Assessment of carnitine excretion and its ratio to plasma free carnitine as a biomarker for primary carnitine deficiency in newborns

In the Netherlands, newborns are referred by the newborn screening (NBS) Program when a low free carnitine (C0) concentration (&lt;5 μmol/l) is detected in their NBS dried blood spot. This leads to ~85% false positive referrals who all need an invasive, expensive and lengthy evaluation. We investigated whether a ratio of urine C0 / plasma C0 (RatioU:P) can improve the follow-up protocol for primary carnitine deficiency (PCD). A retrospective study was performed in all Dutch metabolic centres, using samples from newborns and mothers referred by NBS due to low C0 concentration. Samples were included when C0 excretion and plasma C0 concentration were sampled on the same day. RatioU:P was calculated as (urine C0 [μmol/mmol creatinine])/(plasma C0 [μmol/l]). Data were available for 59 patients with genetically confirmed PCD and 68 individuals without PCD. The RatioU:P in PCD patients was significantly higher (p value &lt; 0.001) than in those without PCD, median [IQR], respectively: 3.4 [1.2–9.5], 0.4 [0.3–0.8], area under the curve (AUC) 0.837. Classified for age (up to 1 month) and without carnitine suppletion (PCD; N = 12, Non-PCD; N = 40), medians were 6.20 [4.4–8.8] and 0.37 [0.24–0.56], respectively. The AUC for RatioU:P was 0.996 with a cut-off required for 100% sensitivity at 1.7 (yielding one false positive case). RatioU:P accurately discriminates between positive and false positive newborn referrals for PCD by NBS. RatioU:P is less effective as a discriminative tool for PCD in adults and for individuals that receive carnitine suppletion.</p

Assessment of carnitine excretion and its ratio to plasma free carnitine as a biomarker for primary carnitine deficiency in newborns

In the Netherlands, newborns are referred by the newborn screening (NBS) Program when a low free carnitine (C0) concentration (<5 μmol/l) is detected in their NBS dried blood spot. This leads to ~85% false positive referrals who all need an invasive, expensive and lengthy evaluation. We investigated whether a ratio of urine C0 / plasma C0 (RatioU:P) can improve the follow-up protocol for primary carnitine deficiency (PCD). A retrospective study was performed in all Dutch metabolic centres, using samples from newborns and mothers referred by NBS due to low C0 concentration. Samples were included when C0 excretion and plasma C0 concentration were sampled on the same day. RatioU:P was calculated as (urine C0 [μmol/mmol creatinine])/(plasma C0 [μmol/l]). Data were available for 59 patients with genetically confirmed PCD and 68 individuals without PCD. The RatioU:P in PCD patients was significantly higher (p value < 0.001) than in those without PCD, median [IQR], respectively: 3.4 [1.2–9.5], 0.4 [0.3–0.8], area under the curve (AUC) 0.837. Classified for age (up to 1 month) and without carnitine suppletion (PCD; N = 12, Non-PCD; N = 40), medians were 6.20 [4.4–8.8] and 0.37 [0.24–0.56], respectively. The AUC for RatioU:P was 0.996 with a cut-off required for 100% sensitivity at 1.7 (yielding one false positive case). RatioU:P accurately discriminates between positive and false positive newborn referrals for PCD by NBS. RatioU:P is less effective as a discriminative tool for PCD in adults and for individuals that receive carnitine suppletion

Accurate discrimination of Hartnup disorder from other aminoacidurias using a diagnostic ratio

