Scholarly article on topic 'A survey of mouth level exposure to cigarette smoke in the United States'

A survey of mouth level exposure to cigarette smoke in the United States Academic research paper on "Health sciences"

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{Tobacco / Smoke / Tar / Nicotine / Yield / "Mouth level exposure" / "Filter analysis"}

Abstract of research paper on Health sciences, author of scientific article — Paul R. Nelson, Peter Chen, Mike Dixon, Thomas Steichen

Abstract Smoke yields determined by a machine-based smoking method cannot adequately predict exposures experienced by human smokers. In this work, a filter analysis technique which addresses this fundamental limitation was used to measure mouth level exposures (MLE) to tar and nicotine in 1330 smokers of 26 brand-styles of US cigarettes covering a wide range of machine-generated yields. Despite the high degree of variability observed among individual smokers, MLEs were significantly correlated with machine-derived tar and nicotine yields (r =0.423 for nicotine MLE/cigarette; r =0.493 for tar MLE/cigarette; p <0.001 for both). Mean tar and nicotine MLE was higher for males than for females. Mean MLE across races was generally similar. Menthol cigarettes tended toward lower MLE than non-menthol cigarettes and King-Size cigarettes (∼83mm) tended toward lower MLE than 100’s cigarettes (∼100mm), though those trends were not statistically significant. There were good agreements between MLEs measured in a group of 159 subjects smoking their usual cigarette brand-style on two separate occasions and between two independent groups of subjects smoking the same brand-styles. The results indicated that the filter analysis method used had sufficient precision to show similarity among groups.

Academic research paper on topic "A survey of mouth level exposure to cigarette smoke in the United States"

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Regulatory Toxicology and Pharmacology

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A survey of mouth level exposure to cigarette smoke in the United States

Paul R. Nelson a* Peter Chena, Mike Dixon Thomas Steichen

aR.J. Reynolds Tobacco Company, P.O. Box 1487, Winston-Salem, NC 27102-1487, USA b Dixon Consultancy, Liphook, UK



Article history:

Received 6 July 2010

Available online 10 October 2010

Keywords: Tobacco Smoke Tar

Nicotine Yield

Mouth level exposure Filter analysis

Smoke yields determined by a machine-based smoking method cannot adequately predict exposures experienced by human smokers. In this work, a filter analysis technique which addresses this fundamental limitation was used to measure mouth level exposures (MLE) to tar and nicotine in 1330 smokers of 26 brand-styles of US cigarettes covering a wide range of machine-generated yields.

Despite the high degree of variability observed among individual smokers, MLEs were significantly correlated with machine-derived tar and nicotine yields (r = 0.423 for nicotine MLE/cigarette; r = 0.493 for tar MLE/cigarette; p < 0.001 for both). Mean tar and nicotine MLE was higher for males than for females. Mean MLE across races was generally similar. Menthol cigarettes tended toward lower MLE than nonmenthol cigarettes and King-Size cigarettes (~83 mm) tended toward lower MLE than 100's cigarettes (~100 mm), though those trends were not statistically significant.

There were good agreements between MLEs measured in a group of 159 subjects smoking their usual cigarette brand-style on two separate occasions and between two independent groups of subjects smoking the same brand-styles. The results indicated that the filter analysis method used had sufficient precision to show similarity among groups.

© 2010 Elsevier Inc. All rights reserved.

1. Introduction

Standardized machine-smoking methods for the measurement of tar and nicotine yields have been in place in many regions of the world since the introduction of the Federal Trade Commission (FTC) method in the US in Public Health Services (1966). The relevance of methods such as the FTC method and the similar International Organization for Standardization (ISO) method has been frequently questioned by the tobacco control community (e.g., National Cancer Institute, 1996; Bates et al., 1999; WHO TobReg 2004, 2008). Recently, the FTC announced they had rescinded their guidance issued in 1966 that generally permitted statements concerning tar and nicotine yields measured using what was termed the FTC method (FTC, 2008).

Alternatives to the ISO and FTC methods have been introduced. These include a method (MA method) introduced by the Commonwealth of Massachusetts in 1997 (Commonwealth of Massachusetts, 1997) and another (Canadian Intense) introduced in Canada in 2000 (Canada, 2000). Both of these methods are based on fixed machine-smoking regimes and thus, as is the case for the ISO and FTC methods, cannot take into account the considerable degree of variability in human smoking behavior styles. Consequently,

* Corresponding author. Fax: +336 728 7708.

E-mail address: (P.R. Nelson). 1 Retired.

0273-2300/$ - see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.yrtph.2010.10.001

efforts have been focused on the development of methods for measuring the emissions from cigarettes when smoked under human smoking conditions.

Methods for determining the exposure of smokers to cigarette smoke constituents tend to fall into three categories:

1. The analysis of biomarkers of smoke constituents, such as nicotine, CO, tobacco specific nitrosamines (TSNAs), and polycyclic aromatic hydrocarbons, in human body fluids or expired breath (e.g., Hecht et al., 2005; Scherer et al., 2007; Shepperd et al., 2009; Mendes et al., 2009).

2. The recording of human puffing topography (e.g., puff volume, duration, and frequency) followed by machine smoking using settings based on the human topography parameters (e.g., Creighton et al., 1978; Djordjevic et al., 2000; Hammond et al., 2006).

3. The analysis of nicotine and tar (or components of tar) deposited in spent cigarette filters and the derivation of human smoke yields from the tar and nicotine retention characteristics of the filters (e.g., Rawbone, 1984; Baker et al., 1998; Watson et al., 2004; Shepperd et al., 2006, 2009; St. Charles et al., 2006, 2010; Polzin et al., 2009; Pauly et al., 2009).

Filter analysis based methods offer a number of advantages over the biomarker or puffing topography approaches in the assessment of the yields of smoke constituents produced during human

smoking. First, spent filters can be easily collected from smokers in their normal smoking environments. Second, they are non-invasive and therefore should not interfere with normal smoking behavior. Third, they can be used in large scale population studies. Fourth, unlike methods based on biomarker analyses, they can provide a direct estimate of the nicotine and tar yields produced by cigarettes when smoked by humans. These represent the maximum amounts the smokers obtain from their cigarette.

The use of filter analysis for estimating nicotine yields to smokers was originally used in the late 1960s and early 1970s (Ashton and Watson, 1970; Schulz and Seehofer, 1978; Forbes et al., 1976). Cigarettes marketed at that time were predominantly unventilated and their tar and nicotine retention characteristics, or filter efficiencies (FE), were relatively constant over the range of puff flow rates produced by smokers. However, this is not the case for modern low yield cigarettes incorporating ventilated filters where FEs tend to decrease with increasing flow-rates through the filter. Consequently, for many years researchers tended not to use filter analysis techniques in smoke exposure studies. This situation changed in 2001 when St. Charles developed a simple but effective means of avoiding the problem of the flow-rate induced changes in FE, thereby improving the accuracy of filter analysis methodologies. The flow-rate through the mouth-end portion of the filter, downstream of the ventilation, is generally above the range of flow-rates where FE is variable. Thus FEs are generally constant in the mouth-end portion of the filter. Additionally, this region of the filter avoids the complications caused by nicotine and other smoke constituents condensing onto the portion of the filter adjacent to the tobacco rod. St. Charles (2001) recommended the analysis of a 10 mm mouth end section of the filter in order to minimize problems caused by flow-dependency of FEs and condensation. This 'part-filter' technique provides a good estimate of the tar and nicotine exiting the cigarette during human smoking (Shepperd et al., 2006) and thus modern filter analysis techniques have the capability of producing robust estimates of mouth level exposures to cigarette smoke in large-scale studies of smokers (Pauly et al., 2009; Polzin et al., 2009; Shepperd et al., 2009; St. Charles et al., 2010).

Table 1

Details of cigarette brand-styles used in the study.

The mechanics of the smoking process involves two phases: puffing and inhaling. The puff is achieved entirely as a result of a mouth action and the smoke generated in the puff process is initially contained within the mouth. This is due to the closure of the soft palate which physically seals the mouth from the airway and allows the smoker to generate sufficient pressures within the mouth to draw smoke from the cigarette. In most smokers, inhalation occurs shortly after the end of the puff. Air is drawn into the mouth, the mouth filling procedure is the same as with drinking through a straw, where it mixes with the smoke and the smoke/air mix is drawn into the respiratory tract (Rodenstein and Stanescu, 1985; Fairweather, 1989; Dixon and Baker, 2003; Bernstein, 2004). The part-filter analysis technique provides a measure of the amounts of smoke constituents exiting the cigarette and entering the smoker's mouth. This is termed mouth level exposure (MLE). Measurement of MLE does not take account of any smoke spilled from the smoker's mouth prior to inhalation or smoke exhaled. Thus filter analysis derived MLE data represent the maximum available to the smoker rather than absolute smoke amounts retained in, and absorbed from, the respiratory system.

