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Metabolites
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Metabolic Profiles in Diseases: From Diagnosis to Characterization and Clinical...
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Metabolic Profiles in Diseases: From Diagnosis to Characterization and Clinical Management Volume 2

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 3680

Special Issue Editor

Dr. Daniel Monleon

E-Mail Website
Guest Editor
Department of Pathology, Medicine and Odontology Faculty, University of Valencia, 46010 Valencia, Spain
Interests: tumor metabolism for precision medicine in cancer; healthy aging; frailty and metabolic age; host- microbiota co-metabolism influence on health; cardiometabolic and cardiovascular disease; biophysical characterization of lipoproteins particles in plasma using NMR spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics, an experimental approach that provides information on a wide range of molecular processes, is gaining more and more importance in the clinical and pharmaceutical fields. The metabolome is closely linked to phenotype, and is an extremely useful tool for diagnosing diseases and evaluating the effects of treatments. The clinical applications of metabolomics in different therapeutic areas include, among others, the diagnosis and prognosis of diseases, the stratification of patients for the application of personalized therapies, the prediction of responses to therapeutic interventions, the monitoring of patients throughout treatment and/or the course of a disease, the detection of relapses/reappearances of disease, etc. This Special Issue of Metabolites, “Metabolic Profiles in Diseases: From Diagnosis to Characterization and Clinical Management”, is dedicated to metabolomic studies dealing with the potential clinical applications of metabolomics. Specific areas include the identification of disease biomarkers, tools for patient follow up, predictive models, technological developments for bringing the field from the lab bench to bedside, etc. Manuscripts dealing with other pertinent challenging issues are also highly desired.

Prof. Dr. Daniel Monleon
Guest Editor

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

  • biomarkers 
  • metabolism and disease 
  • clinical applications
  • translational metabolomics

Published Papers (2 papers)

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Research

16 pages, 2019 KiB  
Article
A Cohort Study of Gastric Fluid and Urine Metabolomics for the Prediction of Survival in Severe Prematurity
by Konstantia Besiri, Olga Begou, Olga Deda, Evmorfia Bataka, Christos Nakas, Helen Gika, Angeliki Kontou, Eleni Agakidou and Kosmas Sarafidis
Metabolites 2023, 13(6), 708; https://doi.org/10.3390/metabo13060708 - 30 May 2023
Cited by 1 | Viewed by 1513
Abstract
Predicting survival in very preterm infants is critical in clinical medicine and parent counseling. In this prospective cohort study involving 96 very preterm infants, we evaluated whether the metabolomic analysis of gastric fluid and urine samples obtained shortly after birth could predict survival [...] Read more.
Predicting survival in very preterm infants is critical in clinical medicine and parent counseling. In this prospective cohort study involving 96 very preterm infants, we evaluated whether the metabolomic analysis of gastric fluid and urine samples obtained shortly after birth could predict survival in the first 3 and 15 days of life (DOL), as well as overall survival up to hospital discharge. Gas chromatography–mass spectrometry (GC-MS) profiling was used. Uni- and multivariate statistical analyses were conducted to evaluate significant metabolites and their prognostic value. Differences in several metabolites were identified between survivors and non-survivors at the time points of the study. Binary logistic regression showed that certain metabolites in gastric fluid, including arabitol, and succinic, erythronic and threonic acids, were associated with 15 DOL and overall survival. Gastric glyceric acid was also associated with 15 DOL survival. Urine glyceric acid could predict survival in the first 3 DOL and overall survival. In conclusion, non-surviving preterm infants exhibited a different metabolic profile compared with survivors, demonstrating significant discrimination with the use of GC-MS-based gastric fluid and urine analyses. The results of this study support the usefulness of metabolomics in developing survival biomarkers in very preterm infants. Full article
(This article belongs to the Special Issue Metabolic Profiles in Diseases: From Diagnosis to Characterization and Clinical Management Volume 2)
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14 pages, 2184 KiB  
Article
Plasma Metabolite Signatures in Male Carriers of Genetic Variants Associated with Non-Alcoholic Fatty Liver Disease
by Lilian Fernandes Silva, Jagadish Vangipurapu, Anniina Oravilahti, Ville Männistö and Markku Laakso
Metabolites 2023, 13(2), 267; https://doi.org/10.3390/metabo13020267 - 13 Feb 2023
Cited by 2 | Viewed by 1812
Abstract
Both genetic and non-genetic factors are important in the pathophysiology of non-alcoholic fatty liver disease (NAFLD). The aim of our study was to identify novel metabolites and pathways associated with NAFLD by including both genetic and non-genetic factors in statistical analyses. We genotyped [...] Read more.
Both genetic and non-genetic factors are important in the pathophysiology of non-alcoholic fatty liver disease (NAFLD). The aim of our study was to identify novel metabolites and pathways associated with NAFLD by including both genetic and non-genetic factors in statistical analyses. We genotyped six genetic variants in the PNPLA3, TM6SF2, MBOAT7, GCKR, PPP1R3B, and HSD17B13 genes reported to be associated with NAFLD. Non-targeted metabolomic profiling was performed from plasma samples. We applied a previously validated fatty liver index to identify participants with NAFLD. First, we associated the six genetic variants with 1098 metabolites in 2 339 men without NAFLD to determine the effects of the genetic variants on metabolites, and then in 2 535 men with NAFLD to determine the joint effects of genetic variants and non-genetic factors on metabolites. We identified several novel metabolites and metabolic pathways, especially for PNPLA3, GCKR, and PPP1R38 variants relevant to the pathophysiology of NAFLD. Importantly, we showed that each genetic variant for NAFLD had a specific metabolite signature. The plasma metabolite signature was unique for each genetic variant, suggesting that several metabolites and different pathways are involved in the risk of NAFLD. The FLI index reliably identifies metabolites for NAFLD in large population-based studies. Full article
(This article belongs to the Special Issue Metabolic Profiles in Diseases: From Diagnosis to Characterization and Clinical Management Volume 2)
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