Predictors of cardiovascular disease among people living with HIV in northern Nigeria

Predictors of cardiovascular disease among people living with HIV in northern Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Predictors of cardiovascular disease among people living with HIV in northern Nigeria Zainab Abdulkadir, Aminatu Ayuba Kwaku, AbdulGaffar Lekan Olawumi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background People living with HIV (PLWH) exhibit two-fold higher incidence of cardiovascular disease compared to HIV-negative persons. However, predictors of cardiovascular disease risk in PLWH are still evolving. The objective of this study is to evaluate the predictors of cardiovascular disease among PLWH in Nigeria. Methods This cross-sectional study was conducted among adult patients attending a large HIV clinic in Kano, northern Nigeria. We used systematic sampling to recruit participants and computed their 5-year projected CVD risk using the Data collection on Adverse effects of Anti-HIV Drugs (DAD) equation. Results The majority of participants were female (70.6%). The estimated median 5-year CVD risk was 0.7% (interquartile range, IQR 0.4, 10). The majority of participants (58.9%) had a low risk of developing cardiovascular disease, while 28.9% had a moderate risk. Cardiovascular disease was associated with elevated high-sensitivity C-reactive protein (hsCRP) > 3.03 mg/L [adjusted odds ratio, aOR: 4.58, 95% CI: 2.09–10.04), p = 0.001], increasing age [aOR 2.38, 95% CI (1.48–4.50), p = 0.020], male sex [aOR 2.16, 95% CI (1.03–4.53), p = 0.040] and hypercholesterolemia [aOR 3.03, 95% CI (1.68–4.86), p = 0.005]. Conclusion The majority of PLWH in our setting have low to moderate risk of developing cardiovascular disease. Cardiovascular disease risk was associated with elevated hsCRP, increasing age, male sex, and hypercholesterolemia. Our findings highlight the importance of early CVD risk stratification to prevent morbidity and mortality among PLWH. Cardiovascular risk DAD equation people living with HIV predictors prevalence Nigeria Figures Figure 1 INTRODUCTION Cardiovascular disease (CVD) is a major global public health concern, responsible for the highest number of deaths worldwide and contributing to over 32% of all deaths in 2023. 1 People living with HIV (PLWH) are at increased risk of developing CVD. 2 Despite significant research, the pathophysiology of CVD among PLWH continues to evolve. 3 The development of CVD in PLWH is due to a complex interplay of traditional CVD risk factors (e.g., hypertension, dyslipidemia, diabetes mellitus, and smoking), HIV-mediated mechanisms, antiretroviral (ARV) medication related cardiometabolic adverse effects, genetic factors, 4–8 and increasing life expectancy among PLWH following the widespread availability/access to antiretroviral therapy (ART) . 9–10 HIV itself can induce and hasten atherosclerosis and endothelial dysfunction by several mechanisms, which include chronic inflammation, residual immune activation driven by viral replication, direct effect of the virus on adipose tissue, and altered cholesterol metabolism. 11 Dyslipidemia, a major CVD risk factor, is highly prevalent among PLWH, with prevalence estimates ranging from 7.7% to 73.4%. 8–10 This dyslipidemia is attributable to various mechanisms, such as increased basal lipolysis, hepatic de-novo lipogenesis, and hypertriglyceridemia especially following ART initiation. 8 , 9 , 10 In addition, ART exposure may cause fat redistribution, manifesting as lipoatrophy in the face, limbs and viscera, which further exacerbates dyslipidemia and hypertriglyceridemia. 8 , 9 , 11 , 12 PLWH have double the risk of CVD compared to HIV-negative individuals. 6 , 8 Sub-Saharan Africa (SSA) bears the highest burden of HIV globally, with approximately 70% of PLWH residing in the region. 5 , 6 However, estimates of absolute CVD risk among PLWH vary significantly across different regions and CVD prediction models . 13–17 In developed countries, higher CVD risk prevalence among PLWH have been documented. 13 – 15 For example, in the United Kingdom, Dhillon et al. reported a prevalence of CVD risk as high as 21.5% using Framingham risk scores (FRS) and 14.8% utilizing the Data Collection on Adverse effects of Anti-HIV Drugs (DAD) risk equation. 13 Similarly, a study from Serbia reported a prevalence of high CVD risk of 27.2% according to FRS, 31.5% with Systematic Coronary Risk Evaluation (SCORE) and 51.6% using DAD. 14 In Portugal, high CVD risk was documented at 20.5% using FRS, 10.3% with DAD, and 4.4% with SCORE. 15 In contrast, studies from developing countries indicate a lower prevalence of CVD risk among PLWH, with regional differences. For example, in Brazil, the prevalence of high CVD risk was reported as 2.8% and 2.1%, using FRS and DAD, respectively. 18 In Nigeria, the CVD risk prevalence was reported as 11.7% using FRS, 12.8% using WHO/ISH prediction models, and 12.8% with SCORE. 16 – 18 Similarly, an Ethiopian study reported high CVD prevalence among adults aged 20 years and older using FRS and the Pooled Cohort Equations (PCE). 17 Among PLWH 40 to 79 years of age, PCE yielded higher prevalence (28%) than FRS (17.7%). 17 In Uganda, the prevalence of high CVD risk was low, at 3.4%, while investigators in Cameroon reported rates of 2.4% utilizing DAD and 8.4% using FRS. 19–21 The variations across studies may be due to the use of varying CVD prediction models, the differences in lifestyle and cultural practices between developed and developing countries. Despite these findings, the prevalence and predictors of CVD among PLWH remain underexplored in northern Nigeria, leaving a critical gap in knowledge. This study aimed to evaluate the prevalence and predictors of CVD among PLWH in Kano, Nigeria. METHODS Study design and population This health facility-based cross-sectional study was conducted at the S.S. Wali HIV outpatient Clinic within Aminu Kano Teaching Hospital (AKTH) in Kano, Nigeria. The study utilized data originally collected for a diagnostic accuracy study of hsCRP versus DAD equation for cardiovascular risk assessment. 23 The current analysis aimed to estimate the prevalence and predictors of cardiovascular disease among PLWH. A total of 180 adults (≥ 18 years of age) living with HIV were systematically recruited over a 6-week period (30th September to 11th November, 2024). We excluded individuals with prior history of cardiovascular events (e.g., known myocardial infarction, stroke, peripheral arterial disease, and/or congestive heart failure), those requiring emergency care, and persons with acute inflammatory illness or moderate to severe cognitive impairment. Sampling technique A total of 180 participants were recruited using systematic sampling, with 10 individuals selected per day using a sampling interval of 5. The first participant was randomly selected through balloting, and every fifth person was subsequently chosen until the minimum sample size was reached. Data collection Data were collected using a pretested questionnaire used in a previous study (available at https://docs.google.com/document/d/1oAEhsbMjn7vNsUTRLj5qyA1lnoxtDqIY/edit?usp=drivesdk&ouid=106491892114506665198&rtpof=true&sd=true ). 23 Trained research assistants administered the questionnaire to obtain sociodemographic information as well as clinical data, including family history of CVD, hypertension, diabetes mellitus, HIV clinical status (CD4 cell count and viral load), medication history (type and duration on ART), weight, height and body mass index (BMI). Blood pressure was measured after 5 minutes rest using a calibrated mercury sphygmomanometer (ERKA)® and an appropriate stethoscope. Participants were instructed to fast overnight for 8–12 hours, and 5ml of blood was collected into Ethylene-Diamine Tetra-acetic Acid (EDTA) vacutainer tubes for analysis of lipid profile, fasting blood glucose (FBG) and hsCRP levels. Blood samples were transferred to the chemical pathology laboratory on-site at AKTH, where they were centrifuged at 3500 rpm and analyzed further per manufacturer’s instructions. High sensitivity (hs) CRP levels were measured using particle-enhanced turbidimetric assay, calibrated and standardized against the WHO reference values. Dyslipidemia was classified based on the National Cholesterol Education Program, Adults Treatment Panel (NCEP-ATP) III guidelines. 24 Hypercholesterolemia was defined as elevated total cholesterol of > 6.2 mmol/L, LDL-C > 4.1mmol/L and/or reduced HDL-C < 1.04mmol/L in men or 3.03 mg/L, based on prior research in the area. 23 , 25 We defined high blood pressure as systolic blood pressure of \(\:\ge\:140\text{m}\text{m}\text{H}\text{g}\) and diastolic blood pressure of \(\:\ge\:90\) mmHg, and/or self-reported history of hypertension or taking antihypertensive medication(s). Diabetes mellitus was defined as fasting blood glucose (FBG) \(\:\ge\:\:7\text{m}\text{m}\text{o}\text{l}/\text{L}\) and/or self-report or history of taking antidiabetic medication(s). We measured participants’ height with stadiometer (Hospitex )® to the nearest 0.1cm. Weight was measured to the nearest 0.1kg with weighing scale (Hospitex )®. We used body mass index (BMI) in kg/m 2 to classify participants as underweight (< 18.5), normal (18.5–24.9), overweight (25–29.9), and obese ( \(\:\ge\:\) 30) based on WHO guidelines. 26 Participants' waist circumference was measured in centimeters at the level of the umbilicus. Using WHO guidelines, truncal obesity was defined as ≥102 cm for men and ≥88 cm in women, while normal waist circumference was <94 cm for men and <80 cm for women. 27 Cardiovascular risk assessment We estimated the 5-year projected CVD risk using the DAD Full (2016) model through a web-based risk calculator. 28 The calculation included variables such as age, sex, smoking history (past/present), family history of CVD, diabetes mellitus, abacavir exposure, protease inhibitor (PI) exposure and duration, CD4 cell count, systolic blood pressure, total cholesterol, and HDL-C levels. The resulting CVD risk classification was as follows: low risk: 10%. 