pooled estimate of prevalence Excel 2010 and transferred to STATA version 11.0. Confidence interval was estimated using Statistics > Binomial CI calculator menu for stu- The pooled estimate of metabolic syndrome varied between coun- dies that not reported confidence interval. Data were pooled for esti- tries ranging from a minimum of 23.55 percent (95% CI 14.49–32.62) mation of prevalence and 95% confidence interval according to the in Kuwait to a maximum of 40.1 percent (95% CI 33.6–46.7) in U.A.E. geographical area using Random and fixed effect method. As shown in Table 2, the prevalence of metabolic syndrome in three Heterogeneity between studies was assessed by I-Square and Tau- countries included Kuwait, Oman, and turkey was found to be at first Square. The variation more than 75% was regarded as high hetero- quartile of prevalence (< 28.19 per cent) compared to the Egypt, Iraq, geneity (Higgins et al., 2003). As significant heterogeneity (P.value less Palestine, U.A.E, and Cyprus where it was at fourth quartile (pre- than 0.05) was found between studies, so random effect model was used valence > 36.09 per cent). In other words, about 75 per cent of studied for designing forest plot. Meta-regression was used for assessing effect countries had a prevalence of more than 28.19 per cent. The overall of sample size, time of study, and diagnostic criteria on considered pooled estimate of metabolic syndrome was 31.2 (95% CI; 28.4–33.9,

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Table 1 Baseline characteristics of studies included in meta-analysis.

Country First author Year of study Sample size Prevalence (%) Lower limit Upper limit Criteria

