Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.
Department of Psychology, College of Arts and Sciences, Saint Louis University, Saint Louis, MO.
Chest. 2024 Nov;166(5):975-986. doi: 10.1016/j.chest.2024.06.3809. Epub 2024 Jul 25.
Although evidence has documented the associations of ambient air pollution with chronic respiratory diseases (CRDs) and lung function, the underlying metabolic mechanisms remain largely unclear.
How does the metabolomic signature for air pollution relate to CRD risk, respiratory symptoms, and lung function?
We retrieved 171,132 participants free of COPD and asthma at baseline from the UK Biobank, who had data on air pollution and metabolomics. Exposures to air pollutants (particulate matter with diameter ≤ 2.5 μm [PM], particulate matter with a diameter ≤ 10 μm, nitrogen oxide [NO], and NO) were assessed for 4 years before baseline considering residential address histories. We used 10-fold cross-validation elastic net regression to identify air pollution-associated metabolites. Multivariable Cox models were used to assess the associations between metabolomic signatures and CRD risk. Mediation and pathway analysis were conducted to explore the metabolic mechanism underlying the associations.
During a median follow-up of 12.51 years, 8,951 and 5,980 incident COPD and asthma cases were recorded. In multivariable Cox regressions, air pollution was positively associated with CRD risk (eg, hazard ratio per interquartile range increment in PM, 1.09; 95% CI, 1.06-1.13). We identified 103, 86, 85, and 90 metabolites in response to PM, particulate matter with a diameter ≤ 10 μm, NO, and NO exposure, respectively. The metabolomic signatures showed significant associations with CRD risk (hazard ratio per SD increment in PM metabolomic signature, 1.11; 95% CI, 1.09-1.14). Mediation analysis showed that peripheral inflammatory and erythrocyte-related markers mediated the effects of metabolomic signatures on CRD risk. We identified 14 and 12 perturbed metabolic pathways (energy metabolism and amino acid metabolism pathways, etc) for PM and NO metabolomic signatures.
Our study identifies metabolomic signatures for air pollution exposure. The metabolomic signatures showed significant associations with CRD risk, and inflammatory- and erythrocyte-related markers partly mediated the metabolomic signatures-CRD links.
尽管有证据表明,环境空气污染与慢性呼吸道疾病(CRD)和肺功能有关,但潜在的代谢机制仍很大程度上不清楚。
空气污染的代谢特征与 CRD 风险、呼吸道症状和肺功能有何关系?
我们从英国生物库中检索了 171132 名在基线时无 COPD 和哮喘的参与者,他们有空气污染和代谢组学的数据。考虑到居住地址历史,在基线前 4 年内评估了空气污染物(直径≤2.5μm 的颗粒物[PM]、直径≤10μm 的颗粒物、氮氧化物[NO]和 NO)的暴露情况。我们使用 10 倍交叉验证弹性网回归来识别与空气污染相关的代谢物。使用多变量 Cox 模型评估代谢组学特征与 CRD 风险之间的关联。进行中介和途径分析以探索关联背后的代谢机制。
在中位随访 12.51 年期间,记录了 8951 例和 5980 例 COPD 和哮喘的发病事件。在多变量 Cox 回归中,空气污染与 CRD 风险呈正相关(例如,PM 每增加一个四分位距的危害比为 1.09;95%CI,1.06-1.13)。我们分别识别出 103、86、85 和 90 种代谢物分别对 PM、直径≤10μm 的颗粒物、NO 和 NO 暴露有反应。代谢组学特征与 CRD 风险有显著关联(PM 代谢组学特征每增加一个标准差的危害比为 1.11;95%CI,1.09-1.14)。中介分析表明,外周炎症和红细胞相关标志物介导了代谢组学特征与 CRD 风险之间的关系。我们确定了 PM 和 NO 代谢组学特征的 14 个和 12 个受扰代谢途径(能量代谢和氨基酸代谢途径等)。
我们的研究确定了空气污染暴露的代谢特征。代谢组学特征与 CRD 风险显著相关,炎症和红细胞相关标志物部分介导了代谢组学特征与 CRD 之间的联系。