29 Statistical Analysis We analyzed collected data using IBM SPSS Statistical software for windows, version 26. Descriptive statistics were reported as means and standard deviations or medians and interquartile ranges for continuous data, while categorical variables were presented as frequencies and percentages. Associations between categorical variables were tested using chi-square or Fisher’s exact test. Multivariate logistic regression analysis was performed for variables that were significant at bivariate level to identify the predictors of CVD risk. Statistical significance was set at p < 0.05. Ethical Considerations We obtained ethical approval from the AKTH Research Ethics Committee (NHREC/28/01/2020/AKTH/EC/3861). The study adhered to the principles outlined in the Helsinki declaration. Signed informed consent was obtained from all participants. RESULTS The majority of participants were female (70.6%), urban residents (88.3%) and Muslim (85.0%) (Table 1 ). Most participants reported never smoking cigarettes (91.7%) or consuming alcohol (96.7%). The most common antiretroviral treatment regimen was Tenofovir, Lamivudine and Dolutegravir (TLD), used by 91.7% of participants. The median duration on ART was 12.0 years (IQR: 8.0, 16.0 years). Participants had a mean BMI \(\:\:\pm\:\:\) standard deviation (SD) of 24.3 \(\:\pm\:\) 3.9 kg/m 2 , with the majority having normal weight (68.9%), while (13.9%) were overweight and (13.9%) were obese. The mean waist circumference ± SD was 84.9 \(\:\pm\:\) 10.4 cm, and 27.2% of participants presented with abdominal obesity. The most prevalent lipid abnormalities were hypercholesterolemia (25%) and hypertriglyceridemia (19.4%). Table 1 Participants’ sociodemographic and clinical characteristics, Kano, Nigeria Variable Frequency N = 180 Age Mean age (years) \(\:\pm\:\varvec{S}\varvec{D}\) Adults 30–49, n (%) 44.03 \(\:\pm\:\) 10.58 107 (59.4) Sex Female n (%) 127 (70.6) Place of residence Urban n (%) 159 (88.3) Religion Islam n (%) 153 (85.0) Marital status Married n (%) 134 (74.4) Tribe Hausa n (%) 144 (80) Occupation Trader n (%) 38 (21.1) Level of education Secondary n (%) 52 (28.9) Monthly income (Naira) <70,000 n (%) 149 (82.8) Family history of CVD Present n (%) 16 (8.9) Smoking History Never smoked n (%) 165 (91.7) Alcohol Use No, n (%) 174 (96.7) ART exposure 1st line, n (%) 165 (91.7) Median years on ART (IQR) 12 (8,16) ART Regimen 2 NRTIs + 1 InSTI (TLD) Protease inhibitors exposed 170 (94.4) 10 (5.6) TDF exposed 170 (94.4) Abacavir Exposed NEV exposed ATV exposed 10 (5.6) 2 (1.1) 4 (2.2) Mean BMI (kg/M 2 ) \(\:\pm\:\:\varvec{S}\varvec{D}\) Normal weight (Kg)n(%) Obesity n(%) Mean waist circumference (cm) \(\:\pm\:\) SD Abdominal obesity n% 24.28 \(\:\pm\:\) 3.90 124 (68.9) 25 (13.9) 84.91 \(\:\pm\:\) 10.42 49 (27.2) Mean T-Chol(mmol/L) \(\:\pm\:\) SD Triglyceride(mmol/L) \(\:\pm\:\varvec{S}\varvec{D}\) Mean LDL-C(mmol/L) \(\:\pm\:\) SD Mean HDL-C(mmol/L) \(\:\pm\:\) SD Hypercholesterolemia n (%) Hypertriglyceridemia n (%) 5.11 \(\:\pm\:\) 1.19 1.17 \(\:\pm\:0.43\) , 3.38 \(\:\pm\:\) 1.27 1.24 \(\:\pm\:\) 0.37 45 (25.0) 35 (19.4) HTN n (%) Mean systolic blood pressure (mmHg) \(\:\pm\:\) SD Mean diastolic blood pressure (mmHg) \(\:\pm\:\) SD 23 (17.8%) 121.67 \(\:\pm\:\) 13.39 80.94 \(\:\pm\:\) 7.22 Diabetes mellitus, yes, n (%) Mean FBS mmol/L \(\:\pm\:\) SD 7 (3.9) 4.76 \(\:\pm\:0.86\) CD4 count (cells/mm 3 ) n(%) \(\:\ge\:\) 200 cell/mm 3 156 (86.7) Viral load (copies/ml) n(%) < 50 172 (95.6) Median DAD Score (IQR) Median hsCRP 0.7 (0.4, 10) 1.91 (1.4, 2.8) Hypertension was observed in 17.8% of participants (classified with JNC-8) while diabetes mellitus was present in 3.9% of participants. Most participants had a CD4 cell count > 200 cells /mm 3 (86.7%) and undetectable viral load (95.6%), as shown in Table 1 . More than half of the participants (58.9%) were classified as having low cardiovascular risk, while 28.9% were categorized as having moderate risk. Only 12.2% of participants were identified as having a high cardiovascular risk (Figure I). The majority of participants in the low-risk category were women (77.4%) as shown in Table 2 . Table 2 Estimated 5-year cardiovascular risk category based on DAD scores by sex, Kano Nigeria DAD CVD Risk Male, n (%) Female, n (%) Total, n (%) P -value Low risk (\) 10) - - - ∗ Statistically significant Tables 3 and 4 depicts factors associated with CVD risk among study participants. Multivariate logistic regression analysis identified age, male sex, hypercholesterolemia, and elevated hsCRP as significant predictors of CVD risk. PLWH who were aged 50 years and older were more than twice as likely to have CVD compared to those under 50 years [adjusted odds ratio, aOR 2.38, 95% confidence interval (CI): 1.48–4.50, p = 0.020]. Male participants had more than double the risk of CVD compared to females [aOR 2.16, 95% CI:1.03–4.53, p = 0.040]. Table 3 a: Participants’ sociodemographic characteristics associated with cardiovascular disease risk by DAD criteria, Kano Variable Low (DAD < 1%) Moderate (DAD 1–5%) High Risk (DAD 5–10%) Total p-value Age <30 30–49 \(\:\ge\:\) 50 19(90.5) 70(65.2) 17(32.7) 2(9.5) 28(26.2) 22(42.3) 0(0.0) 9(8.4) 13(25.0) 21(100.0) 107(100.0) 52(100.0) < 0.001∗ Sex Male Female 24(45.3) 82(64.6) 17(32.1) 35(27.6) 12(22.6) 10(7.9) 53(100.0) 127(100.0) 0.010∗ Place of residence Urban Rural 94(59.1) 12(57.1) 47(29.6) 5(23.8) 18(11.3) 4(19.1) 159(100.0) 21(100.0) 0.57 Religion Islam Christianity Traditional 88(57.5) 17(65.4) 1(100.0) 46(30.1) 6(23.1) 0(0.0) 19(12.4) 3(11.5) 0(0.0) 153(100.0) 26(100.0) 1(100.0) 0.86 Marital status Single Married Divorced /separated Widowed 17(85.0) 76(56.7) 1(100.0) 12(48.0) 2(10.0) 43(32.1) 0(0.0) 7(28.0) 1(5.0) 15(11.2) 0(0.0) 6(24.0) 20(100.0) 134(100.0) 1(100.0) 25(100.0) 0.11 Tribe Hausa Yoruba Igbo Others 84(58.3) 2(66.7) 5(50.0) 15(65.2) 43(29.9) 1(33.3) 2(20.0) 5(21.7) 17(11.8) 0(0.0) 3(30.0) 3(13.1) 144(100.0) 3(100.0) 10(100.0) 23(100.0) 0.61 Occupation Civil servant Trader Housewife Farmer Retiree Unemployed Others 14(45.2) 20(52.6) 45(68.1) 1(25.0) 1(33.3) 11(84.6) 14(56.0) 11(35.5) 3(7.9) 19(28.9) 1(25.0) 2(66.7) 2(15.4) 11(44.0) 3(9.7) 15(39.5) 2(3.0) 2(50.0) 0(0.0) 0(0.0) 0(0.0) 31(100.0) 38(100.0) 66(100.0) 4(100.0) 3(100.0) 13(100.0) 25(100.0) 0.30 Level of Education None Quranic Primary Secondary Tertiary 9(50.0) 28(60.7) 13(54.2) 33(63.5) 23(57.5) 5(27.8) 13(28.3) 8(33.3) 13(25.0) 13(32.5) 4(22.2) 5(10.7) 3(12.5) 6(11.5) 4(10.0) 18(100.0) 46(100.0) 24(100.0) 52(100.0) 40(100.0) 0.94 Income (Naira) 150,00 89(59.7) 14(60.9) 3(37.5) 44(29.5) 5(21.7) 3(37.5) 16(10.8) 4(17.4) 2(25.0) 149(100.0) 23(100.0) 8(100.0 0.54 Family Hx CVD Absent Present 102(62.2) 4(25.0) 45(27.4) 7(43.7) 17(10.4) 5(31.3) 164(100.0) 16(100.0) 0.14 Smoking History Never smoked Current smoker Past smoker 102(61.8) 3(23.1) 1(50.0) 50(30.3) 1(7.7) 1(50.0) 13(7.9) 9(69.2) 0(0.0) 165(100.0) 13(100.0) 2(100.0) 0.18 Alcohol Use No Yes 104(59.8) 2(33.3) 49(28.2) 3(50.0) 21(12.0) 1(16.7) 174(100.0) 6(100.0) 0.42 ∗ Statistically significant Table 3 b: Participants’ clinical characteristics associated with cardiovascular disease risk by DAD criteria, Kano, Nigeria. Variable Low (DAD < 1%) Moderate (DAD 1–5%) High Risk (DAD 5–10%) Total p-value ART Regimen 2 NRTIs + 1nSTI (TDF exposed) 100(58.8) 6(60.0) 50(29.4) 2(20.0) 20(11.8) 2(20.0) 170(100.0) 10(100.0) < 0.001∗ 2 NRTIs + 1 PIs PIs exposed 102(60.0) 4(40.0) 51(30.0) 1(10.0) 17(10.0) 5(50.0) 170(100.0) 10(100.0) < 0.001∗ 2NRTIs + ABV Abacavir Exposed 103(60.6) 3(30.0) 51(30.0) 1(10.0) 16(9.4) 6(60.0) 170(100.0) 10(100.0) < 0.001∗ Duration on ART < 5yrs \(\:\ge\:\) 5yrs 21(75.0) 85(55.9) 4(14.3) 48(31.6) 3(10.7) 19(12.5) 28(100.0) 152(100.0) 0.007∗ CD4 (cells/mm 3 ) < 200 \(\:\ge\:\) 200 10(41.7) 96(61.5) 8(33.3) 44(28.2) 6(25.0) 16(10.3) 24(100.0) 156(100.0) 0.071 Viral load (copies/ml) < 50 \(\:\ge\:\) 50 104(60.5) 2(25.0) 50(29.1) 2(25.0) 18(10.4) 4(50.0) 172(100.0) 8(100.0) 0.045∗ Comorbidities • HTN • DM • HTN/DM • HTN/Asthma • Asthma • Cancer • Hepatitis B • Absent. 5(15.6) 1(14.3) 1(33.3) 0(0.0) 1(50.0) 0(0.0) 1(50.0) 97(71.3) 14(43.8) 2(28.6) 0(0.0) 0(0.0) 1(50.0) 1(100.0) 0(0.0) 38(28.0) 13(40.6) 4(57.1) 2(66.7) 1(100.0) 0(0.0) 0(0.0) 1(50.0) 1(0.7) 32(100.0) 7(100.0) 3(100.0) 1(100.0) 2(100.0) 1(100.0) 2(100.0) 136(100.0) 0.98 BMI Kg/M 2 Underweight < 18.5. Normal ;18.5–24.9 Overweight; 25–29.9 Obesity \(\:\ge\:\:30\) 5(83.3) 77(62.1) 16(64.0) 8(32.0) 1(16.7) 38(30.6) 6(24.0) 7(28.0) 0(0.0) 9(7.3) 3(12.0) 10(40.0) 6(100.0) 124(100.0) 25(100.0) 25(100.0) 0.005 Abdominal obesity Present Absent 24(49.0) 72(55.0) 16(32.7) 46(53.1) 9(18.3) 13(9.9) 49(100.0) 131(100.0) 0.48 Hypercholesterolemia Present Absent 18(40.0) 88(65.2) 13(28.9) 39(28.9) 14(31.1) 8(5.9) 45(100.0) 135(100.0) 0.003 Hypertriglyceridemia Present Absent 18(51.4) 88(60.7) 13(37.2) 39(26.9) 4(11.4) 18(12.4) 35(100.0) 145(100.0) 0.089 High-sensitivity CRP Low \(\:\le\:\) 3.03mg/L High > 3.03mg/L 101(70.1) 5(13.9) 38(26.3) 14(38.9) 5(3.6) 17(47.2) 144(100.0) 36(100.0) < 0.001* ∗ Statistically significant Table 4 Logistic regression analysis showing factors associated with DAD CVD risk, Kano Nigeria Variables aOR OR (95%CI) P value Age(yrs) < 50yrs \(\:\ge\:50\mathbf{y}\mathbf{r}\mathbf{s}\) 1 2.38 (1.48–4.50) 0.020 * Gender Female Male 1 2.17 (1.03–4.53) 0.040* TDF Exposed Unexposed 1 0.84 (0.07–10.74) 0.892 Abacavir Exposed Unexposed 1 0.91 (0.08–10.99) 0.151 Proteases inhibitors Exposed unexposed 1 0.89 (0.09–14.99) 0.189 ART duration \(\:\ge\:\) 5yrs < 5yrs 1 0.46 (0.16 − 1.32) 0.511 Viral Load (copies/ml) < 50 \(\:\ge\:\) 50 1 0.22 (0.03–1.76) 0.159 BMI (Kg/M 2 ) \(\:\ge\:\) 30 3.03 1 4.58 (2.09–10.05) < 0.001* *Statistically significant, OR - Odds ratio, CI - Confidence Interval. The majority of participants with high CVD risk had hypercholesterolemia. Hypercholesterolemia remained a significant predictor of CVD after regression analysis. Participants with hypercholesterolemia have more than three times the odds of developing CVD compared to those without hypercholesterolemia [aOR 3.03, 95% CI:1.68–4.86, p = 0.005]. Similarly, we found elevated levels of hsCRP > 3.03mg/L to be a significant predictor of high CVD risk after controlling confounders. Participants with elevated hsCRP had more than four-fold higher odds of CVD risk than those with lower hsCRP levels [aOR 4.58, 95% CI:2.09–10.05), p < 0.001]. DISCUSSION Our study assessed 5-year estimated CVD risk among PLWH in Nigeria, and found the majority of the participants having low to moderate risk. Our findings are consistent with reports from Brazil, Cameroon, and Togo. 18,20,21 The observed low to moderate risk of CVD could be attributed to the demographic profile of our participants, most of whom were young females (30–49 years of age). Several studies have reported a low risk of CVD among younger age groups and females. 12 , 27 , 29 – 30 However, the proportion of participants categorized as high CVD risk in our study (12.2%) was notably higher compared to reports from Brazil (2.1%), Cameroon (2.2%) and Togo (1.5%). 18,20 This difference may be explained by the longer duration of ART exposure among our study population, with a median of 12 years (IQR: 8, 16 years) compared to median ART durations of 6 years and 4.1 years in the Cameroon and Togo studies, respectively. Additionally, in our study population we documented higher BMI values as 13% of the participants were overweight and additional 13% were obese. 18 , 20 The most prevalent traditional cardiovascular risk factors among the study participants were abdominal obesity (27.2%), hypercholesterolemia (25.0%) hypertriglyceridemia (19.4%), and hypertension (17.8%). Diabetes mellitus (3.9%) was the least reported cardiovascular risk factor. These findings are consistent with Noumegni et al in Cameroon, who similarly reported dyslipidemia, abdominal obesity, and hypertension as the most frequent risk factors, while diabetes mellitus being the least prevalent. 20 The observed prevalence hypercholesterolemia, abdominal obesity, and hypertriglyceridemia could be linked to long-term exposure to ART medications, which are known to induce lipid abnormalities and fat redistribution. 8 – 11 Consistent with existing literature, we found older age to be a significant predictor of CVD risk in our population. 31 – 33 Male sex was also significantly associated with increased CVD risk, with men exhibiting more than two-fold higher risk compared to women. This finding aligns with studies conducted in the general population and among PLWH. 30 – 34 Men faced a heightened CVD risk due to combination of factors, including biological structure and hormonal influences. 35 For instance, women’s heart and blood vessels are smaller and before menopause estrogen provides women with protection against heart disease. 35 , 36 In addition, sex differences in social habits, such as lifestyle choices; smoking, alcohol consumption, and health seeking behavior also contribute to the increased CVD risk among men. 35 , 36 Hypercholesterolemia remained a significant predictor of CVD risk in our study, corroborating findings by others. This association may be related to the direct effects of HIV itself on adipose tissue and its impact on cholesterol metabolism. 