Egypt (Thanopoulou et al., 2006) A Thanopoulou 2006 327 20.6 16.2 25.2 ATP III Egypt (Abd Elaziz et al., 2015) Khaled Mahmoud AbdElaziz 2014 220 55 48.1 61.6 ATPIII Emirate (Hajat and Shather, 2012) CotherHajat 2012 760 50.3 46.7452 53.8548 ATP III Emirate (Khthir and Espina, 2014) RodhanKhathir 2014 575 22 18.614 25.386 ATP III Emirate (Malik and Razig, 2008) Malik M 2008 4097 39.6 38.1024 41.0976 ATPIII Emirate (Malik and Razig, 2008) Malik M 2008 4097 40.5 38.9968 42.0032 IDF Emirate (Hajat and Shather, 2012) CotherHajat 2012 760 48.7 45.1464 52.2536 IDF Iran (Azizi et al., 2003) FeeidonAzizi 2003 9846 30.1 29.194 31.006 ATPIII Iran (Sadrbafoghi et al., 2006) Sadrbafoghi SM 2008 1107 32.1 29.3498 34.8502 ATPIII Iran (Marjani et al., 2012) AbdoljalalMarjuni 2012 160 20.62 14.351 26.889 ATPIII Iran (Marjani et al., 2012) AbdoljalalMarjuni 2012 160 23.75 17.156 30.344 ATPIII Iran (Shahini et al., 2013) NajmehShahini 2013 160 35 27.6093 42.3907 ATPIII Iran (Hajian-Tilaki et al., 2014) k Hajian 2014 1000 42.3 39.2379 45.3621 ATPIII Iraq (Al-Azzawi, 2015) Omar farooq AL-Azawti 2015 300 42 36.3 47.8 ADA Iraq (Ismael et al., 2016) Sherzad A. Ismae 2016 566 30.6 26.7 34.5 IDF Jordan (Khader et al., 2007) YousefKhader 2007 1121 36.3 33.6 39 IDF Jordan (Dajani et al., 2013) Rana Dajani 2013 4025 36.9 35.4 38.4 IDF Jordan (Loizou et al., 2006) TheodorosLoizou, 2005 1200 22.2 19.8 24.6 National Jordan (Tınazlı et al., 2018) MehtapTinazil 2015 324 37.04 31.7 42.5 National Kuwaiti (Badr et al., 2007) Hanan E 2007 434 18 14.3854 21.6146 ATPIII Kuwaiti (Roshdy, 2011) Reda Roshdy 2011 153 18.3 12.173 24.427 ATPIII Kuwaiti (Al-Isa, 2013) AL-Isa 2010 431 14.8 11.4475 18.1525 IDF Kuwaiti (Roshdy, 2011) Reda Roshdy 2011 153 26.1 19.1409 33.0591 IDF Kuwaiti (Al Zenki et al., 2012) Sameer AL Zenki 2012 1830 36.1 33.8994 38.3006 IDF Kuwaiti (Roshdy, 2011) Reda Roshdy 2011 153 28.1 20.9776 35.2224 JIS kyrgyzstan (Polupanov et al., 2016) Polupanov A.G 2016 1672 30.9 28.7 33.1 Not reported Lebnan (Nasreddine et al., 2010) Nasreddine L 2010 87 26.4 17.1373 35.6627 ATPIII Lebnan (Potenza and Mechanick, 2009) Abla- Mehio 2007 499 31.2 27.1348 35.2652 IDF Omani (Al-Lawati et al., 2003) Jawada AL-Lawati 2003 1419 17 15.0455 18.9545 ATPIII Omani (El-Aty et al., 2014) El-Aty MA 2014 3137 23.6 22.1 25.1 ATPIII Omani (Al-Shafaee et al., 2008) AL-Shafaee 2008 281 45.9 40.0735 51.7265 Not reported Pakistan (Jahan et al., 2007) Fridous Jahan 2006 250 35 29.0874 40.9126 ATPIII Pakistan (Faiz Alam et al., 2011) MazharFaizAlam 2010 197 14.95 9.97056 19.9294 ATPIII Pakistan (Ramzan et al., 2010) Ramzan M 2010 86 22.95 14.0624 31.8376 ATPIII Pakistan (Ali et al., 2012) NilufarSoltan Ali 2012 1329 63.7 61.1147 66.2853 IDF palestine (Sirdah et al., 2011) Mohammad M Sirdah 2011 230 23 17.5612 28.4388 ATPIII palestine (Abu Sham'a et al., 2009) Abu Shama 2009 342 33.6 28.5939 38.6061 IDF palestine (Rizkallah-Khader, 2009) Najwa R, et 2010 500 58 55.7 60.2 IDF palestine (Jamee et al., 2013) Amal Jamee 2013 163 59.5 55.6 63.3 NCEP/ATPIII palestine (Abdul-Rahim et al., 2001) Hanan F et al. 2001 992 17 15.8 18.1 WHO Qatar (Musallam et al., 2008) manalmusallam, and et al. 2008 27.7 23.3 32 ATPIII Qatar (Bener et al., 2009) BenerAbdolbari 2009 1204 26.5 24.0071 28.9929 ATPIII Qatar (Musallam et al., 2008) manalmusallam, and et al. 2008 35.4 30.7 40 IDF Qatar (Bener et al., 2009) BenerAbdolbari 2009 1204 33.7 31.03 36.37 IDF Qatar (Al-Thani et al., 2016) Mohamed Hamad Al-Thani 2015 2496 27.7 24.5 30.9 IDF Saudi Arabia (Al-Qahtani and Imtiaz, 2005) Dhafer A.AlQahteni 2005 1079 20.8 18.3 23.2 ATPIII Saudi Arabia (Al-Nozha et al., 2005) Mansour M AL-Nozha 2005 17293 41.4 40.6659 42.1341 ATPIII Saudi Arabia (BahijriRmar et al., 2013) SuhadmBahijri 2012 233 16.7 11.9108 21.4892 ATPIII Saudi Arabia (BahijriRmar et al., 2013) SuhadmBahijri 2012 233 18.9 13.8729 23.9271 IDF Saudi Arabia (Aljohani, 2014) Najij.Aljohmani 2014 4406 28.3 26.9699 29.6301 IDF Turkey (Erem et al., 2001) CihanirErem 2008 4809 26.9 25.6467 28.1533 ATPIII Turkey (Gundogan et al., 2013) KursatGundogan 2012 4309 36.6 35.1617 38.0383 ATPIII Turkey (Gundogan et al., 2013) KursatGundogan 2012 4309 44 42.5179 45.4821 ATPIII Turkey (Kumbasar et al., 2013) BakiKumasar 2013 1106 15.3 13.1784 17.4216 ATPIII Turkey (Sanisoglu et al., 2006) SyavuzSanisoglu 2006 15468 17.91 17.3057 18.5143 IDF Turkey (Gundogan et al., 2013) KursatGundogan 2009 767 34.6 31.2334 37.9666 IDF Turkey (Gundogan et al., 2013) KursatGundogan 2009 767 28.8 25.5952 32.0048 IDF Turkey (Ganime et al., 2011) Hasan Orhan 2011 807 17.5 14.8784 20.1216 IDF Turkey (Sozmen et al., 2013) KaanSozmen 2012 13964 23.4 22.6978 24.1022 IDF Yemni (Almikhlajy et al., 2008) Abdullah A Almikh 2008 274 23.8 18.7575 28.8425 ATPIII Yemni (Bamashmoos et al., 2011) Mohammad A 2011 200 46 39.0926 52.9074 OF5

