Newborn screening (NBS) is one of the United States' largest, most successful preventative public health initiatives, improving outcomes for newborns with inborn errors of metabolism. Most disorders on the Recommended Uniform Screening Panel are screened using triple-quadrupole mass spectrometry and flow injection analysis. While these methods are sensitive and well suited for high-throughput quantitative applications, the breadth of measured analytes is limited to a relatively small number of biomarkers, which often have considerable overlaps between healthy and diseased populations. High-resolution liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is now capable of profiling thousands of metabolites, making it well suited for exploratory and biomarker discovery studies. To this end, we developed a robust workflow for performing nontargeted LC-MS analysis on dried bloodspot (DBS) specimens with coverage across many metabolic pathways relevant to NBS. HILIC chromatography enabled quantitation of amino acid and acylcarnitine species while also retaining lipid species, such as lyso-phosphatidylcholines. We analyzed 810 newborn-derived DBS samples across a wide range of newborn birthweights, identifying correlations with metabolites that help to better account for the lower accuracy observed for some NBS markers (e.g., isovalerylcarnitine). Additionally, we leveraged this nontargeted workflow to capture new biomarkers and metabolic phenotypes in newborns associated with parenteral nutrition administration and maternal nicotine exposure. Two critical biomarkers were identified as useful additions to targeted screening panels: N-acetyltyrosine as a qualitative marker for parenteral nutrition administration and N-acetylputrescine as a quantitative marker for controlling birthweight variability.

Persistent splenomegaly, often an incidental finding, can originate from a number of inherited metabolic disorders (IMDs). Variants of APOE are primarily known as risk factors in terms of cardiovascular disease; however, severe dysfunction of APOE can result in a disease phenotype with considerable overlap with lysosomal storage disorders (LSDs), including splenomegaly and gross elevation of N-palmitoyl-O-phosphocholine-serine (PPCS).

A case study (deep phenotyping, genetic and FACS analysis) and literature study was conducted.

The index patient, with a family history of early-onset cardiovascular disease, presented with splenic infarctions in a grossly enlarged spleen. The identified genetic cause was homozygosity for two APOE variants (c.604C>T, p.(Arg202Cys) and c.512G>A, p.(Gly171Asp); ε1/ε1), resulting in a macrophage storage phenotype resembling an LSD that was also present in the brother of the index patient. A FACS analysis of the circulating monocytes showed increased lipid content and the expression of activation markers (CD11b, CCR2, CD36). This activated state enhances lipoprotein intake, which eventually converts these monocytes/macrophages into foam cells, accumulating in tissues (e.g., spleen and vascular wall). A literature search identified seven individuals with splenomegaly caused by APOE variants (deletion of leucine at position 167). The combined data from all patients identified male gender, splenectomy and obesity as potential modifiers determining the severity of the phenotype (i.e., degree of triglyceride increase in plasma and/or spleen size). Symptoms are (partially) reversible by lipid-lowering medication and energy restricted diets and splenectomy is contra-indicated.

Inherited dyslipidemic splenomegaly caused by disruptive APOE variants should be included in the differential diagnoses of unexplained splenomegaly with abnormal lipid profiles. A plasma lipid profile consistent with dysbetalipoproteinemia is a diagnostic biomarker for this IMD.

Levocarnitine is essential for brain functioning and fatty acid metabolism and stems largely from dietary sources. The Epsilon-Trimethyllysine Hydroxylase (TMLHE) gene encodes the enzyme N-Trimethyllysine hydroxylase (TMLH) which catalyses the first step in the biosynthesis of carnitine. Lack of TMLH enzyme activity is associated with developmental delay and autistic behaviours described as X-linked recessive autism, type 6 (OMIM#300872).

Here, an institutionalized adult male patient with intellectual disability, autism, and challenging behaviours is presented in whom genetic analysis disclosed a novel pathogenic variant in the TMLHE gene. Extensive somatic, neurological, psychiatric, and neuropsychological investigations were performed next to examination of hematological and biochemical parameters including plasma carnitine status. Also, Whole Exome Sequencing (WES) and Next-Generation Metabolic Screening (NGMS) were performed.

