Abstract
BackgroundMajor depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are “melancholic depression” and “anxious depression”. Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression.ResultsWith the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress.ConclusionsWe find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2953-2) contains supplementary material, which is available to authorized users.
Highlights
Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology
Classification performance on MDD subtypes We first compared the performance of the Random Forest classifier using kNN3 imputation using individual metabolites (228 features) on three different classification tasks illustrated in Fig. 1.: (1) MDD vs. Healthy Control, (2) Anxious Depressed vs. Healthy Control, (3) Melancholic Depressed vs. Healthy Control
The classifier performance was higher for Melancholic Depressed patients than for the other two subgroups (Fig. 1b), suggesting that Melancholic Depressed patients may be more homogenous at the biological level and easer to predict using a metabolomics biosignature
Summary
Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Liu et al BMC Genomics (2016) 17:669 behind other diseases This is reflected in a megaanalysis of GWAS data performed by the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium on 9240 MDD cases and 9519 controls which identified no replicable markers, despite the detection of significant markers in 94 % of other disease tested in populations of the same size [3]. Even within individual symptom items there is heterogeneity with criteria such as “gaining weight or losing weight”, “hypersomnia or insomnia”, “psychomotor agitation or retardation” It would be surprising if such clinical heterogeneity were not reflected in the biology of MDD
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