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Inflammation markers after randomization to abacavir/lamivudine or tenofovir/emtricitabine with efavirenz or atazanavir/ritonavir

McComsey, Grace A.a; Kitch, Douglasb; Daar, Eric S.c; Tierney, Camlinb; Jahed, Nasreen C.d; Melbourne, Kathleene; Ha, Belindaf; Brown, Todd T.g; Bloom, Anthonyh; Fedarko, Nealg; Sax, Paul E.i

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doi: 10.1097/QAD.0b013e328354f4fb
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HIV-infected patients experience high rates of non-AIDS complications, including cardiovascular disease (CVD), which has been linked to heightened inflammation. With effective antiretroviral therapy (ART), inflammation markers overall decrease but do not normalize [1–4]. Treatment with abacavir (ABC)-containing regimens has been associated with higher risk of CVD in some studies [5–10] but not others [11–14].

Several attempts have been made to understand this potential association, with one proposed mechanism a deleterious effect of ABC on inflammation. Whereas one observational study found an association between ABC and higher inflammation markers [5], another did not [15]. Prospective randomized studies comparing ABC to non-ABC regimens also have not demonstrated a difference in these markers [16–19]. Because of the remaining uncertainty about the differential effect of ABC vs. other nucleoside reverse transcriptase inhibitors (NRTIs) on inflammation, and the potential effect of the concomitant protease inhibitor or non-nucleoside reverse transcriptase inhibitor (NNRTI) therapy, we sought to compare changes in inflammation markers in the context of a large randomized trial.


AIDS Clinical Trials Group (ACTG) A5224s was a metabolic substudy of ACTG A5202 in which ART-naive patients at least 16 years old with HIV-1 RNA above 1000 copies/ml were randomized in a double-blinded fashion to co-formulated tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) or ABC/lamivudine (ABC/3TC), along with open-labeled efavirenz (EFV) or atazanavir/ritonavir (ATV/r). Randomization was stratified by screening HIV-1 RNA (< vs. ≥100 000 copies/ml). A secondary biomarker substudy of A5224s included all patients with available stored plasma at baseline and week 24 and/or 96, and was designed with the primary objective to compare the effect after 24 weeks of initiating ABC/3TC vs. TDF/FTC on inflammation and endothelial activation markers. Secondary objectives were to compare 24-week biomarker changes between EFV and ATV/r, and compare ABC/3TC vs. TDF/FTC and EFV vs. ATV/r on 96-week biomarker changes. A5224s main exclusion criteria were endocrine diseases including diabetes mellitus. The duration of the study was 96 weeks after the last patient enrolled into A5202. Each patient signed an informed consent, which was approved by each participating site's local Institutional Review Board.

As previously described [20], the NRTI assignment was prematurely unblinded for patients with A5202 screening HIV-1 RNA at least 100 000 copies/ml because of higher rates of virologic failure with ABC/3TC regimens. Baseline smoking status was not collected in A5224s but was available on a subset of patients co-enrolled into the observational cohort ACTG A5001.

Biomarker assays

Plasma samples were stored at −80°C without prior thawing until analysis. Assays were performed at Johns Hopkins Bayview Advanced Chemistry Laboratory, Baltimore, Maryland, USA. We measured high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), TNF-α, and the soluble receptors of TNF-α, (sTNFR-I, II), along with the endothelial activation markers soluble vascular cellular and intercellular adhesion molecules (sVCAM-1 and sICAM-1). hsCRP was measured using a highly sensitive ELISA (ALPCO Diagnostics, Windham, New Hampshire, USA). Other markers were measured using enzyme-immunosorbent assay (R&D Systems, Minneapolis, Minnesota, USA). Markers were measured in duplicate and values averaged for analysis. The intra-assay and inter-assay precisions of these assays were 1.3–7.6% coefficient of variation (CV) (average 3.3%) and 1.83–8.95% CV (average 6.89%), respectively.

Statistical analysis

The primary objective was to compare, between pooled, randomized NRTI components (ABC/3TC vs. TDF/FTC with third drug combined), changes from baseline to week 24 in sVCAM-1 and sTNFR-II. Other objectives were considered secondary. All analyses were initially performed using intent-to-treat principles based on randomized treatment assignment which used all available data and modifications to randomized treatment, and missing values were ignored. Supplemental as-treated analyses were performed which censored values after a change in the randomized NRTI (when comparing NRTI components) or NNRTI/protease inhibitor (when comparing NNRTI/protease inhibitor components). Comparisons used a factorial analysis approach in which, after assessing for treatment effect modification by the other component, the NRTI effect was assessed by combining EFV and ATV/r arms and vice versa. P values below 0.05 (<0.10 for assessing treatment effect modification) were considered statistically significant, and nominal values are reported without adjustment for multiple comparisons. Analyses were performed using SAS, version 9.2 (SAS Institute Inc., Cary, North Carolina, USA).

