Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine

Objectives: The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used. Methods: Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data. Results: The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r(2)>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs. Conclusions: The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Automated summary

In this study, a LC-MS/MS method was developed for the analysis of organic acids, acylcarnitines, and acylglycines in urine. The method simplified the sample preparation process and achieved accurate and reliable measurement of 146 biomarkers. The method has been successfully applied to the diagnosis of metabolic disorders in over 800 urine samples.