8–9,11−12 We also found elevated hsCRP > 3.03mg/L to be a significant predictor of CVD. This finding has been reported by others, 23,25,30 including Koosha et al who found hCRP biomarker to be an independent risk factor for CVD, independent of age, sex, diabetes mellitus, dyslipidemia, hypertension, obesity, and smoking. 30 Our study has several limitations. First, we were unable to ascertain causality between the estimated CVD risk and actual cardiovascular events. Second, we cannot generalize our findings to broader populations since this is a tertiary hospital-based study with a limited sample size that may not be representative of all PLWH. Despite these limitations, the study has numerous strengths. We used a globally validated tool for CVD risk estimation specific to PLWH, ensuring accurate risk estimates. In addition, our use of probability sampling minimized selection bias, and regression analysis accounted for potential confounders, strengthening the reliability of our findings. CONCLUSION We found that the majority of PLWH attending a large HIV clinic in northern Nigeria had low to moderate CVD risk. The risk of developing CVD was associated with established risk factors in other studies, namely elevated hsCRP, increasing age, male sex, and hypercholesterolemia. Our findings highlight the importance of early risk stratification and targeted preventive interventions to mitigate the impact of these risk factors among PLWH. We recommend larger, long-term longitudinal studies to better ascertain the incidence of CVD in similar populations and the role of other risk factors, including specific ART regimens and their impact on cardiovascular health. Abbreviations ART Antiretroviral therapy CVD Cardiovascular-disease CVR Cardiovascular Risk DAD Data collection on adverse effects of anti-HIV drugs HIV Human immunodeficiency syndrome hsCRP High-sensitivity C-reactive protein PCE Pooled cohort equation PLWH People living with HIV/AIDS SCORE Systematic coronary risk evaluation Declarations All participants were asked to voluntarily participate and written informed consent was obtained from participants in this study. The study was conducted according to Helsinki declaration and ethical approval from the Aminu Kano Teaching Hospital Research Ethics committee, reference number (NHREC/28/01/2020/AKTH/EC/3861). Consent for publication Not applicable Data Availability The data set used for this study is available upon reasonable request. The data is not publicly available due to privacy reasons. Funding This work was supported by the Fogarty International Center (FIC) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the U.S. National Institutes of Health (NIH) award number 1D43TW011544. The findings and conclusions are those of the authors and do not necessarily represent the official position of the FIC, NIAAA, NIH, the Department of Health and Human Services, or the government of the United States of America. Acknowledgment We also appreciate the assistance from the African Centre of Excellence for Population Health and Policy (ACEPHAP) at Bayero University, Kano, Nigeria. Conflict of Interest None to declare Authors contribution . ZA, AKA Research conceptualization and initial manuscript writing, ZA, FIT, MB, Data collection and critical manuscript review ZA, OAL, AKS Data analysis and synthesis. ZA, OAL. Reviewed Tables, Figures and critical manuscript review. ZA, GMC, BAG manuscript writing, senior advisory input. MB, MUS, MAH, CWW provided senior advisory input, manuscript review and final approval. All authors reviewed manuscript. Clinical trial number : Not applicable References Murray CJL, Roth G, Stark B, DeCleene N, Hsu J, Johnson C et al. Global, Regional and National Burden of Cardiovascular Diseases and Risk Factors in 204 Conutries and Territories, 1990–2023. JACC.2025; 10.1016/j.jacc.2025.08.015 DelabaysB, Cavassini M, Damas J, Beuret H, Calmy A, Hasse B, et al. Cardiovascular risk assessment in people living with HIV compared to the general population. Eur J Prev Cardiol. 2022;29:689–99. Fragkou P, Moschopoulos CD, Dimopoulou D, Triantafyllidi H, Birmpa D, Benas D. Cardiovascular disease and risk assessment in people living with HIV: Current practices and novel perspectives. Hellenic J Cardiol. 2023;71(42):54. Nou E, Lo J, Hadigan C, Grinspoon SK. Pathophysiology and management of cardiovascular disease in patients with HIV. Lancet Diabetes Endocrinol. 2016;4(7):598–610. Vachiat A, McCutcheon K, Tsabedze N, Zachariah D, Manga P. HIV and Ischemic Heart Disease. J Am Coll Cardiol. 2017;69(1):73–82. Feinstein MJ, Hsue PY, Benjamin LA, Bloomfield GS, Currier JS, Freiberg MS, et al. Characteristics, Prevention, and Management of Cardiovascular Disease in People Living With HIV: a Scientific Statement from the American Heart Association. Circulation . CIR; 2019. p. 069. Hsue PY, Waters DD. HIV infection and coronary heart disease: mechanisms and management. Nat Rev Cardiol. 2019;16(12):745–59. Ballocca F, D’Ascenzo F, Gili S, Grosso Marra W, Gaita F. Cardiovascular disease in patients with HIV. Trends Cardiovasc Med. 2017;27(8):558–63. Muhammad S, Sani MU, Okeahialam BN. Cardiovascular disease risk factors among HIV-infected Nigerians receiving highly active antiretroviral therapy. Niger Med J. 2013;54:185–90. Amusa GA, Awokola B, Akanbi MO, Onuh JA, Uguru SU, Oke DA et al. Burden of Cardiovascular Disease Risk Factors in HIV-infected Adults in North-Central Nigeria. Conference Paper ·May 2015DOI: 10.1164/ajrccm-conference.2015.191.1_MeetingAbstracts.A4521 Bourgeois C, Gorwood J, Olivo A, et al. Contribution of Adipose Tissue to the Chronic Immune Activation and Inflammation Associated with HIV Infection and Its Treatment. Front Immunol. 2021;12:670566. Díaz-Delfín J, Domingo P, Wabitsch M, Giralt M, Villarroya F. HIV-1 Tat protein impairs adipogenesis and induces the expression and secretion of pro-in flammatory cytokines in human SGBS adipocytes. Antivir Ther. 2012;17(3):529–40. Dhillon S, Sabin CA, Alagaratnam J, Bagkeris E, Post FA, Boffito M, et al. Level of agreement between frequently used cardiovascular risk calculators in people living with HIV. HIV Med. 2019;20:347–52. https://doi.org/10.1111/hiv.12731 . Begovac J, Dragović G, Višković K, Kušić J, Perović Mihanović M, Lukas D, et al. Comparison of four international cardio-vascular disease prediction models and theprevalence of eligibility for lipid loweringtherapy in HIV infected patients on antiretroviral therapy. Croat Med J. 2015;56:14–23. https://doi.org/10.3325/cmj . Policarpo S, Rodrigues T, Moreira AC, Valadas E. Cardiovascular risk in HIV-infected individuals: A comparison of three risk prediction algorithms. J Port Soc Cardiol. 2019;38:463–70. Edward AO, Oladayo AA, Omolola AS, Adetiloye AA, Adedayo PA. Prevalence of traditional cardiovascular risk factors and evaluation of cardiovascular risk using three risk equations in Nigerians living with human immunodeficiency virus. North Am J Med Sci. 2013;5:680. https://doi.org/10.4103/1947 2714.123251 . Woldu M, Minzi O, Shibeshi W, Shewaamare A, Engidawork E. Predicting the risk of atherosclerotic cardiovascular disease among adults living with HIV/AIDS in Addis Ababa, Ethiopia: A hospital-based study. PLoS One 2021 Nov 29:16 (11). Nery WM, Turchi MartellI CM, Aparecida Silveira E, Alencar de Sousa C, de Oliveira Falco M. Cássia Oliveira de Castro A. Cardiovascular Risk Assessment: A Comparison of the Framingham, PROCAM, and DAD Equations in HIV-Infected Persons. Sci World J 2013;1–9. Mubiru F, Castelnuovo B, Reynolds SJ, Kiragga A, Tibakabikoba H, Owarwo NC, et al. Comparison of different cardiovascular risk tools used in HIV patient cohorts in sub-Saharan Africa; do we need to include laboratory tests? PLoS ONE. 2021;16(1):e243552. https://doi.org/10.1371/journal.pone.0243552 . Noumegni SR, Ama VJM, Assah FK, Bigna JJ, Nansseu JR, Jenny Arielle M, et al. Assessment of the agreement between the Framingham and DAD risk equations for estimating cardiovascular risk in adult Africans living with HIV infection: a cross- sectional study. Tropical Diseases. Travel Med Vaccines. 2017;3:12. Moukhaila A, Mossi ​ EK, Amadou N, Dzidzonu Nemi K, et al. Cardiovascular Risk Factors and Cardiovascular Risk in People Living with HIV: Comparison of Four Cardiovascular Risk Prediction Algorithms. Global J Med. 2020;20:(5):57–71. Awofala AA, Ogundele OE. HIV epidemiology in Nigeria. Saudi J Biol Sci. 2018; 25(4):697–703. 10.1016/j.sjbs.2016.03.006 .Epub 2016 Apr 9. PMID: 29740232. Abdulkadir Z, Kwaku AA, Ibrahim ZU, Olawumi AL, Abdulazeez ZU, Sheriff S, et al. Validity of high-sensitivity C-reactive protein versus DAD equation for cardiovascular risk assessment in people living with HIV in Nigeria. BMC Infect Dis. 2025;25:978. https://doi.org/10.1186/s12879-025-11378-4 . Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. De Luca A, de Gaetano Donati K, Colafigli M, Cozzi-Lepri A, De Curtis A, et al. The association of high-sensitivity creative protein and other biomarkers with cardiovascular disease in patients treated for HIV: a nested case–control study. BMC Infect Dis. 2013;13:414. World Health Organization Body Mass Index classification. Available at https://wwww.who.int>…items. Waist Circumference and Waist Hip ratio- IRIS- World Health Organization. Available at https://iris.who.int%3Ehandle Accessed 12th October 2024. Friis-Møller N, Ryom L, Smith C, Weber R, Reiss P, Dabis F, et al. An updated prediction model of the global risk of cardiovascular disease in HIV-positive persons: the data-collection on adverse effects of anti-HIV drugs (D:A:D) study. Eur J Prev Cardiol. 2016;23:214–23. 10.1177/2047487315579291 . D;A;D (F.) 5-year CVD risk score. Available at https://www.chip.dk/Tools-standards/Clinical-risk-scores Accessed 12th October 2024. Koosha P, Roohafza H, Sarrafzadegan N, Vakhshoori M, Talaei M, Sheikhbahaei E, Sadeghi M. High Sensitivity C-Reactive Protein Predictive Value for Cardiovascular Disease: A Nested Case Control from Isfahan Cohort Study (ICS). Glob Heart. 2020;15(1):3. 10.5334/gh.367 . Freiberg MS, Chang CC, Kuller LH, Skanderson M, Lowy E, Kraemer KL, Butt AA, Bidwell Goetz M, Leaf D, Oursler KA, et al. HIV infection and the risk of acute myocardial infarction. JAMA Intern Med. 2013;173(8):614–22. Liu Z, Zhang J, Xang X, Gao H, Chen S, Weissman S, et al. The dynamic risk factors of cardiovascular disease among people living with HIV: a real-world data study. BMC PH. 2024;24:1162. Samji H, Cescon A, Hogg RS, Modur SP, Althoff KN, Buchacz K, Burchell AN, Cohen M, Gebo KA, Gill MJ et al. Closing the gap: increases in life expectancy. McLaughlin MM, Ma Y, Scherzer R, Rahalkar S, Martin JN, Mills C, Milush J, Deeks SG, Hsue PY. Association of viral persistence and atherosclerosis in adults with treated HIV infection. JAMA Netw Open. 2020;3(10):e2018099–2018099. Mbakwem AC, Amadi CE, Ajuluchukwu JN, Kushimo OA. Trends and outcomes of cardiovascular disease admissions in Lagos, Nigeria: a 16-year review. Cardiovasc J Afr. 2023;34(3):140–8. Amadi CE, Grove TP, Mbakwem AC, Ozoh OB, Kushimo OA, Wood DA, Akinkunmi M. Prevalence of cardiometabolic risk factors among professional male long-distance bus drivers in Lagos, south-west Nigeria: a cross-sectional study. Cardiovasc J Afr. 2018;29(2):106–14. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8621829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":598642559,"identity":"8e95b162-3f80-4797-aa89-750c44eaaa23","order_by":0,"name":"Zainab Abdulkadir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYPACGx42/uYDQIaEDLFa0uT4JY4lgLTwEKvlsLFkQ44BiEVYi/y0wwcfF9QwJ244cObzqxs1FjwM7IePbsCnhXF2WrLxjGNsiRsO926zzjkGdBhPWtoNfFqYpXPMpHnYeIC2nN1mnMMG1CLBY4ZXC5t0/vffPP8kgFpynhnn/CNCC490Dhszb5sByPvMj3PbiNAiIZ1mLM3blwAKZDPm3D4JoCMJ+EV+dvLDzzzf/oOi8vHnnG91cvzsh4/h1YLiLwkwSaxyEGD+QIrqUTAKRsEoGDkAALvCRGztCj68AAAAAElFTkSuQmCC","orcid":"","institution":"Aminu Kano Teaching Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zainab","middleName":"","lastName":"Abdulkadir","suffix":""},{"id":598642560,"identity":"666a885c-fb04-4218-9410-4be126ef6fe6","order_by":1,"name":"Aminatu Ayuba Kwaku","email":"","orcid":"","institution":"Bayero University Kano","correspondingAuthor":false,"prefix":"","firstName":"Aminatu","middleName":"Ayuba","lastName":"Kwaku","suffix":""},{"id":598642561,"identity":"aa837718-f3b4-4cf8-a36d-9b20ce627662","order_by":2,"name":"AbdulGaffar Lekan Olawumi","email":"","orcid":"","institution":"Aminu Kano Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"AbdulGaffar","middleName":"Lekan","lastName":"Olawumi","suffix":""},{"id":598642562,"identity":"5f437b50-4da7-41ed-a3ee-a30483bb5b74","order_by":3,"name":"Godpower Michael Chinedu","email":"","orcid":"","institution":"Aminu Kano Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Godpower","middleName":"Michael","lastName":"Chinedu","suffix":""},{"id":598642563,"identity":"cc17d620-b0f4-464d-960d-161a3074d997","order_by":4,"name":"Bukar A. Grema","email":"","orcid":"","institution":"Aminu Kano Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bukar","middleName":"A.","lastName":"Grema","suffix":""},{"id":598642564,"identity":"99fcc34f-242e-4e86-ad44-d7e3ae2b8fa7","order_by":5,"name":"Fatimah Tsiga-Ahmed Ismail","email":"","orcid":"","institution":"Bayero University Kano","correspondingAuthor":false,"prefix":"","firstName":"Fatimah","middleName":"Tsiga-Ahmed","lastName":"Ismail","suffix":""},{"id":598642565,"identity":"902300be-f22f-4a19-87c4-7865fc88eb84","order_by":6,"name":"Baba Maiyaki Musa","email":"","orcid":"","institution":"Bayero University Kano","correspondingAuthor":false,"prefix":"","firstName":"Baba","middleName":"Maiyaki","lastName":"Musa","suffix":""},{"id":598642566,"identity":"ef50ecfe-1a9c-4aa9-93f1-cce146b86c05","order_by":7,"name":"William C. Wester","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"C.","lastName":"Wester","suffix":""},{"id":598642567,"identity":"aba40f38-654c-4683-8eb5-a4384a923512","order_by":8,"name":"Mahmoud Umar Sani","email":"","orcid":"","institution":"Bayero University Kano","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"Umar","lastName":"Sani","suffix":""},{"id":598642568,"identity":"0d89bbec-4887-4c26-9a19-f3b732f47c04","order_by":9,"name":"Muktar Hassan Aliyu","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Muktar","middleName":"Hassan","lastName":"Aliyu","suffix":""}],"badges":[],"createdAt":"2026-01-16 19:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8621829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8621829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104176995,"identity":"f402f3e6-aba4-4b4f-bd1d-71d5a59aa52b","added_by":"auto","created_at":"2026-03-08 16:42:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83993,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of cardiovascular disease risk among the study participants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8621829/v1/55a652c6db4dd60df3b68360.png"},{"id":104404192,"identity":"09a8485f-d74b-4e49-b28a-4869996e605c","added_by":"auto","created_at":"2026-03-11 12:19:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1559281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8621829/v1/7f3c04f9-d293-4ab4-a10a-42fada41b7b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of cardiovascular disease among people living with HIV in northern Nigeria","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiovascular disease (CVD) is a major global public health concern, responsible for the highest number of deaths worldwide and contributing to over 32% of all deaths in 2023.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e People living with HIV (PLWH) are at increased risk of developing CVD.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Despite significant research, the pathophysiology of CVD among PLWH continues to evolve.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The development of CVD in PLWH is due to a complex interplay of traditional CVD risk factors (e.g., hypertension, dyslipidemia, diabetes mellitus, and smoking), HIV-mediated mechanisms, antiretroviral (ARV) medication related cardiometabolic adverse effects, genetic factors, \u003csup\u003e4\u0026ndash;8\u003c/sup\u003e and increasing life expectancy among PLWH following the widespread availability/access to antiretroviral therapy (ART) .\u003csup\u003e9\u0026ndash;10\u003c/sup\u003e HIV itself can induce and hasten atherosclerosis and endothelial dysfunction by several mechanisms, which include chronic inflammation, residual immune activation driven by viral replication, direct effect of the virus on adipose tissue, and altered cholesterol metabolism.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Dyslipidemia, a major CVD risk factor, is highly prevalent among PLWH, with prevalence estimates ranging from 7.7% to 73.4%.\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e This dyslipidemia is attributable to various mechanisms, such as increased basal lipolysis, hepatic de-novo lipogenesis, and hypertriglyceridemia especially following ART initiation.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In addition, ART exposure may cause fat redistribution, manifesting as lipoatrophy in the face, limbs and viscera, which further exacerbates dyslipidemia and hypertriglyceridemia.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePLWH have double the risk of CVD compared to HIV-negative individuals.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Sub-Saharan Africa (SSA) bears the highest burden of HIV globally, with approximately 70% of PLWH residing in the region.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e However, estimates of absolute CVD risk among PLWH vary significantly across different regions and CVD prediction models .\u003csup\u003e13\u0026ndash;17\u003c/sup\u003e In developed countries, higher CVD risk prevalence among PLWH have been documented.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e For example, in the United Kingdom, Dhillon et al. reported a prevalence of CVD risk as high as 21.5% using Framingham risk scores (FRS) and 14.8% utilizing the Data Collection on Adverse effects of Anti-HIV Drugs (DAD) risk equation.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Similarly, a study from Serbia reported a prevalence of high CVD risk of 27.2% according to FRS, 31.5% with Systematic Coronary Risk Evaluation (SCORE) and 51.6% using DAD.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e In Portugal, high CVD risk was documented at 20.5% using FRS, 10.3% with DAD, and 4.4% with SCORE.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn contrast, studies from developing countries indicate a lower prevalence of CVD risk among PLWH, with regional differences. For example, in Brazil, the prevalence of high CVD risk was reported as 2.8% and 2.1%, using FRS and DAD, respectively. \u003csup\u003e18\u003c/sup\u003e In Nigeria, the CVD risk prevalence was reported as 11.7% using FRS, 12.8% using WHO/ISH prediction models, and 12.8% with SCORE.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Similarly, an Ethiopian study reported high CVD prevalence among adults aged 20 years and older using FRS and the Pooled Cohort Equations (PCE).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Among PLWH 40 to 79 years of age, PCE yielded higher prevalence (28%) than FRS (17.7%).\u003csup\u003e17\u003c/sup\u003e In Uganda, the prevalence of high CVD risk was low, at 3.4%, while investigators in Cameroon reported rates of 2.4% utilizing DAD and 8.4% using FRS. \u003csup\u003e19\u0026ndash;21\u003c/sup\u003e The variations across studies may be due to the use of varying CVD prediction models, the differences in lifestyle and cultural practices between developed and developing countries.\u003c/p\u003e \u003cp\u003eDespite these findings, the prevalence and predictors of CVD among PLWH remain underexplored in northern Nigeria, leaving a critical gap in knowledge. This study aimed to evaluate the prevalence and predictors of CVD among PLWH in Kano, Nigeria.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis health facility-based cross-sectional study was conducted at the S.S. Wali HIV outpatient Clinic within Aminu Kano Teaching Hospital (AKTH) in Kano, Nigeria. The study utilized data originally collected for a diagnostic accuracy study of hsCRP versus DAD equation for cardiovascular risk assessment.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The current analysis aimed to estimate the prevalence and predictors of cardiovascular disease among PLWH. A total of 180 adults (\u0026ge;\u0026thinsp;18 years of age) living with HIV were systematically recruited over a 6-week period (30th September to 11th November, 2024). We excluded individuals with prior history of cardiovascular events (e.g., known myocardial infarction, stroke, peripheral arterial disease, and/or congestive heart failure), those requiring emergency care, and persons with acute inflammatory illness or moderate to severe cognitive impairment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling technique\u003c/h3\u003e\n\u003cp\u003eA total of 180 participants were recruited using systematic sampling, with 10 individuals selected per day using a sampling interval of 5. The first participant was randomly selected through balloting, and every fifth person was subsequently chosen until the minimum sample size was reached.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected using a pretested questionnaire used in a previous study (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.google.com/document/d/1oAEhsbMjn7vNsUTRLj5qyA1lnoxtDqIY/edit?usp=drivesdk\u0026amp;ouid=106491892114506665198\u0026amp;rtpof=true\u0026amp;sd=true\u003c/span\u003e\u003cspan address=\"https://docs.google.com/document/d/1oAEhsbMjn7vNsUTRLj5qyA1lnoxtDqIY/edit?usp=drivesdk\u0026amp;ouid=106491892114506665198\u0026amp;rtpof=true\u0026amp;sd=true\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003csup\u003e23\u003c/sup\u003e Trained research assistants administered the questionnaire to obtain sociodemographic information as well as clinical data, including family history of CVD, hypertension, diabetes mellitus, HIV clinical status (CD4 cell count and viral load), medication history (type and duration on ART), weight, height and body mass index (BMI). Blood pressure was measured after 5 minutes rest using a calibrated mercury sphygmomanometer (ERKA)\u0026reg; and an appropriate stethoscope.\u003c/p\u003e \u003cp\u003eParticipants were instructed to fast overnight for 8\u0026ndash;12 hours, and 5ml of blood was collected into Ethylene-Diamine Tetra-acetic Acid (EDTA) vacutainer tubes for analysis of lipid profile, fasting blood glucose (FBG) and hsCRP levels. Blood samples were transferred to the chemical pathology laboratory on-site at AKTH, where they were centrifuged at 3500 rpm and analyzed further per manufacturer\u0026rsquo;s instructions. High sensitivity (hs) CRP levels were measured using particle-enhanced turbidimetric assay, calibrated and standardized against the WHO reference values.\u003c/p\u003e \u003cp\u003eDyslipidemia was classified based on the National Cholesterol Education Program, Adults Treatment Panel (NCEP-ATP) III guidelines.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Hypercholesterolemia was defined as elevated total cholesterol of \u0026gt;\u0026thinsp;6.2 mmol/L, LDL-C\u0026thinsp;\u0026gt;\u0026thinsp;4.1mmol/L and/or reduced HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.04mmol/L in men or \u0026lt;\u0026thinsp;1.29mmol/L in women while, Hypertriglyceridemia was defined as triglycerides of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:1.7\\)\u003c/span\u003e\u003c/span\u003emmol/L.