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

2020 ◽  
Vol 21 (3) ◽  
pp. 979 ◽  
Author(s):  
Hanneke A. Haijes ◽  
Maria van der Ham ◽  
Hubertus C.M.T. Prinsen ◽  
Melissa H. Broeks ◽  
Peter M. van Hasselt ◽  
...  
Keyword(s):  
High Resolution Mass Spectrometry ◽  
Correct Diagnosis ◽  
Diagnostic Algorithm ◽  
Data Interpretation ◽  
Dried Blood Spots ◽  
Untargeted Metabolomics ◽  
Plasma Samples ◽  
Inborn Errors ◽  
Diagnostic Screening ◽  
Knowledge Based

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.

scholarly journals Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 206 ◽  
Author(s):  
Marten H. P. M. Kerkhofs ◽  
Hanneke A. Haijes ◽  
A. Marcel Willemsen ◽  
Koen L. I. van Gassen ◽  
Maria van der Ham ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Dried Blood Spots ◽  
Clinical Diagnostics ◽  
Untargeted Metabolomics ◽  
Next Generation ◽  
Primary Reaction ◽  
Inborn Errors ◽  
Whole Exome ◽  
Z Scores ◽  
Metabolic Screening

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.

scholarly journals Novel Biomarkers to Distinguish between Type 3c and Type 2 Diabetes Mellitus by Untargeted Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 423
Author(s):  
Cristina Jimenez-Luna ◽  
Ariadna Martin-Blazquez ◽  
Carmelo Dieguez-Castillo ◽  
Caridad Diaz ◽  
Jose Luis Martin-Ruiz ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Roc Curve ◽  
High Resolution Mass Spectrometry ◽  
Correct Diagnosis ◽  
Principal Component ◽  
Untargeted Metabolomics ◽  
Serum Samples

Pancreatogenic diabetes mellitus (T3cDM) is a highly frequent complication of pancreatic disease, especially chronic pancreatitis, and it is often misdiagnosed as type 2 diabetes mellitus (T2DM). A correct diagnosis allows the appropriate treatment of these patients, improving their quality of life, and various technologies have been employed over recent years to search for specific biomarkers of each disease. The main aim of this metabolomic project was to find differential metabolites between T3cDM and T2DM. Reverse-phase liquid chromatography coupled to high-resolution mass spectrometry was performed in serum samples from patients with T3cDM and T2DM. Multivariate Principal Component and Partial Least Squares-Discriminant analyses were employed to evaluate between-group variations. Univariate and multivariate analyses were used to identify potential candidates and the area under the receiver-operating characteristic (ROC) curve was calculated to evaluate their diagnostic value. A panel of five differential metabolites obtained an area under the ROC curve of 0.946. In this study, we demonstrate the usefulness of untargeted metabolomics for the differential diagnosis between T3cDM and T2DM and propose a panel of five metabolites that appear altered in the comparison between patients with these diseases.

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

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 289 ◽  
Author(s):  
Ramon Bonte ◽  
Michiel Bongaerts ◽  
Serwet Demirdas ◽  
Janneke G. Langendonk ◽  
Hidde H. Huidekoper ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Inborn Errors Of Metabolism ◽  
Correct Diagnosis ◽  
Screening Method ◽  
Current Approach ◽  
Untargeted Metabolomics ◽  
Metabolic Phenotype ◽  
Inborn Errors ◽  
New Biomarkers ◽  
Orbitrap Ms

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.

scholarly journals Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma

Metabolites ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
Hanneke Haijes ◽  
Marcel Willemsen ◽  
Maria van der Ham ◽  
Johan Gerrits ◽  
Mia Pras-Raves ◽  
...  
Keyword(s):  
High Resolution Mass Spectrometry ◽  
Dried Blood Spots ◽  
Sample Analysis ◽  
Direct Infusion ◽  
Clinical Value ◽  
Inborn Errors ◽  
Blood Spots ◽  
Z Scores ◽  
R Programming ◽  
Probable Diagnosis

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.

scholarly journals Ritka örökletes anyagcsere-betegségek diagnosztikája: laboratóriumi vizsgálati megközelítések

