Abstract
Acute exacerbations and worsening of idiopathic pulmonary fibrosis (IPF) have been associated with exposure to ozone (O3), nitrogen dioxide (NO2) and particulate matter, but chronic exposure to air pollution might also affect the incidence of IPF. We investigated the association between chronic exposure to NO2, O3 and particulate matter with an aerodynamic diameter <10 μm (PM10) and IPF incidence in Northern Italy between 2005 and 2010.
Daily predictions of PM10 concentrations were obtained from spatiotemporal models, and NO2 and O3 hourly concentrations from fixed monitoring stations. We identified areas with homogenous exposure to each pollutant. We built negative binomial models to assess the association between area-specific IPF incidence rate, estimated through administrative databases, and average overall and seasonal PM10, NO2, and 8-hour maximum O3 concentrations.
Using unadjusted models, an increment of 10 µg·m−3 in NO2 concentration was associated with an increase between 7.93% (95% CI 0.36–16.08%) and 8.41% (95% CI −0.23–17.80%) in IPF incidence rate, depending on the season. After adjustment for potential confounders, estimated effects were similar in magnitude, but with larger confidence intervals.
Although confirmatory studies are needed, our results trace a potential association between exposure to traffic pollution and the development of IPF.
Abstract
The inhalation of traffic-related pollutants, namely NO2, might be involved in the development of IPF http://ow.ly/qfLE30ge36C
Introduction
Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrotic lung disease with a severe prognosis and a median survival of 3–5 years from diagnosis [1].
Although the mechanisms that cause the development of IPF are still under debate [1, 2], there is evidence that both genetic and environmental factors, such as cigarette smoking [3, 4], occupational exposure to metal and wood dusts [5–7] and exposure to viruses [8], such as Epstein–Barr virus, may play a role [9–12].
Johannson et al. [13] observed an association between the risk of acute exacerbation of IPF and ozone (O3) and nitrogen dioxide (NO2), and two recent studies observed that exposure to particulate matter is associated with increased mortality and decreased forced vital capacity in IPF patients [14, 15], suggesting that air pollution may contribute to worsening disease.
These studies and the large body of evidence supporting the short- and long-term effects of air pollution on respiratory diseases, including chronic obstructive pulmonary disease, asthma and lung cancer [16–21], point to the need for further investigation into the relationship between air pollution and IPF [22]. Such studies are specifically important given the recent translational data that identify air pollution as a trigger for telomere shortening and accelerated senescence, surfactant abnormalities, stress of the endoplasmic reticulum, oxidant–antioxidant dysregulation and chronic inflammation. These mechanisms all potentially cause premature exhaustion and abnormal alveolar re-epithelialisation, which are mechanisms responsible for the development of IPF [23–27].
The aim of this study was to investigate the long-term relationship between exposure to three criteria pollutants, namely particulate matter with an aerodynamic diameter <10 μm (PM10), NO2 and O3, and the incidence of IPF in Lombardy, Northern Italy, from 2005 to 2010.
Lombardy is characterised by high air pollution levels, heterogeneous among subregional areas. The stagnation of air pollutants over its lowland areas [28, 29] is facilitated by weather, climate and orographic characteristics (figure 1) which cause frequent thermal inversion. In a prior study [30], among the ∼10 million inhabitants of the region, we identified >2000 IPF incident cases from 2005 to 2010. The number of IPF cases, together with the variability of environmental exposures makes Lombardy an interesting context for this analysis.
Location of the study area. Elevation raster data of Lombardy downloaded from the website of the network of the Italian national environmental information system (www.sinanet.isprambiente.it/it/sia-ispra/download-mais/dem75/view). Shapefile of the Italian administrative regions downloaded from the website of the Italian National Institute of Statistics (www.istat.it/it/archivio/24613).
Material and methods
Outcome assessment
To identify IPF incident cases, we used the broad case definition (BCD), as described in our previous study [30]: we relied on regional healthcare administrative databases in order to identify all patients who had at least one hospitalisation or outpatient visit with a diagnosis of IPF (International Classification of Diseases, ninth revision, clinical modification (ICD-9-CM) code 516.3 [31]) between 2005 and 2010, and we used the date of the first traceable diagnosis as a proxy for the disease onset. We excluded all patients who had at least one visit with a diagnosis of another interstitial lung disease (ICD-9-CM codes 135, 237.7, 272.7, 277.3, 277.8, 446.21, 446.4, 495, 500–505, 506.4, 508.1, 508.8, 515, 516.0–516.2, 516.8–516.9, 517.2, 517.8, 518.3, 555, 710.0–710.4, 714.81, 720.0 and 759.5) after onset of IPF, and in order to distinguish incident from prevalent cases, we required a 5-year washout period. For each case we had information on the municipality of residence.
Exposure assessment
We retrieved daily predictions of PM10 concentrations in Lombardy on a 10×10 km grid, for the years 2000–2009, from a model based on satellite aerosol optical depth measurements and land-use regression variables [28].
For each municipality, we estimated the yearly average PM10 concentration (online supplementary material).
We obtained hourly NO2 and O3 concentrations measured from 2000 to 2010 at background and traffic monitoring stations [32] that fulfilled predefined completeness criteria (online supplementary material) from ARPA (Regional Environmental Protection Agency) of Lombardy (http://ita.arpalombardia.it/ita/index.asp).
Population average exposures are usually assessed based on background stations [33]; however, it is well known that NO2 is a proxy for traffic-related exposure [18], therefore we decided to also rely on traffic monitors. For each municipality, we estimated the long-term exposure to NO2 by averaging measures from all background monitors located within 10 km of the municipality limits and all traffic monitors located within 5 km.
For O3, we considered only background monitors, and given that it is a seasonal pollutant with very low concentrations during the cold season [34], we estimated the municipality-specific yearly average 8-hour maximum concentration of O3 over the warm season, based on measures from monitors located within 10 km of the municipality limits.
After evaluating regional coverage and average concentrations over different time windows, we decided to use the average concentrations over 2005–2010 for NO2 and O3, and over 2005–2009 for PM10 as proxies for chronic exposures (online supplementary material).
Assessment of confounders
Age is a well-known risk factor for IPF and some studies highlighted that sex might also play a role [30, 35]. In order to account for potential confounding due to these two factors, we retrieved the age and sex distribution of the population on January 1, 2005 and 2011 for each municipality from the Italian National Institute of Statistics (ISTAT; www.istat.it/it/).
Since temperature is another known confounder of the short-term relationship between respiratory morbidity and air pollution [29], we obtained the monthly average temperatures predicted at a 1×1 km grid by the SCIA project (www.scia.isprambiente.it/home_new.asp) developed by the Italian Institute for Environmental Protection and Research (ISPRA). The 2005–2010 municipality-specific average temperatures were assessed using the same methodology used for PM10 (online supplementary material).
In order to account for the potential confounding due to socioeconomic status, we retrieved data on the municipality-specific average per-capita income from 2005 to 2010 from the website of the Italian Ministry of Economy and Finance (www1.finanze.gov.it/finanze2/pagina_dichiarazioni/dichiarazioni.php).
Finally, since there is evidence of a relationship between IPF incidence and exposure to farming or livestock [7], for each municipality we computed the area covered by natural, agricultural and urban/industrial lands, based on data estimated for 2006 through the CORINE land cover model [36] available on the ISPRA website (www.sinanet.isprambiente.it/it/sia-ispra/download-mais/corine-land-cover/corine-land-cover-2006/view).
Statistical analysis
We observed that the distribution of the municipality-specific 2005–2010 cumulative count of IPF incident cases followed a negative binomial distribution, with a high proportion of zeroes, due to the presence of many municipalities with a small population. In order to reduce this proportion, we stratified the distribution of each pollutant in classes of 2 µg·m−3 for PM10 and NO2, and 2 ppb for O3.
For each pollutant, we first aggregated adjacent municipalities belonging to the same class, creating new areas with bigger populations. Then, for each aggregated area, we used the municipality-specific data to compute cumulative number of IPF incident cases, mean age, percentage of males, average per-capita income, main use of the land (natural, agricultural or urban/industrial), average temperature and average exposure to the pollutant of interest over the study period. Finally, we applied negative binomial regression models and investigated the relationship between the logarithm of the outcome of interest and predictors [37].
For each pollutant, we built an unadjusted model accounting for no confounders, a fully adjusted model, accounting for all available confounders, and the best model, chosen based on the Akaike information criterion, including only confounders that improved the model fitting (online supplementary material).
Results are reported as percentage change in the incidence rate, with associated 95% confidence intervals for a 10-unit change in pollutant concentrations.
