Combined atmospheric oxidant capacity and increased levels of exhaled nitric oxide

In this study, we initially recruited 30 retired COPD patients from a central urban community (1.9 km² in area) in Shanghai, China. The inclusion criteria included: (1) doctor-diagnosed COPD based on the results of spirometry; (2) local residents who lived in this Community for at least 3 years; and (3) retirees aged more than 40 years. We excluded those who were currently active or passive smokers (at home); consumed any alcohol; had severe comorbidities or inflammatory diseases; and had received antibiotic treatments during the past month. We further excluded those patients with unstable and severe COPD in this study to reduce the influence of respiratory medication on our results. Subjects who took anti-inflammatory medications during follow-ups would be excluded from final analyses.

From  27th May to  5th July, 2014, we scheduled six repeated follow-ups every week. The subjects were randomly divided into three subgroups and were invited to participate in FeNO examinations on Tuesday, Thursday or Saturday, respectively at the Community Health Service Center. For each patient, examinations were scheduled at the same time (1:30 p.m. to 2:30 p.m.) on the same day of the week to control for potential circadian rhythms and day-of-week variations in FeNO levels (Spanier et al 2008). Self-administered questionnaires were applied to collect individual baseline information including residential address, age, gender, height, weight, educational status, duration of COPD and chronic comorbidities. Spirometry was performed at baseline to assess the severity of airflow limitation in these COPD patients, according to the Global initiative for Chronic Obstructive Lung Disease guidelines (Vestbo et al 2013). These subjects were also asked to record any COPD symptoms and medications, and whether they left the central urban areas of Shanghai during the entire study period. This study was approved by the Institutional Review Board of the School of Public Health, Fudan University. Written consent forms were obtained from all subjects.

FeNO levels were measured with a portable NIOX MINO machine (Aerocrine AB, Solna, Sweden) according to the standardized procedures issued by the American Thoracic Society and the European Respiratory Society (Dweik et al 2011). Briefly, subjects were asked to avoid eating and drinking for at least 1 h before examinations. They were instructed to inhale from the machine as fully as they can and then exhale into the machine for 10 s without a break. A visible indicator was shown during exhalation to help subjects maintain a constant flow rate of 50 ml s⁻¹. At last, the results would display automatically after a 90 s interval. The quality control was autonomically performed and it would not display a reading once the subject exhaled below or above the required speed of flow rate.

We obtained the hourly data on NO₂ and O₃ from a nearby government-controlled monitoring station that was approximately 3 km away from this community. This station was not in the direct vicinity of major air pollution sources including traffic. We calculated O_x as the sum of real-time concentrations on NO₂ and O₃. In order to allow for the adjustment of potential confounding of other environmental variables, we also collected the hourly concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) from the same fixed-site station and weather data (mean temperature and mean relative humidity) from a meteorological station that was 5 km away from the community. The concentrations of PM_2.5, NO₂ and O₃ were measured using the methods based on tapered element oscillating microbalance, chemiluminescence and ultraviolet absorption, respectively.

Environmental and individual health data were linked by the time of physical examinations. FeNO levels were natural log-transformed before statistical analyses because they were approximately right-skewed distributed.

We applied linear mixed-effect models to examine the associations between air pollutants and FeNO. In the basic model, FeNO was a dependent term, and O_x was introduced as an independent fixed-effect term. Several covariates were also incorporated as fixed-effect terms: (1) an indicator variable for 'week' to adjust for any unknown weekly time trends in FeNO levels; (2) an indicator variable for 'day of the week' to exclude any unknown variations in FeNO levels within a week; (3) mean temperature and relative humidity on the concurrent day to adjust for the confounding effects of meteorological conditions; and (4) individual characteristics, including age, sex, body mass index. We did not further adjust for educational attainment, duration of COPD and prevalence of a chronic comorbidity (hypertension, coronary heart disease, diabetes,) because the model fit statistic was not further improved, characterized by smaller Akaike information criterion (AIC) (Akaike 1973). At last, we included a random intercept for each subject to account for the autocorrelations among repeated FeNO measurements.

