Skip to main content

Advertisement

Log in

Plasma lipidomics reveals potential lipid markers of major depressive disorder

  • Research Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Major depressive disorder (MDD) is a grave debilitating mental disease with a high incidence and severely impairs quality of life. Therefore, its physiopathological basis study and diagnostic biomarker discovery are extremely valuable. In this study, a non-targeted lipidomics strategy using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was performed to reveal differential lipids between MDD (n = 60) and healthy controls (HCs, n = 60). Validation of changed lipid species was performed in an independent batch including 75 MDD and 52 HC using the same lipidomic method. Pronouncedly changed lipid species in MDD were discovered, which mainly were lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), 1-O-alkyl-2-acyl-PE (PE O), 1-O-alkyl-2-acyl-PC (PC O), sphingomyelin (SM), diacylglycerol (DG), and triacylglycerol (TG). Among these lipid species, LPC, LPE, PC, PE, PI, TG, etc. remarkably increased in MDD and showed pronounced positive relationships with depression severity, while 1-O-alkyl-2-acyl-PE and SM with odd summed carbon number significantly decreased in MDD and demonstrated negative relationships with depression severity. A combinational lipid panel including LPE 20:4, PC 34:1, PI 40:4, SM 39:1, 2, and TG 44:2 was defined as potential diagnostic biomarker with a good sensitivity and specificity for distinguishing MDD from HCs. Our study brings insights into lipid metabolism disorder in MDD and provides a specific potential biomarker for MDD diagnose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. The epidemiology of major depressive disorder—results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289:3095–105.

    Article  Google Scholar 

  2. Reynolds EH. Brain and mind: a challenge for WHO. Lancet. 2003;361:1924–5.

    Article  CAS  Google Scholar 

  3. Huang TL, Lin CC. Advances in biomarkers of major depressive disorder. Adv Clin Chem. 2015;68:177–204.

    Article  Google Scholar 

  4. Vancampfort D, Correll CU, Wampers M, Sienaert P, Mitchell AJ, De Herdt A, et al. Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychol Med. 2014;44:2017–28.

    Article  CAS  Google Scholar 

  5. Rethorst CD, Bernstein I, Trivedi MH. Inflammation, obesity, and metabolic syndrome in depression: analysis of the 2009–2010 National Health and Nutrition Examination Survey (NHANES). J Clin Psychiatry. 2014;75:E1428–E32.

    Article  Google Scholar 

  6. Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet. 2009;374:609–19.

    Article  Google Scholar 

  7. Huang TL, Chen JF. Lipid and lipoprotein levels in depressive disorders with melancholic feature or atypical feature and dysthymia. Psychiatry Clin Neurosci. 2004;58:295–9.

    Article  CAS  Google Scholar 

  8. Sevincok L, Buyukozturk A, Dereboy F. Serum lipid concentrations in patients with comorbid generalized anxiety disorder and major depressive disorder. Can J Psychiatry. 2001;46:68–71.

    CAS  Google Scholar 

  9. Martins-de-Souza D. Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder. Dialogues Clin Neurosci. 2014;16:63–73.

    Google Scholar 

  10. Shao WH, Chen JJ, Fan SH, Lei Y, Xu HB, Zhou J, et al. Combined metabolomics and proteomics analysis of major depression in an animal model: perturbed energy metabolism in the chronic mild stressed rat cerebellum. OMICS. 2015;19:383–92.

    Article  CAS  Google Scholar 

  11. Zheng P, Gao HC, Qi ZG, Jia JM, Li FF, Chen JJ, et al. Peripheral metabolic abnormalities of lipids and amino acids implicated in increased risk of suicidal behavior in major depressive disorder. Metabolomics. 2013;9:688–96.

    Article  CAS  Google Scholar 

  12. Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, et al. Plasma metabonomics as a novel diagnostic approach for major depressive disorder. J Proteome Res. 2012;11:1741–8.

