Association of short-term exposure to ambient air pollutants with exhaled nitric oxide in hospitalized patients with respiratory-system diseases

Ecotoxicology and Environmental Safety 168 (2019) 394–400

Contents lists available at ScienceDirect

Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Association of short-term exposure to ambient air pollutants with exhaled nitric oxide in hospitalized patients with respiratory-system diseases ⁎

Huibin Guoa,1, Wenlan Yangb,1, Li Jianga, Yan Lyua, Tiantao Chenga, Beilan Gaob, , Xiang Lia,c,

T

⁎⁎

a

Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China Department of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China c Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Exhaled nitric oxide Respiratory system diseases Ambient air pollutants Hospital patients

Background: Previous studies have suggested that exposure to ambient air pollutants may adversely affect human health. However, few studies have examined the health effects of exposure to ambient air pollutants in hospitalized patients. Objectives: To evaluate the association between short-term exposure to ambient air pollutants and exhaled nitric oxide fraction (FeNO) in a large cohort of hospitalized patients. Methods: FeNO was detected for 2986 hospitalized patients (ages 18–88 years). Daily average concentrations of SO2, NO2, O3, CO, PM2.5 and PM10 in 2014 and 2015 were obtained from nine fixed-site monitoring stations. Multiple linear regression models were chosen to assess the associations of exposure to ambient air pollutants with FeNO while adjusting for confounding variables. Lagged variable models were selected to determine the association between FeNO and ambient air pollutants concentrations with lags of up to 7 days prior to FeNO testing. Results: Interquartile-range (IQR) increases in the daily average SO2 (8.00 μg/m3) and PM2.5 (37.0 μg/m3) were strongly associated with increases in FeNO, with increases of 3.41% [95% confidence interval (CI), 0.94–5.93%] and 2.72% (95%CI, −0.09% to 5.61%), respectively. However, FeNO levels were not statistically associated with PM10, NO2, O3 or CO. In the two-pollutant models, the maximum correlation was for ambient SO2. We also found that FeNO was associated with IQR increases in daily average ambient concentrations of SO2 up to 3 and 4 days after the exposure events. Conclusions: Short-term exposure to SO2 and PM2.5 were positively correlated with FeNO levels in hospitalized patients in Shanghai.

1. Introduction Numerous studies have reported a relationship between ambient pollution and high-rate morbidity and mortality from respiratory symptoms and inflammatory lung injury (Khafaie et al., 2017; Olaniyan et al., 2017; Peng et al., 2013). The potential mechanisms accounting for these relationships are not clear. Nevertheless, accumulating data indicate that SO2 may give rise to pulmonary inflammation, appearing as either systemic autonomic effects, and/or the spread of cytokines, a systemic proinflammatory product, that may impact cardiovascular function and vascular tone (Yun et al., 2011). Inflammatory processes play a vital role in many lung diseases, including COPD and asthma (Delfino et al., 2013). A recent, proposal suggests that the

pathophysiology of respiratory disease is likely associated with systemic inflammatory processes (Khafaie et al., 2017; Sarnat et al., 2012). Exhaled nitric oxide fraction (FeNO) has been used as an indicator to evaluate the association between airway inflammation and contact with ambient air pollutants (Delfino et al., 2006; Koenig et al., 2003). Nitric oxide (NO), an intercellular messenger with a brief half-life, plays a key role in several biological functions including host-defense (Ricciardolo et al., 2004), neurotransmission and inflammation (Barnes et al., 2010). Many of these biological functions are associated with the respiratory system and cardiovascular pathophysiology. Furthermore, FeNO is an important biomarker for several respiratory-system diseases, such as asthma and COPD (Kharitonov and Barnes, 2000; Montuschi et al., 2001). In the respiratory system, macrophages, non-cholinergic,

⁎

Corresponding author Corresponding author at: Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China. E-mail address: [email protected] (X. Li). 1 These authors contributed to equally to this work. ⁎⁎

https://doi.org/10.1016/j.ecoenv.2018.10.094 Received 17 August 2018; Received in revised form 21 October 2018; Accepted 24 October 2018 0147-6513/ © 2018 Elsevier Inc. All rights reserved.

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

(NIOX MINO) to maintain a steady flow. A dynamic flow restrictor in the device yielded a continuous flow rate of 50 ml/s. For the NIOX, the action of exhaling was repeated after a brief break until 2 acceptable assessments ( ± 2.5 ppb for measurements being less than 50 ppb and ± 5% for assessment equal to or greater than 50 ppb) were obtained. For the NIOX MINO, sufficient exhalation is compulsory for the assessment to be considered ‘approved’ by the equipment. The average amount of the two acceptable assessments from the NIOX and the assessment that received the first approval from each NIOX MINO test was applied for the detailed investigation. The NO filter of the NIOX and the calibration gas were both replaced at the start of the research and system calibration was performed at the intervals of 14 days. Food, beverages, smoking and intense exercise were not allowed within 1 h before the FeNO measurements.

