DefinePK

Abstract

Background: Inherited metabolic disorders (IMDs) represent a diverse group of genetic conditions affecting essential metabolic pathways. Next-generation sequencing (NGS) technologies have emerged as powerful diagnostic tools for these disorders, yet their comparative effectiveness, implementation challenges, and clinical utility remain incompletely characterized.
Methods: We conducted a systematic review of studies utilizing NGS technologies (targeted panels, whole exome sequencing, whole genome sequencing) for diagnosing IMDs published between January 2010 and October 2024. Data extraction focused on diagnostic yield, technology performance metrics, novel genetic findings, clinical impact, and cost-effectiveness. Study quality was assessed using a modified QUADAS-2 tool.
Results: Eighty-seven studies comprising 4,328 patients were included. The overall diagnostic yield was 46.3% (95% CI: 42.1-50.5%), with significant variation across technologies: targeted panels (41.2%), WES (48.7%), WGS (57.9%), and combined approaches (61.0%). Yield varied by IMD category, with highest rates in amino acid metabolism disorders (58.2%) and glycogen storage disorders (56.7%). WGS demonstrated superior sensitivity for structural variants (85.2%) and non-coding regions (83.4%) compared to WES (68.3%, 7.6%) and targeted panels (42.5%, 12.3%). NGS diagnostics led to management changes in 37.2% of diagnosed cases, specific treatment initiation in 28.5%, and avoidance of unnecessary procedures in 22.3%. Cost-effectiveness analysis revealed targeted panels as most economical for well-defined disorders (ICER: $42,650/QALY), while WGS showed value in complex, previously undiagnosed cases. Meta-regression identified early age at testing (OR 1.84), consanguinity (OR 2.36), and prior biochemical evidence (OR 3.12) as predictors of diagnostic success.
Conclusions: NGS technologies significantly improve diagnostic yield in IMDs compared to conventional approaches, with substantial clinical impact. Technology selection should be guided by disorder characteristics, phenotypic specificity, and resource considerations. Implementation challenges include variant interpretation, bioinformatic standardization, and accessibility in resource-limited settings. Integration with other omics technologies and emerging sequencing methods hold promise for further enhancing diagnostic capabilities for IMDs.