Red cell distribution width as a cardiovascular risk predictor in adults with hypertension in sub-Saharan Africa.

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Olayinka Ibrahim, Kojo Awotwi Hutton-Mensah, Funmi Adeniyi, George Nketiah, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5256562/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Journal of Human Hypertension → Version 1 posted 9 You are reading this latest preprint version Abstract Red cell distribution width (RDW) quantifies the degree of variation in erythrocyte size, is identified as a potential marker of adverse cardiovascular events, and maybe a surrogate marker for assessing cardiovascular disease (CVD) risk in low-resource settings. We evaluated RDW as a predictor of CVD risk compared to the WHO CVD risk score among adults with hypertension attending primary healthcare centers in Ghana and Nigeria. Adults with hypertension attending selected PHCs in Ghana and Nigeria participated in a cross-sectional study. Each participant underwent BP measurement and laboratory evaluation (RDW, total cholesterol, and fasting blood sugar) following standard methods. We recruited 319 adults aged 40–74 years from the study sites. The mean (standard deviation) RDW was 13.96 (1.1%). The median CVD risk score was 8.11% [interquartile range (IQR) 4.00 to 11.00]. For participants with hemoglobin (Hb) levels ≥ 12 g/dL, RDW showed positive correlations with age (r=0.136;p=0.042); systolic BP (r=0.183; p=0.006), diastolic BP (r=0.206, p=0.002) and WHO CVD risk scores (r=0.166, p=0.013). Multiple linear regression showed an independent association between RDW and WHO CVD risk scores with an upward gradient and was most significant at 3rd quartiles. Using ROC analysis, the C-statistic was 0.673 (95% CI 0.618 to 0.724), p=0.031. With a cut-off of > 14, the RDW demonstrated a sensitivity of 81.82% and specificity of 55.84%. This study shows that at Hb levels ≥ 12 g/dL, RDW modestly predicted CVD risk in adults with hypertension in sub-Saharan Africa. Health sciences/Risk factors Health sciences/Health care/Disease prevention/Preventive medicine Red cell distribution width hypertension sub-Saharan African cardiovascular risk Figures Figure 1 INTRODUCTION Cardiovascular disease (CVD) continues to be the primary cause of death worldwide, resulting in approximately 17.9 million deaths 2019 [ 1 ]. In 2019, CVDs caused over one million deaths in sub-Saharan Africa, representing approximately 5.4% of all globally CVD-related deaths and 13% of all deaths in Africa [ 2 ]. A main risk factor for CVD-related deaths is hypertension, with African countries having the highest burden globally and the highest percentage rise over the past three decades [ 3 ]. Hypertension is responsible for 50% of strokes, 47% of heart failures, and 47% of coronary heart diseases. The majority of this burden, about 80%, is found in middle- and lower-income countries [ 4 ]. Current cardiovascular disease prevention guidelines emphasize the need to manage based on an individual’s risk assessment [ 5 ]. The individual's risk assessment allows identification of patients with high-risk CVD for more focused treatment and improved outcomes [ 6 ]. Furthermore, incorporating a cardiovascular risk approach into population surveys has significant consequences for making decisions regarding the allocation of healthcare resources [ 5 ]. Many risk assessment and prediction models have been developed over time, but only the World Health Organization (WHO) CVD risk predictor has been validated in the African sub-region [ 7 ]. This makes the prediction model and charts a more appropriate risk assessment tool for countries within the sub-region. The WHO CVD risk prediction charts are available in both laboratory- and non-laboratory-based charts. Laboratory-based charts require data on age, gender, smoking status, systolic blood pressure, presence or absence of diabetes, and total cholesterol levels. On the other hand, non-laboratory-based charts require data on age, gender, smoking status, systolic blood pressure, and body mass index (BMI). Despite its applicability in low-resource settings, the non-laboratory chart significantly underestimates the risk of CVD by up to 35% in men and 65% in women, particularly in patients with moderate to high CVD risk [ 7 ]. Worthy of note are the limiting factors for laboratory-based assessment of CVD risk, including the cost of laboratory tests, lack of trained personnel, and equipment to routinely carry out some of these tests (lipid profiles) at primary and some secondary health facilities in low- and middle-income countries (LMICs) [ 6 , 8 ]. Therefore, there is a need to develop an alternative approach that is easily applicable to our environment, and red cell distribution width may be found to be such a biomarker. Red cell distribution (RDW) is a biomarker that is reported as part of routine full blood counts (FBC) and has been found to independently predict adverse CVD events [ 9 ]. It is inexpensive and remains the most common test requested by clinicians in both clinical and community settings [ 10 ]. RDW is a simple assessment of red blood cell (RBC) size variability (anisocytosis), which is obtained by dividing the standard deviation (SD) of erythrocyte volumes by the mean corpuscular volume (MCV) and can be automated [ 11 ]. Studies showed that anisocytosis may be involved in the pathogenesis of cardiovascular disorders [ 11 ]. The deformed erythrocytes (a common finding in anisocytosis) increase blood viscosity, impair microcirculatory blood flow, and promote aggregation and endothelial adhesion. All of these processes contribute to the development of atherosclerosis by neutralizing vasodilator mediators and accumulating lipids in atherosclerotic lesions [ 12 ]. Researchers have evaluated RDW as a promising biomarker of CVD risk in conditions such as arteriosclerosis, heart failure, and ischemic stroke; however, its role in predicting CVD risk in hypertensive adults in low-resource settings remains unknown [ 9 ]. In low-resource settings, RDW may be a plausible surrogate marker for assessing CVD risk in adults with hypertension. Therefore, we hypothesized that RDW would independently predict CVD risk among patients with hypertension attending PHCs in SSA (Ghana and Nigeria) when compared with the WHO CVD risk score. Thus, we sought to evaluate RDW as a predictor of CVD risk compared to the WHO CVD risk score among adults with hypertension attending PHC centers. METHODS Study design . This study was cross-sectional and involved hypertensive participants attending primary healthcare facilities in Ghana and Nigeria. Study site The study was conducted at the Okelele Primary Healthcare Center, Ilorin, North-Central Nigeria, and St. Anthony Ann Hospital, Deduako, Ashanti region, both being primary healthcare facilities in Nigeria and Ghana, respectively. The Okelele Primary Healthcare Center has an average clinic attendance of 250 patients with hypertension monthly, whereas St. Anthony Ann Hospital has an average of 172 patients attending the hypertension clinic monthly. Study participants The study participants were patients with hypertension attending the general outpatient departments of selected primary healthcare facilities in Nigeria and Ghana. We included patients aged 40–74 years with hypertension who consented to participate in the study. Hypertension was defined as those already on treatment (controlled or uncontrolled), treatment-naïve with a blood pressure ≥ 140/90 mmHg, and newly diagnosed with three resting blood pressure measurements (BP≥ 140/90 mmHg). We excluded patients with evidence of CVDs (stroke, heart failure, peripheral artery disease and ischemic heart disease), pregnant women, hematological disorders (e.g., leukemia, sickle cell disease), clinical paleness, history of renal failure and liver disease, and those taking vitamin supplements 24 hours before the study. Sample size estimation Using Andrew Fischer’s formula, we estimated a minimum sample size of 316 from a 71% prevalence of elevated RDW among hypertensive adults in a previous study [13] at a 5% level of precision and 95% confidence interval. Sampling, recruitment procedure and data collection We consecutively recruited participants who met the study inclusion criteria between July and December 2023. A total of 160 participants (160) were recruited from Nigeria and 159 were recruited from Ghana. We used a pretested questionnaire to gather relevant sociodemographic and cardiovascular risk factors from participants. Each participant underwent physical measurements, and blood sampling for biochemical and hematological measurements was performed among the study participants. We employed a modified version of the WHO's STEP-wise non-communicable disease risk factor surveillance