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Oxidative damage to dna and lipids as biomarkers of exposure to air pollution.

BACKGROUND: Air pollution is thought to exert health effects through oxidative stress, which causes damage to DNA and lipids.

OBJECTIVE: We determined whether levels of oxidatively damaged DNA and lipid peroxidation products in cells or bodily fluids from humans are useful biomarkers of biologically effective dose in studies of the health effects of exposure to particulate matter (PM) from combustion processes.

DATA SOURCES: We identified publications that reported estimated associations between environmental exposure to PM and oxidative damage to DNA and lipids in PubMed and EMBASE. We also identified publications from reference lists and articles cited in the Web of Science.

DATA EXTRACTION: For each study, we obtained information on the estimated effect size to calculate the standardized mean difference (unitless) and determined the potential for errors in exposure assessment and analysis of each of the biomarkers, for total and stratified formal meta-analyses.

DATA SYNTHESIS: In the meta-analysis, the standardized mean differences (95% confidence interval) between exposed and unexposed subjects for oxidized DNA and lipids were 0.53 (0.29-0.76) and 0.73 (0.18-1.28) in blood and 0.52 (0.22-0.82) and 0.49 (0.01-0.97) in urine, respectively. The standardized mean difference for oxidized lipids was 0.64 (0.07-1.21) in the airways. Restricting analyses to studies unlikely to have substantial biomarker or exposure measurement error, studies likely to have biomarker and/or exposure error, or studies likely to have both sources of error resulted in standardized mean differences of 0.55 (0.19-0.90), 0.66 (0.37-0.95), and 0.65 (0.34-0.96), respectively.

CONCLUSIONS: Exposure to combustion particles is consistenly associated with oxidatively damaged DNA and lipids in humans, suggesting that it is possible to use these measurements as biomarkers of biologically effective dose.

KEY WORDS: biomarker, DNA damage, lipid peroxidation products, oxidative stress, particulate matter. Environ Health Perspect 118:1126-1136 (2010). doi:10.1289/ehp.0901725 [Online 27 April 2010]


Exposure to particulate matter (PM) from combustion processes contributes substantially to cardiovascular and pulmonary ill health and premature mortality globally (Brook 2008; Salvi and Barnes 2009). PM represents highly complex mixtures with large variations in size, chemical composition, shape, surface, reactivity, and charge, in both time and space, due to variable sources, atmospheric chemical reactions, and meteorological conditions (Sioutas et al. 2005). Nevertheless, for exposure assessment for epidemiological association with health outcomes and regulation, PM is usually only considered as mass defined by size cutoff at 2.5 and 10 [micro]m in aerodynamic diameter ([PM.sub.2.5] and [PM.sub.10], respectively). These measures are often little affected by ultrafine particles (UFPs) from, for example, diesel engine emission because of their low mass, although UFPs are thought to have important health effects due to their high alveolar deposition, small size ([less than or equal to] 0.1 [micro]m in aerodynamic diameter), large surface area, and potential to translocate (Delfino et al. 2005). Moreover, in epidemiological studies, exposure levels are often assigned crudely and groupwise according to air monitoring data and sources near the residence, because the modest individual risks require large numbers and long observation times to assess. Personal exposure can be assessed by portable monitors or carefully registered time--activity patterns in well-defined microenvironments of exposure in small numbers of subjects (Zou et al. 2009). However, the internal dose of PM also depends on breathing patterns and airway deposition, and cardiovascular and other systemic effects of PM require further translocation of PM constituents or signaling molecules or cells from the airways (Mills et al. 2009). Oxidative stress with inflammation is thought to be central in the mechanisms of action for both the pulmonary and extrapulmonary health effects of PM (Mills et al. 2009; Risom et al. 2005). Thus, biomarkers of oxidative stress should serve as proxy measures of the true internal exposure to PM to compare potential health impacts of different sources in both small controlled exposure settings and large population approaches. Oxidative modification of DNA and lipids are particularly relevant for cancer and cardiovascular disease where oxidative stress in the circulation is important (Mills et al 2009; Risom et al. 2005). Experimental studies in animals and cell cultures have consistently shown that combustion-related PM induces oxidative stress and DNA damage in relevant organs and cells (Moller et al. 2008a, 2010). In our experience the effects on biomarkers of oxidized DNA and lipids are observed within a lag period < 24 hr after the exposure to PM.

A number of studies of PM exposure in humans have applied biomarkers of oxidative damage to DNA and lipids in the blood compartment or in terms of products excreted in urine or exhaled breath condensate (EBC), as outlined in Table 1. The biomarkers of oxidatively damaged DNA include 8-oxo-7,8-dihydroguanine (8-oxo-Gua) or the corresponding deoxynucleoside 8-oxo-7,8-dihydro-2' -deoxyguanosine (8-OX-odG) measured in DNA and urine, the exo-cyclic [M.sub.1] adduct to guanine ([M.sub.1]dG), and lesions detected as sites in DNA sensitive to formamidopyrimidine DNA glycosylase (FPG) and endonuclease III (ENDOIII). The biomarkers of lipid peroxidation (LPO) products include conjugated dienes (CDs), lipid hydroperoxides, malondialdehyde (MDA), thiobarbituric acid-reactive substances (TBARS), and [F.sub.2]-isoprostanes measured in EBC, plasma, serum, or urine. However, a systematic approach is required to evaluate the validity of their use as biomarkers of biological effective dose in this context. We undertook a systematic review of the published studies to assess the extent and consistency of associations between exposure to combustion-related PM and the biomarkers of oxidative damage to DNA and lipids.
Table 1. Summary of biomarkers of oxidatively damaged DNA, nucleobases,
and LPO products used in studies of the effect of combustion particles.

Oxidatively damaged DNA or nucleobases

Biomolecule             Description                  Assays

ENDOIII/FPG             DNA base lesions detected    Comet assay
                        by bacterial ENDOIII or FPG
                        enzymes, representing
                        mainly oxidized purine
                        (including 8-oxodG) and
                        pyrimidine lesions,

8-oxodG                 Major oxidation product in   HPLC-ECD,
                        nuclear DNA; detection of    LC-MS/MS,
                        8-oxodG in urine or plasma   antibodies
                        mainly originates from
                        oxidation of deoxyguanosine
                        triphosphate in the
                        nucleotide pool

8-oxoGua                Major oxidation product in   HPLC-ECD,
                        nuclear DNA; detection of    LC-MS/MS,
                        8-oxoGua in urine or plasma  antibodies
                        is likely to arise from
                        cleavage of the oxidized
                        base from DNA by repair
                        enzymes (e.g., OGG1)

[M.sub.1]dG             Exocyclic DNA damage formed  LC-MS, antibodies
                        by reactive carbonyl
                        compounds released from
                        oxidized lipids

LPO products CDs        Breakdown products of fatty  Spectrophotometry
                        acids considered to
                        represent an early stage of
                        the LPO process

Lipid hydroperoxides    Reaction product between     Spectrophotometry
MDA/TBARS               [O.sub.2] and carbon         Spectrophotometry
                        radical in lipids Breakdown
                        carbonyl product of LPO;
                        the reaction with
                        thiobarbituric acid forms
                        adducts that can be
                        detected by
                        prepurification of urine or
                        plasma before the reaction
                        with thiobarbituric acid
                        can be considered as a
                        specific measurement of LPO
                        products, whereas the
                        simple TBARS assay is
                        highly unspecific

[F.sub.2]-isoprostanes  Products that arise mainly   GC-MS, LC-MS/MS,
                        from oxidation of            antibodies
                        arachidonic acid in
                        phospholipids, often
                        referred to as
                        8-iso-[PGF.sub.2[alpha]] or

Abbreviations: 8-oxodG, 8-oxo-7,8-dihydro-2'-deoxyguanosine; 8-oxoGua,
8-oxo-7,8-dihydroguanine; CDs, conjugated dienes; ECD, electrochemical
detection; ENDOIII, endonuclease III; FPG, formamidopyrimidine DNA
glycosylase; GC-MS, gas chromatography--mass spectrometry; HPLC,
high-performance liquid chromatography; LC-MS, liquid
chromatography--mass spectrometry; LC-MS/MS, liquid chromatography with
tandem mass spectrometry; LPO, lipid peroxidation; [M.sub.2]dG,
exocyclic [M.sub.1] adduct to guanine; MDA, malondialdehyde; OGG1,
8-oxoguanine DNA glycosylase; TBARS, thiobarbituric acid--reactive
substances. For descriptions and critical assessments of the assays as
biomarkers, see Griffiths et al. (2002) and Halliwell and Whiteman

Materials and Methods

Studies included in meta-analysis. The publications were identified by searches in the PubMed, EMBASE, and Web of Science databases, as well as reference lists in the identified papers [see Supplemental Material for the search strategy (doi:10.1289/ehp.0901725)]. We included studies that investigated measures of effects of environmental air pollution exposure. This encompassed studies of subjects who had been exposed at work to environmental air pollution (e.g., policemen exposed to traffic exhaust). We excluded studies on occupational exposures to air pollution, such as metal smelting or incineration, because these are characterized by exposure to other air pollution components than those found in urban air. We searched for publications with reported data on LPO products and oxidatively damaged DNA by means of the biomarkers outlined in Table 1 in airways, the blood compartment, and urine. Isolated leukocytes, lymphocytes, or mononuclear blood cells are all referred to as white blood cells (WBCs). We used the term "oxidized nucleobases" for the urinary excretion of 8-oxodG and 8-oxoGua because they are not measured in DNA. Tables 2-5 summarize details of the included studies. The results from some of the studies have been reported in multiple publications; we discuss these as studies rather than as individual publications because they originated from the same investigation.