We describe the findings of a part-filter study designed to measure tar and nicotine MLEs for US consumers of commercially available cigarettes covering a range of machine-derived smoke yields and cigarette designs typical of products sold in the US. Of particular interest was the comparison of MLEs from mentholated and non-mentholated cigarettes, as concerns have been expressed that cigarette mentholation may increase the exposure of smokers to cigarette smoke constituents such as nicotine. The study was also designed to investigate the reproducibility and repeatability of measurements obtained in field studies using the part-filter method.

2. Methods

2.1. Test cigarettes

Twenty-six cigarette brand-styles from the US market were used in the study. Manufacturers included RJ Reynolds Tobacco

Brand-style Tar Band Tar (mg/cig) Nicotine (mg/cig) Length Menthol Market Share (%) Subjects (M/F)

Carlton HP TG1 0.6 0.10 KS No 0.03 20/15

Carlton 100's SP TG1 0.7 0.13 100s No 0.06 21/13

Carlton 100's HP TG1 0.8 0.12 100s No 0.13 13/17

Carlton SP TG1 1.1 0.17 KS No 0.04 19/15

Doral ultra lights 100's HP TG2 4.3 0.41 100s No 0.34 24/22

Camel ultra lights HP TG2 5.0 0.45 KS No 0.15 21/20

Marlboro ultra lights 100's HP TG2 5.3 0.49 100s No 1.27 14/29

Marlboro ultra lights HP TG2 5.4 0.51 KS No 2.38 35/51

Newport lights HP TG3 8.0 0.65 KS Yes 0.45 21/21

Marlboro lights menthol HP TG3 8.4 0.71 KS Yes 1.48 17/27

Pall mall lights 100's HP TG3 8.5 0.79 100s No 0.36 23/20

Camel lights menthol HP TG3 8.8 0.71 KS Yes 0.34 21/22

Camel lights HP TG3 9.7 0.83 KS No 2.91 29/11

Marlboro lights 100's HP TG3 9.9 0.79 100s No 3.39 20/33

Camel lights 99 HP TG3 10.2 0.87 100s No 0.13 26/22

Kool milds HP TG3 10.3 0.84 KS Yes 0.48 16/26

Marlboro lights HP TG3 11.2 0.83 KS No 12.46 54/49

Marlboro medium HP TG3 11.2 0.79 KS No 1.60 24/17

Pall mall FF 100's HP TG4 12.8 1.13 100s No 0.47 27/13

Marlboro HP TG4 14.6 1.04 KS No 7.93 65/40

Newport HP TG4 15.2 1.08 KS Yes 3.85 61/44

Marlboro 100's HP TG4 15.5 1.18 100s No 2.14 29/17

Camel filters 100's HP TG4 16.1 1.31 100s No 0.13 32/10

Camel filters HP TG4 16.3 1.26 KS No 1.19 35/13

Camel wides HP TG4 17.1 1.25 KS No 0.42 37/10

Kool filter Kings HP TG4 17.4 1.19 KS Yes 1.00 19/28

HP = Hard Pack, SP = Soft Pack, TG1 = (<2 mg CFM tar), TG2 = (2-8 mg CFM tar), TG3 =(8-14 mg tar), TG4 = (>14 mg tar), KS = King-Size ~83 mm length, M = male, F = female. Market share data obtained from Marlin 2006 database.

Company (RJRT), Philip Morris, USA and Lorillard. Details of these brand-styles are shown in Table 1. Cigarettes with different packaging (hard pack, soft pack), length (King Size [~83 mm], 100 mm), flavor style (menthol and non-menthol) and Cambridge Filter Method (CFM) (previously referred to as the FTC method) smoke yields (0.5-17.9 mg tar) were used in the study. In August 2006, the cigarette brand-styles used in the study accounted for a 45% share of the US market. Philip Morris and Lorillard brand-styles were purchased at retail. R.J. Reynolds brand-styles destined for retail distribution were obtained from the manufacturer.

2.2. Measurement of machine smoked tar, nicotine and CO yields

All of the brand-styles were smoked in R.J. Reynolds smoke lab (ISO 17025 certified) using the following three machine-smoking regimes, and mainstream smoke tar, nicotine and CO yields were measured:

1) The CFM regime: 35 mL puff volume, 2 s puff duration, one puff per minute, filter vents intact. (Pillsbury et al., 1969)

2) The Massachusetts regime (MA): 45 mL puff volume, 2 s puff duration, two puffs per minute, 50% of filter vents blocked (Commonwealth of Massachusetts, 1997).

3) The Canadian Intense regime (CI): 55 mL puff volume, 2 s puff duration, two puffs per minute, 100% of filter vents blocked. (Canada, 2000)

Results of the determinations appear in Table 2.

2.3. Subjects

A target of 40-50 smokers was set for brand-styles with a market share of >0.4%. The target was lowered to 20-40 smokers for brand-styles with a market share <0.4% because of difficulties in recruiting smokers of these less popular brand-styles. Subject recruitment was carried out by a market research agency (Bellomy

Table 2

Yields determined using the Cambridge Filter Method (CFM), Massachusetts (MA) and Canadian Intense (CI) smoking regimes.

Brand-style Tar Nicotine


Carlton 100's SP 0.6 7.0 20.2 0.10 0.74 1.36

Carlton 100's HP 0.7 6.8 20.2 0.13 0.78 1.48

Carlton HP 0.8 7.9 19.4 0.12 0.80 1.48

Carlton SP 1.1 7.3 19.6 0.17 0.79 1.35

Doral ultra lights 100's HP 4.3 15.9 27.6 0.41 1.26 1.78

Camel ultra lights HP 5.0 15.5 17.9 0.45 1.17 1.29

Marlboro ultra lights 100's HP 5.3 16.1 27.0 0.49 1.27 1.76

Marlboro ultra lights HP 5.4 16.0 26.9 0.51 1.22 1.58

Newport lights HP 8.0 18.7 23.1 0.65 1.38 1.52

Marlboro lights menthol HP 8.4 21.2 29.7 0.71 1.58 1.85

Camel lights menthol HP 8.5 23.3 32.8 0.79 1.88 2.30

Camel lights HP 8.8 20.8 28.4 0.71 1.59 1.86

Marlboro lights HP 9.7 21.6 26.3 0.83 1.67 1.92

Marlboro lights 100's HP 9.9 23.9 30.7 0.79 1.75 1.95

Kool milds HP 10.2 24.1 34.0 0.87 1.92 2.37

Camel lights 99 HP 10.3 23.8 31.9 0.84 1.73 2.01

Marlboro medium HP 11.2 22.5 30.0 0.83 1.52 1.83

Pall mall lights 100's HP 11.2 23.3 31.5 0.79 1.65 1.90

Marlboro HP 12.8 29.5 40.6 1.13 2.37 2.85

Marlboro 100's HP 14.6 30.5 35.9 1.04 2.08 2.26

Kool filter Kings HP 15.2 30.2 34.4 1.08 2.25 2.38

Camel filters HP 15.5 31.5 39.4 1.18 2.25 2.58

Pall mall FF 100's HP 16.1 32.4 37.1 1.31 2.50 2.75

Newport HP 16.3 32.1 39.3 1.26 2.45 2.78

Camel wides HP 17.1 34.0 37.4 1.25 2.40 2.51

Camel filters 100's HP 17.4 30.9 39.9 1.19 2.18 2.60

Research, Winston Salem, NC) and eligible smokers were those who smoked at least seven cigarettes per day and had smoked one of the brand-styles listed in Table 1 as their usual brand-style for more than 3 months. Potential participants were excluded if they reported certain medical conditions including heart disease, asthma or other lung disease, kidney disease, liver disease, or pregnancy.

Screening sessions were held during which potential subjects were provided with protocol details. Those who wished to participate in the study were enrolled. Informed consent was obtained from the participants before they commenced the study. The smokers were recruited from 24 sites across the US.

The study consisted of one or two 'test-cycles' depending on the brand-style analyzed. In the first test-cycle, participants visited the test center, completed a smoking history and demographic questionnaire, received three packs of their usual brand-style of cigarette and materials for collecting used cigarette butts, and provided a saliva sample. Two days later each subject revisited the test center to return their used cigarette butts, provide a second saliva sample and complete a brief study survey.

In order to assess the repeatability of the data, smokers of five brand-styles (Marlboro Hard Pack (HP), Marlboro Lights 100s, Marlboro Lights HP, Marlboro Ultra Lights HP and Newport HP) were invited to participate in a second 'test-cycle' involving the same butt and saliva collection procedures. This test-cycle was conducted two weeks after the first test-cycle.

Four additional independent groups of smokers (replicate groups) were recruited to smoke Marlboro Hard Pack (HP), Marlboro Lights HP, Marlboro Ultra Lights HP and Newport HP in order to assess the reproducibility of the data.

Participants received $35 or $50 for participating in a test-cycle dependent on the location of the test center.