Within each regimen, one-sample t-tests were used to assess mean change from baseline, whereas mean comparisons between regimen components used two-sample t-tests. Analyses that adjusted for baseline factors and explored associations with biomarkers used linear regression. Due to the highly skewed distribution of the biomarker data, biomarkers were loge transformed prior to analysis. The estimated mean change from baseline of loge-transformed biomarkers was exponentiated to obtain the estimated mean fold change within a component (or arm). The estimated mean difference between components (or arms) of change from baseline in loge-transformed biomarkers were exponentiated, subtracted by 1, and multiplied by 100 to obtain the estimated percentage difference between the two mean fold changes(Δ), with TDF/FTC and EFV as reference groups for the comparisons.

The comparison of ABC/3TC and TDF/FTC with EFV and ATV/r combined (factorial analysis) was performed at each time point since there was no significant evidence that the NRTI effect differed at 24 or 96 weeks by the NNRTI/protease inhibitor component. Similarly, the comparison of EFV and ATV/r with ABC/3TC and TDF/FTC combined was performed.

In sensitivity analyses the intent-to-treat analyses on change in biomarkers from entry to weeks 24 and 96 were adjusted for the following prespecified baseline covariates that could affect inflammation, first individually, then jointly using linear regression: NNRTI/protease inhibitor (or NRTI components for NNRTI/protease inhibitor analyses), baseline biomarker level, sex, age, race/ethnicity, log10 HIV-1 RNA, CD4, BMI, smoking status (when available), hypertension, fasting glucose, LDL-cholesterol, and family history of coronary artery disease (CAD).


Patient characteristics

As previously detailed [21,22], 269 patients were randomized to one of the four regimens and were included in A5224s analysis. Of these 269 patients, 244 (91%) with available stored plasma from baseline and week 24 and/or 96 were included in this biomarker substudy. Among these 244, 61 were randomized to EFV + TDF/FTC, 64 to EFV + ABC/3TC, 57 to ATV/r + TDF/FTC, and 62 to ATV/r + ABC/3TC. Baseline characteristics are summarized in Table 1. Overall, 85% were men, 48% white non-Hispanics, and among 205 with available data, 41% were smokers. Median age was 39 years, CD4 240 cells/μl, and HIV-1 RNA 4.64 log10 copies/ml. None of the patients had a prior history of myocardial infarction, and only one had a history of stroke. Baseline characteristics were balanced across arms, except that a larger proportion of women were randomized to EFV (18%) than ATV/r (11%), and a larger proportion of hypertensive patients were randomized to ABC/3TC (21%) than TDF/FTC (11%), and similarly, EFV (22%) than ATV/r (11%). Baseline characteristics were similar between the 244 included in the biomarker substudy and the 25 A5224s participants not included (data not shown).

Table 1
Table 1:
Baseline characteristics of study participants by randomized arms.

Overall, 26 (10.7%) patients modified their randomized NRTI [4/118 (3.4%) TDF/FTC; 22/126 (17.5%) ABC/3TC] before week 24 and an additional 39 [16.0%; 13/118 (11.0%) TDF/FTC; 26/126 (20.6%) ABC/3TC] modified between weeks 24 and 96. Also, 22 (9.0%) modified their NNRTI/protease inhibitor [19/125 (15.2%) EFV; 3/119 (2.5%) ATV/r] before week 24 and an additional 40 [16.4%; 17/125 (13.6%) EFV; 23/119 (19.3%) ATV/r] between weeks 24 and 96.

At week 24, 171 (70%) had HIV-1 RNA below 50 copies/ml (70% on TDF/FTC; 70% on ABC/3TC). Among patients who had screening HIV-1 RNA at least 100 000 copies/ml, 55% on TDF/FTC and 64% on ABC/3TC had HIV-1 RNA below 50 copies/ml. Two patients experienced a myocardial infarction during the study – one at week 15 and the other at week 164. Both patients had screening HIV-1 RNA below 100 000 copies/ml and were receiving their randomized regimen of TDF/FTC + EFV at the time of the event.

