1. No category

Uptake of smoke constituents by
UK cigarette smokers
A cross-sectional biomarker study
Gunther Peters , Thomas Pienemann, Wolfram Roeper
Imperial Tobacco Germany
JOINT STUDY GROUPS MEETING
SMOKE SCIENCE AND PRODUCT TECHNOLOGY
November 2008, Shanghai, China
Content
• Background
• Objectives
• Study Design
• Field Work
• Results
• Conclusions
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Background
Biomarker studies are the most often recommended
method to assess the exposure of smokers to
smoke constituents [IoM & LSRO reports].
The knowledge about representative smoke
exposure data is fundamental for the assessment
of potential reduced exposure products.
Because Imperial Tobacco is the leading
manufacturer in the United Kingdom it was
decided in 2005 to conduct such a study.
The clinical trials were carried out between April
and December 2006 and analytical evaluation
was completed in summer 2007.
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3
Background – Exposure Schema
tobacco
smoke
human smoke yield
per cigarette
(yield in-use)
content
or
precursor
in
tobacco
man (human intake & uptake)
varies with individual
smoking behaviour
daily
smoke
intake
daily
smoke
uptake
machine smoke yield
per cigarette
varies with
individual
behaviour,
e.g. daily
consumption
assessed by
biomarker
measurement
in blood or
excretion
in 24-h-urine
varies with product properties
& smoking regimen
mean values from a sufficient
number of study subjects
Main Objective
Measure the levels of selected biomarkers of
cigarette smoke exposure in blood and urine
samples
from regular-smoking & non-smoking healthy
subjects
and compare between smokers of brands with
different tar levels and non-smokers
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Secondary Objectives
• Compare levels of biomarkers with regards to
demographic criteria, e.g. gender.
• Evaluate smoke exposure from collected
smoked filter tips.
• Measure chemical and biological properties of
the most frequently used brands to compare
results from machine smoking with smoke
exposure data and levels of biomarkers.
• Assess a device for measuring CO in exhaled
breath and compare with COHb levels in blood.
•
Measure and evaluate smoking topography data.
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6
Study Design
Cross-sectional representation of the general UK smoking
population of factory-made cigarettes (FMC) at one
point in time (2006)
Group A – smokers of 8-10 mg tar level brands
Group B – smokers of 5-7 mg tar level brands
Group C – smokers of 1-4 mg tar level brands
<population of tar groups by market share>
Group NS – non-smokers (control group)
Gender split 50/50 +/- 10, age > 21,
mean age 40 +/- 5, at 2 recruitment sites
No targets set for social criteria or ethnic origin
Study protocol approved by an ethics committee
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Study Design – Targets for study subjects
ISO tar*)
share**)
target
actual
Group A
8 – 10
75,0 %
600
581
Group B
5–7
22.5 %
180
207
Group C
1–4
2.5 %
20
38
Smokers
total
100 %
800
826
100
103
Non-smokers
*) Classification by declared figures on packs, a few (<20) imported
brands were classified by measured tar values.
**) Average UK markets shares 2005/2006
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Study Design – Selected demographic data
group
(tar level)
A
(8 - 10 mg)
B
(5 - 7 mg)
C
(1 - 4 mg)
mean age
(years)
gender split
male
female
total no.
of
subjects
35
343
238
581
31
112
95
207
41
20
18
38
smokers
34
475
351
826
non-smokers
33
61
42
103
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9
Study Design - Selection of Biomarkers
Biomarker of
exposure
Body
fluid
Nicotine + 5
metabolites
urine
NNAL
urine
1-OHP
Smoke Precurser, related Unique to
phase smoke constituent tobacco
Particu
LSRO
category
Nicotine
Yes
A
P
NNK (TSNA)
Yes
A
urine
P
Pyrene/PaHs
No
A
MHBMA,
DHBMA
urine
Vapour
1,3-Butadiene
No
A/B
SPMA
urine
V
Benzene
No
A
3-HPMA
urine
V
Acrolein
No
A
CoHb
blood
V
CO
No
A
Acetonitrile
blood
V
Acetonitrile
No
B
Thiocyonate
blood
V
Hydrogen cyanide
No
C
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late
10
Field Work – Compliance Checks
Smoking and non-smoking compliance checked at
screening visit (limits for salivary cotinine & COHb)
and cross-checked against biomarker data.
Health check at screening visit, e. g. for
- bmi limits, pregnancy of female smokers, ..
Tar group compliance:
Brand identification
by consistency check of
brand reported,
pack-shot and
smoked cigarette filter study
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Field Work
CO in exhaled breath
analyser
Butt collection for
smoked filter analysis
Consumption data
from diary cards
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Results
Biomarker data were analysed using a fixed effects
analysis of variance (ANOVA) and a mixed model
ANOVA.
No significant differences were found for demographic
criteria, e.g. age and gender.
To present
• Biomarker levels in smokers and non-smokers
• Differences between tar groups
• Compare results with published data
• Nicotine yield in-use from filter analysis
and compare with nicotine uptake
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13
Results – Marker levels in smokers & non-smokers
unit
all smokers
(826)
non-smokers
Nic + 5 metabolites (ur. ex.)
mg eq nic./mmol
creatinine
1.43
0.0032
447
NNAL (urinary excretion)
ng/mmol creat.
