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Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

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Abstract

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

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References

  1. Cui R (2015) Editorial: a systematic review of depression. Curr Neuropharmacol 13(4):480. https://doi.org/10.2174/1570159x1304150831123535

    Article  PubMed  CAS  Google Scholar 

  2. Bains N, Abdijadid S (2023) Major Depressive Disorder. In: StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL)

  3. Rakel RE (1999) Depression. Prim Care 26(2):211–224. https://doi.org/10.1016/s0095-4543(08)70003-4

    Article  PubMed  CAS  Google Scholar 

  4. Albert PR (2015) Why is depression more prevalent in women? J Psychiatry Neurosci 40(4):219–221. https://doi.org/10.1503/jpn.150205

    Article  PubMed  PubMed Central  Google Scholar 

  5. Goodwin RD, Dierker LC, Wu M, Galea S, Hoven CW, Weinberger AH (2022) Trends in U.S. depression prevalence from 2015 to 2020: the widening treatment gap. Am J Prev Med 63(5):726–733. https://doi.org/10.1016/j.amepre.2022.05.014

  6. Yang F, Lodder P, Huang N, Liu X, Fu M, Guo J (2023) Thirty-year trends of depressive disorders in 204 countries and territories from 1990 to 2019: an age-period-cohort analysis. Psychiatry Res 328:115433. https://doi.org/10.1016/j.psychres.2023.115433

  7. Agatisa PK, Matthews KA, Bromberger JT, Edmundowicz D, Chang YF, Sutton-Tyrrell K (2005) Coronary and aortic calcification in women with a history of major depression. Arch Intern Med 165(11):1229–1236. https://doi.org/10.1001/archinte.165.11.1229

    Article  PubMed  Google Scholar 

  8. Cuijpers P, Javed A, Bhui K (2023) The WHO World Mental Health Report: a call for action. Br J Psychiatry 222(6):227–229. https://doi.org/10.1192/bjp.2023.9

    Article  PubMed  Google Scholar 

  9. Malhi GS, Mann JJ (2018) Depression. Lancet (London, England) 392(10161):2299–2312. https://doi.org/10.1016/s0140-6736(18)31948-2

    Article  PubMed  Google Scholar 

  10. Bucciarelli V, Caterino AL, Bianco F, Caputi CG, Salerni S, Sciomer S, Maffei S, Gallina S (2020) Depression and cardiovascular disease: the deep blue sea of women’s heart. Trends Cardiovasc Med 30(3):170–176. https://doi.org/10.1016/j.tcm.2019.05.001

    Article  PubMed  Google Scholar 

  11. Hage MP, Azar ST (2012) The link between thyroid function and depression. J Thyroid Res 2012:590648. https://doi.org/10.1155/2012/590648

    Article  PubMed  CAS  Google Scholar 

  12. National Institute of Mental Health (2024) Depression. https://www.nimh.nih.gov/health/publications/depression. Accessed 17 June 2024

  13. In: England MJ, Sim LJ (eds) (2009) Depression in parents, parenting, and children: opportunities to improve identification, treatment, and prevention. Washington (DC). https://doi.org/10.17226/12565

  14. Dean J, Keshavan M (2017) The neurobiology of depression: an integrated view. Asian J Psychiatr 27:101–111. https://doi.org/10.1016/j.ajp.2017.01.025

    Article  PubMed  Google Scholar 

  15. El Yacoubi M, Vaugeois JM, Marguet D, Sauze N, Guieu R, Costentin J, Fenouillet E (2006) Behavioral characterization of CD26 deficient mice in animal tests of anxiety and antidepressant-like activity. Behav Brain Res 171(2):279–285. https://doi.org/10.1016/j.bbr.2006.04.003

    Article  PubMed  CAS  Google Scholar 

  16. Caspi A, Hariri AR, Holmes A, Uher R, Moffitt TE (2010) Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits. Am J Psychiatry 167(5):509–527. https://doi.org/10.1176/appi.ajp.2010.09101452

