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The acute effects of fine particulate matter constituents on blood inflammation and coagulation Cong Liu, Jing Cai, Renjie Chen, Liping Qiao, Hongli Wang, Wenxi Xu, Huichu Li, Zhuohui Zhao, and Haidong Kan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b00312 • Publication Date (Web): 16 Jun 2017 Downloaded from http://pubs.acs.org on June 18, 2017

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Environmental Science & Technology

The acute effects of fine particulate matter constituents on blood inflammation and coagulation

Authors: Cong Liu1, †, Jing Cai1, 2, †, Liping Qiao3, Hongli Wang3, Wenxi Xu4, Huichu Li1, Zhuohui Zhao1, Renjie Chen1, 2,*, Haidong Kan1, 5,*



These authors contributed equally to this work.

Affiliations: 1. School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai 200032, China; 2. Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China; 3. State Environmental Protection Key Lab of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China; 4. Huangpu District Center for Disease Control and Prevention, Shanghai 200023, China; 5. Key Laboratory of Reproduction Regulation of National Population and Family Planning Commission, Shanghai Institute of Planned Parenthood

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Research, Institute of Reproduction and Development, Fudan University, Shanghai 200032, China

*Address correspondence to: Dr. Haidong Kan, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. Tel/fax: +86 (21) 5423 7908. E-mail: [email protected]; Dr. Renjie Chen, P.O. Box 249, 130 Dong-An Road, Shanghai

200032,

China.

Tel/fax:

+86

(21)

[email protected].

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5423

7908.

E-mail:

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Abstract

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Limited evidence is available on the effects of various fine particulate matter

3

(PM2.5) constituents on blood inflammation and coagulation. We examined

4

the associations between 10 constituents and 10 circulating biomarkers in a

5

panel of 28 urban residents with 4 repeated measurements in Shanghai,

6

China.

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single-constituent models, the constituent-PM2.5 joint models, and the

8

constituent-residual models to evaluate the associations between PM2.5

9

constituents and 8 inflammatory biomarkers (fibrinogen, C-reactive protein,

10

monocyte chemoattractant protein-1, tumor necrosis factor-α, interleukin-1b,

11

intercellular adhesion molecule-1, P-selectin, vascular cell adhesion

12

molecule-1) and 2 coagulation biomarkers (plasminogen activator inhibitor-1

13

and soluble CD40 ligand). We found robust associations of organic carbon

14

(OC), elemental carbon (EC), nitrate (NO3-), and ammonium (NH4+) with at

15

least 1 of 8 inflammatory markers. On average, an interquartile range

16

increase in the four constituents corresponded to increments of 50%, 37%, 25%

17

and 26% in inflammatory biomarkers, respectively. Only sulfate (SO42-) or

18

NH4+ was robustly associated with coagulation markers (corresponding

19

increments: 23% and 20%). Our results provided evidence that some

20

constituents in PM2.5 (OC, EC, NO3-, SO42- and NH4+) might play crucial roles

21

in inducing systematic inflammation and coagulation, but their roles varied by

22

the selected biomarkers.

Based

on

the

linear

mixed-effect

models,

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fitted

the

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Keywords: fine particulate matter; chemical constituent; inflammation;

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coagulation; biomarker; panel study

25 26

Word count: abstract (200 words) + text (3800 words) + 2 tables (600 words)

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and 4 figures (2400 words) = 7000 words

28

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Introduction

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Associations between short-term exposure to fine particulate matter (PM2.5)

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and

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worldwide.1-3 PM2.5 has very complex chemical compositions and it was thus

33

crucial to determine which constituents dominate the effects of PM2.5 on the

34

cardiovascular system.1,

35

effects of specific constituents are scarce and limited to a small fraction of

36

constituents, especially in developing countries.5, 6

cardiovascular

diseases

4

(CVDs)

have

been

well

documented

However, investigations of the cardiovascular

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An increasing number of studies have attempted to elucidate the time

38

courses during which PM2.5 exposure causes adverse cardiovascular

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outcomes. These studies have focused on the effects of sub-daily PM2.5

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exposure on clinical or subclinical outcomes, such as cardiac arrest,

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myocardial infarction, ST-segment depression, arrhythmia, fibrillation, and

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increased blood pressure.7 Systemic inflammation and hypercoagulability are

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two common mechanisms among a number of possible biological pathways

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whereby PM2.5 adversely affects the cardiovascular system,8-10 but such

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evidence has been limited with regard to time course. Our previous studies

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have demonstrated the acute effects of sub-daily PM2.5 exposure on an array

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of relevant biomarkers,11-13 but little knowledge is available on the sub-daily

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exposure to various PM2.5 constituents.

