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Published in final edited form as: HIV Med. 2020 Dec 8;22(4):314–320. doi: 10.1111/hiv.13018

Utilization of Absolute Monocyte Counts to Predict Cardiovascular Events in Persons Living with Human Immunodeficiency Virus

Milana Bogorodskaya 1,2, Asya Lyass 3, Taylor F Mahoney 4, Leila H Borowsky 5, Pritha Sen 6, Filip K Swirski 7, Suman Srinivasa 8, Chris T Longenecker 2, Joseph M Massaro 4, Ralph B D’Agostino Sr 3, Virginia A Triant 5,6,9
PMCID: PMC7954843  NIHMSID: NIHMS1640897  PMID: 33295150

Abstract

Introduction

Cardiovascular risk is increased in persons living with HIV (PLWH). In HIV-uninfected populations, total absolute monocyte counts (AMC) have been shown to be predictive of future cardiovascular events (CVE). We sought to determine whether AMC predicts CVE in PLWH independent of established and HIV-related cardiovascular risk factors.

Methods

We identified all PLWH within the Partners HIV Cohort without factors which could confound the monocyte count. CVE was defined as fatal or non-fatal acute myocardial infarction or ischemic stroke. Baseline measured AMC was defined as the average of all outpatient AMC counts a year before and after the baseline date. Multivariable Cox proportional hazards models were used to assess the association of baseline AMC with CVE.

Results

Our cohort consisted of 1980 patients, with median follow-up of 10.9 years and 182 CVE. Mean (±SD) age was 41.9 years±9.3; 73.0% were male. Mean CD4+ was 506.3cells/mm3±307.1, 48% had HIV viral load (VL) <400 copies/mL, and 87% were on antiretroviral therapy. Mean AMC was 0.38×103cells/μL±0.13. In multivariable modeling adjusted for traditional CV risk factors, CD4+ cell count, and HIV VL, AMC quartile 2 (Q2) (HR 1.01, p=0.98), Q3 (HR 1.07, p=0.76), and Q4 (HR 0.97, p=0.89) were not significantly predictive of CVE compared to Q1.

Discussion

Baseline AMC was not associated with long-term CVE in PLWH. AMC obtained in routine clinical encounters does not appear to enhance CV risk stratification in PLWH.

Keywords: HIV, absolute monocyte count, cardiovascular disease, monocyte immune activation, cardiovascular risk

Introduction

With significant advances in antiretroviral therapy (ART) over the last three decades, patients with human immunodeficiency virus (HIV) are living longer[1] and developing age-associated, chronic, non-communicable disorders such as cardiovascular disease (CVD)[2,3]. In prior studies, HIV has been found to be an independent risk factor for myocardial infarction (MI) and acute ischemic stroke[4,5], even after adjustment for viral load (VL), ART, and CD4+ cell count. The mechanism of this phenomenon is thought to be a complex interplay of increased prevalence of traditional risk factors[6], HIV-related factors [7], and the combination of increased microbial gut translocation and continued HIV replication that leads to ongoing chronic inflammation and immune dysregulation[8–10]. The chronic inflammation in PLWH and its relationship to CVD is thought to be partially mediated by monocytes[8,11–13].

Thus far, research on the involvement of monocytes in the development of CVD in PLWH has focused on biomarkers that are difficult to incorporate into clinical management given their expense and lack of clinical availability. Previous studies in uninfected individuals have shown that higher absolute monocyte counts (AMC) can predict CVE[14,15] as well as 30-day mortality after emergency department visits[16]. This study sought to determine whether AMC predicts incident CVE in PLWH independently of established CVD risk factors and HIV-related parameters.