\u003csup\u003e24\u003c/sup\u003e hsCRP cut-off points were obtained after validity testing. Elevated hsCRP was defined as \u0026gt;3.03 mg/L, based on prior research in the area.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe defined high blood pressure as systolic blood pressure of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:140\\text{m}\\text{m}\\text{H}\\text{g}\\)\u003c/span\u003e\u003c/span\u003e and diastolic blood pressure of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:90\\)\u003c/span\u003e\u003c/span\u003emmHg, and/or self-reported history of hypertension or taking antihypertensive medication(s). Diabetes mellitus was defined as fasting blood glucose (FBG) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\:7\\text{m}\\text{m}\\text{o}\\text{l}/\\text{L}\\)\u003c/span\u003e\u003c/span\u003e and/or self-report or history of taking antidiabetic medication(s). We measured participants\u0026rsquo; height with stadiometer (Hospitex\u003cb\u003e)\u0026reg;\u003c/b\u003e to the nearest 0.1cm. Weight was measured to the nearest 0.1kg with weighing scale (Hospitex\u003cb\u003e)\u0026reg;.\u003c/b\u003e We used body mass index (BMI) in kg/m\u003csup\u003e2\u003c/sup\u003e to classify participants as underweight (\u0026lt;\u0026thinsp;18.5), normal (18.5\u0026ndash;24.9), overweight (25\u0026ndash;29.9), and obese ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 30) based on WHO guidelines.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Participants' waist circumference was measured in centimeters at the level of the umbilicus. Using WHO guidelines, truncal obesity was defined as \u0026ge;102 cm for men and \u0026ge;88 cm in women, while normal waist circumference was \u0026lt;94 cm for men and \u0026lt;80 cm for women.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCardiovascular risk assessment\u003c/h3\u003e\n\u003cp\u003eWe estimated the 5-year projected CVD risk using the DAD Full (2016) model through a web-based risk calculator.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e The calculation included variables such as age, sex, smoking history (past/present), family history of CVD, diabetes mellitus, abacavir exposure, protease inhibitor (PI) exposure and duration, CD4 cell count, systolic blood pressure, total cholesterol, and HDL-C levels. The resulting CVD risk classification was as follows: low risk: \u0026lt;1%, moderate risk: 1\u0026ndash;5%, high risk: 5\u0026ndash;10%, and very high risk: \u0026gt;10%.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe analyzed collected data using IBM SPSS Statistical software for windows, version 26. Descriptive statistics were reported as means and standard deviations or medians and interquartile ranges for continuous data, while categorical variables were presented as frequencies and percentages. Associations between categorical variables were tested using chi-square or Fisher\u0026rsquo;s exact test. Multivariate logistic regression analysis was performed for variables that were significant at bivariate level to identify the predictors of CVD risk. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e We obtained ethical approval from the AKTH Research Ethics Committee (NHREC/28/01/2020/AKTH/EC/3861). The study adhered to the principles outlined in the Helsinki declaration. Signed informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe majority of participants were female (70.6%), urban residents (88.3%) and Muslim (85.0%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Most participants reported never smoking cigarettes (91.7%) or consuming alcohol (96.7%). The most common antiretroviral treatment regimen was Tenofovir, Lamivudine and Dolutegravir (TLD), used by 91.7% of participants. The median duration on ART was 12.0 years (IQR: 8.0, 16.0 years). Participants had a mean BMI\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\pm\\:\\:\\)\u003c/span\u003e\u003c/span\u003estandard deviation (SD) of 24.3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e3.9 kg/m\u003csup\u003e2\u003c/sup\u003e, with the majority having normal weight (68.9%), while (13.9%) were overweight and (13.9%) were obese. The mean waist circumference \u0026plusmn; SD was 84.9\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e10.4 cm, and 27.2% of participants presented with abdominal obesity. The most prevalent lipid abnormalities were hypercholesterolemia (25%) and hypertriglyceridemia (19.4%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eParticipants\u0026rsquo; sociodemographic and clinical characteristics, Kano, Nigeria\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eAge\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMean age (years) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\varvec{S}\\varvec{D}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAdults 30\u0026ndash;49, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e44.03 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 10.58\u003c/p\u003e\n\u003cp\u003e107 (59.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eSex\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFemale n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e127 (70.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003ePlace of residence\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUrban n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e159 (88.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eReligion\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIslam n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e153 (85.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eMarital status\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMarried n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e134 (74.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eTribe\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eHausa n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e144 (80)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eOccupation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTrader n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e38 (21.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eLevel of education\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSecondary n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e52 (28.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eMonthly income (Naira)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lt;70,000 n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e149 (82.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eFamily history of CVD\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ePresent n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eSmoking History\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eNever smoked n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e165 (91.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eAlcohol Use\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eNo, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e174 (96.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eART exposure\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1st line, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e165 (91.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian years on ART (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (8,16)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"Underline\"\u003eART Regimen\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e2 NRTIs\u0026thinsp;+\u0026thinsp;1 InSTI (TLD)\u003c/p\u003e\n\u003cp\u003eProtease inhibitors exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e170 (94.4)\u003c/p\u003e\n\u003cp\u003e10 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTDF exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 (94.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbacavir Exposed\u003c/p\u003e\n\u003cp\u003eNEV exposed\u003c/p\u003e\n\u003cp\u003eATV exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (5.6)\u003c/p\u003e\n\u003cp\u003e2 (1.1)\u003c/p\u003e\n\u003cp\u003e4 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean BMI (kg/M\u003csup\u003e2\u003c/sup\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\:\\varvec{S}\\varvec{D}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eNormal weight (Kg)n(%)\u003c/p\u003e\n\u003cp\u003eObesity n(%)\u003c/p\u003e\n\u003cp\u003eMean waist circumference (cm) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAbdominal obesity n%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.28\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e3.90\u003c/p\u003e\n\u003cp\u003e124 (68.9)\u003c/p\u003e\n\u003cp\u003e25 (13.9)\u003c/p\u003e\n\u003cp\u003e84.91\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e10.42\u003c/p\u003e\n\u003cp\u003e49 (27.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean T-Chol(mmol/L) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e SD\u003c/p\u003e\n\u003cp\u003eTriglyceride(mmol/L) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\varvec{S}\\varvec{D}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMean LDL-C(mmol/L) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSD\u003c/p\u003e\n\u003cp\u003eMean HDL-C(mmol/L) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSD\u003c/p\u003e\n\u003cp\u003eHypercholesterolemia n (%)\u003c/p\u003e\n\u003cp\u003eHypertriglyceridemia n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.11\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e1.19\u003c/p\u003e\n\u003cp\u003e1.17\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:0.43\\)\u003c/span\u003e\u003c/span\u003e,\u003c/p\u003e\n\u003cp\u003e3.38\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 1.27\u003c/p\u003e\n\u003cp\u003e1.24\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e0.37\u003c/p\u003e\n\u003cp\u003e45 (25.0)\u003c/p\u003e\n\u003cp\u003e35 (19.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHTN n (%)\u003c/p\u003e\n\u003cp\u003eMean systolic blood pressure (mmHg) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSD\u003c/p\u003e\n\u003cp\u003eMean diastolic blood pressure (mmHg) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (17.8%)\u003c/p\u003e\n\u003cp\u003e121.67\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e13.39\u003c/p\u003e\n\u003cp\u003e80.94\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e7.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes mellitus, yes, n (%)\u003c/p\u003e\n\u003cp\u003eMean FBS mmol/L\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (3.9)\u003c/p\u003e\n\u003cp\u003e4.76\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:0.86\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD4 count (cells/mm\u003csup\u003e3\u003c/sup\u003e) n(%)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e200 cell/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156 (86.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViral load (copies/ml) n(%)\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172 (95.