Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Bi-allelic NIT1 variants cause a brain small vessel disease characterized by movement disorders, massively dilated perivascular spaces, and intracerebral hemorrhage.

PURPOSE: To describe a recessively inherited cerebral small vessel disease, caused by loss-of-function variants in Nitrilase1 (NIT1). METHODS: We performed exome sequencing, brain magnetic resonance imaging, neuropathology, electron microscopy, western blotting, and transcriptomic and metabolic analyses in 7 NIT1-small vessel disease patients from 5 unrelated pedigrees. RESULTS: The first identified patients were 3 siblings, compound heterozygous for the NIT1 c.727C>T; (p.Arg243Trp) variant and the NIT1 c.198_199del; p.(Ala68∗) variant. The 4 additional patients were single cases from 4 unrelated pedigrees and were all homozygous for the NIT1 c.727C>T; p.(Arg243Trp) variant. Patients presented in mid-adulthood with movement disorders. All patients had striking abnormalities on brain magnetic resonance imaging, with numerous and massively dilated basal ganglia perivascular spaces. Three patients had non-lobar intracerebral hemorrhage between age 45 and 60, which was fatal in 2 cases. Western blotting on patient fibroblasts showed absence of NIT1 protein, and metabolic analysis in urine confirmed loss of NIT1 enzymatic function. Brain autopsy revealed large electron-dense deposits in the vessel walls of small and medium sized cerebral arteries. CONCLUSION: NIT1-small vessel disease is a novel, autosomal recessively inherited cerebral small vessel disease characterized by a triad of movement disorders, massively dilated basal ganglia perivascular spaces, and intracerebral hemorrhage.

Tagged IDS causes efficient and engraftment-independent prevention of brain pathology during lentiviral gene therapy for Mucopolysaccharidosis type II.

Mucopolysaccharidosis type II (OMIM 309900) is a lysosomal storage disorder caused by iduronate 2-sulfatase (IDS) deficiency and accumulation of glycosaminoglycans, leading to progressive neurodegeneration. As intravenously infused enzyme replacement therapy cannot cross the blood-brain barrier (BBB), it fails to treat brain pathology, highlighting the unmet medical need to develop alternative therapies. Here, we test modified versions of hematopoietic stem and progenitor cell (HSPC)-mediated lentiviral gene therapy (LVGT) using IDS tagging in combination with the ubiquitous MND promoter to optimize efficacy in brain and to investigate its mechanism of action. We find that IDS tagging with IGF2 or ApoE2, but not RAP12x2, improves correction of brain heparan sulfate and neuroinflammation at clinically relevant vector copy numbers. HSPC-derived cells engrafted in brain show efficiencies highest in perivascular areas, lower in choroid plexus and meninges, and lowest in parenchyma. Importantly, the efficacy of correction was independent of the number of brain-engrafted cells. These results indicate that tagged versions of IDS can outperform untagged IDS in HSPC-LVGT for the correction of brain pathology in MPS II, and they imply both cell-mediated and tag-mediated correction mechanisms, including passage across the BBB and increased uptake, highlighting their potential for clinical translation.

Gastrointestinal Biomarkers and Their Association with Feeding in the First Five Days of Pediatric Critical Illness.

OBJECTIVES: Predicting the patients' tolerance to enteral nutrition (EN) would help clinicians optimize individual nutritional intake. This study investigated the course of several gastrointestinal (GI) biomarkers and their association with EN advancement (ENA) longitudinally during pediatric intensive care unit (PICU) admission. METHODS: This is a secondary analysis of the Early versus Late Parenteral Nutrition in the Pediatric Intensive Care Unit randomized controlled trial. EN was started early and increased gradually. The cholecystokinin (CCK), leptin, glucagon, intestinal fatty acid-binding protein 2 (I-FABP2), and citrulline plasma concentrations were measured upon PICU admission, day 3 and day 5. ENA was defined as kcal EN provided as % of predicted resting energy expenditure. The course of the biomarkers and ENA was examined in patients with samples on all time points using Friedman and Wilcoxon signed-rank tests. The association of ENA with the biomarkers was examined using a 2-part mixed-effects model with data of the complete population, adjusted for possible confounders. RESULTS: For 172 patients, median age 8.6 years (first quartile; third quartile: 4.2; 13.4), samples were available, of which 55 had samples on all time points. The median ENA was 0 (0; 0) on admission, 14.5 (0.0; 43.8) on day 3, and 28.0 (7.6; 94.8) on day 5. During PICU stay, CCK and I-FABP2 concentrations decreased significantly, whereas glucagon concentrations increased significantly, and leptin and citrulline remained stable. None of the biomarkers was longitudinally associated with ENA. CONCLUSIONS: Based on the current evidence, CCK, leptin, glucagon, I-FABP2, and citrulline appear to have no added value in predicting ENA in the first 5 days of pediatric critical illness.