Methods

Study design/Data source

We identified patients in the Partners HIV Cohort, a prospective observational clinical care cohort derived from the Partners HealthCare System Research Patient Data Registry (RPDR), a centralized clinical data registry containing comprehensive demographic and clinical information for patients seen at Brigham and Women’s Hospital (BWH) since 1996 or Massachusetts General Hospital (MGH) since 1992. Data are derived from several sources, including hospital billing systems and a clinical data repository, and data on demographics, encounters, diagnoses, and laboratories are available. The diagnosis of HIV was based on an individual having at least three encounters (inpatient or outpatient) with International Classification of Diseases (ICD)-9-CM coding of either 042 or ICD-10-CM coding of B20. ICD codes are international medical classifications for diseases, injuries, health encounters, and inpatient procedures which are used for payment reimbursement and national surveillance research in the United States.

Eligibility criteria

Patients with at least two encounters between January 1, 2000 and December 31, 2017 and at least 1 peripheral absolute monocyte value were included. The start of observation was the earliest date after January 1, 2000 that lipid laboratory measurements were available. Patients were followed until they developed a CV event, died or were censored on December 31, 2017. Since peripheral monocytes have a short turnover of 1–7 days [17,18] and can fluctuate significantly with time of day, medication use, acute infections, and changes in physiological stress[18], patients with certain medical conditions and certain medications were excluded. Patients who were under 18 years of age at start of observation or had any diagnosis of malignancy, autoimmune disorder (including systemic lupus erythematosus, rheumatoid arthritis, mixed connective tissue disease, systemic sclerosis, psoriatic arthritis, ankylosing spondylitis, Sjogren’s syndrome, sarcoidosis, inflammatory bowel disease, or any type of vasculitis), had a solid organ or hematopoietic bone marrow transplant, or had a CV event (acute, fatal or non-fatal stroke or myocardial infarction) prior to the start of observation were excluded. All monocyte values associated with white blood cell (WBC) values >11.0×103cells/μL (the upper limit of normal at our institution’s laboratory), monocyte values associated with inpatient hospitalization, and monocyte values +/− 5 standard deviations from the mean[19] were excluded. These exclusions were applied to all monocyte values included in the average of the baseline monocyte count. The exclusions were to avoid elevations in absolute monocyte counts due to acute infections or acute episodes of physiological stress.

Predictor variables

Measured total WBC count and absolute monocyte count were obtained from RPDR laboratory data. Additional baseline variables obtained from the RPDR included data on traditional cardiac risk factors (hypertension, diabetes, dyslipidemia), CD4+ cell count, HIV viral load (VL), and use of ART. Smoking status was ascertained by the application of a validated natural language processing-based algorithm [20]. All variables, unless mentioned otherwise, were obtained at or prior to baseline monocyte count. For HIV viral load, CD4+ cell count, nadir CD4+ cell count, total cholesterol, and blood pressure, the value obtained closest to the baseline date was used. Time on ART was also calculated prior to baseline. Clinical diagnoses (hypertension, diabetes, dyslipidemia, congestive heart failure, peripheral vascular disease, and hepatitis C) were obtained using ICD-9-CM and ICD-10-CM codes. Use of medications was represented as current medication as of baseline. To account for the variability of AMC, baseline absolute monocyte count was defined as the average of all outpatient monocyte counts a year before and after the baseline date.

Ascertainment of outcomes

The primary outcome was major adverse cardiovascular events, defined as fatal or non-fatal acute myocardial infarction or acute ischemic stroke. Acute MI and ischemic stroke events were determined by ICD coding and included all patients with ICD-9-CM code 410 or ICD-10-CM code I21 (acute MI) and ICD-9-CM code 433–434 or ICD-10-CM code I63 (acute ischemic stroke), and all subtypes. In cases of recurrent CVE, only the first event after start of observation was counted in the analysis.