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian DAD Score (IQR)\u003c/p\u003e\n\u003cp\u003eMedian hsCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7 (0.4, 10)\u003c/p\u003e\n\u003cp\u003e1.91 (1.4, 2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHypertension was observed in 17.8% of participants (classified with JNC-8) while diabetes mellitus was present in 3.9% of participants. Most participants had a CD4 cell count\u0026thinsp;\u0026gt;\u0026thinsp;200 cells /mm\u003csup\u003e3\u003c/sup\u003e (86.7%) and undetectable viral load (95.6%), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eMore than half of the participants (58.9%) were classified as having low cardiovascular risk, while 28.9% were categorized as having moderate risk. Only 12.2% of participants were identified as having a high cardiovascular risk (Figure I). The majority of participants in the low-risk category were women (77.4%) as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimated 5-year cardiovascular risk category based on DAD scores by sex, Kano Nigeria\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDAD CVD Risk\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMale, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFemale, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal, n (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow risk (\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (22.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82 (77.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.010\u0026lowast;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerate risk (1\u0026ndash;5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (32.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (67.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh risk (5\u0026ndash;10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (54.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery high (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e 10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003cstrong\u003e\u0026lowast; Statistically significant\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e depicts factors associated with CVD risk among study participants. Multivariate logistic regression analysis identified age, male sex, hypercholesterolemia, and elevated hsCRP as significant predictors of CVD risk. PLWH who were aged 50 years and older were more than twice as likely to have CVD compared to those under 50 years [adjusted odds ratio, aOR 2.38, 95% confidence interval (CI): 1.48\u0026ndash;4.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020]. Male participants had more than double the risk of CVD compared to females [aOR 2.16, 95% CI:1.03\u0026ndash;4.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ea: Participants\u0026rsquo; sociodemographic characteristics associated with cardiovascular disease risk by DAD criteria, Kano\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003cp\u003e(DAD\u0026thinsp;\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003cp\u003e(DAD 1\u0026ndash;5%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh Risk\u003c/p\u003e\n\u003cp\u003e(DAD 5\u0026ndash;10%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003cp\u003e\u0026lt;30\u003c/p\u003e\n\u003cp\u003e30\u0026ndash;49\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 50\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e19(90.5)\u003c/p\u003e\n\u003cp\u003e70(65.2)\u003c/p\u003e\n\u003cp\u003e17(32.7)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2(9.5)\u003c/p\u003e\n\u003cp\u003e28(26.2)\u003c/p\u003e\n\u003cp\u003e22(42.3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e9(8.4)\u003c/p\u003e\n\u003cp\u003e13(25.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e21(100.0)\u003c/p\u003e\n\u003cp\u003e107(100.0)\u003c/p\u003e\n\u003cp\u003e52(100.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026lowast;\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e24(45.3)\u003c/p\u003e\n\u003cp\u003e82(64.6)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17(32.1)\u003c/p\u003e\n\u003cp\u003e35(27.6)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e12(22.6)\u003c/p\u003e\n\u003cp\u003e10(7.9)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e53(100.0)\u003c/p\u003e\n\u003cp\u003e127(100.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.010\u0026lowast;\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrban\u003c/p\u003e\n\u003cp\u003eRural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e94(59.1)\u003c/p\u003e\n\u003cp\u003e12(57.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e47(29.6)\u003c/p\u003e\n\u003cp\u003e5(23.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18(11.3)\u003c/p\u003e\n\u003cp\u003e4(19.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e159(100.0)\u003c/p\u003e\n\u003cp\u003e21(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIslam\u003c/p\u003e\n\u003cp\u003eChristianity\u003c/p\u003e\n\u003cp\u003eTraditional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e88(57.5)\u003c/p\u003e\n\u003cp\u003e17(65.4)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e46(30.1)\u003c/p\u003e\n\u003cp\u003e6(23.1)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e19(12.4)\u003c/p\u003e\n\u003cp\u003e3(11.5)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e153(100.0)\u003c/p\u003e\n\u003cp\u003e26(100.0)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003cp\u003eDivorced /separated\u003c/p\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17(85.0)\u003c/p\u003e\n\u003cp\u003e76(56.7)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e12(48.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2(10.0)\u003c/p\u003e\n\u003cp\u003e43(32.1)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e7(28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1(5.0)\u003c/p\u003e\n\u003cp\u003e15(11.2)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e6(24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e20(100.0)\u003c/p\u003e\n\u003cp\u003e134(100.0)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e25(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTribe\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHausa\u003c/p\u003e\n\u003cp\u003eYoruba\u003c/p\u003e\n\u003cp\u003eIgbo\u003c/p\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e84(58.3)\u003c/p\u003e\n\u003cp\u003e2(66.7)\u003c/p\u003e\n\u003cp\u003e5(50.0)\u003c/p\u003e\n\u003cp\u003e15(65.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e43(29.9)\u003c/p\u003e\n\u003cp\u003e1(33.3)\u003c/p\u003e\n\u003cp\u003e2(20.0)\u003c/p\u003e\n\u003cp\u003e5(21.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17(11.8)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e3(30.0)\u003c/p\u003e\n\u003cp\u003e3(13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e144(100.0)\u003c/p\u003e\n\u003cp\u003e3(100.0)\u003c/p\u003e\n\u003cp\u003e10(100.0)\u003c/p\u003e\n\u003cp\u003e23(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCivil servant\u003c/p\u003e\n\u003cp\u003eTrader\u003c/p\u003e\n\u003cp\u003eHousewife\u003c/p\u003e\n\u003cp\u003eFarmer\u003c/p\u003e\n\u003cp\u003eRetiree\u003c/p\u003e\n\u003cp\u003eUnemployed\u003c/p\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e14(45.2)\u003c/p\u003e\n\u003cp\u003e20(52.6)\u003c/p\u003e\n\u003cp\u003e45(68.1)\u003c/p\u003e\n\u003cp\u003e1(25.0)\u003c/p\u003e\n\u003cp\u003e1(33.3)\u003c/p\u003e\n\u003cp\u003e11(84.6)\u003c/p\u003e\n\u003cp\u003e14(56.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e11(35.5)\u003c/p\u003e\n\u003cp\u003e3(7.9)\u003c/p\u003e\n\u003cp\u003e19(28.9)\u003c/p\u003e\n\u003cp\u003e1(25.0)\u003c/p\u003e\n\u003cp\u003e2(66.7)\u003c/p\u003e\n\u003cp\u003e2(15.4)\u003c/p\u003e\n\u003cp\u003e11(44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3(9.7)\u003c/p\u003e\n\u003cp\u003e15(39.5)\u003c/p\u003e\n\u003cp\u003e2(3.0)\u003c/p\u003e\n\u003cp\u003e2(50.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e31(100.0)\u003c/p\u003e\n\u003cp\u003e38(100.0)\u003c/p\u003e\n\u003cp\u003e66(100.0)\u003c/p\u003e\n\u003cp\u003e4(100.0)\u003c/p\u003e\n\u003cp\u003e3(100.0)\u003c/p\u003e\n\u003cp\u003e13(100.0)\u003c/p\u003e\n\u003cp\u003e25(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLevel of Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003eQuranic\u003c/p\u003e\n\u003cp\u003ePrimary\u003c/p\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9(50.0)\u003c/p\u003e\n\u003cp\u003e28(60.7)\u003c/p\u003e\n\u003cp\u003e13(54.2)\u003c/p\u003e\n\u003cp\u003e33(63.5)\u003c/p\u003e\n\u003cp\u003e23(57.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5(27.8)\u003c/p\u003e\n\u003cp\u003e13(28.3)\u003c/p\u003e\n\u003cp\u003e8(33.3)\u003c/p\u003e\n\u003cp\u003e13(25.0)\u003c/p\u003e\n\u003cp\u003e13(32.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4(22.2)\u003c/p\u003e\n\u003cp\u003e5(10.7)\u003c/p\u003e\n\u003cp\u003e3(12.5)\u003c/p\u003e\n\u003cp\u003e6(11.5)\u003c/p\u003e\n\u003cp\u003e4(10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18(100.0)\u003c/p\u003e\n\u003cp\u003e46(100.0)\u003c/p\u003e\n\u003cp\u003e24(100.0)\u003c/p\u003e\n\u003cp\u003e52(100.0)\u003c/p\u003e\n\u003cp\u003e40(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIncome (Naira)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lt;70,000\u003c/p\u003e\n\u003cp\u003e70,000\u0026ndash;150,000\u003c/p\u003e\n\u003cp\u003e\u0026gt;150,00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e89(59.7)\u003c/p\u003e\n\u003cp\u003e14(60.9)\u003c/p\u003e\n\u003cp\u003e3(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e44(29.5)\u003c/p\u003e\n\u003cp\u003e5(21.7)\u003c/p\u003e\n\u003cp\u003e3(37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e16(10.8)\u003c/p\u003e\n\u003cp\u003e4(17.4)\u003c/p\u003e\n\u003cp\u003e2(25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e149(100.0)\u003c/p\u003e\n\u003cp\u003e23(100.0)\u003c/p\u003e\n\u003cp\u003e8(100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFamily Hx CVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e102(62.2)\u003c/p\u003e\n\u003cp\u003e4(25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e45(27.4)\u003c/p\u003e\n\u003cp\u003e7(43.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e17(10.4)\u003c/p\u003e\n\u003cp\u003e5(31.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e164(100.0)\u003c/p\u003e\n\u003cp\u003e16(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking History\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNever smoked\u003c/p\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003cp\u003ePast smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e102(61.8)\u003c/p\u003e\n\u003cp\u003e3(23.1)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e50(30.3)\u003c/p\u003e\n\u003cp\u003e1(7.7)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13(7.9)\u003c/p\u003e\n\u003cp\u003e9(69.2)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e165(100.0)\u003c/p\u003e\n\u003cp\u003e13(100.0)\u003c/p\u003e\n\u003cp\u003e2(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAlcohol Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e104(59.8)\u003c/p\u003e\n\u003cp\u003e2(33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e49(28.2)\u003c/p\u003e\n\u003cp\u003e3(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e21(12.0)\u003c/p\u003e\n\u003cp\u003e1(16.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e174(100.0)\u003c/p\u003e\n\u003cp\u003e6(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003cstrong\u003e\u0026lowast; Statistically significant\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eb: Participants\u0026rsquo; clinical characteristics associated with cardiovascular disease risk by DAD criteria, Kano, Nigeria.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003cp\u003e(DAD\u0026thinsp;\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003cp\u003e(DAD 1\u0026ndash;5%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh Risk\u003c/p\u003e\n\u003cp\u003e(DAD 5\u0026ndash;10%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eART Regimen\u003c/p\u003e\n\u003cp\u003e2 NRTIs\u0026thinsp;+\u0026thinsp;1nSTI (TDF exposed)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e100(58.8)\u003c/p\u003e\n\u003cp\u003e6(60.