Exploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis

Exploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Exploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis Nelson Musilanga, Hussein Nasib, Given Jackson, Frank Shayo, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3958331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus and metabolic syndrome represent two closely intertwined public health challenges that have reached alarming epidemic proportions in low- and middle-income countries, particularly in sub-Saharan Africa. Therefore, the current study aimed to determine the weighted pooled prevalence of metabolic syndrome and its components among individuals with type 2 diabetes mellitus in sub-Saharan Africa as defined by the 2004 National Cholesterol Education Program- Adult Treatment Panel (NCEP-ATP III 2004) and/or the International Diabetes Federation (IDF) criteria. Methods A systematic search was conducted to retrieve studies published in the English language on the prevalence of metabolic syndrome among type 2 diabetic individuals in sub-Saharan Africa. Searches were carried out in PubMed, Embase, Scopus, Google Scholar, African Index Medicus and African Journal Online from their inception until July 31, 2023. A random-effects model was employed to estimate the weighted pooled prevalence of metabolic syndrome in sub-Saharan Africa. Evidence of between-study variance attributed to heterogeneity was assessed using Cochran’s Q statistic and the I2 statistic. The Joanna Briggs Institute quality appraisal criteria were used to evaluate the methodological quality of the included studies. The summary estimates were presented with forest plots and tables. Publication bias was checked with the funnel plot and Egger’s regression test. Results Overall, 1421 articles were identified and evaluated using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and 30 studies that met the inclusion criteria were included in the final analysis. The weighted pooled prevalence of metabolic syndrome among individuals with type 2 diabetes mellitus in sub-Saharan Africa was 63.1% (95% CI: 57.9–68.1) when using the NCEP-ATP III 2004 criteria and 60.8% (95% CI: 50.7–70.0) when using the IDF criteria. Subgroup analysis, using NCEP-ATP III 2004 and IDF criteria, revealed higher weighted pooled prevalence among females: 73.5% (95% CI: 67.4–79.5), 71.6% (95% CI: 60.2–82.9), compared to males: 50.5% (95% CI: 43.8–57.2), 44.5% (95% CI: 34.2–54.8) respectively. Central obesity was the most prevalent component of metabolic syndrome, with a pooled prevalence of 55.9% and 61.6% using NCEP-ATP III 2004 and IDF criteria, respectively. There was no statistical evidence of publication bias in both the NCEP-ATP III 2004 and IDF pooled estimates. Conclusions The findings underscore the alarming prevalence of metabolic syndrome among individuals with type 2 diabetes mellitus in sub-Saharan Africa. Therefore, it is essential to promote lifestyle modifications, such as regular exercise and balanced diets, prioritize routine obesity screenings, and implement early interventions and robust public health measures to mitigate the risks associated with central obesity. Internal Medicine Metabolic Syndrome Prevalence diabetes mellitus sub-Saharan Africa. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Metabolic Syndrome (MetS), characterized by a constellation of interconnected risk factors such as abdominal obesity, high blood pressure, high blood glucose, and abnormal lipid profiles, poses a significant risk to individuals worldwide [ 1 , 2 ]. When coexisting with type 2 diabetes mellitus (T2DM), this syndrome can exacerbate the progression of the disease and increase the risk of cardiovascular diseases [ 3 , 4 ], which are the leading cause of mortality worldwide [ 5 , 6 ]. Sub-Saharan Africa (SSA), home to over one billion people, is not immune to these global health trends [ 7 ]. Owing to the increase in urbanization, excessive alcohol consumption, unhealthy eating habits, smoking, sedentary lifestyles, and overweight [ 8 , 9 ], SSA like many other regions, is currently witnessing a rapid epidemiological shift characterized by an increasing predominance of non-communicable diseases (NCDs) [ 10 ], contributing to a growing prevalence of both T2DM and MetS in the region. T2DM is the most common chronic metabolic-endocrine disorder affecting adults. It results from a complex interaction between heredity along with other risk factors such as insulin resistance, obesity, physical inactivity, an unhealthy diets, smoking, and excessive alcohol consumption [ 11 ]. It’s multi-systemic nature suggests that complications and comorbidities have the potential to impact various organ systems [ 12 ], particularly in the setting of poor blood glucose control. The burden of T2DM in sub-Saharan Africa has grown into a substantial public health challenge. According to the International Diabetes Federation (IDF) report, the greatest relative increase in the prevalence of diabetes between 2021 and 2045 will occur in low-income countries (11.9%) and middle-income countries (21.1%), which largely includes SSA countries [ 13 ]. Globally, the prevalence of MetS is escalating at an alarming rate, and it is highly prevalent in patients with T2DM [ 14 , 15 ]. It was estimated that 20–25% of the adult general population and 70–80% of T2DM patients had MetS worldwide [ 16 ]. Individuals with MetS are more likely to have a higher risk of heart attacks and cardiovascular diseases (CVD) compared to those without MetS [ 4 ]. Furthermore, it is documented that the risk of CVD development is greater among individuals who have a combination of T2DM and MetS compared to those who have either condition alone [ 17 ]. While the burden of communicable diseases has traditionally been the major focus of public health initiatives in SSA, the rise of non-communicable diseases like T2DM and MetS is now posing a significant threat to the region's health and socio-economic development. Unlike prior studies [ 18 , 19 ] that explored MetS in broader African populations or specific country, the current study aimed to systematically review the available evidence and provide an estimate of the pooled prevalence of MetS among SSA individuals with T2DM. By spotlighting MetS within the context of T2DM in SSA, offers a more targeted understanding of MetS within a unique subset of the African population, providing valuable information for healthcare practitioners and researchers focusing on this demographic. Methods Design and Registration We conducted a systematic review and meta-analysis of observational studies, all of which were cross-sectional study designs done across SSA. This systematic review and meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guideline [ 20 ]. The study protocol was registered in the PROSPERO, an international prospective register of systematic reviews protocols on health related topics CRD42023455576 [ 21 ]. Outcome of Interest The primary outcome of interest for this study was the pooled prevalence of MetS among T2DM patients, as defined by the widely recognized and extensively used criteria’s i.e 2004 National Cholesterol Education Program- Adult Treatment Panel (NCEP-ATP III 2004) [ 1 ] and/or the IDF criteria [ 2 ]. Using NCEP-ATP III 2004 any three of the five metabolic syndrome components while using IDF criteria central Obesity, plus two of the four MetS components (Table 1 ). The secondary aim was to describe the prevalence of individual components of MetS among T2DM patients, according to the specific MetS definition criteria among T2DM individuals in SSA. Table 1 Diagnostic criteria of metabolic syndrome according to NCEP-ATP III 2004 and IDF Criteria Criteria NCEP-ATP III 2004 IDF Central Obesity Hypertriglyceridemia Reduced HDL-Cholesterol Hyperglycemia Hypertension Waist circumference ≥ 102cm in male and ≥ 88cm in female. TG ≥ 150 mg/dl or triglycerides treatment < 40mg/dl in males and < 50mg/ dl in females or HDL-c treatment FBG ≥ 100mg/dL or on treatment Systolic/Diastolic BP ≥ 130/85mmHg or hypertension treatment Waist circumference ≥ 94cm in male and ≥ 80cm in female. TG ≥ 150 mg/dl or triglycerides treatment < 40mg/dl in males and < 50mg/ dl in females or HDL-c treatment FBG ≥ 100mg/dL or on treatment Systolic/Diastolic BP ≥ 130/85mmHg or hypertension treatment BP: Blood pressure; FBG: Fasting blood glucose; HDL-c: High Density Lipoprotein cholesterol; IDF: International Diabetes Federation; NCEP-ATP III: National Cholesterol Education Program–Adult Treatment Panel III; TG: Triglyceride. Data Source and Search Strategy We conducted a comprehensive systematic literature search to identify studies reporting the prevalence of MetS among T2DM patients in the sub-Saharan African population. The search utilized a combination of Medical Subject Headings (MeSH) and free text words across various electronic databases and search engine, including MEDLINE-PubMed, EMBASE, Scopus, African Index Medicus, African Journal Online and Google Scholar. Inclusion criteria were limited to English-language studies published from the inception of databases until July 31, 2023. Additionally, a snowball search was performed on the reference lists of all relevant included studies. The search strategy focused on three key elements: metabolic syndrome, type 2 diabetes mellitus and sub-Saharan Africa. These searches were independently performed by two authors; N.M and H.N. The detailed search strategy used for the databases is presented in the supplementary material S1. To manage references and remove duplicates, we used Rayyan, an online web application. Inclusion and exclusion criteria The inclusion criteria were as follows: All observational studies reporting the prevalence of MetS and its subcomponents among T2DM individuals in sub-Saharan African populations, studies reporting metabolic syndrome using IDF criteria and/or NCEP-ATP III 2004, and publications with full text in English. The full text of studies meeting these criteria was retrieved and screened for eligibility. Whereas, non-original research articles, such as review articles, editorials, case reports, letters, or commentaries, studies describing MetS in populations other than sub-Saharan Africa, T2DM, and