Optimising urinary catecholamine metabolite diagnostics for neuroblastoma.

INTRODUCTION: The analysis of urinary catecholamine metabolites is a cornerstone of neuroblastoma diagnostics. Currently, there is no consensus regarding the sampling method, and variable combinations of catecholamine metabolites are being used. We investigated if spot urine samples can be reliably used for analysis of a panel of catecholamine metabolites for the diagnosis of neuroblastoma. METHODS: Twenty-four-hour urine or spot urine samples were collected from patients with and without neuroblastoma at diagnosis. Homovanillic acid (HVA), vanillylmandelic acid (VMA), dopamine, 3-methoxytyramine, norepinephrine, normetanephrine, epinephrine and metanephrine were measured by high-performance liquid chromatography coupled with fluorescence detection (HPLC-FD) and/or ultra-performance liquid chromatography coupled with electrospray tandem mass spectrometry (UPLC-MS/MS). RESULTS: Catecholamine metabolite levels were measured in urine samples of 400 neuroblastoma patients (24-hour urine, n = 234; spot urine, n = 166) and 571 controls (all spot urine). Excretion levels of catecholamine metabolites and the diagnostic sensitivity for each metabolite were similar in 24-hour urine and spot urine samples (p > .08 and >.27 for all metabolites). The area under the receiver-operating-characteristic curve (AUC) of the panel containing all eight catecholamine metabolites was significantly higher compared to that of only HVA and VMA (AUC = 0.952 vs. 0.920, p = .02). No differences were observed in metabolite levels between the two analysis methods. CONCLUSION: Catecholamine metabolites in spot urine and 24-hour urine resulted in similar diagnostic sensitivities. The Catecholamine Working Group recommends the implementation of spot urine as standard of care. The panel of eight catecholamine metabolites has superior diagnostic accuracy over VMA and HVA.

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy.

Prediction of VLCAD deficiency phenotype by a metabolic fingerprint in newborn screening bloodspots.

PURPOSE: Newborns who test positive for very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) in newborn screening may have a severe phenotype with early onset of life-threatening symptoms but may also have an attenuated phenotype and never become symptomatic. The objective of this study is to investigate whether metabolomic profiles in dried bloodspots (DBS) of newborns allow early phenotypic prediction, permitting tailored treatment and follow-up. METHODS: A metabolic fingerprint was generated by direct infusion high resolution mass spectrometry in DBS of VLCADD patients (n = 15) and matched controls. Multivariate analysis of the metabolomic profiles was applied to differentiate subgroups. RESULTS: Concentration of six acylcarnitine species differed significantly between patients and controls. The concentration of C18:2- and C20:0-carnitine, 13,14-dihydroretinol and deoxycytidine monophosphate allowed separation between mild and severe patients. Two patients who could not be prognosticated on early clinical symptoms, were correctly fitted for severity in the score plot based on the untargeted metabolomics. CONCLUSION: Distinctive metabolomic profiles in DBS of newborns with VLCADD may allow phenotypic prognostication. The full potential of this approach as well as the underlying biochemical mechanisms need further investigation.

Misdiagnosis of CTX due to propofol: The interference of total intravenous propofol anaesthesia with bile acid profiling.

