Inter-individual variation in CYP2A6 activity and COPD in smokers: perspectives for an early predictive marker| Tabaccologia

Abstract

Introduction: Even if cigarette smoke is a well-defined risk factor for Chronic obstructive pulmonary disease (COPD), most smokers do not develop the disease. The individual rate of nicotine metabolism, determined by genetic difference in nicotine metabolic enzymes, has been associated with smoking behavior and with differential efficacy of smoking cessation treatments. We sought to investigate the relation between the rate of nicotine metabolism and the risk for COPD among smokers.

Methods: Subjects (N = 78) referring to the smoking cessation service of the Sant’Andrea Hospital of Rome were clinically evaluated by spirometry, COPD assessment test, Modified Medical Research Council test, Fagerström’s test. The rate of nicotine metabolism was assessed using salivary nicotine metabolite ratio (NMR) determined by liquid chromatography-tandem mass spectrometry. Subjects were categorized in slow, normal and fast metabolizers according to the quartile’s distribution of NMR, and association between NMR and COPD was evaluated. Statistical power was > 0.9 (α = 0.05).

Results: NMR was significantly higher in COPD subjects compared to non-COPD subjects (median 0.89, IQR 0.52-1.17 vs 0.54 IQR 0.41-0.83), and the OR for COPD in fast nicotine metabolizers were 3.62 (95% CI: 1.09-12.05, p = 0.049) and 6.53 (95% CI: 1.61-26.47, p = 0.01) compared to normal and slow metabolizers, respectively. Multinomial logistic regression analysis shows that, in the analysed cohort, NMR is an independent risk factor for COPD.

Conclusion: The potential role of NMR as a predictive factor for COPD is intriguing since it could be easily determined decades before the onset of the pathology, contributing to earlier prevention, and thus deserves further evaluation in larger, multi-centric studies. 

Introduction

Chronic obstructive pulmonary disease (COPD) is defined by the Global Initiative for Chronic obstructive disease (GOLD) as ‘‘a common, preventable, and treatable disease that is characterized by persistent respiratory symptoms and airflow limitation due to airway abnormalities, usually caused by significant exposure to noxious particles or gases” [1,2].

Despite the preventable nature of the disease, it is one of the leading causes of death worldwide, and requires a great deal of effort to implement effective surveillance and prevention [3].

Cigarette smoking is the most well-established single risk factor for COPD [4-6]. Although, among smokers, the incidence of COPD smokers’ is variable [4-7] and early recognition of susceptibility/protective factors could be important to implement earlier and more effective prevention. The duration and intensity of smoking are associated with the relative risk of COPD, and, in fact, smoking cessation is the main recognized tool that can change the course of the disease [8,9].

The effect of smoking on the pathogenesis of COPD is related to the chemical insult provided to the airways by the million particles and airways by the million particles and airways by the million particles and toxic substances contained in a cigarette puff [10]. Nicotine itself, in addition to besides being addictive, acts as a mediator of lung damage. It promotes fibrogenesis through various mechanisms such as damage to epithelial/endothelial barriers, release of TGF-β1, production of reactive oxygen species, collagen-protein activation, reactive oxygen species, activation of collagen-producing cells, and recruitment of inflammatory cells [11].

In addition, exposure to inhaled nicotine directly affects lung parameters associated with the development of COPD [12].

Nicotine is primarily metabolized to cotinine by the cytochrome P450 enzyme CYP2A6, with a minor contribution from CYP2B6.

Cotinine is further metabolized, exclusively by CYP2A6, to 3-hydroxycotinine. Polymorphisms affecting the enzymatic function of both of these proteins, leading to altered nicotine metabolism, are well known [13]. However, both CYP2A6 and CYP2B6 are highly polymorphic, making accurate pharmacogenomic prediction of enzyme function difficult.

An alternative and inexpensive test to assess nicotine metabolism is the following the ratio of 3-hydroxycotinine to cotinine concentration measured in biological samples such as urine, blood or saliva, using liquid chromatography coupled with tandem mass spectrometry (LCMS/MS). The test, known as nicotine metabolite ratio (NMR), is an established and validated phenotypic indicator of CYK [14].

The lower the NMR, the lower the CYP2A6 activity and nicotine clearance.