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As the largest developing country in the world, China is facing enormous

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public health challenges due to severe air pollution problems and the heavy

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burden of CVDs. We therefore designed this longitudinal study in Shanghai,

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China, to explore the short-term associations of PM2.5 constituents on blood

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inflammation and coagulation, and further, to deduce which constituents are

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most deleterious to the cardiovascular system. Elderly patients with chronic

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obstructive pulmonary disease (COPD) were selected because they are

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hypothesized to be susceptible to the adverse cardiovascular effects of air

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pollutants as they have a higher deposition of particles and an inherent

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inflammatory state.6

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Material and methods

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Study Design and Participants

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We initially recruited 30 volunteers from a community-based registry of COPD

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patients in Shanghai. The sample size was determined to be comparable with

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previous panel studies.11, 12, 14, 15 Two patients were excluded because they

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took medication due to exacerbation of COPD condition during the study

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period. Details on the subject recruitment and study design have been

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described in our previous publication.16 Briefly, all COPD patients were

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diagnosed by physicians. We only included the stable patients with

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mild-to-moderate COPD in this study according to the classification of the

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Global Initiative for Chronic Obstructive Lung Disease based on the baseline

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spirometry test, and we excluded those who were current active or passive

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smokers (living with a current smoker), consumed any alcohol, or had severe

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comorbidities or inflammatory diseases. All the participants had a predicted

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forced expiratory volume 1 (FEV1) ≥ 50% and an FEV1/forced vital capacity

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(FVC) < 0.70. Six weekly follow-up visits were scheduled from May 27 to July

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5, 2014, but we only arranged 4 blood collection appointments at 1-week

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intervals due to the subjects’ refusal to have more blood drawn. For each

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patient, blood collection was scheduled for the consecutive 4 weeks at the

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same time (1:30 p.m. to 2:30 p.m.) on the same day of week to control for

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possible circadian rhythms. Data on individual characteristics (such as age,

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gender, height, weight, educational attainment, income, medication use, and

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history of chronic morbidities) were collected at baseline. The study protocol

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was approved by the Institutional Review Board of the School of Public

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Health of Fudan University, and written informed consent form was obtained

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from each subject.

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Blood collection and lab analysis

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During each follow-up, venous peripheral blood samples (5 ml) were drawn

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by a certified nurse using coagulant vacuum tubes and then were rapidly

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separated into serum and plasma by centrifugation at 4,000 rpm for 10

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minutes within 20 minutes of collection. Serum samples were transported

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directly to our laboratory and stored at -80°C before analysis.

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We analyzed 10 circulating biomarkers associated with particulate air

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pollution in at least 1 panel study.11, 14, 16 These included: 1) 8 biomarkers of

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inflammation, including fibrinogen, C-reactive protein (CRP), P-selectin,

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monocyte chemoattractant protein (MCP)-1, interleukin-1b (IL-1β), tumor

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necrosis factor (TNF)-a, intercellular adhesion molecule-1 (ICAM-1), and

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vascular cell adhesion molecule-1 (VCAM-1); and 2) 2 biomarkers of

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coagulation, soluble CD40 ligand (sCD40L) and plasminogen activator

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inhibitor-1 (PAI-1). These biomarkers were measured by a commercial

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Millipore MILLIPLEX MAP human cytokine/chemokine kit (Millipore Corp.,

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Billerica, MA), which is based on the Luminex xMAP technology. The level for

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each biomarker was simultaneously quantified using the MAGPIX system and

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xPONENT software 134 (Luminex, Austin, TX). The lower limits of

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quantitation (LLOQ) of the biomarkers varied from 0.01 pg/ml to 1.00 pg/ml.

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Measurements lower than the LLOQ (8.5%) were replaced by half of the

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LLOQ. All biomarker tests were performed under the same conditions

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according to the manufacturer’s instructions, and all results were within the

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quality control ranges.