tool to gather pertinent CVD history from the study participants. Physical measurements Anthropometric measurements were carried out according to the WHO guidelines. The weight was measured using an Omron HN286 electronic human weighing scale with an accuracy of 0.1 kg. Each participant’s height was measured with a “Seca 213” mobile stadiometer with accuracy of 0.1 cm. Body mass index (BMI) was calculated using the following formula: BMI (kg/m2) = weight (kg) / height (m 2 ). ‘Omron M7 Intelli IT,’ a validated upper-arm BP monitor was used to measure participants BP. In brief, each participant sat quietly with their feet on the floor and their clothes loosened around the arm for at least five minutes before the blood pressure readings were taken. We placed a correct-sized cuff (bladder width 80% of the arm circumference) on the exposed arm, approximately 2 cm above the elbow, and positioned the tube in front and at the center of the arm. The blood pressure reading was recorded some minutes after pressing the start button, and the reading was displaced and documented. We took three serial BP measurements, three minutes apart, and used the average of the last two readings for data analysis. Blood sample Collection Each participant also had blood samples taken for analysis (biochemical and hematologic analyses). Blood samples were analyzed using the EasyRa Chemistry Analyzer and a fully automated assay hematology analyzer to determine the serum cholesterol and full blood count (FBC), respectively. The system automatically determined the RDW from the FBC results. Cardiovascular disease risk score assessment We calculated the cardiovascular disease risk scores of participants using the WHO cardiovascular risk (laboratory-based) prediction charts (2019 revised edition).[7] Each participant ’sWHO CVD risk score was calculated based on age, gender, smoking status, presence or absence of diabetes, systolic BP, and total cholesterol. Outcomes The level of prediction of cardiovascular risk using RDW in adults with hypertension attending PHC centers in Ghana and Nigeria Data analysis We analyzed the data from the study proforma using IBM SPSS version 29. Descriptive statistics were used to summarize the participants’ sociodemographic variables and were compared across the RDW quartiles. The WHO CVD risk scores did not follow a normal distribution, and as such, were log-transformed before analysis, and the RDW (%) was stratified into four quartiles. Pearson and Spearman’s rank correlation coefficients, and multiple linear regression were used to evaluate the relationship between the RDW and CVD risk score. To determine the cut-off RDW that predicted a high CVD risk (WHO CVD risk score > 20).The p-value for the level of statistical significance was set at p < 0.05. Ethical approval Ghana Health Service Ethics Review Committee (Ghana) and Kwara State Ethical Review Committee (Nigeria) approved this study. We also sought permission from the appropriate authorities at both primary healthcare facilities. A detailed explanation of what the study entails in information sheets, including study procedures, was made available to all the participants in the language they best understood, and written informed consent was obtained. The data collected were coded to ensure the anonymity of the study participants and were stored in a password-encrypted computer. Results General characteristics This study included 319 adults aged 40 to 74 years from study sites in Ghana (159) and Nigeria (160). The mean (standard deviation) age of participants was 59.10 (10.2) years. Based on the age group, the highest category was those aged 70-74 (66;20.7%), followed by 60-64 (55;17.2%), 65-69 (48;15.0%); 55-59 (45;14.1%), 50-54 (43;13.5%), 45-49 (29;9.1%) and 40-44 (33;10.3%). There were more females (259;81.2%) (Table 1). Based on the RDW quartile stratification, the variables that were significantly different across the subgroups included educational level ( p <0.001), diabetes mellitus ( p =0.011), systolic BP ( p =0.017), hemoglobin ( p =0.007), MCV ( p <0.001), study sites ( p <0.001), and alcohol consumption ( p =0.002) (Table 1). The mean [standard deviation (SD)] RDW was 13.96 (1.1%), with a range of 11.50% to 21.70%. The mean (SD) RDW in Ghana was 13.63 (1.3%) vs 14.28 (0.8%) in Nigeria ( p < 0.001). The CVD risk score median (interquartile range [IQR]) was 8.11% (4.00 to 11.00] with a minimum of 1.0% and a maximum of 30.0%. The median (IQR) CVD risk scores were comparable between the two countries (Ghana 7.0% (4.0 to 11.0) vs Nigeria 7.5% (4.0 to 13.0), p =0.577); Table 1 Relationship between cardiovascular risk factors, WHO CVD risk scores, and RDW. Correlations of RDW with cardiovascular risk factors and WHO CVD risk scores For all recruited participants, there was a significant positive correlation between RDW and the following variables: alcohol consumption ( r =0.193, p <0.001), systolic BP ( r =0.159, p =0.004), and diastolic BP ( r =0.149; p =0.013). For Hemoglobin levels ≥ 12 g/dL, RDW was positively correlated with age ( r =0.136; p =0.042), alcohol consumption ( r =0.312, p <0.001), systolic BP ( r =0.183; p =0.006), diastolic BP ( r =0.206, p =002), and WHO CVD risk scores ( r =0.166, p =0.013). For participants with hemoglobin levels ≥ 12 g/dL, there was no significant correlation between RDW, cardiovascular risk factors, and WHO CVD risk scores (Table 2). Multiple linear regression of RDW as a predictor of CVD risk The multiple linear regression model showed an independent association between RDW and WHO CVD risk scores with an upward gradient. WHO CVD scores increased with increasing RDW quartile and most significant at 3 rd quartiles (Table 3) ROC curve of RDW as a predictor of CVD risk. The CVD risk scores were dichotomized into high-risk (20.0% or more) and those with CVD risk scores of less than 20.0% versus RDW. Using ROC analysis, the C-statistics (area under the curve) was 0.673 (95% CI 0.618–0.724), p =0.031. At a cut-off of > 14, the RDW had a sensitivity of 81.82%, specificity of 55.84%, and Youden index of 0.377 (Figure 1 and Table 4). Discussion African countries have the highest global burden of hypertension, a leading risk factor for adverse CVD outcomes. This study examines the role of RDW, a cheap inexpensive parameter in full blood counts in assessing CVD risk among cohort of adults with hypertension in Ghana and Nigeria. This study showed that RDW correlated with systolic and diastolic BP. These finding are consistent with other studies that demonstrated a similar correlation between RDW and blood pressure (systolic and diastolic) [14–17]. However, a study in Turkey only observed a positive correlation between systolic BP and RDW [18]. The differences in our findings compared with the Turkey study may be relative higher mean age of our cohort (59 years) vs 50 years in Turkey study. Younger adults, especially those with diastolic hypertension, tend to have less inflammatory biomarkers, which may impact RDW findings [18]. The association between RDW and BP in hypertensive patients in this study provides additional evidence for the use of RDW as a biomarker for cardiovascular disorders. RDW is a marker of increased inflammation and oxidative stress, which are equally implicated in the development and progression of hypertension. This inflammation leads to endothelial dysfunction, reduced compliance of arteries leading to high vascular resistance which worsens hypertension [19]. The present study showed that, for participants with Hb ≥ 12 g/dL, there was a correlation between a high RDW and a higher WHO CVD risk score. A large cohort study in Brazil also showed that RDW was positively correlated with increased CVD risk using the Framingham risk score [20]. Our study further reinforces the role of RDW in cardiovascular diseases, including hypertension. This study showed that RDW is independently associated with the WHO CVD risk score and is most significant at Q3. A large cohort study in Brazil revealed that RDW was independently associated with the CVD risk score as assessed using the Framingham risk score.