By now, NMR has been used primarily as a predictor of individual response to smoking cessation therapies and is a potential tool to guide personalized selection of cessation therapy [14,15]. However, it should be kept in mind that, like most members of the xenobiotic enzyme superfamily members metabolizing cytochromes P450, CYP2A6 has broad substrate specificity and is involved in the biotransformation into toxic compounds of many molecules contained in cigarettes [16,17]. Therefore, while leading to increased nicotine clearance, more rapid CYP2A6 activity could CYP2A6 activity may lead to increased exposure to other toxic substances.

In this study, we sought to investigate the link between individual NMR value and the susceptibility of smokers to develop COPD.

Materials and methods

Patients and clinical evaluation

This is a retrospective cohort study including 78 consecutive outpatients (42 females, 36 males), progressively from September 2018 to September 2019 at the smoking cessation service of Sant’Andrea Hospital in Rome.

Of these, 37 received a diagnosis of COPD. The diagnosis of COPD was established by the Tiffeneau index (TI), which is the rate between FEV1 (forced expiratory volume of one second) and forced vital capacity (FVC) of less than 70%, according to the GOLD guidelines [2].

Spirometry was performed by body plethysmography (Jaeger masterscreen system, Germany) as dynamic flow sand volumes were measured by the pneumotachographic method and volumes and resistances by the plethysmographic method.

Patients were instructed to discontinue bronchodilator 48 hours before the test. The techniques followed the guidelines of the American Thoracic Society and the European Respiratory Society [18].

In addition, the Fagerstrom test was used to assess smoking dependence, the Modified Medical Research Council (mMRC) scale to assess dyspnea, and the COPD Assessment Test (CAT) to detect the risk of exacerbation [19-21].

Exhaled CO was measured with a smoker-lyzer device (Bedfont, U.S.A.).

A saliva sample (2-3 mL) was collected in a sterile 15-mL tube approximately 1 hour after the last cigarette, stored at 4°C for up to 2 hours, and then sent for NMR testing to the Clinical Biochemistry Laboratory at Sant’Andrea in Rome, Italy, where samples were stored at -20°C until analysis.

All participants provided informed consent, and the study was approved by the Institutional Review Board (Azienda Ospedaliera Sant’Andrea- Sapienza University of Rome, del. 656-13/07/2017).

NMR Determination

Nicotine, cotinine, 3-OH-cotinine and internal standard (IS) cotinine-(methyl-d3) standards were provided by Sigma Aldrich (Saint Louis, MO, U.S.A.).

Twenty μL of saliva was added to 60 μL of 10 μg/ml IS in acetonitrile (Carlo Erba, Milan, Italy) containing 0.1% formic acid (Merck, Darmstadt, Germany). After 30 seconds vortex shaking and 10 min incubation at -20°C, the samples were centrifuged for 15 min at 14,000 g, and 20 μL of top layer was transferred into autosampler vials. Five μL were injected into the chromatographic system.

Chromatography was performed using an Agilent 1100 series liquid chromatography system (Agilent Technologies, Santa Clara, CA, USA) using a Phenomenex Luna HILIC column (100 × 2.0 mm, 3 μm), containing the same packing material. The column was maintained at 60°C. The mobile phases were 0.1% aqueous formic acid (eluent A) and acetonitrile (eluent B). Elution was performed at flow rate of 180 μL/min, using an elution gradient as follows: 3 min with 100% eluent B and 2 min of linear gradient to 50% eluent B, followed by an additional period of 2 min in isocratic conditions and finally 3 min of 100 eluent B. The total analysis run time was 10 min. The mass spectrometry method was performed on a 3200 triple quadrupole system (Applied Biosystems, Foster City, CA, U.S.A.) with a turbo ion spray source. The detector was set in positive ion mode, the ion spray voltage was 5500 V, the source temperature was 200°C, and the dissociation gas for collision activation (nitrogen) was at medium value.

Quadrupoles Q1 and Q3 were tuned for unity mass resolution.

The instrument was set in multiple reaction monitoring mode. The specific ions (m/z) monitored were m/z 163.2 ￫ m/z 130.1 for nicotine, m/z 177.1 ￫ m/z 80.1 for cotinine, m/z 193.1 ￫ m/z 80.1 for 3-OH-cotinine, with a residence time of 100 ms. Data were acquired and processed with Analyst 1.5.1. NMR was calculated based on the 3-OH-cotinine/cotinine ratio.

Statistical analysis

NMR determined in the general population, as follows: slow metabolizers (SM) had an NMR value Q1, normal metabolizers (NM) had an NMR value between Q2+Q3, and fast metabolizers (FM), had an NMR value within Q4. Statistical power analysis showed that the available sample size, having a sampling ratio of 1.08 (N = 41: non-COPD group/N = 37: COPD group), would allow for an assumed, a hypothesized incidence of at least 50 percent of non-normal metabolizers (NMR values Q1 or Q4) to be detected in the COPD group compared to the COPD group.

