The introduction of highly active antiretroviral therapy has led to a marked reduction in HIV/AIDS-related mortality [1–3]. Accompanying this increased longevity, cardiovascular disease (CVD) has emerged as a significant cause of morbidity and mortality in those with treated HIV infection [4–6].
Although continuous antiretroviral therapy appears to be associated with a lower rate of CVD than interrupted therapy [7,8], the extent to which CVD is accelerated due to HIV infection is not clear. In one review of a large registry, myocardial infarction (MI) hospitalization rates were increased 1.75-fold in those with HIV infection, with a stronger association seen in women [relative risk (RR) = 2.98] than in men (RR = 1.40) . In a study using MediCal claims data, coronary heart disease prevalence was significantly higher only in younger participants with HIV infection compared with controls (men up to age 34 years and women up to age 44 years) . In contrast, the adjusted risk of MI was lower in some older age groups of HIV-infected participants compared with controls (>age 45 years). A third study of a health maintenance organization database found that the age-adjusted rates of hospitalization for coronary artery disease or MI were approximately 50–70% higher in HIV-infected men compared with control men . One limitation of such studies is the inability to adjust adequately for traditional cardiovascular risk factors such as smoking [9,11]. Several studies have found that the prevalence of smoking is higher in those with HIV infection [11–14], which makes it difficult to interpret studies that relied on registries and databases that did not have complete smoking data.
Furthermore, both HIV infection and the antiretroviral drugs used in HIV therapy have adverse effects on metabolic parameters that are known to contribute to CVD (reviewed in ). HIV infection is associated with decreased high-density lipoprotein (HDL) and low-density lipoprotein (LDL) and increased triglycerides and very low density lipoprotein (VLDL). Some antiretroviral drugs induce hypertriglyceridemia, insulin resistance and diabetes. Most combination drug regimens restore LDL, but not HDL to normal levels.
Measurement of intima-medial thickness (IMT) in the carotid artery by ultrasound has been shown to be strongly associated with MI and coronary artery disease events [16,17]. Although carotid IMT (cIMT) or presence of plaque has been used in many studies to assess factors contributing to atherosclerosis in HIV infection (reviewed in ), only a few studies have included control groups. Although some of those studies found an independent association of HIV infection with cIMT in the common carotid and the bulb region [19,20], most examined only the common carotid, not the internal or bulb region, and did not find a significant association of HIV infection with IMT after adjustment for other CVD risk factors [21–25]. For example, in a study of 168 HIV-infected participants and 68 HIV-uninfected participants, a two-fold higher risk of lesions was found in the common carotid in HIV infection, but the risk ratio was reduced to 1.4-fold (P = 0.32) after adjustment for sex, age, lipids and smoking . However, a key limitation of many of these previous studies is the relatively small sample size of control participants, which limits the ability to detect independent associations with HIV infection. Of the two largest studies, one found an independent association of HIV infection with greater IMT in the common carotid and bulb after adjusting for demographics and traditional CVD risk factors , whereas the other found no evidence for an association of HIV infection with greater common cIMT after adjusting for demographics, traditional CVD risk factors and a variety of nontraditional CVD risk factors .
Therefore, a major unanswered question is whether the effect of HIV infection on atherosclerosis is mediated by traditional CVD risk factors. A primary aim of the second examination of the study of Fat Redistribution and Metabolic Change in HIV Infection (FRAM) was to evaluate this research question using cIMT . To that end, we compared measurements of IMT in the common and internal/bulb regions of the carotid arteries in 433 HIV-infected participants from FRAM with those of 5749 control participants of similar age from FRAM and from the Multi-Ethnic Study of Atherosclerosis (MESA). We assessed the association of HIV infection with cIMT after adjustment for demographics and traditional CVD risk factors (age, sex, race, smoking, diabetes, blood pressure and lipids).
The FRAM study was initially designed to evaluate the prevalence and correlates of changes in fat distribution, insulin resistance and dyslipidemia in a representative sample of HIV-infected participants and HIV-seronegative controls in the United States . The second examination added measurements of cIMT to study preclinical atherosclerosis in HIV infection and incorporated data from the MESA study as additional controls. The methods of the FRAM study have been described in detail previously . FRAM and MESA study protocols were approved by institutional review boards at all respective sites.
