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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: AIDS Behav. 2018 Jun;22(6):2026–2032. doi: 10.1007/s10461-017-1928-x

Baseline cigarette smoking status as a predictor of virologic suppression and CD4 cell count during one-year follow-up in substance users with uncontrolled HIV infection

Theresa Winhusen a, Daniel J Feaster b, Rui Duan b, Jennifer L Brown a, Eric S Daar c, Raul Mandler d, Lisa R Metsch e
PMCID: PMC5899070  NIHMSID: NIHMS913031  PMID: 29030717

Abstract

Cigarette smoking is prevalent in people living with HIV/AIDS (PLHIV) who abuse alcohol and/or illicit substances. This study evaluated whether smoking is predictive of virologic non-suppression (>200 copies/mL) and low CD4 count (<200 cells/mm3) during 1-year follow-up in medically hospitalized, substance-using PLHIV recruited for a multi-site trial. Smoking status was assessed with the Heaviness of Smoking Index (HSI). Analyses revealed that, controlling for baseline differences and adherence to antiretroviral therapy, non-smokers (n=237), compared to smokers scoring in the medium-to-high range on the HSI (n=386), were significantly more likely to achieve viral suppression (OR=1.50, 95% CI 1.02, 2.20). There was a significant smoking-by-time interaction for CD4 cell count (χ2(1)=4.08, p<.05), with smokers less likely to have low CD4 count at baseline and 6-month follow-up, but more likely to have low CD4 count at 12-month follow-up. The results suggest that smoking may play a role in immunological functioning in HIV-infected substance users.

Keywords: Tobacco, HIV, substance abuse, virologic suppression

INTRODUCTION

Cigarette smoking is prevalent in people living with HIV/AIDS (PLHIV) (1–3) and has been found to be significantly associated with viral load and CD4 cell count in some (4–7), but not all studies (8, 9). Evidence that smoking increases viral load through a direct biological effect (10–13) suggests the importance of providing smoking-cessation treatment as part of HIV care. However, two key confounds, which could largely account for the poorer immunological functioning observed in PLHIV who smoke, have been identified. First, there is evidence that cigarette smoking is associated with lower adherence to antiretroviral therapy (ART) (14, 15), which could account for the observed poorer immunological functioning. Second, some research has found that substance use (16), including the use of opioids (17, 18), cocaine (19, 20), and alcohol (21, 22), is associated with accelerated HIV progression and is more prevalent in smokers (1, 23) and, thus, could help account for the association.

It is therefore important that clinical research control for these potential confounds when evaluating the association between cigarette smoking and immunological functioning in PLHIV, as done in studies by Feldman et al. (5) and Hile et al. (4). Feldman and colleagues conducted a multi-year longitudinal study in a sample of women with HIV and found that, in analyses controlling for ART adherence, substance use, and other potential confounding factors, ART was less effective in improving immunological functioning in cigarette smokers than non-smokers as assessed by viral load and CD4 cell count (5). Of interest, cigarette smokers initially had higher CD4 cell counts than non-smokers but this changed during the course of ART treatment such that smokers had significantly lower CD4 cell counts than non-smokers at follow-up (5). In a recently completed, large (N=14,000), retrospective chart review by Hile and colleagues (4), analyses controlling for the receipt of ART, the use of other substances, and other potential confounds, revealed a significant association between cigarette smoking and low CD4 cell count (<200 cells/mm3) and viral non-suppression (> 200 copies/mL).

To our knowledge, a similar analysis has not been conducted in a sample of PLHIV specifically selected for active substance use. The present paper reports findings from a secondary analysis of the National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network (CTN) CTN-0049 (ClinicalTrials.gov: NCT01612169) dataset, a trial which tested interventions to reduce viral load in a sample of hospitalized PLHIV using substances (24). This secondary analysis was designed to test our hypothesis that, in substance-using PLHIV, baseline smoking status would predict unsuppressed viral load (> 200 copies/mL) and low CD4 cell count (<200 cells/mm3) during the 12-month follow-up period when controlling for other substance use and ART adherence.

