Fully Automated Serum LC-MS/MS Platform and Pediatric Reference Intervals for Organic Acids, Amino Acids, and Acylcarnitines in Children (Ages 0–6 Years): Toward Quantitative Diagnosis of Inborn Errors of Metabolism

TL;DR

A new automated blood test platform can detect metabolic disorders in children by measuring multiple metabolites at once, improving diagnosis speed and accuracy.

Contribution

A fully automated serum-based LC–MS/MS platform with pediatric reference intervals for integrated metabolic profiling in children.

Findings

The platform simultaneously quantifies 54 metabolites with high precision and accuracy.

Pediatric reference intervals enabled effective interpretation of metabolic abnormalities in IEM patients.

Z-score models successfully distinguished major IEM categories like organic acidemias and fatty acid oxidation disorders.

Abstract

Background/Objectives: Conventional diagnosis of inborn errors of metabolism (IEMs) requires multiple specimen types—urine organic acids, plasma amino acids, and serum acylcarnitines—analyzed on distinct analytical platforms. This multi-assay approach is labor-intensive and limits timely clinical decision making. We aimed to develop a fully automated serum-based LC–MS/MS platform for integrated quantitative metabolite profiling and to establish pediatric reference intervals (RIs) to support diagnostic interpretation. Methods: A fully automated LC–MS/MS system integrated with the CLAM-2030 automated pretreatment module was developed to enable simultaneous quantification of 25 organic acids, 8 amino acids, and 21 acylcarnitines. Analytical performance was assessed for linearity, limits of detection and quantification, precision and accuracy. Serum samples from 296 non-IEM children aged 0–6 years were analyzed to establish pediatric RIs using Box–Cox transformation and Gaussian modeling. Clinical utility was evaluated in sera from 89 patients diagnosed with IEM using z-score-based logistic regression models. Results: The method demonstrated excellent performance, with linearity (r2 > 0.99) across calibration ranges, limits of detection and quantification defined by signal-to-noise ratios > 3 and >10, and intra- and inter-assay precision < 15% CV for all 54 analytes. Twenty-one analytes met the acceptance criterion of ±20% accuracy at all quality-control levels. Pediatric RIs provided a quantitative framework for interpreting the metabolic abnormalities. In IEM patients, disease-specific metabolites were consistently outside the established ranges, and z-score-based logistic regression models successfully distinguished major IEM categories, including organic acidemias and long-chain fatty acid oxidation disorders. Conclusions: This fully automated, serum-based LC–MS/MS platform provides a clinically practical and quantitative framework for integrated metabolic profiling using pediatric RIs, supporting diagnosis and monitoring of IEMs in pediatric settings.

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