2.4. Butt collection procedure

On day 1 of a test-cycle, each subject was provided with a foam block containing 50 capped polyethylene vials in which sample stability had been demonstrated at room temperature for up to 2 weeks. They were asked to collect all filters from cigarettes smoked from the first cigarette in the morning to the last in the evening of day 2 of the test-cycle. Each butt (filter plus residual tobacco) was placed into an individual vial and the vial was sealed using the screw cap. If a smoker's consumption exceeded 50 cigarettes per day, the excess butts were not collected but the smoker was asked to provide a total cigarette count for the day. The subjects returned the butts to the study center on day 3, where the butts were refrigerated prior to being shipped to Arista Laboratories, Richmond, VA, for analysis.

2.5. Butt analysis procedure

The butts were removed from the vials and were counted and inspected. Butts were not analyzed if they were from an incorrect brand or were damaged, i.e., burnt, excessively crushed or extinguished in liquid. The length of each acceptable butt was measured from the mouth end of the filter to the char line. A 10 mm section was then cut from the mouth end of the filter. The cut tips were used to estimate the tar and nicotine mouth level exposures of the smokers.

The estimation of mouth level exposures (MLEs) relies on using the relationships between the mainstream smoke yields of tar and nicotine, and the amounts of tar and nicotine retained within the filter tips of the cigarettes. The method used in the study was derived from the part-filter analysis technique described in detail by St. Charles et al. (2010).

Calibration curves for tar yield vs. UV absorbance per filter tip and nicotine yield vs. nicotine content per tip were prepared for each of the cigarette brand-styles used by the subjects. Each brand-style was smoked on a 20-port linear smoking machine using six different smoking regimes intended to span the range of human yields of the brand-style. The machine-smoking regimes are described in Table 3.

The regimes were selected to cover the range of puff volumes and flow rates typically produced by human smokers and to produce a wide range of tar and nicotine yields for each cigarette type. Between two and five cigarettes were smoked onto each Cambridge filter (Table 4).

The number of cigarettes per Cambridge filter pad was set such that the total particulate matter collected on the pad would not exceed 150 mg, which would avoid effusion of nicotine through the filter. This procedure was conducted in triplicate for each brandstyle and each machine-smoking regime. The mainstream yields of Total Particulate Matter (TPM), nicotine, water and Nicotine Free Dry Particulate Matter (NFDPM or tar) were determined by validated methods [Arista methods are based upon and reference ISO 10315, 10362-1, 3308, 3402, 4387, and 8454 (International Standards Organization 1995, 1999a,b, 2000a-c)]. The individual values obtained from the repeat smoking procedures were used to generate the calibration curves.

The filter tips from the calibration procedures and from the subjects were extracted using 20 mL methanol incorporating n-hepta-decane as an internal standard and were analyzed for nicotine content by GC with FID detection. The tar content of the filter tips was determined from the extracts using a UV absorbance method (columnless HPLC with UV detection (310 nm)) derived from the methods of Sloan and Curran (1981) and Conner et al. (1990).

Linear regression equations were produced from the calibration curves for mainstream tar vs. UV absorbance per filter tip and mainstream nicotine vs. nicotine content per filter tip. Separate regression equations were produced for each brand-style of cigarette used in the study.

The filter tips from each smoker were extracted and analyzed in batches of 3-6 filters. Estimated tar and nicotine MLE was determined on a per-cigarette basis using the appropriate calibration regression equations for each batch of filters analyzed. Average per-cigarette MLEs were then calculated for each smoker. MLEs per day were obtained for each subject by multiplying the appropriate per cigarette value by the number of returned butts.

2.6. Saliva sample collection and analysis

Table 4

Number of cigarettes smoked per Cambridge filter for each calibration regime/brand-style CFM yield combination.

Regime CFM yield

<8 mg 8-14 mg >14 mg

0 5 5 5

1 5 5 5

2 5 5 3

3 5 - -

4 5 5

5 5 3 3

6 - 5 5

7 5 3 2

8 5 - -

before being transported frozen to the RJRT laboratory for analysis. Samples were stored at -10° C prior to analysis for cotinine.

The cotinine levels in the saliva samples were determined using the method described by Byrd et al. (2005).

2.7. Data analysis

2.7.1. Analysis by individual smoker

Linear regression analyses were conducted using either CFM tar or nicotine yields as the independent variable and individual smoker MLE values as the dependent variable.

2.7.2. Analysis by brand

The individual smoker MLEs, salivary cotinine and daily cigarette consumption values were averaged for each brand. These average values were used as dependent variables in linear regression analyses using either CFM tar or nicotine yields as the independent variable.

2.7.3. Analysis by tar band

The data were grouped by tar band into 4 categories based upon traditional grouping by CFM yield, tar group 1 (TG1, <2 mg CFM tar), tar group 2 (TG2, 2-8 mg CFM tar), tar group 3 (TG3, 814 mg CFM tar), tar group 4 (TG4, >14 mg CFM tar). A series of one-way analysis of variance (ANOVA) tests were conducted in order to determine the statistical significance of tar band effects on the various exposure measures. Tukey-Kramer HSD tests were also conducted when the ANOVA test indicated a significant effect of tar band (p <0.05).

Saliva samples were collected using Salivettes® (Sarstedt Inc., Newton, NC). The respondents viewed a saliva collection instructional video then chewed on the Salivette® cotton swab for one minute before placing the swab into the Salivette tube. The Sali-vettes were placed in sealed plastic bags and stored in a freezer

2.7.4. Menthol and non-menthol comparisons

Separate linear regression analyses were performed for mentholated and non-mentholated cigarettes. CFM tar or nicotine yield was used as the independent variable and individual smoker values for tar or nicotine MLEs was the dependent variable. The slopes

Table 3

Smoking regimes used for part-filter analysis calibration.

Point (regime) Smoking conditions Pack tar Yield (mg/cig)

Volume (mL) Duration (s) Interval (s) Smoked length/puffs 1-3 mg 4 mg plus

0 - - - - U U

1 40 2 60 Four puffs U U

2 40 2 30 Tipping + 3 mm U U

3 50 1.5 60 Four puffs U -

4 50 1.5 60 Tipping + 3 mm - U

5 50 1.5 30 Tipping + 3 mm U U

6 70 1.5 60 Four puffs - U

7 70 1.5 30 Tipping + 3 mm U U

8 70 1.5 20 Tipping + 3 mm U -

and intercepts of the regression lines for the mentholated and non-mentholated brands were compared statistically using a regression analysis method.

2.7.5. Effect of cigarette length

An analysis similar to the one described above for menthol/nonmenthol was used to compare MLEs for the 100 mm and King-Size cigarettes.

2.7.6. Gender effect

The MLEs for the male and female respondents were averaged separately in each of the four tar bands. The male and female mean values for each tar band were compared using independent two-sample t-tests.

2.7.7. Race effect

The MLEs for black and white smokers were averaged separately in each of the four tar bands. Independent two-sample t-tests were used to determine the statistical significances of a race effect in each of the tar bands.

2.7.8. Repeatability and reproducibility

In the context of this work, the terms repeatability and reproducibility are not used in the traditional analytical sense to examine within and between laboratory variability. Here, the terms are used to examine variability within and between groups of study subjects. The data obtained from the group of subjects who participated in two test cycles (repeatability assessment) were subject to a series of paired t-tests to determine the statistical significance of any differences between the two test cycles. Additional correlation coefficients (r values) were obtained for the data obtained from cycle 1 and cycle 2.

Data obtained from the replicate groups smoking the same cigarette brands were compared using independent two-sample t-tests in order to assess the reproducibility of the test methods across different groups of subjects.

3. Results

A total of 1330 respondents completed the first test-cycle of the study. The breakdown of respondents by gender and brand smoked is shown in Table 1.

3.1. Cigarette consumption

Two indices of daily cigarette consumption rates were obtained in the study. Subjects were asked to report their usual daily consumption during their initial visit to the test center. A second measure of consumption was obtained by counting the number of butts collected during day 2 of the test cycle. This latter consumption value was used in the calculation of daily tar and nicotine MLEs. The average 'stated' consumption figure (17.7 cigs/day) was significantly lower (p < 0.0001) than consumption measured using the butt count method (24.1 cigs/day). The average cigarette consumption per brand, as measured by the butt count, is plotted against CFM nicotine yield in Fig. 1. There was no significant correlation between these two variables (r = 0.29, p = 0.21).

Provision of gratis cigarettes may be partially responsible for the difference between the smokers' self-reported consumption and the consumption measured from returned cigarette filters. Despite being instructed to smoke ''normally,'' many smokers appear to have smoked more heavily than their self-reported normal on the collection day. For example, 27% of the smokers who reported smoking 15 or fewer cigarettes per day smoked p25 cigarettes and 10% of them smoked p40 cigarettes on the collection day.

.1 24 &

y = 1.3443X + 22.122 R = 0.29

♦ ♦ ♦ ♦

0.5 0.8 1.1 1.4 1.7

CFM Nicotine (mg/cig)

Fig. 1. Daily cigarette consumption as a function of CFM nicotine yield.

3.2. Tar and nicotine MLEs

Fig. 2 shows the individual tar and nicotine MLEs plotted against CFM tar or nicotine yields of the cigarettes smoked by each subject. Regression lines for the brand-style mean MLEs and yields obtained using the MA and CI machine-smoking methods are also shown. Both nicotine (r = 0.423) and tar MLEs per cigarette (r = 0.493) were significantly correlated with CFM nicotine and tar yields, respectively (p < 0.001 in both cases). Significant correlations (p < 0.001) were also obtained for brand-style mean MLEs per day and CFM tar or nicotine yields (r = 0.280 for per nicotine, r = 0.326 for tar).

The individual smoker MLEs were averaged for each cigarette type and the tar and nicotine MLEs per cigarettes appear as the solid circles in Fig. 2. Tar and nicotine MLEs (per cigarette and per day) for each brand-style are shown in Table 5.

The MLEs for the individual smokers were compared with the CFM, MA and CI yields of their respective brands. The percentage of smokers obtaining lower MLEs than the machine yields of their cigarette brand is shown in Table 6. Less than 25% of smokers obtained lower tar and nicotine MLEs than the CFM yields, more than 75% of non-TG1 smokers obtained tar yields lower than the yields produced by the MA method, and more than 95% of all smokers obtained lower tar MLEs than the yields produced using the CI method.

Grouping the MLEs by cigarette brand-style removed the influence of between-smoker variability within each brand-style; hence, the correlations between MLEs and CFM yields were increased and better-represented the relationship of MLEs and CFM on a brand-style basis. Significant correlations were obtained for nicotine MLE/cig vs CFM nicotine (r = 0.928), tar MLE/cig vs CFM tar (r = 0.915), nicotine MLE/day vs CFM nicotine (r = 0.903) tar MLE/day vs CFM tar (r = 0.893). The individual MLE, cigarette consumption and salivary cotinine data were grouped according to the tar band of the smokers' cigarettes (Table 7). Mean MLE tar and nicotine exposures (both per cigarette and per day) showed significant increases across the tar bands from the TG1 band to the TG4 band. Mean MLEs per cigarette were also higher than the average CFM tar and nicotine yields for all four tar bands.

Although the ANOVA test revealed a statistically significant difference (p < 0.001) in the mean salivary cotinine levels of smokers in the four tar bands this was solely attributed to the higher mean cotinine level of the TG4 smokers compared with the levels for smokers from the other three bands (Table 7). There were no statistically significant differences in the mean cotinine levels for smokers in the TG3, TG2 and TG1 bands. The correlation between

CFM Tar Yield (mg/cig)

0 Individual MLE ---MA regression

• Brand Mean MLE — ■■ — CI regression ...... CFM regression

Fig. 2. Individual smoker and brand-style average tar and nicotine MLEs per cigarette as a function of CFM tar and nicotine yields with regression lines for regulatory and brand-style average MLE yields.

Table 5

Mean tar and nicotine MLE values and CFM yields for the cigarette brand-styles studied (standard deviations are in parenthesis).

Brand-style CFM tar (mg/cig) Tar MLE (mg/cig) Tar MLE (mg/day) CFM nicotine (mg/cig) Nicotine MLE (mg/cig) Nicotine MLE (mg/day)

Carlton HP 0.6 6(3) 128 (122) 0.10 0.7 (0.3) 13 (12)

Carlton 100's SP 0.7 8(4) 178 (143) 0.13 0.9 (0.4) 20(16)

Carlton 100's HP 0.8 8(5) 196 (142) 0.12 0.8 (0.5) 21 (15)

Carlton SP 1.1 8(4) 171 (122) 0.17 1.0 (0.5) 20(15)

Doral ultra lights 100's HP 4.3 11 (6) 262 (214) 0.41 1.1 (0.6) 25 (20)

Camel ultra lights HP 5.0 12 (5) 294 (243) 0.45 1.1 (0.5) 27 (23)

Marlboro ultra lights 100's HP 5.3 15 (6) 385 (300) 0.49 1.3 (0.6) 35 (26)

Marlboro ultra lights HP 5.4 13 (6) 330 (225) 0.51 1.3 (0.6) 31 (22)

Newport lights HP 8.0 13 (4) 291 (196) 0.65 1.2 (0.4) 28 (19)

Marlboro lights menthol HP 8.4 17(8) 347 (260) 0.71 1.4 (0.7) 29 (20)

Pall mall lights 100's HP 8.5 20(8) 515 (388) 0.79 1.8 (0.8) 46 (36)

Camel lights menthol HP 8.8 16 (5) 398 (254) 0.71 1.3 (0.4) 33 (21)

Camel lights HP 9.7 18 (7) 383 (252) 0.83 1.5 (0.6) 32 (21)

Marlboro lights 100's HP 9.9 18 (5) 407 (252) 0.79 1.6 (0.5) 35 (20)

Camel lights 99 HP 10.2 19 (6) 415 (281) 0.87 1.6 (0.6) 36 (24)

Kool milds HP 10.3 17(7) 396 (223) 0.84 1.6 (0.7) 35(19)

Marlboro lights HP 11.2 19 (7) 426 (309) 0.83 1.4 (0.5) 33 (23)

Marlboro medium HP 11.2 17 (6) 412 (339) 0.79 1.4 (0.5) 33 (26)

Pall mall FF 100's HP 12.8 21 (7) 516 (272) 1.13 1.9 (0.6) 46 (24)

Marlboro HP 14.6 19 (7) 485(311) 1.04 1.5 (0.6) 37 (23)

Newport HP 15.2 19 (7) 466 (319) 1.08 1.7 (0.6) 40 (28)

Marlboro 100's HP 15.5 24 (8) 634 (441) 1.18 2.0 (0.7) 52 (36)

Camel filters 100's HP 16.1 23 (8) 521 (376) 1.31 2.0 (0.8) 46 (32)

Camel filters HP 16.3 21 (8) 476 (284) 1.26 1.7 (0.6) 39 (24)

Camel wides HP 17.1 21 (6) 559 (338) 1.25 1.7 (0.6) 46 (27)

Kool filter Kings HP 17.4 19 (8) 433 (315) 1.19 1.5 (0.6) 34 (24)

CFM = Cambridge Filter Method. MLE = Mouth level exposure.

Table 6

Percent of smokers whose tar (nicotine) mouth level exposures was less than yields obtained using the CFM, MA and CI machine-smoking regimes.

Tar band CFM (%) MA (%) CI (%)

TG4 23 (16) 91(85) 97 (91)

TG3 10(9) 80 (67) 96(82)

TG2 8(8) 76 (56) 97 (79)

TG1 2(3) 44 (43) 99(92)

Total 14(11) 80 (69) 97 (86)

Figures in parentheses refer to nicotine mouth level exposure; CFM = Cambridge Filter Method, MA = Massachusetts method, CI = Canadian intense method. Tar band descriptors as for Table 1.

salivary cotinine and nicotine MLE per day was significant (p < 0.001) but weak (r = 0.34).

There were no statistically significant differences (p = 0.194) in mean daily cigarette consumption rates between the tar bands (Table 7).

3.3. Tar:nicotine ratios

The tar: nicotine (T:N) ratios for each cigarette type under human smoking conditions and the three machine-smoking regimes are shown in Fig. 3 along with a second-order polynomial fit through the data.

The T:N ratios of the lowest yield cigarettes increased markedly when moving from the CFM machine-smoking regime to the CI regime. The increase was much less marked for the highest yield products. This resulted in similar T:N ratios for the lowest (TG1) and highest (TG4) yield cigarettes under the CI smoking regime. Although the distributions of the T:N ratios measured under human smoking conditions and under the MA smoking regime were flatter than the one produced under the CFM machine-smoking regime, the T:N ratios of the lowest yield cigarettes (TG1) were less than those for the higher yield cigarettes.

Fig. 3. Tar:nicotine ratio for each brand-style measured using MLE data and data from three regulatory machine-smoking regimes (second-order polynomial fit).

3.5. Racial differences in MLEs

The data were separated according to race and a comparison of the mean ± SD MLEs obtained by black and white smokers is shown in Table 9.

In general, MLEs were similar among the different racial groups. In some cases, black smokers had statistically significantly lower exposure to tar and nicotine on a per-cigarette basis. The differences tended to be relatively small except in the TG2 group where all MLE measures were statistically significantly lower for black smokers than for whites.