METHODS

Study Design

Details of CTN-0049 are provided elsewhere (24). The study was an intent-to-treat, 3-group randomized controlled trial with follow-up visits at 6- and 12-months post-randomization; the primary outcome was viral suppression at 12 months post-randomization. Participants were recruited from 11 study sites and randomly assigned in a 1:1:1 ratio to receive: 1) 6 months of patient navigation, 2) 6 months of patient navigation plus financial incentives, or 3) treatment as usual. Patient navigation included up to 11 sessions of care coordination with case management and motivational interviewing with the goal of increasing linkage to substance use disorder treatment and HIV care. Financial incentives (up to $1160) were provided for increased engagement in HIV care and substance use disorder treatment, reduced substance use, and improved HIV outcomes. Treatment as usual participants received the site’s standard intervention for linking patients to outpatient HIV care and substance use disorder treatment. Hospital staff, including social workers, case managers, attending physicians, and infectious diseases consultants, were typically responsible for scheduling an outpatient HIV care appointment. The most common practice for linkage to substance use disorder treatment was written referral. Participants were reimbursed up to $210 for completing research-related activities.

Participants

Participants were recruited from 11 hospitals located in the following U.S. cities: Atlanta, Georgia; Baltimore, Maryland; Boston, Massachusetts; Birmingham, Alabama; Chicago, Illinois; Dallas, Texas; Los Angeles, California; Miami, Florida; New York, New York; and Philadelphia and Pittsburgh, Pennsylvania. Eligible participants were at least 18 years of age, English speaking, medical inpatients with HIV infection who signed a medical record release. To be eligible, participants were required to: 1) self-report, or have medical record documentation of, any illicit opioid (e.g., including misuse/abuse of prescription opioids), illicit stimulant, or heavy alcohol use as determined by the Alcohol Use Disorders Identification Test (AUDIT-C) (25) within the past year; 2) meet one of the following requirements: had an AIDS-defining illness; had a CD4 cell count less than 350 cells/mm3 at their most recent screen and a viral load of > 200 copies/mL within 6 months; or had a CD4 cell count within 12 months that was 500 cells/mm3 or less and their viral load was more than 200 copies/mL (or their viral load was unknown with clinical indicators that the patient was likely to have a detectable viral load); 3) had functional status of 60 or higher on the Karnofsky performance scale; and 4) lived in the area of the site and provided locator information.

Measures

All measures included in this secondary analysis were obtained at baseline and 6- and 12-month follow-up. At baseline, cigarette smoking status was assessed using the Fagerström Test for Nicotine Dependence (26, 27) modified to include “Do you currently smoke cigarettes?” as an initial question. Participants who responded “no” to the initial question were scored as non-smokers. Smoking severity for those responding “yes” was assessed with the Heaviness of Smoking Index (HSI), which is comprised of two items (time to first cigarette after waking; number of cigarettes per day) from the Fagerström Test for Nicotine Dependence (26, 27). The HSI score has a range of 0–6, with cut-offs for low (0–1), medium (2–4), and high (5–6). Viral load and CD4 cell count were measured by local laboratories. HIV medication adherence was measured by self-report as the percentage of pills taken in the last 30 days (28); “high adherence” was defined as self-reporting taking ≥90% of prescribed ART. Illicit substance use was assessed using the substance module of the Addiction Severity Index (29, 30). Alcohol use was assessed with the AUDIT-C (25).

Data analysis

The present secondary analysis was not specifically outlined in the parent trial (CTN-0049) but was delineated prior to being conducted. The CTN-0049 trial included 801 randomized participants. Past research suggests that effects of smoking on viral load and CD4 cell count may not be found in light smokers (31). Hence, the present analyses compared participants self-reporting no smoking at baseline (n=237) to participants scoring in the medium-to-high range on the HSI (n=386). The remaining 178 CTN-0049 participants (who were light smokers) were excluded from the analyses. Demographic characteristics of the smoking and non-smoking groups were compared using Pearson’s chi-square, t-tests, and the Wilcoxon test. We then used Generalized Estimating Equation (GEE) models to evaluate whether there was a difference in rates of follow-up across the smoking groups. GEE was also used to evaluate the associations between smoking status and the outcomes of unsuppressed viral load (> 200 copies/mL) and low CD4 cell count (<200 cells/mm3) at the 6th and 12th month follow-up. Variables showing significant differences across smoking status in the descriptive analyses, the CTN-0049 treatment arm (i.e., patient navigation, patient navigation plus incentives, or treatment as usual), baseline low CD4 cell status, baseline viral suppression status, and ART adherence were included in the GEE model as control variables. As reported in the study results, the groups did not significantly differ in the proportion of missing data; all observed data were included in the analyses such that participants with missing data at a particular time point had only that time point excluded from the analysis. Analyses were performed using SAS statistical software (Version 9.3; SAS Institute, Cary, NC). All tests were performed at a significance level of 0.05.

