Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2026 Apr 1;39(4):584–595. doi: 10.1021/acs.chemrestox.5c00480

Effect of Cigarette Type and Smoking Behavior on Urinary Metabolite Levels of Tobacco-Associated Toxicants

Milou G Hendriks †, Charlotte G G M Pauwels †,‡, Daniel J Conklin §, Pawel Lorkiewicz §, Agnes W Boots †, Reinskje Talhout ‡, Bjorn Winkens ∥, Antoon Opperhuizen †,⊥, Frederik-Jan van Schooten †, Alexander H V Remels †,*
PMCID: PMC13100950  PMID: 41921978

Abstract

Filter ventilation in cigarettes may alter smoking behavior and impact exposure to harmful chemicals. This study examined the effect of filter ventilation on smoking topography and urinary levels of metabolites of known tobacco-associated toxicants. Twelve male daily Marlboro Red (MR) cigarette smokers (aged 26–34) participated in the study. In a controlled environment, participants smoked regular MR, Marlboro Prime (MP, a low tar, nicotine, carbon monoxide version of MR), or ventilation holes-blocked MP (MPT; where T = taped) ad libitum on separate days. Smoking topography was recorded by using the CReSSmicro device. Urine samples were collected throughout the day, and 27 metabolites of tobacco-associated toxicants (i.e., nicotine, aldehydes, xylene) were analyzed by ultra performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Smoking different types of cigarettes throughout the day resulted in increased urinary metabolites, including those of nicotine, aldehydes, xylene, and others. No differences were observed in intraday increases in these metabolites between smoking MR and MP. However, compared with MR and MP, smoking MPT was associated with a smaller increase in urinary concentrations of cotinine and 3-hydroxycotinine. Switching from the MR to MP or to the MPT did not significantly alter the number of cigarettes smoked. However, puff count, puff duration, and total cigarette volume smoked were significantly lower when smoking MPT. Overall, our data supports existing literature that indicates that smoking high filter-ventilated cigarettes elicits equivalent toxin exposure as conventional cigarettes. Blocking ventilation holes led to lower urinary nicotine metabolite concentrations, which may be partly due to the rapidly altered smoking topography.

graphic file with name tx5c00480_0008.jpg

graphic file with name tx5c00480_0006.jpg

1. Introduction

Smokers are exposed to well over 6000 (toxic) chemicals that are present in cigarette smoke, increasing their risk for developing diseases such as cancer and cardiovascular and pulmonary diseases. In an attempt to make cigarettes appear less harmful, manufacturers introduced filter ventilation. , Filter ventilation is a design feature of cigarettes that can influence smoke composition, smoking behavior, and smoker satisfaction. Filter ventilation involves incorporating miniscule holes into the cigarette filter. While most filter cigarettes have ventilation holes, the position, size, and number of these holes can vary. , Cigarettes with higher filter ventilation, often referred to as light cigarettes, have lower tar, nicotine, and carbon monoxide (TNCO) yields in their smoke as assessed by smoking machines under ISO 3308 conditions. A lower amount of TNCO, and thus a lower level of toxicants, is likely due to the dilution of smoke with air drawn in through the ventilation holes. This could suggest that low TNCO cigarettes are less harmful; however, research has shown that smoking high filter-ventilated cigarettes has been linked to an increased risk of specific lung carcinomas. It has been postulated that this could be a result from changes in smoke composition, differences in lung exposure to toxicants, or altered smoking topography induced by filter ventilation. ,

Smoking topography refers to the characteristics of how individuals smoke cigarettes. This includes various parameters such as puff volume, puff duration, interpuff interval, number of puffs, and puff flow. Filter ventilation influences smoking topography by diluting the smoke. Due to reduced nicotine intake from highly filter-ventilated cigarettes, smokers may exhibit compensatory behavior, such as taking longer and deeper puffs or increasing the number of puffs or cigarettes. − Additionally, filter ventilation creates a smoother and less irritating smoking experience, improving product appeal and potentially facilitating this compensatory behavior. , However, it is important to realize that smokers can cover the ventilation holes with their lips or fingers when holding the cigarette, thereby decreasing dilution of the smoke and subsequently increasing the exposure to smoke-associated chemicals.

Previous studies have investigated the effect of filter ventilation on smoking topography. , It was concluded that smokers hardly change their cigarettes per day (CPD) when switching to or from low highly filter-ventilated cigarettes, and only partially compensate for changes in nicotine yields by adapting smoking topography (particularly puff volume). , However, there is limited data on the interplay among filter ventilation, smoking topography (including the blocking of ventilation holes while holding the cigarette), and the resulting impact on exposure to tobacco-associated toxicants. In this context, a cross-sectional study investigated the relation between filter ventilation and biomarkers of tobacco-associated exposure in smokers. The study concluded that filter ventilation was not associated with lower levels of exposure to these chemicals. However, because of the observational nature of the study, the impact of filter ventilation could not be isolated, as other differences between cigarette brands (e.g., tobacco type, additives) included in the study also influenced the results. Furthermore, CPD or smoking topography was not directly assessed. Thus, a more controlled intervention is needed to examine the effects of filter ventilation and smoking behavior on exposure to tobacco-associated toxicants.

Previously, we performed a human intervention study using three types of cigarettes; Marlboro Red (MR), Marlboro Prime (MP), and Marlboro Prime with taped ventilation holes (MPT). These cigarettes were chosen because the regular king-size MR is the most popular brand in The Netherlands with claimed TNCO yields of 10, 0.7, and 10 mg per cigarette based on machine smoking under ISO 3308 conditions. The highly filter-ventilated MP has the lowest claimed TNCO levels (1, 0.1, 2 mg/cigarette) available on the market in The Netherlands at the time of the study. The urine samples of this study were used to investigate the effect of filter ventilation on exposure to well-known tobacco-associated toxicants present in cigarette smoke, as assessed by urinary metabolites of these compounds, and to assess the impact of potential changes in smoking topography on the levels of these urinary metabolites. We hypothesized that filter ventilation will alter smoking topography, resulting in urinary metabolite levels comparable to those of different types of cigarettes.

2. Methods

2.1. Cigarette Brands of Interest

This study was previously described in detail, and a brief summary of the methods is included for context. MR king-size and MP king-size cigarettes with a filter ventilation of 43% and 83%, respectively, were used. Cigarette packs were bought in 2017 at a tobacconist in The Netherlands to guarantee matching batch number. The MP cigarette with the ventilation holes taped (MPT) was used as a third “cigarette variant” in this study. A 100% filter vent blocking was achieved by taping with 19 mm width Scotch Magic TM tape (Cat no: 810, 3M, USA).

2.2. Participant Characteristics

Twelve Caucasian/Europe-originated men (25–34 years old) smoking on average 15–25 MR cigarettes per day (CPD) were recruited and participated in the study. To avoid brand-dependent variables in puffing parameters, only smokers who smoked MR cigarettes for at least three years were selected. Subjects suffering from respiratory diseases or chronic illness, using daily medication, or experiencing adverse health effects due to smoking were excluded. This study was approved by the accredited medical ethical committee (NL63420.068.17/METC173038) in Maastricht (The Netherlands) and registered at Clinicaltrials.gov under NCT03498053. All participants gave written informed consent before participation.

2.3. Study Design

This study was a controlled crossover study. The study consisted of three identical experimental days of 10 h, differing only by the cigarette type smoked. On the first experimental day, participants smoked MR ad libitum. Following the first experimental day, participants switched to smoking MP during an adjustment period of 6 days at home. Participants were provided with six packs of MP (one per day) and one spare pack and were required to return all spent butts and unsmoked cigarettes. After the adjustment period, the second experimental day involved smoking MP ad libitum at the study location. This was immediately followed by the third experimental day, where participants smoked the MPT variant ad libitum. The study location was an apartment to create a homelike environment, where the participants had unrestricted access to the same food products for breakfast, lunch, dinner, snacks, and drinks. During the day, participants could smoke cigarettes ad libitum through a CReSSmicro device. Participants arrived at 8 AM and were instructed to refrain from smoking before the start of the experiment. At the beginning of the first experimental day, participants signed the informed consent and were given instructions on using the CReSSmicro device. Participants were required to collect all their urine throughout the experimental days; urine was sampled in the morning; and total urine was collected after 5 h and after 10 h. Urine samples were kept on ice and stored at minus 20 at the end of the experimental day.

