Solid fuel combustion is an important source of the release of rare earth elements (REEs) into the ambient environment, resulting in potential adverse effects on human cardiovascular health. Our study aimed to identify reliable exposure biomarkers of REE intake and their potential role in blood pressure change. A total of 24 rats were administered with 14 REE chlorides at four doses (six rats per group). Fur samples were collected both before and after administration. Blood samples were collected after 12 weeks of REE intake. The REE concentrations in rat fur and blood samples were measured by inductively coupled plasma mass spectrometry. For each week, blood pressure, as well as heart rate and pulse pressure, were measured. The linear mixed-effect model was used to analyze the relationship between REE administration dose and blood pressure change. We found that the REE concentration in fur, but not blood, samples exhibited significant dose–response relationships with administration dose. It suggested that hair samples are a more efficient matrix for indicating the exposure level of a population to REEs than blood samples. However, there was no dose–response relationships between the administration dose and blood pressure change of rats, or with heart rate and pulse pressure for the 14 REEs. We also did not find a dose–response relationship between REE administration levels and plasma concentration of 8-hydroxy-2’-deoxyguanosine, as an important DNA oxidative stress damage biomarker. In conclusion, hair samples are more suitable as a sample type to reliably assess exposure to REEs than blood samples, and REEs did not have a direct adverse effect on blood pressure in our rat model.

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/).