We stratified the studies into three broad categories: controlled exposures, panel studies, and cross-sectional studies. Studies with controlled exposure to air pollution PM are the most robust type of design as either crossover studies or parallel groups of subjects exposed to air pollution constituents or filtered air. In panel studies, samples are collected from the same individuals at different times of the year in order to exploit contrasts in exposure due to temporal changes. This study design minimizes the influence of interindividual variation because the subjects are their own controls. However, the design is vulnerable to confounding because other factors such as diet and sunlight show temporal (e.g., seasonal) variation, which can affect the value of the biomarker as shown, for instance, for DNA damage in WBCs detected by the comet assay (Moller and Loft 2006; Moller et al. 2002). In addition, the quality of the panel study depends on the exposure characterization; personal exposure characterization shows a closer association with biomarker levels than does exposure assessed from stationary monitoring stations (Sorensen et al. 2003a, 2003b, 2003c; Vinzents et al. 2005). Cross-sectional studies have a less controlled design than do the panel studies because the exposure gradient is obtained by collecting samples from subjects from different geographical areas or occupations. The cross-sectional studies can have optimal exposure characterization, but confounding can be a problem because individual factors such as lifestyle, including diet, may influence the biomarker and covary with the exposure. This problem typically arises, for instance, when policemen and office personal or subjects from rural and urban areas are being compared.

We critically analyzed the studies with special focus on suboptimal exposure assessment and the quality of biomarkers. We referred to these problems as potential measurement error in the exposure assessment and biomarkers because they can be regarded as systematic errors that may affect the validity. Figure 1 outlines the relationship between the measurement error in exposure assessment and biomarker. We should emphasize that specific studies included in our analysis may have other sources of bias, including selection bias and small numbers of observations, which can affect the estimated effect size in a particular study.

Measurement error in exposure assessment. The primary exposure assessment in our analysis is the mass concentration of particles as [PM.sub.2.5] or [PM.sub.10] or the number concentration of UFPs. The exposure characterization encompasses data obtained from stationary monitor stations and personal monitors. We regarded personal exposure to PM as the optimal exposure assessment; for studies without PM measurements we regarded data on ambient gasses [nitrogen oxides ([NO.sub.x]), ozone ([O.sub.3]), or sulfur dioxide], polycyclic aromatic hydrocarbons (PAHs), and benzene as indirect estimates of PM exposure with greater potential for error. Similarly, the urinary excretion of metabolites of PAHs [e.g., 1-hydroxypyrene (1-HOP)] and benzene [S-phenylmercapturic acid (S-PMA) and trans, trans-muconic acid (tt-MA)] generated by their biotransformation are potentially biased estimates of the ambient concentration of PM, although they may be important biomarkers of internal dose of the parent compound. The measured PM showed the strongest association with the biomarkers of oxidized DNA and lipids in studies that measured the exposure as both PM and nitrogen dioxide ([NO.sub.2]), 1-HOP, S-PMA, and tt-MA (Avogbe et al. 2005; Brauner et al. 2007; Sorensen et al. 2003a, 2003b). We expect that the measurement error will be nondifferential, which usually biases effect estimates toward the null because they tend to obscure contrasts between the exposed and unexposed and those with or without the outcome of interest.

Measurement error related to biomarkers. The potential for biomarker measurement error originates from unspecific measurements or analytic flaws due to poor assay conditions. For instance, suboptimal assay procedures used to detect oxidatively damaged DNA may cause spurious oxidation that increases the apparent level of DNA damage. The level of 8-oxodG in DNA from unexposed mammals is approximately 1 lesion/[10.sup.6] dG (deoxyguanosine); the European Standards Committee on Oxidative DNA Damage (2003a, 2003b; Gedik et al. 2005) recommended that publications that report levels of 8-oxodG above a threshold of 5 lesions/[10.sup.6] dG should be interpreted with caution. The comet assay detects DNA damage by migration of DNA in agarose gels, and the end points are usually reported as extent of migration, although they can be transformed to lesions per unaltered nucleotides by calibration with ionizing radiation (Forchhammer et al. 2008; Moller et al. 2004). The level of oxidatively damaged DNA measured by the comet assay in WBCs of humans is < 1 lesion/[10.sup.6] nucleotides (Moller 2006). Oxidatively damaged DNA, nucleo-bases, and LPO products can be measured by antibody-based methods, but artificially high background levels can occur because of unspecific binding of the antibodies to other biomolecules (Halliwell and Whiteman 2004; Moller et al. 2008b). The simple assay of TBARS and CDs has been seriously criticized and is not recommended for in vivo detection of LPO products, whereas improved methods using high-performance liquid chromatography (HPLC) purification steps are more reliable assays of TBARS (Halliwell and Whiteman 2004). We classified bio-markers with suboptimal biochemical analysis as follows: (1) simple spectrophotometric measurement of TBARS without a prepurification step; (2) simple assays for CDs and lipid hydroperoxides, (3) levels of 8-oxodG exceeding a threshold of 5 lesions/[10.sup.6] dG in the unexposed group, and (4) detection of oxidatively damaged DNA, nucleobases, or lipids by antibody-based methods without prepurification steps. We expect that the biomarker measurement error will result in reduced effect estimates for both nonspecific biomarkers and assays having low sensitivity or a high limit of detection.

Assessment of estimated effect size. The studies differ considerably in design, and the results are reported in ways and units that preclude direct comparison of the estimated effect size in the studies. Thus, we have estimated the effect of exposure on biomarkers as standardized mean differences with 95% confidence intervals (CIs) between exposed subjects and referents in a random effects meta-analysis by means of Review Manager (RevMan; version 5.0; Nordic Cochrane Centre, Cochrane Collaboration 2008, Kobenhavn [empty set], Denmark). The standardized mean difference is the difference in means of groups divided by the pooled standard deviation (SD). It is a measurement of estimated effect, which can be used in a meta-analysis when all studies assess the same outcome (level of oxidized biomolecules in this analysis), but it is measured in a variety of ways with different scales. We assessed the estimated effect size in a random model meta-analysis because it incorporates heterogeneity among studies. The heterogeneity between studies was analyzed by tau squared, chi squared, and [I.sup.2] tests; tests for subgroup differences were carried out using the chi-square test. We obtained means, SDs, and the number of subjects from the studies, or we calculated these values from data reported in the publications (see Tables 2-5). The variance was reported in different ways in the original publications; thus, our estimates of 95% CIs may be biased, but the central estimates (means) should not be. We calculated the mean and SD from regression analyses for studies that modeled continuous data and defined the exposure gradient as equal to either the interquartile range (Kelishadi et al. 2009; Liu et al. 2009a, 2009b) or a 10 [micro]g/[m.sup.3] increase in PM (Liu et al. 2007). The interquartile range in [PM.sub.2.5] was in the same range as a 10-[micro]g/m(3) increase in [PM.sub.2.5] in the studies carried out in Windsor, Ontario, Canada (Liu et al. 2007, 2009a, 2009b), whereas the interquartile range (66.5 [mu]g/[m.sup.3] measured as [PM.sub.10]) measured by Kelishadi et al. (2009) in Iran was substantially higher, which could reflect different sources of exposure or PM fraction. We calculated overall means and SD for studies that included more than one group of exposed subjects or investigated the same subjects under different exposure scenarios (Avogbe et al. 2005; De Coster et al. 2008; Mills et al. 2008; Novotna et al. 2007; Rundell et al. 2008; Singh et al. 2007; Staessen et al. 2001; Sorensen et al. 2003a, 2003b). We assumed that the interquartile ranges would be equal to the SDs found in studies that used nonparametric analyses and reported variation as ranges (Chen et al. 2007; Romieu et al. 2008; Sorensen et al. 2003a, 2003b).


Oxidative damage reported in controlled exposure studies. Table 2 summarizes studies on the association between air pollution exposure and oxidized DNA and lipids in controlled exposure studies. The number of subjects in these studies is rather low (3-41 subjects; mean [+ or -] SD, 18 [+ or -] 12), which may be because carrying out controlled exposure on a large number of subjects is demanding. A study of air pollution exposure to UFPs in persons bicycling for approximately 90 min in a laboratory or on traffic-intense streets reported that the level of FPG sites in WBCs was associated with the number concentration of UFPs (Vinzents et al. 2005). A subsequent investigation by the same group had a similar correlation between particulate fractions with median particle sizes of 23 nm and 57 nm (consistent with semivolatile organic compounds from diesel exhaust and carbonaceous soot emissions into the air of a busy street) and the level of FPG sites in WBCs (Brauner et al. 2007). A subsequent study in elderly subjects showed a statistically nonsignificant decrease in urinary excretion of the [F.sub.2]-isoprostane 8-iso-prostaglandin-[F.sub.2] after a period of home air filtration (Brauner et al. 2008). In another study of elderly patients with coronary heart disease, Mills et al. (2008) reported that inhalation of concentrated air pollution particles (CAPs) was associated with increased concentration of 8-isoprostanes in EBC. This finding is in keeping with that found by Rundell et al. 2008) who observed that healthy young subjects had elevated level of MDA in EBC after intensive exercise at a location with high-traffic intensity compared with the same type of exercise at a location with less traffic.
Table 2. Summary of controlled exposure studies on exposure to air
pollution PM from combustion processes.

