Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia

Gebremedhin Gebreegziabiher; Tefera Belachew; Kibrti Mehari; Dessalegn Tamiru

doi:10.1371/journal.pone.0243103

. 2021 Feb 9;16(2):e0243103. doi: 10.1371/journal.pone.0243103

Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia

Gebremedhin Gebreegziabiher ^1,^*, Tefera Belachew ², Kibrti Mehari ³, Dessalegn Tamiru ²

Editor: Frank T Spradley⁴

PMCID: PMC7872241 PMID: 33561153

Abstract

Introduction

Dyslipidemia is a major risk factor for cardiovascular diseases (CVD). The prevalence of dyslipidemia is not known among Ethiopian adults. The prevalence is expected to rise due to the socio-economic development accompanied by lifestyle changes. This study was conducted to estimate the prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City.

Methods

A community-based cross-sectional study was conducted among 321 randomly selected subjects. Data were collected on sociodemographic, anthropometric, lifestyle, and clinical characteristics of the participants using the WHO STEPS survey instrument. Data were analyzed using SPSS software version 24.0. Student’s t-test and Pearson’s Chi-square test were used to assessing the interrelationship between each factor and outcome variables. Bivariate and multivariable logistic regression analysis were used to identify risk factors associated with dyslipidemia. All statistical significance was considered at p ≤0.05.

Results

The prevalence of dyslipidemia in this study was 66.7%. The prevalence of high low-density lipoprotein cholesterol (LDL-C), elevated triglyceride, elevated total cholesterol, and low high-density lipoprotein cholesterol (HDL-C) was 49.5%, 40.2%, 30.8%, and 16.5%, respectively. Being above 64 years (aOR: 2.196, 95% CI: 1.183–4.078) and 40–64 years old (aOR: 2.196, 95% CI: 1.183–4.078), overweight (aOR: 2.50, 95% CI: 1.314–4.756) and obesity (aOR: 15.489, 95% CI: 3.525–68.070), walking <150 minutes per week (aOR: 1.722, 95% CI: 1.004–2.953), raised fasting blood glucose (FBG) (aOR: 4.804, 95% CI: 1.925–11.988), and medium socio-economic status (aOR: 2.017, 95% CI: 1.044–3.899) were identified as significant predictors of dyslipidemia.

Conclusions

The finding of this study indicated that the prevalence of dyslipidemia is unacceptably high among adult residents of Mekelle City, which underlines an urgent need for early detection and public health interventions through the integrated involvement of public, governmental, and non-governmental organizations.

Introduction

Dyslipidemia is either one or a combination of elevated total cholesterol, high LDL-C, low HDL-C, and elevated triglyceride [1]. Dyslipidemia is a major risk factor for coronary heart disease (CHD) [2]. People with dyslipidemia are at a twofold increased risk of developing CVD as compared to those with normal lipid levels [3]. CVD is becoming more prevalent globally and is one of the prominent causes of death [4]. Raised levels of certain lipids in the blood increase the risk of atherosclerosis, which is recognized as the primary risk factor for stroke, peripheral vascular, and CHD [5].

Both LDL-C and HDL-C regulate the amount of cholesterol in the body. An imbalance between the two can increase the risk of myocardial infarction and stroke. High LDL-C is associated with an increased risk of atherosclerotic CVD due to the buildup of plaques within the arteries [1]. LDL-C carried cholesterol is potentially atherosclerotic. However, the HDL-C carried one has a protecting role against atherosclerosis [6]. HDL-C helps to remove cholesterol from the body, which decreases the risk of atherosclerotic CVD [1].

Most (80%) of the lipid disorders are associated with diet and lifestyle [7]. Modifiable risk factors, including a diet high in saturated or trans fats, sedentary lifestyle, smoking, and obesity increase the risk of dyslipidemia [3]. Lipid profile and CVD have a linear relationship. Dyslipidemia aggravates the development of atherosclerosis [8]. The prevalence of dyslipidemia is much higher among patients with coexisting cardiovascular risk factors such as hypertension, diabetes, or human immunodeficiency virus [9].

Dyslipidemia is associated with more than half of the global cases of ischemic heart disease and more than 4 million deaths per annum [10]. The pooled prevalence of dyslipidemia among the African general adult population was 25.5%. Besides, the overall prevalence of elevated total cholesterol, high LDL-C, low HDL-C, and elevated triglyceride was 25.5%, 21.4%, 19.5%, and 17.0%, respectively [9]. Advanced age, raised FBG, drinking coffee, and vegetable intake were identified as significant predictors of dyslipidemia among women contraceptive users in Eastern Ethiopia [11].

There is no literature showing the prevalence and factors associated with dyslipidemia among the Ethiopian general adult population. Only one facility-based cross-sectional study was conducted in Eastern Ethiopia among contraceptive users, which reported a high prevalence (34.8%) of dyslipidemia. However, it cannot represent the general adult population [11]. Therefore, this study aimed to assess the prevalence of dyslipidemia and associated risk factors among adult residents in Mekelle City, Northern Ethiopia.

Materials and methods

Study design, setting, and population

A community-based cross-sectional study was conducted among 321 adult residents in Mekelle City from July to September 2019. Mekelle is the second-largest city in Ethiopia, which is the capital city of Tigray regional state. Mekelle is located at 783 km to the north of the capital city, Addis Ababa. The city is divided into seven sub-cities. Being an adult aged 20 years and above and residents who lived at least 6 months in the city were considered as the inclusion criteria. Whereas, pregnant women and the first 6 months of lactating mothers were excluded from the study.

Ethical issues

Ethical clearance and approval were obtained from the institutional review board of Jimma University. Besides, a support letter was obtained from the local administrators. All participants were informed of what is expected from them and their rights. Written informed consent was obtained from each participant. Illiterate participants put their fingerprint as a signature in the written consent form voluntarily after data collectors read the information. Participants with abnormal lipid profiles, FBG, and blood pressure were linked to their nearby healthcare facilities for further investigation, counseling, and treatment.

Sample size determination and sampling procedure

A single population proportion formula was used [12] to determine the sample size with the assumption of the prevalence of dyslipidemia among the African adults (25.5%) [9], 95% level of confidence, 5% margin of error, and 80% power. Thus, the calculated sample size was 292. After 10% (n = 29) of the calculated sample size was added to consider the non-response rate, the total sample size was 321.

Probability of having dyslipidemia increases with age. Hence, a stratified sampling technique was used using age. Two age groups (strata) were created (20–39 years and ≥40 years). Then, the total sample size was proportionally allocated based on the number of the adult population in each stratum. Besides, the total sample size was proportionally allocated to the seven sub-cities. The list of households from each municipality was used as a sampling frame. Households were selected using a simple random sampling technique from each sub-city. Likewise, a single eligible participant was selected using a lottery method from each selected household.

Data collection and quality control

A data collection team was established. The team consisted of three public health professionals, one medical laboratory technologist, and one supervisor. The team was trained for two days on how to conduct face-to-face interviews, anthropometric measurements, how to measure blood pressure, and how to collect and handle the blood sample. A structured questionnaire adapted from the WHO STEPS survey instrument was used to collect the data [13]. The questionnaire was translated into the local language (Tigrigna). Besides, it was translated back to English to check the consistency. The data collection tools were pretested to check completeness, consistency, sensitivity, and applicability and were ratified accordingly.

Height was measured using a stadiometer (UNICEF SECA) to the nearest 0.1 cm without shoes. The participant was positioned in the Frankfurt plane and the measurer checked the four contact points (heel, calf, buttock, and shoulder) against the vertical stand. Waist Circumference (WC) was taken to the nearest 0.1 cm at the midway between the lowest costal margin, midclavicular line, and the anterior superior iliac spine using fixed tension tape. Hip circumference was also taken to the nearest 0.1 cm at the level of the greater trochanter of the femur with the subjects wearing a pant. Weight was measured using a digital scale (UNICEF SECA) to the nearest 0.1 kg with light closes and without shoes.

Blood pressure was measured in triplicate after 5 minutes of rest and the subsequent measurements were done 5 minutes apart. The mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) were used for analyses. The validity of the weighing scale was checked using a known weight before each measurement. All anthropometric measurements were collected in triplicate and the average values were used for analyses. All measurement data were collected using standardized techniques and calibrated equipment [14].

