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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

ACS Paragon Plus Environment


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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].


5423

7908.

E-mail:

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1

Abstract

2

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.

7

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,


we

fitted

the


23

Keywords: fine particulate matter; chemical constituent; inflammation;

24

coagulation; biomarker; panel study

25 26

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

27

and 4 figures (2400 words) = 7000 words

28


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29

Introduction

30

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

31

and

32

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

37

An increasing number of studies have attempted to elucidate the time

38

courses during which PM2.5 exposure causes adverse cardiovascular

39

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

40

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

41

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

42

increased blood pressure.7 Systemic inflammation and hypercoagulability are

43

two common mechanisms among a number of possible biological pathways

44

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

45

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

46

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

47

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

48

exposure to various PM2.5 constituents.

49

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

50

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



51

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

52

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

53

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

54

most deleterious to the cardiovascular system. Elderly patients with chronic

55

obstructive pulmonary disease (COPD) were selected because they are

56

hypothesized to be susceptible to the adverse cardiovascular effects of air

57

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

58

inflammatory state.6

59 60

Material and methods

61

Study Design and Participants

62

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

63

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

64

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

65

took medication due to exacerbation of COPD condition during the study

66

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

67

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

68

diagnosed by physicians. We only included the stable patients with

69

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

70

Global Initiative for Chronic Obstructive Lung Disease based on the baseline

71

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

72

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

74

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

75

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

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

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

84

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

85

from each subject.

86

Blood collection and lab analysis

87

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

88

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

89

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

90

minutes within 20 minutes of collection. Serum samples were transported

91

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

92

We analyzed 10 circulating biomarkers associated with particulate air

93

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

94

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



95

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

96

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

97

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

98

coagulation, soluble CD40 ligand (sCD40L) and plasminogen activator

99

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

100

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

101

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

102

each biomarker was simultaneously quantified using the MAGPIX system and

103

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

104

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

105

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

106

LLOQ. All biomarker tests were performed under the same conditions

107

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

108

quality control ranges.

109

Environmental Data

110

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

111

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

112

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

113

Academy of Environmental Sciences (approximately 4 km away from the

114

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

115

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

116

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,

118

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

119

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

120

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

121

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

122

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

123

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

124

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

125

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

126

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

127

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

128

routinely conducted, including maintenance/cleaning for this instrument as

129

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

130

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

131

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

132

provided in detail elsewhere.17-19

133

Daily mean temperature and mean relative humidity were collected from

134

the Shanghai Meteorological Bureau to allow for the adjustment of weather

135

conditions. We also collected hourly concentrations of gaseous pollutants,

136

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

137

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

138

km away from the community.



139

Statistical Analyses

140

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

141

biomarker measurements were natural log-transformed to improve the

142

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

143

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

144

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

145

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

146

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

147

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

148

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

149

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

150

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

151

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

152

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

153

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

154

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

155

constituents, we fit the above models using multiple separate intervals

156

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

157

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

158

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

159

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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161

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

162

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

163

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

164

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

165

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

166

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

167

each constituent by establishing a linear regression model between total

168

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

169

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

170

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

171

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

172

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

173

exposure to gaseous pollutants, we performed a sensitivity analysis by

174

including

175

single-constituent models individually.

4

gaseous

pollutants

(CO,

NO2, SO2

and

O3 )

in

the

176

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

177

considered statistically significant. All models were performed using R

178

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

179

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

180

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

181

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

182



183

Results

184

Descriptive Statistics

185

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

186

descriptive characteristics of the participants have been provided in our

187

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

188

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

189

hypertension, and they all had a regular intake of antihypertensive

190

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

191

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

192

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

193

Shanghai 3 days before the scheduled blood collection.

194

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

195

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

196

coagulation biomarkers. There are considerable variations of these cytokines

197

in and between subjects.

198

Table 2 provides the descriptive statistics on the daily average

199

concentrations of PM2.5 constituents, weather variables and gaseous

200

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

201

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

202

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

203

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

204

which is much higher than the World Health Organization Air Quality


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205

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

206

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

207

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

208

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

209

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

210

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

211

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

212

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

213

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

214

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

215

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

216

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

217

constituents.

218

Regression Results

219

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

220

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

221

observed significantly positive associations between PM2.5 and most

222

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

223

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

224

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

225

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

226

between each biomarker and constituent, regardless of the statistical



227

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

228

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

229

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

230

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

231

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

232

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

233

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

234

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

235

single-constituent model. We observed significantly positive associations of

236

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

237

EC were consistently associated with all 8 inflammatory biomarkers. On

238

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

239

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

240

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

241

PAI-1 and 34% increments in sCD40L.

242

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

243

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

244

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

245

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

246

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

247

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

248

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


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249

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

250

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

251

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

252

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

253

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

254

became less significantly associated with almost all cytokines. The

255

associations of OC and EC with inflammatory cytokines were strengthened

256

and statistical significance was lost for some cytokines when adjusting for

257

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

258

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

259

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

260 261

Discussion

262

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

263

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

264

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

265

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

266

associations were restricted within 24 hours. We further identified some

267

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

268

associations with blood inflammation or coagulation than the remaining 5

269

constituents. Our findings were generally insensitive to the adjustment for

270

gaseous pollutants.



271

Abundant human evidence has demonstrated that short-term inhalation of

272

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

273

coagulation.1,

274

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

275

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

276

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

277

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

278

exposure.23,

279

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

280

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

281

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

282

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

283

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

284

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

285

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

286

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

287

among healthy young students in Beijing observed a significant increase of

288

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

289

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

290

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

291

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

292

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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293

potential production of effects.

294

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

295

on systemic inflammation and coagulation were limited and inconsistent. OC

296

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

297

were independently associated with inflammatory biomarkers, but not with

298

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

299

demonstrated significant increases in inflammatory cytokines (fibrinogen,

300

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

301

Another study among a panel of COPD patients in Germany reported

302

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

303

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

304

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

305

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

306

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

307

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

308

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

309

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

310

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

311

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

312

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

313

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

314

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



315

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

316

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

317

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

318

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

319

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

320

inflammation.16 The independent cardiovascular effects of the 3 components

321

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

322

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

323

Our findings may have implications for developing air pollution abatement

324

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

325

observed independent associations of carbonaceous components and

326

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

327

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

328

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

329

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

330

biomass burning that merit further investigations against sea salt and

331

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

332

across constituents may lead to challenges in interpreting these results.

333

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

334

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

335

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

336

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


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337

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

338

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

339

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

340

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

341

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

342

dust, which are less uniformly distributed within the city.

343

Because temperature is an important confounder when evaluating the

344

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

345

with temperature may be helpful to partly explain the differentiated

346

associations between constituents and biomarkers. In this analysis, we

347

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.



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


Page 20 of 42

Page 21 of 42


381

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



399

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


Page 22 of 42

Page 23 of 42


421

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



431

References

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590



591

Page 32 of 42

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


Page 33 of 42


598

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



Page 34 of 42

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


Page 35 of 42


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.



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)


Page 36 of 42

Page 37 of 42


612

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



617 618


619


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)


Page 38 of 42

Page 39 of 42


622


623


624


625


626



627 628


629


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)


Page 40 of 42

Page 41 of 42


632


633


634


635


636



637

638 639

Table of Contents or Abstract Figure

640


Page 42 of 42

The Acute Effects of Fine Particulate Matter ... - ACS Publications

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