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MAJOR ARTICLE
Monocyte-Activation Phenotypes Are Associated
With Biomarkers of Inflammation and
Coagulation in Chronic HIV Infection
Eleanor M. P. Wilson,1,a Amrit Singh,1,a Katherine Huppler Hullsiek,2 Dave Gibson,5 W. Keith Henry,3,5 Ken Lichtenstein,6
Nur F. Önen,5 Erna Kojic,7 Pragna Patel,8 John T. Brooks,8 Irini Sereti,1 and Jason V. Baker,3,4 for the Study to Understand
the Natural History of HIV/AIDS in the Era of Effective Therapy (SUN Study) Investigators
1
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland; 2Division of Biostatistics, 3Department of
Medicine, University of Minnesota, and 4Division of Infectious Diseases, Hennepin County Medical Center, Minneapolis, Minnesota; 5Washington
University School of Medicine, St. Louis, Missouri; 6National Jewish Health, Denver, Colorado; 7Miriam Hospital, Providence, Rhode Island; and 8Division
of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia
Background. Soluble biomarkers of inflammation predict non-AIDS related morbidity and mortality among
human immunodeficiency virus (HIV)–infected persons. Exploring associations between plasma biomarkers and
cellular phenotypes may identify sources of excess inflammation.
Methods. Plasma biomarkers (interleukin 6 [IL-6] level, D-dimer level, high-sensitivity C-reactive protein
[hsCRP] level, soluble CD14 [sCD14] level, and soluble CD163 [sCD163] level) were measured from cryopreserved
samples from the Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy (SUN
Study). We performed immunophenotyping of peripheral blood mononuclear cells for markers of T-cell and
monocyte activation, maturation, and migration. We evaluated associations between cellular phenotypes and soluble
biomarkers by Spearman rank correlation and multivariate linear regression.
Results. Participants’ (n = 670) median age was 41 years, 88% were prescribed antiretroviral therapy, 72% had a
plasma HIV RNA load of <400 copies/mL, and the median CD4+ T-lymphocyte count was 471 cells/µL. After adjustment, CD14++CD16+ monocytes were associated with higher levels of IL-6, hsCRP, and sCD163; associations
with IL-6 and hsCRP persisted in persons with suppressed HIV replication. While CCR5+ monocytes positively associated with D-dimer levels, CCR2+ monocytes were inversely associated with hsCRP levels.
Conclusions. Plasma inflammatory biomarkers that predict morbidity and mortality were strongly associated
with monocyte activation and migration, modestly associated with T-cell maturation, and not associated with
CD8+ T-cell activation phenotypes. These findings suggest that strategies to control monocyte activation warrant
further investigation.
Keywords. monocytes; HIV; immune activation; IL-6; D-dimer; C-reactive protein.
Effective combination antiretroviral therapy (ART)
has dramatically increased but not fully restored the
Received 25 February 2014; accepted 2 May 2014; electronically published 9 May
2014.
Presented in part: D2 HIV Pathogenesis Keystone Symposium, Beaver Run, Colorado, 3–8 April 2013; Conference on Retroviruses and Opportunistic Infections,
Boston, Massachusetts, 3–6 March 2014.
a
E. M. P. W. and A. S. contributed equally to this work.
Correspondence: Jason V. Baker, MD, MS, 701 Park Ave (MC G5), Minneapolis,
MN 55415 ([email protected]).
The Journal of Infectious Diseases® 2014;210:1396–406
Published by Oxford University Press on behalf of the Infectious Diseases Society of
America 2014. This work is written by (a) US Government employee(s) and is in the
public domain in the US.
DOI: 10.1093/infdis/jiu275
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life expectancy of people with human immunodeficiency (HIV) infection [1, 2]. With increasing CD4 +
T-lymphocyte counts and control of viral replication,
serious non-AIDS events, such as premature cardiovascular disease and cancer, are now the dominant causes
of morbidity and mortality in treated populations [3, 4].
This excess non–AIDS-related morbidity and mortality
may be, in part, a consequence of ongoing systemic inflammation related to HIV infection [5–7]. Data from
multiple epidemiologic studies have demonstrated that
plasma markers of inflammation (interleukin 6 [IL-6]
and high sensitivity C-reactive protein [hsCRP] levels),
coagulation (D-dimer), and monocyte activation (soluble
CD14 [sCD14] and soluble CD163 [sCD163] levels) predict increased rates of cardiovascular disease, non-AIDS events, and
all-cause mortality [6, 8–12].