Sample collection, laboratory analysis, and definition of terms

After conducting the face-to-face interviews and anthropometric measurements, participants were appointed to the next morning (8:00 am–9:00 am) at their nearest health facility to give fasting venous blood. Around 5 ml of venous blood sample was collected after overnight fasting for FBG, HDL-C, triglyceride, LDL-C, and total cholesterol tests. The blood sample was clotted for 30 minutes. Then, the sample was centrifuged for 5 minutes at 4000 revolutions per minute. Around 2.5 ml of pure serum sample was separated to the Nunc tube. The sample was analyzed using Bio-system A25 automated clinical chemistry machine (Spain). Before sample analysis, the machine was checked using controls and blank on a daily basis.

Raised FBG was defined as ≥5.6 mmol/L (≥100 mg/dL) or on diabetes treatment and raised WC was defined as ≥94 cm for men and ≥80 cm for women [15]. Dyslipidemia was defined as a lipid profile that consists of the following abnormalities, either singly or in combination. Elevated total cholesterol ≥5.17 mmol/L (≥200 mg/dL); high LDL-C ≥3.36 mmol/L (≥130 mg/dL); low HDL-C <1.03 mmol/L (<40 mg/dL) for men, <1.3 mmol/L (<50 mg/dL) for women; and elevated triglyceride ≥1.7 mmol/L (≥150 mg/dL) [16].

Participants were considered as normotensive (<130/85 mmHg), pre-hypertensive (≥130-139/85-89 mmHg), and hypertensive (≥140/90 mmHg) for SBP and DBP, respectively. BMI was calculated by dividing weight in kilograms (kg) by the square meters (m²) of height. Participants with a BMI lower than 18.5 kg/m² were considered as underweight; between 18.5 and 24.9 kg/m² as normal; between 25.0 and 29.9 kg/m² as overweight and 30.0 kg/m² and above as obese [17].

Data analysis

Data were checked for completeness and consistency in the hard copy, double entered into EPI data software version 3.1 to check clerical errors. Then, the data were exported to the Statistical Package for the Social Sciences (SPSS) for Windows version 24 program for analyses. A descriptive analysis of the background characteristics was performed. Besides, the normality of the continuous variables was checked. Bivariate and multivariable logistic regression analyses were performed to identify factors independently associated with dyslipidemia. Backward stepwise elimination was used to remove non-significant variables until only statistically significant variables remained in the final logistic model. Crude and adjusted odds ratios and their corresponding 95% Confidence Intervals (CI) were computed in the bivariate and multivariable logistic regression analysis, respectively. The goodness of fit of the model was checked using the Hosmer-Lemeshow test at p >0.05. All statistical significance was declared at p ≤0.05.

Results

Characteristics of the participants

A total of 321 adults participated in the study. Men and women were significantly different in terms of educational status, marriage, occupation, smoking, and alcohol consumption (p <0.005). A higher percentage of women were ever been measured their blood pressure and blood glucose (p <0.005). Whereas, a higher percentage of men did formal exercise and walked ≥150 minutes per week (p <0.005). Only 13.1% of the participants did formal exercise. The intensity of activity in daily work was significantly different across gender (p = 0.044). Women consumed significantly more vegetables per week compared to men (P = 0.007) (Table 1).

Table 1. Background characteristics of the participants stratified by gender (n = 321).

Variables	Categories	Men, n (%)	Women, n (%)	Total, n (%)	p-value
Variables	Categories	145 (45.2)	176 (54.8)	321 (100.0)	(X²)
Age (mean ± SD)		39.98±14.52	38.18±13.96	38.99±14.22	0.259^¶
Educational status	Unable to read and write	6 (4.1)	29 (16.5)	35 (10.9)	0.002^*
	Primary school	20 (13.8)	31 (17.6)	51 (15.9)
	Secondary school	70 (48.3)	64 (36.4)	134 (41.7)
	Tertiary	49 (33.8)	52 (29.5)	101 (31.5)
Marital status	Single	49 (33.8)	30 (17.0)	79 (24.6)	0.001^*
	Married	91 (62.8)	133 (75.6)	224 (69.8)
	Others ^a	5 (3.4)	13 (7.4)	18 (5.6)
Occupation	Employed	125 (86.2)	75 (42.6)	200 (62.3)	<0.001^*
	Housewife	0 (0.0)	79 (44.9)	79 (24.6)
	Unemployed	20 (13.8)	22 (12.5)	42 (13.1)
Household monthly income (ranked)	Poor	61 (42.1)	61 (34.7)	122 (38.0)	0.067
	Medium	42 (29.0)	42 (23.9)	84 (26.2)
	Rich	42 (29.0)	73 (41.5)	115 (35.8)
Smoking	Yes	16 (11.0)	0 (0.0)	16 (5.0)	<0.001^*
Smoking	No	129 (89.0)	176 (100.0)	305 (95.0)	<0.001^*
Living with smoker	Yes	9 (6.2)	7 (4.0)	16 (5.0)	0.361
Living with smoker	No	136 (93.8)	169 (96.0)	305 (95.0)	0.361
Alcohol consumption	Yes	123 (84.8)	125 (71.0)	248 (77.3)	0.003^*
Alcohol consumption	No	22 (15.2)	51 (29.0)	73 (22.7)	0.003^*
Ever measured blood pressure	Yes	75 (51.7)	118 (67.0)	193 (60.1)	0.005^*
Ever measured blood pressure	No	70 (48.3)	58 (33.0)	128 (39.9)	0.005^*
Ever told having hypertension	Yes	12 (8.3)	20 (11.4)	32 (10.0)	0.358
Ever told having hypertension	No	133 (91.7)	156 (88.6)	289 (90.0)	0.358
On treatment for hypertension	Yes	5 (3.4)	5 (2.8)	10 (3.1)	0.759^b
On treatment for hypertension	No	140 (96.6)	171 (97.2)	311 (96.9)	0.759^b
Ever measured blood glucose	Yes	57 (39.3)	100 (56.8)	157 (48.9)	0.002^*
Ever measured blood glucose	No	88 (60.7)	76 (43.2)	164 (51.1)	0.002^*
Ever told having diabetes	Yes	8 (5.5)	7 (4.0)	15 (4.7)	0.515
Ever told having diabetes	No	137 (94.5)	169 (96.0)	306 (95.3)	0.515
On treatment for diabetes	Yes	1 (0.7)	3 (1.7)	4 (1.2)	0.630^b
On treatment for diabetes	No	144 (99.3)	173 (98.3)	317 (98.8)	0.630^b
Formal exercise	Yes	38 (26.2)	4 (2.3)	42 (13.1)	<0.001^*
Formal exercise	No	107 (73.8)	172 (97.7)	279 (86.9)	<0.001^*
Walking minutes per week	None	6 (4.1)	20 (11.4)	26 (8.1)	<0.001^*
	1–149	18 (12.4)	87 (49.4)	105 (32.7)
	≥150	121 (83.5)	69 (39.2)	190 (59.2)
Intensity of activity of daily work	Low	15 (10.3)	17 (9.7)	32 (10.0)	0.044^*
	Moderate	109 (75.2)	148 (84.1)	257 (80.0)
	Vigorous	21 (14.5)	11 (6.2)	32 (10.0)
Fruit intake per week (mean ± SD)		1.55±2.16	1.53±1.77	1.54±1.95	0.915^¶
vegetable intake per week (mean ± SD)		1.43±1.48	1.90±1.60	1.69±1.56	0.007^*^¶

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Note: *significant difference,

^a Divorced, Widowed, Separated,

^b Fisher’s Exact Test,

^¶ student’s t-test, X²: Chi-square, SD: Standard Deviation.

Prevalence of dyslipidemia and other CVD risk factors

The prevalence of dyslipidemia in this study was 66.7%. A higher percentage of men had elevated triglyceride (P <0.001). Whereas, a higher percentage of women had low HDL-C, central obesity, and raised waist to hip ratio (P <0.01). The mean waist to hip ratio was significantly higher among men (p = 0.001). The mean values of total cholesterol and HDL-C were significantly higher among women (p<0.001). Whereas, the mean triglyceride was significantly higher among men (p<0.001) (Table 2).

Table 2. Prevalence of dyslipidemia and other CVD risk factors with their mean values stratified by gender (n = 321).