P < 0.001) in 15 middle east-countries. There were no significant (Q = 10.34 and df = 1, Q = 72.99 and df = 5, Q = 148.14 and df = 5, differences among prevalence in different countries. Meta-regression and Q = 25.89 and df = 1, respectively; P < 0.001), for Emirates, test showed that the year of study is a significant explainer for variation Jordan, and Oman; it was 97 percent (Q = 164 and df = 4, Q = 110 between studies (0.02). In comparison, diagnostic criteria (IDF, ATP III, and df = 3, Q = 94 and df = 2, respectively; P < 0.001), and for and et al.) and sample size had no significant effect on the variance Egypt, Turkey, Saudi Arabia, Palestine, and Pakistan was more than 98 between studies (P > 0.05). percent (Q = 69 and df = 1, Q = 1563 and df = 9, Q = 587 and The Q statistics for heterogeneity of studies were significantly large df = 4, Q = 1387 and df = 5, and Q = 358 and df = 3, respectively; regarding pooled estimation of each country. I-square index of pooled P < 0.001). Thus the random effect model was done for synthesis of estimations for studies done in Qatar was 82 per cent (Q = 23, df = 4; prevalence data (Fig. 1). P < 0.001), for Iraq, Iran, Kuwait, and Yemen was between 90 and 96

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Table 2 The combined prevalence of MS by country & method of estimation.

Countries No. of studies (sample size) Pooled estimate of MS P.value

Random eﬀect Fixed eﬀect

Egypt 2 (547) 37.70 (3.99–71.40) 31.18 (27.44–34.92) 0.02 U. A Emirates 5 (10289) 40.10 (33.60–46.70) 39.98 (39.04–40.91) < 0.001 Iran 6 (12433) 31.02 (26.00–36.00) 30.93 (30.12–31.74) Iraq 2 (866) 36.09 (24.93–47.26) 34.19 (30.96–37.42) Jordan 3 (6346) 31.81 (22.60–41.02) 33.41 (32.26–34.56) Kuwait 6 (3154) 23.55 (14.49–32.62) 26.71 (25.20–28.22) Kyrgyzstan 1 (1672) 30.90 (28.70–33.10) 30.90 (28.70–33.10) Cyprus 1 (324) 37.04 (31.64–42.44) 37.04 (31.64–42.44) Lebanon 2 (586) 30.42 (26.70–31.14) 30.42 (26.70–34.14) Oman 3 (4837) 28.19 (18.88–37.49) 22.14 (20.97–23.31) Pakistan 4 (1862) 34.20 (7.22–61.27) 49.41 (47.33–51.49) 0.01 Palestine 5 (2227) 38.20 (16.00–60.40) 27.80 (26.85–28.76) < 0.001 Qatar 5 (4904) 30.08 (26.54–33.62) 29.71 (28.30–31.13) Saudi Arabia 5 (23244) 25.37 (15.59–35.15) 36.60 (35.99–37.21) Turkey 9 (46306) 27.21 (21.38–33.04) 23.66 (23.28–24.04) Yemen 2 (474) 34.76 (13.01–56.52) 31.51 (27.44–35.59) 0.002

