https://orcid.org/0000-0002-5232-4731
Figures
Abstract
Introduction
Dyslipidemia is a well-established, modifiable risk factor for cardiovascular disease. This study aimed to determine the pooled prevalence of dyslipidemia, its components, and associated factors among adult patients residing in Ethiopia.
Method
A comprehensive search of articles was conducted using MEDLINE, Embase, African Journals Online, Google Scholar, and University repositories. The quality of the studies was evaluated using the Joanna Briggs Institute quality appraisal criteria. Dyslipidemia was defined based on the National Cholesterol Education Program Adult Treatment Panel III cut-off points. We pooled the effect sizes using a random-effects model in Stata (v.16). Subgroup and sensitivity analysis were also performed. Small study effect was assessed by Luis Furuya-Kanamori asymmetry index, Doi plot, funnel plot, and Egger’s tests.
Results
Out of 640 initially identified articles, 104 underwent full-text review, and 62 met the inclusion criteria for the final analysis. The pooled prevalence of overall dyslipidemia was 64% (95% CI: 54–74%; I2 = 98.74; P < 0.001). The prevalence of elevated total cholesterol was 32% (95% CI: 27–36%; I2 = 97.09; P < 0.001), elevated total triglycerides was 43% (95% CI: 38–47%; I2 = 97.56; P < 0.001), elevated low-density lipoprotein was 30% (95% CI: 24–36%; I2 = 97.24; P < 0.001), and low high-density lipoprotein was 48% (95% CI: 42–54%; I2 = 98.53; P < 0.001). Having high wrist circumference, older age, being overweight or obese, residing in urban, being an alcohol consumer, being a smoker, and being physically inactive were associated with increased odds of dyslipidemia.
Citation: Zeleke TM, Liben FE, Tesfaye TS, Munea AL, Kumsa FA (2025) Prevalence and associated factors of dyslipidemia among adults with coexisting chronic disease in Ethiopia: A systematic review and meta-analysis. PLoS One 20(4): e0320119. https://doi.org/10.1371/journal.pone.0320119
Editor: Paolo Magni, Università degli Studi di Milano, ITALY
Received: January 13, 2024; Accepted: February 13, 2025; Published: April 29, 2025
Copyright: © 2025 Zeleke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Dyslipidemia refers to the presence of lipid disorders characterized by either one or any combination of elevated levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), or low levels of high-density lipoprotein cholesterol (HDL-c) [1]. The global burden of lipid abnormalities has increased over the past 30 years [2]. These abnormalities have been strongly linked to higher mortality rates, increased morbidity, and substantial healthcare expenses [3].
Globally, dyslipidemia remains a largely underdiagnosed and undertreated condition [2]. Studies indicate a high prevalence of dyslipidemia among adults with coexisting chronic diseases such as mental disorders [4], hypertension, diabetes mellitus, metabolic syndrome, and HIV (human immunodeficiency virus) patients [5]. The World Health Organization (WHO) estimated in 2008 that Africa had a lower prevalence of elevated TC 23.1% in adults [6]. However, hospital-based studies in Ethiopia have reported a prevalence of dyslipidemia as high as 93.2% among adults with chronic disease [7–9].
Dyslipidemia is a well-established modifiable risk factor for cardiovascular diseases (CVD) [10,11]. Individuals with dyslipidemia face a twofold higher risk of developing CVD compared to those with normal lipid levels [11]. CVD is a chronic non-communicable disease (NCD) and one of the leading causes of global mortality. According to the American Heart Association, the annual number of CVD-related deaths was 19 million in 2020 and is estimated to reach 22.2 million in 2030 [12,13]. Over the past four decades, there has been a significant surge in the prevalence of cardiovascular diseases (CVD) in Africa. And it is anticipated to overtake infectious diseases as the leading cause of death by 2030 [14]. Ethiopia has made inadequate progress in reducing cardiovascular and other non-communicable diseases, with CVDs are one of the leading causes of premature mortality and death [15,16]. Access to public healthcare facilities and availability of health services in Ethiopia are generally limited. Furthermore, there are substantial disparities in accessibility and inequalities in the distribution of healthcare resources [17–19]. These healthcare challenges might also contribute to the rising prevalence of dyslipidemia and present significant barriers to its prevention and management.
This review will enable researchers to evaluate the burden of dyslipidemia, where existing evidence is often hospital-based, fragmented and inconsistent. Understanding the prevalence and factors associated with dyslipidemia can also provide valuable insights for researchers and policymakers, in designing targeted interventions and strategies for effective disease management and the prevention of further complications. Therefore, this study aimed to systematically review and pool the prevalence of dyslipidemia, its components, and associated factors among adults with coexisting chronic disease in Ethiopia.
Methods
The study followed Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA-2020) guideline for design and reporting (S1 Table). The review protocol is registered with International Prospective Register of Systematic Reviews (CRD42023438436) and can be accessed online at https://www.crd.york.ac.uk/PROSPERO/#myprospero.
Search strategy
We systematically searched MEDLINE, Embase, and African Journals Online for articles published in Englishchildren between January 1, 2014, and October 23, 2023. Reference lists of included studies were also reviewed for additional studies. Furthermore, Grey literature was identified through Google Scholar, and University repositories. The search strategy included: “dyslipid*”, “dyslipidemia”, “dyslipidaemia”, “lipid profile”, “metabolic syndrome*” “Hypercholesterol*”, “hyperlipidemia”, “lipid disorder”, “Prevalence”, “associated factors”, and “Ethiopia”. A detailed list of search terms is provided in Supplementary Information (S2 Table).
Eligibility criteria
We included original studies reporting the prevalence and factors associated with overall dyslipidemia, elevated TC, LDL-c, TG, or low HDL-c among adult patients (aged ≥ 18 years) in Ethiopia, as no eligible studies on dyslipidemia in children and adolescents with coexisting chronic illnesses were identified. Studies on metabolic syndrome were also included if dyslipidemia or its components were independently identified. Only studies meeting the Adult Treatment Panel III case definition of dyslipidemia were considered. We excluded conference abstracts, editorials, commentaries, reviews, letters, case reports, and studies without primary data. Additionally, studies on the general population and Ethiopians living abroad were excluded.
Outcome measurement
The primary outcome of this review was dyslipidemia and its components. Definitions of dyslipidemia varied across studies. Therefore, for this review, it was defined as one or more of the following: elevated TC ≥ 200 mg/dl (5.17 mmol/L), elevated LDL-c ≥130 mg/dl (3.36 mmol/L), elevated TG ≥150 mg/dl (1.7 mmol/L), and low HDL-c <40 mg/dl (1.03 mmol/L) or low HDL-c <40 mg/dl (1.03 mmol/L) for men and <50 mg/dl (1.3 mmol/L) for women [20,21].