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cai S, Huang S, Hao W (2015) New hypothesis and treatment targets of depression: an integrated view of key findings. Neurosci Bull 31(1):61–74. https://doi.org/10.1007/s12264-014-1486-4

    Article  PubMed  PubMed Central  Google Scholar 

  18. Duman RS, Monteggia LM (2006) A neurotrophic model for stress-related mood disorders. Biol Psychiat 59(12):1116–1127. https://doi.org/10.1016/j.biopsych.2006.02.013

    Article  PubMed  CAS  Google Scholar 

  19. Castillo MFR, Cohen A, Edberg D, Hoppensteadt D, Fareed J, Martin B, Halaris A (2020) Vascular endothelial growth factor in bipolar depression: a potential biomarker for diagnosis and treatment outcome prediction. Psychiatry Res 284:112781. https://doi.org/10.1016/j.psychres.2020.112781

    Article  PubMed  CAS  Google Scholar 

  20. Skibinska M, Kapelski P, Dmitrzak-Weglarz M, Lepczynska N, Pawlak J, Twarowska-Hauser J, Szczepankiewicz A, Rajewska-Rager A (2021) Elevated epidermal growth factor (EGF) as candidate biomarker of mood disorders-longitudinal study in adolescent and young adult patients. J Clin Med 10 (18). https://doi.org/10.3390/jcm10184064

  21. Li WZ, Li WP, Yao YY, Zhang W, Yin YY, Wu GC, Gong HL (2010) Glucocorticoids increase impairments in learning and memory due to elevated amyloid precursor protein expression and neuronal apoptosis in 12-month old mice. Eur J Pharmacol 628(1–3):108–115. https://doi.org/10.1016/j.ejphar.2009.11.045

    Article  PubMed  CAS  Google Scholar 

  22. Black PH (2002) Stress and the inflammatory response: a review of neurogenic inflammation. Brain Behav Immun 16(6):622–653. https://doi.org/10.1016/s0889-1591(02)00021-1

    Article  PubMed  CAS  Google Scholar 

  23. Repple J, Mauritz M, Meinert S, de Lange SC, Grotegerd D, Opel N, Redlich R, Hahn T et al (2020) Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder. Mol Psychiatry 25(7):1550–1558. https://doi.org/10.1038/s41380-019-0603-1

    Article  PubMed  Google Scholar 

  24. Hagan CC, Graham JM, Tait R, Widmer B, van Nieuwenhuizen AO, Ooi C, Whitaker KJ, Simas T et al (2015) Adolescents with current major depressive disorder show dissimilar patterns of age-related differences in ACC and thalamus. NeuroImage Clin 7:391–399. https://doi.org/10.1016/j.nicl.2014.12.019

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fang Y, Mao R (2019) Introduction. Adv Exp Med Biol 1180:1–17. https://doi.org/10.1007/978-981-32-9271-0_1

    Article  PubMed  CAS  Google Scholar 

  26. Korgaonkar MS, Williams LM, Song YJ, Usherwood T, Grieve SM (2014) Diffusion tensor imaging predictors of treatment outcomes in major depressive disorder. Br J Psychiatry 205(4):321–328. https://doi.org/10.1192/bjp.bp.113.140376

    Article  PubMed  Google Scholar 

  27. Pan JX, Xia JJ, Deng FL, Liang WW, Wu J, Yin BM, Dong MX, Chen JJ et al (2018) Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Transl Psychiatry 8(1):130. https://doi.org/10.1038/s41398-018-0183-x

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Mao R, Zhang C, Chen J, Zhao G, Zhou R, Wang F, Xu J, Yang T et al (2018) Different levels of pro- and anti-inflammatory cytokines in patients with unipolar and bipolar depression. J Affect Disord 237:65–72. https://doi.org/10.1016/j.jad.2018.04.115