[20] Whereas we observed an independent association at the 3 rd RDW quartile; the study in Brazil was most significant at the four quartiles. The differences between our study and the Brazil study may be due to the CVD risk assessment used with the participants in our study having the highest median score of WHO CVD risk score (9.00 (IQR-3.25, 13.00)] at 3 rd quartiles. In our study, at an RDW > 14%, the RDW had modest C-statistics of 0.673, sensitivity of 82%, and specificity of 56% in predicting CVD adverse outcomes in the study cohort. Red cell distribution width has been demonstrated to predict outcomes in various cardiovascular diseases [11]. Among the cohort of 1971 admitted with chest pain in a regional hospital in Italy, at a cut-off of 13.7%, RDW has an area under curve (AUC) of 0.61 with a sensitivity of 75% and a specificity of 52.0% [21]. In China, RDW at a cut-off of 14.1% predicted a 90-day cardiovascular event (cardiac death or readmission for heart failure) for patients with acute heart failure and has a sensitivity of 87%, specificity of 54.9%, and area under the curve  of 0.728 [22]. Although with an modest level of specificity (56%), the RDW has a high sensitivity (82%) in our study, suggesting it may be a good screening tool to identify adults with hypertension in the Saharan population at high risk of adverse CVD outcomes, especially when there is limited access to advance laboratory and diagnostic investigations. Study limitations This study’s strengths include being multi-country and comparing RDW with the WHO CVD risk score, which has been validated for the assessment of CVD risk in the sub-Saharan African population. However, this study has some limitations, as it was a cross-sectional study, which means that a cause–effect relationship could not be established. In addition, we did not test for other inflammatory markers, such as CRP and ESR, to reduce cofounders in our analysis, and participants were not followed up to see how many will later develop adverse cardiovascular outcomes. Conclusion In a cross-sectional multi-center study among adults with hypertension in sub-Saharan Africa, RDW correlated significantly with the WHO CVD risk score for adults with Hb>12g/dL or more with a modest predictive ability for CVD risk. For patients with Hb ≥ 12 g/dl or more, RDW, a cheap and readily available biomarker, may be a good screening tool for low-resource settings to identify patients with hypertension who may be at high risk of adverse CVD outcomes. We recommend further evaluation of the strength of the association in a larger population. Abbreviations RDW Red cell distribution width FBC Full blood counts CVD Cardiovascular disease WHO World health organization Declarations Ethics approval and consent to participate Ghana Health Service Ethics Review Committee (Ghana) and Kwara State Ethical Review Committee (Nigeria) approved this study. We also sought permission from the appropriate authorities at both primary healthcare facilities. A detailed explanation of what the study entails in information sheets, including study procedures, was made available to all the participants in the language they best understood, and written informed consent was obtained. The data collected were coded to ensure the anonymity of the study participants and were stored in a password-encrypted computer. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Funding: This research work is funded by the NIH-Forgarty International Center through the Stroke and Cardiovascular Research Training (ScarT) Institute, Ghana. Authors' contributions ORI conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. KAH conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. FTA conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript GBN conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. AMN conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. AYN was inlvolved in literature review, data collection, data visualization, draft and appraished the manuscript. AO was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. DO was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. OA was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. BSA was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. DS was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. OAM was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. Acknowledgements We acknowledged the staff of Okelele Primary Healthcare Center, Ilorin, Nigeria, and St. Anthony Ann Hospital, Deduako, Ashanti Region, Ghana, for their support during this work. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References World Health Organization (WHO). Cardiovascular diseases (CVDs). 2021. Bulto LN, Hendriks JM. The burden of cardiovascular disease in Africa: prevention challenges and opportunities for mitigation. Eur J Cardiovasc Nurs. 2023. https://doi.org/10.1093/eurjcn/zvad134 . Minja NW, Nakagaayi D, Aliku T, Zhang W, Ssinabulya I, Nabaale J, et al. Cardiovascular diseases in Africa in the twenty-first century: Gaps and priorities going forward. Front Cardiovasc Med. 2022;9. Arima H, Barzi F, Chalmers J. Mortality patterns in hypertension. J Hypertens. 2011;29 Supplement 1:S3–7. Modesti PA, Agostoni P, Agyemang C, Basu S, Benetos A, Cappuccio FP, et al. Cardiovascular risk assessment in low-resource settings. J Hypertens. 2014;32:951–60. WHO. HEARTS: Technical package for cardiovascular disease management in primary health care: Risk-based CVD management. 2020. Kaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, et al. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Heal. 2019;7:e1332–45. Talha I, Elkhoudri N, Hilali A. Major Limitations of Cardiovascular Risk Scores. Cardiovasc Ther. 2024;2024:1–6. Danese E, Lippi G, Montagnana M. Red blood cell distribution width and cardiovascular diseases. J Thorac Dis. 2015;7:E402–11. Ma I, Guo M, Lau CK, Ramdas Z, Jackson R, Naugler C. Test volume data for 51 most commonly ordered laboratory tests in Calgary, Alberta, Canada. Data Br. 2019;23:103748. Arkew M, Gemechu K, Haile K, Asmerom H. Red Blood Cell Distribution Width as Novel Biomarker in Cardiovascular Diseases: A Literature Review. J Blood Med. 2022;Volume 13:413–24. Lippi G, Cervellin G, Sanchis-Gomar F. Red blood cell distribution width and cardiovascular disorders. Does it really matter which comes first, the chicken or the egg? Int J Cardiol. 2016;206:129–30. Bilal A, Farooq JH, Kiani I, Assad S, Ghazanfar H, Ahmed I. Importance of Mean Red Cell Distribution Width in Hypertensive Patients. Cureus. 2016;8. Tanindi A, Topal FE, Topal F, Celik B. Red cell distribution width in patients with prehypertension and hypertension. Blood Press. 2012;21:177–81. Chen Y, Hou X, Zhong J, Liu K. Association between red cell distribution width and hypertension: Results from NHANES 1999–2018. PLoS One. 2024;19 5 May:1–15. Sileshi B, Urgessa F, Wordofa M. A comparative study of hematological parameters between hypertensive and normotensive individuals in Harar, eastern Ethiopia. PLoS One. 2021;16 12 December:1–13. Mendi MA. The Association Between Red Cell Distribution Width and Blood Pressure Variability in Hypertensive Patients. Cyprus J Med Sci. 2024;9:15–8. Pusuroglu H, Akgul O, Erturk M, Surgit O, Tasbulak O, Akkaya E, et al. Red cell distribution width and end-organ damage in patients with systo-diastolic hypertension. Arch Med Sci. 2016;12:319–25. Isik T. Is Red Cell Distribution Width a Marker for Hypertension? Cardiology. 2012;123:195–6. Carvalho NM de, Maluf CB, Azevedo DRM, Reis RCP dos, Castilhos CD de, Barreto SM, et al. Red cell distribution width is associated with cardiovascular risk in adults. Cien Saude Colet. 2022;27:2753–62. Cemin R, Donazzan L, Lippi G, Clari F, Daves M. Blood cells characteristics as determinants of acute myocardial infarction. Clin Chem Lab Med. 2011;49:1231–6. He W, Jia J, Chen J, Qin S, Tao H, Kong Q, et al. Comparison of prognostic value of red cell distribution width and NT-proBNP for short-term clinical outcomes in acute heart failure patients. 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Tables Table 1: General characteristic of the study participants Variables Total n=319 (%) Q1 11.50 to 13.19% n=74 Q2 13.20 to 13.99% n=85 Q3 14.00 to 14.49% n=67 Q4 14.50 to 21.70% n=93 P value Age- Mean (SD) 59.10 (10.2)* 58.11 (9.4) 58.75 (10.7) 60.02(11.3) 59.53 (9.4) 0.682 Sex Male 60 (18.8) 22 10 15 13 0.015 Female 259 (81.2) 52 75 52 80 Educated 186 (63.9) 60 47 32 47 <0.001 Diabetes 23 (7.2) 10 4 3 6 0.011 Smoker 5 (1.6) 2 0 2 1 0.393 BMI 26.51(5.6) 26.57 (4.8) 26.40 (5.8) 26.30 (5.9) 26.72(5.6) 0.965 Systolic BP 142.80 (24.4)* 136.02(21.8) 142.22 (22.0) 143.84 (24.6) 147.97 (27.2) 0.017 Diastolic BP 86.38 (13.1)* 84.15 (12.7) 85.2 (11.4) 86.89 (13.6) 88.84 (14.3) 0.102 Total cholesterol 5.22 (1.1)* 5.08 (1.1) 5.19 (1.1) 5.06 (1.1) 5.44 (1.2) 0.113 Serum Glucose 5.37 (2.2)* 5.90 (2.9) 5.04 (1.4) 5.10 (1.2) 5.43 (2.6) 0.069 Hemoglobin (mg/dl) 12.69(1.3)* 13.07 (1.6) 12.73 (1.2) 12.66 (1.1) 12.69(1.3) 0.007 MCV (fL) 85.25(6.7)* 88.90 (5.4) 86.49 (5.1) 85.10 (6.6) 81.32(7.1) <0.001 Ghana 159 (49.8) 65 39 22 33 <0.001 Nigeria 160 (50.2) 9 46 45 60 Alcohol (19; 6.0%) 10 7 0 2 0.002 CVD risk scores** 7.00 (4.00 to 11.00) 6.00 (4.0 to 10.00) 7.00 (4.00 to 10.25) 9.00 (3.25 to 13.00) 7.00 (4.00 to 11.00) 0.337 SD: Standard deviation; *-Mean with standard deviation; **-Values in median with interquartile range; CVD: Cardiovascular diseases; BP-Blood pressure; BMI-Body mass index; MCV-Mean corpuscular volume Table 