The degree to which innate or adaptive immunological pathways contribute to the systemic inflammation that, in turn, predicts serious non-AIDS events in persons with chronic HIV
infection has not been clearly defined. Some studies suggested
that persistent T-cell activation is associated with impaired immune recovery and development of atherosclerotic disease
among HIV-infected persons [13–15]. The observation that
elevated plasma levels of IL-6 and sCD14, which reflect innate
immune activation, and of D-dimer, a marker of coagulation,
independently predict mortality suggests that monocyte activation and coagulopathy may contribute to disease risk [8, 9]. Indeed, an increased frequency of activated, proinflammatory
monocyte subsets (also designated CD14++CD16+ monocytes)
has been associated with an increased risk for cardiovascular
events in the general population [16], and an increased frequency of these monocytes has been noted in blood specimens
from HIV-infected persons [17]. We have previously reported
that greater frequencies of CD14++CD16+ monocytes predict
more-rapid progression of coronary atherosclerosis among patients in the Study to Understand the Natural History of HIV/
AIDS in the Era of Effective Therapy (SUN Study) [18].
The administration of ART improves but does not always
fully normalize levels inflammatory biomarkers [9, 19] that remain associated with cardiovascular disease and mortality despite long-term ART and virologic suppression [20]. While
combination ART has been the standard of care in the treatment of HIV infection since 1996, many longitudinal studies
of chronically infected, treated patients have been complicated
by inclusion of nonstandard or outdated management practices
(ie, structured treatment interruptions, sequential monotherapy,
or dual protease inhibitor therapy) that may not reflect how
current standards of care influence ongoing immune activation.
The SUN Study is a prospective, observational study of a contemporary cohort of HIV-infected adults at low risk for AIDS, who, if
treated, were exposed only to combination ART [21].
Here, we have characterized associations between plasma biomarkers previously shown to predict risk for non-AIDS clinical
events and phenotypes of activated immune cells. We have
found that these biomarkers tended to associate with phenotypes reflecting monocyte activation, highlighting the potential
role of innate immune activation in long-term complications of
treated HIV infection.
METHODS
Study Design
The SUN Study was a Centers for Disease Control and Prevention (CDC)–funded prospective observational cohort study of
HIV-infected participants enrolled in 4 US cities (Denver,
Minneapolis, Providence, and St. Louis) between March 2004
and June 2006 [21]. Human subjects research committees at
the CDC and each clinical site approved the protocol, and all
participants provided written informed consent. Eligible participants were expected to survive for at least 2 years and were
either treatment naive or exposed solely to combination ART
(≥3 nucleoside reverse transcriptase inhibitors or ≥3 antiretroviral drugs from at least 2 different classes).
Clinical data, including all medications and diagnoses, were extracted from medical charts and entered into a single database
(Clinical Practice Analyst; Cerner, Vienna, VA). Additional data
were collected through study-specific physical examinations, laboratory testing, and an audio computer-assisted self-interview [21].
Specimen Collection and Laboratory Measurements
At study entry, fasting whole blood and plasma specimens were
collected and shipped overnight to the CDC. Peripheral blood
mononuclear cells (PBMCs) were isolated and cryopreserved
centrally at a CDC laboratory within 30 hours of blood collection. All specimens were then stored in liquid nitrogen at −70°C
until analyzed. Clinical site laboratory testing included measurement of fasting serum lipid levels, plasma HIV RNA load,
and CD4+ T-cell counts.
Measurement of Biomarkers in Cryopreserved Plasma
The following soluble biomarkers were measured at the Diabetes Research and Training Center Radioimmunoassay Core
Laboratory (Washington University School of Medicine):
high-sensitivity C-reactive protein (hsCRP; Kamiya Biomedical,
Seattle WA) and D-dimer (Roche Diagnostics, Indianapolis IN)
levels, using immunoturbidometric assays on a Hitachi 917 analyzer; soluble CD14 (sCD14) levels were measured using an
enzyme-linked immunosorbent assay (ELISA)–based assay
(R&D Systems, Minneapolis MN). In addition, we used electroluminescence immunossays to measure interleukin 6 (IL-6) levels (Meso Scale Discovery, Rockville MD) levels and ELISA to
measure soluble CD163 (sCD163) levels (Aviscera Bioscience,
Santa Clara CA), according to the manufacturers’ instructions.
Immunophenotyping of PBMCs
Immunophenotyping was performed on cryopreserved PBMCs
using multicolor flow cytometry. Panels of fluorochromeconjugated antibodies for cell surface markers (and viability
dye to exclude nonviable cells) have been validated previously
[6]. The fluorochrome-conjugated antibodies used were antiCD2 efluor450 (clone RPA-2.10), anti-CD3 efluor450 (clone
UCHT1), anti-CD19 efluor450 (clone HIB19), anti-CD20
APC (clone 2H7), anti-CD38 PE-Cy7 (clone HIT2), antiCX3CR1 PE (clone 2A9-1), anti-CD28 PE-Cy7 (clone
CD28.2), anti-CD4 efluor605 (clone OKT4), and anti-HLADR efluor605 (clone LN3), all from eBioscience; antiCD57 APC (clone HCD57), anti-CD27 Ax700 (clone O323),
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anti-CX3CR1 APC (clone 2A9-1), anti-CD16 PE-Cy7 (clone
3G8), and anti-CCR2 PerCp-Cy5.5 (clone TG5), all from BioLegend; anti-HLA-DR PE (clone G46-6), anti-CD3 APC-Cy7
(clone SK7), anti-CD8 PB (clone RPA-T8), anti-CD14 PE
(clone M5E2), anti-CCR5 APC-Cy7 (clone 2D7), and antiCD56 PB (clone B159), all from BD Biosciences; anti-TF FITC
(clone VIC7) from American Diagnostica; anti-CD45RO ECD
(clone UCHL1) from Beckman Coulter; and Live/Dead Fixable
Blue Dead Cell Stain Kit with UV excitation from Invitrogen.
