Illicit drug use is one of the most prevalent and major risk factors for HIV transmission.1 Intravenous drug use is the predominant reason for the increased transmission of HIV-1. However, other routes of cocaine administration (inhalation, snorting, smoking, and injection) are also correlated with an almost 3-fold increased association with HIV-1 transmission2 and with accelerated progression to AIDS.3 Cocaine has been designated as an immunomodulator based on studies showing that acute exposure can alter the expression of proinflammatory cytokines, tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6), in monocytes of cocaine-dependent individuals and that chronic exposure may result in greater suppression.4 Cocaine exerts its effects through the dopamine receptors and the sigma-1 receptor, which is involved in altered immune function and has been identified on peripheral blood mononuclear cells (PBMCs) and cells within the central nervous system.5,6 Studies in murine peritoneal macrophages treated acutely with selected dosages of cocaine have also shown a suppression of immunological parameters such as IL-1 and TNF-α.7
Based on these studies, cocaine has been proposed to disrupt the homeostatic balance between type 1 and type 2 helper T cytokines (Th1 and Th2, respectively).6 CD4+ T-helper cells are the major sources of cytokines and chemokines.8 Th1 cells produce proinflammatory cytokines such as IL-1, IL-2, IL-6, TNF-α, interferon γ (IFN-γ), IL-12, and IL-23, and Th2 cells produce IL-4, IL-5, IL-6, IL-10, and IL-13.8,9 The balance between Th1 and Th2 cytokines plays an influential role in many pathological processes.8 Cytokines and chemokines play an important role during viral infection in both clearance of the virus and pathogenesis of the infection.10 Responses from Th1 cells are associated with cell-mediated immunity, whereas responses from Th2 cells are associated with humoral immune responses. Both subsets of cells have distinct profiles of cytokines that are activated by different immune responses.11 IL-2 and IFN-γ, along with IL-1β and TNF-α, share proinflammatory properties and play an important role in the pathogenesis of autoimmune diseases and infections.12
Several cohort studies of HIV-1–infected individuals, have indicated that use of cocaine is associated with decreased adherence to antiretroviral therapy (ART) and with accelerated disease progression,1,13–15 demonstrating that the results from in vitro studies could be corroborated with in vivo studies. In a 6-month cohort study examining adherence to ART among 85 HIV-1–positive individuals, active cocaine use was significantly associated with lower adherence to therapeutic regimens. Adherence declined 41% among active cocaine users, and the decline was associated with failure to maintain viral suppression.13 Another prospective, 30-month clinical study enrolled 222 HIV-1–positive drug-abusing individuals. Crack cocaine users were twice as likely to have a decrease in their CD4+ T-cell count below 200 cells per microliter, as compared with individuals abusing other drugs such as marijuana, heroin, and alcohol [the study controlled for use of highly active antiretroviral therapy (HAART)]. In addition to the observed association with CD4+ T-cell count, crack cocaine was significantly associated with a higher level of HIV-1 RNA.16 Another retrospective cohort study showed similar results for crack cocaine abusers and also found that crack cocaine abusers developed more AIDS-defining illnesses, such as bacterial pneumonia, pneumocystosis, histoplasmosis, and cerebral toxoplasmosis.17
Several cohort studies have shown the impact of cocaine on HIV-1 infection and selected clinical parameters. However, fewer studies have shown correlations between cocaine use and cytokine profiles or immunopathogenesis in HIV-1–infected individuals. One of the few cohort studies analyzed a plasma biomarker signature of immune activation, comparing 57 HIV-1–positive patients who were on combination ART with 29 HIV-1–negative individuals.18 Approximately 55% of the HIV-1–infected individuals were abusing cocaine, some of whom were simultaneously abusing opiates. Given this observation, a small number of individuals were abusing cocaine alone; however, drug abuse was determined by self-reporting or urine toxicology and documented longitudinal histories of drug abuse were lacking.18 A cluster of viremic cocaine users were found to have elevated levels of soluble IL-2R (sIL-2R), a cytokine that can promote the growth and proliferation of T cells.18 sIL-2R has been shown to be increased in HIV-1–seropositive individuals and a predictor of progression of HIV-1 infection to AIDS; it correlates with response to therapy and remains unaffected by the presence of opportunistic infections, as previously reviewed.19 In addition to sIL-2R, cytokines that were elevated in viremic as compared with viremic noncocaine users included CXCL9, CXCL10 (IFN-γ–induced protein 10), and CCL4 (macrophage inflammatory protein 1β).18 These findings raise the likelihood that cocaine is responsible for the deregulation of the immune response.