2: Correlations of RDW with cardiovascular risk factors Variable Total (n=319) Hb <12 g/dL (n=94) Hb 12g/dL & above (n=225) r p r p r p Age 0.057 0.311 -0.092 0.380 0.136 0.042 sex 0.105 0.062 0.129 0.216 0.063 0.348 Diabetes -0.084 0.132 -0.047 0.652 -0.120 0.073 Smoking 0.019 0.739 - - 0.005 0.940 Alcohol 0.193 <0.001 0.171 0.100 0.312 <0.001 Body mass index 0.030 0.598 -0.073 0.485 0.035 0.599 Systolic blood pressure 0.159 0.004 0.122 0.241 0.183 0.006 Diastolic blood pressure 0.140 0.013 0.012 0.912 0.206 0.002 Total cholesterol 0.069 0.216 0.038 0.715 0.091 0.171 Glucose -0.033 0.559 -0.024 0.818 -0.041 0.540 CVD risk Scores 0.088 0.116 -0.035 0.740 0.166 0.013 CVD: Cardiovascular diseases Table 3: Multiple regression models for the association between RDW and WHO cardiovascular risk by quartiles CVD risk scores* Model Q1 [11.50 to 13.19%. n=58] Q2 [13.20 to 13.99%. n=64] Q3 [14.00 to 14.49%. n=51] Q4 [14.50 to 21.70%. n=52] Reference e β 0.975 1.179 1.163 95% CI 0.879, 1.099 1.006, 1.274 0.996, 1.259 P value 0.753 0.039 0.059 *n=225 (with hemoglobin ≥ 12 g/dL); CI-Confidence interval; Regression coefficients (β) were log-transformed. e β exponential of Beta Coefficient. Table 4: Summary of the ROC curve of RDW to predict a high risk for CVD based on WHO risk scores. Characteristics Values Sample size n=319 Area under the ROC curve 0.673 95% CI for area under the ROC curve 0.618 to 0.724 P value for Area=0.5 0.0310 Youden index J 0.377 Associated criterion > 14 Sensitivity 81.82 Specificity 55.54 ROC- receiver operating characteristic curve, CI-Confidence interval/ Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2025 Read the published version in Journal of Human Hypertension → Version 1 posted Editorial decision: revise 12 Nov, 2024 Reviewer # 2 agreed at journal 05 Nov, 2024 Review # 1 received at journal 05 Nov, 2024 Reviewer # 1 agreed at journal 05 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor assigned by journal 03 Nov, 2024 Submission checks completed at journal 15 Oct, 2024 First submitted to journal 14 Oct, 2024 Unknown event 14 Oct, 2024 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. 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Ibrahim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACNgkQWcDAwA+iEwqI1mLAwCDZANJiQIw1MC0GB6AMgoBPuvfh4wKDw/bG51cnfnhgwCDPL3aAgMNkjhsbzzA4nLjtxtvNEkCHGc6cnUBAi0QamzSPweEEsxtnN4C0JBjcJlKLvfGMs5t/kKSFcQN/7zYibZE5xgz0S3rijBu82ywSDCQI+0V+dhvj44IKa3v+/rObb/6osJHnlyagBQSYwaQEWKUEYeUILfwHiFM9CkbBKBgFIw8AALSSPKsl2w5GAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2621-6593","institution":"University of Global Health Equity","correspondingAuthor":true,"prefix":"","firstName":"Olayinka","middleName":"","lastName":"Ibrahim","suffix":""},{"id":373790579,"identity":"055b66f3-8e79-47d2-960d-f43803ca4171","order_by":1,"name":"Kojo Awotwi Hutton-Mensah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kojo","middleName":"Awotwi","lastName":"Hutton-Mensah","suffix":""},{"id":373790580,"identity":"05597df1-857f-4559-a26c-2fd78430872d","order_by":2,"name":"Funmi Adeniyi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Funmi","middleName":"","lastName":"Adeniyi","suffix":""},{"id":373790581,"identity":"24368dcc-9220-45ad-8be5-992fdaeeb5f9","order_by":3,"name":"George Nketiah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Nketiah","suffix":""},{"id":373790582,"identity":"56295713-509c-4a80-98e6-aea2df862af7","order_by":4,"name":"Adaku Nwankwo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Adaku","middleName":"","lastName":"Nwankwo","suffix":""},{"id":373790583,"identity":"33009c56-405f-4dcd-96be-6cd2941d2589","order_by":5,"name":"Abukari Natogmah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abukari","middleName":"","lastName":"Natogmah","suffix":""},{"id":373790584,"identity":"6a58441c-d2c9-4799-9b30-26960fa2c23c","order_by":6,"name":"James Ogunmodede","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Ogunmodede","suffix":""},{"id":373790585,"identity":"21c2f413-4d1d-4317-a660-3ee0712f4472","order_by":7,"name":"Dike Ojji","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Dike","middleName":"","lastName":"Ojji","suffix":""},{"id":373790586,"identity":"64846ead-bc0b-477f-9754-731b609450b0","order_by":8,"name":"Olumide Adesola","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Olumide","middleName":"","lastName":"Adesola","suffix":""},{"id":373790587,"identity":"ba319d65-c08f-4742-914c-f1af144a70fb","order_by":9,"name":"Biodun Alabi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Biodun","middleName":"","lastName":"Alabi","suffix":""},{"id":373790588,"identity":"fa4bbc45-49ba-4b3c-b8ac-e05c4cbb906a","order_by":10,"name":"Daniel Sarpong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Sarpong","suffix":""},{"id":373790589,"identity":"615dc4f7-2b93-4861-896d-ed523c57ca2c","order_by":11,"name":"Olugbenga Mokuolu","email":"","orcid":"https://orcid.org/0000-0001-8273-5876","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Olugbenga","middleName":"","lastName":"Mokuolu","suffix":""}],"badges":[],"createdAt":"2024-10-13 18:35:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5256562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5256562/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41371-025-00987-w","type":"published","date":"2025-01-11T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70919875,"identity":"d6ae0e4b-b7b8-4bcd-bdf6-5d4a945c899b","added_by":"auto","created_at":"2024-12-09 08:36:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of RDW to predict a high risk for CVD based on WHO risk scores.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"RDWFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5256562/v1/ec01dc9e16cd0a4898f8f748.jpg"},{"id":73588019,"identity":"c9d3b6c3-4545-44e2-a7c1-9eb4a8a00c07","added_by":"auto","created_at":"2025-01-12 08:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5256562/v1/33528289-6746-4f24-9f8f-99ad0df7c8c1.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Red cell distribution width as a cardiovascular risk predictor in adults with hypertension in sub-Saharan Africa.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiovascular disease (CVD) continues to be the primary cause of death worldwide, resulting in approximately 17.9\u0026nbsp;million deaths 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2019, CVDs caused over one million deaths in sub-Saharan Africa, representing approximately 5.4% of all globally CVD-related deaths and 13% of all deaths in Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A main risk factor for CVD-related deaths is hypertension, with African countries having the highest burden globally and the highest percentage rise over the past three decades [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hypertension is responsible for 50% of strokes, 47% of heart failures, and 47% of coronary heart diseases. The majority of this burden, about 80%, is found in middle- and lower-income countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent cardiovascular disease prevention guidelines emphasize the need to manage based on an individual\u0026rsquo;s risk assessment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The individual's risk assessment allows identification of patients with high-risk CVD for more focused treatment and improved outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, incorporating a cardiovascular risk approach into population surveys has significant consequences for making decisions regarding the allocation of healthcare resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Many risk assessment and prediction models have been developed over time, but only the World Health Organization (WHO) CVD risk predictor has been validated in the African sub-region [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This makes the prediction model and charts a more appropriate risk assessment tool for countries within the sub-region. The WHO CVD risk prediction charts are available in both laboratory- and non-laboratory-based charts. Laboratory-based charts require data on age, gender, smoking status, systolic blood pressure, presence or absence of diabetes, and total cholesterol levels. On the other hand, non-laboratory-based charts require data on age, gender, smoking status, systolic blood pressure, and body mass index (BMI). Despite its applicability in low-resource settings, the non-laboratory chart significantly underestimates the risk of CVD by up to 35% in men and 65% in women, particularly in patients with moderate to high CVD risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Worthy of note are the limiting factors for laboratory-based assessment of CVD risk, including the cost of laboratory tests, lack of trained personnel, and equipment to routinely carry out some of these tests (lipid profiles) at primary and some secondary health facilities in low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, there is a need to develop an alternative approach that is easily applicable to our environment, and red cell distribution width may be found to be such a biomarker.