The new frontier of newborn screening: integrating genomics, metabolomics, and data science for inborn errors of metabolism

Newborn screening (NBS) for inborn errors of metabolism (IEMs) represents one of the most successful public health initiatives of the past century. Originating with the simple bacterial inhibition assay for phenylketonuria, the field was revolutionized by tandem mass spectrometry (MS/MS). Today, we stand at the precipice of a second, more profound revolution driven by the convergence of high-throughput genomics, next-generation metabolomics, and advanced data science. This review articulates this paradigm shift, detailing how MS/MS is being augmented by targeted DNA analysis and how whole genome sequencing and untargeted metabolomics, integrated with artificial intelligence, are expanding the detectable universe of disorders and enabling precision interventions. However, this powerful multi-omic approach raises critical challenges: incomplete penetrance, overdiagnosis, economic burden, psychosocial impacts, algorithm biases, and the genomic data gap that exacerbates health disparities. We critically examine the practical limitations of AI, the lack of evidence for cost-effectiveness, the slow pace of database updates, and the need for short-term reanalysis systems to “rescue” cases left undiagnosed. Global policy variations and data-sharing governance frameworks are also addressed. We conclude that the future of NBS lies not in sequential technology replacement, but in the responsible creation of a sophisticated, multi-omic biomedical information system to transform NBS into a foundational pillar of precision pediatric medicine. Not applicable.