The short and long term associations of particulate matter 2020 environmen

Bạn đang xem bản rút gọn của tài liệu. Xem và tải ngay bản đầy đủ của tài liệu tại đây (3.75 MB, 18 trang )

Environmental Pollution 267 (2020) 115630

Contents lists available at ScienceDirect

Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol

Review

The short- and long-term associations of particulate matter with
inﬂammation and blood coagulation markers: A meta-analysis*
Hong Tang a, b, Zilu Cheng c, Na Li a, b, Shuyuan Mao a, b, Runxue Ma a, Haijun He a,
Zhiping Niu a, b, Xiaolu Chen a, b, Hao Xiang a, b, *
a
b
c

Department of Global Health, School of Health Sciences, Wuhan University, 115# Donghu Road, Wuhan, China
Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan, China
School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, 122# Luoshi Road, Wuhan, China

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 17 June 2020
Received in revised form
31 August 2020
Accepted 7 September 2020
Available online 10 September 2020

Inﬂammation and the coagulation cascade are considered to be the potential mechanisms of ambient
particulate matter (PM) exposure-induced adverse cardiovascular events. Tumor necrosis factor-alpha
(TNF-a), interleukin-6 (IL-6), interleukin-8 (IL-8), and ﬁbrinogen are arguably the four most commonly
assayed markers to reﬂect the relationships of PM with inﬂammation and blood coagulation. This review
summarized and quantitatively analyzed the existing studies reporting short- and long-term associations
of PM2.5(PM with an aerodynamic diameter 2.5 mm)/PM10 (PM with an aerodynamic diameter 10 mm)
with important inﬂammation and blood coagulation markers (TNF-a, IL-6, IL-8, ﬁbrinogen). We reviewed
relevant studies published up to July 2020, using three English databases (PubMed, Web of Science,
Embase) and two Chinese databases (Wang-Fang, China National Knowledge Infrastructure). The OHAT
tool, with some modiﬁcation, was applied to evaluate risk of bias. Meta-analyses were conducted with
random-effects models for calculating the pooled estimate of markers. To assess the potential effect
modiﬁers and the source of heterogeneity, we conducted subgroup analyses and meta-regression analyses where appropriate. The assessment and correction of publication bias were based on Begg’s and
Egger’s test and “trim-and-ﬁll” analysis. We identiﬁed 44 eligible studies. For short-term PM exposure,
the percent change of a 10 mg/m3 PM2.5 increase on TNF-a and ﬁbrinogen was 3.51% (95% conﬁdence
interval (CI): 1.21%, 5.81%) and 0.54% (95% conﬁdence interval (CI): 0.21%, 0.86%) respectively. We also
found a signiﬁcant short-term association between PM10 and ﬁbrinogen (percent change ¼ 0.17%, 95% CI:
0.04%, 0.29%). Overall analysis showed that long-term associations of ﬁbrinogen with PM2.5 and PM10
were not signiﬁcant. Subgroup analysis showed that long-term associations of ﬁbrinogen with PM2.5 and
PM10 were signiﬁcant only found in studies conducted in Asia. Our ﬁndings support signiﬁcant shortterm associations of PM with TNF-a and ﬁbrinogen. Future epidemiological studies should address the
role long-term PM exposure plays in inﬂammation and blood coagulation markers level change.
© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
( />
Keywords:
Particulate matter
Inﬂammation
Blood coagulation
Meta-analysis

1. Introduction

Inﬂammation and the coagulation cascade are considered as
potential mechanisms of ambient particulate matter exposure
induced adverse cardiovascular events (Hamanaka and Mutlu,
2018). TNF-a (tumor necrosis factor-a), IL-6 (interleukin-6), IL-8
(interleukin-8), and ﬁbrinogen are arguably the four most

*
This paper has been recommended for acceptance by Dr. Da Chen.
* Corresponding author. Department of Global Health, School of Health Sciences,
Wuhan University, 115# Donghu Road, Wuhan, China.
E-mail address: (H. Xiang).

commonly assayed markers to reﬂect the associations of ambient
particulate matter with inﬂammation and blood coagulation (Fang
et al., 2012).
There are close links between inﬂammation and blood coagulation. Inﬂammation is thought to regulate blood coagulation and
activate the ﬁbrinolytic system (Esmon, 2003). For example, acute
inﬂammation can lead to an increase in ﬁbrinogen (Luyendyk et al.,
2019). Fibrinogen is a blood coagulation biomarker with proinﬂammatory effect, which not only play a signiﬁcant role in platelet
aggregation and thrombosis (Kattula et al., 2017), but also increases
in response to inﬂammation (Hoppe, 2014). A study reported that
ﬁbrinogen is up-regulated after being stimulated by inﬂammatory

/>0269-7491/© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( />

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

examined the short-term and long-term associations of PM2.5/PM10

with inﬂammation and blood coagulation markers up to July 2019.
Supplemental Table S1 showed the PECOS statement of all included
studies (Morgan et al., 2018). Keywords included (1) “air pollution”,
“air pollutants”, “air environmental pollutants”, “environmental air
pollutants”, “pollution”, “pollutant*", “particulate matter”, “particulate air pollutants”, “particulate matters”, “particulate*", “particle*", “PM”, “PM2.5”, “PM10”; (2) “ﬁbrinogen”, “blood coagulation
factor I"; (3) “tumor necrosis factor-alpha”, “tumor necrosis factor
alpha”, “tumor necrosis factor”, “TNFalpha”, “TNF-alpha”; (4)
“Interleukin-6”, “IL-6”, “Interleukin 6”, “IL6”, “Interleukin-8”, “IL-8”,
“Interleukin 8”, “IL8”. Also, synonyms of relative markers and particulate matter were searched using Medical Subjects Headings
terms. Search strings were summarized in the supplementary
material.

cytokines, such as interleukin 6 (Ridker et al., 2000). Blood coagulation, in turn, play an important role in inﬂammation. Fibrinogen is
one of the most effective contributors to inﬂammation among all
proteins of the coagulation system (Castell et al., 1990). Fibrinogen
is considered a potential driver of inﬂammation-related diseases
(sepsis, endotoxemia, encephalomyelitis or multiple sclerosis)
(Davalos and Akassoglou, 2012). Studies have shown that ﬁbrinogen can activate inﬂammation, leading to the release of inﬂammatory cytokines, such as TNF-a (Jensen et al., 2007). Herein, we
focus on four typical biomarkers, which have not only been widely
studied in air pollution research to reﬂect the role of particulate
matter in inducing inﬂammation and blood coagulation, but also
related to cardiovascular diseases.
Fibrinogen is regarded as a risk factor and predictor of cardiovascular disease (De Luca et al., 2011; Kunutsor et al., 2016). Studies
indicated that ﬁbrinogen was associated with cardiovascular
morbidity and mortality (D’Angelo et al., 2006). A meta-analysis
reported a signiﬁcant association of ﬁbrinogen with myocardial
infarction (Fibrinogen Studies et al., 2005). In addition, studies also
reported that the additional measurement of ﬁbrinogen could help
prevent cardiovascular events (Emerging Risk Factors et al., 2012;
Maresca et al., 1999). TNF-a, IL-6, and IL-8 are regarded as critical

inﬂammation markers and play a signiﬁcant role in inﬂammation
(Ghasemi et al., 2011; Mehaffey and Majid, 2017; Unver and
McAllister, 2018). Moreover, TNF-a is closely related to atherosclerosis as it contributes to inﬂammation as well as promoting
insulin resistance (Popa et al., 2007). Studies also reported that IL-6
and IL-8 are associated with multiple cardiovascular diseases, such
as coronary artery disease, atherosclerosis, sudden cardiac death
(Apostolakis et al., 2009; Hussein et al., 2013).
Current epidemiological studies reported inconsistent effects of
PM2.5 and PM10 on the above markers. Among 6589 nonsmoking
subjects in South Korea, for short-term PM exposure, Lee et al. reported 0.44% (95%CI: 0.15%, 0.73%) higher ﬁbrinogen levels with
10.4 mg/m3 increment of PM2.5 and 0.61% (95%CI: 0.33%, 0.90%)
higher ﬁbrinogen levels with 20.1 mg/m3 increment of PM10 (Lee
et al., 2018). In healthy college students, for short-term PM exposure, Wang et al. reported the percent change of a 10 mg/m3 PM2.5
increase on IL-6 and TNF-a was 4.1% (95%CI: 1.2%, 6.9%) and 4.4%
(95%CI: 1.7%, 7.0%), respectively (Wang et al., 2018). However, there
were studies reported inconsistent ﬁndings. A study conducted on
general population reported an insigniﬁcant short-term association
between PM10 and ﬁbrinogen (Liao et al., 2005). Among healthy
humans, Kumarathasan et al. reported insigniﬁcant changes of TNFa, IL-6, and IL-8 with short-term PM2.5 exposure (Kumarathasan
et al., 2018).
To date, there has been no meta-analysis to summarize associations of PM (PM2.5, PM10) with inﬂammation and blood coagulation markers (TNF-a, IL-6, IL-8, ﬁbrinogen). To ﬁll this gap, this
review summarized and quantitatively analyzed the existed
studies, which could provide healthcare professionals and researchers with a comprehensive overview of the effect of shortterm and long-term exposure to particulate air pollution on TNF,
IL-6, IL-8, and ﬁbrinogen.

2.2. Inclusion and exclusion criteria
We evaluated the effects of short-term (for days or weeks) (Lee
et al., 2017) and long-term PM exposure (more than six months)
(Rodosthenous et al., 2018) on inﬂammation and blood coagulation
markers. The included articles should be epidemiologic studies

focusing on the associations of inﬂammation and blood coagulation
markers with PM exposure and reported associations and 95%
conﬁdence intervals directly or data could be used to calculate. We
excluded in vivo studies, in vitro studies, case reports, summaries,
reviews, editorials, commentaries, and studies that reported
inﬂammation and coagulation markers in nasal lavage, induced
sputum and exhaled breath condensate (EBC). Studies restricted to
pregnant women (Braithwaite et al., 2019) and focusing on PM size
fractions, concentrated ambient particles (CAPs), occupational
exposure, indoor exposure, and cigarette smoke exposure were not
included.
2.3. Study selection
We downloaded all studies identiﬁed from ﬁve databases into a
reference manager (Endnote X8) and removed duplicates. The
remaining studies were screened for eligibility by two investigators. First, two investigators screened titles and abstracts to
select eligible studies. Then, the remaining studies were reviewed
in full texts. Two investigators selected studies independently, and
a third investigator adjudicated disagreements. References of
included studies were searched to ﬁnd more relevant studies.
2.4. Data extraction and synthesis
Two investigators extracted data from each study, including
authors, publication year, characters of subjects (disease status,
age), sample size, study design, study location, study period, an
average of markers level (TNF-a, IL-6, IL-8, ﬁbrinogen), average
levels of PM, exposure assessment methods, effect estimates
(percent change, coefﬁcient(b), relative change, fold change) and
standard error or a 95% conﬁdence interval. The data extraction was
performed by two investigators and any disagreements were
adjudicated by a third investigator.
We used the percent change as effect estimates. All estimates

were converted into percent change of a 10 mg/m3 PM increase.
Beta-coefﬁcients from linear regression models were normalized
using an equation b Â 10÷M Â 100%to calculate the percent change,
and another equation ẵb 1:96 SEị 10 ữM 100% to calculate
95% conﬁdence intervals (CIs) (Yang et al., 2015), where b represents the regression coefﬁcient, M represents the mean of markers
level, and SE represents the standard error associated with b. Stata

2. Methods
Details of a PRISMA checklist (Moher et al., 2009) were present
in the Supplementary material.
2.1. Search methods
We searched three English databases (PubMed, Web of Science,
Embase) and two Chinese databases (Wang-Fang, China National
Knowledge Infrastructure) to identify epidemiological studies that
2

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Each study was removed in turn to investigate the sensitivity of
pooled results. The assessment and correction of publication bias
were based on Begg’s and Egger’s test (Egger et al., 1997) and “trimand-ﬁll” analysis.

software (version 12.0; Stata Corp, U.S.) was used to conduct the
meta-analysis.
2.5. Risk of bias evaluation

3. Results

The OHAT tool, with some modiﬁcation, was applied to evaluate
risk of bias (Rooney et al., 2014). We considered some related reviews when formulating standards for the risk of bias used in this
study (Supplemental Table S2) (Kirrane et al., 2019; Luben et al.,
2017; Rooney et al., 2014). We assessed the following aspects: selection bias, disease misclassiﬁcation, exposure assessment, confounding, detection bias, and selective reporting. Each aspect is
rated as “high”, “probably high”, “probably low”, “low”, or “not
applicable” based on speciﬁc criteria.