3.4. Gender differences in MLEs

The male and female smoker MLE data were analyzed separately and the results are shown in Table 8. For the total group of smokers, male smokers had significantly higher mean tar and nicotine MLEs than female smokers (p < 0.001). The mean CFM tar and nicotine yields were also higher for the all male smokers compared with all female smokers. This was due to there being fewer female smokers in the TG4 category. Within each tar band, the CFM yield was similar for males and females. The differences in MLE were statistically significantly greater for males than females for all bands other than TG1 (Table 8). These results suggest that the difference by gender in MLE is more likely attributable to differences in smoking behavior than choice of cigarette within a tar band.

Table 7

CFM nicotine and tar yields, MLEs, salivary cotinine and cigarette consumption rate data (mean ± standard deviation) grouped by tar band of cigarette.

3.6. Effect of menthol on MLEs

Six menthol brand-styles (four TG3 and two TG4 brand-styles) were included in the study. In order to determine whether cigarette mentholation influenced tar and nicotine MLEs the slopes of the regression lines for individual smoker MLE vs. CFM yield for the menthol brand-styles were compared with those for the nonmenthol TG3 and TG4 brand-styles. The results for tar and nicotine MLE per cigarette are shown in Fig. 4.

The regression lines for the menthol cigarettes fell slightly below those for the non-menthol cigarettes suggesting a trend towards slightly lower MLEs per cigarette for the menthol brand-styles. However, differences in the slopes and intercepts of the regression lines were not statistically significant. These results indicate that the mentholated cigarettes were not associated with

TG1 TG2 TG3 TG4 ANOVA p value

N =133 N = 218 N = 498 N = 481

Tar CFM (mg/cig) 0.8 ± 0.2a 5.1 ± 0.4b 9.8 ± 1.1c 15.5 ± 1.2d <0.001

Tar MLE (mg/cig) 7.7 ± 3.9a 12.9 ± 6.0b 17.6 ± 6.8c 20.5 ± 7.7d <0.001

Tar MLE (mg/day) 167.3 ± 133.1a 319.5 ± 244.6b 403.1 ± 285.9c 502.0 ± 332.9d <0.001

Nic CFM (mg/cig) 0.13 ± 0.03a 0.47 ± 0.04b 0.79 ± 0.06c 1.15 ± 0.09d <0.001

Nic MLE (mg/cig) 0.85 ± 0.45a 1.19 ± 0.58b 1.48 ± 0.58c 1.69 ± 0.64d <0.001

Nic MLE (mg/day) 18.5 ± 14.7a 29.6 ± 22.7b 33.9 ± 23.6b 41.5 ± 27.4c <0.001

Cigs/day 22.7 ± 14.1a 24.9 ± 14.1a 23.5 ± 13.3a 24.8 ± 13.1a 0.194

Salivary Cot. (ng/mL) 246.6 ± 169.7a 242.6 ± 179.2a 246.0 ± 173.7a 295.1 ± 174.8b <0.001

N = number of smokers, MLE = Mouth level exposure, CFM = Cambridge Filter Method, TG1 = (<2 mg CFM tar), TG2 = (2-8 mg CFM tar), TG3 = (8-14 mg tar), TG4 = (>14 mg tar).

A suffix (a-d) shared in a row indicates no significant difference between those tar bands (Tukey-Kramer HSD test, p > 0.05).

Table 8

Effect of gender on tar and nicotine MLEs (mean ± standard deviation).

TG1 n = 73#, 60$ TG2 n = 96#, 122$ TG3 n = 251#, 248$ TG4 n = 305#, 175$ All n = 725#, 605$

Tar MLE (mg/cig) M 7.9 ±3.9 13.4 ±6.1 18.8 ±6.8*** 21.7 ±7.9*** 18.2 ±8.2***

F 7.4 ±4.0 12.5 ±5.9 16.3 ±6.6*** 18.3 ±7.0*** 15.2 ±7.2***

Tar MLE (mg/day) M 167.1 ±139.3 360.3 ± 268.0* 440.4 ± 300.0*** 554.9 ± 347.7*** 450.7 ±327.1***

F 167.6 ±126.4 287.4 ± 220.4* 365.4 ± 266.3*** 410.0 ±283.6*** 342.9 ± 262.0***

Nic MLE (mg/cig) M 0.87 ± 0.44 1.23 ±0.57 1.58 ±0.58*** 1.79 ±0.66*** 1.55 ±0.67***

F 0.83 ± 0.46 1.16 ±0.58 1.38 ±0.56*** 1.52 ±0.57*** 1.32 ±0.59***

Nic MLE (mg/day) M 18.1 ±14.7 33.2 ± 24.4* 36.7 ± 24.5** 45.5 ± 28.3*** 38.1 ±26.7***

F 18.9 ±14.8 26.8 ±21.0* 31.1 ±22.3** 34.4 ± 24.3*** 30.0 ± 22.4***

CFM tar (mg/cig) M 0.8 ± 0.2 5.0 ± 0.5 9.9 ±1.1 15.5 ±1.2 10.7 ±5.0***

F 0.8 ± 0.2 5.1 ± 0.4 9.8 ±1.1 15.5 ±1.3 9.6 ± 4.8***

CFM nic (mg/cig) M 0.13 ±0.03 0.47 ± 0.04 0.79 ± 0.06 1.16 ±0.10 0.84 ± 0.30***

F 0.13 ±0.03 0.48 ± 0.04 0.78 ± 0.06 1.14 ±0.08* 0.76 ±0.30***

M, # - male smokers, F, $ - female smokers, MLE - mouth level exposure, CFM - Cambridge Filter Method, TG1 = (<2 mg CFM tar), TG2 = (2-8 mg CFM tar), TG3 = (8-14 mg tar), TG4 = (>14 mg tar).

* p < 0.05 denotes significance of gender difference within each tar band (2-sample independent t-test). ** p < 0.01 denotes significance of gender difference within each tar band (2-sample independent t-test). *** p < 0.001 denotes significance of gender difference within each tar band (2-sample independent t-test).

Table 9

Effect of race on tar and nicotine MLEs (mean ± standard deviation).

TG1 n = 26B, 91 W TG2 n = 34B,158 W TG3 n = 75B, 369 W TG4 n = 98B, 332 W All n = 223B, 950 W

Tar MLE (mg/cig) B 6.5 ±4.5 9.5 ± 6.8*** 15.9 ±8.7* 20.3 ± 8.2 15.8 ±9.2*

W 7.9 ±3.6 13.2 ±5.5*** 17.8 ±6.2* 20.5 ± 7.5 17.1 ±7.4*

Tar MLE (mg/day) B 154.9 ±151.3 210.6 ± 209.4** 381.4 ±289.1 482.7 ± 358.5 374.2 ±321.7

W 173.6 ±130.0 338.1 ± 240.2** 412.0 ±284.1 513.4 ±324.3 412.3 ±298.5

Nic MLE (mg/cig) B 0.71 ± 0.46 0.88 ± 0.64** 1.37 ±0.73 1.70 ±0.68 1.36 ±0.76*

W 0.89 ± 0.42 1.23 ±0.55** 1.50 ±0.54 1.69 ± 0.62 1.46 ±0.61*

Nic MLE (mg/day) B 17.0 ±15.6 19.7 ±19.3** 33.3 ± 23.6 40.1 ± 29.8 32.3 ± 26.5

W 19.3 ±14.6 31.5 ±22.9** 34.4 ± 23.4 42.5 ± 26.7 35.3 ± 24.8

CFM tar (mg/cig) B 0.8 ± 0.2 4.9 ± 0.5* 9.5 ±1.2 15.8 ±1.1** 10.5 ±5.3

W 0.8 ± 0.2 5.1 ± 0.4* 9.9 ±1.1 15.4 ±1.3** 10.1 ±4.8

CFM nic (mg/cig) B 0.13 ±0.03 0.46 ± 0.04* 0.76 ±0.07*** 1.14 ±0.08 0.81 ± 0.35

W 0.13 ±0.02 0.48 ± 0.04* 0.79 ± 0.06*** 1.16 ±0.09 0.80 ± 0.33

B - black smokers, W - white smokers, MLE - mouth level exposure, CFM - Cambridge Filter Method, TG1 = (<2 mg CFM tar), TG2 = (2-8 mg CFM tar), TG3 = (8-14 mg tar), TG4 = (>14 mg tar).

* p < 0.05 denotes significance of race difference within each tar band (2-sample independent t-test). ** p < 0.01 denotes significance of race difference within each tar band (2-sample independent t-test). *** p < 0.001 denotes significance of race difference within each tar band (2-sample independent t-test).

increased tar or nicotine MLEs per cigarette. Similar trends were observed for MLEs per day (data not shown).

3.7. Effect of cigarette length on MLEs

Ten of the 26 brand-styles used in the study were 100's style (typically 96-99 mm in length) whereas the remainder were King Size (KS, typically 80-84 mm in length). The nicotine and tar MLEs from the 100's cigarettes tended to fall above the regression lines of MLEs vs. CFM yields. Thus for a given CFM yield 100 mm cigarettes tended to produce higher MLEs than the KS cigarettes. Regression analyses were conducted for the individual smoker tar and nicotine MLEs vs. CFM yields from the 100 mm and KS cigarettes. Although the slopes of the regression lines were marginally steeper for the 100 mm cigarettes, the differences between the two lengths in regression slopes and intercepts were not statistically significant (data not shown).

