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Metabolomics analysis of post-traumatic stress disorder symptoms in World Trade Center responders.
Kuan, Pei-Fen; Yang, Xiaohua; Kotov, Roman; Clouston, Sean; Bromet, Evelyn; Luft, Benjamin J.
Affiliation
  • Kuan PF; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. PeiFen.Kuan@stonybrook.edu.
  • Yang X; Department of Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Kotov R; Department of Psychiatry, Stony Book University, Stony Brook, NY, USA.
  • Clouston S; Department of Family, Population and Preventive Medicine, Stony Book University, Stony Brook, NY, USA.
  • Bromet E; Department of Psychiatry, Stony Book University, Stony Brook, NY, USA.
  • Luft BJ; Department of Medicine, Stony Brook University, Stony Brook, NY, USA. Benjamin.Luft@stonybrookmedicine.edu.
Transl Psychiatry ; 12(1): 174, 2022 04 28.
Article in En | MEDLINE | ID: mdl-35484105
ABSTRACT
Metabolomics has yielded promising insights into the pathophysiology of post-traumatic stress disorder (PTSD). The current study expands understanding of the systems-level effects of metabolites by using global metabolomics and complex lipid profiling in plasma samples from 124 World Trade Center responders (56 PTSD, 68 control) on 1628 metabolites. Differential metabolomics analysis identified hexosylceramide HCER(261) associated with PTSD at FDR < 0.1. The multi-metabolite composite score achieved an AUC of 0.839 for PTSD versus unaffected control classification. Independent component analysis identified three metabolomic modules significantly associated with PTSD. These modules were significantly enriched in bile acid metabolism, fatty acid metabolism and pregnenolone steroids, which are involved in innate immunity, inflammatory process and neuronal excitability, respectively. Integrative analysis of metabolomics and our prior proteomics datasets on subsample of 96 responders identified seven proteomic modules significantly correlated with metabolic modules. Overall, our findings shed light on the molecular alterations and identify metabolomic-proteomic signatures associated with PTSD by using machine learning and network approaches to enhance understanding of the pathways implicated in PTSD. If present results are confirmed in follow-up studies, they may inform development of novel treatments.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Stress Disorders, Post-Traumatic / Emergency Responders Type of study: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Aspects: Patient_preference Limits: Humans Language: En Journal: Transl Psychiatry Year: 2022 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Stress Disorders, Post-Traumatic / Emergency Responders Type of study: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Aspects: Patient_preference Limits: Humans Language: En Journal: Transl Psychiatry Year: 2022 Document type: Article Affiliation country: