Long-Term Exposure to Ultrafine Particles and Particulate Matter Constituents and the Risk of Amyotrophic Lateral Sclerosis
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 9 Linguagem: Inglês
10.1289/ehp9131
ISSN1552-9924
AutoresZhebin Yu, Susan Peters, Loes van Boxmeer, George S. Downward, Gerard Hoek, Marianthi‐Anna Kioumourtzoglou, Marc G. Weisskopf, Johnni Hansen, Leonard H. van den Berg, Roel Vermeulen,
Tópico(s)Air Quality and Health Impacts
ResumoVol. 129, No. 9 Research LetterOpen AccessLong-Term Exposure to Ultrafine Particles and Particulate Matter Constituents and the Risk of Amyotrophic Lateral Sclerosis Zhebin Yu, Susan Peters, Loes van Boxmeer, George S. Downward, Gerard Hoek, Marianthi-Anna Kioumourtzoglou, Marc G. Weisskopf, Johnni Hansen, Leonard H. van den Berg, and Roel C.H. Vermeulen Zhebin Yu Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Department of Epidemiology and Health Statistics, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China , Susan Peters Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands , Loes van Boxmeer Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, UMC Utrecht, Utrecht, Netherlands , George S. Downward Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands , Gerard Hoek Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands , Marianthi-Anna Kioumourtzoglou Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA , Marc G. Weisskopf Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA , Johnni Hansen Danish Cancer Society Research Center, Copenhagen, Denmark , Leonard H. van den Berg Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, UMC Utrecht, Utrecht, Netherlands , and Roel C.H. Vermeulen Address correspondence to Roel C.H. Vermeulen, Division of Environmental Epidemiology, Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, 3508 TD, Utrecht, Netherlands. Telephone: 31 302539448. Email: E-mail Address: [email protected] Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands Published:9 September 2021CID: 097702https://doi.org/10.1289/EHP9131Cited by:2AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit IntroductionThe etiology of amyotrophic lateral sclerosis (ALS) remains unknown but is considered to be an interplay of environmental exposures and genetic predisposition (van Es et al. 2017). Few epidemiological studies have examined the association between ambient air pollution and ALS. We previously reported an increased risk of developing ALS for long-term exposure to traffic-related air pollution in a Dutch case–control study (917 cases and 2,662 controls) (Seelen et al. 2017). Increased knowledge about the possible associations between particulate matter (PM) and its constituents and ALS will provide additional insight into the potential pathophysiology of ALS. We aimed to extend on our previous analyses by including 2,081 more cases and controls and by extending the exposure assessment to a broader range of air pollutants [ultrafine particles (particulate matter less than or equal to 0.1 micrometersPM≤0.1μm in aerodynamic diameter or UFPs), PM elemental components, and oxidative potentials (OPs)].MethodsPresent analyses were based on ALS patients and controls enrolled in the Prospective ALS in the Netherlands (PAN) study (Huisman et al. 2011) from 1 January 2006 to 31 December 2018. All patients with a diagnosis of possible, probable, or definite ALS according to the revised El Escorial criteria (Brooks et al. 2000) were included. Population-based controls selected from the registers of the patients' general practitioners were frequency matched by sex and age (plus or minus 5 years±5y). Information including sex, date of birth, education level, body mass index, smoking, alcohol consumption, residential history, and area-level socioeconomic status (SES) was collected. Annual concentrations of air pollution constituents were estimated at the geocoded residential addresses of each participant based on land-use regression (LUR) models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Exposomics projects (Beelen et al. 2013; de Hoogh et al. 2013; Eeftens