Background

Substantial research has investigated the adverse effects of traffic-related air pollutants (TRAP) on human health. Convincing associations between TRAP and respiratory and cardiovascular diseases are known, but the underlying biological mechanisms are not well established. High-resolution metabolomics (HRM) is a promising platform for untargeted characterization of molecular mechanisms between TRAP and health indexes.

Objectives

We examined metabolic perturbations associated with short-term exposures to TRAP, including carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), fine particulate matter (PM_2.5), organic carbon (OC), and elemental carbon (EC) among 180 participants of the Center for Health Discovery and Well-Being (CHDWB), a cohort of Emory University-affiliated employees.

Methods

A cross-sectional study was conducted on baseline visits of 180 CHDWB participants enrolled during 2008–2012, in whom HRM profiling was determined in plasma samples using liquid chromatography-high-resolution mass spectrometry with positive and negative electrospray ionization (ESI) modes. Ambient pollution concentrations were measured at an ambient monitor near downtown Atlanta. Metabolic perturbations associated with TRAP exposures were assessed following an untargeted metabolome-wide association study (MWAS) framework using feature-specific Tobit regression models, followed by enriched pathway analysis and chemical annotation.

Results

Subjects were predominantly white (76.1%) and non-smokers (95.6%), and all had at least a high school education. In total, 7821 and 4123 metabolic features were extracted from the plasma samples by the negative and positive ESI runs, respectively. There are 3421 features significantly associated with at least one air pollutant by negative ion mode, and 1691 features by positive ion mode. Biological pathways enriched by features associated with the pollutants are primarily involved in nucleic acids damage/repair (e.g., pyrimidine metabolism), nutrient metabolism (e.g., fatty acid metabolism), and acute inflammation (e.g., histidine metabolism and tyrosine metabolism). NO₂ and EC were associated most consistently with these pathways. We confirmed the chemical identity of 8 metabolic features in negative ESI and 2 features in positive ESI, including metabolites closely linked to oxidative stress and inflammation, such as histamine, tyrosine, tryptophan, and proline.

Conclusions

We identified a range of ambient pollutants, including components of TRAP, associated with differences in the metabolic phenotype among the cohort of 180 subjects. We found Tobit models to be a robust approach to handle missing data among the metabolic features. The results were encouraging of further use of HRM and MWAS approaches for characterizing molecular mechanisms underlying exposure to TRAP.

中文翻译：

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应用高分辨率代谢组学来识别受交通相关空气污染干扰的生物途径

 背景

大量研究调查了交通相关空气污染物 (TRAP) 对人类健康的不利影响。 TRAP 与呼吸系统和心血管疾病之间令人信服的关联是已知的，但潜在的生物学机制尚未明确。高分辨率代谢组学 (HRM) 是一个有前途的平台，可用于非针对性地表征 TRAP 和健康指数之间的分子机制。

 目标

我们检查了与短期暴露于 TRAP 相关的代谢扰动，包括一氧化碳 (CO)、二氧化氮 (NO ₂ )、臭氧 (O ₃ )、细颗粒物 (PM _2.5 )、有机碳 (OC) 和元素碳(EC) 是健康发现与福祉中心 (CHDWB) 的 180 名参与者之一，该中心由埃默里大学附属员工组成。

 方法

对 2008 年至 2012 年期间注册的 180 名 CHDWB 参与者进行基线访问进行横断面研究，使用液相色谱-高分辨率质谱法以及正负电喷雾电离 (ESI) 模式测定血浆样本中的 HRM 分析。环境污染浓度是在亚特兰大市中心附近的环境监测仪上测量的。根据非靶向代谢组范围关联研究 (MWAS) 框架，使用特定特征的 Tobit 回归模型评估与 TRAP 暴露相关的代谢扰动，然后进行丰富的通路分析和化学注释。

 结果

受试者主要是白人（76.1%）和非吸烟者（95.6%），并且所有人都至少受过高中教育。通过阴性和阳性 ESI 运行，总共分别从血浆样本中提取了 7821 个和 4123 个代谢特征。负离子模式有3421个与至少一种空气污染物显着相关的特征，正离子模式有1691个特征。与污染物相关的特征丰富的生物途径主要涉及核酸损伤/修复（例如嘧啶代谢）、营养代谢（例如脂肪酸代谢）和急性炎症（例如组氨酸代谢和酪氨酸代谢）。 NO ₂和 EC 与这些途径的相关性最为一致。我们确认了阴性 ESI 中的 8 个代谢特征和阳性 ESI 中的 2 个特征的化学特性，包括与氧化应激和炎症密切相关的代谢物，如组胺、酪氨酸、色氨酸和脯氨酸。

 结论

我们确定了一系列环境污染物，包括 TRAP 成分，与 180 名受试者的代谢表型差异相关。我们发现 Tobit 模型是处理代谢特征中缺失数据的有效方法。结果鼓励进一步使用 HRM 和 MWAS 方法来表征 TRAP 暴露的分子机制。