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Environmental Data

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During the study period (from May 27 to July 5, 2014), we obtained real-time

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(hourly) concentrations of PM2.5 and its constituents from a fixed-site monitor,

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which was located on the rooftop of a 5-story building at the Shanghai

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Academy of Environmental Sciences (approximately 4 km away from the

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community). The two sites were mostly surrounded by commercial properties

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and residential dwellings, and were not in the direct vicinity of main roadways,

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industrial pollution, or other local pollution sources. The mass concentration

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of PM2.5 was measured by an online particulate monitor (FH 62 C14 series,

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Thermo Fisher Scientific, Inc.) equipped with a verified PM2.5 cyclone using

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beta attenuation techniques. Organic carbon (OC) and elemental carbon (EC)

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were measured using a semi-continuous OC/EC analyzer (model RT-4,

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Sunset Laboratory, Inc.) equipped with a PM2.5 cyclone and an upstream

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parallel-plate organic denuder (Sunset Laboratory Inc.). The concentrations

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of 8 major water-soluble inorganic ions, including chlorine (Cl−), nitrate (NO3−),

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sulfate (SO42−), ammonium (NH4+), sodium (Na+), potassium (K+), magnesium

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(Mg2+), and calcium (Ca2+), were measured by a commercial instrument for

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online monitoring of aerosols and gases (MARGA, model ADI 2080, Applikon

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Analytical B.V.). The quality assurance/quality control procedures were

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routinely conducted, including maintenance/cleaning for this instrument as

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well as calibrations for air flow rate, mass foil, and temperature/pressure. The

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time resolution was 1 hour for each sample, with 45 min of sampling and 15

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min of analysis. The principle and operation of this instrument have been

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provided in detail elsewhere.17-19

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Daily mean temperature and mean relative humidity were collected from

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the Shanghai Meteorological Bureau to allow for the adjustment of weather

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conditions. We also collected hourly concentrations of gaseous pollutants,

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including sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon

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monoxide (CO), from one fixed-site national monitoring station, which is 1.8

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km away from the community.

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Statistical Analyses

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Environmental and health data were linked by the time of blood sampling. All

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biomarker measurements were natural log-transformed to improve the

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normality before statistical analyses. We applied the linear mixed-effect

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model to evaluate the associations between biomarkers and PM2.5. In the

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basic model, PM2.5 and its components were incorporated one at a time as

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the fixed-effect terms. We also incorporated several covariates as fixed-effect

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terms: (1) an indicator variable of “week” of blood collections to exclude any

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unknown weekly time trends; (2) an indicator variable of “day of the week” to

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control for the potential day-of-week effects; (3) the moving average of mean

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temperature and relative humidity on the current day and previous 3 days to

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adjust for the confounding effects of weather conditions; and (4) individual

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characteristics, such as age, gender, body mass index, education, and the

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history of morbidities. Finally, a random intercept was introduced to account

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for the within-subject correlations due to repeated measurements. To fully

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capture the time-lag patterns in the effects of PM2.5 and its various

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constituents, we fit the above models using multiple separate intervals

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preceding the blood draw: 0 to 6 h, 7 to 12 h, 13 to 24 h, 0 to 24 h (lag 0 day),

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25 to 48 h (lag 1 day), 49 to 72 h (lag 2 days) and 3 to 7 days.

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In addition to the basic single-constituent model described above, we

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built a “constituent-PM2.5 joint model” with the adjustment of total PM2.5 mass

160

to account for potential confounding by PM2.5 and other constituents that

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co-vary with PM2.5. However, it usually leads to underestimation of the effects

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for a specific constituent due to the over-adjustment with respect to the strong

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correlations with a constituent and PM2.5.20 We thus further fitted a

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“constituent-residual model”, which has the advantage of eliminating

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confounding and extraneous variation by total PM2.5, as well as collinearity

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with the remaining constituents. In this model, we first obtained the residual of

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each constituent by establishing a linear regression model between total

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PM2.5 and the constituent, and then introduced the residual into the basic

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model replacing this constituent. The constituent residual can be regarded as

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a crude measure of the independent contribution of a constituent to the

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effects of PM2.5 after excluding its collinearity of the remaining constituents.

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To test the robustness of our results on the adjustment for concomitant

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exposure to gaseous pollutants, we performed a sensitivity analysis by

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including

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single-constituent models individually.

