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. Author manuscript; available in PMC: 2025 Dec 15.
Published in final edited form as: J Neuroimmunol. 2024 Oct 28;397:578470. doi: 10.1016/j.jneuroim.2024.578470

Influence of Cocaine Use Reduction on Markers of Immune Function

William W Stoops 1,2,3,4, Thomas P Shellenberg 1,3, Sean D Regnier 1, David H Cox 1, Reuben Adatorwovor 5, Lon R Hays 2, Danielle M Anderson 2, Joshua A Lile 1,2,3, Joy M Schmitz 6, Jennifer R Havens 1,4, Suzanne C Segerstrom 7
PMCID: PMC11620913  NIHMSID: NIHMS2034111  PMID: 39504756

Abstract

This study determined the effects of reduced cocaine use on immune function. Treatment seeking participants with Cocaine Use Disorder enrolled in a 12-week contingency management trial to reduce cocaine use. Participants were randomly assigned 1:1:1 to High Value Reinforcers (i.e., $55/negative urine sample) for cocaine abstinence (n=41), Low Value Reinforcers (i.e., $13/negative urine sample) for cocaine abstinence (n=33) or Non-Contingent Control (n=33). Immune measures were collected at 6-week intervals. The High Value group had greatest use reductions, increased erythema and IL-6 and decreased IL-10 and CCL5, suggesting an activated immune response. Cocaine use reduction may promote changes in immune health.

Keywords: cocaine, immune, human, clinical trial

Graphical Abstract

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1. Introduction

Cocaine Use Disorder (CUD) remains a public health concern. Over 5 million Americans used cocaine and approximately 1.4 million people met criteria for CUD in 2022 (Substance Abuse and Mental Health Services Administration [SAMHSA], 2023). Despite expending considerable resources to develop a medication for CUD, none have met the expectation that treatments promote continuous and complete abstinence from cocaine use (for reviews, see Czoty et al., 2016; Kampman, 2019; Tardelli et al., 2020). Experts in the field have suggested that objectively verified reductions in drug use are clinically meaningful and may be a more viable treatment target (Donovan et al., 2012; Volkow, 2022). Indeed, the recently updated FDA guidance for stimulant use disorder medication development now recognizes change in pattern of cocaine use (e.g., reduction in frequency of use) as an acceptable non-abstinence endpoint (FDA, 2023). Although some retrospective analyses have demonstrated that reduced cocaine use benefits psychosocial outcomes like craving or quality of life (Aminesmaeili et al., 2024; Carroll et al., 2014; Loya et al., 2023; Roos et al., 2019; Votaw et al., 2024), few prospective studies have evaluated the benefits of cocaine use reduction across a range of other physical and psychosocial health outcomes. The analyses reported here originate from a prospective trial (NCT03224546) that used Contingency Management (CM) to promote reductions in cocaine use and evaluate consequent changes on numerous health outcomes.

Mounting evidence suggests that cocaine use negatively affects immune health (for reviews, see Assis et al., 2021; Coller and Hutchinson, 2012; Gipson et al., 2021; Karimi-Haghighi et al., 2023; Pellegrino and Bayer, 1998). Cocaine use increases the likelihood of HIV infection above and beyond the risks associated with injection drug use (Anthony et al., 1991; Chaisson et al., 1989) and exacerbates HIV severity (Macmadu et al., 2023; Parikh et al., 2014). The exact mechanisms underlying this increased risk are unknown, but multiple clinical studies have shown that chronic cocaine use produces a pro-inflammatory state, which may increase vulnerability to contracting infections or exacerbate existing infections (Carrico et al., 2008; Fox et al., 2012; Ruiz et al., 1994; Van Dyke et al., 1986). Those studies show that people who use cocaine or other stimulants have altered levels of inflammation biomarkers such as interleukin (IL)-10, IL-6 and tumor necrosis factor-α (TNF-α) following stressor exposure (Carrico et al., 2008; Ersche et al., 2014; Fox et al., 2012) or when compared to controls (Funchal et al, 2024; Levandowski et al., 2016). Moreover, levels of natural killer cells, which are markers of cell-mediated immunity, are elevated by cocaine administration (Ruiz et al., 1994; Van Dyke et al., 1986). Cocaine abstinence promotes normalization of these biomarkers in preclinical and clinical studies (e.g., Araos et al., 2014; Kubera et al., 2008) but the effect of reduced cocaine use remains to be determined.

Although these biomarkers of immune function are dysregulated in people who use cocaine, physiological, in vivo laboratory evaluation of cell-mediated immunity (e.g., delayed-type hypersensitivity [DTH]; Segerstrom and Sephton, 2010) has not yet been conducted in this group. Cell-mediated immunity is the primary mechanism for protection against viruses and other intracellular pathogens. The present trial included a DTH measure to determine the effects of reduced cocaine use on cell-mediated immunity and to facilitate interpretation of the functional significance of immune biomarker changes accompanying reduced use.