The first FRAM examination enrolled 1183 HIV-infected participants and 297 HIV-uninfected controls from 2000 to 2002. Control participants were recruited from two centers of the Coronary Artery Risk Development in Young Adults (CARDIA) study. The second examination, which included cIMT was conducted approximately 5 years later and examined 581 HIV-infected participants and 246 HIV-uninfected controls that had been seen at the first examination (three control participants were excluded, because they were found to be HIV infected). Controls from the MESA study who had IMT preformed and matched inclusion criteria were also used for controls in this analysis. All three cohorts were nationally representative and the control groups were population based.
Fat Redistribution and Metabolic Change in HIV Infection HIV-infected study sample
HIV-infected participants were initially recruited from 16 HIV or infectious disease clinics or cohorts, and were demographically nationally representative . By the second examination, the FRAM HIV-infected participants were highly treated, with 97% having received some form of antiretroviral therapy and 94% having been on highly active antiretroviral therapy.
Fat Redistribution and Metabolic Change in HIV Infection Coronary Artery Risk Development in Young Adults study sample
CARDIA participants were originally recruited in 1985–1986 as a population-based sample of healthy 18–30-year-old white and African–American women and men to longitudinally study cardiovascular risk factors . At the time of the second FRAM examination, the CARDIA controls from the original FRAM study were 37–50 years old.
Fat Redistribution and Metabolic Change in HIV Infection Multi-Ethnic Study of Atherosclerosis study sample
An additional pool of control participants from the MESA study was included to supplement the control participant pool with individuals in the upper age range (45–78 years) of the HIV-infected FRAM participants. MESA was initiated in July 2000 to investigate the prevalence, correlates and progression of CVD in a population-based sample of 6814 men and women aged 45–84 years who were free of clinical CVD at enrollment; they were recruited from six US field centers .
To ensure comparability of the HIV-infected and control groups, participants included in this analysis were restricted to white, African–American, and Hispanic men and women aged 37–78 years who were free of clinical CVD at the time of the ultrasound scan. All HIV-infected (n = 433), MESA control (n = 5521) and FRAM control (n = 228) participants who met these criteria and had IMT measurements available were included in the analysis. Rather than age-matching participants from MESA, we used the entire cohort with available IMT measurements in this age range and adjusted for age as a covariate. Sensitivity analysis revealed similar findings for the association between HIV infection and IMT when the analysis was limited to HIV-infected participants and controls matched for age, sex and race/ethnicity (data not shown).
Demographic information, personal and family medical history, smoking and current medication use were assessed by structured questionnaires in the HIV-infected participants and controls [26,28]. Height, weight and blood pressure were measured by standardized protocols. A fasting venous blood sample was collected from participants for measurement of glucose and lipids. We classified participants as having diabetes if they had a fasting blood glucose level of 126 mg/dl (7.0 mmol/l) or more or reported use of insulin or oral hypoglycemic medication.
Assessment of carotid intima-medial wall thickness
Trained sonographers at each field center performed B-mode ultrasonography of the right and left near and far walls of the common carotid and the internal carotid plus bulb region of participants from MESA (visit 1: 2002), FRAM HIV-infected participants (visit 2: 2004–2007) and FRAM controls (visit 2: 2005–2006). A standardized protocol for all three studies was developed by the Ultrasound Reading Center (Tufts Medical Center) [16,29]. All sonographers were required to be trained and certified by the Tufts Ultrasound Reading Center, including review of scans. Manual tracings were used to compare segments. Ultrasound images were analyzed centrally at the Ultrasound Reading Center to calculate maximum near-wall and far-wall cIMT at each arterial site. The maximal wall thickness of the common carotid artery was computed as the mean of the maximum cIMT of the near and far walls of the right and left sides; available measurements of the common carotid ranged from one to four wall locations. Maximal cIMT of the internal carotid artery was computed in the same way and included the bulb region; available measurements ranged from one to 12 locations for FRAM HIV-infected participants and MESA participants and one to 16 locations for FRAM-Control participants. The greater number of measurements on FRAM-Control participants is due to a slightly different FRAM-Control reading protocol whereby separate measurements were attempted for the internal carotid artery and the carotid artery bulb (eight locations each), compared with MESA and FRAM HIV-infected, which analyzed these anatomic sites as a single entity using 12 measurements.