neutrophils, endothelial cells, non-adrenergic, and epithelial cells have the ability to produce FeNO (Gaston et al., 1994). The generation of endogenous NO is dominated by various isoforms of nitric oxide synthase (NOS), including inducible NOS (iNOS) and constitutive NOS (cNOS). The activation of iNOS by pro-inflammatory cytokines is likely responsible for the increased levels of NO in asthma patients (Ricciardolo et al., 2004). Previous researches indicated that the evaluation of NO concentrations of exhaled air might represent a non-invasive assessment of airway inflammation (Flamant-Hulin et al., 2010; Kim et al., 2016). In particular, FeNO has been shown to be a related marker in asthma, COPD and chronic cough (de Laurentiis et al., 2008; Sarnat et al., 2012). Additional pathways have been suggested for the observed increases in FeNO. For instance, recent evidence shows that the acidification of the respiratory system exerts a serious impact on human health and potentially fosters the generation of NO in the human lung (Barnes et al., 2010). Few studies have focused on the impact of air pollution exposure on subclinical inflammatory signals such as NO. Van Amsterdam et al. compared ambient air pollutants levels with the FeNO level in 16 nonsmoking adults for a period of 14 days (Van Amsterdam et al., 1999). The most powerful relationship was detected between FeNO and ambient CO. In children between 8 and 13 years old, Steerenberg et al. found that exposure to nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter smaller than 10 µm (PM10) or black smoke was associated with an increase in FeNO level (Steerenberg et al., 2001). These pollutants were also associated with a decreased peak expiration flow and increased inflammatory signals in nasal lavage cases. However, no studies have assessed the relationship between air pollution exposure and FeNO in hospitalized patients with respiratory diseases, a population that may be particularly vulnerable to the effects of pollution. We tested the relationship between variations in short-term FeNO and ambient air pollution exposure in hospitalized patients with respiratory disease over a one-year period.

2.3. Exposure assessment Exposure was estimated through outside PM10 (particulate matter less than 10 µm in aerodynamic diameter), PM2.5 (particulate matter less than 2.5 µm in aerodynamic diameter), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) concentrations documented by the Shanghai City government at 9 fixedpoint surveillance centre locations (Putuo, Yangpu, Luwan, Hongkou, Jin’an, Xuhui, Pudong, Chuansha, Zhangjiang). To ensure quality of air pollution data, we obtained PM10, PM2.5, CO, NO2, SO2 and O3 concentrations data from 9 fixed-point surveillance centre locations with the study area. The monitoring stations are regulated by the Shanghai Environmental Protection Bureau. These stations are mandated to be located away from major roads, industrial sources, buildings, or residential sources of emissions from the burning of coal, waste, or oil; thus, our monitoring results reflect the background urban air pollution level in Shanghai rather than local sources such as traffic or industrial combustion. Thus, the monitoring data from these stations generally reflected the background urban air pollution of Shanghai rather than pollution from local sources. Daily average concentrations of PM10, PM2.5, O3, SO2, NO2, and CO and meteorological condition (temperature and humidity) were assessed for all 647 days in the course of the study. The residences of all the hospital patients were georeferenced by applying a geographic information system (GIS), and the closest surveillance site was assigned to the participant. We also monitored meteorological statistics (daily average temperature and relative humidity) from the Fudan super meteorological station (< 1 km) to allow consideration of the impact of weather on the hospitalized patients. The station is located on the rooftop (20 m from the ground) of school building No.4 at the Fudan University campus (121.50° E, 31.30° N), approximately 150 m south of Shanghai Lung Hospital and approximately 5 km northeast of downtown Shanghai (the elevation is approximately 4 m a.s.l.).

2. Materials and methods 2.1. Hospitalized patients Data on hospitalized patients aged 18 years and older were extracted from the Shanghai Lung Hospital (SLH) electronic medical administration documents kept from 2014 to 2015. Medical care data included personal characteristics such as age, gender, smoking, career, permanent residence and length of stay. Using this information, we selected 2986 admissions of persons to ensure the selection of patient residing in Shanghai and to provide sufficient data on activities related to air pollution exposure. The disease data were selected in accordance with the codes in International Classification of Diseases, 10th revision (ICD-10) for all patients with asthma (ICD-10: J45, J45.0, J45.9, J 45.1) and/or COPD (ICD-10: J44, J44.1, J44.9). We did not obtain consent from individuals because the data did not contain personal identification. The research draft was tested and approved by the Ethics Committee of Fudan University and Shanghai Lung Hospital.

2.4. Statistical analysis Before statistical modeling was conducted, all of the FeNO concentrations were log-transformed because the data was extremely distorted. The basic characteristics (gender, age and smoking) of the hospitalized patients were compared via bivariate analysis applying the chi-square test or t-test, depending on the variable type. Associations among air pollution, meteorological causes and the research process were analyzed by applying Spearman rank order correlation with IBM SPSS Statistics 22 because of abnormal distributions of several variables. All duplicates were analyzed by applying the R data operating system (R Project for Statistical Computing, Vienna, Austria). All hypothesis testing applied a two tailed test and a 0.05 significance level. Multiple linear regression models were applied to examine the associations between FeNO concentrations and the concentrations of ambient air pollutants to which the patients were exposed. After the consideration of underlying confounders and impact modifiers, the

2.2. FeNO measurement FeNO assessments were completed by applying NIOX and NIOX MINO® airway inflammation monitors (Aerocrine AB, Solna, Sweden) (Khalili et al., 2007) following the ATS recommended procedures [American Thoracic Society/European Respiratory Society (ATS/ERS) 2005] (ATS/ERS, 2005). The assessments were performed in a random order. To determine the outcomes of the two types of monitors, all participants were tested when they were placed into the fixed seat without a nose clip. With the help of a mouthpiece, participants achieved one full inspiration and breathed out at a continuous exhalation rate, under the guidance of visual cues and auditory cues 395