3.1. Study characteristics
Fig. 1 shows the selection process of literature. We identiﬁed 44
studies from citations screened (Chen et al., 2018; Chuang et al.,
2007; Cole et al., 2018; Croft et al., 2017; Dadvand et al., 2014;
Delﬁno et al., 2010; Deng et al., 2020; Dubowsky et al., 2006;
Emmerechts et al., 2012; Forbes et al., 2009; Green et al., 2016;
Habre et al., 2018; Hajat et al., 2015; Hassanvand et al., 2017;
Hildebrandt et al., 2009; Hoffmann et al., 2009; Huttunen et al.,
2012; Kumarathasan et al., 2018; Lanki et al., 2015; Lee et al.,
2018; Liao et al., 2005; Mirowsky et al., 2015; Pekkanen et al.,
2000; Pope et al., 2016; Puett et al., 2019; Rich et al., 2012;
Ruckerl et al., 2007; Rückerl et al., 2014; Rudez et al., 2009;
Schneider et al., 2010; Schwartz, 2001; Seaton et al., 1999; Steinvil
et al., 2008; Strak et al., 2013; Su et al., 2017; Sullivan et al., 2007;
Tsai et al., 2012; Viehmann et al., 2015; Wang et al., 2018; Wu et al.,
2012; Zeka et al., 2006; Zhang et al., 2016, 2020; Zuurbier et al.,
2011). Supplemental Table S3 provides the characteristics of
included studies. Thirteen studies were conducted on patients with
speciﬁc diseases, thirty on general populations, and one on patients
and the general population. Sample size ranged from 22 to 20,000
for short-term studies, and from 242 to 25,000 for long-term
studies. Seven studies assessed exposure using air pollution exposure models (land-use regression modeling, kriging interpolation

modeling, and air dispersion modeling), and the rest based on ﬁxed
site or personal exposure measurement. Eighteen studies were
performed in North America, sixteen in Europe, and ten in Asia. No
study was conducted in South America or Africa.

2.6. Statistical analysis
2.6.1. Meta-analysis
Meta-analyses were conducted only when four or more eligible
studies examined the association between the same pollutant and
the same marker (Vrijheid et al., 2011). When studies reported the
data of multi-pollutant models and single-pollutant models, we
only analyzed the data of single-pollutant models. If only subgroup
data were available in the study, then all subgroup results were
included. When some studies provided several adjusted models,
we used the “main model” or fully-adjusted model in our metaanalysis. If multiple lags were reported, we chose one based on
the following criteria: (1) the lag that the investigators focused on
or stated as a priority; (2) the lag that was statistically signiﬁcant;
(3) the lag with the largest effect estimate (Atkinson et al., 2012). In
addition, for short-term studies, we pooled the effect estimates
according to lag patterns when four or more estimates were
available.
Meta-analyses based on the random-effects model were conducted to estimate the association between PM and inﬂammation
and blood coagulation markers. I2, representing the proportion of
heterogeneity in the total variation of effect, was used to quantify
the heterogeneity among included studies. I2 values in the range of
50e100% indicate large or extreme heterogeneity (Higgins et al.,
2003).

3.2. Risk of bias evaluation
The evaluation for risk of bias was shown in Fig. 2. Most of the

studies were evaluated as ‘low’ or ‘probably low’ risk except four
studies (Deng et al., 2020; Huttunen et al., 2012; Liao et al., 2005;
Seaton et al., 1999). We considered that the included studies are of
sufﬁcient quality to evaluate the association between these
markers and particulate air pollution. More details can be found in
the supplementary materials (Table S4).

2.6.2. Subgroup analysis
The heterogeneity among all included studies exists due to the
differences in population characteristics, sample size, study designs, exposure assessment techniques, study locations, and
pollution levels. To conﬁrm the potential confounders, we performed subgroup analyses by disease status (general population or
patients) (Liu et al., 2019), age (<60 years or !60 years) (Schneider
et al., 2010), gender proportion (male 50% or male >50%)
(Clougherty, 2010), sample size (<1000 or !1000) (Liu et al., 2019),
study design (panel study, cross-sectional study, others (time-series study, case-crossover study, semi-experimental design)), study
location (Europe, Asia or North America), PM level (low or high
according to WHO guidelines) (Krzyzanowski and Cohen, 2008),
and exposure assessment techniques (ﬁxed site monitors or
others).

3.3. Associations between PM2.5 and markers
3.3.1. Overall meta-analysis for PM2.5 and markers
Our meta-analysis showed signiﬁcant changes of TNF-a and
ﬁbrinogen and insigniﬁcant changes of IL-6 and IL-8 with shortterm PM2.5 exposure (Fig. 3(A), 3(E), 3(B), and 3(D)). For shortterm PM exposure, the percent change of a 10 mg/m3 PM2.5 increase on TNF-a and ﬁbrinogen were 3.51% (95% CI: 1.21%, 5.81%)
and 0.54% (95% CI: 0.21%, 0.86%). Fibrinogen was not signiﬁcantly
associated with long-term PM2.5 exposure (Fig. 3(F)). Meta-analysis
according to lag pattern showed that the percent change of a 10 mg/
m3 PM2.5 increase on TNF-a (n ¼ 4 studies) and ﬁbrinogen (n ¼ 12
studies) were 4.19% (95%CI: 0.36%, 8.03%) and 0.26% (95%CI: 0.05%,
0.47%) at lag 1 day respectively (Fig. 4).

2.6.3. Meta-regression, sensitivity analyses, and publication bias
To investigate the source of heterogeneity, we performed a
meta-regression analysis (Higgins et al., 2011). Factors included
disease status, age, gender proportion, sample size, study design,
study location, average level of pollutants, and exposure assessment techniques.

3.3.2. Subgroup-analysis for PM2.5 and markers
Sub-stratiﬁed analysis by study location showed that signiﬁcant

3

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Fig. 1. PRISMA 2009 ﬂow diagram of study selection.

associations of PM2.5 with ﬁbrinogen, TNF-a, and IL-6 in studies
conducted in Asia compared to that conducted in Europe (Table 1).
For example, we found a statistically signiﬁcant association between short-term PM2.5 exposure and IL-6 in studies conducted in
Asia (percent change ¼ 11.65%, 95%CI: 3.02%, 20.28%), while an
insigniﬁcant association in studies conducted in Europe (percent
change ¼ 0.32%, 95%CI: À1.61%, 2.25%) (Table 1).

3.4. Associations between PM10 and markers
3.4.1. Overall meta-analysis for PM10 and markers
Our meta-analysis showed a signiﬁcant short-term association
between PM10 and ﬁbrinogen (Fig. 3(G); n ¼ 16 studies) and an

insigniﬁcant long-term association between PM10 and ﬁbrinogen
(Fig. 3(H); n ¼ 5 studies). The percent change of a 10 mg/m3 PM10
increase on ﬁbrinogen was 0.17% (95% CI: 0.44%, 0.29%). The pooled
estimate of IL-6 with short-term PM10 exposure was not signiﬁcant
(Fig. 3(C); n ¼ 5 studies). Meta-analysis stratiﬁed by lag pattern
showed a 0.08% (95%CI: 0.02%, 0.13%) increase in ﬁbrinogen (n ¼ 5
studies) per 10 mg/m3 exposure to PM10 at lag 0 day (Fig. 4).

3.3.3. Studies not included in meta-analysis
There are only one, two, and one studies investigated the associations of long-term PM2.5 exposure with TNF-a (Dadvand et al.,
2014), IL-6 (Dadvand et al., 2014; Hajat et al., 2015), and IL-8
(Dadvand et al., 2014), which was too small to permit us to
perform a meta-analysis.
4

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

followed by general population (percent change ¼ 0.11%, 95%CI:
0.00%, 0.21%).
3.4.3. Studies not included in meta-analysis
There are only two studies investigated the associations of
short-term PM10 exposure with TNF-a (Tsai et al., 2012; Zuurbier
et al., 2011) and IL-8 (Mirowsky et al., 2015; Zuurbier et al., 2011),
which was too small to permit us to perform a meta-analysis.
3.5. Meta-regression analysis, sensitivity analyses and publication
bias
Meta-regression analysis showed air pollutants levels, age,

study location, disease status, and study design may be the source
of heterogeneity (Table 2). Sensitivity analyses supported the results of meta-analyses for all inﬂammation and blood coagulation
markers (Fig. 5). Begg’s funnel plots of PM2.5 and TNF-a, IL-8 show
general symmetry (Fig. 6). Also, P-values of Begg’s and Egger’s tests
indicated no publication bias of analyses on PM2.5 and TNF-a, IL-8
(Table 3). For IL-6, the P-value of Egger’s test in studies reporting
short-term association between PM2.5 and IL-6 was 0.02. Trim-andﬁll analysis shows the change in the overall analysis for studies
reporting the short-term association between PM2.5 and IL-6 is
0.90% (95%CI: À0.02%, 2.00%) (Table 3, Figure S1 (A)). For shortterm PM exposure, we did not observe publication bias of analyses on ﬁbrinogen and PM2.5, PM10. However, the P-value of Egger’s
test in studies reporting long-term association for PM2.5-ﬁbrinogen
was 0.05 (n ¼ 7 studies). Trim-and-ﬁll analysis shows no change in
the overall analysis for studies reporting the long-term association
for PM2.5-ﬁbrinogen (Figure S1(B)).
4. Discussion
To our knowledge, we conducted this ﬁrst review to comprehensively summarize and quantitatively analyze short- and longterm association of PM2.5/PM10 with key inﬂammation and blood
coagulation markers. Our meta-analysis showed signiﬁcant shortterm associations between PM2.5 and ﬁbrinogen (percent
change ¼ 0.44%, 95%CI: 0.11%, 0.77%) and PM10-ﬁbrinogen (percent
change ¼ 0.17%, 95%CI: 0.04%, 0.29%). For short-term PM exposure,
the overall analysis showed the percent change of a 10 mg/m3 PM2.5
increase on TNF-a was 3.67% (95%CI: 0.97%, 6.36%). However, in
long-term studies, the pooled estimates of ﬁbrinogen with PM2.5,
and PM10 were insigniﬁcant.
Given the important role of TNF-a and ﬁbrinogen for inﬂammation and coagulation cascade in cardiovascular disease, our results support that short-term PM exposure might cause adverse
effects on the human body through inﬂammation and coagulation
cascade. When human bodies are exposed to particulate air
pollution, particles can cause an acute-phase response and
inﬂammation indicated by increments of ﬁbrinogen and inﬂammatory cytokines (Fiordelisi et al., 2017; Franchini and Mannucci,
2007). Particles can cause an acute-phase response when it reach
the bronchi and alveolar cells (Brook et al., 2010). Fibrinogen, a
marker of acute-phase response, is not only a blood coagulation but

also play a role in inﬂammation (Kattula et al., 2017). Fibrinogen can
activate inﬂammation, leading to the release of inﬂammatory cytokines, such as TNF-a (Jensen et al., 2007). TNF-a is an inﬂammatory marker and involved in the development of atherosclerosis
(Popa et al., 2007). Inﬂammatory cytokines due to air pollution
exposure can also trigger ﬁbrinogen production (Mutlu et al., 2007).
Fibrinogen due to air pollution may increases plasma viscosity and
induces platelet adhesion and aggregation, which could enhance
coagulation potential and increase the risk of venous thrombosis
leading to the development of cardiovascular disease (Brook et al.,

Fig. 2. Risk of bias rating for each study.