Although there are many HIV-1 cohort studies, very few of them monitor subjects' use of illicit or recreational drugs, test subjects systematically and longitudinally for illicit drug use, and analyze the results accordingly. Determining the effects of cocaine use in HIV-1–infected individuals by comparing a nondrug using population with a population of individuals preferentially abusing cocaine is an immediate priority. To this end, we have established the DREXELMED HIV/AIDS Genetic Analysis Cohort which assesses a subjects drug, alcohol, and tobacco histories to more accurately assess the impact of single substances such as cocaine on the severity of HIV-1 disease in the absence of overt polydrug use.
MATERIALS AND METHODS
Patient Recruitment, Clinical Data, and Sample Collection
Patients in the DREXELMED HIV/AIDS Genetic Analysis Cohort have been recruited from the Partnership Comprehensive Care Practice of the Division of Infectious Diseases and HIV Medicine at Drexel University College of Medicine, which is located in center city Philadelphia, Pennsylvania, as previously described.20 The study was approved by the Drexel University College of Medicine Institutional Review Board under protocol 16,311 (B.W., principal investigator) in compliance with Declaration of Helsinki of 1975 as revised in 2000. All participants were required to provide informed consent. Once a subject was enrolled, an anonymous identifier was assigned and the patient's clinical data were collected as previously described.20 Blood samples were drawn from each patient at the initial visit and at each return visit (approximately every 6 months but at least 1 recall per year) to allow for longitudinal study. Patients were considered preferential nonusers (PN) if they tested negative for all drugs at all visits and never admitted to drug use. Preferential cocaine users (PCo) were patients who tested positive for only cocaine at all visits; patients were dropped from consideration if they tested negative for their preferential drug on 2 consecutive visits or positive for a different drug at any visit. Preferential users of cannabinoids and benzodiazepines (PBe) were also identified using the same parameters as were used for the PCo group. Multidrug users (MDU) had to test positive for the same combination of drugs with identical requirements as the PCo group. Patients were classified into drug-user groups based solely on drug testing results; their verbal admissions or denials were not considered.
Patient Drug Screening
Blood samples from each patient's first and every subsequent visit were screened using a standard 7-drug profile drug screen (LabCorp., Burlington, NC), which included amphetamines (amphetamine and methamphetamine); barbiturates (amobarbital, butalbital, pentobarbital, phenobarbital, and secobarbital); benzodiazepines (desalkylflurazepam, flurazepam, and diazepam); cannabinoids (tetrahydrocannabinol and tetrahydrocannabinolic acid); cocaine (cocaine and benzoylecgonine); opiates (codeine and morphine); and phenyl cyclidine.
PBMC Isolation and Plasma Sample Storage
Three purple-top blood collection tubes (Vacutainer; Becton, Dickinson, Franklin Lakes, NJ) containing EDTA were used to collect 50 mL of whole blood from patients for plasma and PBMC isolation as described previously.20,21
Luminex 30-Plex Assay
The Human cytokine 30-plex panel for the Luminex platform (Life Technologies, Grand Island, NY) was used to quantify the indicated cytokines, chemokines, and growth factors present in the plasma samples. The multiplex immunoassay was performed as described by the manufacturer and the plate was read on the Luminex 200 system (Luminex Corp., Austin, TX). Each multiplex immunoassay was performed twice in duplicate, giving 4 values for each cytokine concentration.
To begin the experimental analysis, all values that fell off of the standard curve were discarded (5%). Subsequently, all remaining raw cytokine values were quantile transformed for each array using the normalized quantiles function in the Bioconductor package.22 The difference in each cytokine between drug-user groups was examined using the categorical contribution model (CCM) and the dosage response was tested using the weighted linear contribution model (WLCM). Both models were based on a linear mixed-effects model and included terms for age, gender, HAART status, and hepatitis C virus (HCV coinfection) as confounding variables. Age was considered as a linear variable with the age range of participants between 20 and 71 years, whereas gender, HAART status (continuous, discontinuous, and naive), and HCV coinfection were treated as categorical variables.
In CCM, patients were grouped into nonusers, single-drug users, and MDU and dummy variables for each category were included in the model. Any patient who did not fall into these categories was excluded from this section of the analysis.
The algorithm used for the CCM model was as follows: cytokine = κ (age) + κ (gender) + κ (HAART) + κ (HCV) + κ (logviralload) + κ (druggroup).
The WLCM attempts to model the effect of drug use on the dependent variable by considering each drug as a linear contributor. Data from all patients with results from the cytokine analysis were used to build the WLCM model, regardless of their drug use. This approach provided a methodology to analyze patients with varying levels of usage of cocaine and other drugs. The inclusion of patients using only cannabinoids or benzodiazepines allows the algorithm to estimate the effect of cocaine within a multidrug use scenario.