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

whole models were controlled for testing period, smoking status, gender, age, temperature, relative humidity and respiratory-system disease status (asthma/COPD). To determine the potential non-linear exposure-response relationships, generalized additive models (GAMs) were applied to estimate the internal connection of ambient air pollutants and ln(FeNO) by examining the degrees of freedom of the fluent connection for every variable as one part of the model fixation procedure. The regulation variables were similar to those obtained by the linear regression. When we selected the final models for each ambient air pollutant, the potential confounding and modifying effect were examined. To avoid potential multi-collinearity of ambient air pollutants, two-pollutant models were fitted to examine co-dependency according to the correlation structure. These modellings assessed the single- and multi-pollutant connection with FeNO data gathered for hospital patients. Our model also tested the connection of FeNO levels and the lag effect exerted by air pollution levels exceeding the air quality standards. In each analysis, we examined the effect of ambient air pollutants with different lag structures. A lag of 0 day (Lag 0), is equal to the ambient air pollutants concentration at the time of FeNO measurement, and a lag of 1 day (Lag 1) indicates the concentration on the previous day. In this way, the lag day with the largest t-statistic for each ambient air pollutant was shown to be the most important lag day for that pollutant. Finally, we carried out sensitivity analysis for all modellings to assess the influence of significant points during our investigation. Total influence assessments (β) and their 95% confidence intervals (CIs) were converted into percent variations and reported for every interquartile range (IQR) of a pollutant by applying Eq. (1) (Cao et al., 2009)：

Percent change in FeNO=

(e (IQR * (β ± 1.96SE ))−1) × 100%

Table 1 Study population and health outcomes summary. Characteristic

Subjects [N (%)]

Mean (ppb)

Median (ppb)

Range (ppb)

All patients Gender Female Male Age (years) 18–60 60–70 70–88 Cigarette smoking No Yes Diseasea Asthma COPD Bronchiectasis Lung Cancer Pneumonia Pulmonary infection

2986 (100)

21.93

17.00

7.00–50.00

1223 (41) 1763 (59)

19.19 23.83

15.00 19.00

6.00–46.00 8.00–52.00

1345 (45) 1030 (35) 604 (20)

21.03 21.60 24.45

16.00 17.00 19.00

6.30–48.00 7.00–48.00 7.00–62.25

1794 (60) 1192 (40)

21.56 22.49

16.00 18.00

6.00–51.00 7.00–48.00

289 601 949 241 332 1069

32.11 23.10 21.79 19.00 20.00 22.44

19.00 18.00 16.00 16.00 16.00 18.00

7.00–91.00 6.00–54.00 6.00–52.00 6.00–41.80 7.65–39.35 7.00–52.50

(10) (20) (32) (8) (11) (36)

COPD, chronic obstructive pulmonary disease. Remark: ICD-10 Version:2016. Bronchiectasis, J47. X01-J47. X03. COPD, J44, J44.1, J44.9. Asthma, J45, J45.0, J45.9, J 45.1. a As defined by report of doctor diagnosis.

3.2. Air pollution data The daily concentrations of ambient air pollutants estimated during the research periods are provided in Table 2. The daily mean PM2.5 concentration ranged from 17.0 to 114 μg/m3 (95% CI; IQR = 37.0 μg/ m3) with an average of 49.7 μg/m3, which was approximately 5 times higher than the Global Guidelines established by the WHO (annual average: 10 μg/m3). The daily average SO2 concentration ranged from 8.00 to 35.4 μg/m3 (95% CI; IQR = 8.00 μg/m3) with a mean of 16.41 μg/m3. The daily average concentrations of PM10, O3, NO2 and CO were 68.7 μg/m3 (IQR = 41.8 μg/m3), 106 μg/m3 (IQR = 81.0 μg/ m3), 44.9 μg/m3 (IQR = 22.0 μg/m3), and 0.78 mg/m3 (IQR = 0.30 mg/m3), respectively. Generally, ambient air pollutants were negatively correlated with temperature and RH, except for a positive correlation between O3 and temperature (r = 0.526, p < 0.001). The correlation between PM2.5 and PM10, PM10 and SO2, and PM2.5 and CO were r = 0.895 (p < 0.001), r = 0.805 (p < 0.001), r = 0.825 (p < 0.001) and r = 0.821 (p = 0.000), respectively. Since Spearman rank order correlation analyses indicated that daily temperature and relative humidity had highly significant connections with several pollutants (shown in Table 3), we included these variables in models to assess their underlying confounding impact. Because pollutants were associated with each other, we also ran two-pollutant models to adjust for confounding effects.

(1)

In descriptive statistics, the IQR is the 1st quartile values minus that of the 3rd quartile; β is the estimated coefficient of the effects of air pollutants and SE is the standard error.