Variables	Categories	Prevalence				Mean ± SD
		All	Men	Women	p-value^g	All	Men	Women	p-value^h
		All	n (%)	n (%)	p-value^g	All	Men	Women	p-value^h
Dyslipidemia	Yes	214 (66.7)	99 (68.3)	115 (65.3)	0.579	NA	NA	NA	NA
Dyslipidemia	No	107 (33.3)	46 (31.7)	61 (34.7)	0.579	NA	NA	NA	NA
Total cholesterol	<200 mg/dL	222 (69.2)	103 (71.0	119 (67.6)	0.509	182.8±49.1	176.6±45.4	187.9±51.6	0.04^*
Total cholesterol	≥200 mg/dL	99 (30.8)	42 (29.0)	57 (32.4)	0.509	182.8±49.1	176.6±45.4	187.9±51.6	0.04^*
Triglyceride	<150 mg/dL	192 (59.8)	71 (49.0)	121 (68.8)	<0.001^*	165.5±138.8	200.4±161.3	136.7±109.4	<0.001^*
Triglyceride	≥150 mg/dL	129 (40.2)	74 (51.0)	55 (31.2)	<0.001^*	165.5±138.8	200.4±161.3	136.7±109.4	<0.001^*
LDL-C	<130 mg/dL	162 (50.5)	80 (55.2)	82 (46.6)	0.126	144.4±48.2	138.4±48.7	149.1±47.4	0.051
LDL-C	≥130 mg/dL	159 (49.5)	65 (44.8)	94 (53.4)	0.126	144.4±48.2	138.4±48.7	149.1±47.4	0.051
HDL-C (M/W)	<40/50 mg/dL	53 (16.5)	15 (10.3)	38 (21.6)	0.007^*	57.7±12.7	53.6±10.7	61.1±13.3	<0.001^*
HDL-C (M/W)	≥40/50 mg/dL	268 (83.5)	130 (89.7)	138 (78.4)	0.007^*	57.7±12.7	53.6±10.7	61.1±13.3	<0.001^*
FBG	Normal ^a	251 (78.2)	107 (73.8)	144 (81.8)	0.221	97.4±38.0	101.0±38.9	94.4±37.2	0.124
	Pre-diabetes ^b	40 (12.5)	22 (15.2)	18 (10.2)
	Diabetes ^c	30 (9.3)	16 (11.0)	14 (8.0)
BMI	Underweight	26 (8.1))	10 (6.9)	16 (9.1)	0.138	24.4±4.9	24.1±4.2	24.7±5.3	0.261
	Normal	166 (51.7)	77 (53.1)	89 (50.6)
	Overweight	87 (27.1)	45 (31.0)	42 (23.8)
	Obese	42 (13.1)	13 (9.0)	29 (16.5)
WC (M/W)	<94/80 cm	161 (50.2)	95 (65.5)	66 (37.5)	<0.001^*	85.6±13.6	86.6±12.6	84.7±14.4	0.212
WC (M/W)	≥94/80 cm	160 (49.8)	50 (34.5)	110 (62.5)	<0.001^*	85.6±13.6	86.6±12.6	84.7±14.4	0.212
Waist to hip ratio (M/W)	<0.9/0.8	87 (27.1)	53 (36.6)	34 (19.3)	0.001^*	0.91±0.10	0.92±0.09	0.89±0.1	0.001^*
Waist to hip ratio (M/W)	≥0.9/0.8	234 (72.9)	92 (63.4)	142 (80.7)	0.001^*	0.91±0.10	0.92±0.09	0.89±0.1	0.001^*
SBP/DBP (mmHg)	Normotensive ^d	163 (50.8)	75 (51.7)	88 (50.0)	0.305	128.4±20.2/ 81.5±11.3	129.0±19.8/ 82.7±12.2	127.8±20.5/ 80.5±10.6	0.618/ 0.094
	Prehypertension ^e	60 (18.7)	22 (15.2)	38 (21.6)
	Hypertension ^f	98 (30.5)	48 (33.1)	50 (28.4)

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Note: *significant difference,

^a FBG: <100.0 mg/dL,

^b FBG: 100.0–125.9 mg/dL,

^c FBG: ≥126.0 mg/dL,

^d SBP/DBP: <130/85 mmHg,

^e SBP/DBP: 130-139/85-89 mmHg,

^f SBP/DBP: ≥140/90 mmHg,

^g Chi-square test,

^h Student’s t-test.

Abbreviations: BMI: Body Mass Index, CVD: Cardiovascular Disease, DBP: Diastolic Blood Pressure, FBG: Fasting Blood Glucose, HDL-C: High-Density Lipoprotein Cholesterol, LDL-C: Low-Density Lipoprotein Cholesterol, M: Men, SBP: Systolic Blood Pressure, WC: Waist Circumference, W: Women.

The risk of dyslipidemia and its component lipid abnormalities, except low HDL-C, were consistently increased with WC, waist to hip ratio, waist to height ratio, BMI, FBG, and blood pressure (p <0.005). Similarly, dyslipidemia, total cholesterol, and high LDL-C were consistently increased with age (p <0.001). Though elevated triglyceride was significantly different with age, the rise was not consistent (p = 0.003). Likewise, dyslipidemia, elevated total cholesterol, and high LDL-C was significantly different with the wealth index, the intensity of activity in daily work, and weekly walking time, respectively (p <0.05). Besides, the risk of low HDL-C was consistently increased with WC, waist to height ratio, and BMI (p <0.05). A higher percentage of non-alcohol consumers had low HDL-C (p = 0.033) (Table 3).

Table 3. Factors affecting the prevalence of dyslipidemia and its lipid components (n = 321).

Variables	Categories	Dyslipidemia (n (%))	Elevated TC (≥200 mg/dL) (n (%))	Elevated TG (≥150 mg/dL) (n (%))	High LDL-C (≥130 mg/dL) (n (%))	Low HDL-C (M/W<40/50 mg/dL) (n (%))
Age	20–39	107 (56.3)	41 (21.6)	63 (33.2)	75 (39.5)	26 (13.7)
	40–59	81 (80.2)	40 (39.6)	54 (53.5)	59 (58.4)	21 (20.8)
	≥60	26 (86.7)	18 (60.0)	12 (40.0)	25 (83.3)	6 (20.0)
p-value		<0.001^*	<0.001^*	0.003^*	<0.001^*	0.258
Alcohol consumption	Yes	169 (68.1)	80 (32.3%)	105 (42.3)	127 (51.2)	35 (14.1)
Alcohol consumption	No	45 (61.6)	19 (26.0)	24 (32.9)	32 (43.8)	18 (24.7)
p-value		0.346	0.382	0.147	0.306	0.033^*
WC in cm (M/W)	<94/80	86 (53.4)	35 (21.7)	47 (29.2)	59 (36.6)	16 (9.9)
WC in cm (M/W)	≥94/80	128 (80.0)	64 (40.0)	82 (51.3)	100 (62.5)	37 (23.1)
p-value		<0.001^*	<0.001^*	<0.001^*	<0.001^*	0.001^*
Waist to hip ratio (M/W)	<0.9/0.8	40 (46.0)	16 (18.4)	23 (26.4)	25 (28.7)	10 (11.5)
Waist to hip ratio (M/W)	≥0.9/0.8	174 (74.4)	83 (35.5)	106 (45.3)	134 (57.3)	43 (18.4)
p-value		<0.001^*	0.003^*	0.002^*	<0.001^*	0.140
Waist to height ratio (M/W)	<0.49/0.50	49 (42.6)	20 (17.4)	26 (22.6)	36 (31.3)	10 (8.7)
Waist to height ratio (M/W)	≥0.49/0.50	165 (80.1)	79 (38.3)	103 (50.0)	123 (59.7)	43 (20.9)
p-value		<0.001^*	<0.001^*	<0.001^*	<0.001^*	0.005^*
Body mass index	Underweight	9 (34.6)	4 (15.4)	4 (15.4)	6 (23.1)	3 (11.5)
	Normal	96 (57.8)	41 (24.7)	52 (31.3)	71 (42.8)	21 (12.7)
	Overweight	69 (79.3)	32 (36.8)	49 (56.3)	48 (55.2)	15 (17.2)
	Obese	40 (95.2)	22 (52.4)	24 (57.1)	34 (81.0)	14 (33.3)
p-value		<0.001^*	0.001^*	<0.001^*	<0.001^*	0.012^*
FBG	Normal ^a	150 (59.8)	58 (23.1)	82 (32.7)	109 (43.4)	39 (15.5)
	Pre-diabetes ^b	36 (90.0)	21 (52.5)	26 (65.0)	28 (70.0)	7 (17.5)
	Diabetes ^c	28 (93.3)	20 (66.7)	21 (70.0)	22 (73.3)	7 (23.3)
p-value		<0.001^*	<0.001^*	<0.001^*	<0.001^*	0.545
Blood pressure (SBP/DBP (mmHg))	<130/85	90 (55.2)	33 (20.2)	51 (31.3)	59 (36.2)	22 (13.5)
Blood pressure (SBP/DBP (mmHg))	≥130/85	124 (78.5)	66 (41.8)	78 (49.4)	100 (63.3)	31 (19.6)
p-value		<0.001^*	<0.001^*	0.001^*	<0.001^*	0.140
Monthly income (ranked)	Low	74 (60.7)	36 (29.5)	47 (38.5)	53 (43.4)	13 (10.7)
	Medium	59 (70.2)	22 (26.2)	36 (42.9)	39 (46.4)	17 (20.2)
	High	81 (70.4)	41 (35.7)	46 (40.0)	67 (58.3)	23 (20.0)
p-value		0.202	0.333	0.822	0.060	0.086
Wealth index ^d	Low	58 (54.7)	25 (23.6)	35 (33.0)	45 (42.5)	13 (12.3)
	Medium	87 (77.0)	43 (38.1)	53 (46.9)	66 (58.4)	22 (19.5)
	High	69 (67.6)	31 (30.4)	41 (40.2)	48 (47.1)	18 (17.6)
p-value		0.002^*	0.068	0.112	0.051	0.333
Intensity of daily work activity	Vigorous	18 (56.3)	4 (12.5)	13 (40.6)	11 (34.4)	4 (12.5)
	Moderate	175 (68.1)	81 (31.5)	102 (39.7)	132 (51.4)	43 (16.7)
	Low	21 (65.6)	14 (43.8)	14 (43.8)	16 (50.0)	6 (18.8)
p-value		0.239	0.010^*	0.676	0.281	0.742
Sitting time per day (ranked)	Low	59 (60.8)	25 (25.8)	35 (36.1)	48 (49.5)	11 (11.3)
	Medium	93 (70.5)	38 (28.8)	51 (38.6)	67 (50.8)	24 (18.2)
	High	62 (67.4)	36 (39.1)	43 (46.7)	44 (47.8)	18 (19.6)
p-value		0.370	0.111	0.293	0.911	0.250
Walking time per week in minutes	<150	95 (72.5)	47 (35.9)	48 (36.6)	74 (56.5)	27 (20.6)
Walking time per week in minutes	≥150	119 (62.6)	52 (27.4)	81 (42.6)	85 (44.7)	26 (13.7)
p-value		0.056	0.105	0.287	0.038*	0.100

