Link Between Ambient Air Pollution & the Development of Parkinsons Disease Essay

Environment International 129 (2019) 28–34 Contents lists available at ScienceDirect Environment International journal homepage: www.elsevier.com/locate/envint Parkinson’s disease and long-term exposure to outdoor air pollution: A matched case-control study in the Netherlands T ⁎ Rosario Toroa,1, George S. Downwardb, ,1, Marianne van der Markb, Maartje Brouwerb, Anke Hussb, Susan Petersb,c, Gerard Hoekb, Peter Nijssend, Wim M. Mullenerse, Antonetta Sasf, Teus van Laarg, Hans Kromhoutb, Roel Vermeulenb,h a Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands c Department of Neurology, University Medical Centre Utrecht, Utrecht, the Netherlands d Department of Neurology, Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands e Department of Neurology, Canisius-Wilhelmina Hospital, Nijmegen, the Netherlands f Department of Neurology, Vlietland Hospital, Schiedam, the Netherlands g Department of Neurology, University Medical Centre Groningen, Groningen, the Netherlands h Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands b A R T I C LE I N FO A B S T R A C T Handling Editor: Zorana Jovanovic Andersen Background: There is some evidence to suggest an association between ambient air pollution and development of Parkinson’s disease (PD). However, the small number of studies published to date has reported inconsistent findings. Objectives: To assess the association between long-term exposure to ambient air pollution constituents and the development of PD. Methods: Air pollution exposures (particulate matter with aerodynamic diameter < 10 μm [PM10], < 2.5 μm [PM2.5], between 2.5 μm and 10 μm [PMcoarse], black carbon, and nitrogen oxides [NO2 and NOx]) were predicted based on land-use regression models developed within the “European Study for Air Pollution Effects” (ESCAPE) study, for a Dutch PD case-control study. A total of 1290 subjects (436 cases and 854 controls). were included and 16 years of exposure were estimated (average participant starting age: 53). Exposures were categorized and conditional logistic regression models were applied to evaluate the association between ambient air pollution and PD. Results: Overall, no significant, positive relationship between ambient air pollutants and PD was observed. The odds ratio (OR) for PD associated with an increase from the first quartile of NO2 (< 22.8 μg/m3) and the fourth (> 30.4 μg/m3) was 0.87 (95% CI: 0.54, 1.41). For PM2.5 where the contrast in exposure was more limited, the OR associated with an increase from the first quartile PM2.5 (< 21.2 μg/m3) to the fourth (> 22.3 μg/m3) was 0.50 (95% CI: 0.24, 1.01). In a subset of the population with long-term residential stability (n = 632), an increased risk of PD was observed (e.g. OR for Q4 vs Q1 NO2:1.37, 95% CI: 0.71, 2.67). Conclusions: We found no clear association between 16 years of residential exposure to ambient air pollution and the development of PD in The Netherlands. Keywords: Air pollution Parkinson’s disease Long-term exposure Land-use regression 1. Background Parkinson’s disease (PD) is one of the most common neurodegenerative diseases in the world, with a global prevalence of 6 million people in the year 2016 (Vos et al., 2017) and is estimated to affect over 9 million individuals by the year 2030 (Dorsey et al., 2007; Rossi et al., 2018). In addition to being a source of premature mortality, the progressive nature of PD means that it causes significant disability, ultimately being responsible for over 3 million lost disability-adjusted life years (DALYs) globally in 2016 (Hay et al., 2017). ⁎ Corresponding author at: Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology (EEPI), Utrecht University, Yalelaan 2, 3584 CM Utrecht, The Netherlands. E-mail address: g.s.downward@uu.nl (G.S. Downward). 1 Co-first author. https://doi.org/10.1016/j.envint.2019.04.069 Received 9 January 2019; Received in revised form 26 April 2019; Accepted 28 April 2019 Available online 16 May 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). Environment International 129 (2019) 28–34 R. Toro, et al. neuropathy, ulnar nerve neuropathy, thoracic and lumbar disc disease and sciatica), which were assumed to not share the same pathological mechanisms as PD. Cases and controls resided in the same region as the hospital they visited. The participation rate among cases and controls was 45% and 35%, respectively. If all controls assigned to a case declined to participate or did not respond, the two next best-matched potential controls were invited. Therefore it was possible that one control served as a control for multiple cases, which occurred 83 times in the current study. Initially, 448 cases were enrolled, however, for four of them, no suitable controls were found and were consequently excluded, resulting in 444 cases and 876 controls. The medical ethics committee of St. Elisabeth Hospital Tilburg provided ethical approval for the study. All participants gave written informed consent for inclusion in the study. The aetiology of PD is largely unknown, with a combination of different environmental and genetic factors being considered to be responsible (Kalia and Lang, 2015). Previous work has associated occupational exposure to pesticides as a risk factor for development of PD while cigarette smoking has been found to have a protective effect (van der Mark et al., 2012; van et al., 2014). Exposure to ambient air pollution has previously been suggested as a potential risk factor in the development of PD via mechanisms including inflammatory processes, oxidative stress, white matter abnormalities, and microglia activation (Block and Calderon-Garciduenas, 2009; Block et al., 2012). Despite a potentially plausible biological basis for PD aetiology, the studies performed on exposure to ambient air pollution thus far have been inconsistent in their findings. Chen et al. reported an increased odds ratio (OR) for PD development in association with PM10 (OR comparing maximum exposure category [ > 65 μg/m3] to lowest [ < 54 μg/m3]: 1.35, 95% CI: 1.12, 1.62) in a nested case-control study in Taiwan (Chen et al., 2017). A different Taiwanese study by Lee et al. also reported elevated ORs in association with exposure to traffic-derived NOx (OR per interquartile range [IQR] increase: 1.08, 95% confidence interval [CI]: 1.04–1.12) in a large case-control study of Taiwanese men and women (Lee et al., 2016). Ritz et al. reported similar results with exposure to traffic-derived NO2 (OR per IQR increase: 1.09, 95% CI: 1.03:1.16) in a Danish case-control study (Ritz et al., 2016). By contrast, Finkelstein et al. reported no association between traffic generated air pollution and PD, however a 10 ng/m3 increase in ambient manganese was associated with an OR of 1.03 (95%CI: 1.00–1.07) (Finkelstein and Jerrett, 2007). Liu et.al reported no significant association between PM or NO2 exposures and PD risk in a nested casecontrol study in the USA (Liu et al., 2016). However, in subgroup analyses, they did report increased risks of PD among women who were exposed to high levels of PM10 (OR for highest quintile: 1.65, 95% CI: 1.11, 2.45) and non-smokers who were exposed to high levels of PM2.5 (OR: 1.29, 95% CI: 1.01, 3.17). Prospective studies based in the USA by Palacios et al. reported no relationship between air pollution constituents and PD risk in their studies of 50,000 men and 115,000 women (Palacios et al., 2014; Palacios et al., 2017). Similar findings were reported by Cerza et al. who, in a cohort study of over a million people in Rome, reported no positive association between PM or NO2 and PD (Cerza et al., 2018). Using a multi-centre PD case-control study, we aimed to investigate the relationship between long-term exposure to ambient air pollutants (particulate matter with aerodynamic diameter < 10 μm [PM10], < 2.5 μm [PM2.5], between 2.5 μm and 10 μm [PMcoarse], black carbon, and nitrogen oxides [NO2 and NOx]), and the development of PD. 2.2. Data collection Cases and controls were interviewed via telephone by trained interviewers between April 2010 and June 2012. Information on demographics, medical history, lifestyle factors, diet, occupational pesticide use, occupation, and a detailed residential history of the participants was collected. The residential history listed all addresses the participant lived at for at least one year, and the first and last year the participant inhabited each address. The addresses were subsequently geocoded to assess residency-based environmental exposures (Brouwer et al., 2017). 2.3. Exposure assessment Exposure to air pollutants at the residential address(es) of study participants were predicted using Land Use Regression (LUR) models developed within the ESCAPE project (Beelen et al., 2013; Eeftens et al., 2012). In brief, concentrations of NO2, NOx, PM2.5, PM10, PMcoarse, and PM2.5 absorbance were measured at multiple sites (40 sites for particulate matter and 80 sites for NO2 and NOx) within The Netherlands. Measurements were conducted in 2009 across three 14-day periods, spread over the seasons to derive their annual average. Subsequently, LUR models were developed by including geographic variables such as traffic intensity, land use and population density in order to explain the spatial variability of the pollutants’ concentrations. The final models for each of the pollutants included three to seven predictor variables and explained 86% (NO2), 87% (NOx), 92% (PM2.5absorbance), 51% (PMcoarse), 67% (PM2.5) and 68% (PM10) of concentration variability. Using the geocoded residential history of study participants, the LUR models were used to estimate the annual ambient air pollution at each participant’s