RESULTS

Sample Characteristics

Table I provides participant characteristics as a function of baseline smoking status. The 623 participants were approximately 44 years of age, 68% were male, and 72% were African American. A greater proportion of smokers, compared to non-smokers, were female, had a lower level of educational achievement, and were less likely to be employed. A greater proportion of smokers, compared to non-smokers, used illicit drugs, including both stimulants and non-stimulants. At baseline, non-smokers were more likely to have a low CD4 cell count and to have met lifetime criteria for an AIDS defining illness relative to smokers. These differences were controlled for in the analyses. The results of a GEE analysis revealed no statistically significant group differences on proportion of missing data across the two follow-up times (χ2(1)=2.4, p=0.12). At 6-month follow-up, 88.9% of smokers and 88.6% of non-smokers completed the visit. At 12-month follow-up, 82.1% of smokers and 83.1% of non-smokers completed the visit. Frequency counts revealed little change in smoking status between baseline and 12-month follow-up, with 3.84% of baseline smokers becoming non-smokers and 6.47% of baseline non-smokers scoring in the medium/high range of the HSI at 12-month follow-up.

Table I.

Participant demographic and baseline characteristics as a function of baseline smoking status

Non-smoker
(N=237)		 Smokera
(N=386)		 Total
(N=623)		 Group Analysis
Statisticb 		P value
Age, mean (SD), years 44.1 (10.7) 44.2 (9.7) 44.1 (10.1) T=−0.14 0.8864
Gender, female 65 (27.4) 136 (35.2) 201 (32.3) χ2=4.10 0.0430
Race/ethnicity χ2=6.04 0.1096
  African American 170 (72.3) 277 (71.8) 447 (72.0)
  Caucasian 21 (8.9) 56 (14.5) 77 (12.4)
  Hispanic 29 (12.3) 36 (9.3) 65 (10.5)
  Other 15 (6.4) 17 (4.4) 32 (5.2)
Education χ2=27.63 <0.0001
  <High school 72 (30.4) 182 (47.2) 254 (40.8)
  High school, GED 78 (32.9) 130 (33.7) 208 (33.4)
  >High school 87(36.7) 74 (19.2) 161 (25.8)
Marital status χ2=3.41 0.1814
  Married or cohabitating 27 (11.4) 49 (12.7) 76 (12.2)
  Separated/divorced/widowed 46 (19.4) 97 (25.1) 143 (23.0)
  Never married 164 (69.2) 240 (62.2) 404 (64.8)
Employment status χ2=20.05 0.0002
  Working 43 (18.1) 27 (7.0) 70 (11.2)
  Unemployed 78 (32.9) 143 (37.0) 221 (35.5)
  Disabled 105 (44.3) 204 (52.8) 309 (49.6)
  Other status 11 (4.6) 12 (3.1) 23 (3.7)
Substance use eligiblec
  Alcohol use eligible 140 (59.1) 220 (57.0) 360 (57.8) χ2=0.26 0.6104
  Drug use eligible 150 (63.3) 325 (84.2) 475 (76.2) χ2=35.43 <0.0001
Stimulant use 139 (58.7) 301 (78.0) 440 (70.6) χ2=26.45 <0.0001
Non-stimulant use 125 (52.7) 277 (71.8) 402 (64.5) χ2=23.21 <0.0001
Currently take antiretrovirals 101 (55.5) 169 (56.0) 270 (55.8) χ2=0.01 0.9204
High ART adherenced 41 (17.5) 50 (13.0) 91 (14.7) χ2=2.35 0.1257
AIDS defining illness 113 (48.9) 142 (37.9) 255 (42.1) χ2=7.16 0.0074
HIV viral loade 60.5 (13.6–228.0) 53.7 (6.7–218.2) 55.5 (7.8–221.9) W=70129.50 0.3691
HIV viral load >200 copies/mL 211(89.0) 347 (89.9) 558 (89.6) χ2=0.12 0.7311
CD4 count <200 cells/mm3 171 (72.2) 248 (64.2) 419 (67.3) χ2=4.16 0.0413
CTN-0049 Treatment arm χ2=3.73 0.1550
  Patient navigation 83 (35.0) 122 (31.6) 205 (32.9)
  Patient navigation+ financial 67 (28.3) 138 (35.8) 205 (32.9)
Treatment as usual 87 (36.7) 126 (32.6) 213 (34.2)
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Note: Where not specifically indicated, numbers represent number (%).

a

Smoker=Participants scoring in the medium-to-high range on the Heaviness of Smoking Index;

b

T= t test value; W=Wilcoxon two-sample test statistics value.

c

To be substance use eligible the patient had to be alcohol-use eligible (Alcohol Use Disorders Identification Test (AUDIT)–C score of ≥3 for women and ≥4 for men) or drug-use eligible (used stimulants or opiates within the past 12 months);

d

Self-report taking ≥ 90% of prescribed ART;

e

Median (Q25–Q75).