    Article  PubMed  CAS  Google Scholar 

  29. Villas Boas GR, Boerngen de Lacerda R, Paes MM, Gubert P, Almeida W, Rescia VC, de Carvalho PMG, de Carvalho AAV et al (2019) Molecular aspects of depression: a review from neurobiology to treatment. Eur J Pharmacol 851:99–121. https://doi.org/10.1016/j.ejphar.2019.02.024

    Article  PubMed  CAS  Google Scholar 

  30. O’Donnell C, Demler TL, Trigoboff E (2022) Selective serotonin reuptake inhibitors (SSRIs) and their effect on patient aggression in adult patients in a state psychiatric facility: a retrospective analysis. Innov Clin Neurosci 19(1–3):33–38

    PubMed  PubMed Central  Google Scholar 

  31. Moraczewski J, Awosika AO, Aedma KK (2023) Tricyclic Antidepressants. In: StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL). Available from: https://www.ncbi.nlm.nih.gov/books/NBK557791/

  32. Anderson HD, Pace WD, Libby AM, West DR, Valuck RJ (2012) Rates of 5 common antidepressant side effects among new adult and adolescent cases of depression: a retrospective US claims study. Clin Ther 34(1):113–123. https://doi.org/10.1016/j.clinthera.2011.11.024

    Article  PubMed  CAS  Google Scholar 

  33. Szuhany KL, Simon NM (2022) Anxiety disorders: a review. JAMA 328(24):2431–2445. https://doi.org/10.1001/jama.2022.22744

    Article  PubMed  CAS  Google Scholar 

  34. Chamberlain SR, Baldwin DS (2021) Monoamine oxidase inhibitors (MAOIs) in psychiatric practice: how to use them safely and effectively. CNS Drugs 35(7):703–716. https://doi.org/10.1007/s40263-021-00832-x

    Article  PubMed  CAS  Google Scholar 

  35. Mann JJ, Aarons SF, Wilner PJ, Keilp JG, Sweeney JA, Pearlstein T, Frances AJ, Kocsis JH et al (1989) A controlled study of the antidepressant efficacy and side effects of (-)-deprenyl. A selective monoamine oxidase inhibitor. Arch Gen Psychiatry 46(1):45–50. https://doi.org/10.1001/archpsyc.1989.01810010047007

    Article  PubMed  CAS  Google Scholar 

  36. Marwaha S, Palmer E, Suppes T, Cons E, Young AH, Upthegrove R (2023) Novel and emerging treatments for major depression. Lancet (London, England) 401(10371):141–153. https://doi.org/10.1016/S0140-6736(22)02080-3

    Article  PubMed  CAS  Google Scholar 

  37. Yang S, Wang J, Li X, Wang T, Xu Z, Xu X, Zhou X, Chen G (2022) Adverse effects of esketamine for the treatment of major depression disorder: findings from randomized controlled trials. Psychiatr Q 93(1):81–95. https://doi.org/10.1007/s11126-020-09871-x

    Article  PubMed  Google Scholar 

  38. Goodwin GM, Aaronson ST, Alvarez O, Arden PC, Baker A, Bennett JC, Bird C, Blom RE et al (2022) Single-dose psilocybin for a treatment-resistant episode of major depression. N Engl J Med 387(18):1637–1648. https://doi.org/10.1056/NEJMoa2206443

    Article  PubMed  CAS  Google Scholar 

  39. Fox MA, French HT, LaPorte JL, Blackler AR, Murphy DL (2010) The serotonin 5-HT(2A) receptor agonist TCB-2: a behavioral and neurophysiological analysis. Psychopharmacology 212(1):13–23. https://doi.org/10.1007/s00213-009-1694-1

    Article  PubMed  CAS  Google Scholar 

  40. Liu L, Wang H, Chen X, Zhang Y, Zhang H, Xie P (2023) Gut microbiota and its metabolites in depression: from pathogenesis to treatment. EBioMedicine 90:104527. https://doi.org/10.1016/j.ebiom.2023.104527