Study selection
Studies were identified using search terms and database filters, then imported them into covidence, a systematic review software (Melbourne, Australia, Veritas Health Innovation) to remove duplicates and conduct screening. Two investigators (TMZ and TST) independently screened the titles, abstracts and full texts of potentially eligible studies and assessed their quality. Discrepancies were resolved through consensus. Articles meeting both eligibility and quality criteria were included in the review.
Data extraction
Data were extracted using a structured template in Microsoft Excel. Extracted variables included the first authors’ name, year of publication, region, study settings, study design, sample size, population-specific diseases, age, frequency of overall dyslipidemia, frequency of dyslipidemia components (elevated TC, LDL-c, TG, and low HDL-c), diagnostic cut-off, and associated factors. To ensure data quality and reliability, two independent reviewers (TMZ and FEL) conducted the extraction process. Any discrepancies were resolved through consensus (S1 File).
Quality assessments
The quality of the included studies was independently assessed by two researchers (TMZ and TST) using Joanna Briggs Institute’s (JBI’s) critical appraisal checklist for prevalence studies [22], cohort studies [23], and case control [24] studies. These tools comprised nine, ten, and eleven questions, respectively, with response options of “yes,” “no,” “unclear,” or “not applicable.” Based on the raters’ evaluations, studies were categorized as “include,” “exclude,” or “seek further information.” Discrepancies between the two reviewers were resolved through discussion (S3 Table).
Statistical analysis
Data analysis was performed using Stata version 16.0 (Stata Corp LP, College Station, TX, USA). The prevalence of dyslipidemia and its components was pooled using a random-effects model, with pooled prevalence presented in a forest plot. We conducted a heterogeneity assessment using Higgins’ I-squared (I²) statistics, performed subgroup analyses, and evaluated small-study effects, including tests for publication bias. Small-study effects were initially assessed through funnel plot asymmetry inspection and Egger’s regression p-value. However, the combined studies only partially met the criteria for employing a funnel plot [25]. Consequently, the Luis Furuya-Kanamori asymmetry(LFK) index and Doi plot were used alongside the funnel plot to examine asymmetry and detect small study effects [26]. Sensitivity analysis was conducted to evaluate the influence of individual studies on the overall estimate. Statistical significance was defined as a P-value of less than 0.05.
Results
Literature search
The initial literature searches yielded 640 records. Of these, 170 duplicated were excluded. A total of 470 articles were screened based on their titles and abstracts, and 104 were underwent for full text assessment. Finally, 62 articles were included in this systematic review and meta-analysis (Fig 1 and S2 File).
Characteristics of the included studies
The included 62 studies consist of 18,939 adult patients. These studies consisted of 59 cross-sectional studies (out of which eight studies were comparative cross-sectional studies), two cohort studies, and one case-control study. Publication dates for the included studies range from 2014 to 2023. Most of the studies (43 out of 62) were conducted in one city (Addis Ababa) and two Ethiopian regional (Amhara and Oromia) administrations. These studies examined the prevalence of overall dyslipidemia or dyslipidemia components (elevated TC, TG, LDL-c, or low HDL-c). Seventeen studies examined dyslipidemia among 6,076 HIV patients, while another 17 studies investigated dyslipidemia in 5431 diabetic patients. Table 1 presents the basic characteristics of the included studies.
Prevalence of overall dyslipidemia
Among Eighteen studies that examined the prevalence of overall dyslipidemia, nine studies [7,27–34] comprising 2672 patients’ assessed overall dyslipidemia using the NCEP III criteria (i.e., at least one of the following abnormal lipid profiles: TC ≥ 200 mg/dl, TGs ≥ 150 mg/dl, LDL-c ≥ 130 mg/dl, or HDL-c < 40 mg/dl). In a similar manner, the remaining nine studies [8,35–42] consisting of 2919 patients, also assessed dyslipidemia using NCEP III criteria but further considered a sex difference in HDL-c cut-off (HDL < 40 mg/dl for males and < 50 mg/dl for female). Forty-one studies reporting on elevated TC (≥ 200 mg/dl) [7–9,27–35,37,40,43–69], forty-nine on TG (≥ 150 mg/dl) [7–9,27–35,37,38,40,41,43,45–49,51–56,59,60,62–67,69–81], Twenty six on LDL-c (≥ 130 mg/dl) [7–9,27–35,37,40,44,45,47,55,58,60,61,65–67,69,82] and forty-nine on low HDL-c using either HDL-c cut-offs < 40 mg/dl for both sexes [7–9,27–34,43,49,52–56,61,64,66,67,69,71,83] or < 40 mg/dl for male and < 50 mg/dl for female [35,37,38,40,41,45,48,51,59,60,62,65,72–81,84,85]. Accordingly, the prevalence of overall dyslipidemia ranges from 27.3% [42] to 93.2% [7], with a pooled prevalence of 64% (95% CI: 54–74%; I2 = 98.74; P < 0.001). The prevalence of TC ranges from 3.6% in psychiatric patients [62] to 58.1% [52] in hypertensive patients, with the pooled estimate of 32% (95% CI: 27–36%; I2 = 97.09; P < 0.001). Similarly, the prevalence of TG ranges from 10.3% among helicobacter pylori infected patients [37] to 84.4% in diabetic patients [45], with a pooled prevalence of 43% (95% CI: 38–47%; I2 = 97.56; P < 0.001). Likewise, the prevalence of elevated LDL-c ranges from 4.9% [35] among HIV patients to 58.1% [29] among dyspeptic patients, with a pooled prevalence of 30% (95% CI: 24–36%; I2 = 97.24; P < 0.001). In a similar fashion, the prevalence of low HDL-c ranges from 10.2% [28] to 89.8% [9]. The pooled prevalence of HDL-c was 48% (95% CI: 42–54%; I2 = 98.53; P < 0.001). Regardless of which HDL-c cut-off is used, the prevalence of HDL-c remained the same with HDL-c cut-off value < 40 mg/dl in both sexes 48% (95% CI: 38–58%; I2 = 98.99; P < 0.001) and with HDL-c cut-off value < 40 mg/dl for male and < 50 mg/dl for female, 48% (95% CI: 41–55%; I2 = 97.38; P < 0.001). The heterogeneity between groups was statistically significant (P < 0.001) (Figs 2 and 3).
A, Forest plot displaying the pooled prevalence of overall dyslipidemia. B, Forest plot displaying the pooled prevalence of TC. C, Forest plot displaying the pooled prevalence of TG. D, Forest plot displaying the pooled prevalence of LDL-c.
