Risk factors and assessment for cardiovascular disease among HIV-positive patients attending a Nigerian tertiary hospital

Risk factors and assessment for cardiovascular disease among HIV-positive patients attending a Nigerian tertiary hospital

Ifeyinwa Dorothy Osegbe^1,&, Oyetunji Olukayode Soriyan¹, Abiola Ann Ogbenna², Henry Chima Okpara³, Elaine Chinyere Azinge¹

 

¹Department of Clinical Pathology, Lagos University Teaching Hospital, Idi-araba, Lagos state, Nigeria, ²Department of Haematology and Blood Transfusion, Lagos University Teaching Hospital, Idi-araba, Lagos state, Nigeria, ³Department of Chemical Pathology, University of Calabar Teaching Hospital, Cross-Rivers state, Nigeria

 

 

^&Corresponding author
Ifeyinwa Dorothy Osegbe, Department of Clinical Pathology, Lagos University Teaching Hospital, Idi-araba, Lagos state, Nigeria

 

 

 

 

Cardiovascular disease (CVD) isa group of disorders of the heart and blood vessels which can manifest as coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis and pulmonary embolism [1]. It is a primary cause of death worldwide [2] with atherosclerosis being the most common pathological process that leads to it, involving a combination of vascular endothelial dysfunction, chronic inflammation, and dyslipidaemia [3]. According to the Global burden of disease study, current predictions estimate that by the year 2020, CVD, notably coronary heart disease (CHD), will become the leading global cause of total disease burden [4].

 

Several risk factors for CVD have been identified and can be categorized into two groups: Modifiable (by lifestyle and/or pharmacotherapy) and Unmodifiable. The traditional risk factors for CVD recognized by the current National Cholesterol Education Project (NCEP) Adult Treatment Panel III (ATP III) guidelines include [3]: age: men ≥45yrs, women ≥55yrs, cigarette smoking, hypertension or use of antihypertensive medication, dyslipidaemia, diabetes mellitus, obesity, family history of premature CHD in a first degree relative male ≤55yrs or female ≤65yrs, and prior CVD in the index individual.

 

In addition, avariety of novel biochemical markers have been suggested to identify individuals at increased risk for CVD, such as: markers of inflammation e.g. high sensitivity C-reactive protein (hsCRP)[5], Homocysteine [6-8], markers of fibrinolytic and haemostatic function e.g.tissue type plasminogen activator antigen and fibrinogen respectively [9].

 

Sub-Saharan Africa is still scourged bythe Human Immunodeficiency Virus (HIV) infection, and this remains a major health concern with a prevalence of 4.9% equating to 23.5 million people,of which 874,000 are plagued with infectious diseases such as tuberculosis [10]. But with the introduction of highly active antiretroviral therapy (HAART),HIV-infected patients are surviving AIDS-related causes of death [10]. Unfortunately, this population is faced with the new challenge of non-communicable diseases because the virus as well as HAART now predisposes them to endothelial dysfunction, chronic inflammation, and dyslipidaemia [11 -13] which are necessary for atherosclerosis that may ultimately lead to CVD.

 

Metabolic abnormalities have been well-described in HIV positive patients onHAART especially those on Protease inhibitors. They include:hypertriglyceridemia, hypercholesterolemia, lipodystrophy and insulin resistance/type2 diabetes mellitus [14, 15], as well as fat redistribution, lipoatrophy or fat accumulation [16] referred to asHAART-associated morphologic and metabolic abnormality syndrome (HAMMAS) or HIV- associated lipodystrophy syndrome (HLS) [17]. These features can cause atherosclerotic CVD, thereforetreated HIV patients may develop cardiovascular complications.

 

Similarly, untreated HIV positive patients are at risk for CVD. They have increased production of pro-inflammatory cytokines e.g. C-reactive protein, tumour necrosis factor, interleukin 6 (IL-6); with concomitant decrease in anti-inflammatory cytokines e.g. IL-10, adiponectin; which promotes endothelial activation and chronic inflammation [11, 12]. These are pathophysiological factors in the development of CVD. These cytokines can also have effects on different enzymes of lipid metabolism resulting in dyslipidaemia [13], a major CVD risk factor. Therefore, it is important to identify CVD risk factors and conduct risk assessments bearing in mind the differences in the aetiological processes of CVD among treated and untreated HIV positive patients. The outcome will be beneficial in prompting early intervention. The aim of this study was to determine the risk factors and assessment for CVD among HIV-positive patients attending a Nigerian tertiary hospital.