Samples were acquired on an LSR-II flow cytometer (BD), and
data were analyzed using FlowJo software, version 9.3.3 (Treestar,
Ashland, OR). The proportion of cells are expressed as percentages, with the following cell types characterized: classical
monocyte phenotype (CD14++CD16−); intermediate monocyte
subset (CD14++CD16+); patrolling monocytes (CD14dimCD16+);
CD14varCD16+, which encompasses both intermediate and
patrolling subsets; monocytes expressing tissue factor (TF);
monocytes expressing markers of tissue migration (CCR5+,
CCR2+, and CX3CR1+); and CD4+ and CD8+ T cells with activated (HLA-DR+CD38+), antigen-responsive (CD28+), senescent
Table 1. Characteristics of 670 Participants at Entry Into the
Study to Understand the Natural History of HIV/AIDS in the Era
of Effective Therapy
Characteristic
Value
Age, y
41 (35–47)
Male sex
510 (76.1)
Ethnicity
Non-Hispanic white
390 (58.2)
Non-Hispanic black
201 (30.0)
Other
BMIa
79 (11.8)
25.8 (23.0–29.0)
Current tobacco use
288 (44.1)
Injection drug use
HBV or HCV coinfection
89 (13.6)
112 (17.0)
Diabetes mellitus
59 (8.8)
Hypertension
Present
213 (31.8)
Received treatment
127 (19.0)
Treatment for hyperlipidemia
Previous AIDS diagnosis
57 (8.5)
164 (24.5)
ART
Prescribed
Duration
590 (88.1)
32.5 (13.0–64.9)
Plasma HIV RNA load <400 copies/mL
CD4+ T-cell count, cells/µL
Current
489 (73.2)
471 (336–680)
Nadir
206 (90–319)
Data are median value (interquartile range) or no. (%) of study participants.
Abbreviations: ART, antiretroviral therapy; HBV, hepatitis B virus; HCV, hepatitis
C virus; HIV, human immunodeficiency virus.
a
Body mass index (BMI) is defined as the weight in kilograms divided by the
height in square meters
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(CD57+ or CD57+CD38+), or migratory (CD4+CX3CR1+) phenotypes. Representative flow cytometry plots are shown in Supplementary Figure 1. All samples had >75% viability; only live
cells were included in all analyses.
Statistical Methods
Baseline characteristics were summarized with frequencies or
medians and interquartile ranges as appropriate. Spearman
rank correlations and linear regression were used to explore associations between markers of cellular activation and levels of
selected soluble inflammatory biomarkers—IL-6, D-dimer,
hsCRP, sCD14, and sCD163—before and after adjustment for
clinical and HIV-related characteristics. Fully adjusted models
included the following covariates obtained at study entry: age,
sex, race, ethnicity, tobacco smoking status, diagnosis of diabetes, diagnosis of hepatitis B virus or hepatitis C virus coinfection, receipt of treatment to decrease blood pressure or lipid
Table 2. Distribution of Soluble Plasma Biomarkers and
Immunophenotypes Among Participants in the Study to
Understand the Natural History of HIV/AIDS in the Era of
Effective Therapy
Biomarker or
Immunophenotype
Participants,
No.
Median (IQR)
Biomarker
IL-6 level, pg/mL
D-dimer level, mg/L
598
662
1.1 (0.7–1.7)
0.1 (0.1–0.2)
hsCRP level, mg/L
664
1.8 (0.8–4.5)
sCD14 level, mg/mL
sCD163 level, pg/mL
670
583
1.1 (0.9–1.4)
532.5 (370.1–796.3)
T-cell phenotype
CD4+ T cells
CD28+
670
84.8 (73.3–91.9)
HLA-DR+CD38+
670
8.0 (4.5–14.2)
CD57+CD28−
CD57+CD38+
670
670
7.4 (3.3–14.9)
3.6 (1.6–8.1)
CX3CR1+
670
2.7 (0.8–6.0)
CD8+ T cells
CD28+
670
27.2 (17.0–38.6)
HLA-DR+CD38+
670
20.6 (11.6–34.9)
CD57+CD28−
CD57+CD38+
670
670
33.9 (26.4–43.8)
22.1 (13.3–30.9)
670
670
89.2 (83.9–93.5)
4.4 (2.1–9.1)
Monocyte phenotype
CCR2+
CCR5+
CX3CR1+
670
7.3 (3.6–11.6)
CD14++CD16−
CD14++CD16+
670
670
79.4 (72.3–83.8)
5.2 (3.4–7.9)
CD14dimCD16+
670
6.1 (3.7–10.4)
CD14varCD16+
TF+
670
670
12.2 (8.4–17.9)
1.5 (0.8–3.2)
Abbreviations: hsCRP, high-sensitivity C-reactive protein; IL-6, interleukin 6;
IQR, interquartile range; sCD14, soluble CD14; sCD163, soluble CD163.