4

gaseous

pollutants

(CO,

NO2, SO2

and

O3 )

in

the

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The statistical tests were two-sided, and values of P < 0.05 were

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considered statistically significant. All models were performed using R

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software (Version 3.3.0, R Foundation for Statistical Computing, Vienna,

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Austria) with the “lme4” package. The estimates for blood biomarkers were

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calculated as the percent changes and their 95% confidence intervals (CIs)

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associated with an interquartile range (IQR) increase in PM2.5 concentrations.

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Results

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Descriptive Statistics

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We obtained all the scheduled blood samples for 28 subjects. Details of the

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descriptive characteristics of the participants have been provided in our

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previous publication.16 Briefly, on average, the participants were 64 years old

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with a body mass index of 24.7 kg/m2. Twelve patients had comorbid

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hypertension, and they all had a regular intake of antihypertensive

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medications. According to self-reported questionnaires, none of the subjects

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participated in strenuous physical activities, had an exacerbation of COPD,

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took anti-COPD medication, or traveled out of the central urban areas of

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Shanghai 3 days before the scheduled blood collection.

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We tested the levels of 10 biomarkers in a total of 112 blood samples.

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Table 1 provides the summary statistics of 8 inflammatory biomarkers and 2

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coagulation biomarkers. There are considerable variations of these cytokines

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in and between subjects.

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Table 2 provides the descriptive statistics on the daily average

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concentrations of PM2.5 constituents, weather variables and gaseous

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pollutants. There are no missing hourly data for PM2.5, but a small fraction

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(about 5%) of missing data in the hourly measurements of some metal ions.

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The 24-h mean concentrations of PM2.5 before the scheduled blood collection

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varied substantially from 14.4 to 105.1 µg/m3, with an average of 38.4 µg/m3,

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which is much higher than the World Health Organization Air Quality

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Guidelines (20 µg/m3).21 SO42- accounted for the largest proportion of PM2.5

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(32% on average), followed by NO3- (25%), OC (18%), and NH4+ (16%).

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In general, there were weak to high correlations among PM2.5

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constituents (SI Table S1). For instance, there were weak correlations

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between Cl- and Mg2+, Ca2+, and K+ (Pearson r: 0.05-0.21), but there were

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strong correlations between OC and EC (Pearson r=0.97). We did not

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observe large variations in weather conditions during the study period, but

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they were moderately-to-strongly correlated with PM2.5 constituents. For

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example, temperature was mildly or moderately positively correlated with OC,

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EC, Cl-, NO3-, and NH4+ (Pearson r: 0.03-0.47) and strongly positively

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correlated with other water-soluble ions (Pearson r: 0.72-0.83, for Na+, K+,

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Mg2+, and Ca2+). Relative humidity was negatively correlated with most PM2.5

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constituents.

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Regression Results

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Figure 1 illustrates the lag patterns of percent changes in 10 blood

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biomarkers associated with an IQR increase in PM2.5 total mass. We

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observed significantly positive associations between PM2.5 and most

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biomarkers within 24 hours. These associations occurred within 0 to 6 hours

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and became strongest between 13 and 24 hours, but attenuated greatly and

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lost statistical significance at lag 1 day and longer lag days (data not shown).

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This kind of lag pattern was not appreciably changed in most associations

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between each biomarker and constituent, regardless of the statistical

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significance (SI Figure S11-S20). We therefore used the exposure averaged

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at lags of 0 to 24 hours to capture almost all effects caused by PM2.5 in our

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main analyses. An IQR (27.4 µg/m3) increase in total PM2.5 was significantly

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associated with increments of 22%, 14%, 6.6%, 4.5%, 12%, 16%, 12%, 8.7%,

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and 27% in serum levels of fibrinogen, CRP, MCP-1, TNF-α, ICAM-1,

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P-selectin, VCAM-1, PAI-1, and sCD40L, respectively.

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Figure 2 presents the percent changes in 10 cytokines per an IQR

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increase in various constituents at 0 to 24 hours (lag 0 day) in the

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single-constituent model. We observed significantly positive associations of

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all PM2.5 constituents, except K+ and Mg2+, with at least one cytokine. OC and

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EC were consistently associated with all 8 inflammatory biomarkers. On

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average, an IQR increase in OC and EC corresponded to 50% and 37%

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increments in these biomarkers, respectively. SO42- was associated with the 2

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coagulation biomarkers, and an IQR increase resulted in 11% increments in

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PAI-1 and 34% increments in sCD40L.

