As mortality from HIV infection declines and the HIV-infected population ages, concerns about elevated cardiovascular disease (CVD) risk have increased. Several studies have indicated an elevated risk for CVD in HIV-infected persons compared with uninfected persons.1–3 Traditional risk factors are considered major determinants of cardiovascular risk in this group.4–6 HIV infection itself also contributes to this increased risk,7 and recently, investigators have cited chronic immune activation and inflammation as possible mechanisms for this elevated propensity for CVD.8
Surrogate markers of CVD have been used in the general population to identify persons at risk before clinical signs of CVD develop, and carotid intima-media thickness (IMT) has been associated with CVD incidence.9 In HIV-infected persons, surrogate markers have been used in conjunction with the Framingham risk score (FRS) in an attempt to better quantify cardiovascular risk.10,11
We followed a cohort of 211 HIV-infected adults for 6 years, measuring 2 different surrogate markers, carotid IMT and coronary artery calcium (CAC), and lifestyle factors, C-reactive protein (CRP), and HIV disease parameters at baseline, 3 years, and 6 years, to better understand cardiovascular risk progression over time in this population. We previously published the results of our 3-year analysis of surrogate marker progression in this cohort.12 We herein report the findings from this cohort at 6 years.
SUBJECTS AND METHODS
From an original cohort of HIV-infected persons enrolled in the longitudinal study, Nutrition for Healthy Living (NFHL), 211 participants completed both a cardiovascular substudy (CARE) and its continuation study (CARE II). The NFHL study, begun in 1995, aimed to investigate nutrition and metabolism in HIV-infected adults; exclusion criteria included diabetes, uncontrolled hypertension, and myocardial infarction or stoke within the past 6 months, but participants who developed these conditions continued in the study. CARE, begun in 2000, included any consenting NFHL participant. Details of the original study are described elsewhere.13 Subjects completing baseline ultrasonography and computed tomography (CT) were recruited for CARE II, with repeat examinations at 6 years. Informed consent and approval by the Tufts Medical Center/Tufts University Institutional Review Board were obtained for NFHL, CARE, and CARE II studies.
Clinical information was collected at baseline and every 12 months (initially every 6 months) for 6 years. Clinical and laboratory data were obtained during the same visit, or closest study visit, to surrogate marker measurements. Demographic data were assessed via interviewer-administered questionnaires. Participants chose 1 designation that best defined their race from an investigator-defined list and investigators then categorized participants as white or nonwhite for the purposes of characterizing the cohort. Blood pressure measurements were obtained using a digital automatic blood pressure monitor. Highly active antiretroviral therapy (HAART) was defined as the use of 3 or more drugs, including 1 or more protease inhibitors (PIs) or nonnucleoside reverse transcriptase inhibitors. All baseline and 6-year study visits were within 6 months of baseline and 6-year imaging visits, respectively. Framingham risk was defined as low if FRS <10%, moderate if 10%–20%, and high if >20%.
Plasma levels of total cholesterol, triglycerides, and high-density lipoprotein cholesterol were measured via standard enzymatic methods; low-density lipoprotein cholesterol was measured directly via Roche Diagnostics kit (Roche Inc, Indianapolis, IN). High-sensitivity CRP, fasting glucose, and insulin were measured simultaneously with lipids. The quantitative insulin sensitivity check index (QUICKI) was calculated according to the following formula: QUICKI = 1/[log(insulin) + log(glucose)].14 CD4+ cell counts were determined by flow cytometry; HIV RNA was quantified by Roche Amplicor (Roche Inc) (lower limit of detection, 400 copies/mL).