This analysis sought to determine whether reduced cocaine use benefits both biomarkers of immune health (e.g., IL-10) and an indicator of cell-mediated immunity (i.e., DTH) over a 12-week intervention. The biomarkers of immune function were the anti-inflammatory cytokine IL-10, the pro-inflammatory cytokine TNF-α and the pleiotropic cytokine IL-6, as well as C Reactive Protein (CRP), a general indicator of inflammation. TNF-α, IL-6 and IL-10 were selected because they are altered in people who use cocaine, as described above. CRP was selected for testing because it represents a later stage of the inflammatory cascade (e.g., TNF-α to IL-6 to CRP). Granulate-Colony Stimulating Factor (G-CSF) and Chemokine Ligand 5 (CCL5, also known as RANTES) were also analyzed in a subset of participants because these immune markers have been influenced by stimulant exposure (Calipari et al., 2018; Pereira et al., 2011, but also see Manini et al., 2021). The overarching hypothesis of this work was that reduced cocaine use confers benefit on these indicators of immune health.

2. Materials and Methods

The Institutional Review Board of the University of Kentucky approved the conduct of this trial and all participants provided signed informed consent prior to study enrollment. This trial was registered on clinicaltrials.gov(NCT03224546). Participants were enrolled as outpatients at the University of Kentucky Psychopharmacology of Addiction Laboratory (PAL) for the duration of the trial and were escorted by staff to the Outpatient Clinic of the University of Kentucky Center for Clinical and Translational Science (CCTS) and the Kentucky Clinic of University of Kentucky Healthcare for subcutaneous candida yeast administration and blood collection.

2.1. Participants

See Table 1 for a CONSORT diagram of study recruitment and allocation. A total of 357 potential participants were assessed for inclusion in this single-blind trial, with 127 randomly assigned to one of three intervention groups. Twenty participants did not attend their first day of treatment, leaving a total of 107 people included in this trial for data analysis. Potential study participants met the following inclusion/exclusion criteria: 1) at least 18 years old; 2) self-reported cocaine use in the week prior to screening; 3) provided a benzoylecgonine (BE)-positive (the primary metabolite of cocaine) urine sample prior to consenting; 4) met Diagnostic and Statistical Manual 5 (DSM 5) criteria for moderate to severe CUD and 5) seeking treatment for their cocaine use. Those who had a current or past medical or psychiatric illness that would have interfered with study participation (e.g., physical dependence on any drug requiring medically managed detoxification, uncontrolled cardiac arrhythmia) or poor venous access precluding blood draws were excluded.

Table 1.

CONSORT Diagram

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2.2. Randomization

Following baseline screening, participants were randomized 1:1:1 to one of three groups (see Intervention) using stratified block randomization: 1) High Value CM (n = 41 who attended the first treatment visit), 2) Low Value CM (n = 33 who attended the first treatment visit) or 3) non-contingent Control (n = 33 who attended the first treatment visit). Strata were based on sex (male/female), age (>35/≤35 years) and cocaine use (>15/≤15 days used in the past 30) based on prior research (Walsh et al., 2013). These data were gathered from the Addiction Severity Index-Lite (Cacciola et al., 2007), which was completed during their baseline visit.

2.3. Procedure

Participants visited the PAL three days per week (Monday, Wednesday, Friday) across the 12-week intervention period, totaling 36 scheduled clinic visits. Participants were withdrawn from the trial if they expressly asked to be withdrawn or missed eight consecutive clinic visits, excluding medical (e.g., sickness) or personal (e.g., family emergency) circumstances. Additionally, participants completed four follow-up appointments over a 24-week period following the completion of treatment (i.e., 4-, 8-, 12- and 24-weeks post-intervention). Immune assays were completed at baseline, weeks 6 and 12 of the intervention period, and at 4-, 12- and 24-weeks post-intervention (see Primary Outcome Variables and Measurement, below). Participants provided observed urine samples during each visit. Samples were coded as positive or negative for BE using qualitative urine screens with a 300 ng/ml cutoff, which is commonly used in clinical trials (e.g., Johnson et al., 2020; Levin et al., 2020). Participants received incentives for providing negative urine samples, which were based on their group assignment (excluding the control group). Urine was also tested for recent use of other drugs (i.e., cannabis, opioids, amphetamine, methamphetamine, phencyclidine, barbiturates, benzodiazepines, methadone, MDMA, tricyclic antidepressants, oxycodone and buprenorphine). Participants also completed Timeline Followback Assessments for self-report of other drug use. These results will be reported elsewhere.