et al. 2012; van Nunen et al. 2017) (see supporting information at https://github.com/kevininef/Airpollution-ALS). We averaged the air pollutant concentrations from 1992 to the date of onset for cases or recruitment for controls as the main exposure.Unconditional logistic regression models were used to estimate the association between exposure to air pollution and ALS in single-pollutant models. Two-pollutant models were performed for each air pollutant by additionally adjusting for the other pollutants one by one. All analyses were performed within R software (version 3.6.1; R Development Core Team). Supporting information is available at https://github.com/kevininef/Airpollution-ALS. The PAN study was approved by the institutional review board of the University Medical Center Utrecht.Results and DiscussionA total of 1,636 patients with ALS and 4,024 controls were included (see supporting information at https://github.com/kevininef/Airpollution-ALS), covering all of the Netherlands. We observed increased odds ratios (ORs) for ALS in association with most air pollutants, with the strongest associations for (particulate matter less than or equal to 2.5 micrometers in aerodynamic diameterPM≤2.5μm absorbance) {odds ratios equals 1.19OR=1.19 [95% confidence interval (CI): 1.10, 1.28], nitrogen dioxide [nitrogen dioxide])NO2] [odds ratios equals 1.25OR=1.25 (95% CI: 1.15, 1.34)], and nitrogen oxides [nitrogen oxidesNOx] [odds ratios equals 1.14OR=1.14 (95% CI: 1.07, 1.22)]} (Table 1). For UFPs, an elevated OR of 1.11 (95% CI: 1.05, 1.16) was observed. For particle elements, road traffic non-tailpipe emissions of copper (Cu), iron (Fe), nickel (Ni), sulfur (S), silicon (Si), and vanadium (V) were associated with significantly higher ORs for ALS in both fine particulate matterPM2.5 and particulate matter begin subscript 10 end subscriptPM10 fractions. Marginal effects for all air pollutants are presented in the supporting information at https://github.com/kevininef/Airpollution-ALS.Table 1 Association between long-term exposure to air pollution and ALS in single-pollutant models.Table 1 has four main columns, namely, Exposure (Interquartile range) begin superscript lowercase a end superscript, Average exposure level begin superscript lowercase a end superscript, Odds ratios (95 percent confidence intervals) begin superscript lowercase b end superscript, and lowercase italic p Value begin superscript lowercase c end superscript. The Average exposure level begin superscript lowercase a end superscript column is subdivided into two columns, namely, Case (uppercase italic n equals 1,636) and control (uppercase italic n equals 4,024).Exposure (IQR)aAverage exposure levelaOR (95% CI)bp ValuecCase (uppercase italic n equals 1,636N=1,636)Control (uppercase italic n equals 4,024N=4,024)particulate matter begin subscript 10 end subscriptPM10 (2.0)32.8 plus or minus 2.232.8±2.232.6 plus or minus 2.232.6±2.21.10 (1.04, 1.16)0.001fine particulate matterPM2.5 (1.5)21.9 plus or minus 1.521.9±1.521.8 plus or minus 1.521.8±1.51.05 (0.92, 1.10)0.153coarse particulate matterPMcoarse (0.9)11.0 plus or minus 1.011.0±1.010.9 plus or minus 1.010.9±1.01.06 (1.00, 1.12)less than 0.001<0.001fine particulate matterPM2.5 absorbance (0.3)1.49 plus or minus 0.241.49±0.241.46 plus or minus 0.241.46±0.241.19 (1.10, 1.28)less than 0.001<0.001nitrogen dioxideNO2 (7.4)27.1 plus or minus 6.027.1±6.026.3 plus or minus 5.626.3±5.61.25 (1.15, 1.34)less than 0.001<0.001nitrogen oxidesNOx (10.7)46.2 plus or minus 9.546.2±9.545.2 plus or minus 9.645.2±9.61.14 (1.07, 1.22)less than 0.001<0.001UFPs (1,240)9,450 plus or minus 1,5209,450±1,5209,280 plus or minus 1,3709,280±1,3701.11 (1.05, 1.16)less than 0.001<0.001fine particulate matterPM2.5 Cu (1.1)3.28 plus or minus 0.953.28±0.953.17 plus or minus 0.883.17±0.881.18 (1.10, 1.27)less than 0.001<0.001particulate matter begin subscript 10 end subscriptPM10 Cu (3.6)12.7 plus or minus 3.6512.7±3.6512.5 plus or minus 3.412.5±3.41.08 (1.02, 1.15)0.019fine particulate matterPM2.5 Fe (27.1)82.1 plus or minus 23.782.1±23.778.9 plus or minus 