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Metabolites
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Metabolomics: An Emerging Potential Approach to Study Critical Illnesses
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Metabolomics: An Emerging Potential Approach to Study Critical Illnesses

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 12493

Special Issue Editors

Dr. Elizabeth Lusczek

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Guest Editor
Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
Interests: metabolomics; NMR; mass spectrometry; trauma; burn; critical illness; biomarker
Dr. Helen G. Gika

E-Mail Website
Guest Editor
Laboratory of Forensic Medicine and Toxicology, School of Medicine, Aristotle University Thessaloniki, Thessaloniki, Greece
Interests: bioanalysis of small molecules; metabolomics; QA/QC strategies in metabolomics; LC-MS; GC-MS; biomarker discovery; disease biomarkers; diagnostic/prognostic
Special Issues, Collections and Topics in MDPI journals
Dr. Olga Deda

E-Mail Website
Guest Editor
1. School of Medicine, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece
2. Greece & Biomic_AUTh, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, 10th km Thessaloniki-Thermi Rd., 57001 Thessaloniki, Greece
Interests: LC-MS/MS; GC-MS and NMR metabolic profiling; biochemical interpretation of metabolomics data; designing and carrying out procedures on rodents
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The reasons for the disproportionately high number of negative randomized controlled trials in critical illness extend beyond improving methodologies, power, and appropriate outcome measurements. Rather, the heterogeneous and dynamic nature of critical illness makes it challenging to prospectively design observational research and clinical trials, limiting our ability to find high-quality therapies to improve outcomes. Instead, precision medicine approaches rooted in translational -omics science, health informatics, and predictive analytics are required to develop targeted, effective therapies and improve patient outcomes.

Recently, efforts have been made to phenotype critical illnesses such as sepsis, a shift away from previous efforts to evaluate treatments and prognostic markers in broadly chosen groups of patients. These efforts suggest that disease phenotypes are not as uniform as previously believed, and as such, different treatments are required for patients with different disease phenotypes. Metabolomics and metabolic phenotyping are reflective of interactions between an individual’s genetics, gut microbiome, and environment (e.g., diet, pharmaceuticals/medical interventions, and exposures) and are well suited to characterizing an individual’s health status at a given point in time or a patient’s response to therapeutic interventions.

Topics of particular interest include, but are not limited to, studies using metabolomics to phenotype critically ill patients, to evaluate patients’ clinical trajectories, to evaluate specific interventions, and to evaluate prognostic and/or diagnostic biomarkers.

Dr. Elizabeth Lusczek
Dr. Helen G. Gika
Dr. Olga Deda
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

  • metabolomics
  • metabolic phenotype
  • critical illness
  • sepsis
  • precision medicine
  • pharmacometabolomics

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Related Special Issue

Published Papers (4 papers)

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Research

15 pages, 2145 KiB  
Article
An NMR-Based Model to Investigate the Metabolic Phenoreversion of COVID-19 Patients throughout a Longitudinal Study
by Rubén Gil-Redondo, Ricardo Conde, Maider Bizkarguenaga, Chiara Bruzzone, Ana Laín, Beatriz González-Valle, Milagros Iriberri, Carlos Ramos-Acosta, Eduardo Anguita, Juan Ignacio Arriaga Lariz, Pedro Pablo España Yandiola, Miguel Ángel Moran, Mario Ernesto Jiménez-Mercado, Leire Egia-Mendikute, María Luisa Seco, Hartmut Schäfer, Claire Cannet, Manfred Spraul, Asís Palazón, Nieves Embade, Shelly C. Lu, Julien Wist, Jeremy K. Nicholson, José M. Mato and Oscar Milletadd Show full author list remove Hide full author list
Metabolites 2022, 12(12), 1206; https://doi.org/10.3390/metabo12121206 - 1 Dec 2022
Cited by 6 | Viewed by 3460
Abstract
After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination [...] Read more.
After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts. Full article
(This article belongs to the Special Issue Metabolomics: An Emerging Potential Approach to Study Critical Illnesses)
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18 pages, 3243 KiB  
Article
Metabolic Phenotyping Study of Mouse Brain Following Microbiome Disruption by C. difficile Colonization
by Olga Deda, Melina Kachrimanidou, Emily G. Armitage, Thomai Mouskeftara, Neil J. Loftus, Ioannis Zervos, Ioannis Taitzoglou and Helen Gika
Metabolites 2022, 12(11), 1039; https://doi.org/10.3390/metabo12111039 - 28 Oct 2022
Cited by 4 | Viewed by 1948
Abstract
Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. [...] Read more.
Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. In the present study, a murine model was used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the effects of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the brain metabolome for the first time. Four different metabolomic-based methods (targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS, targeted GC-MS/MS, and untargeted GC-MS) were applied, resulting in the identification of 217 unique metabolites in the brain extracts, mainly glycerophospholipids, glycerolipids, amino acids, carbohydrates, and fatty acids. Univariate and multivariate statistical analysis revealed that CDI, as well as the subsequent treatments, altered significantly several brain metabolites, probably due to gut dysbiosis, and affected the brain through the gut–brain axis. Notably, none of the therapeutic approaches completely restored the brain metabolic profile to the original, healthy, and non-infected phenotype, even after 10 days of treatment. Full article
(This article belongs to the Special Issue Metabolomics: An Emerging Potential Approach to Study Critical Illnesses)
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16 pages, 1401 KiB  
Article
Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial
by Eleftherios Panteris, Olga Deda, Andreas S. Papazoglou, Efstratios Karagiannidis, Theodoros Liapikos, Olga Begou, Thomas Meikopoulos, Thomai Mouskeftara, Georgios Sofidis, Georgios Sianos, Georgios Theodoridis and Helen Gika
Metabolites 2022, 12(9), 816; https://doi.org/10.3390/metabo12090816 - 30 Aug 2022
Cited by 7 | Viewed by 2636
Abstract
Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels [...] Read more.
Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691–0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD. Full article
(This article belongs to the Special Issue Metabolomics: An Emerging Potential Approach to Study Critical Illnesses)
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16 pages, 2248 KiB  
Article
Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients
by Sultan S. Abdelhamid, Jacob Scioscia, Yoram Vodovotz, Junru Wu, Anna Rosengart, Eunseo Sung, Syed Rahman, Robert Voinchet, Jillian Bonaroti, Shimena Li, Jennifer L. Darby, Upendra K. Kar, Matthew D. Neal, Jason Sperry, Jishnu Das and Timothy R. Billiar
Metabolites 2022, 12(9), 774; https://doi.org/10.3390/metabo12090774 - 23 Aug 2022
Cited by 7 | Viewed by 3657
Abstract
Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers [...] Read more.
Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights. Full article
(This article belongs to the Special Issue Metabolomics: An Emerging Potential Approach to Study Critical Illnesses)
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