Metabolomics Profiling before, during, and after the Beijing Olympics: A Panel Study of Within-Individual Differences during Periods of High and Low Air Pollution

Abstract

Background:The metabolome is a collection of exogenous chemicals and metabolites from cellular processes that may reflect the body’s response to environmental exposures. Studies of air pollution and metabolomics are limited.Objectives:To explore changes in the human metabolome before, during, and after the 2008 Beijing Olympics Games, when air pollution was high, low, and high, respectively.Methods:Serum samples were collected before, during, and after the Olympics from 26 participants in an existing panel study. Gas and ultra-high performance liquid chromatography/mass spectrometry were used in metabolomics analysis. Repeated measures ANOVA, network analysis, and enrichment analysis methods were employed to identify metabolites and classes associated with air pollution changes.Results:A total of 886 molecules were measured in our metabolomics analysis. Network partitioning identified four modules with 65 known metabolites that significantly changed across the three time points. All known molecules in the first module (n=33) were lipids (e.g., eicosapentaenoic acid, stearic acid). The second module consisted primarily of dipeptides (n=24, e.g., isoleucylglycine) plus 8 metabolites from four other classes (e.g., hypoxanthine, 12-hydroxyeicosatetraenoic acid). Most of the metabolites in Modules 3 (19 of 23) and 4 (5 of 5) were unknown. Enrichment analysis of module-identified metabolites indicted significantly overrepresented pathways, including long- and medium-chain fatty acids, polyunsaturated fatty acids (n3 and n6), eicosanoids, lysolipid, dipeptides, fatty acid metabolism, and purine metabolism [(hypo) xanthine/inosine–containing pathways].Conclusions:We identified two major metabolic signatures: one consisting of lipids, and a second that included dipeptides, polyunsaturated fatty acids, taurine, and xanthine. Metabolites in both groups decreased during the 2008 Beijing Olympics, when air pollution was low, and increased after the Olympics, when air pollution returned to normal (high) levels. https://doi.org/10.1289/EHP3705

Serum cardiovascular-related metabolites disturbance exposed to different heavy metal exposure scenarios

“…Metabolomics technology can be used to identify early health effects of heavy metals in PM and to reveal the underlying mechanisms, which has been applied in metabolic disturbance research associated with PM exposures ( Du et al, 2020 , Zhang et al, 2019 ). For example, a quasi-experiment during the Beijing Olympics showed significant differences in serum metabolites between high-exposure and low-exposure periods ( Mu et al, 2019 ). A crossover trial in Shanghai reported exposure to PM 2.5 was correlated with obvious changes in both serum and urine metabolites ( Li et al, 2017a , Chen et al, 2019 ).…”

A framework for stability‐based module detection in correlation graphs

“…The application of the bootstrapping approach to module stability to the metabolomics dataset from an air pollution study [28] demonstrates the utility of this approach for omics data, high‐dimensional data, and network analysis in general. Although the stability levels were in the low range, we were able to determine an appropriate threshold and modules that resulted in biologically meaningful modules.…”

“…The approach to bootstrapping stability was also applied to a high‐throughput metabolomics dataset. The data were collected from 26 participants at three‐time points, which were before, during and after, the 2008 Beijing Olympics [28]. During the Olympics, there were government‐mandated restrictions that aimed to reduce pollution levels and thus decreased exposure.…”

“…During the Olympics, there were government‐mandated restrictions that aimed to reduce pollution levels and thus decreased exposure. The data consists of a total of p = 886 metabolites (nodes) measured by Metabolon using mass spectrometry, see [28] for data processing details. Eight metabolites were eliminated from the analysis based on severe missing data (<50%).…”

“…This novel approach is benchmarked on several simulations that examine performance as a function of the number of parameters, sample size, correlation across inputs, and the number of modules. This framework is also applied to a high‐dimensional air pollution exposure metabolomics dataset collected during the Beijing Olympics [28]. The module stability framework has been implemented in the open‐source R package bootcluster available on CRAN.…”