Changes in plasma TNF-α and soluble TNF receptors (sTNFR-II co-primary endpoint)

At weeks 24 and 96, there was a statistically significant decrease in TNF-α, sTNFR-I, and sTNFR-II levels within all arms (P < 0.001) (Fig. 1 and Table 2), without differences between ABC/3TC and TDF/FTC or ATV/r and EFV by intent-to-treat (P ≥ 0.44) or as-treated analyses.

Fig. 1
Fig. 1:
Mean fold change in sICAM-1, sVCAM-1, TNF-α, sTNFR-I, and sTNFR-II by intent-to-treat analysis with 95% confidence intervals (CIs) for the means at weeks 24 and 96 for the four treatment arms.sICAM-1, soluble intercellular adhesion molecule; sVCAM-1, soluble vascular cellular adhesion molecule.
Table 2
Table 2:
Changes from baseline to weeks 24 and 96 in loge transformed inflammation and endothelial activation markers for all four treatment arms.

There was some evidence that the NRTI effect differed by screening HIV-1 RNA stratum for week 24 sTNFR-II (P = 0.069) and TNF-α (P = 0.093), and that the NNRTI/protease inhibitor effect differed for week 96 sTNFR-I (P = 0.048); thus analyses were conducted within each stratum for these markers. For sTNFR-II, within the low stratum, the ABC/3TC mean fold change was marginally significantly smaller than TDF/FTC at 24 weeks {0.57 vs. 0.65; Δ = −11.4% [95% confidence intervals (CI) −22.3%, 1.2%]; P = 0.073}, whereas within the high stratum it was not significantly different [0.50 vs. 0.47; Δ = 7.4% (−8.6%, 26.1%); P = 0.38]. As-treated analyses showed similar results. For week 24 TNF-α, in both the low stratum and the high stratum, the estimated mean fold change was not significantly different between ABC/3TC and TDF/FTC [0.61 vs. 0.68; Δ = −10.9% (−22.6%, 2.6%); P = 0.11 within the low stratum and 0.57 vs. 0.53; Δ = 6.6% (−8.1%, 23.8%); P = 0.39 within the high stratum]. As-treated analyses showed similar results. For week 96 sTNFR-I, within the low stratum the ATV/r estimated mean fold change was marginally significantly larger than EFV [0.95 vs. 0.89; Δ = 6.5% (−0.6%, 14.2%); P = 0.074], but similar within the high stratum [0.81 vs. 0.86; Δ = −5.6% (−14.9%, 4.8%); P = 0.28]. As-treated yielded similar results.

Changes in endothelial activation markers (sVCAM-1 co-primary endpoint)

At weeks 24 and 96, there was a statistically significant decrease in sVCAM-1 and sICAM-1 within all arms (P ≤ 0.001) (Fig. 1 and Table 2), without differences between ABC/3TC and TDF/FTC or ATV/r and EFV at either time point by intent-to-treat (P ≥ 0.14) or as-treated analyses. There was no significant evidence of an interaction between the NRTI components and the HIV-1 RNA stratum. However, for sVCAM-1 at week 24, there was evidence of an interaction between the NNRTI/protease inhibitor component and HIV-1 RNA stratum (P = 0.047). Within the low stratum, the ATV/r estimated mean fold change was marginally larger than EFV [0.72 vs. 0.66; Δ = 9.3% (95% CI −0.8%, 20.4%); P = 0.071], whereas within the high stratum, it was similar [0.55 vs. 0.60; Δ = −7.0% (−18.6%, 6.2%); P = 0.28]. As-treated analysis showed similar results.

Changes in C-reactive protein

As shown in Table 2 and Fig. 2, at week 24, within the ABC/3TC + EFV arm there was a statistically significant increase in hsCRP [estimated mean fold change 1.77 (95% CI 1.24, 2.52); P = 0.002], whereas within the TDF/FTC + ATV/r arm, there was a marginally significant decrease [0.69 (0.47, 1.00); P = 0.051]. In TDF/FTC + EFV and ABC/3TC + ATV/r arms, hsCRP did not change significantly [1.11 (0.78, 1.61); P = 0.55 and 1.13 (0.82, 1.54); P = 0.45, respectively]. At 96 weeks, hsCRP increased significantly for only ABC/3TC + EFV [1.54 (1.06, 2.23); P = 0.026].