those with unclear or unspecified methods of diagnosing metabolic syndrome were excluded. Study Selection and Quality Assessment Two authors (N.M. and H.N.) independently conducted the literature search and screened the titles, abstracts and keywords of all the studies retrieved from online database searches for possible inclusion in the review. Furthermore, the relevant articles were obtained in full text, and after a thorough reading of the full-text articles, the included studies were identified based on the assessment of inclusion and exclusion criteria. Any discrepancies during the entire selection process between the two authors were resolved either through consensus or consultation with third author (G.J). The search, screening, and study identification process are summarized in Fig. 1 . The methodological quality and risk of bias of the included studies was assessed using eight aspects of the Joanna Brigg’s Institute (JBI) quality checklist for analytical cross-sectional studies [ 22 , 23 ]. Two authors (N.M. and H.N.) independently used the tool to evaluate the inclusion criteria, measurement of exposure and outcome variables, confounding adjustment, and appropriateness of statistical analysis. Studies that scored 50% or higher on the quality assessment were considered to be of good quality. Full details regarding the appraisal checklist are provided in Table 2 . Table 2 Methodological quality assessment of included studies using Joanna Brigg’s Institute quality appraisal (JBI). Author[Year] Were the criteria for inclusion in the sample clearly defined? Were the study participants and setting described in detail? Was the exposure measured in a valid and reliable way? Were objective, standard criteria used for measurement of the condition? Was appropriate statistical analysis used? Were the outcomes measured in a valid and reliable way? Were confounding factors identified? Were strategies to deal with confounding factors stated? Overall appraisal Kalk et al. [ 24 ] 2008 Yes Yes Yes Yes Yes Yes Yes Yes Good Titty et al. [ 25 ] 2008 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Titty et al. [ 26 ] 2009 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Puepet et al. [ 27 ] 2009 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Unadike et al. [ 28 ] 2009 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Chanda et al. [ 29 ] 2010 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Titty et al. [ 30 ] 2010 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Ogbera et al. [ 31 ] 2011 Yes Yes Yes Yes Yes Yes Yes Yes Good Kangne et al. [ 32 ] 2012 Yes Yes Yes Yes Yes Yes Yes Yes Good Osuji et al. [ 33 ] 2012 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Mogre et al. [ 34 ] 2014 Yes Yes Yes Yes Yes Yes Yes Yes Good Nsiah et al. [ 35 ] 2015 Yes Yes Yes Yes Yes Yes Yes Yes Good Ejiofor et al. [ 36 ] 2015 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Onyenekwu et al. [ 37 ] 2017 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Abban et al. [ 38 ] 2017 Yes Yes Yes Yes Yes Yes Yes Yes Good Amidu et al. [ 39 ] 2017 Yes Yes Yes Yes Yes Yes Unclear Unclear Good Osei-Yeboah et al. [ 40 ] 2017 Yes Yes Yes Yes Yes Yes Yes Yes Good Woyesa et al. [ 41 ] 2017 Yes Yes Yes Yes Yes Yes Yes Yes Good Tadewos et al. [ 42 ] 2017 Yes Yes Yes Yes Yes Yes Yes Yes Good Biadgo et al.[ 43 ] 2018 Yes Yes Yes Yes Yes Yes Yes Yes Good Birarra et al.[ 44 ] 2018 Yes Yes Yes Yes Yes Yes Yes Yes Good Obirikorang et al. [ 45 ] 2018 Yes Yes Yes Yes Yes Yes Yes Yes Good Agyemang-Yeboah et al. [ 46 ] 2019 Yes Yes Yes Yes Yes Yes Yes Yes Good Gebremeskel et al. [ 47 ] 2019 Yes Yes Yes Yes Yes Yes Yes Yes Good Wube et al. [ 48 ] 2019 Yes Yes Yes Yes Yes Yes Yes Yes Good Zerga et al. [ 49 ] 2020 Yes Yes Yes Yes Yes Yes Yes Yes Good Anto et al. [ 50 ] 2022 Yes Yes Yes Yes Yes Yes Yes Yes Good Gebreyesus et al. [ 51 ] 2022 Yes Yes Yes Yes Yes Yes Yes Yes Good Gemeda et al.[ 52 ] 2022 Yes Yes Yes Yes Yes Yes Yes Yes Good Charkos et al. [ 53 ] 2023 Yes Yes Yes Yes Yes Yes Yes Yes Good Data Extraction: Extraction of relevant data from the included studies was independently performed by two authors (N.M and H.N). Information regarding authors; year of publication; geographical location; year(s) of survey; study design; sample size; gender; mean age; sampling techniques; diagnostic criteria for defining metabolic syndrome and relevant clinic outcomes of interest were collected using a standardized data extraction form. Extracted data were then checked for its accuracy and consistency by a third author (G.J). Statistical Analysis The extracted data were exported to computer software RStudio version 2023.06.1 + 524 for data synthesis, analysis, and generation of forest and funnel plots. Evidence of between study variance due to heterogeneity was assessed using Cochran’s Q statistic and the I 2 statistic [ 54 , 55 ]. Furthermore, in order to explore potential sources of heterogeneity across the included studies, subgroup and sensitivity analyses were performed to comprehensively assess the overall effect size within the included studies. A random-effects model with inverse variance was used to obtain an overall summary estimate of the prevalence across studies. Point estimation with a confidence interval of 95% was used. The presence of publication bias was examined through the utilization of funnel plots, further enhanced by formal statistical assessment using Egger’s test [ 56 ]. Results Study Selection As shown in Fig. 1 , a preliminary search of online databases using a combination of MeSH and free text words retrieved a total of 1418 potential articles, and an additional 3 articles were found through manual citation searching. After removing duplicates, 928 articles remained, which were then screened based on their titles and abstracts, resulting in the elimination of a further 872 articles that were irrelevant to the research question. Among the 56 articles that underwent full-text review, ultimately 30 articles met the inclusion criteria and were included in this review. Characteristics of Included Studies. A characteristic summary of thirty articles included in this study involving 8879 individuals is illustrated in Table 3 . All were of cross-sectional study design conducted in six sub-Saharan African countries namely Cameroon, Ethiopia, Ghana, Nigeria, Zambia and South Africa as demonstrated in Fig. 2 . In these studies, the prevalence of MetS was estimated based on the IDF and/or NCEP-ATP III 2004 criteria. Among the articles, eleven studies reported the prevalence of MetS based on both NCEP-ATP III 2004 and IDF criteria [ 32 , 38 , 53 , 39 , 40 , 43 – 45 , 48 , 49 , 51 ], fourteen studies reported based solely on NCEP-ATP III 2004 criteria [ 25 , 26 , 42 , 46 , 50 , 52 , 28 – 31 , 33 , 35 , 36 , 41 ] and five studies reported based on IDF criteria alone [ 24 , 27 , 34 , 37 , 47 ]. Additionally, nine studies reported the prevalence of MetS subcomponents based on NCEP-ATP III 2004 criteria [ 25 , 26 , 31 , 35 , 40 – 44 ] and six studies based on IDF criteria [ 24 , 27 , 40 , 43 , 44 , 47 ]. Table 3 Characteristics of the included studies that evaluated the prevalence of MetS among T2DM in sub-Saharan population Author[Year] Country Study design Sampling method Survey Period Sample size Sex Mean age Diagnostic criteria Overall prevalence (NCEP/ATP-III) (IDF) Kalk et al. [ 24 ] 2008 South Africa Cross-sectional study Convenience sampling 1994–2002 500 Both 48.3 ± 8.7 IDF - 69.0% Titty et al. [ 25 ] 2008 Ghana Cross-sectional study Convenience sampling January 2006 to May 2007 456 Both 55.8 ± 12.3 NCEP-ATP III 55.9% - Titty et al. [ 26 ] 2009 Ghana Cross-sectional study Unspecified June 2006 to May 2007 300 Both 57.8 ± 11.3 NCEP-ATP III 60.3% - Puepet et al. [ 27 ] 2009 Nigeria Cross-sectional study Convenience sampling January 2006 to December 2008 634 Both 54.2 ± 9.1 IDF - 63.6% Unadike et al. [ 28 ] 2009 Nigeria Cross-sectional study Unspecified January to August 2008 240 Both 50.8 ± 11 NCEP-ATP III 62.5% - Chanda et al. [ 29 ] 2010 Zambia Cross-sectional study Unspecified Unspecified 400 Both 59.3 ± 11.13 NCEP-ATP III 73.0% - Titty et al. [ 30 ] 2010 Ghana Cross-sectional study Convenience sampling September 2006 to August 2007 240 Both 47.2 ± 12.3 NCEP-ATP-III 43.3% - Ogbera et al. [ 31 ] 2011 Nigeria Cross-sectional study Unspecified Unspecified 201 Female 62.4 ± 7.7 NCEP-ATP III 69.0% - Kangne et al. [ 32 ] 2012 Cameroon Cross-sectional study Convenience sampling 2006–2008 308 Both 55.8 ± 10.5 NCEP-ATP III, IDF 60.4% 71.7% Osuji et al. [ 33 ] 2012 Nigeria Cross-sectional study Unspecified Unspecified 93 Both 55.27 ± 12.55 NCEP-ATP III 66.7% - Mogre et al. [ 34 ] 2014 Ghana Cross- sectional study Convenience sampling Unspecified 200 Both 56.2 ± 12.13 IDF - 24.0% Ejiofor et al. [ 36 ] 2015 Nigeria Cross-sectional study Simple random sampling March to September 2006 366 Both Unspecified NCEP-ATP III 67.8% - Nsiah et al. [ 35 ] 2015 Ghana Cross-sectional study Unspecified February to April 2013 150 Both 51.3 ± 0.97 NCEP-ATP III 58.0% - Abban et al. [ 38 ] 2017 Ghana Cross-sectional study Convenience sampling March to April 2015 103 Both 56.24 ± 9.77 NCEP-ATP III, IDF 59.09% 75.0% Amidu et al. [ 39 ] 2017 Ghana Cross-sectional study Convenience sampling November 2010-March 2011 274 Male 59.9 ± 11.3 NCEP-ATP III, IDF 65.3% 43.1% Onyenekwu et al. [ 37 ] 2017 Nigeria Cross-sectional study Systematic sampling Unspecified 108 Both Unspecified IDF - 97.2% Osei-Yeboah et al. [ 40 ] 2017 Ghana Cross-sectional study Convenience sampling February to April 2016 162 Both 56.4 ± 10.6 NCEP-ATP III, IDF 43.8% 69.1% Woyesa et al. [ 41 ] 2017 Ethiopia Cross-sectional study Simple random sampling February to May 2017 314 Both 49.8 ± 9.8 NCEP-ATP III 70.1% - Tadewos et al. [ 42 ] 2017 Ethiopia Cross-sectional study Systematic random sampling March to November 2014 270 Both 48.8 ± 11.9 NCEP-ATP III 45.9% - Biadgo et al. [ 43 ] 2018 Ethiopia Cross-sectional study Unspecified June to July 2015 159 Both 49.8 ± 8.7 NCEP-ATP III, IDF 66.7% 53.5% Birarra et al. [ 44 ] 2018 Ethiopia Cross-sectional study Systematic random sampling March to May 2017 256 Both Unspecified NCEP-ATP III, IDF 70.3% 57.0% Obirikorang et al. [ 45 ] 2018 Ghana Cross-sectional study