Differential oxidative stress response in young children and the elderly following exposure to PM2.5
Environ Health Prev Med
Differential oxidative stress response in young children and the elderly following exposure to PM2.5
Kyoungwoo Kim 0 1 2 3 4 5
Eun-Young Park 0 1 2 3 4 5
Kwan-Hee Lee 0 1 2 3 4 5
Jung-Duck Park 0 1 2 3 4 5
Yong-Dae Kim 0 1 2 3 4 5
Yun-Chul Hong 0 1 2 3 4 5
0 K.-H. Lee Department of Occupational and Environmental Medicine, Inha University Hospital , Incheon , South Korea
1 E.-Y. Park Y.-C. Hong (&) Department of Preventive Medicine, College of Medicine, Seoul National University , 28 Yeongeon Dong, Jongro Gu, Seoul 110-799 , South Korea
2 K. Kim Department of Family Medicine, Inje University, Seoul Paik Hospital , Seoul , South Korea
3 Y.-C. Hong Institute of Environmental Medicine, Seoul National University Medical Research Center , Seoul , South Korea
4 Y.-D. Kim Department of Preventive Medicine, College of Medicine, Chungbuk National University , Cheongju , South Korea
5 J.-D. Park Department of Preventive Medicine, College of Medicine, Chung-Ang University , Seoul , South Korea
s Objectives The mechanism of the adverse health effects of ambient particulate matter on humans has not been wellinvestigated despite many epidemiologic association studies. Measurement of personal exposure to particulate pollutants and relevant biological effect markers are necessary in order to investigate the mechanism of adverse health effects, particularly in fragile populations considered to be more susceptible to the effects of pollutants. Methods We measured personal exposure to PM2.5 and examined oxidative stress using urinary malondialdehyde three times in 51 preschoolers and 38 elderly subjects.
Particulate matter; Oxidative stress; Repeated measurement; Malondialdehyde; Biological markers
A linear mixed-effects model was used to estimate PM2.5
effects on urinary MDA levels.
Results Average personal exposure of the children and
elderly to PM2.5 was 80.5 ± 29.9 and 20.7 ± 12.7 lg/m3,
respectively. Mean urinary MDA level in the children and
the elderly was 3.6 ± 1.9 and 4.0 ± 1.6 lmol/g creatinine.
For elderly subjects the PM2.5 level was significantly
associated with urinary MDA after adjusting for age, sex,
BMI, passive smoking, day-care facility site, alcohol
consumption, cigarette smoking, and medical history (heart
disease, hypertension and bronchial asthma). However,
there was no significant relationship for children.
Conclusions The elderly were more susceptible than
young children to oxidative stress as a result of ambient
exposure to PM2.5. Identification of oxidative stress
induced by PM2.5 explains the mechanism of adverse
health effects such as cardiovascular or respiratory
diseases, particularly in the elderly.
Recent evidence from epidemiological studies has shown
that air pollution is associated with adverse health effects
such as an increase in cardiovascular and pulmonary
]. In particular, increased risk of hospitalization
for cardiovascular and pulmonary diseases has also been
reported to be associated with particulate air pollution [
]. However, most epidemiological studies have used
outdoor central monitoring data as the exposure estimates,
and evidence supporting relevant biological mechanisms of
how ambient particulate matter causes adverse health
effects is insufficient.
Although the pathophysiological mechanisms linking
inhalation of air pollution to acute exacerbation of
cardiovascular or pulmonary disease are not completely
understood in humans, inflammation has been reported to
be induced by particulate air pollution in several
humanexposure studies, but it is uncertain whether oxidative
stress is mediated by particle-induced inflammation [
Although results from cell-culture and animal experiments
suggest that oxidative stress is involved in its pathogenesis,
evidence of the relationship between human exposure to
ambient particulate matter and oxidative stress is still
]. This lack of clear understanding arises in
part from the difficulty of measuring human exposure to
ambient concentrations of pollutants and evaluating the
resulting health effects. Monitoring personal exposure
instead of using outdoor central monitoring data and
utilizing relevant biomarkers instead of disease or mortality
record could be one of the more precise methods for
characterizing individual exposure and early effects, which
are expected to explain their mechanisms.