The algorithm for the WLCM model is as follows: cytokine = κ (age) + κ (gender) + κ (HAART) + κ (HCV) + κ (logviralload) + κ (cocaine) + κ (cannabinoid) + κ (benzodiazepine).
Within the WLCM model, 3 different methods were constructed to represent the effects of drug use. The first method involved using the positive/negative results of the drug test at the sampled visit as binary variables (termed the at-visit analysis). The second method involved using the fraction of positive tests for each drug up to and including the sampled visit as linear variables (termed the upto-visit analysis). The third method used the fraction of positive tests for each drug at all visits for a particular patient as linear variables (termed the all-visits analysis; visit is denoted as the visit of the patient at which the plasma sample was used for the Luminex assay).
Statistical analysis was performed using R2.15.1 (The R Foundation for Statistical Computing Vienna, Austria); P ≤ 0.05 was considered significant. Multiple testing comparison was performed using the Benjamini–Hochberg correction (q ≤ 0.05).
DREXELMED HIV/AIDS Genetic Analysis Cohort Demographics
At the time of this report, the DREXELMED HIV/AIDS Genetic Analysis Cohort comprised 504 patients infected with HIV-1 (subtype B). From this cohort, 80 black/African American patients were identified and placed into the drugs-of-abuse subcohort using stringent definitions of drug abuse described in the Materials and Methods. The 80 patients were categorized into the following groups, 29 PN, 27 PCo users, 8 MDU, 11 preferential cannabinoid users, and 5 preferential benzodiazepine users. These 80 patients were used for the CCM analysis. An additional 23 black/African American patients were selected for the WLCM model because they had varying levels of cocaine and had never tested positive for any other drug. Thus, the total number of black/African American patients in the drugs-of-abuse subcohort was 103 (Table 1).
Health of the Patients in the CCM Analysis
To assess the impact of drug use on the overall health of the patients in the CCM model, a comparison of CD4+ and CD8+ T-cell counts, CD4:CD8 ratio, and viral load was performed. A normal CD4+ T-cell count ranges from approximately 500 to 1600 cells per microliter, and a normal CD8+ T-cell count ranges from approximately 300 to 1000 cells per microliter.23 Although decreased levels of CD4+ T cells and measurable viral loads are classical indicators of HIV disease severity, studies have shown that an elevated CD8+ T-cell count while a patient is on HAART, as is the case for the majority of the patients reported in the present study, can be a warning of treatment failure and is associated with accelerated HIV disease progression.24 The PN population was healthier than either the PCo or MDU populations. The PN group exhibited a higher mean CD4+ T-cell count and lower mean CD8+ T-cell counts (Fig. 1). The CD4:CD8 ratios of the PCo and MDU populations were therefore lower than in the PN population (Fig. 1). The PCo and MDU populations also had higher mean viral loads than the PN population (Fig. 1).
Impact of Drugs of Abuse on Cytokine Profiles in Patients Infected With HIV-1
To understand the impact of drugs of abuse on cytokine profiles in patients within the drugs-of-abuse subcohort, plasma samples from the subjects were subjected to a cytokine human 30-plex immunoassay. The 30 cytokines were examined with respect to their association with cocaine use using the 2 biostatistical models, CCM (n = 80) and WLCM (n = 103) (Fig. 2). CCM has been the model traditionally used for this type of analysis and in this regard it served as an informative control for comparison against the WLCM model, which was developed to allow for any measured drug (cocaine in this study) to have an effect on the value of each cytokine in the 30-plex panel. To understand the complicated nature of drug abuse, the WLCM analysis was performed in 3 different ways (at-visit, upto-visit, and all-visits, as outlined above). This allowed us to define the effect of cocaine use over a long period on the levels of the 30 cytokines examined in the study, given the longitudinal sampling with the associated drug testing and detailed history of drug abuse available at every visit for every patient in the DREXELMED HIV/AIDS Genetic Analysis Cohort. Because the WLCM model allows patients to not be grouped, it gave us the potential to add more patients into the analysis. In addition, the WLCM restricted at-visit analysis was performed with the same number of patients as in the CCM analysis to compare the results obtained from both models. Analysis using the CCM model identified epidermal growth factor (EGF) as being significantly associated with cocaine use (P = 0.0203). Analysis using the same group of patients with the WLCM model (designated WLCM RESTRICTED AT-VISIT) also revealed EGF (P = 0.0267) to be significantly associated with cocaine use as well as IL-4 (P = 0.0134) and monokine induced by gamma interferon (P = 0.0480). Analysis using the WLCM model for the AT-VISIT identified eotaxin (P = 0.0336) and IL-4 (P = 0.0021) to be positively associated with cocaine use, trended positively associated as a group with cocaine use (P = 0.0651). Analysis for the UPTO-VISIT identified IL-4 (P = 0.0261) as being positively associated with cocaine use. In addition, in analyzing the effects of cocaine on the Th2 cytokine panel, the values of 3 cytokines, IL-4, IL-5, and IL-10, when added together were positively associated with cocaine use (P = 0.0364). Finally, analysis for ALL-VISITS identified IL-10 (P = 0.0208) and granulocyte-macrophage colony-stimulating factor (P = 0.0437) as being positively associated with cocaine use, as was the Th2 panel (P = 0.0191) (Fig. 2 and Table 2).
