FAST GCMS DETERMINATION OF URINARY ORGANIC ACIDS FOR DIAGNOSIS OF METABOLISM INBORN ERRORS

Significance of the Topic

The analysis of urinary organic acids plays a critical role in diagnosing inborn errors of metabolism. Early detection of abnormal metabolite accumulation can guide timely therapeutic interventions and prevent irreversible damage. Conventional GC–MS methods, while reliable, often suffer from long runtimes and limited daily throughput, impeding large-scale screening in busy clinical laboratories.

Objectives and Study Overview

This study aimed to develop and validate a fast GC–MS approach using the Shimadzu GCMS-QP2010 system to accelerate the analysis of a broad panel of urinary organic acids. The goal was to reduce cycle times by a factor of three to four without sacrificing chromatographic resolution or quantitative performance for routine metabolic screening.

Methodology and Instrumentation Used

Sample preparation involved addition of internal standards (tridecanoic acid trimethylester and margaric acid), followed by extraction and trimethylsilylation derivatization. Key instrumental parameters:

• Instrument: Shimadzu GCMS-QP2010 with AOC-20i autosampler
• Column: Supelco SLB-5 ms, 15 m × 0.1 mm × 0.1 µm
• Injection: Split mode (1/100), inlet at 280 °C
• Oven program: 100 °C for 1.6 min, ramp at 14 °C/min to 280 °C, hold 3 min
• MS conditions: EI mode, ion source 200 °C, interface 280 °C, scan m/z 50–500 at 0.2 s/event

Main Results and Discussion

Comparison of conventional and fast GC–MS revealed a three- to fourfold decrease in analysis time. Despite the shorter run, critical analytes such as 3-hydroxy-3-methylglutaric and pimelic acids maintained baseline separation, owing to the narrow-bore column. Calibration curves for key metabolites (e.g., glutaric acid) exhibited linearity (R2 = 0.996) across 50–1000 µmol/L. Library searches aided identification of over 135 organic acids. Incorporation of Linear Retention Index (LRI) matching drastically reduced false hits, improving confidence in automated screening.

Benefits and Practical Applications

• High throughput: Enables processing of more samples per day, supporting large-scale newborn or clinical screening programs.
• Robust quantitation: Accurate internal standard calibration ensures reliable results for critical organic acids.
• Enhanced identification: AART and LRI functions streamline data review and reduce manual confirmation steps.
• Clinical impact: Faster turnaround time accelerates diagnosis and treatment decisions in metabolic disorders.

Future Trends and Applications

Advancements may include integration with high-resolution mass spectrometry for expanded analyte coverage, automated sample preparation robotics for minimal hands-on time, and enhanced software algorithms leveraging machine learning for pattern recognition. Growing compound libraries and retention index databases will further refine identification confidence.

Conclusion

The fast GC–MS method on the Shimadzu GCMS-QP2010 platform delivers a rapid, reliable workflow for urinary organic acid analysis, tripling to quadrupling laboratory throughput. By preserving chromatographic performance and leveraging advanced data processing, this approach offers a practical solution for clinical and research laboratories focused on metabolic disorder screening.