3.8. Length of cigarette rod smoked

The lengths of the butts (filter plus unsmoked tobacco) were measured in the study. This enabled the calculation of the amounts of tobacco consumed for each cigarette. The mean lengths (±SD) of tobacco consumed were 57.7 ± 4.3 mm for the 100's and

46.8 ± 4.0 mm for the KS cigarettes. There were no relationships between the amounts of tobacco consumed and CFM tar yields for either the 100's (r = 0.008) or the KS (r = 0.011) cigarettes. On average, the cigarettes were smoked to within 5.5 ± 4.6 mm of the overwrap.

3.9. Repeatability testing

To evaluate repeatability of the MLE measurement, smokers of five brand-styles were asked to participate in a second test cycle two weeks after completing the first test cycle. Of those eligible to participate in a second test-cycle, 57% smokers completed a second test-cycle. The mean values for MLEs, cigarette consumption and length of cigarette consumed for the two test-cycles are shown in Table 10. There were statistically significant correlations between test-cycles 1 and 2 for the individual smoker measures (p < 0.001 in all cases). Additionally, there were no significant differences between the two cycles for tar and nicotine MLEs per cigarette and cigarette length consumed. This indicates a high degree of repeatability for the variables measured on a per-cigarette basis.

There was a small but statistically significant reduction (-6.7%, p = 0.04) in mean cigarette consumption when the smokers moved from test-cycle 1 to test-cycle 2. This was reflected in small

^ 23-ra о

га 20-1 j:

Ш 17 S

ic 14 -I I-

11 13 15 CFM Tar (mg/cig)

2.2 -,

ig) ci

ra 1.9-1 m

0.8 1.0 1.2 CFM Nicotine (mg/cig)

A - — Non-menthol

• - - - - Menthol

Fig. 4. Mean per cigarette tar and nicotine MLEs for both menthol and non-menthol TG3 and TG4 cigarette brand-styles.

Table 10

Mean MLEs, cigarette consumption and length of cigarette consumed for repeat testing of the same group of smokers at a two-week interval.

Parameter N Test-cycle Test-cycle r value P

1 2 value

Nicotine MLE (mg/cig) 157 1.55 1.54 0.684* 0.692

Nicotine MLE (mg/day) 157 38.4 35.6 0.777* 0.053

Tar MLE (mg/cig) 157 18.9 18.7 0.722* 0.512

Tar MLE (mg/day) 157 469 433 0.795* 0.036

Cigarettes per day 159 25.2 23.5 0.845* 0.040

Length consumed 155 51.1 51.0 0.715* 0.852

* p < 0.0001 for significance of r value; p values are from a two-tailed paired t-test.

reductions (about 7%) in the mean MLE per day values. The reduction was statistically significant for tar MLE per day (p = 0.036).

3.10. Reproducibility testing

Replicate groups of smokers (panels) were recruited for four brand-styles to evaluate reproducibility of MLE measurements

across independently recruited groups. The differences in the mean MLE and cigarette consumption values between the two panels for each of the four brand-styles are shown in Table 11. There were small MLE differences (2-7%) between the groups for all brand-styles except Marlboro Lights, where there were 12-13% differences between the groups for MLEs per day. In viewing the table, keep in mind that the mean MLE per day is calculated per subject and is not simply the product of the mean MLE per cigarette and number of cigarettes smoked.

4. Discussion

There have been a number of publications on the use of the part-filter method in recent years. Shepperd et al. (2006) reported the results of a validation study of the method. They compared estimates of tar and nicotine MLEs from part-filter analysis with measures obtained from human smoke duplication and obtained good correlations between the data produced by the two methods. St. Charles et al. (2006) reported a highly significant correlation between nicotine MLE and 24hr urinary nicotine metabolites in a clinical study of 74 smokers. Based on these validation and correlation studies, Pauly et al. (2009) stated the part-filter method may be useful in providing proxy measures of mouth level exposure to tobacco smoke constituents.

The part-filter technique has been used in consumer studies in Germany (Shepperd et al., 2009) and the US (St. Charles et al., 2010). The results from our current study are consistent with many of the findings of these previous studies. All of these consumer studies showed statistically significant correlations between ISO or CFM nicotine yields and nicotine MLEs. As in the current study, there was considerable variability in MLE exposures for smokers of each cigarette type, and many smokers obtained higher nicotine MLEs than the ISO or CFM nicotine yields of their brand-styles, especially smokers of the lower yield products. In two of the studies, Shepperd et al. (2009) and St. Charles et al. (2010), the differences in the mean MLEs (tar and nicotine for St. Charles et al., 2010, nicotine only for Shepperd et al., 2009) were assessed for smokers of cigarettes from different tar band categories. Shepperd et al. (2009) reported statistically significant differences between the mean nicotine MLEs for smokers of German ultra light (1 mg ISO tar yield), light (4 mg ISO tar yield) and full flavor (10 mg tar yield). Significant differences between the mean tar and nicotine MLEs of smokers of US ultra-low low (1-3 mg CFM tar yield), ultra-low high (4-6 mg CFM tar yield), light (7-12 mg CFM tar yield) and full flavor (13 mg + CFM tar yield) were reported by St. Charles et al. (2010).

As the current study and St. Charles et al. (2010) were both conducted in the US using similar size groups of smokers of US products, it is worthwhile directly comparing the results from the two studies.

St. Charles et al. (2010) reported nicotine and tar MLE values from 784 smokers of 17 brand-styles of cigarettes yielding 1 mg-18 mg CFM tar yield. Our study covered a similar range of CFM tar yields but incorporated a higher number of subjects (n = 1330) and brand-styles (n = 26) in the first test-cycle. Six cigarette brand-styles were common to both studies. Daily cigarette

Table 11

Differences between mean MLE and cigarettes consumption for two independently recruited groups smoking the same cigarettes.

Brand-style Nic MLE/cig (%) Nic MLE/day (%) Tar MLE/cig (%) Tar MLE/day (%) Cig consumption (%)

Marlboro FF -3.0 +3.3 -4.1 +3.4 +6.9

Marlboro Lt +5.7 +12.3 +4.9 +13.3 -2.6

Marlboro ULT +4.1 -1.3 +2.3 -3.0 +2.4

Newport FF +3.7 +5.9 +3.1 +3.9 +3.0

% Refer to the differences between the mean scores of panel 2 and panel 1.

consumption rates were not related to the CFM tar or nicotine yields of the cigarettes in either study. Individual smoker tar MLEs per cigarette were significantly correlated with CFM tar yield in both studies (r = 0.493 for the current study, 0.546 for St. Charles et al., 2010). Significant correlations were also obtained for individual smoker nicotine MLEs vs. CFM nicotine yields (r = 0.423 for current study, 0.505 for St. Charles et al. (2010). The magnitudes of these correlation coefficients increased in both studies when mean MLE values per brand-style instead of MLEs per subject were used in the regression analyses. This was due to the removal of the subject-to-subject MLE variability within each brand-style. Both studies included 100 mm and King-Size cigarettes and the trend towards slightly higher MLEs from the longer length cigarettes was present in both studies.

The main differences between the current and St. Charles et al. (2010) studies were in the magnitudes of the tar and nicotine MLEs. The mean tar and nicotine MLEs per brand-style and per tar band were higher in the current study than in St. Charles et al. (2010) (see Fig. 5).

This is also reflected in the fact that St. Charles et al. reported only 0.3% of their smokers had tar MLEs and 1.4% of smokers had nicotine MLEs greater than their corresponding Canadian Intense yields; whereas, more smokers had MLEs higher than the CI yields in the current study (see Table 6). It is possible that differences in the compositions of the subject pools and/or cigarette brand-styles used in the two studies may be factors responsible for the differences in MLE magnitudes. However, as MLEs for the six brand-styles common to both studies were also higher in the current than in the St. Charles et al. (2010) study, differences in brand-style composition are unlikely to be a major factor. Differences in calibration procedures and composition of the groups of smokers may have been contributing factor to the observed differences in the mean MLEs.

CFM Tar (mg/cig)

CFM Nicotine (mg/cig)

■ -This study

♦ ..........St. Charles, et al. (2010)

Fig. 5. Comparison of tar and nicotine MLEs with data from St. Charles et al. (2010).

Previously published part-filter analysis studies (e.g., Shepperd et al., 2009; St. Charles et al., 2009) have not addressed the issues of repeatability and reproducibility for groups of smokers. The results from the current study show excellent agreements between tar and nicotine MLEs per cigarette obtained from the same smokers participating in two test-cycles separated by a period of 2 weeks (Table 10). However, the differences between the two test-cycles were larger for the MLEs per day and were statistically significant for tar MLE per day. This effect can be attributed to a small but statistically significant reduction in measured cigarette consumption during the 2nd test-cycle.