Fig. 2
Fig. 2:
Mean fold change in hsCRP (panel a) and IL-6 (panel b) by intent-to-treat analysis with 95% confidence intervals (CIs) for the means at weeks 24 and 96 by NRTI and NNRTI/PI components, and for the four treatment arms.hsCRP, high-sensitivity C-reactive protein; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor. Week 0 and week 24 hsCRP for ABC/3TC and TDF/FTC are shown in panel c as distribution by hsCRP (mg/liter) values, with black bars representing median values, and in panel d as distribution by American Heart Association Risk categories. From bottom to top: hsCRP less than 1 mg/l (low risk), 1–3 mg/l (average risk), above 3 mg/l (high risk), and above 10 mg/l. ABC/3TC, abacavir/lamivudine; hsCRP, high-sensitivity C-reactive protein; IL6, interleukin-6; TDF/FTC, tenofovir/emtricitabine.

Changes by nucleoside reverse transcriptase inhibitor components

At weeks 24 and 96, in the ABC/3TC arms (third drugs combined) there was a statistically significant increase in hsCRP [estimated mean fold change 1.43 (95% CI 1.12, 1.83); P = 0.004 at 24 weeks and 1.36 (95% CI 1.05, 1.75); P = 0.016 at 96 weeks], whereas within the TDF/FTC arms there was no significant change from baseline at either time points (P ≥ 0.35). At weeks 24 and 96, the change in hsCRP was significantly larger for ABC/3TC than TDF/FTC [1.43 vs. 0.88; Δ = 61.5% (13.6%, 129.5%); P = 0.008 at week 24 and 1.36 vs. 0.88; Δ = 53.5% (6.9%, 120.4%); P = 0.021 at week 96]. As-treated analyses showed similar results.

At week 24 (but not 96), there was evidence of an interaction between the NRTI component and HIV-1 RNA stratum (P = 0.054). Within the high stratum the ABC/3TC mean fold change was larger than TDF/FTC [1.82 vs. 0.74; Δ = 142.5% (39.8%, 330.3%); P = 0.002], whereas within the low stratum no statistically significant difference was detected [1.22 vs. 1.00; Δ = 21.5% (−22.6%, 90.9%); P = 0.40]. As-treated analyses showed similar results.

Changes by non-nucleoside reverse transcriptase inhibitor/protease inhibitor components

At week 24, in the EFV arms (NRTIs combined), there was a statistically significant increase in hsCRP [estimated mean fold change 1.41 (95% CI 1.11, 1.80); P = 0.008] without change in the ATV/r arms [estimated mean fold change 0.88 (0.68, 1.14); P = 0.31]; the difference was significant between the arms [Δ = −37.6% (−56.1%, −11.2%); P = 0.009]. At week 96, hCRP did not change significantly in either the EFV [1.21 (0.95, 1.56); P = 0.15] or ATV/r [0.98 (0.75, 1.28); P = 0.85] arms, and no difference was seen between the arms [Δ = −19.5% (−44.2%, 16.1%); P = 0.24]. As-treated analyses showed similar results. There was no evidence of an interaction between the NNRTI/protease inhibitor component and HIV-1 RNA stratum.

Three post-hoc sensitivity analyses were performed; the first excluding patients with suspected hypersensitivity reaction, the second excluding patients with HIV-1 RNA at least 50 copies/ml at week 24, and the third excluding the six patients with immune reconstitution syndrome. Similar results were seen by intent-to-treat and as-treated analyses for NRTI and NNRTI/protease inhibitor component comparisons. Among all patients, no differential NRTI effect was detected between patients with (n = 168) and without (n = 68) HIV-1 RNA below 50 copies/ml at week 24 (P = 0.68). In addition, after adjustment for changes in CD4 cell counts and in BMI, the results remained unchanged.

Changes in IL-6

At week 24, there was a statistically significant decrease in IL-6 within the TDF/FTC + EFV [estimated mean fold change 0.74 (95% CI 0.58, 0.95); P = 0.020] and TDF/FTC + ATV/r arm [0.76 (0.62, 0.92); P = 0.005] (Fig. 2 and Table 2). For ABC/3TC + EFV and ABC/3TC + ATV/r, the estimated mean fold change was 0.97 [(0.80, 1.18); P = 0.75] and 0.95 [(0.77, 1.18); P = 0.67], respectively. At week 96, there was a statistically significant decrease in IL-6 within all arms (P ≤ 0.026).