Statistical analysis

Demographic and clinical characteristics were compared between those with and without incident CVE using two sample t-test for continuous variables and Chi-Squared test for categorical variables. Univariate Cox proportional hazards model was used to assess the relationship between quartiles of absolute monocyte count and incident CVE. Then, a stepwise multivariate Cox proportional hazards model was run, using a set of demographic and clinical characteristics to predict incident CVE (age, sex, race, total cholesterol, HDL, diabetes, systolic blood pressure, hypertension treatment, smoking and statin medication use). Predictors from this model significant at 0.10 alpha level were then included as covariates in a stepwise Cox proportional hazards model assessing the relationship between quartiles of absolute monocyte counts and CVE, additionally adjusting for CD4+ cell count and viral load. Due to a change in the absolute monocyte laboratory test and reference range in 2008, analysis was also internally adjusted for the time period (prior to vs. after 2008). All analyses were performed using SAS 9.4 (Cary, NC).

Results

The study population consisted of 1980 patients with median follow up of 10.9 years. The median start of observation was in 2005. Mean age was 41.9 years, 73% were male, and 50% were Caucasian. Hypertension was present in 16%, dyslipidemia in 17%, diabetes mellitus in 8%, heart failure in 2%, and peripheral vascular disease in 1%; 42% were smokers. Mean CD4+ was 506.3cells/mm3±307.1, 48% of all included patients had an HIV VL < 400 copies/mL, and 87% were on antiretroviral therapy. Mean length of time on ART was 3.4±3.8 years prior to the date of baseline AMC. Mean nadir CD4+ prior to baseline was 280 cells/mm3±260.4. The median and range of monocyte values used to generate the baseline monocyte count were 6 and 1–166, respectively. Mean WBC was 5.66×103cells/μL ±1.55. Mean AMC was 0.38×103/μL ±0.13. There was no difference in intra-individual variance in AMC between those who did have CVE and those who did not. There was no difference in the level of medication use by quartile of AMC for any class of ART medications (PI, p=0.72; NRTI, p=0.21; NNRTI, p=0.07).

A total of 182 patients had a CVE; 89 developed an acute MI, 4 of which were fatal and 93 developed a non-fatal acute ischemic stroke. Individuals with events were significantly more likely to be older, have hypertension, dyslipidemia, diabetes mellitus, heart failure, and be current smokers. Those with events also had a significantly lower CD4+ cell count, were more likely to be on protease inhibitors (PIs), less likely to be on integrase strand transfer inhibitors (INSTIs), and were more likely to be co-infected with hepatitis C virus (HCV). (Table 1).

Table 1.

Baseline Characteristics

Characteristic Overall (N=1,980) No Event (N=1,798) Event (N=182)
Demographics
Age (years), mean (SD) * 41.9 (9.3) 41.5 (9.37) 45.6 (8.2)
Male sex (%) 1,452 (73%) 1,316 (73%) 136 (74%)
Race
 African-American (%) 547 (28%) 495 (27%) 52 (29%)
 Caucasian (%) 984 (50%) 892 (50%) 92 (51%)
 Hispanic (%) 257 (13%) 226 (13%) 31 (17%)
 Other (%) 134 (7%) 128 (7%) 6 (3%)
 Unknown (%) 58 (3%) 57 (3%) 1 (1%)
Laboratory values
WBC (x103/μL), mean (SD) 5.66 (1.55) 5.66 (1.54) 5.65 (1.61)
AMC (x103/μL), mean (SD) 0.38 (0.13) 0.38 (0.13) 0.37 (0.14)
Monocyte percentage, mean (SD) 6.82 (2.18) 6.84 (2.19) 6.65 (2.11)
CVD-related factors
Total cholesterol (mg/dL)* 175.8 (47.9) 174.6 (45.3) 187.0 (67.3)
Systolic blood pressure (mmHg)* 122.6 (16.0) 122.4 (15.6) 125.1 (19.6)
Diastolic blood pressure (mmHg) 77.6 (11.4) 77.5 (11.3) 78.8 (11.8)
Hypertension (%)* 324 (16%) 266 (15%) 58 (32%)
Dyslipidemia (%)* 329 (17%) 283 (16%) 46 (25%)
Diabetes mellitus (%)* 156 (8%) 110 (6%) 46 (25%)
Heart failure (%)* 40 (2%) 30 (2%) 10 (5%)
Peripheral vascular disease (%) 16 (1%) 14 (1%) 2 (1%)
Current cigarette use (%)* 828 (42%) 726 (40%) 102 (56%)
Statin use (%)* 628 (32%) 511 (28%) 117 (64%)
Hepatitis C (%)* 247 (12%) 209 (12%) 38 (21%)
HIV-related factors
Antiretroviral therapy (%) 1718 (87%) 1562 (87%) 156 (86%)
 NRTIs (%) 1672 (84%) 1518 (84%) 154 (85%)
 NNRTIs (%) 1043 (53%) 938 (52%) 105 (58%)
 PIs (%)* 1021 (52%) 912 (51%) 109 (60%)
 INSTIs (%)* 195 (10%) 189 (11%) 6 (3%)
 Others (%) 11 (1%) 11 (1%) 0
HIV viral load < 400 (copies/ml) 953 (48%) 869 (48%) 84 (46%)
CD4+ count (cells/mm3)* 506.3 (307.1) 511.4 (308.8) 455.4 (285.0)
Nadir CD4+ count (cells/mm3) 280.8 (260.4) 284.3 (262.7) 248.3 (236.1)
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*

