CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Area of Science:

• Environmental Health
• Computational Biology
• Metabolomics
• Exposomics

Background:

• Inter-chemical correlations in human metabolomics and exposomics data offer insights into chemical relationships.
• Interpreting complex correlation structures in large datasets is challenging.
• A systematic approach is needed to manage and visualize these correlations.

Purpose of the Study:

• To develop a comprehensive database (CCDB) cataloging inter-chemical correlations from public metabolomics and exposomics studies.
• To provide an accessible online interface for exploring these correlations.
• To facilitate the understanding of chemical interactions and exposures in humans.

Main Methods:

• Compilation of inter-chemical correlation data from 35 human studies, including NHANES and untargeted metabolomics datasets.
• Development of the Chemical Correlation Database (CCDB) as a systematic catalogue.
• Implementation of a user-friendly web interface for data retrieval and visualization.

Main Results:

• The CCDB provides single compound-centric views of chemical correlations.
• Demonstrated applications include exploring correlations within chemical classes (PFAS, PAHs, PCBs, phthalates), xenobiotics (caffeine, acetaminophen), endogenous metabolites (acyl-carnitines), and unannotated PFAS peaks.
• The database integrates data from diverse human studies.

Conclusions:

• The CCDB serves as a valuable computational resource for metabolomics and exposomics research.
• It enhances the understanding of biological and chemical relationships among metabolites and exposures.
• The CCDB facilitates the exploration of complex chemical interactions in the human body.