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Ruhland S, Hauser J, Kaunzinger I, Nakamura Y, Stollberg E, Lange KW (2020) Effects of omega-3 fatty acids on working memory in rats with increased sugar intake. J Funct Foods 69:103951. https://doi.org/10.1016/j.jff.2020.103951

    Article  CAS  Google Scholar 

  42. Nakagawa A, Mitsuda D, Sado M, Abe T, Fujisawa D, Kikuchi T, Iwashita S, Mimura M et al (2017) Effectiveness of supplementary cognitive-behavioral therapy for pharmacotherapy-resistant depression: a randomized controlled trial. J Clin Psychiatry 78(8):1126–1135. https://doi.org/10.4088/JCP.15m10511

    Article  PubMed  Google Scholar 

  43. Santarsieri D, Schwartz TL (2015) Antidepressant efficacy and side-effect burden: a quick guide for clinicians. Drugs Context 4:212290. https://doi.org/10.7573/dic.212290

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhang ZJ, Zhang SY, Yang XJ, Qin ZS, Xu FQ, Jin GX, Hou XB, Liu Y et al (2023) Transcutaneous electrical cranial-auricular acupoint stimulation versus escitalopram for mild-to-moderate depression: an assessor-blinded, randomized, non-inferiority trial. Psychiatry Clin Neurosci 77(3):168–177. https://doi.org/10.1111/pcn.13512

    Article  PubMed  CAS  Google Scholar 

  45. Croarkin PE, Elmaadawi AZ, Aaronson ST, Schrodt GR Jr, Holbert RC, Verdoliva S, Heart KL, Demitrack MA et al (2021) Left prefrontal transcranial magnetic stimulation for treatment-resistant depression in adolescents: a double-blind, randomized, sham-controlled trial. Neuropsychopharmacology 46(2):462–469. https://doi.org/10.1038/s41386-020-00829-y

    Article  PubMed  CAS  Google Scholar 

  46. Jankovic J (2002) Levodopa strengths and weaknesses. Neurology 58(4 Suppl 1):S19-32. https://doi.org/10.1212/wnl.58.suppl_1.s19

    Article  PubMed  CAS  Google Scholar 

  47. Krauss JK, Lipsman N, Aziz T, Boutet A, Brown P, Chang JW, Davidson B, Grill WM et al (2021) Technology of deep brain stimulation: current status and future directions. Nat Rev Neurol 17(2):75–87. https://doi.org/10.1038/s41582-020-00426-z

    Article  PubMed  Google Scholar 

  48. Etminan M, Gill S, Samii A (2003) Comparison of the risk of adverse events with pramipexole and ropinirole in patients with Parkinson’s disease. Drug Saf 26(6):439–444. https://doi.org/10.2165/00002018-200326060-00005

    Article  PubMed  CAS  Google Scholar 

  49. Marazziti D, Rutigliano G, Baroni S, Landi P, Dell’Osso L (2014) Metabolic syndrome and major depression. CNS Spectr 19(4):293–304. https://doi.org/10.1017/s1092852913000667

    Article  PubMed  Google Scholar 

  50. Stunkard AJ, Faith MS, Allison KC (2003) Depression and obesity. Biol Psychiat 54(3):330–337. https://doi.org/10.1016/s0006-3223(03)00608-5

    Article  PubMed  Google Scholar 

  51. Campayo A, Gómez-Biel CH, Lobo A (2011) Diabetes and depression. Curr Psychiatry Rep 13(1):26–30. https://doi.org/10.1007/s11920-010-0165-z

    Article  PubMed  Google Scholar 

  52. Gadad BS, Jha MK, Czysz A, Furman JL, Mayes TL, Emslie MP, Trivedi MH (2018) Peripheral biomarkers of major depression and antidepressant treatment response: current knowledge and future outlooks. J Affect Disord 233:3–14. https://doi.org/10.1016/j.jad.2017.07.001