Figure 1. Representative flow cytometry graphs depicting selected cellular phenotypes. Representative plots showing selected phenotypes. Selected monocytes subsets are shown, defined by CD14 and CD16 expression (A) and by CCR2 and CX3CR1 expression (B). Selected T-cell activation phenotypes are also
depicted, defined by HLA-DR and CD38 expression (C ) and by CD57 and CD28 expression (D ). For compete gating methods, see Supplementary Figure 1.
levels, CD4+ T-cell count, and HIV RNA load of <400 copies/
mL. Regression coefficients were transformed to express the
percentage change (and 95% confidence intervals [CIs]) in
the level of each soluble biomarker for every 5% absolute
increase in a cellular phenotype. We chose a 5% absolute difference as a sole-standard increment to allow comparison of the
regression coefficients across all the cellular phenotypes studied.
To compensate for multiple comparisons, we defined statistical
significance as a P value ≤ .01.
RESULTS
Participant Characteristics
Of all 691 SUN Study participants, 670 had baseline plasma
specimens and PBMCs available and were included in our
investigation. Their characteristics at study entry are presented
in Table 1. Characteristics of subjects without baseline samples
available were similar to those of included subjects, with the exception of tobacco use, which was less frequent among the
omitted subjects (data not shown). The median age was 41
years, and the majority of subjects were male (76%), nonHispanic white (58%), and slightly overweight (median body
mass index [BMI; defined as the weight in kilograms divided by
the height in square meters], 25.8). Consistent with other studies
of HIV-infected individuals, 44% of this cohort used tobacco at
enrollment, and 14% used injection drugs. Prevalent comorbidities included diabetes mellitus (9%), hypertension (32%), and
chronic viral hepatitis (17%). Nearly a quarter had a previous
AIDS diagnosis. Median CD4+ T-cell count was 471 cells/µL.
Of 590 participants receiving combination ART at the time of
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blood specimen collection, 482 (72%) had plasma HIV RNA
levels of <400 copies/mL. The distribution of plasma and cellular biomarkers are shown in Table 2. Representative flow cytometry graphs depicting selected cellular phenotypes are shown in
Figure 1.
Correlations Between Expression of Cellular Markers and
Plasma Biomarkers
Spearman rank correlations were used to explore associations between levels of soluble biomarkers and cellular phenotypes. Figure 2 shows associations between plasma levels of biomarkers
and expression of selected monocyte activation and migration
phenotypes (Figure 2A) and T-cell phenotypes (Figure 2B).
Monocyte CCR5 expression correlated with IL-6 and D-dimer
levels, and TF expression correlated with IL-6, hsCRP, and
sCD14 levels. The frequency of the CD14++CD16+ proinflammatory activated subset of monocytes was positively correlated with
plasma levels of IL-6, D-dimer, hsCRP, and sCD163. So-called
patrolling (ie, CD14dimCD16+) monocytes correlated with
sCD14 but not with sCD163. In contrast, a higher percentage frequency of CCR2+ monocytes, signifying monocytes with tissue
migratory potential, was inversely correlated with hsCRP level.
The expression of CD28, a marker expressed on resting naive
and central memory T cells that is lost on more differentiated
effector cells [22], correlated inversely with levels of inflammatory biomarkers. An inverse correlation was observed
between the frequency of CD8+CD28+ subsets and D-dimer
and sCD163 levels; CD8+CD28−CD57− subsets were positively
correlated with IL-6, D-dimer, and sCD163 levels; and
CD4+CD28 −CD57− subsets were correlated with D-dimer
Figure 2. Correlations between phenotypes of immune cells and soluble plasma biomarker levels among 670 participants in the Study to Understand the
Natural History of HIV/AIDS in the Era of Effective Therapy. Spearman rank correlations of soluble biomarkers with monocyte activation and migration
markers (A) and T-cell activation and maturation phenotypes (B ) with soluble biomarkers. Colored bars represent statistically significant correlations
(P ≤ .01) between the cellular marker and plasma biomarker levels. Open bars represent nonsignificant correlations. Abbreviations: hsCRP, high-sensitivity
C-reactive protein; IL-6, interleukin 6; sCD14, soluble CD14; sCD163, soluble CD163.
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Figure 2
level only. Activated CD4+ and CD8+ T-cell subsets, defined by
coexpression of HLA-DR and CD38, and so-called senescent
CD8+ T cells (ie, those coexpressing CD57 and CD38) were associated only with sCD163 level (not shown). We observed no
significant associations between levels of other markers and the
remaining T-cell or monocyte phenotypes.
continued.