Changes by nucleoside reverse transcriptase inhibitor components

At week 24, there was a statistically significant decrease in IL-6 within the TDF/FTC arms (combining the third drug) [estimated mean fold change 0.75 (95% CI 0.64, 0.87); P < 0.001] without significant change from baseline in the ABC/3TC arms [0.96 (0.83, 1.11); P = 0.59]. The change in IL-6 was significantly larger for ABC/3TC vs. TDF/FTC [Δ = 28.5% (4.2%, 58.4%); P = 0.019]. As-treated analyses showed similar results. At 96 weeks, both ABC/3TC and TDF/FTC arms (third drugs combined) had a statistically significant decrease in IL-6 levels (both P < 0.001), and there was no difference between the NRTI components in IL-6 change by intent-to-treat analysis [0.61 vs. 0.75; Δ = 22.2% (−4.3%, 56.1%); P = 0.11], but there was by as-treated analysis [0.79 vs. 0.60; Δ = 32.6% (1.2%, 73.6%); P = 0.040].

At week 24 (but not 96), there was evidence of an interaction between the NRTI component and HIV-1 RNA stratum (P = 0.012). Within the high stratum, there was a significantly different change in IL-6 for ABC/3TC vs. TDF/FTC [1.06 vs. 0.60; Δ = 76.3% (31.3%, 136.8%); P < 0.001], but not within the low stratum [0.91 vs. 0.88; Δ = 2.8% (−22.9%, 36.9%); P = 0.85]. As-treated analyses showed similar results.

Changes by non-nucleoside reverse transcriptase inhibitor/protease inhibitor components

At weeks 24 and 96, by intent-to-treat and as-treated analyses, IL-6 decreased significantly (P ≤ 0.043) in both EFV and ATV/r arms (when NRTIs combined), without significant differences for ATV/r vs. EFV (P ≥ 0.80). There was no interaction between the NNRTI/protease inhibitor component and HIV-1 RNA stratum at either time point.

Changes in biomarkers adjusted for baseline covariates

The intent-to-treat analyses on change in biomarkers from entry to weeks 24 and 96 were adjusted as detailed in the statistical section. For analyses of the NRTI or NNRTI/protease inhibitor effect, all the adjusted models, alone and jointly, yielded similar results as the unadjusted analyses for all biomarkers.

Association between baseline factors and changes in biomarkers

Linear regression analyses assessed the association of baseline factors with changes in biomarkers (Table 3). The covariates were the same as those used in adjusted analyses, except for baseline marker level.

Table 3
Table 3:
Results of the regression analyses to assess the baseline factors associated with 24 and 96 weeks changes in all biomarkers.

For CRP and IL-6, by multivariable analysis, in addition to the ABC/3TC and ATV/r effects, lower CD4 cell count was independently associated with increased 24-week hsCRP change, whereas greater HIV-1 RNA was associated with decreased IL-6. Additionally, a significant interaction between the NRTI component and HIV-1 RNA was seen for hsCRP and IL-6. Specifically, compared to TDF/FTC, per log10 copies/ml, HIV-1 RNA was associated with a 69.5% (1.7%, 182.6%) and 36.6% (-0.4%, 87.5%) larger mean fold change in ABC/3TC for hsCRP (1.19 vs. 0.70) and IL-6 (0.96 vs. 0.70), respectively. At 96 weeks, for both hsCRP and IL-6, male sex was associated with decreases. For the subset with smoking data (n = 205), smoking was associated with 24 week increases in hsCRP and IL-6.


This study details changes in inflammation and endothelial activation markers among patients randomized to one of the four commonly used ART regimens. As seen in prior studies, we demonstrated that ART initiation led to a decrease in markers of TNF-α activation and endothelial activation, but not hsCRP. We also found that on average with ABC/3TC there were short-term (24 weeks) and longer-term (96 weeks) increases in hsCRP and short-term differences in IL-6 compared to TDF/FTC, and that EFV induced increases in hsCRP compared to ATV/r at week 24. Adjusting for potential confounders/imbalances, including virologic efficacy, did not change these results and as-treated analyses yielded similar results to intent-to-treat analyses.

Cardiovascular disease recently emerged as a major cause of morbidity and mortality in HIV. In addition to traditional CVD risk factors, inflammation appears to be a major component of this enhanced risk. So far, this has been shown in mostly cross-sectional studies in which higher antigen-specific T-cell responses [23], CRP [4,24,25], TNF-α [4] and IL-6 [26] have been associated with carotid intima–media thickness, and higher CD4+CD38+HLA-DR+ T-cells% with arterial stiffness [27].

