High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a non-invasive biofluid that is rich in polar metabolites and well-suited for metabolomic epidemiology. However, due to individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted LC-MS metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity – SG), most are manual and therefore not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014-0.0011) relative to a hand held refractometer. Using this RID-based SG normalization, we developed an automated LC MS workflow for untargeted urinary metabolomics in 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data independent acquisition (DIA) mode at 3 collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) <10% in the quality controls (QCs) and <20% in the samples for a small cohort (n=87 samples, n=22 QCs). Application in a large cohort (n=842 urine samples, n=248 QCs), demonstrated CVQC<5% and CVsamples<16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.