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Abstract

Context

In the general population, high-density lipoprotein (HDL) cholesterol efflux capacity (HCEC) relates inversely to incident cardiovascular events. Previous studies have suggested that HCEC is decreased in HIV and that antiretroviral therapy (ART) initiation might improve HCEC.

Objective

To evaluate HCEC in the context of ART initiation and immune activation in HIV.

Design and Outcome Measures

Baseline HCEC from 10 ART-naive HIV-infected males and 12 prospectively matched non–HIV-infected males were analyzed. In the HIV cohort, HCEC 6 months after elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate (E/C/F/TDF) therapy was evaluated. HCEC served as the primary outcome and was measured by the ability of J774 mouse macrophages to efflux cholesterol. Our ex vivo assay used two cholesterol acceptors [apolipoprotein B (apoB)-depleted sera or purified HDL] and modulation of cellular efflux pathways using a liver X receptor (LXR) agonist.

Results

The median age was 34 years [interquartile range (IQR), 27 to 51], and baseline HDL was 46 mg/dL (IQR, 38 to 61). HCEC was significantly greater in the non–HIV-infected subjects than in the HIV-infected subjects at baseline. HCEC, assessed using apoB-depleted sera, significantly increased after ART (no LXR agonist, baseline: median, 8.1%; IQR, 7.0% to 11.9%; after ART: median, 12.9%; IQR, 10.4% to 21.1%; P = 0.006; LXR agonist, baseline, 1.3% ± 1.3%; after ART, 2.5% ± 1.0%; P = 0.02), although not to the levels in the non–HIV-infected subjects (no LXR agonist: median, 14.9%; IQR, 11.5% to 19.1%; LXR agonist: 5.8% ± 1.3%). HCEC, assessed using purified HDL, did not significantly increase after ART. The change in HCEC with ART related inversely to the change in the percentage of CD14−CD16+ (nonclassical) monocytes (ρ = −0.74, P = 0.04) and directly to the change in the percentage of CD14+CD16− (classical) monocytes (ρ = 0.72, P = 0.045).

Conclusions

Our data suggest improvement of HCEC with E/C/F/TDF and a relationship between the ART-induced decrease in immune activation and ART-induced improvement in HCEC.

Individuals living with HIV are at an increased risk of cardiovascular disease (CVD), and this increased risk persists even after controlling for traditional cardiovascular risk factors (1, 2). Systemic immune activation in the presence of HIV infection is thought to be an important contributor to this increased CVD risk. HIV-infected individuals, in particular, have increased levels of markers of monocyte activation and percentages of nonclassical (CD14−CD16+) monocytes (3–6). The effect of HIV infection on high-density lipoprotein (HDL) activity could also contribute to the increased atherogenesis in HIV. HDL cholesterol, in particular, has both antiatherogenic (7) and anti-inflammatory (8) functions. One of its antiatherogenic functions occurs through the process of reverse cholesterol transport—a process whereby cholesterol is transported from peripheral tissues such as the vessel wall to the liver, where it is eventually metabolized and/or excreted through the intestines. As part of this process, cholesterol is exported from cells onto HDL or nascent HDL particles through either aqueous diffusion or actively by transmembrane proteins. The ability of HDL particles to accept cholesterol in this process is known as HDL cholesterol efflux capacity (HCEC) (9). HCEC, in particular, has been inversely associated in the general population with the likelihood of angiographic coronary artery disease, carotid intima-media thickness, and incident CVD events (10, 11).

The antiatherogenic properties of HDL can be affected by a variety of factors, including inflammation and HIV infection. In vitro studies, for example, have shown that inflammatory milieus (8) and HIV infection (12) can induce structural changes to the HDL particle that affect the antiatherogenic function of the HDL particle. HIV infection through the virion-encoded Nef protein also has been shown to directly impair cholesterol efflux by downregulating one of the key transmembrane proteins in human monocytes/macrophages, adenosine triphosphate-binding cassette transporter A1 (ABCA1) (13). In a longitudinal study from our group, we previously reported the effect of antiretroviral therapy (ART) initiation with elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate (E/C/F/TDF) on arterial inflammation among ART-naive HIV-infected subjects (14). In the present analysis, we used subjects from that previous study and compared the HCEC among subjects with newly diagnosed HIV infection and non–HIV-infected controls. We then assessed the effects of ART initiation with E/C/F/TDF on HCEC. HCEC was assessed using an ex vivo mouse macrophage model. The experimental conditions were varied to include either apolipoprotein B (apoB)-depleted sera (which contains the HDL particles and also any inflammatory cytokines or chemokines present in the sera of each subject) or purified HDL (which is free of sera factors and hence captures a measure of HDL functionality specific only to the HDL particles) from each study subject. Given that HDL cholesterol efflux in human cholesterol-laden macrophages occurs predominantly through the ABCA1 pathway and the downregulation of the ABCA1 pathway in HIV, variable stimulation of this pathway in our mouse macrophage model was performed using a liver X receptor (LXR) agonist. Additionally, given the viral suppression and decrease in immune activation with ART initiation, we hypothesized that HCEC would increase after 6 months of therapy with E/C/F/TDF and that the changes in HCEC with ART would relate significantly to the changes in select markers of inflammation and immune activation and immune cell subpopulations.

Methods

Study design

We conducted a prospective study to investigate the effects of initiation of E/C/F/TDF on arterial inflammation using cardiac 18F-flurodeoxyglucose positron emission tomography scanning from 24 July 2012 to 7 May 2015, as previously reported (14). Twelve ART-naive men with HIV infection who were scheduled to begin E/C/F/TDF therapy by their clinical physician were recruited. Of these 12 men, 10 had been diagnosed with HIV within the past 2 years and were included in the present analysis. Also, 12 HIV-negative individuals were recruited and underwent blood testing only at baseline and did not receive combined ART (cART). Each non–HIV-infected individual was matched to a single HIV-infected subject according to his CVD risk factors. The 10-year atherosclerotic CVD risk scores of the enrolled subjects were 2.1 [interquartile range (IQR), 0.9 to 3.8] vs 1.8 (IQR, 0.7 to 5.3; P = 1.00) for the HIV-infected vs non–HIV-infected subjects, respectively. The Framingham risk scores of the HIV-infected and non–HIV-infects subjects were 1 (IQR, 0.5 to 3.5) and 3.5 (IQR, 0.5 to 7.5; P = 0.27), respectively. The subjects in both groups were enrolled using similar criteria, including age >18 years and no diabetes, current or previous coronary artery disease, or relevant autoimmune or inflammatory disease. For the HIV-infected subjects, the study procedures were performed at baseline and after ~6 months of cART. The Massachusetts General Hospital institutional review board approved the study, and all subjects provided written informed consent. The study is registered at ClinicalTrials.gov (NCT01766726).