Readers overlapped between both FRAM and MESA images and FRAM-Control and MESA images, which enabled us to adjust our statistical models for possible reader effects . We had replicate readings (the same ultrasound scan read by two different readers) for a subset of HIV-infected participants (n = 175) and controls (n = 134); we included these replicates in the analysis, using generalized estimating equations (GEEs) to account for the repeated measures. We tested for residual differences between study control populations in a combined MESA/FRAM-Control multivariable model for the participants who fell in the shared age range. This estimated adjusted differences between the two control groups of −0.032 mm [95% confidence interval (CI) −0.138 to 0.074, P = 0.56] for internal and −0.001 mm (95% CI −0.044 to 0.042, P = 0.96) for common. Although the CIs are too wide to completely exclude a substantial difference between the two groups, the estimates suggest that any difference is likely to be too small to be of practical importance. We, therefore, combined the two control groups in the primary HIV vs. control multivariable models.
We used linear GEEs (with robust CIs) to model the association between HIV infection and both common and internal/bulb cIMT. All models were adjusted for IMT reader. We applied a staged modeling approach for each cIMT outcome, first fitting a model with HIV infection alone, next fitting a model including HIV infection and demographic characteristics (age, sex and race/ethnicity) and finally fitting a model including demographics and traditional CVD risk factors (smoking status, diabetes, SBP, DBP, total cholesterol and HDL cholesterol). The small amount of missing data (∼2%) was handled using a complete case approach. Nonlinear age transformations were tested to confirm that a linear adjustment for age was adequate. All analyses were performed using PROC GENMOD in SAS version 9.1.3 (SAS Institute Inc. Cary, North Carolina, USA).
The characteristics of the participants studied are presented in Table 1. The age range of the participants in this analysis was restricted to 37–78 years, where there was overlap between the HIV-infected participants and controls. The mean age of HIV-infected participants was 49 years and that of controls was 61 years. Among HIV-infected participants, 70% were men, 51% white, 42% African–American and 7.2% Hispanic. Due to the design of the control cohorts, the controls were evenly divided by sex and had a higher proportion of Hispanics than the HIV-infected cohort.
Current smoking was more common in HIV-infected participants (36%) than in controls (15%, P < 0.0001). Prevalence of diabetes and use of medications for hypertension were similar in the HIV-infected participants and controls. Differences in lipids and lipoproteins between HIV and controls are similar to previous reports [31,32]. HDL and total cholesterol levels were lower and triglyceride levels were higher in HIV infection.
Carotid intima-media thickness measurements
Despite their younger age, mean unadjusted IMT for the internal carotid artery including the bulb region (Table 2) was higher in HIV-infected participants than in controls (1.17 ± 0.50 vs. 1.06 ± 0.58 mm, P < 0.0001). After multivariable adjustment for IMT reader and demographic factors (age, sex and race), the association of HIV infection with greater internal IMT was strengthened (0.188 mm, P < 0.0001). This HIV association was somewhat attenuated after adjusting for the remaining traditional CVD risk factors (smoking, diabetes, blood pressure, total cholesterol and HDL cholesterol), but the HIV-infected participants still had greater IMT (0.148 mm, P < 0.0001). Although HIV infection was associated with greater internal cIMT in both women (0.200 mm) and men (0.128 mm), this association was stronger in women (P = 0.046, test for interaction).
A similar pattern was seen in the common carotid artery, although the differences were smaller in magnitude (Table 2). Unadjusted mean common IMT levels were similar in HIV-infected participants and controls, but after adjustment for IMT reader and demographic characteristics, HIV infection was associated with 0.043 mm greater IMT (P = 0.0004). The HIV association was again somewhat attenuated after controlling for traditional risk factors to 0.033 mm, but remained statistically significant (P = 0.005). The observed HIV association was substantially stronger in women (0.075 mm) than in men for common cIMT (0.013 mm, test for interaction: P = 0.0034). The association of HIV infection with cIMT was similar when analysis was limited to either the MESA controls or the CARDIA controls and HIV-infected participants of the same age range (data not shown).
