Transcript Comparison of biomarkers for use in assessing smoke

Biological monitoring of
exposure to woodsmoke
Christopher Simpson, Ph.D.
Department of Environmental and Occupational
Health Sciences
University of Washington, Seattle
For presentation at the Georgia Air Quality and Climate Summit: May 7, 2008
Outline
• Rationale for methoxyphenols as a
biomarker of woodsmoke exposure
• Biomonitoring of woodsmoke exposure
– Managed exposure study
– Wildland firefighter exposure study
• Conclusions and Future prospects
Exposure monitoring issues
• Biomass smoke exhibits significant spatial and
temporal variability
• Central monitoring may be a poor surrogate for
personal exposure
• Traditional personal exposure monitoring
(pumps and filters) may be too expensive, or
impractical for some populations
• A biomarker approach may provide a better
measure of personal exposure than traditional
monitors.
Selected markers for biomass combustion
Relative proportions of MPs, vary depending on type of wood
OH
HO
OH
OH
OO
OC H 3
HO
OH
O CH 3
OH
O CH 3
OH
O CH 3
OCH 3
OCH3
OH
Guaiacol
Levoglucosan
Methylgua iacol
OH
H3 C O
OCH 3
Ethylguaiacol
Propylguaiacol Eugenol
OH
H 3 CO
OH
H3 CO
OH
OH
OCH 3
H3 CO
O CH 3
cis-Isoeugenol
H3 CO
OCH 3
OCH 3
OH
OCH3
Syringol
Ethylsyringol
Methylsyringol
OH
O CH 3
Propylsyringol
OH
H3 CO
O
Acetosyringone
OH
OCH 3
OH
OCH 3
OH
O CH 3
OH
O
Sinapylaldehyde
OH
H3 CO
OCH 3
OCH3
O
O
Coniferylaldehyde
O
Propylsyringone
O CH 3
H3 CO
O
Acetovanillone
H 3 CO
O
Vanillin
Allylsyringol
O
Syringaldehy d e
G uaiacylaceton e
Methoxyphenols as biomarkers of
woodsmoke
• Unique to woodsmoke
– Derived from lignin pyrolysis
• Abundant in woodsmoke
– 2.5 % relative to PM, 2500 mg/kg
• Readily excreted in urine
– minimal phase 1 metabolism for LMWT
compounds
• Rapid urinary elimination (t1/2 ~2-6 hr)
I. ‘Campfire’ exposures
Study design
• Nine healthy subjects
• 2 hour managed exposure to mixed hardwood
and softwood smoke
• Personal monitoring of integrated PM2.5, LG,
MPs (filter samples)
• Real-time monitoring of PM and CO on one
subject
• Collect serial urine samples for 72 hours
centered on exposure
• Dietary restrictions imposed
I. ‘Campfire’ exposures
3000
2500
2000
1500
1000
500
0
1
2
3
4
5
6
Subject #
2 hr TWA values
7
8
9
Excretion rates for syringol and
guaiacol
1.2
syringol
1
Excetion rate (µg/min) Normalized
Excretion Rate (µg/min) Normalized
1.2
0.8
0.6
0.4
0.2
0
-40
-20
0
20
-0.2
hours post exposure
40
1
guaiacol
0.8
0.6
0.4
0.2
60
0
-40
-20
0
20
hours post exposure
40
60
Dose-response for methoxyphenol
biomarker
Biomarker is sum of 12-hr average creatinine adjusted urinary concentration for 5
methoxyphenols that showed maximum response to woodsmoke exposure
Conclusions from managed
exposure study
• Urinary concentrations of multiple syringyls and
guaiacols increased after acute (2hr) exposure
to woodsmoke.