2.3.1. CM Intervention

Group assignment was associated with one of three possible incentive schedules. The first 17 participants in this trial received an escalating and resetting abstinence contingency in which the value of each incentive increased with every consecutive BE-negative urine sample (starting at $2.50 or $5.00 depending on group assignment). Participants who provided a BE-positive urine sample received no payment for that sample (excluding the Control group) and the incentive size for the next negative urine was reset to the initial level. Providing three consecutive negative samples after a positive sample resulted in a return to the value earned prior to the reset. Although this is a common arrangement (Higgins et al., 2007), atypically low levels of abstinence and poor retention in these initial participants prompted us to revise the intervention (see Regnier et al. 2022 for further details). All remaining participants received fixed value incentives, with the same maximum earning potential as under the original CM arrangement: participants assigned to the High Value group received $55 for providing a BE-negative urine sample and a maximum possible total of $1980. Participants assigned to the Low Value group received $13 for providing a BE-negative urine sample and participants assigned to the Control group received $13 per urine sample regardless of urine screen results; participants in both of those groups could earn a maximum of $468.

All participants received manual-guided, weekly Matrix Intensive Outpatient Treatment (SAMHSA, 2013) offered by a graduate level counselor who was blinded to all assigned study conditions. Independent of their urine test results, participants could also be compensated an additional $10 for travel and between $15 and $60 per visit, which ranged between thirty minutes and four hours, based on outcome variables being measured (see Primary Outcome Variables and Measurement) if they completed the 12-week treatment period. These payments were the same across all groups. All payments were made by check, which could be cashed for no charge at a local bank.

2.3.2. Primary Outcome Variables and Measurement

2.3.2.1. Participant Demographics.

Participant demographics (e.g., age, race, sex, years of education) were collected using the Addiction Severity Index-Lite (Cacciola et al., 2007) and a questionnaire assessing the number of DSM 5 CUD criteria met (American Psychiatric Association, 2013).

2.3.2.2. Cocaine Use.

Cocaine use was monitored at the PAL by qualitative urine drug screening at every visit. Urine samples were tested for the presence of BE using Instant-View BE urine cassette tests and coded positive or negative as described above. These cups included a specimen validity test strip to determine urine temperature and adulteration tests (i.e., pH, specific gravity, oxidants, creatinine, nitrite, glutaraldehyde). For data analysis purposes, percent BE-negative urines were calculated to match the intervals for which the immune measures were aggregated or collected during treatment (i.e., at 6-week intervals during treatment).

2.3.2.4. Immune Function

Immune function was measured using a physiological outcome of cellular mediated immunity, DTH to subcutaneous candida yeast, as well as immune biomarkers (i.e., TNF-α, IL-6, IL-10, CRP, G-CSF and CCL5). DTH and blood samples for measurement of biomarkers were collected at the baseline visit and weeks 6 and 12 of the intervention period. They were also collected at each post-treatment follow up visit, except for the 8-week post visit.

To determine DTH, 0.1 ml of antigen derived from candida yeast (Candin®, Allermed, San Diego, CA) was injected subcutaneously into subjects’ non-dominant forearms. DTH responses, which reflect recruitment and migration of immune cells to the skin and T cell and inflammatory response competence (Rabin, 1999), were evaluated 48 hours later by study staff calculating two standardized scores for each participant: one for erythema (i.e., skin redness/rash) and one for induration (i.e., skin hardness). For each of these, we used the ball-point pen method (Longfield et al., 1984). When resistance due to the induration was encountered, the pen was lifted, defining the margins of the orthogonal boundaries of the immune reaction (measured in millimeters). We found the mean of participants’ measurements and then found the mean and standard deviation of those means. Those means were then converted to z-scores by batch for analysis (M=0, SD=1; Segerstrom and Sephton, 2010). Larger DTH responses were associated with reduced morbidity and mortality in a range of patient populations (Christou et al., 1995; Dolan et al., 1995; Wayne et al., 1990).

Blood for biomarker analysis was drawn into tubes with clot activator or EDTA via venipuncture from an antecubital vein by a research nurse or phlebotomist in the CCTS Outpatient Unit or Kentucky Clinic. Blood for serum was allowed to clot 30 minutes. All tubes were centrifuged at 1300×g, 10 minutes, 4°C. To obtain platelet-free plasma, the top layer from the EDTA tube was transferred to a fresh microcentrifuge tube and further centrifuged at 10,000×g, 10 minutes, 4°C. Serum and platelet-free plasma aliquots were stored at −80°C prior to assay. Assays were performed by the CCTS Biomarker Analysis Lab using commercially available kits according to the manufacturer’s instructions. Platelet-free plasma CCL5 (R&D Systems cat. DCS50) was measured by enzyme-linked immunoassay and signals were captured with a Biotek Synergy plate reader and analyzed using Gen 5 software. Serum IL-10, TNF-α, IL-6, CRP and G-CSF (Meso Scale cat. K15049—custom multiplex IL-6/IL-10/TNF-α, K15198—CRP, K151VKG—GCSF) was measured by electrochemiluminescent immunoassay, signal was captured with a Meso Scale QuickPlex SQ 120MM instrument and analyzed using Discovery Work Bench software. A standard curve shift was observed for G-CSF analyses, so low standards were dropped and 4-parameter curves were used to reduce variability for this outcome (Feng et al., 2019). Data were also log transformed prior to analysis.

Other outcomes were assessed in the trial (e.g., biomarkers of cardiac health and psychosocial function) and will be reported elsewhere.