8. Geographic distribution of metabolic syndrome in middle - east may be due to low awareness towards food based dietary guidelines countries (Yehia et al., 2015; Berry et al., 2011). Moreover changes in life style and socio-economic status in this area had a significant effect on phy- Geographic Information System software was used to demonstrate sical activity, which are protective for diabetes, hypertension, over- the prevalence of MS of the map of Middle – East. As presented in Map weight and other components of metabolic syndrome (Abdulrahman 1, Middle - East countries were divided into three classes for MS pre- and Musaiger, 2018). Based on evidences, lifestyle changes such as valence. Countries included Egypt, Israel, Iraq, Palestine, Cyprus, and physical inactivity, skipping breakfast, decreasing dietary fiber, and U.A.E reported high prevalence of MS (red colour). There were no data eating high fat diet contribute to development the overweight/obesity. available for the prevalence of MS in Afghanistan, Syria, and Bahrain. Accordingly, the high frequency of obesity in some middle - east Also, gathered data for Palestine was used for both Israel and Palestine. countries could explain the high prevalence of MS to some extent. Countries in the east of the map show lower prevalence than other area (Alnohair, 2014; Pengpid et al., 2015). According to the last report by (Map 1). Middle East Internet Users total population of Middle – East is 254,438,981 people. Therefore, about 79,384,962 people (31.2 percent – 9. PAR for non-communicable diseases of total population) have metabolic syndrome in the Middle East. It seems that overall prevalence rate of MS in the middle-east (31.3 per Cardiovascular Disease (CVD) and CVD mortality: Attributed risk of cent) and some of the middle-east countries are more than the Europe ff metabolic syndrome for cardiovascular disease varied between coun- countries (24.3 per cent) (Scuteri et al., 2015). These di erences may ff tries from 17.9 per cent (95% CI: 11.8–23.2) in Kuwait to 27.1 per cent be attributed to the di erences in engagement of inhabitants in physical (95% CI: 23.8–30.2) in United Arab Emirates. This measure for mor- activity, sports, and full-time or part-time work (change in lifestyle) in tality due to CVD was found 29.6 in United Arab Emirates and 19.8 in Europe and middle-east countries (Sibai et al., 2008; de Groot et al., Kuwait. 2004). In addition, the frequency of the MS's components (i.e. diabetes), ffi ff Stroke and coronary heart disease: The population attributable risk of the existing e cient health care system, and using di erent criteria for fi stroke found more than 23.4 in Egypt, U.A.E, Iraq, Jordan, Pakistan, MS veri cation might explain some variation in estimation of MS in the – Palestine, and Yemen. The maximum PAR of coronary heart disease was Europe and Middle East countries (Basit et al., 2015). 17.2 (95%CI: 14.8–19.5). As shown in Table 3, compare to other dis- Population Attributable Risk (PAR) for CVD and CVD mortality cussed diseases, the lowest estimated PAR was allocated to coronary were approximately 30 per cent for most of countries. It means that, heart disease. However, the middle-east countries have various PAR one third of CVD and CVD mortality in the region are preventable by fi ranged from 10.9 to 17.2 per cent. modi cation in MS components. Moreover, at least 18.9 per cent and Myocardial infarction and all-cause mortality: Nearly, more than 20 maximum 26.4 per cent of stroke and myocardial infraction were at- per cent of myocardial infarction can be attributed to the metabolic tributed to MS. Therefore, the healthy actions for prevention of devel- fi syndrome in middle-east countries. Moreover, Egypt, U.A. E, and opment of the metabolic syndrome components speci cally hypergly- Palestine had higher PAR (≥17.9 per cent) than other countries for the caemia may lead to decrease in hospital outcome as well as risk of mortality due to all-causes. severe heart failure (Zeller et al., 2005). The findings also demonstrated that the frequency of MS might be raised in general population if the current situation remained un- 10. Discussion changed. Therefore, data highlight the importance of preventive actions like physical activity and diet improvement for controlling MS cases in This study provided a comprehensive view about the prevalence of population. In addition, compilation of preventive plans and increasing metabolic syndrome and it's Attributable Risk for cardiovascular, the awareness of people about the MS risk factors should be regarded as stroke, and coronary heart diseases as well as myocardial infarc- the first priority for the health care systems. On the other hand, there tion and all-cause mortality in middle-east-countries overall and by has been an increasing trend in occurrence of cardiovascular disease country. The combined prevalence rates of metabolic syndrome were (CVD), stroke, and other risk factors in middle-east countries (Tran high on average and in some countries, so that countries included; et al., 2010; Ramahi, 2010). So, it is predictable that percentage of U.A.E, Palestine, and Egypt had the highest frequency (≥37.7 per cent) Attributable risk to metabolic syndrome will increase among popula- of MS. However, Mediterranean diet has been associated with lower tion in the future years while there is no appropriate efforts for rate of CVD and metabolic diseases but the high prevalence rate of MS

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Fig. 1. Forest plot of pooled estimates of MS prevalence and its 95% CI.

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Map 1. Geographic distribution of metabolic syndrome.

Table 3 Estimated Population Attributable Risk by disease and country.