addresses. For participants who lived at different addresses at the same year, concentrations were averaged to give the concentration for that year. The modelled concentrations obtained from 2009 were assumed constant for the years 2010 through 2011 while the concentrations from 1992 to 2008 were back extrapolated using data from routine background monitoring network sites in The Netherlands. Back extrapolation was performed using the absolute and relative differences in measurements between the back extrapolated years (1992 to 2008) and 2009 (Beelen et al., 2014). We did not back extrapolate further than 1992 because there was no routine monitoring data available for all pollutants before 1992. The annual air pollutant concentrations for each participant from 1992 to the year before case diagnosis (cases) or the index year of the matched case diagnosis (controls) were averaged to provide a mean ambient concentration for each participant. 2. Methods 2.1. Study population The present study was an analysis of a multi-centre case control study on PD, designed to study the potential association between lifestyle, occupational and environmental risk factors, and PD, in The Netherlands. Details of the study and the subject selection are described elsewhere (van et al., 2014). In brief, PD cases and their matched controls were identified and recruited from five hospitals located in four cities (Groningen, Rotterdam, Nijmegen, and Tilburg) in The Netherlands between April 2010 and June 2012 (Fig. S1). Within each hospital, a neurologist reviewed the medical records of patients with a diagnosis of PD. Individuals were eligible as cases if they were initially diagnosed between January 2006 to December 2011 and were still alive at time of recruitment (n = 1001). Each enrolled case was paired with two controls, who visited the same hospital within the same time-frame as cases ( ± 3 years). Controls were matched by hospital, visiting date, gender, and age. Controls were patients diagnosed with non-neurodegenerative and peripheral neurological diseases (i.e. median nerve 2.4. Statistical analysis Subjects for whom > 50% of the addresses between 1992 until the year before onset/index year were missing, were excluded (6 cases, 6 controls). If the subject excluded was a case, its matched controls were also excluded. If the subject excluded was a control, however, it was 29 Environment International 129 (2019) 28–34 R. Toro, et al. excluded with its case only if this was the only control available. This resulted in a total of 7 cases and 18 controls being excluded. Additionally, one case had missing data for one of the covariates and was removed with its matched controls. Average annual air pollution exposure was divided in quartiles based on the exposure distribution among the controls. We used conditional logistic regression models to calculate the odds ratio (OR) and 95% confidence intervals (CI) to determine the association between average residential exposure to air pollutants and PD. Additionally, pvalues for linear trend were calculated using the category value () of each quartile as a continuous variable. We specified two levels of adjustments for confounding covariates in our models. Model 1 was adjusted for educational level, smoking status and family history of PD. In model 2, we additionally adjusted for area social economic status (SES, percentage high income at the neighbourhood level of residency). Subjects with complete information on variables for models 1 and 2 were included in the analysis (436 cases and 854 controls). Several sensitivity analyses were performed. First, we assessed the possible linear association between residential air pollution and PD by using the exposure as a continuous variable. Second, as pesticide exposure has previously been found to be relevant to PD risk, we evaluated the impact of residential exposure to specific pesticides – selected on the basis of prior relevance to PD risk (Benomyl, Lindane, Paraquat, and Maneb) – used on commercial crops within 100 m of a participant’s residence on any relationship between air pollution and PD risk (Brouwer et al., 2017). Third, we explored the possible selection bias among the controls and their diseases by excluding each control disease one at a time. Fourth, assuming that residential mobility could result in exposure misclassification, we restricted the analyses to participants who did not move for either 14 years before or after 1992, separately. This time period was selected so as to maximize available sample size while still representing long term (i.e. > 10 years) residential stability. Fifth, we assessed possible effect modification of smoking status and sex via stratified analysis. Last, we studied the heterogeneity in stratified analysis by hospital to determine whether results were influenced unduly by a specific hospital in the