Non-probability convenience sampling Unspecified 384 Both 56.4 ± 13.1 NCEP-ATP III, IDF 77.1% 76.3% Agyemang-Yeboah et al. [ 46 ] 2019 Ghana Cross-sectional study Simple random sampling Unspecified 405 Both 58.5 ± 9.9 NCEP-ATP III 90.6% - Gebremeskel et al. [ 47 ] 2019 Ethiopia Cross-sectional study Simple random sampling February to June 2018 419 Both 56.39 ± 10.18 IDF - 51.1% Wube et al. [ 48 ] 2019 Ethiopia Cross-sectional Simple random sampling February to May 2017 314 Both 49.8 ± 9.8 NCEP-ATP III, IDF 70.1% 52.9% Zerga et al. [ 49 ] 2020 Ethiopia Cross-sectional study Simple random sampling February to March 2017 330 Both Unspecified NCEP-ATP III, IDF 59.4% 50.3% Anto et al. [ 50 ] 2022 Ghana Cross-sectional study Convenience sampling March to June 2021 241 Both Unspecified NCEP-ATP III 42.7% - Gebreyesus et al. [ 51 ] 2022 Ethiopia Cross-sectional study Systematic sampling September to November 2019 421 Both 58.2 ± 11 NCEP-ATP III, IDF 67.9% 57.0% Gemeda et al. [ 52 ] 2022 Ethiopia Cross-sectional study Simple random sampling September 2020 to August 2021 394 Both Unspecified NCEP-ATP III 68.3% - Charkos et al. [ 53 ] 2023 Ethiopia Cross-sectional study Systematic random sampling September to October 2022 237 Both Unspecified NCEP-ATP III, IDF 41.3% 41.8% IDF: International Diabetes Federation; NCEP-ATP-III: National Cholesterol Education Program–Adult Treatment Panel I Burden of Metabolic syndrome Using NCEP-ATP III 2004 and IDF Criteria. The weighted pooled prevalence of MetS among T2DM individuals in sub-Saharan Africa using NCEP-ATP III 2004 criteria is 63.1% (95% CI: 57.9–68.1), with significant heterogeneity I 2 = 94% and Cochran Q-statistic p < 0.01 as graphically depicted in Fig. 3 . While using IDF criteria yielded a pooled prevalence of 60.8% (95% CI: 50.7–70.0), with an I 2 of 95% and Cochran Q-statistic p < 0.01 as shown in Fig. 4 . The random-effects model was assumed due to the considerable heterogeneity observed across the included studies in the meta-analysis. Prevalence of the Metabolic Syndrome Components. In the current systematic review, the prevalence of the individual components of MetS other than hyperglycemia among the sub-Saharan Africa T2DM population was reported in ten studies based on NCEP-ATP III 2004 criteria, and six studies reported based on IDF criteria. The overall pooled prevalence of metabolic syndrome component by NCEP-ATP III 2004 criteria was as follows: central obesity 55.9% [95% CI: 47.6, 64.2], low HDL-c 43.3% [95% CI: 33.5, 53.2], hypertriglyceridemia 48.0% [95% CI: 35.2, 60.7] and hypertension 54.8% [95% CI: 43.2, 66.4]. These values are summarized in Table 4 . Whereas the overall pooled prevalence of MetS component by IDF criteria was as follows: central obesity 61.6% [95% CI: 47.9, 75.3], low HDL-c 49.9% [95% CI: 37.3, 62.6], hypertriglyceridemia 49.2% [95% CI: 34.1, 64.4] and hypertension 56.1% [95% CI: 46.7, 65.4] as summarized in Table 5 . Table 4 Pooled prevalence of metabolic syndrome component based on NCEP-ATP III 2004 Prevalence of metabolic syndrome component Author [Year] Sample Central Obesity Low-HDL-c High-TG Hypertension Titty et al. [ 25 ] 2008 456 43.6 47.4 37.5 46.9 Titty et al. [ 26 ] 2009 300 69.6 58.5 56.4 69.6 Unadike et al. [ 28 ] 2009 240 74.4 17.3 48.0 86.7 Ogbera et al. [ 31 ] 2011 201 75.0 59.0 19.0 64.0 Nsiah et al. [ 35 ] 2015 150 48.6 41.3 32.7 60.0 Osei-Yeboah et al. [ 40 ] 2017 162 48.2 23.5 16.7 66.7 Woyesa et al. [ 41 ] 2017 314 61.3 39.2 70.4 28.0 Tadewos et al. [ 42 ] 2017 270 40.7 47.0 68.1 28.1 Birarra et al. [ 44 ] 2018 256 53.5 67.2 68.8 43.4 Biadgo et al. [ 43 ] 2018 159 43.4 32.7 62.3 55.4 Pooled prevalence [95% CI] 55.9 [47.6, 64.2] 43.3 [33.5, 53.2] 48.0 [35.2, 60.7] 54.8 [43.2, 66.4] CI: Confidence Interval; HDL-c: High density lipoprotein cholesterol; TG: Triglyceride; NCEP-ATP III: National Cholesterol Education Program–Adult Treatment Panel III. Table 5 Pooled prevalence of metabolic syndrome component based on IDF criteria Prevalence of metabolic syndrome component Author [Year] Sample Central Obesity Low-HDL-c High-TG Hypertension Birarra et al. [ 44 ] 2018 256 61.7 66.8 67.6 43.0 Biadgo et al. [ 43 ] 2018 159 61.0 32.7 62.3 55.4 Osei-Yeboah et al. [ 40 ] 2017 162 30.8 47.5 16.7 66.7 Kalk et al. [ 24 ] 2008 500 75.2 47.6 42.0 67.0 Puepet et al. [ 27 ] 2009 634 80.0 70.0 62.9 63.1 Gebremeskel et al. [ 47 ] 2019 419 59.7 34.4 45.1 41.3 Pooled Prevalence [95% CI] 61.6 [47.9, 75.3] 49.9 [37.3, 62.6] 49.2 [34.1, 64.4] 56.1 [46.7, 65.4] CI: Confidence Interval; HDL-c: High density lipoprotein cholesterol; TG: Triglyceride ; IDF: International Diabetes Federation. Subgroup and sensitivity analysis Subgroup analyses were conducted based on gender, country, sample size, and mean age. According to the NCEP-ATP III 2004, a total of 17 studies reported prevalence based on gender, revealing that the pooled prevalence of MetS among females in SSA was significantly higher compared to males (73.5% vs. 50.5%). Meanwhile, the results of subgroup analysis based on sample size showed the highest prevalence in studies with ≥ 250 subjects compared to those with < 250 subjects (67.0% vs. 55.2%), as depicted in supplementary table 2. Furthermore, subgroup analysis based on IDF criteria, as shown in supplementary table 3, revealed a higher pooled prevalence among females (71.6%) compared to males (44.5%) among the 11 studies that reported prevalence based on gender. Among the 12 reports that specified participant mean age, the pooled prevalence was comparable across the two categories of mean age: <50 years and ≥ 50 years. Additionally, sensitivity analyses were conducted using the leave-one-out approach to evaluate the influence of individual studies on the overall estimate of MetS, based on the NCEP-ATP III 2004 and IDF criteria. The results indicated no substantial evidence for the influence of any single study on the overall pooled prevalence of MetS among individuals with T2DM in SSA (Figs. 5 and 6 ). To further explore the observed heterogeneity in the study, we conducted a meta-regression to account for this. The analysis revealed that gender had a significant influence on the overall effect sizes in both NCEP-ATP III 2004 and IDF (p < 0.0001, 0.0007 respectively) and studies with a sample size ≥ 250 for NCEP-ATP III 2004 there was a significant influence observed at p value 0.0106. Publication bias A funnel plot of the pooled prevalence of MetS and Begg’s statistical tests at a 5% significance level were used to assess the presence of potential publication bias among the included studies. The funnel plots were almost symmetrical for the NCEP-ATP III 2004 criteria and IDF criteria, as graphically represented in Figs. 7 and 8 , respectively. Furthermore, separate analyses of the linear regression test of funnel plot asymmetry based on NCEP-ATP III 2004 and IDF criteria resulted in statistically non-significant p-values of 0.7800 and 0.6686, respectively, indicating the absence of publication bias. Discussion The association between T2DM and MetS has been thoroughly investigated. To our knowledge, this is the first systematic review and meta-analysis that evaluated the weighted pooled prevalence of MetS in individuals with T2DM in sub-Saharan Africa using specific diagnostic criteria for metabolic syndrome. The findings of this systematic review indicate that the weighted pooled prevalence of MetS was 63.1% (95% CI: 57.9–68.1) and 60.8% (95% CI: 50.7–70.0) using NCEP-ATP III 2004 and IDF criteria, respectively. The observed disparities in the prevalence of MetS when applying the NCEP-ATP III 2004 criteria versus the IDF criteria are noteworthy. The prevalence was slightly higher (63.1%) when the NCEP-ATP III 2004 criteria were used, compared to the IDF criteria (60.8%). These differences can be attributed to variations in the diagnostic components and thresholds employed by each set of criteria [ 57 ]. Similar findings regarding the variation in MetS prevalence based on diagnostic criteria have been reported in many studies conducted in different parts of the world [ 58 , 59 ]. Interestingly, when we compare our findings with those from other regions and study populations, we observe divergent outcomes. For instance, our findings are somewhat consistent with results reported in a systematic review among African T2DM patients (66.9%) [ 18 ] and Ethiopian T2DM patients (63.78%) [ 19 ]. However, the current weighted pooled prevalence of MetS using IDF criteria (60.8%) was higher than the prevalence estimated globally, which typically ranges between 20% and 25% when using similar diagnostic criteria [ 16 ]. Notably, subgroup analysis by gender revealed a considerably higher pooled prevalence of MetS in females, at 73.5% (95% CI: 67.4–79.5), compared to males at 50.5% (95% CI: 43.8–57.2) according to the NCEP-ATP III 2004. Similarly, a higher pooled prevalence was observed according to the IDF criteria among females, reaching 71.6% (95% CI: 60.2–82.9), compared to males at 44.5% (95% CI: 34.2–54.8). This finding aligns with reports from systematic reviews conducted among various populations, including SSA African [ 60 ], Ghanaian [ 61 ], Bangladesh [ 62 ], and mainland China [ 63 ]. The possible reason for the higher prevalence in females could be gender-specific increased MetS risk factors among women, such as menopause, contraceptive therapy use, elevated body weight, and increased waist circumference, in comparison to men [ 64 ]. Based on IDF criteria, among the included studies, the highest weighted pooled prevalence was observed in Nigeria at 80.2% (95% CI: 47.1–99.9), while Ethiopia had the lowest at 52.0% (95% CI: 48.3–55.8). This contrasts with a review by Shiferaw et al. [ 65 ]that identified the highest prevalence of MetS in Ethiopia. However, their study combined studies with varying diagnostic criteria, unlike our report, which might account for this variation. The differences in MetS prevalence between Nigeria and Ethiopia found on the current review stem from a blend of varying dietary patterns, lifestyle distinctions, disparities in healthcare infrastructure, and cultural influences. Generally, our findings differ from those of many other studies around the world. In a systematic review conducted among healthy South Asians, a prevalence