Special Issue "Using Metabolomics to Subphenotype Disease and Therapeutic Response"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Dr. Brent Warren Winston
E-Mail Website
Guest Editor
Departments of Critical Care, Medicine and Biochemistry and Molecular Biology, Health Research Innovation Center (HRIC), University of Calgary, Room 4C64, 3280 Hospital Drive N.W., Calgary, AB T2N 4Z6, Canada
Interests: metabolomics in human diseases including: sepsis, ARDS and traumatic brain injury
Dr. Angela Rogers
E-Mail Website
Co-Guest Editor
Medicine-Med/Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
Interests: ARDS; sepsis; ICU outcomes; COVID-19
Dr. Kathleen A. Stringer
E-Mail Website
Co-Guest Editor
1. Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, 428 Church St, Ann Arbor, MI 48109, USA
2. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USA
Interests: translational metabolomics and pharmacometabolomics in critical care
Dr. Chel Hee Lee
E-Mail Website
Co-Guest Editor
Department of Critical Care Medicine, Alberta Health Services & University of Calgary, Calgary, AB T2N 4Z6, Canada
Interests: methodologies in clustering and classification

Special Issue Information

Dear Colleagues, 

To understand subgrouping of disease, one has to first understand the subgrouping terminology. As described by Prescott et al. (2016), “subphenotype is defined as a subgroup among a disease entity that (a) is at highest risk for poor outcome (prognostic enrichment) or (b) shares similar underlying biological factors and/or a different reaction to medical measures (predictive enrichment)”. Enrichment strategies offer the potential to reduce heterogeneity and, hence, allow one to use an approach to precision medicine by selecting the subgroup most likely to benefit. Of note, there is a difference in the definition of subtype, endotype, and phenotype.

Defining subgroups is often achieved by clustering. Exactly how the clustering methodology is being used as a means of determining subgroups is a very important topic currently undergoing much discussion, but there is a need to include metabolomics in this discussion. Various clustering techniques can be used in metabolomics to help subgroup disease. Clustering techniques are unsupervised learning algorithms widely discussed in machine learning and artificial intelligence. Clustering analysis is based on distance, centroid, and density. K-means, hierarchical algorithm, DBSCAN, OPTICS, spectral clustering, network clustering, latent cluster analysis, affinity propagation, and BIRCH are popular clustering methods. Many studies have been conducted with hard-type clustering (i.e., one object has only one cluster membership). Recent advances allow for soft-type clustering (i.e., one object may have more than two cluster memberships). Central to clustering is appropriate feature selection methods. Feature selection is an emerging issue since the result depends on the input used for the algorithm. Importantly, validation indices, performance measures, and sample size requirements are not well studied with clustering algorithms, especially in terms of metabolomics, and it is difficult to find good practical guidance. Moreover, few studies have been conducted to compare algorithms. Finally, classical algorithms use only continuous features while recent algorithms have started utilizing mixed data of continuous, ordinal, nominal, and count features.

The purpose of this Metabolites Special Issue is to discuss the methodologies used in metabolomics to help subgroup disease, to show how metabolomics can be used to enrich the process of subphenotyping of disease, and to demonstrate how this is being accomplished for several disease processes.

Dr. Brent Warren Winston
Guest Editor
Dr. Angela Rogers
Dr. Kathleen A. Stringer
Dr. Chel Hee Lee
Co-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 papers will be 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 2000 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
  • feature selection
  • disease clustering methods
  • disease subgrouping/subphenotyping
  • prognostic enrichment
  • predictive enrichment
  • ARDS subphenotypes
  • sepsis subphenotypes
  • asthma subphenotypes
  • possibly TBI subphenotypes

Published Papers (1 paper)

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Research

Article
Exhaled Metabolite Patterns to Identify Recent Asthma Exacerbations
Metabolites 2021, 11(12), 872; https://doi.org/10.3390/metabo11120872 - 15 Dec 2021
Viewed by 427
Abstract
Asthma is a chronic respiratory disease that can lead to exacerbations, defined as acute episodes of worsening respiratory symptoms and lung function. Predicting the occurrence of these exacerbations is an important goal in asthma management. The measurement of exhaled breath by electronic nose [...] Read more.
Asthma is a chronic respiratory disease that can lead to exacerbations, defined as acute episodes of worsening respiratory symptoms and lung function. Predicting the occurrence of these exacerbations is an important goal in asthma management. The measurement of exhaled breath by electronic nose (eNose) may allow for the monitoring of clinically unstable asthma and exacerbations. However, data on its ability to perform this is lacking. We aimed to evaluate whether eNose could identify patients that recently had asthma exacerbations. We performed a cross-sectional study, measuring exhaled breath using the SpiroNose in adults with a physician-reported diagnosis of asthma. Patients were randomly divided into a training (n = 252) and validation (n = 109) set. For the analysis of eNose signals, principal component (PC) and linear discriminant analysis (LDA) were performed. LDA, based on PC1-4, reliably discriminated between patients who had a recent exacerbation from those who had not (training receiver operating characteristic (ROC)–area under the curve (AUC) = 0.76,95% CI 0.69–0.82), (validation AUC = 0.76, 95% CI 0.64–0.87). Our study showed that, exhaled breath analysis using eNose could accurately identify asthma patients who recently had an exacerbation, and could indicate that asthma exacerbations have a specific exhaled breath pattern detectable by eNose. Full article
(This article belongs to the Special Issue Using Metabolomics to Subphenotype Disease and Therapeutic Response)
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