Our study supports the findings of earlier studies that initiation of effective ART results in an overall decrease in inflammation markers, with the exception of hsCRP, which remains unchanged or even increases [2,17,28,29]. The reason hsCRP behaves differently from other markers remains elusive, with one potential explanation being HIV-associated subclinical hepatocyte dysfunction, as CRP is mainly produced by hepatocytes in response to IL-6. Results were unchanged after adjusting for several factors known to affect CRP, including hepatitis C [30]. Oral contraceptives have been shown to increase hsCRP [31,32], but excluding the four women who were on oral contraceptives (all on EFV; three on ABC/3TC) did not modify the results. Likewise, statins were shown to decrease hsCRP [33], but excluding the 23 statin users did not affect hsCRP or IL-6 results. Regardless, hsCRP appears to be an important marker, independently associated with CVD [4,24], HIV progression [34], and mortality [35], even after adjusting for CD4 and HIV-1 RNA. However, it is unclear whether the American Heart Association (AHA) CVD risk stratification cut-offs established for HIV-uninfected populations [36] are valid in HIV. Figure 2 (c, d) shows the baseline and week 24 AHA hsCRP risk categories, without apparent shift to higher risk categories in either group.

Only limited data exist regarding the effect of specific ART on inflammation, and most focus on ABC, because of its potential link to increased CVD in some studies [5–9]. Others [11–14] did not find such link, including A5001 [11] that included data from A5202. Studies investigating the effect of ABC on inflammation yielded conflicting results. The International Network for Strategic Initiatives in Global HIV Trials group found higher IL-6 and hsCRP, but similar TNF-α, with ABC regimens [5], whereas the Multicenter AIDS Cohort Study cohort found similar biomarker levels, including IL-6 and hsCRP, in ABC vs. non-ABC-treated patients [15]. Moreover, studies of virologically suppressed individuals who switched their NRTIs to ABC or TDF found no differences in inflammation markers after switching [18,19]. Furthermore, the HEAT study, which prior to the current study was the only randomized study [16] that measured inflammation markers in patients initiating their first ART regimen with ABC vs. TDF-based therapy, found no difference in these markers between the regimens. However, HEAT investigated only lopinavir/ritonavir-based therapies, whereas our study had both EFV and ATV/r as third drugs, and EFV (but not ATV/r) was found to be associated with hsCRP increase.

We found an interaction between screening HIV-1 RNA stratum and the NRTI effect so that the differences seen between ABC/3TC and TDF/FTC were larger in the high screening viral load stratum than in the lower. This observation may explain the discrepancies seen in prior biomarker studies, and specifically in prior randomized studies of virologically suppressed patients in whom no difference in markers were found between ABC and TDF regimens [18,19].

The mechanism by which ABC affects inflammatory pathways is not clear. In one study, ABC induced dose-dependent increases in neutrophil adhesion through the activation of Mac-1, which interacts with its endothelial ligand ICAM-1 [37]. Some have suggested that analogous to other drug toxicity [38], ABC may interfere with purine signaling pathways leading to impairment of lymphocyte activation [39]. A recent study found that ABC up-regulates pro-inflammatory cytokine mRNA transcription by stimulation of Toll-like receptor 8 signaling [40]. Others have suggested that ABC can induce a low-level hypercoagulable state by increasing platelets aggregation due to hyperactive platelets [41], or increase fibrinolytic capacity by raising plasminogen activator inhibitor-1 levels [42].

Although several studies have shown that CD4 cell recovery is similar after initiation of protease inhibitor vs. NNRTI regimens [43,44], only one study found similar decreases in sTNFR-I and II [45] with protease inhibitor vs. NNRTI regimens, but hsCRP and IL-6 were not measured. Our finding of higher CRP with EFV vs. ATV/r is consistent with a study showing that in adipocytes, EFV induces the release of higher levels of cytokines than protease inhibitors [46].

Our study has several limitations, including decreased sample size at week 96, possible selection bias of A5224s patients who have available week 24 and/or 96 samples (possibly healthier individuals with lower inflammation), and the large number of analysis performed without adjustment for multiple comparisons, which may increase the risk of a type I error. Although the co-primary endpoints (sTNFR-II and VCAM-1) were chosen because our prior studies showed them to be associated with clinically relevant outcomes, such as incident diabetes and carotid IMT [4,25,28], other markers chosen as secondary endpoints (e.g. IL-6 and hsCRP) have also been associated with mortality and CVD [24,26].