HDL cholesterol efflux capacity

HCEC was assessed at baseline for all subjects and at 6 months for the HIV-infected subjects only. The blood samples used for the measurement of HCEC were stored at −80°C. J774 mouse macrophages (cell line authentication in 2014) were seeded into 24-well plates at 100,000 cells/well in Dulbecco’s modified Eagle medium (DMEM) with 10% fetal bovine serum and 0.5 μg/mL penicillin-streptomycin. After the macrophages had been cultured for 24 hours, the cells were incubated for an additional 24 hours in media with 1.0 mCi/mL 3H-cholesterol and then washed twice with warm phosphate-buffered saline. HCEC was assessed under four different experimental conditions, which varied in the cholesterol acceptor (apoB-depleted sera vs purified HDL from the study subjects) and upregulation of ABCA1 using the LXR agonist T0901317 (Sigma-Aldrich). The four experimental conditions were as follows: (1) 1.5% apoB-depleted sera with LXR agonist incubation overnight before incubation with apoB-depleted sera; (2) 2.5% apoB-depleted sera with LXR agonist incubation overnight and during incubation with apoB-depleted sera; (3) purified HDL with LXR agonist incubation overnight before incubation with purified HDL; and (4) purified HDL with LXR agonist incubation overnight and during incubation with purified HDL. For upregulation of ABCA1, the cells were incubated in serum-free DMEM with 1 μM of the LXR agonist T0901317 (Sigma-Aldrich) overnight for 18 hours in the first and third experimental conditions and for an additional 6 hours during incubation with apoB-depleted sera or purified HDL in the second and fourth experimental conditions. To measure the cholesterol efflux acceptor activity of the apoB-depleted sera (or purified HDL) from the study subjects, macrophages were placed in serum-free DMEM and incubated with apoB-depleted sera (or purified HDL) samples for 6 hours. The media were then collected and clarified of cell debris by spinning for 5 minutes at 1000 rpm. A 300-μL aliquot of clarified media was then mixed with 270 mL of scintillation fluid. A cell lysate was prepared by adding 1 mL of 0.1 N sodium hydroxide to the wells, followed by 30 minutes of incubation. Next, 300 μL of lysate was mixed with 270 mL of scintillation fluid. The derived media and cell counts were then used to calculate the percentage of efflux: [(media counts)/(media counts + cell-associated counts)]. Twenty-four well plates were used, with three wells used as a negative control (media) and three wells used for each participant sample (either apoB-depleted sera or purified HDL). A mean percentage of efflux for the control wells was calculated for each plate, and a mean HCEC for each participant was calculated from the three wells incubated with the participant sample. The mean percentage of efflux of the control wells was then subtracted from the mean HCEC for each study participant that was measured on the same 24-well plate as the corresponding control wells. Therefore, if the mean percentage of efflux of the control wells was greater than the mean HCEC for the wells from a particular study participant, a negative percentage of efflux capacity was reported.

After obtaining the results from the four experimental conditions described, substantial between-group and within-group differences before and after cART were noted in the apoB-depleted experimental conditions. However, these differences were not seen with the experimental conditions using purified HDL. As such, to explore further the HCEC using apoB-depleted sera, an additional experimental condition using apoB-depleted sera with no LXR agonist was performed. During this experiment, minimal HCEC was observed at 6 hours of incubation with apoB-depleted sera; therefore, the incubation period was increased to 12 hours. We conducted dose–response assays to confirm that under the conditions of no LXR stimulation and 12 hours of incubation with either apoB-depleted sera (1.5%, 2.5%, and 3.5%) or purified HDL (5, 10, and 15 µg/mL), the final media concentrations elicited a linear efflux response, indicating that we were measuring HCEC in the dynamic range of the assay. In particular, for the apoB-depleted sera at these concentrations, the corresponding percentages of efflux were 10.4% ± 1.1%, 13.4% ± 0.8%, and 16.8% ± 0.6% compared with 2.3% ± 0.6% for the control cells that had received no depleted sera (average of efflux from three wells ± standard error of the mean). For the purified HDL at the stated concentrations, the corresponding percentages of efflux were 29.3% ± 1.7%, 33.0% ± 1.7%, and 41.5% ± 3.0%.

ApoB-depleted sera

Thawed sera were depleted of apoB-containing lipoproteins as follows. First, polyethylene glycol (20% w/v; in 200 mM glycine; pH 7.4) was added to the sera (40:100 v/v). The samples were mixed and incubated at room temperature for 20 minutes and then centrifuged at 10,000 relative centrifugal force for 30 minutes at 4°C. The resulting supernatant containing the HDL fraction was used in the cholesterol efflux assays at a final concentration of 1.5% total media volume or 2.5% total media volume (15).

HDL purification

HDL was isolated from plasma samples using ultracentrifugation, as previously described (16).

Metabolic, lipid, immune, and HIV-specific laboratory assessments

Creatinine and lipid levels were assessed using standard methods. HIV viral loads were assessed using ultrasensitive reverse transcription polymerase chain reaction with a lower limit of detection of 20 copies/mL (Cobas AmpliPrep; Roche Molecular Diagnostics). The CD4+ T-cell counts were assessed using flow cytometry. Flow cytometric analysis of the following lymphocytes was performed: CD4+ T cells, CD8+ T cells, CD4+HLA-DR (antigen D related)+CD38+ T cells (i.e., activated CD4+T cells), and CD8+HLA-DR+CD38+ T cells (i.e., activated CD8+T cells) (17). Flow cytometric analysis of the following monocytes were performed: CD14+CD16− monocytes [i.e., classical monocytes, which represent the largest percentage of monocytes in humans (18)], CD14+CD16+ monocytes [i.e., inflammatory monocytes, which have a proinflammatory cytokine production profile (19)], and CD14−CD16+ monocytes (i.e., nonclassical/homing monocytes, which home to the vascular endothelium (20)]. The levels of the following immune and inflammatory biomarkers, which are elevated in the setting of HIV and/or associated with atherosclerotic plaque burden or progression of atherosclerosis in HIV, were also assessed (3, 21–25): sCD163, sCD14, CXCL-10, MCP-1, hsIL-6, CRP, and Lp-PLA2, as previously reported by our group (14).