3. Results 3.1. Study population and concentrations of FeNO A total of 2986 breath samples were collected. All of the practices had sufficient exhaled breath volume for research. The connection of FeNO levels with the subject features are shown in Table 1. The average age of the research subjects was approximately 59 years old; approximately three-fifths were male, and approximately two-fifths had a history of smoking. The median FeNO level was 17.00 ppb with a range of 7.00–50.00 ppb among all participants. Although approximately 10% of the targets reported a medical diagnosis of asthma and an additional 20% reported a diagnosis of COPD, tests of discrepancies between these subgroups failed to reveal significant variations in FeNO levels at the 95% confidence level. Fig. 1 illustrates the outcomes of the stratified analysis to assess the change in impact by sex for each age group (Fig. 1A) and smoking group (Fig. 1B) on FeNO concentration. Notably, old age (70‒88 years) had a significant impact upon the FeNO concentration. In subjects in the age group of 70‒88 years, the mean FeNO concentration was 20.9 ppb (7.00–42.00ppb) in females and 26.1 ppb (7.00–62.25ppb, p = 0.000) in males. The mean FeNO concentration was 22.49 ppb (7.00–48.00 ppb) in the smoking group and 21.56 ppb (6.00–51.00 ppb) in the non-smoking group for males. When the stratified outcomes were connected, no important heterogeneity was found. No other impact modifiers indicated evidence for potential effect modification with the exception of sex, with a suggested difference between males and females.

3.3. Association between ambient air pollutants and FeNO Fig. 2 presents the percent changes between changes in FeNO and ambient air pollutant levels using single- and two-pollutant models. Adjusting for CO and SO2 significantly reduced the association between FeNO per IQR increase in PM2.5; however, adjusting for NO2 and O3 only slightly changed the association with PM2.5. Adjusting for CO only slightly reduced the association between FeNO per IQR increase in SO2; similarly, adjusting for NO2, O3, PM10 and PM2.5 only slightly reduced the association with SO2. The adjustment of PM2.5 in the two-pollutant models did not significantly change the CO effect estimate, but the estimated percent change in FeNO per IQR increase in CO was reduced to 0.89% (95% confidence interval, −2.91–4.54%) when adjusted for 396

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

A

B 80

80

Female Male

60

P = 0.000

P = 0.000

P = 0.000

P = 0.000

FeNO Con. (ppb)

FeNO Con. (ppb)

Female Male

40

20

0

40

20

0 18-60

60-70

70-88

Total

P = 0.000

60

Smkoing Nonsmoking Smkoing Nonsmoking

Smoking group

Age Groups

Fig. 1. FeNO in different group and p - value in different group. (A) Age and Gender group. (B) Smoking group. Table 2 Descriptive statistics for air pollutant concentrations, temperature (Temp) and relative humidity (RH) across the study period. Ambient factors PM2.5 (μg/ m3) PM10 (μg/ m3) O3 (μg/m3) SO2 (μg/m3) NO2 (μg/m3) CO (μg/m3) Temp (°C) RH (%)

Mean

5th percentile

Median

95th percentile

Single pollutant Two pollutant

Interquartile range

49.7

17.0

42.0

114

37.0

68.7

26.0

57.0

155

41.8

106 16.4 44.9 780 18.1 69.8

99.0 8.00 21.0 490 4.34 45.8

43.8 13.0 42.0 600 20.2 71.7

193 35.4 80.2 1280 29.5 91.8

48.0 8.00 22.0 300 13.1 19.2

* All parameters are their average values. O3 means 8-h daily average concentration.

-12

-9

-6

-3

0

3

6

9

12

15

18

Change in FeNO (%) Fig. 2. Single-pollutant and two-pollutant models for percent change in FeNO per IQR increase in pollutants, using daily average pollutant concentrations (mean and 95% confidence interval). All estimates are adjusted for disease, temperature and relative humidity.

SO2. Overall, SO2 had the most robust association with FeNO when adjusted for confounding by other pollutants. Fig. 3 shows the lag structure of changes in FeNO associated with an interquartile range increase in SO2 (Fig. 3A) and PM2.5 (Fig. 3B). The FeNO changes per IQR increase (effect estimates) in SO2 and PM2.5 were largest at lag 0. The effect estimates generally decreased with each increasing lag. Statistically significant associations were also observed for FeNO with the current day (Lag 0), 3-day (Lag 3) and 4-day lagged (Lag 4) pollution for SO2. For Lag 0, the increase in IQR with the average ambient SO2 concentration had a relationship with an increase of FeNO of 3.41% (p < 0.01; 95%CI: 0.94–5.93%). An IQR increases in the daily average concentration of SO2 corresponded to a 0.42% (p < 0.05; 95%CI: −1.77–2.66%) increases in FeNO at Lag 3. For Lag 4, an IQR increases in the daily average concentration of SO2

corresponded to a 0.51% (p < 0.01; 95%CI: −1.61–2.68%) increases in FeNO. There was no statistically significant association with SO2 on other days. Similarly, the single-lag day models indicated that IQR increases in PM2.5 with the current-day (Lag 0) had the strongest significant associations with an increase in FeNO. For Lag 0, the IQR increases in the daily average concentration of PM2.5 was equal to a 2.72% (p < 0.05; 95%CI: −0.09–5.61%) increases in FeNO.