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Note: *significant difference,

^a FBG: <100.0 mg/dL,

^b FBG: 100.0–125.9 mg/dL,

^c FBG: ≥126.0 mg/dL,

^d ranked using Principal Component Analysis.

Abbreviations: DBP: Diastolic Blood Pressure, FBG: Fasting Blood Glucose, HDL-C: High-Density Lipoprotein Cholesterol, LDL-C: Low-Density Lipoprotein Cholesterol, M: Men, SBP: Systolic Blood Pressure, WC: Waist Circumference, W: Women.

Effect of dyslipidemia on the mean value of different CVD risk factors

The mean values of LDL-C, total cholesterol, triglyceride, FBG, BMI, WC, SBP, DBP, age, and weight were higher among dyslipidemia positive subjects compared to negatives. The mean FBG (102.8 mg/dL) was higher among subjects with dyslipidemia compared to dyslipidemia negatives (86.7 mg/dL). However, the mean value of HDL-C was higher among dyslipidemia negative subjects compared to positives (Fig 1).

Fig 1 — LDL-C, TC, TG, HDL-C, and FBG in mg/dl; BMI in kg/m²; WC in cm; SBP and DBP in mm Hg; Age in year; Weight in kg. **Abbreviations**: BMI: Body Mass Index, DBP: Diastolic Blood Pressure, FBG: Fasting Blood Glucose, HDL-C: High-Density Lipoprotein Cholesterol, LDL-C: Low-Density Lipoprotein Cholesterol, SBP: Systolic Blood Pressure, TG: Triglyceride, TC: Total Cholesterol, WC: Waist Circumference.

Correlation of lipid components and other CVD risk factors

The most frequently occurred combination of lipid abnormalities in both sexes was TC+TG+LDL-C followed by TC+LDL-C. The combination of TC+TG+LDL-C was the most common among men followed by TG alone. Whereas, TC+LDL-C was the most common combination among women followed by TC+TG+LDL-C. More than one-fourth (27.1%) of the subjects with dyslipidemia have two lipid abnormalities, while 17.4% of them have three lipid abnormalities. All CVD risk factors were positively correlated with each other except with HDL-C. A strong correlation was observed between LDL-C and total cholesterol (r = 0.83), WC and BMI (r = 0.82) and total cholesterol and triglyceride (r = 0.49). HDL-C was positively correlated only with total cholesterol (r = 0.24) and LDL-C (r = 0.18) (Table 4 and Figs 2 and 3).

Table 4. Co-occurrence of the four lipid abnormalities stratified by gender (n = 321).

Lipid abnormalities	Men	Women	Total
Lipid abnormalities	n (%)	n (%)	n (%)
Negative	46 (31.7)	61 (34.7)	107 (33.3)
TC+TG+LDL-C	26 (17.9)	20 (11.4)	46 (14.3)
TC+LDL-C	9 (6.2)	26 (14.8)	35 (10.9)
LDL-C	9 (6.2)	18 (10.2)	27 (8.4)
TG+LDL-C	16 (11.0)	8 (4.5)	24 (7.5)
TG	18 (12.4)	5 (2.8)	23 (7.2)
TG+HDL-C	6 (4.1)	7 (4.0)	13 (4.1)
HDL-C	4 (2.8)	7 (4.0)	11 (3.4)
LDL+HDL-C	2 (1.4)	8 (4.5)	10 (3.1)
TC+TG+LDL-C+HDL-C	1 (0.7)	8 (4.5)	9 (2.8)
TG+LDL-C+HDL-C	2 (1.4)	5 (2.8)	7 (2.2)
TC+TG	5 (3.4)	0 (0.0)	5 (1.6)
TC+TG+HDL-C	0 (0.0)	2 (1.1)	2 (0.6)
TC	1 (0.7)	0 (0.0)	1 (0.3)
TC+LDL-C+HDL-C	0 (0.0)	1 (0.6)	1 (0.3)
TC+HDL-C	0 (0.0)	0 (0.0)	0 (0.0)

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Abbreviations: LDL-C: Low-Density Lipoprotein Cholesterol, TC: Total Cholesterol, TG: Triglyceride, HDL-C: High-Density Lipoprotein Cholesterol.

Fig 3 — **Abbreviations:** BMI: Body Mass Index, DBP: Diastolic Blood Pressure, FBG: Fasting Blood Glucose, HDL-C: High-Density Lipoprotein Cholesterol, LDL-C: Low-Density Lipoprotein Cholesterol, SBP: Systolic Blood Pressure, TG: Triglyceride, TC: Total Cholesterol, WC: Waist Circumference.

Factors associated with dyslipidemia

In the multivariable logistic regression model, advanced age, higher BMI, walking less than 150 minutes per week, raised FBG, and medium socio-economic status were significantly associated with dyslipidemia (p <0.05). The odds of dyslipidemia was 2.2 (aOR: 2.196, 95% CI: 1.183–4.078) and 4.3 (aOR: 4.334, 95% CI: 1.183–15.877) times higher among 40–64 years and ≥65 years, respectively compared to subjects aged below 40 years. Similarly, the odds of dyslipidemia was 2.5 (aOR: 2.50, 95% CI: 1.314–4.756) and 15.5 (aOR: 15.489, 95% CI: 3.525–68.070) times higher among overweight and obese subjects, respectively compared to normal and underweight subjects. Adults who walked less than 150 minutes per week had a 1.7 (aOR: 1.722, 95% CI: 1.004–2.953) times higher risk of dyslipidemia compared to their counterparts. The likelihood of dyslipidemia among adults with raised FBG was 4.8 (aOR: 4.804, 95% CI: 1.925–11.988) times higher compared to adults with normal FBG. Besides, participants with medium socioeconomic status had a 2.0 (aOR: 2.017, 95% CI: 1.044–3.899) times higher risk of dyslipidemia compared to participants with low socioeconomic status (Table 5).