2.4. Urinary Metabolite Analysis

Mass concentration of tobacco alkaloids and volatile organic compounds metabolites (VOCm) was measured using UPLC-MS/MS using a two-run method as described by Srivastava et al. The method was adapted from Alwis et al. and validated in accordance with US Food and drug administration bioanalytical method validation guidelines, as described in detail by Lorkiewicz et al. For the positive ion mode, 2 μL injections were made for five analytes (nicotine (NIC), cotinine (COT), 3-hydroxycotinine (3-HC), anabasine (ANB), and anatabine (ANTB)). For the negative ion mode, 2 μL injections were made for the remaining analytes. Measurements were performed by using a Premier XE mass spectrometer with an electrospray ionization (ESI) source and an ACQUITY UPLC core system equipped with a T3 column. Additionally, 10 μL injections were used for the negative ion mode, with 20 injections performed under these conditions.

For UPLC-MS/MS analysis, urine samples were diluted with solvent A of the UPLC gradient with isotopic labeled internal standard and then applied on an UPLC-MS/MS instrument. Separation was performed on an Acquity UPLC HSS T3 (150 × 2.1 mm, 1.8 μm) column (Waters Inc., MA) with a binary gradient (Solvent A was 15 mM ammonia acetate, pH 6.8, and solvent B was acetonitrile) at a flow rate of 0.45 mL/min. Mass spectrometric detection was carried out on a Premier XE triple quadrupole mass spectrometer with an electrospray ionization source. Optimized cone voltages and collision energies were used for each of the individual analytes. For each analyte, three multiple reaction monitoring (MRM) transitions were set up: one for quantification, one for confirmation, and one for the labeled internal standard. These MRMs were scheduled around the retention time of the analytes. No fewer than 12 data points were collected for each peak. Analytes in urine samples were quantified using peak area ratio based on 10 point-standard curves that were run before and after the urine samples. TargetLynx quantification application manager software (Waters Inc., MA) was used for peak integration, calibration, and quantification.

Method linearity and limits of detection (LOD) were evaluated following CDC and FDA guidance. , Calibration curves were generated using 1/x-weighted linear regression of analyte-to-internal-standard peak area ratios, with coefficients of determination (R 2) exceeding 0.990 for all measured analytes. Analytical LODs were calculated using Taylor’s method, based on the standard deviation of the y-intercepts and the slope derived from six calibration curves generated across independent analytical runs.

Quality control (QC) samples were prepared by spiking pooled urine with known concentrations of analytes at two levels. To account for the endogenous background, six replicate measurements were performed prior to spiking. Accuracy and precision were assessed across three independent analytical runs, each consisting of five replicate injections per QC level, yielding a total of 30 injections per level. Accuracy was calculated by comparing measured concentrations to expected values, while precision was expressed as the coefficient of variation (CV %). Most CV values did not exceed 15%, except for 2CaHEMA (22%). Validation metrics are summarized in the Supporting Information Tables.

Analytical standards, stable-isotope-labeled internal standards, supplier information, MRM transitions, retention times, and limits of detection for all analytes are provided in Supporting Information Tables 1 and 2.

For the measurement of creatinine levels, urine samples were 1:20 diluted with water. Creatinine levels were measured on an ace axcel clinical chemistry system (Alfa Wassermann, West Caldwell, NJ) using ACE Creatinine reagent (Alfa Wassermann, West Caldwell, NJ). If the measured creatinine concentration was not within the range of 0.33 to 25 mg/dL, then the concentration was remeasured with a different dilution of the urine sample. Urinary metabolite concentrations were normalized to urinary creatinine. −

In total, 27 compounds were measured in the urine. Tobacco alkaloids nicotine, cotinine, 3-HC, anabasine, and anatabine and the following metabolites were measured because they represent key biomarkers of exposure to VOCs present in cigarette smoke; Trichlorovinyl mercapturic acid (122CVMA), 1,2-Dichlorovinyl mercapturic acid N-Acetyl-S-(1,2-dichloroethenyl)-l-cysteine (12CVMA), 1-Hydroxymethyl-2-propenyl mercapturic acid N-Acetyl-S-[1-(hydroxymethyl)-2-propenyl]-l-cysteine (1HMPeMA), 1-Propylmercapturic acid N-Acetyl-S-propyl-l-cysteine (1PMA), 2,2-Dichlorovinyl mercapturic acid N-Acetyl-S-(2,2-dichlorovinyl)-l-cysteine (22CVMA), 2,4-Dimethylphenyl mercapturic acid N-Acetyl-S-(2,4-dimethylphenyl)-l-cysteine (24MPhMA), 2-Hydroxy-1-phenylethyl mercapturic acid (2HPhEMA), N-Acetyl-S-(carbamoylethyl)-l-cysteine (2CaEMA), N-Acetyl-S-(2-carboxyethyl)-l-cysteine (2CoEMA), N-Acetyl-S-(2-cyanoethyl)-l-cysteine (2CyEMA), N-Acetyl-S-(2-hydroxypropyl)­cysteine (2HPMA), 2-Methyl Hippuric Acid (2MHA), 3,4-Dihydroxybutyl mercapturic acid N-Acetyl-S-(3,4-dihydroxybutyl)-l-cysteine (34HMBMA), 4-Methyl hippuric acid (34MHA), N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine (3HMPMA), N-Acetyl-S-(3-hydroxypropyl)­cysteine (3HPMA), N-Acetyl-S-benzyl-l-cysteine (BzMA), Carbamoyl-2-hydroxy-ethyl mercapturic acid N-Acetyl-S-(3-amino-2-hydroxy-3-oxopropyl)-l-cysteine (CaHEMA), N-Acetyl-S-(4-hydroxy-2-buten-1-yl)-l-cysteine ((E)-4HBeMA), Mandelic acid (MADA), Methylcarbamoyl mercapturic acid N-Acetyl-S-(Nmethylcarbamoyl)-l-cysteine (MCaMA), Phenylglyoxylic acid (PhGA). Of these metabolites, 13 were excluded for analysis due to a substantial amount of missing data, primarily caused by limitations in the sensitivity and reliability of the detection method (Supporting Information Table 3). Many of these excluded metabolites were below the limit of detection (LOD) or had missing data due to quality control rejection.

2.5. Smoking Topography Assessment by CReSSmicro Analysis

Puff parameters of all cigarettes smoked during the experimental days were monitored and recorded with the hand-held portable version of the clinical research support system (CReSSmicro v2.0.0; Plowshare Technologies, Baltimore, MD). The device has a sterilized flow meter mouthpiece connected to a pressure transducer, which converts pressure into a digital signal that is sampled at 50 Hz. CReSSmicro computer software transforms the signal to a flow rate (mL/s) to compute smoking topography data. The three CReSSmicro devices used were calibrated according to procedures described in the manufacturer’s user manuals. The calibration was verified at the end of every experimental day. The software of the CReSSmicro, designed by Borgwaldt, uses the 50 Hz raw data to show a summary of puff profiles in the viewer of the program. A puff cleanup procedure (using the CReSS CleanUp program) was followed to correct machine-generated artifacts in the data. In case of interpuff interval (IPI) < 300 ms, the volume and duration were combined with the previous puff, and remaining puffs with duration <100 ms or volume <5 mL were deleted as they are most likely noise from the machine. Furthermore, erroneous cigarette data beyond the normal physiologic measures was deleted. Criteria were average puff volume >150 mL, total puff volume >2 L, and duration >2.8 s 38. Furthermore, as reported earlier, failures in flow-rate recordings of de CReSS device are not detected and corrected by the CReSS CleanUp program. We observed a relation between flow-rate dropouts and loose, and thus wrong, insertion of the cigarette into the device. Loose insertion was shown to be characterized by flow-rate dropouts in the raw data (i.e., the record signal dropped to 0 mL/s in the middle of a puff, and then went back up to the predropout flow rate), a high puff count, and high total puff volume, and thus fails to represent the smoking topography. To prevent unrepresentative smoking topography data, we deleted individual cigarettes displaying flow-rate dropouts in >45% of the puffs. The flow-rate dropout percentage was calculated as a fraction of the total puff count of the particular cigarette and is based on a smoking machine experiment with the CReSS device. Puff count, puff volume (mL), puff flow (mL/s), puff duration (sec), and inter puff interval (sec) were measured with the CReSSmicro device. In addition, the total volume per cigarette was calculated (mean and standard deviation) per participant.