GAA variants associated with reduced enzymatic activity but lack of Pompe-related symptoms, incidentally identified by exome sequencing.

Pompe disease is a rare metabolic myopathy caused by pathogenic variants affecting the activity of the lysosomal glycogen-degrading enzyme acid alpha-glucosidase (GAA). Impaired GAA function results in the accumulation of undegraded glycogen within lysosomes in multiple tissues but predominantly affects the skeletal, smooth and cardiac muscle. The degree of residual enzymatic activity appears to roughly correlate with the age of onset and the severity of the clinical symptoms. Here, we report four siblings in which the GAA variants NM_000152.5:c.2237G > C p.(Trp746Ser) and NM_000152.5:c.266G > A p.(Arg89His) were identified as an incidental finding of clinical exome sequencing. These variants are listed in the ClinVar and the Pompe disease GAA variant databases but are reported here for the first time in compound heterozygosity. All four siblings displayed normal urine tetrasaccharide levels and no clinical manifestations related to Pompe disease. Nevertheless, GAA enzymatic activity was within the range for late onset Pompe patients. Our report shows an association between a novel genotype and attenuated GAA enzymatic activity. The clinical significance can only be established by the regular monitoring of these individuals. The study highlights the major challenges for clinical care arising from incidental findings of next generation sequencing.

AMFR dysfunction causes autosomal recessive spastic paraplegia in human that is amenable to statin treatment in a preclinical model.

Hereditary spastic paraplegias (HSP) are rare, inherited neurodegenerative or neurodevelopmental disorders that mainly present with lower limb spasticity and muscle weakness due to motor neuron dysfunction. Whole genome sequencing identified bi-allelic truncating variants in AMFR, encoding a RING-H2 finger E3 ubiquitin ligase anchored at the membrane of the endoplasmic reticulum (ER), in two previously genetically unexplained HSP-affected siblings. Subsequently, international collaboration recognized additional HSP-affected individuals with similar bi-allelic truncating AMFR variants, resulting in a cohort of 20 individuals from 8 unrelated, consanguineous families. Variants segregated with a phenotype of mainly pure but also complex HSP consisting of global developmental delay, mild intellectual disability, motor dysfunction, and progressive spasticity. Patient-derived fibroblasts, neural stem cells (NSCs), and in vivo zebrafish modeling were used to investigate pathomechanisms, including initial preclinical therapy assessment. The absence of AMFR disturbs lipid homeostasis, causing lipid droplet accumulation in NSCs and patient-derived fibroblasts which is rescued upon AMFR re-expression. Electron microscopy indicates ER morphology alterations in the absence of AMFR. Similar findings are seen in amfra-/- zebrafish larvae, in addition to altered touch-evoked escape response and defects in motor neuron branching, phenocopying the HSP observed in patients. Interestingly, administration of FDA-approved statins improves touch-evoked escape response and motor neuron branching defects in amfra-/- zebrafish larvae, suggesting potential therapeutic implications. Our genetic and functional studies identify bi-allelic truncating variants in AMFR as a cause of a novel autosomal recessive HSP by altering lipid metabolism, which may potentially be therapeutically modulated using precision medicine with statins.

Uveal Melanoma Patients Have a Distinct Metabolic Phenotype in Peripheral Blood.

Uveal melanomas (UM) are detected earlier. Consequently, tumors are smaller, allowing for novel eye-preserving treatments. This reduces tumor tissue available for genomic profiling. Additionally, these small tumors can be hard to differentiate from nevi, creating the need for minimally invasive detection and prognostication. Metabolites show promise as minimally invasive detection by resembling the biological phenotype. In this pilot study, we determined metabolite patterns in the peripheral blood of UM patients (n = 113) and controls (n = 46) using untargeted metabolomics. Using a random forest classifier (RFC) and leave-one-out cross-validation, we confirmed discriminatory metabolite patterns in UM patients compared to controls with an area under the curve of the receiver operating characteristic of 0.99 in both positive and negative ion modes. The RFC and leave-one-out cross-validation did not reveal discriminatory metabolite patterns in high-risk versus low-risk of metastasizing in UM patients. Ten-time repeated analyses of the RFC and LOOCV using 50% randomly distributed samples showed similar results for UM patients versus controls and prognostic groups. Pathway analysis using annotated metabolites indicated dysregulation of several processes associated with malignancies. Consequently, minimally invasive metabolomics could potentially allow for screening as it distinguishes metabolite patterns that are putatively associated with oncogenic processes in the peripheral blood plasma of UM patients from controls at the time of diagnosis.