Table 3 Spearman rank order correlation coefficients of variables. Variable Date Temp RH PM2.5 PM10 O3 SO2 NO2

Temp 0.262

RH **

PM2.5 **

0.096 0.351**

PM10 *

- 0.088 - 0.158** - 0.277**

- 0.067 - 0.199** - 0.520** 0.895**

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). 397

O3

SO2 **

0.186 0.526** - 0.201** 0.156** 0.195**

- 0.056 - 0.392** - 0.619** 0.730** 0.805** - 0.006

NO2

CO **

- 0.103 - 0.413** - 0.313** 0.678** 0.663** - 0.137** 0.726**

0.105** - 0.148** - 0.086* 0.825** 0.718** - 0.028 0.665** 0.683**

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

9

A

FeNO change (%) per IQR PM2.5 increase

FeNO change (%) per IQR SO2 increase

8

6

4

2

0

-2

B 6

3

0

-3

-6

-4 Lag 0

Lag 1

Lag 2

Lag 3

Lag 4

Lag 5

Lag 6

Lag 0

Lag 7

Lag 1

Lag 2

Lag days

Lag 3

Lag 4

Lag 5

Lag 6

Lag 7

Lag days

Fig. 3. Mean change in FeNO per IQE change in air pollutants at lag days. A) SO2, B) PM2.5. All estimates are adjusted for temperature, relative humidity and respiratory system disease.

estimates for non-COPD admission were significantly higher than for COPD admission (7.38% per IQR increase, p < 0.05; 95%CI:1.16–13.9%). In the models specific to both, O3 was negatively and statistically significant association with an increase in FeNO of nonasthma and non-COPD hospitalized patients, and an IQR increase in the daily average concentrations of O3 corresponded to a −3.81% (p < 0.05; 95%CI: −7.26% to −0.23%) increase in FeNO.

3.4. FeNO and disease In previous studies, FeNO has been regarded as a useful and noninvasive marker for the estimation of clinical respiratory-system disease. In all hospitalized patients in the present study, the mean FeNO concentrations were significantly higher in the asthmatic subgroup (32.1 ± 37.4 ppb) than in the non-asthmatic subgroup (20.9 ± 19.0 ppb, p < 0.001), as shown in Table 1. Meanwhile, the mean FeNO concentrations of the COPD subgroup (23.1 ± 19.7 ppb) were slightly higher than those of the non-COPD subgroup (21.6 ± 19.2 ppb; p < 0.05). This finding indicated that both asthma and COPD were associated with FeNO. The effects of the ambient air pollutants on COPD and/or asthma status are shown in Table 4. In the asthma-specific models, SO2 and CO were significantly associated with increases in the FeNO of non-asthma hospital admissions. An IQR increases in the daily average concentrations of SO2 was associated with an increase of 3.42% (p < 0.01; 95%CI: 1.00–5.90%) in FeNO, and an IQR increase in the daily average concentrations of CO corresponded to a 3.09% (p < 0.01; 95%CI: 0.40–5.86%) increase in FeNO. In the COPD-specific models, SO2 and CO were statistically significant associated with an increase of FeNO in the COPD subgroup. An IQR increases in the daily average concentration of SO2 was associated with an increase of 2.47% (p < 0.05; 95%CI: −0.17–5.19%) in FeNO, and an IQR increases in the daily average concentrations of CO corresponded to a 3.09% (p < 0.05; 95%CI: 0.40–5.86%) increase in FeNO. Meanwhile, the SO2 effect

4. Discussions The research indicates that exposure to ambient air pollution in hospitalized patients with respiratory-system disease is associated with an increase of FeNO, a sign of pulmonary inflammation. In particular, ambient SO2 and PM2.5 were associated with FeNO through certain exposure windows during previous days. The two-pollutant models showed that NO2, CO and O3 did not confound the SO2 impact. Previous studies showed close associations between ambient pollution and FeNO. An advanced panel study also suggested an association between FeNO and ambient SO2, particulate matter, NO2, and black smoke in children those who live in urban areas (Delfino et al., 2015; Herbarth et al., 2001; Poynter et al., 2006). The present research pioneers in assessments of the association between air pollutant exposure and FeNO in an underlying sensitive human subgroup, i.e., the hospitalized patients with respiratory-system disease. We can estimate the fraction of SO2, which has been related to health impact in previous researches, although ambient air often includes many pollutants at high

Table 4 Percent change (95% CI) in FeNO per IQR increases in air pollutants over different respiratory system disease. Modifying factors Asthma Yes No p-interaction COPD Yes No p-interaction Botha No Yes

PM2.5+

PM10+

O3+

SO2+

NO2+

CO+

4.30 (−9.41, 20.1) 2.51 (−0.24, 5.34)1 0.65

3.04 (−11.6, 20.2) 2.18 (−0.50, 4.93) 0.53

13.6 (−4.30, 34.9) −2.78 (−5.91, 0.44)1 0.00

5.12 (−8.13, 20.3) 3.42 (1.00, 5.90)** 0.97

2.08 (−12.1, 18.5) 2.97 (−0.03, 6.07)1 0.62

4.09 (−8.81, 18.8) 3.09 (0.40, 5.86)* 0.92

0.63 (−6.61, 8.43) 2.78 (−0.24, 5.88)1 0.89

−0.05 (−6.74, 7.12) 2.28 (−0.72, 5.23) 0.93

2.38 (−5.34, 10.7) −2.57 (−6.13, 1.13) 0.74

7.38 (1.16, 13.9)* 2.47 (−0.17, 5.19)* 0.13

4.09 (−3.62, 12.4) 2.39 (−0.89, 5.79) 0.50

2.88 (−4.23, 10.5) 3.28 (0.29, 6.35)* 0.46

2.64 (−0.33, 5.70)1 2.72 (−0.09, 5.61)*

2.18 (−0.78, 5.23) 2.19 (−0.58, 5.03)1

−3.81 (−7.26, −0.23)* −1.96 (−5.30, 1.50)

2.47 (−0.17, 5.18)1 3.41 (0.94, 5.93)**

2.16 (−1.11, 5.54) 2.91 (−0.15, 6.06)1

3.00 (0.07, 6.02) 3.45 (0.66, 6.31)1

a

Asthma and COPD. p < 0.1. * p < 0.05. ** p < 0.01.