Multivariate Associations
Percentage changes in soluble biomarkers for every 5% absolute
increase in cellular phenotypes observed in univariate models
were similar to observed Spearman rank correlations and are
shown in Supplementary Table 1. Results of multivariate modeling adjusted for clinical and HIV-related factors, shown in
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Tables 3 and 4, demonstrated that intermediate CD14++CD16+
monocytes remained statistically associated with levels of multiple inflammatory biomarkers; an absolute increase of 5% in
the frequency of CD14++ CD16 + monocytes was associated
with an 11.4% higher level of IL-6 (95% CI, 4.9%–18.3%;
P < .001), a 27.4% higher level of hsCRP (95% CI, 16.3%–
39.7%; P < .001), and a 10.9% higher level of sCD163 (95%
CI, 3.3%–19.1%; P < .01). Associations between biomarker levels and T-cell activation and maturation phenotypes, although
statistically significant, were of modest magnitude. For example,
after adjustment, an absolute increase by 5% in the frequency
of CD8+CD28 + cells was associated with a 3.0% lower
sCD163 level (95% CI, 1.4%–4.6%; P < .001) and a 1.2% lower
D-dimer level (95% CI, .5%–1.9%; P < .01). A higher frequency
by 5% of CD4+HLA-DR+CD38+ cells was associated with a
5.1% (95% CI, 2.3%–8.0%; P < .001) higher sCD163 level. We
observed no other significant associations with any other
T-cell activation markers.
Finally, when we restricted comparisons to participants with
suppression of viral replication (Supplementary Table 2), associations were similar; an absolute increase by 5% in the frequency of the CD14++CD16+ monocyte subset was associated with
higher levels of both hsCRP (28.5% [95% CI, 14.3%–44.5%];
P < .001) and IL-6 (10.7% [95% CI, 2.4%–19.7%]; P = .01). Corresponding associations were present, but to a more modest
degree, for CCR5+ monocytes with a 1.9% higher D-dimer
level (95% CI, 1.0%–2.7%; P < .001); for CD8+CD28−CD57−
T-cells with a 4.0% higher sCD163 level (95% CI, 1.5%–6.6%;
P < .01) and a 2.9% higher D-dimer level (95% CI, 1.9%–
3.9%, P < .001); and for CD4+CD28 −CD57− T-cells with a
2.1% higher D-dimer level (95% CI, .6%–3.5%; P < .01).
DISCUSSION
In this study, we explored associations between cellular phenotypes of innate and adaptive immunity and soluble biomarkers
of inflammation that predict clinical risk among participants in
a contemporary cohort of HIV-infected persons at low risk for
AIDS. As the population of chronically infected adults receiving
effective treatment grows, the frequencies of serious non–AIDSrelated events are projected to increase [23, 24]. The SUN Study
reflects US HIV clinic populations at risk for these long-term
complications: these individuals are predominantly male, middle aged, and have comorbidities that include hypertension, tobacco use, and diabetes mellitus. Here, we report that levels of
inflammatory biomarkers known to be associated with increased clinical risk (IL-6, D-dimer, hsCRP, sCD14, and
sCD163) showed more consistent and stronger associations
with markers of monocyte activation and migration and with
T-cell maturation, rather than with markers of T-cell activation.
These data suggest that monocytes may play a prominent role in
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Table 3. Multivariate Analysis of Percentage Change in Plasma
Biomarkers for Each Absolute 5% Increase in Monocyte
Phenotype Among 670 Participants in the Study to Understand
the Natural History of HIV/AIDS in the Era of Effective Therapy
Monocyte Phenotype,
Biomarker
Percentage Change
(95% CI)
P value
+
CCR2
IL-6 level
−2.1 (−5.0 to .8)
.16
D-dimer level
−0.8 (−2.3 to .7)
.30
hsCRP level
sCD14 level
−6.9 (−11.2 to −2.4)
−0.0 (−1.4 to 1.4)
sCD163 level
−4.2 (−7.5 to −.7)
CCR5+
IL-6 level
D-dimer level
hsCRP level
sCD14 level
sCD163 level
CX3CR1+
IL-6 level