• T1/2 for urinary excretion 2-6 hrs
• Biomarker levels increased proportionately with
exposure
– exposure to LG explained ~80% of variability in
urinary biomarker
• Threshold to detect exposure event ~600 g/m3
III. Wildland firefighter study
Study data
• 20 shifts worked by 13 firefighters
– Part of dataset collected by UGA, CDC
– Chosen to cover range of PM2.5 exposures
• Personal TWA levels of CO, PM2.5, LG
– CO measured via datalogging monitor
– PM2.5, LG measured from single filter
– Qxr re: smoked/grilled foods, smoking
• Pre- /post-shift urinary measures
PM2.5, CO, and LG correlations
7
160
140
120
100
80
60
40
20
0
7
Pearson r =0.077
p = 0.0006
6
CO concentration (ppm)
Spearman rho =0.002
p = 0.99
CO concentration (ppm)
LG concentration (ug/m
3
)
180
5
4
3
2
1
0
0
500
1000
1500
PM2.5 concentration (ug/m3)
2000
Spearman rho -0.27
p = 0.41
6
5
4
3
2
1
0
0
50
100
150
200
0
LG concentration (ug/m3)
500
1000
1500
2000
PM2.5 concentration (ug/m 3)
Full-shift exposure data only (n=11)
Pearson correlations for LG and CO; Spearman for PM
Significant creatinine-adjusted
urinary MP correlations
• Four guaiacol-type MPs
– Guaiacol, methylguaiacol, ethylguaiacol and
propylguaiacol (Pearson r >0.6, p<0.01)
• Three syringol-type MPs
– Syringol, methylsyringol, and ethylsyringol
(Pearson r >0.6, p<0.01)
• Levels for these MPs combined into
summed guaiacol and syringol variables
– For summed variables only, ND values
assigned method LOD/2 and used
CO vs. change in creatinine-adjusted
summed guaiacols
CO (ppm) vs Summed Guaiacolsg/mg
(
creatinine)
Cross-Shift difference in summed
guaiacol concentration (mg/mg
creatinine)
10
y = 0.283x - 0.051
p = 0.002
r^2 = 0.63
8
6
4
2
0
-2
0
2
4
6
8
CO concentration (ppm)
10
12
Conclusions: exposure
measurements
• LG and PM2.5 significantly correlated
• LG and CO significantly correlated
• PM2.5 and CO not correlated
– Literature generally shows strong correlation
between PM2.5 and CO for firefighters
– Lack of correlation in our study possibly due
to small sample size
Conclusions: urinary MPs vs.
exposures
• Cross-shift urinary MPs
– Significant changes in 14 of 22 urinary MPs
• Exposures. vs. MPs
– Individual and summed creatinine-adjusted guaiacols
highly associated with CO levels
(softwoods predominant tree species in this forest)
– Smaller association with LG; none with PM2.5
– In regression models, LG and CO exposures explain
up to 80% the variance in urinary MP concentrations
Overall evaluation of urinary
methoxyphenols as biomarkers of
woodsmoke exposure
• Urinary MPs were associated with woodsmoke
exposures in 3 studies where exposure to
woodsmoke were high
– They were not associated with low woodsmoke
exposures in Seattle!
• Dietary confounding and baseline variability limit
application of this biomarker to high exposure
situations
– Questionnaires useful to identify confounding
– In acute exposure situations calculate changes in
biomarker levels to reduce importance of baseline
variability
Woodsmoke exposure biomarkers: next
steps
• Further research required to:
– Quantify the influence of fuel type and
combustion conditions on biomarker response
– Evaluate population heterogeneity in
woodsmoke exposure-biomarker response
relationship
Acknowledgements
UW researchers
Collaborators
David Kalman, PhD
Russell Dills, PhD
Michael Paulsen
Sally Liu, PhD
Jacqui Ahmad
Rick Neitzel
Meagan Yoshimoto
Elizabeth Grey
Bethany Katz
Kirk Smith, PhD (UCB)
Michael Clarke (UCB)
Luke Naeher, PhD (UGA)
Alison Stock (CDC)
Dana Barr (CDC)
Kevin Dunn (CDC)
USFS Savannah River Site
Funding
USEPA, NIOSH