, p<0.05 comparing No Event vs. Event

WBC, white blood cell count; AMC, absolute monocyte count; CVD, cardiovascular disease; NRTI, nucleoside reverse transcriptase inhibitors; NNRTI, non-nucleoside reverse transcriptase inhibitors; PIs, protease inhibitors; INSTI, integrase strand transfer inhibitor.

In univariate analysis, there was no association between AMC and incident CVE (Q2 hazard ratio (HR) 0.96, 95% CI [0.64, 1.44], p=0.83; Q3 HR 1.08, 95% CI [0.72, 1.61], p=0.71; and Q4 HR 1.16, 95% CI [0.76, 1.76], p=0.50 versus Q1). In multivariable modeling adjusted for age, sex, CD4+ cell count (per 100 cells/mm3 increments), diabetes mellitus, smoking status, statin use, and HIV VL, Q2 AMC (HR 1.01, p=0.98), Q3 (HR 1.07, p=0.76), and Q4 (HR 0.97, p=0.89) were not significantly associated with CVE compared to Q1 AMC. (Table 2). Analysis of AMC and of monocyte percentage as continuous variables yielded similar results (data not shown). The most recent AMC prior to CV event also did not show any association with future CV events (data not shown). Traditional risk factors for cardiovascular disease such as age (HR 1.05, p<0.0001), diagnosis of diabetes mellitus (HR 3.02, p<0.0001), and tobacco use (HR 1.99, p<0.0001) were significantly associated with CVE. CD4+ cell count (per 100 cells/mm3 increments) (HR 0.93, p=0.01) was also associated with CVE while HIV VL was not (HR 1.00, p=0.98).

Table 2.

Multivariable model for cardiovascular events

Variables Hazard Ratio (95% CI) p value
AMC (Q2 vs. Q1) 1.01 (0.67, 1.52) 0.98
AMC (Q3 vs. Q1) 1.07 (0.71, 1.61) 0.76
AMC (Q4 vs. Q1) 0.97 (0.63, 1.50) 0.89
Age (years) 1.05 (1.03, 1.07) <0.0001
Sex 1.08 (0.76, 1.52) 0.67
CD4+ (per 100 cells/mm3 increments) 0.93 (0.88, 0.98) 0.01
HIV VL > 400 (copies/mL) 1.00 (0.74, 1.37) 0.98
Diabetes mellitus 3.02 (2.12, 4.32) <0.0001
Smoking status 1.99 (1.48, 2.68) <0.0001
Statin use 2.51 (1.82, 3.47) <0.0001
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CI, confidence interval; AMC, absolute monocyte count; Q, quartile

Monocyte quartile 1 range (0.05–0.28 cells x103/μL), monocyte quartile 2 range (0.28–0.35 cells x103/μL), monocyte quartile 3 range (0.36–0.44 cells x103/μL), and monocyte quartile 4 range (0.45–1.56 cells x103/μL)

In additional models, we added time on ART prior to baseline and nadir CD4+ cell count prior to baseline individually and together, and the inclusions of these variables did not significantly alter the association of AMC with CVE (data not shown).