Biomarker               Subjects (n)   Sex,      Exposure assessment
                                       age,      (a)

8-oxodG (ELISA)         Subjects with  MF        [PM.sub.2.5], 4.8 and
[F.sub.2]-isoprostanes  metabolic      18-49     205 [micro]g/[m.sup.3]
(LC-MS/MS)              syndrome       years     NO, 38.6 and 1,516 ppb
                        exposed to     NS        [NO.sub.2],15.5 and
                        diesel                   25.5 ppb
                        exhaust or FA
                        for 2 hr

8-iso-[PGF.sub.2]       Healthy        MF        [PM.sub.2.5],261 and
(ELISA)                 subjects       20-56     14-27
8-oxodG, 8-oxoGua       exposed        years     [micro]g/[m.sup.3]
(HPLC/GC-MS)            to wood smoke  NS        UFPs, 137,500 and
FPG sites (comet)       in a                     5,950
MDA(HPLC-FD)            chamber for 4            particles/[cm.sup.3]
                        hr (13)                  [NO.sub.2], 10 and 8.5

FPG sites (comet)       Healthy        MF        Personal UFPs, 6,
                        subjects       20-40     169-15, 362
                        exposed        years     particles/[cm.sup.3]
                        in a chamber   NS        (non-FA), and 91-542
                        for 24 hr                particles/[cm.sup.3]
                        (29)                     (FA)
                                                 [NO.sub.x], 25.3 ppb
                                                 (non-FA), 28.3 ppb
                                                 (FA), 11.6 ppb (back
                                                 ground), and 59.5 ppb
                                                 (busy street)
                                                 [O.sub.3], 12.1 ppb
                                                 (non-FA), 4.3 ppb
                                                 (FA), 30.1 ppb (back
                                                 ground), and 19.5 ppb
                                                 (busy street)

8-iso-[PGF.sub.2]       Elderly        MF        Personal UFPs, 10,016
(ELISA)                 subjects       60-75     particles/[cm.sup.3]
                        exposed in     years     (non-FA) and 3,206
                        the homes      VS        particles/[cm.sup.3]
                        (41)                     (FA)
                                                 [PM.sub.2.5], 12.6
                                                 (non-FA) and 4.7
                                                 [NO.sub.2], 20.0
                                                 (non-FA) and 20.0

8-lsoprostane (ELISA)   Subjects with  M         UFPs, 99,400 and zero
                        stable         54        particles/[cm.sup.3]
                        coronary       [+ or -]  [NO.sub.x], 7.2 and
                        heart disease  2 and 59  6.3 ppb
                        (12) and       [+ or -]  [SO.sub.2], 0.13 and
                        controls (12)  2         0.13 ppb
                        exposed to     years     [O.sub.3], 5.0 and 6.0
                        CAPs for 2     NS        ppb

MDA (HPLC)              Subjects       M         Personal UFPs, 252,290
                        exercising in  21        and 7,382
                        location with  [+ or -]  particles/[cm.sup.3]
                        low and high   2
                        traffic        years
                        intensity      NS

8-oxoGua (HPLC-ECD)     Healthy        M         None (49,000 cars/12
                        subjects       22-25     hr)
                        exposed to     years
                        traffic        NS
                        exhaust at a
                        for 4 hr(3)

FPG sites (comet)       Subjects       MF        Personal UFPs (32,400
                        bicycling in   25        and 13,400
                        Copenhagen     [+ or -]  particles/[cm.sup.3])
                        (15)           3         [PM.sub.10], 23.5
                                       years     [micro]g/[m.sup.3]
                                       NS        (street) and 16.9
                                                 [NO.sub.2], 32.1 and
                                                 (street) and 11.3

Biomarker               Potential            Findings     Study
                        measurement error

8-oxodG (ELISA)         Biomarker            No           Allen et al.
[F.sub.2]-isoprostanes  (8-oxodG)            difference   2009 (b)
(LC-MS/MS)                                   in urinary

8-iso-[PGF.sub.2]       Biomarker            Increased    Barregard
(ELISA)                 (8-iso-[PGF.sub.2])  urinary      et al.
8-oxodG, 8-oxoGua                            excretion    2006,
(HPLC/GC-MS)                                 of           2008;
FPG sites (comet)                            8-iso-PGF    Danielsen
MDA(HPLC-FD)                                 and MDA      et al.2008
                                             levels in    (c)
                                             FPG sites
                                             (WBCs) and
                                             8-oxodG and

FPG sites (comet)       No                   Decreased    Brauner et
                                             levels in    al. 2007
                                             WBCs by
                                             exposure to

8-iso-[PGF.sub.2]       Biomarker            Unaltered    Brauner et
(ELISA)                                      urinary      al. 2008

8-lsoprostane (ELISA)   Biomarker            Increased    Mills et
                                             in EBC by    al. 2008

MDA (HPLC)              No                   Increased    Rundell et
                                             after        al. 2008 (d)
                                             exercise at
                                             with high

8-oxoGua (HPLC-ECD)     Exposure             Increased    Suzuki et
                                             urinary      al. 1995 (e)
                                             during the
                                             first 12 hr
                                             and 24 hr
                                             levels at
                                             36 and 48
                                             hr after

FPG sites (comet)       No                   Increased    Vinzents
                                             after        et al.
                                             cycling in   2005
                                             the traffic
                                             cycling in

Abbreviations: ECD, electrochemical detection; ELISA, enzyme-linked
immunosorbent assay; FA, filtered air; FD, fluorescence detection;
GC-MS, gas chromatography--mass spectrometry; LC-MS, liquid
chromatography--mass spectrometry; LC-MS/MS, liquid chromatography with
tandem mass spectrometry; M, male; MF, male and female; NO, nitric
oxide; NS, nonsmoker; iso-[PGF.sub.2], 8-iso-[PGF.sub.2],
8-iso-prostaglandin [F.sub.2]; [SO.sub.2], sulfur dioxide.

(a) The values represent exposure assessment in the high-exposure and
low-exposure group, respectively, unless stated otherwise by specific
footnotes. (b) We calculated the mean and SD from the mean difference
and 95% Cl assuming no missing data in the pair analysis. (c) We
calculated the net difference in MDA from preexposure values and
baseline-adjusted the data according to the level of MDA in the group
of subjects exposed to filtered air. The SD was calculated from 90% Cl.
(d) We used the mean level of MDA from the exercises at the locations
with low and high PM concentration. (e) The data correspond to the mean
of the whole exposure period (0-48 hr).

Controlled exposure to wood smoke containing very high mass concentration of particles has been associated with increased levels of LPO products in serum, urine, and EBC (Barregard et al. 2006, 2008). However, Danielsen et al. (2008) observed no association between exposure and EPG sites in WBCs and suggested that this result may be due to increased DNA repair activity of oxidized nucleobases because urinary excretion of 8-oxoGua and WBC expression levels of the 8-oxoguanine DNA glycosylase (OGGI) base excision repair enzyme, which removes 8-oxoGua from DNA, were increased after exposure to wood smoke but not after exposure of the same subjects to clean air. Increased urinary excretion of 8-oxoGua was also observed in a study where subjects were exposed to exhaust on a traffic-intense street for 4 hr (Suzuki et al. 1995), but 2 hr of exposure to a high concentration of diesel exhaust was not associated with urinary excretion of 8-oxodG or [F.sub.2]-isoprostanes in subjects with metabolic syndrome (Allen et al. 2009).

Oxidative damage reported in panel studies. Table 3 summarizes studies on the association between air pollution exposure and oxidized DNA and lipids in panel studies. These studies involved multiple measurements over time, and the number of subjects in these studies was higher than the number of subjects in controlled exposure studies (2-182 subjects; mean [+ or -] SD, 62 [+ or -] 59). Several panel studies showed that concurrent air pollution exposure was associated with elevated levels of LPO products in EBC (Liu et al. 2009a; Romieu et al. 2008) and plasma (Liu et al. 2007; Medina-Navarro et al. 1997), as well as elevated levels of 8-oxodG in plasma (Chuang et al. 2007). Subjects without doctor-diagnosed cardiovascular diseases or who did not take medication for diabetes showed an association between outdoor levels of [PM.sub.2.5] and TBARS in plasma, although the analysis of all subjects in the study only indicated statistically nonsignificant associations between personal exposure to [PM.sub.2.5] and TBARS or 8-isoprostanes in plasma (Liu et al. 2009b). Personal exposure to [PM.sub.2.5] was associated with increased levels of 8-oxodG in WBCs of students living in the center of Copenhagen, whereas the exposure was only associated with the MDA levels of women and not with the level of FPG sites in WBCs in any group (Sorensen et al. 2003a, 2003b). Interestingly, Sorensen et al. (2003a, 2003b) observed no correlation between 8-oxodG in WBCs and the background mass concentration of [PM.sub.2.5] measured at stationary monitoring stations, suggesting that a relatively clean urban air may provide too little contrast in the long-range transported fractions of PM to be a reliable indicator of traffic-generated exposure. Moreover, they found significant association between the biomarkers and personal exposure to [NO.sub.2] supporting the key role of PM. This is in keeping with observations from a controlled exposure study with constant [NO.sub.2] exposure, which showed a strong effect of change in PM exposure on DNA oxidation (Brauner et al. 2007).
Table 3. Summary of panel studies on exposure to air pollution PM from
combustion processes.