of MetS was reported as 26.1% (ATP III), 29.8% (IDF), and 32.5% (modified ATP III) [ 66 ]. Similarly, a quantitative synthesis of 111 studies conducted among the Indian adult general population reported a prevalence of 29% (NCEP ATP-III) and 34% (IDF) [ 67 ]. The observed discrepancies in the prevalence of MetS reported among different studies around the world are significant. These discrepancies might be due to differences in intrinsic study design, sample size, and characteristics of the study participants, such as comorbidities, geographical locations, urbanization, and lifestyle factors, including physical inactivity and unhealthy eating habits [ 68 , 69 ]. Moreover, the current review focused on Sub-Saharan African Type 2 Diabetes Mellitus individuals. T2DM appears to play a pivotal role in the pathogenesis and exacerbation of MetS, such that individuals with T2DM are more likely to have MetS, increasing their susceptibility to cardiovascular complications[ 11 , 70 ]. According to the data compiled in this review, the pooled prevalence of MetS components was as follows: central obesity at 55.9% and 61.6%; low HDL-c at 43.3% and 49.9%; hypertriglyceridemia at 48.0% and 49.2%; and hypertension at 54.8% and 56.1%, according to NCEP-ATP III 2004 and IDF criteria, respectively. Central obesity emerged as the most frequent metabolic syndrome component in this systematic review. Visceral adiposity has long been recognized as a central player in insulin resistance and is linked to a heightened risk of type 2 diabetes mellitus and cardiovascular diseases [ 71 ]. Moreover, high blood pressure and abnormal lipid profiles were also found to be prevalent in our review. Thus, our findings underscore the importance of a holistic approach to patient care, integrating strategies to mitigate MetS components alongside T2DM management to prevent adverse health effects such as CVD [ 72 , 73 ]. The strengths of the present study include its comprehensive database search using varying combinations of keywords and well-defined inclusion/exclusion criteria. However, we wish to acknowledge several limitations in the current study. Firstly, significant heterogeneity was observed across the included studies, and this heterogeneity persisted even after stratification for diagnostic criteria. Secondly, the diversity in sub-Saharan African populations, as SSA is home to various ethnic, cultural, and socio-economic groups, may exhibit different risk factors and disease profiles. Therefore, the generalizability of findings across this region may be limited, as the prevalence and associations of MetS in T2DM can vary among these subpopulations. Conclusion Although limited in scope, the findings presented here underscore the alarming prevalence of MetS among individuals with T2DM in sub-Saharan Africa. This trend may be directly linked to the rapid economic development and urbanization occurring in the region. This swift industrialization can lead to significant changes in lifestyle patterns and overnutrition, resulting in overweight and obesity, emphasizing the urgent need for comprehensive, region-specific prevention and management strategies. Encouraging lifestyle modifications, including regular exercise and balanced diets, is essential. Moreover, it is crucial to develop routine obesity screening procedures. Implementing early interventions and robust public health initiatives are crucial in mitigate the risks associated with central obesity. Sub-Saharan Africa faces unique health challenges, including limited healthcare resources and the dual burden of communicable and non-communicable diseases, which must be taken into account when developing effective interventions. Moving forward, it is imperative to prioritize research efforts that not only elucidate the underlying mechanisms of MetS and T2DM but also explore culturally sensitive and sustainable approaches for prevention and treatment. We hope that this systematic review will serve as a foundation for further studies, ultimately leading to more effective strategies and improved health outcomes for individuals in sub-Saharan Africa who are grappling with the challenges of metabolic syndrome and T2DM. Declarations Acknowledgments We would like to thank all authors of studies included in this systematic review and meta-analysis. Data Availability The data used to support the findings of this study are available from the corresponding author upon request. Conflicts of Interest All the authors declare that they have no conflicts of interest relevant for this study. Funding Statement The authors received no funding for publication of this study. 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S2TablesforSubgroupanalysis.docx Supplementary S2: Subgroup Analysis S2 Tables: Subgroup analysis results based on NCEP-ATP III 2004 and IDF criteria Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/9c841592393150884b7a321e.png"},{"id":51238926,"identity":"9cb9e1a1-2911-41b1-8eb5-e585b7fcc3c7","added_by":"auto","created_at":"2024-02-16 16:57:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115587,"visible":true,"origin":"","legend":"\u003cp\u003eA map of Africa showing the locations of the included studies (created with paintmaps.com)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/3322bfaa733c00b55d7da5f5.png"},{"id":51238378,"identity":"a226e698-d1a8-4b84-9a23-e985eed46f69","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117704,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating the pooled prevalence of MetS with corresponding 95% CIs in sub-Saharan Africa based on NCEP-ATP III 2004 criteria.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/e590a2a397ebef3f9196cd60.png"},{"id":51238928,"identity":"19f7c47e-c0fc-460e-b83e-a2a4351a1ad8","added_by":"auto","created_at":"2024-02-16 16:57:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99104,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating the pooled prevalence of MetS with corresponding 95% CIs in sub-Saharan Africa based on IDF criteria.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/a13fc5ed2f04cd5f76401754.png"},{"id":51238385,"identity":"2ef9783c-ae94-452f-b290-555bd3b64b26","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157040,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis based on NCEP-ATP III 2004 criteria.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/42e5156d8254182154de73eb.png"},{"id":51238384,"identity":"0c402ebf-3eb3-4392-a65c-2b60ad1f1100","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":111183,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis based on IDF criteria.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/cb76c35ce8138b34d1a7f3f7.png"},{"id":51238383,"identity":"bd594164-185a-4698-a6a0-d7fe9b551c42","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24010,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot for the publication bias based on NCEP-ATP III 2004 criteria\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/c2679769a5da3860528ae94e.png"},{"id":51238381,"identity":"54d6535f-eda9-413b-8348-673596ae64cb","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27836,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot for the publication bias based on IDF criteria\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/6a66e266e268b5e7b52181c9.png"},{"id":51239262,"identity":"eeac4c1f-cde0-44f3-bba4-03c69d1d623d","added_by":"auto","created_at":"2024-02-16 17:05:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358708,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/6984acd6-a7c6-461a-a543-004df146953d.pdf"},{"id":51238924,"identity":"30fd389d-3afd-4181-affc-e1db454c5dfb","added_by":"auto","created_at":"2024-02-16 16:57:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15387,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary S1: Database Search Strategy\u003c/p\u003e\n\u003cp\u003eS1 Tables: Search Strategies used for final search of databases.\u003c/p\u003e","description":"","filename":"S1TablesDatabaseSearchStrategy.docx","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/3c40178ee594650d2ee41a95.docx"},{"id":51238376,"identity":"9ddbddf1-0e43-4256-8895-193ea78a36e0","added_by":"auto","created_at":"2024-02-16 16:49:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16262,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary S2: Subgroup Analysis\u003c/p\u003e\n\u003cp\u003eS2 Tables: Subgroup analysis results based on NCEP-ATP III 2004 and \u0026nbsp;IDF criteria\u003c/p\u003e","description":"","filename":"S2TablesforSubgroupanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-3958331/v1/796c65b871e1541a02ac6f3d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic Syndrome (MetS), characterized by a constellation of interconnected risk factors such as abdominal obesity, high blood pressure, high blood glucose, and abnormal lipid profiles, poses a significant risk to individuals worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. When coexisting with type 2 diabetes mellitus (T2DM), this syndrome can exacerbate the progression of the disease and increase the risk of cardiovascular diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which are the leading cause of mortality worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Sub-Saharan Africa (SSA), home to over one billion people, is not immune to these global health trends [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Owing to the increase in urbanization, excessive alcohol consumption, unhealthy eating habits, smoking, sedentary lifestyles, and overweight [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], SSA like many other regions, is currently witnessing a rapid epidemiological shift characterized by an increasing predominance of non-communicable diseases (NCDs) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], contributing to a growing prevalence of both T2DM and MetS in the region.