    Article  PubMed  CAS  Google Scholar 

  53. Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17(7):451–459. https://doi.org/10.1038/nrm.2016.25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Kaddurah-Daouk R, Krishnan KR (2009) Metabolomics: a global biochemical approach to the study of central nervous system diseases. Neuropsychopharmacology 34(1):173–186. https://doi.org/10.1038/npp.2008.174

    Article  PubMed  CAS  Google Scholar 

  55. Newgard CB (2017) Metabolomics and metabolic diseases: where do we stand? Cell Metab 25(1):43–56. https://doi.org/10.1016/j.cmet.2016.09.018

    Article  PubMed  CAS  Google Scholar 

  56. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R et al (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27(6):747–751. https://doi.org/10.1097/01.ftd.0000179845.53213.39

    Article  PubMed  CAS  Google Scholar 

  57. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R et al (2013) HMDB 3.0–the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801-807. https://doi.org/10.1093/nar/gks1065

    Article  PubMed  CAS  Google Scholar 

  58. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom: JMS 45(7):703–714. https://doi.org/10.1002/jms.1777

    Article  PubMed  CAS  Google Scholar 

  59. Vinaixa M, Schymanski EL, Neumann S, Navarro M, Salek RM, Yanes O (2016) Mass spectral databases for LC/MS-and GC/MS-based metabolomics: state of the field and future prospects. TrAC, Trends Anal Chem 78:23–35

    Article  CAS  Google Scholar 

  60. Li B, He X, Jia W, Li H (2017) Novel applications of metabolomics in personalized medicine: a mini-review. Molecules (Basel, Switzerland) 22 (7). https://doi.org/10.3390/molecules22071173

  61. Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R (2015) Metabolomics for laboratory diagnostics. J Pharm Biomed Anal 113:108–120. https://doi.org/10.1016/j.jpba.2014.12.017

    Article  PubMed  CAS  Google Scholar 

  62. Belujon P, Grace AA (2017) Dopamine system dysregulation in major depressive disorders. Int J Neuropsychopharmacol 20(12):1036–1046. https://doi.org/10.1093/ijnp/pyx056

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Hammen C (2018) Risk factors for depression: an autobiographical review. Annu Rev Clin Psychol 14:1–28. https://doi.org/10.1146/annurev-clinpsy-050817-084811

    Article  PubMed  Google Scholar 

  64. Vancampfort D, Correll CU, Wampers M, Sienaert P, Mitchell AJ, De Herdt A, Probst M, Scheewe TW et al (2014) Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychol Med 44(10):2017–2028. https://doi.org/10.1017/s0033291713002778

    Article  PubMed  CAS  Google Scholar 

  65. Pan A, Keum N, Okereke OI, Sun Q, Kivimaki M, Rubin RR, Hu FB (2012) Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes Care 35(5):1171–1180. https://doi.org/10.2337/dc11-2055

    Article  PubMed  PubMed Central  Google Scholar 

  66. Conway B, Rene A (2004) Obesity as a disease: no lightweight matter. Obes Rev: An Off J Int Assoc Stud Obes 5(3):145–151. https://doi.org/10.1111/j.1467-789X.2004.00144.x

    Article  CAS  Google Scholar 

  67. Preiss K, Brennan L, Clarke D (2013) A systematic review of variables associated with the relationship between obesity and depression. Obes Rev: An Off J Int Assoc Stud Obes 14(11):906–918. https://doi.org/10.1111/obr.12052

    Article  CAS  Google Scholar 

  68. Xu Q, Anderson D, Lurie-Beck J (2011) The relationship between abdominal obesity and depression in the general population: a systematic review and meta-analysis. Obes Res Clin Pract 5(4):e267-360. https://doi.org/10.1016/j.orcp.2011.04.007

    Article  PubMed  Google Scholar 

  69. van Strien T (2018) Causes of emotional eating and matched treatment of obesity. Curr DiabRep 18(6):35. https://doi.org/10.1007/s11892-018-1000-x