1

398

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

FeNO for up to 3 and 4 days (Fig. 3). The findings are in accordance with the fact that the signal of systemic inflammation (FeNO) generally does not increase immediately after the ambient air pollutants exposure event and a lag effect may exist for hours to days (Berhane et al., 2011; Mar et al., 2005). Huang et al. (2012) found that SO2 had the largest impact at Lag 3 in young adults. In summary, exposure to SO2 has a significant association with FeNO at Lag 0 (current days), Lag 3 and Lag 4. SO2 may react with the mucus layer of the upper airways, and induced an inflammatory response in the lung. However, these reactions might not be immediate, because rapid responses of FeNO variation have a relationship with the nervous system, which tends to vary with certain nerve receptors or synaptic mediators. Thus, postponed reactions in some instances account for the adjustments of gene expression and enzyme synthesis (inflammation response) (Mar et al., 2005). Mechanistic studies focusing on the impacts of ambient air pollutants on human fitness are mainly dependent on the intake of the highly concentrated air pollution. The present research provides an approach for exploring the mechanisms accounting for the fitness impact of actual exposures to ambient air pollution in humans by adopting a simple, non-invasive, and repeatable technique. Our study has several limitations, including the patients’ disease status and the concentrations of ambient air pollutants to which patients were exposed. Even after medical diagnosis, uncertainty still existed regarding the true status of the disease of patients. Thus, hospitalized patients in this study were simply classified as diseased or nondiseased, neglecting the severity of these diseases, which made it impossible for us to assess the association between exposure to ambient air pollutants and FeNO under different disease severities.

concentrations that are relatively connected because of their common sources (such as transportation and factories) and local meteorological conditions that control atmospheric transport (Meng et al., 2003). The results indicated that ambient SO2 is consistently associated with FeNO levels. However, few epidemiological studies have suggested that SO2 has a significantly statistical association with FeNO (Lin et al., 2011; Liu et al., 2009). One interpretation of the results might be that hospitalized patients have higher susceptibility to SO2 than healthy humans. Brown et al. (2003) suggested that the trigger concentrations of SO2 for acute bronchoconstriction in asthmatic subjects were much lower than those for healthy subjects. Another possible explanation might be that the exposure concentrations of SO2 were slightly high in the current study (Table 2). For example, Liu et al. (2009), in a study of 182 children with asthma, found ambient SO2 at a daily average concentration of 12.8 μg/ m3. Pathophysiology studies showed that inhaled SO2 tends to hydrate easily in the respiratory tract to generate sulfurous acid. This acid will give rise the generation of an excessive amount of reactive oxygen species (ROS) in the airways, inducing inflammatory reactions via oxidative stress (Meng et al., 2003). Liu et al. (2009) also suggested that SO2 was significantly positively associated with thiobarbituric acid reactive substances (TBARS), which are oxidative stress markers. Yun et al. (2011) proposed that SO2 inhalation enhanced iNOS levels in the lungs of rats. Furthermore, Koksal et al. (2003) found that the concentrations of cytokines, direct-total nitrite and nitrate showed notably (p < 0.0001) higher values in the subjects (high SO2 exposure) than in the controls. The results indicated that NO has the possibility of functioning with a significant role in the pathogenesis of bronchoconstriction in asthma-like syndrome due to SO2 exposure. It was also observed that a negative and non-significant relationship existed between either the current hour or the 8-h ozone exposures and FeNO (Fig. 2). Nevertheless, Balmes et al. (1996) and Frampton et al. (1997) reported that high concentrations (200–400 ppb) of O3 caused human airway inflammations during laboratory studies, indicating that O3 has a positive association with FeNO. In the present study, an IQR increases in the daily average concentration of O3 corresponded to a 1.85% (p < 0.05; 95%CI: −1.15–4.95%) increases in FeNO at Lag 3 (Fig. 3). Thus, we assume that exposure to O3 may have a lagged effect on FeNO, and the negative associations between O3 and FeNO on the current day were probably due to the rapid reaction between FeNO and the ambient O3 (Adamkiewicz et al., 2004). The two-pollutant models suggested that the estimated impact of PM2.5 on FeNO was attributable mainly to confounding by SO2 and O3. In contrast, the association between FeNO and SO2 was not confounded by the other ambient air pollutants assessed (PM10, PM2.5, NO2, CO). Our study concluded that SO2 may predominantly affect respiratory inflammation in hospitalized patients, in contrast with other common pollutants. The relationships we have explored may support sub-clinical variations that elucidate the biological mechanisms accounting for the established epidemiological connection between air pollution exposure and health (Table 4). Nevertheless, the absolute variations investigated here may be regarded as very small in clinical terms. For instance, hospitalized patients with acute asthma have been shown to display average FeNO levels higher than those in comparable targets (32.1 vs 21.9 ppb). The discrepancies between patients with stable asthma and ordinary targets have been reported to be smaller (for example, 13.9 vs 6.2 ppb), in accordance with the research outcomes (Table 1) (published FeNO levels differ among various ranges because of differences in both data gathering techniques and the populations under investigation). The outcomes show that the impact of air pollution on FeNO might not be immediate. This finding is in accordance with the fact that the signal of inflammation generally does not increase immediately after the precipitating event and may remain increased for days afterward. Specifically, proinflammatory cytokines such as TNF-a and IL-6 initiate reactions that involve the generation of acute-phase proteins and the induction of NO synthase. Exposure to SO2 may have a lagged effect on

