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

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• metabolomics
• feature selection
• disease clustering methods
• disease subgrouping/subphenotyping
• prognostic enrichment
• predictive enrichment
• ARDS subphenotypes
• sepsis subphenotypes
• asthma subphenotypes
• possibly TBI subphenotypes