Spontaneous preterm birth (SPB) is affected by various
environmental
exposures. However, there is still an urgent need to efficiently integrate
exposomic information to build its prediction model and unveil the
potential toxic pathways. Here, we conducted a nested case-control
study by recruiting 30 women with SPB delivery (cases) and 30 women
without (controls) at their early pregnancy. We analyzed various biomarkers
of external chemical exposure, lipidomics, and immunity, resulting
in 1081 exposure features. A logistic regression model (LR) was used
to screen potential risk factors, and four statistical learners were
used to establish SPB prediction models. Overall, the serum lipid
concentration in cases was higher than in controls, while this was
not the case for chemical and immune biomarkers. Random forest (RF)
and extreme gradient boosting (XGboost) models had a relatively higher
prediction accuracy of > 90%. Glycerophospholipids (GP) were the
most
abundant lipidomic features screened by LR, RF, and XGboost models,
followed by glycerolipids and sphingolipids, shown as well as by enrichment
analysis. Moreover, FA(21:0) had the largest contribution to the prediction
performance. Maternal exposure to various elements can contribute
to SPB risk due to their interaction with GP metabolism. Therefore,
it is promising to use exposomic data to predict SPB risk and screen
key molecular events.
Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this study, we aim to propose one new framework of exposomic analysis and build up one novel integrated platform “ExposomeX” to expediate the discovery of the “Exposure-Biology-Disease” nexus. We have developed 13 standardized modules to accomplish six major functions including statistical learning (E-STAT), exposome database search (E-DB), mass spectrometry data processing (E-MS), meta-analysis (E-META), biological link via pathway integration and protein-protein interaction (E-BIO) and data visualization (E-VIZ). Using ExposomeX, we can effectively analyze the multiple-dimensional exposomics data and investigate the “Exposure-Biology-Disease” nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. The performance of ExposomeX has been well validated by re-analyzing two previous multi-omics studies. Additionally, ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis. In sum, we have proposed a novel framework for standardized exposomic analysis, which can be accessed using both R and online interactive platform (http://www.exposomex.cn/).
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