Biomarker        Subjects (n)   Sex,      Exposure          Potential
                                age,      assessment (a)    measurement
                                smoking                     error

8-oxodG (ELISA)  Students       MF        [PM.sub.2.5],     Biomarker
                 followed for   18-25     12.7-59.5         exposure
                 3 months       years     [mu]g/[m.sup.3]
                 (76)           NS        [PM.sub.10],
                                          2.8-39.4 ppb
                                          2.8-33.3 ppb
                                          22.5-48.3 ppb

TBARS (FD)       Subjects with  MF        Personal          Biomarker
                 diabetes       28-63     [PM.sub.10],
                 mellitus (25)  years     25.5(9.8-133)
                 followed for   NS        [mu]g/[m.sup.3]
                 7 weeks

TBARS (FD)       Asthmatics     MF        [PM.sub.2.5],     Biomarker
8-lsoprostanes   (182)          9-14      2.7-14.3          exposure
(immunoassay)    followed for   years     [mu]g/[m.sup.3]
                 4 weeks        NS        [SO.sub.2],
                                          1.3-13.8 ppb
                                          12.3-27.0 ppb
                                          7.5-21.0 ppm

TBARS (SPM)      Normal         MF        [PM.sub.2.5],     Biomarker
8-lsoprostanes   subjects       64-96     6.3 (0.9-16.6)
(ELISA)          living in      years     [mu]g/[m.sup.3]
                 Windsor,       NS        (personal
                 Ontario,                 exposure) and
                 Canada (29)              15.3 (10.4-24.2)
                 followed for             [mu]g/[m.sup.3]
                 maximally 50             (outdoor)

TBARS (SPM)      Medical        NR        [O.sub.3], 141    Biomarker
CDs (SPM)        doctors        27-32     ppb (no report    exposure
                 investigated   years     of the [O.sub.3]
                 1 or 16 weeks  NS        level in
                 after arrival            original
                 in Mexico                residence)
                 City (21)

MDA(FD)          Asthmatics     MF        [PM.sub.2.5],     Biomarker
                 (107)          10        27.4 (4.2-89.5)   exposure
                 followed for   [+ or -]  [mu]g/[m.sup.3]
                 2-16 weeks     2         [NO.sub.2], 35.3
                 (average 8     years     (13.9-73.5) ppb
                 weeks)         NS        [O.sub.3], 31.1
                                          (9.8-60.7) ppb

8-oxodG          Students       MF        Personal          No
(HPLC-ECD)       living in      20-33     [PM.sub.2.5],
FPG sites        Copenhagen,    years     16.1 (10-24.5)
(comet)          Denmark (50)   NS        [mu]g/[m.sup.3]
MDA (HPLC)       followed for             [PM.sub.2.5],
                 1 year                   9.2 (5.3-14.8

8-oxodG (ELISA)  Security       NR        [PM.sub.2.5],     Biomarker
                 guards         18-20     243 (199-460)
                 analyzed       years     [mu]g/[m.sup.3]
                 before and     NS
                 after a work
                 shift (2)
                 followed for
                 2 months

Biomarker        Findings                        Study

8-oxodG (ELISA)  Positive association            Chuang et al. 2007
                 between 8-oxodG in plasma
                 and [SO.sub.2] and [O.sub.3];
                 no association with
                 [PM.sub.2.5], [PM.sub.10],
                 and [NO.sub.2]

TBARS (FD)       Positive association            Liu et al. 2007 (b)
                 between [PM.sub.10] levels
                 and TBARS in plasma

TBARS (FD)       Positive association            Liu et al. 2009a (c)
8-lsoprostanes   between TBARS in EBC and
(immunoassay)    [SO.sub.2], [NO.sub.2] and
                 [PM.sub.2.5], but not with
                 [O.sub.3]; the concentration
                 of 8-isoprostanes in EBC was
                 only associated with
                 [SO.sub.2] concentration

TBARS (SPM)      No association with             Liu et al.
8-lsoprostanes   personal [PM.sub.2.5]           2009b (d)
(ELISA)          exposure and LPO products
                 in plasma; an association
                 with outdoor [PM.sub.2.5]
                 and TBARS in a subset of
                 subjects without
                 cardiovascular disease or
                 who did not take diabetic

TBARS (SPM)      Increased TBARS in serum        Medina-Navarro
CDs (SPM)        after the first week of         et al. 1997 (e)
                 of the stay, normalized
                 in samples obtained
                 16 weeks after

MDA(FD)          Positive association            Romieu et al. 2008 (f)
                 between ambient
                 [PM.sub.2.5] levels
                 and MDA in EBC

8-oxodG          Correlation between             Sorensen et al. 2003a,
(HPLC-ECD)       personal exposure to            2003b (g)
FPG sites        [PM.sub.2.5] and 8-oxodG
(comet)          in WBCs and MDA in plasma
MDA (HPLC)       (only women); no correlation
                 between [PM.sub.2.5] and
                 FPG sites in WBCs or 24-hr
                 urinary excretion of 8-oxodG;
                 no correlation between
                 biomarkers and stationary
                 (urban background)
                 measurements of

8-oxodG (ELISA)  Increased in urine after        Wei et al. 2009 (h)
                 the work shift

Abbreviations: ECD, electrochemical detection; ELISA, enzyme-linked
immunosorbent assay; FD, fluorescence detection; M, male; MF, male and
female; NR, not reported; NS, non-smoker; [SO.sub.2], sulfur dioxide;
SPM, spectrophotometry.

(a) The values represent exposure assessment in the high-exposure and
low-exposure group, respectively, unless stated otherwise by specific
footnotes. (b) We calculated means and SD from the regression analysis
in the study, based on 10-[mu]g/[m.sup.3] increase in [PM.sub.10] and a
coefficient of variation of 100%. (c) We calculated the mean level of
LPO from TBARS and 8-isoprostanes, and the SD from the lower 95% Cl
assuming that it is similar to the 5th percentile and the coefficient
of variation is the same in the exposed and reference group. (d) We
calculated data from the regression model reported in the original
publication. The data correspond to the difference in LPO products
from the interquartile range in personal [PM.sub.2.5] exposure. We
calculated the SD from the coefficient of variation of data reported
as 5th and 95th percentile, assuming that it is equivalent to 95% Cl,
and the mean level of LPO products from data of TBARS and
8-isoprostanes. (e) We calculated the mean level of LPO products from
TBARS and CDs. (f) We calculated data (means) by regression analysis
assuming that the SD is the same as the interquartile range. (g) We
assumed that the SD and interquartile is the same value for the
analysis of 8-oxodG and calculated the mean level of ENDOIII/FPG sites.
(h) The study encompassed samples from two subjects analyzed on 29
working days.

Oxidative damage reported in cross-sectional studies. The design of the cross-sectional studies can be grouped into two main categories. The first category is characterized by studies that achieved the exposure contrast by studying subjects in occupations with different ambient air pollution levels (Table 4). The other category is characterized by studies of subjects, often with comparable occupations or ages, from geographical areas with different ambient air pollution levels (Table 5). The cross-sectional studies have generally included more subjects than the controlled exposure studies and panel studies. The number of subjects in the cross-sectional studies on different occupations has been in the range of 31-356 subjects (mean [+ or -] SD = 109 [+ or -] 95 subjects), whereas the studies that have contrasted exposure in different geographical areas have used even higher number of subjects (43-894 subjects; mean [+ or -] SD = 222 [+ or -] 234).
Table 4. Summary of cross-sectional studies on exposure to air
pollution PM from combustion processes in humans in different

Biomarker                   Subjects     Sex, age,  Exposure assessment
                            (n)          smoking    (a)

8-oxodG (HPLC-ECD)          Bus drivers  MF         1-HOP (urine)
MDA (HPLC)                  (107)        27-60

Lipid hydroperoxides (SPM)  Traffic      M          None
                            officers     38-52
                            and          years
                            controls     S/NS

FPG sites (comet)           Airport      NR         Stationary sampling
                            personnel    43         of PAHs
                            (41)and      [+ or -]   Urinary 1-HOP
                            controls     9          (urine)
                            (31)         years

8-oxodG (ELISA)             Taxi         M          1-HOP (urine)
                            drivers      40
                            (95) and     [+ or -]
                            controls     4 and 44
                            (75)         [+ or -]

8-oxodG (HPLC-ECD)          Taxi-motor   M          Ambient (stationary)
                            drivers and  36         concentration of
                            rural        [+ or -]   PAHs and benzene
                            controls     5          Urinary excretion of
                            (41)         years      S-PMA and 1-HOP

8-oxodG (ELISA)             Highway      F          Traffic intensity
                            toll         26         and urinary 1-HOP
                            workers and  [+ or -]   glucuronide
                            controls     6 and 27   excretion
                            (74)         [+ or -]

ENDOIII/FPG sites (comet)   Policemen    M          [PM.sub.2.5]
                            from         31 and 35  (stationary
                            Prague,      years      monitoring data, 33
                            Czech        (median)   [+ or -] 40 and 15
                            Republic     NS         [+ or -] 9
                            (65)                    [mu]g/[m.sup.3])
                                                    PAHs (personal
                                                    8.5 [+ or -]
                                                    9 and 3.0
                                                    [+ or -] 3.4

FPG sites (comet)           Subjects     MF         None
                            exposed to   35-64
                            traffic      years
                            (44) and     S/NS

8-oxodG (ELISA)             Bus drivers  M          [PM.sub.2.5] and
15-[F.sub.2t]-isoprostanes  (50) and     50         [PM.sub.10]
(immunoassay)               controls     [+ or -]   (stationary
                            (50)         10 and 51  monitoring station)
                                         [+ or -]   and PAHs (personal
                                         11         exposure)
                                         years      [PM.sub.2.5], 32.1
                                         NS         [+ or -] 8.1 and
                                                    20.9 [+ or -] 6.8
                                                    [PM.sub.10], 38.6 [+
                                                    or -] 8.2 and 24.1
                                                    []+ or -] 6.5

8-oxodG (LC-MS/MS)          Policemen,   M          Concentration of
[M.sub.1]dG (immunoslot     bus          34.1       PAHs in personal
blot)                       drivers,     [+ or -]   [PM.sub.2.5]
                            and          9          samples
                            controls     years
                            (356)        S/NS

Biomarker                   Potential    Findings

8-oxodG (HPLC-ECD)          Exposure     Bus drivers in the
MDA (HPLC)                               city center had
                                         higher levels of
                                         urinary 8-oxodG
                                         excretion than did
                                         bus drivers from
                                         the rural/suburban
                                         area; no clear
                                         differences between
                                         urinary excretions
                                         on working days and
                                         on days off
                                         observed; unaltered
                                         MDA in plasma
                                         between bus drivers
                                         in the city center
                                         and rural/suburban

Lipid hydroperoxides (SPM)  Biomarker    No difference
                            exposure     between exposed and

FPG sites (comet)           Exposure     Higher level in
                                         WBCs of exposed

8-oxodG (ELISA)             Biomarker    Highest level in
                            exposure     urine of exposed

8-oxodG (HPLC-ECD)          Biomarker    Highest level in
                            exposure     WBCs of exposed
                                         subjects (high
                                         background level of
                                         8-oxodG, 11.1

8-oxodG (ELISA)             Biomarker    Highest level in
                            exposure     urine of exposed