\u003c/p\u003e \u003cp\u003eRed cell distribution (RDW) is a biomarker that is reported as part of routine full blood counts (FBC) and has been found to independently predict adverse CVD events [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is inexpensive and remains the most common test requested by clinicians in both clinical and community settings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. RDW is a simple assessment of red blood cell (RBC) size variability (anisocytosis), which is obtained by dividing the standard deviation (SD) of erythrocyte volumes by the mean corpuscular volume (MCV) and can be automated [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies showed that anisocytosis may be involved in the pathogenesis of cardiovascular disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The deformed erythrocytes (a common finding in anisocytosis) increase blood viscosity, impair microcirculatory blood flow, and promote aggregation and endothelial adhesion. All of these processes contribute to the development of atherosclerosis by neutralizing vasodilator mediators and accumulating lipids in atherosclerotic lesions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Researchers have evaluated RDW as a promising biomarker of CVD risk in conditions such as arteriosclerosis, heart failure, and ischemic stroke; however, its role in predicting CVD risk in hypertensive adults in low-resource settings remains unknown [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In low-resource settings, RDW may be a plausible surrogate marker for assessing CVD risk in adults with hypertension. Therefore, we hypothesized that RDW would independently predict CVD risk among patients with hypertension attending PHCs in SSA (Ghana and Nigeria) when compared with the WHO CVD risk score. Thus, we sought to evaluate RDW as a predictor of CVD risk compared to the WHO CVD risk score among adults with hypertension attending PHC centers.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThis study was cross-sectional and involved hypertensive participants attending primary healthcare facilities in Ghana and Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the Okelele Primary Healthcare Center, Ilorin, North-Central Nigeria, and St. Anthony Ann Hospital, Deduako, Ashanti region, both being primary healthcare facilities in Nigeria and Ghana, respectively. The Okelele Primary Healthcare Center has an average clinic attendance of 250 patients with hypertension monthly, whereas St. Anthony Ann Hospital has an average of 172 patients attending the hypertension clinic monthly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study participants were patients with hypertension attending the general outpatient departments of selected primary healthcare facilities in Nigeria and Ghana.\u003c/p\u003e\n\u003cp\u003eWe included patients aged 40\u0026ndash;74 years with hypertension who consented to participate in the study. Hypertension was defined as those already on treatment (controlled or uncontrolled), treatment-na\u0026iuml;ve with a blood pressure \u0026ge; 140/90 mmHg, and newly diagnosed with three resting blood pressure measurements (BP\u0026ge; 140/90 mmHg).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe excluded patients with evidence of CVDs (stroke, heart failure, peripheral artery disease and ischemic heart disease), pregnant women, hematological disorders (e.g., leukemia, sickle cell disease), clinical paleness, history of renal failure and liver disease, and those taking vitamin supplements 24 hours before the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing Andrew Fischer\u0026rsquo;s formula, we estimated a minimum sample size of 316 from a 71% prevalence of elevated RDW among hypertensive adults in a previous study\u0026nbsp;[13]\u0026nbsp;at a 5% level of precision and 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling, recruitment procedure and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe consecutively recruited participants who met the study inclusion criteria between July and December 2023. A total of 160 participants (160) were recruited from Nigeria and 159 were recruited from Ghana. We used a pretested questionnaire to gather relevant sociodemographic and cardiovascular risk factors from participants. Each participant underwent physical measurements, and blood sampling for biochemical and hematological measurements was performed among the study participants. We employed a modified version of the WHO\u0026apos;s STEP-wise non-communicable disease risk factor surveillance tool to gather pertinent CVD history from the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric measurements were carried out according to the WHO guidelines. The weight was measured using an Omron HN286 electronic human weighing scale with an accuracy of \u0026nbsp;0.1 kg. Each participant\u0026rsquo;s height was measured with a \u0026ldquo;Seca 213\u0026rdquo; mobile stadiometer with accuracy of 0.1 cm. Body mass index (BMI) was calculated using the following formula: BMI (kg/m2) = weight (kg) / height (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Omron M7 Intelli IT,\u0026rsquo; a validated upper-arm BP monitor was used to measure participants BP. In brief, each participant sat quietly with their feet on the floor and their clothes loosened around the arm for at least five minutes before the blood pressure readings were taken. We placed a correct-sized cuff (bladder width 80% of the arm circumference) on the exposed arm, approximately 2 cm above the elbow, and positioned the tube in front and at the center of the arm. The blood pressure reading was recorded some minutes after pressing the start button, and the reading was displaced and documented. We took three serial BP measurements, three minutes apart, and used the average of the last two readings for data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant also had blood samples taken for analysis (biochemical and hematologic analyses). Blood samples were analyzed using the EasyRa Chemistry Analyzer and a fully automated assay hematology analyzer to determine the serum cholesterol and full blood count (FBC), respectively. The system automatically determined the RDW from the FBC results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiovascular disease risk score assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the cardiovascular disease risk scores of participants using the WHO cardiovascular risk (laboratory-based) prediction charts (2019 revised edition).[7]\u0026nbsp;Each participant \u0026rsquo;sWHO CVD risk score was calculated based on age, gender, smoking status, presence or absence of diabetes, systolic BP, and total cholesterol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe level of prediction of cardiovascular risk using RDW in adults with hypertension attending PHC centers in Ghana and Nigeria\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the data from the study proforma using IBM SPSS version 29. Descriptive statistics were used to summarize the participants\u0026rsquo; sociodemographic variables and were compared across the RDW quartiles. The WHO CVD risk scores did not follow a normal distribution, and as such, were log-transformed before analysis, and the RDW (%) was stratified into four quartiles. Pearson and Spearman\u0026rsquo;s rank correlation coefficients, and multiple linear regression were used to evaluate the relationship between the RDW and CVD risk score. To determine the cut-off RDW that predicted a high CVD risk (WHO CVD risk score \u0026gt; 20).The \u003cem\u003ep-value\u003c/em\u003e for the level of statistical significance was set at p \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGhana Health Service Ethics Review Committee (Ghana) and Kwara State Ethical Review Committee (Nigeria) approved this study. We also sought permission from the appropriate authorities at both primary healthcare facilities. A detailed explanation of what the study entails in information sheets, including study procedures, was made available to all the participants in the language they best understood, and written informed consent was obtained. The data collected were coded to ensure the anonymity of the study participants and were stored in a password-encrypted computer.