Statistical analysis was performed using IBM SPSS version 25.0. The significance level was set p<0.05. Normality of the data was assessed by the Kolmogorov-Smirnov test, and values were expressed as mean and standard deviation (SD) or median and interquartile range, as appropriate.

Each patient was assigned to a nicotine metabolic class based on the quartile distribution of the NMR determined in the population at the expected incidence of 25% (defined by quartile determination) with a power of 0.92 (α = 0.05).

The association of NMR with COPD and other patient characteristics was analyzed by Mann-Whitney U-test and Spearman’s correlation, while categorical variables were analyzed by χ2 test or Fisher’s exact test.

Multinomial logistic regression analysis was used to explore the association between NMR and covariates with COPD, adjusting for confounding factors such as age and sex.

Results

Subjects with COPD were older, thinner and more frequent smokers according to the number of years of smoking (Table 1).

In the overall population, the NMR value presented a non-normal distribution, with a median of 0.61 (range 0.23-3.19) and a distribution in quartiles as follows: Q1 ≤ 0.47; 0.47 < Q2 ≤ 0.61; 0.61 < Q3 ≤ 0.98; NMR value showed no significant correlations with age, sex, BMI, onset, pack-year, CAT, mMRC or Fagerström index. COPD was determined by detecting FEV₁/FVC < 70%, no history of asthma, and no reversibility observed after bronchodilation with 400 μg salbutamol.

Comparing COPD patients with non-COPD patients, we found that the two groups were homogeneous in age of smoking initiation, Fagerstrom index, and exhaled CO.

In contrast, significant differences were found in age and pack-year: COPD patients were older, with a greater smoking exposure and lower BMI than non-CPD patients (Table 1).

A significant difference was found in NMR value between COPD and non-CPD patients, that was higher in COPD (p = 0.004).

To evaluate the association of NMR with COPD development, subjects were grouped according to NMR value as slow metabolizers (SM) with Q1≤ 0.47, normal metabolizers (NM) with Q2-Q3 and fast metabolizers (FM) if they were in the Q4 quartile.

We found that COPD was present in the majority of FMs (73.7%), compared with non-FM where 39% had COPD with a frequency of 43.6% in NM and 30% MS, respectively (Table 2). Indeed, in the population, the difference between FM and non-FM was significant (p = 0.016).

The odds ratio (OR) for FMs compared with non-FMs was 4.38 (p < 0.016) regarding the risk of developing COPD, specifically there was an OR of 6.53 compared with MSs and 3.52 compared with NMs (Table3). Multinomial logistic regression analysis shows that, in the study population, NMR is an independent risk factor for COPD. A significant but slight association with COPD was also found for BMI (Table 4).

Discussion

Chronic obstructive pulmonary disease is currently the fourth leading cause of death worldwide, but is expected to become the third by 2030 [4,5].

It is a multifactorial disease resulting from complex interactions between genetic and environmental factors, among which smoking has a clear etiological role, although only a marginal proportion of smokers develop the disease [4-7]. Understanding the biological factors underlying this variability could help to recognize high-risk individuals early, improving preventive strategies. In the case of smokers, this is particularly relevant because the disease arises with a delay of decades after the first cigarette. Assessment of the rate of nicotine metabolism is attracting increasing interest as it is assumed to influence the extent of individual nicotine exposure. The NMR, defined as the ratio of 3-hydroxycotinine to cotinine concentration, is a reproducible marker of nicotine metabolism,[22] clearly associated with smoking behavior and response to smoking cessation treatments such as nicotine replacement therapy (NRT), varenicline and bupropion. In the case of NRT, MSs show higher rates of rapidity than FMs, while the efficacy of bupropion and varenicline appears to be higher in FMs [15,23-26]. While the better efficacy of NRT in MS may be related to lower nicotine clearance, the efficacy of nicotine-free treatments in FM has been related to their dopaminergic effect [27].

The starting hypothesis of the present study is that the individual rate of nicotine metabolism, reflecting the level of CYP2A6 activity, may influence susceptibility to COPD due to increased exposure to harmful CYP2A6 molecules including nicotine. Therefore, we determined the NMR in a cohort of patients who referred to a smoking cessation center and evaluated its association with COPD.