2017 ◽  
Vol 158 (48) ◽  
pp. 1903-1907
Author(s):  
Eszter Szabó ◽  
Lídia Balogh ◽  
Attila Szabó ◽  
Ildikó Szatmári
Keyword(s):  
Early Diagnosis ◽  
Genetic Disorders ◽  
Correct Diagnosis ◽  
Clinical Signs ◽  
Diagnostic Algorithm ◽  
Signs And Symptoms ◽  
Laboratory Studies ◽  
Laboratory Measurements ◽  
Inborn Errors ◽  
Appropriate Therapy

Abstract: Inherited errors of metabolism are rare genetic disorders characterized by diverse clinical and biochemical phenotypes. The complexity of signs and symptoms often presents a challenge for both clinicians and laboratory specialists. In many cases, prevention of permanent neurological symptoms or death in patients presenting these disorders is dependent on early diagnosis and introduction of appropriate therapy. For professionals it is indispensable to be familiar with the major clinical signs of inborn errors of metabolism and with the necessary and available laboratory studies to achieve an early diagnosis. The review tries to give a way of approach, diagnostic algorithm of laboratory measurements for the correct diagnosis in inherited errors of metabolism. The combination of biochemical and clinical signs, results of special metabolic investigations represent a portentous challenge in general practice. For the correct diagnosis of an inherited error of metabolism, the teamwork between clinicians and laboratory specialists is indispensable. Orv Hetil. 2017; 158(48): 1903–1907.

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

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  
Keyword(s):  
Regression Model ◽  
Inborn Errors Of Metabolism ◽  
Untargeted Metabolomics ◽  
Detection Accuracy ◽  
Inborn Errors ◽  
Mixed Effect ◽  
Metabolomics Data ◽  
Normalization Methods ◽  
Metabolite Concentrations ◽  
Reference Samples

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.

Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics

Metabolomics ◽  
2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Kelli Goodman ◽  
Matthew Mitchell ◽  
Anne M. Evans ◽  
Luke A. D. Miller ◽  
Lisa Ford ◽  
...  
Keyword(s):  
Untargeted Metabolomics ◽  
Plasma Samples

Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers

2021 ◽  
Vol 135 ◽  
pp. 116161
Author(s):  
Leticia Lacalle-Bergeron ◽  
David Izquierdo-Sandoval ◽  
Juan V. Sancho ◽  
Francisco J. López ◽  
Félix Hernández ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Untargeted Metabolomics ◽  
High Resolution Mass ◽  
Resolution Mass

scholarly journals Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study

Nutrients ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1916
Author(s):  
Leticia Lacalle-Bergeron ◽  
Tania Portolés ◽  
Francisco J. López ◽  
Juan Vicente Sancho ◽  
Carolina Ortega-Azorín ◽  
...  
Keyword(s):  
Ion Mobility ◽  
New Technologies ◽  
Biomarker Discovery ◽  
High Resolution Mass Spectrometry ◽  
Collision Cross Section ◽  
Ultra Performance Liquid Chromatography ◽  
Untargeted Metabolomics ◽  
Mobility Separation ◽  
Randomized Controlled ◽  
Ion Mobility Separation

A major problem with dietary assessments is their subjective nature. Untargeted metabolomics and new technologies can shed light on this issue and provide a more complete picture of dietary intake by measuring the profile of metabolites in biological samples. Oranges are one of the most consumed fruits in the world, and therefore one of the most studied for their properties. The aim of this work was the application of untargeted metabolomics approach with the novel combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) and study the advantages that this technique can bring to the area of dietary biomarker discovery, with the specific case of biomarkers associated with orange consumption (Citrus reticulata) in plasma samples taken during an acute intervention study (consisting of a randomized, controlled crossover trial in healthy individuals). A total of six markers of acute orange consumption, including betonicines and conjugated flavonoids, were identified with the experimental data and previous literature, demonstrating the advantages of ion mobility in the identification of dietary biomarkers and the benefits that an additional structural descriptor, as the collision cross section value (CCS), can provide in this area.

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