Moderate intellectual disability along with obsessive and aggressive behaviour in the context of autism spectrum disorders was established as well as symptoms from the catatonic spectrum. With WES, a novel variant in the TMHLE gene was identified and using NGMS, increased concentration of trimethyllysine and decreased concentration of γ-butyrobetaine were found resulting in a significantly decreased BB/TML ratio, confirming the pathogenicity of this variant.

X-linked autism type 6 is characterized by moderate intellectual disability and symptoms from the autism spectrum in the absence of any dysmorphisms. To prevent regressive autistic episodes in young children, it is highly recommended to consider next-generation sequencing techniques as the first step in the differential diagnostic process of autism.

Metabolomics can be used for a multitude of purposes, including monitoring of treatment effects and for increasing the knowledge of the pathophysiology of a wide range of diseases. Global (commonly referred to as "untargeted") metabolomics is hypothesis-generating and provides the opportunity to discover new biomarkers. Being versatile and having a high degree of selectivity and sensitivity, liquid chromatography-mass spectrometry (LC-MS) is the most common technique applied for metabolomics. We here present our global metabolomics LC-electrospray ionization-MS/MS method. The sample preparation procedures for plasma, serum, dried blood spots, urine, and cerebrospinal fluid are simple and nonspecific to reduce the risk of analyte loss. The method is based on reversed-phase chromatography using a diphenyl column. The high-resolution Q Exactive Orbitrap MS with data-dependent acquisition provides MS/MS spectra of a wide range of analytes. Our method covers a large part of the metabolome regarding hydrophobicity and compound class.

Inborn errors of metabolism constitute a set of hereditary diseases that impose severe medical and physical challenges in the affected individual, in particular, for the pediatric patient population. Timely diagnosis is crucial for these patients, as any delay could result in irreversible health damage, underscoring the importance of early initiation of personalized treatment. Current routine diagnostic screening for inborn errors of metabolism relies on various targeted analyses of established biomarkers. However, this approach is time-consuming, focuses on a limited number of tests (based on clinical information) with a relatively small number of biomarkers, and does not facilitate the identification of new markers. In contrast, untargeted metabolomics-based screening offers a more efficient diagnostic solution, by assessing thousands of metabolites across multiple metabolic pathways in a single test. This not only saves time but also conserves resources for clinicians, the diagnostic laboratory, and for patients.This chapter describes the computational workflow of our "Next Generation Metabolic Screening" approach, which is a metabolomics-based method that is currently applied at the Translational Metabolic Laboratory of the Radboud University Medical Center (the Netherlands) for the diagnosis of inborn errors of metabolism.

Childhood-onset mitochondrial disorders are rare genetic diseases that often manifest with neurological impairment due to altered mitochondrial structure or function. To date, pathogenic variants in 373 genes across the nuclear and mitochondrial genomes have been linked to mitochondrial disease, but the ensuing genetic and clinical complexity of these disorders poses considerable challenges to their diagnosis and management. Nevertheless, despite the current lack of curative treatment, recent advances in next generation sequencing and -omics technologies have laid the foundation for precision mitochondrial medicine through enhanced diagnostic accuracy and greater insight into pathomechanisms. This holds promise for the development of targeted treatments in this group of patients. Against a backdrop of inherent challenges and recent technological advances in mitochondrial medicine, this review discusses the current diagnostic approach to a child with suspected mitochondrial disease and outlines management considerations of particular relevance to paediatric neurologists. We highlight the importance of mitochondrial expertise centres in providing the laboratory infrastructure needed to supplement uninformative first line genomic testing with focused and/or further unbiased investigations where needed, as well as coordinating an integrated multidisciplinary model of care that is paramount to the management of patients affected by these conditions.