3.4.2. Subgroup analysis for PM10 and markers
Sub-stratiﬁed analysis by exposure assessment technique, subjects, study location, and study design showed that a signiﬁcant or
stronger short-term association between PM10 and ﬁbrinogen in
studies assigning exposure based on ﬁxed-site, for patients, conducted in Asia and panel design compared to that assigning exposure using other methods, for the general population, performed in
Europe and cross-sectional design (Table 1). For example, we found
a signiﬁcant short-term association between PM10 and ﬁbrinogen
in studies for patient (percent change ¼ 0.79%, 95%CI: 0.15%, 1.42%),
5

Fig. 3. Forest plot of the meta-analysis: (A) short-term expose to PM2.5 and TNF-a (B) short-term exposure to PM2.5 and IL-6 (C) short-term exposure to PM10 an d IL-6 (D) shortterm exposure to PM2.5 and IL-8 (E) short-term exposure to PM2.5 and ﬁbrinogen (F) long-term exposure to PM2.5 and ﬁbrinogen (G) short-term exposure to PM10 and ﬁbrinogen (H)
long-term exposure to PM10 and ﬁbrinogen.

6

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Fig. 4. Meta-analyses stratiﬁed by varying lag patterns (A) short-term expose to PM2.5 and TNF-a (B) short-term exposure to PM2.5 and IL-6 (C) short-term exposure to PM2.5 and
ﬁbrinogen(D)short-term exposure to PM10 and ﬁbrinogen.

7

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Fig. 4. (continued).

8

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Table 1
Subgroup analysis of percent change in inﬂammation and blood coagulation markers in association with a 10 mg/m3 increase in ambient PM concentration.
Biomarker

Subgroup

Exposure

Disease status
Fibrinogen PM2.5

Short-term

PM10

Long-term
Short-term

TNF-a
IL-6

PM2.5
PM2.5

Short-term
Short-term

Age
Fibrinogen

PM2.5

Short-term
Long-term

PM10

Short-term

TNF-a

IL-6

PM2.5
PM2.5

Short-term
Short-term

Sex
Fibrinogen

PM2.5

Short-term

PM10

Long-term
Short-term

PM2.5
PM2.5

Long-term
Short-term
Short-term

Sample size
Fibrinogen PM2.5

Short-term

PM10

Long-term
Short-term

TNF-a
IL-6

TNF-a
PM2.5
IL-6
PM2.5
Study design
Fibrinogen PM2.5

Long-term
Short-term
Short-term
Short-term

Long-term

TNF-a
IL-6

PM10

Short-term

PM2.5
PM2.5

Long-term
Short-term
Short-term

Study location
Fibrinogen PM2.5

Short-term

PM10

Long-term
Short-term

PM2.5
PM2.5

Long-term
Short-term
Short-term

Pollution level
Fibrinogen PM2.5

Short-term

TNF-a
IL-6

PM10
TNF-a
IL-6

PM2.5
PM2.5

Long-term
Short-term
Long-term
Short-term
Short-term

Grouping criteria

Pooled percent-changes
(95% CI)

P value

No. of effect
estimates

No. of studies

General population
Patients

General population
General population
Patients
General population
General population
Patients

0.31 (-0.01, 0.63)
0.88 (0.20, 1.55)
2.44 (-2.67, 7.54)
0.11 (0.00, 0.21)
0.79 (0.15, 1.42)
2.98 (0.85, 5.12)
1.22(0.15, 2.28)
0.29 (-3.44, 4.01)

0.061
0.011
0.349
0.041
0.015
0.006
0.025
0.881

9
13
8
13
6

7
11
8

<60
!60
<60
!60
<60
!60
<60
<60
!60

0.32 (0.00, 0.64)
0.57 (-0.40, 1.54)
2.28 (0.06,4.50)
À0.99 (-2.92, 0.95)
0.12 (-0.03, 0.26)
0.23 (-0.18, 0.65)
2.98 (0.85, 5.12)
2.47 (-0.17, 5.12)
0.70 (-0.88, 2.29)

0.047
0.253
0.044
0.319
0.113
0.274

0.006
0.067
0.384

male 50%
male>50%
male 50%
male 50%
male>50%
male 50%
male>50%
male 50%
male>50%

0.41
0.54
2.20
0.16
0.25
0.50
3.63
1.86
1.42

(0.11,0.72)
(-0.01, 1.10)
(-3.60, 8.00)
(-0.02,0.34)
(0.00,0.49)
(-1.04, 2.03)

(0.27, 6.99)
(-1.31, 5.03)
(-0.42, 3.27)

<1000
!1000
!1000
<1000
!1000
!1000
<1000
<1000

0.36
0.64
2.20
0.34
0.15
0.50
3.51
1.04

(-0.12, 0.84)
(0.33, 0.96)
(-3.60, 8.00)
(0.00, 0.68)
(0.01, 0.29)
(-1.04, 2.03)
(1.21, 5.81)
(-0.15, 2.24)

Heterogeneity
P-value for heterogeneity

I2

9
11
6
11
5
7
11
7

0.005
0.009
<0.001
0.014
0.031
<0.001
<0.001
<0.001

64.00%
54.7%
94.60%
52.10%
59.40%
81.7%

76.4%
77.2%

8
12
4
4
13
4
7
11
8

8
10
3
4
12
4
7
11
8

.041
0.003
0.724
0.918
0.002
0.072
<0.001

<0.001
0.005

52.2%
60.90%
0.00%
0.00%
60.70%
57.10%
81.7%
79.0%
65.50%

0.008
0.056
0.457
0.084
0.046
0.525
0.034
0.250
0.130

7
14
7
9
9
6
6

7
12

7
12
5
9
8
4
6
7
11

0.211
<0.001
<0.001
<0.001
0.026
<0.001
<0.001
0.001
<0.001

28.50%
65.0%
95.30%
72.10%
53.90%
79.00%
86.60%

72.8%
76.5%

0.14
<0.001
0.457
0.05
0.031
0.525
0.003
0.088

16
6
7
10
9
6
9
16

13
6
5
9
7
4
9
15

0.001
0.245
<0.001
0.005
0.004
<0.001
<0.001
<0.001

60.70%
25.2%
95.30%
62.00%
65.00%
79.00%
81.2%
77.8%

0.62 (0.22, 1.02)
0.33 (-2.13, 2.80)
0.25 (-0.17, 0.68)
3.27 (-4.55, 11.09)
1.65 (-0.89, 4.20)
0.48 (0.17, 0.78)
0.07 (-0.07,0.21)
À0.01 (-1.33, 1.31)
4.06 (1.24, 6.89)
1.72 (-0.11, 3.54)
5.24 (0.77, 9.71)
0.28 (-2.72, 3.29)

0.002
0.790
0.241
0.413
0.203
0.003
0.323
0.99
0.005
0.065
0.021
0.853

13
4
5
4
5
9
7
5
5
12
2
5

10
4
5

4
3
8
5
3
5
11
2
5

0.087
0.005
0.022
<0.001
0.568
0.024
0.06
0.175
<0.001
<0.001
0.384
0.013

37.10%
76.6%
65.2%
97.30%
0.00%
54.70%
50.30%

37.00%
88.5%
79.1%
0.0%
68.50%

Europe
Asia
North America
Europe
Europe
Asia
Europe
Asia
Europe
Asia
North America

0.21 (-0.10, 0.51)
1.09 (0.06, 2.13)
1.04 (0.08, 2.00)
0.13 (-2.54, 2.79)
0.19 (-0.05, 0.42)
0.15 (0.03, 0.28)
À0.02 (-1.18, 1.14)
2.56 (0.62, 4.49)
0.32 (-1.61, 2.25)
11.65 (3.02, 20.28)
0.54 (-0.50, 1.57)

0.189
0.038
0.034
0.926
0.12
0.019
0.968
0.01
0.745
0.008
0.310

7
4
11
5
12
4
5
4
5
4
10

6
4
9
3
10
3

3
4
4
4
10

0.033
0.104
0.021
0.955
0.004
0.024
0.199
0.055
0.087
0.002
<0.001

56.10%
51.4%
52.5%
0.00%
59.90%
68.20%
33.30%
60.6%
50.70%
79.6%
71.4%

Low
High
High
Low
Low
High
Low
High

0.62 (0.19, 1.05)
0.83 (-0.44, 2.09)
2.47 (-3.08, 8.02)
0.28 (0.04, 0.52)
À0.61 (-1.69, 0.46)
2.94 (0.43, 5.44)
0.38 (-0.50, 1.26)
11.71 (3.82, 19.60)

0.004
0.201
0.384
0.021
0.264
0.022
0.401
0.004

16
5
7

16
4
5
12
5

13
5
7
14
3
5
11
5

0.015
0.035
<0.001
0.001
0.723
0.009
<0.001
0.004

48.8%
61.2%
95.20%
61.60%
0.00%
70.4%

68.7%
73.8%

Panel study
Cross-sectional
Others
Panel study
Cross-sectional
Panel study
Cross-sectional
Cross-sectional
Panel study
Panel study
Cross-sectional
Others

study

study
study
study

study

(continued on next page)
9

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Table 1 (continued )
Biomarker

Subgroup

Exposure assessment
Fibrinogen PM2.5

TNF- a
IL-6

Exposure

Short-term

PM10

Long-term
Short-term

PM2.5
PM2.5

Long-term
Short-term
Short-term

Grouping criteria

Fixed site
Others
Others
Fixed site
Others
Others
Fixed site
Fixed site
Others

Pooled percent-changes
(95% CI)

P value

0.65 (0.27, 1.03)
0.18 (-0.58, 0.95)
1.34 (-0.86, 3.54)
0.20 (0.08, 0.32)
À0.08 (-0.68, 0.53)
0.07 (-0.99, 1.14)
3.91 (1.01, 6.80)
1.28 (-0.61, 3.18)
2.09 (-0.30, 4.48)

0.001
0.639
0.232
0.001

0.803
0.891
0.008
0.184
0.087

No. of effect
estimates

No. of studies

19
3
6
15
4
6
6
13
6

16
3
4
13
3
4
6
12
6

Heterogeneity
P-value for heterogeneity

I2

0.004
0.05
0.674
0.002
0.034
0.259
<0.001
<0.001
0.016

52.2%
66.5%
0.00%
59.90%
65.40%
23.30%
84.2%
78.4%
64.3%

Abbreviations: PM2.5:particulate matter with aerodynamic diameter equal to or less than 2.5 mm; PM10: particulate matter with aerodynamic diameter equal to or less than
10 mm; TNF- a: tumor necrosis factor-alpha, IL-6: interleukin-6; NA: not applicable.

