Pushing the Boundaries of Biomarker Discovery in Phenylketonuria: Metabolomic Profiling Reveals Novel Biomarkers and Their Associations with Phenylalanine.

BACKGROUND

Phenylketonuria (PKU) is a rare genetic disorder caused by mutations in the phenylalanine hydroxylase (PAH) gene, resulting in a deficiency of the enzyme responsible for metabolizing phenylalanine (Phe) and its accumulation. PKU can be identified through newborn screening (NBS) or genetic sequencing; however, both approaches have limitations, including high false-discovery rates and variants of uncertain significance (VUS). This study aims to identify a PKU metabolomic profile using unique biomarkers to enhance early diagnosis and improve treatment outcomes.

METHODS

Dried blood spot (DBS) samples from 65 patients diagnosed with PKU and matched healthy controls were collected through the NBS program. An untargeted metabolomics analysis was conducted using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to profile metabolites and investigate altered metabolic pathways in patients with PKU.

RESULTS

A total of 418 significantly dysregulated metabolites were identified in PKU patients. Among them, 90 metabolites were identified as endogenous human metabolites. The most significantly affected pathways were those related to the metabolism of aromatic amino acids and polysaccharides. Moreover, lipid metabolic pathways were dysregulated, including those involved in fatty acid and phospholipid biosynthesis. In addition to phenylalanine (AUC = 0.994), 1,11-Undecanedicarboxylic acid (UDCA) (AUC = 0.969) was significantly elevated in patients with PKU, suggesting it is a promising potential biomarker for PKU.

CONCLUSIONS

Untargeted metabolomics revealed distinct metabolic alterations in patients with PKU, providing insights into disease pathophysiology. The identification of UDCA as a consistently elevated metabolite supports its potential utility as a supplementary biomarker for PKU diagnosis and monitoring. Further validation in larger cohorts, using a targeted metabolomics approach, is warranted.