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therapeutic options, making it challenging to remain up to date. A centralized therapy knowledgebase is needed for early diagnosis and targeted treatment. This study aimed to identify all treatable IEMs through a scoping literature review, followed by data extraction and analysis according to the Treatabolome principles. Knowledge of treatable IEMs, therapeutic categories, efficacy, and evidence was integrated into the Inborn Errors of Metabolism Knowledgebase (IEMbase), an online database encompassing all IEMs. The study identified 275 treatable IEMs, 18% of all currently known 1564 IEMs, according to the International Classification of Inherited Metabolic Disorders. Disorders of fatty acid and ketone body metabolism had the highest treatability (67%), followed by disorders of vitamin and cofactor metabolism (60%), and disorders of lipoprotein metabolism (42%). The most common treatment strategies were pharmacological therapy (34%), nutritional therapy (34%), and vitamin and trace element supplementation (12%). Treatment effects were most commonly observed in nervous system abnormalities (34%), metabolism/homeostasis abnormalities (33%), and growth (7%). Predominant evidence sources included case reports with evidence levels 4 (48%) and 5 (12%), and individual cohort studies with evidence level 2b (12%). Our study generated the Metabolic Treatabolome 2024. IEMs are the largest group of monogenic disorders amenable to disease-modifying therapy. With drug repurposing efforts and advancements in gene therapies, this number will expand. IEMbase now provides up-to-date, comprehensive information on clinical and biochemical symptoms and therapeutic options, empowering patients, families, healthcare professionals, and researchers in improving patient outcomes.

Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

Huppke-Brendel syndrome (HBS) is an autosomal recessive disorder caused by SLC33A1 mutations, a gene coding for the acetyl-CoA transporter-1 (AT-1). So far it has been described in nine pediatric and one adult patient. Therapeutic trials with copper histidinate failed to achieve any clinical improvement. Here, we describe the clinical characteristics of two novel patients, one of them diagnosed by gene analysis and his sib postmortally based on clinical characteristics. We demonstrate a therapeutic trial with acetylation therapy, consisting of N-acetylcysteine and ketogenic diet, in one of them. We provide biochemical data on N-acetylated amino acids in cerebrospinal fluid (CSF) and plasma before and after starting this treatment regimen. Our results indicate that ketogenic diet and N-acetylcysteine do not seem to normalize the concentrations of N-acetylated amino acids in CSF or plasma. The overall metabolic pattern shows a trend toward lowered levels of N-acetylated amino acids in CSF and to a lesser extent in plasma. Although there are some assumptions, the function of AT-1 is still not clear and further studies are needed to better understand mechanisms underlying this complex disorder.

Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA).

Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring.

The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included.

Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU).

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.

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.

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.

There are many different genetic diseases called inborn errors of metabolism (IEM) which result from defective enzymes in the metabolic pathway. As a result, these defects either cause a harmful accumulation of substances or lead to a lack of certain types of molecule. The present review traces the origin and development of IEMs from Sir Archibald Garrod's theory in the early 20th century to current diagnostic and therapeutic approaches. It also involves a systematic literature review complying with PRISMA which included studies sourced from PubMed, Scopus, Web of Science and Google Scholar. It points out that high rates of consanguinity are associated with high prevalence rates for IEMs especially in the Eastern Mediterranean area. IEMS are classified as energy deficiency disorders, intoxication disorders, and storage disorders. Each category has a variety of clinical manifestations. This study incorporates different diagnostic methods ranging from simple biochemical tests to tandem mass spectrometry and next generation sequencing; while management approaches such as dietary modifications, enzyme replacement therapy and gene therapy were assessed for their efficacy. Specific attention is paid to Pakistan where there exists considerable consanguinity among people coupled with inadequate health care services which have seriously affected delivery of health care services thereby leading to numerous challenges for the country healthcare system during service provision.