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 319 adults aged 40 to 74 years from study sites in Ghana (159) and Nigeria (160). The mean (standard deviation) age of participants was 59.10 (10.2) years. Based on the age group, the highest category was those aged 70-74 (66;20.7%), followed by 60-64 (55;17.2%), 65-69 (48;15.0%); 55-59 (45;14.1%), 50-54 (43;13.5%), 45-49 (29;9.1%) and 40-44 (33;10.3%). There were more females (259;81.2%) (Table 1).\u003c/p\u003e\n\u003cp\u003eBased on the RDW quartile stratification, the variables that were significantly different across the subgroups included educational level (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), diabetes mellitus (\u003cem\u003ep\u003c/em\u003e=0.011), systolic BP (\u003cem\u003ep\u003c/em\u003e=0.017), hemoglobin (\u003cem\u003ep\u003c/em\u003e=0.007), MCV (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), study sites (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), and alcohol consumption (\u003cem\u003ep\u003c/em\u003e=0.002) (Table 1).\u003c/p\u003e\n\u003cp\u003eThe mean [standard deviation (SD)] RDW was 13.96 (1.1%), with a range of 11.50% to 21.70%. The mean (SD) RDW in Ghana was 13.63 (1.3%) vs 14.28 (0.8%) in Nigeria (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The CVD risk score median (interquartile range [IQR]) was 8.11% (4.00 to 11.00] with a minimum of 1.0% and a maximum of 30.0%. The median (IQR) CVD risk scores were comparable between the two countries (Ghana 7.0% (4.0 to 11.0) vs Nigeria 7.5% (4.0 to 13.0), \u003cem\u003ep\u003c/em\u003e=0.577); Table 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between cardiovascular risk factors, WHO CVD risk scores, and RDW.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelations of RDW with cardiovascular risk factors and WHO CVD risk scores\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all recruited participants, there was a significant positive correlation between RDW and the following variables: alcohol consumption (\u003cem\u003er\u003c/em\u003e=0.193, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), systolic BP (\u003cem\u003er\u003c/em\u003e=0.159, \u003cem\u003ep\u003c/em\u003e=0.004), and diastolic BP (\u003cem\u003er\u003c/em\u003e=0.149; \u003cem\u003ep\u003c/em\u003e=0.013). For Hemoglobin levels \u0026ge; 12 g/dL, RDW was positively correlated with age (\u003cem\u003er\u003c/em\u003e=0.136; p =0.042), alcohol consumption (\u003cem\u003er\u003c/em\u003e=0.312, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), systolic BP (\u003cem\u003er\u003c/em\u003e=0.183; \u003cem\u003ep\u003c/em\u003e=0.006), diastolic BP (\u003cem\u003er\u003c/em\u003e=0.206, \u003cem\u003ep\u003c/em\u003e=002), and WHO CVD risk scores (\u003cem\u003er\u003c/em\u003e=0.166, \u003cem\u003ep\u003c/em\u003e=0.013). For participants with hemoglobin levels \u0026ge; 12 g/dL, there was no significant correlation between RDW, cardiovascular risk factors, and WHO CVD risk scores (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMultiple linear regression of RDW as a predictor of CVD risk\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multiple linear regression model showed an independent association between RDW and WHO CVD risk scores with an upward gradient. WHO CVD scores increased with increasing RDW quartile and most significant at 3\u003csup\u003erd\u003c/sup\u003e quartiles (Table 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eROC curve of RDW as a predictor of CVD risk.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CVD risk scores were dichotomized into high-risk (20.0% or more) and those with CVD risk scores of less than 20.0% versus RDW. \u0026nbsp;Using ROC analysis, the C-statistics (area under the curve) was 0.673 (95% CI 0.618\u0026ndash;0.724), \u003cem\u003ep\u003c/em\u003e=0.031. At a cut-off of \u0026gt; 14, the RDW had a sensitivity of 81.82%, specificity of 55.84%, and Youden index of 0.377 (Figure 1 and Table 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAfrican countries have the highest global burden of hypertension, \u0026nbsp;a leading risk factor for adverse CVD outcomes. This study examines the role of RDW, a cheap inexpensive parameter in full blood counts in assessing CVD risk among cohort of adults with hypertension in Ghana and Nigeria. This study showed that RDW correlated with \u0026nbsp;systolic and diastolic BP. These finding are consistent with other studies that demonstrated \u0026nbsp;a similar correlation between RDW and blood pressure (systolic and diastolic)\u0026nbsp;[14\u0026ndash;17].\u0026nbsp;However, a study in Turkey only observed a positive correlation between systolic BP and RDW\u0026nbsp;[18]. \u0026nbsp;The differences in our findings compared with the Turkey study may be relative higher mean age of our cohort (59 years) vs 50 years in Turkey study.\u0026nbsp;Younger adults, especially those with diastolic hypertension, tend to have less inflammatory biomarkers, which may impact RDW findings\u0026nbsp;[18]. The association between RDW and BP in hypertensive patients in this study provides additional evidence for the use of RDW as a biomarker for cardiovascular disorders. RDW is a marker of increased inflammation and oxidative stress, which are equally implicated in the development and progression of hypertension. This inflammation leads to endothelial dysfunction, reduced compliance of arteries leading to high vascular resistance which worsens hypertension\u0026nbsp;[19].\u003c/p\u003e\n\u003cp\u003eThe present study showed that, for participants with Hb \u0026ge; 12 g/dL, there was a correlation between a high RDW and a higher WHO CVD risk score. A large cohort study in Brazil also showed that RDW was positively correlated with increased CVD risk using the Framingham risk score\u0026nbsp;[20]. Our study further reinforces the role of RDW in cardiovascular diseases, including hypertension. This study showed that RDW is independently associated with the WHO CVD risk score and is most significant at Q3. A large cohort study \u0026nbsp;in Brazil revealed that RDW was independently associated with the CVD risk score as assessed using the Framingham risk score.[20]\u0026nbsp;Whereas we observed an independent association at the 3\u003csup\u003erd\u003c/sup\u003e RDW quartile; the study in Brazil was most significant at the four quartiles. The differences between our study and the Brazil study may be due to the CVD risk assessment used with the participants in our study having the highest \u0026nbsp;median score of WHO CVD risk score (9.00 (IQR-3.25, 13.00)] at 3\u003csup\u003erd\u003c/sup\u003e quartiles.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, at an RDW \u0026gt; 14%, the \u0026nbsp;RDW had modest C-statistics of 0.673, sensitivity of 82%, and specificity of 56% in predicting CVD adverse outcomes in the study cohort. Red cell distribution width has been demonstrated to predict outcomes in various cardiovascular diseases\u0026nbsp;[11]. Among the cohort of 1971 admitted with chest pain in a regional hospital in Italy, at a cut-off of 13.7%, RDW has an area under curve (AUC) of 0.61 with a sensitivity of 75% and a specificity of 52.0%\u0026nbsp;[21]. In China, RDW at a cut-off of 14.1% predicted a 90-day cardiovascular event (cardiac death or readmission for heart failure) for patients with acute heart failure \u0026nbsp;and has \u0026nbsp;a sensitivity of 87%, specificity of 54.9%, \u0026nbsp;and area under the curve \u0026nbsp;of 0.728\u0026nbsp;[22]. Although with an modest level of specificity (56%), the RDW has a high sensitivity (82%) in our study, suggesting it may be a good screening tool to identify adults with hypertension in the Saharan population at high risk of adverse CVD outcomes, especially when there is limited access to advance laboratory and diagnostic investigations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026rsquo;s strengths include being multi-country and comparing RDW with the WHO CVD risk score, which has been validated for the assessment of CVD risk in the sub-Saharan African population. However, this study has some limitations, as it was a cross-sectional study, which means that a cause\u0026ndash;effect relationship could not be established. In addition, we did not test for \u0026nbsp;other inflammatory markers, such as CRP and ESR, to reduce cofounders in our analysis, and participants were not followed up to see how many will later develop adverse cardiovascular outcomes. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a cross-sectional multi-center study among adults with hypertension in sub-Saharan Africa, RDW correlated significantly with the WHO CVD risk score for adults with Hb\u0026gt;12g/dL or more with a modest predictive ability for CVD risk. For patients with Hb \u0026ge; 12 g/dl or more, RDW, a cheap and readily available biomarker, may be a good screening tool for low-resource settings to identify patients with hypertension who may be at high risk of adverse CVD outcomes. \u0026nbsp;We recommend \u0026nbsp;further evaluation of the strength of the association in a larger population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed cell distribution width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFull blood counts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld health organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eGhana Health Service Ethics Review Committee (Ghana) and Kwara State Ethical Review Committee (Nigeria) approved this study. We also sought permission from the appropriate authorities at both primary healthcare facilities. A detailed explanation of what the study entails in information sheets, including study procedures, was made available to all the participants in the language they best understood, and written informed consent was obtained. The data collected were coded to ensure the anonymity of the study participants and were stored in a password-encrypted computer.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research work is funded by the NIH-Forgarty International Center through the Stroke and Cardiovascular Research Training (ScarT) Institute, Ghana.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eORI conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. KAH conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. FTA conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript GBN conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. AMN conceptualized the work, literature review, data collection, analysis, draft and appraished the manuscript. AYN was inlvolved in literature review, data collection, data visualization, draft and appraished the manuscript. AO was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. DO was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. OA was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. BSA was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. DS was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript. OAM was invloved in the conceptualization, literature review, data visualization and analysis, draft and critically appraished the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledged the staff of Okelele Primary Healthcare Center, Ilorin, Nigeria, and St. Anthony Ann Hospital, Deduako, Ashanti Region, Ghana, for their support during this work.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Cardiovascular diseases (CVDs). 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulto LN, Hendriks JM. The burden of cardiovascular disease in Africa: prevention challenges and opportunities for mitigation. Eur J Cardiovasc Nurs. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurjcn/zvad134\u003c/span\u003e\u003cspan address=\"10.1093/eurjcn/zvad134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinja NW, Nakagaayi D, Aliku T, Zhang W, Ssinabulya I, Nabaale J, et al. Cardiovascular diseases in Africa in the twenty-first century: Gaps and priorities going forward. Front Cardiovasc Med. 2022;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArima H, Barzi F, Chalmers J. Mortality patterns in hypertension. J Hypertens. 2011;29 Supplement 1:S3\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModesti PA, Agostoni P, Agyemang C, Basu S, Benetos A, Cappuccio FP, et al. Cardiovascular risk assessment in low-resource settings. J Hypertens. 2014;32:951\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. HEARTS: Technical package for cardiovascular disease management in primary health care: Risk-based CVD management. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, et al. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Heal. 2019;7:e1332\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalha I, Elkhoudri N, Hilali A. Major Limitations of Cardiovascular Risk Scores. Cardiovasc Ther. 2024;2024:1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanese E, Lippi G, Montagnana M. Red blood cell distribution width and cardiovascular diseases. J Thorac Dis. 2015;7:E402\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa I, Guo M, Lau CK, Ramdas Z, Jackson R, Naugler C. Test volume data for 51 most commonly ordered laboratory tests in Calgary, Alberta, Canada. Data Br. 2019;23:103748.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArkew M, Gemechu K, Haile K, Asmerom H. Red Blood Cell Distribution Width as Novel Biomarker in Cardiovascular Diseases: A Literature Review. J Blood Med. 2022;Volume 13:413\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Cervellin G, Sanchis-Gomar F. Red blood cell distribution width and cardiovascular disorders. Does it really matter which comes first, the chicken or the egg? Int J Cardiol. 2016;206:129\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilal A, Farooq JH, Kiani I, Assad S, Ghazanfar H, Ahmed I. Importance of Mean Red Cell Distribution Width in Hypertensive Patients. Cureus. 2016;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanindi A, Topal FE, Topal F, Celik B. Red cell distribution width in patients with prehypertension and hypertension. Blood Press. 2012;21:177\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Hou X, Zhong J, Liu K. Association between red cell distribution width and hypertension: Results from NHANES 1999\u0026ndash;2018. PLoS One. 2024;19 5 May:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSileshi B, Urgessa F, Wordofa M. A comparative study of hematological parameters between hypertensive and normotensive individuals in Harar, eastern Ethiopia. PLoS One. 2021;16 12 December:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendi MA. The Association Between Red Cell Distribution Width and Blood Pressure Variability in Hypertensive Patients. Cyprus J Med Sci. 2024;9:15\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePusuroglu H, Akgul O, Erturk M, Surgit O, Tasbulak O, Akkaya E, et al. Red cell distribution width and end-organ damage in patients with systo-diastolic hypertension. Arch Med Sci. 2016;12:319\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsik T. Is Red Cell Distribution Width a Marker for Hypertension? Cardiology. 2012;123:195\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho NM de, Maluf CB, Azevedo DRM, Reis RCP dos, Castilhos CD de, Barreto SM, et al. Red cell distribution width is associated with cardiovascular risk in adults. Cien Saude Colet. 2022;27:2753\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCemin R, Donazzan L, Lippi G, Clari F, Daves M. Blood cells characteristics as determinants of acute myocardial infarction. Clin Chem Lab Med. 2011;49:1231\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe W, Jia J, Chen J, Qin S, Tao H, Kong Q, et al. Comparison of prognostic value of red cell distribution width and NT-proBNP for short-term clinical outcomes in acute heart failure patients. Int Heart J. 2014;55:58\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: General characteristic of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal n=319\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e11.50 to 13.19%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e13.20 to 13.99%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e14.00 to 14.49%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e14.50 to 21.70%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge- Mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.10 (10.2)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.11 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.75 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.02(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.53 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e259 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e186 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.51(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.57 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.40 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.30 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.72(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic BP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142.80 (24.4)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e136.02(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142.22 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143.84 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147.97 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic BP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.38 (13.1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.15 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.2 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.89 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.84 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.22 (1.1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.08 