Introduction: Hartnup disorder is caused by a deficiency of the sodium dependent B 0 AT1 neutral amino acid transporter in the proximal kidney tubules and jejunum. Biochemically, Hartnup disorder is diagnosed via amino acid excretion patterns. However, these patterns can closely resemble amino acid excretion patterns of generalized aminoaciduria, which may induce a risk for misdiagnosis and preclusion from treatment. Here we explore whether calculating a diagnostic ratio could facilitate correct discrimination of Hartnup disorder from other aminoacidurias. Methods: 27 amino acid excretion patterns from 11 patients with genetically confirmed Hartnup disorder were compared to 68 samples of 16 patients with other aminoacidurias. Amino acid fold changes were calculated by dividing the quantified excretion values over the upper limit of the age-adjusted reference value. Results: Increased excretion of amino acids is not restricted to amino acids classically related to Hartnup disorder ("Hartnup amino acids", HAA), but also includes many other amino acids, not classically related to Hartnup disorder ("other amino acids", OAA). The fold change ratio of HAA over OAA was 6.1 (range: 2.4-9.6) in the Hartnup cohort, versus 0.2 (range: 0.0-1.6) in the aminoaciduria cohort ( p < .0001), without any overlap observed between the cohorts. Discussion: Excretion values of amino acids not classically related to Hartnup disorder are frequently elevated in patients with Hartnup disorder, which may cause misdiagnosis as generalized aminoaciduria and preclusion from vitamin B3 treatment. Calculation of the HAA/OAA ratio improves diagnostic differentiation of Hartnup disorder from other aminoacidurias

Cross-omics: Integrating genomics with metabolomics in clinical diagnostics

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: A method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction

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

Direct infusion based metabolomics identifies metabolic disease in patients’ dried blood spots and plasma

In metabolic diagnostics, there is an emerging need for a comprehensive test to acquire a complete view of metabolite status. Here, we describe a non-quantitative direct-infusion high-resolution mass spectrometry (DI-HRMS) based metabolomics method and evaluate the method for both dried blood spots (DBS) and plasma. 110 DBS of 42 patients harboring 23 different inborn errors of metabolism (IEM) and 86 plasma samples of 38 patients harboring 21 different IEM were analyzed using DI-HRMS. A peak calling pipeline developed in R programming language provided Z-scores for ~1875 mass peaks corresponding to ~3835 metabolite annotations (including isomers) per sample. Based on metabolite Z-scores, patients were assigned a ‘most probable diagnosis’ by an investigator blinded for the known diagnoses of the patients. Based on DBS sample analysis, 37/42 of the patients, corresponding to 22/23 IEM, could be correctly assigned a ‘most probable diagnosis’. Plasma sample analysis, resulted in a correct ‘most probable diagnosis’ in 32/38 of the patients, corresponding to 19/21 IEM. The added clinical value of the method was illustrated by a case wherein DI-HRMS metabolomics aided interpretation of a variant of unknown significance (VUS) identified by whole-exome sequencing. In summary, non-quantitative DI-HRMS metabolomics in DBS and plasma is a very consistent, high-throughput and nonselective method for investigating the metabolome in genetic disease

Dried blood spot analysis: an easy and reliable tool to monitor the biochemical effect of hematopoietic stem cell transplantation in Hurler Syndrome patients

Hurler syndrome (HS), the most severe phenotype in the spectrum of mucopolysaccharidosis type I, is caused by a deficiency of the lysosomal enzyme alpha-L-iduronidase (IDUA). At present, hematopoietic stem cell transplantation (HSCT) is the only treatment able to prevent disease progression in the central nervous system, and therefore considered the treatment of choice in HS patients. Because IDUA enzyme activities after HSCT have been suggested to influence the prognosis of HS patients, monitoring these activities after HSCT remains highly important. The use of dried blood spots (DBS) for enzyme analysis can be a useful alternative to the conventional leukocyte assay. Importantly, this method allows for convenient worldwide shipment, and can therefore be applied to monitor patients from larger areas of the world, or during large-scale international studies. Furthermore, this method requires only a minimal amount of blood. From 13 HS patients receiving HSCT, 36 paired whole blood and DBS samples were analyzed to assess leukocyte and DBS IDUA activities, respectively. To correct for potential interfering factors, simultaneous assay of the alpha-Galactosidase-A (AGA) activity was performed in the DBS samples and an IDUA/AGA ratio was calculated. A strong linear correlation was demonstrated between the DBS IDUA/AGA ratio and the leukocyte IDUA activity (r2 = .875, P < .001). This correlation was applicable to all enzyme activities, including the activities measured early after HSCT as well as heterozygous activities because of mixed chimerism or the use of a carrier donor. These results demonstrate that the DBS method is reliable to monitor the biochemical effect of HSCT in HS patients