ENDOIII/FPG sites (comet)   Exposure     Highest level in
                                         WBCs of exposed
                                         subjects; positive
                                         correlation between
                                         PAH exposure and
                                         DNA damage in
                                         samples collected
                                         in January

FPG sites (comet)           Exposure     Statistically
                                         higher level in
                                         WBCs of exposed

8-oxodG (ELISA)             Biomarker    Highest levels in
15-[F.sub.2t]-isoprostanes  exposure     urine of exposed
(immunoassay)                            subjects

8-oxodG (LC-MS/MS)          Biomarker    Policemen in
[M.sub.1]dG (immunoslot     exposure     Kosice, Slovakia,
blot)                                    had higher levels
                                         of 8-oxodG in WBCs
                                         than did controls;
                                         no effect in
                                         policemen from
                                         Prague; 8-oxodG
                                         levels were very
                                         high (i.e., 53.6
                                         corresponding to
                                         dG); significantly
                                         higher levels of
                                         [M.sub.1]dG in
                                         exposed subjects in

Biomarker                   Study

8-oxodG (HPLC-ECD)          Autrup et al. 1999, Loft et al. 1999 (b)

Lipid hydroperoxides (SPM)  Bonina et al. 2008 (c)

FPG sites (comet)           Cavallo et al. 2006 (d)

8-oxodG (ELISA)             Chuang et al. 2003

8-oxodG (HPLC-ECD)          Ayi Fanou et al. 2006

8-oxodG (ELISA)             Lai et al. 2005

ENDOIII/FPG sites (comet)   Novotna et al. 2007 (e)

FPG sites (comet)           Palli et al. 2009

8-oxodG (ELISA)             Rossner et al. 2007, 2008a, 2008b (f)

8-oxodG (LC-MS/MS)          Singh et al. 2007
[M.sub.1]dG (immunoslot

Abbreviations: ECD, electrochemical detection; ELISA, enzyme-linked
immunosorbent assay; F, female; LC-MS, liquid chromatography--mass
spectrometry; LC-MS/MS, liquid chromatography with tandem mass
spectrometry; M, male; MF, male and female; NR, not reported; NS,
nonsmoker; S, smoker; SPM, spectrophotometry.

(a) The values represent exposure assessment in the high-exposure and
low-exposure group, respectively, unless stated otherwise by specific
footnotes. (b) We used data from bus drivers on working days. (c) We
pooled means and SD from smokers and nonsmokers of controls and
traffic officers at the sampling before the intervention with
phytochemicals (day 0). (d) The publication reports the mean level DNA
damage without indication of the SD, whereas later studies by the same
group showed a coefficient of variation of 40%. (e) The data represent
the variation between sampling in January and September. The personal
exposure to PAHs in the exposed and control group was 6.0 and 4.5
ng/[m.sup.3], respectively. The study had sampling of [PM.sub.2.5]
and [PM.sub.10] by personal monitors, but the low amount of material
precluded the assessment of individual exposure.

Table 5. Summary of cross-sectional studies on exposure to air
pollution PM from combustion processes in different areas.

Biomarker               Subjects (n)     Sex, age, smoking

FPG sites (comet)       Taxi-motor       M 34 [+ or -] 10 years
                        drivers, people  NS
                        near busy roads
                        and rural
                        controls (135)

8-oxodG (HPLC-ECD)      Children living  M 9-13 years NS
                        in rural and
                        urban area

8-oxodG                 Children living  MF 6-13 years NR
(immunohistochemistry)  in a
                        area and Mexico
                        City (98)

8-iso-PGF(ELISA)        Subjects living  MF 18-22 years NS
                        in areas of
                        high and low

8-oxodG (ELISA)         Subjects living  MF 50-65 years S/NS
                        in Flanders,
                        Belgium (399)

TBARS (SPM) and CDs     Medical doctors  NR 17-32 years NS
(SPM)                   who lived in
                        (24) or who
                        recently moved
                        to (21) Mexico
                        City and
                        controls (17)

TBARS (SPM)             Subjects         F 31-63 years NS
                        exposed to
                        biomass smoke
                        (28) and
                        controls (15)

CDs MDA                 Children living  MF 10-18 years NR
                        in Isfahan,
                        Iran (374)

TBARS (SPM)             Subjects living  MF 34 [+ or -] 6 and 69
                        in rural (125)   [+ or -] 8 years NS
                        and urban (167)
                        areas of

8-oxodG (HPLC-ECD)      Subjects living  MF 17.2 [+ or -] 0.8
                        in a rural       years S/NS
                        village (100)
                        and two suburbs
                        of Antwerp,
                        Belgium (100)

8-oxodG (ELISA)         Children living  MF 6-11 years NS
                        in areas of low
                        and high air
                        exposure (894)

ENDOIII/FPG sites       Subjects living  MF 36.5 (27-46 years) S/NS
(comet)                 in Copenhagen,
8-oxodG (HPLC-ECD)      Denmark (40)

TBARS (SPM)             Children living  NR 12-15 years NR
                        in Pancevo
                        area) and
                        (village) in
                        Serbia (128)

Biomarker               Exposure assessment     Potential
                        (a)                     measurement

FPG sites (comet)       Ambient (stationary)    Exposure
                        sampling of UFPs
                        (201,691 and 6,961
                        (midday 1-hr
                        concentration in a
                        busy street
                        intersection and town
                        square in a rural
                        village, respectively)
                        and urinary excretion
                        of S-PMA

8-oxodG (HPLC-ECD)      Benzene (ambient        Exposure
                        monitoring and
                        personal exposure)

8-oxodG                 [O.sub.3] (stationary   Biomarker
(immunohistochemistry)  monitoring data)        exposure

8-iso-PGF(ELISA)        [PM.sub.10], 42.3       Biomarker
                        (25.7-67.9) and 25.6    exposure
                        (17.8-28.6) ppb
                        [NO.sub.2], 39.7
                        (8.3-49.9) and 21.6
                        (11.4-29.6) ppb
                        [O.sub.3], 42.9
                        (28.5-65.3) and 26.9
                        (17.6-33.5) ppb
                        (stationary monitoring
                        stations with
                        subsequent modeling)

8-oxodG (ELISA)         1-HOP (urine)           Biomarker
                        tt-MA (urine)           exposure

TBARS (SPM) and CDs     [O.sub.3], 152 and 29   Biomarker
(SPM)                   ppb (stationary         exposure

TBARS (SPM)             None                    Biomarker

CDs MDA                 [PM.sub.10], 122        Biomarker
                        [+ or -] 34 [mu]g/      exposure
                        [m.sup.3] [NO.sub.2],
                        34 [+ or -] 13 ppb
                        [O.sub.3], 38 [+ or -]
                        12 ppb [SO.sub.2],
                        36  [+ or -] 14 ppb

TBARS (SPM)             [O.sub.3] (155 vs. 46   Biomarker
                        ppb)                    exposure
                        [PM.sub.10] (122 vs.
                        104 [mu]g/[m.sup.3])
                        Stationary monitoring

8-oxodG (HPLC-ECD)      1-HOP (urine)           Exposure
                        tt-MA (urine)

8-oxodG (ELISA)         [PM.sub.2.5], 22.7 and  Biomarker
                        16.8 [mu]g/[m.sup.3]    exposure
                        [PM.sub.10], 30.0 and
                        20.4 [mu]g/[m.sup.3]
                        Stationary monitoring

ENDOIII/FPG sites       Benzene (personal       Exposure
(comet)                 exposure and urinary
8-oxodG (HPLC-ECD)      S-PMA excretion)

TBARS (SPM)             None                    Biomarker

Biomarker               Findings         Study

FPG sites (comet)       Association      Avogbe et al. 2005
                        between S-PMA
                        excretion and
                        FPG sites in

8-oxodG (HPLC-ECD)      Increased in     Buthbumrung et al.
                        WBCs and urine   2008

8-oxodG                 Higher level in  Calderon-Garciduehas
(immunohistochemistry)  nasal biopsies   et al. 1999
                        from children
                        in Mexico City
                        compared with
                        children in the
                        low polluted

8-iso-PGF(ELISA)        Highest level    Chen et al. 2007 (b)
                        in plasma of
                        subjects living
                        in the most
                        polluted area

8-oxodG (ELISA)         Association      De Coster et al. 2008
                        between          (c)
                        (1-HOP and
                        tt-MA) and
                        excretion in

TBARS (SPM) and CDs     No difference    Hicks et al. I996
(SPM)                   in serum level   (d)
                        subjects who
                        had permanently
                        or who had
                        never lived in
                        Mexico City;
                        subjects who
                        had recently
                        (within one
                        week) moved to
                        Mexico City had
                        elevated levels
                        in serum

TBARS (SPM)             Highest level    Isik et al. 2005
                        in serum of

CDs MDA                 Association      Kelishadi et al. 2009
                        between          (e)
                        [PM.sub.10] and
                        CDs in plasma

TBARS (SPM)             Highest level    Sanchez-Rodriguez et
                        in plasma of     al. 2005 (f)
                        subjects living
                        in Mexico City

8-oxodG (HPLC-ECD)      Highest level    Staessen et al. 2001
                        in urine from    (g)
                        subjects; no
                        markers (1-HOP
                        and tt-MA) and
                        excretion in

8-oxodG (ELISA)         Positive         Svecova et al. 2009
                        between air
                        exposure and
                        excretion of
                        8-oxodG in the
                        area with high
                        air pollution
                        (Teplice, Czech
                        Republic); same
                        in the area
                        with low level
                        of air

ENDOIII/FPG sites       Positive         Sorensen et al. 2003c
(comet)                 association      (h)
8-oxodG (HPLC-ECD)      between urinary
                        S-PMA excretion
                        and 8-oxodG
                        (WBCs); no
                        sites (WBCs) or
                        excretion of

TBARS (SPM)             Highest level    Vujovic et al. 2009
                        in plasma from   (i)

Abbreviations: CDs, conjugated dienes; ECD, electrochemical detection;
ELISA, enzyme-linked immunosorbent assay; F, female; M, male; MF, male
and female; NR, not reported; NS, nonsmoker; SPM, spectrophotometry;
iso-[PGF.sub.2], 8-iso-[PGF.sub.2], 8-iso-prostaglandin [F.sub.2]; MDA,
malondialdehyde; [SO.sub.2], sulfur dioxide.