Viral Load Suppression

The results of a GEE analysis revealed a statistically significant effect for baseline smoking status across both follow-up time points (OR=1.50, 95% CI 1.02, 2.20) and a non-significant smoking-by-time interaction effect (χ2(1)=1.17, p=0.28) on viral load suppression. As can be seen in Figure 1, more smokers were not virally suppressed at the 6- and 12-month follow-up assessments than non-smokers.

Figure 1.

Figure 1

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Proportion of participants not virally suppressed (>200 copies/mL) ± SE (model implied) as a function of baseline smoking status and time.

Low CD4 Count

The results of the GEE analysis revealed a non-significant effect for baseline smoking status (OR=0.83, 95% CI 0.48, 1.41) and a statistically significant baseline smoking-by-time interaction effect (χ2(1)=4.08, p<0.05) for low CD4 cell count over the 12-month follow-up. As can be seen in Figure 2, a higher proportion of non-smokers, relative to smokers, met criteria for low CD4 count at baseline and 6-month follow-up, but at 12-month follow-up this reversed with more smokers meeting criteria for low CD4 count.

Figure 2.

Figure 2

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Proportion of participants with low CD4 cell count (<200 cells/mm3) ± SE (model implied) as a function of baseline smoking status and time

DISCUSSION

This secondary analysis of the CTN-0049 dataset tested the hypothesis that, in PLHIV who use substances, baseline smoking status would predict poorer virologic and immunological response as measured by lack of viral load suppression (> 200 copies/mL) and low CD4 cell count (<200 cells/mm3) during a 12-month follow-up period when controlling for ART adherence, other substance use, and other potential confounding factors. Consistent with our hypothesis, baseline smoking status was a significant predictor of not achieving virologic suppression during follow-up, with smokers being less likely to be suppressed at 6- and 12-month follow-up. For low CD4 cell count, there was a significant smoking-by-time interaction, with smokers more likely to have high CD4 cell count at baseline and 6-month follow-up but more likely to have low CD4 cell count at 12-month follow-up. The latter finding is consistent with a study by Feldman and colleagues (5) which found that female PLHIV smokers were healthier than non-smokers initially but, over the course of ART, non-smokers evidenced significantly greater benefits as reflected in both CD4 cell count and viral load compared to smokers.

In the current era of ART, smoking in PLHIV is associated with significant morbidity and mortality (32–36), with a recent study estimating that the risk of death associated with smoking is greater than 60% for PLHIV (37). The potential mechanisms accounting for the worse outcomes are unclear and may include lower ART receipt/adherence and the impact of other substance use (4). The present study adds to evidence from clinical studies finding an association between cigarette smoking and immunologic response when controlling for ART adherence and other substance use (4, 5). However, other research evaluating this potential association has failed to find a significant effect for smoking on viral load and CD4 cell count. For example, no evidence of an association between HIV progression and smoking was found in a sample of PLHIV who had a history of alcohol abuse (9). The discrepant findings may reflect the impact of factors that have not been assessed. For example, there is evidence that oxidative stress, which is an imbalance between reactive oxygen species and antioxidant defense, may play a role in HIV progression (10, 38–41). While cigarette smoking is associated with a significant increase in oxidative stress (42–45), not all smokers evidence an increase (45). Hence, the negative impact of smoking on immunological function in PLHIV may, for example, be limited to those experiencing a significant increase in oxidative stress resulting from cigarette smoking.

In addition to the potential negative impact on immunological functioning, cigarette smoking is a significant concern due to its prevalence in PLHIV. An estimated 40 – 60% of PLHIV smoke (46), a rate two to three times higher than the general population (47). Of the 801 participants randomized into CTN-0049, 70.4% reported being cigarette smokers; this elevated rate is consistent with the greater prevalence of smoking in substance using patient populations (48). The interventions tested in CTN-0049 were designed to engage the participants in substance use disorder treatment. Unfortunately, smoking-cessation is not a priority in most substance use disorder treatment programs (49) and, not surprisingly, very few of the participants in the present study stopped smoking between baseline and 1-year follow-up (3.84%). A recent review revealed that there has been limited research on effective smoking-cessation interventions for PLHIV and noted the importance of addressing this research gap (50). The present findings serve to reinforce the importance of addressing this gap in future research.