2.6. Data Analysis

To assess the effect of type of cigarette on the tobacco-associated metabolites in urine at baseline (morning of each experimental day), a marginal model for repeated measures was used with type of cigarette (MR, MP, MPT) as fixed categorical factor and an unstructured covariance structure for repeated measures. Furthermore, a paired t-test was applied to examine the increase in urinary metabolite concentrations throughout the day by comparing baseline concentrations to concentrations measured cumulatively over the 10 h. In addition, to investigate the effect of filter ventilation on exposure, the intraday increases in urinary metabolite concentrations between three different types of cigarettes were examined and compared. To achieve this, the absolute changes during the day were first calculated (Δ = absolute concentration after 10 habsolute concentration at baseline) and subsequently corrected for the number of cigarettes smoked by dividing by the total cigarette count of the respective participants on the specific day. The resulting absolute change per cigarette of certain metabolites was subsequently compared between the three experimental days using the same aforementioned marginal model for repeated measures. Furthermore, the same marginal model for repeated measures was used to assess the effect of type of cigarette (independent variable) on smoking topography parameters (dependent variables). Smoking topography variables were averaged across all cigarettes within a participant. Additionally, to explore the effects of type of cigarette and smoking topography parameters on the urinary metabolite concentrations, marginal models for repeated measures were used with type of cigarette and one of the smoking topography parameters in the fixed part of the model and an unstructured covariance structure for repeated measures. Due to sample size, we could not include all smoking topography parameters in one model. Two-sided p-values <0.05 were used as threshold for statistical significance. Data were processed and analyzed using Microsoft Excel (version 2410, part of Microsoft 365) and IBM SPSS Statistics for Windows (version 28.0, Armonk, NY: IBM Corp).

3. Results

3.1. Subjects

Mean age of the 12 participants was 29.8 years (standard deviation (SD) 3.3; 26–34 years old), and they started smoking at the mean age of 16.3 years old (SD 2.8; 13–22 years old). Participants smoked on average 19 CPD (SD 2.4; 15–20 CPD) since the age of 20 years (SD 4.9; 16–28 years old), as self-reported. Participants smoked on average 15.9 (SD 5.4) CPD during the adjustment period to MP. Butt compliance (i.e., number of butts collected) indicated that participants adhered to the protocol. Participants did not report the use of other tobacco products during the adjustment period. Participants maintained their CPD with a maximum fluctuation of four cigarettes throughout the experimental days.

3.2. Tobacco-Associated Metabolites in Urine at Baseline

For the metabolites that could be reliably detected, no significant differences were observed in baseline metabolite measurements across the three experimental days except for MADA (Table ). Urinary MADA concentrations were significantly higher at the start of the day of smoking MP (p = 0.043) and MPT (p = 0.019) compared with the start of the day smoking when smoking MR. Furthermore, there was a large interpersonal variation in metabolite levels detected in morning urine.

1. Baseline Urinary Levels of Tobacco-Associated Metabolites (Mean ± SD).

average concentration (ng/mg creatinine)
parent compound analyte MR MP MPT
Tobacco Alkaloids
nicotine NIC 871.26 (605.77) 723.92 (266.44) 921.85 (462.73)
cotinine COT 914.93 (436.67) 741.47 (335.64) 948.10 (865.76)
3-hydroxy-cotinine 3-HC 2749.70 (1779.71) 2412.27 (1409.65) 2347.41 (1681.23)
Volatile Organic Compounds
acrolein 3HPMA 915.57 (642.85) 916.07 (356.72) 1077.40 (476.72)
acrolein 2CoEMA 239.20 (186.47) 247.39 (116.38) 271.33 (183.73)
crotonaldehyde 3HMPMA 728.49 (497.32) 867.70 (311.23) 838.56 (500.43)
xylene 2MHA 40.14 (55.14) 44.07 (28.85) 57.01 (63.80)
xylene 34MHA 211.05 (189.44) 282.17 (100.65) 256.66 (133.98)
acrylamide 2CaEMA 183.01 (73.58) 177.01 (53.93) 184.04 (100.98)
propylene oxide 2HPMA 57.76 (31.12) 59.50 (31.54) 80.10 (31.71)
styrene MADA 344.71 (147.97) 499.11 (202.78) 574.62 (277.57)
acrylonitrile 2CyEMA 121.12 (96.23) 130.32 (65.56) 115.35 (59.54)
1,3-butadiene (E)-4HBeMA 22.65 (17.04) 27.54 (14.95) 27.10 (12.95)
toluene BzMA 6.40 (4.55) 8.04 (7.22) 6.60 (3.74)
Open in a new tab
a

p < 0.05 compared with MR. MP = Marlboro Prime; MPT = Marlboro Prime Taped; MR = Marlboro ed. 2CaEMA = N-Acetyl-S-(carbamoylethyl)-l-cysteine; 2CoEMA = N-Acetyl-S-(2-carboxyethyl)-l-cysteine; 2CyEMA = N-Acetyl-S-(2-cyanoethyl)-l-cysteine; 2HPMA = N-Acetyl-S-(2-hydroxypropyl)­cysteine; 2MHA = 2-Methyl hippuric acid; 34MHA = 4-Methyl hippuric acid; 3-HC = 3-hydroxycotinine; 3HMPMA = N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine; 3HPMA = N-Acetyl-S-(3-hydroxypropyl)­cysteine; BzMA = N-Acetyl-S-benzyl-l-cysteine; COT = cotinine; (E)-4HBeMA = N-Acetyl-S-(4-hydroxy-2-buten-1-yl)-l-cysteine; MADA = Mandelic acid; NIC = nicotine. VOCm abbreviations based on acronym harmonization.

3.3. Increase in Urinary Metabolite Concentrations when Smoking Marlboro Red

Smoking MR resulted in a significant increase in average urinary nicotine concentrations throughout the day (2.63-fold; p = 0.009). At the individual level, nicotine concentrations increased for all participants except for one, who displayed a decrease in urinary nicotine concentration (Figure D). Furthermore, smoking MR led to significant increases in urinary nicotine metabolites: cotinine (3.75-fold; p < 0.001) and 3-hydroxycotinine (3-HC) (2.29-fold; p = 0.002) (Figure A–C). Furthermore, there was a large interindividual variation in the magnitude of increases in urinary nicotine metabolite concentrations (Figure D–F).

1.

1

Open in a new tab

Urinary nicotine metabolites before and after smoking Marlboro Red. Absolute urinary metabolite concentrations of nicotine (A,D), cotinine (B,E), and 3-hydroxycotinine (3-HC) (C,F) at baseline and after 10 h when smoking Marlboro Red. Horizontal lines represent means with corresponding 95% confidence intervals (A–C). Colored lines indicate different participants (n = 11) (D–F). P-values were derived from paired t tests. **p < 0.01, ***p < 0.001.

In addition to nicotine metabolites, aldehyde metabolites were measured in urine to assess the exposure to these toxicants. Urinary concentrations of aldehyde metabolites increased throughout the day for all participants when smoking MR (Figure ). On average, smoking MR throughout the day resulted in a significant increase in urinary concentrations of 3-HMPMA (3.39-fold; p < 0.001), 3-HPMA (2.76-fold; p < 0.001), and CoEMA (2.15-fold; p < 0.001), which represent metabolites of crotonaldehyde (3HMPMA) and acrolein (3HPMA and CoEMA) (Figure A–C). Like the urinary nicotine metabolites, aldehyde metabolite levels increased in all participants throughout the day; however, the magnitude of this increase varied considerably between individuals.

2.

2

Open in a new tab

Urinary aldehyde metabolites before and after smoking Marlboro Red. Absolute urinary metabolite concentrations of the crotonaldehyde metabolite 3HMPMA (A,D), and acrolein metabolites 3HPMA (B,E) and 2CoEMA (C,F) at baseline and after 10 h when smoking Marlboro Red. Horizontal lines represent means with corresponding 95% confidence intervals (A–C). Colored lines indicate different participants (n = 11) (D–F). P-values were derived from paired t tests. ***p < 0.001. 2CoEMA = N-Acetyl-S-(2-carboxyethyl)-l-cysteine; 3HMPMA = N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine; 3HPMA = N-Acetyl-S-(3-hydroxypropyl)­cysteine.

In addition to metabolites of nicotine and aldehydes, the absolute increases of the other metabolites upon smoking MR are depicted in Supporting Information Figures 1–3. All urinary metabolite concentrations increased significantly during the day. In addition, similar to nicotine and aldehyde metabolites, there was substantial interindividual variation not only at baseline levels but also in magnitude of change in these metabolites throughout the day. Among all metabolites measured, xylene metabolites showed the most potent increase throughout the day (Supporting Information Figure 1). The xylene metabolite 2MHA exhibited a significant 5.7-fold increase (p = 0.002) and 34MHA exhibited an increase of 4.6-fold (p < 0.001).

