Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The metabolome-wide signature of major depressive disorder

Abstract

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e−07) and saturated (P = 3.2e−05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e−4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Forest plot of the 34 internally replicated metabolites.

Similar content being viewed by others

Data availability

The data used to support the findings of this study are available upon reasonable request from NESDA, Amsterdam: nesda@amsterdamumc.nl. Information on how to request the study data, including the data sharing policy, can be found at https://www.nesda.nl/nesda-english/.

References

  1. MacDonald K, Krishnan A, Cervenka E, Hu G, Guadagno E, Trakadis Y. Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review. Am J Med Genet B Neuropsychiatr Genet. 2019;180:122–37.

    Article  PubMed  Google Scholar 

  2. Bot M, Milaneschi Y, Al-Shehri T, Amin N, Garmaeva S, Onderwater GLJ, et al. Metabolomics Profile in Depression: A Pooled Analysis of 230 Metabolic Markers in 5283 Cases With Depression and 10,145 Controls. Biological Psychiatry. 2020;87:409–18.

    Article  CAS  PubMed  Google Scholar 

  3. Julkunen H, Cichońska A, Tiainen M, Koskela H, Nybo K, Mäkelä V, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14:604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pan A, Keum N, Okereke OI, Sun Q, Kivimaki M, Rubin RR, et al. Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes Care. 2012;35:1171–80.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Milaneschi Y, Simmons WK, van Rossum EFC, Penninx BW. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24:18–33.

    Article  CAS  PubMed  Google Scholar 

  6. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care. 2008;31:2383–90.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Van der Kooy K, van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A. Depression and the risk for cardiovascular diseases: systematic review and meta analysis. Int J Geriatr Psychiatry. 2007;22:613–26.

    Article  PubMed  Google Scholar 

  8. Zacharias HU, Hertel J, Johar H, Pietzner M, Lukaschek K, Atasoy S, et al. A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression. Mol Psychiatry. 2021;26:7372–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. van der Spek A, Stewart ID, Kühnel B, Pietzner M, Alshehri T, Gauß F, et al. Circulating metabolites modulated by diet are associated with depression. Mol Psychiatry. 2023;28:3874–87.

  10. Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Mol Psychiatry. 2021;26:7393–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Milaneschi Y, Arnold M, Kastenmüller G, Dehkordi SM, Krishnan RR, Dunlop BW, et al. Genomics-based identification of a potential causal role for acylcarnitine metabolism in depression. J Affect Disord. 2022;307:254–63.

    Article  CAS  PubMed  Google Scholar 

  12. Milaneschi Y, Peyrot WJ, Nivard MG, Mbarek H, Boomsma DI, Penninx WJH. B. A role for vitamin D and omega-3 fatty acids in major depression? An exploration using genomics. Transl Psychiatry. 2019;9:219.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Surendran P, Stewart ID, Au Yeung VPW, Pietzner M, Raffler J, Wörheide MA, et al. Rare and common genetic determinants of metabolic individuality and their effects on human health. Nat Med. 2022;28:2321–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience. 2019;22:343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Penninx BWJH, Eikelenboom M, Giltay EJ, van Hemert AM, Riese H, Schoevers RA, et al. Cohort profile of the longitudinal Netherlands Study of Depression and Anxiety (NESDA) on etiology, course and consequences of depressive and anxiety disorders. J Affect Disord. 2021;287:69–77.

    Article  PubMed  Google Scholar 

  16. Penninx BWJH, Beekman ATF, Smit JH, Zitman FG, Nolen WA, Spinhoven P, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 2008;17:121–40.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Do KT, Wahl S, Raffler J, Molnos S, Laimighofer M, Adamski J, et al. Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics. 2018;14:128.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rush AJ, Giles DE, Schlesser MA, Fulton CL, Weissenburger J, Burns C. The Inventory for Depressive Symptomatology (IDS): preliminary findings. Psychiatry Res. 1986;18:65–87.

    Article  CAS  PubMed  Google Scholar 

  19. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exercise. 2003;35:1381–95.

    Article  Google Scholar 

  20. Gerrits MM, van Oppen P, van Marwijk HW, van der Horst H, Penninx BW. The impact of chronic somatic diseases on the course of depressive and anxiety disorders. Psychother Psychosom. 2013;82:64–66.

    Article  PubMed  Google Scholar 

  21. Gaspersz R, Lamers F, Beekman ATF, van Hemert AM, Schoevers RA, Penninx BWJH. The Impact of Depressive Disorder Symptoms and Subtypes on 6-Year Incidence of Somatic Diseases. Psychother Psychosom. 2018;87:308–10.

    Article  PubMed  Google Scholar 

  22. Liu J, Lahousse L, Nivard MG, Bot M, Chen L, van Klinken JB, et al. Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas. Nat Med. 2020;26:110–7.

    Article  CAS  PubMed  Google Scholar 

  23. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40:597–608.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bowden J, Del Greco M F, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783–802.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52.