\u003c/p\u003e \u003cp\u003eT2DM is the most common chronic metabolic-endocrine disorder affecting adults. It results from a complex interaction between heredity along with other risk factors such as insulin resistance, obesity, physical inactivity, an unhealthy diets, smoking, and excessive alcohol consumption [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It\u0026rsquo;s multi-systemic nature suggests that complications and comorbidities have the potential to impact various organ systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], particularly in the setting of poor blood glucose control. The burden of T2DM in sub-Saharan Africa has grown into a substantial public health challenge. According to the International Diabetes Federation (IDF) report, the greatest relative increase in the prevalence of diabetes between 2021 and 2045 will occur in low-income countries (11.9%) and middle-income countries (21.1%), which largely includes SSA countries [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, the prevalence of MetS is escalating at an alarming rate, and it is highly prevalent in patients with T2DM [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It was estimated that 20\u0026ndash;25% of the adult general population and 70\u0026ndash;80% of T2DM patients had MetS worldwide [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Individuals with MetS are more likely to have a higher risk of heart attacks and cardiovascular diseases (CVD) compared to those without MetS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, it is documented that the risk of CVD development is greater among individuals who have a combination of T2DM and MetS compared to those who have either condition alone [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the burden of communicable diseases has traditionally been the major focus of public health initiatives in SSA, the rise of non-communicable diseases like T2DM and MetS is now posing a significant threat to the region's health and socio-economic development. Unlike prior studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] that explored MetS in broader African populations or specific country, the current study aimed to systematically review the available evidence and provide an estimate of the pooled prevalence of MetS among SSA individuals with T2DM. By spotlighting MetS within the context of T2DM in SSA, offers a more targeted understanding of MetS within a unique subset of the African population, providing valuable information for healthcare practitioners and researchers focusing on this demographic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Registration\u003c/h2\u003e \u003cp\u003eWe conducted a systematic review and meta-analysis of observational studies, all of which were cross-sectional study designs done across SSA. This systematic review and meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guideline [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The study protocol was registered in the PROSPERO, an international prospective register of systematic reviews protocols on health related topics CRD42023455576 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome of Interest\u003c/h2\u003e \u003cp\u003eThe primary outcome of interest for this study was the pooled prevalence of MetS among T2DM patients, as defined by the widely recognized and extensively used criteria\u0026rsquo;s i.e 2004 National Cholesterol Education Program- Adult Treatment Panel (NCEP-ATP III 2004) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and/or the IDF criteria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Using NCEP-ATP III 2004 any three of the five metabolic syndrome components while using IDF criteria central Obesity, plus two of the four MetS components (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The secondary aim was to describe the prevalence of individual components of MetS among T2DM patients, according to the specific MetS definition criteria among T2DM individuals in SSA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic criteria of metabolic syndrome according to NCEP-ATP III 2004 and IDF Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCEP-ATP III 2004\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Obesity\u003c/p\u003e \u003cp\u003eHypertriglyceridemia\u003c/p\u003e \u003cp\u003eReduced HDL-Cholesterol\u003c/p\u003e \u003cp\u003eHyperglycemia\u003c/p\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102cm in male and \u0026ge; 88cm in female.\u003c/p\u003e \u003cp\u003eTG\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl or triglycerides treatment\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40mg/dl in males and \u0026lt;\u0026thinsp;50mg/ dl in females or HDL-c treatment\u003c/p\u003e \u003cp\u003eFBG\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL or on treatment\u003c/p\u003e \u003cp\u003eSystolic/Diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130/85mmHg or hypertension treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaist circumference\u0026thinsp;\u0026ge;\u0026thinsp;94cm in male and \u0026ge; 80cm in female.\u003c/p\u003e \u003cp\u003eTG\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl or triglycerides treatment\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40mg/dl in males and \u0026lt;\u0026thinsp;50mg/ dl in females or HDL-c treatment\u003c/p\u003e \u003cp\u003eFBG\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL or on treatment\u003c/p\u003e \u003cp\u003eSystolic/Diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;130/85mmHg or hypertension treatment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBP: Blood pressure; FBG: Fasting blood glucose; HDL-c: High Density Lipoprotein cholesterol; IDF: International Diabetes Federation; NCEP-ATP III: National Cholesterol Education Program\u0026ndash;Adult Treatment Panel III; TG: Triglyceride.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Search Strategy\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive systematic literature search to identify studies reporting the prevalence of MetS among T2DM patients in the sub-Saharan African population. The search utilized a combination of Medical Subject Headings (MeSH) and free text words across various electronic databases and search engine, including MEDLINE-PubMed, EMBASE, Scopus, African Index Medicus, African Journal Online and Google Scholar. Inclusion criteria were limited to English-language studies published from the inception of databases until July 31, 2023. Additionally, a snowball search was performed on the reference lists of all relevant included studies. The search strategy focused on three key elements: metabolic syndrome, type 2 diabetes mellitus and sub-Saharan Africa. These searches were independently performed by two authors; N.M and H.N. The detailed search strategy used for the databases is presented in the supplementary material S1. To manage references and remove duplicates, we used Rayyan, an online web application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were as follows: All observational studies reporting the prevalence of MetS and its subcomponents among T2DM individuals in sub-Saharan African populations, studies reporting metabolic syndrome using IDF criteria and/or NCEP-ATP III 2004, and publications with full text in English. The full text of studies meeting these criteria was retrieved and screened for eligibility. Whereas, non-original research articles, such as review articles, editorials, case reports, letters, or commentaries, studies describing MetS in populations other than sub-Saharan Africa, T2DM, and those with unclear or unspecified methods of diagnosing metabolic syndrome were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection and Quality Assessment\u003c/h2\u003e \u003cp\u003eTwo authors (N.M. and H.N.) independently conducted the literature search and screened the titles, abstracts and keywords of all the studies retrieved from online database searches for possible inclusion in the review. Furthermore, the relevant articles were obtained in full text, and after a thorough reading of the full-text articles, the included studies were identified based on the assessment of inclusion and exclusion criteria. Any discrepancies during the entire selection process between the two authors were resolved either through consensus or consultation with third author (G.J). The search, screening, and study identification process are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The methodological quality and risk of bias of the included studies was assessed using eight aspects of the Joanna Brigg\u0026rsquo;s Institute (JBI) quality checklist for analytical cross-sectional studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Two authors (N.M. and H.N.) independently used the tool to evaluate the inclusion criteria, measurement of exposure and outcome variables, confounding adjustment, and appropriateness of statistical analysis. Studies that scored 50% or higher on the quality assessment were considered to be of good quality. Full details regarding the appraisal checklist are provided in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethodological quality assessment of included studies using Joanna Brigg\u0026rsquo;s Institute quality appraisal (JBI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor[Year]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWere the criteria for inclusion in the sample clearly defined?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWere the study participants and setting described in detail?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWas the exposure\u003c/p\u003e \u003cp\u003emeasured in a valid and reliable way?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWere objective,\u003c/p\u003e \u003cp\u003estandard criteria used for measurement of the condition?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWas appropriate statistical analysis used?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWere the outcomes\u003c/p\u003e \u003cp\u003emeasured in a valid and reliable way?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWere confounding factors identified?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWere strategies to deal with confounding factors stated?