Statistical analysis

As part of a prespecified analysis, we examined the change in HCEC in the HIV-infected study subjects after 6 months of combined ART and the between-group difference in HCEC in the HIV-infected group at baseline and the non–HIV-infected control group. The normality of the data was assessed using a Shapiro-Wilk test, and normally distributed data are presented as the mean ± standard deviation and non-normally distributed data as the median and IQR. Comparisons between the HIV-infected group and the non–HIV-infected group were performed using the Student t test for normally distributed continuous variables, the Wilcoxon rank sum test for non-normally distributed continuous variables, and the χ2 test for categorical variables. A Wilcoxon signed rank test was used to assess the change in HCEC in response to cART in the HIV-infected group. Bivariate analyses were performed using the Pearson correlation coefficient for normally distributed continuous variables and the Spearman correlation coefficient if at least one variable was non-normally distributed. Multivariate regression modeling was performed using HDL cholesterol and HIV status as covariates. Sensitivity analyses were performed using HIV-infected subjects matched to non–HIV-infected subjects by age and body mass index (BMI). HCEC was then compared between non–HIV-infected subjects and HIV-infected subjects from this matched cohort. All statistical analyses were performed using SAS JMP software, version 11.0 (SAS Institute).

Results

Baseline demographic and cardiometabolic parameters

Overall, the median age for the study subjects was 34 years (IQR, 27 to 51). All the subjects were men, and 18% were Hispanic or Latino (Table 1). The baseline total cholesterol was 167 ± 32 mg/dL, HDL cholesterol was 46 mg/dL (IQR, 38 to 61), and LDL cholesterol was 99 ± 25 mg/dL. The median BMI was 26.0 ± 3.3 kg/m2, and the Framingham risk score was 1 (IQR, 1 to 5). The baseline demographic data, including CVD risk scores did not differ significantly between the HIV-infected group and non–HIV-infected group.

Table 1.
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Baseline Demographic Data for HIV-Infected Group and Control Group

CharacteristicHIV-Infected Group (n = 10)Non–HIV-Infected Group (n = 12)P Value
Traditional risk factorsa
 Race0.32
  White70 (7/10)92 (11/12)
  Black10 (1/10)8 (1/12)
  Asian10 (1/10)0 (0/12)
  Other10 (1/10)0 (0/12)
 Age, y29 (26–43)48 (27–51)0.29
 BMI, kg/m225.7 ± 4.026.2 ± 2.90.74
 Total cholesterol, mg/dL157 ± 30175 ± 320.18
 LDL cholesterol, mg/dL95 ± 27102 ± 240.51
 HDL cholesterol, mg/dL40 (36–47)52 (45–64)0.07
 Triglycerides, mg/dL83 ± 2989 ± 340.66
 Total/HDL cholesterol ratio3.7 ± 1.03.3 ± 0.70.37
 Framingham risk score1 (0.5–3.5)3.5 (0.5–7.5)0.27
 Current HTN20 (2/10)17 (2/12)0.84
 Current smoker50 (5/10)25 (3/12)0.22
 Current hepatitis C10 (1/10)0 (0/12)0.20
 Family history of premature CHDb0 (0/9)9 (1/11)0.27
HIV-specific factors
 Time since HIV diagnosis, y0.73 ± 0.62NANA
 CD4+ T cell count, cells/mm3440 ± 143NANA
 Viral load, copies/mL32,000 (22,715–80,925)NANA
Monocyte subpopulations
 CD14+CD16−, % as a % of monocytes85.8 (83.8–91.3)88.0 (85.2–89.7)0.67
 CD14+CD16+, % as a % of monocytes8.0 (4.3–13.7)8.7 (6.7–12.2)0.82
 CD14−CD16+, % as a % of monocytes4.4 (2.7–6.5)3.2 (2.6–3.7)0.33
Markers of immune activation and arterial inflammation
 sCD163, ng/mL1253 (860–1930)956 (608–1281)0.20
 sCD14, ng/mL2735 ± 7172151 ± 10210.13
 CXCL10, pg/mL234 (115–650)78 (59–112)0.005
 Lp-PLA2, ng/mL181 ± 47208 ± 290.14
 MCP-1, pg/mL181.1 (129.9–195.1)153.9 (131.1–198.8)0.95
 hsIL-6, pg/mL1.5 (0.9–2.3)1.3 (0.9–2.1)0.72
 hsCRP, ng/mL110 (42–513)147 (86–369)0.72
CharacteristicHIV-Infected Group (n = 10)Non–HIV-Infected Group (n = 12)P Value
Traditional risk factorsa
 Race0.32
  White70 (7/10)92 (11/12)
  Black10 (1/10)8 (1/12)
  Asian10 (1/10)0 (0/12)
  Other10 (1/10)0 (0/12)
 Age, y29 (26–43)48 (27–51)0.29
 BMI, kg/m225.7 ± 4.026.2 ± 2.90.74
 Total cholesterol, mg/dL157 ± 30175 ± 320.18
 LDL cholesterol, mg/dL95 ± 27102 ± 240.51
 HDL cholesterol, mg/dL40 (36–47)52 (45–64)0.07
 Triglycerides, mg/dL83 ± 2989 ± 340.66
 Total/HDL cholesterol ratio3.7 ± 1.03.3 ± 0.70.37
 Framingham risk score1 (0.5–3.5)3.5 (0.5–7.5)0.27
 Current HTN20 (2/10)17 (2/12)0.84
 Current smoker50 (5/10)25 (3/12)0.22
 Current hepatitis C10 (1/10)0 (0/12)0.20
 Family history of premature CHDb0 (0/9)9 (1/11)0.27
HIV-specific factors
 Time since HIV diagnosis, y0.73 ± 0.62NANA
 CD4+ T cell count, cells/mm3440 ± 143NANA
 Viral load, copies/mL32,000 (22,715–80,925)NANA
Monocyte subpopulations
 CD14+CD16−, % as a % of monocytes85.8 (83.8–91.3)88.0 (85.2–89.7)0.67
 CD14+CD16+, % as a % of monocytes8.0 (4.3–13.7)8.7 (6.7–12.2)0.82
 CD14−CD16+, % as a % of monocytes4.4 (2.7–6.5)3.2 (2.6–3.7)0.33
Markers of immune activation and arterial inflammation
 sCD163, ng/mL1253 (860–1930)956 (608–1281)0.20
 sCD14, ng/mL2735 ± 7172151 ± 10210.13
 CXCL10, pg/mL234 (115–650)78 (59–112)0.005
 Lp-PLA2, ng/mL181 ± 47208 ± 290.14
 MCP-1, pg/mL181.1 (129.9–195.1)153.9 (131.1–198.8)0.95
 hsIL-6, pg/mL1.5 (0.9–2.3)1.3 (0.9–2.1)0.72
 hsCRP, ng/mL110 (42–513)147 (86–369)0.72