Analysis of urinary oligosaccharide excretion patterns by UHPLC/HRAM mass spectrometry for screening of lysosomal storage disorders.

Oligosaccharidoses, sphingolipidoses and mucolipidoses are lysosomal storage disorders (LSDs) in which defective breakdown of glycan-side chains of glycosylated proteins and glycolipids leads to the accumulation of incompletely degraded oligosaccharides within lysosomes. In metabolic laboratories, these disorders are commonly diagnosed by thin-layer chromatography (TLC) but more recently also mass spectrometry-based approaches have been published. To expand the possibilities to screen for these diseases, we developed an ultra-high-performance liquid chromatography (UHPLC) with a high-resolution accurate mass (HRAM) mass spectrometry (MS) screening platform, together with an open-source iterative bioinformatics pipeline. This pipeline generates comprehensive biomarker profiles and allows for extensive quality control (QC) monitoring. Using this platform, we were able to identify α-mannosidosis, ß-mannosidosis, α-N-acetylgalactosaminidase deficiency, sialidosis, galactosialidosis, fucosidosis, aspartylglucosaminuria, GM1 gangliosidosis, GM2 gangliosidosis (M. Sandhoff) and mucolipidosis II/III in patient samples. Aberrant urinary oligosaccharide excretions were also detected for other disorders, including NGLY1 congenital disorder of deglycosylation, sialic acid storage disease, MPS type IV B and GSD II (Pompe disease). For the latter disorder, we identified heptahexose (Hex7), as a potential urinary biomarker, in addition to glucose tetrasaccharide (Glc4), for the diagnosis and monitoring of young onset cases of Pompe disease. Occasionally, so-called "neonate" biomarker profiles were observed in young patients, which were probably due to nutrition. Our UHPLC/HRAM-MS screening platform can easily be adopted in biochemical laboratories and allows for simple and robust screening and straightforward interpretation of the screening results to detect disorders in which aberrant oligosaccharides accumulate.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data.

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.

Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores.

The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing genetic variant(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in genes associated with metabolic processes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is deficient or not. When calculating this score, Reafect takes multiple factors into account: the magnitude and sign of alterations in the metabolite levels, the reaction distances between metabolites and reactions in the pathway, and the biochemical directionality of the reactions. We applied Reafect to untargeted metabolomics data of 72 patient samples with a known IEM and found that in 81% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with Combined Annotation Dependent Depletion (CADD) scores (a measure for gene variant deleteriousness) and ranked the metabolic genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the genes containing the disease-causing variant when compared with the two approaches individually. For 15/27 IEM patients the correct affected gene was ranked within the top 0.25% of the set of potentially affected genes. Together, our findings suggest that metabolomics data improves the identification of affected genes in patients suffering from IEM.

The role of ERNDIM diagnostic proficiency schemes in improving the quality of diagnostic testing for inherited metabolic diseases.

External quality assurance (EQA) is crucial to monitor and improve the quality of biochemical genetic testing. ERNDIM (www.erndim.org), established in 1994, aims at reliable and standardized procedures for diagnosis, treatment and monitoring of inherited metabolic disease (IMD) by providing EQA schemes and educational activities. Currently, ERNDIM provides 16 different EQA schemes including quantitative schemes for various metabolite groups, and interpretive schemes such as diagnostic proficiency testing (DPT). DPT schemes focus on the ability of laboratories to correctly identify and interpret abnormalities in authentic urine samples across a wide range of IMDs. In the DPT schemes, six samples each year are distributed together with clinical information. Laboratories choose and perform the tests needed to reach a diagnosis. Data were collected on 345 samples, distributed to up to 105 laboratories worldwide. Diagnostic proficiency (the % of total points possible for all participating laboratories within a scheme for analysis and interpretation) ranged widely: amino acid disorders (n = 20), range 33%-100%, mean 84%; organic acid disorders (n = 35), range 14%-100%, mean 84%; lysosomal storage disorders (n = 13), range 20%-97%, mean 73%; purine/pyrimidine disorders (n = 9), range 37%-100%, mean 70%; miscellaneous disorders (n = 8), range 17%-100%, mean 65%; no IMD, range 65%-95%, mean 85%. When a sample with the same disorder was distributed in a subsequent survey, performance improved in 75 cases with no improvement seen in 32, suggesting overall improvement of performance. ERNDIM diagnostic proficiency testing is a valuable activity which can help to assess laboratory performance, identify methodological/technical challenges, be informative during quality audits and contribute to a better clinical appreciation of diagnostic uncertainty.