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e50(29.4)\u003c/p\u003e\n\u003cp\u003e2(20.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e20(11.8)\u003c/p\u003e\n\u003cp\u003e2(20.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e170(100.0)\u003c/p\u003e\n\u003cp\u003e10(100.0)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026lowast;\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 NRTIs\u0026thinsp;+\u0026thinsp;1 PIs\u003c/p\u003e\n\u003cp\u003ePIs exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102(60.0)\u003c/p\u003e\n\u003cp\u003e4(40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51(30.0)\u003c/p\u003e\n\u003cp\u003e1(10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17(10.0)\u003c/p\u003e\n\u003cp\u003e5(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170(100.0)\u003c/p\u003e\n\u003cp\u003e10(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u0026lowast;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2NRTIs\u0026thinsp;+\u0026thinsp;ABV\u003c/p\u003e\n\u003cp\u003eAbacavir Exposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103(60.6)\u003c/p\u003e\n\u003cp\u003e3(30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51(30.0)\u003c/p\u003e\n\u003cp\u003e1(10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16(9.4)\u003c/p\u003e\n\u003cp\u003e6(60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170(100.0)\u003c/p\u003e\n\u003cp\u003e10(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u0026lowast;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration on ART\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;5yrs\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 5yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e21(75.0)\u003c/p\u003e\n\u003cp\u003e85(55.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4(14.3)\u003c/p\u003e\n\u003cp\u003e48(31.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3(10.7)\u003c/p\u003e\n\u003cp\u003e19(12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e28(100.0)\u003c/p\u003e\n\u003cp\u003e152(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.007\u0026lowast;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCD4 (cells/mm\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lt; 200\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e10(41.7)\u003c/p\u003e\n\u003cp\u003e96(61.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e8(33.3)\u003c/p\u003e\n\u003cp\u003e44(28.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6(25.0)\u003c/p\u003e\n\u003cp\u003e16(10.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e24(100.0)\u003c/p\u003e\n\u003cp\u003e156(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eViral load (copies/ml)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lt; 50\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e104(60.5)\u003c/p\u003e\n\u003cp\u003e2(25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e50(29.1)\u003c/p\u003e\n\u003cp\u003e2(25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18(10.4)\u003c/p\u003e\n\u003cp\u003e4(50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e172(100.0)\u003c/p\u003e\n\u003cp\u003e8(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.045\u0026lowast;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; HTN\u003c/p\u003e\n\u003cp\u003e\u0026bull; DM\u003c/p\u003e\n\u003cp\u003e\u0026bull; HTN/DM\u003c/p\u003e\n\u003cp\u003e\u0026bull; HTN/Asthma\u003c/p\u003e\n\u003cp\u003e\u0026bull; Asthma\u003c/p\u003e\n\u003cp\u003e\u0026bull; Cancer\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hepatitis B\u003c/p\u003e\n\u003cp\u003e\u0026bull; Absent.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5(15.6)\u003c/p\u003e\n\u003cp\u003e1(14.3)\u003c/p\u003e\n\u003cp\u003e1(33.3)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003cp\u003e97(71.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e14(43.8)\u003c/p\u003e\n\u003cp\u003e2(28.6)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e38(28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13(40.6)\u003c/p\u003e\n\u003cp\u003e4(57.1)\u003c/p\u003e\n\u003cp\u003e2(66.7)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e1(50.0)\u003c/p\u003e\n\u003cp\u003e1(0.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e32(100.0)\u003c/p\u003e\n\u003cp\u003e7(100.0)\u003c/p\u003e\n\u003cp\u003e3(100.0)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e2(100.0)\u003c/p\u003e\n\u003cp\u003e1(100.0)\u003c/p\u003e\n\u003cp\u003e2(100.0)\u003c/p\u003e\n\u003cp\u003e136(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBMI Kg/M\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eUnderweight\u0026thinsp;\u0026lt;\u0026thinsp;18.5.\u003c/p\u003e\n\u003cp\u003eNormal ;18.5\u0026ndash;24.9\u003c/p\u003e\n\u003cp\u003eOverweight; 25\u0026ndash;29.9\u003c/p\u003e\n\u003cp\u003eObesity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\:30\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5(83.3)\u003c/p\u003e\n\u003cp\u003e77(62.1)\u003c/p\u003e\n\u003cp\u003e16(64.0)\u003c/p\u003e\n\u003cp\u003e8(32.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1(16.7)\u003c/p\u003e\n\u003cp\u003e38(30.6)\u003c/p\u003e\n\u003cp\u003e6(24.0)\u003c/p\u003e\n\u003cp\u003e7(28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0(0.0)\u003c/p\u003e\n\u003cp\u003e9(7.3)\u003c/p\u003e\n\u003cp\u003e3(12.0)\u003c/p\u003e\n\u003cp\u003e10(40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6(100.0)\u003c/p\u003e\n\u003cp\u003e124(100.0)\u003c/p\u003e\n\u003cp\u003e25(100.0)\u003c/p\u003e\n\u003cp\u003e25(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAbdominal obesity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e24(49.0)\u003c/p\u003e\n\u003cp\u003e72(55.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e16(32.7)\u003c/p\u003e\n\u003cp\u003e46(53.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9(18.3)\u003c/p\u003e\n\u003cp\u003e13(9.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e49(100.0)\u003c/p\u003e\n\u003cp\u003e131(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHypercholesterolemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18(40.0)\u003c/p\u003e\n\u003cp\u003e88(65.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13(28.9)\u003c/p\u003e\n\u003cp\u003e39(28.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e14(31.1)\u003c/p\u003e\n\u003cp\u003e8(5.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e45(100.0)\u003c/p\u003e\n\u003cp\u003e135(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHypertriglyceridemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18(51.4)\u003c/p\u003e\n\u003cp\u003e88(60.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e13(37.2)\u003c/p\u003e\n\u003cp\u003e39(26.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4(11.4)\u003c/p\u003e\n\u003cp\u003e18(12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e35(100.0)\u003c/p\u003e\n\u003cp\u003e145(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-sensitivity CRP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e3.03mg/L\u003c/p\u003e\n\u003cp\u003eHigh\u0026thinsp;\u0026gt;\u0026thinsp;3.03mg/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e101(70.1)\u003c/p\u003e\n\u003cp\u003e5(13.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e38(26.3)\u003c/p\u003e\n\u003cp\u003e14(38.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5(3.6)\u003c/p\u003e\n\u003cp\u003e17(47.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e144(100.0)\u003c/p\u003e\n\u003cp\u003e36(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003cstrong\u003e\u0026lowast; Statistically significant\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" style=\"width: 516.266px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression analysis showing factors associated with DAD CVD risk, Kano Nigeria\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003eaOR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003eOR (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eAge(yrs)\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50yrs\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:50\\mathbf{y}\\mathbf{r}\\mathbf{s}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1.48\u0026ndash;4.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.020 *\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e2.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1.03\u0026ndash;4.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.040*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eTDF\u003c/p\u003e\n\u003cp\u003eExposed\u003c/p\u003e\n\u003cp\u003eUnexposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(0.07\u0026ndash;10.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.892\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eAbacavir\u003c/p\u003e\n\u003cp\u003eExposed\u003c/p\u003e\n\u003cp\u003eUnexposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(0.08\u0026ndash;10.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eProteases inhibitors\u003c/p\u003e\n\u003cp\u003eExposed\u003c/p\u003e\n\u003cp\u003eunexposed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(0.09\u0026ndash;14.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.189\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eART duration\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e5yrs\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;5yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(0.16 \u0026minus;\u0026thinsp;1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.511\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eViral Load (copies/ml)\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(0.03\u0026ndash;1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.159\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eBMI (Kg/M\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e30\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e(0.42\u0026ndash;2.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e0.592\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003eHypercholesterolemia\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e3.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1.68\u0026ndash;4.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e0.005 *\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 265px;\" align=\"left\"\u003e\n\u003cp\u003ehsCRP(mg/L)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e3.03\u003c/p\u003e\n\u003cp\u003e\u0026gt;3.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 22px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e4.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2.09\u0026ndash;10.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 127.266px;\" align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 491.266px;\" colspan=\"4\"\u003e\u003cstrong\u003e*Statistically significant, OR\u003c/strong\u003e- Odds ratio, \u003cstrong\u003eCI\u003c/strong\u003e- Confidence Interval.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe majority of participants with high CVD risk had hypercholesterolemia. Hypercholesterolemia remained a significant predictor of CVD after regression analysis. Participants with hypercholesterolemia have more than three times the odds of developing CVD compared to those without hypercholesterolemia [aOR 3.03, 95% CI:1.68\u0026ndash;4.