21.878.9±21.81.22 (1.13, 1.31)less than 0.001<0.001particulate matter begin subscript 10 end subscriptPM10 Fe (125.0)383 plus or minus 119383±119368 plus or minus 10368±101.16 (1.09, 1.24)less than 0.001<0.001fine particulate matterPM2.5 K (13.3)114 plus or minus 9.26114±9.26114 plus or minus 9.44114±9.440.98 (0.90, 1.07)0.764particulate matter begin subscript 10 end subscriptPM10 K (17.3)204 plus or minus 15.8204±15.8203 plus or minus 15.0203±15.01.09 (1.02, 1.17)0.008fine particulate matterPM2.5 Ni (1.0)1.96 plus or minus 0.701.96±0.701.91 plus or minus 0.671.91±0.671.15 (1.05, 1.25)0.004particulate matter begin subscript 10 end subscriptPM10 Ni (1.1)2.34 plus or minus 0.812.34±0.812.28 plus or minus 0.762.28±0.761.17 (1.07, 1.28)0.001fine particulate matterPM2.5 S (63.8)888 plus or minus 52.3888±52.3885 plus or minus 51.2885±51.21.10 (1.02, 1.18)0.021particulate matter begin subscript 10 end subscriptPM10 S (47.3)1,010 plus or minus 44.21,010±44.21,010 plus or minus 42.41,010±42.41.08 (1.01, 1.15)0.034particulate matter begin subscript 10 end subscript siliconPM10Si (12.2)82.4 plus or minus 11.882.4±11.881.5 plus or minus 11.181.5±11.11.12 (1.05, 1.19)0.003particulate matter begin subscript 10 end subscript siliconPM10Si (80.7)368 plus or minus 87.4368±87.4356 plus or minus 72.3356±72.31.18 (1.11, 1.25)less than 0.001<0.001fine particulate matterPM2.5 V (1.5)3.04 plus or minus 1.123.04±1.122.96 plus or minus 1.072.96±1.071.15 (1.05, 1.25)0.004particulate matter begin subscript 10 end subscriptPM10 V (1.6)3.86 plus or minus 1.263.86±1.263.77 plus or minus 1.193.77±1.191.14 (1.05, 1.23)0.004fine particulate matterPM2.5 Zn (18.8)25.8 plus or minus 12.925.8±12.926.1 plus or minus 13.126.1±13.10.96 (0.88, 1.04)0.315particulate matter begin subscript 10 end subscriptPM10 Zn (25.8)35.3 plus or minus 17.935.3±17.935.4 plus or minus 18.235.4±18.20.99 (0.91, 1.08)0.857OP ESR (171.9)901 plus or minus 133901±133889 plus or minus 128889±1281.14 (1.06, 1.23)0.032OP DTT (0.2)0.81 plus or minus 0.160.81±0.160.81 plus or minus 0.160.81±0.161.01 (0.93, 1.09)0.343Note: ALS, amyotrophic lateral sclerosis; CI, confidence interval; Cu, copper; Fe, iron; IQR, interquartile range; K, potassium; Ni, nickel; nitrogen dioxideNO2, nitrogen dioxide; nitrogen oxidesNOx, nitrogen oxides; fine particulate matterPM2.5, particulate matter with aerodynamic diameter less than or equal to 2.5 micrometersdiameter≤2.5μm; particulate matter begin subscript 10 end subscriptPM10, particulate matter with aerodynamic diameter less than or equal to 10 micrometersdiameter≤10μm; fine particulate matter absorbancePM2.5 absorbance, particulate matter less than or equal to 2.5 micrometers in aerodynamic diameter absorbancePM≤2.5μm absorbance; coarse particulate matterPMcoarse, particulate matter with aerodynamic diameter between 2.5 micrometers2.5μm and 10 micrometers10μm; OP DTT, oxidative potential metric with dithiothreitol; OP ESR, oxidative potential metric with electron spin resonance; OR, odds ratio; S, sulfur; SES, socioeconomic status; Si, silicon; UFPs, ultrafine particles; V, vanadium; Zn, zinc.aUnits are micrograms per meter cubedμg/m3 for particulate matter begin subscript 10 end subscriptPM10, fine particulate matterPM2.5, coarse particulate matterPMcoarse, nitrogen dioxideNO2 and nitrogen oxidesNOx; 10 begin superscript negative 5 per meter10−5/m for fine particulate matterPM2.5 absorbance; particle numbers per centimeter cubednumbers/cm3 for UFPs; nanograms per meter cubedng/m3 for all PM elemental constituents; atomic units per meter cubed/m3 for OP ESR; and mol dithiothreitol per minute per meter cubedDTT/min per meter cubed for OP DTT.bResults were adjusted for sex, age (age in y at diagnosis for cases and at recruitment for controls), education level, body mass index, smoking status, alcohol consumption, and area SES using unconditional logistic regression models; ORs are presented as per IQR increment.cp-Values corrected for multiple testing using Benjamini and Hochberg method (Benjamini and Hochberg 1995) are presented.In two-pollutant models adjusted for PM