22.6
not calc.
102<LOQ
(>20)
Acetonitrile (blood conc.)
ng/ml
80.6
4.0
20,2
SPMA (urinary excretion)
ng/mmol creat.
268
24
11,2
COHb (blood saturation)
(%)
4.14
1.16
3,6
HPMA (urinary excretion)
µg/mmol creat.
192
61
3,1
Thiocyanate (serum conc.)
µmol/l
116
39
3,0
Butadiene equiv. calculated
µg/mmol creat.
79
32
2,5
biomarker
ratio
DHBMA (urin. ex.) µg/mmol creat.
345
133
2,6
MHBMA (urin. ex.) µg/mmol creat.
19
14
1,4
1-OHP (urinary excretion)
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(103)
ng/mmol creat.
42
20
2,1
14
SSPT 51
Co
ns
Hb
HP
tio
n
1-O
ien
-E
q.
ya
na
te
HP
MA
CO
um
p
Bu
t ad
Th
ioc
ni t
ri le
AL
SP
MA
Ac
eto
NN
Ni
c+
5
Results – Comparison of tar groups
100%
50%
A
B
C
NS
0%
15
Results – Comparison with published data
Demographic data
unit
Mean age
years
Gender split
% m/f
UK
this study
GER
*)
34.3
29.3
57/43
37/63
Number of study subjects per group (ISO smoke yields)
Group A
8 – 10 mg tar
581
0.7 – 1.0 mg N
Group B
5 – 7 mg tar
78
207
0.5 – 0.6 mg N
Group C
1 – 4 mg tar
0.1 – 0.4 mg N
122
38
66
*) G. Scherer et al., „Relationship between machine-derived
smoke yields and biomarkers in cigarette smokers in Germany“,
Regulatory Toxicology and Pharmacology 47 (2007), 171-183
Results – Comparison with published data
marker
unit
group
UK (this study)
GER *)
NS
A
B
C
NS
A
B
C
-
15
12
18
-
13.4
11.7
16.8
3.8
28.9
21.7
19.0
2.6
17.1
15.9
20.7
consumption
/d
CO ex
ppm
Nic+5**
mg
-
14.3
8.7
9.7
-
11.5
10.6
12.8
NNAL**
µg
-
0.22
0.17
0.12
-
0.37
0.27
0.14
HPMA**
mg
0.71
1.80
1.36
1.41
0.34
1.33
1.32
0.94
SPMA**
µg
0.32
2.54
1.80
2.83
0.29
2.00
1.90
1.55
1-OHP**
µg
0.24
0.40
0.33
0.33
0.10
0.21
0.19
0.17
*) A.a.O.
**) total in 24h-urine
Results – Comparison with published data
Nicotine excretion per cigarette
UK*
GER**
Group A
0,95 mg
0,86 mg
Group B
0,73 mg
0,91 mg
Group C
0,54 mg
0,76 mg
Nicotine +5 metabolites in 24h-urine / daily consumption
*) this study: mean of ratios
**) A.a.O.: ratios of means
Results – Filter analysis
Smoked filter tips
collected from all
smokers
A
B
C
all
1,4
1,2
1,14
1,05
1
Calibration curves from
top 14 brands:
group A: 7, B: 5, C: 2,
0,85
0,8
0,78
0,6
> 75% of UK market,
mean calibration curves
0,4
used for each group
0,2
0
mg nic/cig.
mg nicotine / cigarette
Results – Comparison of mouth level nicotine
exposure from filter analysis with 24h-urine
nic+5 markers per cigarette
1,20
‘intake’
‘uptake’
1,00
84%
0,80
83%
85%
0,60
0,40
70%
A
B
C
all
0,20
0,00
filter analysis
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nic. marker
20
Background – Nicotine Exposure
tobacco/product
smoking
topography
tobacco
content
~2%
+
design
properties
e.g.
filter
retention
smoke
man (human intake & uptake)
human smoke yield
per cigarette (mg)
A: 1.14
B: 0.85
C: 0.78
all: 1.05
daily
cons.
A: 15
B: 12
C: 18
all:14
?
Design
model
machine smoke yield
per cigarette
mean of top 14 brands
ISO: 0.8
Intense: 2.2
?
daily
intake
(mg)
A: 17.0
B: 10.2
C: 14.1
All: 15.2
uptake
factor
A:0.84
B:0.85
C:0.70
all:0.83
daily
uptake
(mg
nic eq.)
A: 14.3
B: 8.7
C: 9.7
all: 12.6
Conclusions
• Clear differences between smokers and
non-smokers for most markers.
• No significant demographic differences,
e.g. gender and age.
• Ranking of marker levels in tar groups: A > C > B;
daily consumption C > A > B.
• Nicotine uptake correlates with smoke yields
from smoked filter analysis.
• Smoke exposure is higher in comparison to ISO smoke yields
but significantly lower than indicated by intense yields.
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Acknowledgments
I would like to thank my co-authors and our partners from Covance and
ABF for their contributions and fruitful discussions.
Thank you for your
attention
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