    Article  CAS  Google Scholar 

  13. Zheng P, Wang Y, Chen L, Yang D, Meng H, Zhou D, et al. Identification and validation of urinary metabolite biomarkers for major depressive disorder. Mol Cell Proteomics. 2013;12:207–14.

    Article  Google Scholar 

  14. Liu XJ, Li ZY, Li ZF, Gao XX, Zhou YZ, Sun HF, et al. Urinary metabonomic study using a CUMS rat model of depression. Magn Reson Chem. 2012;50:187–92.

    Article  CAS  Google Scholar 

  15. Ni Y, Su M, Lin J, Wang X, Qiu Y, Zhao A, et al. Metabolic profiling reveals disorder of amino acid metabolism in four brain regions from a rat model of chronic unpredictable mild stress. FEBS Lett. 2008;582:2627–36.

    Article  CAS  Google Scholar 

  16. Liu X, Zheng P, Zhao X, Zhang Y, Hu C, Li J, et al. Discovery and validation of plasma biomarkers for major depressive disorder classification based on liquid chromatography-mass spectrometry. J Proteome Res. 2015;14:2322–30.

    Article  CAS  Google Scholar 

  17. Holcapek M. Lipidomics. Anal Bioanal Chem. 2015;407:4971–2.

    Article  CAS  Google Scholar 

  18. Adibhatla RM, Hatcher JF, Dempsey RJ. Lipids and lipidomics in brain injury and diseases. AAPS J. 2006;8:E314–E21.

    Article  Google Scholar 

  19. Suhre K, Roemisch-Margl W, Hrabe de Angelis M, Adamski J, Luippold G, Augustin R. Identification of a potential biomarker for fabp4 inhibition: the power of lipidomics in preclinical drug testing. J Biomol Screen. 2011;16:467–75.

    Article  CAS  Google Scholar 

  20. Hu C, van der Heijden R, Wang M, van der Greef J, Hankemeier T, Xua G. Analytical strategies in lipidomics and applications in disease biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:2836–46.

    Article  CAS  Google Scholar 

  21. Demirkan A, Isaacs A, Ugocsai P, Liebisch G, Struchalin M, Rudan I, et al. Plasma phosphatidylcholine and sphingomyelin concentrations are associated with depression and anxiety symptoms in a Dutch family-based lipidomics study. J Psychiatr Res. 2013;47:357–62.

    Article  Google Scholar 

  22. Faria R, Santana MM, Aveleira CA, Simoes C, Maciel E, Melo T, et al. Alterations in phospholipidomic profile in the brain of mouse model of depression induced by chronic unpredictable stress. Neuroscience. 2014;273:1–11.

    Article  CAS  Google Scholar 

  23. Uher R, Payne JL, Pavlova B, Perlis RH. Major depressive disorder in DSM-5: implications for clinical practice and research of changes from DSM-IV. Depress Anxiety. 2014;31:459–71.

    Article  Google Scholar 

  24. Lewis CP, Nakonezny PA, Ameis SH, Vande Voort JL, Husain MM, Emslie GJ, et al. Cortical inhibitory and excitatory correlates of depression severity in children and adolescents. J Affect Disord. 2016;190:566–75.

    Article  Google Scholar 

  25. Berglund L, Sacks F, Brunzell JD. Risk factors for cardiovascular disease: renewed interest in triglycerides. Clin Lipidol. 2013;8:1–4.

    Article  CAS  Google Scholar 

  26. Austin MA, Hokanson JE, Edwards KL. Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol. 1998;81:7B–12B.

    Article  CAS  Google Scholar 

  27. Sarwar N, Danesh J, Eiriksdottir G, Sigurdsson G, Wareham N, Bingham S, et al. Triglycerides and the risk of coronary heart disease—10158 incident cases among 262525 participants in 29 Western prospective studies. Circulation. 2007;115:450–8.

    Article  CAS  Google Scholar 

  28. Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet. 2014;384:626–35.