Factors associated with overall dyslipidemia
This study showed that patients with higher WC (≥ 94 cm) had 3 times more odds to develop dyslipidemia than patients with WC < 94 cm. With respect to age, the pooled odds ratio from eight studies revealed that patients aged > 40 years or > 30 years have higher odds of dyslipidemia than their respective younger age groups. Our study also identified that patients who were overweight or obese, urban residents, and alcohol consumers had more odds to experience dyslipidemia. Similarly, smokers and patients who were physically inactive were associated with having higher odds of dyslipidemia. In contrast, this meta-analysis reported that gender, educational status, and marital status had no association with dyslipidemia (Table 2).
Sensitivity and sub-group analyses
Due to heterogeneity among studies, we performed several analyses to identify the source of heterogeneity. Initially, we carried out sensitivity analysis by excluding outliers from overall dyslipidemia [7,8], TC [62], TG [37,45], LDL-c [35,45], and HDL-c [7,28,67] to check their effect on the degree of variability. However, the heterogeneity among studies remained high (I2 ≥ 95). Consequently, we included all the studies in the final meta-analysis model. Following this, we conducted sub-group analyses based on region, HDL-c cut-off, and study population.
Our finding suggested dyslipidemia and its components were highly prevalent across some of regional administrations in Ethiopia. We observed variation in the pooled estimate of overall dyslipidemia ranges from 82% (95% CI: 79–85%) in Sidama to 58% (95% CI: 36–80%; I2 = 98.64; P < 0.001) in Oromia region. Additionally, the pooled estimate of elevated TC ranges from 39% (95% CI: 27–51%; I2 = 97.05; P < 0.001) in Amhara to 28% (95% CI: 19–38%; I2 = 98.15; P < 0.001) in Oromia region. Furthermore, the pooled estimate of elevated TG ranges from 61% (95% CI: 58–64%) in Harari and Dire Dawa to 31% (95% CI: 27–36%; I2 = 88.58; P < 0.001) in Addis Ababa. Moreover, the pooled estimate of elevated LDL-c ranges from 39% (95% CI: 27–52%; I2 = 96.72; P < 0.001) in Amhara to 25% (95% CI: 14–36%; I2 = 96.50; P < 0.001) in Oromia. Regarding HDL-c, the pooled prevalence ranged from 56% (95% CI: 53–59%) in Tigray to 41% (95% CI: 31–51%; I2 = 97.47; P < 0.001) in Addis Ababa. The degree of heterogeneity between groups in each subgroup analysis was statistically significant (S1 Fig). Our sub-group analysis also suggested that a higher prevalence of overall dyslipidemia, 68% (95% CI: 56–81%; I2 = 98.39; P < 0.001), was seen among studies with HDL-c cut-off value < 40 mg/dl.
Moreover, there was a significant variation in the pooled prevalence of dyslipidemia and its components among the various study populations. The highest pooled prevalence of elevated TC has been observed among hypertensive patients, 49% (95% CI: 42–56%; I2 = 73.42; P < 0.001), while the least was seen among psychiatric patients, 23% (95% CI: 2–44%). The pooled prevalence of elevated TG ranged from 55% (95% CI: 46–63%; I2 = 97.19; P < 0.001) among diabetic patients to 27% (95% CI: 24–30%; I2 = 41.37; P = 0.13) among psychiatric patients. The pooled prevalence of elevated LDL-c ranged from 39% (95% CI: 21–58%) among hypertensive patients to 25% (95% CI: 17–32%; I2 = 96.99; P < 0.001) among HIV patients. The pooled prevalence of low HDL-c ranged from 61% (95% CI: 57–64%) among patients with metabolic syndrome to 42% (95% CI: 30–54%; I2 = 98.23; P < 0.001) among diabetic patients. The heterogeneities between groups were statistically significant. Fig 4 and S2 Fig present findings of the subgroup analyses by study population, and HDL-c cut-off.
Fig 4 depicts the prevalence of overall dyslipidemia, elevated total cholesterol, elevated triglyceride, low HDL-c, and elevated LDL-c in patients with HIV, hypertension, diabetes, H. pylori, psychiatric illness and those with metabolic syndrome. The prevalence of dyslipidemia among HIV patients ranges from 25% (elevated LDL-c) to 61% (overall dyslipidemia). Among hypertensive patients, the prevalence of dyslipidemia varies from 39% (elevated LDL-c) to 70% (overall dyslipidemia). For diabetic patients, the highest and lowest prevalence of dyslipidemia is 55% and 32%, respectively.
Small study effects
To assess small-study effects, we initially visually inspected the funnel plots, which revealed asymmetry for elevated TC and LDL-C. Subsequently, Doi plots and LFK indices were computed, alongside with Egger’s regression p-values. Similarly, Egger’s regression test confirmed the presence of asymmetry among the included studies for elevated TC (P < 0.001) and LDL-c (P < 0.001). The LFK index also indicated a major asymmetry for elevated TC (LFK index = 2.47) and LDL-c (LFK index = 2.32). The LFK index for overall dyslipidemia (LFK index= -1.65) showed a minor asymmetry. However, no asymmetry was identified for elevated TG (LFK index= 0.53) and low HDL-c (LFK index = 0.43) (S3 Fig and S4 Fig).
Discussion
This is the first systematic review and meta-analysis study on dyslipidemia, its components and associated factors among adults with coexisting chronic disease in Ethiopia. Our result showed 64% of the patients have at least one lipid abnormality, and pooled prevalence of elevated TC, elevated TG, elevated LDL-c, and low HDL-c were 32%, 43%, 30%, and 48%, respectively. In this study, the most prominent form of dyslipidemia component was HDL-c, an independent risk factor for CVD. The highest prevalence of low HDL-c phenomenon might reflect a sedentary lifestyle accompanied by high consumption of refined and unhealthy foods that are rich in saturated fat and trans-fatty acids [86]. Having high wrist circumference, older age, being overweight or obese, residing in urban, being alcohol consumers, being smokers, and being physically inactive were significant risk factors for overall dyslipidemia. Additionally, the geographical stratification of results revealed that regional administrations like Sidama, Amhara, and Tigray had a higher prevalence of dyslipidemia compared to the rest of the regional administrations in Ethiopia. Furthermore, our analysis also showed that patients with hypertension and diabetes had a higher prevalence of dyslipidemia. In the present review, the prevalence of overall dyslipidemia (64%) is significantly higher than those reported in china (42.1%) [87], Sub-Saharan Africa (38.38%) [88], Italy (17.5%) [89] and Africa (16.5%) [90]. Our finding also showed a substantially higher prevalence of overall dyslipidemia across different parts of Africa: East Africa (60.8%), Southern (53.1%), west (46.7%), and Central Africa (21%) [90]. Likewise, the prevalence of dyslipidemia components are markedly higher than those observed in Africa [5], China [91], and Malaysia [6]. However, the Malaysian study reported a higher prevalence of elevated LDL-c (73%) and TC (52%). Moreover, our study also revealed a higher prevalence of overall dyslipidemia and its components among patients with hypertension, HIV, and diabetes. Similarly, the prevalence of dyslipidemia components in hypertensive and HIV patients were notably higher compared to the findings of a meta-analysis on dyslipidemia among adults in Africa, which found a prevalence of dyslipidemia components among adult hypertensive patients (elevated TC 38%, elevated TG 22.2%, elevated LDL-c 32.7%, low HDL-c 39.4%) and (elevated TC 24.2%, elevated TG 22.7%, elevated LDL-c 20.8%) among HIV patients [5].