Technical Aspects of Coenzyme Q10 Analysis: Validation of a New HPLC-ED Method.

The biochemical measurement of the CoQ status in different tissues can be performed using HPLC with electrochemical detection (ED). Because the production of the electrochemical cells used with the Coulochem series detectors was discontinued, we aimed to standardize a new HPLC-ED method with new equipment. We report all technical aspects, troubleshooting and its performance in different biological samples, including plasma, skeletal muscle homogenates, urine and cultured skin fibroblasts. Analytical variables (intra- and inter-assay precision, linearity, analytical measurement range, limit of quantification, limit of detection and accuracy) were validated in calibrators and plasma samples and displayed adequate results. The comparison of the results of a new ERNDIM external quality control (EQC) scheme for the plasma CoQ determination between HPLC-ED (Lab 1) and LC-MS/MS (Lab 2) methods shows that the results of the latter were slightly higher in most cases, although a good consistency was generally observed. In conclusion, the new method reported here showed a good analytical performance. The global quality of the EQC scheme results among different participants can be improved with the contribution of more laboratories.

Maternal Body Mass Index, Early-Pregnancy Metabolite Profile, and Birthweight.

CONTEXT: Maternal prepregnancy body mass index (BMI) has a strong influence on gestational metabolism, but detailed metabolic alterations are unknown. OBJECTIVE: First, to examine the associations of maternal prepregnancy BMI with maternal early-pregnancy metabolite alterations. Second, to identify an early-pregnancy metabolite profile associated with birthweight in women with a higher prepregnancy BMI that improved prediction of birthweight compared to glucose and lipid concentrations. DESIGN, SETTING, AND PARTICIPANTS: Prepregnancy BMI was obtained in a subgroup of 682 Dutch pregnant women from the Generation R prospective cohort study. MAIN OUTCOME MEASURES: Maternal nonfasting targeted amino acids, nonesterified fatty acid, phospholipid, and carnitine concentrations measured in blood serum at mean gestational age of 12.8 weeks. Birthweight was obtained from medical records. RESULTS: A higher prepregnancy BMI was associated with 72 altered amino acids, nonesterified fatty acid, phospholipid and carnitine concentrations, and 6 metabolite ratios reflecting Krebs cycle, inflammatory, oxidative stress, and lipid metabolic processes (P-values < 0.05). Using penalized regression models, a metabolite profile was selected including 15 metabolites and 4 metabolite ratios based on its association with birthweight in addition to prepregnancy BMI. The adjusted R2 of birthweight was 6.1% for prepregnancy BMI alone, 6.2% after addition of glucose and lipid concentrations, and 12.9% after addition of the metabolite profile. CONCLUSIONS: A higher maternal prepregnancy BMI was associated with altered maternal early-pregnancy amino acids, nonesterified fatty acids, phospholipids, and carnitines. Using these metabolites, we identified a maternal metabolite profile that improved prediction of birthweight in women with a higher prepregnancy BMI compared to glucose and lipid concentrations.

Associations of maternal and infant metabolite profiles with foetal growth and the odds of adverse birth outcomes.