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The magnitude of the associations between PM2.5 constituents (lag 0 day)

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and cytokines attenuated appreciably, and the 95% CIs became larger in

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constituent-PM2.5 joint models and constituent-residual models (Figures 3 and

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4). We found relatively robust associations of OC, EC, NO3- and NH4+ on at

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least 3 inflammation markers. Only SO42- or NH4+ was relatively robustly

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associated with the 2 coagulation markers. Using CRP as an example, the

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graphic abstract illustrated the associations with five constituents (OC, EC,

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NO3-, SO42-, and NH4+) in all 3 models (see Table of Contents).

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In the sensitivity analyses, by controlling for gaseous pollutants (SI

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Figure S1-S10), the associations between constituents and cytokines were

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almost unchanged when adjusting for O3. After controlling for NO2, SO2, and

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CO, the associations of NO3-, SO42-, and NH4+ decreased slightly and

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became less significantly associated with almost all cytokines. The

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associations of OC and EC with inflammatory cytokines were strengthened

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and statistical significance was lost for some cytokines when adjusting for

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SO2 and CO. The associations of the 5 constituents on coagulation were also

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strengthened after controlling for NO2 and SO2, but became insignificant for

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NO3-, SO42-, and NH4+ in some cases.

260 261

Discussion

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This study provided a relatively comprehensive analysis of the short-term

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associations of PM2.5 chemical constituents (2 carbonaceous fractions and 8

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inorganic ions) on blood biomarkers of inflammation and coagulation. We

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found significant associations between PM2.5 and all cytokines, and these

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associations were restricted within 24 hours. We further identified some

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constituents, including OC, EC, SO42, NO3-, and NH4+, have more robust

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associations with blood inflammation or coagulation than the remaining 5

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constituents. Our findings were generally insensitive to the adjustment for

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gaseous pollutants.

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Abundant human evidence has demonstrated that short-term inhalation of

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PM2.5 would result in elevations of circulating biomarkers of inflammation and

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coagulation.1,

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PM2.5 on the concurrent day and most biomarkers we selected. The effects of

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PM2.5 occurred within 0 to 6 hours, became strongest within 13 to 24 hours,

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and disappeared after 24 hours. Previous studies have also reported that the

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acute effects of PM2.5 were restricted on the current day or sub-day after

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exposure.23,

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biomarkers. For example, we estimated that an IQR increase in 24-h average

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PM2.5 concentrations was associated with increments of 22%, 14%, 6.6%,

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4.5%, 12%, 16%, 12%, 8.7% and 27% in fibrinogen, CRP, MCP-1, TNF-a,

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ICAM-, P-selectin, VCAM-1, PAI-1, and sCD40L, respectively. These results

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were generally comparable to previous estimates. For example, in our

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previous panel study with a crossover design, we found that an IQR increase

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(64 µg/m3) in PM2.5 concentrations was associated with significant increases

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of 16.1% and 71.3% in MCP-1 and sCD40L, respectively.15 A panel study

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among healthy young students in Beijing observed a significant increase of

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7.1% in TNF-α per an IQR (63.4 µg/m3) increase in PM2.5.14 Another panel

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study in the US reported a 7.6% increase in CRP associated with an IQR

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(19.6 µg/m3) increase in PM2.5 concentrations.25 The similar lag patterns in

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the effects of constituents with PM2.5 total mass suggested that various

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constituents of PM2.5 have similar time courses from entering the body to

9, 22

24

We observed significantly positive associations between

The magnitude of associations varied slightly among

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potential production of effects.

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Nevertheless, previous studies on the acute effects of PM2.5 constituents

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on systemic inflammation and coagulation were limited and inconsistent. OC

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and EC are two major components in PM2.5 total mass. We found that they

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were independently associated with inflammatory biomarkers, but not with

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coagulation biomarkers. Similarly, a panel study on the 2008 Beijing Olympics

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demonstrated significant increases in inflammatory cytokines (fibrinogen,

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sCD40L, etc.) associated with EC and OC among healthy young adults.26

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Another study among a panel of COPD patients in Germany reported

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increased levels of fibrinogen by exposure to EC and OC.6 The independent

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effects of OC and EC on the cardiovascular system were also broadly

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supported by time-series or long-term studies.27-29

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Soluble ions (such as SO42-, NO3-, and NH4+) typically constitute the