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOverall appraisal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalk et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePuepet et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadike et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanda et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOgbera et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKangne et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsuji et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMogre et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNsiah et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjiofor et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnyenekwu et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbban et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmidu et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsei-Yeboah et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoyesa et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTadewos et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiadgo et al.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirarra et al.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObirikorang et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgyemang-Yeboah et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebremeskel et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWube et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZerga et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnto et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebreyesus et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGemeda et al.[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharkos et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Extraction:\u003c/h2\u003e \u003cp\u003eExtraction of relevant data from the included studies was independently performed by two authors (N.M and H.N). Information regarding authors; year of publication; geographical location; year(s) of survey; study design; sample size; gender; mean age; sampling techniques; diagnostic criteria for defining metabolic syndrome and relevant clinic outcomes of interest were collected using a standardized data extraction form. Extracted data were then checked for its accuracy and consistency by a third author (G.J).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe extracted data were exported to computer software RStudio version 2023.06.1\u0026thinsp;+\u0026thinsp;524 for data synthesis, analysis, and generation of forest and funnel plots. Evidence of between study variance due to heterogeneity was assessed using Cochran\u0026rsquo;s Q statistic and the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e statistic [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Furthermore, in order to explore potential sources of heterogeneity across the included studies, subgroup and sensitivity analyses were performed to comprehensively assess the overall effect size within the included studies. A random-effects model with inverse variance was used to obtain an overall summary estimate of the prevalence across studies. Point estimation with a confidence interval of 95% was used. The presence of publication bias was examined through the utilization of funnel plots, further enhanced by formal statistical assessment using Egger\u0026rsquo;s test [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a preliminary search of online databases using a combination of MeSH and free text words retrieved a total of 1418 potential articles, and an additional 3 articles were found through manual citation searching. After removing duplicates, 928 articles remained, which were then screened based on their titles and abstracts, resulting in the elimination of a further 872 articles that were irrelevant to the research question. Among the 56 articles that underwent full-text review, ultimately 30 articles met the inclusion criteria and were included in this review.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacteristics of Included Studies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA characteristic summary of thirty articles included in this study involving 8879 individuals is illustrated in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All were of cross-sectional study design conducted in six sub-Saharan African countries namely Cameroon, Ethiopia, Ghana, Nigeria, Zambia and South Africa as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In these studies, the prevalence of MetS was estimated based on the IDF and/or NCEP-ATP III 2004 criteria. Among the articles, eleven studies reported the prevalence of MetS based on both NCEP-ATP III 2004 and IDF criteria [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], fourteen studies reported based solely on NCEP-ATP III 2004 criteria [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and five studies reported based on IDF criteria alone [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, nine studies reported the prevalence of MetS subcomponents based on NCEP-ATP III 2004 criteria [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and six studies based on IDF criteria [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the included studies that evaluated the prevalence of MetS among T2DM in sub-Saharan population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor[Year]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSampling method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurvey Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDiagnostic criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOverall prevalence\u003c/p\u003e \u003cp\u003e(NCEP/ATP-III) (IDF)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalk et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1994\u0026ndash;2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2006 to May 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune 2006 to May 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePuepet et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary 2006 to December 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e63.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadike et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJanuary to August 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanda et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember 2006 to August 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOgbera et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKangne et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCameroon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsuji et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.27\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMogre et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross- sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjiofor et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch to September 2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e67.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNsiah et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to April 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e58.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbban et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch to April 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmidu et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNovember 2010-March 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e65.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnyenekwu et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e97.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsei-Yeboah et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to April 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoyesa et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to May 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTadewos et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch to November 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiadgo et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune to July 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirarra et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch to May 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObirikorang et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-probability convenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgyemang-Yeboah et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e90.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebremeskel et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to June 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWube et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to May 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZerga et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFebruary to March 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e59.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnto et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGhana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMarch to June 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebreyesus et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember to November 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e67.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e57.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGemeda et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember 2020 to August 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharkos et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthiopia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic random sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeptember to October 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNCEP-ATP III, IDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eIDF: International Diabetes Federation; NCEP-ATP-III: National Cholesterol Education Program\u0026ndash;Adult Treatment Panel I\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBurden of Metabolic syndrome Using NCEP-ATP III 2004 and IDF Criteria.