Because people spend most of their time indoors, using
outdoor environmental data to represent a subject’s
exposure may not be an accurate method of evaluating the
effects of air pollution exposure [
]. In addition, different
exposures are expected individually according to
considerable spatial variations, various indoor sources and
composition of ambient particulate pollutants, and different
personal activity levels [
]. Therefore, measuring
personal exposure and its biological effects on an
individual basis is an important issue in air pollution
Biomarkers can be used to relate exposure to biological
effects such as oxidative stress, which is thought to be
involved in the mechanisms of adverse health effects of
particulate pollutants. We measured urinary
malondialdehyde (MDA) concentrations in spot urine samples as a
relevant biomarker representing oxidative stress,
considering times between exposure and its biological effects. As
for human biomarkers of oxidative stress, MDA, one of the
aldehyde products of lipid peroxidation, is known to be an
end-product of free radical reactions and elevated MDA
levels are found in many diseases including cardiovascular
disease and cancer [
As for human exposure and oxidative damage, some
studies regarding outdoor central monitoring data as
exposure estimates in young subjects found a significant
association between particulate air pollution and oxidative
stress, but others found no association [
as measurement of personal exposure and issue of
monitoring devices have progressed, researchers have shown
associations between individual exposure to particulate
pollutants and biomarkers of oxidative damage [
Further, because populations susceptible to the effects of
particulate matter could be identified by personal
monitoring, it helps to determine target populations for air
pollution control .
The primary objective of this study was to examine the
link between exposure to fine particulate matter and
oxidative stress, with the hypothesis that not all groups of
people respond to particulate pollutant exposure in the
same way. In particular, young children and the elderly
were recruited for this study with the assumption that they
have different susceptibility to oxidative stress induced by
particulate air pollution.
A total of 38 elderly volunteers at two day-care facilities
and 51 children in one kindergarten class in Seoul
participated in this study. General information about
sociodemographic factors, past medical history, and family
history were acquired by means of interviews, and
anthropometric measurements were obtained during the
first visit. We also obtained smoking history (no smoking/
past smoking/current smoking) and alcohol consumption
(no drinking/less than 2–3 times per month/1–2 times per
week/3–4 times per week/almost daily) from the elderly
participants. The ethical committee of the Seoul National
University Hospital approved this study, and informed
consent was obtained from each participant.
PM2.5 sampling and analysis
We measured personal exposure to PM2.5 of participating
subjects at two day-care facilities for elderly and at a
kindergarten for children. To account for temporal
variation, the measurements were repeated three times in
August, October, and December 2005 for the elderly
participants and in October and December 2006 and February
2007 for the children.
The particles were sampled with a PM2.5 cyclon
(Personal Environmental Monitor, SKC, USA) containing a
pump (Mine Safety Appliances, USA), and a battery for
12 h operation. The air flow rate (2 l/min) of the pump was
adjusted before each sampling. The PM2.5 cyclon was
attached with a pin between the chest and head, and the
pump was placed in a small bag that the subjects carried
The suspended fine particles were sampled over 6 h for
each subject. The 37 mm, 2.0 lm Teflon filters (PTFE,
USA) were weighed before and after sampling using a
microbalance and the concentration of ambient particulate
matter was calculated considering air flow and
Measurement of urinary MDA
For each participant, 30 ml urine was collected at each
visit between 4:00 and 5:00 pm; these samples were kept
frozen at -70 C until analysis. Urinary MDA was
determined by measurement of MDA-thiobarbituric acid
(TBA) adducts. Aliquots of the upper layers of
centrifuged urine samples were mixed with phosphoric acid and
TBA reagent in an iced methanol glass tube, and the
mixtures were boiled for 60 min. After the mixture had
been boiled it was cooled in ice water for 5 min and
centrifuged after addition of methanol. The absorbance
was measured by high-performance liquid
chromatography (HPLC), using a system equipped with an SP930D
solvent-delivery pump and a UV730D absorbance
detector (Youngin, Korea). Compounds were separated on a
Nova-Pak C18 column (150 9 3.9 nm) with 50 mM
KH2PO4 (pH 6.8)–methanol 58:42 (v/v) as mobile phase.
