Christopher R Brydges1, Sudeepa Bhattacharyya2, Siamak Mahmoudian Dehkordi3, Yuri Milaneschi4, Brenda Penninx5, Rick Jansen6, Bruce S Kristal7, Xianlin Han8, Matthias Arnold9, Gabi Kastenmüller10, Mandakh Bekhbat11, Helen S Mayberg12, W Edward Craighead13, A John Rush14, Oliver Fiehn1, Boadie W Dunlop11, Rima Kaddurah-Daouk15. 1. West Coast Metabolomics Center, University of California, Davis, USA. 2. Arkansas Biosciences Institute, Department of Biological Sciences, Arkansas State University, AR, USA. 3. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA. 4. Amsterdam UMC / GGZ inGeest Research & Innovation, Amsterdam, Netherlands. 5. Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands. 6. Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Netherlands; Department of Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, the Netherlands. 7. Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA. 8. University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. 9. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 10. Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 11. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA. 12. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 13. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA; Department of Psychology, Emory University, Atlanta, GA, USA. 14. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Psychiatry, Health Sciences Center, Texas Tech University, Permian Basin, TX, USA; Duke-National University of Singapore, Singapore. 15. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. Electronic address: kaddu001@mc.duke.edu.
Abstract
BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.
BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.
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