Consistent with this, we observed a higher prevalence of elevated triglycerides (55%) among diabetic patients compared to finding from a study conducted in Ethiopia on diabetic individuals [92]. Our findings, however, were lower than a finding from a study in Ethiopia, which reported TC, LDL-c, and HDL-c levels of 34.08%, 41.13%, and 44.36%, respectively [92]. Discrepancy in prevalence estimates across studies can be attributed to the differences in diagnostic cut-offs, dyslipidemia components assessed, study settings, populations, period, and differences in lifestyle.
Another important aspect of this study is that 19 variables were extracted from 11 studies to determine factors associated with dyslipidemia. Our data demonstrated that 11 of the variables (age, residence, physical activity, current alcohol consumption, history of alcohol consumption, smoking, history of smoking, being hypertensive, high HC, high WC, and BMI) were significantly associated with overall dyslipidemia. This was in agreement with the findings of a study on determinants of dyslipidemia in Africa, where patients with hypertension, BMI, and high WC were significantly associated with dyslipidemia [90]. No association was found between sex, educational status, marital status, chat chewing, helicobacter pylori infection, family history of DM, family history of DM, and duration of DM with overall dyslipidemia. On the contrary, Obsa et al. reported that sex, educational status, and marital status were significantly associated with the odds of having dyslipidemia [90]. As most of risk factors are modifiable, excluding age, it is crucial to deliver behavioral interventions targeting the identified risk factors. It is highly recommended that performing physical activity, reducing alcohol consumption, and quitting smoking will help to prevent and control dyslipidemia [21].
In this meta-analysis, the finding of high overall dyslipidemia and its components in adults with coexisting chronic disease is hardly wondering given the rapid urbanization, dietary and behavioral changes, and widespread adoption of unhealthy lifestyles occurring across Ethiopia [93, 94]. Literacy status and lack of awareness of common cardiovascular risk factors may also play an important roles and should be considered in prevention and treatment approaches of dyslipidemia [95]. As dyslipidemia is a well-established risk factor for cardiovascular diseases [91], these results highlight the urgent need for appropriate interventions, both clinical and non-clinical. If left uncontrolled, it could result in serious health problems [96]. In this context, dyslipidemia not only fuels the epidemic of CVD but also promotes the progressiveness of adverse outcomes. Access to lipid-lowering drugs remains limited for substantial portions of the world’s population, especially in low-income countries such as Ethiopia [2]. Our findings underscore the critical need to address dyslipidemia among adults with coexisting chronic diseases in Ethiopia. This highlights the urgent need for an integrated approach to the prevention, detection, and management of dyslipidemia in the country. This finding is believed to inform clinicians, public health programmers, and policy makers about dyslipidemia and its components burden in adults with coexisting chronic disease in Ethiopia.
This novel study has several strengths including a large sample size and the assessment of dyslipidemia and its components across various patient groups. This approach is particularly valuable as it goes beyond limiting the analysis to a single patient subgroup, such as those with diabetes or hypertension. Despite the important evidence generated by this meta-analysis, our study should be interpreted in light of some limitations. The considerable heterogeneity observed among the included studies was not fully explained through sensitivity or subgroup analyses based on the cut-off used to define dyslipidemia, region and types of coexisted chronic diseases. The heterogeneity between studies is inevitable [97] and could arise from difference in study settings, sample sizes, and the method used to measure dyslipidemia. Another potential limitation is the generalizability of the study findings may be limited, as the included participants were not representative of the national population.
Conclusions
Our study highlights an alarmingly higher prevalence of lipid abnormalities in adult patients with coexisting chronic diseases in Ethiopia. Consequently, dyslipidemia and its components represent a significant public health challenge in the country. To address this issue effectively, it is crucial to implement measures that improve the prevention, early detection, treatment, and management of dyslipidemia in adults living in Ethiopia. These actions will help mitigate the health and socioeconomic consequences associated with this condition.
Supporting information
S2 Table. Search strategy used for the electronic databases.
https://doi.org/10.1371/journal.pone.0320119.s002
(DOCX)
S3 Table. Final Joanna Briggs Institute’s (JBI’s) critical appraisal checklist of included studies.
https://doi.org/10.1371/journal.pone.0320119.s003
(PDF)
S1 Fig. Sub-group analysis by region.
Forest plots of pooled prevalence of overall dyslipidemia and dyslipidemia components among patients in Ethiopia by region. A, Forest plot displaying the pooled prevalence of overall dyslipidemia by region. B, Forest plot displaying the pooled prevalence of TC by region. C, Forest plot displaying the pooled prevalence of TG by region. D, Forest plot displaying the pooled prevalence of LDL-c by region. E, Forest plot displaying the pooled prevalence of HDL-c by region.
https://doi.org/10.1371/journal.pone.0320119.s004
(TIF)
S2 Fig. Sub-group analysis by HDL-c cut-off and study population.
Forest plots of pooled prevalence of overall dyslipidemia and dyslipidemia components among patients in Ethiopia by sub-groups. A, Forest plot displaying the pooled prevalence of overall dyslipidemia by HDL-c cut-off. B, Forest plot displaying the pooled prevalence of TC by study population. C, Forest plot displaying the pooled prevalence of TG by study population. D, Forest plot displaying the pooled prevalence of LDL-c by study population. E, Forest plot displaying the pooled prevalence of HDL-c by study population.
https://doi.org/10.1371/journal.pone.0320119.s005
(TIF)
S3 Fig. Small study effect (Funnel plot).
Small study effect assessed by using funnel plot for overall dyslipidemia and dyslipidemia components among patients in Ethiopia, 2023. A, Small study effect assessed by using funnel plot for overall dyslipidemia. B, Small study effect assessed by using funnel plot for TC. C, Small study effect assessed by using funnel plot for TG. D, Small study effect assessed by using funnel plot for LDL-c. E, Small study effect assessed by using funnel plot for HDL-c.
https://doi.org/10.1371/journal.pone.0320119.s006
(TIF)
S4 Fig. Small study effect (Doi plot and LFK index).
