The study was conducted in Hawassa University comprehensive specialized Hospital, Southern Nations Nationalities and Peoples Region (SNNPR) from September 01/2015 to June 30/2016. Hawassa is the capital city of the region and located 275 km from Addis Ababa, which is the capital city of Ethiopia. This institution based cross sectional study was conducted in hypertensive patients. All hypertensive subjects age older than 18 years those who had a regular follow-up in the chronic disease clinic were eligible for the study. However, patients using lipid-altering drugs, women with confirmed pregnancy, previously diagnosed diabetic individuals, known cardiac and renal failures were excluded from the study.

The sample size was calculated based on single population proportion formula using a 95% confidence interval (CI) and 81.3% prevalence of low HDL-c among hypertensive patients according to NCEP-ATP III [11].where, P = proportion of low HDL-c, Z_α/2 = Critical value at 95% level of confidence (Z = 1.96) d = Margin of error (5%). Based on the above formula with considering 5% non-response rate, the final sample size was calculated to be 244. To select participants from the study population, direct patient flow was checked for one week in the chronic diseases clinic and patients’ logbook was assessed additionally for its confirmation. Thus, the trend showed that the average weekly patient flow was approximately 70–80 cases for hypertensive patients. Finally, every fourth hypertensive patients were selected using systematic random sampling technique.

Socio-demographic, anthropometric and other relevant clinical data of the study participants were collected by pre-tested structured questionnaires. Following this, trained nurse who were working at chronic diseases clinic recorded the physical/anthropometric examinations. Blood pressure (BP) of the patients was measured using a standard adult arm cuff of mercury type sphygmomanometer after a minimum of 5–10 min rest in the clinic. The precision of the measurment was maintained by using two readings within 2–3 min differences and finally the average blood pressure (BP) was taken and recorded to assess pressure(BP) status. In addition the third measurment was taken, when the two BP readings differed by 10 mmHg within a single individual, and finally the average of three readings was used. WHO stepwise approach was applied to collect data of weight, height and waist circumference [13]. Individuals Weight and height was measured when they stood without shoes and wearing light garments. Body weight was measured using an electronic scale to the nearest 0.1 kg, and standing height was measured using a wall stadiometer to the nearest 0.1 cm. Moreover, body mass index (BMI) was calculated as the weight (in kilogram) divided by the square root of height (in meter) and it is classified according to international conventions: underweight (<18.5 Kg/m²), normal weight (18.5–24.9Kg/m²), overweight (25–29.9 kg/m²), and obesity (≥30 kg/m²) [14]. Furthermore, waist circumference (WC) was measured at the umbilicus using a non-elastic tape to the nearest 0.1 cm with patients standing erect position after the end of a normal exhalation.

According to NCEP-ATP III guideline the intensity of physical activity explained as follows: moderate intensity of physical activities include: brisk walking for 30–40 min; 2-swimming-laps for 20 min; bicycling for pleasure or transportation around 5 miles in 30 min; Raking leaves for 30 min and other related activities. If an individual physical activity performance is lower than mentioned above, it is categorized under low intensity of physical activity [15]. In addition, high intensity of physical activities include: walking after work for 30 min before getting in the car/ reaching home; walking up or down 1–2 trips of stairs instead of always taking the elevator; doing daily heavy house cleaning, push a stroller/take walks with your children; or other activities which require more energy [15].

A morning 4–5 ml blood was collected from each patient after overnight fast, and then serum was obtained for biochemical tests. Serum samples were analyzed for fasting blood sugar (FBS), high density lipoprotein cholesterol (HDL-c) and triglycerides (TGs) using A25™ BioSystem chemistry analyzer [BioSystems S.A. Costa Brava 30, Barcelona (Spain) (BioSystems™, Spain)]. TGs and FBS were analyzed using enzymatic colorimetric assay method, while a quantitative determination of HDL-c was done using direct homogeneous enzymatic assay technique. All reagents used for these analytes were from Germany (Human Gesellschaftfu^¨r Biochemica und Diagnostica mBH).

The presence of MetS was defined using the NCEP-ATP III guideline. The guideline illustrated that participants should have at least three of the following risk factors to be categorized under the group of MetS. This include abdominal obesity [defined as waist circumference (WC) >102 cm in men and >88 cm in women]; raised TGs level (≥150 mg/dl); low HDL-c (<40 mg/dl in men and <50 mg/dl in women); increased BP (systolic BP ≥130/diastolic BP ≥85 mmHg) and increased FBS (≥110 mg/dl) [15].

All questionnaires were checked visually, coded and entered into EPI INFO version 3.1 and exported to Statistical Package for Social Sciences (SPSS) Version 20 for analysis. Descriptive statistics were used to describe study population in relation to relevant variables. Chi-square and or fisher’s exact test was used for categorical variables. In addition, mean differences in quantitative continuous data of the study groups was evaluated by student’s t-test. The variation in the distribution of categorical variables in study groups was assessed using univariate and multivariate binary logistic regression. Furthermore, P-value <0.2 was used as a cut-off to comprise variables for multivariate binary logistic regression and finally results were considered as statistically significant when the p-value is < 5%.