The present study has several strengths and a few limitations. First, the parent trial was conducted at 11 sites, which enhances the generalizability of the results, and included a relatively large sample of substance-using PLHIV. A limitation is that the findings are correlational in nature and, thus, cause and effect determinations cannot be made. In addition, smoking may be related to other potential confounding factors that could not be accounted for in the models; these factors could include baseline differences that, while controlled for in the model, could still impact the outcomes of interest. Another limitation is the use of self-report, which is open to social desirability and other biases, for both smoking status and level of ART-adherence.

In conclusion, the results of this exploratory analysis suggest that smoking may play a role in immunologic response in HIV-infected substance users. Future research to replicate this finding and to delineate the potential mechanisms by which smoking may affect HIV progression seems warranted.

Acknowledgments

Funding for the parent trial and analysis was provided for the study’s principal investigators by the National Institute on Drug Abuse under the following awards: U10DA013720 and UG1DA013720 (Drs José Szapocznik and Lisa R. Metsch); U10DA013035 and UG1DA013035 (Drs John Rotrosen and Edward V. Nunes, Jr); U10DA013034 and UG1DA013034 (Drs Maxine Stitzer and Robert Schwartz); U10DA013727 and UG1DA013727 (Drs. Kathleen T. Brady and Matthew Carpenter); U10DA020024 and UG1DA020024 (Dr Madhukar H. Trivedi); U10DA013732, UG1DA013732, and 5UG1DA013732 (Dr Theresa Winhusen); U10DA015831 and UG1DA015831 (Drs. Roger D. Weiss and Kathleen Carroll); U10DA015815 and UG1DA015815 (Drs James L. Sorensen and Dennis McCarty); U10DA020036 (Dr Dennis Daley); U10DA013043 (Dr George Woody); U10DA013045 (Dr Walter Ling); HHSN271200900034C/ N01DA92217 and HHSN271201400028C/N01DA142237 (Dr Paul VanVeldhuisen); and HHSN271201000024C/N01DA102221 (Dr Robert Lindblad). Support from the University of Miami Center for AIDS Research (CFAR) (P30AI073961; Dr Savita Pahwa), the Emory University CFAR (P30AI050409; Drs Carlos del Rio, James W. Curran, and Eric Hunter), the Atlanta Clinical and Translational Science Institute (UL1TR000454; Dr David Stephens), and the HIV Center for Clinical and Behavioral Studies at the New York State Psychiatric Institute/Columbia University Medical Center (P30MH043520; Dr Robert Remien) is also acknowledged. The National Institute on Drug Abuse (NIDA) had no further role in study design or in the collection, analysis and interpretation of data. NIDA Center for the Clinical Trials Network (CCTN) personnel contributed to the design of the original study and CCTN contractors played a role in the collection and analysis of data from the original study. NIDA had no further role in the design of this analysis, in the manuscript writing of the report, or in the decision to submit the paper for publication.

Footnotes

Conflict of Interest: The authors declare that they have no conflict of interest.

Compliance with Ethical Standards

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent: Informed consent was obtained from all individual participants included in the study.