short-term PM2.5 exposure and C-reactive protein was greater in

the subgroup with PM2.5 lower than 25 mg/m3 (Liu et al., 2019).
Subgroup analysis by study location showed that the change of
inﬂammation and blood coagulation markers were signiﬁcant in
Asia. For example, we found a signiﬁcant short-term association
between PM2.5 and IL-6 in studies conducted in Asia (percent
change: 19.82%, 95%CI: 2.94%, 36.70%), but an insigniﬁcant association in studies conducted in Europe (percent change: 0.32%, 95%
CI: À1.61%, 2.25%) or North America (percent change:
0.32%, À0.68%, 1.32%). We also found signiﬁcant pooled estimates of
ﬁbrinogen with PM2.5 and PM10 exposure in studies conducted in
Asia, but not in Europe or North America. A study conducted in 10
cities around the world found that the cities located in Europe
(except Milan) all met the EU PM2.5 annual mean standard (25 mg/
m3), while the cites located in Asia have the highest PM2.5 annual
mean concentrations (de Jesus et al., 2019). Pollution level in Asia is
higher than in Europe, which may contribute to this ﬁnding. The
lack of study in Africa is concerning because these areas may have a
more signiﬁcant impact (Li et al., 2018). In our meta-analysis, there
is no study conducted in Africa.
There are differences in the sources of particulate matter in
different regions, which may be the main reason for the differences
in biomarkers. Some countries in Asia have serious industrial
pollution (Zhang et al., 2019), while in Europe, the proportion of
particulate matter caused by industrial emissions is relatively small.
The main sources of particulate matter are vehicular source, crustal
source, sea-salt source and secondary aerosol source (Viana et al.,
2008). A study conducted in France showed that the highest
source of PM10 is secondary inorganic aerosols (28%), while the
lowest source is heavy oil combustion (4%) (Waked et al., 2014). A
study conducted in China reported that the main sources of PM2.5
are coal combustion, industrial emissions and vehicular exhaust

(Zhang et al., 2019). Moreover, the pollutant concentration in Asia
has been above the WHO threshold for longer than in Europe,
which may also contribute to the continental differences (de Jesus
et al., 2019).
The variation of components in different regions may be a
reason for inconsistent ﬁndings among studies (Steenhof et al.,
2011). In China, Wu et al. found that an increase of 3.91% (95%CI:
0.31%, 7.63%) in ﬁbrinogen per 0.51 mg/m3 exposure to the iron of
PM2.5 at lag 1 day among healthy adults (Wu et al., 2012). Lei et al.
reported a signiﬁcant short-term relationship between lead of
PM2.5 and TNF-a (percent change ¼ 65.20%, 95% CI: 37.07%, 99.10%)
(Lei et al., 2019). A meta-analysis of European cohorts reported a
signiﬁcant long-term association between ﬁbrinogen and zinc of
PM2.5 (percent change ¼ 1.2%, 95%CI: 0.1%, 2.4%), but an insigniﬁcant association for PM2.5 mass (Hampel et al., 2015). A review
reported that metals in particulate matter play different roles in
prothrombotic status (Signorelli et al., 2019). These ﬁndings suggest
that particles mass alone can’t fully reﬂect the toxicity of particles.

2010; Tousoulis et al., 2011).
Not all studies included in this review showed signiﬁcant
changes of TNF-a and ﬁbrinogen with short-term PM exposure.
Zuurbier et al. reported insigniﬁcant changes of TNF-a and ﬁbrinogen with PM2.5 exposure during commuting, which may be due to
exercise (Zuurbier et al., 2011). Exercise is considered to be a
method in controlling the expression of inﬂammation markers
(Neves Miranda et al., 2015) and coagulation markers (Kupchak
et al., 2017). Exercise has an effect on anti-inﬂammatory,
including reduced IL-6 and TNF-a (Woods et al., 2009). Exercise
also has effects on coagulation and ﬁbrinolysis, which could signiﬁcant degrade ﬁbrinogen (El-Sayed et al., 2004). Moreover, the
study by Zuurbier only measured blood markers at lag 6 h, which
did not allow more time windows of response (Zuurbier et al.,

2011). If we observe multiple time windows, we could ﬁnd more
changes of markers.
The short-term associations of exposure to PM with inﬂammation and blood coagulation markers are different at lag length, and
such effects might be different in populations. A study conducted in
COPD patients reported that the percent change of a 10.8 mg/m3
PM2.5 increase on TNF-a was 52.2% (95%CI: 16.1%, 99.4%) at lag 1 day
(Dadvand et al., 2014). Among healthy college students, Wang et al.
reported that, at lag 1 day, the percent change of a 10 mg/m3 PM2.5
increase on TNF-a was 4.37% (95% CI: 11.68%, 7.13%) (Wang et al.,
2018). Also, some studies reported the signiﬁcant change of these
markers at a longer lag interval. Among patients undergoing cardiac rehabilitation, Rich et al. found that the percent change of a
6.5 mg/m3 PM2.5 increase on ﬁbrinogen was 0.082 g/L (95%CI:
0.006 g/L, 0.159 g/L) at lag 2 day (Rich et al., 2012). Study on male
patients showed that the percent change of a 11.43 mg/m3 PM10
increase on ﬁbrinogen were 2.4% (95%CI: 0.6%, 4.1%) and 1.8% (95%
CI: 0.1%, 3.5%) at lag 3 day and lag 4 day, respectively (Hildebrandt
et al., 2009). Rückerl et al. reported a signiﬁcant change of ﬁbrinogen with 5-day average PM2.5 exposure in impaired glucose
tolerance patients or type 2 diabetes mellitus patients, but not in
genetically susceptible subjects (Rückerl et al., 2014). To investigate
the lag effect of PM exposure on changes of these markers, we
conducted meta-analyses according to lag patterns. Meta-analysis
stratiﬁed by lag pattern showed that the percent change of a
10 mg/m3 PM2.5 increase on TNF-a and ﬁbrinogen were 4.19% (95%
CI: 0.36%, 8.03%) and 0.26% (95% CI: 0.02%, 0.51%) at lag 1 day
respectively, and 0.08% (95%CI: 0.02%, 0.13%) higher ﬁbrinogen
levels per 10 mg/m3 exposure to PM10 at lag 0 day.
Subgroup analysis by PM concentrations showed that signiﬁcant
associations of short-term PM2.5 exposure with TNF-a and IL-6 in
higher PM levels. Interestingly, it was found signiﬁcant associations
of short-term PM2.5 and PM10 exposure with ﬁbrinogen in lower

PM levels. Liang et al. also reported that the change of von Willebrand factor was more sensitive in the subgroup with PM2.5 <
25 mg/m3 (Liang et al., 2020). Similarly, the association between
10

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Table 2
Meta-regression analysis by potential modiﬁer.
Biomarker

Subgroup

Disease status
Fibrinogen
PM2.5

Exposure

Short-term
Long-term

PM10

Short-term

TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Age
Fibrinogen

PM2.5

Short-term

Long-term

PM10

Short-term

Long-term

TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Sex
Fibrinogen

PM2.5

Short-term

Long-term
PM10

Short-term

Long-term
TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Sample size
Fibrinogen

PM2.5

Short-term
Long-term

PM10

Short-term
Long-term

IL-6

PM2.5

Short-term

Study design
Fibrinogen

PM2.5

Short-term

Long-term
PM10

Short-term

Long-term
TNF-a

IL-6

PM2.5

PM2.5

Short-term

Short-term

Grouping criteria

No. of effect estimates

No. of studies

Meta-regression
Coef.

P value

I2

0.196

58.96%

0.563

94.63%

0.051

54.53%

0.064

80.45%

0.377

76.73%

0.8007

59.89%

0.0001

0.00%

0.1795

57.60%

0.6703

71.48%

0.064

80.45%

0.744

75.00%

0.3688

58.26%

0.953

94.59%

0.7892

65.24%

0.712

78.96%

0.88

81.51%

0.701

75.30%

0.204

55.37%

0.953

94.59%

0.844

63.45%

0.712

78.96%

0.983

76.17%

0.4046

56.21%

0.521

93.95%

0.0476

49.95%

0.167

44.31%

0.1101

83.36%

0.7533

75.80%

General population
Patients
General population
Patients
General population
Patients
General population
Patients
General population

Patients

9
13
8
1
13
6
7
2
11
8

9
11
6
1
11
5
7
2
11
7

Reference
0.45 (-0.25, 1.16)
Reference
À4.56 (-22.35,13.22)
Reference
0.46 (0.00,0.93)

Reference
13.30 (-1.04, 27.64)
Reference
À3.20 (-10.65, 4.25)

<60
!60
NA
<60
!60
NA
<60
!60
NA
<60
!60
NA
<60
!60
<60
!60

8
12
2
4
4
1
13
4

2
3
2
2
7
2
11
8

8
10
2
3
4
1
12
4
2
2
2
2
7
2
11
8

À0.20 (-1.23, 0.82)
Reference
0.15 (-1.17, 1.48)
3.27 (-0.41,6.94)

Reference
12.81 (9.86, 15.76)
À0.18 (-0.61, 0.25)
Reference
0.03 (-0.45, 0.51)
1.38 (-3.49, 6.25)
Reference
1.45 (-3.23, 6.14)
À13.30 (-27.64, 1.04)
Reference
1.22 (-6.53, 8.97)
Reference

male 50%
male>50%

7
14

7
12

Reference
À0.03 (-0.80, 0.73)

NA
male 50%
male>50%
male 50%
Male>50%

NA
male 50%
male>50%
male 50%
male>50%
male 50%
male>50%

1
7
2
9
9
1
6
1
3
6
7
12

1
5
2
9
8
1
4
1
3

6
7
11

1.38 (-0.69, 3.46)
Reference
À0.29 (-11.22,10.65)
Reference
0.06 (-0.34,0.46)
À0.26 (-1.22,0.71)
Reference
1.04 (-5.79,7.86)
Reference
À0.45 (-7.31, 6.41)
Reference
1.57 (-6.90, 10.03)

<1000
!1000
<1000
!1000
<1000
!1000
<1000
!1000
<1000
!1000

16
6

2
7
10
9
1
6
16
3

13
6
2
5
9
7
1
4
15
3

À0.41 (-1.07, 0.24)
Reference
À0.29 (-11.22,10.65)
Reference
0.04 (-0.36,0.43)
Reference
1.04 (-5.79,7.86)
Reference
0.10 (-9.46, 9.65)
Reference

13
4
5
4
5
9
7
3
2
5
5
1
3
12
2
5

10
4
5
4
3
8
5
3
2
3
5
1

3
11
2
5

Reference
0.09 (-1.11, 1.30)
À0.39 (-1.03, 0.25)
Reference
À2.36 (-10.63,5.91)
Reference
À0.26 (-0.47, À0.05)
À0.27 (-0.52, À0.03)
Reference
À1.82 (-4.71,1.08)
Reference
43.68 (-3.27, 90.63)
À2.47 (-8.03, 3.09)
Reference
4.02 (-11.07, 19.11)
À1.85 (-11.48, 7.78)

Panel study
Cross-sectional
Others
Panel study
Cross-sectional
Panel study
Cross-sectional
Others

Panel study
Cross-sectional
Panel study
Cross-sectional
Others
Panel study
Cross-sectional
Others

study

study
study

study
study

study

(continued on next page)
11

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Table 2 (continued )
Biomarker

Subgroup

Study location
Fibrinogen
PM2.5

Exposure

Short-term

Long-term

PM10

Short-term

Long-term
TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Pollution level

Fibrinogen
PM2.5

Short-term

Long-term
PM10

Short-term
Long-term

TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Exposure assessment
Fibrinogen
PM2.5

Short-term
Long-term

PM10

Short-term
Long-term

TNF-a

PM2.5

Short-term

IL-6

PM2.5

Short-term

Grouping criteria

No. of effect estimates

No. of studies

Meta-regression
Coef.