The multivariable adjusted associations of demographic factors, traditional CVD risk factors and HIV infection with both IMT measures are presented in Table 3. For the internal carotid, the association of HIV infection (0.148 mm) was similar to that of current smoking (0.173 mm), of diabetes (0.117 mm) and of a 30 mm higher SBP (0.162 mm = 3× 0.054/10 mm blood pressure). Similar results were found in the model for common carotid; the association of HIV infection (0.033 mm) with common cIMT was similar to that of smoking (0.020 mm), diabetes (0.027 mm) and a 10 mm higher SBP (0.025 mm). For both common and internal cIMT models, after SBP entered the model, the DBP coefficient became negative, consistent with the known effects of pulse pressure on cIMT [33,34].
Although some effects, such as that of HIV infection, were stronger in one sex than in the other (Supplementary Tables 1 and 2), we report results from the pooled model in Table 3 because the directions of their associations with IMT were the same in men and women for most factors. Within each sex, the magnitude of the HIV association was also similar to that of traditional risk factors such as smoking. Indeed, for the common carotid in women, the association of HIV infection (0.077 mm) with IMT was four times that of smoking (0.018 mm). The weakest association of HIV infection with IMT was found in the common carotid for men (0.013 mm), where the association of HIV did not reach significance and was similar in magnitude to that of smoking (0.017 mm), but less than that of diabetes (0.036 mm).
As IMT is known to be right skewed, we performed a sensitivity analysis to address the question of whether a small group of individuals is driving the overall mean. We excluded participants with large dfbetas (a measure of how much impact each observation has on the predictor of interest). When we removed the 1% of individuals with the largest outliers (identified by the highest absolute value of cluster dfbeta), the HIV effect remained strong: 0.180 mm for internal cIMT (95% CI 0.111, 0.249; P < 0.0001) and 0.047 mm for common cIMT (95% CI 0.027, 0.067; P < 0.0001). Additionally, after removing outliers, the fully adjusted association between HIV and common cIMT remained statistically significant for both men (0.028 mm; 95% CI 0.004, 0.053; P = 0.0235) and women (0.080; 95% CI 0.046, 0.115; P < 0.0001).
We also considered the association of HIV infection with dichotomized IMT (internal or common IMT > 1.5 mm). Similar to the analysis considering IMT as a continuous measure, we found that the association of HIV infection with elevated cIMT was attenuated somewhat after adjustment for traditional CVD risk factors, but that it remained strong and statistically significant [relative risk (RR) = 1.82, 95% CI 1.20, 2.77; P = 0.0047 for internal or common IMT > 1.5 mm]. We also looked at the prevalence of stenosis and found that after adjustment for demographic and traditional risk factors, there was a higher risk of stenosis in HIV-infected participants compared with controls (RR = 1.73, 95% CI 1.30, 2.30; P = 0.0002).
We have shown that preclinical atherosclerosis as quantified by ultrasound measurements of cIMT is increased in HIV-infected participants compared with controls, even after adjusting for demographics and traditional CVD risk factors. Thus, although HIV infection and its therapies are associated with increases in several traditional CVD risk factors (e.g., decreased HDL cholesterol, increased non-HDL cholesterol and diabetes) , there is an additional effect of HIV infection beyond that of traditional CVD risk factors. The association of HIV infection with cIMT is similar in magnitude to that of traditional risk factors such as diabetes and smoking. The HIV association is of the magnitude of a 5–9-year increase in age. The association of HIV infection with cIMT was stronger in women than in men. These results have several implications for both research and clinical practice.