mass, the associations of most air pollutant elements with ALS remained positive, whereas the association of PM mass became null (Figure 1). In two-pollutant models corrected for nitrogen dioxideNO2, the associations of most air pollutants were reduced toward the null, except for Si in the particulate matter begin subscript 10 end subscriptPM10 Si fraction (particulate matter begin subscript 10 end subscript siliconPM10Si), whereas the estimated positive association for nitrogen dioxideNO2 remained, indicating independent associations between nitrogen dioxideNO2, particulate matter begin subscript 10 end subscript siliconPM10Si, and the risk of ALS. Sensitivity analyses showed the associations of nitrogen dioxideNO2 and particulate matter begin subscript 10 end subscript siliconPM10Si with ALS were robust (see supporting information at https://github.com/kevininef/Airpollution-ALS).Figure 1. Two-pollutant model with the main effects of PM mass, absorbance, nitrogen dioxideNO2, nitrogen oxidesNOx, UFPs, PM OP, and PM elemental compositions. The x-axis represents the estimate of certain air pollution constituents, the y-axis represents the pollutants adjusted in the two-pollutant models. All results were adjusted for sex, age, education level, body mass index, smoking status, alcohol consumption, and area SES using unconditional logistic regression models. The particulate matter begin subscript 10 end subscriptPM10 model adjusted for fine particulate matterPM2.5 and coarse particulate matterPMcoarse is difficult to interpret because particulate matter begin subscript 10 end subscriptPM10 is the sum of these two. The models including both nitrogen dioxideNO2 and nitrogen oxidesNOx are also difficult to interpret because nitrogen dioxideNO2 is included in nitrogen oxidesNOx. Red dots stand for single-pollutant models; blue triangles stand for two-pollutant models. Numeric data of this figure are presented in supporting information at https://github.com/kevininef/Airpollution-ALS. Note: Cu, copper; Fe, iron; K, potassium; Ni, nickel; nitrogen dioxideNO2, nitrogen dioxide; nitrogen oxidesNOx, nitrogen oxides; OP DTT, oxidative potential metric with dithiothreitol; OP ESR, oxidative potential metric with electron spin resonance; PM, particulate matter; fine particulate matterPM2.5, particulate matter with aerodynamic diameter less than or equal to 2.5 micrometersdiameter≤2.5μm; particulate matter begin subscript 10 end subscriptPM10, particulate matter with aerodynamic diameter less than or equal to 10 micrometersdiameter≤10μm; fine particulate matter absorbancePM2.5 absorbance, particulate matter less than or equal to 2.5 micrometers in aerodynamic diameter absorbancePM≤2.5μm absorbance; coarse particulate matterPMcoarse, particulate matter with aerodynamic diameter between 2.5 micrometers2.5μm and 10 micrometers10μm; PM OP, particulate matter oxidative potential; S, sulfur; SES, socioeconomic status; Si, silicon; UFPs, ultrafine particles; V, vanadium; Zn, zinc.With an extended sample [nearly twice the size of the previous analyses by Seelen et al. (2017)], we here confirm the positive associations for nitrogen dioxideNO2 (see supporting information at https://github.com/kevininef/Airpollution-ALS). Moreover, restricting the analysis to the participants who were recruited after the previous publication showed consistent associations for air pollution and ALS, speaking to the robustness of the associations (see supporting information at https://github.com/kevininef/Airpollution-ALS). We also broadened our previously published analyses to a wider range of air pollutants and found that the association between long-term air pollution exposure and ALS, as previously hypothesized (Seelen et al. 2017), is mainly driven by local traffic-related constituents. nitrogen dioxideNO2 primarily comes from tailpipe emissions and predictors in the Si LUR models were also traffic variables. The nitrogen dioxideNO2 concentrations were already below the current World Health Organization air quality guidelines (40 micrograms per meter cubed40 μg/m3), suggesting potential