P value

I2

0.2133

53.33%

0.0447

64.46%

0.9245

65.46%

0.056

18.79%

0.2211

61.54%

0.1641

70.57%

Europe
Asia
North America
Europe
Asia
North America

Europe
Asia
North America
Europe
Asia
Europe
Asia
North America
Europe
Asia
North America

7
4
11
5
2
2
12
4
3
5
2
2
4
3
5
4
10

6
4
9
3
2
2
10
3
3
3
2
2
4
3
4
4
10

À0.50 (-1.36, 0.36)
Reference
0.18 (-0.88, 1.24)
À8.50 (-15.51,-1.49)
Reference
À8.49 (-15.94,-1.04)
À0.01 (-0.47,0.45)
Reference
0.14 (-0.68,0.96)
À2.37 (-4.84,0.09)
Reference
À2.08 (-9.06, 4.90)

Reference
3.73 (-1.87, 9.32)
À8.35 (-17.78, 1.07)
Reference
À7.68 (-16.48, 1.12)

Low
High
NA
Low
High
Low
High
Low
High
Low
High
NA
Low
High
NA

16
5
1
2
7
16
3
4

3
2
5
2
12
5
2

13
5
1
1
7
14
2
3
3
2
5
2
11
5
2

0.11 (-0.68, 0.91)
Reference
À0.47 (-1.40, 0.46)
À2.32 (-13.19,8.55)
Reference
0.17 (-0.03,0.36)

Reference
À2.95 (-4.58, À1.33)
Reference
4.53 (-8.98, 18.03)
Reference
0.18 (-7.46, 7.83)
À8.63 (-16.84, À0.41)
Reference
À7.40 (-17.98, 3.18)

0.2968

52.05%

0.629

94.41%

0.086

56.55%

0.006

0.00%

0.7233

78.10%

0.1147

69.94%

Fixed site
Other
Fixed site
Other
Fixed site
Other
Fixed site
Other
Fixed site
Other
Fixed site
Other

19
3
3
6
15
4
1
6
6
3
13
6

16
3
3
4
13
3
1
4
6
3
12
6

Reference
À0.42 (-1.25, 0.41)
Reference
À3.36 (-11.51,4.79)
Reference
À0.32 (-0.83,0.18)
Reference
À2.37 (-5.03, 0.29)
Reference
À1.14 (-8.18, 5.90)
Reference
1.14 (-7.10, 9.39)

0.300

54.21%

0.362

93.36%

0.195

60.97%

0.071

23.26%

0.713

83.36%

0.774

75.59%

Abbreviations: PM2.5:particulate matter with aerodynamic diameter equal to or less than 2.5 mm; PM10: particulate matter with aerodynamic diameter equal to or less than
10 mm; TNF-a: tumor necrosis factor-alpha, IL-6: interleukin-6; NA: not applicable.

modeling. Each exposure assessment technique has its advantages
and disadvantages (Beelen et al., 2010). Differences in the accuracy
of exposure assessment techniques may contribute to heterogeneities between studies (Sellier et al., 2014). Two studies investigated the long-term association for PM2.5-ﬁbrinogen on the same
population (Hoffmann et al., 2009; Viehmann et al., 2015). The
study modeling PM concentrations on a grid of 5 km reported a
signiﬁcant change of ﬁbrinogen with PM2.5 exposure (percent
change ¼ 3.9%, 95%CI: 0.3%, 7.7%) (Hoffmann et al., 2009). However,

the other study modeling PM concentrations on a grid of 1 km
reported an insigniﬁcant change of ﬁbrinogen (Viehmann et al.,
2015).
Our study had some limitations. First, few studies investigated
long-term associations of PM with TNF-a, IL-6, and IL-8. Also, there
are few reports on long-term associations of ﬁbrinogen with PM2.5
and PM10, which leads to lower statistical power. Second, most
studies used a single-pollutant model, although there may be interactions between pollutants (Mustaﬁc et al., 2012). It is difﬁcult to
implement and validate the multi-pollutant model. Therefore, we

The components of particle contribute to the changes of inﬂammation and blood coagulation markers with PM exposure. For
example, black carbon is reported that more reﬂected adverse
health effect of particulate air pollution compared with PM mass
(Janssen et al., 2011). Fang et al. reported that an increase of 37.4%
(95%CI: 2.0%, 85.0%) in IL-6, 19.9% (95%CI: 5.3%, 36.4%) in IL-8 and
27.8% (95%CI: 10.0%, 48.4%) in TNF-a per 0.36 mg/m3 exposure to
black carbon at lag 4 day among patients with diabetes (Fang et al.,
2012). Delﬁno et al. also found black carbon was signiﬁcant associated with IL-6 in patients with coronary artery disease (Delﬁno
et al., 2008). In an elderly cohort, Zhang et al. reported that IL-6
was signiﬁcant associated with black carbon, but not with PM2.5
(Zhang et al., 2016). Future research should be conducted to
investigate associations of PM constituents with inﬂammation and
blood coagulation markers, which can accurately assess the impacts of particulate air pollution on these markers (Pedersen et al.,
2016).
Studies used different exposure assessment techniques such as
land-use regression, kriging interpolation, and air dispersion
12

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Fig. 5. Sensitivity analyses for the association between PM and inﬂammation and blood coagulation level change excluding studies one by one (A) short-term expose to PM2.5 and
TNF-a (B) short-term exposure to PM2.5 and IL-6 (C) short-term exposure to PM10 an d IL-6 (D) short-term exposure to PM2.5 and IL-8 (E) short-term exposure to PM2.5 and
ﬁbrinogen (F) long-term exposure to PM2.5 and ﬁbrinogen (G) short-term exposure to PM10 and ﬁbrinogen (H) long-term exposure to PM10 and ﬁbrinogen.

13

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Fig. 6. Begg’s funnel plots of publication bias analyses (A) short-term expose to PM2.5 and TNF-a (B) short-term exposure to PM2.5 and IL-6 (C) short-term exposure to PM10 an
d IL-6 (D) short-term exposure to PM2.5 and IL-8 (E) short-term exposure to PM2.5 and ﬁbrinogen (F) long-term exposure to PM2.5 and ﬁbrinogen (G) short-term exposure to PM10
and ﬁbrinogen (H) long-term exposure to PM10 and ﬁbrinogen (s.e.: standard error).

14

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630

Table 3
Publication bias analyses.
Biomarker

Pollutant

Exposure

Begg’s test (P-value)

Eggr’s test (P-value)

Trim-and-ﬁll estimate Pooled %-changes (95% CI)

TNF-a
IL-6

PM2.5
PM2.5
PM10
PM2.5
PM2.5

Short-term
Short-term
Short-term
Short-term
Short-term
Long-term
Short-term
Long-term

0.47
0.18
0.45

0.76
0.69
0.47
0.17
0.76

0.07
0.02
0.15
0.64
0.09
0.05
0.11
0.07

e
0.90 (-0.20, 2.00)
e
e
e
e
e
e

IL-8
ﬁbrinogen

PM10

Abbreviations: PM2.5:particulate matter with aerodynamic diameter equal to or less than 2.5 mm; PM10: particulate matter with aerodynamic diameter equal to or less than

10 mm; TNF-a: tumor necrosis factor-alpha, IL-6: interleukin-6.

estimated the association between inﬂammation and blood coagulation markers and each pollutant based on the single-pollutant
model and did not evaluate the interactions between the air pollutants. Third, signiﬁcant heterogeneity may come from study
design, study location, exposure assessment technique, and population characteristics.

Holguin, F., Hong, Y., Luepker, R.V., Mittleman, M.A., Peters, A., Siscovick, D.,
Smith Jr., S.C., Whitsel, L., Kaufman, J.D., 2010. Particulate matter air pollution
and cardiovascular disease: an update to the scientiﬁc statement from the
American Heart Association. Circulation 121, 2331e2378. American Heart Association Council on, E., Prevention, C.o.t.K.i.C.D., Council on Nutrition, P.A.,
Metabolism.
Castell, J.V., Gomezlechon, M.J., David, M., Fabra, R., Trullenque, R., Heinrich, P.C.,
1990. ACUTE-PHASE response OF human hepatocytes - regulation OF acutephase protein-synthesis BY INTERLEUKIN-6. Hepatology 12, 1179e1186.
Chen, R., Li, H., Cai, J., Wang, C., Lin, Z., Liu, C., Niu, Y., Zhao, Z., Li, W., Kan, H., 2018.
Fine particulate air pollution and the expression of microRNAs and circulating
cytokines relevant to inﬂammation, coagulation, and vasoconstriction. Environ.
Health Perspect. 126, 017007.
Chuang, K.J., Chan, C.C., Su, T.C., Lee, C.T., Tang, C.S., 2007. The effect of urban air
pollution on inﬂammation, oxidative stress, coagulation, and autonomic
dysfunction in young adults. Am. J. Respir. Crit. Care Med. 176, 370e376.
Clougherty, J.E., 2010. A growing role for gender analysis in air pollution epidemiology. Environ. Health Perspect. 118, 167e176.
Cole, C.A., Carlsten, C., Koehle, M., Brauer, M., 2018. Particulate matter exposure and
health impacts of urban cyclists: a randomized crossover study. Environ. Health
17, 78.
Croft, D.P., Cameron, S.J., Morrell, C.N., Lowenstein, C.J., Ling, F., Zareba, W.,
Hopke, P.K., Utell, M.J., Thurston, S.W., Thevenet-Morrison, K., Evans, K.A.,
Chalupa, D., Rich, D.Q., 2017. Associations between ambient wood smoke and
other particulate pollutants and biomarkers of systemic inﬂammation, coagulation and thrombosis in cardiac patients. Environ. Res. 154, 352e361.
D’Angelo, A., Ruotolo, G., Garancini, P., Sampietro, F., Mazzola, G., Calori, G., 2006.
Lipoprotein(a), ﬁbrinogen and vascular mortality in an elderly northern Italian

population. Haematologica 91, 1613e1620.
Dadvand, P., Nieuwenhuijsen, M.J., Agusti, A., de Batlle, J., Benet, M., Beelen, R.,
Cirach, M., Martinez, D., Hoek, G., Basagana, X., Ferrer, A., Ferrer, J., RodriguezRoisin, R., Sauleda, J., Guerra, S., Anto, J.M., Garcia-Aymerich, J., 2014. Air
pollution and biomarkers of systemic inﬂammation and tissue repair in COPD
patients. Eur. Respir. J. 44, 603e613.
Davalos, D., Akassoglou, K., 2012. Fibrinogen as a key regulator of inﬂammation in
disease. Semin. Immunopathol. 34, 43e62.
de Jesus, A.L., Rahman, M.M., Mazaheri, M., Thompson, H., Knibbs, L.D., Jeong, C.,
Evans, G., Nei, W., Ding, A., Qiao, L., Li, L., Portin, H., Niemi, J.V., Timonen, H.,
Luoma, K., Petaja, T., Kulmala, M., Kowalski, M., Peters, A., Cyrys, J., Ferrero, L.,
Manigrasso, M., Avino, P., Buonano, G., Reche, C., Querol, X., Beddows, D.,
Harrison, R.M., Sowlat, M.H., Sioutas, C., Morawska, L., 2019. Ultraﬁne particles
and PM2.5 in the air of cities around the world: are they representative of each
other? Environ. Int. 129, 118e135.
De Luca, G., Verdoia, M., Cassetti, E., Schaffer, A., Cavallino, C., Bolzani, V., Marino, P.,
2011. High ﬁbrinogen level is an independent predictor of presence and extent
of coronary artery disease among Italian population. J. Thromb. Thrombolysis
31, 458e463.
Delﬁno, R.J., Staimer, N., Tjoa, T., Arhami, M., Polidori, A., Gillen, D.L., George, S.C.,
Shafer, M.M., Schauer, J.J., Sioutas, C., 2010. Associations of primary and secondary organic aerosols with airway and systemic inﬂammation in an elderly
panel cohort. Epidemiology 21, 892e902.
Delﬁno, R.J., Staimer, N., Tjoa, T., Polidori, A., Arhami, M., Gillen, D.L., Kleinman, M.T.,
Vaziri, N.D., Longhurst, J., Zaldivar, F., Sioutas, C., 2008. Circulating biomarkers of
inﬂammation, antioxidant activity, and platelet activation are associated with
primary combustion aerosols in subjects with coronary artery disease. Environ.
Health Perspect. 116, 898e906.
Deng, Z., Tan, C., Xiang, Y., Pan, J., Shi, G., Huang, Y., Xiong, Y., Xu, K., 2020. Association between ﬁne particle exposure and common test items in clinical laboratory: a time-series analysis in Changsha, China. Sci. Total Environ. 723,
137955.
Dubowsky, S.D., Suh, H., Schwartz, J., Coull, B.A., Gold, D.R., 2006. Diabetes, obesity,
and hypertension may enhance associations between air pollution and markers

of systemic inﬂammation. Environ. Health Perspect. 114, 992e998.
Egger, M., Smith, G.D., Schneider, M., Minder, C., 1997. Bias in meta-analysis detected
by a simple, graphical test. BMJ (Clin. Res. ed.) 315, 629e634.
El-Sayed, M.S., Ali, Z.E., Ahmadizad, S., 2004. Exercise and training effects on blood

