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Metabolites
Special Issues
Metabolomic Applications in Medical Strategy for Inborn Errors of Metabolism
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Metabolomic Applications in Medical Strategy for Inborn Errors of Metabolism

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 3931

Special Issue Editors

Dr. Yana Sandlers

E-Mail Website
Guest Editor
Department of Chemistry, Cleveland State University, Cleveland, OH 44115, USA
Interests: untargeted and targeted metabolomics; inborn errors of metabolism; assays development
Special Issues, Collections and Topics in MDPI journals
Dr. Jessica M. Colon-Franco

E-Mail Website
Guest Editor
Laboratory Medicine, Cleveland Clinic Main Campus, Cleveland, OH 44195, USA
Interests: clinical biochemistry

Special Issue Information

Dear Colleagues,

Timely diagnosis is crucial for outcomes and management of inborn errors of metabolism disorders. Newborn screening is a public health program that focuses on quantitative profiling of clinically relevant blood biomarkers and facilitates early disease recognition. However, the human metabolome is a dynamic environment affected by various genetic, physiological, and nutritional factors. Recent technical and data analysis advances in omics technologies set the stage for metabolomics integration into clinical chemistry and biochemical genetics laboratories. Undoubtedly, metabolomics analysis combined with a vigorous statistical and pathway analysis is a powerful approach in the identification of novel disease biomarkers, new therapeutic targets and, a tool to monitor the long-term metabolic effects of existing therapies. However, the lack of standardization for samples collection and processing, lack of harmonization and proficiency testing programs for metabolomics significantly challenge metabolomics integration into routine clinical laboratories workflows. In this Special Issue of Metabolites, we welcome original research and review manuscripts that explore current challenges in integrating metabolomics into laboratory medicine and/or expand our knowledge in current and emerging techniques to facilitate new insights and management of inherited metabolic disorders.

Dr. Yana Sandlers
Dr. Jessica M. Colon-Franco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  inborn errors of metabolism
  •  targeted and untargeted metabolomics
  •  laboratory medicine
  •  pathophysiology mechanism
  •  diagnostic markers
  •  novel therapeutic targets
  •  preanalytical and analytical phase challenges
  •  standartization in metabolomics

Published Papers (2 papers)

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Research

11 pages, 1486 KiB  
Article
Low Fasting Concentrations of Glucagon in Patients with Very Long-Chain Acyl-CoA Dehydrogenase Deficiency
by Rasmus Stenlid, Hannes Manell, Rikard Seth, Sara Y. Cerenius, Azazul Chowdhury, Camilla Roa Cortés, Isabelle Nyqvist, Thomas Lundqvist, Maria Halldin and Peter Bergsten
Metabolites 2023, 13(7), 780; https://doi.org/10.3390/metabo13070780 - 22 Jun 2023
Viewed by 1199
Abstract
(1) Background: Deficiencies of mitochondrial fatty acid oxidation (FAO) define a subgroup of inborn errors of metabolism, with medium-chain acyl-CoA dehydrogenase deficiency (MCAD) and very long-chain acyl-CoA dehydrogenase deficiency (VLCAD) being two of the most common. Hypoketotic hypoglycemia is a feared clinical complication [...] Read more.
(1) Background: Deficiencies of mitochondrial fatty acid oxidation (FAO) define a subgroup of inborn errors of metabolism, with medium-chain acyl-CoA dehydrogenase deficiency (MCAD) and very long-chain acyl-CoA dehydrogenase deficiency (VLCAD) being two of the most common. Hypoketotic hypoglycemia is a feared clinical complication and the treatment focuses on avoiding hypoglycemia. In contrast, carnitine uptake deficiency (CUD) is treated as a mild disease without significant effects on FAO. Impaired FAO has experimentally been shown to impair glucagon secretion. Glucagon is an important glucose-mobilizing hormone. If and how glucagon is affected in patients with VLCAD or MCAD remains unknown. (2) Methods: A cross-sectional study was performed with plasma hormone concentrations quantified after four hours of fasting. Patients with VLCAD (n = 10), MCAD (n = 7) and CUD (n = 6) were included. (3) Results: The groups were similar in age, sex, weight, and height. The glucagon and insulin levels were significantly lower in the VLCAD group compared to the CUD group (p < 0.05, respectively). The patients with CUD had glucagon concentrations similar to the normative data. No significant differences were seen in GLP-1, glicentin, glucose, amino acids, or NEFAs. (4) Conclusions: Low fasting concentrations of glucagon are present in patients with VLCAD and cannot be explained by altered stimuli in plasma. Full article
(This article belongs to the Special Issue Metabolomic Applications in Medical Strategy for Inborn Errors of Metabolism)
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31 pages, 9822 KiB  
Article
Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data
by Michiel Bongaerts, Purva Kulkarni, Alan Zammit, Ramon Bonte, Leo A. J. Kluijtmans, Henk J. Blom, Udo F. H. Engelke, David M. J. Tax, George J. G. Ruijter and Marcel J. T. Reinders
Metabolites 2023, 13(1), 97; https://doi.org/10.3390/metabo13010097 - 7 Jan 2023
Cited by 2 | Viewed by 1705
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Metabolomic Applications in Medical Strategy for Inborn Errors of Metabolism)
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