There is evidence of seasonal differences in the short-term effects of PM10 and NO2. This could be due to differential sources in the case of PM10, the presence of unaccounted effect modifiers or differences in the accuracy of measured concentrations [38]. In order to verify if such differences persist when addressing long-term effects, analyses were repeated considering the average warm (April–September) and cold (October–March) season concentrations of these two pollutants.
All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and R version 3.1.3 (R Project for Statistical Computing, www.R-project.org); maps were created using ESRI ArcMAP 10.5 (ESRI, Redlands, CA, USA).
Sensitivity analysis
In order to test how the inclusion of NO2 traffic monitors affected the estimated municipality-specific exposures, we compared these measures with those assessed based only on background monitors located within 10 km of the city limits.
Furthermore, we tested whether our results were robust to case definition by repeating the analysis using two alternative definitions: one less stringent (generic case definition), and one more stringent (narrow case definition) (online supplementary material).
Given the results observed for NO2, as a descriptive analysis we defined two macro-areas based on the average 2005–2010 NO2 concentration using a cut-off of 40 µg·m−3, which corresponds to the European Union (EU) yearly average standard (http://ec.europa.eu/environment/air/quality/standards.htm). We then compared age- and sex-adjusted IPF incidence rates between the two areas. The same analysis was repeated using a cut-off of 20 µg·m−3, the limit recommended by the World Health Organization [39].
Results
In our previous study [30], we identified 2093 incident cases satisfying the BCD, distributed among 1545 municipalities (online supplementary table S1). We identified a mean±sd 1.4±11.3 cases per municipality, but intermunicipality variability was very high, as shown by the coefficient of variation, which was 8.4. This was due to the variability of the municipality-specific population: 6249±34 855 inhabitants with a coefficient of variation of 5.6.
Average exposures over the time windows are reported in online supplementary tables S2–S4. Using the window 2005–2010, we could assign PM10 exposure to 99.1% of the municipalities, NO2 exposure to 68.7% and O3 exposure to 57.7%. The location of monitoring stations selected for the analysis and the average municipality-specific exposure are reported in online supplementary figures S1–S5.
The aggregation procedure significantly lowered the number of areas with no IPF cases: the percentage of zeroes fell from 56.6% to 36.2% for PM10, from 51.1% to 21.5% for NO2 and from 49.8% to 12.5% for O3 (online supplementary figures S6–S8).
In table 1, we report the description of outcomes and confounders after the aggregation process for each pollutant: we identified 398 areas based on PM10 concentration, 303 based on NO2 and 112 based on O3. The average number of IPF cases per area was five for the aggregation based on PM10, six for NO2 and 15 for O3.
Distribution of the outcome and of the potential confounders in the aggregated areas
The 2005–2009 average PM10 concentration at an aggregated area level (table 2) was 39 µg·m−3, the 2005–2010 average NO2 concentration was 41 µg·m−3 and the 2005–2010 average 8-hour maximum O3 concentration during the warm season was 107 ppm. Estimated PM10 and NO2 concentrations were significantly higher during the cold season. Figures 2–4 report the estimated pollutant concentrations in the aggregated areas covered by the exposure assessment, clearly showing the heterogeneity of the levels registered over the study area.
Distributions of daily concentrations of air pollutants at an aggregated area level over the study period
Map of the estimated daily average particulate matter with an aerodynamic diameter <10 μm concentration for the period 2005–2009 at an aggregated area level. Shapefile of the Italian administrative regions and municipalities downloaded from the website of the Italian National Institute of Statistics (www.istat.it/it/archivio/24613).
Map of the estimated daily average nitrogen dioxide concentration for the period 2005–2010 at an aggregated area level. Shapefile of the Italian administrative regions and municipalities downloaded from the website of the Italian National Institute of Statistics (www.istat.it/it/archivio/24613).
Map of the estimated daily average 8-h maximum ozone concentration for the period 2005–2010 at an aggregated area level. Shapefile of the Italian administrative regions and municipalities downloaded from the website of the Italian National Institute of Statistics (www.istat.it/it/archivio/24613).
Table 3 presents the results of the association between incidence of IPF and chronic exposure to each pollutant. The incidence of IPF was not associated with the concentration of PM10 and O3.