2.4. Data Analysis Plan

2.4.1. Demographic Comparisons

Baseline characteristics were summarized and compared among the 107 participants who completed their first treatment appointment. Chi-square test statistics were used to compare the proportion of participants within each intervention group across the contingency tables for the categorical variables. Similarly, t-tests and ANOVA models were used to compare the mean differences between the intervention groups for each of the continuous variables.

2.4.2. Assessing changes in cocaine use

Missed visits (i.e., no-shows) were treated as missing data and excluded from the denominator when analyzing overall abstinence. Data were also imputed with the last visit carried forward (LVCF), with no significant changes in results. Therefore, those results were not included. Descriptive statistics and logistic regression models using Generalized Linear Mixed Effect (GLME) Models were used to analyze urine test results based on group membership. Urine test results were dichotomous outcome measures (positive/negative), which were assessed over 36 possible treatment period timepoints for each participant. The within-subject variable was Clinic Visit number (Continuous; 1–36) while Group (Ordinal; High, Low, Control) was the primary fixed effect. The Control group served as a reference. The primary outputs for this analysis were the main effects of the model (i.e., F statistic), estimated marginal means (EMMs) for each group (i.e., proportion of negative samples across groups, expressed as percentages) with pairwise group contrasts of EMMs using Bonferroni correction, and odds ratios (OR). Despite stratifying on past 30 days of cocaine use, participants in the Low Value group were more likely to be BE-positive during their baseline appointment than participants in the other groups. Therefore, baseline urine test results at baseline were included in the model as a covariate.

Odds ratios were interpreted as the odds of providing a BE-negative urine sample for participants exposed to one of the intervention conditions (High Value and Low Value) compared to the odds of providing a negative urine sample for participants exposed to the Control condition.

2.4.3. Evaluating Effect of Changes in Cocaine Use on Immune Outcomes

Generalized linear models (GzLM) were first used to evaluate the relationship between each immune outcome as a function of group assignment while adjusting for intervention time (i.e., baseline, week 6, and week 12). By accounting for the potential confounding effects of the intervention timepoint, this approach allowed for a more nuanced understanding of the relationship between the predictor and outcomes over time. Each model was then repeated to include the follow-up time points (i.e., 4-, 12-, and 24-weeks post) to explore if any differences emerged, or persisted, into the post-intervention follow-ups. Parameter estimates for models fit with group assignment can be interpreted as the associated incremental change in the outcome based on being in the High Value group or Low Value group compared to controls, when controlling for the effect of time.

To better isolate the effects of cocaine use reduction, the same data analysis process was then repeated for each outcome and percent BE-negative urine samples across matching timepoints to explore how reducing cocaine use was associated with changes in immune function, independent of intervention group assignment. Parameter estimates for models fit with percent BE-negative urine samples can be interpreted as the incremental change in the outcome based on having one percent increase in BE-negative urine samples, when controlling for the effect of time.

The GzLM methodology is robust in handling missing data. Thus, missing data in this study were assumed to be missing at random due to the LVCF analysis noted above. All statistical tests were conducted at a significance level of 5% using SPSS Version 28 (IBM Corporation, Armonk, NY) for cocaine use and SAS version 9.4 (SAS Institute, Cary, NC) for DTH and biomarkers. All graphs were created in PRISM 10 (GraphPad, La Jolla, CA).

3. Results

3.1. Demographics and Retention

Given the stratification parameters, there were no statistically significant differences in the proportion of participants assigned to each group by gender, age and cocaine use (Table 2). The proportion of Black participants was significantly greater in the Low Value group compared to the High Value and Control groups (p=0.020). Number of pre-intervention days using cocaine and alcohol was also similar across groups, as was the proportion of participants meeting criteria for moderate or severe CUD, as defined by the DSM-5. The mean number of years of education did not differ significantly across groups.

Table 2.

Characteristics of Enrolled Participants by Group Assignment

High Value
n = 41		 Low Value
n = 33		 Control
n = 33
n % n % n % P-value
Sex 0.999
Male 26 63.4 21 63.6 68 63.6
Female 15 36.6 12 36.4 12 36.4
Race 0.020*
Black 32 78.0 30 90.9 26 78.8
White 3 7.3 1 3.0 7 21.2
Hispanic 1 2.4 2 6.0 0 0.0
Multiracial 4 9.8 0 0.0 0 0.0
American Indian/Alaska Native 1 2.4 0 0.0 0 0.0
Age in Years, mean (SD) 53.7 (9.0) 50.4 (7.8) 50.5 (10.0) 0.196
Education in Years, mean (SD) 12.9 (1.9) 12.6 (1.6) 12.5 (1.5) 0.568
DSM-5 Cocaine Use Disorder 0.167
Moderate 2 4.9 6 18.2 3 9.1
Severe 39 95.1 27 81.8 30 90.9
Days of Use in Past 30, mean (SD)
Cocaine 11.5 (8.7) 11.1 (7.5) 11.9 (7.6) 0.936
Alcohol 7.8 (9.2) 8.3 (8.3) 6.0 (8.4) 0.604
Number of Cigarettes Smoked in Past 30 Days, mean (SD) 291.7 (285.5) 165.5 (185.4) 248.4 (246.7) 0.142
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Note. N=107.