Surrogate Marker Measurement
Baseline ultrasonography and CT images were obtained January 2002 through January 2004; 6-year images were obtained September 2009 through December 2010. Ultrasound protocols were adapted from the Cardiovascular Health Study,15 performed by centrally trained and certified ultrasonographers, recorded on super-VHS tape, and read at a central facility. Carotid IMT measurements were obtained via high-resolution B ultrasound images of the bilateral common carotid artery (CCA) and internal carotid artery (ICA). One longitudinal lateral view of the distal 10 mm of the common carotids and 3 longitudinal views of the internal carotids were used. The mean of the maximum of the near- and far-wall carotid IMT was used for the final analysis, given its association with CVD risk.16 Baseline and 6-year ultrasounds were read after study completion by a single reader (paired replicate scans) to control for temporal drift and interreader variability.
CAC scores were obtained by ultrafast CT scan of the heart with synchronized electrocardiogram to select images least affected by cardiac motion as described elsewhere.17,18 After initial automatic image selection, pixel regions within these images were considered for calcium scoring. Scores were computed using standardized scoring techniques.19 For this analysis, CAC was analyzed as a binary variable (detectable/undetectable) and as a multichotomous variable: CAC = 0, CAC >0 but <100, or CAC ≥100. In multivariate models, we defined CAC progression as a binary variable (progression/no progression) as in our 3-year analysis, using the method of Berry et al20, as follows.12 Progression was defined as CAC score >0 if CAC = 0 at baseline. If CAC score was greater than 0 but less than 100 at baseline, then progression was defined as >10 Agatston units annualized change at follow-up. If CAC ≥100 at baseline, then progression was defined as >10% annualized percent change (annualized change divided by baseline CAC score) at follow-up. At baseline and 3 years, all participants received their imaging reports; participants with >50% carotid stenosis or CAC scores >400 were informed of their results, along with their care providers.
Nonnormally distributed variables, including internal carotid IMT, are reported as median (interquartile range). Comparisons between those with and without CAC progression were conducted using χ2 or the Fisher exact test for categorical variables, the Student t test for continuous normally distributed variables, and the the Wilcoxon rank sum test for all continuous nonnormal variables. Separate multivariate regression models were generated to determine independent factors associated with change or progression of each surrogate marker. Changes in both carotid IMT measures from baseline to year 6 were entered as outcome variables in linear regression models (common carotid IMT change was normally distributed and internal carotid IMT change was nonnormally distributed); CAC progression (yes/no) was used as the outcome variable in logistic regression models. CVD-related risk factors (age, gender, smoking, lipids, glucose, FRS, and metabolic syndrome), HIV-specific risk factors (HAART, CD4+, nadir CD4+, HIV RNA, and PI use), and nontraditional risk factors (CRP and Apo-E lipoprotein) were evaluated as exposures. Because many of the exposure variables were correlated, we ran preliminary analyses on groups of related variables to identify the exposures most strongly associated with each outcome. For example, we compared models using the FRS with models using the individual components of the FRS to determine whether the FRS or its individual components were more strongly associated with change or progression. For each model, exposure variables with P < 0.20 in bivariate analyses were included in initial multivariate models. Final models of IMT change and CAC progression were then determined by stepwise regression techniques. We used baseline measures for all exposures, except pack-years of cigarette smoking and nadir CD4+, which was determined by self-report at enrollment or by laboratory measurement throughout the study, whichever was lowest. Statistical significance was defined as a P value of <0.05. Statistical analyses were performed using SAS for Windows (version 9.2; SAS Institute, Inc., Cary, NC).
These results include data on 211 participants completing both baseline and 6-year imaging studies. Baseline and 6-year characteristics are shown in Table 1. Of 345 original participants with IMT data, 134 were lost to follow-up: 55 participants died, 32 declined to further participate, 2 were missing 6-year imaging studies, and 45 could not be contacted. Participants lost to follow-up had more advanced HIV disease, were more likely to use IV drugs, had lower total cholesterol, and were less likely to have metabolic syndrome than participants who continued (Table 2). Baseline IMT values and CAC scores for the entire cohort were measured at the baseline visit and were not significantly different between those lost to follow-up and those who continued. The mean time between baseline and 6-year imaging studies was 6.5 ± 0.5 years. Interreader variability for ICA IMT at 6 years was 0.81; intrareader variability was 0.96. Interreader variability at 6 years for CCA IMT was 0.75; intrareader variability was 0.92. Surrogate markers at baseline and 6 years are shown in Table 3.