First, we noted some baseline characteristics that were not homogeneous between the groups of patients with COPD and those without COPD.

Probably this difference is related to the clinical setting in which the study was conducted, a smoking cessation center, with patient access driven primarily by a desire to quit smoking and not directly related to the presence of lung disease. The higher median age and number of cigarette packs and lower BMI in the COPD group could be explained by the natural history of COPD, which is more prevalent among long-time and intensive smokers.

However, the correlation analysis showed that neither age, gender, BMI or other smoking-related variables significantly influence the NMR, while the regression analysis showed that higher NMR was an independent risk factor for COPD. The same analysis showed a significant, albeit slight, association of BMI with COPD. While this result is consistent with previous reports on the effect of BMI on the onset and prognosis of COPD [23,30] we report for the first time, to our knowledge, a higher significance of COPD development among people with higher nicotine metabolism (FM) than among normal or slow metabolizers (NM and SM). Interestingly, the odds of COPD in FMs are higher than in NMs, suggesting a protective effect of slow metabolism toward the development of COPD.

Although this effect is lost when comparing MS with NM, the mirror role of slow and fast metabolism makes sense and deserves further evaluation in larger studies.

The biological mechanisms underlying the increased rate of COPD in FM may be different. Although fast metabolism also increases the rate of nicotine elimination, it is associated with a change in smoking topology that induces an increase in total puff volume (to compensate for increased nicotine extraction) and increased exposure to harmful chemicals [31].

Recently, a large study that analyzed data from the Population Assessment of Tobacco use and Health, analyzed total puff volume (to compensate for increased nicotine extraction), found a significant association between higher NMR and increased exposure to tobacco-derived chemicals [32]. Notably, this study also analyzed correlations of biological markers with NMR, finding higher levels of the inflammatory markers ICAM-1 (intracellular adhesion molecule ) and PGE-M (prostaglandin E2 metabolite) in FM compared with MS. ICAM-1 is a known inflammatory marker[33] that shows higher expression in small airway epithelial cells derived from smokers than in cells derived from nonsmokers[34]. The specific induction of ICAM-1 by cigarette smoke extract has been confirmed using cultured rat tracheal tissues[35] as an evaluation model.

In addition, ICAM-1 expression increases in both large and small airways in smokers with chronic airflow limitation [36,37] and up-regulation promotes lung damage in COPD model rats [38].

PGE2 is also a mediator of smoking-induced inflammation through activation of cyclooxygenase 2 and prostaglandin E2 synthase [39] and is markedly increased in the sputum of current COPD smokers [40,41].

In addition to the increased exposure to primary toxicants, higher NMR, which is actually a marker of CYP2A6 activity, probably increases exposure to secondary smoke toxicants produced specifically by CYP2A6.

CYP2A6 has broad substrate specificity, and in addition to nicotine, the amount of many other chemicals in cigarettes may be affected by its activity.

CYP2A6 is critical for the activation of N-diethylnitrosamine and other tobacco-related nitrosamines [16,17]

Thus, increased CYP2A6 activity leads to an increased rate of conversion of cigarette chemicals into toxic compounds.

toxic compounds. Identification of the determinants of damage, lung generated by CYP2A6 may help elucidate the molecular patterns specifically triggered in FM.

Our results should be confirmed by larger, multicenter studies, as this research has limitations.

Although the effect of higher NMR on COPD onset is large enough to be detected with adequate statistical power in this study, the small sample size did not allow us to investigate the possible association between fast metabolism and COPD features such as dyspnea and exacerbation risk.

In addition, we cannot exclude that some inhomogeneity between COPD and non-COPD patient groups may have influenced the results. Unfortunately, case-control matching for age, sex, and other relevant parameters was not possible due to constraints in patient enrollment resulting from the size and type of size and type of clinical setting in which the study took place (smoking cessation center,130-150 patients/year). Finally, as done in previous reports, we used the quartile distribution of NMR to define cut-off values.

Currently, a precise definition of NMR cut-off levels is not available: NMR measurements are quite comparable across studies

Measurement variability brought about by differences in biological sampling and analytical protocols still requires one to refer to one’s threshold values.

Conclusions

Nicotine metabolite ratio is a potential predictive marker of COPD development in smokers. Fast metabolizers are prevalent in COPD population meaning that regardless of exposure to tobacco smoke these individuals are more prone to airway remodeling. Fast metabolizers have a six times greater risk than slow metabolizers of developing COPD.

Figures and tables

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