Data presented as % (n/N), mean ± standard deviation for normally distributed data, or median (IQR) for non-normally distributed data.

Abbreviations: CHD, coronary heart disease; HTN, hypertension; LDL, low-density lipoprotein.

a

All the subjects enrolled in the present study were men.

b

The family history was unknown for 2 study participants.

Table 1.
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Baseline Demographic Data for HIV-Infected Group and Control Group

CharacteristicHIV-Infected Group (n = 10)Non–HIV-Infected Group (n = 12)P Value
Traditional risk factorsa
 Race0.32
  White70 (7/10)92 (11/12)
  Black10 (1/10)8 (1/12)
  Asian10 (1/10)0 (0/12)
  Other10 (1/10)0 (0/12)
 Age, y29 (26–43)48 (27–51)0.29
 BMI, kg/m225.7 ± 4.026.2 ± 2.90.74
 Total cholesterol, mg/dL157 ± 30175 ± 320.18
 LDL cholesterol, mg/dL95 ± 27102 ± 240.51
 HDL cholesterol, mg/dL40 (36–47)52 (45–64)0.07
 Triglycerides, mg/dL83 ± 2989 ± 340.66
 Total/HDL cholesterol ratio3.7 ± 1.03.3 ± 0.70.37
 Framingham risk score1 (0.5–3.5)3.5 (0.5–7.5)0.27
 Current HTN20 (2/10)17 (2/12)0.84
 Current smoker50 (5/10)25 (3/12)0.22
 Current hepatitis C10 (1/10)0 (0/12)0.20
 Family history of premature CHDb0 (0/9)9 (1/11)0.27
HIV-specific factors
 Time since HIV diagnosis, y0.73 ± 0.62NANA
 CD4+ T cell count, cells/mm3440 ± 143NANA
 Viral load, copies/mL32,000 (22,715–80,925)NANA
Monocyte subpopulations
 CD14+CD16−, % as a % of monocytes85.8 (83.8–91.3)88.0 (85.2–89.7)0.67
 CD14+CD16+, % as a % of monocytes8.0 (4.3–13.7)8.7 (6.7–12.2)0.82
 CD14−CD16+, % as a % of monocytes4.4 (2.7–6.5)3.2 (2.6–3.7)0.33
Markers of immune activation and arterial inflammation
 sCD163, ng/mL1253 (860–1930)956 (608–1281)0.20
 sCD14, ng/mL2735 ± 7172151 ± 10210.13
 CXCL10, pg/mL234 (115–650)78 (59–112)0.005
 Lp-PLA2, ng/mL181 ± 47208 ± 290.14
 MCP-1, pg/mL181.1 (129.9–195.1)153.9 (131.1–198.8)0.95
 hsIL-6, pg/mL1.5 (0.9–2.3)1.3 (0.9–2.1)0.72
 hsCRP, ng/mL110 (42–513)147 (86–369)0.72
CharacteristicHIV-Infected Group (n = 10)Non–HIV-Infected Group (n = 12)P Value
Traditional risk factorsa
 Race0.32
  White70 (7/10)92 (11/12)
  Black10 (1/10)8 (1/12)
  Asian10 (1/10)0 (0/12)
  Other10 (1/10)0 (0/12)
 Age, y29 (26–43)48 (27–51)0.29
 BMI, kg/m225.7 ± 4.026.2 ± 2.90.74
 Total cholesterol, mg/dL157 ± 30175 ± 320.18
 LDL cholesterol, mg/dL95 ± 27102 ± 240.51
 HDL cholesterol, mg/dL40 (36–47)52 (45–64)0.07
 Triglycerides, mg/dL83 ± 2989 ± 340.66
 Total/HDL cholesterol ratio3.7 ± 1.03.3 ± 0.70.37
 Framingham risk score1 (0.5–3.5)3.5 (0.5–7.5)0.27
 Current HTN20 (2/10)17 (2/12)0.84
 Current smoker50 (5/10)25 (3/12)0.22
 Current hepatitis C10 (1/10)0 (0/12)0.20
 Family history of premature CHDb0 (0/9)9 (1/11)0.27
HIV-specific factors
 Time since HIV diagnosis, y0.73 ± 0.62NANA
 CD4+ T cell count, cells/mm3440 ± 143NANA
 Viral load, copies/mL32,000 (22,715–80,925)NANA
Monocyte subpopulations
 CD14+CD16−, % as a % of monocytes85.8 (83.8–91.3)88.0 (85.2–89.7)0.67
 CD14+CD16+, % as a % of monocytes8.0 (4.3–13.7)8.7 (6.7–12.2)0.82
 CD14−CD16+, % as a % of monocytes4.4 (2.7–6.5)3.2 (2.6–3.7)0.33
Markers of immune activation and arterial inflammation
 sCD163, ng/mL1253 (860–1930)956 (608–1281)0.20
 sCD14, ng/mL2735 ± 7172151 ± 10210.13
 CXCL10, pg/mL234 (115–650)78 (59–112)0.005
 Lp-PLA2, ng/mL181 ± 47208 ± 290.14
 MCP-1, pg/mL181.1 (129.9–195.1)153.9 (131.1–198.8)0.95
 hsIL-6, pg/mL1.5 (0.9–2.3)1.3 (0.9–2.1)0.72
 hsCRP, ng/mL110 (42–513)147 (86–369)0.72

Data presented as % (n/N), mean ± standard deviation for normally distributed data, or median (IQR) for non-normally distributed data.