There were 198 participants with data on IMT at baseline and 6 years. Of the entire cohort, 13 participants were excluded from this analysis: 11 were missing either baseline or follow-up ultrasound and 2 had uninterpretable or unavailable baseline or follow-up ultrasounds. ICA IMT progression occurred in 43% (n = 85). The median change in ICA IMT per year of follow-up was 0.023 mm (0.013–0.045). Yearly progression was greater if ICA IMT was abnormal at baseline (0.04 vs. 0.02 mm; P < 0.001). In multivariate analysis, factors that were positively associated with ICA IMT change were age, baseline triglycerides ≥150 mg/dL (1.69 mmol/L), and pack-years of tobacco smoking (Table 4). For example, if a man of median age (45 years) with median number of pack-years of tobacco use (11) had triglycerides ≥150 mg/dL, his predicted ICA IMT change over 6 years would be 0.30 µm, on average. The same man with triglycerides <150 mg/dL (1.69 mmol/L) would have a predicted ICA IMT change of only 0.16 µm over 6 years.
CCA IMT progression occurred in 59% (n = 116). The median change in CCA IMT per year of follow-up was 0.019 mm (0.014–0.024). Yearly progression was not different if CCA IMT was abnormal at baseline (0.016 vs. 0.018 mm; P = 0.21). Table 4 shows the predictors of 6-year change in CCA IMT from the final multivariate regression model. Cholesterol, age, nadir CD4+ count, and PI use were all independently and positively associated with change in CCA IMT. Every 1-unit increase in cholesterol and age was associated with a 0.002-mm difference in 6-year change in CCA IMT. Each 100 cells per cubic millimeter increase in nadir CD4+ count was associated with a difference in 6-year change in CCA IMT of 0.005 mm. Participants using PIs at baseline had a 0.02-mm greater 6-year change in CCA IMT than those not using PIs at baseline.
CORONARY CALCIUM SCORE
There were 205 participants with data on CAC change. Six participants from the entire cohort were excluded from this analysis: 4 had coronary stents placed before follow-up, 1 lacked a baseline CT, and 1 died before follow-up CT. CAC progression occurred in 33% of the cohort. Of those with undetectable CAC at baseline, 43% (n = 41) had detectable CAC at follow-up. Of those with CAC >0 at baseline, 24% had worse CAC at follow-up (n = 26). The percentage of those with CAC >100 rose from 5% to 16% over 6 years. CAC was undetectable in 46% of the cohort at baseline and in 41% at 6 years. In multivariate analysis, baseline diabetes, HIV viral load, and HAART duration were significantly associated with increased odds of CAC progression (Table 4). Population attributable risk for CAC progression was 30% for diabetes, 25% for detectable viral load, and 40% for HAART duration >2 years.
In our cohort of HIV-infected participants, rates of ICA and CCA IMT progression have been remarkably consistent over time, from our 3-year observation (0.020 and 0.016 mm/year, respectively)12 to our current 6-year observation (0.023 and 0.019 mm/year, respectively). We also noted segment-specific differences in associations between risk factors and IMT measurements in the CCA and ICA. In addition, we observed that changes in IMT and CAC over time are associated not only with traditional risk factors but also with HIV-related factors and that these associations vary with type of surrogate marker evaluated. Differences in predictors between the 3-year and 6-year data are likely due to increased length of follow-up, the inclusion of pack-years smoking as an exposure variable, and changes in medication use over time. For example, the use of both antihypertensives and lipid-lowering agents was uncommon in our cohort at baseline (3% and 8%, respectively) but increased to 19% and 18%, respectively, at 6 years. Individual components of the FRS were more strongly associated with surrogate marker change than the composite score; however, this may be due to the overwhelming effect of age: when age at 6 years was imputed into baseline FRS, the imputed 6-year FRS was not statistically different from the FRS measured at year 6.