Estimated percentage change (95% CI) in the incidence rate for a 10-unit increase in average concentrations of pollutants
As for the effect of NO2, in unadjusted models we detected a significant association between the incidence of IPF and cold season NO2 concentration: an increment of 10 µg·m−3 in NO2 was associated with a 7.93% (95% CI 0.36–16.08%) increase in the incidence rate of IPF; in addition, we observed a marginally significant (p<0.10) association with incidence rate (95% CI) for the overall (8.41%, −0.23–17.80%) and the warm season (8.22%, −1.44–18.82%) concentrations. A marginally significant association with the cold season concentration persisted when using the model with the best fit (7.55%, −0.76–16.56%). Even though the estimated effect did not reach statistical significance, point estimates were quite robust to adjustment, and only the width of the confidence interval varied (figure 5). This was confirmed by the sensitivity analyses involving cases identified using the generic and narrow case definitions (online supplementary figures S9 and S10).
Estimated percentage change in the incidence rate (95% CI) of a 10-µg·m−3 increase in average nitrogen dioxide concentration. #: adjusted for area-specific proportion of males, mean age, average income, temperature and main land use; ¶: adjusted for area-specific mean age and main land use.
Finally, descriptive analyses on adjusted rates confirmed that IPF incidence rates are significantly higher where the average concentration of NO2 is >40 µg·m−3. In addition, an increase emerges using a 20 µg·m−3 cut-off, but it is not significant due to the limited size of the population exposed to a concentration below this level (online supplementary figures S11 and S12).
Discussion
In our study, we investigated the association between the incidence of IPF and chronic exposure to PM10, NO2 and O3 in Northern Italy. While no association was detected with PM10 and O3, we observed a positive, although often marginally significant, association with overall and cold season NO2 concentration.
Although estimated effects do not always reach statistical significance, they should be interpreted as a strong signal that the association exists: point estimates are quite robust, as they are moderately affected by different model specifications and case definitions; only precision decreases when using adjusted models, as confirmed by wider confidence intervals (figure 5 and online supplementary figures S9 and S10). This probably means that the model power is insufficient to estimate a significant effect of the pollutant while simultaneously accounting for all confounders of interest. If we could assess the exposure for the whole region, instead of covering only 68% of the municipalities, the number of areas included in the models would grow, as would the power of the model. This might also be true for O3. Furthermore, the descriptive analysis of adjusted rates confirmed that IPF incidence is significantly higher in areas where the average NO2 concentration is above the EU limit (online supplementary figures S11 and S12).
Finally, effects estimated using the cold season concentrations were similar, but slightly more precise than those of the overall analyses (figure 5 and online supplementary figures S9 and S10). These results suggest that the cold season concentrations better represent the overall real exposure. Indeed, instruments used in ARPA stations to monitor NO2 have been shown to overestimate concentrations during the warm season, because they are sensitive to other products of the oxidation of nitrogen oxides that are more abundant during this season [40].
To our knowledge this is the first study that aimed at investigating the association between exposure to air pollution and IPF incidence. Only a few previous studies have addressed the relationship between criteria pollutants and disease progression in affected patients [13–15], showing associations between exposure to NO2, O3 and particulate matter and the risk of disease worsening or mortality. We observed no significant association between O3 or PM10 and the incidence of IPF, suggesting that exposure to these pollutants may trigger adverse events only in susceptible subjects, who already have the disease.
Reports in the literature have presented possible mechanisms by which NO2 might have a role in the development of IPF, and they include proinflammatory response in bronchial epithelial cells, alteration of the distribution of leukocyte subsets in both blood and bronchoalveolar lavage and telomere shortening [23–27, 41]. Recent findings show that the telomeric DNA regions at the ends of chromosomes are particularly sensitive to the damage caused by reactive oxygen species [42, 43], which are also generated by NO2, and telomere shortening can be accelerated by chronic inflammation and increased oxidative stress [44–46], two major mechanisms involved in mediating NO2 effects on human health [46–50]. Other mechanisms resulting from exposure to air pollution include a dose-dependent upregulation of proteins associated with epithelial–mesenchymal transition [51], and a deregulation of the transforming growth factor-β signalling pathway [52], both proposed mechanisms of IPF.
Our study had some limitations. Since we relied on administrative databases for the identification of incident cases of IPF, we list the major limitations of this approach.