*

The proportion of Black participants was statistically significantly greater in the Low Value group compared to the High Value and Control groups.

Figure 1 shows retention rates for each group throughout each week of the trial. The High Value group retained 71% of participants, the Low Value group retained 58% of participants, and the Control group retained 79% of participants by Week 12.

Figure 1.

Figure 1

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Retention Rates by Group

Note. Retention rates by group are plotted as a percentage of randomized participants that remained in the trial by the end of each week. Participants who missed eight consecutive visits without a medical or personal exemption were removed from the trial.

3.2. Changes in Cocaine Use as a Function of Group Assignment

Figure 2 shows percent BE-negative urine samples weekly for each group. When controlling for baseline abstinence, the GLME revealed a statistically significant main effect of group (F = 4.36; p = 0.013) and baseline abstinence (F = 8.03; p = 0.005) on the provision of BE-negative urine samples. Participants in the High Value (EMM = 46% negative) group were significantly more likely to provide a BE-negative urine sample throughout the course of the study compared to controls (EMM = 24% negative; p = 0.012; OR = 2.72). Participants in the Low Value group (EMM = 23% negative) were not more likely to provide a BE-negative urine sample compared to Controls (p = 0.874; OR = .926).

Figure 2.

Figure 2

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Average Percent BE-Negative Urine by Group

Note. Average percent BE-negative urine samples over the 12-week intervention and follow-up period for participants assigned to the High Value Alternative Reinforcer (circles), Low Value Alternative Reinforcer (squares) or Control (triangles) groups. X Axis: weeks in study (weeks 16–36 are post-treatment follow-up visits). 0 indicates baseline visit results. Values were calculated by dividing the number of negative urine samples collected during each week by the number of positive urine samples collected during the same week. Missed visits were counted as missing data and not included in the denominator.

3.3. Effects of Intervention Group on Immune Function

The results from the model for each outcome and intervention group are summarized in Table 3 and in the text below.

Table 3.

Associations between Intervention Group and Immune Outcomes

Intervention Time Points All Time Points
Outcome Group Effect Size Chi-Square P-value Effect Size Chi-Square P-value
Biomarkers
IL-10 High −0.24 6.5 0.011 −0.20 8.4 0.004
Low 0.14 1.8 0.174 0.11 2.0 0.157
TNF-α High −0.09 2.7 0.100 −0.05 1.1 0.289
Low −0.11 3.1 0.080 −0.08 2.9 0.087
IL-6 High 0.25 4.7 0.030 0.17 3.5 0.061
Low 0.02 0.0 0.899 0.06 0.4 0.533
CRP High 0.26 1.6 0.212 0.19 1.1 0.299
Low 0.18 0.6 0.422 0.32 2.7 0.101
G-CSF High 0.00 0.0 0.976 0.04 0.5 0.476
Low 0.02 0.1 0.785 0.02 0.2 0.680
CCL5 High −0.59 6.9 0.009 −0.72 15.7 <0.001
Low −0.29 1.5 0.228 −0.50 6.4 0.012
DTH
Induration High 0.14 0.7 0.379 0.18 2.3 0.131
Low 0.28 2.7 0.098 0.37 8.0 0.005
Erythema High 0.33 4.6 0.031 0.27 5.1 0.024
Low 0.17 1.1 0.307 0.20 2.3 0.128
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Note. Effect size estimates use the Control group as the reference. All associations are adjusted for the effect of intervention timepoint.

3.3.1. IL-10

There was a significant relationship between the High Value group and IL-10 values during the intervention timepoints (Chi-Square = 6.5, p = 0.011) when controlling for time which retained significance when including all follow-up timepoints (Chi-Square = 8.4, p = 0.004; Figure 3). During the intervention timepoints, being in the High Value group was associated with a 0.24 pg/mL decrease in IL-10 values.

Figure 3.

Figure 3

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Average log IL-10, IL-6, and CCL5 values by Group

Note. Average log IL-10 (top panel), IL-6 (middle panel) and CCL5 (bottom panel) over the 12-week intervention and follow-up period for participants assigned to the High Value Alternative Reinforcer (circles), Low Value Alternative Reinforcer (squares) or Control (triangles) groups. X Axis: weeks in treatment. Brackets: Standard deviation

3.3.2. TNF-α

The relationships between Group and TNF-α did not reach statistical significance.

3.3.3. IL-6

There was a significant relationship between being in the High Value group and IL-6 values during the intervention timepoints (Chi-Square = 4.7, p = 0.030), when controlling for time (Figure 3). During the intervention timepoints, being in the High Value group was associated with a 0.25 pg/mL increase in IL-6 values.