In a large group of 10,914 participants from the general population enrolled between ages 45 and 64 in the Atherosclerosis Risk in Communities study, average change in CCA IMT was 0.035 mm over 3 years, yielding an estimated change of 0.012 mm/yr.21 A subsequent evaluation of 12,085 participants from the Atherosclerosis Risk in Communities cohort demonstrated CCA IMT increases of 0.036–0.047 mm over 5 years, depending on gender and race, yielding an estimated change of 0.007–0.009 mm/yr.22 The 6-year progression of CCA IMT in an uninfected Finnish cohort (mean age: 37.7 years) was 0.046 mm (0.084) or an estimated change of 0.008 mm/yr.23 In a similarly aged group, 30% nonwhite, yearly CCA IMT progression was 0.016 ± 0.002 mm/yr over 5.8 years.24 In an older population (mean age: 60 years) evaluated for 3 years during a randomized trial of folate supplementation, yearly progression of CCA IMT in the control group was 0.0013 mm/yr.25 As increases in CCA IMT of as little as 1 SD have been associated with an increased hazard ratio for CVD,26 the 0.019 mm/yr CCA IMT progression in our cohort may have clinical implications for HIV-infected populations.
Of those in our cohort with undetectable CAC at baseline, 33% had detectable CAC at 3 years12 and 43% had detectable CAC at 6 years. In a study of uninfected adults aged 33–45 years, 9.6% had CAC27; in a subset of 2415, 11% had detectable CAC at baseline, 18.4% had detectable CAC 5 years later, and 16.1% had CAC progression.28 Thus, our data suggest that CVD risk surrogate marker progression occurs more rapidly and frequently in HIV infection and is not completely accounted for in this cohort by increasing age. Strikingly, many participants developed CAC scores >100.
As in our prior analyses, factors associated with IMT and CAC progression at 6 years were predominately traditional risk factors, with a few notable exceptions. In our cohort, baseline duration of HAART increased the odds ratio for CAC progression over 6 years, albeit minimally. Because calcium deposition occurs later in the atherosclerotic process than increased IMT, the effect of HAART duration may be better assessed through this more distal measure. Alternatively, as very low CAC scores may be less accurate, minimal CAC deposition may be considered progression. As reported by the Multicenter AIDS cohort study, HAART may increase the likelihood of CAC deposition but limit its extent.29 Last, duration of HAART at baseline may simply reflect cumulative exposure to HIV virus. HIV viral load was also associated with a higher odds ratio of CAC progression in our cohort; as expected, the odds ratio for viral load exceeds that obtained for HAART duration because HAART is usually required for viral load suppression.
Very similar progression of CAC, 34%, was found in an HIV-infected cohort for whom CAC progression was associated with age, low-density lipoprotein, visceral adipose tissue, and CD4 count.30 The authors used the homeostasis model assessment rather than diabetes as a measure of insulin resistance and the follow-up (11 months) was markedly less than in our study30; this may account for differences in the associations reported. Others reported an association between epicardial adipose tissue and CAC progression, which occurred in 10.4% of their cohort over a median of 18.7 months.31 Participants with metabolic syndrome had less CAC progression over 1 year if treated with metformin, particularly if CAC was >0 at baseline.32 Interestingly, metformin had no effect on CCA or on visceral, subcutaneous, or extremity adipose tissue.32 We did not evaluate fat accumulation in this study; however, we did evaluate metabolic syndrome and/or its components, including waist circumference, and these were not significantly associated with CAC progression. Although 30% of our cohort had metabolic syndrome and 9% had diabetes, only 1% used glucose-lowering agents at baseline (9% at 6 years). We did note CAC regression in some subjects; therefore, the increase in medications we observed for lipids, glucose, and hypertension may have affected CAC progression in certain participants.
