Cigarette-Smoking Intensity and Interferon-Gamma Release Assay Conversion among Persons Who Inject Drugs: A Cohort Study

We analyzed data from a longitudinal cohort study of persons who inject drugs (PWID) in Tijuana, Mexico, to explore whether cigarette smoking increases the risk of interferon gamma release assay (IGRA) conversion. PWID were recruited using respondent driven sampling (RDS). QuantiFERON-TB Gold In-Tube (QFT) assay conversion was defined as interferon-gamma concentrations <0.35 IU/mL at baseline and ≥0.7 IU/mL at 18 months. We used multivariable Poisson regression adjusted for RDS weights to estimate risk ratios (RRs). Of 129 eligible participants, 125 (96.9%) smoked at least one cigarette during followup with a median of 11 cigarettes smoked daily, and 52 (40.3%) had QFT conversion. In bivariate analysis, QFT conversion was not associated with the number of cigarettes smoked daily (P = 0.716). Controlling for age, gender, education, and alcohol use, the RRs of QFT conversion for smoking 6–10, 11–15, and ≥16 cigarettes daily compared to smoking 0–5 cigarettes daily were 0.9 (95% confidence interval (CI), 0.5–1.6), 0.5 (95% CI, 0.3–1.2), and 0.7 (95% CI, 0.3–1.6), respectively. Although this study did not find an association between self-reported smoking intensity and QFT conversion, it was not powered sufficiently to negate such an association. Larger longitudinal studies are needed to fully explore this relationship.


Introduction
Evidence has accumulated over the years which demonstrates a causal relationship between tobacco use and increased tuberculosis (TB) morbidity and mortality [1][2][3][4][5][6]. However, the strength of evidence for this relationship varies by TB outcome [3]. For example, while high-quality longitudinal cohort studies provide strong evidence that tobacco use increases the risk of TB disease, the evidence for the relationship between tobacco use and the risk of Mycobacterium tuberculosis infection is relatively weak [3,7,8]. Previous studies exploring this relationship utilized cross-sectional or case-control methodologies to determine the association between "ever" or "current" smoking and lifetime infection with M. tuberculosis as determined by a single tuberculin skin test (TST) result [9][10][11][12][13][14]. Therefore, these studies were not able to assess the temporality between tobacco use and M. tuberculosis infection. For example, a participant infected with M. tuberculosis as a child who subsequently began smoking years later would contribute to the positive association between smoking and TST positivity.
An improved understanding of the relationship between cigarette smoking and M. tuberculosis infection would help inform the implementation of tobacco control efforts as a 2 Pulmonary Medicine part of global TB interventions. However, due to the low incidence of M. tuberculosis infection in most populations, conducting longitudinal cohort studies to strengthen the evidence regarding this relationship would necessitate the enrollment and long-term followup of a large number of participants. Furthermore, while interferon gamma release assays (IGRAs) have been shown to have higher specificity than TSTs for the diagnosis of latent TB infection (LTBI), no study has explored the effect of tobacco use on serial IGRA test results [15,16].
The objective of the present study was to investigate the association between level of cigarette smoking and IGRA conversion among persons who inject drugs (PWID) in Tijuana, Mexico, a population at high risk for M. tuberculosis infection. Previous studies using this cohort showed a high baseline LTBI prevalence LTBI of 67% and an 18-month IGRA conversion rate of 28.7% to 51.9%, depending on the definition of conversion used [17,18]. We hypothesized that higher levels of cigarette smoking in this population would be associated with increased risk for IGRA conversion in a dose-response relationship.

Study Design and Population.
We analyzed data from a longitudinal cohort study of PWID in Tijuana, Mexico that sought to determine risk factors for incident HIV, TB, and syphilis [19]. Study recruitment and data collection methods have been described in detail previously [19]. Briefly, eligible study participants were ages 18 years or older, had injected illicit drugs within the previous month, and had no plans to move from Tijuana during the followup period. Participants were recruited through respondent-driven sampling (RDS), which relies on recruiting participants through referrals from previously enrolled participants [20,21]. RDS allows for the derivation of population-representative estimates of prevalence and risk factors by adjusting for the information collected on the participants' social networks during analysis [20,21]. Enrolled participants made study visits at baseline and at 6, 12, and 18 months. To increase retention, community outreach workers actively contacted participants to remind them of their followup appointments. Participants were also provided with $10 at baseline and $5 at followup visits as compensation for their time and travel expenses. Only participants who tested IGRA negative at baseline and who had IGRA results available at 18 months were included in the present analysis. Institutional Review Boards at University of California, San Diego and the Tijuana General Hospital reviewed and approved the study protocol, and informed consent was obtained from all participants.