Previous findings from analyses of registries and administrative databases have suggested that the rates of CVD or MI are higher in HIV-infected patients than in uninfected patients [9,11]. Such studies, however, are unable to fully adjust for differences in the distribution of traditional CVD risk factors between HIV-infected participants and control participants. Given the many effects of HIV infection and antiretroviral drugs on metabolic parameters , the finding that there is an association of HIV infection with atherosclerosis beyond that explained by metabolic disturbances has important implications for risk assessment in patients. The residual increase in atherosclerosis as measured by IMT implies increased independent risk of coronary artery disease and stroke associated with HIV infection and/or its therapies. Now that patients with HIV infection are living longer, these data suggest that clinicians should consider HIV infection as a candidate CVD risk factor. This may be especially important for HIV-infected patients who have an intermediate level of risk as determined by predictive equations such as that from the Framingham Study [35,36].
The finding that a stronger association of HIV infection with cIMT is seen in women compared with men is consistent with one registry study . In that study, HIV-infected women had significantly more cardiovascular events than women controls, but the association in men was much smaller and did not reach statistical significance. Our data suggest that HIV infection may confer increased CVD risk in both sexes, but to a greater extent in women. In women, the association of HIV infection with IMT was greater than that of smoking; in men, the association of HIV infection with IMT was similar to that of smoking.
In the non-HIV-infected population, the effect of some risk factors, such as diabetes, on CVD are also more marked in women than men . For example, after adjustment for other risk factors, diabetes was associated with a 70% higher risk of death from CVD in diabetic men compared with men without diabetes, and 230% higher risk in diabetic women compared with women without diabetes. Sex differences have also been observed for associations of risk factors with cIMT; for example, metabolic syndrome is more of an independent risk factor for greater cIMT in women than in men .
Furthermore, we have previously shown that white women have the most atherogenic lipid profile among HIV-infected demographic groups, as LDL levels are not decreased by HIV infection [31,32]. Those findings raise the possibility of additional increased risk in HIV-infected women relative to HIV-infected men mediated by traditional CVD risk factors.
We found that the HIV association was stronger with internal cIMT (including the bulb region) than with common cIMT. It is, therefore, of interest that the studies which found little association of HIV infection with IMT made those measurements only in the common carotid [21–25]. For example, one study of triads of 45 participants, 89% men, found the common carotid to be 0.002 mm greater (95% CI −0.013, 0.017; P = 0.80) in HIV-infected participants not on protease inhibitors and 0.010 greater (95% CI −0.011, 0.031; P = 0.34) in HIV-infected participants continuously on protease inhibitors compared with controls . A larger study of both men and women that included 1510 controls found evidence that HIV infection was not associated with greater common cIMT after adjustment for demographic, traditional CVD risk factors and lifestyle factors, as the adjusted association was −0.007 mm for both HIV-infected men (95% CI −0.027, 0.012; P = 0.47) and women (95% CI −0.018, 0.003; P = 0.12) compared with controls . However, the other large study that included 1168 controls did find an independent association of HIV infection with cIMT, with a much larger association seen at the bifurcation (HIV = 0.250 mm, 95% CI 0.198, 1.303; P < 0.0001) than in the common carotid (HIV = 0.044 mm, 95% CI 0.021, 0.066; P = 0.0001) . The later results are comparable to our finding of an adjusted HIV association of 0.148 mm (95% CI 0.072, 0.224; P = 0.0001) in the internal carotid including the bulb and 0.033 mm (95% CI 0.010, 0.056; P = 0.005) in the common carotid. Furthermore, the earlier, smaller study finding an independent association of HIV infection with IMT also measured IMT in the bulb . The reasons for the discrepancies among these reports deserve further study, but our results suggest that future studies in HIV infection should include the internal carotid and the bulb, which may be the locations where increased atherosclerosis due to HIV infection begins.
Limitations of our study include the use of cross-sectional data for both risk factors and cIMT. Given that the effects of HIV and antiretroviral therapy on CVD risk factors are dynamic and cumulative, it might have been ideal to have longitudinal data for risk factors such as cholesterol, HDL and blood pressure. However, examination of the data from the first and second FRAM examinations shows surprisingly little change in metabolic parameters despite switches in antiretroviral therapy (data not shown). Controls ideally would have been identical except for the presence of HIV infection, but such a cohort would be impractical and perhaps impossible to develop. However, a major strength of this study was a large control group with extensive data on CVD risk factors that enabled us to adjust for relevant traditional CVD risk factors, including age, which was our major aim. Furthermore, we measured both the common carotid and the internal/bulb regions, which enabled us to explain the discrepancies in earlier reports.