 

 

Study design

 

This was a cross-sectional study of cardiovascular disease risk factors in HIV-positive adult patientsattending the HIV outpatient clinic of the Lagos University Teaching Hospital (LUTH), which is a tertiary hospital in Lagos, Southwest Nigeria. The study protocol was reviewed and approved by the Hospital’s Research and Ethics Committee (ADM/ DCST/HREC/200).

 

Study population

 

Male and female adult patients aged 21 to 60 years with confirmed HIV seropositivity by double enzyme linked immunosorbent assay (ELISA) and confirmatory Western blot were included as the subjects in the study.The sampling technique was by stratified, random sampling, where stratification was by whether patients had been taking antiretroviral therapy for over one year or not at all. Patients were randomly selected using a table of random numbers.Those with CD4+ T cell counts ≥350cells/mm³ and who had notreceived HAART were designated as the ‘naive’ group, while patients who had commenced HAART for at least six months were designated the ‘treated’ group.

 

Considering that there are several CVD risk factors with their respective prevalence rates [7, 18] the sample size was notcalculated using the formula: n = z²pq/d² where n = sample size, z= critical value at 95% confidence level, usually set at 1.96, p= Prevalence, q= 1-p, d= precision of 5% (0.05) [19]. Literature review showed that the sample size of 171 was used in a similar study [18].

 

But to increase the validity of our study, anextra 17% were included to cater for non-responders, giving a sample size of 200 for the subject cases. The controls were age and sex-matched adult HIV negative participants recruited during community outreaches after voluntary counseling and testing. They were includedin acase:controlratio of 2:1; however some controls were lost to follow up. Patients with secondary causes of dyslipidaemia and hyperglycaemia were excluded, including pregnant women and nursing mothers.Participants were recruited for the study after they were fully informed and written consent obtained.Confidentiality was ensured.

 

Data collection

 

Questionnaires were administered to the subjects to obtain basic demographic data and history of:HAART use- type and duration, cigarette smoking, antihypertensive and diabetic medication use. Thereafter, blood pressure was taken in the sitting position after five minutes of rest using a digital sphygmanometer. Weight in kilograms (kg) was measured to the nearest 0.1kg using a calibrated measuring scale, while height in metres (m) was measured to nearest 0.1m using a calibrated stadiometerfor the calculation of body mass index (BMI= weight/ (height)2.Waist circumference (WC) in centimetres (cm) was measured to the nearest 0.1cm using a tape rule at midway between the subcostal plane and the iliac crest. Hip circumference (HC) was also measured at the largest width and used to calculate the waist-to-hip ratio (WHR = WC/HC).

 

Specimen collection

 

The participants were requested to return in the morning after an overnight 10-12 hr fast when antecubital venous blood collection was performed.Five mls of blood was drawn into a plain vacutainer for lipid profile and hsCRP; 3mls into a fluoride oxalate vacutainer for glucose assayand 3mls into an EDTA vacutainer for homocysteine determination.The specimens were taken to the laboratory where they were centrifuged at 4000rpm for 10minutes. The supernatant, plasma or serum as the case may be, was separated out. The fluoride oxalate plasma was analyzed for glucose daily. While the EDTA plasma was aliquoted into a cryogenic storage tube for homocysteine, also the serum was aliquoted into two cryogenic storage tubes (one for lipid profile and hsCRP each) and stored at -70°C in a well-monitored freezer (NuAire, USA), for one month until the analyses were performed. Haemolysed, icteric and lipaemic samples were excluded.

 

Biochemical analysis Glucose oxidase method was used to estimate fasting plasma glucose concentration, Total cholesterol (TC), Triglyceride (TG) and High Density Lipoprotein-Cholesterol (HDL-C) were analyzed with standard enzymatic methods, while Low Density Lipoprotein-Cholesterol (LDL-C) was determined by a direct, homogenous assay on a Roche Hitachi 902 analyzer (Roche diagnostics, Germany). The LDL-C method was employed to circumvent the limitation of the routinely used Friedewald equation [20], which cannot be applied when TG >400mg/dl,as this may be seen in HIV patients on HAART. A sandwich, solid-phase ELISA was used to determinehsCRP concentrations (Diagnostic Automation Inc.,USA), as well as homocysteine (Axis Shield, Germany) and readoutspectrophotometrically with a Biorad microwell reader (Biorad, USA).

 

Quality control

 

Precision studies were carried out for the lipids and glucose using bovine precision sera (RANDOX, UK) level 2 (lot no 407SN, expiry date 2014/03) and level 3(lot no 324SE, expiry date 2013/04); and coefficient of variation (%CV) was calculated for within and between assay runs. Liquichek liquid control (BIORAD, UK) medium level was used to control the hsCRP assay(lot no 0906296, expiry date 2012-10), while a tri-level control set (Axis Shield, Germany) was used to quality control the homocysteine assay (lot no 802883611, expiry date 2012-08-08).

 

Statistical analysis

 

Data from the completed questionnaires and laboratory results were categorized into: Subjects (HIV-positive naive, HIV-positive treated) and Controls (HIV negative). They were entered unto a spreadsheet (Microsoft Office Excel 2007) where BMI and CVD risk ratios were calculated. Ten-year risk assessment for CVD was performed using Framingham risk score calculator [21]. Statistical analyses were performed using SPSS version 20 (Chicago IL, USA). Kolmogorov-Smirnov test was used to test for normality and results for hsCRP and homocysteine were log-transformed.One-way ANOVA was used to compare the mean values between the three subgroups, while the Student’s t-test was used for comparison of mean of continuous variables between the controls and each subject subgroupto distinguish their individual contributing differences.Prevalence and 95% confidence intervalsof high risk factors for CVD were performed.Z-test was performed to compare prevalences between the naïve and treated subjects. The level of statistical significance was established at p-value of <0.05.

 

 

Two hundred and eighty-three (283) participants consisting of 170 (60%) females and 113(40%) males were included in this study. The HIV naïve patients were 100 with mean ± sd age of 35.6 ± 8.1years, the HIV treated patients were 100 with mean ± sd age of 37.5± 7.8years, and the age- and sex-matched HIV negative controls were 83 with mean ± sd age of 36.6 ± 7.2 yrs. Of the treated patients, eighty-five (85) had been administered NRTI-based therapy, while 15 received PI-based regimen.

 

The mean values of CVD risk factor variables are shown in Table 1 highlighting significant differences between the 3 groups, while in Table 2 the means were compared between the controls and each subject subgroup. The prevalence of risk factor variables using cut off levels of high risk for CVD are shown in Table 3.

 

Risk assessment for CVD using Grover’s risk ratio LDL-C/HDL-C [22] revealed mean ± sd of HIV naïve 3.6 ± 2.5 and HIV treated 2.5 ± 1.4 which fall in the range of 3.3 – 3.7 that signifies increased risk of death from CVD [23]. Similarly, TC/HDL-C [22] showed mean ± sd of HIV naïve 5.9 ± 3.7 and HIV treated 4.8 ± 5.2 which are >5.6 that signifies high risk for CVD [24]. Atherogenic Index (log TG/HDL-C) [25] showed mean ± sd of HIV naïve +0.18 ± 0.3 and HIV treated -0.03 ± 0.4, wherethe more positive the ratio, the higher the risk for CVD, and vice versa [26].

 

Ten year risk assessment for CVD using Framingham risk score performed on patients that had ≥2 risk factors showedmedian (interquartile range) of HIV naïve women n=6, 1.5% (1 - 3.75%); HIV naïve men n= 9, 6.5% (2 - 11.25%); HIV treated women n=16, 1.0% (1.0 - 4.5%);and HIV treated men n=18, 7.5% (4.25 - 16.5%).Three patient data sets were excluded because they exceeded the equation variables’ limits (age=20 – 99years, TC= 130 - 320mg/dl, HDL-C= 20 – 100mg/dl, Systolic blood pressure= 90 – 200mmHg) [21].

 

 

In this study, the major independent risk factors for CVD were identified in the HIV positive patients, although their mean values were not exceptionally elevated as similarly reported by Oduola et al [27]. Despite this, measures of obesity (WC, WHR), diabetes mellitus (fasting plasma glucose), hypertension (systolic blood pressure), dyslipidaemia (hypertriglyceridaemia, low HDL-C), hsCRP, and homocysteine in the subjects were significantly higher than the HIV negative controls.

 

High TG with low HDL-C in HIV naïve subjects compared to the HIV negative controlswas demonstrated by Rose et al [28]. This corroborates our findings and may suggest that HIV infection is associated with modified HDL metabolism re-directing cholesterol to the apo B-containing lipoprotein and likely reduces the functionality of reverse cholesterol transport [29]. Also, the presence of endothelial lipase and phospholipase A2 during the vascular inflammation induced by the viral infection results in low HDL-C [30], therefore a dyslipidaemic pattern is associated with HIV infection itself. On the other hand, the metabolic changes we observed in the HIV-treated patients were in line with the features of HAMMAS, with women having greater prevalence of obesity.

 

The prevalence of dyslipidaemia among HIV treated subjects observed in another Nigerian study were lower than ours except hypertriglyceridaemia.Theirs revealed hypercholesterolaemia =28%, elevated LDL-C= 24% and hypertriglyceridaemia =35%, using desirable cut-off values of TC-200mg/dl (5.2mmol/L), LDL-C-130mg/dl (3.3mmol/L), TG-150mg/dl (1.7mmol/L) respectively [31]. The difference to our finding of hypertriglyceridaemia = 24% may be explained by the specific HAART medication the patients may be taking.

 

Protease inhibitors (PIs) are antiretroviral drugs that target the catalytic region of HIV protease and prevent it from cleaving translated precursor polyprotein to individual proteins in order to form mature viral particles, resulting in non-infectious viral particles. However, this region is homologous with regions of two human proteins that regulate lipid metabolism: cytoplasmic retinoic-acid binding protein-1 (CRABP-1) and low density lipoprotein-receptor-related protein (LRP) [16], which would result in increased apoptosis of peripheral adipocytes, decreased pre-adipocyte differentiation, decreased triglyceride storage in adipose tissue, increased VLDL production, impaired hepatic chylomicron uptake and endothelial triglyceride clearance, resulting in hypertriglyceridaemia [16,32].

 

Although patients on nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI) may develop lipodystrophy, insulin resistance, high TC and LDL-C, only modest elevations in TG have been described [33,34]. The increased prevalence of hypertriglyceridaemia in HIV patients treated with PI versus the NRTI was also demonstrated in a study by Salami et al [35]. In our study, only 15 HIV treated patients were on PIs because it is second-line medication, administered when the NRTI-based regimen fails [36]. Higher mean hsCRP concentration and prevalence were observed in the HIV naïve subjects when compared to treated patients and the controls (p <0.001), similar to reports from other studies [37,38]. This could be due to the unhindered chronic inflammation by the viral infection in these subjects which may be alleviated with HAART and antioxidants.

 

Plasma Homocysteine concentrations were significantly elevated in both naive and treated HIV subjects than the controls, corroborating findings in other studies [39,40]. We demonstrated hyperhomocysteinaemia prevalence of HIV naïve= 35% and HIV treated=39%; which were higher than what was observed in a study conducted in Italy, where the HIV treated prevalence was 28.3%, using the same cut-off value of > 12µmol/L [41]. This may be due to the reduced intake of folic acid, vitamin B6 and vitamin B12 supplements in our African population which are required for the metabolism of homocysteine.

 

Risk assessment

 

It is recommended to examine for CVD traditional risk factors in adults 20 -79 years of age who are free of atherosclerotic CVD in a bid to assess their risk for future coronary heart disease [3]. Absolute cutoff values have been assigned for each risk factor (Table 3) which give an indication of high risk for CVD.

 

Although quantitative measurements of the lipid profile can be used to ascribe risk and target treatment strategies [3], Gotto et al claim that that approach is more helpful for extreme values and not for marginal values [42] like those seen in our subjects. The thirty-person/ten-country study recommends using apoB/apoA1 ratio to evaluate lipoprotein risk for CVD [43], but a more readily available correlate would be LDL-C/HDL-C which has proven to be the best lipid-related predictor of future cardiovascular event [22, 44] than LDL-C or HDL-C alone [45]. Unfortunately, these ratios have not been validated in HIV-infected populations and may be limited because it excludes TG which is a common feature in them, especially those on HAART. Therefore, atherogenic index (AI) which is derived from log10 of serum (TG/HDL-C) and has been shown to be a surrogate of small, dense LDL particle size that predicts coronary artery disease independently, as well as type 2 diabetes mellitus, high blood pressure and metabolic syndrome [25] may be useful.

 

Using these risk indices, we observed an increasedrisk for CVD, which was exaggerated in the HIV naïve group, corroborated by a study conducted in India [46]. Similarly, a study conducted in Uganda demonstrated increased TC/HDL-C ratio in the HIV naïve (4.6) versus the HIV treated (3.4) [47]. These findings are attributed to the relatively lower HDL-C levels, which havealso been independently associated with increased CVD risk [3].

 

Although published guidelines do not recommend measurement of any of the emerging novel risk factors for the purpose of routine evaluation [48], nor for risk assessment [3]; hsCRP ≥ 2mg/L can be applied to revise risk assessment upward when risk-based treatment decisions are uncertain after quantitative risk assessments [48]. Similarly, in a meta-analysis of the association between homocysteine and CVD, it was found that for every 5umol/L increase in serum homocysteine concentration, the risk of ischaemic heart disease increased 20% to 30% [49]. Thus, clinically, the measurement of total homocysteine is considered important as a risk factor for CVD and other disorders [9, 50, 51].

 

The Framingham risk-assessment tool is a coronary prediction algorithm that provides estimates of total CHD risk (risk of developing one of the following: angina pectoris, myocardial infarction, or coronary disease death) over the course of 10 years [52]. It is used for individuals in the general population who have two or more risk factors for CVD. The factors used to estimate risk include: age, gender, TC, HDL-C, systolic blood pressure and antihypertensive medication, and cigarette smoking. Relative risk for CHD is estimated by comparison to low risk Framingham participants, and standard practice is to initiate some form of intervention when the 10-year Framingham risk exceeds 20% [52]. Our subjects had a median range of 1.0 - 7.5%. Limitations in the use of the Framingham tool for HIV-infected patients include: underestimation of cardiovascular events because key factorssuch as hypertriglyceridemia, are not used in the Framingham calculations. Also, there are direct effects of HIV and HAART on CVD risk that are not captured in the calculations [52]. Based on these, Law et alsuggest that lower 10-year CVD risk calculations should be used to guide CVD interventions among HIV-treated patients than those used for the general population [53]. If this is adopted, maybe our subjects risk scores would fall within adverse ranges.

 

The cross-sectional design of this study was a limitation because a prospective study of HIV patients followed up after commencement of HAART would have enabled us to monitor their distinct metabolic changes. Alsothe Framingham risk calculator could not compute variables beyond certain limits.

 

 

Considering the high prevalence of CVD risk factorsin both the HIV naïve and HIV treated subjects observed in this study,it would be difficult to identify which patients are at risk for CVD simply from their HIV status or their antiretroviral therapy alone. It is suggested that regular biochemical examination of the cardiovascular system in all HIV-infected patients be performed to prevent atherosclerotic vascular complications and to reduce the risk for future cardiovascular events.

 

 

The authors declare no competing interest.

 

 

IDO and OOS conceived the research topic, IDO and HCO collected the data and analyzed it, IDO and AAO drafted the manuscript, while OOS, AAO and ECA critically reviewed the manuscript. All authors consented to the final manuscript for publication.

 

 

The Principal Investigator- Dr. A.A. Akamu, and staff of the HIV/AIDS Prevention Initiative in Nigeria (APIN), at the Lagos University Teaching Hospital.

 

 

Table 1: comparison of the mean values of CVD risk factors in subjects and controls

Table 2: comparison of mean values of CVD risk factors of each subject subgroup versus controls

Table 3: comparison of prevalence of high risk factors for CVD in HIV subjects

 

 

1. World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization.Global atlas on cardiovascular disease prevention and control. 201 http://www.world-heart-federation.org/fileadmin/user_upload/documents/Publications/Global_CVD_Atlas.pdf. Accessed 22nd February 2015. PubMed | Google Scholar


2. World Health Organization. Causes of death 2008: data sources and methods. 2011. http://www.who.int/healthinfo/global_burden_disease/cod_2008_sources_ methods.pdf. Accessed 7th April 2015. PubMed | Google Scholar


3. 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):2486-2947. PubMed | Google Scholar


4. World Health Organization. The Global Burden of Disease: 2004 Update. 2008.http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf.Accessed 15th March 2015. PubMed | Google Scholar


5. Koenig W, Sund M, Frohlich M, Fischer HG, Lowel H, Doring A et al. C-reactive protein, a sensitive marker of inflammation, predicts future risk of coronary heart disease in initially healthy middle-aged men: results from the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) Augsburg Cohort Study, 1984 to 1992. Circulation. 1999; 99(2):237-242. PubMed | Google Scholar


6. Osunkalu VO, Onajole AT, Odeyemi KA, Ogunnowo BA, Sekoni AD, Ayoola GA et al. Homocysteine and folate levels as indicators of cerebrovascular accident. J Blood Med. 2010; 1:131-134. PubMed | Google Scholar


7. Okubadejo NU, Oladipo OO, Adeyemoye AA, Awosanya GO, Danesi MA. Exploratory study of plasma total homocysteine and its relationship to short term outcome in acute ischaemic stroke in Nigerians. BMC Neurol. 2008; 8(1):26-32. PubMed | Google Scholar


8. Akpalu AK, Nyame PK. Plasma homocysteine as a risk factor for strokes in Ghanaian adults. Ghana Med J. 2009; 43(4):157-163. PubMed | Google Scholar


9. Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the production of cardiovascular disease in women. N Eng J Med. 2000; 342(12):836-843. PubMed | Google Scholar


10. Joint United Nations Programme on HIV/AIDS (UNAIDS). UNAIDS report on global AIDS epidemic. 2012. http://www.unaids.org/sites/default/files/media_asset/20121120_UNAIDS_Global_Report_2012_with_annexes_en_1.pdf. Accessed 22nd February 2015. PubMed | Google Scholar


11. Chi D, Henry J, Kelley J, Thorpe R, Smith JK, Krishnaswamy G. The effect of HIV infection on endothelial function. Endothelium. 2000; 7(4):223-242. PubMed | Google Scholar


12. Guzik TJ, Mangalat D, Korbut R. Adipocytokines-novel link between inflammation and vascular function. J Physiol Pharmacol. 2006; 57(4):505-528. PubMed | Google Scholar


13. Keréveur A, Cambillau M, Kazatchkine M, Moatti N. Lipoprotein anomalies in HIV infections. Ann Med Interne (Paris). 1996; 147(5):333-343. PubMed | Google Scholar


14. Vergis EN, Paterson DL, Wagener MM, Swindells S, Singh N. Dyslipidaemia in HIV-infected patients: association with adherence to potent antiretroviral therapy. Int J STD AIDS. 2001;12(7):463-468. PubMed | Google Scholar


15. Carr A, Samaras K, Burton S, Law M, Freund J, Chrisholm DJ et al. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS. 1998; 12(7):F51-58. PubMed | Google Scholar


16. Panse I, Vasseur E, Raffin-Sanson ML, Staroz F, Rouveix E. Lipodystrophy associated with protease inhibitors. Br J Dermatol. 2000; 142(3):496-500. PubMed | Google Scholar


17. Fauci AS, Lane HC. Human immunodeficiency virus (HIV) disease: AIDS and related disorders- diseases of the endocrine and metabolic disorders. In: Harrison’s principles of internal medicine -15th ed. 2001. New York. McGraw-Hill. Google Scholar


18. Guimaraes MM, Greco DB, Fiqueiredo SM, Foscolo RB, Oliviera AR Jr, Machado LJ. High sensitivity C-reactive protein levels in HIV-infected patients treated or not with antiretroviral drugs and their correlation with factors related to cardiovascular risk and HIV infection. Atherosclerosis. 2008; Dec 201(2): 434-439. PubMed | Google Scholar


19. Areoye MO. Research methodology with statistics for health and social sciences. 2004. Nathadex publishers. Ilorin. Google Scholar


20. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without the use of the preparative ultracentrifuge. Clin Chem. 1972 Jun; 18(6):499-502. PubMed | Google Scholar