3.4. Examining the Difference in Urinary Metabolite Concentrations between Different Types of Cigarettes

To investigate the effect of filter ventilation on exposure, the intraday increases in urinary metabolite concentrations between three different types of cigarettes were examined and compared. The increase in urinary nicotine concentration per cigarette was not statistically significantly different among the MR, MP, and MPT (Figure A). However, the increase in urinary cotinine concentration during the day was significantly lower when smoking MPT compared to MR (0.52-fold; p = 0.009). Furthermore, increases in urinary 3-HC were lower when smoking MPT compared with both MR (0.55-fold; p = 0.027) and MP (0.63-fold; p = 0.038) (Figure B,C).

3.

3

Open in a new tab

Changes in urinary nicotine metabolites per type of cigarette. Changes in urinary concentrations (ng/mg creatinine) per cigarette for nicotine (A), cotinine (B), and 3-hydroxycotinine (3-HC) (C) for three types of cigarettes. Horizontal lines represent estimated means with corresponding 95% confidence intervals derived from a repeated-measures marginal model (n = 12). P-values reflect within-subject comparisons between cigarette types from the repeated-measures model. *p < 0.05; **p < 0.01. MR = Marlboro Red; MP = Marlboro Prime; MPT = Marlboro Prime Taped.

For the aldehyde metabolites, increases in urinary metabolite concentration during the day were not statistically significant between the different types of cigarettes (Figure A–C).

4.

4

Open in a new tab

Changes in urinary aldehyde metabolites per type of cigarette. Changes in urinary concentrations (ng/mg creatinine) per cigarette for the crotonaldehyde metabolite 3HMPMA (A), and acrolein metabolites 3HPMA (B) and 2CoEMA (C) for three types of cigarettes. Horizontal lines represent estimated means with corresponding 95% confidence intervals derived from a repeated-measures marginal model (n = 12). 2CoEMA = N-Acetyl-S-(2-carboxyethyl)-l-cysteine; 3HMPMA = N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine; 3HPMA = N-Acetyl-S-(3-hydroxypropyl)­cysteine; MP = Marlboro Prime; MPT = Marlboro Prime Taped; MR = Marlboro Red.

With regard to other metabolites investigated, the absolute change per cigarette was not statistically different between the different types of cigarettes (Supporting Information Figures 4–6). Furthermore, absolute changes in urinary metabolite concentrations per type of cigarette are presented in the supplement (Supporting Information Figures 7–11).

3.5. Smoking Topography

As it has been shown that smoking topography can influence smoke composition (type of compounds and levels of compounds) and hence may impact exposure to tobacco-associated toxicants, smoking topography of all participants on all three experimental days was assessed. Participants smoked on average almost the same number of MR (11.8 ± 2.7), MP (12.1 ± 2.5), and MPT cigarettes (12.2 ± 2.6). Overall, smoking topography parameters were not significantly different between MR and MP. Puff count was significantly lower when smoking the MPT cigarette compared with MR (0.78-fold, p = 0.001) and MP (0.83-fold, p = 0.002) (Figure A). Additionally, puff duration was significantly shorter when smoking MPT compared with MP (0.90-fold, p = 0.013) (Figure D). Furthermore, total cigarette volume was significantly lower when smoking the MPT cigarettes compared with MR (0.74-fold, p = 0.001) and MP (0.69-fold, p = 0.001) (Figure F). Puff volume (Figure B), puff flow (Figure C), and inter puff interval (Figure E) were not significantly different between the different types of cigarettes.

5.

5

Open in a new tab

Smoking topography parameters across different types of cigarettes. Smoking topography parameters puff count (A), puff volume (B), puff flow (C), puff duration (D), inter puff interval (E), and total cigarette volume (F) of individuals smoking different types of cigarettes (n = 12). Horizontal lines represent estimated means with corresponding 95% confidence intervals derived from a repeated-measures marginal model. MP = Marlboro Prime; MPT = Marlboro Prime Taped; MR = Marlboro Red. P-values reflect within-subject comparisons between cigarette types from the repeated-measures model*p < 0.05, **p < 0.01.

3.6. Effect of Smoking Topography on Urinary Metabolite Concentrations

A marginal model for repeated measures was used to explore the effects of the type of cigarette and smoking topography parameters on the urinary metabolite concentrations. For nicotine, when adjusting for smoking topography parameters, the effect of type of cigarette on the urinary concentration was not significant (Supporting Information Table 5). Furthermore, the smoking topography parameters did not have a significant effect on urinary nicotine concentrations. Given that the total cigarette volume was significantly different between the types of cigarettes (Figure ) and more directly represents the amount of smoke inhaled, it is the most relevant parameter to include in the model. Cotinine concentrations were significantly influenced by type of cigarette (p = 0.027), and this effect remained significant when adjusting for total cigarette volume (p = 0.033) (Supporting Information Table 5). Total cigarette volume did not have a significant effect on urinary cotinine concentration (p = 0.843). For 3-HC, the type of cigarette had a significant effect on the concentration without covariates (p = 0.042) (Supporting Information Table 5). However, when adjusting for total cigarette volume, the effect of type of cigarette on urinary 3-HC concentration was no longer statistically significant (p = 0.094). Nevertheless, total cigarette volume did not have a significant effect on urinary 3-HC concentration (p = 0.083) (Supporting Information Table 5).

4. Discussion

To our knowledge, this is the first study investigating the interplay between filter ventilation, smoking topography (including the blocking of ventilation holes while holding the cigarette), and the resulting impact on exposure to tobacco-associated toxicants. We observed significant increases in urinary tobacco-associated metabolite levels throughout the day when smoking MR, MP, and MPT cigarettes, with certain metabolites showing more potent increases compared with other metabolites. There was substantial interindividual variation, not only in baseline metabolite concentrations but also in the intraday increases induced by smoking. Despite the large differences in TNCO yields of the regular (MR) and highly filter-ventilated cigarette (MP) on machine smoking under ISO 3308 condition, , these increases in urinary metabolite concentrations did not significantly differ between these cigarettes. Regarding smoking topography, there were no significant differences in smoking topography between MR and MP. In contrast, differences in puff count, puff duration, and total cigarette volume were observed when smoking the taped highly filter-ventilated cigarette. Research with larger sample sizes is needed to evaluate the combined effects of smoking parameters on urinary metabolite concentrations.

The baseline (morning) levels of urinary metabolites did not significantly differ between the experimental days. When comparing baseline urinary metabolite levels reported in the present study with the literature, we observed that these are largely in line with other studies assessing these metabolites in urine of smokers ,− but there are some differences. For example, Lorkiewicz et al. reported lower urinary baseline concentrations of nicotine (0.59-fold) and its metabolites cotinine (0.08-fold) and 3-HC (0.21-fold). In addition, this study also reported lower baseline metabolite levels of acrolein (3HPMA 0.55-fold; 2CoEMA 0.44-fold), crotonaldehyde (3HMPMA; 0.44-fold), acrylamide (0.47-fold), xylene (0.8-fold), acrylonitrile (0.27-fold), 1,3-butadiene (0.56-fold), and styrene (0.40-fold) compared with our study. The participants in that study were instructed to refrain from smoking for 48 h before the visit, whereas in our study, participants only abstained from smoking in the morning. This difference in smoking abstinence could explain the observed differences in baseline metabolite concentrations. On the contrary, other studies reported higher morning urinary concentrations of nicotine (1.5- and 1.4-fold) and cotinine (3.2- and 2.4-fold), , 3-HC (1.9-fold), 3HPMA (2.3-fold), and 3HMPMA (3.7-fold). These two studies did not have strict regulations for refraining from smoking. In addition, differences in legal limits of TNCO between countries may have contributed to these differences. Overall, baseline measurements of all metabolites in this study were within the expected range when compared with other studies. ,,

Interestingly, MADA, a metabolite of styrene, was significantly higher at the baseline of the second and third experimental days compared with the first day. Nonsmokers have MADA concentrations of approximately 130 ng/mg creatinine, which is approximately one-third of the levels observed in our study population. ,, This suggests that there are alternative exposure pathways besides smoking. Dietary patterns may influence the metabolism and excretion of MADA. , For example, ethanol inhibits the excretion of MADA. It cannot be determined if dietary intake, including alcohol consumption, plays a role in MADA levels measured in this study as dietary intake from the previous day was not recorded. It has to be noted that, although no statistically significant differences were observed in their baseline measurements between the different experimental days, dietary patterns can also alter the excretion of other smoke-associated metabolites. , Furthermore, variations in baseline levels could be influenced by the timing of the last cigarette smoked the previous day.

