High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Area of Science:

• Metabolomics
• Analytical Chemistry
• Epidemiology

Background:

• Urine metabolomics is valuable for epidemiology but challenged by hydration variability.
• Existing urine normalization methods are often manual and impractical for large studies.
• Accurate normalization is crucial for reliable untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics.

Purpose of the Study:

• To develop an automated, high-throughput method for untargeted urinary metabolomics.
• To establish a reliable urine specific gravity (SG) measurement for normalization in 96-well plates.
• To validate an automated LC-MS workflow for population-based metabolomic studies.

Main Methods:

• Developed a 96-well plate method to measure urine SG using a refractive index detector (RID).
• Implemented an automated LC-MS workflow with HILIC chromatography (positive/negative ionization) and data-independent acquisition (DIA).
• Utilized technical internal standards (tISs) for data quality monitoring and cubic spline regression for signal drift correction.

Main Results:

• The RID-based SG measurement showed high accuracy (85-115%) and precision (<3.4%).
• Automated workflow demonstrated good data quality with low coefficients of variation (CVs) in both small and large cohorts (CV_QC < 5%, CV_samples < 16% after correction).
• Successfully identified >540 urinary metabolites, including endogenous and exogenous compounds.

Conclusions:

• The developed automated workflow and SG normalization method are suitable for large-scale urinary metabolomic epidemiology.
• This platform facilitates population-based molecular phenotyping by enabling high-throughput, standardized urine analysis.
• The method addresses key challenges in urine metabolomics, improving its applicability in epidemiological research.