3.3.4. CRP

The relationships between Group and CRP did not reach statistical significance.

3.3.5. G-CSF

The relationships between Group and G-CSF did not reach statistical significance.

3.3.6. CCL5

There was a statistically significant relationship between being in the High Value group and CCL5 values during the intervention timepoints (Chi-Square = 6.9, p = 0.009) when controlling for time, which retained significance when including all follow-up timepoints (Chi-Square = 15.7, p <0.001) (Figure 3). During the intervention timepoints, being in the High Value group was associated with a 0.59 ng/mL decrease in CCL5 values and a 0.72 ng/mL decrease when considering all timepoints.

There was also a significant relationship between being in the Low Value group and CCL5 values when including all timepoints (Chi Square = 6.4, p = 0.012), but not during the intervention. For all timepoints, being in the Low Value group was associated with 0.50 ng/mL decrease in CCL5 values.

3.3.7. DTH

There was a significant relationship between being in the Low Value group and induration z-scores when including all timepoints (Chi Square = 8.0, p = 0.005), but not during the intervention. For all timepoints, being in the Low Value group was associated with 0.37 ng/mL increase in induration z-score.

There was a significant relationship between being in the High Value group and erythema z-scores during the intervention timepoints (Chi-Square = 4.6, p = 0.031) when controlling for time, which retained significance when including all follow-up timepoints (Chi-Square = 5.1, p = 0.024). During the intervention timepoints, being in the High Value group was associated with a 0.33 increase in erythema z-score and a 0.27 pg/mL increase when considering all timepoints.

3.4. Effects of Cocaine Use Reduction on Immune Function

The results from the model for each outcome and percent BE-negative urine are summarized in Table 4 and in the text below.

Table 4.

Associations between Percent BE-Negative Urine Samples and Immune Outcomes

Intervention Time Points All Time Points
Outcome Effect Size Chi-Square P-value Effect Size Chi-Square P-value
Biomarkers
IL-10 −0.14 1.7 0.192 −0.09 1.5 0.225
TNF-α 0.05 0.6 0.433 0.08 3.3 0.070
IL-6 0.24 3.6 0.058 0.18 3.6 0.057
CRP 0.40 2.9 0.088 0.49 7.3 0.007
G-CSF 0.08 1.4 0.240 0.10 3.4 0.067
CCL5 −0.01 0.0 0.955 0.00 0.0 0.994
DTH
Induration 0.44 6.8 0.009 0.20 2.6 0.107
Erythema 0.56 11.0 0.001 0.30 5.8 0.016
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Note. Effect size estimates use the Control group as the reference. All associations are adjusted for the effect of intervention timepoint.

3.4.1. IL-10

The relationships between percent BE-negative urine and IL-10 did not reach statistical significance.

3.4.2. TNF-α

The relationships between percent BE-negative urine and TNF-α did not reach statistical significance.

3.4.3. IL-6

The relationships between percent BE-negative urine and IL-6 did not reach statistical significance.

3.4.4. CRP

There was a significant relationship between percent BE-negative urine and CRP values when including all timepoints (Chi Square = 7.3, p = 0.007), but not during the intervention only. For all timepoints, a one percent increase in BE-negative urine was associated with 0.49 ng/mL increase in CRP values.

3.4.5. G-CSF

The relationships between percent BE-negative urine and G-CSF did not reach statistical significance.

3.4.6. CCL5

The relationships between percent BE-negative urine and CCL5 did not reach statistical significance.

3.4.7. DTH

There was a significant relationship between percent BE-negative urine and induration z-scores during the intervention timepoints (Chi Square = 6.8, p = 0.009) which did not retain significance when including all timepoints (Figure 4). During the intervention, a one percent increase in BE-negative urine was associated with a 0.44 increase in induration z-score.

Figure 4.

Figure 4

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DTH Measures at Baseline, Week 6, and Week 12

Note. DTH reactions (induration [left panels] and erythema [right panels] to Candida Yeast transformed as z-scores for all participants across three intervention timepoints (Baseline [top panels], Week 6 [middle panels], and Week 12 [bottom panels]). X-axis: percent of all urine samples up to each of the three timepoints that were benzoylecgonine-negative for each participant for each timeframe.

There was a significant relationship between percent BE-negative urine and erythema z-scores during the intervention timepoints (Chi-Square = 11.0, p = 0.001) when controlling for time (Figure 4), which retained significance when including all follow-up timepoints (Chi-Square = 5.8, p = 0.016). During the intervention timepoints, a one percent increase in BE-negative urine was associated with a 0.56 increase in erythema z-score and a 0.30 increase when considering all timepoints.

4. Discussion

This analysis sought to demonstrate that reduced cocaine use over a 12-week CM intervention would improve markers of immune health. As has been shown in numerous studies (Bentzley et al., 2021), CM reduced cocaine use, with the highest magnitude reinforcers promoting significantly greater reductions in use relative to lower value or control conditions. Two primary findings merit further focus and consideration: 1) group differences were observed on biomarker and DTH outcomes and 2) reduced cocaine use, as indicated by percent BE-negative urine results, was associated with increased CRP in follow-up and increased DTH response, primarily during the intervention period.