5. Conclusions Our data show that FeNO levels are positively associated with shortterm exposure to SO2 and PM2.5, and the robust association between FeNO and SO2 was not confounded by the other ambient air pollutants assessed. Exposure to SO2 exhibited a lagged effect on FeNO for up to 3 and 4 days. These adverse effects of air pollutants were observed in a cohort of hospitalized patients. These results provide preliminary information on the association between ambient air pollutants exposure and potential health risk in this susceptible population. The impacts of PM2.5 and SO2 on FeNO level exhibited the positive variation, especially for these susceptible population, suggesting that China not only should reduce the atmospheric pollutant emissions by implementing ambient air pollutants emission control strategies, but also concern ambient air pollutants accumulation in the various regions and lag effect of ambient air pollutants. Acknowledgments This work was supported by the National Natural Science Foundation of China (Nos. 21876029, 21577021, 41475109). References Adamkiewicz, G., Ebelt, S., Syring, M., Slater, J., Speizer, F.E., Schwartz, J., et al., 2004. Association between air pollution exposure and exhaled nitric oxide in an elderly population. Thorax 59 (3), 204–209. ATS/ERS, 2005. Ats/ers recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am. J. Respir. Crit. Care Med. 171 (8), 912–930. Balmes, J.R., Chen, L.L., Scannell, C., Tager, I., Christian, D., Hearne, P.Q., et al., 1996. Ozone-induced decrements in fev(1) and fvc do not correlate with measures of inflammation. Am. J. Respir. Crit. Care Med. 153 (3), 904–909. Barnes, P.J., Dweik, R.A., Gelb, A.F., Gibson, P.G., George, S.C., Grasemann, H., et al., 2010. Exhaled nitric oxide in pulmonary diseases a comprehensive review. Chest 138 (3), 682–692. Berhane, K., Zhang, Y., Linn, W.S., Rappaport, E.B., Bastain, T.M., Salam, M.T., et al., 2011. The effect of ambient air pollution on exhaled nitric oxide in the children's health study. Eur. Respir. J. 37 (5), 1029–1036. Brown, T.P., Rushton, L., Mugglestone, M.A., Meechan, D.F., 2003. Health effects of a

399

Ecotoxicology and Environmental Safety 168 (2019) 394–400

H. Guo et al.

Measurement of offline exhaled nitric oxide in a study of community exposure to air pollution. Environ. Health Perspect. 111 (13), 1625–1629. Koksal, N., Yildirim, Z., Gokirmak, M., Hasanoglu, H.C., Mehmet, N., Avci, H., 2003. The role of nitric oxide and cytokines in asthma-like syndrome induced by sulfur dioxide exposure in agricultural environment. Clin. Chim. Acta 336 (2), 115–122. Lin, W.W., Huang, W., Zhu, T., Hu, M., Brunekreef, B., Zhang, Y.H., et al., 2011. Acute respiratory inflammation in children and black carbon in ambient air before and during the 2008 beijing olympics. Environ. Health Perspect. 119 (10), 1507–1512. Liu, L., Poon, R., Chen, L., Frescura, A.M., Montuschi, P., Ciabattoni, G., et al., 2009. Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. Environ. Health Perspect. 117 (4), 668–674. Mar, T.F., Jansen, K., Shepherd, K., Lumley, T., Larson, T.V., Koenig, J.Q., 2005. Exhaled nitric oxide in children with asthma and short-termpm2.5 exposure in seattle. Environ. Health Perspect. 113 (12), 1791–1794. Meng, Z.Q., Qin, G.H., Zhang, B., Geng, H., Bai, Q.L., Bai, W., et al., 2003. Oxidative damage of sulfur dioxide inhalation on lungs and hearts of mice. Environ. Res. 93 (3), 285–292. Montuschi, P., Kharitonov, S.A., Barnes, P.J., 2001. Exhaled carbon monoxide and nitric oxide in copd. Chest 120 (2), 496–501. Olaniyan, T., Jeebhay, M., Roosli, M., Naidoo, R., Baatjies, R., Kunzil, N., et al., 2017. A prospective cohort study on ambient air pollution and respiratory morbidities including childhood asthma in adolescents from the western cape province: study protocol. Bmc Public Health 17. Peng, R.D., Samoli, E., Pham, L., Dominici, F., Touloumi, G., Ramsay, T., et al., 2013. Acute effects of ambient ozone on mortality in europe and north america: results from the aphena study. Air Qual. Atmos. Health 6 (2), 445–453. Poynter, M.E., Persinger, R.L., Irvin, C.G., Butnor, K.J., van Hirtum, H., Blay, W., et al., 2006. Nitrogen dioxide enhances allergic airway inflammation and hyperresponsiveness in the mouse. Am. J. Physiol. Lung C. 290 (1), 144–152. Ricciardolo, F.L.M., Sterk, P.J., Gaston, B., Folkerts, G., 2004. Nitric oxide in health and disease of the respiratory system. Physiol. Rev. 84 (3), 731–765. Sarnat, S.E., Raysoni, A.U., Li, W.W., Holguin, F., Johnson, B.A., Luevano, S.F., et al., 2012. Air pollution and acute respiratory response in a panel of asthmatic children along the U.S.-mexico border. Environ. Health Perspect. 120 (3), 437–444. Steerenberg, P.A., Nierkens, S., Fischer, P.H., Van Loveren, H., Opperhuizen, A., Vos, J.G., et al., 2001. Traffic-related air pollution affects peak expiratory flow, exhaled nitric oxide, and inflammatory nasal markers. Arch. Environ. Health 56 (2), 167–174. Van Amsterdam, J.G.C., Verlaan, B.P.J., Van Loveren, H., Elzakker, B.G.V., Vos, S.G., Opperhuizen, A., et al., 1999. Air pollution is associated with increased level of exhaled nitric oxide in nonsmoking healthy subjects. Arch. Environ. Health 54 (5), 331–335. Yun, Y., Hou, L., Sang, N., 2011. SO2 inhalation modulates the expression of pro-inflammatory and pro-apoptotic genes in rat heart and lung. J. Hazard Mater. 185 (1), 482–488.

