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.
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References
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.
Reynolds EH. Brain and mind: a challenge for WHO. Lancet. 2003;361:1924–5.
Huang TL, Lin CC. Advances in biomarkers of major depressive disorder. Adv Clin Chem. 2015;68:177–204.
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.
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.
Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. Lancet. 2009;374:609–19.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Holcapek M. Lipidomics. Anal Bioanal Chem. 2015;407:4971–2.
Adibhatla RM, Hatcher JF, Dempsey RJ. Lipids and lipidomics in brain injury and diseases. AAPS J. 2006;8:E314–E21.
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.
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.
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.
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.
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.
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.
Berglund L, Sacks F, Brunzell JD. Risk factors for cardiovascular disease: renewed interest in triglycerides. Clin Lipidol. 2013;8:1–4.
Austin MA, Hokanson JE, Edwards KL. Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol. 1998;81:7B–12B.
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.
Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet. 2014;384:626–35.
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.
Elderon L, Whooley MA. Depression and cardiovascular disease. Prog Cardiovasc Dis. 2013;55:511–23.
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.
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.
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.
Slusarski DC, Corces VG, Moon RT. Interaction of Wnt and a frizzled homologue triggers G-protein-linked phosphatidylinositol signalling. Nature. 1997;390:410–3.
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.
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.
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.
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.
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.
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.
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.
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Xinyu Liu, Jia Li and Peng Zheng contributed equally to this work.
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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
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DOI: https://doi.org/10.1007/s00216-016-9768-5