In all experimental days, urinary metabolite concentrations increased throughout the day. This is expected upon smoking and has been reported previously. , In addition, baseline measurements in smokers typically show elevated levels of these metabolites compared with nonsmokers. ,− The study of Lorkiewicz et al. showed that smoking one Marlboro Red cigarette increased urinary concentrations of metabolites of nicotine, aldehydes, and other tobacco-associated metabolites within just 3 h. Interestingly, increases in these urinary metabolites are larger 3 h after smoking one cigarette compared with smoking multiple cigarettes throughout the day as measured in this study. Furthermore, the study of St Helen et al. in which 24 h urine was collected during smoking, showed lower urinary aldehyde and 1,3-butadiene concentrations compared with our study. Different study designs may explain the discrepancies in the outcomes. In our study, we opted for a controlled and homogeneous design to minimize the effects of race, sex, and cigarette brand on metabolite yield, which can influence urinary metabolite concentrations. ,,

The increase in tobacco-associated metabolites in urine throughout the day in our study was highly variable between individuals. These interindividual variations may be influenced by genetic factors, particularly those that affect the enzymes involved in the metabolism of nicotine and other related compounds. Tobacco-associated compounds are primarily metabolized in the liver ,− and it is known that polymorphisms in the P450 enzyme CYP2A6 can cause differences in nicotine metabolism in humans. Furthermore, lifestyle factors could also account for interindividual differences. Alcohol, caffeine, and diet could all affect liver enzyme activity and thereby metabolism of smoke-associated metabolites. , Furthermore, some compounds, such as acrolein and acrylamide, are present in food products and can subsequently be metabolized which can contribute to urinary levels. −

The current study found no significant differences in urinary metabolite concentrations between smoking regular cigarettes and smoking highly filter-ventilated cigarettes. This finding aligns with previous research indicating that exposure to harmful chemicals is similar when smoking highly filter-ventilated cigarettes and regular cigarettes. ,, Interestingly, although metabolite levels of most tobacco-associated chemicals were not lower when smoking a highly filter-ventilated cigarette, urinary concentrations of nicotine metabolites were significantly lower when smoking the taped cigarette compared with the regular cigarette. As previously reported, TNCO yields of the taped cigarette are 9.4, 0.5, and 13.3 mg per cigarette based on machine smoking under ISO 3308 conditions. These yields are comparable to TNCO yields of the regular cigarette and are higher than TNCO yields of the highly filter-ventilated cigarette. The lower cotinine and 3-HC concentrations when smoking the taped cigarette that we observed could be caused by changes in the smoke topography. It has to be noted, however, that other urinary metabolites were not significantly lower when smoking the taped cigarette compared with the regular cigarette. This could indicate that the presence of nicotine metabolites in urine is more dependent on smoking behavior compared to other metabolites. Nicotine concentrations did not differ significantly between cigarettes possibly likely due to its short half-life of approximately 2 h compared with the longer half-lives of cotinine and 3-HC, which are approximately 16 and 6 h, respectively. In conclusion, smoking highly filter-ventilated cigarettes is not less harmful than smoking regular cigarettes based on exposure to VOCs. Furthermore, lower concentrations of cotinine and 3-HC may be attributed to differences in smoking topography among types of cigarettes.

In the current study, no significant differences were observed in CPD between the different types of cigarettes. This finding aligns with previous research, which has consistently reported that smokers do not significantly increase their CPD when switching to low-yield or highly filter-ventilated cigarettes. ,,, These studies suggest that smokers may compensate for the lower nicotine yield by altering their smoking behavior, such as taking deeper or more frequent puffs, rather than by increasing the number of cigarettes smoked per day. However, we did not observe a difference in puff frequency or puff flow when smoking the highly filter-ventilated cigarette compared with that of the regular cigarette.

In this context, smoking topography is important in assessing exposure to toxic chemicals in cigarette smoke. Variations in smoking topography can lead to significant differences in the intake of harmful constituents among individuals. Although there is some variation, the measurements of smoking topography parameters found in this study are generally within the range of other published research. − In this study, no significant differences were observed in smoking topography parameters between regular and highly filter-ventilated cigarettes. This is in contrast with previous studies which showed an increase in total puff volume when switching from a regular to a low-yield cigarette. , However, significant decreases were observed in puff count, puff duration, and total cigarette volume when smoking the taped cigarette. Importantly, in our study, participants reported an aversion to the taped cigarette, which may account for the observed significant decrease in puff count, puff duration, and total cigarette volume when smoking the taped cigarette. The absence of a significant difference in smoking topography between regular and highly filter-ventilated cigarettes suggests that compensatory smoking is unlikely to account for the lack of variation in urinary metabolite concentrations between the cigarettes.

To assess the impact of smoking topography parameters and the type of cigarette on urinary metabolite concentrations, we applied a marginal model for repeated measures. After adjusting for smoking topography for nicotine, the cigarette type had no significant effect on urinary concentrations. Furthermore, smoking topography parameters did not have a significant effect on urinary nicotine concentrations. This suggests that urinary nicotine levels were not strongly influenced by the cigarette type or smoking behavior. This highlights that smoking highly filter-ventilated cigarettes has the same health risks as smoking regular cigarettes. For 3-HC concentrations, the effect of the type of cigarette was no longer significant after adjusting for total cigarette volume, suggesting that total cigarette volume may act as a confounder. This implies that the differences in urinary 3-HC concentrations across cigarettes could be influenced by the total cigarette volume. However, the total cigarette volume itself did not have a significant effect on urinary 3-HC concentration. A study with a larger sample size would allow for a more comprehensive analysis of all smoking topography parameters, providing deeper insights into their combined effects.

Finally, we hypothesized that smoking a highly filter-ventilated cigarette would influence smoking topography, thereby leading to similar urinary metabolite levels compared with a regular MR cigarette. However, this was not observed in this study. Smokers largely maintained their topography, as assessed by the parameters that we measured. Although not statistically significant, likely due to a low power of our study, urinary nicotine metabolite levels were lower in MP compared with MR. In contrast, and unexpectedly, urinary levels of other metabolites, including those from aldehydes, were not different between these two types of cigarettes. Several factors may explain the absence of differences in urinary metabolite levels between MP and MR cigarettes. One possible explanation is that we assessed common smoke topography variables but did not assess postinhalation dynamics. For example, the depth of inhalation as well as pulmonary retention time of the smoke were not assessed. It could be speculated that smokers increase these variables to compensate for lower nicotine yield from highly filter-ventilated cigarettes. , In addition, side-stream smoke, the smoke from a smoldering cigarette, contains much higher concentrations of toxic chemicals (e.g., aldehydes) than mainstream smoke, with levels being up to 10-fold higher. − Studies in nonsmokers showed elevated levels of urinary biomarkers for tobacco exposure as a result of secondhand exposure. , Smokers often breathe in a fair amount of their own side stream and secondhand smoke, but this has not been quantified before. Therefore, the inhalation of side-stream smoke may have influenced urinary metabolite concentrations, specifically those of the aldehydes, contributing to no differences between types of cigarettes.

4.1. Limitations and Strengths

Our study confirms previous reports in the literature regarding the impact of filter ventilation on toxicant exposure and smoke topography. However, to our knowledge, no previous studies have combined the assessment of a comprehensive set of urinary metabolites and smoking topography in a crossover design using three different cigarette products (regular, highly filter-ventilated, and ventilation-blocked). In addition, we assessed real-world smoking behavior (in a home-like setting) and tracked metabolite increases throughout a day of ad libitum smoking instead of a single time point or cigarette. Furthermore, the design of a controlled within-subject comparison in which the same participants were using three different types of cigarettes represents a strength of this study. Additionally, we used a marginal repeated-measures model to explore the combined effects of the type of cigarette and smoking topography parameters on the urinary metabolite concentrations. This model was used to assess if changes in smoking topography have an impact on variations in urinary metabolite concentrations, thereby providing a more integrated understanding of the relation between type of cigarette, smoking behavior, and exposure rather than just the impact of cigarette type on behavior or metabolites. By using a homogeneous group of participants, we intended to minimize the effects of age, race, hormones, and metabolism as these factors could influence metabolite concentrations. , Furthermore, studies indicated that menstrual cycle phase and ovarian hormones can influence smoking behavior in women. With this design, we opted to increase the sensitivity of the effect of the types of cigarettes and strengthen internal validity.

This study has some limitations that should be considered in interpreting the results. In this study, only a subset of nicotine metabolites was measured. Measuring more nicotine metabolites could provide more complete insight into nicotine exposure and metabolism. Furthermore, urine was collected throughout the day and pooled for analysis. More frequent sampling time points and longer sampling periods could provide deeper insights into patterns of smoke-associated metabolites over time. In addition, as in many other studies, smoking topography is measured using the CReSSmicro device. − However, the device may have caused variability due to improper insertion of the cigarette into the device, leading to anomalous data. Additionally, the unfamiliarity of smoking through a machine may have caused participants to change their natural smoking behavior when using the device, which could have affected the accuracy of the behavioral data. This study was homogeneous and had a small sample size, and further research with a larger and heterogeneous study group is needed to confirm the relevance and validity of our findings. Furthermore, the study did not completely control for dietary intake on the days prior to measurement days, which could influence urinary metabolite concentrations.