References

  • 1.Duval X, Baron G, Garelik D, et al. Living with HIV, antiretroviral treatment experience and tobacco smoking: results from a multisite cross-sectional study. Antivir Ther. 2008;13(3):389–97. [PMC free article] [PubMed] [Google Scholar]
  • 2.Burkhalter JE, Springer CM, Chhabra R, Ostroff JS, Rapkin BD. Tobacco use and readiness to quit smoking in low-income HIV-infected persons. Nicotine Tob Res. 2005;7(4):511–22. doi: 10.1080/14622200500186064. [DOI] [PubMed] [Google Scholar]
  • 3.Pacek LR, Latkin C, Crum RM, Stuart EA, Knowlton AR. Current cigarette smoking among HIV-positive current and former drug users: associations with individual and social characteristics. AIDS Behav. 2014;18(7):1368–77. doi: 10.1007/s10461-013-0663-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hile SJ, Feldman MB, Alexy ER, Irvine MK. Recent Tobacco Smoking is Associated with Poor HIV Medical Outcomes Among HIV-Infected Individuals in New York. AIDS Behav. 2016;20(8):1722–9. doi: 10.1007/s10461-015-1273-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Feldman JG, Minkoff H, Schneider MF, et al. Association of cigarette smoking with HIV prognosis among women in the HAART era: a report from the women's interagency HIV study. Am J Public Health. 2006;96(6):1060–5. doi: 10.2105/AJPH.2005.062745. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Miguez-Burbano MJ, Burbano X, Ashkin D, Pitchenik A, Allan R, Pineda L, et al. Impact of tobacco use on the development of opportunistic respiratory infections in HIV seropositive patients on antiretroviral therapy. Addict Biol. 2003;8(1):39–43. doi: 10.1080/1355621031000069864. [DOI] [PubMed] [Google Scholar]
  • 7.Wojna V, Robles L, Skolasky RL, Mayo R, Selnes O, de la Torre T, et al. Associations of cigarette smoking with viral immune and cognitive function in human immunodeficiency virus-seropositive women. J Neurovirol. 2007;13(6):561–8. doi: 10.1080/13550280701620747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Gritz ER, Vidrine DJ, Lazev AB, Amick BC, 3rd, Arduino RC. Smoking behavior in a low-income multiethnic HIV/AIDS population. Nicotine Tob Res. 2004;6(1):71–7. doi: 10.1080/14622200310001656885. [DOI] [PubMed] [Google Scholar]
  • 9.Kabali C, Cheng DM, Brooks DR, Bridden C, Horsburgh CR, Samet JH. Recent cigarette smoking and HIV disease progression: no evidence of an association. AIDS Care. 2011;23(8):947–56. doi: 10.1080/09540121.2010.542128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Rao P, Ande A, Sinha N, Kumar A, Kumar S. Effects of Cigarette Smoke Condensate on Oxidative Stress, Apoptotic Cell Death, and HIV Replication in Human Monocytic Cells. PLoS One. 2016;11(5):e0155791. doi: 10.1371/journal.pone.0155791. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Masia M, Padilla S, Fernandez M, et al. Oxidative Stress Predicts All-Cause Mortality in HIV-Infected Patients. PLoS One. 2016;11(4):e0153456. doi: 10.1371/journal.pone.0153456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ande A, McArthur C, Ayuk L, et al. Effect of mild-to-moderate smoking on viral load, cytokines, oxidative stress, and cytochrome P450 enzymes in HIV-infected individuals. PLoS One. 2015;10(4):e0122402. doi: 10.1371/journal.pone.0122402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ande A, McArthur C, Kumar A, Kumar S. Tobacco smoking effect on HIV-1 pathogenesis: role of cytochrome P450 isozymes. Expert Opin Drug Metab Toxicol. 2013;9(11):1453–64. doi: 10.1517/17425255.2013.816285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Webb MS, Vanable PA, Carey MP, Blair DC. Cigarette Smoking among HIV+ Men and Women: Examining Health, Substance Use, and Psychosocial Correlates across the Smoking Spectrum. J Behav Med. 2007;30(5):371–83. doi: 10.1007/s10865-007-9112-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Shuter J, Bernstein SL. Cigarette smoking is an independent predictor of nonadherence in HIV-infected individuals receiving highly active antiretroviral therapy. Nicotine Tob Res. 2008;10(4):731–6. doi: 10.1080/14622200801908190. [DOI] [PubMed] [Google Scholar]