Detection was at 532 nm.
Associations between exposure to PM2.5 and urinary
MDA concentrations were assessed. A linear
mixedeffects model was used to estimate the effects of
pollutants on urinary MDA, controlling for age, sex, BMI,
passive smoking, and measurement session. For the
elderly, day-care facility site, alcohol consumption,
cigarette smoking, and medical history (heart disease,
hypertension, and bronchial asthma) were also included
in the model. Because the distribution of urinary MDA
concentrations was skewed, log-transformed data were
used for these measurements in the linear-mixed models.
Age, sex, height, weight, alcohol consumption, and
smoking exposure were treated as fixed effects, whereas
each subject was treated as a random effect in the
models. Statistical analyses were conducted with SAS
(version 9.1). All tests of statistical significance were
two-sided (a-error 0.05).
General characteristics of the study participants are shown
in Table 1. Mean subject age was 5.9 ± 0.2 for the
children and 78.6 ± 6.0 for the elderly. In this study
population 72.5% of the preschoolers and 42.1% of the
elderly were male; 39.5% of the elderly subjects were
current smokers. In addition, 42.1% of the elderly reported
the presence of hypertension. The proportion of subjects
that had the heart disease and bronchial asthma were 7.9
and 5.3% (not shown in Table 1).
Personal exposure to PM2.5 and concentrations of
urinary MDA are shown in Table 2. The mean personal
PM2.5 was 80.5 ± 29.9 lg/m3 in the children and
20.7 ± 12.7 lg/m3 in the elderly. However, the mean
urinary MDA concentrations were not significantly
different between two groups, with means of 3.6 ± 1.9 lmol/g
cr for the children and 4.0 ± 1.6 lmol/g cr for the elderly.
The relationship between personal exposure to PM2.5
and urinary MDA concentrations is shown in Fig. 1. The
horizontal axis is the level of personal exposure to PM2.5
assessed over 6 h and the vertical axis is the
log-transformed urinary concentration of MDA adjusted by
urinary creatinine (cr) level from spot urine collected in
three sessions from 51 preschoolers and 38 elderly
The mixed-effects model of log MDA concentration for
personal PM2.5 exposure showed a statistically significant
increase of 0.3% log MDA concentration per 1 lg/m3
increase of personal PM2.5 exposure in the elderly. This
study showed that estimates of change for urinary MDA
concentrations attributable to personal PM2.5 exposure
were quite different for children and the elderly. In
children, the mixed-effects model of log MDA concentration
for personal PM2.5 exposure showed no association
between them, controlling for age, sex, BMI, passive
smoking, and measurement session.
This study found a significant association between fine
particulate pollution and urinary MDA concentration in the
elderly, but not in children, demonstrating that the elderly
are more susceptible than young children to oxidative
stress induced by ambient fine particulates.
We used personal exposure to PM2.5 as exposure
variable. The mean personal PM2.5 was 80.5 ± 29.9 lg/m3 for
the children and 20.7 ± 12.7 lg/m3 for the elderly
subjects. Air monitoring data for ambient air pollutants in
Seoul showed that annual mean values for PM10 were 58
and 60 lg/m3 in 2005 and 2006 [
]. Ambient PM2.5
concentrations were not monitored regularly in Seoul, but a
report showed that 24-h means of outdoor and indoor PM2.5
were 56.0 and 43.2 lg/m3 [
Personal concentrations of PM2.5 may reflect the level of
indoor PM2.5 rather than outdoor levels [
despite the higher PM2.5 level in children, there is little
difference in the oxidative stress concentrations between
the elderly and the preschoolers. Urinary MDA
concentrations in the elderly (4.0 lmol/g cr) and in children
(3.6 lmol/g cr) in this study were comparable with the
levels of MDA in asthmatic children under stable
conditions (3.0 lmol/g cr) and during asthma attacks (4.4 lmol/
g cr) [
Mechanisms by which the particulate matter induces
oxidative stress have been investigated in cell and animal
experiments. Soluble transition metals and organic
molecules adsorbed on to the surface of particles are capable
of generating reactive oxygen intermediates in lung cells
and activating redox-sensitive transcription factors to
enhance inflammatory reactions [
]. Moreover, soluble
constituents of PM2.5 that can cross the pulmonary
epithelium into the circulation may directly affect the
cardiovascular system via imbalance of the autonomic
nervous system, systemic oxidative stress, and
inflammatory response [
The production of reactive oxygen species and
subsequent biomolecular damage can be repaired or removed