Country CVD CVD mortality Stroke Coronary Heart Disease MI All-cause mortality

PAR (95% CI) PAR (95% CI) PAR (95% CI) PAR (95% CI) PAR (95% CI) PAR (95% CI)

Egypt 25.9(3.57–39.9) 28.3(4.0–42.8) 27.1(3.8–41.4) 16.3(2.0–27.0) 27.1(3.8–41.4) 17.9(2.2–29.2) U.A. E 27.1(23.8–30.2) 29.6(26.0–32.9) 28.4(24.9–31.6) 17.2(14.8–19.5) 28.4(24.9–31.6) 18.8(16.3–21.3) Iran 22.3(19.4–25.0) 24.5(21.4–27.4) 23.4(20.4–26.2) 13.8(11.9–15.7) 23.4(20.4–26.2) 15.2(13.1–17.2) Iraq 25.1(18.8–30.5) 27.4(20.7–33.1) 26.3(19.7–31.8) 15.8(11.4–19.7) 26.3(19.7–31.8) 17.3(12.6–21.5) Jordan 22.8(17.3–27.6) 25.0(19.1–30.1) 23.9(18.2–28.8) 14.19(10.5–17.5) 23.9(18.2–28.8) 16.0(12.8–19.1) Kuwait 17.9(11.8–23.2) 19.8(13.2–25.5) 18.9(12.5–24.4) 10.9(7.0–14.5) 18.9(12.5–24.4) 12(7.7–15.9) Kyrgyzstan 22.3(21.0–23.5) 24.4(23.1–25.7) 23.4(22.1–24.6) 13.8(12.9–14.6) 23.4(22.1–24.6) 15.9(14.2–16.1) Cyprus 25.6(22.7–28.2) 28(24.9–30.8) 268(23.8–29.5) 16.1(14.1–18.0) 26.8(23.8–29.5) 17.6(15.5–19.7) Lebanon 22.0(19.8–24.0) 24.2(21.8–26.3) 23.1(20.9–25.2) 13.6(12.1–15.0) 23.1(20.9–25.2) 14.9(13.4–16.5) Oman 20.7(14.9–25.8) 22.8(16.5–28.2) 21.8(15.7–27.0) 12.7(8.9–16.3) 21.8(15.7–27.0) 14(9.8–17.8) Pakistan 24.1(6.2–36.2) 26.4(7.0–39.1) 25.3(6.6–37.7) 15.1(3.6–24.1) 25.3(6.6–37.7) 16.5(4–26.7) Palestine 26.2(12.9–35.9) 28.6(14.3–38.8) 27.4(13.6–37.4) 16.5(7.6–23.9) 27.4(13.6–37.4) 18.1(8.4–25.9) Qatar 21.8(19.7–23.8) 24.0(21.7–26.0) 22.9(20.8–24.9) 13.5(12.1–14.8) 22.9(20.8–24.9) 14.8(13.3–16.3) Saudi Arabia 19.0(12.6–24.6) 21.0(14.0–26.9) 20.0(13.3–25.8) 11.6(7.4–15.4) 20.0(13.3–25.8) 12.8(8.2–16.9) Turkey 20.1(16.5–23.5) 22.2(18.3–25.7) 21.2(17.4–24.6) 12.3(10.0–14.6) 21.2(17.4–24.6) 13.6(11.0–16.0) Yemen 24.4(10.7–34.4) 26.7(12.0–37.2) 25.6(11.4–35.8) 15.3(6.3–22.7) 25.6(11.4–35.8) 16.77(7–24.6) controlling the adverse effects of lifestyle changes and nutrition tran- analysis and the meta-analysis. A.H.A performed the GIS processing. Z.I sition in this area. contributed to the interpretation of the results. F.KH designed the tables The strengths of this study is its systematic nature and Meta – and performed some calculation. All authors were involved in writing analysis of massive data in a wide region which provided a good picture and reviewing the final manuscript. of MS frequency and its population attributable fractions overall, by country and disease for the Middle – East countries. However, the re- Appendix A. Supplementary data sults of the study may have been affected by the diagnostic criteria of MS, the place of studies, and the time of study in each country. Supplementary data to this article can be found online at https:// Nevertheless, the random effect model and sensitivity/subgroup ana- doi.org/10.1016/j.obmed.2019.100086. lysis as well as Meta regression used to find out any sources of heterogeneity. References

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