Abstract
BACKGROUND AND AIM: Unraveling the exposome may be a cornerstone towards precision prevention in public health. The presented approach was applied to a cohort of 50 mother-child pairs in the framework of HEALS project. METHODS: The links between in utero exposure to metals, PFOS, PFOA, organophosphates, and organochlorines, metabolic pathway deregulation, and clinically observed phenotypes were drawn through a urinary and serum untargeted metabolomics analysis using UPLC-Q-TOF/MS and NMR, followed by integrative bioinformatics and exposome-wide association algorithms. Spectral pre-processing was performed using the Bioconductor R packages XCMS and CAMERA. The databases HMDB, Metlin, and Lipid Maps, were used for metabolites identification. Enrichment and pathway analyses were performed using GeneSpring GX, which mapped significant biomarkers to known biochemical pathways based on the information contained in public databases (MetaCyc, Wikipathways, and KEGG). The Exposome-Wide Association Study (EWAS) approach was adopted to comprehensively and systematically associate multiple exposure factors discovering robust correlations with metabolites levels and dysregulated pathways. RESULTS:Metabolite identification revealed that the total number of unique annotated metabolites in urine and serum samples analysis using LC-HRMS was 751, and 7830, respectively. The detected metabolites on serum samples were mapped on 246 pathways, while urinary metabolites on 163. According to EWAS analysis, birth weight is positively affected by S-Adenosylhomocysteine levels during the first trimester of pregnancy, and negatively associated with the levels of Citrulline, and DEAMPY, at delivery. In addition, higher exposure levels to Hexachlorocyclohexane (HCH), 2,2',4,5,5'-Pentachlorobiphenyl (PCB101) and 2.4’-DDT, can lead to height increasement. The same outcome is associated to citric acid levels. Head circumference is positively associated with exposure to 4.4’-DDT at the first trimester. CONCLUSIONS:Overall, functionally coupling advanced bioinformatics algorithms applied on omics data with exposome-derived information on exposures and health indicators can support the high-dimension-biology-based association of environmental exposures and adverse health outcomes in early life. KEYWORDS: Exposome, Metabolomics, Birth outcomes
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