Although we cannot rule out an association of HIV-related factors with IMT, we did not identify any HIV-related factor that could explain much of the HIV effect. The effects of HIV infection reported here include any effects of antiretroviral drugs that are independent of their metabolic effects. Unlike in Maggi et al., who found a higher prevalence of lesions in those with protease inhibitor therapy, we did not find a substantial association of protease inhibitor exposure (or other antiretroviral drugs or classes) with increased IMT.
In summary, even after adjusting for traditional CVD risk factors, HIV infection is associated with increased preclinical atherosclerosis as measured by cIMT. The association of HIV infection with IMT is stronger in the internal and bulb regions than in the common carotid. The association of HIV infection is more pronounced in women than in men. These results should be considered when clinicians assess CVD risk in HIV-infected patients.
Supported by NIH grants RO1-DK57508, RO1-HL74814, RO1-HL 53359, K24-AI56933, M01-RR00036, M01-RR00051, M01-RR00052, M01-RR00054, M01-RR00083, M01-RR0636, M01-RR00865 and UL1-RR024131 and contracts N01-HC-95159 through N01-HC-95165 and N01-HC-95169 from the NHLBI and with resources and the use of facilities of the VA Medical Centers in San Francisco, California, Atlanta, Georgia, New York, New York, and Washington, District of Columbia. The funding agency had no role in the collection or analysis of the data.
The funding agency played no role in the conduct of the study, collection of the data, management of the study, analysis of data, interpretation of the data or preparation of the manuscript. A representative of the funding agent participated in planning the protocol. As part of the standard operating procedures of CARDIA, the manuscript was reviewed at the NHLBI, but no revisions were requested.
Sites and investigators: University Hospitals of Cleveland (Barbara Gripshover, MD); Tufts University (Abby Shevitz, MD (deceased) and Christine Wanke, MD); Stanford University (Andrew Zolopa, MD); University of Alabama at Birmingham (Michael Saag, MD); John Hopkins University (Joseph Cofrancesco, MD, and Adrian Dobs, MD); University of Colorado Heath Sciences Center (Lisa Kosmiski, MD, and Constance Benson, MD); University of North Carolina at Chapel Hill (David Wohl, MD, and Charles van der Horst, MD*); University of California at San Diego (Daniel Lee, MD, and W. Christopher Mathews, MD*); Washington University (E. Turner Overton, MD, and William Powderly, MD); VA Medical Center, Atlanta (David Rimland, MD); University of California at Los Angeles (Judith Currier, MD); VA Medical Center, New York (Michael Simberkoff, MD); VA Medical Center, Washington DC (Cynthia Gibert, MD); St Luke's-Roosevelt Hospital Center (Donald Kotler, MD and Ellen Engelson, PhD); Kaiser Permanente, Oakland (Stephen Sidney, MD); University of Alabama at Birmingham (Cora E. Lewis, MD).
FRAM 2 Data Coordinating Center: University of Washington, Seattle (Richard A. Kronmal, PhD, Mary Louise Biggs, PhD, J.A. Christopher Delaney, PhD, and John Pearce).
Image reading centers: St Luke's-Roosevelt Hospital Center (Steven Heymsfield, MD, Jack Wang, MS, and Mark Punyanitya). Tufts New England Medical Center, Boston (Daniel H. O'Leary, MD, Joseph Polak, MD, Anita P. Harrington).
Office of the principal investigator: University of California, San Francisco, Veterans Affairs Medical Center and the Northern California Institute for Research and Development (Carl Grunfeld, MD, PhD, Phyllis Tien, MD, Peter Bacchetti, PhD, Michael Shlipak, MD, Rebecca Scherzer, PhD, Mae Pang, RN, MSN, Heather Southwell, MS, RD).
Clinicaltrials.gov ID: NCT00331448.
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