5. Conclusion
Our meta-analysis showed ﬁbrinogen and TNF-a were signiﬁcantly associated with short-term PM exposure. The current study
is too limited to draw an appropriate conclusion about long-term
associations of PM with the above markers. Future epidemiological studies should address the role long-term PM exposure plays in
inﬂammation and blood coagulation markers level change.
Declaration of competing interest
The authors declare that they have no known competing
ﬁnancial interests or personal relationships that could have
appeared to inﬂuence the work reported in this paper.
Acknowledgments
The authors acknowledge all the participants and administrators of this study.
Appendix A. Supplementary data
Supplementary data related to this article can be found at
/>Funding
This work was supported by the Bill & Melinda Gates Foundation (Grant No. OOP1148464) and the Natural Science Fund of
Hubei Province (Granter number: 2018CFB634).
References
Apostolakis, S., Vogiatzi, K., Amanatidou, V., Spandidos, D.A., 2009. Interleukin 8 and
cardiovascular disease. Cardiovasc. Res. 84, 353e360.
Atkinson, R.W., Cohen, A., Mehta, S., Anderson, H.R., 2012. Systematic review and
meta-analysis of epidemiological time-series studies on outdoor air pollution
and health in Asia. Air Quality Atmosphere and Health 5, 383e391.
Beelen, R., Voogt, M., Duyzer, J., Zandveld, P., Hoek, G., 2010. Comparison of the
performances of land use regression modelling and dispersion modelling in
estimating small-scale variations in long-term air pollution concentrations in a

Dutch urban area. Atmos. Environ. 44, 4614e4621.
Braithwaite, I., Zhang, S., Kirkbride, J.B., Osborn, D.P.J., Hayes, J.F., 2019. Air pollution
(particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis. Environ.
Health Perspect. 127, 126002.
Brook, R.D., Rajagopalan, S., Pope 3rd, C.A., Brook, J.R., Bhatnagar, A., Diez-Roux, A.V.,
15

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630
Hassanvand, M.S., Naddaﬁ, K., Kashani, H., Faridi, S., Kunzli, N., Nabizadeh, R.,
Momeniha, F., Gholampour, A., Arhami, M., Zare, A., Pourpak, Z., Hoseini, M.,
Yunesian, M., 2017. Short-term effects of particle size fractions on circulating
biomarkers of inﬂammation in a panel of elderly subjects and healthy young
adults. Environ. Pollut. 223, 695e704.
Higgins, J.P.T., Green, S., 2011. Selection of study characteristics for subgroup Analyses and meta-regression. In: Cochran Handbook for Systematic Reviews of
Interventions, Version 5.1.0.
Higgins, J.P., Thompson, S.G., Deeks, J.J., Altman, D.G., 2003. Measuring inconsistency in meta-analyses. BMJ 327, 557e560.
Hildebrandt, K., Ruckerl, R., Koenig, W., Schneider, A., Pitz, M., Heinrich, J.,
Marder, V., Frampton, M., Oberdorster, G., Wichmann, H.E., Peters, A., 2009.
Short-term effects of air pollution: a panel study of blood markers in patients
with chronic pulmonary disease. Part. Fibre Toxicol. 6.
Hoffmann, B., Moebus, S., Dragano, N., Stang, A., Mohlenkamp, S., Schmermund, A.,
Memmesheimer, M., Brocker-Preuss, M., Mann, K., Erbel, R., Jockel, K.H., 2009.
Chronic residential exposure to particulate matter air pollution and systemic
inﬂammatory markers. Environ. Health Perspect. 117, 1302e1308.
Hoppe, B., 2014. Fibrinogen and factor XIII at the intersection of coagulation,
ﬁbrinolysis and inﬂammation. Thromb. Haemostasis 112, 649e658.
Hussein, A.A., Gottdiener, J.S., Bartz, T.M., Sotoodehnia, N., DeFilippi, C., See, V.,

Deo, R., Siscovick, D., Stein, P.K., Lloyd-Jones, D., 2013. Inﬂammation and sudden
cardiac death in a community-based population of older adults: the Cardiovascular Health Study. Heart Rhythm 10, 1425e1432.
Huttunen, K., Siponen, T., Salonen, I., Yli-Tuomi, T., Aurela, M., Dufva, H., Hillamo, R.,
Linkola, E., Pekkanen, J., Pennanen, A., Peters, A., Salonen, R.O., Schneider, A.,
Tiittanen, P., Hirvonen, M.R., Lanki, T., 2012. Low-level exposure to ambient
particulate matter is associated with systemic inﬂammation in ischemic heart
disease patients. Environ. Res. 116, 44e51.
Janssen, N.A.H., Hoek, G., Simic-Lawson, M., Fischer, P., van Bree, L., ten Brink, H.,
Keuken, M., Atkinson, R.W., Anderson, H.R., Brunekreef, B., Cassee, F.R., 2011.
Black carbon as an additional indicator of the adverse health effects of airborne
particles compared with PM10 and PM2.5. Environ. Health Perspect. 119,
1691e1699.
Jensen, T., Kierulf, P., Sandset, P.M., Klingenberg, O., Joo, G.B., Godal, H.C.,
Skjonsberg, O.H., 2007. Fibrinogen and ﬁbrin induce synthesis of proinﬂammatory cytokines from isolated peripheral blood mononuclear cells.
Thromb. Haemostasis 97, 822e829.
Kattula, S., Byrnes, J.R., Wolberg, A.S., 2017. Fibrinogen and ﬁbrin in hemostasis and
thrombosis. Arterioscler. Thromb. Vasc. Biol. 37, e13ee21.
Kirrane, E.F., Luben, T.J., Benson, A., Owens, E.O., Sacks, J.D., Dutton, S.J., Madden, M.,
Nichols, J.L., 2019. A systematic review of cardiovascular responses associated
with ambient black carbon and ﬁne particulate matter. Environ. Int. 127,
305e316.
Krzyzanowski, M., Cohen, A., 2008. Update of WHO air quality guidelines. Air
Quality Atmosphere and Health 1, 7e13.
Kumarathasan, P., Vincent, R., Blais, E., Bielecki, A., Guenette, J., Filiatreault, A.,
Brion, O., Cakmak, S., Thomson, E.M., Shutt, R., Kauri, L.M., Mahmud, M., Liu, L.,
Dales, R., 2018. Cardiovascular and inﬂammatory mechanisms in healthy
humans exposed to air pollution in the vicinity of a steel mill. Part. Fibre Toxicol.
15, 34.
Kunutsor, S.K., Kurl, S., Zaccardi, F., Laukkanen, J.A., 2016. Baseline and long-term
ﬁbrinogen levels and risk of sudden cardiac death: a new prospective study

and meta-analysis. Atherosclerosis 245, 171e180.
Kupchak, B.R., Kraemer, W.J., Hooper, D.R., Saenz, C., Dulkis, L.L., Secola, P.J.,
Brown, L.E., Galpin, A.J., Coburn, J.W., DuPont, W.H., Caldwell, L.K., Volek, J.S.,
Maresh, C.M., 2017. The effects of a transcontinental ﬂight on markers of
coagulation and ﬁbrinolysis in healthy men after vigorous physical activity.
Chronobiol. Int. 34, 148e161.
Lanki, T., Hampel, R., Tiittanen, P., Andrich, S., Beelen, R., Brunekreef, B., Dratva, J., De
Faire, U., Fuks, K.B., Hoffmann, B., Imboden, M., Jousilahti, P., Koenig, W.,
Mahabadi, A.A., Kunzli, N., Pedersen, N.L., Penell, J., Pershagen, G., ProbstHensch, N.M., Schaffner, E., Schindler, C., Sugiri, D., Swart, W.J., Tsai, M.Y.,
Turunen, A.W., Weinmayr, G., Wolf, K., Yli-Tuomi, T., Peters, A., 2015. Air
pollution from road trafﬁc and systemic inﬂammation in adults: a crosssectional analysis in the European escape project. Environ. Health Perspect.
123, 785e791.
Lee, H., Myung, W., Jeong, B.H., Choi, H., Jhun, B.W., Kim, H., 2018. Short- and longterm exposure to ambient air pollution and circulating biomarkers of inﬂammation in non-smokers: a hospital-based cohort study in South Korea. Environ.
Int. 119, 264e273.
Lee, H., Myung, W., Kim, D.K., Kim, S.E., Kim, C.T., Kim, H., 2017. Short-term air
pollution exposure aggravates Parkinson’s disease in a population-based
cohort. Sci. Rep. 7, 44741.
Lei, X., Chen, R., Wang, C., Shi, J., Zhao, Z., Li, W., Yan, B., Chillrud, S., Cai, J., Kan, H.,
2019. Personal ﬁne particulate matter constituents, increased systemic
inﬂammation, and the role of DNA hypomethylation. Environ. Sci. Technol. 53,
9837e9844.
Li, J., Wang, N., Wang, J., Li, H., 2018. Spatiotemporal evolution of the remotely
sensed global continental PM2.5 concentration from 2000-2014 based on
Bayesian statistics. Environ. Pollut. 238, 471e481.
Liang, Q., Sun, M., Wang, F., Ma, Y., Lin, L., Li, T., Duan, J., Sun, Z., 2020. Short-term
PM2.5 exposure and circulating von Willebrand factor level: a meta-analysis.
Sci. Total Environ. 737, 140180.
Liao, D., Heiss, G., Chinchilli, V.M., Duan, Y., Folsom, A.R., Lin, H.M., Salomaa, V.,

haemostasis in health and disease - an update. Sports Med. 34, 181e200.