    Article  CAS  Google Scholar 

  70. Zarouna S, Wozniak G, Papachristou AI (2015) Mood disorders: a potential link between ghrelin and leptin on human body? World J Exp Med 5(2):103–109. https://doi.org/10.5493/wjem.v5.i2.103

    Article  PubMed  PubMed Central  Google Scholar 

  71. Unnikrishnan R, Anjana RM, Mohan V (2016) Diabetes mellitus and its complications in India. Nat Rev Endocrinol 12(6):357–370. https://doi.org/10.1038/nrendo.2016.53

    Article  PubMed  Google Scholar 

  72. Pearson-Stuttard J, Holloway S, Polya R, Sloan R, Zhang L, Gregg EW, Harrison K, Elvidge J et al (2022) Variations in comorbidity burden in people with type 2 diabetes over disease duration: a population-based analysis of real world evidence. EClinicalMedicine 52:101584. https://doi.org/10.1016/j.eclinm.2022.101584

    Article  PubMed  PubMed Central  Google Scholar 

  73. Park M, Reynolds CF, 3rd (2015) Depression among older adults with diabetes mellitus. Clinics in geriatric medicine 31 (1):117–137, ix. https://doi.org/10.1016/j.cger.2014.08.022

  74. Chan KL, Cathomas F, Russo SJ (2019) Central and peripheral inflammation link metabolic syndrome and major depressive disorder. Physiology (Bethesda) 34(2):123–133. https://doi.org/10.1152/physiol.00047.2018

    Article  PubMed  CAS  Google Scholar 

  75. Oxenkrug G (2013) Insulin resistance and dysregulation of tryptophan-kynurenine and kynurenine-nicotinamide adenine dinucleotide metabolic pathways. Mol Neurobiol 48(2):294–301. https://doi.org/10.1007/s12035-013-8497-4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Palego L, Betti L, Rossi A, Giannaccini G (2016) Tryptophan biochemistry: structural, nutritional, metabolic, and medical aspects in humans. J Amino Acids 2016:8952520. https://doi.org/10.1155/2016/8952520

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Wang P, Kong FZ, Hong XH, Zhang L, Zhao WH, Yang JC, Zhang H (2022) Neuronal nitric oxide synthase regulates depression-like behaviors in shortening-induced obese mice. Nutrients 14 (20). https://doi.org/10.3390/nu14204302

  78. Lontchi-Yimagou E, Sobngwi E, Matsha TE, Kengne AP (2013) Diabetes mellitus and inflammation. Curr DiabRep 13(3):435–444. https://doi.org/10.1007/s11892-013-0375-y

    Article  CAS  Google Scholar 

  79. Hotamisligil GS, Shargill NS, Spiegelman BM (1993) Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science (New York, NY) 259(5091):87–91. https://doi.org/10.1126/science.7678183

    Article  CAS  Google Scholar 

  80. Shoelson SE, Lee J, Goldfine AB (2006) Inflammation and insulin resistance. J Clin Investig 116(7):1793–1801. https://doi.org/10.1172/jci29069

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Akimoto H, Oshima S, Sugiyama T, Negishi A, Nemoto T, Kobayashi D (2019) Changes in brain metabolites related to stress resilience: metabolomic analysis of the hippocampus in a rat model of depression. Behav Brain Res 359:342–352. https://doi.org/10.1016/j.bbr.2018.11.017

    Article  PubMed  CAS  Google Scholar 

  82. Phillips C (2017) Brain-derived neurotrophic factor, depression, and physical activity: making the neuroplastic connection. Neural Plast 2017:7260130. https://doi.org/10.1155/2017/7260130

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Eyileten C, Kaplon-Cieslicka A, Mirowska-Guzel D, Malek L, Postula M (2017) Antidiabetic effect of brain-derived neurotrophic factor and its association with inflammation in type 2 diabetes mellitus. J Diabetes Res 2017:2823671. https://doi.org/10.1155/2017/2823671