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe weighted pooled prevalence of MetS among T2DM individuals in sub-Saharan Africa using NCEP-ATP III 2004 criteria is 63.1% (95% CI: 57.9\u0026ndash;68.1), with significant heterogeneity \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;94% and Cochran Q-statistic p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 as graphically depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. While using IDF criteria yielded a pooled prevalence of 60.8% (95% CI: 50.7\u0026ndash;70.0), with an \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of 95% and Cochran Q-statistic p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The random-effects model was assumed due to the considerable heterogeneity observed across the included studies in the meta-analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrevalence of the Metabolic Syndrome Components.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the current systematic review, the prevalence of the individual components of MetS other than hyperglycemia among the sub-Saharan Africa T2DM population was reported in ten studies based on NCEP-ATP III 2004 criteria, and six studies reported based on IDF criteria. The overall pooled prevalence of metabolic syndrome component by NCEP-ATP III 2004 criteria was as follows: central obesity 55.9% [95% CI: 47.6, 64.2], low HDL-c 43.3% [95% CI: 33.5, 53.2], hypertriglyceridemia 48.0% [95% CI: 35.2, 60.7] and hypertension 54.8% [95% CI: 43.2, 66.4]. These values are summarized in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhereas the overall pooled prevalence of MetS component by IDF criteria was as follows: central obesity 61.6% [95% CI: 47.9, 75.3], low HDL-c 49.9% [95% CI: 37.3, 62.6], hypertriglyceridemia 49.2% [95% CI: 34.1, 64.4] and hypertension 56.1% [95% CI: 46.7, 65.4] as summarized in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled prevalence of metabolic syndrome component based on NCEP-ATP III 2004\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePrevalence of metabolic syndrome component\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor [Year]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitty et al. 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[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOgbera et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNsiah et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsei-Yeboah et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoyesa et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTadewos et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirarra et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiadgo et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePooled prevalence [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55.9 [47.6, 64.2]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e43.3 [33.5, 53.2]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e48.0 [35.2, 60.7]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e54.8 [43.2, 66.4]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI: Confidence Interval; HDL-c: High density lipoprotein cholesterol; TG: Triglyceride; NCEP-ATP III: National Cholesterol Education Program\u0026ndash;Adult Treatment Panel III.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled prevalence of metabolic syndrome component based on IDF criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePrevalence of metabolic syndrome component\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor [Year]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-HDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirarra et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiadgo et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsei-Yeboah et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalk et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePuepet et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGebremeskel et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePooled Prevalence [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e61.6 [47.9, 75.3]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49.9 [37.3, 62.6]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e49.2 [34.1, 64.4]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e56.1 [46.7, 65.4]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI: Confidence Interval; HDL-c: High density lipoprotein cholesterol; TG: Triglyceride ; IDF: International Diabetes Federation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and sensitivity analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted based on gender, country, sample size, and mean age. According to the NCEP-ATP III 2004, a total of 17 studies reported prevalence based on gender, revealing that the pooled prevalence of MetS among females in SSA was significantly higher compared to males (73.5% vs. 50.5%). Meanwhile, the results of subgroup analysis based on sample size showed the highest prevalence in studies with \u0026ge;\u0026thinsp;250 subjects compared to those with \u0026lt;\u0026thinsp;250 subjects (67.0% vs. 55.2%), as depicted in supplementary table 2. Furthermore, subgroup analysis based on IDF criteria, as shown in supplementary table 3, revealed a higher pooled prevalence among females (71.6%) compared to males (44.5%) among the 11 studies that reported prevalence based on gender. Among the 12 reports that specified participant mean age, the pooled prevalence was comparable across the two categories of mean age: \u0026lt;50 years and \u0026ge;\u0026thinsp;50 years. Additionally, sensitivity analyses were conducted using the leave-one-out approach to evaluate the influence of individual studies on the overall estimate of MetS, based on the NCEP-ATP III 2004 and IDF criteria. The results indicated no substantial evidence for the influence of any single study on the overall pooled prevalence of MetS among individuals with T2DM in SSA (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). To further explore the observed heterogeneity in the study, we conducted a meta-regression to account for this. The analysis revealed that gender had a significant influence on the overall effect sizes in both NCEP-ATP III 2004 and IDF (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, 0.0007 respectively) and studies with a sample size\u0026thinsp;\u0026ge;\u0026thinsp;250 for NCEP-ATP III 2004 there was a significant influence observed at p value 0.0106.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePublication bias\u003c/h2\u003e \u003cp\u003eA funnel plot of the pooled prevalence of MetS and Begg\u0026rsquo;s statistical tests at a 5% significance level were used to assess the presence of potential publication bias among the included studies. The funnel plots were almost symmetrical for the NCEP-ATP III 2004 criteria and IDF criteria, as graphically represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, respectively. Furthermore, separate analyses of the linear regression test of funnel plot asymmetry based on NCEP-ATP III 2004 and IDF criteria resulted in statistically non-significant p-values of 0.7800 and 0.6686, respectively, indicating the absence of publication bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe association between T2DM and MetS has been thoroughly investigated. To our knowledge, this is the first systematic review and meta-analysis that evaluated the weighted pooled prevalence of MetS in individuals with T2DM in sub-Saharan Africa using specific diagnostic criteria for metabolic syndrome. The findings of this systematic review indicate that the weighted pooled prevalence of MetS was 63.1% (95% CI: 57.9\u0026ndash;68.1) and 60.8% (95% CI: 50.7\u0026ndash;70.0) using NCEP-ATP III 2004 and IDF criteria, respectively. The observed disparities in the prevalence of MetS when applying the NCEP-ATP III 2004 criteria versus the IDF criteria are noteworthy. The prevalence was slightly higher (63.1%) when the NCEP-ATP III 2004 criteria were used, compared to the IDF criteria (60.8%). These differences can be attributed to variations in the diagnostic components and thresholds employed by each set of criteria [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Similar findings regarding the variation in MetS prevalence based on diagnostic criteria have been reported in many studies conducted in different parts of the world [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Interestingly, when we compare our findings with those from other regions and study populations, we observe divergent outcomes. For instance, our findings are somewhat consistent with results reported in a systematic review among African T2DM patients (66.9%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Ethiopian T2DM patients (63.78%) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the current weighted pooled prevalence of MetS using IDF criteria (60.8%) was higher than the prevalence estimated globally, which typically ranges between 20% and 25% when using similar diagnostic criteria [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, subgroup analysis by gender revealed a considerably higher pooled prevalence of MetS in females, at 73.5% (95% CI: 67.4\u0026ndash;79.5), compared to males at 50.5% (95% CI: 43.8\u0026ndash;57.2) according to the NCEP-ATP III 2004. Similarly, a higher pooled prevalence was observed according to the IDF criteria among females, reaching 71.6% (95% CI: 60.2\u0026ndash;82.9), compared to males at 44.5% (95% CI: 34.2\u0026ndash;54.8). This finding aligns with reports from systematic reviews conducted among various populations, including SSA African [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], Ghanaian [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], Bangladesh [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and mainland China [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The possible reason for the higher prevalence in females could be gender-specific increased MetS risk factors among women, such as menopause, contraceptive therapy use, elevated body weight, and increased waist circumference, in comparison to men [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Based on IDF criteria, among the included studies, the highest weighted pooled prevalence was observed in Nigeria at 80.2% (95% CI: 47.1\u0026ndash;99.9), while Ethiopia had the lowest at 52.0% (95% CI: 48.3\u0026ndash;55.8). This contrasts with a review by Shiferaw et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]that identified the highest prevalence of MetS in Ethiopia. However, their study combined studies with varying diagnostic criteria, unlike our report, which might account for this variation. The differences in MetS prevalence between Nigeria and Ethiopia found on the current review stem from a blend of varying dietary patterns, lifestyle distinctions, disparities in healthcare infrastructure, and cultural influences.