0.8 (−.7 to 2.4)
1.4 (.7–2.2)
<.01
.98
.02
.29
<.001
−0.1 (−2.5 to 2.3)
−0.0 (−.7 to .7)
.92
.96
0.4 (−1.5 to 2.2)
.70
0.8 (−2.8 to 4.5)
.68
D-dimer level
0.4 (−1.4 to 2.2)
.65
hsCRP level
sCD14 level
4.5 (−1.3 to 10.7)
−0.9 (−2.5 to .8)
.13
.31
sCD163 level
4.2 (−.1 to 8.8)
.06
CD14++CD16−
IL-6 level
−0.8 (−3.0 to 1.4)
.46
D-dimer level
−1.1 (−2.1 to −.0)
.05
hsCRP level
sCD14 level
−0.5 (−3.9 to 3.0)
0.4 (−.6 to 1.4)
.76
.41
sCD163 level
−0.3 (−2.8 to 2.4)
.84
CD14++CD16+
IL-6 level
11.4 (4.9–18.3)
<.001
D-dimer level
2.3 (−.7 to 5.4)
.13
hsCRP level
sCD14 level
27.4 (16.3–39.7)
2.2 (−.6 to 5.0)
<.001
.12
sCD163 level
10.9 (3.3–19.1)
<.01
CD14dimCD16+
IL-6 level
−0.3 (−3.9 to 3.4)
.88
D-dimer level
0.9 (−1.0 to 2.7)
.35
2.0 (−3.8 to 8.0)
−1.3 (−2.9 to .4)
.51
.13
0.5 (−3.7 to 5.0)
.81
hsCRP level
sCD14 level
sCD163 level
TF+
IL-6 level
−0.5 (−4.2 to 3.4)
.81
D-dimer level
−0.1 (−2.0 to 1.8)
.93
hsCRP level
sCD14 level
1.8 (−4.0 to 8.0)
−0.1 (−1.8 to 1.7)
.55
.93
sCD163 level
−2.6 (−6.9 to 1.9)
.25
Data are from adjusted linear regression models. Regression coefficients were
transformed to reflect the percentage change in plasma biomarker for each
absolute 5% increase in cellular phenotype. Models were adjusted for age,
sex, race, current smoking, diabetes, receipt of treatment for hypertension,
receipt of treatment to decrease lipid levels, viral hepatitis, CD4+ T-cell
count, and human immunodeficiency virus RNA load of <400 copies/mL.
Abbreviations: CI, confidence interval; hsCRP, high-sensitivity C-reactive
protein; IL-6, interleukin 6; sCD14, soluble CD14; sCD163, soluble CD163.
Table 4. Multivariate Analysis of Percentage Change in Plasma Biomarkers for Each Absolute 5% Increase in T-Cell Phenotype Among
670 Participants in the Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy
CD4+ T Cells
CD8+ T Cells
Percentage Change
(95% CI)
P value
Percentage Change
(95% CI)
P value
IL-6 level
D-dimer level
0.0 (−2.2 to 2.3)
−0.1 (−1.2 to 1.0)
.97
.87
0.5 (−.9 to 1.9)
0.7 (.0–1.4)
.50
.05
hsCRP level
−0.9 (−4.3 to 2.7)
.63
−0.9 (−3.1 to 1.4)
.44
sCD14 level
sCD163 level
0.4 (−.7 to 1.4)
5.1 (2.3–8.0)
.49
<.001
0.5 (−.1 to 1.2)
0.4 (−1.3 to 2.1)
.12
.65
−0.4 (−1.6 to .8)
−0.6 (−1.2 to −.0)
.49
.04
−1.5 (−2.9 to −.1)
−1.2 (−1.9 to −.5)
.04
<.01
T-Cell Phenotype, Biomarker
+
+
HLA-DR CD38
CD28+
IL-6 level
D-dimer level
hsCRP level
1.3 (−.6 to 3.3)
.17
−0.2 (−2.4 to 2.1)
sCD14 level
sCD163 level
−0.1 (−.6 to .5)
−0.4 (−1.8 to 1.0)
.81
.56
−0.5 (−1.1 to .2)
−3.0 (−4.6 to −1.4)
−0.3 (−2.4 to 1.7)
0.6 (−.4 to 1.6)
.74
.24
1.0 (−.7 to 2.8)
−0.3 (−1.2 to .5)
.89
.15
<.001
CD57+
IL-6 level
D-dimer level
.25
.44
hsCRP level
−1.4 (−4.4 to 1.8)
.39
1.8 (−1.0 to 4.6)
.21
sCD14 level
sCD163 level
−0.7 (−1.6 to .2)
−0.4 (−2.8 to 2.1)
.11
.75
0.1 (−.6 to .9)
0.7 (−1.4 to 2.8)
.73
.52
IL-6 level
D-dimer level
2.0 (−.5 to 4.5)
1.5 (.2–2.8)
.12
.02
1.4 (−.3 to 3.1)
2.8 (1.9–3.6)
.10
<.001
hsCRP level
−0.5 (−4.4 to 3.5)
.80
0.1 (−2.6 to 2.8)
1.0 (−.2 to 2.1)
1.7 (−1.2 to 4.7)
.10
.26
0.1 (−.6 to .9)
3.3 (1.3–5.4)
1.0 (−2.0 to 4.1)
0.3 (−1.2 to 1.8)
.52
.70
1.4 (−.3 to 3.1)
−0.6 (−1.4 to .2)
CD28−CD57−
sCD14 level
sCD163 level
.97
.71
<.01
CX3CR1+ or CD28−CD57+a
IL-6 level
D-dimer level
.12
.15
hsCRP level
−1.1 (−5.6 to 3.6)
.64
1.0 (−1.7 to 3.7)
.47
sCD14 level
sCD163 level
0.2 (−1.2 to 1.6)
−0.8 (−4.2 to 2.9)
.78
.68
0.1 (−.6 to .9)
1.0 (−1.0 to 3.1)
.72
.32
Data are from adjusted linear regression models. Regression coefficients were transformed to reflect the percentage change in plasma biomarker for each absolute
5% increase in cellular phenotype. Models were adjusted for age, sex, race, current smoking, diabetes, receipt of treatment for hypertension, receipt of treatment to
decrease lipid levels, viral hepatitis, CD4+ T-cell count, and human immunodeficiency virus RNA load of <400 copies/mL.