Discussion

This study is the first to our knowledge to investigate the association between peripheral AMC and future CVE in PLWH. Our results demonstrate that baseline AMC averaged over two years is not associated with cardiovascular events over an average of 11 years of follow up in PLWH. The findings suggest that while monocyte activation has been specifically implicated in HIV-associated atherosclerosis, the AMC obtained in routine clinical care is not independently associated with downstream cardiovascular events and may not be a reliable clinical indicator of cardiovascular risk.

Studies assessing the relationship between baseline AMC and CVE in the general population have produced varied results. Both observational data and a sub-study of randomized controlled trial data demonstrated AMC to be predictive of future CVE[14,15] while other observational studies have shown no difference in CVE based on AMC[19].

Monocyte activation plays a central role in atherogenesis and progression of CVD in uninfected patients[21–23], as atherosclerotic plaques form by the accumulation of macrophage-laden foam cells and lipoproteins[22]. Circulating monocyte subsets have been demonstrated to be independent predictors of cardiovascular events[24] and associated with presence of non-calcified coronary plaque in asymptomatic individuals[25]. Furthermore, studies have shown that blockade of monocyte-stimulating factors and receptors significantly reduces plaque burden in mice[26].

Similarly, the monocyte-mediated chronic inflammatory response is thought to play a role in early development of CVD in PLWH. Monocyte turnover has been implicated in the pathogenesis of acquired immunodeficiency syndrome independent of T-cell counts. Elevations in certain markers of monocyte activation (soluble CD14, soluble CD163, MCP-1) are seen in PLWH[9,27–29] and have been independently associated with cardiovascular and all-cause mortality[30], subclinical atherosclerosis[13,28,31], and vascular inflammation[32–34]. Furthermore, studies have demonstrated an association between non-classical monocytes with coronary artery calcium score progression on CT angiography[35]. However, these biomarkers and monocyte subsets are not available in the clinical setting. No study to date has assessed the association of AMC with future CVE in PLWH. Peripheral AMC are readily available in the clinical setting and obtained with every complete blood count (CBC) measurement. The peripheral absolute monocyte count measures the steady state of monocytes in the blood at a given time and can fluctuate diurnally, with stress, and other infections. Therefore, they do not necessarily provide a consistent illustration of the degree of monocyte activation or turnover that is occurring within organ tissues. This may explain why AMC was not associated with future CVE in our study, while markers of monocyte activation (sCD14 and sCD163) and certain monocyte subsets have shown an association in prior studies.

Our results are consistent with some of the previously published data. The Rana et al.[19] study among a non-HIV population, found no association between monocytes and CVE, and had a similar follow-up period to our study of 8 years. In contrast, the studies that found monocytes to be predictive of CVE in the general population looked at a shorter follow-up period of 1–3 years. Since peripheral monocytes have a short turnover of 1–7 days [17,18], absolute monocyte levels may be better able to predict short-term events. The low number of events in our cohort precluded us from looking at shorter follow up periods. However, we did evaluate whether the most recent AMC available or most recent prior to a CVE in those who had events was associated with having a CVE and we did not find a statistically significant association. We applied rigorous exclusion criteria to ensure the monocyte counts were not attributed to acute periods of inflammation, and we attempted to account for acute fluctuations in levels by using an average of multiple monocyte counts drawn routinely over a span of two years. Our results may also reflect a differing mechanism of monocyte involvement in PLWH compared to uninfected individuals. Certain monocyte subsets are known to be elevated in PLWH compared to controls[36], yet these may not translate into a change in total circulating absolute monocyte levels.