BACKGROUND: Adaptations in maternal and foetal metabolic pathways may predispose to altered foetal growth and adverse birth outcomes. OBJECTIVE: To assess the associations of maternal early-pregnancy metabolite profiles and infant metabolite profiles at birth with foetal growth from first trimester onwards and the odds of adverse birth outcomes. METHODS: In a prospective population-based cohort among 976 Dutch pregnant women and their children, serum concentrations of amino acids, non-esterified fatty acids (NEFA), phospholipids (PL) and carnitines in maternal early-pregnancy blood and in cord blood were obtained by liquid-chromatography tandem mass spectrometry. Information on foetal growth was available from first trimester onwards. RESULTS: After false discovery rate correction for multiple testing, higher infant total and individual NEFA concentrations were associated with a lower weight, length, and head circumference at birth. Higher infant total and individual acyl-lysophosphatidylcholine (lyso.PC.a) and alkyl-lysophosphatidylcholine concentrations were associated with higher weight and head circumference (lyso.PC.a only) at birth, higher odds of LGA and lower odds of SGA. Few individual maternal metabolites were associated with foetal growth measures in third trimester and at birth, but not with the odds of adverse birth outcomes. CONCLUSIONS: Our results suggest that infant metabolite profiles, particularly total and individual lyso.PC.a and NEFA concentrations, were strongly related to growth measures at birth and the odds of adverse birth outcomes. Few individual maternal early-pregnancy metabolites, but not total metabolite concentrations, are associated with foetal growth measures in third trimester and at birth.

Abnormal VLCADD newborn screening resembling MADD in four neonates with decreased riboflavin levels and VLCAD activity.

Early detection of congenital disorders by newborn screening (NBS) programs is essential to prevent or limit disease manifestation in affected neonates. These programs balance between the detection of the highest number of true cases and the lowest number of false-positives. In this case report, we describe four unrelated cases with a false-positive NBS result for very-long-chain acyl-CoA dehydrogenase deficiency (VLCADD). Three neonates presented with decreased but not deficient VLCAD enzyme activity and two of them carried a single heterozygous ACADVL c.1844G>A mutation. Initial biochemical investigations after positive NBS referral in these infants revealed acylcarnitine and organic acid profiles resembling those seen in multiple acyl-CoA dehydrogenase deficiency (MADD). Genetic analysis did not reveal any pathogenic mutations in the genes encoding the electron transfer flavoprotein (ETF alpha and beta subunits) nor in ETF dehydrogenase. Subsequent further diagnostics revealed decreased levels of riboflavin in the newborns and oral riboflavin administration normalized the MADD-like biochemical profiles. During pregnancy, the mothers followed a vegan, vegetarian or lactose-free diet which probably caused alimentary riboflavin deficiency in the neonates. This report demonstrates that a secondary (alimentary) maternal riboflavin deficiency in combination with reduced VLCAD activity in the newborns can result in an abnormal VLCADD/MADD acylcarnitine profile and can cause false-positive NBS. We hypothesize that maternal riboflavin deficiency contributed to the false-positive VLCADD neonatal screening results.

Associations of maternal bisphenol urine concentrations during pregnancy with neonatal metabolomic profiles.

BACKGROUND: Fetal exposure to bisphenols is associated with altered fetal growth, adverse birth outcomes and childhood cardio-metabolic risk factors. Metabolomics may serve as a tool to identify the mechanisms underlying these associations. We examined the associations of maternal bisphenol urinary concentrations in pregnancy with neonatal metabolite profiles from cord blood. METHODS: In a population-based prospective cohort study among 225 mother-child pairs, maternal urinary bisphenol A, S and F concentrations in first, second and third trimester were measured. LC-MS/MS was used to determine neonatal concentrations of amino acids, non-esterified fatty acids (NEFA), phospholipids (PL), and carnitines in cord blood. RESULTS: No associations of maternal total bisphenol concentrations with neonatal metabolite profiles were present. Higher maternal average BPA concentrations were associated with higher neonatal mono-unsaturated alkyl-lysophosphatidylcholine concentrations, whereas higher maternal average BPS was associated with lower neonatal overall and saturated alkyl-lysophosphatidylcholine (p-values < 0.05).Trimester-specific analyses showed that higher maternal BPA, BPS and BPF were associated with alterations in neonatal NEFA, diacyl-phosphatidylcholines, acyl-alkyl-phosphatidylcholines, alkyl-lysophosphatidylcholine, sphingomyelines and acyl-carnitines, with the strongest effects for third trimester maternal bisphenol and neonatal diacyl-phosphatidylcholine, sphingomyeline and acyl-carnitine metabolites (p-values < 0.05). Associations were not explained by maternal socio-demographic and lifestyle characteristics or birth characteristics. DISCUSSION: Higher maternal bisphenol A, F and S concentrations in pregnancy are associated with alterations in neonatal metabolite profile, mainly in NEFA, PL and carnitines concentrations. These findings provide novel insight into potential mechanisms underlying associations of maternal bisphenol exposure during pregnancy with adverse offspring outcomes but need to be replicated among larger, diverse populations.