Small study effect assessed by using the LFK index for overall dyslipidemia and dyslipidemia components among patients in Ethiopia, 2023. A, Small study effect assessed by using the LFK index for overall dyslipidemia. B, Small study effect assessed by using the LFK index for TC. C, Small study effect assessed by using the LFK index for TG. D, Small study effect assessed by using the LFK index for LDL-c.E, Small study effect assessed by using the LFK index for HDL-c.
https://doi.org/10.1371/journal.pone.0320119.s007
(TIF)
S1 File. Raw data on dyslipidemia prevalence and risk factors in Ethiopian adults with chronic diseases.
https://doi.org/10.1371/journal.pone.0320119.s008
(XLSX)
S2 File. All studies identified in the literature search.
https://doi.org/10.1371/journal.pone.0320119.s009
(XLSX)
References
- 1. Cooney MT, Dudina A, De Bacquer D, Wilhelmsen L, Sans S, Menotti A, et al. HDL cholesterol protects against cardiovascular disease in both genders, at all ages and at all levels of risk. Atherosclerosis. 2009;206(2):611–6. pmid:19375079
- 2. Pirillo A, Casula M, Olmastroni E, Norata GD, Catapano AL. Global epidemiology of dyslipidaemias. Nat Rev Cardiol. 2021;18(10):689–700. pmid:33833450
- 3. Najafipour H, Shokoohi M, Yousefzadeh G, Sarvar Azimzadeh B, Moshtaghi Kashanian G, Bagheri MM, et al. Prevalence of dyslipidemia and its association with other coronary artery disease risk factors among urban population in Southeast of Iran: results of the Kerman coronary artery disease risk factors study (KERCADRS). J Diabetes Metab Disord. 2016;15:49. pmid:27777902
- 4. Gardner-Sood P, Lally J, Smith S, Atakan Z, Ismail K, Greenwood KE, et al. Cardiovascular risk factors and metabolic syndrome in people with established psychotic illnesses: baseline data from the IMPaCT randomized controlled trial. Psychol Med. 2015;45(12):2619–29. pmid:25961431
- 5. Noubiap JJ, Bigna JJ, Nansseu JR, Nyaga UF, Balti EV, Echouffo-Tcheugui JB, et al. Prevalence of dyslipidaemia among adults in Africa: a systematic review and meta-analysis. Lancet Glob Health. 2018;6(9):e998–1007. pmid:30103999
- 6. Mohamed-Yassin M-S, Rosman N, Kamaruddin KN, Miptah HN, Baharudin N, Ramli AS, et al. A systematic review and meta-analysis of the prevalence of dyslipidaemia among adults in Malaysia. Sci Rep. 2023;13(1):11036. pmid:37419924
- 7. Mohammed O, Alemayehu E, Ebrahim E, Fiseha M, Gedefie A, Ali A, et al. Atherogenic dyslipidemia and associated risk factors among hypertensive patients of five health facilities in Northeast Ethiopia. PLoS One. 2023;18(2):e0277185. pmid:36787318
- 8. Agete TH, Eshetu NG. Factors associated with atherogenic dyslipidemia among hypertensive patients at southern Ethiopia. Int J Med Med Sci. 2018;10(7):86–93.
- 9. Mohammed AE, Shenkute TY, Gebisa WC. Diabetes mellitus and risk factors in human immunodeficiency virus-infected individuals at Jimma University Specialized Hospital, Southwest Ethiopia. Diabetes Metab Syndr Obes. 2015;8:197–206. pmid:25926749
- 10. Nelson RH. Hyperlipidemia as a risk factor for cardiovascular disease. Prim Care. 2013;40(1):195–211. pmid:23402469
- 11. Yao YS, Li TD, Zeng ZH. Mechanisms underlying direct actions of hyperlipidemia on myocardium: An updated review. Lipids Health Dis. 2020;19(1):23. pmid:32035485
- 12. Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. Heart disease and stroke statistics-2023 update: A report from the american heart association. Circulation. 2023;147(8):e93–621. pmid:36695182
- 13. Banach M, Penson PE, Farnier M, Fras Z, Latkovskis G, Laufs U, et al. Bempedoic acid in the management of lipid disorders and cardiovascular risk. 2023 position paper of the International Lipid Expert Panel (ILEP). Prog Cardiovasc Dis. 2023;79:2–11. pmid:36889490
- 14. World Health Organization. Global status report on noncommunicable diseases 2014. Available from: https://www.who.int/nmh/publications/ncd-status-report-2014/en/
- 15. Misganaw A, Haregu TN, Deribe K, Tessema GA, Deribew A, Melaku YA, et al. National mortality burden due to communicable, non-communicable, and other diseases in Ethiopia, 1990–2015: findings from the Global Burden of Disease Study 2015. Population health metrics. 2017 Dec;15:1–7.
- 16. Ali S, Misganaw A, Worku A, Destaw Z, Negash L, Bekele A, et al. The burden of cardiovascular diseases in Ethiopia from 1990 to 2017: Evidence from the Global Burden of Disease Study. Int Health. 2021;13(4):318–26. pmid:32945840
- 17. Hierink F, Oladeji O, Robins A, Muñiz MF, Ayalew Y, Ray N. A geospatial analysis of accessibility and availability to implement the primary healthcare roadmap in Ethiopia. Commun Med (Lond). 2023;3(1):140. pmid:37805668
- 18. Woldemichael A, Takian A, Akbari Sari A, Olyaeemanesh A. Inequalities in healthcare resources and outcomes threatening sustainable health development in Ethiopia: panel data analysis. BMJ Open. 2019;9(1):e022923. pmid:30705237
- 19. Hendrix N, Warkaye S, Tesfaye L, Woldekidan MA, Arja A, Sato R, et al. Estimated travel time and staffing constraints to accessing the Ethiopian health care system: A two-step floating catchment area analysis. J Glob Health. 2023;13:04008. pmid:36701563
- 20. Scuteri A, Cunha PG, Agabiti Rosei E, Badariere J, Bekaert S, Cockcroft JR, et al. Arterial stiffness and influences of the metabolic syndrome: a cross-countries study. Atherosclerosis. 2014;233(2):654–60. pmid:24561493
- 21. Expert Panel on Detection, , and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III). JAMA. 2001;285(19):2486–97. pmid:11368702
- 22.
Institute J. Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. 2017.
- 23.
Institute J. Joanna Briggs Institute Critical Appraisal Checklist for Cohort Studies. 2017.
- 24.
Institute JB. JBI critical appraisal checklist for case control studies. 2020.