Reproducibility of the MLE per cigarette results was excellent for all four brand-styles assessed, as there were only very small differences (ranging from 2.3% to 5.7%) between MLEs from the two sets of independent panels. Good agreements between the panels for MLEs per day were also observed for three of the four brand-styles (ranging from 1.3% to 5.9%). Larger differences of 12-13% were seen in the MLEs per day for one brand-style.

The part-filter analysis method provides a measure of MLE to smoke constituents. Lower respiratory tract exposure, retention, and absorption of smoke constituents into the systemic circulation may be influenced by post-puff inhalation factors such as inhalation depth and duration. Consequently, as MLEs provide estimates of the maximum amounts available for deposition and absorption, they may not necessarily correlate with other measures of exposure such as levels biomarkers in blood, urine or saliva.

Attempts have been made to examine the relationships between part-filter derived measures of nicotine MLE and blood, urinary and salivary biomarkers of nicotine exposure. St. Charles et al. (2006) compared nicotine MLEs with measures of 24 h urinary nicotine metabolites and salivary cotinine in an in-clinic study of a group of 74 subjects smoking their own brand-styles of US cigarettes over a period of 5 days, during which, samples were obtained each day. They reported significant correlations between part-filter derived nicotine MLEs per day and urinary nicotine equivalents (r = 0.81) and salivary cotinine (r = 0.67). The correlation between nicotine MLEs per day and urinary nicotine equivalents (r = 0.81) was stronger than the one between the latter and salivary cotinine (r = 0.70). More recently, in their in-clinic study of 150 German smokers, Shepperd et al. (2009) reported significant correlations between nicotine MLEs/day and 24 h urinary nicotine equivalents (r = 0.83), plasma cotinine (r = 0.83), and salivary cotinine (r = 0.79). Cigarette consumption rates were closely monitored in both the St. Charles et al. (2006) and Shepperd et al. (2009) studies, thus allowing accurate calculations of daily MLEs from the measured MLE per cigarette data.

Although we obtained a significant correlation between nicotine MLE per day and salivary cotinine, the strength of the correlation (r = 0.34) was considerably weaker than those reported by St. Charles et al. (2006) and Shepperd et al. (2009). This may reflect differences in saliva collection techniques. In both the St. Charles et al. (2006) and Shepperd et al. (2009) studies, saliva samples were collected at the same time of day for each subject and the collections were supervised by clinically trained staff. There was no specified time for saliva sampling in the current study and sample collection followed video instructions with limited supervision. This reduced level of control of saliva sampling may have increased the variability in the saliva cotinine data, thereby degrading the correlation between nicotine MLE per day and saliva cotinine.

A further indication of the relationships between part-filter derived nicotine MLEs and biomarkers of nicotine uptake was provided by St. Charles et al. (2010). In Fig. 10 of their article, they compared part-filter derived nicotine MLEs per cigarette obtained from 784 US smokers with nicotine uptake values per cigarette derived from either plasma nicotine, plasma cotinine, salivary coti-nine or 24 h urinary nicotine metabolite measures reported in 8

publications (Gori et al., 1983; Benowitz et al., 1984; Gori et al., 1985; Benowitz et al., 1988; Byrdet al., 1995; Byrdet al., 1998; Jar-vis et al., 2001 and Mendes et al., 2008). With one exception (Jarvis et al., 2001), the trend of an increase in mean nicotine MLE with increasing CFM nicotine yield was similar for the trends in nicotine uptakes derived from the published biomarker data.

One of the reasons for the good agreement between nicotine MLEs and nicotine exposure derived from biomarkers of systemi-cally absorbed nicotine is the fact that nicotine uptake from the respiratory tract is relatively insensitive to changes in inhalation depth and/or duration, as virtually all of the nicotine inhaled is deposited within the respiratory tract when inhalation occurs (Armitage et al., 1975; Zacny et al., 1987; Armitage et al., 2004; Baker and Dixon, 2006; Feng et al., 2007). However, such a relationship between MLE and biomarkers of systemic absorption may not hold for other smoke constituents, where their respiratory deposition and systemic uptake may be influenced by changes in post-puff inhalation/exhalation characteristics.

Although part-filter derived measures of smoke component MLEs may not be suitable as proxy measures of respiratory exposure or systemic uptake of some smoke components, they can provide a more reliable indication of smoke yields produced under actual human smoking conditions than the derivation of yields from traditional biomarkers of smoke exposure or from the monitoring and duplication of human puffing topography. There are a number of reasons why this is so:

1) The majority of biomarkers of smoke component exposure are metabolites of the parent constituent. There may be considerable inter-individual variations in the rates of metabolism and clearance of the smoke constituent, and the fractional recoveries of some metabolites may be low. Consequently, a comparison of the levels of a smoke constituent metabolite between smokers may not provide a reliable measure of the differences in the yields of the constituent delivered to the smokers.

2) There may not be a linear relationship between the amounts of smoke constituents taken from the cigarette by the smoker and the levels of biomarkers in body fluids. Melikian et al. (2007) compared the emissions of the smoke constituents: nicotine, NNK and benzo[a]pyrene, measured under human smoking conditions, with the levels of their respective metabolites: cotinine, NNAL and 1-hydroxypyrene in urine. They observed marked decreases in the level of bio-marker per unit of exposure for all three constituents with increasing amounts of constituents delivered to the smoker.

3) Many of the biomarkers used in the assessment of cigarette smoke exposure provide indications of daily exposure to cigarette smoke constituents, e.g., plasma or salivary cotinine as a biomarker for nicotine exposure. Frequently, the levels of these biomarkers are divided by daily consumption rates in order to provide an indication of exposure on a per-ciga-rette basis. Unless cigarette consumption rates are closely monitored, as was the case in St. Charles et al. (2006) and Shepperd et al. (2009), errors can occur as result of inaccuracies in the reporting of daily consumption rates. Many studies simply rely on self-reported consumption rates and, as was seen in our current study, self-reported and actual consumption figures may differ considerably. However, it must be stressed that the apparent consumption difference in the current study may have been influenced by the provision of free cigarettes in the filter collection phase of the study.

4) The recording of human puffing topographies in the laboratory or clinic, followed by machine smoking using the human topography parameters (smoke duplication), can provide good measures of cigarette emissions during human

smoking (Creighton et al., 1978; Djordjevic et al., 2000; Hammond et al., 2006). However, the equipment used to measure puffing topography and/or the laboratory or clinic setting may interfere with the normal smoking behavior patterns of the smokers and, hence, alter cigarette emissions by changing crucial parameters such as puff numbers and puff volumes (Comer and Creighton, 1978; Tobin and Sackner, 1982; Ossip-Klein et al., 1983). Recently, Nelson et al. (2009) reported results showing the influence of both the laboratory setting and the cigarette holder used for puff profile measurement on nicotine and tar MLEs. In a study of 44 smokers of 6 mg CFM tar yield cigarettes, mean MLEs were highest for 'profiled' smoking in the laboratory (18.0 mg tar, 1.57 mg nicotine), intermediate for 'un-profiled' smoking in the laboratory (16.9 mg tar, 1.45 mg nicotine) and lowest for 'un-profiled' smoking in the field (13.4 mg tar, 1.34 mg nicotine). In addition the equipment required to record and duplicate human smoking profiles is complex and not suitable for use in large-scale monitoring studies (Polzin et al., 2009).

The part-filter technique is immune to the problems listed above and thus can provide reliable measures of the emissions from different cigarette types under actual human smoking conditions. Polzin et al. (2009) recently described the part-filter method as providing excellent versatility and throughput for the estimation of mouth level exposure to cigarette smoke constituents, particularly when an accurate assessment of exposure on a per-cigarette basis is required. While Polzin et al. (2009) measured the levels of solanesol in part-filters to estimate mouth level exposures to nicotine and TSNAs, the merits described by Polzin et al. (2009) also apply to mouth level exposures derived from the measurement of nicotine and UV tar in part-filters.

The trends observed with tar and nicotine MLEs per cigarette vs. corresponding CFM yields were reflected in the MLE per day trends. An absence of an effect of CFM yield on daily cigarette consumption rates is the reason why the MLE per cigarette and MLE per day trends were very similar.

