Magnitude of metabolic syndrome among middle age adulthood urban residents in west Ethiopia

Background: Nowadays, most of the people die due to metabolic impairment. Metabolic syndrome is a major health threat facing all people worldwide. However it is a silent killer; community based screening was not practiced, especially in west Ethiopia. Thus, this study aimed to assess magnitude of metabolic syndrome among middle aged urban residents. Methods: A community based cross sectional study design was applied on 266 healthy adults in March 2019. Data was collected with questionnaires, anthropometric measures and biomarkers. By using SPSS version 24, magnitude of metabolic was indentified; statistical associated significant of variables were considered at p ≤0.05 on multivariable logistic regression analysis. Results: Using the new clinical definition of the metabolic syndrome given by International Diabetes Federation the prevalence of metabolic syndrome was 12.6% while 15.4% as of Ethiopian cut off point. The prevalence of central obesity was 47.1% as of International Diabetes Federation, 2018 while 58.6% as of Ethiopian. By separate components the most frequent metabolic syndrome parameters; blood pressure, hyperglycemia and low density lipoprotein were 18.4%, 24.4%, 20.4% and 19.5% respectively. Majority (91.7%) of participants had unhealthy lifestyles. Findings of binary analysis showed those who had serum triglycerides level ≥150 mg/dL was found to increased risks of metabolic syndrome by more than one hundred thirteen times (OR=113.18 CI=36.05-355.29). On multivariate analysis those who had body mass index ≥25Kg/m was found to increased risks of metabolic syndrome by more than twenty one times (AOR: 25.67; 95% CI 7.18, 94.59; p<0.001), more than twelve times those found with central obesity (AOR:12.74; p, 0.015), more than nine times for systolic blood pressure ≥130mmHg (AOR: 9.30; P, 0.039), and fourfold greater for FBS≥ 100 mg/dl (AOR: 4.42; P<0.001). Conclusion: This study reveals magnitude of metabolic syndrome was high among middle aged urban residents of west Ethiopia. Therefore, educating community on metabolic syndrome prevention was significant to mitigate complication from degenerative diseases.


INTRODUCTION
The global prevalence of NCDs is increasing rapidly, including low and middle income countries and in 2012 almost 75% of NCD-related deaths where took place [1]. In developing countries, over-nutrition is second 'silent emergency' due to rapid urbanization and shifting of diet [2] and 79% of deaths attributable to chronic diseases are occurring mostly during middle aged [3].
In 2000WHO reported that about 800,000 cases were recorded in Ethiopia, and this number projected to rise to 1.8 million by 2030. Also WHO reported in 2013 showed about 1.9 million diabetic patients seen in Ethiopia [4,5]. Findings from Tikur Anbessa Specialized Hospital also reveals 9.9% of undiagnosed employees in that hospital were at high risk for developing T2DM and about 13.9% of the female had gestational diabetic mellitus [6].
Risks of NCDs are increasing as a result of unhealthy lifestyles, such as tobacco, unhealthy diet, risky alcohol drinking and physical inactivity [7]. Metabolic syndrome is of the result of interconnected physiological, biochemical, clinical, and metabolic factors. It also defined as having abdominal obesity plus any two or more of the following risk factors: elevated blood pressure, reduced high-density lipoprotein cholesterol (HDL-C), elevated triglyceride levels and raised fasting plasma glucose [8][9][10].
Unhealthy lifestyles are the most common modifiable risk factors of metabolic syndrome. The impact of unhealthy lifestyles not only undermines quality of life and productivity, but also contributes death and economic losses. Morbidity costs represent lost income from reduced productivity, restricted physical activity, and absenteeism and bed days. Mortality costs encompass lost future income due to premature death [11]. According to Ethiopian Public Health Institute report in 2016, the cumulative economic losses due to NCD burden in low & middle income countries between 2011 and 2025 have been estimated US$7 trillion. Population norms for HRQoL are an essential ingredient in health economics and in the evaluation of population health [12].
Degenerative diseases can be delayed, prevented, or managed through healthy behaviors [13], yet lifestyles-related health risk factors is still lack focus. In 2013, the American Medical Association classified obesity as a disease. It is most commonly caused by a combination of unhealthy diet, physical in activities and genetic susceptibility. Dieting and exercising is medicine for obesity and reducing disability and death from chronic diseases [14,15]. Promotion of healthy diets and of physical activity should be a core competence of primary care providers, who play an important role in providing individual services to tackle the burden of NCDs [16].
Nutrition transition exacerbated by decreased physical activity, stressful lifestyle, and risky alcohol drinking and tobacco use [17,18].
The prevalence of metabolic syndrome increases could be due to a rapid nutrition transition such as high-calorie intake, high fat consumption, and low consumption of dietary fiber foods, as well as behavioral factors like sedentary lifestyles, increase in tobacco use, and excessive alcohol consumption [19]. Also migration from rural to urban [20] and nature of blackness in habitant mostly risk for prevalence of metabolic syndrome [21].
Study conducted in Addis Ababa revealed, the prevalence of metabolic syndrome was 14.0% in men and 24.0% in women [22]. Similarly; according to IDF definition in the study population the overall prevalence rate of Metabolic Syndrome (Met S-IDF) in Jimma Town was estimated to be (16.7%) [23].
Even though metabolic syndrome is prevalent in urban areas particularly workforces and women, in Ethiopia the effect not yet registered nationwide. Also Ethiopia had no optimal cut-off points for defining metabolic syndrome and risk components of adults, rather than using cot-off points from Caucasian population [25].Screening for metabolic disorders among healthy individuals is ignored as the literatures showed [26]; evidence also suggests that an abnormal metabolic profile, rather than high BMI, is associated with higher risk of diabetes and cardiovascular diseases [27].
In line with the concept, because of chronic degenerative diseases most of young age adults are dying due to metabolic age than chronological ageing. Unless risk factors of metabolic syndrome are prevented earlier, the consequences range from serious chronic conditions to premature death. Therefore, this study aimed to assess prevalence of metabolic syndrome and its associated factors among middle aged urban residents in West Ethiopia.

