In inborn errors of intermediate protein metabolism (IEM), the effect of special low-protein foods (SLPFs) on dietary intake has been scarcely studied. The aim of this study was to compare the nutritional profile of SLPFs with usual foods and to assess whether their intake determines the dietary pattern and affects the plasma biochemical profile in children with IEMs with different protein restrictions. A database with the nutritional composition of 250 SLPFs was created. A total of 59 children with IEMs were included in this cross-sectional observational study. The greatest significant differences in macronutrient composition were observed between dairy, meat, fish, and egg SLPFs and regular foods. After stratifying subjects by SLPFs, the participants with the highest intake (>32%) had a higher total energy intake and lower intake of natural protein than those in the lowest tertile (<24%) (p < 0.05). However, when stratifying subjects by dairy SLPF intake, children in the highest tertile (>5%) showed a higher intake of sugars, total and saturated fats, and higher plasma levels of total and low-density lipoprotein cholesterol than those in the first tertile (<1%) (p < 0.05). The variability in the nutritional composition of SLPFs highlights the need for up-to-date databases which would greatly assist in optimizing individualized recommendations for children with IEMs and protein restrictions.

Background: Phenylketonuria (PKU) is a rare metabolic disorder caused by a deficiency in the enzyme phenylalanine hydroxylase, leading to the accumulation of phenylalanine (Phe). Raised Phe levels can result in neurocognitive deficits, intellectual disabilities, and behavioral or psychiatric disorders. Aim of review: To conduct a systematic review of human studies on metabolites identified through metabolomics in individuals with PKU, compared to healthy controls, and to provide insights into their biological significance. Key scientific concepts of review: A total of 26 human studies analyzing metabolites in urine and blood met the inclusion criteria. In total, 544 metabolites that differed between patients with PKU and healthy controls were identified through different metabolomic techniques (LC-MS, GC-MS, NMR). Differences were primarily observed in blood samples, which accounted for 95% of the total metabolites, with only 5% detected in urine samples, reflecting the limited use of this body fluid in only five studies. We found 60% of blood metabolites upregulated in patients with PKU, including Phe, Phe-related metabolites, lipids, and other amino acids, while tryptophan and kynurenine, among others, were downregulated (40%). Additionally, 35 metabolites (6% of the total) exhibited inconsistent directions of change (both up- and downregulated), including amino acids, carnitine derivatives, and lipids. These findings may be attributed to clinical factors (dietary adherence, supplementation, and treatment) and methodological differences in blood-derived matrices. Consequently, the high heterogeneity across studies, biological matrices and analytical platforms represents limitations for establishing a unique metabolomic signature. Overall, these results emphasize the metabolic complexity of PKU and highlight the potential of metabolomics to advance disease monitoring and management.

Phenylketonuria (PKU) is a rare inherited metabolic disorder caused by phenylalanine hydroxylase deficiency, leading to phenylalanine (Phe) accumulation and neurological dysfunction if untreated. While metabolomics holds promise for biomarker discovery in PKU, few studies have examined urinary metabolites using untargeted approaches. This study applied untargeted metabolomics using HPLC-QTOF-MS to analyze urine from 36 adult patients with PKU and 34 healthy controls. Biomarker Analysis was performed with MetaboAnalyst 6.0. A total of 73 significant metabolites (FDR < 0.05; VIP > 1) were identified, with 29 upregulated and 44 downregulated in PKU. A 23% of these metabolites were related to Phe metabolism, while 77% were associated with alterations across more than 10 metabolic pathways, including leucine and tryptophan metabolism, acylcarnitines, vitamins, and diet- or microbiota-derived compounds, among others. Specifically, upregulated metabolites with an AUC > 0.9 included several Phe-derived compounds, the nucleoside 8-hydroxy-7-methylguanine, and indole compounds (1H-indole-3-carboxaldehyde). Conversely, downregulated metabolites with an AUC > 0.9 included N-acetyl(iso)leucine and a heptenoylcarnitine isomer. The Random Forest-based model demonstrated enhanced predictive performance when integrating 10 metabolites, supporting their potential utility as biomarkers for PKU. These findings improve the biological understanding of metabolic disturbances beyond Phe, and may support the development of new therapeutic and dietary strategies.

Dietary interventions are essential for managing phenylketonuria (PKU) and may influence metabolic regulation beyond phenylalanine control. We characterized the urinary metabolomic fingerprint of pediatric participants (n=82) recruited into clinical phenotypes: PKU, PKU with response to tetrahydrobiopterin (BH4), and hyperphenylalaninemia (HPA), as well as sex- and age-matched healthy children controls. Untargeted metabolomics (HPLC-Q-TOF-MS/MS) revealed 59 discriminant metabolites across multiple biochemical pathways, including phenylalanine, tryptophan, and caffeine metabolisms, as well as metabolites related to dietary exposure or gut microbial metabolism. Distinct urinary signatures were described across phenotypes. Phenylalanine-related pathways predominated in PKU, accompanied by increased excretion of vitamin derivatives, consistent with protein substitute supplementation. In contrast, reduced levels of non-phenylalanine amino acid derivatives, methylhistidines, creatine, and branched-chain amino acid-related metabolites were observed in PKU, suggesting alterations in muscle metabolism or natural protein intake. Microbiota-derived metabolites were also less represented in PKU, indicating potential effects of dietary restrictions on gut-host metabolic interactions. HPA individuals showed a urinary fingerprint closer to controls, whereas the BH4 subgroup exhibited the greatest metabolic heterogeneity, reflecting variability in dietary and pharmacological treatment responses. These findings reveal metabolic diversity within the pediatric PKU spectrum driven by clinical phenotype and nutritional management. Urinary metabolomics may support more precise monitoring of metabolic health status and guide precision nutrition strategies in PKU and HPA from early life.