  • 16.Atluri VS. Editorial: HIV and Illicit Drugs of Abuse. Frontiers in microbiology. 2016;7:221. doi: 10.3389/fmicb.2016.00221. Epub 2016/03/26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Roy S, Ninkovic J, Banerjee S, et al. Opioid drug abuse and modulation of immune function: consequences in the susceptibility to opportunistic infections. J Nneuroimmune Pharmacol. 2011;6(4):442–65. doi: 10.1007/s11481-011-9292-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Wang X, Ma TC, Li JL, et al. Heroin inhibits HIV-restriction miRNAs and enhances HIV infection of macrophages. Front Microbiol. 2015;6:1230. doi: 10.3389/fmicb.2015.01230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Swepson C, Ranjan A, Balasubramaniam M, Pandhare J, Dash C. Cocaine Enhances HIV-1 Transcription in Macrophages by Inducing p38 MAPK Phosphorylation. Front Microbiol. 2016;7:823. doi: 10.3389/fmicb.2016.00823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Dash S, Balasubramaniam M, Villalta F, Dash C, Pandhare J. Impact of cocaine abuse on HIV pathogenesis. Front Microbiol. 2015;6:1111. doi: 10.3389/fmicb.2015.01111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Williams EC, Hahn JA, Saitz R, Bryant K, Lira MC, Samet JH. Alcohol Use and Human Immunodeficiency Virus (HIV) Infection: Current Knowledge, Implications, and Future Directions. Alcohol Clin Exp Res. 2016;40(10):2056–72. doi: 10.1111/acer.13204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Baum MK, Rafie C, Lai S, Sales S, Page JB, Campa A. Alcohol use accelerates HIV disease progression. AIDS Res Hum Retroviruses. 2010;26(5):511–8. doi: 10.1089/aid.2009.0211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Humfleet GL, Delucchi K, Kelley K, Hall SM, Dilley J, Harrison G. Characteristics of HIV-positive cigarette smokers: a sample of smokers facing multiple challenges. AIDS Educ Prev. 2009;21(3 Suppl):54–64. doi: 10.1521/aeap.2009.21.3_supp.54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Metsch LR, Feaster DJ, Gooden L, et al. Effect of Patient Navigation With or Without Financial Incentives on Viral Suppression Among Hospitalized Patients With HIV Infection and Substance Use: A Randomized Clinical Trial. JAMA. 2016;316(2):156–70. doi: 10.1001/jama.2016.8914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Int Med. 1998;158(16):1789–95. doi: 10.1001/archinte.158.16.1789. [DOI] [PubMed] [Google Scholar]
  • 26.Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86(9):1119–27. doi: 10.1111/j.1360-0443.1991.tb01879.x. [DOI] [PubMed] [Google Scholar]
  • 27.Kozlowski LT, Porter CQ, Orleans CT, Pope MA, Heatherton T. Predicting smoking cessation with self-reported measures of nicotine dependence: FTQ, FTND, and HSI. Drug Alcohol Depend. 1994;34(3):211–6. doi: 10.1016/0376-8716(94)90158-9. [DOI] [PubMed] [Google Scholar]
  • 28.Reynolds NR, Sun J, Nagaraja HN, Gifford AL, Wu AW, Chesney MA. Optimizing measurement of self-reported adherence with the ACTG Adherence Questionnaire: a cross-protocol analysis. J Acquir Immune Defic Syndr. 2007;46(4):402–9. doi: 10.1097/qai.0b013e318158a44f. [DOI] [PubMed] [Google Scholar]
  • 29.McLellan AT, Kushner H, Metzger D, et al. The Fifth Edition of the Addiction Severity Index. J Subst Abuse Treat. 1992;9(3):199–213. doi: 10.1016/0740-5472(92)90062-s. [DOI] [PubMed] [Google Scholar]
  • 30.McLellan AT, Luborsky L, Woody GE, O'Brien CP. An improved diagnostic evaluation instrument for substance abuse patients. The Addiction Severity Index. J Nerv Ment Dis. 1980;168(1):26–33. doi: 10.1097/00005053-198001000-00006. [DOI] [PubMed] [Google Scholar]
  • 31.Brown JL, Winhusen T, DiClemente RJ, et al. The association between cigarette smoking, virologic suppression, and CD4+ lymphocyte count in HIV-Infected Russian women. AIDS Care. 2017;29(9):1102–6. doi: 10.1080/09540121.2017.1327645. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Gordin FM, Roediger MP, Girard PM, et al. Pneumonia in HIV-infected persons: increased risk with cigarette smoking and treatment interruption. Am J Respir Crit Care Med. 2008;178(6):630–6. doi: 10.1164/rccm.200804-617OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Lifson AR, Neuhaus J, Arribas JR, et al. Smoking-related health risks among persons with HIV in the Strategies for Management of Antiretroviral Therapy clinical trial. Am J Public Health. 2010;100(10):1896–903. doi: 10.2105/AJPH.2009.188664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Pines H, Koutsky L, Buskin S. Cigarette smoking and mortality among HIV-infected individuals in Seattle, Washington (1996–2008) AIDS Behav. 2011;15(1):243–51. doi: 10.1007/s10461-010-9682-3. [DOI] [PubMed] [Google Scholar]