Changes as a function of group were largely observed in the High Value group, the group that experienced the greatest reduction in cocaine use. Specifically, CCL5 and IL-10 were decreased, while IL-6 was increased, in that group, relative to Control. We also observed increased erythema in the High Value group. The Low Value group had decreased CCL5 and increased induration relative to Control, but only during follow-up. We found that erythema and induration were also increased in positive association with percent BE-negative urine results. These data suggest that as individuals reduced their cocaine use, their cell-mediated immunity and inflammatory response increased overall.

When considering associations between verified changes in use (i.e., with percent BE-negative urines) and our outcomes, we found that CRP was positively associated with percent BE-negative urine during follow-up only while induration and erythema were positively associated with percent BE-negative urine during treatment, with an effect that persisted into follow-up for erythema only.

Prior clinical research showed that levels of CCL5, a pro-inflammatory cytokine that attracts immune cells to sites of inflammation (Krensky and Ahn, 2007), are increased in individuals with CUD and return to control levels after four weeks of abstinence (Pereira et al., 2011). We found that CCL5 levels decreased over the course of the trial in the High and Low Value groups. The High Value group had consistently higher percent BE-negative urine results throughout the trial, but the Low Value group did not differ from the Control group in terms of use, with the exception that they reduced their use relative to baseline. These findings suggest that a modest change in use from baseline may reduce CCL5 and are consistent with those of Pereira and colleagues in that groups that reduced their cocaine use had reductions on this measure. It is worth noting that another study did not find differences in CCL5 levels between people who use cocaine and controls), but that study collected samples from individuals experiencing acute cocaine overdose and could have missed delayed changes in this marker associated with chronic use or abstinence/reduction in use (Manini et al., 2021).

On the other hand, levels of IL-10, an anti-inflammatory cytokine, decreased, and levels of IL-6, a pleiotropic cytokine (Tanaka et al., 2014), increased in the High Value group during the trial. Mixed findings have been observed regarding levels of IL-10 following cocaine exposure (Iyer and Cheng, 2012). Studies, consistent with ours, have shown increased IL-10 levels relative to individuals who do not use cocaine (e.g., Funchal et al., 2024; Levandowski et al., 2016b), whereas others found that cocaine exposure decreased IL-10 levels relative to those who do not use (e.g., Anier et al., 2022; Fox et al., 2012; Moreira et al., 2016). IL-6 was elevated in people who use cocaine relative to controls (Levandowski et al., 2016a; 2016b; Moreira et al., 2014; Pianca et al., 2017). However, cocaine abstinence did not change IL-6 levels in at least one study (Pianca et al., 2017) even though acute cocaine dosing decreased IL-6 response in humans with histories of cocaine use (Halpern et al., 2003). Overall, although our findings are consistent with some prior research, they do not necessarily clarify the relationship between cocaine use, IL-10 and IL-6. Discrepancies may be driven by biological or design factors, including evaluating reduced cocaine use versus complete abstinence, chronic compared to acute effects of cocaine or longitudinal instead of cross-sectional assessment. The lack of a clear relationship found here is also reflected in a prior meta-analysis that found limited evidence for the association between chronic cocaine use and these markers (Doggui et al., 2021).

We observed that CRP, a general marker of inflammation that occurs later in the inflammatory cascade (Sproston and Ashworth, 2018) was positively associated with increased percent BE-negative urine results in follow-up. This finding is inconsistent with prior research showing that cocaine use increases CRP levels relative to controls (Cherenack et al., 2023; Ramasamy et al., 2023). A sensitivity analysis that excluded individuals who had exceptionally high levels of CRP indicative of acute infection (i.e., > 10) revealed no statistically significant effects on the relationship between CRP and percent BE-negative results, suggesting that the effect we observed may be driven by those individuals. Those analyses also revealed that only the significant effect of group on IL-6 was no longer statistically significant when excluding individuals with CRP > 10, suggesting that acute infection was not driving most of the other results described above.

Most of the biomarkers assessed (e.g., IL-10, CCL5) do not currently have generally accepted “normal” ranges, so the clinical implications of changes in these outcomes cannot be fully determined with these results. However, when evaluating CRP values in the context of the generally accepted low cardiovascular risk range (i.e., < 0.3 mg/dL; Nehring et al., 2023), we found the following proportions of subjects with normal values across groups and time in the trial. In the High Value group, approximately 48% of participants had low risk CRP scores at baseline, 48% had low risk scores at week 6 and 39% had low risk scores at week 12. In the Low Value group, 44% of participants had low risk CRP scores at baseline, 50% had low risk scores at week 6 and 44% had low risk scores at week 12. In the Control group, approximately 48% of participants had low risk CRP values, 70% had low risk scores at week 6 and 67% had low risk scores at week 12. Further, although normal DTH values have not been established, we compared baseline DTH values to those collected from healthy young adults (Segerstrom and Septhon, 2010). Over 80% of our sample was above the 75th percentile for erythema and 39% of our sample was above the 75th percentile for induration using ranges found by Segerstrom and Sephton (2010). Given further observed increases in CRP, erythema and induration as a function of cocaine use reduction over the trial, these results suggest hypergic responses indicative of a highly reactive immune system potentially rebounding from long-term modulation of immune response associated with chronic cocaine use.

