Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression

Abstract

Background: Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.

Objectives: To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.

Methods: A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.

Results: In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.

Conclusions: The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.

Keywords: Gene expression; Knowledge Discovery in Databases; Longitudinal data; Shape-based clustering; Type 2 Diabetes.

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Figure 1:

IL-1β (2-^{Δ Ct}) expression over time, grouped according to the slopes between time points using the three clustering algorithms described in Section 3.3: (a) K-Means (Upper panel), (b) Kernel K-Means (Center panel) and (c) K-Medoids (Lower panel).