To fully explore the time windows for the short-term effects of O_x on FeNO, we evaluated the models using multiple lag periods of O_x exposure preceding FeNO measurements, i.e., single lags of 0–6 h (h), 7–12 h, 13–24 h, 0–24 h (0 d), 25–48 h (1 d) and 49–72 h (2 d). We did not explore lags longer than 72 h because most previous studies found that the short-term effects of air pollution on respiratory outcomes were limited in the first 3 days after exposure (Dominici et al 2006, Williams et al 2014, Chen et al 2015).

To compare the individual effects of NO₂ and O₃ with combined O_x, we fitted the same models as described above for NO₂ and O₃, and further evaluated the mode fit based on the AIC statistic (Akaike 1973).

As a sensitivity analysis, we fitted two-pollutant models to evaluate whether the effects of O_x were sensitive to the adjustment of PM_2.5, another source of atmospheric oxidative activity.

At last, in order to explore the potential effect modification of individual characteristics, we further performed stratification analyses by sex, age (<65 and ≥65), BMI (<24 and ≥24 kg m⁻²), educational status (low: illiteracy, primary school and junior middle school; high: senior middle school and above), presence of comorbidities (hypertension, coronary heart disease or diabetes) and severity of COPD (mild and moderate).

Statistical tests were two-sided, and the effects of p-values ≤0.05 were considered to be statistically significant. All analyses were performed in R software (Version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria) with the 'lme4' package. The effect estimates were presented as the percent change and their 95% confidence intervals (95% CI) associated with a 10 μg m⁻³ increase of air pollutant concentrations.

Two subjects were excluded from the analysis because they took anti-inflammatory medications during follow-ups. The descriptive statistics on individual characteristics of the remaining 28 subjects were shown in a previous publication of this project (Chen et al 2015). According to the results from self-administered questionnaires, they did not go out of the central urban area of Shanghai, have an exacerbation of COPD or take anti-COPD medication during the study period. Based on the results of spirometry at baseline, there were 12 mild and 16 moderate COPD patients.

Table 1 shows the summary statistics of FeNO measurements, 24 h averages of air pollutant concentrations and meteorological variables preceding each follow-up visit. In total, we obtained 168 valid measurements of FeNO. The mean level of FeNO is 24.4 ppb with apparent variations (standard deviation: 23.2 ppb). During the study period, the averaged 24 h mean concentrations for NO₂, O₃, O_x and PM_2.5 are 47.2 μg m⁻³, 82.4 μg m⁻³, 129.6 μg m⁻³ and 56.1 μg m⁻³, respectively. The daily mean temperature and relative humidity are 23.8 °C and 71.7%, respectively. There are no missing data in our study.

Table 2 shows the spearman correlation coefficients among environmental variables. Generally, the 24 h average concentrations of O_x are more strongly correlated with O₃ than with NO₂. O_x is moderately positively correlated with PM_2.5, moderately positively correlated with temperature and weakly negatively correlated with relative humidity. There were moderate or strong correlations of FeNO (r = 0.44–0.92) among the subgroups with male sex, older age, higher BMI, the prevalence of comorbidity and higher educational status.

Table 3 provides the associations between O_x and FeNO using different lag periods. The associations are statistically significant within the first 24 h after exposure with the strongest effects occurring in the sub-period of 13–24 h. Then, the associations attenuate substantially and turn to be non-significant from lag 0 d to 2 d. For example, FeNO increase by 4.28% (95%CI: 1.19%, 7.37%) corresponding to a 10 μg m⁻³ increase in 24 h average concentrations of O_x.

Table 3 also shows the results of sensitivity analyses after adjusting for PM_2.5. In comparison with the unadjusted results, the adjusted estimate increases and turned to be statistically significant within the first 12 h after exposure; it is quite similar using the 24 h averaged exposure; it remains non-significant on lag 1 or 2 days.

The effects of O₃ and NO₂ are not statistically significant at almost all lags using single-pollutant models, but increase substantially and become significant in the first 24 h when adjusting for each other (table 4). The AIC values obtained in single-pollutant models with O_x is smaller than with NO₂ and O₃ (i.e., 139.6 versus 143.4 and 143.4, respectively, for 24 h averages). The values are also obviously lower than those obtained in two-pollutant models with NO₂ and O₃.

Figure 1 illustrates the effects of 24 h average O_x on FeNO levels in different subgroups. The effect estimates were statistically significant only among males, elders, and those with body mass index ≥24 kg m⁻², a comorbidity, higher educational attainment, or moderate airflow limitation.

Zoom In Zoom Out Reset image size