    Article  PubMed  Google Scholar 

  31. Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51:1339–48.

    Article  CAS  PubMed  Google Scholar 

  32. Reynolds LM, Dutta R, Seeds MC, Lake KN, Hallmark B, Mathias RA, et al. FADS genetic and metabolomic analyses identify the ∆5 desaturase (FADS1) step as a critical control point in the formation of biologically important lipids. Sci Rep. 2020;10:15873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Galli C, Calder PC. Effects of fat and fatty acid intake on inflammatory and immune responses: a critical review. Ann Nutr Metab. 2009;55:123–39.

    Article  CAS  PubMed  Google Scholar 

  34. Bazinet RP, Layé S. Polyunsaturated fatty acids and their metabolites in brain function and disease. Nat Rev Neurosci. 2014;15:771–85.

    Article  CAS  PubMed  Google Scholar 

  35. Hillard CJ. Circulating Endocannabinoids: From Whence Do They Come and Where are They Going? Neuropsychopharmacology. 2018;43:155–72.

    Article  CAS  PubMed  Google Scholar 

  36. zu Heringdorf DM. Lysophospholipids. In: Offermanns S, Rosenthal W, editors. Encyclopedia of Molecular Pharmacology. Berlin, Heidelberg: Springer; 2008. p. 710–6.

  37. Chen S, Wei C, Gao P, Kong H, Jia Z, Hu C, et al. Effect of Allium macrostemon on a rat model of depression studied by using plasma lipid and acylcarnitine profiles from liquid chromatography/mass spectrometry. J Pharm Biomed Anal. 2014;89:122–9.

    Article  CAS  PubMed  Google Scholar 

  38. Liu X, Li J, Zheng P, Zhao X, Zhou C, Hu C, et al. Plasma lipidomics reveals potential lipid markers of major depressive disorder. Anal Bioanal Chem. 2016;408:6497–507.

    Article  CAS  PubMed  Google Scholar 

  39. Chan P, Suridjan I, Mohammad D, Herrmann N, Mazereeuw G, Hillyer LM, et al. Novel Phospholipid Signature of Depressive Symptoms in Patients With Coronary Artery Disease. J Am Heart Assoc. 2018;7:e008278.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pinto B, Conde T, Domingues I, Domingues MR. Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review. Int J Mol Sci. 2022;23:2032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Baloni P, Arnold M, Buitrago L, Nho K, Moreno H, Huynh K, et al. Multi-Omic analyses characterize the ceramide/sphingomyelin pathway as a therapeutic target in Alzheimer’s disease. Commun Biol. 2022;5:1074.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang G, Bieberich E. Sphingolipids in neurodegeneration (with focus on ceramide and S1P). Adv Biol Regul. 2018;70:51–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hao Y, Guo M, Feng Y, Dong Q, Cui M. Lysophospholipids and Their G-Coupled Protein Signaling in Alzheimer’s Disease: From Physiological Performance to Pathological Impairment. Front Mol Neurosci. 2020;13:58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Thesing CS, Bot M, Milaneschi Y, Giltay EJ, Penninx BWJH. Omega-3 and omega-6 fatty acid levels in depressive and anxiety disorders. Psychoneuroendocrinology. 2018;87:53–62.

    Article  CAS  PubMed  Google Scholar 

  45. Grosso G, Micek A, Marventano S, Castellano S, Mistretta A, Pajak A, et al. Dietary n-3 PUFA, fish consumption and depression: A systematic review and meta-analysis of observational studies. J Affect Disord. 2016;205:269–81.

    Article  CAS  PubMed  Google Scholar 

  46. Accinni T, Panfili M, Tarsitani L, Biondi M, Pasquini M, Maraone A. A Revision on the Effectiveness of Omega-3 Polyunsaturated Fatty Acids as Monotherapy in the Treatment of Major Depressive Disorder. Int J Clin Pract. 2022;2022:3801235.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lamon-Fava S, So J, Mischoulon D, Ziegler TR, Dunlop BW, Kinkead B, et al. Dose- and time-dependent increase in circulating anti-inflammatory and pro-resolving lipid mediators following eicosapentaenoic acid supplementation in patients with major depressive disorder and chronic inflammation. Prostaglandins Leukot Essent Fatty Acids. 2021;164:102219.

    Article  CAS  PubMed  Google Scholar 

  48. Mischoulon D, Dunlop BW, Kinkead B, Schettler PJ, Lamon-Fava S, Rakofsky JJ, et al. Omega-3 Fatty Acids for Major Depressive Disorder With High Inflammation: A Randomized Dose-Finding Clinical Trial. J Clin Psychiatry. 2022;83:21m14074.