\u003c/p\u003e \u003cp\u003eGenerally, our findings differ from those of many other studies around the world. In a systematic review conducted among healthy South Asians, a prevalence of MetS was reported as 26.1% (ATP III), 29.8% (IDF), and 32.5% (modified ATP III) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Similarly, a quantitative synthesis of 111 studies conducted among the Indian adult general population reported a prevalence of 29% (NCEP ATP-III) and 34% (IDF) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The observed discrepancies in the prevalence of MetS reported among different studies around the world are significant. These discrepancies might be due to differences in intrinsic study design, sample size, and characteristics of the study participants, such as comorbidities, geographical locations, urbanization, and lifestyle factors, including physical inactivity and unhealthy eating habits [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Moreover, the current review focused on Sub-Saharan African Type 2 Diabetes Mellitus individuals. T2DM appears to play a pivotal role in the pathogenesis and exacerbation of MetS, such that individuals with T2DM are more likely to have MetS, increasing their susceptibility to cardiovascular complications[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the data compiled in this review, the pooled prevalence of MetS components was as follows: central obesity at 55.9% and 61.6%; low HDL-c at 43.3% and 49.9%; hypertriglyceridemia at 48.0% and 49.2%; and hypertension at 54.8% and 56.1%, according to NCEP-ATP III 2004 and IDF criteria, respectively. Central obesity emerged as the most frequent metabolic syndrome component in this systematic review. Visceral adiposity has long been recognized as a central player in insulin resistance and is linked to a heightened risk of type 2 diabetes mellitus and cardiovascular diseases [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, high blood pressure and abnormal lipid profiles were also found to be prevalent in our review. Thus, our findings underscore the importance of a holistic approach to patient care, integrating strategies to mitigate MetS components alongside T2DM management to prevent adverse health effects such as CVD [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strengths of the present study include its comprehensive database search using varying combinations of keywords and well-defined inclusion/exclusion criteria. However, we wish to acknowledge several limitations in the current study. Firstly, significant heterogeneity was observed across the included studies, and this heterogeneity persisted even after stratification for diagnostic criteria. Secondly, the diversity in sub-Saharan African populations, as SSA is home to various ethnic, cultural, and socio-economic groups, may exhibit different risk factors and disease profiles. Therefore, the generalizability of findings across this region may be limited, as the prevalence and associations of MetS in T2DM can vary among these subpopulations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAlthough limited in scope, the findings presented here underscore the alarming prevalence of MetS among individuals with T2DM in sub-Saharan Africa. This trend may be directly linked to the rapid economic development and urbanization occurring in the region. This swift industrialization can lead to significant changes in lifestyle patterns and overnutrition, resulting in overweight and obesity, emphasizing the urgent need for comprehensive, region-specific prevention and management strategies. Encouraging lifestyle modifications, including regular exercise and balanced diets, is essential. Moreover, it is crucial to develop routine obesity screening procedures. Implementing early interventions and robust public health initiatives are crucial in mitigate the risks associated with central obesity.\u003c/p\u003e \u003cp\u003eSub-Saharan Africa faces unique health challenges, including limited healthcare resources and the dual burden of communicable and non-communicable diseases, which must be taken into account when developing effective interventions. Moving forward, it is imperative to prioritize research efforts that not only elucidate the underlying mechanisms of MetS and T2DM but also explore culturally sensitive and sustainable approaches for prevention and treatment. We hope that this systematic review will serve as a foundation for further studies, ultimately leading to more effective strategies and improved health outcomes for individuals in sub-Saharan Africa who are grappling with the challenges of metabolic syndrome and T2DM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all authors of studies included in this systematic review and meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflicts of interest relevant for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNM, HN and GJ developed the protocol and involved in the design, selection of study, data extraction, quality assessment, statistical analysis, results from interpretation, and developing the initial and final drafts of the manuscript. FS, CN, SG, AM, SH, KK and EM Involved in statistical analysis and revising subsequent drafts. All authors read and approved the final draft of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThird Report of the National Cholesterol Education Program (NCEP) (2002) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 106(25):3143\u0026ndash;3421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmet P, Alberti G, Shaw J (2005) A new IDF worldwide definition of the metabolic syndrome: of the metabolic syndrome: the rationale and the results. 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Metab Syndr Relat Disord 7(2):83\u0026ndash;88\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Maranatha Hospital","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic Syndrome, Prevalence, diabetes mellitus, sub-Saharan Africa.","lastPublishedDoi":"10.21203/rs.3.rs-3958331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3958331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eType 2 diabetes mellitus and metabolic syndrome represent two closely intertwined public health challenges that have reached alarming epidemic proportions in low- and middle-income countries, particularly in sub-Saharan Africa. Therefore, the current study aimed to determine the weighted pooled prevalence of metabolic syndrome and its components among individuals with type 2 diabetes mellitus in sub-Saharan Africa as defined by the 2004 National Cholesterol Education Program- Adult Treatment Panel (NCEP-ATP III 2004) and/or the International Diabetes Federation (IDF) criteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA systematic search was conducted to retrieve studies published in the English language on the prevalence of metabolic syndrome among type 2 diabetic individuals in sub-Saharan Africa. Searches were carried out in PubMed, Embase, Scopus, Google Scholar, African Index Medicus and African Journal Online from their inception until July 31, 2023. A random-effects model was employed to estimate the weighted pooled prevalence of metabolic syndrome in sub-Saharan Africa. Evidence of between-study variance attributed to heterogeneity was assessed using Cochran\u0026rsquo;s Q statistic and the I2 statistic. The Joanna Briggs Institute quality appraisal criteria were used to evaluate the methodological quality of the included studies. The summary estimates were presented with forest plots and tables. Publication bias was checked with the funnel plot and Egger\u0026rsquo;s regression test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, 1421 articles were identified and evaluated using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and 30 studies that met the inclusion criteria were included in the final analysis. The weighted pooled prevalence of metabolic syndrome among individuals with type 2 diabetes mellitus in sub-Saharan Africa was 63.1% (95% CI: 57.9\u0026ndash;68.1) when using the NCEP-ATP III 2004 criteria and 60.8% (95% CI: 50.7\u0026ndash;70.0) when using the IDF criteria. Subgroup analysis, using NCEP-ATP III 2004 and IDF criteria, revealed higher weighted pooled prevalence among females: 73.5% (95% CI: 67.4\u0026ndash;79.5), 71.6% (95% CI: 60.2\u0026ndash;82.9), compared to males: 50.5% (95% CI: 43.8\u0026ndash;57.2), 44.5% (95% CI: 34.2\u0026ndash;54.8) respectively. Central obesity was the most prevalent component of metabolic syndrome, with a pooled prevalence of 55.9% and 61.6% using NCEP-ATP III 2004 and IDF criteria, respectively. There was no statistical evidence of publication bias in both the NCEP-ATP III 2004 and IDF pooled estimates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe findings underscore the alarming prevalence of metabolic syndrome among individuals with type 2 diabetes mellitus in sub-Saharan Africa. Therefore, it is essential to promote lifestyle modifications, such as regular exercise and balanced diets, prioritize routine obesity screenings, and implement early interventions and robust public health measures to mitigate the risks associated with central obesity.\u003c/p\u003e","manuscriptTitle":"Exploring the Prevalence and Components of Metabolic Syndrome in Sub-Saharan African Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:49:32","doi":"10.21203/rs.3.rs-3958331/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5242d351-cf08-4a76-9288-9ded127b8d02","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28777238,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2024-02-16T16:49:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 16:49:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3958331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3958331","identity":"rs-3958331","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}