We observed an association between CD4+ cell count and CVE independent of absolute monocyte count, consistent with multiple prior studies showing an association between lower CD4+ cell count and increased CVE [37,38]. Immune dysregulation, as demonstrated by the association of low CD4+ cell counts with CVE, is a known risk factor for CVD in HIV. Although higher viral load[39,40] has also been associated with increased CVE, we did not observe this association in the current study. Traditional CVD risk factors, including age, diabetes mellitus, and smoking, were significantly associated with CVE.

Our study was observational in nature and subject to limitations. Ascertainment of clinical variables may have been limited by reliance on using diagnostic codes and limitations in capturing outside-system events. Confounding bias is also a common limitation of observational data. We controlled for all variables that were feasible to obtain accurately from an electronic medical record that could affect the incidence of a CVE. We were not able to adjust for family history or obesity, nor for many behaviors such as drug use, dietary intake, and physical activity level, which can also increase risk for future CVE. The possibility of other unmeasured confounders also exists. To optimize power to detect an association, we employed a long period of follow up from 2000 to 2017 to capture CVE events; this period spanned changes in contemporary HIV care resulting in a heterogenous HIV population and might have the possibility of diluting the association between AMC and future CVE. Nevertheless, this is the first study, to our knowledge, to assess the association between peripheral absolute monocyte counts and CVE in PLWH.

Conclusions and implications

In summary, our study demonstrated that baseline AMC may not be a useful marker to predict future CVE in PLWH over a span of 11 years. Future studies are needed to evaluate whether AMC is associated with CVE within shorter follow-up intervals. Although monocyte activation is known to promote atherogenesis in PLWH, AMC obtained in routine clinical encounters do not appear to enhance CVD risk stratification in this group. Additional specific markers of inflammation and immune dysregulation may be necessary to help identify PLWH at heightened CVD risk.

Key Summary:

Peripheral absolute monocyte count is not associated with future cardiovascular events accounting for cardiovascular and HIV-related factors in persons living with HIV.

Acknowledgments

We would like to acknowledge Susan Regan, PhD for her contribution to the Partners HIV Cohort used for this study. This work was also conducted with support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic health care centers. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic health care centers, or the National Institutes of Health.

Funding: This work was supported by National Institutes of Health [R01HL132786 to VAT and RBD], [R01AG062393 to VAT], and [K23 HL136262 to SS]. Funding sources had no role in the design of the study, data analysis, or writing of the manuscript.

Footnotes

Data: Data not publicly available.

Disclosure Statement:

MB – has nothing to declare.

AL – has nothing to declare.

TFM – has nothing to declare

PS – has nothing to declare

FKS – has nothing to declare

SS – has nothing to declare

CTL – has received a research grant from Gilead Sciences and has served on an advisory board for Esperion Therapeutics.

JMM – has nothing to declare

RBD – has nothing to declare

VAT – has nothing to declare.