Taken together, the data suggest that reductions in cocaine use alter biomarkers of immune response and DTH. Statistically significant results were largely observed in participants who experienced the greatest changes in use (i.e., the High Value group, who had approximately 50% negative urine results on average), and not solely as a function of percent BE-negative urine results in the overall sample, so it is possible that there are reduction thresholds at which beneficial effects of cocaine use reduction emerge. For example, in some clinical trials participants who achieve at least a 75% reduction in cocaine use experience longer treatment retention and improved psychosocial functioning compared to participants who do not meet that threshold (Loya et al., 2023). Data from this trial suggest that thresholds may vary between biomarkers, where effects were primarily observed in group analyses (i.e., in the High Value group) suggesting a higher threshold (e.g., 50% reductions), and cell-mediated immunity (i.e., DTH), where effects were observed in both types of analyses suggesting a lower threshold. These results could indicate an early alleviation of inflammation (i.e., with reduced CCL5) produced by reduced cocaine use that also reduces the need for IL-10 as an anti-inflammatory cytokine, with remnants of inflammation evidenced by elevated IL-6 during treatment and increased CRP levels in follow-up. More research is needed, however, to better understand the meaning of these findings, especially considering a recent systematic review and meta-analysis suggesting that the relationship between chronic drug use and markers of inflammation is neither clear nor well understood (Doggui et al., 2021).

Several limitations should be acknowledged. First, we did not observe statistically significant effects of reduced use on other markers previously shown to be affected by cocaine use (e.g., TNF-α; Araos et al., 2014; Kubera et al., 2008) but this could be explained by the fact that prior work has focused largely on complete abstinence, whereas the present project evaluated the effects of reduced cocaine use. The inability to detect effects of cocaine use reduction on immune biomarkers may also be because we failed to enroll our target sample size, largely due to the COVID-19 pandemic, which is another limitation of the trial. Lastly, polydrug use could have influenced the immune outcomes reported here. The High Value group smoked nearly twice as many cigarettes per day as those in the Low Value group at baseline. This was not a statistically significant difference but given that cigarette smoking increases inflammation and affects immune biomarkers (Doggui et al., 2021; Liu et al., 2023), it is possible that this difference blunted any benefits of cocaine use reduction observed in the High Value group.

5. Conclusion

In this trial, we observed that biomarkers of immune function and cell-mediated immunity changed as a function of reduced cocaine use. Although the full implications of these changes cannot be understood given the relative paucity of mechanistic information about immune function in people with CUD or other substance use disorders, these results support the idea that reducing cocaine use, rather than achieving complete abstinence, may improve immune response. These data add to a growing body of literature suggesting that cocaine use reduction is a viable treatment target with multiple beneficial biopsychosocial health benefits (e.g., Aminesmaeili et al., 2024; Carroll et al., 2014; Loya et al., 2023; Roos et al., 2019; Votaw et al., 2024).

Highlights.

  • Improved cell-mediated immunity is associated with reduced cocaine use

  • CCL5 and IL-10 decreased in association with reduced cocaine use

  • CRP increased in follow-up in association with reduced cocaine use

Acknowledgment:

This research was supported by grants from the National Institute on Drug Abuse (R01DA043938; T32DA035200) and the National Center for Advancing Translational Science (TL1TR001997; UL1TR001998). The funding agencies had no role in study design, data collection or analysis, or preparation and submission of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no relevant conflicts of interest to declare.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Clinical Trial Registration:

This trial is registered on clinicaltrials.gov under NCT03224546.

CRediT:

Stoops (Conceptualization, Methodology, Investigation, Resources, Writing-Original Draft, Writing-Review & Editing, Visualization, Supervision, Project Administration, Funding Acquisition), Shellenberg (Validation, Formal Analysis, Investigation, Data Curation, Writing-Review & Editing), Regnier (Validation, Formal Analysis, Investigation, Data Curation, Writing-Review & Editing, Visualization, Project Administration), Cox (Software, Validation, Investigation, Data Curation, Writing-Review & Editing, Project Administration), Adatorwovor (Validation, Formal Analysis, Writing-Review & Editing), Hays (Investigation, Writing-Review & Editing), Anderson (Investigation, Writing-Review & Editing), Lile (Conceptualization, Methodology, Investigation, Writing-Review & Editing, Supervision, Project Administration), Schmitz (Conceptualization, Writing-Review & Editing), Havens (Conceptualization, Methodology, Validation, Writing-Review & Editing), Segerstrom (Investigation, Writing-Review & Editing)

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