We have shown in a randomized study a differential treatment effect on some inflammation markers, with differences in hsCRP and IL-6 in those receiving ABC compared to TDF and EFV compared to ATV/r-containing regimens. Further investigations are needed to duplicate these findings and clarify their clinical significance.


Role of authors: All authors played a role in editing the manuscript and approved the text as submitted. G.A.McC. designed the study and wrote the manuscript. P.E.S. and E.S.D. assisted in the design of the study, reviewed and edited the manuscript. D.K. and C.T. performed the data analysis and assisted in the interpretation of statistical data. K.M., N.C.J., A.B., B.H., and T.T.B. reviewed and edited the manuscript. N.F. ran the biomarker assays, and reviewed and edited the manuscript. Data in this manuscript were collected by AIDS Clinical Trials Group. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

Acknowledgement Appendix for A5224s

Sadia Shaik, MD and Ruben Lopez, MD – Harbor-UCLA Medical Center (Site 603) CTU Grant #: AI0694241, UL1-RR033176.

Susan L. Koletar, MD and Diane Gochnour, RN – The Ohio State University Medical Center (Site 2301) CTU Grant #: AI069474.

Geyoul Kim, RN and Mark Rodriguez, RN – Washington University (Site 2101) CTU Grant #: U01AI069495; GCRC Grant: UL1 RR024992.

Elizabeth Lindsey, RN and Tamara James, BS – Alabama Therapeutics CRS (Site 5801) CTU Grant #: U01 AI069452.

Ann C. Collier, MD and Jeffrey Schouten, MD, JD – University of Washington (Site 1401) CTU Grant #: AI069434; UL1 RR025014.

Jorge L. Santana Bagur, MD and Santiago Marrero, MD – Puerto Rico-AIDS Clinical Trials Unit (Site 5401) CTU Grant #: 5 U0I AI069415-03.

Jenifer Baer, RN, BSN and Carl Fichtenbaum, MD – University of Cincinnati (Site 2401) CTU Grant #: AI069513.

Patricia Walton, BSN, RN and Barbara Philpotts, BSN, RN – Case Western Reserve (Site 2501) CTU Grant #: AI69501.

Princy Kumar, MD and Joseph Timpone, MD – Georgetown University (Site 1008) CTU Grant#: ACTG grant #: 5U01AI069494.

Donna Pittard, RN BSN and David Currin, RN – University of North Carolina (Site 3201) CTU Grant #: 5-U01 AI069423-03; UNC CFAR #: P30 AI050410 (-11); UNC CTRC #: UL 1RR 025747.

Julie Hoffman, RN and Edward Seefried, RN – San Diego Medical Center UC (Site 701) CTU Grant #: AI69432.

Susan Swindells, MBBS and Frances Van Meter, APRN – University of Nebraska (Site 1505) CTU Grant #: AI 27661.

Deborah McMahon, MD and Barbara Rutecki, MSN, MPH, CRNP – University of Pittsburgh (Site 1001) CTU Grant #: 1 U01 AI069494-01.

Michael P. Dube, MD and Martha Greenwald, RN, MSN – Indiana University (Site 2601) CTU Grant #: 5U01AI025859; GCRC #: M01 RR00750.

Ilene Wiggins, RN, and Eric Zimmerman, RN – Johns Hopkins University (Site 201) CTU Grant #: AI27668; CTSA Grant #: UL1 RR025005.

Judith Aberg, MD and Margarita Vasquez, RN – New York University/NYC HHC at Bellevue Hospital Center (Site 401) CTU Grant #: AI27665, New grant number: AI069532.

Martin McCarter and M. Graham Ray, RN, MSN – Colorado AIDS Clinical Trials Unit, (Site 6101) CTU Grant #: AI69450; RR025780.

Mamta Jain, MD – PI and Tianna Petersen, MS – University of Texas Southwestern Medical Center (Site 3751) CTU Grant #: 3U01AI046376-05S4.

Emily Stumm, BS and Pablo Tebas, MD – University of Pennsylvania, Philadelphia (Site 6201) CTU Grant #: P30-AI0450008-11; CFAR Grant #: UO1-AI069467-04.

Mary Albrecht, MD and Neah Kim, NP – Beth Israel Deaconess (Partners/Harvard) CRS (Site 103) CTU Grant #: U01 AI069472-04.

Paul Edward Sax, MD and Joanne Delaney, RN – Brigham and Women's Hospital (Site 107) CTU Grant #: UOI AI 069472.