Emerging Risk Factors, C., Kaptoge, S., Di Angelantonio, E., Pennells, L., Wood, A.M.,
White, I.R., Gao, P., Walker, M., Thompson, A., Sarwar, N., Caslake, M.,
Butterworth, A.S., Amouyel, P., Assmann, G., Bakker, S.J., Barr, E.L., BarrettConnor, E., Benjamin, E.J., Bjorkelund, C., Brenner, H., Brunner, E., Clarke, R.,
Cooper, J.A., Cremer, P., Cushman, M., Dagenais, G.R., D’Agostino, R.B., Sr
Dankner, R., Davey-Smith, G., Deeg, D., Dekker, J.M., Engstrom, G., Folsom, A.R.,
Fowkes, F.G., Gallacher, J., Gaziano, J.M., Giampaoli, S., Gillum, R.F., Hofman, A.,
Howard, B.V., Ingelsson, E., Iso, H., Jorgensen, T., Kiechl, S., Kitamura, A.,
Kiyohara, Y., Koenig, W., Kromhout, D., Kuller, L.H., Lawlor, D.A., Meade, T.W.,
Nissinen, A., Nordestgaard, B.G., Onat, A., Panagiotakos, D.B., Psaty, B.M.,
Rodriguez, B., Rosengren, A., Salomaa, V., Kauhanen, J., Salonen, J.T., Shaffer, J.A.,
Shea, S., Ford, I., Stehouwer, C.D., Strandberg, T.E., Tipping, R.W., Tosetto, A.,
Wassertheil-Smoller, S., Wennberg, P., Westendorp, R.G., Whincup, P.H.,
Wilhelmsen, L., Woodward, M., Lowe, G.D., Wareham, N.J., Khaw, K.T., Sattar, N.,
Packard, C.J., Gudnason, V., Ridker, P.M., Pepys, M.B., Thompson, S.G., Danesh, J.,
2012. C-reactive protein, ﬁbrinogen, and cardiovascular disease prediction.
N. Engl. J. Med. 367, 1310e1320.
Emmerechts, J., Jacobs, L., Van Kerckhoven, S., Loyen, S., Mathieu, C., Fierens, F.,
Nemery, B., Nawrot, T.S., Hoylaerts, M.F., 2012. Air pollution-associated procoagulant changes: the role of circulating microvesicles. J. Thromb. Haemostasis
10, 96e106.
Esmon, C.T., 2003. Coagulation and inﬂammation. J. Endotoxin Res. 9, 192e198.
Fang, S.C., Mehta, A.J., Alexeeff, S.E., Gryparis, A., Coull, B., Vokonas, P.,
Christiani, D.C., Schwartz, J., 2012. Residential black carbon exposure and
circulating markers of systemic inﬂammation in elderly males: the normative
aging study. Environ. Health Perspect. 120, 674e680.
Fibrinogen Studies, C., Danesh, J., Lewington, S., Thompson, S.G., Lowe, G.D.,
Collins, R., Kostis, J.B., Wilson, A.C., Folsom, A.R., Wu, K., Benderly, M.,
Goldbourt, U., Willeit, J., Kiechl, S., Yarnell, J.W., Sweetnam, P.M., Elwood, P.C.,
Cushman, M., Psaty, B.M., Tracy, R.P., Tybjaerg-Hansen, A., Haverkate, F., de
Maat, M.P., Fowkes, F.G., Lee, A.J., Smith, F.B., Salomaa, V., Harald, K., Rasi, R.,
Vahtera, E., Jousilahti, P., Pekkanen, J., D’Agostino, R., Kannel, W.B., Wilson, P.W.,

Toﬂer, G., Arocha-Pinango, C.L., Rodriguez-Larralde, A., Nagy, E., Mijares, M.,
Espinosa, R., Rodriquez-Roa, E., Ryder, E., Diez-Ewald, M.P., Campos, G.,
Fernandez, V., Torres, E., Marchioli, R., Valagussa, F., Rosengren, A.,
Wilhelmsen, L., Lappas, G., Eriksson, H., Cremer, P., Nagel, D., Curb, J.D.,
Rodriguez, B., Yano, K., Salonen, J.T., Nyyssonen, K., Tuomainen, T.P., Hedblad, B.,
Lind, P., Loewel, H., Koenig, W., Meade, T.W., Cooper, J.A., De Stavola, B.,
Knottenbelt, C., Miller, G.J., Cooper, J.A., Bauer, K.A., Rosenberg, R.D., Sato, S.,
Kitamura, A., Naito, Y., Palosuo, T., Ducimetiere, P., Amouyel, P., Arveiler, D.,
Evans, A.E., Ferrieres, J., Juhan-Vague, I., Bingham, A., Schulte, H., Assmann, G.,
Cantin, B., Lamarche, B., Despres, J.P., Dagenais, G.R., Tunstall-Pedoe, H.,
Woodward, M., Ben-Shlomo, Y., Davey Smith, G., Palmieri, V., Yeh, J.L.,
Rudnicka, A., Ridker, P., Rodeghiero, F., Tosetto, A., Shepherd, J., Ford, I.,
Robertson, M., Brunner, E., Shipley, M., Feskens, E.J., Kromhout, D., Dickinson, A.,
Ireland, B., Juzwishin, K., Kaptoge, S., Lewington, S., Memon, A., Sarwar, N.,
Walker, M., Wheeler, J., White, I., Wood, A., 2005. Plasma ﬁbrinogen level and
the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. J. Am. Med. Assoc. 294, 1799e1809.
Fiordelisi, A., Piscitelli, P., Trimarco, B., Coscioni, E., Iaccarino, G., Sorriento, D., 2017.
The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail. Rev. 22, 337e347.
Forbes, L.J., Patel, M.D., Rudnicka, A.R., Cook, D.G., Bush, T., Stedman, J.R.,
Whincup, P.H., Strachan, D.P., Anderson, R.H., 2009. Chronic exposure to outdoor air pollution and markers of systemic inﬂammation. Epidemiology 20,
245e253.
Franchini, M., Mannucci, P.M., 2007. Short-term effects of air pollution on cardiovascular diseases: outcomes and mechanisms. J. Thromb. Haemostasis 5,
2169e2174.
Ghasemi, H., Ghazanfari, T., Yaraee, R., Faghihzadeh, S., Hassan, Z.M., 2011. Roles of
IL-8 in ocular inﬂammations: a review. Ocul. Immunol. Inﬂamm. 19, 401e412.
Green, R., Broadwin, R., Malig, B., Basu, R., Gold, E.B., Qi, L., Sternfeld, B.,
Bromberger, J.T., Greendale, G.A., Kravitz, H.M., Tomey, K., Matthews, K.,
Derby, C.A., Jackson, E.A., Green, R., Ostro, B., 2016. Long- and short-term
exposure to air pollution and inﬂammatory/hemostatic markers in midlife
women. Epidemiology 27, 211e220.

Habre, R., Zhou, H., Eckel, S.P., Enebish, T., Fruin, S., Bastain, T., Rappaport, E.,
Gilliland, F., 2018. Short-term effects of airport-associated ultraﬁne particle
exposure on lung function and inﬂammation in adults with asthma. Environ.
Int. 118, 48e59.
Hajat, A., Allison, M., Diez-Roux, A.V., Jenny, N.S., Jorgensen, N.W., Szpiro, A.A.,
Vedal, S., Kaufman, J.D., 2015. Long-term exposure to air pollution and markers
of inﬂammation, coagulation, and endothelial activation a repeat-measures
analysis in the multi-ethnic study of atherosclerosis (MESA). Epidemiology
26, 310e320.
Hamanaka, R.B., Mutlu, G.M., 2018. Particulate matter air pollution: effects on the
cardiovascular system. Front. Endocrinol. 9, 680.
Hampel, R., Peters, A., Beelen, R., Brunekreef, B., Cyrys, J., de Faire, U., de Hoogh, K.,
Fuks, K., Hoffmann, B., Huls, A., Imboden, M., Jedynska, A., Kooter, I., Koenig, W.,
Kunzli, N., Leander, K., Magnusson, P., Mannisto, S., Penell, J., Pershagen, G.,
Phuleria, H., Probst-Hensch, N., Pundt, N., Schaffner, E., Schikowski, T., Sugiri, D.,
Tiittanen, P., Tsai, M.Y., Wang, M., Wolf, K., Lanki, T., 2015. Long-term effects of
elemental composition of particulate matter on inﬂammatory blood markers in
European cohorts. Environ. Int. 82, 76e84.
16

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630
Soukup, J., Peters, A., Schneider, A., 2014. Associations between ambient air
pollution and blood markers of inﬂammation and coagulation/ﬁbrinolysis in
susceptible populations. Environ. Int. 70, 32e49.
Rudez, G., Janssen, N.A., Kilinc, E., Leebeek, F.W., Gerlofs-Nijland, M.E., Spronk, H.M.,
ten Cate, H., Cassee, F.R., de Maat, M.P., 2009. Effects of ambient air pollution on
hemostasis and inﬂammation. Environ. Health Perspect. 117, 995e1001.

Schneider, A., Neas, L.M., Graff, D.W., Herbst, M.C., Cascio, W.E., Schmitt, M.T.,
Buse, J.B., Peters, A., Devlin, R.B., 2010. Association of cardiac and vascular
changes with ambient PM2.5 in diabetic individuals. Part. Fibre Toxicol. 7, 14.
Schwartz, J., 2001. Air pollution and blood markers of cardiovascular risk. Environ.
Health Perspect. 109 (Suppl. 3), 405e409.
Seaton, A., Soutar, A., Crawford, V., Elton, R., McNerlan, S., Cherrie, J., Watt, M.,
Agius, R., Stout, R., 1999. Particulate air pollution and the blood. Thorax 54,
1027e1032.
Sellier, Y., Galineau, J., Hulin, A., Caini, F., Marquis, N., Navel, V., Bottagisi, S., GiorgisAllemand, L., Jacquier, C., Slama, R., Lepeule, J., Group, E.M.-C.C.S., 2014. Health
effects of ambient air pollution: do different methods for estimating exposure
lead to different results? Environ. Int. 66, 165e173.
Signorelli, S.S., Oliveri Conti, G., Zanobetti, A., Baccarelli, A., Fiore, M., Ferrante, M.,
2019. Effect of Particulate Matter-Bound Metals Exposure on Prothrombotic
Biomarkers: A Systematic Review. Environmental Research.
Steenhof, M., Gosens, I., Strak, M., Godri, K.J., Hoek, G., Cassee, F.R., Mudway, I.S.,
Kelly, F.J., Harrison, R.M., Lebret, E., Brunekreef, B., Janssen, N.A.H.,
Pieters, R.H.H., 2011. In vitro toxicity of particulate matter (PM) collected at
different sites in The Netherlands is associated with PM composition, size
fraction and oxidative potential - the RAPTES project. Part. Fibre Toxicol. 8.
Steinvil, A., Kordova-Biezuner, L., Shapira, I., Berliner, S., Rogowski, O., 2008. Shortterm exposure to air pollution and inﬂammation-sensitive biomarkers. Environ.
Res. 106, 51e61.
Strak, M., Hoek, G., Godri, K.J., Gosens, I., Mudway, I.S., van Oerle, R., Spronk, H.M.,
Cassee, F.R., Lebret, E., Kelly, F.J., Harrison, R.M., Brunekreef, B., Steenhof, M.,
Janssen, N.A., 2013. Composition of PM affects acute vascular inﬂammatory and
coagulative markers - the RAPTES project. PloS One 8, e58944.
Su, T.C., Hwang, J.J., Yang, Y.R., Chan, C.C., 2017. Association between long-term
exposure to trafﬁc-related air pollution and inﬂammatory and thrombotic
markers in middle-aged adults. Epidemiology 28 (Suppl. 1). S74es81.
Sullivan, J.H., Hubbard, R., Liu, S.L., Shepherd, K., Trenga, C.A., Koenig, J.Q.,
Chandler, W.L., Kaufman, J.D., 2007. A community study of the effect of particulate matter on blood measures of inﬂammation and thrombosis in an