Abbreviations: CHD, coronary heart disease; HTN, hypertension; LDL, low-density lipoprotein.

a

All the subjects enrolled in the present study were men.

b

The family history was unknown for 2 study participants.

HCEC using apoB-depleted sera and LXR agonism

After the experimental condition using apoB-depleted sera with LXR agonist incubation overnight for 18 hours, HCEC was significantly greater in the non–HIV-infected subjects compared with that in the HIV-infected subjects at baseline (5.8% ± 1.3% vs 1.3% ± 1.3%; P < 0.0001; Fig. 1a). After 6 months of cART, HCEC had also significantly increased in the HIV-infected subjects from 1.3% ± 1.3% to 2.5% ± 1.0% (P = 0.02), although not to the levels seen in the non–HIV-infected subjects (P < 0.0001). In the experimental condition using apoB-depleted sera with LXR agonist incubation overnight for 18 hours and then an additional 6 hours with macrophages incubated with the apoB-depleted sera, the HCEC was overall greater for the non–HIV-infected subjects and HIV-infected subjects compared with the condition in which the LXR agonist incubation period was only 18 hours. At baseline, HCEC again was greater in the non–HIV-infected subjects compared with that in the HIV-infected subjects (15.3% ± 3.8% vs 11.6% ± 4.0%; P = 0.04; Fig. 1b). After 6 months of cART, HCEC did not significantly increase in the HIV-infected subjects under maximal stimulation of efflux using an LXR agonist (from 11.6% ± 4.0% to 12.7% ± 2.3%; P = 0.30).

HCEC with apoB-depleted sera using (a) 18 hours of LXR agonism and (b) 24 hours of LXR agonism. (a) HCEC was assessed with apoB-depleted sera and 18 hours of LXR agonism. HCEC was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline (5.8% ± 1.3% vs 1.3% ± 1.3%, P < 0.0001). After 6 months of E/C/F/TDF, HCEC significantly increased in the HIV-infected subjects from 1.3% ± 1.3% to 2.5% ± 1.0% (P = 0.02), although not to the levels seen in the non–HIV-infected subjects. (b) HCEC was assessed with apoB-depleted sera and 24 hours of LXR agonism. At baseline, HCEC again was greater in the non–HIV-infected subjects compared with the HIV-infected subjects (15.3% ± 3.8% vs 11.6% ± 4.0%, P = 0.04). After 6 months of cART, HCEC did not significantly increase in the HIV-infected subjects under maximal stimulation of efflux using an LXR agonist (11.6% ± 4.0% to 12.7% ± 2.3%, P = 0.30).
Figure 1.

HCEC with apoB-depleted sera using (a) 18 hours of LXR agonism and (b) 24 hours of LXR agonism. (a) HCEC was assessed with apoB-depleted sera and 18 hours of LXR agonism. HCEC was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline (5.8% ± 1.3% vs 1.3% ± 1.3%, P < 0.0001). After 6 months of E/C/F/TDF, HCEC significantly increased in the HIV-infected subjects from 1.3% ± 1.3% to 2.5% ± 1.0% (P = 0.02), although not to the levels seen in the non–HIV-infected subjects. (b) HCEC was assessed with apoB-depleted sera and 24 hours of LXR agonism. At baseline, HCEC again was greater in the non–HIV-infected subjects compared with the HIV-infected subjects (15.3% ± 3.8% vs 11.6% ± 4.0%, P = 0.04). After 6 months of cART, HCEC did not significantly increase in the HIV-infected subjects under maximal stimulation of efflux using an LXR agonist (11.6% ± 4.0% to 12.7% ± 2.3%, P = 0.30).

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HCEC using purified HDL particles and LXR agonism

In the setting of LXR agonist incubation with macrophages overnight only, there was a borderline statistically significant difference between the HCEC with purified HDL in the non–HIV-infected subjects and that in the HIV-infected subjects (15.1%, IQR, 13.7% to 18.0%; vs 11.2%, IQR, 5.5% to 15.2%; P = 0.046; Fig. 2a). HCEC did not increase significantly with 6 months of ART under this condition (from 11.2%, IQR, 5.5% to 15.2%; to 11.6%, IQR, 4.2% to 15.2%; P = 1.00). Under conditions using LXR agonist incubation overnight and during incubation with purified HDL, no statistically significant difference was found in HCEC between the non–HIV-infected subjects and HIV-infected subjects at baseline (10.8% ± 4.6% vs 11.3% ± 5.1%; P = 0.82; Fig. 2b) and no statistically significant difference was found in HCEC after 6 months of cART (from 11.3% ± 5.1% to 12.5% ± 3.4%; P = 0.52).

HCEC with purified HDL using (a) 18 hours of LXR agonist and (b) 24 hours of LXR agonism. (a) HCEC was assessed with purified HDL and 18 hours of LXR agonism. A borderline statistically significant difference was seen in HCEC between the non–HIV-infected subjects and the HIV-infected subjects (15.1%, IQR, 13.7% to 18.0%; vs 11.2%, IQR, 5.5% to 15.2%; P = 0.046). HCEC did not increase substantially with 6 months of E/C/F/TDF under this condition (11.2%, IQR, 5.5% to 15.2% to 11.6%, IQR, 4.2% to 15.2%; P = 1.00). (b) HCEC was assessed with purified HDL and 24 hours of LXR agonism. No statistically significant difference was seen in HCEC between non–HIV-infected subjects and HIV-infected subjects at baseline (10.8% ± 4.6% vs 11.3% ± 5.1%; P = 0.82). After 6 months of cART, also no statistically significant increase in HCEC was seen using purified HDL and maximal stimulation with an LXR agonist (11.3% ± 5.1% to 12.5% ± 3.4%, P = 0.52).
Figure 2.