2.2.
Measures. An in-depth questionnaire was administered via person-to-person interview at each visit. The questionnaire contained items on demographic characteristics and substance use behavior, including injection and noninjection use of illicit drugs, alcohol consumption, and cigarette smoking. Cigarette smoking was ascertained by first asking, "Have you smoked cigarettes in the past 6 months?" Participants who responded "Yes" were asked, "In the past 6 months, how many cigarettes did you usually smoke per day?" Based on preliminary analysis, we anticipated a high prevalence of cigarette smoking in this population and, consequently, insufficient number of nonsmokers for categorization. Therefore, we used the average number of cigarettes smoked daily during the 18-month study period as the exposure of interest. This exposure was stratified into quartiles (0-5, 6-10, 11-15, and ≥16 cigarettes) for the primary analysis.
IGRA conversion at 18 months was ascertained using QuantiFERON-TB Gold In-Tube ((QFT) Cellestis, VIC, Australia). For this test, whole blood samples were collected in three separate tubes: a Nil Control tube, TB Antigen tube, and a Mitogen Control tube. The tubes were incubated at 37 • C for 16 to 24 hours and centrifuged. The interferongamma (IFN-γ) released in the Nil Control tube was then measured using enzyme-linked immunosorbent assay (ELISA) and subtracted from that found in the TB Antigen Tube. QFT was administered at baseline for all participants. However, because of an unexpected delay in procuring the supplies necessary for specimen collection and testing, only a subset of the participants who were QFT negative using the manufacturer recommended cutoff of <0.35 IU/mL at baseline were retested at 18 months. For the primary analysis, we used a previously published conservative definition of QFT conversion (i.e., baseline IFN-γ < 0.35 IU/mL and IFNγ ≥ 0.70 IU/mL at followup), which reduces false positive conversions that could potentially arise due to within-subject variability observed in serial QFT tests [22]. In a secondary sensitivity analysis, we used the cutoff of 0.35 IU/mL at 18 months to define conversion.

Statistical Analysis.
The Pearson's χ 2 test was used for comparisons involving categorical variables, and the Wilcoxon rank-sum and the Kruskal-Wallis tests were used for continuous variables. We considered statistical tests to be significant at α of 0.05. We constructed Poisson regression models with robust variance estimation, via generalized estimating equation (GEE), to determine risk ratios (RRs) for QFT conversion for participants in each smoking exposure quartile compared to those in the first quartile [23,24]. The models were weighted by inverse probability weights derived using the RDS Analytical Tool [25]. The GEE algorithm also accounted for clustering by recruiter assuming an exchangeable correlation structure.
The base model included covariates representing established risk factors for M. tuberculosis infection, including age, gender, education, and alcohol use, regardless of their association with QFT conversion in our study population. We also evaluated the effect of drug use behavior using the "change-in-estimate" approach; drug use variables were added to the base model only if their inclusion changed the RRs between smoking and QFT by >10% [26]. Drug use variables evaluated included frequency and duration of heroin, methamphetamine, cocaine and marijuana use, including smoking of these substances. To account for the
In multivariable analysis adjusted for RDS, inclusion of drug use variables to the base model did not change the association between cigarette smoking quartiles and QFT conversion. Therefore, the final model consisted of daily cigarette smoking quartiles, age, gender, education, and alcohol use as independent variables ( Table 3). The adjusted RRs for QFT conversion for each quartile of daily number of cigarettes smoked compared to the lowest exposure quartile were 0.86 (95% CI 0.46-1.63), 0.54 (95% CI 0.25-1.17), and 0.74 (95% CI 0.33-1.64), respectively (Table 3; Figure 2). There was no statistically significant difference in the risk of QFT conversion at any of the quartiles of daily cigarettes smoked compared with that of the lowest exposure quartile. Furthermore, age, gender, education, and alcohol use were not statistically significant predictors of QFT conversion. In the reduced model that included the smoking variables and educational attainment only, having attained less than high school education compared with higher education was found to increase the risk of QFT conversion (RR = 2.83; 95% CI 1.08-7.42). As with the full model, higher levels of daily  cigarette smoking exposure quartiles were not associated with QFT conversion risk in this model (Table 3).
All tolerance estimates were greater than 0.10, and the highest condition index was 11.8 in the final model, indicating that multicollinearity did not affect our findings. We found five potential outliers based on residual and influence statistics, but removing these had no effect on our findings. Additionally, using a cutoff of 0.35 IU/mL instead of 0.70 IU/mL at 18 months to define conversion and fitting the final model with daily number of cigarettes smoked as a continuous variable did not alter our findings. In post hoc power analysis, assuming 43.3% QFT conversion risk that we found among participants in the lowest cigarettesmoking quartile, our sample size of 129 provided 28.1%, 55.6%, and 82.6% power to detect a RR of 1.4, 1.6, and 1.8, respectively, for QFT conversion among participants in the highest exposure quartile.