benefits of tightening the guidelines and regulatory limits of nitrogen dioxideNO2 (World Health Organisation Fact Sheet 2018).A potential limitation might be that we used the disease onset date for cases in calculating the exposure period, subsequently resulting in a slightly different etiological time window for cases than controls. However, we reestimated the average concentrations for controls from 1992 to 1 y prior to the recruitment date (see supporting information at https://github.com/kevininef/Airpollution-ALS) and generated essentially the same exposure values.Using the air pollution models developed in 2010 for PM elements and in 2014 for UFPs to predict historical exposure might also be a concern, but this is supported by previous studies that reported that the spatial contrasts in measured and modeled annual average levels were stable over time (Eeftens et al. 2011; Downward et al. 2018). Sensitivity analysis of the present study using concentrations without back- extrapolation rendered essentially similar results (see supporting information at https://github.com/kevininef/Airpollution-ALS). Possible residual confounding cannot be excluded given that data regarding medical comorbidities, for example, were not included in the present analysis.Overall, we found that long-term exposures to nitrogen dioxideNO2 and particulate matter begin subscript 10 end subscript siliconPM10Si were independently associated with ALS in a large population-based case–control study. These associations hint toward the potential health relevance of both tailpipe and non-tailpipe emissions of motorized traffic contributing to ALS risk.AcknowledgmentsS.P., L.v.B., L.v.d.B., and R.V. contributed to the study concept and design and participated in data collection and processing. S.P., G.D., G.H., M.-A.K., M.G.W., J.H., and R.V. contributed to the analyses plan. Z.Y. performed the statistical analyses. S.P., G.D., G.H., M.-A.K., M.G.W., J.H., and R.V. contributed to the analyses and interpretation of data. Z.Y. drafted the manuscript. S.P., L.v.B., G.D., G.H., M.-A.K., M.G.W., J.H., L.v.d.B., and R.V. revised the manuscript for intellectual content. L.v.d.B. obtained the funding for the case–control study.This case–control study was funded by the ALS Foundation Netherlands; Prinses Beatrix Spierfonds; the European Community's Health Seventh Framework Programme (grant agreements 259867 and 211250); Netherlands Organisation for Health Research and Development (ZonMW) under the frame of E-Rare-2, the European Research Area Network on Rare Diseases; EU Joint Programme Neurodegenerative Disease Research project [Sampling and biomarker OPtimization and Harmonization In ALS and other motor neuron diseases (SOPHIA) and Survival, Trigger and Risk, Epigenetic, eNvironmental and Genetic Targets for motor neuron Health (STRENGTH) projects]; and the ZonMW Vici scheme to L.v.d.B. Z.Y. was supported by a scholarship from the Chinese Scholarship Council. 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Google ScholarThe authors declare they have no actual or potential competing financial interests.FiguresReferencesRelatedDetailsCited by Saucier D, Registe P, Bélanger M and O'Connell C (2023) Urbanization, air pollution, and water pollution: Identification of potential environmental risk factors associated with amyotrophic lateral sclerosis using systematic reviews, Frontiers in Neurology, 10.3389/fneur.2023.1108383, 14 Parks R, Nunez Y, Balalian A, Gibson E, Hansen J, Raaschou-Nielsen O, Ketzel M, Khan J, Brandt J, Vermeulen R, Peters S, Goldsmith J, Re D, Weisskopf M and Kioumourtzoglou M (2022) Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis, Epidemiology, 10.1097/EDE.0000000000001536, 33:6, (757-766), Online publication date: 1-Nov-2022. Vol. 129, No. 9 September 2021Metrics About Article Metrics Publication History Manuscript received11 February 2021Manuscript revised20 July 2021Manuscript accepted21 July 2021Originally published9 September 2021 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.
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