title: Metabolomic biosignature differentiates melancholic depressive patients from healthy controls.
abstract: 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. 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.
  
  
  
  With 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.
  
  
  
  We 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.
authors.affiliation: Department of Computer Science and Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA.
authors.forename: Yashu
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Janssen Research & Development, LLC, 3210 Merryfield Row, San Diego, CA, 92121, USA.
authors.forename: Lynn
authors.initials: L
authors.lastname: Yieh
authors.affiliation: Department of Computer Science and Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Yang
authors.affiliation: Janssen Research & Development, LLC, Turnhoutseweg 30, 2340, Beerse, Belgium.
authors.forename: Wilhelmus
authors.initials: W
authors.lastname: Drinkenburg
authors.affiliation: Janssen Research & Development, LLC, Turnhoutseweg 30, 2340, Beerse, Belgium.
authors.forename: Pieter
authors.initials: P
authors.lastname: Peeters
authors.affiliation: Janssen Research & Development, LLC, Turnhoutseweg 30, 2340, Beerse, Belgium.
authors.forename: Thomas
authors.initials: T
authors.lastname: Steckler
authors.affiliation: Janssen Research & Development, LLC, 1125 Trenton-Harbourton Road, Titusville, NJ, USA.
authors.forename: Vaibhav A
authors.initials: VA
authors.lastname: Narayan
authors.affiliation: Janssen Research & Development, LLC, 1125 Trenton-Harbourton Road, Titusville, NJ, USA.
authors.forename: Gayle
authors.initials: G
authors.lastname: Wittenberg
authors.affiliation: Department of Computer Science and Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, 85287, USA. jieping.ye@asu.edu.
authors.forename: Jieping
authors.initials: J
authors.lastname: Ye
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015415:Biomarkers / Q000097:blood*; D001774:Blood Chemical Analysis / Q000379:methods*; D003865:Major Depressive Disorder / Q000378:metabolism / Q000523:psychology*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D055815:Young Adult
chemical_list: D015415:Biomarkers
keywords: Biomarker; Classification; Major depressive disorder; Melancholic depression; Metabolomics