21. National Heart, Lung, and Blood Institute. Ten year cardiovascular risk calculator-risk assessment tool for estimating ten year risk of having a heart attack. 2008. http://www.nhlbi.nih.gov/health-pro/guidelines/current/cholesterol-guidelines/quick-desk-reference-html/10-year-risk-framingham-table. Accessed 17th July 2015. PubMed | Google Scholar


22. Grover SA, Levington C, Paquet S. Identifying adults at low risk for significant hyperlipidemia: a validated clinical index. J ClinEpidemiol. 1999;52(1):49-55. PubMed | Google Scholar


23. Cullen P, Schulte H, Assmann G. The Munster Heart Study (PROCAM): Total mortality in middle-aged men is increased at low total and LDL-cholesterol concentration in smokers but not in nonsmokers. Circulation. 1997; 96(7): 2128-2136. PubMed | Google Scholar


24. Kinosian B, Glick H, Garland G. Cholesterol and coronary heart disease: predicting risks by levels and ratios. Ann Intern Med. 1994; 121(9): 641-647. PubMed | Google Scholar


25. Onat A, Can G, Kaya H, Hergen? G. Atherogenic index of plasma (log10 triglyceride/high-density lipoprotein-cholesterol) predicts high blood pressure, diabetes, and vascular events. J Clin Lipidol. 2010; 4(2):89-98. PubMed | Google Scholar


26. Dobiáová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FERHDL). ClinBiochem. 2001 Oct; 34(7):583-588. PubMed | Google Scholar


27. Oduola T, Akinbolade AA, Oladokun LO, Adeosun OG, Bello IS, Ipadeola TI. Lipid profiles in people living with HIV/AIDS on ARV therapy in an urban area of Osun state, Nigeria. World J Med Sciences. 2009; 4(1):18-21. PubMed | Google Scholar


28. Rose H, Hoy J, Woolley I, Tchoua U, Bukrinsky M, Dart A et al. HIV Infection and High Density Lipoprotein Metabolism. Atherosclerosis. 2008; 199(1): 79-86. PubMed | Google Scholar


29. Anastos K, Lu D, Shi Q, Tien PC, Kaplan RC, Hessol NA et al. Association of serum lipid levels with HIV serostatus, specific antiretroviral agents, and treatment regimens. J Acquir Immune Defic Syndr. 2007; 45(1):34-42. PubMed | Google Scholar


30. Rader DJ. Molecular regulation of HDL metabolism and function: implications for novel therapies. J Clin Invest. 2006; 116(12):3090-3100. PubMed | Google Scholar


31. Lesi OA, Soyebi KS, Eboh CN. Fatty liver and hyperlipidemia in a cohort of HIV-positive Africans on highly active antiretroviral therapy. J Natl Med Assoc. 2009;101(2):151-155. PubMed | Google Scholar


32. Mooser V, Carr A. Antiretroviral therapy-associated hyperlipidaemia in HIV disease. Curr Opin Lipidol. 2001; 12(3):313-319. PubMed | Google Scholar


33. Lundgren J, Reiss P, Worm S, Weber R, El-Sadir W, De Wit S et al. Risk of myocardial infarction with exposure to specific antiretrovirals from the PI, NNRTI and NRTI drug classes: the D:A:D study. In:16th conference on Retroviruses and opportunistic infections. 2009. Feb 8-11. Montreal, Canada. Google Scholar


34. Friis-Møller N, Weber R, Reiss P, Thiébaut R, Kirk O, d’Arminio Monforte et al. DAD study group: Cardiovascular disease risk factors in HIV patients-association with antiretroviral therapy. AIDS. 2003; 17(8):1179-1193. PubMed | Google Scholar


35. Salami AK, Akande AA, Olokoba AB. Serum lipids and glucose abnormalities in HIV/AIDS patients on antiretroviral therapies. West Afr J Med. 2009; 28(1):10-15. PubMed | Google Scholar


36. Federal Ministry of Health. Guidelines for the use of antiretroviral (ARV) drugs in Nigeria. 2007; 38-41. Google Scholar


37. Hsue PL, Younes N, Martin J, Deeks S, Waters D. C-reactive protein levels in patients with HIV: A marker of cardiovascular risk or chronic infection?In:12th conferences on retroviruses and opportunistic infections. 2005. Feb 22-25. Boston. Abstract 864. Google Scholar