This study prioritized cumulative effects, measured as the total levels of metabolites excreted in urine throughout the day, as this reflects the overall daily exposure. This aligns with the habitual nature of smoking, which typically involves consistent, repeated use throughout the day. While blood concentrations can provide insights into immediate physiological effects, our study focused on understanding how smoking topography influences systemic exposure over repeated use. This perspective is more relevant for assessing the long-term risks and patterns associated with smoking behavior.

5. Conclusions

Our findings suggest that neither filter ventilation nor smoking topography influences urinary metabolite concentrations, indicating that these factors may have minimal influence on long-term systemic exposure to smoking-associated compounds. In conclusion, smoking highly filter-ventilated cigarettes is as harmful as smoking regular cigarettes.

Supplementary Material

tx5c00480_si_001.pdf (833.6KB, pdf)

Acknowledgments

The authors gratefully thank the participants for their time and commitment, and we want to thank Bo Van Engelen and Christy Tulen for their assistance with the human study.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemrestox.5c00480.

  • Additional tables with analytical parameters, excluded metabolites, smoking topography data, statistical model outputs, and metabolite correlations; and additional figures with changes in urinary metabolites by type of cigarette (PDF)

#.

M.G.H. and C.G.G.M.P. contributed equally for the current manuscript. CRediT: Milou G. Hendriks data curation, formal analysis, investigation, visualization, writing - original draft, writing - review & editing; Charlotte G.G.M. Pauwels conceptualization, investigation, methodology, project administration, writing - review & editing; Daniel J. Conklin investigation, methodology, resources, validation, writing - review & editing; Pawel Lorkiewicz investigation, methodology, validation, writing - review & editing; Agnes W. Boots conceptualization, supervision, writing - review & editing; Reinskje Talhout conceptualization, supervision, writing - review & editing; Bjorn Winkens formal analysis, software, validation, writing - review & editing; Antoon Opperhuizen conceptualization, funding acquisition, supervision, writing - review & editing; Frederik-Jan van Schooten conceptualization, resources, supervision, writing - review & editing; Alexander H. Remels data curation, supervision, validation, writing - review & editing.

This research was funded by The Netherlands Food and Consumer Product Safety Authority (NVWA). This research was supported by grants of the U.S. National Institutes of Health: U54HL120163 (DJC, PL), HL171763 (DJC), and S10OD026840 (PL).

The authors declare no competing financial interest.