Christine Hurley, RN and Roberto Corales, DO – AIDS Care (Site 1108) CTU Grant #: U01AI069511-02 (as of 2/12/08); GCRC: UL1 RR 024160.

Keith Henry, MD and Bette Bordenave, RN – Hennepin County Medical Center (Site 1502) CTU Grant #: N01 AI72626.

Wendy Armstrong, MD and Ericka R. Patrick, RN, MSN, CCRC – Emory University HIV/AIDS Clinical Trails Unit (Site 5802) CTU Grant #: UO1Al69418-01/CFAR Grant Number: P30Al050409.

Jane Reid RNC MS and Mary Adams RN MPh – University of Rochester (Site 1101) CTU Grant #: U01AI069511-02 (as of 2/12/08); GCRC: UL1 RR 024160.

Gene D. Morse, PharmD, FCCP, BCPS, SUNY – Buffalo, Erie County Medical Ctr. (Site 1102) CTU Grant #: AI27658.

Michael P. Dube, MD and Martha Greenwald, RN, MSN – Wishard Memorial Hospital Indiana University (Site 2603) CTU Grant #: 5U01AI025859; GCRC #: M01 RR00750.

Kimberly Y. Smith, MD, MPH and Joan A. Swiatek, APN – Rush University Medical Center (Site 2702) CTU Grant #: U01 AI069471.

Nancy Hanks, RN, and Debra Ogata-Arakaki, RN – University of Hawaii at Manoa, Leahi Hospital (Site 5201) CTU Grant #: AI34853.

Ardis Moe, MD and Maria Palmer, PA-C – UCLA Medical Center (Site 601) CTU Grant #: 1U01AI069424-01.

Jeffery Meier, MD and Jack T. Stapleton, MD – University of Iowa Hospitals and Clinics (Site 1504) CTU Grant #: UL1RR024979.

Gary Matthew Cox, MD and Martha Silberman, RN – Duke University Medical Center Adult CRS (Site 1601) CTU Grant #: 5U01 AI069 484-02.

2705 – Cook County Hospital

Gerianne Casey, RN and William O’Brien MD – University of Texas, Galveston (Site 6301) CTU Grant #: AI32782.

Valery Hughes, FNP and Todd Stroberg, RN – Cornell CRS (Site 7803, 7804) – CTU Grant#: U01 AI069419; CTSC #: UL1 RR024996.

Nyef El-Daher, MD – McCree McCuller Wellness Center at the Connection (Site 1107) CTU Grant #: U01AI069511-02 (as of 2/12/08); GCRC: UL1 RR 024160.

Rebecca J. Basham, BS and Husamettin Erdem, MD – Vanderbilt Therapeutics CRS (Site 3652) CTU Grant #: AI46339-01; MO1 RR 00095.

The project described was supported by Award Numbers U01AI068636, AI068634, AI38855, AI065348 from the National Institute of Allergy and Infectious Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. GlaxoSmithKline and Gilead funded the cost of the inflammation marker assays. Study medications were provided by Abbott Pharmaceuticals, Bristol-Myers Squibb, Gilead Sciences, and GlaxoSmithKline.

Conflicts of interest

G.A.McC. has served as a scientific advisor or speaker for Bristol Myers Squibb, GlaxoSmithKline, Tibotec, and Gilead Sciences, has received research grants from Bristol Myers Squibb, GlaxoSmithKline, and Gilead Sciences, and is currently serving as the DSMB Chair for a Pfizer-sponsored study. D.K. has no conflict. E.S.D. serves as a consultant for Bristol Myers Squibb, Gilead, GlaxoSmithKline, Merck, ViiV and receives research grant support from Abbott Laboratories, Merck, Gilead, ViiV, and Pfizer. C.T. is a member of a DSMB for Tibotec. Nasreen Jahed has no conflict. K.M. is an employee of Gilead Sciences and owns stock in Gilead Sciences. B.H. is an employee of GlaxoSmithKline. T.T.B. has served as a scientific consultant for Bristol Myers Squibb, GlaxoSmithKline, Abbott, Tibotec, and Gilead Sciences, has received research grants from GlaxoSmithKline and Merck. N.F. has no conflict. S.B. has no conflict. P.E.S. serves as a consultant for Abbott, BMS, Gilead, GSK, Merck, Tibotec, and ViiV and receives grant Support from BMS, Gilead, Merck, Tibotec, and ViiV.


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abacavir; C-reactive protein; endothelial activation markers; inflammation markers; interleukin-6; TNF alpha

© 2012 Lippincott Williams & Wilkins, Inc.