38. Kristoffersen US, Kofoed K, Kronborg G, Giger AK, Kjaer A, Lebech AM. Reduction in circulating markers of endothelial dysfunction in HIV-infected patients during antiretroviral therapy. HIV Med. 2009; 10(2):79-87. PubMed | Google Scholar


39. Abdollahi A, Shoar TS. Hyperhomocysteinemia in HIV-Infected Individuals: Correlation of a frequent prothrombotic factor with CD4+ Cell Count. Oman Med J. 2012; 27(3):224-227. PubMed | Google Scholar


40. Bernasconi E, Uhr M, Magenta L, Ranno A, Telenti A; Swiss HIV Cohort Study. Homocysteinaemia in HIV-infected patients treated with highly active antiretroviral therapy. AIDS. 2001; 15(8):1081-1082. PubMed | Google Scholar


41. Guaraldi G, Ventura P, Garlassi E, Orlando G, Squillace N, Nardini G et al. Hyperhomocysteinaemia in HIV-infected patients: determinants of variability and correlations with predictors of cardiovascular disease. HIV Medicine. 2009;10(1):28-34. PubMed | Google Scholar


42. Gotto Am, Whitney E, Stein EA, Shapiro DR, Clearfield M, Weis S et al. Relation between baseline and on-treatment lipid parameters and first acute major coronary events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). Circulation. 2000; 101(5):477-484. PubMed | Google Scholar


43. Barter PJ, Ballantyne CM, Carmena R, Cabezas MC, Chapman MJ, Couture P et al. Apo B versus cholesterol in estimating cardiovascular risk and in guiding therapy: report of the thirty-person/ten-country panel. J Intern Med. 2006; 259(3):247-258. PubMed | Google Scholar


44. Fernandez ML, Webb D. The LDL to HDL cholesterol ratio as a valuable tool to evaluate coronary heart disease risk. Jour Am Col of Nutr. 2008; 27(1):1-5. PubMed | Google Scholar


45. Manninen V, Tenkanen L, Koskinen P, Huttunen JK, Mantarri M, Heinonen OP et al. Joint effects of serum triglyceride and LDL-cholesterol and HDL-cholesterol concentration on coronary heart disease risk in the Helsinki Heart study: Implications for treatment. Circulation. 1992; 85(1):37-45. PubMed | Google Scholar


46. Kumar A, Sathian B. Assessment of lipid profile in patients with human immunodeficiency virus without antiretroviral therapy. Asian Pacific J tropical disease. 2011; 1(1):24-27. PubMed | Google Scholar


47. Buchacz K, Weidle PJ, Moore D, Were W, Mermin J, Downing R et al. Changes in lipid profile over 24 months among adults on first-line highly active antiretroviral therapy in the home-based AIDS care program in rural Uganda. J Acquir Immune Defic Syndr. 2008; 47(3):304-311. PubMed | Google Scholar


48. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, DÁgostino RB, Gibbons R et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/ American Heart Association task force on practice guidelines. Circulation. 2014; 129:S49-S73. Google Scholar


49. Wald DS, Law M, Morris JK. Homocysteine and cardiovascular disease: evidence on causality from a meta-analysis. BMJ. 2002; 325(7374):1202-1206. PubMed | Google Scholar


50. Christen WG, Ajani UA, Glynn RJ, Hennekens CH. Blood levels of homocysteine and increased risks of cardiovascular disease: causal or casual? Arch Intern Med. 2000;160(4): 422-434. PubMed | Google Scholar


51. Refsum H, Smith AD, Ueland PM, Nexo E, Clarke R, McPartlin J et al. Facts and recommendations about total homocysteine determinations: an expert opinion. Clin Chem. 2004; 50(1):30-32. PubMed | Google Scholar


52. Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97(18):1837-1847. PubMed | Google Scholar


53. Law MG, D'Armino Monforte A, Friis-Moller N, Weber R, El-Sadir W, Reiss P et al. Cardio- and cerebrovascular events and predicted rates of myocardial infarction in the D:A:D Study. In: Program and abstracts of the 11th Conference on Retroviruses and Opportunistic Infections. 2004. February 8-11. San Francisco, California. Abstract 737. Google Scholar