References

  1. Rodgman, A. ; Perfetti, T. A. . The Chemical Components of Tobacco and Tobacco Smoke; CRC press, 2008. [Google Scholar]
  2. Kozlowski L. T., O’Connor R. J.. Cigarette filter ventilation is a defective design because of misleading taste, bigger puffs, and blocked vents. Tob Control. 2002;11(Suppl 1):I40–I50. doi: 10.1136/tc.11.suppl_1.i40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. King B., Borland R., Le Grande M., O’Connor R., Fong G., McNeill A., Hatsukami D., Cummings M.. Smokers’ awareness of filter ventilation, and how they believe it affects them: findings from the ITC Four Country Survey. Tob Control. 2023;32:93. doi: 10.1136/tobaccocontrol-2020-056134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Talhout R., Richter P. A., Stepanov I., Watson C. V., Watson C. H.. Cigarette Design Features: Effects on Emission Levels, User Perception, and Behavior. Tob Regul Sci. 2018;4(1):592–604. doi: 10.18001/TRS.4.1.6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. National Institute for Public Health and the Environment (RIVM) . Filter Ventilation. RIVM, 2017. https://www.rivm.nl/en/tobacco/filter-ventilation. [Google Scholar]
  6. Bialous S. A., Yach D.. Whose standard is it, anyway? How the tobacco industry determines the International Organization for Standardization (ISO) standards for tobacco and tobacco products. Tob Control. 2001;10(2):96–104. doi: 10.1136/tc.10.2.96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. National Institute for Public Health and the Environment (RIVM) . What Exactly do Cigarette Smokers Inhale? A Comparison between the WHO Intense Method and the ISO Method.; Bilthoven, The Netherlands, 2023. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.rivm.nl/sites/default/files/2023-07/Comparison-between-WHO-Intense-method-and-ISO-method_0.pdf. [Google Scholar]
  8. Song, M. A. ; Benowitz, N. L. ; Berman, M. ; Brasky, T. M. ; Cummings, K. M. ; Hatsukami, D. K. ; Marian, C. ; O’Connor, R. ; Rees, V. W. ; Woroszylo, C. ; Shields, P. G. . Cigarette Filter Ventilation and its Relationship to Increasing Rates of Lung Adenocarcinoma. J. Natl. Cancer Inst. 2017, 109 (12).: 10.1093/jnci/djx075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Frederiksen L. W., Miller P. M., Peterson G. L.. Topographical components of smoking behavior. Addict. Behav. 1977;2(1):55–61. doi: 10.1016/0306-4603(77)90009-0. [DOI] [PubMed] [Google Scholar]
  10. Djordjevic M. V., Fan J., Ferguson S., Hoffmann D.. Self-regulation of smoking intensity. Smoke yields of the low-nicotine, low-‘tar’cigarettes. Carcinogenesis. 1995;16(9):2015–2021. doi: 10.1093/carcin/16.9.2015. [DOI] [PubMed] [Google Scholar]
  11. Scherer G.. Smoking behaviour and compensation: a review of the literature. Psychopharmacology. 1999;145(1):1–20. doi: 10.1007/s002130051027. [DOI] [PubMed] [Google Scholar]
  12. Scherer G., Lee P. N.. Smoking behaviour and compensation: a review of the literature with meta-analysis. Regul. Toxicol. Pharmacol. 2014;70(3):615–628. doi: 10.1016/j.yrtph.2014.09.008. [DOI] [PubMed] [Google Scholar]
  13. Hammond D., Collishaw N. E., Callard C.. Secret science: tobacco industry research on smoking behaviour and cigarette toxicity. Lancet. 2006;367(9512):781–787. doi: 10.1016/S0140-6736(06)68077-X. [DOI] [PubMed] [Google Scholar]
  14. Rees V. W., Hatsukami D., Talhout R.. Cigarette filter ventilation, product appeal and regulatory options: a review of the influence of ventilation on consumers’ sensory and risk perceptions. Tob. Control. 2025:058921. doi: 10.1136/tc-2024-058921. [DOI] [PubMed] [Google Scholar]
  15. Carroll D. M., Stepanov I., O’Connor R., Luo X., Cummings K. M., Rees V. W., Bickel W. K., Berman M. L., Ashley D. L., Bansal-Travers M.. et al. Impact of Cigarette Filter Ventilation on U.S. Smokers’ Perceptions and Biomarkers of Exposure and Potential Harm. Cancer Epidemiol., Biomarkers Prev. 2021;30(1):38–44. doi: 10.1158/1055-9965.EPI-20-0852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Pauwels C., Hintzen K. F. H., Talhout R., Cremers H., Pennings J. L. A., Smolinska A., Opperhuizen A., Van Schooten F. J., Boots A. W.. Smoking regular and low-nicotine cigarettes results in comparable levels of volatile organic compounds in blood and exhaled breath. J. Breath Res. 2021;15(1):016010. doi: 10.1088/1752-7163/abbf38. [DOI] [PubMed] [Google Scholar]
  17. Pauwels C., Klerx W. N. M., Pennings J. L. A., Boots A. W., van Schooten F. J., Opperhuizen A., Talhout R.. Cigarette Filter Ventilation and Smoking Protocol Influence Aldehyde Smoke Yields. Chem. Res. Toxicol. 2018;31(6):462–471. doi: 10.1021/acs.chemrestox.7b00342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Srivastava S., Krivokhizhina T., Keith R., Bhatnagar A., Srivastava S., Xie Z., Lorkiewicz P.. High-throughput UPLC-ESI/MSMS method for simultaneous measurement of the urinary metabolites of volatile organic compounds and tobacco alkaloids. J. Chromatogr. B. 2025;1252:124463. doi: 10.1016/j.jchromb.2025.124463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Alwis K. U., Blount B. C., Britt A. S., Patel D., Ashley D. L.. Simultaneous analysis of 28 urinary VOC metabolites using ultra high performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (UPLC-ESI/MSMS) Anal. Chim. Acta. 2012;750:152–160. doi: 10.1016/j.aca.2012.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. U.S. Food and Drug Administration Bioanalytical Method Validation Guidance for Industry; U.S. Food and Drug Administration, 2018. [Google Scholar]
  21. Lorkiewicz P., Riggs D. W., Keith R. J., Conklin D. J., Xie Z., Sutaria S., Lynch B., Srivastava S., Bhatnagar A.. Comparison of Urinary Biomarkers of Exposure in Humans Using Electronic Cigarettes, Combustible Cigarettes, and Smokeless Tobacco. Nicotine Tob. Res. 2019;21(9):1228–1238. doi: 10.1093/ntr/nty089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Srivastava S., Sithu S. D., Vladykovskaya E., Haberzettl P., Hoetker D. J., Siddiqui M. A., Conklin D. J., D’Souza S. E., Bhatnagar A.. Oral exposure to acrolein exacerbates atherosclerosis in apoE-null mice. Atherosclerosis. 2011;215(2):301–308. doi: 10.1016/j.atherosclerosis.2011.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Xie Z., Sutaria S. R., Chen J. Y., Gao H., Conklin D. J., Keith R. J., Srivastava S., Lorkiewicz P., Bhatnagar A.. Evaluation of urinary limonene metabolites as biomarkers of exposure to greenness. Environ. Res. 2024;245:117991. doi: 10.1016/j.envres.2023.117991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Mikheev V. B., Buehler S. S., Brinkman M. C., Granville C. A., Lane T. E., Ivanov A., Cross K. M., Clark P. I.. The Application of Commercially Available Mobile Cigarette Topography Devices for E-cigarette Vaping Behavior Measurements. Nicotine Tob. Res. 2020;22(5):681–688. doi: 10.1093/ntr/nty190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Pauwels C., Boots A. W., Visser W. F., Pennings J. L. A., Talhout R., Van Schooten F. J., Opperhuizen A.. Characteristic Human Individual Puffing Profiles Can Generate More TNCO than ISO and Health Canada Regimes on Smoking Machine When the Same Brand Is Smoked. Int. J. Environ. Res. Public Health. 2020;17(9):3225. doi: 10.3390/ijerph17093225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Tevis D. S., Flores S. R., Kenwood B. M., Bhandari D., Jacob P., Liu J., Lorkiewicz P. K., Conklin D. J., Hecht S. S., Goniewicz M. L.. et al. Harmonization of acronyms for volatile organic compound metabolites using a standardized naming system. Int. J. Hyg. Environ. Health. 2021;235:113749. doi: 10.1016/j.ijheh.2021.113749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. De Jesús V. R., Bhandari D., Zhang L., Reese C., Capella K., Tevis D., Zhu W., Del Valle-Pinero A. Y., Lagaud G., Chang J. T.. et al. Urinary Biomarkers of Exposure to Volatile Organic Compounds from the Population Assessment of Tobacco and Health Study Wave 1 (2013–2014) Int. J. Environ. Res. Public Health. 2020;17(15):5408. doi: 10.3390/ijerph17155408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Feng J., Sosnoff C. S., Bernert J. T., Blount B. C., Li Y., Del Valle-Pinero A. Y., Kimmel H. L., van Bemmel D. M., Rutt S. M., Crespo-Barreto J.. et al. Urinary Nicotine Metabolites and Self-Reported Tobacco Use Among Adults in the Population Assessment of Tobacco and Health (PATH) Study, 2013–2014. Nicotine Tob. Res. 2022;24(5):768–777. doi: 10.1093/ntr/ntab206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gallart-Mateu D., Dualde P., Coscollà C., Soriano J. M., Garrigues S., de la Guardia M.. Biomarkers of exposure in urine of active smokers, non-smokers, and vapers. Anal. Bioanal. Chem. 2023;415(27):6677–6688. doi: 10.1007/s00216-023-04943-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Keith R. J., Fetterman J. L., Orimoloye O. A., Dardari Z., Lorkiewicz P. K., Hamburg N. M., DeFilippis A. P., Blaha M. J., Bhatnagar A.. Characterization of Volatile Organic Compound Metabolites in Cigarette Smokers, Electronic Nicotine Device Users, Dual Users, and Nonusers of Tobacco. Nicotine Tob. Res. 2020;22(2):264–272. doi: 10.1093/ntr/ntz021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Smith D. M., Shahab L., Blount B. C., Gawron M., Kosminder L., Sobczak A., Xia B., Sosnoff C. S., Goniewicz M. L.. Differences in Exposure to Nicotine, Tobacco-Specific Nitrosamines, and Volatile Organic Compounds among Electronic Cigarette Users, Tobacco Smokers, and Dual Users from Three Countries. Toxics. 2020;8(4):88. doi: 10.3390/toxics8040088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Directive 2014/40/EU; European Parliament and the council of the European Union, 2014. https://health.ec.europa.eu/system/files/2016-11/dir_201440_en_0.pdf (accessed Dec 21, 2023). [Google Scholar]