(a) The values represent exposure assessment in the high-exposure and
low-exposure group, respectively, unless stated otherwise by specific
footnotes. (b) We used the median and interquartile range as surrogates
for the mean and SD. (c) We used data from Antwerp, Belgium, and a
rural area in the analysis because they had emissions of PAHs, and
we estimated the SD from the 95% Cl. (d) We calculated the mean level
of LPO products from TBARS and CDs. (e) We used data based on the
difference in interquartile range of the exposure ([PM.sub.10]) and
assuming that the median concentration of exposure (122
[mu]g/[m.sup.3]) corresponds to the mean level of MDA (0.7 [mu]M) and
CDs (2.5 [mu]M). We calculated the SD from the mean coefficient of
variation (11%) of the LPO products. (f) We pooled mean and SD from
adult and elderly subjects. (g) We pooled data from Wilrijk and
Hoboken, Belgium, for the analysis and calculated the SD from 95% Cl.
(h) We used the mean and SD in the groups of subjects being either
higher or lower than the median urinary excretion of S-PMA. (i) We
estimated the SD from 95% Cl.

Using job titles as the basis for stratification of exposure, studies showed that subjects in occupations with high exposure to traffic emissions had higher levels of FPG sites (Avogbe et al. 2005; Cavallo et al. 2006; Palli et al. 2009) and 8-oxodG and [M.sub.1]dG (Ayi Fanou et al. 2006; Singh et al. 2007) in WBCs than did referents. However, the latter two studies reported levels of 8-oxodG that were above the threshold of 5 lesions/[10.sup.6] dG, suggesting the potential for spurious oxidation of the samples. Another study showed higher levels of FPG and ENDOIII sites in WBCs of traffic emission exposed policemen compared with other policemen working indoor during a month when air pollution was relatively high (i.e., January) but no association during a month with low air pollution exposure (i.e., September) (Novotna et al. 2007). In contrast, Bonina et al. (2008) found no difference in serum lipid hydroperoxides concentration when comparing traffic officers with healthy indoor workers; the primary purpose of their study appears to have been to compare lipid hydroperoxide levels between subjects that received phytochemicals and subjects that did not, and they evaluated associations with air pollution in a secondary analysis. Evidence of null associations in studies that evaluate air pollution as a secondary exposure suggests the possibly of a general trend toward publication bias favoring studies that report positive associations when air pollution is the primary exposure of interest, but the findings of the Bonina et al. (2008) study may also have been due to the use of a nonspecific spectro-photometric assay for the detection of LPO products, which can bias the estimated effect toward null. Studies using urinary biomarkers have also shown increased levels of 8-oxodG and 15- [F.sub.2t] isoprostanes in subjects exposed to high concentrations of traffic-vehicle exhausts (Chuang et al. 2003; Lai et al. 2005; Rossner et al. 2008a, 2008b).

Cross-sectional studies of subjects living, working, or going to school in locations with different ambient air pollution levels encompass investigations of subjects with predefined age groups, such as children, adolescents, adults, or elderly. Studies of children living in areas with different levels of exposure have shown positive associations with 8-oxodG levels in nasal cells (Calderon-Garciduenas et al. 1999), 8-oxodG in WBCs (Buthbumrung et al. 2008), LPO products in plasma (Kelishadi et al. 2009; Vujovic et al. 2009), and urinary excretion of 8-oxodG (Svecova et al. 2009). Studies of air pollution exposure in adults have provided more mixed results: benzene as a marker of urban air pollution exposure was associated with urinary excretion of S-PMA and 8-oxodG in WBCs, but not with ENDOIII and FPG sites in WBCs or urinary excretion of 8-oxodG (Sorensen et al. 2003c). Other studies of urinary excretion of 8-oxodG have shown positive associations (De Coster et al. 2008; Loft et al. 1999; Staessen et al. 2001; Wei et al. 2009), but studies of LPO products in plasma have indicated both positive associations with oxidative damage (Chen et al. 2007; Isik et al. 2005; Sanchez-Rodriguez et al. 2005) and no apparent effects (Autrup et al. 1999; Hicks et al. 1996).

Combined effect estimates for markers in airways, blood, and urine. The qualitative assessment in the preceding sections indicates that most of the reports showed associations between air pollution exposure and oxidatively damaged DNA, nucleobases, and lipids. Most studies measured the biomarkers in surrogate tissue cells such as WBCs or noncellular bodily fluids such as plasma, urine, and EBC. The data on biomarkers of the airways mainly encompass measurements of LPO products in EBC, whereas only one study examined 8-oxodG in nasal cells (Calderon-Garciduenas et al. 1999). Figure 2 shows study-specific and overall estimates of effect for exposure to PM on oxidatively damaged DNA, nucleobases, and lipids in the airways, blood, and urine. In general there is considerable heterogeneity between the studies and between subgroups; methodological diversity between studies may explain the heterogeneity. In addition, the categorization of the exposure does not take into account that exposure gradients most likely differ between the studies. It is not possible to calculate a standardized exposure unit (e.g., effect per 10-[mu]g/[m.sup.3] increase [PM.sub.2.5]) because the studies reported exposure measurements in different fractions of PM, or they contained no information about the concentration of PM. The overall standardized mean differences between exposed subjects and nonexposed referents for the oxidized DNA and for LPO products in the blood were 0.53 (95% CI, 0.29-0.76) and 0.73 (95% CI, 0.18-1.28), respectively. In the urine the estimated effect size by PM exposure versus nonexposed referents for oxidatively damaged DNA and nucleobases and for LPO products was 0.52 (95% CI, 0.22-0.82) and 0.49 (95% CI, 0.01-0.97), respectively. This suggests that exposure to PM is associated with comparable increases in oxidized DNA and lipids, although it should be emphasized that the heterogeneity between subgroups might mask real differences between the biomarkers. The effect on DNA damage in the airways is presently difficult to assess because there was only one study of oxidized DNA (Calderon-Garciduenas et al. 1999). The estimated effect on LPO products in EBC was 0.64 (95% CI, 0.07-1.21), which is comparable to overall standardized mean differences in LPO products in the blood and urine with PM exposure. This finding suggests that LPO products in plasma and urine are suitable biomarkers of biologically effective PM dose reflecting oxidative stress in the airways.

Study or subgroup                    Mean          SD     Total

Airways/DNA damage

Calderon-Garciduenas                  602.0        195.0     87
et al. 1999

Subtotal (95% Cl)                                            87

Heterogeneity: Not applicable

Test for overall effect Z= 6.04 (p < 0.00001)

Airways/LPO products

Barregaard et al.                    0.0053       0.0865     13

Liu et al. 2009a                        0.9         4.97    182

Mills et al. 2008                      16.6          7.6      8

Romieu et al. 2008                     14.0         16.3    107

Rundell et al. 2008                    27.2          6.9     12

Subtotal (95% Cl)                                           322

[Tau.sup.2] = 0.29; [X.sup.2] = 28.40, df = 4 (p < 0.0001);
[/.sup.2] = 86%

Test for overall effect: Z= 2.21 (p = 0.03)

Blood/DNA damage

Avogbe et al. 2005                      9.2         3.11    108

Brauner et al. 2007                    0.53         0.28     29

Buth bumrung et al.                    0.25         0.13     40

Cavallo et al. 2006                   12.85         5.14     41

Chuang et al. 2007                     0.57          0.2     76

Danielsen et al.                      0.225         0.13     12

Fanou et al. 2006                      2.02         1.25     35

Novotna et al. 2007                    2.52         1.73     54

Palli et al. 2008                       5.0         3.06     44

Singh et al. 2007                      58.3         34.9     98

Singh et al. 2007                      33.0         33.1    198

Sorensen et al. 2003a                  1.51          6.8     49

Sorensen et al. 2003a                  0.58         0.33     49

Sorensen et al. 2003c                  9.76         17.5     20

Sorensen et al. 2003c                  1.03         1.07     14

Vinzents et al. 2005                   0.08         0.08     14

Subtotal (95% Cl)                                           881

Heterogeneity: [Tau.sup.2] = 0.15; [X.sup.2] = 61.54, df = 15
(p < 0.00001); [I.sup.2] = 76%

Test for overall effect: Z= 4.42 (p < 0.00001)

Blood/LPO products

Autrup et al. 1999                     0.87         0.19     55

Bonina et al. 2008                   340.17         41.4     20

Chen et al. 2007                      195.3         46.0     61

Hicks et al. 1996                      5.05         1.55     24

Isik et al. 2005                       3.28         0.79     28

Kelishadi et al.                       1.52         0.17    187

Liu et al. 2007                       0.572        0.572     24

Liu et al. 2009b                        5.9         5.16     28

Medina-Navarro et al.                  5.68          1.2     21

Sanches-Rodriguez et                   0.38         0.19    167
al. 2005

Sorensen et al.2003b                   35.9          7.4     47

Vujovic et al. 2009                    1.25         0.43     42

Subtotal (95% Cl)                                           704

Heterogeneity: [Tau.sup.2] = 0.88; [X.sup.2] = 219.65, df = 11
(p < 0.00001); [I.sup.2] = 95%

Test for overall effect: Z= 2.59 (p = 0.009)