Fungicides undergo rapid metabolism and are excreted in the urine. There are few methods for screening these ubiquitous compounds, which have a high potential for human exposure. High-resolution mass spectrometry (HRMS) is a suitable technique to assess fungicide exposures; however, there is a lack of spectral libraries for fungicide annotation and in particular for downstream metabolites. We created spectral libraries for 32 fungicides for suspect screening. Fungicide standards were administered to mice, and 24-h urine was analyzed using hydrophilic interaction and reversed-phase chromatography coupled to hybrid quadrupole-orbitrap mass spectrometry. Suspect metabolite MS2 spectra for library creation were selected based on the ratio of exposed-to-control mouse urine. MS2 libraries were applied to urine collected from female university students (n = 73). Several tetraconazole and tebuconazole metabolites were detected in 3% (2/73) of the samples. The creation of comprehensive suspect screening MS2 libraries is a useful tool to detect fungicide exposure for human biomonitoring.

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Recent studies in PKU patients identified alternative biomarkers in blood using untargeted metabolomics. To test the added clinical value of these novel biomarkers, targeted metabolomics of 11 PKU biomarkers (phenylalanine, glutamyl-phenylalanine, glutamyl-glutamyl-phenylalanine, N-lactoyl-phenylalanine, N-acetyl-phenylalanine, the dipeptides phenylalanyl-phenylalanine and phenylalanyl-leucine, phenylalanine-hexose conjugate, phenyllactate, phenylpyruvate, and phenylacetate) was performed in stored serum samples of the well-defined PKU patient-COBESO cohort and a healthy control group. Serum samples of 35 PKU adults and 20 healthy age- and sex-matched controls were analyzed using ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry. Group differences were tested using the Mann-Whitney U test. Multiple linear regression analyses were performed with these biomarkers as predictors of (neuro-)cognitive functions working memory, sustained attention, inhibitory control, and mental health. Compared to healthy controls, phenylalanine, glutamyl-phenylalanine, N-lactoyl-phenylalanine, N-acetyl-phenylalanine, phenylalanine-hexose conjugate, phenyllactate, phenylpyruvate, and phenylacetate were significant elevated in PKU adults (p < 0.001). The remaining three were below limit of detection in PKU and controls. Both phenylalanine and N-lactoyl-phenylalanine were associated with DSM-VI Attention deficit/hyperactivity (R2 = 0.195, p = 0.039 and R2 = 0.335, p = 0.002, respectively) of the ASR questionnaire. In addition, N-lactoyl-phenylalanine showed significant associations with ASR DSM-VI avoidant personality (R2 = 0.265, p = 0.010), internalizing (R2 = 0.192, p = 0.046) and externalizing problems (R2 = 0.217, p = 0.029) of the ASR questionnaire and multiple aspects of the MS2D and FI tests, reflecting working memory with R2 between 0.178 (p = 0.048) and 0.204 (p = 0.033). Even though the strength of the models was not considered strong, N-lactoyl-phenylalanine outperformed phenylalanine in its association with working memory and mental health outcomes.

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Mitochondria are semi-autonomous organelles present in several copies within most cells in the human body that are controlled by the precise collaboration of mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) encoding mitochondrial proteins. They play important roles in numerous metabolic pathways, such as the synthesis of adenosine triphosphate (ATP), the predominant energy substrate of the cell generated through oxidative phosphorylation (OXPHOS), intracellular calcium homeostasis, metabolite biosynthesis, aging, cell cycles, and so forth. Previous studies revealed that dysfunction of these multi-functional organelles, which may arise due to mutations in either the nuclear or mitochondrial genome, leads to a diverse group of clinically and genetically heterogeneous disorders. These diseases include neurodegenerative and metabolic disorders as well as cardiac and skeletal myopathies in both adults and newborns. The plethora of phenotypes and defects displayed leads to challenges in the diagnosis and treatment of mitochondrial diseases. In this regard, the related literature proposed several diagnostic options, such as high throughput mitochondrial genomics and omics technologies, as well as numerous therapeutic options, such as pharmacological approaches, manipulating the mitochondrial genome, increasing the mitochondria content of the affected cells, and recently mitochondrial diseases transmission prevention. Therefore, the present article attempted to review the latest advances and challenges in diagnostic and therapeutic options for mitochondrial diseases.