  33. Capella K. M., Roland K., Geldner N., Rey deCastro B., De Jesús V. R., van Bemmel D., Blount B. C.. Ethylbenzene and styrene exposure in the United States based on urinary mandelic acid and phenylglyoxylic acid: NHANES 2005–2006 and 2011–2012. Environ. Res. 2019;171:101–110. doi: 10.1016/j.envres.2019.01.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wilson H., Robertson S., Waldron H., Gompertz D.. Effect of alcohol on the kinetics of mandelic acid excretion in volunteers exposed to styrene vapour. Br. J. Ind. Med. 1983;40(1):75–80. doi: 10.1136/oem.40.1.75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Benowitz N. L., Hukkanen J., Jacob P. 3rd. Nicotine chemistry, metabolism, kinetics and biomarkers. Handb. Exp. Pharmacol. 2009;192:29–60. doi: 10.1007/978-3-540-69248-5_2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hodges R. E., Minich D. M.. Modulation of Metabolic Detoxification Pathways Using Foods and Food-Derived Components: A Scientific Review with Clinical Application. J. Nutr. Metab. 2015;2015:1–23. doi: 10.1155/2015/760689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. St Helen G., Liakoni E., Nardone N., Addo N., Jacob P. 3rd, Benowitz N. L.. Comparison of Systemic Exposure to Toxic and/or Carcinogenic Volatile Organic Compounds (VOC) during Vaping, Smoking, and Abstention. Cancer Prev Res. 2020;13(2):153–162. doi: 10.1158/1940-6207.Capr-19-0356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Jain R. B.. Distributions of selected urinary metabolites of volatile organic compounds by age, gender, race/ethnicity, and smoking status in a representative sample of U.S. adults. Environ. Toxicol. Pharmacol. 2015;40(2):471–479. doi: 10.1016/j.etap.2015.07.018. [DOI] [PubMed] [Google Scholar]
  39. Rubinstein M. L., Shiffman S., Rait M. A., Benowitz N. L.. Race, Gender, and Nicotine Metabolism in Adolescent Smokers. Nicotine Tob. Res. 2013;15(7):1311–1315. doi: 10.1093/ntr/nts272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Benowitz N. L., Lessov-Schlaggar C. N., Swan G. E.. Genetic influences in the variation in renal clearance of nicotine and cotinine. Clin Pharmacol Ther. 2008;84(2):243–247. doi: 10.1038/clpt.2008.54. [DOI] [PubMed] [Google Scholar]
  41. Ahmed Laskar A., Younus H.. Aldehyde toxicity and metabolism: the role of aldehyde dehydrogenases in detoxification, drug resistance and carcinogenesis. Drug Metab. Rev. 2019;51(1):42–64. doi: 10.1080/03602532.2018.1555587. [DOI] [PubMed] [Google Scholar]
  42. Langman J. M.. Xylene: its toxicity, measurement of exposure levels, absorption, metabolism and clearance. Pathology. 1994;26(3):301–309. doi: 10.1080/00313029400169711. [DOI] [PubMed] [Google Scholar]
  43. Dearfield K. L., Abernathy C. O., Ottley M. S., Brantner J. H., Hayes P. F.. Acrylamide: its metabolism, developmental and reproductive effects, genotoxicity, and carcinogenicity. Mutat. Res. 1988;195(1):45–77. doi: 10.1016/0165-1110(88)90015-2. [DOI] [PubMed] [Google Scholar]
  44. Nakajima T., Wang R.-S., Elovaara E., Gonzalez F. J., Gelboin H. V., Raunio H., Pelkonen O., Vainio H., Aoyama T.. Toluene metabolism by cDNA-Expressed human hepatic cytochrome P450. Biochem. Pharmacol. 1997;53(3):271–277. doi: 10.1016/S0006-2952(96)00652-1. [DOI] [PubMed] [Google Scholar]
  45. Dearfield K. L., Abernathy C. O., Ottley M. S., Brantner J. H., Hayes P. F.. Acrylamide: its metabolism, developmental and reproductive effects, genotoxicity, and carcinogenicity. Mutat. Res. Rev. Genet. Toxicol. 1988;195(1):45–77. doi: 10.1016/0165-1110(88)90015-2. [DOI] [PubMed] [Google Scholar]
  46. Faller T. H., Csanády G. A., Kreuzer P. E., Baur C. M., Filser J. G.. Kinetics of Propylene Oxide Metabolism in Microsomes and Cytosol of Different Organs from Mouse, Rat, and Humans. Toxicol. Appl. Pharmacol. 2001;172(1):62–74. doi: 10.1006/taap.2001.9135. [DOI] [PubMed] [Google Scholar]
  47. Elfarra A. A., Krause R. J., Selzer R. R.. Biochemistry of 1,3-butadiene metabolism and its relevance to 1,3-butadiene-induced carcinogenicity. Toxicology. 1996;113(1):23–30. doi: 10.1016/0300-483X(96)03423-3. [DOI] [PubMed] [Google Scholar]
  48. Nakajima M., Yokoi T.. Interindividual variability in nicotine metabolism: C-oxidation and glucuronidation. Drug Metab. Pharmacokinet. 2005;20(4):227–235. doi: 10.2133/dmpk.20.227. [DOI] [PubMed] [Google Scholar]
  49. Jiang K., Huang C., Liu F., Zheng J., Ou J., Zhao D., Ou S.. Origin and Fate of Acrolein in Foods. Foods. 2022;11(13):1976. doi: 10.3390/foods11131976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Alvarenga G. F., de Resende Machado A. M., Barbosa R. B., Ferreira V. R. F., Santiago W. D., Teixeira M. L., Nelson D. L., Cardoso M. D. G.. Correlation of the presence of acrolein with higher alcohols, glycerol, and acidity in cachaças. J. Food Sci. 2023;88(4):1753–1768. doi: 10.1111/1750-3841.16523. [DOI] [PubMed] [Google Scholar]
  51. Stadler R. H., Blank I., Varga N., Robert F., Hau J., Guy P. A., Robert M. C., Riediker S.. Acrylamide from Maillard reaction products. Nature. 2002;419(6906):449–450. doi: 10.1038/419449a. [DOI] [PubMed] [Google Scholar]
  52. Hecht S. S., Murphy S. E., Carmella S. G., Li S., Jensen J., Le C., Joseph A. M., Hatsukami D. K.. Similar uptake of lung carcinogens by smokers of regular, light, and ultralight cigarettes. Cancer Epidemiol., Biomarkers Prev. 2005;14(3):693–698. doi: 10.1158/1055-9965.EPI-04-0542. [DOI] [PubMed] [Google Scholar]
  53. Donny E. C., Hatsukami D. K.. Randomized Trial of Reduced-Nicotine Standards for Cigarettes. N. Engl. J. Med. 2016;374(4):396–397. doi: 10.1056/NEJMc1513886. [DOI] [PubMed] [Google Scholar]
  54. Mercincavage M., Souprountchouk V., Tang K. Z., Dumont R. L., Wileyto E. P., Carmella S. G., Hecht S. S., Strasser A. A.. A Randomized Controlled Trial of Progressively Reduced Nicotine Content Cigarettes on Smoking Behaviors, Biomarkers of Exposure, and Subjective Ratings. Cancer Epidemiol., Biomarkers Prev. 2016;25(7):1125–1133. doi: 10.1158/1055-9965.EPI-15-1088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Diaz D., Luo X., Hatsukami D. K., Donny E. C., O’Connor R. J.. Cigarette filter ventilation, smoking topography, and subjective effects: A mediational analysis. Drug Alcohol Depend. 2022;241:109683. doi: 10.1016/j.drugalcdep.2022.109683. [DOI] [PubMed] [Google Scholar]
  56. Kassel J. D., Greenstein J. E., Evatt D. P., Wardle M. C., Yates M. C., Veilleux J. C., Eissenberg T.. Smoking Topography in Response to Denicotinized and High-Yield Nicotine Cigarettes in Adolescent Smokers. J. Adolesc. Health. 2007;40(1):54–60. doi: 10.1016/j.jadohealth.2006.08.006. [DOI] [PubMed] [Google Scholar]
  57. Romero D. R., Appolon G., Novotny T. E., Pulvers K., Tracy L., Satybaldiyeva N., Magraner J., Oren E.. Switching people who smoke to unfiltered cigarettes: Effects on smoking topography. Addict Behav Rep. 2024;19:100548. doi: 10.1016/j.abrep.2024.100548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yang J., Hashemi S., Han W., Lee C., Song Y., Lim Y.. Study on the daily Ad Libitum smoking habits of active Korean smokers and their effect on urinary smoking exposure and impact biomarkers. Biomarkers. 2021;26(8):691–702. doi: 10.1080/1354750X.2021.1981448. [DOI] [PubMed] [Google Scholar]
  59. Hammond D., Fong G. T., Cummings K. M., Hyland A.. Smoking topography, brand switching, and nicotine delivery: results from an in vivo study. Cancer Epidemiol., Biomarkers Prev. 2005;14(6):1370–1375. doi: 10.1158/1055-9965.EPI-04-0498. [DOI] [PubMed] [Google Scholar]
  60. Djordjevic M. V., Stellman S. D., Zang E.. Doses of nicotine and lung carcinogens delivered to cigarette smokers. J. Natl. Cancer Inst. 2000;92(2):106–111. doi: 10.1093/jnci/92.2.106. [DOI] [PubMed] [Google Scholar]
  61. National Research Council Committee on Passive, S Environmental Tobacco Smoke: Measuring Exposures and Assessing Health Effects; National Academies Press (US) Copyright © 1986 by the National Academy of Sciences, 1986. [PubMed] [Google Scholar]
  62. Schick S., Glantz S.. Philip Morris toxicological experiments with fresh sidestream smoke: more toxic than mainstream smoke. Tob Control. 2005;14(6):396–404. doi: 10.1136/tc.2005.011288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Charles S. M., Batterman S. A., Jia C.. Composition and emissions of VOCs in main- and side-stream smoke of research cigarettes. Atmos. Environ. 2007;41(26):5371–5384. doi: 10.1016/j.atmosenv.2007.02.020. [DOI] [Google Scholar]
  64. Kuang H., Feng J., Li Z., Tan J., Zhu W., Lin S., Pang Q., Ye Y., Fan R.. Volatile organic compounds from second-hand smoke may increase susceptibility of children through oxidative stress damage. Environ. Res. 2022;207:112227. doi: 10.1016/j.envres.2021.112227. [DOI] [PubMed] [Google Scholar]
  65. Matsukura S., Taminato T., Kitano N., Seino Y., Hamada H., Uchihashi M., Nakajima H., Hirata Y.. Effects of environmental tobacco smoke on urinary cotinine excretion in nonsmokers. Evidence for passive smoking. N. Engl. J. Med. 1984;311(13):828–832. doi: 10.1056/NEJM198409273111305. [DOI] [PubMed] [Google Scholar]
  66. Weinberger A. H., Smith P. H., Allen S. S., Cosgrove K. P., Saladin M. E., Gray K. M., Mazure C. M., Wetherington C. L., McKee S. A.. Systematic and Meta-Analytic Review of Research Examining the Impact of Menstrual Cycle Phase and Ovarian Hormones on Smoking and Cessation. Nicotine Tob. Res. 2015;17(4):407–421. doi: 10.1093/ntr/ntu249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Blank M. D., Disharoon S., Eissenberg T.. Comparison of methods for measurement of smoking behavior: mouthpiece-based computerized devices versus direct observation. Nicotine Tob. Res. 2009;11(7):896–903. doi: 10.1093/ntr/ntp083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Lee E. M., Malson J. L., Waters A. J., Moolchan E. T., Pickworth W. B.. Smoking topography: reliability and validity in dependent smokers. Nicotine Tob. Res. 2003;5(5):673–679. doi: 10.1080/1462220031000158645. [DOI] [PubMed] [Google Scholar]
  69. Perkins K. A., Karelitz J. L., Giedgowd G. E., Conklin C. A.. The reliability of puff topography and subjective responses during ad lib smoking of a single cigarette. Nicotine Tob. Res. 2012;14(4):490–494. doi: 10.1093/ntr/ntr150. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

tx5c00480_si_001.pdf (833.6KB, pdf)

Articles from Chemical Research in Toxicology are provided here courtesy of American Chemical Society

RESOURCES