Early diagnosis of inborn errors of metabolism (IEM)—a large group of congenital disorders—is critical, given that many respond well to targeted therapy. Newborn screening programs successfully capture a proportion of patients enabling early recognition and prompt initiation of therapy. For others, the heterogeneity in clinical presentation often confuses diagnosis with more common conditions. In the absence of family history and following clinical suspicion, the laboratory diagnosis typically begins with broad screening tests to circumscribe specialised metabolite and/or enzyme assays to identify the specific IEM. Confirmation of the biochemical diagnosis is usually achieved by identifying pathogenic genetic variants that will also enable cascade testing for family members. Unsurprisingly, this diagnostic trajectory is too often a protracted and lengthy process resulting in delays in diagnosis and, importantly, therapeutic intervention for these rare conditions is also postponed. Implementation of mass spectrometry technologies coupled with the expanding field of metabolomics is changing the landscape of diagnosing IEM as numerous metabolites, as well as enzymes, can now be measured collectively on a single mass spectrometry-based platform. As the biochemical consequences of impaired metabolism continue to be elucidated, the measurement of secondary metabolites common across groups of IEM will facilitate algorithms to further increase the efficiency of diagnosis.

Preterm birth remains a leading cause of newborn death, disability, and long-term health challenges globally, with a substantial impact on families, the health system, and society. Omega-3 supplementation in women with low omega-3 status in early pregnancy has been shown to reduce the risk of preterm birth, particularly early preterm birth (<34 weeks of gestation). The cost implications of incorporating omega-3 testing and targeted supplementation into routine antenatal care to reduce early preterm birth in Australia are unclear. A decision analytic model was developed using Australian epidemiological, clinical, and cost data to compare routine omega-3 testing and targeted supplementation with current practice. The model predicted the number of early preterm births prevented and associated cost savings over an 18-year time horizon. Deterministic sensitivity analyses were conducted to examine uncertainty in key model inputs. Among 289,195 singleton pregnancies in 2022, an estimated 17.5% of women would have low omega-3 status and be eligible for supplementation. Making conservative assumptions, the model predicted that, relative to current practice, omega-3 testing and supplementation would be a dominant strategy, preventing 640 early preterm births, with projected savings of $26.1 million in direct healthcare costs. Sensitivity analyses indicated that the results were robust across most scenarios and identified the treatment effect estimate as the strongest driver of model predictions. Omega-3 testing in early pregnancy with targeted supplementation may offer a scalable and cost-effective strategy to reduce early preterm birth with potential benefits for mothers, infants, and the health system. The modelling suggests meaningful health and economic benefits. Cost savings were robust across most plausible scenarios, supporting the potential value of this approach on a national scale.