Mitapivat is an investigational, oral, small-molecule allosteric activator of pyruvate kinase (PK). PK is a regulatory glycolytic enzyme that is key in providing the red blood cell (RBC) with sufficient amounts of adenosine triphosphate (ATP). In sickle cell disease (SCD), decreased 2,3-DPG levels increase the oxygen affinity of hemoglobin, thereby preventing deoxygenation and polymerization of sickle hemoglobin. The PK activator mitapivat has been shown to decrease levels of 2,3-DPG and increase levels of ATP in RBCs in patients with SCD. In this phase 2, investigator-initiated, open-label study (https://www.clinicaltrialsregister.eu/ NL8517; EudraCT 2019-003438-18), untargeted metabolomics was used to explore the overall metabolic effects of 8-week treatment with mitapivat in the dose-finding period. In total, 1773 unique metabolites were identified in dried blood spots of whole blood from ten patients with SCD and 42 healthy controls (HCs). The metabolic phenotype of patients with SCD revealed alterations in 139/1773 (7.8%) metabolites at baseline when compared to HCs (false discovery rate-adjusted p < 0.05), including increases of (derivatives of) polyamines, purines, and acyl carnitines. Eight-week treatment with mitapivat in nine patients with SCD altered 85/1773 (4.8%) of the total metabolites and 18/139 (12.9%) of the previously identified altered metabolites in SCD (unadjusted p < 0.05). Effects were observed on a broad spectrum of metabolites and were not limited to glycolytic intermediates. Our results show the relevance of metabolic profiling in SCD, not only to unravel potential pathophysiological pathways and biomarkers in multisystem diseases but also to determine the effect of treatment.

Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development of additional tools for the diagnosis/screening of MUSD. Recently, untargeted metabolomics has been used to explore metabolic profiling and discover the potential biomarkers/pathways of inherited metabolic diseases. Thus, we aimed to discover a distinctive metabolic profile and biomarkers/pathways for MSUD newborns using untargeted metabolomics. Herein, untargeted metabolomics was used to analyze dried blood spot (DBS) samples from 22 MSUD and 22 healthy control newborns. Our data identified 210 altered endogenous metabolites in MSUD newborns and new potential MSUD biomarkers, particularly L-alloisoleucine, methionine, and lysoPI. In addition, the most impacted pathways in MSUD newborns were the ascorbate and aldarate pathways and pentose and glucuronate interconversions, suggesting that oxidative and detoxification events may occur in early life. Our approach leads to the identification of new potential biomarkers/pathways that could be used for the early diagnosis/screening of MSUD newborns but require further validation studies. Our untargeted metabolomics findings have undoubtedly added new insights to our understanding of the pathogenicity of MSUD, which helps us select the appropriate early treatments for better health outcomes.

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a rare neurometabolic disorder caused by disruption of the gamma-aminobutyric acid (GABA) pathway. A more detailed understanding of its pathophysiology, beyond the accumulation of GABA and gamma-hydroxybutyric acid (GHB), will increase our understanding of the disease and may support novel therapy development. To this end, we compared biochemical body fluid profiles from SSADHD patients with controls using next-generation metabolic screening (NGMS). Targeted analysis of NGMS data from cerebrospinal fluid (CSF) showed a moderate increase of aspartic acid, glutaric acid, glycolic acid, 4-guanidinobutanoic acid, and 2-hydroxyglutaric acid, and prominent elevations of GHB and 4,5-dihydroxyhexanoic acid (4,5-DHHA) in SSADHD samples. Remarkably, the intensities of 4,5-DHHA and GHB showed a significant positive correlation in control CSF, but not in patient CSF. In an established zebrafish epilepsy model, 4,5-DHHA showed increased mobility that may reflect limited epileptogenesis. Using untargeted metabolomics, we identified 12 features in CSF with high biomarker potential. These had comparable increased fold changes as GHB and 4,5-DHHA. For 10 of these features, a similar increase was found in plasma, urine and/or mouse brain tissue for SSADHD compared to controls. One of these was identified as the novel biomarker 4,5-dihydroxyheptanoic acid. The intensities of selected features in plasma and urine of SSADHD patients positively correlated with the clinical severity score of epilepsy and psychiatric symptoms of those patients, and also showed a high mutual correlation. Our findings provide new insights into the (neuro)metabolic disturbances in SSADHD and give leads for further research concerning SSADHD pathophysiology.