    Article  CAS  Google Scholar 

  29. Rosenson RS, Davidson MH, Hirsh BJ, Kathiresan S, Gaudet D. Genetics and causality of triglyceride-rich lipoproteins in atherosclerotic cardiovascular disease. J Am Coll Cardiol. 2014;64:2525–40.

    Article  CAS  Google Scholar 

  30. Elderon L, Whooley MA. Depression and cardiovascular disease. Prog Cardiovasc Dis. 2013;55:511–23.

    Article  Google Scholar 

  31. Goldstein BI, Carnethon MR, Matthews KA, McIntyre RS, Miller GE, Raghuveer G, et al. Major depressive disorder and bipolar disorder predispose youth to accelerated atherosclerosis and early cardiovascular disease. A scientific statement from the American Heart Association. Circulation. 2015;132:965–86.

    Article  Google Scholar 

  32. Ferketich AK, Schwartzbaum JA, Frid DJ, Moeschberger ML. Depression as an antecedent to heart disease among women and men in the NHANES I study. Arch Intern Med. 2000;160:1261–8.

    Article  CAS  Google Scholar 

  33. Van Marwijk HWJ, Van der Kooy KG, Stehouwer CDA, Beekman ATF, van Hout HPJ. Depression increases the onset of cardiovascular disease over and above other determinants in older primary care patients, a cohort study. BMC Cardiovasc Disord. 2015;15:40.

    Article  Google Scholar 

  34. Slusarski DC, Corces VG, Moon RT. Interaction of Wnt and a frizzled homologue triggers G-protein-linked phosphatidylinositol signalling. Nature. 1997;390:410–3.

    Article  CAS  Google Scholar 

  35. Tomita H, Ziegler ME, Kim HB, Evans SJ, Choudary PV, Li JZ, et al. G protein-linked signaling pathways in bipolar and major depressive disorders. Front Genet. 2013;4:297.

    Article  Google Scholar 

  36. Kim H, McGrath BM, Silverstone PH. A review of the possible relevance of inositol and the phosphatidylinositol second messenger system (PI-cycle) to psychiatric disorders—focus on magnetic resonance spectroscopy (MRS) studies. Hum Psychopharmacol. 2005;20:309–26.

    Article  CAS  Google Scholar 

  37. Karege F, Perroud N, Burkhardt S, Fernandez R, Ballmann E, La Harpe R, et al. Alterations in phosphatidylinositol 3-kinase activity and PTEN phosphatase in the prefrontal cortex of depressed suicide victims. Neuropsychobiology. 2011;63:224–31.

    Article  CAS  Google Scholar 

  38. Fuchikami M, Morinobu S, Segawa M, Okamoto Y, Yamawaki S, Ozaki N, et al. DNA methylation profiles of the brain-derived neurotrophic factor (BDNF) gene as a potent diagnostic biomarker in major depression. PLoS One. 2011;6, e23881.

    Article  CAS  Google Scholar 

  39. Lo LH, Huang TL, Shiea J. Acid hydrolysis followed by matrix-assisted laser desorption/ionization mass spectrometry for the rapid diagnosis of serum protein biomarkers in patients with major depression. Rapid Commun Mass Spectrom. 2009;23:589–98.

    Article  CAS  Google Scholar 

  40. Ding X, Yang S, Li W, Liu Y, Li Z, Zhang Y, et al. The potential biomarker panels for identification of major depressive disorder (MDD) patients with and without early life stress (ELS) by metabonomic analysis. PLoS One. 2014;9, e97479.

    Article  Google Scholar 

Download references

Acknowledgments

The study has been supported by the key foundation (No. 21435006), the foundation (nos. 81401140 and 81370906), and the creative research group project (No. 21321064) from the National Natural Science Foundation of China.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Peng Xie or Guowang Xu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Xinyu Liu, Jia Li and Peng Zheng contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 901 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Li, J., Zheng, P. et al. Plasma lipidomics reveals potential lipid markers of major depressive disorder. Anal Bioanal Chem 408, 6497–6507 (2016). https://doi.org/10.1007/s00216-016-9768-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-016-9768-5

Keywords

Navigation