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Background: Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU). Methods: To find new biomarkers, we compared the results of untargeted lipidomics and metabolomics in treated adult PKU patients to those of matched controls. Samples (lipidomics in EDTA-plasma (22 PKU and 22 controls) and metabolomics in serum (35 PKU and 20 controls)) were analyzed using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Data were subjected to multivariate (PCA, OPLS-DA) and univariate (Mann–Whitney U test, p < 0.05) analyses. Results: Levels of 33 (of 20,443) lipid features and 56 (of 5885) metabolite features differed statistically between PKU patients and controls. For lipidomics, findings include higher glycerolipids, glycerophospholipids, and sphingolipids species. Significantly lower values were found for sterols and glycerophospholipids species. Seven features had unknown identities. Total triglyceride content was higher. Higher Phe and Phe catabolites, tryptophan derivatives, pantothenic acid, and dipeptides were observed for metabolomics. Ornithine levels were lower. Twenty-six metabolite features were not annotated. Conclusions: This study provides insight into the metabolic phenotype of PKU patients. Additional studies are required to establish whether the observed changes result from PKU itself, diet, and/or an unknown reason.

Two siblings showed increased galactose and galactose-related metabolites in neonatal screening. Diagnostic workup did not reveal abnormalities in any of the known disease-causing enzymes involved in galactose metabolism. Using whole-exome sequencing, we identified a homozygous missense variant in PPA1 encoding the cytosolic pyrophosphatase 1 (PPA1), c.557C>T (p.Thr186Ile). The enzyme activity of PPA1 was determined using a colorimetric assay, and the protein content was visualized via western blotting in skin fibroblasts from one of the affected individuals. The galactolytic activity of the affected fibroblasts was determined by measuring extracellular acidification with a Seahorse XFe96 analyzer. PPA1 activity decreased to 22% of that of controls in the cytosolic fraction of homogenates from patient fibroblasts. PPA1 protein content decreased by 50% according to western blot analysis, indicating a reduced stability of the variant protein. The extracellular acidification rate was reduced in patient fibroblasts when galactose was used as a substrate. Untargeted metabolomics of blood samples revealed an elevation of other metabolites related to pyrophosphate metabolism. Besides hyperbilirubinemia in the neonatal period in one child, both children were clinically unremarkable at the ages of 3 and 14 years, respectively. We hypothesize that the observed metabolic derangement is a possible mild manifestation of PPA1 deficiency. Unresolved abnormalities in galactosemia screening might result in the identification of more individuals with PPA1 deficiency, a newly discovered inborn metabolic disorder (IMD).

The Wilson and Jungner (W&J) and Andermann criteria are meant to help select diseases eligible for population-based screening. With the introduction of next-generation sequencing (NGS) methods for newborn screening (NBS), more inherited metabolic diseases (IMDs) can technically be included, and a revision of the criteria was attempted. This study aimed to formulate statements and investigate whether those statements could elaborate on the criterion of treatability for IMDs to decide on eligibility for NBS. An online Delphi study was started among a panel of Dutch IMD experts (EPs). EPs evaluated, amended, and approved statements on treatability that were subsequently applied to 10 IMDs. After two rounds of Delphi, consensus was reached on 10 statements. Application of these statements selected 5 out of 10 IMDs proposed for this study as eligible for NBS, including 3 IMDs in the current Dutch NBS. The statement: ‘The expected benefit/burden ratio of early treatment is positive and results in a significant health outcome’ contributed most to decision-making. Our Delphi study resulted in 10 statements that can help to decide on eligibility for inclusion in NBS based on treatability, also showing that other criteria could be handled in a comparable way. Validation of the statements is required before these can be applied as guidance to authorities.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.