    Article  PubMed  Google Scholar 

  49. Kyriacou DN, Lewis RJ. Confounding by Indication in Clinical Research. JAMA. 2016;316:1818–9.

    Article  PubMed  Google Scholar 

  50. Yang K, Zhao Z, Gross RW, Han X. Identification and quantitation of unsaturated fatty acid isomers by electrospray ionization tandem mass spectrometry: a shotgun lipidomics approach. Anal Chem. 2011;83:4243–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Davyson E, Shen X, Gadd DA, Bernabeu E, Hillary RF, McCartney DL, et al. Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids. Biol Psychiatry. 2023;S0006-3223:00055–0.

    Google Scholar 

  52. Bot M, Brouwer IA, Roca M, Kohls E, Penninx BWJH, Watkins E, et al. Effect of Multinutrient Supplementation and Food-Related Behavioral Activation Therapy on Prevention of Major Depressive Disorder Among Overweight or Obese Adults With Subsyndromal Depressive Symptoms: The MooDFOOD Randomized Clinical Trial. JAMA. 2019;321:858–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Okereke OI, Vyas CM, Mischoulon D, Chang G, Cook NR, Weinberg A, et al. Effect of Long-term Supplementation With Marine Omega-3 Fatty Acids vs Placebo on Risk of Depression or Clinically Relevant Depressive Symptoms and on Change in Mood Scores: A Randomized Clinical Trial. JAMA. 2021;326:2385–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

RJ received funding from ZonMw: The Netherlands Organization for Health Research and Development (project number: 636310017, research program GGZ). This project is partially supported by funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 848146. In addition, the work was funded through NIMH grant number R01MH108348 and through a series of grants issued to Dr. KaddurahDaouk (PI) through NIA including U01AG061359, RF1AG057452, and RF1AG051550 that supported large number of scientists working on metabolomics and neuropsychiatric disorders and role for acylcarnitines. MA and GK received funding (through their institutions) from the National Institutes of Health/National Institute on Aging through grants RF1AG058942, RF1AG059093, U01AG061359, U19AG063744, and R01AG069901. We would like to thank the Psychiatric Genomics Consortium and 23andMe, including its research participants and employees, for making this work possible by sharing GWAS summary statistics. BWD has received research support from Boehringer Ingelheim, Compass Pathways, NIMH, Otsuka, Sage, Usona Institute, and Takeda and has served as a consultant for Biohaven, Cerebral Therapeutics, Greenwich Biosciences, Myriad Neuroscience, NRx Pharmaceuticals, Otsuka, Sage, and Sophren Therapeutics. AJR has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk Technologies, Inc., ICON, PLC, Johnson and Johnson (Janssen), Liva-Nova, MindStreet, Inc., Neurocrine Biosciences Inc., Otsuka-US; speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Wolters Kluwer Health, Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co-inventor on two patents: U.S. Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and U.S. Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. The infrastructure for the NESDA study (http://www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (ZonMw, grant number 10-000-1002) and financial contributions by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Leiden University Medical Center, Leiden University, GGZ Rivierduinen, University Medical Center Groningen, University of Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Rob Giel Onderzoekscentrum). We thank Dr. Najaf Amin for sharing results.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

RJ and YM performed analysis and wrote the manuscript. DS, GK and MA performed pre processing of the metabolite data. XH, BD, AR, RK and BP designed the study and helped writing and interpreting.

Corresponding authors

Correspondence to Rick Jansen or Rima Kaddurah-Daouk.

Ethics declarations

Competing interests

AJR has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk, Johnson and Johnson (Janssen), Liva-Nova, Neurocrine Biosciences Inc., Otsuka-US; speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co-inventor on two patents: U.S. Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and U.S. Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. M.A. and G.K. are co-inventors (through Duke University/Helmholtz Zentrum München) on patents on applications of metabolomics in diseases of the central nervous system and hold equity in Chymia LLC and IP in PsyProtix and Atai that are exploring the potential for therapeutic applications targeting mitochondrial metabolism in depression. RKD is funded by National Institute on Aging [U19AG063744, U01AG061359, 1RF1AG058942, 1RF1AG057452, RF1AG051550, and R01AG046171] and National Institute of Mental Health [R01MH108348]. This funding enabled consortia that she leads including the Mood Disorder Precision Medicine Consortium, the Alzheimer’s Disease Metabolomics Consortium, and the Alzheimer Gut Microbiome Project that contributed to acylcarnitine discoveries. She is an inventor on key patents in the field of Metabolomics and hold equity in Metabolon, a biotech company in North Carolina. In addition, she holds patents licensed to Chymia LLC and PsyProtix with royalties and ownership. The funders listed above had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication. BWD has received research support from Acadia, Compass, Aptinyx, NIMH, Sage, Otsuka, and Takeda, and has served as a consultant to Greenwich Biosciences, Myriad Neuroscience, Otsuka, Sage, and Sophren Therapeutics. All the other authors declare no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jansen, R., Milaneschi, Y., Schranner, D. et al. The metabolome-wide signature of major depressive disorder. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02613-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-024-02613-6

Search

Quick links