Dear Colleagues,

Large-scale metabolomics studies are gaining interest, as is the need to develop metabolome-wide association studies (MWAS). Large-scale initiatives such as NIH “All of Us” and extensive clinical studies will provide the metabolomics community with more reliable biomarkers of specific diseases. However, the path to achieving MWAS as a precision medicine tool for clinics needs to be paved, following the FAIR principles.

We will cover all steps to achieve reliable and cost-effective MWAS for precision medicine with the current Special Issue. Specifically:

1) Samples and Experimentally related issues: Biofluid collection and preservation, including POC devices; the need for fast, robust, and reliable high throughput methods; strategies for removing multiple batch variations (internal standards, QC’s, statistically, RUV algorithms); raw spectra processing issues (larger files, different formats).

2) Meta-data collection: Informed consent and data protection (such as HIPPA) compliances; public and digital health collaborations (personalized tools); minimum of anthropometrical, prescription and health status data needed.

3) Progression of Profiling to Identification: Metabolites identification to support iterative downstream investigations; appropriate levels of compound identification (MSI); software tools for processing and identification; the need to report databases used (in silico, public or in-house libraries).

4) Insights of the Metabolism: Statistic (univariate vs. multivariate) and normalization in MWAS; pathway analysis validity for biological fluids (as opposed to cells or tissue); data reporting for further re-analysis with meta-analysis; repositories needed for MWAS; reliability of MWAS results.

Dr. Raquel Cumeras
Dr. Jung H. Suh
Prof. Dr. Stephen Barnes
Guest Editors

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• large scale metabolomics
• biofluid collection and preservation
• high throughput screening methods
• batch variations
• raw spectras processing issues
• informed consent and data protection
• public and digital health collaborations (personalized tools)
• clinical metadata
• metabolites identification
• software tools for processing and identification
• metabolism
• large-scale statistic and normaltization
• pathway analysis
• data reporting
• meta-analysis
• large-scale repositiories