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Nonomura T, Tsuchida A, Ono-Kishino M, Nakagawa T, Taiji M, Noguchi H (2001) Brain-derived neurotrophic factor regulates energy expenditure through the central nervous system in obese diabetic mice. Int J Exp Diabetes Res 2(3):201–209. https://doi.org/10.1155/edr.2001.201

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Hanyu O, Yamatani K, Ikarashi T, Soda S, Maruyama S, Kamimura T, Kaneko S et al (2003) Brain-derived neurotrophic factor modulates glucagon secretion from pancreatic alpha cells: its contribution to glucose metabolism. Diabetes Obes Metab 5(1):27–37. https://doi.org/10.1046/j.1463-1326.2003.00238.x

    Article  PubMed  CAS  Google Scholar 

  86. Yu M, Zhang X, Lu F, Fang L (2015) Depression and risk for diabetes: a meta-analysis. Can J Diabetes 39(4):266–272. https://doi.org/10.1016/j.jcjd.2014.11.006

    Article  PubMed  Google Scholar 

  87. Vaccarino V, McClure C, Johnson BD, Sheps DS, Bittner V, Rutledge T, Shaw LJ, Sopko G et al (2008) Depression, the metabolic syndrome and cardiovascular risk. Psychosom Med 70(1):40–48. https://doi.org/10.1097/PSY.0b013e31815c1b85

    Article  PubMed  Google Scholar 

  88. Carney RM, Freedland KE, Veith RC (2005) Depression, the autonomic nervous system, and coronary heart disease. Psychosom Med 67(Suppl 1):S29-33. https://doi.org/10.1097/01.psy.0000162254.61556.d5

    Article  PubMed  Google Scholar 

  89. Jones DJ, Bromberger JT, Sutton-Tyrrell K, Matthews KA (2003) Lifetime history of depression and carotid atherosclerosis in middle-aged women. Arch Gen Psychiatry 60(2):153–160. https://doi.org/10.1001/archpsyc.60.2.153

    Article  PubMed  Google Scholar 

  90. Hashimoto K (2018) Metabolomics of major depressive disorder and bipolar disorder: overview and future perspective. Adv Clin Chem 84:81–99. https://doi.org/10.1016/bs.acc.2017.12.005

    Article  PubMed  CAS  Google Scholar 

  91. Czysz AH, South C, Gadad BS, Arning E, Soyombo A, Bottiglieri T, Trivedi MH (2019) Can targeted metabolomics predict depression recovery? Results from the CO-MED trial. Transl Psychiatry 9(1):11. https://doi.org/10.1038/s41398-018-0349-6

    Article  PubMed  PubMed Central  Google Scholar 

  92. Huang TL, Lin CC (2015) Advances in biomarkers of major depressive disorder. Adv Clin Chem 68:177–204. https://doi.org/10.1016/bs.acc.2014.11.003

    Article  PubMed  CAS  Google Scholar 

  93. Strawbridge R, Young AH, Cleare AJ (2017) Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr Dis Treat 13:1245–1262. https://doi.org/10.2147/NDT.S114542

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Huang TL, Chen JF (2004) Lipid and lipoprotein levels in depressive disorders with melancholic feature or atypical feature and dysthymia. Psychiatry Clin Neurosci 58(3):295–299. https://doi.org/10.1111/j.1440-1819.2004.01235.x

    Article  PubMed  CAS  Google Scholar 

  95. Liu X, Li J, Zheng P, Zhao X, Zhou C, Hu C, Hou X, Wang H et al (2016) Plasma lipidomics reveals potential lipid markers of major depressive disorder. Anal Bioanal Chem 408(23):6497–6507. https://doi.org/10.1007/s00216-016-9768-5