Study design and setting
A community based cross-sectional study design was adopted in accordance with approaches of WHO to conduct the research on middle age urban residents in West Ethiopia. Because of hub center for western towns (Assosa, Ambo, Bure-Bahirdar, Metu and Jimma) we selected Nekemte. It is 328 km far from Addis Ababa (Finfinne) and has six kebeles. Its city projection in 2017 is estimated to be 117,819 and out of this adults 51 %( 117,819= 60,088) [27]. This Town has one specialized Hospital, one referral Hospital, and three Health centers. Community based education on healthy lifestyle adoption, prevention of risk factors of metabolic syndrome and improvement of quality of life was being given from 1February-1August 2019, so these results serve as baseline and the data was collected in February 2019.

Source and study population
All middle aged adults (41-64years) among Nekemte residents during the study time were the source of population. While all middle aged (41-64 years) adults in the selected communes and registered as a resident and had lived in community for greater than six months were be included in study population.

Inclusion and exclusion criteria
Participants who lived at least six months and aged 41 to 64 years who were eligible to participate in the study However, those on medication and have known cardiovascular disease; attended behavioral change communication program; pregnant & lactating; bariatric surgery; us anti-psychotics and physically disables were excluded

Sample and Sampling techniques
Two hundred sixty six participants calculated using a single finite population proportion formula by using Epi Info™ 7 by considering with the following assumptions: margin of error of 5%, confidence level of 95%, 10% non-response rate and central obesity (19.6%) the most common prevalent metabolic syndrome component Ethiopian adults [40].
From six kebeles (small administrative unit), two kebeles which were not adjacent but homogeneous in terms of socioeconomically and geographically were selected. Since data was used as baseline for an intervention, one kebele was randomly selected and the other was purposively allocated with buffering zone through natural geography to avoid data contamination.

Data collection
Data were gathered using structured questionnaires through interview of adults in the local language that translated from English version by trained research assistants. Self administered questionnaires and anthropometric and biochemical measurements were used to collect data.
Anthropometric assessment of adults carried out using standardized techniques. Weight and height measurements will be taken using calibrated equipment (Taylor Lithium Electronic Scale for weight and a portable stadiometer/sac-Germany) with light clothes and no shoes.
For laboratory analysis, 5ml of venous blood samples from the ante-capital vein was taken after.
The study participants were advised to take an overnight fasting of 10-12 hours before collecting the blood samples for the determination of FBG and lipid profiles. Fasting blood (plasma) glucose, serum total cholesterol, HDL-C and triglycerides were determined by Auto analyzer (Human Star Model 80) method by using specific reagents (Human). LDL-C was calculated using the Freidwald formula. VLDL = Triglycerides ÷5; LDL = Total cholesterol -(HDL + VLDL) [28].
The anthropometric assessment was done according to the standardized procedures stipulated by the Food and Nutrition Technical Assistance (FANTA) Anthropometric Indicators Measurement Guide by seca Germany [29]. Glucose (FPG) ≥100 mg/dl (5.6 m mol/L). In addition BMI≥25kg/m 2 was overweight [16,48].
Diet diversity was constructed according to FAO 2013, by counting the intake of the food groups over a period of one week based on the sum of food groups consumed over the reference period.
For instance, an adult who consumed one item from each of the food groups at least once during the week would have the maximum DDS.