Table 5. Bivariate and multivariable logistic regression analysis result (n = 321).

Variables	Categories	Dyslipidemia		cOR (95%CI)	aOR (95%CI)	p-value
Variables	Categories	Yes (n (%))	No (n (%))	cOR (95%CI)	aOR (95%CI)	p-value
Sex	Men	99 (68.3)	46 (31.7)	1.142 (0.715,1.822)
Sex	Women	115 (65.3)	61 (34.7)	1
Age	<40years	107 (56.3)	83 (43.7)	1	1
	40-64years	88 (80.7)	21 (19.3)	3.251 (1.865,5.666)^*	2.196 (1.183,4.078)	0.013^*
	≥65years	19 (86.4)	3 (13.6)	4.913 (1.406,17.163)^*	4.334 (1.183,15.877)	0.027^*
WC in cm (M/W)	<94/80	86 (53.4)	75 (46.6)	1	1
WC in cm (M/W)	≥94/80)	128 (80.0)	32 (20.0)	3.488 (2.124,5.728)^*	1.040 (0.498,2.176)	0.916
BMI	Underweight & normal	105 (54.7)	8 (45.3)	1	1
	Overweight	69 (79.3)	18 (20.7)	3.176 (1.758,5.738)^*	2.500 (1.314,4.756)	0.005^*
	Obese	40 (95.2)	2 (4.8)	16.571 (3.894,70.524)^*	15.489 (3.525,68.070)	<0.001^*
Educational status	Can’t read and write	30 (85.7)	5 (14.3)	3.182 (1.134,8.927)^*	1.853 (0.521,6.593)	0.341
	Elementary	36 (70.6)	15 (29.4)	1.273 (0.614,2.637)	1.078 (0.455,2.554)	0.865
	Secondary	82 (61.2)	52 (38.8)	0.836 (0.489,1.431)	0.927 (0.488,1.761)	0.816
	Tertiary	66 (65.3)	35 (34.7)	1	1
Intensity of activity in daily work	Vigorous	18 (56.3)	14 (43.7)	1
	Moderate	175 (68.1)	82 (31.9)	1.660 (0.787,3.500)
	Low	21 (65.6)	11 (34.4)	1.485 (0.541,4.077)
Walking time per week	<150 minutes	95 (72.5)	36 (27.5)	1.574 (0.971,2.553)^*	1.722 (1.004,2.953)	0.048^*
Walking time per week	≥150 minutes	119 (62.6)	71 (37.4)	1	1
Sitting time (ranked)	Lowest	59 (60.8)	38 (39.2)	1
	Medium	93 (70.5)	39 (29.5)	1.536 (0.883,2.6700
	Highest	62 (67.4)	30 (32.6)	1.331 (0.733,2.418)
FBG	<100.00 mg/dl	150 (59.8)	101 (40.2)	1	1
FBG	≥100.00 mg/dl	64 (91.4)	6 (8.6)	7.182 (2.997,17.212)^*	4.804 (1.925,11.988)	0.001^*
Waist to Hip ratio (M/W)	<0.9/0.8	40 (46.0)	47 (54.0)	1	1
Waist to Hip ratio (M/W)	≥0.9/0.8)	174 (74.4)	60 (25.6)	3.408 (2.039,5.695)^*	1.423 (0.755,2.684)	0.275
Blood pressure	Normotensive	90 (55.2)	73 (44.8)	1	1
Blood pressure	Raised blood pressure	124 (78.5)	34 (21.5)	2.958 (1.814,4.825)^*	1.599 (0.887,2.881)	0.118
Heart rate (ranked)	Low	70 (63.1)	41 (36.9)	1
	Medium	67 (65.0)	36 (35.0)	1.090 (0.623,1.907)
	High	77 (72.0)	30 (28.0)	1.503 (0.849,2.662)
Fruit intake (at least per week)	Yes	129 (65.5)	68 (34.5)	1
Fruit intake (at least per week)	No	85 (68.5)	39 (31.5)	1.149 (0.711,1.856)
Vegetable intake (at least per week)	Yes	156 (65.5)	82 (24.5)	1
Vegetable intake (at least per week)	No	58 (69.9)	25 (30.1)	1.219 (0.711,2.092)
Formal exercise	Yes	26 (61.9)	16 (38.1)	1
Formal exercise	No	188 (67.4)	91 (32.6)	1.271 (0.650,2.487)
Wealth index	Low	58 (54.7)	48 (45.3)	1	1
	Medium	87 (77.0)	26 (23.0)	2.769 (1.548,4.953)^*	2.017 (1.044,3.899)	0.037^*
	High	69 (67.6)	33 (32.4)	1.730 (0.984,3.042)	1.350 (0.716,2.545)	0.353
House servant	Yes	60 (77.9)	17 (22.1)	2.063 (1.134,3.751)^*	1.088 (0.461,2.568)	0.847
House servant	No	154 (63.1)	90 (36.9)	1	1
Laundry machine	Yes	74 (77.1)	22 (22.9)	2.018 (1.167,3.489)^*	1.055 (0.517,2.154)	0.883
Laundry machine	No	140 (62.5)	84 (37.5)	1	1
House ownership	Yes	116 (72.0)	45 (28.0)	1.631 (1.021,2.606)^*	0.704 (0.377,1.314)	0.270
House ownership	No	98 (61.3)	62 (38.7)	1	1
Type of oil	Liquid	109 (68.1)	51 (31.9)	1.140 (0.716,1.814)
Type of oil	Solid	105 (65.2)	56 (34.8)	1
Alcohol consumption	Yes	169 (68.1)	79 (31.9)	1.315 (0.743,2.325)
Alcohol consumption	No	45 (61.6)	28 (38.4)	1

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Note: Maximum SE: 0.671; Hosmer-Lemeshow: 0.572,

*significant association.

Abbreviations: cOR: crude Odds Ratio; aOR: adjusted Odds Ratio, FBG: Fasting Blood Glucose, BMI: Body Mass Index, WC: Waist Circumference, M: Men, W: Women.

Discussion

In this study, a high prevalence of dyslipidemia (66.7%) was found among adults residing in Mekelle city. This high prevalence of dyslipidemia might be attributed to rapid urbanization, improved socioeconomic status, change in dietary habits, decreased physical activity, and change in intensity of work. The present finding is consistent with the results reported in Palestine (66.4%) [18] and South Africa (67.3%) [19]. However, the prevalence of dyslipidemia in this study is higher than the previous findings reported in Eastern Ethiopia (34.8%) [11], Africa (25.5%) [9], China (32.2%) [20], Iran (30.0%) [21], India (50.7%) [22], and Uganda (63.3%) [23]. Contrary to this, the prevalence is lower than previous studies reported in Lithuania (89.7%) [24], South Africa (85.0%) [25], India (78.4%) [26] and Poland (77.2%) [27]. This difference might be due to variation in the cutoffs, stage of urbanization in the various study settings, study period, socioeconomic status, and lifestyles of the study subjects.

High LDL-C was the most prevalent (49.5%) component of dyslipidemia followed by elevated triglyceride (40.2%), which is consistent with the previous findings reported in India [28] and China [29]. This phenomenon may reflect the growing high intake of simple carbohydrates and high saturated fat diets parallel to rapid urbanization. The prevalence of high LDL-C (49.5%) in this study is higher than the previous finding reported in Ethiopia (14.1%) [30]. It is almost similar to study findings reported in India (47.8%) [26] and Iran (50.0%) [31]. But lower than the findings reported in Thailand (56.5%) [32], Uganda (60.9%) [33], Ghana (61.0%) [34], Senegal (66.3%) [35], and Jordan (75.9%) [36]. These differences might be attributed to the variations in the cutoffs, level of urbanization, study settings, lifestyle, and socioeconomic status.

The prevalence of elevated triglyceride (40.2%) in this study is higher than the previous findings reported in Senegal (7.1%) [37], Nigeria (9.9%) [38], Ethiopia (21.0%) [30], and Malawi (28.7%) [39]. However, it is consistent with the study findings reported in Venezuela (39.7%) [40], Jordan (41.9%) [36], and Uganda (42.1%) [33]. But lower than the findings documented in Thailand (49.9%) [32], India (56.1%) [28], South Africa (59.3%) [19], and Brazil (65.3%) [41].