Allen et a. 2009                      0.087        0.193      6

Buthbumrung et al.                     2.16         1.84     43

Chuang et al. 2003                     13.4          4.7     95

Danielsen et al. 2008                  8.43         6.48     10

Danielsen et al. 2008                  0.84         0.35     10

De Coster et al.                      14.53         5.33     89

Lai et al. 2005                        13.3          7.1     47

Loft et al. 1999                      190.0        108.0     29

Rossner Jr et al.                      6.66         2.41     50

Staessen et al. 2001                   0.52         0.27    100

Suzuki et al. 1995                     9.93         2.48      3

Svecova et al. 2009                    14.6          5.8    495

Sorensen et al.                        0.23         0.14     49

Sorensen et al.                      222.03        126.2     20

Wei et al. 2009                        6.91         3.67     58

Subtotal (95% Cl)                                          1104

Heterogeneity: [Tau.sup.2] = 0.26; [X.sup.2] = 110.20, df = 14
(p < 0.00001); [I.sup.2] = 87%

Test for overall effect: Z= 3.37 (p = 0.0007)

Urine/LPO products

Allen et al. 2009                       0.0        0.234      6

Barregaard et al.                     0.036        0.051     11

Brauner et al. 2008                     0.5          0.1     41

Rossner Jr et al.                      0.77         0.27     50

Subtotal (95% Cl)                                           108

Heterogeneity: [Tau.sup.2] = 0.13; [X.sup.2] = 7.09, df = 3
(p = 0.07); [/.sup.2] = 58%

Test for overall effect: Z= 2.00 (p = 0.05)

Total (95% Cl)                                             3206

Heterogeneity: [Tau.sup.2] = 0.40; [X.sup.2] = 549.65, df = 52
(p < 0.00001); [/.sup.2] = 91%

Test for overall effect: Z= 6.39 (p < 0.00001)

Test for subgroup differences: [X.sup.2] = 122.77, df = 5 (p < 0.00001),
[/.sup.2] = 95.9%


Study or subgroup                    Mean          SD     Total  Weight

Airways/DNA damage

Calderon-Garciduenas                  210.0        122.0     12    1.8%
et al. 1999

Subtotal (95% Cl)                                            12    1.8%

Heterogeneity: Not applicable

Test for overall effect Z= 6.04 (p < 0.00001)

Airways/LPO products

Barregaard et al.                       0.0        0.022     13    1.7%

Liu et al. 2009a                       0.85         4.65    182    2.3%

Mills et al. 2008                       4.5          1.3      8    1.1%

Romieu et al. 2008                     12.4         16.3    107    2.2%

Rundell et al. 2008                    12.2          3.9     12    1.3%

Subtotal (95% Cl)                                           322    8.6%

Heterogeneity: [Tau.sup.2] = 0.29; [X.sup.2] = 28.40, df = 4
(p < 0.0001); [/.sup.2] = 86%

Test for overall effect: Z= 2.21 (p = 0.03)

Blood/DNA damage

Avogbe et al. 2005                     5.39         2.47     27    2.1%

Brauner et al. 2007                    0.38         0.22     29    2.0%

Buth bumrung et al.                    0.08         0.34     32    2.0%

Cavallo et al. 2006                    7.97         3.19     31    2.0%

Chuang et al. 2007                      0.6          0.2     76    2.2%

Danielsen et al.                      0.255        0.119     12    1.6%

Fanou et al. 2006                      1.11         0.82      6    1.5%

Novotna et al. 2007                    1.29         1.21     11    1.8%

Palli et al. 2008                      4.11         3.96     27    2.0%

Singh et al. 2007                      49.2         30.4    105    2.2%

Singh et al. 2007                      29.2         21.1    156    2.3%

Sorensen et al. 2003a                   0.5          3.5     49    2.1%

Sorensen et al. 2003a                  0.27         0.18     49    2.1%

Sorensen et al. 2003c                  5.01          5.2     19    1.8%

Sorensen et al. 2003c                 0.376         0.66     14    1.7%

Vinzents et al. 2005                   0.02         0.04     14    1.7%

Subtotal (95% Cl)                                           657   31.1%

Heterogeneity: [Tau.sup.2] = 0.15; [X.sup.2] = 61.54, df = 15
(p < 0.00001); [I.sup.2] = 76%

Test for overall effect: Z= 4.42 (p < 0.00001)

Blood/LPO products

Autrup et al. 1999                     0.96         0.25     45    2.1%

Bonina et al. 2008                   348.91        31.67     12    1.7%

Chen et al. 2007                       97.2         34.7     59    2.0%

Hicks et al. 1996                      5.23          0.9     17    1.9%

Isik et al. 2005                       1.47         0.63     15    1.6%

Kelishadi et al.                       1.22         0.13    187    2.2%

Liu et al. 2007                       0.411        0.411     24    1.9%

Liu et al. 2009b                       5.22         4.57     28    2.0%

Medina-Navarro et al.                  4.77         1.22     21    1.9%

Sanches-Rodriguez et                   0.23         0.12    125    2.2%
al. 2005

Sorensen et al.2003b                   36.3          8.0     47    2.1%

Vujovic et al. 2009                    0.99         0.32     82    2.1%

Subtotal (95% Cl)                                           662   23.8%

Heterogeneity: [Tau.sup.2] = 0.88; [X.sup.2] = 219.65, df = 11
(p < 0.00001); [I.sup.2] = 95%

Test for overall effect: Z= 2.59 (p = 0.009)


Allen et a. 2009                        0.0        0.193      6    1.2%

Buthbumrung et al.                     1.32         1.24     32    2.0%

Chuang et al. 2003                     11.5          4.7     75    2.2%

Danielsen et al. 2008                  4.24         2.31     10    1.5%

Danielsen et al. 2008                  0.73         0.19     10    1.5%

De Coster et al.                       14.0         5.25     76    2.2%

Lai et al. 2005                         8.4          6.2     24    2.0%

Loft et al. 1999                      146.0         89.0     27    2.0%

Rossner Jr et al.                      5.21         2.23     50    2.1%

Staessen et al. 2001                   0.44          0.2    100    2.2%

Suzuki et al. 1995                     4.22         1.97      3    0.4%

Svecova et al. 2009                    15.2          6.1    399    2.3%

Sorensen et al.                       0.122         0.09     49    2.1%

Sorensen et al.                       280.2        142.5     20    1.9%

Wei et al. 2009                        1.83         0.52     58    2.1%

Subtotal (95% Cl)                                           939   27.7%

Heterogeneity: [Tau.sup.2] = 0.26; [X.sup.2] = 110.20, df = 14
(p < 0.00001); [I.sup.2] = 87%

Test for overall effect: Z= 3.37 (p = 0.0007)

Urine/LPO products

Allen et al. 2009                      0.05        0.234      6    1.3%

Barregaard et al.                     0.016        0.018     11    1.6%

Brauner et al. 2008                     0.4          0.1     41    2.0%

Rossner Jr et al.                      0.68         0.38     50    2.1%

Subtotal (95% Cl)                                           108    7.0%

Heterogeneity: [Tau.sup.2] = 0.13; [X.sup.2] = 7.09, df = 3 (p = 0.07);
[/.sup.2] = 58%

Test for overall effect: Z= 2.00 (p = 0.05)

Total (95% Cl)                                             2700  100.0%

Heterogeneity: [Tau.sup.2] = 0.40; [X.sup.2] = 549.65, df = 52
(p < 0.00001); [/.sup.2] = 91%

Test for overall effect: Z= 6.39 (p < 0.00001)

Test for subgroup differences: [X.sup.2] = 122.77, df = 5 (p < 0.00001),
[/.sup.2] = 95.9%

                       Std. Mean Difference,   Std. Mean Difference,

Study or subgroup                    95% Cl       95% Cl

Airways/DNA damage

Calderon-Garciduenas      2.07 (1.40, 2.74)
et al. 1999

Subtotal (95% Cl)         2.07 (1.40, 2.74)

Heterogeneity: Not applicable

Test for overall effect Z= 6.04 (p < 0.00001)

Airways/LPO products

Barregaard et al.        0.08 (-0.69, 0.85)

Liu et al. 2009a         0.01 (-0.20, 0.22)

Mills et al. 2008         2.10 (0.81, 3.39)

Romieu et al. 2008      0.101 (-0.17, 0.37)

Rundell et al. 2008       2.58 (1.45, 3.72)

Subtotal (95% Cl)         0.64 (0.07, 1.21)

Heterogeneity: [Tau.sup.2] = 0.29; [X.sup.2] = 28.40, df = 4
(p < 0.0001); [/.sup.2] = 86%

Test for overall effect: Z= 2.21 (p = 0.03)

Blood/DNA damage

Avogbe et al. 2005        1.26 (0.82, 1.71)

Brauner et al. 2007       0.59 (0.06, 1.11)

Buth bumrung et al.       0.68 (0.20, 1.16)

Cavallo et al. 2006       1.09 (0.59, 1.60)

Chuang et al. 2007       0.15 (-0.47, 0.17)

Danielsen et al.         0.23 (-1.04, 0.57)

Fanou et al. 2006        0.74 (-0.14, 1.62)

Novotna et al. 2007       0.73 (0.07, 1.39)

Palli et al. 2008        0.26 (-0.22, 0.74)

Singh et al. 2007         0.28 (0.00, 0.55)

Singh et al. 2007        0.13 (-0.08, 0.34)

Sorensen et al. 2003a    0.19 (-0.21, 0.58)

Sorensen et al. 2003a     1.16 (0.73, 1.59)

Sorensen et al. 2003c    0.36 (-0.28, 0.99)

Sorensen et al. 2003c    0.71 (-0.05, 1.48)

Vinzents et al. 2005      0.92 (0.14, 1.71)

Subtotal (95% Cl)         0.53 (0.29, 0.76)

Heterogeneity: [Tau.sup.2] = 0.15; [X.sup.2] = 61.54, df = 15
(p < 0.00001); [I.sup.2] = 76%

Test for overall effect: Z= 4.42 (p < 0.00001)

Blood/LPO products

Autrup et al. 1999     -0.41 (-0.81, -0.01)

Bonina et al. 2008      -0.22 (-0.94, 0.49)