- 25. Ioannidis JPA, Trikalinos TA. The appropriateness of asymmetry tests for publication bias in meta-analyses: A large survey. CMAJ. 2007;176(8):1091–6. pmid:17420491
- 26. Furuya-Kanamori L, Barendregt JJ, Doi SAR. A new improved graphical and quantitative method for detecting bias in meta-analysis. Int J Evid Based Healthc. 2018;16(4):195–203. pmid:29621038
- 27. Addisu B, Bekele S, Wube TB, Hirigo AT, Cheneke W. Dyslipidemia and its associated factors among adult cardiac patients at Ambo university referral hospital, Oromia region, west Ethiopia. BMC Cardiovasc Disord. 2023;23(1):321. pmid:37355585
- 28. Woldeyes E, Fisseha H, Mulatu HA, Ephrem A, Benti H, Alem MW, et al. Prevalence of clinical cardiovascular disease risk factors among HIV infected patients on anti-retroviral treatment in a tertiary hospital in Ethiopia. HIV AIDS (Auckl). 2022;14:297–309. pmid:35747268
- 29. Hashim M, Mohammed O, G/Egzeabeher T, Wolde M. The association of Helicobacter Pylori infection with dyslipidaemia and other atherogenic factors in dyspeptic patients at St. Paul’s Hospital Millennium Medical College. Heliyon. 2022;8(5):e09430. pmid:35600431
- 30. Hirigo AT, Teshome T, Abera Gitore W, Worku E. Prevalence and associated factors of dyslipidemia among psychiatric patients on antipsychotic treatment at Hawassa University comprehensive specialized hospital. Nutr Metab Insights. 2021;14:11786388211016842. pmid:34035653
- 31. Fiseha T, Alemu W, Dereje H, Tamir Z, Gebreweld A. Prevalence of dyslipidaemia among HIV-infected patients receiving combination antiretroviral therapy in North Shewa, Ethiopia. PLoS One. 2021;16(4):e0250328. pmid:33905435
- 32. Yazie TS. dyslipidemia and associated factors in tenofovir disoproxil fumarate-based regimen among human immunodeficiency virus-infected ethiopian patients: A hospital-based observational prospective cohort study. Drug Healthc Patient Saf. 2020;12:245–55. pmid:33304107
- 33. Duguma F, Gebisa W, Mamo A, Tamiru D, Woyesa S. Diabetes mellitus and associated factors among adult HIV patients on highly active anti-retroviral treatment. HIV AIDS (Auckl). 2020;12:657–65. pmid:33162756
- 34. Abdu A, Cheneke W, Adem M, Belete R, Getachew A. Dyslipidemia and associated factors among patients suspected to have helicobacter pylori infection at jimma university medical center, Jimma, Ethiopia. Int J Gen Med. 2020;13:311–21. pmid:32606896
- 35. Assefa A, Abiye AA, Tadesse TA, Woldu M. Prevalence and factors associated with dyslipidemia among people living with HIV/AIDS on follow-up care at a tertiary care hospital in Ethiopia: A cross-sectional study. Drug Healthc Patient Saf. 2023;15:93–102. pmid:37546170
- 36. Woldeyes E, Fisseha H, Mulatu HA, Ephrem A, Benti H, Alem MW, et al. Echocardiographic findings and associated factors in HIV-infected patients at a tertiary hospital in Ethiopia. Medicine (Baltimore). 2022;101(33):e30081. pmid:35984157
- 37. Nigatie M, Melak T, Asmelash D, Worede A. Dyslipidemia and its associated factors among helicobacter pylori-infected patients attending at university of gondar comprehensive specialized hospital, Gondar, North-West Ethiopia: A comparative cross-sectional study. J Multidiscip Healthc. 2022;15:1481–91. pmid:35873092
- 38. Fanta K, Daba FB, Asefa ET, Chelkeba L, Melaku T. Prevalence and impact of metabolic syndrome on short-term prognosis in patients with acute coronary syndrome: Prospective cohort study. Diabetes Metab Syndr Obes. 2021;14:3253–62. pmid:34295168
- 39. Timerga A, Kelta E, Kenenisa C, Zawdie B, Habte A, Haile K. Serum electrolytes disorder and its associated factors among adults admitted with metabolic syndrome in Jimma Medical Center, South West Ethiopia: Facility based crossectional study. PLoS One. 2020;15(11):e0241486. pmid:33152003
- 40. Kemal A, Teshome MS, Ahmed M, Molla M, Malik T, Mohammed J, et al. Dyslipidemia and associated factors among adult patients on antiretroviral therapy in armed force comprehensive and specialized hospital, addis ababa, Ethiopia. HIV AIDS (Auckl). 2020;12:221–31. pmid:32753977
- 41. Bune GT, Yalew AW, Kumie A. The global magnitude of metabolic syndrome among antiretroviral therapy (ART) exposed and ART-naïve adult HIV-infected patients in gedio-zone, southern Ethiopia: Comparative cross-sectional study, using the Adult Treatment Panel III criteria. Diabetes Metab Syndr. 2019;13(5):2833–41. pmid:31425944
- 42. Bahrey D, Gebremedhn G, Mariye T, Girmay A, Aberhe W, Hika A, et al. Prevalence and associated factors of chronic kidney disease among adult hypertensive patients in Tigray teaching hospitals: a cross-sectional study. BMC Res Notes. 2019;12(1):562. pmid:31500655
- 43. Fentie D, Yibabie S. Magnitude and associated factors of dyslipidemia among patients with severe mental illness in dire Dawa, Ethiopia: neglected public health concern. BMC Cardiovasc Disord. 2023;23(1):298. pmid:37312056
- 44. Seifu L, Gashaye Y, Taye M. Renal function abnormalities among patients with acute stroke at a tertiary hospital in Ethiopia. Ethiop J Health Sci. 2022;32(6):1175–82. pmid:36475245
- 45. Sahiledengle B, Assefa T, Negash W, Tahir A, Regasa T, Tekalegn Y, et al. Prevalence and factors associated with diabetic retinopathy among adult diabetes patients in Southeast Ethiopia: A hospital-based cross-sectional study. Clin Ophthalmol. 2022;16:3527–45. pmid:36274673
- 46. Letta S, Aga F, Yadeta TA, Geda B, Dessie Y. Poor self-care practices and being urban resident strongly predict chronic complications among patients with type 2 diabetes in Eastern Ethiopia: A hospital-based cross-sectional study. Diabetes Metab Syndr Obes. 2022;15:2095–106. pmid:35898445
- 47.
Endale Kabtu AT. Prevalence of diabetic macular edema and risk factors among diabetic patients at the University of Gondar Tertiary Eye Care and Training Center, North West Ethiopia. 2022.