Abbreviations: CI, confidence interval; hsCRP, high-sensitivity C-reactive protein; IL-6, interleukin 6; sCD14, soluble CD14; sCD163, soluble CD163.
a
The CX3CR1+ phenotype is associated with CD4+ T cells, and the CD28−CD57+ phenotype is associated with CD8+ T cells.
chronic and serious non-AIDS events, such as premature cardiovascular disease and cancer.
Mounting evidence suggests that HIV-mediated activation of
the coagulation and inflammatory cascade places treated HIVinfected individuals at greater risk of cardiovascular diseases,
compared with uninfected persons [8, 25–27]. This excess risk
is attributable to residual inflammation associated with chronic
HIV disease, independent of HIV disease progression and after
controlling for traditional risk factors, such as age, sex, race,
BMI, use of antihypertensive therapy or treatment to lower
lipid levels, and tobacco use [28].
We specifically studied monocyte subsets because of recent
data highlighting the role of activated monocytes in cardiovascular disease among HIV-infected and HIV-uninfected populations
[16, 29]. We were able to show that an activated monocyte subset,
identified by CD14++CD16+ expression, was associated with
higher levels of IL-6, hsCRP, and sCD163, associations that
were robust after adjustment for clinical and HIV-related factors
and that persisted among the subset of virologically suppressed
patients. The clinical relevance of these robust associations is unknown, but elevated levels of IL-6 and/or D-dimer have been
previously shown to predict the risk of non–AIDS-related
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complications, including all-cause mortality, over many years of
follow-up in large clinical trials of HIV-infected persons receiving ART [20]. Using data from these trials, the degree of the associations with IL-6 levels we described for an absolute 5% higher
frequency of CD14++CD16+ cells corresponds to a 15% increased
risk for serious non-AIDS events or all-cause mortality [20].
We also found that CCR5 expression was positively associated with D-dimer levels, whereas CCR2 expression was inversely
associated with hsCRP levels. It has been shown that CCR2 expression on monocytes is downregulated in response to inflammatory cytokines [30, 31] and that a switch from CCR2 to CCR5
expression may represent a maturation step of monocytes as
they become more differentiated toward resident macrophages
[32]. While monocyte phenotypes are complex and overlapping,
our findings support the hypothesis that systemic inflammation
may result in less CCR2 expression, with a shift toward CCR5
expression. In support of this hypothesis, in a recent study we
found lower levels of CCR2 expression and higher plasma
CRP levels in effectively treated HIV-infected patients (ie,
those who were receiving ART and had an HIV RNA load of
< 400 copies/mL), as well as in elite controllers, compared
with HIV-uninfected controls [31]. Alternatively, upon binding,
the decrease in observed expression of CCR2 on circulating
monocytes may reflect the binding of monocytes expressing
CCR2 to its ligand, MCP-1, and the subsequent trafficking of
these monocytes into inflamed tissues [33]. We also described
associations between monocyte TF expression and elevated
IL-6 and CRP levels, consistent with the premise that systemic
inflammation may activate coagulation through TF expression
on monocytes; however, we did not find associations with the
sCD14 level, as previously reported [17].
In contrast, we found no evidence suggesting that activation
of the adaptive immune system, as measured by T-cell activation markers, was linked to the increased levels of inflammatory
markers observed in our cohort. We were able to show that
CD8+ T-cell subsets expressing the costimulatory molecule
CD28, a phenotype previously shown to predict improved proliferative and functional CD4+ T-cell T-cell responses to immunization with novel antigens [34, 35], were inversely associated
with D-dimer and sCD163 levels. This finding may be a sign
that these cells retain a greater functional capacity to respond
to new pathogens and protect against serious non-AIDS events.
The proportion of CD8+CD28− T cells expressing CD57 was
also independently associated with lower D-dimer levels. This
is consistent with recent reports by Lee et al that HIV infection
leads to the abnormal accumulation of CD8+CD28− cells not
expressing CD57 and that the frequency of this CD8+ T-cell
phenotype predicts mortality in treated patients [36, 37]. We believe our findings support the hypothesis that CD14++CD16+
monocytes contribute to an overall inflammatory environment
through production of proinflammatory mediators that, in turn,
halt T-cell differentiation and maturation.
