Long-Term Exposure to Fine Particle Elemental Components and Natural and Cause-Specific Mortality—a Pooled Analysis of Eight European Cohorts within the ELAPSE Project,Environmental Health Perspectives

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Long-Term Exposure to Fine Particle Elemental Components and Natural and Cause-Specific Mortality—a Pooled Analysis of Eight European Cohorts within the ELAPSE Project
Environmental Health Perspectives ( IF 10.1 ) Pub Date : 2021-4-12 , DOI: 10.1289/ehp8368
Jie Chen ₁ , Sophia Rodopoulou ₂ , Kees de Hoogh _{3,

4} , Maciej Strak _{1,

5} , Zorana J Andersen ₆ , Richard Atkinson ₇ , Mariska Bauwelinck ₈ , Tom Bellander _{9,

10} , Jørgen Brandt ₁₁ , Giulia Cesaroni ₁₂ , Hans Concin ₁₃ , Daniela Fecht ₁₄ , Francesco Forastiere _{12,

15} , John Gulliver _{14,

16} , Ole Hertel ₁₁ , Barbara Hoffmann ₁₇ , Ulla Arthur Hvidtfeldt ₁₈ , Nicole A H Janssen ₅ , Karl-Heinz Jöckel ₁₉ , Jeanette Jørgensen ₆ , Klea Katsouyanni _{2,

15} , Matthias Ketzel _{11,

20} , Jochem O Klompmaker _{5,

21} , Anton Lager ₂₂ , Karin Leander ₉ , Shuo Liu ₆ , Petter Ljungman _{9,

23} , Conor J MacDonald _{24,

25} , Patrik K E Magnusson ₂₆ , Amar Mehta ₂₇ , Gabriele Nagel ₂₈ , Bente Oftedal ₂₉ , Göran Pershagen _{9,

10} , Annette Peters _{30,

31} , Ole Raaschou-Nielsen ₁₈ , Matteo Renzi ₁₂ , Debora Rizzuto _{32,

33} , Evangelia Samoli ₂ , Yvonne T van der Schouw ₃₄ , Sara Schramm ₁₉ , Per Schwarze ₂₉ , Torben Sigsgaard ₃₅ , Mette Sørensen ₁₈ , Massimo Stafoggia _{9,

12} , Anne Tjønneland ₁₈ , Danielle Vienneau _{3,

4} , Gudrun Weinmayr ₂₈ , Kathrin Wolf ₃₀ , Bert Brunekreef ₁ , Gerard Hoek ₁

Affiliation

Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands.
Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
Swiss Tropical and Public Health Institute, Basel, Switzerland.
University of Basel, Basel, Switzerland.
National Institute for Public Health and the Environment, Bilthoven, Netherlands.
Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Population Health Research, St George's, University of London, London, UK.
Interface Demography, Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium.
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Centre for Occupational and Environmental Medicine, Stockholm, Sweden.
Department of Environmental Science, Aarhus University, Denmark.
Department of Epidemiology, Lazio Region Health Service, Rome, Italy.
Agency for Preventive and Social Medicine, Bregenz, Austria.
Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
Science Policy and Epidemiology Environmental Research Group, King's College London, London, UK.
Centre for Environmental Health and Sustainability, School of Geography, Geology and the Environment, University of Leicester, Leicester, UK.
Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
Danish Cancer Society Research Center, Copenhagen, Denmark.
Institute for Medical Informatics, Biometry and Epidemiology, Medical Faculty, University of Duisburg-Essen, Essen, Germany.
Global Centre for Clean Air Research, University of Surrey, Guildford, UK.
Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
Department of Cardiology, Danderyd University Hospital, Stockholm, Sweden.
Centre de recherche en Epidémiologie et Santé des Populations, Faculté de Medicine, Université Paris-Saclay, Villejuif, France.
Department of Statistics, Computer Science and Applications, University of Florence, Florence, Italy.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany.
Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.
Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
Department of Epidemiology, Ludwig Maximilians Universität München, Munich, Germany.
Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.
Stockholm Gerontology Research Center, Stockholm, Sweden.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Department of Public Health, Section of Environment Occupation and Health, Danish Ramazzini Centre, Aarhus University, Aarhus, Denmark.