The prevalence of elevated total cholesterol (30.8%) in this study is almost similar to the study reported in Iran (29.6%) [21]. However, it is lower than the previous study reported in Ethiopia (33.7%) [11]. On the other hand, the prevalence of total cholesterol in this study is higher than the study findings reported in different African countries [9, 30, 33–35, 37]. The prevalence of low HDL-C (16.5%) in the present study is almost similar to the previous studies done in different African countries including Malawi (15.9%) [39], Ghana (17.0%) [34], and Africa (18.5%) [9]. Unlike many previous studies [22, 30, 38, 42–45], low HDL-C is the least prevalent component of dyslipidemia in this study.

Advanced age, higher BMI, waking less than 150 minutes per week, raised FBG, and medium socio-economic status were significantly associated with a higher risk of dyslipidemia. The prevalence of dyslipidemia markedly increased with age, peaking at the peak age range (≥65 years). The prevalence of dyslipidemia was 56.3%, 80.7%, and 86.4% among <40 years, 40–64 years, and 65 years and above, respectively. This is consistent with the findings documented elsewhere [11, 21, 46–52]. However, studies conducted in China [53] and Thailand [33] reported contradictory findings. The possible explanation for this result might be, as age increases the level of activity and intensity of work decreases, which leads to excessive fat accumulation. Besides, the socio-economic status might be improved with age, which may lead to a dietary shift.

The prevalence of dyslipidemia was also significantly increased with BMI. Around 54.7% of underweight and normal adults were dyslipidemia positive. Whereas, the prevalence was 79.3% and 95.2% among overweight and obese subjects, respectively. Many previous studies documented consistent findings [19, 23, 33, 47, 48, 51, 52, 54, 55]. This might be due to the high tendency of increasing the concentration of different lipid components as increased BMI.

An inverse relationship was observed between weekly walking time and dyslipidemia, which is similar to many previous study findings [24, 37, 46, 47, 49, 52–54]. The prevalence of dyslipidemia among subjects with a mean weekly walking time of ≥150 minutes and <150 minutes was 62.6% and 72.5%, respectively. The possible explanation for this finding might be, since the energy share of activity is increased with increasing walking time, consumed energy may not be stored in the form of lipids. Besides, stored lipids might be burned for energy to fill the energy deficit during walking, which leads to a decreased ratio of fat mass to fat-free mass.

Raised FBG was positively associated with dyslipidemia in this study. The prevalence of dyslipidemia was 59.8% and 91.4% among normal and hyperglycemic subjects, respectively. This is in line with many previous studies [11, 20, 24, 26, 32, 34, 47, 51–56]. This might be due to a close relationship between blood glucose and lipid metabolism. Because both increase with increasing body weight [57].

Medium socioeconomic status was significantly associated with a higher risk of dyslipidemia compared to low socioeconomic status. Contrary to this, subjects in the high socioeconomic status were not significantly different from subjects in the low socioeconomic status. The prevalence of dyslipidemia among subjects with low, medium, and high socioeconomic status was 54.7%, 77.0%, and 67.6%, respectively, which is in line with the previous study reported in China [51]. This might be related to better economic access to alcoholic drinks, energy-dense foods, refined carbohydrates, and physical inactivity. The poor cannot afford energy-dense foods and are engaged in energy-demanding daily work, and the rich can afford healthy foods.

The prevalence of dyslipidemia among individuals who had house servant (77.9%), laundry machine (77.1%), and private house (72.0%) was higher than in individuals who had no house servant (63.1%), laundry machine (62.5%), and private house (61.3%). If an individual has a house servant or laundry machine, s/he may stop household chores. This may lead to physical inactivity, less energy expenditure, and more weight gain. Similarly, house ownership may be associated with better economic access to energy-dense foods, physical inactivity, engagement in low-intensity work, and low energy expenditure. This may cause weight gain and accumulation of excess fat, which leads to dyslipidemia. However, the effect of having a house servant, laundry machine, and house ownership on dyslipidemia was not statistically significant in this study.

As a limitation, the prevalence of dyslipidemia was based on a single laboratory test, which may lead to minor inaccuracies. As all cross-sectional study designs, limits the ability to address causal relationships between dyslipidemia and its identified associated risk factors. Since the data were collected through a questionnaire, this may lead to a recall bias.

Conclusion

In this study, the prevalence of dyslipidemia and its lipid components particularly high LDL-C, elevated triglyceride, and elevated total cholesterol were unacceptably high. Advanced age, increased BMI, walking less than 150 minutes per week, hyperglycemia, and medium socioeconomic status were significantly associated with increased risk of dyslipidemia. All are modifiable risk factors except age. This result highlights an urgent need to develop and implement appropriate intervention programs aimed at controlling the risk factors and introducing routine screening programs in the urban areas of Ethiopia. Besides, it is necessary to improve the awareness of individuals on the risk factors, and the use of proper therapeutics like nutritional, exercise, and behavioral interventions.

Supporting information

S1 File. Used dataset for dyslipidemia 2020.

(SAV)

Click here for additional data file.^{(149.8KB, sav)}

Acknowledgments

We would like to express our heartfelt gratitude to all study participants, data collectors, Tigray Health Research Institute, Jimma University, and Adigrat University for their support.

Data Availability

All relevant data are within the manuscript and its Supporting Informationw files.

Funding Statement

The award was received by GGG. No grant number for the award. website of Jimma University: www.ju.edu.et Website of Adigrat University: http://adu.edu.et.usitestat.com/ Jimma University and Adigrat Universities have funded this research in collaboration. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0243103.r001

Decision Letter 0

Frank T Spradley

25 Aug 2020

PONE-D-20-23153

Prevalence of dyslipidemia and associated risk factors among adults living in Mekelle city, Northern Ethiopia, a community-based cross-sectional study.

PLOS ONE

Dear Dr. Gebrehiwot,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Two experts in the field handled your manuscript, and we are very thankful for their time and efforts. Although some interest was found in your study, there were major concerns that arose during review that overshadowed this enthusiasm. Notably, there were several comments about the experimental design, including the need for describing the inclusion criteria; the data presentation needs work; and the text needs to be rewritten to simplify the results and explanations. Someone should be hired to proof the manuscript for spelling and grammar. Please address ALL of the reviewers' comments in your revised manuscript.

Please submit your revised manuscript by Oct 09 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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We look forward to receiving your revised manuscript.

Kind regards,

Frank T. Spradley

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1- Given the definition of the prevalence of the disease in a community, it cannot be said that this study examined the prevalence of dyslipidemia in Ethiopia.

2- What was the inclusion criteria?

3- From line 219 to line 241 the text is added because most of the explanations are in Table 1 and there is no need for explanation in the text.

More appropriate and concise sentences can be used instead. For example, most people were married and the marital status of men and women was significantly different (P = 0.001), women consumed significantly more vegetables during the week than men (P = 0.007).

4- In line 204, the name of the test is Hosmer, not Hesmer.

5- Many parts of an article have a very long text.

Reviewer #2: This manuscript provides critical information on dyslipidemic rates in the Northern Ethiopian population. Overall, the study was well designed and written, but a few points should be addressed.

1). Carefully check for spelling errors and proper grammatical usage throughout the manuscript and correct. Lines 124-128 read somewhat disjointed and are confusing to the reader (possible footnotes for further description?). Please clarify this information.

2). Under Methods: Move ' Ethical Issues' subsection information into the 'Study Design and Participants' subsection at the beginning of the Methods section.

3) Considering that roughly ~10% of participants could not read/write, how was consent given from these participants? Was proxy or next-of-kin consent given for these participants? Were they read the consent form specifically? Mention how the study gained consent in this subpopulation and list this important detail in the Methods.

4). While table 3 does a great job addressing the covariable relationship between several characteristics of the participants and dyslipidemia, an interesting comparison should be made to include "Alcohol use" which was significantly greater in the cohort. Because alcohol can dramatically alter lipid metabolism, this comparison should be made.

5). TABLE 1: The far left column is confusing to read. Recommend a headline for the far left column as "Characteristics" and show each characteristic in the subcolumn either outlined with separate borders or place break lines between characteristics. As written, the wording seems to run together and is difficult to distinguish transitions to the next characteristic listed.