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majority of PM2.5 mass. We found independent associations between NO3-

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and/or NH4+ with inflammatory biomarkers and between SO42- or NH4+ and

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coagulation biomarkers, which were also comparable to previous findings.30

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For example, a panel study in Taiwan reported that both SO42- and NO3- were

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positively associated with CRP, fibrinogen, and PAI-1 in single-pollutant

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models, but only the association between SO42- and fibrinogen and PAI-1

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remained significant when controlling for PM2.5.9 SO42- was also robustly

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associated with sCD40L in a panel of healthy young adults surrounding the

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Beijing Olympics.26 Another panel study among healthy young adults in

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Beijing only demonstrated significant associations of TNF-α with SO42- and

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NO3- in single-constituent models. However, the associations of SO42- and

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NO3- were null or inverse with other inflammatory biomarkers.14 In our

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previous study, we demonstrated significant effects of PM2.5 and its

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constituents of SO42-, NH4+, OC, and EC on an indicator of airway

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inflammation.16 The independent cardiovascular effects of the 3 components

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of PM2.5 (such as SO42-, NO3-, and NH4+) were also supported by other

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time-series or long-term studies.29, 31, 32

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Our findings may have implications for developing air pollution abatement

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strategies to maximize public health benefits. As mentioned above, we

325

observed independent associations of carbonaceous components and

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several soluble ions with circulating biomarkers, which may reflect the public

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health importance from one or a set of sources.33 We found the independent

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associations of OC, EC, SO42-, NO3-, and NH4+, rather than Cl-, Na+, K+, Mg2+,

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and Ca2+, suggesting the relative importance of fossil combustion and

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biomass burning that merit further investigations against sea salt and

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wind-blown dust.34-36 However, potential differential measurement errors

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across constituents may lead to challenges in interpreting these results.

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There were few data available concerning the intra-city spatial distribution of

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PM2.5 constituents in China. Therefore, it may still be plausible that the

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observed stronger effects of combustion-related constituents (OC, EC, SO42-,

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NO3-, and NH4+) might be attributable to lesser extent of exposure

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measurement errors in that they are enriched more in the finer size range of

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PM2.5 size distribution and thus are more spatially uniformly distributed in the

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city. In contrast, the non-significant associations of constituents (Cl-, Na+,

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Mg2+, and Ca2+) with biomarkers might be explained by the larger

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measurement errors due to their closer relations with sea salt and wind-blown

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dust, which are less uniformly distributed within the city.

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Because temperature is an important confounder when evaluating the

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health effects of air pollutants,30, 37 the different correlations of constituents

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with temperature may be helpful to partly explain the differentiated

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associations between constituents and biomarkers. In this analysis, we

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analyzed the associations between temperature and biomarkers using the

348

same models with PM2.5 constituents and found almost null or non-significant

349

associations. For OC, EC, and NO3-, which are weakly or moderately

350

correlated with temperature, their associations with biomarkers may be not

351

substantially confounded by temperature. For Na+, K+, Mg2+, Ca2+, which are

352

strongly correlated with temperature, their non-significant associations with

353

biomarkers might actually reflect the weak associations between temperature

354

and

355

correlations in modifying the effects of constituents on adverse health

356

outcomes merited further investigation because temperature and PM2.5

357

constituents were not measured at the individual level.

358

biomarkers.

Nonetheless,

the

roles

of

temperature-constituent

It remains unclear how PM2.5 constituents affect cardiovascular function.

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359

Our results supported the hypotheses that short-term exposure to PM2.5 and

360

some of its constituents was significantly associated with increments of CRP,

361

TNF-α, MCP-1, ICAM-1, VCAM-1, sP-selectin, sCD40L, and PAI-1. These

362

cytokines are well-established biomarkers of blood inflammation and

363

coagulation that is heavily involved in the development of a number of

364

adverse cardiovascular outcomes.38-42 Our findings indicated that some

365

constituents may be primarily responsible for the blood inflammation and

366

coagulation caused by PM2.5, which may aid in further investigations, for

367

example, on the genetic and epigenetic mechanisms whereby PM2.5

368

constituents affect biomarkers.

369

Our study has several strengths. First, we obtained real-time

370

concentrations of PM2.5 constituents, which allowed us to explore their

371

sub-daily effects and time courses. Second, the longitudinal panel design with

372

repeated-measures allowed the study subjects to serve as their own controls

373

and thus increased the statistical power. Third, we comprehensively

374

examined the effects of various PM2.5 constituents on a series of circulating

375

biomarkers, which avoided potential publication bias. Our results provided

376

abundant evidence linking air pollution with CVDs.