Glutaric aciduria type-1 (GA-1) is a rare metabolic disorder due to glutaryl coenzyme A dehydrogenase deficiency, causing elevated levels of glutaryl-CoA and its derivatives. GA-1 exhibits symptoms like macrocephaly, developmental delays, and movement disorders. Timely diagnosis through genetic testing and newborn screening is crucial. However, in some cases, transiently elevated level of glutarylcarnitine (C5DC) challenges accurate diagnosis, highlighting the need for alternative diagnostic methods, like mass spectrometry-based untargeted metabolomics, to identify additional biomarkers for distinguishing falsely suspected GA-1 from healthy newborns.

DBS samples from falsely suspected GA-1 newborns (n = 47) and matched control were collected through the NBS program. Untargeted metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was performed to enable biomarker and pathway investigations for significantly altered metabolites.

582 and 546 were up- and down-regulated metabolites in transient GA-1. 155 endogenous metabolites displayed significant variations compared to the control group. Furthermore, our data identified novel altered metabolic biomarkers, such as N-palmitoylcysteine, heptacarboxyporphyrin, 3-hydroxylinoleoylcarnitine, and monoacylglyceride (MG) (0:0/20:1/0:0), along with perturbed metabolic pathways like sphingolipid and thiamine metabolism associated with the transient elevated C5DC levels in DBS samples.

A distinct metabolic pattern linked to the transient C5DC elevation in newborns was reported to enhance the prediction of the falsely positive cases, which could help avoiding unnecessary medical treatments and minimizing the financial burdens in the health sector.

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

Mitochondrial disorders are a group of rare and heterogeneous genetic diseases characterized by dysfunctional mitochondria leading to deficient adenosine triphosphate synthesis and chronic energy deficit in patients. The majority of these patients exhibit a wide range of phenotypic manifestations targeting several organ systems, making their clinical diagnosis and management challenging. Bridging translational to clinical research is crucial for improving the early diagnosis and prognosis of these intractable mitochondrial disorders and for discovering novel therapeutic drug candidates and modalities. This review provides the current state of clinical testing in mitochondrial disorders, discusses the challenges and opportunities for converting basic discoveries into clinical settings, explores the most suited patient-centric approaches to harness the extraordinary heterogeneity among patients affected by the same primary mitochondrial disorder, and describes the current outlook of clinical trials.

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

Background: Early diagnosis of inherited metabolic diseases (IMDs) is important because treatment may lead to reduced mortality and improved prognosis. Due to their diversity, it is a challenge to diagnose IMDs in time, effecting an emerging need for a comprehensive test to acquire an overview of metabolite status. Untargeted metabolomics has proven its clinical potential in diagnosing IMDs, but is not yet widely used in genetic metabolic laboratories. Methods: We assessed the potential role of plasma untargeted metabolomics in a clinical diagnostic setting by using direct infusion high resolution mass spectrometry (DI-HRMS) in parallel with traditional targeted metabolite assays. We compared quantitative data and qualitative performance of targeted versus untargeted metabolomics in patients suspected of an IMD (n = 793 samples) referred to our laboratory for 1 year. To compare results of both approaches, the untargeted data was limited to polar metabolites that were analyzed in targeted plasma assays. These include amino acid, (acyl)carnitine and creatine metabolites and are suitable for diagnosing IMDs across many of the disease groups described in the international classification of inherited metabolic disorders (ICIMD). Results: For the majority of metabolites, the concentrations as measured in targeted assays correlated strongly with the semi quantitative Z-scores determined with DI-HRMS. For 64/793 patients, targeted assays showed an abnormal metabolite profile possibly indicative of an IMD. In 55 of these patients, similar aberrations were found with DI-HRMS. The remaining 9 patients showed only marginally increased or decreased metabolite concentrations that, in retrospect, were most likely to be clinically irrelevant. Illustrating its potential, DI-HRMS detected additional patients with aberrant metabolites that were indicative of an IMD not detected by targeted plasma analysis, such as purine and pyrimidine disorders and a carnitine synthesis disorder. Conclusion: This one-year pilot study showed that DI-HRMS untargeted metabolomics can be used as a first-tier approach replacing targeted assays of amino acid, acylcarnitine and creatine metabolites with ample opportunities to expand. Using DI-HRMS untargeted metabolomics as a first-tier will open up possibilities to look for new biomarkers.