Abstract

Background:

Inconsistent associations between long-term exposure to particles with an aerodynamic diameter $less than or equal to 2.5 micrometers$ [fine particulate matter ( ${PM}_{2.5}$ )] components and mortality have been reported, partly related to challenges in exposure assessment.

Objectives:

We investigated the associations between long-term exposure to ${PM}_{2.5}$ elemental components and mortality in a large pooled European cohort; to compare health effects of ${PM}_{2.5}$ components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms.

Methods:

We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985–2005). Residential exposure to 2010 annual average concentration of eight ${PM}_{2.5}$ components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a $100 by 100 meter$ scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for ${PM}_{2.5}$ mass and nitrogen dioxide ( ${NO}_{2}$ ) separately.

Results:

We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both ${PM}_{2.5}$ and ${NO}_{2}$ . HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR- and RF-modeled exposures, respectively, per $2 nanograms per meter cubed$ , adjusting for ${PM}_{2.5}$ mass. Associations with cause-specific mortality were less consistent in two-pollutant models.

Conclusion:

Long-term exposure to V in ${PM}_{2.5}$ was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368

中文翻译：

长期接触细颗粒元素成分与自然死亡率和特定原因死亡率——对 ELAPSE 项目中八个欧洲队列的汇总分析

抽象的

背景：

长期接触颗粒物与空气动力学直径之间的关联不一致 $\leq 2.5 μ 米$ [细颗粒物（ ${下午}_{2.5}$ )] 已报道成分和死亡率，部分与暴露评估中的挑战有关。

目标：

我们调查了长期暴露于 ${下午}_{2.5}$ 大型欧洲队列中的元素成分和死亡率；比较对健康的影响 ${下午}_{2.5}$ 使用两种曝光建模方法估计的成分，即监督线性回归（SLR）和随机森林（RF）算法。

方法：

我们汇集了 8 个欧洲队列的数据，共有 323,782 名参与者，基线时（1985-2005 年）平均年龄为 49 岁。住宅暴露2010年平均集中度为8 ${下午}_{2.5}$ 成分[铜 (Cu)、铁 (Fe)、钾 (K)、镍 (Ni)、硫 (S)、硅 (Si)、钒 (V) 和锌 (Zn)] 使用欧洲范围的 SLR 估算和射频模型 $100 \times 100 米$ 规模。我们应用 Cox 比例风险模型来研究成分与自然死亡率和特定原因死亡率之间的关联。此外，通过调整各成分进行双污染物分析 ${下午}_{2.5}$ 质量和二氧化氮（ $不_{2}$ ）分别地。

结果：

我们观察到 46,640 例自然原因死亡，涉及 6,317,235 人年，平均随访时间为 19.5 年。所有 SLR 模型组成部分均与单一污染物模型中的自然原因死亡率显着相关，风险比 (HR) 为 1.05 至 1.27。 RF 模型的 Cu、Fe、K、S、V 和 Zn 也观察到类似的 HR，且置信区间 (CI) 更宽。 SLR 模型的 Ni、S、Si、V 和 Zn 的 HR 仍然高于统一值，并且在对两者进行调整后（几乎）显着 ${下午}_{2.5}$ 和 $不_{2}$ 。在两种污染物模型中，HR 仅对 RF 建模的 K 和 V 保持（几乎）显着性。对于每个 SLR 和 RF 模型暴露，V 的 HR 分别为 1.03 (95% CI: 1.02, 1.05) 和 1.06 (95% CI: 1.02, 1.10)。 $2 {吴 / 米}^{3}$ ，调整为 ${下午}_{2.5}$ 大量的。在两种污染物模型中，与特定原因死亡率的关联不太一致。

结论：

更新日期：2021-04-13

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