6) TABLE 3: Add borders around the p-value row. As mentioned above, it is difficult to identify and read the table as presented. Consider alternative ways to organize the data, or at the very least, add more break lines and subcolumns to organize the presentation in a clearer manner.

7). Discussion: Line 453: Discuss how characteristics related to household income (e.g. House ownership, wealth, house servant, laundry machine) are related to the prevalence of dyslipidemia. Also, significant alcohol use in the cohort is a limitation that should be addressed.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Feb 9;16(2):e0243103. doi: 10.1371/journal.pone.0243103.r002

Author response to Decision Letter 0

22 Sep 2020

Editor's comments

Please ensure that your manuscript meets PLOS ONE's style requirements:- Manuscript prepared according to PLOS ONE's style.

Your ethics statement must appear in the Methods section of your manuscript:- Ethics statement incorporated in the Methods and materials section.

Reviewer 1

1. Given the definition of the prevalence of the disease in a community, it cannot be said that this study examined the prevalence of dyslipidemia in Ethiopia:- We admit the comment and addressed as follows.

This study was aimed to estimate the prevalence of dyslipidemia and its associated risk factors among adults residing in Mekelle city, Northern Ethiopia.

2. What was the inclusion criteria?:- Being an adult aged 20 years and above and residents who lived at least 6 months in the city were considered as the inclusion criteria.

3. From line 219 to line 241 the text is added because most of the explanations are in Table 1 and there is no need for explanation in the text. More appropriate and concise sentences can be used instead. For example, most people were married and the marital status of men and women was significantly different (P = 0.001), women consumed significantly more vegetables during the week than men (P = 0.007):- Addressed as recommended. We have indicated only the most important findings from table 1 in the text.

4. In line 204, the name of the test is Hosmer, not Hesmer:- Addressed as recommended. The goodness of fit of the model was checked using the Hosmer-Lemeshow test at p>0.05.

5. Many parts of an article have a very long text:- We make the article short and precise without missing the most important content.

Reviewer 2

1. Carefully check for spelling errors and proper grammatical usage throughout the manuscript and correct. Lines 124-128 read somewhat disjointed and are confusing to the reader (possible footnotes for further description?). Please clarify this information:- We made extensive revision for spelling and grammatical errors and modified accordingly.

The information in Lines 124-128 is clarified as recommended.

2. Under Methods: Move ' Ethical Issues' subsection information into the 'Study Design and Participants' subsection at the beginning of the Methods section:- ‘Ethical issues’ moved to the recommended subsection (under study design, setting, and participants).

3. Considering that roughly ~10% of participants could not read/write, how was consent given from these participants? Was proxy or next-of-kin consent given for these participants? Were they read the consent form specifically? Mention how the study gained consent in this subpopulation and list this important detail in the Methods:- Data collectors read the information to each illiterate participant and asked him or her for voluntary participation. If voluntary, they put their fingerprint as a signature in the written consent form. This is the usual practice in developing countries, where many people are illiterate. This information is indicated in the Ethical issues subsection under materials and methods.

4. While table 3 does a great job addressing the covariable relationship between several characteristics of the participants and dyslipidemia, an interesting comparison should be made to include "Alcohol use" which was significantly greater in the cohort. Because alcohol can dramatically alter lipid metabolism, this comparison should be made:- We have included alcohol use in Table 3 and Table 5 according to reviewer’s recommendation. However, the effect is not statistically significant as expected. The possible reason might be the frequency, type, and amount of alcohol consumed matters. Since alcohol is expensive, most people in the study area consumed few numbers of bottle of beer per week, which may not have significant effect on the lipid profile of individuals.

5. TABLE 1: The far left column is confusing to read. Recommend a headline for the far left column as "Characteristics" and show each characteristic in the subcolumn either outlined with separate borders or place break lines between characteristics. As written, the wording seems to run together and is difficult to distinguish transitions to the next characteristic listed:- Addressed as recommended. Two separate columns have been created (named as ‘variables’ and ‘categories’).

6. TABLE 3: Add borders around the p-value row. As mentioned above, it is difficult to identify and read the table as presented. Consider alternative ways to organize the data, or at the very least, add more break lines and subcolumns to organize the presentation in a clearer manner:- Table 3 has been re-organized as recommended.

7. Discussion: Line 453: Discuss how characteristics related to household income (e.g. House ownership, wealth, house servant, laundry machine) are related to the prevalence of dyslipidemia. Also, significant alcohol use in the cohort is a limitation that should be addressed:- Having better household assets is related with better access to energy dense foods, physical inactivity, engagement in low intensity work, low energy expenditure all of which may lead to weight gain and accumulation of excessive fat, which in turn may lead to dyslipidemia.

More than one-quarters of participants in the present study were alcohol consumers and we tried to assess the association with dyslipidemia according to your recommendation. However, the effect is not significant as expected. The possible reason might be the frequency, type, and amount of alcohol consumed matters. Since alcohol is expensive, most people consume few numbers of bottle of beer per week, which may not have significant effect on the lipid profile of individuals.

NB A rebuttal letter that responds to each point raised by the academic editor and reviewers is uploaded together with other necessary documents.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(19.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0243103.r003

Decision Letter 1

Frank T Spradley

14 Oct 2020

PONE-D-20-23153R1

Prevalence of dyslipidemia and associated risk factors among adults living in Mekelle city, Northern Ethiopia.

PLOS ONE

Dear Dr. Gebrehiwot,

There are still major revisions that require the attention of the authors. Notably, these revisions relate to English grammar and syntax. The reviewer has kindly offered suggestions in this regard, but the manuscript must be critically reviewed by the authors for readability. Moreover, and importantly, there are concerns about discrepancies in the findings. Please address ALL of the reviewer's comments in your revised manuscript.

Please submit your revised manuscript by Nov 28 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Frank T. Spradley

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

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6. Review Comments to the Author

Reviewer #1: Generally

Although the statistical methods used in this article are good and the analysis is well done, but is poorly written in terms of the principles of article writing.

I have mentioned many corrections in the findings section and other parts of the findings also need corrections similar to the ones mentioned, and this type of bug has been repeated throughout the findings section. The authors need to modify and resubmit the entire section of the findings as noted

......................................................................

It is best to add a reference to line 102, To determine what is the formula for calculating the sample size.

The “Materials and methods” section is very long and should be summarized. For example, if possible,” Study design and setting” And “Study population”, it is better to combine them and write more briefly.

Also, if possible,” Selection and training sections of data collectors” And “Data collection and quality control”, it is better to combine them and write more briefly.

Also, if possible,” Blood sample collection and laboratory analysis” And “Definition of terms”, it is better to combine them and write more briefly.

Please shorten the text wherever possible in all parts of the manuscript. For example in line 172, the phrase “Sample characteristics were expressed as mean ± standard deviation (SD) for continuous variables, and frequencies and percentages for categorical variables” can be omitted because this is specified in the results section.

In “Result” section, As mentioned in the previous review, items that are clear from the tables should not be repeated in the text. Please provide brief explanations in the text only in cases where the difference is significant.

It is better to remove the phrase “the mean + SD age of the participants was 38.99 + 14.22 years” in line 197.

It is better to remove the phrase “More than half (54.8%) of participants were women.” in line 197.

In line 199 to 203, additional text has been written about marital status, occupation, smoking and alcohol, and a brief explanation can be written instead. For example, please briefly explain that men and women were significantly different in terms of marriage, occupation, smoking and alcohol (P<0.005). And only give a brief explanation if you see an interesting result for each of them.

Please correct lines 203 to 210 as described above.

In line 207, in the table of this frequency, 13.1 is written, but in the text, 13.4.

In line 221, the phrase "slightly higher among men (68.3%) compared to women (65.3%)" It is better to remove it because it is not more common in men.

Please delete these phrases because they are clear from the table:

Line 222 “Half (49.5%) of the study participants had elevated LDL-C while 30.8% had an elevated total cholesterol level”

Line 226 “More than one fourth (27.1%) of the participants were overweight.”

Line 230 “Almost one-thirds (30.5%) and 18.7% of the participants were hypertensive and pre-hypertensive respectively”

Line 233 “The mean + SD BMI and WC were 24.4+4.9 kg/m2 and 85.6+13.6 cm respectively, with no significant difference across gender”

Line 259 “The prevalence of dyslipidemia was 57.8%, 79.3%, and 95.2% among normal, overweight, and obese participants respectively”

Also is in lines 260 to 264.