Chen et al. 2007          2.39 (1.92, 2.86)

Hicks et al. 1996        0.13 (-0.76, 0.49)

Isik et al. 2005          2.40 (1.58, 3.23)

Kelishadi et al.          1.98 (1.73, 2.23)

Liu et al. 2007          0.32 (-0.25, 0.89)

Liu et al. 2009b         0.14 (-0.39, 0.66)

Medina-Navarro et al.     0.74 (0.11, 1.37)

Sanches-Rodriguez et      0.91 (0.67, 1.16)
al. 2005

Sorensen et al.2003b    -0.05 (-0.46, 0.35)

Vujovic et al. 2009       0.72 (0.33, 1.10)

Subtotal (95% Cl)         0.73 (0.18, 1.28)

Heterogeneity: [Tau.sup.2] = 0.88; [X.sup.2] = 219.65, df = 11
(p < 0.00001); [I.sup.2] = 95%

Test for overall effect: Z= 2.59 (p = 0.009)


Allen et a. 2009         0.42 (-0.73, 1.57)

Buthbumrung et al.        0.52 (0.05, 0.98)

Chuang et al. 2003        0.40 (0.10, 0.71)

Danielsen et al. 2008    0.82 (-0.10, 1.75)

Danielsen et al. 2008    0.37 (-0.51, 1.26)

De Coster et al.         0.10 (-0.21, 0.41)

Lai et al. 2005           0.71 (0.21, 1.22)

Loft et al. 1999         0.44 (-0.09, 0.97)

Rossner Jr et al.         0.62 (0.22, 1.02)

Staessen et al. 2001      0.34 (0.06, 0.61)

Suzuki et al. 1995       2.04 (-0.50, 4.58)

Svecova et al. 2009      0.10 (-0.23, 0.03)

Sorensen et al.           0.91 (0.49, 1.33)

Sorensen et al.          0.42 (-1.05, 0.20)

Wei et al. 2009           1.93 (1.48, 2.37)

Subtotal (95% Cl)         0.52 (0.22, 0.82)

Heterogeneity: [Tau.sup.2] = 0.26; [X.sup.2] = 110.20, df = 14
(p < 0.00001); [I.sup.2] = 87%

Test for overall effect: Z= 3.37 (p = 0.0007)

Urine/LPO products

Allen et al. 2009       -0.20 (-1.33, 0.94)

Barregaard et al.        0.50 (-0.35, 1.35)

Brauner et al. 2008       0.99 (0.53, 1.45)

Rossner Jr et al.        0.27 (-0.12, 0.66)

Subtotal (95% Cl)         0.49 (0.01, 0.97)

Heterogeneity: [Tau.sup.2] = 0.13; [X.sup.2] = 7.09, df = 3 (p = 0.07);
[/.sup.2] = 58%

Test for overall effect: Z= 2.00 (p = 0.05)

Total (95% Cl)            0.62 (0.43, 0.80)

Heterogeneity: [Tau.sup.2] = 0.40; [X.sup.2] = 549.65, df = 52
(p < 0.00001); [/.sup.2] = 91%

Test for overall effect: Z= 6.39 (p < 0.00001)

Test for subgroup differences: [X.sup.2] = 122.77, df = 5
(p < 0.00001), [/.sup.2] = 95.9%

Figure 2. Forest plot of air pollution exposure on biomarkers of
oxidized DNA, nucleobases, and lipids. Specific biomarkers in studies
that have measured multiple assays of oxidized DNA and lipids are
(1) 8-oxodG, (2) M1dG, (3) 8-oxodG, (4) ENDOIII/FPG sites,
(5) ENDOIII/FPG sites, (6) 8-oxodG, (7) 8-oxodG, and (8) 8-oxoGua
(the numbers in parentheses refer to references citations that are
listed by first author/year only).

Combined estimates according to the potential for exposure or outcome measurement error. The focus of our analysis was on the estimated effect of exposure to PM on biomarkers of oxidized DNA, nucleobases, and lipids. A number of studies have not measured personal exposure to PM, which may be because of their focus on other air pollution constituents or lack of resources or because the study was too large to do personal exposure measurements. Ideally, all studies should have included personal measurements of PM, and the analysis should have included mutual adjustment for coexposures to other air pollutants. We assumed that PM is the most important contributor to oxidative stress among the air pollutants and that personal exposure can be estimated from the personal exposure to the other pollutants or ambient PM levels, although with potential bias. Of the studies identified in our meta-analysis, only Chen et al. (2007) has estimated personal exposure levels using mathematical modeling of data from stationary monitoring stations. We have categorized the studies according to whether or not they characterized exposure using personal versus ambient PM measurements. Based on the categorization of the studies according to the potential measurement error in Tables 2-5, the estimated effect size is 0.55 (95% CI, 0.19-0.90), 0.66 (95% CI, 0.37-0.95), and 0.65 (95% CI, 0.34-0.96) for studies categorized as having no potential measurement error, potential measurement error in either biomarker analysis or exposure assessment, and potential measutement error in both biomarker analysis or exposure assessment, respectively [see Supplemental Material for the Forest plots (doi:10.1289/ehp.0901725)]. The effect size is essentially identical in the three groups, which could be caused by opposite acting effects of potential measurement errors (regression toward null effect) and uncontrolled confounding factors (increased effect size) in panel and cross-sectional studies. The percentages of studies that did not use personal measurements to assess PM exposure were 8% (1 of 12), 36% (4 of 11), and 100% (29 of 29) in the studies categorized as controlled exposures, panel studies, and cross-sectional studies, respectively ([x.sup.2] = 36.7, p < 0.001). The use of error-prone biomarker assays was less likely in the controlled exposure studies (31%; 4 of 13) than in the panel studies (64%; 7 of 11) and cross-sectional studies (59%; 17 of 29), although no differences were found between the distributions ([x.sup.2] = 3.4, p> 0.05). Overall, the quality of the studies, in terms of the likelihood of exposure and outcome measurement error, appears highest in the controlled studies and lowest in the cross-sectional studies. Both of these measurement errors may bias the effect estimate toward null, whereas uncontrolled confounding factors in panel studies and cross-sectional studies most likely increased the estimated effect size. In addition, we emphasize that the studies that measured personal PM exposure and used more accurate biomarker assays mainly investigated the effect of air pollution particles in realistic urban air concentrations (Brauner et al. 2007, 2008; Rundell et al. 2008; Sorensen et al. 2003a, 2003b; Vinzents et al. 2005), although one study used high concentrations of wood smoke particles (Barregard et al. 2006, 2008; Danielsen et al. 2008). Collectively, associations between PM exposure and biomarkers of oxidative stress estimated by studies likely to have more accurate exposure and outcome measurements cannot be explained by exposure to excessive concentrations of PM. Yet, the controlled exposure studies in our metaanalysis had few numbers of observations, which might reduce the precision with which effects have been estimated. In addition, controlled exposure studies may be more prone to selection bias due to random errors in selection and have limited generalizability because they are restricted to participants with specific characteristics.


Our analysis shows that exposure to combustion particles is consistently associated with elevated levels of oxidatively damaged DNA and nucleobases and LPO products in human blood cells, plasma, urine, and EBC. The association is seen across studies with optimum designs, including controlled or personal exposure assessment and biomarkers with low potential measurement error due to indirect exposure assessment and/or use of biomarkers prone to artifacts. Still, we emphasize that the identified studies are inhomogeneous in design and quality of biomarkers, which weakens our conclusions about specific exposure-effect relationships for particulate air pollution. In addition, few numbers of subjects, especially in the controlled exposure studies, is a minor limitation that might affect the generalizability of our meta-analysis results.

Our critical analysis indicates that the range in exposure to realistic ambient concentrations of combustion particles is associated with a 50% increase in the level of oxidatively damaged DNA, nucleobases, and lipids, supporting the notion that they are suitable biomarkers of the biologically effective dose of PM. However, we caution against the use of suboptimal biomarkers; ideal biomarkers of oxidative damage should detect a major part of the total ongoing oxidative damage in vivo, have small assay variation, have smaller intraindividual than interindividual variation, not be confounded by diet, be stable on storage, and have the same level obtained in target and surrogate tissue (Halliwell and Whiteman 2004). They should also have predictive value of risk of disease, which can be firmly assessed only in prospective studies (Loft and Moller 2006). The biomarkers of urinary excretion of 8-oxodG and TBARS in plasma are among the few biomarkers that have been studied in prospective cohort studies; they have predictive value regarding development of lung cancer and cardiovascular diseases, respectively (Loft et al. 2006; Walter et al. 2004). Further development of oxidized DNA and LPO products as biomarkers of biological effective dose of air pollution exposure should focus on the most reliable and well-validated assays, including assays for the measurement of isoprostanes and techniques that consistently measure low background levels of oxidatively damaged DNA and nucleobases. The relevant biomarkers with low potential of measurement error are constantly developed to increase assay capacity toward high throughput, for instance, the comet assay and urinary excretion of 8-oxodG (Henriksen and Poulsen 2009; Stang and Witte 2009). This development will allow the use of these biomarkers of exposure to PM in large-scale population studies.


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Address correspondence to P. Moller, Oster Farimagsgade 5A, Postbox 2099, DK-1014, Copenhagen K, Denmark. Telephone: 45-35-32-76-54. Fax: 45-35-32-76-86. E-mail:

Supplemental Material is available online (doi:10.1289/ehp.0901725 via

This work was supported by grants from the Danish Research Councils.

The authors declare they have no actual or potential competing financial interests.

Received 24 November 2009; accepted 27 April 2010.

Peter Moller and Steffen Loft

Department of Public Health, Section of Environment Health, University of Copenhagen, Copenhagen, Denmark
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Title Annotation:Review
Author:Moller, Peter; Loft, Steffen
Publication:Environmental Health Perspectives
Article Type:Report
Geographic Code:4EUDE
Date:Aug 1, 2010
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