Endocrinology, Diabetes & Metabolism · 13 authors, 3 centres
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This retrospective cohort study analyzed PAH gene variants in 826 patients with hyperphenylalaninemia from a single Italian center, identifying 166 unique variants and assessing genotype-phenotype predictions using the APV/GPV system. The main finding is that the predicted phenotype matched the actual phenotype in 85% of cases, but significant discrepancies exist for specific variants, highlighting limitations of current predictive models for personalized treatment.
This study provides a comprehensive genetic overview of hyperphenylalaninemia (HPA) in one of Europe's largest patient cohorts. Researchers retrospectively analyzed 826 patients followed at a single Italian center over 60 years, focusing on PAH gene mutations and their correlation with clinical phenotype (classic PKU, mild PKU, or mild HPA) as defined by pre-treatment blood phenylalanine levels. Methods involved molecular analysis using evolving techniques (DGGE, Sanger, NGS) and phenotype prediction via the APV/GPV system.
Key results included the identification of 166 different PAH variants, with p.Ala403Val being the most frequent (9.81% of alleles). Seven previously undescribed pathogenic variants were found. The GPV-based prediction matched the actual phenotype in 85% of cases (638/748). However, significant discrepancies were observed, particularly for patients with GPV ≤2.7 (expected cPKU), where 24% presented with milder forms. Specific variants like p.Leu48Ser and p.Tyr414Cys were frequently associated with phenotypes milder than predicted.
Limitations include the retrospective nature of the study, the focus on a single ethnic population (predominantly Caucasian), and the acknowledged biological complexity of PAH function (e.g., multimeric structure, allosteric regulation) that current predictive models may oversimplify. The study did not account for factors like interallelic complementation or epigenetic influences.
Implications are that while the APV/GPV system is a useful predictive tool, it is not definitive. The findings support the need for clinical management based on individual metabolic data rather than genotype prediction alone and highlight the potential for personalized medicine as understanding of variant-specific effects improves.