Nadir CD4+ was associated with change in CCA IMT over 6 years. Although this association may seem counterintuitive, the effect size is quite small, and this may reflect the presence of more traditional risk factors associated with healthier HIV-infected persons. It has been reported that higher nadir CD4+ predicts less arterial stiffness in HIV-infected men33 and that nadir CD4+ ≤200 predicts IMT progression at 1 year34; nevertheless, to our knowledge, our study has the longest follow-up for this measure in HIV-infected persons. Baseline PI use was also associated with greater change in CCA IMT, which was also observed as a trend in the 3-year analysis. As we previously noted,12 this finding, not observed with CAC or ICA IMT progression, may reflect varying responses to HAART by arterial segment. The Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy (SUN study) group also found reduced CCA IMT progression with a nonnucleoside reverse transcriptase inhibitors–based versus PI-based regimen at 2 years.35 Nevertheless, there is limited difference in progression by any other HAART parameter to suggest that PI use is a primary determinant of CVD risk. Similarly, traditional risk factors eclipsed HIV-specific parameters in a prospective cohort of persons using PI therapy for ≥2 years.36 In addition, the use of specific PIs changed over time: amprenavir, saquinavir, indinavir, and nelfinavir use declined during the study, whereas ritonavir use rose from 8% to 34% over 6 years. The overall percentage of participants using PIs did not differ significantly between baseline and year 6.
We did not find any associations between baseline CRP and surrogate marker progression at 6 years in our cohort. Inflammation contributes to the atherosclerotic plaque, representing the first lesion in CVD.37 Prior studies have shown that CRP values are higher in HIV-infected persons.38 Recently, Hsue et al39 reported that high-sensitivity CRP was associated with IMT progression at the carotid bifurcation; this association was not found for internal or carotid IMT. Our group previously reported an association between CRP and mortality in HIV-infected persons,40 and others reported that statin use reduced CRP in HIV-infected persons on PI regimens.41 Therefore, interventions such as statins, aimed at reducing dyslipidemia, may have wider repercussions due to their effect on chronic inflammation.
This study has several limitations. Given the prospective cohort design, there is no HIV-uninfected control group. However, several other groups have reported similar increases in cardiovascular risk in HIV infection11 and we have discussed data from HIV-uninfected cohorts for these same measures above. Some studies referenced above did not report method of IMT measurement or used a different measure, such as mean–mean IMT, which may limit direct comparisons with our cohort. CAC takes time to accumulate, so categorical definitions may limit detection of changes on a finer scale; however, although the percentage of persons with detectable CAC did not markedly differ over 6 years, the percentage of those with CAC >100 tripled. In addition, we used 2 distinct imaging modalities and report 3 surrogate markers to compensate for the limitations of each individual marker. New factors related to HIV infection, immunity, and inflammation have emerged that may impact cardiovascular risk, which we were unable to explore, because these were not available at the design and funding of the original study. Differences between those participants lost to follow-up and those who continued may limit generalizability of the results to cohorts with these attributes; however, we collected demographic and clinical information for a large group over 6 years to thoroughly describe our study population and to limit confounding. Last, cause of death for all participants who died during the study is not available; nevertheless, the purpose of this study was to evaluate progression of surrogate markers of CVD risk, not clinical outcomes.
In conclusion, our data suggest that HIV-infected persons accumulate CVD risk over time beyond that experienced in the general population. Although traditional risk factors contribute to this risk, some HIV-specific factors may continue to emerge as important predictors over time. Interventions to reduce CVD risk may be effective even in those with the highest risk by FRS or surrogate markers. Continued attention to CVD risk modification in all individuals with HIV infection is essential for mitigating risk even at the earliest stages of HIV disease.
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