study (Higgins and Thompson, 2002). In these sensitivity analyses, to include the largest possible number of cases and controls when excluding diseases, restricting analyses and stratifying by covariates, matching was discarded and we used unconditional logistic regression (matching variables were used as additional adjustment variables). All statistical analyses were performed using SAS version 9.4 with p values < 0.05 being used to indicate statistical significance. Table 1 Demographic characteristics of the study population. Characteristics Male, n (%) Age, y, median (IQR)a Education, n (%) Elementary school Middle school/high school College/university Currentb smoking, n (%) Never Former Current Family history of PD, n (%) None At least one first-degree relative diagnosed with PD Occupational exposure to pesticides, n (%)d Never Low High Area SES, median (IQR)c PD patients Controls (n = 436) (n = 854) 275 (63.1) 68.8 (62.5–74.0) 540 (63.2) 69.2 (62.9–74.4) 120 (27.5) 186 (42.7) 130 (29.8) 297 (34.8) 338 (39.6) 219 (25.6) 203 (46.6) 214 (49.1) 19 (4.4) 240 (28.1) 488 (57.1) 126 (14.8) 375 (86.0) 61 (14.0) 797 (93.3) 57 (6.7) P value 0.95 0.48 0.02 < 0.001 < 0.001 0.45 337 (77.3) 25 (5.7) 74 (17.0) 20.3 (18.7–20.3) 676 (79.2) 55 (6.4) 123 (14.4) 20.3 (18.7–20.3) 0.39 Abbreviations: PD = Parkinson’s disease; SES = socioeconomic status. a Age on which questionnaire was complete in cases and controls. b Current, meaning before onset of PD. c Social economic status is based on area level, percentage of high income is depicted. d Occupational pesticide exposure assigned using a job exposure matrix (JEM). highest exposed individuals compared to those in the reference category for both the unadjusted and adjusted models (Table 3). For the fully adjusted model (model 2), PM2.5 absorbance showed significantly decreased ORs in the highest exposure category (OR 0.57, 95% CI 0.35, 0.96). An increased OR was observed comparing the second quartile of NO2 exposure to the first quartile (OR for model 2: 1.14, 95% CI: 0.76,1.70) however for the higher exposure categories, the ORs attenuated to the null (OR for highest quartile: 0.87, 95%: 0.54, 1.41). For PM2.5, the OR associated with an increase from the first quartile PM2.5 (< 21.2 μg/m3) to the fourth (> 22.3 μg/m3) was 0.50 (95% CI: 0.24, 1.01). No significant exposure-response trends were observed for any models or pollutants. 3. Results 3.1. Sensitivity analyses In total, 436 patients with PD and 854 matched controls were included for analysis. Demographic characteristics are presented in Table 1. As has been previously described (Kalia and Lang, 2015), a higher prevalence of PD was seen among men versus women (63.1% vs 36.9%). The median age of PD diagnosis was approximately 69 years old. Cases had a higher level of education than controls (72.5% had high level of education compared to 65.2% in controls, p < 0.05) and were more often never-smokers (46.6% vs 28.1%, p < 0.05). A family history of PD was also higher in cases than controls (14.0% vs 6.7%, p < 0.05). On average, approximately 16 years of air pollution exposure (prior to diagnosis) was estimated per person. The mean annual concentration for each pollutant is presented in Table 2, stratified by disease status. Mean concentrations of NO2, NOx, PM2.5, PM10, PMcoarse and PM2.5 absorbance did not differ significantly between cases and controls. Contrast in exposure was lower for PM2.5 and PM10 compared to NO2 and NOx. Correlations between pollutants were moderate to high (0.49 to 0.87). The highest correlation observed was between NO2 and PM2.5 absorbance (0.87 – Table S1). The ORs of nearly all air pollutants were decreased among the Including residential exposure to pesticides in alternative models had no appreciable effect on the main findings of the current paper. For example, the OR for NO2 and PD risk was not materially changed if exposure to pesticides was included in regression models as either a continuous variable (OR:0.87, 95% CI: 0.53, 1.41) or as a binary variable (ever/never exposed; OR: 0.88, 95% CI: 0.53, 1.45, Table S2). Further sensitivity analyses, including considering exposure as a continuous variable did not show any notable difference as compared to the overall results (Table 3) nor did excluding each control disease one at a time (data not shown). Further, no heterogeneity was observed between the study centres. Stratification by smoking status revealed that ORs for never-smokers were similar to the ORs for the overall population, with the exceptions of PM2.5 absorbance and PM2.5 where we observed lower and more significant ORs in the never smokers population than in the smokers population (Table 4). However, in both groups the ORs remained below the null as in the overall analysis. When we stratified by smoking and gender (Supplement Table S3), an increase in the ORs were detected am …