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Wilson et al
The only T-cell activation marker significantly associated with
any of the soluble biomarkers of inflammation we explored after
adjustment for clinical and HIV-associated factors was coexpression of HLA-DR and CD38 on CD4+ T cells, which correlated
with only one biomarker, sCD163, consistent with previous
reports that sCD163 levels directly correlate with lymphocyte activation markers [38]. It is interesting to note that other markers
of monocyte activation, including sCD14, were not associated
with this T-cell phenotype. While both sCD14 and sCD163 are
reported as markers of myeloid activation, they have different origins and may represent distinct, although perhaps overlapping,
biological pathways. LPS binding can induce the shedding of
sCD14 by monocytes, but some studies have suggested that
appreciable levels may be achieved via hepatocyte production
and could be considered an acute phase reactant under inflammatory conditions [39]. CD163 is a scavenger of hemoglobinhaptoglobin complexes that is shed from the cell surface of
monocytes and macrophages in response to inflammatory stimuli, but once shed, sCD163 acts to attenuate immune activation
by preventing T-cell proliferation and activation [40, 41]. Because
of measurement variability and our inability to test the underlying mechanisms (given our nonexperimental design), we cannot
infer causation in our analysis, but we believe our data highlight
an important potential inconsistently among the widely studied
and clearly not interchangeable myeloid markers that should be
the focus of future studies.
It is worth noting that the described associations, particularly
those with T-cell phenotypes and monocyte migration phenotypes, represented small effects overall. However, chronic diseases, such as cardiovascular disease and cancer, develop over
long periods, and therefore small contributions to an overall inflammatory milieu may have an important effect over many
years of continuous exposure. It has been suggested that even
modest elevations in D-dimer levels may reflect increases in
coagulability, thrombin formation, and turnover of cross-linked
intravascular fibrin, all of which may be associated with microvascular complications and end-organ disease [8]. Finally, the
strongest associations we observed were between the CD14++
CD16+ monocyte phenotype and IL-6, hsCRP, and sCD163
levels, highlighting the potential importance of innate immune
activation; the clinical implications of this finding warrant
future research.
There are limitations to our study, particularly the absence of
an HIV-uninfected control group to determine whether these
immunologic associations differ by infection status. We could
neither infer causation nor control for unmeasured confounding
because of the cross-sectional and nonrandomized study design,
nor could we account for any relative contribution of ART toxicity. The SUN Study cohort also did not have sufficient numbers
of participants or follow-up time to study associations directly
with clinical event risk. Finally, these analyses were intended to
generate hypotheses, with a focus on identifying patterns, given
that plasma inflammatory biomarkers may reflect overlapping
pathways. However, the relatively large degree of both measurement and biologic variability for these markers may have contributed to a given phenotype (eg, CD14++CD16+) demonstrating
associations that did not reach statistical significance for one
inflammatory marker (eg, sCD14) but did for another (eg,
sCD163).
In conclusion, it remains unclear to what extent biomarkers
of inflammation that are associated with risk for serious nonAIDS conditions (eg, IL-6, sCD14, sCD163, CRP, and D-dimer)
represent modifiable risk factors. Our data suggest a potential relationship between cellular innate immune activation and systemic inflammation that may contribute to serious non-AIDS events,
such as cardiovascular disease. Monocyte activation may therefore
provide candidate intermediate immunologic end points for future interventional studies targeting persistent inflammation.
Supplementary Data
Supplementary materials are available at The Journal of Infectious Diseases
online (http://jid.oxfordjournals.org). Supplementary materials consist of
data provided by the author that are published to benefit the reader. The
posted materials are not copyedited. The contents of all supplementary
data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.
Notes
Acknowledgments. We thank the SUN Study participants; the clinical
site investigators and staff; Adam Rupert (Leidos Biomedical Research),
for help with soluble biomarker measurements; and Bruno B. Andrade,
for help with figure design.
E. M. P. W. and A. S. contributed to laboratory data acquisition, data analysis, and manuscript writing. K. H. H. performed statistical analysis and assisted in manuscript preparation. D. G. participated in laboratory data
acquisition and manuscript preparation. W. K. H., K. L., N. O., E. K.,
P. P., and J. T. B. contributed to data acquisition and manuscript
writing. I. S. contributed to study design, laboratory data acquisition, and
interpretation of data and assisted in manuscript preparation.
J. V. B. designed the study and contributed to data analysis and manuscript
writing.
Disclaimer. The content of this publication does not necessarily reflect
the views or policies of the Department of Health and Human Services, nor
does mention of trade names, commercial products, or organizations imply
endorsement by the US government.
Financial support. This work was supported by the Centers for Disease
Control and Prevention (contracts 200-2002-00610, 200-2002-00611, 2002002-00612, 200-2002-00613, 200-2007-23633, 200-2007-23634, 2002007-23635, and 200-2007-23636), the National Institutes of Health
(grant 1KL2RR033182-01), and the National Institute of Allergy and Infectious Diseases (intramural program funds to E. M. P. W., A. S., and I. S.).
Potential conflicts of interest. All authors: No reported conflicts.
All authors have submitted the ICMJE Form for Disclosure of Potential
Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
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