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005]. Similarly, we found elevated levels of hsCRP\u0026thinsp;\u0026gt;\u0026thinsp;3.03mg/L to be a significant predictor of high CVD risk after controlling confounders. Participants with elevated hsCRP had more than four-fold higher odds of CVD risk than those with lower hsCRP levels [aOR 4.58, 95% CI:2.09\u0026ndash;10.05), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study assessed 5-year estimated CVD risk among PLWH in Nigeria, and found the majority of the participants having low to moderate risk. Our findings are consistent with reports from Brazil, Cameroon, and Togo. \u003csup\u003e18,20,21\u003c/sup\u003e The observed low to moderate risk of CVD could be attributed to the demographic profile of our participants, most of whom were young females (30\u0026ndash;49 years of age). Several studies have reported a low risk of CVD among younger age groups and females.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e However, the proportion of participants categorized as high CVD risk in our study (12.2%) was notably higher compared to reports from Brazil (2.1%), Cameroon (2.2%) and Togo (1.5%).\u003csup\u003e18,20\u003c/sup\u003e This difference may be explained by the longer duration of ART exposure among our study population, with a median of 12 years (IQR: 8, 16 years) compared to median ART durations of 6 years and 4.1 years in the Cameroon and Togo studies, respectively. Additionally, in our study population we documented higher BMI values as 13% of the participants were overweight and additional 13% were obese.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe most prevalent traditional cardiovascular risk factors among the study participants were abdominal obesity (27.2%), hypercholesterolemia (25.0%) hypertriglyceridemia (19.4%), and hypertension (17.8%). Diabetes mellitus (3.9%) was the least reported cardiovascular risk factor. These findings are consistent with Noumegni et al in Cameroon, who similarly reported dyslipidemia, abdominal obesity, and hypertension as the most frequent risk factors, while diabetes mellitus being the least prevalent.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The observed prevalence hypercholesterolemia, abdominal obesity, and hypertriglyceridemia could be linked to long-term exposure to ART medications, which are known to induce lipid abnormalities and fat redistribution.\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsistent with existing literature, we found older age to be a significant predictor of CVD risk in our population.\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Male sex was also significantly associated with increased CVD risk, with men exhibiting more than two-fold higher risk compared to women. This finding aligns with studies conducted in the general population and among PLWH.\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Men faced a heightened CVD risk due to combination of factors, including biological structure and hormonal influences.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e For instance, women\u0026rsquo;s heart and blood vessels are smaller and before menopause estrogen provides women with protection against heart disease.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e In addition, sex differences in social habits, such as lifestyle choices; smoking, alcohol consumption, and health seeking behavior also contribute to the increased CVD risk among men.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHypercholesterolemia remained a significant predictor of CVD risk in our study, corroborating findings by others. This association may be related to the direct effects of HIV itself on adipose tissue and its impact on cholesterol metabolism.\u003csup\u003e8\u0026ndash;9,11\u0026minus;12\u003c/sup\u003e We also found elevated hsCRP\u0026thinsp;\u0026gt;\u0026thinsp;3.03mg/L to be a significant predictor of CVD. This finding has been reported by others,\u003csup\u003e23,25,30\u003c/sup\u003e including Koosha et al who found hCRP biomarker to be an independent risk factor for CVD, independent of age, sex, diabetes mellitus, dyslipidemia, hypertension, obesity, and smoking.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, we were unable to ascertain causality between the estimated CVD risk and actual cardiovascular events. Second, we cannot generalize our findings to broader populations since this is a tertiary hospital-based study with a limited sample size that may not be representative of all PLWH. Despite these limitations, the study has numerous strengths. We used a globally validated tool for CVD risk estimation specific to PLWH, ensuring accurate risk estimates. In addition, our use of probability sampling minimized selection bias, and regression analysis accounted for potential confounders, strengthening the reliability of our findings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eWe found that the majority of PLWH attending a large HIV clinic in northern Nigeria had low to moderate CVD risk. The risk of developing CVD was associated with established risk factors in other studies, namely elevated hsCRP, increasing age, male sex, and hypercholesterolemia. Our findings highlight the importance of early risk stratification and targeted preventive interventions to mitigate the impact of these risk factors among PLWH. We recommend larger, long-term longitudinal studies to better ascertain the incidence of CVD in similar populations and the role of other risk factors, including specific ART regimens and their impact on cardiovascular health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eART\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntiretroviral therapy\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\"\u003eCVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eData collection on adverse effects of anti-HIV drugs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman immunodeficiency syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehsCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePooled cohort equation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLWH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeople living with HIV/AIDS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCORE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystematic coronary risk evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll participants were asked to voluntarily participate and written informed consent was obtained from participants in this study. The study was conducted according to Helsinki declaration and ethical approval from the Aminu Kano Teaching Hospital Research Ethics committee, reference number (NHREC/28/01/2020/AKTH/EC/3861).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set used for this study is available upon reasonable request. The data is not publicly available due to privacy reasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fogarty International Center (FIC) and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the U.S. National Institutes of Health (NIH) award number 1D43TW011544. The findings and conclusions are those of the authors and do not necessarily represent the official position of the FIC, NIAAA, NIH, the Department of Health and Human Services, or the government of the United States of America.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also appreciate the assistance from the African Centre of Excellence for Population Health and Policy (ACEPHAP) at Bayero University, Kano, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;None to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eZA, AKA Research conceptualization and initial manuscript writing,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZA, FIT, MB, Data collection and critical manuscript review\u003c/p\u003e\n\u003cp\u003eZA, OAL, AKS Data analysis and synthesis.\u003c/p\u003e\n\u003cp\u003eZA, OAL. Reviewed Tables, Figures and critical manuscript review.\u003c/p\u003e\n\u003cp\u003eZA, GMC, BAG manuscript writing, senior advisory input.\u003c/p\u003e\n\u003cp\u003eMB, MUS, MAH, CWW provided senior advisory input, manuscript review and final approval.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMurray CJL, Roth G, Stark B, DeCleene N, Hsu J, Johnson C et al. Global, Regional and National Burden of Cardiovascular Diseases and Risk Factors in 204 Conutries and Territories, 1990\u0026ndash;2023. 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Closing the gap: increases in life expectancy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLaughlin MM, Ma Y, Scherzer R, Rahalkar S, Martin JN, Mills C, Milush J, Deeks SG, Hsue PY. Association of viral persistence and atherosclerosis in adults with treated HIV infection. JAMA Netw Open. 2020;3(10):e2018099\u0026ndash;2018099.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMbakwem AC, Amadi CE, Ajuluchukwu JN, Kushimo OA. Trends and outcomes of cardiovascular disease admissions in Lagos, Nigeria: a 16-year review. Cardiovasc J Afr. 2023;34(3):140\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmadi CE, Grove TP, Mbakwem AC, Ozoh OB, Kushimo OA, Wood DA, Akinkunmi M. Prevalence of cardiometabolic risk factors among professional male long-distance bus drivers in Lagos, south-west Nigeria: a cross-sectional study. Cardiovasc J Afr. 2018;29(2):106\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular risk, DAD equation, people living with HIV, predictors, prevalence, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-8621829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePeople living with HIV (PLWH) exhibit two-fold higher incidence of cardiovascular disease compared to HIV-negative persons. However, predictors of cardiovascular disease risk in PLWH are still evolving. The objective of this study is to evaluate the predictors of cardiovascular disease among PLWH in Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted among adult patients attending a large HIV clinic in Kano, northern Nigeria. We used systematic sampling to recruit participants and computed their 5-year projected CVD risk using the Data collection on Adverse effects of Anti-HIV Drugs (DAD) equation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe majority of participants were female (70.6%). The estimated median 5-year CVD risk was 0.7% (interquartile range, IQR 0.4, 10). The majority of participants (58.9%) had a low risk of developing cardiovascular disease, while 28.9% had a moderate risk. Cardiovascular disease was associated with elevated high-sensitivity C-reactive protein (hsCRP)\u0026thinsp;\u0026gt;\u0026thinsp;3.03 mg/L [adjusted odds ratio, aOR: 4.58, 95% CI: 2.09\u0026ndash;10.04), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001], increasing age [aOR 2.38, 95% CI (1.48\u0026ndash;4.50), p\u0026thinsp;=\u0026thinsp;0.020], male sex [aOR 2.16, 95% CI (1.03\u0026ndash;4.53), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040] and hypercholesterolemia [aOR 3.03, 95% CI (1.68\u0026ndash;4.86), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005].\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe majority of PLWH in our setting have low to moderate risk of developing cardiovascular disease. Cardiovascular disease risk was associated with elevated hsCRP, increasing age, male sex, and hypercholesterolemia. Our findings highlight the importance of early CVD risk stratification to prevent morbidity and mortality among PLWH.\u003c/p\u003e","manuscriptTitle":"Predictors of cardiovascular disease among people living with HIV in northern Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 16:42:36","doi":"10.21203/rs.3.rs-8621829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4290388-1a8f-4575-83a5-8ea804b01e7e","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T16:42:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 16:42:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8621829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8621829","identity":"rs-8621829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}