elderly population. Environ. Health 6, 3.
Tousoulis, D., Papageorgiou, N., Androulakis, E., Briasoulis, A., Antoniades, C.,
Stefanadis, C., 2011. Fibrinogen and cardiovascular disease: genetics and biomarkers. Blood Rev. 25, 239e245.
Tsai, D.H., Amyai, N., Marques-Vidal, P., Wang, J.L., Riediker, M., Mooser, V.,
Paccaud, F., Waeber, G., Vollenweider, P., Bochud, M., 2012. Effects of particulate
matter on inﬂammatory markers in the general adult population. Part. Fibre
Toxicol. 9, 24.
Unver, N., McAllister, F., 2018. IL-6 family cytokines: key inﬂammatory mediators as
biomarkers and potential therapeutic targets. Cytokine Growth Factor Rev. 41,
10e17.
Viana, M., Kuhlbusch, T.A.J., Querol, X., Alastuey, A., Harrison, R.M., Hopke, P.K.,
v o
^ t, A.S.H., Hueglin, C., Bloemen, H.,
Winiwarter, W., Vallius, M., Szidat, S., Pre
Wåhlin, P., Vecchi, R., Miranda, A.I., Kasper-Giebl, A., Maenhaut, W.,
Hitzenberger, R., 2008. Source apportionment of particulate matter in Europe: a
review of methods and results. J. Aerosol Sci. 39, 827e849.
Viehmann, A., Hertel, S., Fuks, K., Eisele, L., Moebus, S., Mohlenkamp, S.,
Nonnemacher, M., Jakobs, H., Erbel, R., Jockel, K.H., Hoffmann, B., 2015. Longterm residential exposure to urban air pollution, and repeated measures of
systemic blood markers of inﬂammation and coagulation. Occup. Environ. Med.
72, 656e663.
Vrijheid, M., Martinez, D., Manzanares, S., Dadvand, P., Schembari, A., Rankin, J.,
Nieuwenhuijsen, M., 2011. Ambient air pollution and risk of congenital anomalies: a systematic review and meta-analysis. Environ. Health Perspect. 119,
598e606.
Waked, A., Favez, O., Alleman, L.Y., Piot, C., Petit, J.E., Delaunay, T., Verlinden, E.,
Golly, B., Besombes, J.L., Jaffrezo, J.L., Leoz-Garziandia, E., 2014. Source apportionment of PM10 in a north-western Europe regional urban background site
(Lens, France) using positive matrix factorization and including primary
biogenic emissions. Atmos. Chem. Phys. 14, 3325e3346.
Wang, C., Chen, R., Shi, M., Cai, J., Shi, J., Yang, C., Li, H., Lin, Z., Meng, X., Liu, C.,
Niu, Y., Xia, Y., Zhao, Z., Kan, H., Weinberg, C.R., 2018. Possible mediation by

methylation in acute inﬂammation following personal exposure to ﬁne particulate air pollution. Am. J. Epidemiol. 187, 484e493.
Woods, J.A., Vieira, V.J., Keylock, K.T., 2009. Exercise, inﬂammation, and innate
immunity. Immunol. Allergy Clin. 29, 381eỵ.
Wu, S., Deng, F., Wei, H., Huang, J., Wang, H., Shima, M., Wang, X., Qin, Y., Zheng, C.,
Hao, Y., Guo, X., 2012. Chemical constituents of ambient particulate air pollution
and biomarkers of inﬂammation, coagulation and homocysteine in healthy
adults: a prospective panel study. Part. Fibre Toxicol. 9, 49.
Yang, T.H., Masumi, S., Weng, S.P., Chen, H.W., Chuang, H.C., Chuang, K.J., 2015.
Personal exposure to particulate matter and inﬂammation among patients with
periodontal disease. Sci. Total Environ. 502, 585e589.
Zeka, A., Sullivan, J.R., Vokonas, P.S., Sparrow, D., Schwartz, J., 2006. Inﬂammatory
markers and particulate air pollution: characterizing the pathway to disease.
Int. J. Epidemiol. 35, 1347e1354.

2005. Association of criteria pollutants with plasma hemostatic/inﬂammatory
markers: a population-based study. J. Expo. Anal. Environ. Epidemiol. 15,
319e328.
Liu, Q., Gu, X., Deng, F., Mu, L., Baccarelli, A.A., Guo, X., Wu, S., 2019. Ambient particulate air pollution and circulating C-reactive protein level: a systematic review and meta-analysis. Int. J. Hyg Environ. Health 222, 756e764.
Luben, T.J., Nichols, J.L., Dutton, S.J., Kirrane, E., Owens, E.O., Datko-Williams, L.,
Madden, M., Sacks, J.D., 2017. A systematic review of cardiovascular emergency
department visits, hospital admissions and mortality associated with ambient
black carbon. Environ. Int. 107, 154e162.
Luyendyk, J.P., Schoenecker, J.G., Flick, M.J., 2019. The multifaceted role of ﬁbrinogen
in tissue injury and inﬂammation. Blood 133, 511e520.
Maresca, G., Di Blasio, A., Marchioli, R., Di Minno, G., 1999. Measuring plasma
ﬁbrinogen to predict stroke and myocardial infarction - an update. Arterioscler.
Thromb. Vasc. Biol. 19, 1368e1377.
Mehaffey, E., Majid, D.S.A., 2017. Tumor necrosis factor-a, kidney function, and
hypertension. Am. J. Physiol. 313. F1005ef1008 Renal physiology.
Mirowsky, J.E., Peltier, R.E., Lippmann, M., Thurston, G., Chen, L.C., Neas, L., DiazSanchez, D., Laumbach, R., Carter, J.D., Gordon, T., 2015. Repeated measures of

inﬂammation, blood pressure, and heart rate variability associated with trafﬁc
exposures in healthy adults. Environ. Health: A Global Access Science Source 14.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., Group, T.P., 2009. Preferred reporting
items for systematic reviews and meta-analyses: the PRISMA statement. PLoS
Med. 6.
Morgan, R.L., Whaley, P., Thayer, K.A., Schunemann, H.J., 2018. Identifying the PECO:
a framework for formulating good questions to explore the association of
environmental and other exposures with health outcomes. Environ. Int. 121,
1027e1031.
Mustaﬁc, H., Jabre, P., Caussin, C., Murad, M.H., Escolano, S., Tafﬂet, M., Perier, M.C.,
Marijon, E., Vernerey, D., Empana, J.P., Jouven, X., 2012. Main air pollutants and
myocardial infarction A systematic review and meta-analysis. Jama-Journal of
the American Medical Association 307, 713e721.
Mutlu, G.M., Green, D., Bellmeyer, A., Baker, C.M., Burgess, Z., Rajamannan, N.,
Christman, J.W., Foiles, N., Kamp, D.W., Ghio, A.J., Chandel, N.S., Dean, D.A.,
Sznajder, J.I., Budinger, G.R.S., 2007. Ambient particulate matter accelerates
coagulation via an IL-6-dependent pathway. J. Clin. Invest. 117, 2952e2961.
Neves Miranda, V.P., Gouveia Peluzio Mdo, C., Rodrigues de Faria, E., Castro Franceschini Sdo, C., Eloiza Priore, S., 2015. Inﬂammatory markers in relation to
body composition, physical activity and assessment of nutritional status of the
adolescents. Nutr. Hosp. 31, 1920e1927.
Pedersen, M., Gehring, U., Beelen, R., Wang, M., Giorgis-Allemand, L.,
Andersen, A.M., Basagana, X., Bernard, C., Cirach, M., Forastiere, F., de Hoogh, K.,
Grazulevicviene, R., Gruzieva, O., Hoek, G., Jedynska, A., Klumper, C., Kooter, I.M.,
Kramer, U., Kukkonen, J., Porta, D., Postma, D.S., Raaschou-Nielsen, O., van
Rossem, L., Sunyer, J., Sorensen, M., Tsai, M.Y., Vrijkotte, T.G., Wilhelm, M.,
Nieuwenhuijsen, M.J., Pershagen, G., Brunekreef, B., Kogevinas, M., Slama, R.,
2016. Elemental constituents of particulate matter and newborn’s size in eight
European cohorts. Environ. Health Perspect. 124, 141e150.
Pekkanen, J., Brunner, E.J., Anderson, H.R., Tiittanen, P., Atkinson, R.W., 2000. Daily
concentrations of air pollution and plasma ﬁbrinogen in London. Occup. Environ. Med. 57, 818e822.

Popa, C., Netea, M.G., van Riel, P.L., van der Meer, J.W., Stalenhoef, A.F., 2007. The role
of TNF-alpha in chronic inﬂammatory conditions, intermediary metabolism,
and cardiovascular risk. J. Lipid Res. 48, 751e762.
Pope 3rd, C.A., Bhatnagar, A., McCracken, J.P., Abplanalp, W., Conklin, D.J., O’Toole, T.,
2016. Exposure to ﬁne particulate air pollution is associated with endothelial
injury and systemic inﬂammation. Circ. Res. 119, 1204e1214.
Puett, R.C., Yanosky, J.D., Mittleman, M.A., Montresor-Lopez, J., Bell, R.A., Crume, T.L.,
Dabelea, D., Dolan, L.M., D’Agostino, R.B., Marcovina, S.M., Pihoker, C.,
Reynolds, K., Urbina, E., Liese, A.D., 2019. Inﬂammation and acute trafﬁc-related
air pollution exposures among a cohort of youth with type 1 diabetes. Environ.
Int. 132.
Rich, D.Q., Zareba, W., Beckett, W., Hopke, P.K., Oakes, D., Frampton, M.W.,
Bisognano, J., Chalupa, D., Bausch, J., O’Shea, K., Wang, Y., Utell, M.J., 2012. Are
ambient ultraﬁne, accumulation mode, and ﬁne particles associated with
adverse cardiac responses in patients undergoing cardiac rehabilitation? Environ. Health Perspect. 120, 1162e1169.
Ridker, P.M., Rifai, N., Stampfer, M.J., Hennekens, C.H., 2000. Plasma concentration of
interleukin-6 and the risk of future myocardial infarction among apparently
healthy men. Circulation 101, 1767e1772.
Rodosthenous, R.S., Kloog, I., Colicino, E., Zhong, J., Herrera, L.A., Vokonas, P.,
Schwartz, J., Baccarelli, A.A., Prada, D., 2018. Extracellular vesicle-enriched
microRNAs interact in the association between long-term particulate matter
and blood pressure in elderly men. Environ. Res. 167, 640e649.
Rooney, A.A., Boyles, A.L., Wolfe, M.S., Bucher, J.R., Thayer, K.A., 2014. Systematic
review and evidence integration for literature-based environmental health
science assessments. Environ. Health Perspect. 122, 711e718.
Ruckerl, R., Greven, S., Ljungman, P., Aalto, P., Antoniades, C., Bellander, T.,
Berglind, N., Chrysohoou, C., Forastiere, F., Jacquemin, B., von Klot, S., Koenig, W.,
Kuchenhoff, H., Lanki, T., Pekkanen, J., Perucci, C.A., Schneider, A., Sunyer, J.,
Peters, A., 2007. Air pollution and inﬂammation (interleukin-6, C-reactive
protein, ﬁbrinogen) in myocardial infarction survivors. Environ. Health Perspect. 115, 1072e1080.

Rückerl, R., Hampel, R., Breitner, S., Cyrys, J., Kraus, U., Carter, J., Dailey, L.,
Devlin, R.B., Diaz-Sanchez, D., Koenig, W., Phipps, R., Silbajoris, R., Soentgen, J.,
17

H. Tang, Z. Cheng, N. Li et al.

Environmental Pollution 267 (2020) 115630
306e319.
Zhang, Y., Yang, J., Chen, X., Yin, H., 2019. Levels, inﬂuencing factors, and chemical
constituents of atmospheric particulates in 10 cities with poor air quality in
China in 2005-2017. J. Environ. Occup. Med. 36, 970e978.
Zuurbier, M., Hoek, G., Oldenwening, M., Meliefste, K., Krop, E., van den Hazel, P.,
Brunekreef, B., 2011. In-trafﬁc air pollution exposure and CC16, blood coagulation, and inﬂammation markers in healthy adults. Environ. Health Perspect.
119, 1384e1389.

Zhang, Q., Niu, Y., Xia, Y., Lei, X., Wang, W., Huo, J., Zhao, Q., Zhang, Y., Duan, Y., Cai, J.,
Ying, Z., Li, S., Chen, R., Fu, Q., Kan, H., 2020. The acute effects of ﬁne particulate
matter constituents on circulating inﬂammatory biomarkers in healthy adults.
Sci. Total Environ. 707, 135989.
Zhang, X., Staimer, N., Gillen, D.L., Tjoa, T., Schauer, J.J., Shafer, M.M.,
Hasheminassab, S., Pakbin, P., Vaziri, N.D., Sioutas, C., Delﬁno, R.J., 2016. Associations of oxidative stress and inﬂammatory biomarkers with chemicallycharacterized air pollutant exposures in an elderly cohort. Environ. Res. 150,

18

The short and long term associations of particulate matter 2020 environmen

Tài liệu liên quan

Tài liệu bạn tìm kiếm đã sẵn sàng tải về