First, IPF patients who never require hospitalisation or whose outpatient visits cannot be traced might have led to an underestimation of incident cases, even if it is unlikely that a subject with such a severe disease never accesses health services. Second, the selection algorithm is based on ICD-9-CM diagnosis codes reported in administrative records, but the accuracy of such codes is unknown and it was impossible to obtain a clinician's validation [30, 53]. However, the BCD algorithm has been used in some of the largest studies on IPF epidemiology [54–56]. Third, we used the date of the incident diagnosis of IPF as a proxy for onset, which might have occurred years earlier; therefore, relevant exposures probably precede 2005. However, considering that in Lombardy the concentrations of the pollutants of interest are declining slowly over time [57], and that we observed only slight variations when computing average exposures over different time-windows (online supplementary tables S2–S4), we are confident that the average 2005–2010 air pollutant concentrations are good indicators of patients’ average chronic exposures. Furthermore, we only had data on the municipality of residence during the study period, therefore the estimated exposure could be biased for people who moved before 2005.
Finally, there might be residual confounding due to risk factors for which there were no available municipality-specific data: for example, we had no information on subjects’ occupations, on how far they lived from major roads and industries or on the proportion of commuters. Most importantly, we could not assess the proportion of smokers in each municipality and smoking is a major risk factor for the development of IPF [10]. However, we are confident that the observed association is not entirely due to smoking, as this could happen only if the municipalities with higher proportions of smokers are also characterised by higher NO2 concentrations; there is no evidence of such covariation in the literature.
Despite these limitations, the use of healthcare administrative databases allowed us to study a large and unselected population: Lombardy has almost 10 million inhabitants, and all of them are covered by the Italian healthcare system. In addition to being one of the most polluted areas of Europe, Lombardy is characterised by high variability of exposure, an essential condition for statistical models to succeed in identifying the target association.
Furthermore, even though traffic monitors are usually excluded from the exposure assessment process, their inclusion appears to improve the estimation of NO2 concentration: our sensitivity analysis shows that concentrations estimated using such monitors are closer to those recently estimated through a model based on emissions within the project called VIIAS, funded by the Italian Ministry of Health (www.viias.it/pagine/concentrazione-degli-inquinanti) (online supplementary figures S4 and S13).
In conclusion, we investigated for the first time the relationship between the incidence of IPF and chronic exposure to air pollution, and we found a positive association with NO2 concentrations.
Longitudinal investigations focusing on subject-specific rather than aggregated data, preferably considering exposures at the subjects’ addresses, could substantially improve the reliability of the estimates. Furthermore, study power could be improved by increasing the number of IPF patients involved, which could be accomplished by extending the time window for recruitment or the study population. Our study encourages further investigation of the association between air pollution and IPF, as it provides a strong signal of traffic pollution being involved in the fibrotic process, thereby identifying a potentially removable risk factor that could be controlled for reducing the incidence of the disease.
Supplementary material
Supplementary Material
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material ERJ-00397-2017_supplement
Disclosures
Supplementary Material
A. Caminati ERJ-00397-2017_Caminati
G. Cesana ERJ-00397-2017_Cesana
S. Conti ERJ-00397-2017_Conti
S. Harari ERJ-00397-2017_Harari
F. Madotto ERJ-00397-2017_Madotto
J.D. Schwartz ERJ-00397-2017_Schwartz
A. Zanobetti ERJ-00397-2017_Zanobetti
Acknowledgements
S. Harari and G. Cesana developed the study concept. S. Conti, A. Zanobetti and J. Schwartz developed the study design. S. Conti and F. Madotto performed the statistical analysis and they interpreted the results from an epidemiological point of view, together with G. Cesana, P. Bertazzi and A. Zanobetti. S. Harari and A. Caminati studied the biological plausibility of the findings and they interpreted the results from a clinical point of view.
All authors contributed to data interpretation and to drafting the paper, revised it critically and approved the final version before the submission. They also checked that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Thomas Piraino (Dept of Respiratory Therapy, St Michael's Hospital, Toronto, ON, Canada and Dept of Anesthesia, Division of Critical Care, McMaster University, Hamilton, ON) revised the language of the final draft.
Footnotes
This article has supplementary material available from erj.ersjournals.com
Support Statement: This study was supported by an unrestricted grant by Hoffmann–La Roche Std. The sponsor had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the report for publication. Funding information for this article has been deposited with the Crossref Funder Registry.
Conflict of interest: Disclosures can be found alongside this article at erj.ersjournals.com
- Received February 24, 2017.
- Accepted October 23, 2017.
- Copyright ©ERS 2018
References