BACKGROUND: Cerebrotendinous xanthomatosis (CTX) is a rare genetic disorder, characterised by chronic diarrhoea, xanthomas, cataracts, and neurological deterioration. CTX is caused by CYP27A1 deficiency, which leads to abnormal cholesterol and bile acid metabolism. Urinary bile acid profiling (increased m/z 627: glucuronide-5ß-cholestane-pentol) serves as diagnostic screening for CTX. However, this led to a false positive CTX diagnosis in two patients, who had received total intravenous anaesthesia (TIVA) with propofol. METHODS: To determine the influence of propofol on bile acid profiling, 10 urinary samples and 2 blood samples were collected after TIVA with propofol Fresenius 7 to 10 mg/kg/h from 12 subjects undergoing scoliosis correction. Urinary bile acids were analysed using flow injection negative electrospray mass spectrometry. Propofol binding to recombinant CYP27A1, the effects of propofol on recombinant CYP27A1 activity, and CYP27A1 expression in liver organoids were investigated using spectral binding, enzyme activity assays, and qPCR, respectively. Accurate masses were determined with high-resolution mass spectrometry. RESULTS: Abnormal urinary profiles were identified in all subjects after TIVA, with a trend correlating propofol dose per kilogramme and m/z 627 peak intensity. Propofol only induced a weak CYP27A1 response in the spectral binding assay, minimally affected CYP27A1 activity and did not affect CYP27A1 expression. The accurate mass of m/z 627 induced by propofol differed >10 PPM from m/z 627 observed in CTX. CONCLUSIONS: TIVA with propofol invariably led to a urinary profile misleadingly suggestive of CTX, but not through CYP27A1 inhibition. To avoid further misdiagnoses, propofol administration should be considered when interpreting urinary bile acid profiles.

Plasma free metanephrines for diagnosis of neuroblastoma patients.

INTRODUCTION: A substantial number of patients with neuroblastoma (NB) have increased excretion of catecholamines and metanephrines. Here, we have investigated the diagnostic role of plasma free metanephrines (PFM), metanephrine (MN), normetanephrine (NMN) and 3-methoxytyramine (3MT) for NB, the most common extra-cranial solid tumour in children. METHODS: PFM were quantified by using a commercial IVD-CE LC-MS/MS method on a TSQ Quantiva coupled to an Ultimate 3000. The method was further validated on 103 samples from pediatric subjects (54 patients with histologically confirmed NB and 49 age and sex matched controls). Correlations between PFM concentrations with clinical factors were tested. We directly compared MN, NMN, and 3MT concentrations in matched plasma and urine samples of NB patients (n = 29). RESULTS: 3MT and NMN showed an excellent diagnostic performance with very high specificity (100% and 95.8%, respectively) and sensitivity (88.2% and 80.4%). ROC curves were obtained (AUC of 0.93 and 0.91 for 3MT and NMN, respectively) and optimal cut-offs that could discriminate between controls and NB patients were defined. A positive correlation between NMN levels in urine and plasma (p = .0017) was found. DISCUSSION: The determination of plasma 3MT and NMN should be taken in consideration as a new diagnostic tool for NB. Validation in prospective clinical studies in comparison to urinary catecholamines and metanephrines is warranted.

Liquid chromatography-tandem mass spectrometry assay for the quantification of free and total sialic acid in human cerebrospinal fluid.

BACKGROUND: Analysis of sialic acid (SA) metabolites in cerebrospinal fluid (CSF) is important for clinical diagnosis. In the present study, a high-performance liquid chromatography-tandem mass spectrometry (HPLC/MS/MS) method for free sialic acid (FSA) and total sialic acid (TSA) in human CSF was validated. METHODS: The method utilized a simple sample-preparation procedure of protein precipitation for FSA and acid hydrolysis for TSA. Negative electrospray ionisation was used to monitor the transitions m/z 308.2-->87.0 (SA) and m/z 311.2--> 90.0 ((13)C(3)-SA). Conjugated sialic acid (CSA) was calculated by subtracting FSA from TSA. We established reference intervals for FSA, TSA and CSA in CSF in 217 control subjects. The method has been applied to patients' samples with known differences in SA metabolites like meningitis (n=6), brain tumour (n=2), leukaemia (n=5), and Salla disease (n=1). RESULTS: Limit of detection (LOD) was 0.54 microM for FSA and 0.45 mM for TSA. Intra- and inter-assay variation for FSA (21.8 microM) were 4.8% (n=10) and 10.4% (n=40) respectively. Intra- and inter-assay variation for TSA (35.6 microM) were 9.7% (n=10) and 12.8% (n=40) respectively. Tested patients showed values of TSA above established reference value. CONCLUSION: The validated method allows sensitive and specific measurement of SA metabolites in CSF and can be applied for clinical diagnoses.