HCEC with purified HDL using (a) 18 hours of LXR agonist and (b) 24 hours of LXR agonism. (a) HCEC was assessed with purified HDL and 18 hours of LXR agonism. A borderline statistically significant difference was seen in HCEC between the non–HIV-infected subjects and the HIV-infected subjects (15.1%, IQR, 13.7% to 18.0%; vs 11.2%, IQR, 5.5% to 15.2%; P = 0.046). HCEC did not increase substantially with 6 months of E/C/F/TDF under this condition (11.2%, IQR, 5.5% to 15.2% to 11.6%, IQR, 4.2% to 15.2%; P = 1.00). (b) HCEC was assessed with purified HDL and 24 hours of LXR agonism. No statistically significant difference was seen in HCEC between non–HIV-infected subjects and HIV-infected subjects at baseline (10.8% ± 4.6% vs 11.3% ± 5.1%; P = 0.82). After 6 months of cART, also no statistically significant increase in HCEC was seen using purified HDL and maximal stimulation with an LXR agonist (11.3% ± 5.1% to 12.5% ± 3.4%, P = 0.52).

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HCEC using apoB-depleted sera without LXR agonism

Given the substantial difference in HCEC using apoB-depleted sera using partial LXR agonist stimulation, an additional experiment was performed using apoB-depleted sera but no LXR agonist stimulation throughout the experimental condition. Under these conditions, HCEC was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline (14.9%, IQR, 11.5% to 19.1%; vs 8.1%, IQR, 7.0% to 11.9%; P = 0.009; Fig. 3). Additionally, 6 months of cART led to a statistically significant increase in HCEC in the HIV-infected subjects (from 8.1%, IQR, 7.0% to 11.9%; to 12.9%, IQR, 10.4% to 21.1%; P = 0.006), although not to the levels seen in the non–HIV-infected subjects (P = 0.45).

HCEC with apoB-depleted sera and no LXR agonism was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline (14.9%, IQR, 11.5% to 19.1% vs 8.1%, IQR, 7.0% to 11.9%; P = 0.009). Additionally, 6 months of cART led to a statistically significant increase in HCEC in HIV-infected subjects from 8.1% (IQR, 7.0% to 11.9%) to 12.9% (IQR, 10.4% to 21.1%; P = 0.006).
Figure 3.

HCEC with apoB-depleted sera and no LXR agonism was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline (14.9%, IQR, 11.5% to 19.1% vs 8.1%, IQR, 7.0% to 11.9%; P = 0.009). Additionally, 6 months of cART led to a statistically significant increase in HCEC in HIV-infected subjects from 8.1% (IQR, 7.0% to 11.9%) to 12.9% (IQR, 10.4% to 21.1%; P = 0.006).

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Association analysis of changes in flow cytometry, immune/inflammatory markers, and HIV-specific parameters to changes in HCEC with cART

Within the HCEC condition using apoB-depleted sera and partial stimulation with an LXR agonist, the change in HCEC with ART was inversely related to the change in the percentage of CD14−CD16+ (nonclassical/homing) monocytes [Spearman rho (ρ) = −0.74, P = 0.04; Supplemental Fig. 1a] and was directly related to the change in the percentage of CD14+CD16− (classical) monocytes (ρ = 0.72, P = 0.045; Supplemental Fig. 1b). The inverse relationship between HCEC and CD14−CD16+ monocytes and direct relationship between HCEC and CD14+CD16− was not relevant in a sensitivity analysis that excluded the participant with the greatest HCEC. Within the HCEC condition using apoB-depleted sera and no LXR stimulation, a trend was found toward an inverse relationship between the change in HCEC and the change in sCD14 (ρ = −0.60, P = 0.067; Supplemental Fig. 2).

HDL cholesterol levels and change in HDL cholesterol levels in relation to HCEC

Among all study subjects, both HIV status and baseline HDL cholesterol levels were significantly related to the baseline HCEC in the condition using apoB-depleted sera and partial stimulation with an LXR agonist (R2 = 0.84, P < 0.0001 for the entire model; P < 0.0001 for HIV status, and P = 0.01 for baseline HDL; Supplemental Fig. 3). In the HIV-infected subjects, a small, but nonsignificant, increase occurred in HDL cholesterol levels with 6 months of cART. In the two experimental conditions in which HCEC significantly increased with cART (apoB-depleted sera with partial stimulation with an LXR agonist and apoB-depleted sera with no LXR agonism), the change in HCEC did not relate to the change in HDL cholesterol levels with cART (data not shown).

Sensitivity analyses for baseline HCEC using subjects matched by age and BMI

Among the overall cohorts, a sensitivity analysis was performed in which subgroups from the HIV and non-HIV groups were specifically matched by age (median, 28 years; IQR, 26 to 49; vs 29 years; IQR, 27 to 50; P = 0.60; for non–HIV-infected vs HIV-infected subjects) and BMI (26.9 ± 3.8 kg/m2 vs 25.4 ± 4.2 kg/m2; P = 0.58) for 5 HIV-infected and for 5 non–HIV-infected subjects. HCEC was significantly greater in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline under the condition of apoB-depleted sera and LXR agonism for 18 hours (5.8% ± 1.2% vs 1.6% ± 1.4%; P = 0.001). We also found a statistically significant difference in HCEC in the non–HIV-infected subjects compared with the HIV-infected subjects at baseline using apoB-depleted sera with LXR agonism for 24 hours (16.7% ± 4.2% vs 11.2% ± 2.3%; P = 0.04). Also, a trend was found toward significantly greater HCEC in the non–HIV-infected subjects compared with the HIV-infected subjects with the apoB-depleted sera and no LXR agonism (data not shown). No substantial differences were found in HCEC in the non–HIV-infected group compared with the HIV-infected group at baseline using the purified HDL in experimental conditions.

