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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 1396 • JID 2014:210 (1 November) • Wilson et al 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), Monocytes and Biomarkers in Chronic HIV • JID 2014:210 (1 November) • 1397 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 1398 • JID 2014:210 (1 November) • Wilson et al (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 Monocytes and Biomarkers in Chronic HIV • JID 2014:210 (1 November) • 1399 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. 1400 • JID 2014:210 (1 November) • Wilson et al 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 Monocytes and Biomarkers in Chronic HIV • JID 2014:210 (1 November) • 1401 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 1402 • JID 2014:210 (1 November) • Wilson et al 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 Monocytes and Biomarkers in Chronic HIV • JID 2014:210 (1 November) • 1403 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. 1404 • JID 2014:210 (1 November) • 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. 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