377

However, our results should be treated with caution because of the

378

following limitations. First, exposure measurement errors are inevitable

379

because all exposure data (including air pollutants and weather conditions)

380

were obtained from a nearby fixed-site monitor. Second, the sample size of

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the present study is relatively small, and some important associations might

382

have been underestimated or missed. Third, as all the participants were

383

elderly COPD patients, the generalizability of our results was limited, but the

384

impacts were not large because they were all stable patients with

385

mild-to-moderate COPD without any medications. Fourth, because of the

386

limitations of our instruments, we failed to evaluate the effects of metals,

387

which may also cause systemic inflammation and coagulation.14

388

In summary, this panel study added to the existing evidence that

389

short-term exposure to particulate air pollution could result in significant

390

increase in circulating biomarkers of inflammation and coagulation in China.

391

Furthermore, some chemical constituents in PM2.5, for instance, OC, EC,

392

SO42-, NO3-, and NH4+, might play crucial roles in inducing the systemic

393

inflammation and coagulation, but their roles varied according to the selected

394

biomarkers. Further investigations with a larger sample size, personal

395

exposure measurements, and more comprehensive measurements of PM2.5

396

constituents are needed to replicate our findings and characterize the

397

pathophysiological pathways whereby PM2.5 affect the cardiovascular system.

398

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Supporting Information

400

Table S1. Pearson correlation coefficients between 24-h average (lag 0 day)

401

concentrations of PM2.5 constituents, weather conditions and gaseous

402

pollutants.

403 404

Figure S1-Figure S10. Percent changes in 10 biomarkers associated with an

405

interquartile range increase in 24-h average (lag 0 day) concentrations of

406

PM2.5 constituents after adjusting for gaseous pollutants in 2-pollutant models.

407

Abbreviations as in Table 1.

408 409

Figure S11-Figure S20. Percent changes in the 10 blood biomarkers

410

associated with an interquartile range increase in sub-daily concentrations of

411

PM2.5 constituents. Abbreviations as in Table 1.

412 413

Author contributions

414

CL and JC performed the statistical analysis and drafted the manuscript. RC

415

and HK revised the manuscript. LQ and HW collected the environmental data.

416

WX, HL and AZ collected the health data. HK and RC designed the study and

417

takes responsibility for the integrity of the data and the accuracy of the data

418

analysis.

419 420

Notes

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The authors declared that they had no competing financial interests.

422 423

Acknowledgements

424

The authors appreciated the contributions of all volunteers in this study. The

425

study was supported by the Public Welfare Research Program of National

426

Health and Family Planning Commission of China (201502003), National

427

Natural Science Foundation of China (91643205 and 81502774), China

428

Medical Board Collaborating Program (13-152), and Cyrus Tang Foundation

429

(CTF-FD2014001).

430

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Table 1. Summary of the health indicators over the study period. Biomarkers

Mean

SD

Min

Median

Max

Fibrinogen, ng/ml

0.96

0.53

0.3

0.86

3.4

CRP, mg/L

4.5

6.4

0.3

1.9

16.9

MCP-1, pg/ml

596

180

263

566

1302

TNF-α, pg/ml

15.0

18.6

3.6

10.9

114.5

IL-1β, pg/ml

4.4

3.3

0.0

5.1

9.2

ICAM-1, ng/ml

105

36

38

100

206

P-selectin, ng/ml

42.7

17.3

23.3

38.5

106.5

VCAM-1, ng/ml

383

102

213

357

705

PAI-1, ng/ml

126

44

57

119

283

sCD40L, µg/ml

3.6

2.5

0.3

3.0

11.2

Blood inflammation

Blood coagulation

592

Definition of abbreviations: SD = standard deviation; IQR = interquartile range;

593

CRP = C-reactive protein; MCP-1= monocyte chemoattractant protein-1;

594

TNF-α= tumor necrosis factor-α; IL-1β= interleukin-1β; ICAM-1= intercellular

595

adhesion molecule-1; VCAM-1= vascular cell adhesion molecule-1; PAI-1=

596

plasminogen activator inhibitor-1; and sCD40L= soluble CD40 ligand.

