A review of multiple diagnostic approaches in the undiagnosed diseases network to identify inherited metabolic diseases

Background: The number of known inherited metabolic diseases (IMDs) has been expanding, and the rate of diagnosis is improving with the development of innovative approaches including next generation sequencing (NGS). However, a substantial proportion of IMDs remain undetected by traditional diagnostic approaches. We aim to highlight the spectrum of IMDs diagnosed by the Undiagnosed Diseases Network (UDN) and to learn from the UDN diagnostic processes that were able to detect IMDs.

Methods: We conducted a retrospective analysis of 757 UDN participants diagnosed from 2015 until 2023 using the cohort database, which were divided into a cohort with IMDs (n = 194; 27%) and a cohort whose phenotypes were not explained by an IMD (n = 563; 73%), based on the International Classification of Inherited Metabolic Disorders (ICIMD). Then, we divided the causes of the metabolic 194 diagnoses into seven groups that included all the ICIMD categories. We inspected which clinical and laboratory approaches contributed to a final UDN diagnosis. We also present a UDN case example from each group to highlight the diagnostic yields that resulted from combining newer diagnostic approaches in the UDN and illustrate potential pitfalls of current NGS methods.

Results: These 194 cases of IMDs included examples from 21/25 (84%) of the ICIMD categories. Of the UDN subjects 164/194 (85%) were diagnosed with IMDs through NGS.

Conclusion: The spectrum of IMDs detected in the UDN cohort is large and growing and appropriate use of newer multiple diagnostic approaches should further increase diagnosis of IMDs that are presently missed by the traditional laboratory screening methods.