Early diagnosis of patients with urolithiasis or hypouricemia owing to inborn errors of hypoxanthine metabolism is important in preventing renal failure or drug-induced toxicity.

We identified three patients with xanthinuria using gas chromatography/mass spectrometry-based urine metabolomics: a 72-year-old male with bladder stone, a severe hypouricemic 59-year-old female with type 2 diabetes mellitus, and an 8-year and 9-month-old female who was first discovered to harbor a mutation in the xanthine dehydrogenase gene using whole-exome sequencing, but had a normal molybdenum cofactor sulfurase gene. Hydantoin-5-propionate was detected in the first and third patients but not in the second, suggesting that the first and second patients had type I and II xanthinuria, respectively.

Gas chromatography/mass spectrometry-based metabolomics can be used for undiagnosed patients with xanthinuria, identification of the type of xanthinuria without allopurinol loading, and the quick functional evaluation of mutations in the xanthinuria-related genes.

The determination of the functional impact of variants of uncertain significance (VUS) is one of the major bottlenecks in the diagnostic workflow of inherited genetic diseases. To face this problem, we set up a CRISPR/Cas9-based strategy for knock-in cellular model generation, focusing on inherited metabolic disorders (IMDs). We selected variants in seven IMD-associated genes, including seven reported disease-causing variants and four benign/likely benign variants. Overall, 11 knock-in cell models were generated via homology-directed repair in HAP1 haploid cells using CRISPR/Cas9. The functional impact of the variants was determined by analyzing the characteristic biochemical alterations of each disorder. Functional studies performed in knock-in cell models showed that our approach accurately distinguished the functional effect of pathogenic from non-pathogenic variants in a reliable manner in a wide range of IMDs. Our study provides a generic approach to assess the functional impact of genetic variants to improve IMD diagnosis and this tool could emerge as a promising alternative to invasive tests, such as muscular or skin biopsies. Although the study has been performed only in IMDs, this strategy is generic and could be applied to other genetic disorders.

Early diagnosis of cerebral palsy (CP) is important to enable intervention at a time when neuroplasticity is at its highest. Current mean age at diagnosis is 13 months in Denmark. Recent research has documented that an early-diagnosis set-up can lower diagnostic age in high-risk infants. The aim of the current study is to lower diagnostic age of CP regardless of neonatal risk factors. Additionally, we want to investigate if an early intervention program added to standard care is superior to standard care alone.

The current multicentre study CP-EDIT (Early Diagnosis and Intervention Trial) with the GO-PLAY intervention included (Goal Oriented ParentaL supported home ActivitY program), aims at testing the feasibility of an early diagnosis set-up and the GO-PLAY early intervention. CP-EDIT is a prospective cohort study, consecutively assessing approximately 500 infants at risk of CP. We will systematically collect data at inclusion (age 3-11 months) and follow a subset of participants (n = 300) with CP or at high risk of CP until the age of two years. The GO-PLAY early intervention will be tested in 80 infants with CP or high risk of CP. Focus is on eight areas related to implementation and perspectives of the families: early cerebral magnetic resonance imaging (MRI), early genetic testing, implementation of the General Movements Assessment method, analysis of the GO-PLAY early intervention, parental perspective of early intervention and early diagnosis, early prediction of CP, and comparative analysis of the Hand Assessment for Infants, Hammersmith Infant Neurological Examination, MRI, and the General Movements method.

Early screening for CP is increasingly possible and an interim diagnosis of "high risk of CP" is recommended but not currently used in clinical care in Denmark. Additionally, there is a need to accelerate identification in mild or ambiguous cases to facilitate appropriate therapy early. Most studies on early diagnosis focus on identifying CP in infants below five months corrected age. Little is known about early diagnosis in the 50% of all CP cases that are discernible later in infancy. The current study aims at improving care of patients with CP even before they have an established diagnosis.

ClinicalTrials.gov ID 22013292 (reg. date 31/MAR/2023) for the CP-EDIT cohort and ID 22041835 (reg. date 31/MAR/2023) for the GO-PLAY trial.

The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used.

Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data.

The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs.

The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

Movement disorders, particularly chorea, are uncommon in inborn errors of metabolism, but their identification is essential for improved clinical outcomes. In this context, comprehensive descriptions of movement disorders are limited and primarily derived from single cases or small patient series, highlighting the need for increased awareness and additional research in this field.

A systematic review was conducted using the MEDLINE database and GeneReviews. The search included studies on inborn errors of metabolism associated with chorea, athetosis, or ballismus. The review adhered to PRISMA guidelines.

The systematic review analyzed 76 studies out of 2350 records, encompassing the period from 1964 to 2022. Chorea was observed in 90.1% of the 173 patients, followed by athetosis in 5.7%. Various inborn errors of metabolism showed an association with chorea, with trace elements and metals being the most frequent. Cognitive and developmental abnormalities were common in the cohort. Frequent neurological features included seizures, dysarthria, and optic atrophy, whereas non-neurological features included, among others, facial dysmorphia and failure to thrive. Neuroimaging and biochemical testing played crucial roles in aiding diagnosis, revealing abnormal findings in 34.1% and 47.9% of patients, respectively. However, symptomatic treatment efficacy for movement disorders was limited.

This study emphasizes the complexities of chorea in inborn errors of metabolism. A systematic approach with red flags, biochemical testing, and neuroimaging is required for diagnosis. Collaboration between neurologists, geneticists, and metabolic specialists is crucial for improving early detection and individualized treatment. Utilizing genetic testing technologies and potential therapeutic avenues can aid in the improvement of patient outcomes.

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Bibliometric studies on inherited metabolic diseases (IMDs) do not exist in the literature. Therefore, our research aims to conduct a bibliometric study to determine the current status, trending topics, and missing points of publications on IMDs. Between 1968 and 2023, we conducted a literature search with the keyword "inherited metabolic disease" in the SCOPUS database. We included research articles in medicine written in English and published in the final section. We created our data pool using VOSviewer, SciMAT, and Rstudio software programs for the bibliometric parameters of the articles that met the inclusion criteria. We performed a bibliometric analysis of the data with the R package "bibliometrix" and BibExcel programs. We included 2702 research articles published on IMDs. The top three countries that have written the most articles in this field are the USA (n = 501), the United Kingdom (n = 182), and China (n = 172). The most preferred keywords by the authors were: newborn screening (n = 54), mutation (n = 43), phenylketonuria (n = 42), children (n = 35), genetics (n = 34), and maple syrup urine disease (n = 32). Trending topics were osteoporosis, computed tomography, bone marrow transplantation in the early years of the study, chronic kidney disease, urea cycle disorders, next-generation sequencing, newborn screening, and familial hypercholesterolemia in the final years of the study. This study provides clinicians with a new perspective, showing that molecular and genetic studies of inherited metabolic diseases will play an essential role in diagnosis and treatment in the future.