The lack of a significant relationship between daily cigarette consumption and CFM yields in our study is not consistent with the findings reported by Burns et al. (2001). They analyzed self-reported cigarette consumption data from the 1990 and 1996 California Tobacco Surveys and reported an inverse relationship between average daily cigarette consumption data and CFM nicotine yield of the cigarettes smoked by participants in the survey. Burns et al. (2001) claimed daily cigarette consumption increased as the CFM nicotine yield fell below approximately 0.95 mg/ciga-rette. However, our results are consistent with those from a large number of studies reporting no significant relationship between machine-derived smoke yields and consumption from cross-sectional studies of groups of smokers of cigarettes differing in ISO or CFM tar and nicotine yields (e.g., Russell et al., 1980; Wald et al., 1981; Rawbone, 1984; Folsom et al., 1984; Gori and Lynch, 1985; Benowitz et al., 1986; Russell et al., 1986; Bridges et al., 1990; Sepkovic et al., 1990; Hofer et al., 1991; Rosa et al., 1992; Woodward and Tunstall-Pedoe, 1992; Hee et al., 1995; Djordjevic et al., 2000; Jarvis et al., 2001; Bowman et al., 2002; Ueda et al., 2002; Nakazawa et al., 2004; Hecht et al., 2005; St. Charles et al., 2006, 2010; Mendes et al., 2009; Shepperd et al., 2009). It should be stressed for most, but not all, of these studies, cigarette consumption rates were self-reported and therefore may be subject to errors. The exceptions were St. Charles et al. (2006) and Shep-perd et al. (2009), where exact accounting for daily consumption rates was assured.

It has also been suggested that smoking a cigarette down to the filter may be a mechanism where smokers compensate for a

reduction machine-derived nicotine yields (Wilkenfeld et al., 2000). Consequently, if this was a commonly practiced behavioral phenomenon one would expect to see an inverse relationship between the length of tobacco column consumed and the CFM tar or nicotine yield of the cigarette. Such a relationship was not observed in our study, as the lengths of tobacco rod consumed for either the 100's or KS cigarettes were not significantly correlated with CFM tar or nicotine yield. St. Charles et al. (2005) reported no trends between butt length and CFM tar yield in a study of 803 consumers of 17 brand-styles of US cigarettes with CFM tar yields in the range of 1-18 mg. They concluded that smoking down to the filter does not appear to be a method of compensatory smoking. Our results are in agreement with those produced by St. Charles et al. (2005).

The mean tar and nicotine MLEs per cigarette and per day for male smokers were significantly higher than the corresponding values for female smokers for cigarettes in the TG3 and TG4 tar bands. The gender difference for MLEs per day was also significant in the TG2 tar band (see Table 8). As there were no significant gender differences in the mean CFM tar and nicotine yields of the cigarettes in each of the four tar bands, the MLE exposure results imply that, in general, males smoke cigarettes more intensively than females. This is consistent with the results of puffing topography studies in which male smokers are reported to have larger average and total puff volumes than female smokers when smoking the same cigarette types (Battig et al., 1982; Hofer et al., 1991; Hee et al., 1995; Eissenberg et al., 1999). These differences in puffing topography and MLEs highlight the importance of accounting for potential gender effects in smoker behavior and exposure studies. For example, if one brand-style of cigarette in a study is predominantly smoked by females and another is predominantly smoked by males, differences between the cigarettes in smoke exposure may reflect a gender effect rather than an effect of cigarette design differences. Although, the male/female ratios differed across the four tar bands in our study, the trends in tar and nicotine MLEs across the tar bands for all smokers (Table 7) were present for both the male and female groups of smokers (Table 8).

Concerns have been raised that menthol cigarettes contribute to the health disparities between black and white smokers in the US (Clark et al., 2004). Menthol cigarettes are more popular in the black than in the white populations of smokers in the US (USDHHS, 1998). It has been suggested that cigarette mentholation may result in smokers taking larger puffs and deeper inhalations as a result of the purported cooling effect of menthol in the mouth and throat (Henningfield et al., 2003; Clark et al., 2004). If the addition of menthol were to enhance puff volumes in smokers then one would expect to see higher tar and nicotine MLEs for mentholated products than for non-mentholated products with similar CFM tar and nicotine yields. Additionally, one would expect to see higher MLEs for black smokers than for white smokers. There was no evidence of higher MLEs for mentholated cigarettes in our study, as the MLEs per cigarette vs CFM yields regression lines for the mentholated cigarettes fell slightly below those for the non-menthol brand-styles (Fig. 4). This indicates a trend towards slightly less exposure for mentholated products. However, the small differences in the slopes and intercepts of these regression lines were statistically insignificant. Similarly, we found no evidence of higher MLEs in black smokers. Indeed, mean MLEs per cigarette for black smokers tended to be lower than the corresponding values for white smokers (see Table 9).

Our results on mentholated cigarettes are consistent with published scientific data concerning the effect of cigarette menthola-tion on smoker behavior. Direct comparisons of puff numbers taken from mentholated and non-mentholated cigarettes were reported in seven studies. No significant difference between menthol

and non-mentholated cigarettes was reported in four of the studies (Caskey et al., 1993; Miller et al., 1994; Ahijevych et al., 1996; Pickworth et al., 2002). A significantly reduced puff number was associated with mentholated products in three of the studies (Nil and Battig, 1989; Jarvik et al., 1994; McCarthy et al., 1995). Puff volumes were reported in six studies. A decrease in puff volume with mentholated cigarettes was reported in four studies, three of which were statistically significant (Nil and Battig, 1989; Jarvik et al., 1994 and McCarthy et al., 1995) and one statistically insignificant (Ahijevych et al., 1996). One study reported similar puff volumes for menthol and non-menthol cigarettes (Miller et al., 1994) and one reported a significant increase in puff volume associated with menthol cigarettes (Ahijevych and Parsley, 1999). These studies clearly indicate there is no consistent experimental evidence to support the view that the mentholation of cigarettes increases the number and/or size of puffs taken by smokers. By contrast, the experimental evidence tends to show the opposite effect: i.e., mentholation is associated with a small reduction in puffing intensity which would be consistent with reduction in tar and nicotine MLEs. The reason for this behavioral effect is unclear but, as pointed out by Jarvik et al. (1994), the additional smoke sensory properties introduced by menthol may create the impression of a higher amount of smoke taken into the mouth and thereby reduce puff volumes taken by smokers.

Our MLE results are also consistent with the results from two studies on the effect of cigarette mentholation on the systemic uptake of nicotine and CO (Benowitz et al., 2004; Heck et al., 2009). Both studies reported similar nicotine and CO uptakes from mentholated and non-mentholated cigarettes matched in CFM tar and nicotine yields. Heck et al. (2009) also reported a lack of a menthol effect on the systemic uptake of NNK.

Currently, there are some authorities (e.g., the European Union) who set cigarette smoke emissions regulations based on the measurement of tar, nicotine and CO using the ISO machine-smoking regime. The WHO Study group on tobacco regulation (TobReg) have recommended that the ISO regime should not be used as a basis for regulating cigarette smoke emissions (Burns et al., 2008). They proposed a system whereby cigarettes should be smoked using the CI smoking regime, and upper levels of the emissions of cigarette smoke constituents should be set on the basis of constituents per mg of nicotine rather than on absolute levels of constituents.

The validity of the WHO TobReg proposals to regulate smoke constituents on a per mg of nicotine hinges upon two key assumptions:

1) All cigarettes, irrespective of their standard machine smoked yields, are assumed to produce similar levels of exposure to nicotine in smokers.

2) The relative composition of mainstream smoke (e.g., the ratios of toxicants to nicotine) produced using the CI regime is assumed to closely resemble that produced under human smoking conditions.

Our results indicate that neither of these conditions is met. First, there were significant differences in nicotine MLEs between smokers of the different tar band cigarettes. Secondly, there were absolute and relative differences in the T:N ratios of the cigarettes when smoked by humans and when smoked using the CI regime. This finding is not surprising given that smoke composition has been seen to vary substantially based upon machine-smoking regime applied (Dixon and Borgerding, 2006). Thus, the TobReg proposal is not relevant to smokers' exposure.

Recently, Marian et al. (2009) commented on the fact that different machine-smoking regimes cause cigarettes to burn differently resulting in differences in smoke composition and

biological activity. They stressed the need to use human smoking behavior patterns in the development of better methods to compare cigarettes and product design features. The measurement of tar and nicotine MLEs by part-filter analysis offers a relatively simple, non-invasive and reproducible means of obtaining information on the yields of cigarettes delivered to smokers in their normal smoking environment. Consequently, the part-filter technique may provide a beneficial alternative to the use of machine-smoking methods for regulatory purposes.

Disclosure statement

Paul Nelson and Peter Chen are employees of R.J. Reynolds Tobacco Company. Thomas Steichen was an employee of R.J. Reynolds Tobacco Company at the time that the research was completed. Mike Dixon is a paid consultant to R.J. Reynolds Tobacco Company and has appeared as an expert witness for the defense in tobacco litigation.


The authors wish to gratefully acknowledge the contributions of the following individuals for their efforts toward the completion of this project: Joy Bodnar for collecting the CFM, MA, and CI yield data; Mike Borgerding for assistance with the final manuscript; Gary Byrd, Mary Dennis, and Weiying Yan for analysis of the saliva samples; Alma Campbell for preparing the sample collection kits; and, Mike Conner for assisting with site visits.


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