title: Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm.
abstract: Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and the autonomic nervous system. This study applied a machine-learning method to classify MDD and control groups by incorporating data from serum proteomic analysis and heart rate variability (HRV) analysis for the identification of novel peripheral biomarkers.
  
  
  
  The study subjects consisted of 25 drug-free female MDD patients and 25 age- and sex-matched healthy controls. First, quantitative serum proteome profiles were analyzed by liquid chromatography-tandem mass spectrometry using pooled serum samples from 10 patients and 10 controls. Next, candidate proteins were quantified with multiple reaction monitoring (MRM) in 50 subjects. We also analyzed 22 linear and nonlinear HRV parameters in 50 subjects. Finally, we identified a combined biomarker panel consisting of proteins and HRV indexes using a support vector machine with recursive feature elimination.
  
  
  
  A separation between MDD and control groups was achieved using five parameters (apolipoprotein B, group-specific component, ceruloplasmin, RMSSD, and SampEn) at 80.1% classification accuracy. A combination of HRV and proteomic data achieved better classification accuracy.
  
  
  
  A high classification accuracy can be achieved by combining multimodal information from heart rate dynamics and serum proteomics in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD. Further studies using larger, independent cohorts are needed to verify the role of these candidate biomarkers for MDD diagnosis.
authors.affiliation: Department of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Kim
authors.affiliation: Institute for Systems Biology, Seattle, WA, United States; Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yongin, Republic of Korea.
authors.forename: Min Young
authors.initials: MY
authors.lastname: Lee
authors.affiliation: Department of Neuropsychiatry, Dongguk University Medical School, Dongguk University International Hospital, Goyang, Republic of Korea.
authors.forename: Se Hyun
authors.initials: SH
authors.lastname: Kim
authors.affiliation: Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
authors.forename: Kyooseob
authors.initials: K
authors.lastname: Ha
authors.affiliation: Department of Applied Chemistry, College of Applied Science, Kyung Hee University, Yongin, Republic of Korea.
authors.forename: Kwang Pyo
authors.initials: KP
authors.lastname: Kim
authors.affiliation: Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea. Electronic address: aym@snu.ac.kr.
authors.forename: Yong Min
authors.initials: YM
authors.lastname: Ahn
mesh_terms: D000328:Adult; D015415:Biomarkers; D003865:Major Depressive Disorder / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008875:Middle Aged; D020543:Proteome / Q000378:metabolism*
chemical_list: D015415:Biomarkers; D020543:Proteome
keywords: Biomarker; Heart rate variability; Machine-learning; Major depressive disorder; Proteomics