Monitoring phenylalanine concentrations in the follow-up of phenylketonuria patients: An inventory of pre-analytical and analytical variation.

BACKGROUND: Reliable measurement of phenylalanine (Phe) is a prerequisite for adequate follow-up of phenylketonuria (PKU) patients. However, previous studies have raised concerns on the intercomparability of plasma and dried blood spot (DBS) Phe results. In this study, we made an inventory of differences in (pre-)analytical methodology used for Phe determination across Dutch laboratories, and compared DBS and plasma results. METHODS: Through an online questionnaire, we assessed (pre-)analytical Phe measurement procedures of seven Dutch metabolic laboratories. To investigate the difference between plasma and DBS Phe, participating laboratories received simultaneously collected plasma-DBS sets from 23 PKU patients. In parallel, 40 sample sets of DBS spotted from either venous blood or capillary fingerprick were analyzed. RESULTS: Our data show that there is no consistency on standard operating procedures for Phe measurement. The association of DBS to plasma Phe concentration exhibits substantial inter-laboratory variation, ranging from a mean difference of -15.5% to +30.6% between plasma and DBS Phe concentrations. In addition, we found a mean difference of +5.8% in Phe concentration between capillary DBS and DBS prepared from venous blood. CONCLUSIONS: The results of our study point to substantial (pre-)analytical variation in Phe measurements, implicating that bloodspot Phe results should be interpreted with caution, especially when no correction factor is applied. To minimize variation, we advocate pre-analytical standardization and analytical harmonization of Phe measurements, including consensus on application of a correction factor to adjust DBS Phe to plasma concentrations.

Hip disease in Mucopolysaccharidoses and Mucolipidoses: A review of mechanisms, interventions and future perspectives.

The hips are frequently involved in inheritable diseases which affect the bones. The clinical and radiological presentation of these diseases may be very similar to common hip disorders as developmental dysplasia of the hip, osteoarthritis and avascular necrosis, so the diagnosis may be easily overlooked and treatment may be suboptimal. Mucopolysaccharidosis (MPS) and Mucolipidosis (ML II and III) are lysosomal storage disorders with multisystemic involvement. Characteristic skeletal abnormalities, known as dysostosis multiplex, are common in MPS and ML and originate from intra-lysosomal storage of glycosaminoglycans in cells of the cartilage, bones and ligaments. The hip joint is severely affected in MPS and ML. Hip pathology results in limitations in mobility and pain from young age, and negatively affects quality of life. In order to better understand the underlying process that causes hip disease in MPS and ML, this review first describes the normal physiological (embryonic) hip joint development, including the interplay between the acetabulum and the femoral head. In the second part the factors contributing to altered hip morphology and function in MPS and ML are discussed, such as abnormal development of the pelvic- and femoral bones (which results in altered biomechanical forces) and inflammation. In the last part of this review therapeutic options and future perspectives are addressed.

Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism.

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5-37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Untargeted Metabolomics-Based Screening Method for Inborn Errors of Metabolism using Semi-Automatic Sample Preparation with an UHPLC- Orbitrap-MS Platform.

Routine diagnostic screening of inborn errors of metabolism (IEM) is currently performed by different targeted analyses of known biomarkers. This approach is time-consuming, targets a limited number of biomarkers and will not identify new biomarkers. Untargeted metabolomics generates a global metabolic phenotype and has the potential to overcome these issues. We describe a novel, single platform, untargeted metabolomics method for screening IEM, combining semi-automatic sample preparation with pentafluorophenylpropyl phase (PFPP)-based UHPLC- Orbitrap-MS. We evaluated analytical performance and diagnostic capability of the method by analysing plasma samples of 260 controls and 53 patients with 33 distinct IEM. Analytical reproducibility was excellent, with peak area variation coefficients below 20% for the majority of the metabolites. We illustrate that PFPP-based chromatography enhances identification of isomeric compounds. Ranked z-score plots of metabolites annotated in IEM samples were reviewed by two laboratory specialists experienced in biochemical genetics, resulting in the correct diagnosis in 90% of cases. Thus, our untargeted metabolomics platform is robust and differentiates metabolite patterns of different IEMs from those of controls. We envision that the current approach to diagnose IEM, using numerous tests, will eventually be replaced by untargeted metabolomics methods, which also have the potential to discover novel biomarkers and assist in interpretation of genetic data.