Discussion
In our analysis of longitudinal cohort data from PWID in Tijuana, we were not able to detect a dose-response relationship between the number of cigarettes smoked per day and QFT conversion. Previous studies evaluating doseresponse relationships between cigarette-smoking and M. tuberculosis infection have shown mixed results for this putative association. A study of population survey data in South Africa found no evidence of a dose-response relationship between pack-years and TST positivity [9]. Likewise, a study of people with silicosis in Hong Kong found no relationship between the number of cigarettes smoked per day or cigarette pack-years and TST positivity [10]. In contrast, a study of prisoners in Pakistan found that TST prevalence increased with number of cigarettes smoked per day [11]. However, these previous studies employed crosssectional or case-control study designs, which limit their ability to evaluate temporality between cigarette-smoking exposures and M. tuberculosis infection. Cigarette smoking has been hypothesized to increase the risk of M. tuberculosis infection by adversely affecting the innate immune system of the host and/or causing structural damage to the respiratory tract [28]. First, expo-sure to cigarette smoke might impair the ability of alveolar macrophages to clear the M. tuberculosis bacilli before T cells are primed for adaptive immunity. Under this model, increased exposure to cigarette smoke in the lungs would result in increased acute susceptibility to M. tuberculosis infection. We were unable to generate evidence to support this model in our study. Smoking also impairs the mucociliary clearance of pathogens and causes other changes to the respiratory tract that could increase the risk for M. tuberculosis infection over time [28][29][30]. Because we did not collect information regarding lifetime history of smoking, we were not able to evaluate the long-term effect of cigarette smoking on M. tuberculosis infection.
Our findings should be interpreted with consideration of the following limitations. First, we were not able to compare QFT conversion risk between smokers and nonsmokers because nearly all of our study participants reported smoking during the study followup period. If even low levels of cigarette smoking increase IGRA conversion substantially, there might have been minimal increased risk for higher frequency smokers, and our study might not have had sufficient power to detect a dose-response relationship. While we had adequate sample size to detect a RR of 1.8 or greater for QFT conversion between participants in the lowest and highest smoking exposure quartiles, the study was underpowered to conclude that there is no association between smoking and QFT conversion. We were also unable to control for history of exposure to persons with TB disease, which is a necessary causal factor for incident M. tuberculosis infection. The inclusion of participants who were not exposed to persons with TB disease in our analysis could have biased our results towards the null. However, controlling for a proxy variable "Have you ever known someone who had TB?" did not alter our findings (data not shown). Future longitudinal studies should investigate the risk of cigarette smoking on M. tuberculosis infection among study participants recruited from persons with known history of exposure to someone with TB disease.
It is also possible that our study participants were already at high risk for M. tuberculosis infection due to other risk factors, which might have overshadowed an incremental increase in risk due to cigarette smoking. In addition, as with TSTs, QFT assays have significant within-subject variability such that conversions and reversions often occur around the 0.35 IU/mL cutoff during serial testing even among persons who are at low risk for M. tuberculosis infection [31,32]. While we used a conservative definition of QFT conversion to minimize misclassification in our analysis, the conversions observed in our study might not represent incident M. tuberculosis infection. Furthermore, since the recommended QFT cutoff of 0.35 IU/mL was derived to maximize specificity for M. tuberculosis infection, the use of this cutoff as an inclusion criterion could have resulted in the inclusion of some participants who were already infected with M. tuberculosis at baseline. However, restricting the analysis to only those participants with baseline QFT of <0.20 did not alter our findings (data not shown). Participants included in our study smoked fewer cigarettes than the participants who were excluded due to unavailable QFT results at 18 months. Therefore, our findings might not be generalizable to all PWID at risk for M. tuberculosis infection. Smoking levels were ascertained by self-report, which might have insufficient precision to evaluate a doseresponse relationship. Lastly, we did not collect information regarding secondhand smoke exposure, which has been shown to be associated with M. tuberculosis infection among children [33].

Conclusions
The present study is the first longitudinal cohort study to explore the relationship between cigarette-smoking intensity and M. tuberculosis infection, and the first to use IGRA conversion as the outcome. Given our findings and the limitations of previous research on this topic, additional research is needed to determine whether there is a causal relationship between smoking and M. tuberculosis infection. For example, a recent mathematical modeling study concluded that intensified tobacco control efforts could prevent 27 million TB-related deaths by 2050 [34]. However, the authors of that study assumed a RR of 2.0 for the effect of smoking on M. tuberculosis infection in their model to arrive at this conclusion. Stronger evidence from larger longitudinal studies is needed to justify such assumptions. Ideally, such a study would be conducted among persons at high risk for M. tuberculosis infection, such as those with household exposure to persons with TB disease, and consists of sufficient numbers of smokers and nonsmokers. While the evidence of a causal relationship between smoking and M. tuberculosis infection is weak, substantial evidence exists that implicates smoking as an independent risk factor for the development of TB disease [1][2][3][4][5][6][7][8]. Therefore, integration of tobacco and TB control interventions remains a high priority for global health [1].