- 48. Gebreyesus HA, Abreha GF, Besherae SD, Abera MA, Weldegerima AH, Gidey AH, et al. High atherogenic risk concomitant with elevated HbA1c among persons with type 2 diabetes mellitus in North Ethiopia. PLoS One. 2022;17(2):e0262610. pmid:35104300
- 49. Abdissa D, Hirpa D. Dyslipidemia and its associated factors among adult diabetes outpatients in West Shewa zone public hospitals, Ethiopia. BMC Cardiovasc Disord. 2022;22(1):39. pmid:35148683
- 50. Kebede WM, Gizachew KD, Mulu GB. Prevalence and risk factors of dyslipidemia among type 2 diabetes patients at a referral hospital, North Eastern Ethiopia. Ethiop J Health Sci. 2021;31(6):1267–76. pmid:35392337
- 51. Haile K, Haile A, Timerga A. Predictors of lipid profile abnormalities among patients with metabolic syndrome in Southwest Ethiopia: A cross-sectional study. Vasc Health Risk Manag. 2021;17:461–9. pmid:34393487
- 52. Gebremedhin MH, Lake EA, Gebrekirstos LG. Heavy khat (Catha edulis) chewing and dyslipidemia as modifiable hypertensive risk factors among patients in Southwest, Ethiopia: Unmatched case-control study. PLoS One. 2021;16(10):e0259078. pmid:34699551
- 53. Bogale K, Aderaw A. Blood pressure control with reference to intensive blood pressure targets among hypertension patients on chronic follow-up at dessie referral hospital, Northeast Ethiopia. Integr Blood Press Control. 2021;14:1–7. pmid:33519231
- 54. Zewudie AZ, Regasa T, Hambisa S, Nureye D, Mamo Y, Aferu T, et al. Treatment outcome and its determinants among patients admitted to stroke unit of jimma university medical center, Southwest Ethiopia. Stroke Res Treat. 2020;2020:8817948. pmid:33489080
- 55. Kumie G, Melak T, Wondifraw Baynes H. The association of serum lipid levels with breast cancer risks among women with breast cancer at felege hiwot comprehensive specialized hospital, Northwest Ethiopia. Breast Cancer (Dove Med Press). 2020;12:279–87. pmid:33363407
- 56. Haile K, Timerga A. Dyslipidemia and its associated risk factors among adult type-2 diabetic patients at Jimma University Medical Center, Jimma, Southwest Ethiopia. Diabetes Metab Syndr Obes. 2020;13:4589–97. pmid:33273834
- 57. Bizuayehu Wube T, Mohammed Nuru M, Tesfaye Anbese A. A comparative prevalence of metabolic syndrome among type 2 diabetes mellitus patients in Hawassa university comprehensive specialized hospital using four different diagnostic criteria. Diabetes Metab Syndr Obes. 2019;12:1877–87. pmid:31571962
- 58. Gebrie A, Gnanasekaran N, Menon M, Sisay M, Zegeye A. Evaluation of lipid profiles and hematological parameters in hypertensive patients: laboratory-based cross-sectional study. SAGE Open Med. 2018;6:2050312118756663. pmid:29468066
- 59. Bosho DD, Dube L, Mega TA, Adare DA, Tesfaye MG, Eshetie TC. Prevalence and predictors of metabolic syndrome among people living with human immunodeficiency virus (PLWHIV). Diabetol Metab Syndr. 2018;10:10. pmid:29483948
- 60. Biadgo B, Melak T, Ambachew S, Baynes HW, Limenih MA, Jaleta KN, et al. The Prevalence of Metabolic Syndrome and Its Components among Type 2 Diabetes Mellitus Patients at a Tertiary Hospital, Northwest Ethiopia. Ethiop J Health Sci. 2018;28(5):645–54. pmid:30607080
- 61. Ataro Z, Ashenafi W, Fayera J, Abdosh T. Magnitude and associated factors of diabetes mellitus and hypertension among adult HIV-positive individuals receiving highly active antiretroviral therapy at Jugal Hospital, Harar, Ethiopia. HIV AIDS (Auckl). 2018;10:181–92. pmid:30349400
- 62. Asaye S, Bekele S, Tolessa D, Cheneke W. Metabolic syndrome and associated factors among psychiatric patients in Jimma University Specialized Hospital, South West Ethiopia. Diabetes Metab Syndr. 2018;12(5):753–60. pmid:29729979
- 63. Adal M, Howe R, Kassa D, Aseffa A, Petros B. Malnutrition and lipid abnormalities in antiretroviral naïve HIV-infected adults in Addis Ababa: A cross-sectional study. PLoS One. 2018;13(4):e0195942. pmid:29672576
- 64. Bekele S, Yohannes T, Mohammed AE. Dyslipidemia and associated factors among diabetic patients attending Durame General Hospital in Southern Nations, Nationalities, and People’s Region. Diabetes Metab Syndr Obes. 2017;10:265–71. pmid:28790859
- 65. Hirigo AT, Tesfaye DY. Influences of gender in metabolic syndrome and its components among people living with HIV virus using antiretroviral treatment in Hawassa, southern Ethiopia. BMC Res Notes. 2016;9:145. pmid:26945987
- 66. Abebe SM, Getachew A, Fasika S, Bayisa M, Girma Demisse A, Mesfin N. Diabetes mellitus among HIV-infected individuals in follow-up care at University of Gondar Hospital, Northwest Ethiopia. BMJ Open. 2016;6(8):e011175. pmid:27540099
- 67. Henock A, Techalew S, Kinfe L. Dyslipidemia among diabetic patients in Southern Ethiopia: Cross-sectional study. J Diabetes Endocrinol. 2015;6(4):19–24.