Prior to actual data collection, the quality of data was assured by pre-testing of questionnaires in Sidama Zone “Bushulo” health center, which is 5 Km away from the study area. The questionnaires were evaluated for clarity and finally amendment was done after pre-testing. Socio-demographic, clinical and physical/anthropometric data of the study subjects was collected by trained nurses. In addition, the lab performance was managed daily through running quality control samples. If results fall outside established value, the run was repeated. Furthermore, the standard operating procedures manual was used to perform all the laboratory procedures.

A total of 238 hypertensive patients approached, six of them (2.5%) refused to take part in the study. Of whom 105 (44.1%) men and 133 (55.9%) were women. The mean (±SD) age of the study participants was 53.2 (14.5) years. The prevalence of Mets was 116(48.7%) and the mean age was significantly higher among MetS group when compared to non-MetS group [55.5(13.7) vs.51 (14.9); p = 0.02], respectively. About 73.9%, 89.9%, 36.6%, 35.3% of the study participants were urban inhabitants, married, educationally primary level and housewives, respectively. Besides urban dwellers had significantly higher MetS when compared to rural inhabitants (82.8% vs. 17.2%), p = 0.003, respectively. Around 11.8% had a vigorous intensity working habits in their day-to-day activities that include farming, walking long distances on foot and doing tough activities. Furthermore, 26.9% of the participants had experiences of performing physical exercises regularly and 1.7% had a history of alcoholism (Table 1).

About 50(21%) of the study participants had a family history of chronic illnesses (diabetes mellitus, 7.6% and hypertension, 13.4%). Majority (95%) of the participants had been using at least one anti-hypertensive agents. The range of ant-hypertensives treatment duration was two months to forty years, and 9.2% of patients were on treatment for more than ten years. In addition the mean systolic BP, diastolic BP, FBS, TGs and BMI were significantly higher in MetS group when compared to non-MetS group (p < 0.0001 for all). However, the mean HDL-c was significantly lower in MetS group when compared to non-MetS group (39.4 vs. 47.6), respectively, p < 0.0001 (Table 2).

MetS was higher in women when compared to men (54.1% vs. 41.9%; p = 0.06), respectively. The prevalence of abdominal obesity, raised FBS, raised TGs and low HDL-c were 37.8%, 35.7%, 62.2% and 60.9%, respectively. In addition the prevalence of low HDL-c and abdominal obesity were significantly higher in women compared to men (69.9% vs. 49.5%; p = 0.001) and (18.1% vs. 53.4%; p < 0.0001), respectively. In contrast BP ≥130 mmHg was significantly higher in men (78.1%) compared to women (64.7%), p = 0.02 (Table 3). Furthermore, three or more abnormal components of MetS within a single individual were higher in urban dwellers compared to rural one (Fig. 1).

Based on NCEP-ATP III criteria: being an urban, the crude odds ratio [COR (95% CI):2.3 (1.25–4.2); p = 0.007] and being hypertensive for more than 10 years [COR (95% CI): 2.5(1.1–5.7); p = 0.02] were associated with MetS. In addition, being a housewife [COR (95% CI): 3.36 (1.4–7.9); p = 0.006], a retirement pension [COR (95% CI):4.1(1.25–13.5); p = 0.02], married [COR (95% CI):2.5(1.1–7.4); p = 0.03], and divorced/widowed (95% CI):13(1.2–140.7); p = 0.02] were also associated with MetS. However multivariate analysis was maintained for possible confounding factors, being married [AOR (95% CI):7.2(1.4–35.9); p = 0.02], divorced/widowed [AOR (95% CI):16.4(1.1–244.2); p = 0.04], and a retirement pension [AOR (95% CI):5.3(1.1–25.9); p = 0.04] were significantly and positively associated risk factors of MetS. Furthermore, monthly income rates, age over 60 years, overweight, and obesity were also associated risk factors of MetS (Table 4).

First, our study was a cross-sectional suggesting that it cannot provide adequate evidence of causality regarding MetS and its risk factors. Second, we used only one classification was used to evaluate the frequency of MetS; a different prevalence rate might be detected if other MetS criteria like the world health organization (WHO) and or, international diabetes federation (IDF) were used. Third, our study included only hypertensive patients and that was not wide-ranged. Fourth, we did not assess the nutritional status of the individuals because of its difficulties in our situations and we have not analyzed the data of cigarette smoking because we got only one individual who had a history of smoking. Regardless of the depicted limitations, this study ultimately adds supportive information on MetS in a limited data situation of sub-Saharan Africa, mainly in Ethiopia including the study area.