In line 229, it is better to write that the difference between men and women was significant.

In line 257 It cannot be said that the behavior of TG and HDL with the age variable, is as mentioned.

In line 264, this phrase is the type of phrase at the beginning of the paragraph. It is better to combine them and bring them in one sentence: "The risk of dyslipidemia and its component lipid abnormalities were also consistently increased with FBG and blood pressure."

In Table 2, there is a discrepancy in the findings! For the “Waist to hip ratio” in qualitative state, it is observed that a higher percentage of women have high values of Waist to hip ratio. But in quantitative state, we see that the average of this variable is higher in men. It’s not logical.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. 2021 Feb 9;16(2):e0243103. doi: 10.1371/journal.pone.0243103.r004

Author response to Decision Letter 1

3 Nov 2020

Response letter

Dear Editor:

I have sent the revised manuscript entitled “Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia.” I really appreciate for your critical review process. All the raised comments are very helpful in improving the manuscript. We have addressed all the comments raised by an editor and the reviewer in the manuscript and we have summarized in the following table. Besides, we tried to shorten the whole manuscript and major revision has been made on grammar and syntax.

1. It is best to add a reference to line 102, To determine what is the formula for calculating the sample size:-

� Lwanga S, Lemeshow S. Sample size determination in health studies: a practical manual. England: World Health Organization; 1991. (Page 5, Line 102)

2. The “Materials and methods” section is very long and should be summarized. For example,

� if possible,” Study design and setting” And “Study population”, it is better to combine them and write more briefly:-

� We have revised the Materials methods section. We have combined and briefly wrote the sub-sections based on your recommendation. As much as possible we make it short and precise. (Page 4-5, Line 84-90)

� if possible,” Selection and training sections of data collectors” And “Data collection and quality control”, it is better to combine them and write more briefly:-

� We have combined and briefly wrote the sub-sections based on your recommendation. (Page 6-7, Line 116-137)

� if possible,” Blood sample collection and laboratory analysis” And “Definition of terms”, it is better to combine them and write more briefly:_

� We have combined and briefly wrote the sub-sections based on your recommendation. (Page 7-8, Line 140-158)

3. Please shorten the text wherever possible in all parts of the manuscript. For example in line 172, the phrase “Sample characteristics were expressed as mean ± standard deviation (SD) for continuous variables, and frequencies and percentages for categorical variables” can be omitted because this is specified in the results section:-

� The indicated phrase is deleted from the data analysis part. Besides, the whole manuscript is revised to minimize the content. Unnecessary repetitions have been deleted. (Page 8, Line 161-171)

4. In “Result” section, As mentioned in the previous review, items that are clear from the tables should not be repeated in the text. Please provide brief explanations in the text only in cases where the difference is significant. It is better to remove the phrase “the mean + SD age of the participants was 38.99 + 14.22 years” in line 197. It is better to remove the phrase “More than half (54.8%) of participants were women.” in line 197.

� Unnecessary phrases are deleted from all table descriptions based on your recommendation. Explanation is provided only for variables with significant difference throughout the manuscript. (Page 9, Line 179-185)

5. In line 199 to 203, additional text has been written about marital status, occupation, smoking and alcohol, and a brief explanation can be written instead.

� For example, please briefly explain that men and women were significantly different in terms of marriage, occupation, smoking and alcohol (P<0.005).

� We have addressed the comment as you have recommended. (page 9, Line 179-180)

� And only give a brief explanation if you see an interesting result for each of them. Please correct lines 203 to 210 as described above.

� We have addressed the comment as you have recommended. (Page 9, Line 179-185)

6. In line 207, in the table of this frequency, 13.1 is written, but in the text, 13.4.

� Thank you for your critical observation. We have corrected it. (13.1%) (Page 9, Line 183)

7. In line 221, the phrase "slightly higher among men (68.3%) compared to women (65.3%)" It is better to remove it because it is not more common in men.

� Removed as recommended. (Page 10, Line 192-197)

8. Please delete these phrases because they are clear from the table:

� Line 222 “Half (49.5%) of the study participants had elevated LDL-C while 30.8% had an elevated total cholesterol level”

� Line 226 “More than one fourth (27.1%) of the participants were overweight.”

� Line 230 “Almost one-thirds (30.5%) and 18.7% of the participants were hypertensive and pre-hypertensive respectively”

� Line 233 “The mean + SD BMI and WC were 24.4+4.9 kg/m2 and 85.6+13.6 cm respectively, with no significant difference across gender”

� Line 259 “The prevalence of dyslipidemia was 57.8%, 79.3%, and 95.2% among normal, overweight, and obese participants respectively”

� Also is in lines 260 to 264.

� All phrases indicated in your comment have been deleted based on your recommendation. We really appreciate your critical review. (Page 10, Line 192-197)

9. In line 229, it is better to write that the difference between men and women was significant.

� Addressed (Page 10, Line 193-195)

10. In line 257 It cannot be said that the behavior of TG and HDL with the age variable, is as mentioned.

� Though the difference in TG was significant with age, there was inconsistency. Whereas, HDL-C was not significantly different with age. Addressed as recommended. (Page 11, Line 215-216)

11. In line 264, this phrase is the type of phrase at the beginning of the paragraph. It is better to combine them and bring them in one sentence: "The risk of dyslipidemia and its component lipid abnormalities were also consistently increased with FBG and blood pressure."

� We have combined the two sentences as you have commented. (Page 11, Line 212-214)

12. In Table 2, there is a discrepancy in the findings! For the “Waist to hip ratio” in qualitative state, it is observed that a higher percentage of women have high values of Waist to hip ratio. But in quantitative state, we see that the average of this variable is higher in men. It’s not logical.

� This is due to the difference in the cutoff point. Men have greater cutoff (0.9) than women (0.8). The same is true for waist circumference, with a cutoff 94 cm and 80 cm for men and women, respectively. Besides, women have naturally greater hip circumference relative to their waist circumference due to excessive fat accumulation around the hip compared to men, which makes women to have lower cutoff. Therefore, significantly higher proportion of women have raised waist to hip ratio (>0.8) and men have significantly higher mean value of waist to hip ratio. (Page 10, Line 193-195)

� Sincerely yours.

� Gebremedhin Gebreegziabiher (corresponding author)

� Email: ghingherg@gmail.com,

� Mobile: +251914754562,

� Nov-03/2020

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PLoS One. doi: 10.1371/journal.pone.0243103.r005

Decision Letter 2

Frank T Spradley

16 Nov 2020

Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia

PONE-D-20-23153R2

Dear Dr. Gebrehiwot,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Frank T. Spradley

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0243103.r006

Acceptance letter

Frank T Spradley

21 Dec 2020

PONE-D-20-23153R2

Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia

Dear Dr. Gebreegziabiher:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Frank T. Spradley

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Used dataset for dyslipidemia 2020.

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Informationw files.

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PERMALINK

Prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City, Northern Ethiopia

Gebremedhin Gebreegziabiher

Tefera Belachew

Kibrti Mehari

Dessalegn Tamiru

Roles

Abstract

Introduction

Methods

Results

Conclusions

Introduction

Materials and methods

Study design, setting, and population

Ethical issues

Sample size determination and sampling procedure

Data collection and quality control

Sample collection, laboratory analysis, and definition of terms

Data analysis

Results

Characteristics of the participants

Table 1. Background characteristics of the participants stratified by gender (n = 321).

Prevalence of dyslipidemia and other CVD risk factors

Table 2. Prevalence of dyslipidemia and other CVD risk factors with their mean values stratified by gender (n = 321).

Table 3. Factors affecting the prevalence of dyslipidemia and its lipid components (n = 321).

Effect of dyslipidemia on the mean value of different CVD risk factors

Fig 1. Difference in the mean value of different CVD risk factors with dyslipidemia status of the participants (n = 321).

Correlation of lipid components and other CVD risk factors

Table 4. Co-occurrence of the four lipid abnormalities stratified by gender (n = 321).

Fig 2. The co-occurrence of lipid abnormalities and their respective proportions (n = 321).

Fig 3. Correlation of different cardiovascular disease risk factors among the participants (n = 321).

Factors associated with dyslipidemia

Table 5. Bivariate and multivariable logistic regression analysis result (n = 321).

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

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