- 68. Tesfaye DY, Kinde S, Medhin G, Megerssa YC, Tadewos A, Tadesse E, et al. Burden of metabolic syndrome among HIV-infected patients in Southern Ethiopia. Diabetes Metab Syndr. 2014;8(2):102–7. pmid:24907175
- 69. Abebe M, Kinde S, Belay G, Gebreegziabxier A, Challa F, Gebeyehu T, et al. Antiretroviral treatment associated hyperglycemia and dyslipidemia among HIV infected patients at Burayu Health Center, Addis Ababa, Ethiopia: a cross-sectional comparative study. BMC Res Notes. 2014;7:380. pmid:24950924
- 70. Kelem A, Shiferaw E, Adane T. Hematological abnormalities and associated factors among metabolic syndrome patients at the University of Gondar comprehensive specialized hospital, Northwest Ethiopia. PLoS One. 2023;18(5):e0286163. pmid:37228109
- 71. Timerga A, Haile K, Dessu S. Anemia and associated factors among patients admitted with metabolic syndromes at Worabe Comprehensive Specialized Hospital, Southern Ethiopia: A cross-sectional study. PLoS One. 2022;17(4):e0266089. pmid:35377899
- 72. Beyene Kassaw A, Tezera Endale H, Hunie Tesfa K, Derbew Molla M. Metabolic syndrome and its associated factors among epileptic patients at Dessie Comprehensive Specialized Hospital, Northeast Ethiopia; A hospital-based comparative cross-sectional study. PLoS One. 2022;17(12):e0279580. pmid:36580471
- 73. Challa F, Getahun T, Sileshi M, Geto Z, Kelkile TS, Gurmessa S, et al. Prevalence of metabolic syndrome among patients with schizophrenia in Ethiopia. BMC Psychiatry. 2021;21(1):620. pmid:34895175
- 74. Amogne G, Alemu T, Tadesse T, Mullu A. Magnitude of metabolic syndrome and its predictors among patients on second-generation antipsychotic drugs at six psychiatry clinics and mental hospitals, in Addis Ababa, Ethiopia, 2019; Multicenter cross-sectional study. Diabetes Metab Syndr. 2021;15(4):102187. pmid:34214901
- 75. Zerga AA, Bezabih AM. Metabolic syndrome and lifestyle factors among type 2 diabetes mellitus patients in Dessie Referral Hospital, Amhara region, Ethiopia. PLoS One. 2020;15(11):e0241432. pmid:33137150
- 76. Teshome T, Kassa DH, Hirigo AT. Prevalence and associated factors of metabolic syndrome among patients with severe mental illness at Hawassa, Southern-Ethiopia. Diabetes Metab Syndr Obes. 2020;13:569–79. pmid:32161483
- 77. Gebremeskel GG, Berhe KK, Belay DS, Kidanu BH, Negash AI, Gebreslasse KT, et al. Magnitude of metabolic syndrome and its associated factors among patients with type 2 diabetes mellitus in Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia: a cross sectional study. BMC Res Notes. 2019;12(1):603. pmid:31533851
- 78. Birarra MK, Gelayee DA. Metabolic syndrome among type 2 diabetic patients in Ethiopia: A cross-sectional study. BMC Cardiovasc Disord. 2018;18(1):149. pmid:30016936
- 79. Woyesa SB, Hirigo AT, Wube TB. Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. BMC Endocr Disord. 2017;17(1):76. pmid:29233152
- 80. Tadewos A, Egeno T, Amsalu A. Risk factors of metabolic syndrome among hypertensive patients at Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia. BMC Cardiovasc Disord. 2017;17(1):218. pmid:28789613
- 81. Tachebele B, Abebe M, Addis Z, Mesfin N. Metabolic syndrome among hypertensive patients at University of Gondar Hospital, North West Ethiopia: a cross sectional study. BMC Cardiovasc Disord. 2014;14:177. pmid:25481843
- 82. Gebre BB, Assefa ZM. Magnitude and associated factors of diabetic complication among diabetic patients attending Gurage zone hospitals, South West Ethiopia. BMC Res Notes. 2019;12(1):780. pmid:31783906
- 83. Kifle ZD, Alehegn AA, Adugna M, Bayleyegn B. Prevalence and predictors of dyslipidemia among hypertensive patients in Lumame Primary Hospital, Amhara, Ethiopia: a cross-sectional study. Metabol Open. 2021;11:100108. pmid:34355158
- 84. Woldu MA, Minzi O, Engidawork E. Dyslipidemia and associated cardiovascular risk factors in HIV-positive and HIV-negative patients visiting ambulatory clinics: a hospital-based study. JRSM Cardiovasc Dis. 2022;11:20480040221114651. pmid:35898404
- 85. Woyesa S, Mamo A, Mekonnen Z, Abebe G, Gudina EK, Milkesa T. Lipid and lipoprotein profile in HIV-infected and non-infected diabetic patients: a comparative cross-sectional study design, Southwest Ethiopia. HIV AIDS (Auckl). 2021;13:1119–26. pmid:34992470
- 86. Bahadoran Z, Mirmiran P, Hosseini-Esfahani F, Azizi F. Fast food consumption and the risk of metabolic syndrome after 3-years of follow-up: Tehran Lipid and Glucose Study. Eur J Clin Nutr. 2013;67(12):1303–9. pmid:24193228
- 87. Xia Q, Chen Y, Yu Z, Huang Z, Yang Y, Mao A, et al. Prevalence, awareness, treatment, and control of dyslipidemia in Chinese adults: a systematic review and meta-analysis. Front Cardiovasc Med. 2023;10:1186330. pmid:37476570
- 88. Karaye KM, Habib AG. Dyslipidaemia in patients with established cardiovascular disease in Sub-Saharan Africa: A systematic review and meta-analysis. Eur J Prev Cardiol. 2014;21(6):682–91. pmid:22952291
- 89.
Marco Zuin GR, Claudio B, Carlo C, Giovanni Z, Loris R. Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis. 2021.
- 90. Obsa MS, Ataro G, Awoke N, Jemal B, Tilahun T, Ayalew N, et al. Determinants of Dyslipidemia in Africa: A systematic review and meta-analysis. Front Cardiovasc Med. 2022;8:778891. pmid:35284497
- 91. Huang Y, Gao L, Xie X, Tan SC. Epidemiology of dyslipidemia in Chinese adults: meta-analysis of prevalence, awareness, treatment, and control. Popul Health Metr. 2014;12(1):28. pmid:25371655
- 92. Dagnew B, Yeshaw Y, Geremew D, Angaw DA, Dagne H, Alemayehu M, et al. Hypertriglyceridemia and other plasma lipid profile abnormalities among people living with diabetes mellitus in Ethiopia: A systematic review and meta-analysis. Biomed Res Int. 2021;2021:7389076. pmid:34056001
- 93. Gelaw YA, Koye DN, Alene KA, Ahmed KY, Assefa Y, Erku DA, et al. Socio-demographic correlates of unhealthy lifestyle in Ethiopia: A secondary analysis of a national survey. BMC Public Health. 2023;23(1):1528. pmid:37568091
- 94. Motuma A, Demissie Regassa L, Gobena T, Teji Roba K, Berhane Y, Worku A. Almost all working adults have at least one risk factor for non-communicable diseases: Survey of working adults in Eastern Ethiopia. PLoS One. 2022;17(2):e0264698. pmid:35226698
- 95. Belete AK, Kassaw AT, Yirsaw BG, Taye BA, Ambaw SN, Mekonnen BA, et al. Prevalence of hypercholesterolemia and awareness of risk factors, prevention and management among adults visiting referral hospital in Ethiopia. Vasc Health Risk Manag. 2023;19:181–91. pmid:37020462
- 96. Azizi F, Hadaegh F, Hosseinpanah F, Mirmiran P, Amouzegar A, Abdi H, et al. Metabolic health in the Middle East and north Africa. Lancet Diabetes Endocrinol. 2019;7(11):866–79. pmid:31422063
- 97. Higgins JPT. Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epidemiol. 2008;37(5):1158–60. pmid:18832388