To understand the impact of the results reported herein with respect to other clinical studies performed in HIV-1–infected individuals within the context of substance abuse, the literature was also reviewed in an attempt to identify previous studies performed on human plasma, serum, or blood or ex vivo studies of human PBMCs to determine cytokines/chemokines associated with HIV-1 and/or cocaine use/exposure (Fig. 2). Interestingly, to our knowledge no studies on the scale reported herein have analyzed human samples for the combined effects of HIV-1 and cocaine, the closest being Kamat et al.18 Of the studies performed concerning HIV-1 pathogenesis and disease in the absence or presence of cocaine, the statistical analyses used in previous studies ranged from relying on subjects' verbal admission of drug use to studies performed without the use of longitudinal sampling. In addition to the human studies, the single animal model study that has examined the effects of HIV and cocaine together, using a simian immunodeficiency virus macaque model, reported no effect of cocaine on the levels of IFN-β, monocyte chemoattractant protein-1, IL-6, TNF-α, or IFN-γ.25
Cocaine Significantly Impacts Disease Severity in HIV-1–Infected Cocaine Users
Because of the longitudinal nature of the cohort and the novel WLCM model that has been developed and reported herein, it was possible to associate percentages of cocaine-positive HIV-1–infected clinical samples used with levels of cytokines that were shown previously to be impacted by cocaine use and clinical parameters of disease severity. These analyses are distinct from the clinical parameters shown in Figure 2 because of the use of this novel model. This analysis was performed with patients who had tested positive for only cocaine and no other drug and patients who had not tested positive for any drug (n = 64). This patient selection allowed analyzing the impact of pure cocaine use, which was varying in percentages of cocaine-positive HIV-1–infected clinical samples, on the immunomodulatory profile. Percentages of cocaine-positive HIV-1–infected clinical samples were calculated by taking into account a patient's longitudinal drug use history as explained in Figure 3. Panel A indicates that as the percentage of cocaine-positive HIV-1–infected clinical samples increased, CD4+ T-cell counts decreased significantly and viral load increased significantly. With respect to the cytokines, panel B includes all the cytokines that were significantly associated with cocaine use using the WLCM at-visit, upto-visit, or all-visits analyses and showed IL-10 and granulocyte-macrophage colony-stimulating factor as well as the Th2 panel of cytokines were all positively associated as the percentage of cocaine-positive HIV-1–infected clinical samples increased. Because this analysis was performed for all the 30 cytokines, panel C shows additional cytokines that were significantly associated with cocaine use such as EGF, IL-1β, IL-2, IL-7, vascular endothelial growth factor, granulocyte colony-stimulating factor as well as the Th1 to Th2 ratio (Fig. 3).
Using an exhaustive multiplex approach and a novel biostatistical model, we identified several cytokines that are associated with PCo use in the plasma of a longitudinal cohort of HIV-1–infected individuals who have detailed histories of drug abuse. Because most of the cohort was black/African American, data from only these individuals were presented. However, additional cytokine profiling data from a limited number of individuals of different races showed differential regulation of these cytokines based on race (data not shown). These studies also emphasize the detrimental impact of cocaine use on HIV-1 disease progression. Because of this link, the data were further analyzed with respect to individual cytokines and their association with viral load and/or CD4 T-cell counts. This analysis was performed with 2 methods. First, the same analysis was performed while considering cocaine as a confounder to VL and CD4 counts. Second, subcohorts of patients were analyzed that either exclusively use cocaine (the PC population) or never use cocaine (the PN population). In both cases, no cytokines or groups of cytokines were linked in the WLCM model after multiple testing correction (data not shown). This result is likely because of the fact that the patients selected for the analyses reported herein were patients who have been seen by the clinic 3 or more times. Consequently, their CD4 T-cell counts were shown to be consistently elevated and their viral loads were shown to be well controlled as a result of effective therapy (Fig. 1). Consequently, the small level of variation in CD4 cell counts and/or viral load measurements cannot be accurately captured based on the small amount of variation in cytokine expression.
