OSCA-finder: Redefining the assay of kidney disease diagnostic through metabolomics and deep learning

Abstract

Liquid chromatography-mass spectrometry (LC-MS) is a platform for urine and blood sample analysis. However, the high variability in the urine sample reduced the confidence of metabolite identification. Therefore, pre and post-calibration operations are inevitable to ensure an accurate urine biomarker analysis. In this study, the phenomenon of a higher creatinine concentration variable in ureteropelvic junction obstruction (UPJO) patient urine samples than in healthy people was revealed, indicating the urine biomarker discovery of UPJO patients is not adapted to the creatinine calibrate strategy. Therefore, we proposed a pipeline “OSCA-Finder” to reshape the urine biomarker analysis. First, to ensure a more stable peak shape and total ion chromatography, we applied the product of osmotic pressure and injection volume as a calibration principle and integrated it with an online mixer dilution. Therefore, we obtained the most peaks and identified more metabolites in a urine sample with peak area group CV<30%. A data-enhanced strategy was applied to reduce the overfit while training a neural network binary classifier with an accuracy of 99.9%. Finally, seven accurate urine biomarkers combined with a binary classifier were applied to distinguish UPJO patients from healthy people. The results show that the UPJO diagnostic strategy based on urine osmotic pressure calibration has more potential than ordinary strategies.