sulphur dioxide air pollution episode. J. Pub Health Med. 25 (4), 369–371. Cao, J., Li, W., Tan, J., Song, W., Xu, X., Jiang, C., et al., 2009. Association of ambient air pollution with hospital outpatient and emergency room visits in shanghai, china. Sci. Total Environ. 407 (21), 5531–5536. de Laurentiis, G., Maniscalco, M., Cianciulli, F., Stanziola, A., Marsico, S., Lundberg, J.O., et al., 2008. Exhaled nitric oxide monitoring in copd using a portable analyzer. Pulm. Pharmacol. Ther. 21 (4), 689–693. Delfino, R.J., Staimer, N., Gillen, D., Tjoa, T., Sioutas, C., Fung, K., et al., 2006. Personal and ambient air pollution is associated with increased exhaled nitric oxide in children with asthma. Environ. Health Persp. 114 (11), 1736–1743. Delfino, R.J., Staimer, N., Tjoa, T., Gillen, D.L., Schauer, J.J., Shafer, M.M., 2013. Airway inflammation and oxidative potential of air pollutant particles in a pediatric asthma panel. J. Expo. Sci. Environ. Epid 23 (5), 466–473. Delfino, R.J., Staimer, N., Tjoa, T., Gillen, D.L., 2015. Relations of exhaled nitric oxide and fev1 to personal endotoxin exposure in schoolchildren with asthma. Occup. Environ. Med. 72 (12), 830–836. Flamant-Hulin, M., Caillaud, D., Sacco, P., Penard-Morand, C., Annesi-Maesano, I., 2010. Air pollution and increased levels of fractional exhaled nitric oxide in children with no history of airway damage. J. Toxicol. Environ. Health A 73 (4), 272–283. Frampton, M.W.B.J., Cox, C., Krein, P.M., Speers, D.M., Tsai, Y., et al., 1997. Effects of ozone on normal and potentially sensitive human subjects. Part iii: mediators of inflammation in bronchoalveolar lavage fluid from nonsmokers, smokers, and asthmatic subjects exposed to ozone: a collaborative study. Res. Rep. Health Eff. Inst. 78, 73–79. Gaston, B., Drazen, J.M., Loscalzo, J., Stamler, J.S., 1994. The biology of nitrogen-oxides in the airways. Am. J. Respir. Crit. Care Med. 149 (2), 538–551. Herbarth, O., Fritz, G., Krumbiegel, P., Diez, U., Franck, U., Richter, M., 2001. Effect of sulfur dioxide and particulate pollutants on bronchitis in children - a risk analysis. Environ. Toxicol. 16 (3), 269–276. Huang, W., Wang, G.F., Lu, S.E., Kipen, H., Wang, Y.D., Hu, M., et al., 2012. Inflammatory and oxidative stress responses of healthy young adults to changes in air quality during the beijing olympics. Am. J. Respir. Crit. Care Med. 186 (11), 1150–1159. Khafaie, M.A., Salvi, S.S., Yajnik, C.S., Ojha, A., Khafaie, B., Gore, S.D., 2017. Air pollution and respiratory health among diabetic and non-diabetic subjects in pune, india-results from the wellcome trust genetic study. Environ. Sci. Pollut. Res. 24 (18), 15538–15546. Khalili, B., Boggs, P.B., Bahna, S.L., 2007. Reliability of a new hand-held device for the measurement of exhaled nitric oxide. Allergy 62 (10), 1171–1174. Kharitonov, S.A., Barnes, P.J., 2000. Clinical aspects of exhaled nitric oxide. Eur. Respir. J. 16 (4), 781–792. Kim, H.B., Eckel, S.P., Kim, J.H., Gilliland, F.D., 2016. Exhaled NO: determinants and clinical application in children with allergic airway disease. Allergy Asthma Immunol. Res. 8 (1), 12–21. Koenig, J.Q., Jansen, K., Mar, T.F., Lumley, T., Kaufman, J., Trenga, C.A., et al., 2003.

400