Studies have indicated that the phenomenon of cocaine regulating cytokine levels is mediated by corticosterone in spleen cells of cocaine-treated mice. In animals treated with cocaine, levels of IL-4 and IL-10 were increased but levels of IL-2 and IFN-γ were not changed, suggesting that while cocaine led to an increase in the Th2 response, it did so by not inhibiting the Th1 response, thus allowing the Th2 response to proceed unrestricted.26 Another study, with chronically cocaine-treated rats, showed a rapid and sustained suppression of lymphocyte proliferation, which was supported by a decrease in the levels of IL-2, integral in the proliferation of lymphocytes and activated T lymphocytes. IL-10 also increased concomitantly, and the proinflammatory cytokine TNF-α and anti-inflammatory cytokines IL-10 and transforming growth factor β1 were dysregulated. Transforming growth factor β1 has been shown to shift the Th1/Th2 response toward Th2 cytokines. The investigators concluded that cocaine allowed for the dysregulation of cytokine expression.27
Thus, the identification of cytokines such as IL-4 and IL-10 that have been significantly altered by cocaine use in the present study is consistent with previous cohort studies of more limited size as well as animal studies.28–31 Both cytokines are Th2-type cytokines secreted by Th2 cells. Whereas IL-4 is involved in differentiation of naive helper T cells (Th0) to Th2 cells, IL-10 is known to downregulate Th1 cytokines.32–37 In fact, when comparing the results obtained with the Th1 and Th2 cytokine panels with clinical samples derived from HIV-1–infected patients in the DREXELMED cohort, there was a significant decrease in the ratio of Th1:Th2 cytokines that correlated with an increase in percentage of cocaine-positive HIV-1–infected clinical samples (Fig. 3). In conjunction with this information, cocaine has been implicated in dysregulation of the Th1/Th2 cytokine balance and in driving the immune response toward a Th2-type response.26,38,39 Preferential Th1- and Th2-type responses account for several immunopathological disorders, such as allergic responses and fetal transplantation tolerance. During HIV-1 infection in particular, the imbalance between Th1/Th2 cytokines was significant, especially given that the bias toward a Th2-type response and an inhibition of Th1-type response has been associated with the immune system failing to control HIV-1 infection, leading to progression of the infection toward AIDS.31,40,41 Clerici and Shearer40 have proposed that a switch from a Th1- to a Th2-type (specifically, IL-4 and IL-10) response could lead to increased intercellular susceptibility to HIV-1 as well as transmission, possibly owing to the upregulation of coreceptors such as CXCR4 by cocaine exposure by signaling through the sigma-1 receptor.42,43 This would favor the replication of CXCR4-utilizing HIV-1 strains or X4 viruses, specifically within Th2 cells that are able to secrete IL-4, which has also been shown to further upregulate CXCR4, whereas Th1 cytokines such as IFN-γ have been shown to downregulate CXCR4 expression.44
This study has also shown that clinical parameters such as CD4+ T-cell counts, and viral load correlate with percentage of cocaine-positive HIV-1–infected clinical samples, further supporting the conclusion that cocaine is detrimental to the immune system and facilitates the acceleration of HIV-1 disease progression. Building on the analysis of results performed in previously published studies, we have analyzed our data using a modern, robust biostatistical model that we developed to define the singular effect of cocaine on cytokine profiles. The presence of 30 cytokine expression levels suggests that some false-positive findings are possible. This possibility has been minimized by further confirmation using dosage in the WLCM, and future validation studies in replication cohorts will help eliminate these false positives. The WLCM can be used in future studies to define the effects of other drugs on specific individual cytokines. Interestingly, while reviewing the results in the literature, we did encounter HIV-1–positive-only cohorts, which used plasma, serum, and PBMC supernatants and drugs-of-abuse (cocaine) cohorts, which used plasma and PBMC supernatants for cytokine profiling.4,7,18,28–31,45–59 However, to our knowledge there were no reports of cytokine profiles on HIV-1–positive cohorts that included detailed longitudinal histories of cocaine use by members of a given cohort, as demonstrated here with the DREXELMED HIV/AIDS Genetic Analysis Cohort.
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Keywords:© 2014 by Lippincott Williams & Wilkins
HIV; cocaine; cytokines; drug use; interleukin-4; interleukin-10