References

  • 1.Centers for Disease Control and Prevention. Death rates among persons aged 25–44 and 45–64, by leading causes of death: United States, 2006 – 2016. 2017. Available at: https://www.cdc.gov/nchs/hus.htm.
  • 2.Feinstein MJ, Bahiru E, Achenbach C, et al. Patterns of Cardiovascular Mortality for HIV-Infected Adults in the United States: 1999 to 2013. Am J Cardiol 2016; 117:214–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Palella FJ, Baker RK, Moorman AC, et al. Mortality in the highly active antiretroviral therapy era: changing causes of death and disease in the HIV outpatient study. J Acquir Immune Defic Syndr 2006; 43:27–34. [DOI] [PubMed] [Google Scholar]
  • 4.Triant VA, Lee H, Hadigan C, Grinspoon SK. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J Clin Endocrinol Metab 2007; 92:2506–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Freiberg MS, Chang C-CH, Kuller LH, et al. HIV infection and the risk of acute myocardial infarction. JAMA Intern Med 2013; 173:614–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hadigan C, Jeste S, Anderson EJ, Tsay R, Cyr H, Grinspoon S. Modifiable dietary habits and their relation to metabolic abnormalities in men and women with human immunodeficiency virus infection and fat redistribution. Clin Infect Dis 2001; 33:710–7. [DOI] [PubMed] [Google Scholar]
  • 7.Bavinger C, Bendavid E, Niehaus K, et al. Risk of Cardiovascular Disease from Antiretroviral Therapy for HIV: A Systematic Review. PLoS One 2013; 8:e59551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Duprez DA, Neuhaus J, Kuller LH, et al. Inflammation, Coagulation and Cardiovascular Disease in HIV-Infected Individuals. PLoS One 2012; 7:e44454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wada NI, Jacobson LP, Margolick JB, et al. The effect of HAART-induced HIV suppression on circulating markers of inflammation and immune activation. AIDS 2015; 29:463–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hsue PY, Waters DD. HIV infection and coronary heart disease: mechanisms and management. Nat Rev Cardiol 2019; 16:745–759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Merlini E, Luzi K, Suardi E, et al. T-Cell Phenotypes, Apoptosis and Inflammation in HIV+ Patients on Virologically Effective cART with Early Atherosclerosis. PLoS One 2012; 7:e46073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Fitch KV, Srinivasa S, Abbara S, et al. Noncalcified Coronary Atherosclerotic Plaque and Immune Activation in HIV-Infected Women. J Infect Dis 2013; 208:1737–1746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.McKibben RA, Margolick JB, Grinspoon S, et al. Elevated Levels of Monocyte Activation Markers Are Associated With Subclinical Atherosclerosis in Men With and Those Without HIV Infection. J Infect Dis 2015; 211:1219–1228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Grau AJ, Boddy AW, Dukovic DA, et al. Leukocyte count as an independent predictor of recurrent ischemic events. Stroke 2004; 35:1147–1152. [DOI] [PubMed] [Google Scholar]
  • 15.Horne BD, Anderson JL, John JM, et al. Which white blood cell subtypes predict increased cardiovascular risk? J Am Coll Cardiol 2005; 45:1638–1643. [DOI] [PubMed] [Google Scholar]
  • 16.Hensel M, Grädel L, Kutz A, et al. Peripheral monocytosis as a predictive factor for adverse outcome in the emergency department. Medicine (Baltimore) 2017; 96:e7404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Patel AA, Zhang Y, Fullerton JN, et al. The fate and lifespan of human monocyte subsets in steady state and systemic inflammation. J Exp Med 2017; 214:1913–1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.van Furth R, Beekhuizen H. Monocytes. In: Encyclopedia of Immunology. Elsevier, 1998: 1750–1754. [Google Scholar]
  • 19.Rana JS, Boekholdt SM, Ridker PM, et al. Differential leucocyte count and the risk of future coronary artery disease in healthy men and women: The EPIC-Norfolk Prospective Population Study. J Intern Med 2007; 262:678–689. [DOI] [PubMed] [Google Scholar]
  • 20.Regan S, Meigs JB, Grinspoon SK, Triant VA. Determinants of Smoking and Quitting in HIV-Infected Individuals. PLoS One 2016; 11:e0153103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hansson GK, Libby P. The immune response in atherosclerosis: a double-edged sword. Nat Rev Immunol 2006; 6:508–519. [DOI] [PubMed] [Google Scholar]
  • 22.Hilgendorf I, Swirski FK, Robbins CS. Monocyte fate in atherosclerosis. Arterioscler Thromb Vasc Biol 2015; 35:272–9. [DOI] [PubMed] [Google Scholar]