Discussion

In the present study, we found that the HCEC was significantly decreased in ART-naive HIV-infected subjects with a recent diagnosis of HIV compared with non–HIV-infected subjects in a comprehensive analysis of varying experimental conditions to determine the potential mechanisms of this effect. Additionally, we have presented data that HCEC had significantly increased after 6 months of a contemporary, integrase inhibitor-based cART regimen but not to the levels seen in non–HIV-infected subjects. Although the HDL levels of our subjects might have affected the HCEC measured in our experimental condition using apoB-depleted sera, the relevant increase in HCEC seen with cART was not related to the change in HDL levels with cART. Our study also found evidence relating the change in HCEC to the change in monocyte subpopulations in the HIV-infected subjects receiving cART; a finding that provides a possible mechanism whereby cART initiation through its effects on immune activation might in turn improve HDL functionality. The persistent impairment in HCEC seen in HIV-infected subjects after 6 months of cART could be similarly related to the persistent immune activation seen in virally suppressed HIV-infected subjects receiving cART, with clinical implications for the treatment of such patients with additional anti-inflammatory strategies.

Previous in vitro studies have shown that HIV infection via the accessory protein Nef affects HDL cholesterol efflux through downregulation of the transporter protein that mediates the transfer of cholesterol across the cellular membrane to HDL particles, ABCA1 (13). HDL cholesterol efflux occurs predominantly through this ABCA1 pathway in human cholesterol-laden macrophages, in contrast to mouse macrophages, in which the aqueous diffusion pathway predominates (9). Although the HDL cholesterol efflux model in our study used J774 mouse macrophages, the ABCA1 pathway was stimulated in our experimental conditions using an LXR agonist, which, together with the LXR promotes ABCA1 transcription (26). Additionally, a J774 mouse macrophage in vitro assay with upregulation of the ABCA1 pathway was similarly used in previous studies by Khera et al. (10) and Rohatgi et al. (11), which related HCEC to carotid intima-media thickness and angiographic coronary artery disease and CVD events, respectively. Our study used variable levels of stimulation of the ABCA1 pathway and demonstrated an important difference in HCEC among HIV-infected subjects and non–HIV-infected subjects, specifically in the condition of low LXR agonist stimulation and no LXR agonism—conditions that mirror the downregulation of ABCA1 in HIV infection.

Our study also evaluated the cholesterol efflux capacity of HDL in the context of apoB-depleted sera (which removes LDL subfractions of the sera) and the purified HDL particles from the subjects. Proteomic analyses performed on purified HDL from HIV-infected subjects have shown decreased levels of paraoxonase 1 and paraoxonase 3 relative to those of the non–HIV-infected subjects (12). Paraoxonase 1 and paraoxonase 3, which are located on the surface of HDL particles, increase HDL cholesterol efflux from macrophages via the ABCA1 pathway (27). In both of these experimental conditions, substantial differences were noted between the ART-naive HIV-infected subjects and the non–HIV-infected subjects. HCEC, however, did not significantly change after 6 months of cART with purified HDL. Although a statistically significant change was not seen in HCEC assessed using purified HDL, a subset of subjects did have an increase in HCEC after ART. Those who did not have an increase in HCEC when assessed using purified HDL, in general, had had a qualitatively longer duration since their HIV diagnosis and higher levels of markers of immune activation. Given that HIV infection and inflammation have both been shown to affect the composition and/or structure of HDL, it is possible that differences in the response to ART among subjects when HCEC was assessed using purified HDL could be related to differences in the duration of HIV and levels of immune activation.

Our study found that the changes in HCEC using apoB-depleted sera correlated substantially with the changes in the percentage of CD14+CD16− (classical) monocytes and was inversely related to the changes in the percentage of CD14−CD16+ (nonclassical/homing) monocytes. Studies have shown that within the HIV-infected population, the percentages of CD14+CD16− monocytes were lower compared with those in non–HIV-infected control subjects and the percentage of CD14−CD16+ monocytes were greater in the HIV-infected population compared with those in the non–HIV-infected subjects (14, 28). CD14−CD16+ monocytes are also known as homing monocytes owing to their ability to home to the vascular endothelium via expression of the CX3C chemokine receptor 1 and C-C chemokine receptor type 5 (29–31). In our study, the decrease in the proportion of these homing monocytes with cART was inversely related to the improvement in HCEC with cART measured using apoB-depleted sera. Given that monocyte subpopulations are characterized by distinct cytokine/chemokine production profiles (32), systemic changes in these monocyte subpopulations with ART could have led to changes in the cytokines/chemokines present in the sera of our HIV-infected subjects. The change in the cytokines/chemokines present in the sera, in turn, could have contributed to the improvement in HCEC seen in our HIV-infected cohort. This relationship between inflammation/immune activation and HCEC was further supported by our results showing improvement in HCEC using apoB-depleted sera and not in the experiments using purified HDL only. Although a statistically significant relationship was not found between the change in HCEC and the change in select markers of immune activation measured (sCD163, sCD14, CXCL-10, MCP-1, hsIL-6, CRP, and Lp-PLA2), a trend was found toward an inverse relationship in the change in HCEC and the change in sCD14, a marker of monocyte activation that has been associated with both mortality and atherosclerosis in HIV (33, 34). Moreover, the change in HCEC seen in our HIV-infected subjects could have resulted from cytokines and/or chemokines not assessed in our study. Together, these results suggest that the immune activation in HIV infection might affect HDL cholesterol efflux and that the improvement in HCEC with cART might, in turn, relate to the decrease in immune activation seen with the suppression of viremia by cART.

Several other studies have assessed HCEC in HIV-infected subjects and the effect of ART on HCEC using various ex vivo macrophage models. A previous study within our group by Lo et al. (15) assessed HCEC in ART-naive HIV-infected subjects using apoB-depleted sera. In that previous study, HCEC was assessed in subjects acutely infected with HIV who were then randomized to either ART—the combination of which varied among subjects—or no ART (15). In contrast to the present study, which used an LXR agonist to variably stimulate the ABCA1 pathway, Lo et al. (15) used macrophages derived from either ABCA1−/− and ABCA1+/+ mice to evaluate HCEC. In their study, HCEC increased significantly after 12 weeks of ART with HCEC assessed using ABCA1+/+ macrophages but not in the ART-untreated HIV-infected subjects (15). In another study among HIV-infected subjects with a recent diagnosis of HIV, who were ART naive, HCEC was assessed using THP-1 human macrophages, together with total plasma or HDL subfractions from subjects (35). In contrast to the present study, the study by El Khoury et al. (35) found that HCEC improved after cART—the combination of which varied among subjects, just as in the study by Lo et al. (15)—when HCEC was assessed using both the total plasma and the purified HDL subfractions. A more recent study by Funderburg et al. (36), which also used J774 macrophages, also found an improvement in HCEC after 48 weeks of an integrase inhibitor-based ART regimen. In their study, the HDL particle number also increased after ART initiation (36). Although these studies, similar to our study, demonstrated that HCEC improves after ART, our findings add substantially to the published data showing the relationships of these effects to changes in detailed indexes of monocyte activation using flow cytometry. Future studies are necessary to determine the relative contributions that inflammation/immune activation and HDL structure/composition have on the ART-induced changes in HCEC.

