Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis
Abstract
Highlights What are the main findings? Diagnostic Technologies Demonstrate Excellent Performance: Tandem mass spectrometry (MS/MS) achieved a pooled sensitivity of 99.1% and specificity of 99.8% for newborn screening of inborn errors of metabolism (IEMs) across 54 studies (8.23 million individuals, 35 countries). In comparison, next-generation sequencing (NGS) yielded a diagnostic rate of 42.8% in suspected cases—rising to 58–65% when integrated with multi-omics. Emerging artificial intelligence (AI)-powered tools achieved an area under the curve (AUC) > 0.95 for specific IEMs (e.g., glycogen storage disease type Ia (GSD Ia): 0.955; citrin deficiency: 0.993). IEM Prevalence and Classification Are Well-Defined but Underappreciated: The pooled global IEM prevalence is 50.9 per 100,000 live births (~1 in 1965), with 16 historical milestones identified from Garrod’s 1902 “chemical individuality” concept to 2025 AI-powered diagnostics. Four major classification systems were characterized: pathophysiological, biochemical pathway-based, organelle-based, and Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology, each serving complementary clinical and research purposes. What are the implications of the main findings? Clinical Practice: Tiered, Technology-Integrated Diagnostic Algorithms Are Now Essential: The high performance of MS/MS (99.1% sensitivity) supports its continued role as the cornerstone of universal newborn screening, but the low positive predictive…
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Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 8,234,567 participants or records.
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