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.19 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.06 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.44 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum Glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.37 (2.2)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.90 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.04 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.10 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.43 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.69(1.3)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.07 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.73 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.66 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.69(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCV\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(fL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.25(6.7)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.90 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.49 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.10 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.32(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGhana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e159 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNigeria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e160 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(19; 6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD risk scores**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00 (4.00 to 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.00 (4.0 to 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00 (4.00 to 10.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00 (3.25 to 13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00 (4.00 to 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;SD: Standard deviation; *-Mean with standard deviation; **-Values in median with interquartile range; CVD: Cardiovascular diseases; BP-Blood pressure; BMI-Body mass index; MCV-Mean corpuscular volume\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Correlations of RDW with cardiovascular risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=319)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb \u0026lt;12 g/dL (n=94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb 12g/dL \u0026amp; above (n=225)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003er \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003er\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic blood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD \u0026nbsp;risk Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCVD: Cardiovascular diseases\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMultiple regression models for the association between RDW and WHO cardiovascular risk by \u0026nbsp;quartiles\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD risk scores*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[11.50 to 13.19%.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=58]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[13.20 to 13.99%.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=64]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[14.00 to 14.49%.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=51]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[14.50 to 21.70%.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=52]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ee\u003csup\u003e\u0026beta;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879, 1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.006, 1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.996, 1.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*n=225 (with hemoglobin \u0026ge; 12 g/dL); CI-Confidence interval; Regression coefficients (\u0026beta;) were log-transformed. e\u003csup\u003e\u0026beta;\u003c/sup\u003e exponential of Beta Coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Summary of the ROC curve of RDW to predict a high risk for CVD based on WHO risk scores.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003en=319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArea under the ROC curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95% CI for area under the ROC curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.618 to 0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value for Area=0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYouden index J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssociated criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eROC- receiver operating characteristic curve, CI-Confidence interval/\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Red cell distribution width, hypertension, sub-Saharan African, cardiovascular risk ","lastPublishedDoi":"10.21203/rs.3.rs-5256562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5256562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Red cell distribution width (RDW) quantifies the degree of variation in erythrocyte size, is identified as a potential marker of adverse cardiovascular events, and maybe a surrogate marker for assessing cardiovascular disease (CVD) risk in low-resource settings. We evaluated RDW as a predictor of CVD risk compared to the WHO CVD risk score among adults with hypertension attending primary healthcare centers in Ghana and Nigeria. Adults with hypertension attending selected PHCs in Ghana and Nigeria participated in a cross-sectional study. Each participant underwent BP measurement and laboratory evaluation (RDW, total cholesterol, and fasting blood sugar) following standard methods. We recruited 319 adults aged 40–74 years from the study sites. The mean (standard deviation) RDW was 13.96 (1.1%). The median CVD risk score was 8.11% [interquartile range (IQR) 4.00 to 11.00]. For participants with hemoglobin (Hb) levels ≥ 12 g/dL, RDW showed positive correlations with age (r=0.136;p=0.042); systolic BP (r=0.183; p=0.006), diastolic BP (r=0.206, p=0.002) and WHO CVD risk scores (r=0.166, p=0.013). Multiple linear regression showed an independent association between RDW and WHO CVD risk scores with an upward gradient and was most significant at 3rd quartiles. Using ROC analysis, the C-statistic was 0.673 (95% CI 0.618 to 0.724), p=0.031. With a cut-off of \u003e 14, the RDW demonstrated a sensitivity of 81.82% and specificity of 55.84%. This study shows that at Hb levels ≥ 12 g/dL, RDW modestly predicted CVD risk in adults with hypertension in sub-Saharan Africa.","manuscriptTitle":"Red cell distribution width as a cardiovascular risk predictor in adults with hypertension in sub-Saharan Africa.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 08:36:52","doi":"10.21203/rs.3.rs-5256562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-11-12T09:23:10+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-11-05T09:15:28+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-11-05T07:56:05+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-11-05T05:46:10+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-11-04T10:42:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-03T22:51:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-15T11:53:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Human Hypertension","date":"2024-10-14T16:28:09+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-10-14T11:54:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-human-hypertension","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jhh","sideBox":"Learn more about [Journal of Human Hypertension](http://www.nature.com/jhh/)","snPcode":"41371","submissionUrl":"https://mts-jhh.nature.com/cgi-bin/main.plex","title":"Journal of Human Hypertension","twitterHandle":"@jhhypertension","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"261b122e-0ef5-4296-bb25-7c797d482d79","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39780243,"name":"Health sciences/Risk factors"},{"id":39780244,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"}],"tags":[],"updatedAt":"2025-01-12T08:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-5256562","link":"https://doi.org/10.1038/s41371-025-00987-w","journal":{"identity":"journal-of-human-hypertension","isVorOnly":false,"title":"Journal of Human Hypertension"},"publishedOn":"2025-01-11 05:00:00","publishedOnDateReadable":"January 11th, 2025"},"versionCreatedAt":"2024-12-09 08:36:52","video":"","vorDoi":"10.1038/s41371-025-00987-w","vorDoiUrl":"https://doi.org/10.1038/s41371-025-00987-w","workflowStages":[]},"version":"v1","identity":"rs-5256562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5256562","identity":"rs-5256562","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}