  • 23.Woollard KJ, Geissmann F. Monocytes in atherosclerosis: subsets and functions. Nat Rev Cardiol 2010; 7:77–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Rogacev KS, Cremers B, Zawada AM, et al. CD14++CD16+ monocytes independently predict cardiovascular events: a cohort study of 951 patients referred for elective coronary angiography. J Am Coll Cardiol 2012; 60:1512–20. [DOI] [PubMed] [Google Scholar]
  • 25.Kashiwagi M, Imanishi T, Tsujioka H, et al. Association of monocyte subsets with vulnerability characteristics of coronary plaques as assessed by 64-slice multidetector computed tomography in patients with stable angina pectoris. Atherosclerosis 2010; 212:171–6. [DOI] [PubMed] [Google Scholar]
  • 26.Combadière C, Potteaux S, Rodero M, et al. Combined inhibition of CCL2, CX3CR1, and CCR5 abrogates Ly6C(hi) and Ly6C(lo) monocytosis and almost abolishes atherosclerosis in hypercholesterolemic mice. Circulation 2008; 117:1649–57. [DOI] [PubMed] [Google Scholar]
  • 27.Joven J, Coll B, Tous M, et al. The influence of HIV infection on the correlation between plasma concentrations of monocyte chemoattractant protein-1 and carotid atherosclerosis. Clin Chim Acta 2006; 368:114–9. [DOI] [PubMed] [Google Scholar]
  • 28.Burdo TH, Lo J, Abbara S, et al. Soluble CD163, a Novel Marker of Activated Macrophages, Is Elevated and Associated With Noncalcified Coronary Plaque in HIV-Infected Patients. J Infect Dis 2011; 204:1227–1236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Castley A, Williams L, James I, Guelfi G, Berry C, Nolan D. Plasma CXCL10, sCD163 and sCD14 Levels Have Distinct Associations with Antiretroviral Treatment and Cardiovascular Disease Risk Factors. PLoS One 2016; 11:e0158169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Sandler NG, Wand H, Roque A, et al. Plasma Levels of Soluble CD14 Independently Predict Mortality in HIV Infection. J Infect Dis 2011; 203:780–790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kelesidis T, Kendall MA, Yang OO, Hodis HN, Currier JS. Biomarkers of microbial translocation and macrophage activation: association with progression of subclinical atherosclerosis in HIV-1 infection. J Infect Dis 2012; 206:1558–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Anzinger JJ, Butterfield TR, Angelovich TA, Crowe SM, Palmer CS. Monocytes as regulators of inflammation and HIV-related comorbidities during cART. J Immunol Res 2014; 2014:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Lo J, Abbara S, Shturman L, et al. Increased prevalence of subclinical coronary atherosclerosis detected by coronary computed tomography angiography in HIV-infected men. AIDS 2010; 24:243–253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Subramanian S, Tawakol A, Burdo TH, et al. Arterial Inflammation in Patients With HIV. JAMA 2012; 308:379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zungsontiporn N, Tello RR, Zhang G, et al. Non-Classical Monocytes and Monocyte Chemoattractant Protein-1 (MCP-1) Correlate with Coronary Artery Calcium Progression in Chronically HIV-1 Infected Adults on Stable Antiretroviral Therapy. PLoS One 2016; 11:e0149143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Funderburg NT, Zidar DA, Shive C, et al. Shared monocyte subset phenotypes in HIV-1 infection and in uninfected subjects with acute coronary syndrome. Blood 2012; 120:4599–4608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Friis-Møller N, Thiébaut R, Reiss P, et al. Predicting the risk of cardiovascular disease in HIV-infected patients: the data collection on adverse effects of anti-HIV drugs study. Eur J Cardiovasc Prev Rehabil 2010; 17:491–501. [DOI] [PubMed] [Google Scholar]
  • 38.Triant VA, Regan S, Lee H, Sax PE, Meigs JB, Grinspoon SK. Association of immunologic and virologic factors with myocardial infarction rates in a US healthcare system. J Acquir Immune Defic Syndr 2010; 55:615–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Kuller LH, Tracy R, Belloso W, et al. Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection. PLoS Med 2008; 5:e203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Chow FC, Bacchetti P, Kim AS, Price RW, Hsue PY. Effect of CD4+ cell count and viral suppression on risk of ischemic stroke in HIV infection. AIDS 2014; 28:2573–7. [DOI] [PMC free article] [PubMed] [Google Scholar]

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