The limitations of our study included the use of an ex vivo model to assess HCEC and our relatively small sample size. Additionally, all subjects enrolled in the present study were men; thus, our findings might not be generalizable to women, given the known sex differences in lipoprotein concentration and particle size and distribution (37). The strengths of our study included the use of a contemporary integrase inhibitor-based ART regimen that was uniformly taken by all HIV-infected study subjects. Our study also used a population of HIV-infected subjects with a new diagnosis of HIV and who were ART naive. Although our study did use an ex vivo model, the mouse macrophage model used was similar to that used in studies that have related HCEC to CVD events in the general population (11). Additionally, the use of experimental conditions using both apoB-depleted sera and purified HDL allowed us to investigate whether the changes seen in HCEC with ART are related to changes in the HDL particles and/or other factors present in the subject sera such as inflammatory cytokines/chemokines.

In conclusion, to the best of our knowledge, the present study is the first to evaluate HCEC in newly diagnosed ART-naive HIV-infected subjects using both apoB-depleted sera and purified HDL. We demonstrated an improvement in HCEC after 6 months of a contemporary integrase inhibitor-based ART regimen. Our study findings have extended those of previous studies through the use of diverse experimental conditions to assess HCEC, including conditions that used two distinct cholesterol acceptors and variable LXR agonism. Our study also demonstrated an important association between improvement in HCEC and changes in monocyte subpopulations after ART initiation among our HIV-infected cohort. The substantial relationship between improvement in HDL functionality, together with improvement in the monocyte phenotype seen in HIV-infected subjects with this integrase inhibitor-based ART, suggests a mechanism in which immune activation could affect atherosclerotic plaque progression in HIV through its effects on HDL functionality/antiatherogenic properties of HDL. Although HCEC improved after ART initiation among our HIV group, it did not improve to the level seen in our non–HIV-infected subjects. Given the association between HCEC and CVD events, future studies should evaluate adjunctive strategies to ART to improve this persistent impairment in HCEC seen in virally suppressed HIV-infected subjects.

Abbreviations

     
  • ABCA1

    adenosine triphosphate-binding cassette transporter A1

    •  
  • apoB

    apolipoprotein B

    •  
  • ART

    antiretroviral therapy

    •  
  • BMI

    body mass index

    •  
  • cART

    combined antiretroviral therapy

    •  
  • CVD

    cardiovascular disease

    •  
  • E/C/F/TDF

    elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate

    •  
  • HCEC

    high-density lipoprotein cholesterol efflux capacity

    •  
  • HDL

    high-density lipoprotein

    •  
  • IQR

    interquartile range

    •  
  • LXR

    liver X receptor.

Acknowledgments

Financial Support: This work was supported by an investigator-initiated grant from Gilead Sciences to S.K.G. M.T. was supported by the National Institutes of Health (NIH) Institutional National Research Service Award (grant T32DK007028-41) and the NIH Ruth L. Kirschstein National Research Service Award (F32AI129700-01A1). M.V.Z. was supported by a Medical Research Investigator Training award from the Harvard Catalyst/The Harvard Clinical and Translational Sciences Center (National Center for Research Resources and the National Center for Advancing Translational Sciences, NIH; grant 8KL2TR000168-05). M.L.F. was supported by a grant from the NIH (grants R01 HL101274 and R01 HL112661). This work was also supported by the Nutrition Obesity Research Center at Harvard (grant DK00561) and by grants to the Harvard Clinical and Translational Science Center from the National Center for Research Resources (grants 8 UL 1TR000170 and 1 UL 1TR001102). The sponsors funded the study but had no role in the analysis of the data or in the decision to publish data.

Clinical Trial Information: ClinicalTrials.gov no. NCT01766726 (registered 9 January 2013).

Author Contributions: M.T. was involved in data analysis, data interpretation, reference search, figure and table preparation, and writing. M.H.P. was involved in data acquisition, data analysis, and data interpretation. M.V.Z. was involved in performing study visits, study concept, study design, and data interpretation. G.K.R. was involved in subject recruitment and data interpretation. T.H.B. was involved in data acquisition and data interpretation. M.N.F. was involved in performing study visits and data interpretation. L.S. was involved in data interpretation. K.M. was involved in reference search and data interpretation. S.K.G. was involved in study concept, study design, data interpretation, and writing. M.L.F. was involved in study concept, study design, data acquisition, data analysis, data interpretation, reference search, and writing. All the authors contributed to critical revision of the manuscript.

Current Affiliation: T.H. Burdo’s current affiliation is the Department of Neuroscience, Temple University School of Medicine, Philadelphia, Pennsylvania 19140.

Disclosure Summary. M.V.Z. participated in a scientific advisory board meeting for Roche Diagnostics and received grant support from Gilead Sciences, all unrelated to the present study. G.K.R. received clinical trial grant support from XBiotech and Gilead Sciences, both unrelated to the present study. K.C.W. served on the scientific advisory board and was paid by Macrophage Therapeutics LLC, unrelated to the present study. K.M. is an employee of Gilead Sciences. S.K.G. served as a paid consultant to Gilead Sciences, Theratechnologies, Bristol-Meyer Squibb, NovoNordisk, Merck, Navidea, and AstraZeneca and received grant support from Amgen, Bristol-Meyer Squibb, Gilead Sciences, KOWA Pharmaceuticals America, Inc., and Theratechnologies, unrelated to the present study. S.K.G. also received a grant to perform this project from Gilead Sciences.

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Author notes

*

These authors contributed equally to the present study.

Copyright © 2017 Endocrine Society
Issue Section:
Clinical Research Articles > Lipids and Cardiovascular
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