  • 35.Rahmanian S, Wewers ME, Koletar S, Reynolds N, Ferketich A, Diaz P. Cigarette smoking in the HIV-infected population. Proc Am Thorac Soc. 2011;8(3):313–9. doi: 10.1513/pats.201009-058WR. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Crothers K, Griffith TA, McGinnis KA, et al. The impact of cigarette smoking on mortality, quality of life, and comorbid illness among HIV-positive veterans. J Gen Intern Med. 2005;20(12):1142–5. doi: 10.1111/j.1525-1497.2005.0255.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Helleberg M, Afzal S, Kronborg G, et al. Mortality attributable to smoking among HIV-1-infected individuals: a nationwide, population-based cohort study. Clin Infect Dis. 2013;56(5):727–34. doi: 10.1093/cid/cis933. [DOI] [PubMed] [Google Scholar]
  • 38.Colado Simao AN, Victorino VJ, Morimoto HK, Reiche EM, Panis C. Redox-driven events in the human immunodeficiency virus type 1 (HIV-1) infection and their clinical implications. Curr HIV Res. 2015;13(2):143–50. doi: 10.2174/1570162x13666150313152422. [DOI] [PubMed] [Google Scholar]
  • 39.Porter KM, Sutliff RL. HIV-1, reactive oxygen species, and vascular complications. Free Radic Biol Med. 2012;53(1):143–59. doi: 10.1016/j.freeradbiomed.2012.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Suresh DR, Annam V, Pratibha K, Prasad BV. Total antioxidant capacity--a novel early bio-chemical marker of oxidative stress in HIV infected individuals. J Biomed Sci. 2009;16:61. doi: 10.1186/1423-0127-16-61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Sharma B. Oxidative stress in HIV patients receiving antiretroviral therapy. Curr HIV Res. 2014;12(1):13–21. doi: 10.2174/1570162x12666140402100959. [DOI] [PubMed] [Google Scholar]
  • 42.Reilly M, Delanty N, Lawson JA, FitzGerald GA. Modulation of Oxidant Stress In Vivo in Chronic Cigarette Smokers. Circulation. 1996;94(1):19–25. doi: 10.1161/01.cir.94.1.19. [DOI] [PubMed] [Google Scholar]
  • 43.Pilz H, Oguogho A, Chehne F, Lupattelli G, Palumbo B, Sinzinger H. Quitting Cigarette Smoking Results in a Fast Improvement of in vivo Oxidation Injury (Determined via Plasma, Serum and Urinary Isoprostane) Thromb Res. 2000;99(3):209–21. doi: 10.1016/s0049-3848(00)00249-8. [DOI] [PubMed] [Google Scholar]
  • 44.Chehne F, Oguogho A, Lupattelli G, Palumbo B, Sinzinger H. Effect of giving up cigarette smoking and restarting in patients with clinically manifested atherosclerosis. Prostaglandins Leukot Essent Fatty Acids. 2002;67(5):333–9. doi: 10.1054/plef.2002.0438. [DOI] [PubMed] [Google Scholar]
  • 45.Morrow JD, Frei B, Longmire AW, et al. Increase in circulating products of lipid peroxidation (F2-isoprostanes) in smokers. Smoking as a cause of oxidative damage. N Engl J Med. 1995;332(18):1198–203. doi: 10.1056/NEJM199505043321804. [DOI] [PubMed] [Google Scholar]
  • 46.Tesoriero JM, Gieryic SM, Carrascal A, Lavigne HE. Smoking among HIV positive New Yorkers: prevalence, frequency, and opportunities for cessation. AIDS Behav. 2010;14(4):824–35. doi: 10.1007/s10461-008-9449-2. [DOI] [PubMed] [Google Scholar]
  • 47.Kalman D, Morissette SB, George TP. Co-morbidity of smoking in patients with psychiatric and substance use disorders. Am J Addict. 2005;14(2):106–23. doi: 10.1080/10550490590924728. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Winhusen T. Introduction to Special Issue on Tobacco and Other Substance Use Disorders: Links and Implications. Am J Drug Alcohol Abuse. 2017;43(2):129–31. doi: 10.1080/00952990.2016.1257635. [DOI] [PubMed] [Google Scholar]
  • 49.Knudsen HK. Implementation of smoking cessation treatment in substance use disorder treatment settings: a review. Am J Drug Alcohol Abuse. 2017;43(2):215–25. doi: 10.1080/00952990.2016.1183019. [DOI] [PubMed] [Google Scholar]
  • 50.Ledgerwood DM, Yskes R. Smoking Cessation for People Living With HIV/AIDS: A Literature Review and Synthesis. Nicotine Tob Res. 2016;18(12):2177–84. doi: 10.1093/ntr/ntw126. [DOI] [PMC free article] [PubMed] [Google Scholar]

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