Data Analysis
Data analyses were done using SPSS for windows version 24(Chicago, Illinois) after checking for missing values and outliers. Descriptive analysis was carried out using risk score as dependent variable, with age, gender, smoking, physical activity, BMI, WC, ethnicity, educational status, religion, income, fruits and vegetables eating habit, previously measured history of blood glucose & pressure, presence of metabolic syndrome.
Regression analysis was used to identify the factors associated with metabolic syndrome. Binary logistic regression was used to determine the association between lifestyles with modifiable risk factors of metabolic syndrome & health related quality of life. Variables with P-value ≤0.25 in binary analysis was selected as candidate variables multivariable logistic regression model to identify association of independently factors. Odds ratio together with their corresponding 95% CI was computed. Multi-co linearity among independent variables was assessed using the standard errors. The standard errors for regression coefficients <2.0, as a familiar cutoff value, showed that there was no multicollinearity among independent variables. Normality was checked for continuous variables. A P-value <0.05 was considered as statistically significant.

Socio demographic characteristics of participants
From two hundred sixty six undiagnosed populations 62.8% were females. On average the age of study participants were 52.5 years and largest population (89.5%) was Oromo followed by Amhara (6%). Likewise, 41.4% of the respondents were live in lowest wealth quintiles.
Similarly, almost all (92.5%) of respondents did not have urban farming/home gardening and 54.9% of them living below poverty threshold (

DISCUSSION
The study reveals unscreened metabolic syndrome and its associated factors among urban residents of west Ethiopia. It demonstrated that high ORs of metabolic syndrome increased with its component factors; both ORs & AOR increased with presence of mobility problem.
The magnitude of metabolic syndrome was 12.6% IDF [48] and15.4% according to [25]; its component like raised triglyceride, hypertension, and raised fasting blood sugar were 20.3, 18.4 and 7.1% respectively. From the participants had metabolic syndrome, 67.69 % of them were females and of those had poorer, adults' aged 41-48 years accounts 50.77%.
Inline to our study, other studies result demonstrated that the prevalence was significantly higher in the middle aged, and sky rocket in middle age [9,[30][31] and typically declines in the elderly [32]. The magnitude of metabolic syndrome in the present study was less than previously documented findings from some urban areas of Ethiopia like Addis Ababa (17.9%) [49].
The prevalence of undiagnosed FBS≥100mg/dl (7.1%) was less comparatively with pooled results of urban areas of Sudan (7.7%) [42], urban Egypt (20%) [9] and red delta river of middleaged Vietnam (40%) [33]. Study conducted in north Indian, prevalence of metabolic syndrome was 32.5% which is higher than this finding [46]. Worldwide, one in four (23%) of adults do not currently meet the global recommendations for physical activity [16] which was less compared to this study (91.0%). This result was highly prevalent. We found physical inactivity was associated with metabolic syndrome, this confirms the study done in other country [34,35].
Waist circumference is a measure of central obesity as per IDF as well as ATP-III criteria was 48.1%. But based on the cut off points [24], this study reveals 58. 6  it needs other formulation to define their association.

CONCLUSIONS
The magnitude of metabolic syndrome in urban residents of west Ethiopia was 15.4% while 12.6% based on new clinical IDF [48]. Association found between the variables. Therefore, to reduce the magnitude, curb burden of NCD and improve health adults, it is recommended: applying holistic lifestyle intervention approach, launching health and nutrition information system or community-based service delivery system and active screening for early detection of risk factors would bring significant changes. To sum up, establishing healthy lifestyles training, diet therapy and NCD prevention center is crucial for awareness creation, capacity building, rehabilitation and job creation at all.

Limitations of the Study
The study is subject to recall bias because of the cress sectional nature and unhealthy lifestyles practices were hidden by respondents due to socially unacceptable behaviors which might under or overestimate the actual levels of risk factors. Any other bottle neck was insufficient biochemical reagent to take large study population.

Ethical approval
This study was approved by the Institutional Review Board of Jimma University (Approval No. IHRPGD/596/2019; January 1, 2019). Support letter was also taken from all concerned bodies.
Prior to the first interview, participants were informed about objectives of the study and privacy during the interview. Written consent was also obtained from individual participants.

Consent for Publication
 Not applicable Disclosure  We declare that there is no competing interest.

Funding
The authors were not received financial support for the publication, but fund for facilitating data collection allocated by Jimma University.

Author Contributions
All made substantial to conception from proposal construction, data collection and manuscript writing to final approval of the version to be published.