597

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Table 2. Descriptive statistics on the 24-h average ambient air pollutants,

599

PM2.5 chemical constituents, and weather variables for the study participants

600

over the study period. Variables

Mean

SD

Min

Median

Max

IQR

Total mass

44.4

25.9

14.4

41.6

105.1

27.4

Cl-

0.69

0.38

0.11

0.66

1.34

0.63

NO3-

9.41

6.38

2.43

8.40

24.97

7.44

SO42-

13.65

7.98

2.81

11.67

34.32

8.38

NH4+

6.40

4.01

0.90

5.38

15.08

5.40

Na+

0.09

0.13

0.00

0.00

0.43

0.16

K+

0.22

0.32

0.00

0.02

0.95

0.41

Mg2+

0.23

0.14

0.08

0.21

0.50

0.23

Ca2+

1.88

1.32

0.55

1.33

4.66

2.28

OC

7.59

3.53

3.78

6.96

13.29

7.94

EC

2.01

0.82

0.78

1.97

3.28

1.61

Temperature (℃)

24.7

1.7

22.7

24.2

27.8

3.2

Relative humidity (%)

68.5

12.5

45.0

72.3

84.0

25.3

49.5

13.3

30.8

46.9

70.0

21.7

PM2.5 (µg/m3)

Weather a

Gaseous pollutants (µg/m3) NO2

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SO2

9.6

5.9

3.2

7.7

20.8

13.3

O3

84.9

31.6

29.9

75.9

143.5

49.5

CO

0.81

0.19

0.49

0.83

1.20

0.30

601

Definition of abbreviations: SD= standard deviation; IQR= interquartile range.

602

a

603

and previous 3 days.

Data are presented as the average of weather conditions on the present day

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604 605

Figure 1. Percent changes (mean and 95% confidence intervals) in blood biomarkers associated with an interquartile range

606

increase in PM2.5 mass concentration using different lag periods. Abbreviations as in Table 1.

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607 608

Figure 2. Percent changes (mean and 95% confidence intervals) in blood

609

biomarkers associated with an interquartile range increase in 24-h average

610

(lag 0 days) concentrations of PM2.5 constituents in the single-constituent

611

model. Label abbreviations: (A) Fibrinogen; (B) CRP, C-reactive protein; (C)

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MCP-1, monocyte chemoattractant protein-1; (D) TNF-α, tumor necrosis

613

factor-α; (E) IL-1β, interleukin-1β; (F) ICAM-1, intercellular adhesion

614

molecule-1; (G) P-selectin; (H) VCAM-1, vascular cell adhesion molecule-1; (I)

615

PAI-1, plasminogen activator inhibitor-1; (J) sCD40L, soluble CD40 ligand.

616

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617 618

Figure 3. Percent changes (mean and 95% confidence intervals) in blood

619

biomarkers associated with an interquartile range increase in 24-h average

620

(lag 0 days) concentrations of PM2.5 constituents in the constituent-PM2.5 join

621

model. Label abbreviations: (A) Fibrinogen; (B) CRP, C-reactive protein; (C)

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MCP-1, monocyte chemoattractant protein-1; (D) TNF-α, tumor necrosis

623

factor-α; (E) IL-1β, interleukin-1β; (F) ICAM-1, intercellular adhesion

624

molecule-1; (G) P-selectin; (H) VCAM-1, vascular cell adhesion molecule-1; (I)

625

PAI-1, plasminogen activator inhibitor-1; (J) sCD40L, soluble CD40 ligand.

626

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627 628

Figure 4. Percent changes (mean and 95% confidence intervals) in blood

629

biomarkers associated with an interquartile range increase in 24-h average

630

(lag 0 days) concentrations of PM2.5 constituents in the constituent-residual

631

model. Labels abbreviations: (A) Fibrinogen; (B) CRP, C-reactive protein; (C)

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MCP-1, monocyte chemoattractant protein-1; (D) TNF-α, tumor necrosis

633

factor-α; (E) IL-1β, interleukin-1β; (F) ICAM-1, intercellular adhesion

634

molecule-1; (G) P-selectin; (H) VCAM-1, vascular cell adhesion molecule-1; (I)

635

PAI-1, plasminogen activator inhibitor-1; (J) sCD40L, soluble CD40 ligand.

636

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638 639

Table of Contents or Abstract Figure

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