Screened at Birth
A new metabolomic profiling approach improves diagnosis of inborn errors of metabolism
Olivia Gaskill | | Quick Read
In the US, every newborn baby is screened at birth for rare (but serious) disorders; however, screening for inborn errors of metabolism (IEM) is not covered in standard panels or first-line biochemical testing. “Clinical genomic sequencing was becoming the norm, and I suspected that inborn errors were more common and not readily identified with current approaches,” says Sarah Elsea, Professor at Baylor College of Medicine. “Genomic testing was identifying patients that should be diagnosed with metabolic screening, but the right tests were not ordered (or not available). It was clear that metabolic testing and screening were not keeping up with genomic screening, which delays diagnosis.”
Recognizing the need for improved diagnostics, Elsea’s team investigated whether untargeted metabolomic profiling was associated with increased diagnostic rates of IEMs compared with traditional metabolic screening approaches (1). “Our data show that some conditions are much more common than originally thought and that the phenotype spectrum can be quite broad,” says Elsea. “We knew the overall diagnostic rate would be low, but we were somewhat surprised at the discrepancy between the traditional targeted testing and the global metabolomic approach. However, we also knew that serial testing, typically done for patients who tested negative using traditional approaches, delayed diagnosis.”
So what’s the next step? Elsea says, “New goals emerged from the analysis of our diagnostic testing data. Several conditions are clear candidates for newborn screening and we will be focusing on these genetic conditions – as well as targeting additional disorders – to identify disease-specific biomarkers and assess the phenotypic spectrum.”
But the team won’t stop there. “We will also use this testing approach to monitor treatment and management of individuals with IEMs, and we are investigating other types of biological samples that may be required for diagnosis and monitoring of some conditions – as well as correlation of metabolomics data to genomic variants to improve interpretation of variants of uncertain significance,” Elsea says. “We only know what we know. If we do not look beyond those limits and assess where we are, then we will never discover anything new, and we will not improve diagnosis, treatment, or quality of life.”
- N Liu et al., JAMA Netw Open, 4, e2114155 (2021). PMID: 34251446.