by defense systems in normal metabolism. Therefore, the
balance between production and degradation of reactive
oxygen metabolites determines oxidative stress and health
]. However, the functional capacity of
antioxidant defense may be reduced with aging; aging is,
therefore, a contributing factor for induction of oxidative
]. This mechanism was supported by the
results of our study, which showed that exposure to
particulate matter significantly produced metabolites of
oxidative stress in the aged group, regarded as more
vulnerable to oxidative stress, than in the young children
Biological markers are useful in explaining pathogenic
mechanisms and recognizing early effects before
development of disease. Therefore, through personal monitoring
of exposure to particulate matter and use of biomarkers, it
is possible to characterize individual exposure and explore
the mechanisms leading to relevant biological effects [
Sørensen et al. [
] measured personal exposure to PM2.5
and 8-oxodG in the blood and urine of students in
Copenhagen, and found that personal PM2.5 exposure was
associated with 8-oxodG concentrations in lymphocyte
DNA, but not in urine.
MDA used as biomarker of oxidative stress in this
study is known to be associated with many diseases
including cardiovascular disease, cancer, Alzheimer’s
disease, liver disease, and adverse pregnancy outcomes
]. There are numerous factors that are known to
affect the formation of lipid peroxides. The level of
MDA may be associated with age, body mass index and
dietary factors. The consumption of some vegetables
may have an inverse association with MDA
]. On the other hand, alcohol and meat
consumption and smoking behavior may increase the
]. In statistical analysis, we considered these
factors and determined the final model including alcohol
consumption, BMI, smoking behavior, passive smoking,
and medical history (heart disease, hypertension, and
Elderly people spend most of their time indoors and
personal exposure to PM2.5 does not correlate strongly with
outdoor central site PM2.5 concentrations [
]. Sources of
indoor ambient particles are not only from outdoor
contributions, but also from indoor sources such as smoking,
cooking, combustion, indoor resuspension of dusts by
human activities, and transformation processes [
reduce errors in the measurement and bias of the
exposureeffect relationship, we repeatedly measured personal
exposure instead of using outdoor or indoor area
monitoring data as an exposure estimate.
In this study, personal exposure to PM2.5 was four
times higher for the children than for the elderly. Personal
exposure to PM2.5 has been reported to be associated with
activities such as walking or cycling and proximity to
]. Therefore, assuming the indoor
and outdoor environments were not so different between
two subgroups, but the elderly at day-care facilities are
relatively inactive compared with the preschoolers in
kindergarten, the reason for the differences in personal
exposure level to PM2.5 may be resuspension of
particulate matter and more particle collection in the filter
because of the greater physical activity of the preschool
We found that the elderly are at a higher risk of
oxidative stress associated with fine particulate pollutant
exposure compared with children. Ambient concentrations
of particles may be sufficient to cause oxidative stress in
elderly subjects with compromised defense mechanisms
against the attack of oxygen-containing free radicals,
whereas young children will be able to tolerate higher
exposure levels. However, it is unclear whether elderly
people are more vulnerable to the effects of all components
in the air pollution mixture. When studying associations
between particulate matter and oxidative stress other
considerations must be included, for example concomitant
personal exposure to other pollutants, e.g. ozone, nitrous
oxides, polycyclic aromatic hydrocarbons, heavy metals,
and volatile organic chemicals.
In conclusion, the association between personal
exposure to fine particulates and oxidative stress markers
suggests that oxidative stress may be involved in the
disease processes induced by particulate air pollution. This
result explains the link between toxicological mechanisms
in in-vitro experiments on particulate exposure and
epidemiological evidence of particulate air pollution. This
finding also suggests the need for air
pollution-management policies to focus on susceptible populations,
particularly the elderly.
Acknowledgments This study was supported by the Eco-technopia
21 project of the Korea Institute of Environmental Science and
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