    Article  PubMed  CAS  Google Scholar 

  96. Liu L, Zhou X, Zhang Y, Liu Y, Yang L, Pu J, Zhu D, Zhou C et al (2016) The identification of metabolic disturbances in the prefrontal cortex of the chronic restraint stress rat model of depression. Behav Brain Res 305:148–156. https://doi.org/10.1016/j.bbr.2016.03.005

    Article  PubMed  CAS  Google Scholar 

  97. Garcia-Garcia AL, Elizalde N, Matrov D, Harro J, Wojcik SM, Venzala E, Ramírez MJ, Del Rio J et al (2009) Increased vulnerability to depressive-like behavior of mice with decreased expression of VGLUT1. Biol Psychiatry 66(3):275–282. https://doi.org/10.1016/j.biopsych.2009.02.027

    Article  PubMed  CAS  Google Scholar 

  98. Raison CL, Lowry CA, Rook GA (2010) Inflammation, sanitation, and consternation: loss of contact with coevolved, tolerogenic microorganisms and the pathophysiology and treatment of major depression. Arch Gen Psychiatry 67(12):1211–1224. https://doi.org/10.1001/archgenpsychiatry.2010.161

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Howren MB, Lamkin DM, Suls J (2009) Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosom Med 71(2):171–186. https://doi.org/10.1097/PSY.0b013e3181907c1b

    Article  PubMed  CAS  Google Scholar 

  100. Liu LY, Zhang HJ, Luo LY, Pu JB, Liang WQ, Zhu CQ, Li YP, Wang PR et al (2018) Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder. Chin Med 13:53. https://doi.org/10.1186/s13020-018-0211-z

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Zhou X, Liu L, Zhang Y, Pu J, Yang L, Zhou C, Yuan S et al (2017) Metabolomics identifies perturbations in amino acid metabolism in the prefrontal cortex of the learned helplessness rat model of depression. Neuroscience 343:1–9. https://doi.org/10.1016/j.neuroscience.2016.11.038

    Article  PubMed  CAS  Google Scholar 

  102. Zhang Y, Yuan S, Pu J, Yang L, Zhou X, Liu L, Jiang X, Zhang H et al (2018) Integrated metabolomics and proteomics analysis of hippocampus in a rat model of depression. Neuroscience 371:207–220. https://doi.org/10.1016/j.neuroscience.2017.12.001

    Article  PubMed  CAS  Google Scholar 

  103. Shao WH, Chen JJ, Fan SH, Lei Y, Xu HB, Zhou J, Cheng PF, Yang YT et al (2015) Combined metabolomics and proteomics analysis of major depression in an animal model: perturbed energy metabolism in the chronic mild stressed rat cerebellum. Omics : A J Integr Biol 19(7):383–392. https://doi.org/10.1089/omi.2014.0164

    Article  CAS  Google Scholar 

  104. Xi G, Hui J, Zhang Z, Liu S, Zhang X, Teng G, Chan KC, Wu EX et al (2011) Learning and memory alterations are associated with hippocampal N-acetylaspartate in a rat model of depression as measured by 1H-MRS. PloS one 6(12):e28686. https://doi.org/10.1371/journal.pone.0028686

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Wu Y, Fu Y, Rao C, Li W, Liang Z, Zhou C, Shen P, Cheng P et al (2016) Metabolomic analysis reveals metabolic disturbances in the prefrontal cortex of the lipopolysaccharide-induced mouse model of depression. Behav Brain Res 308:115–127. https://doi.org/10.1016/j.bbr.2016.04.032

    Article  PubMed  CAS  Google Scholar 

  106. Hassan AM, Mancano G, Kashofer K, Fröhlich EE, Matak A, Mayerhofer R, Reichmann F, Olivares M et al (2019) High-fat diet induces depression-like behaviour in mice associated with changes in microbiome, neuropeptide Y, and brain metabolome. Nutr Neurosci 22(12):877–893. https://doi.org/10.1080/1028415x.2018.1465713

    Article  PubMed  CAS  Google Scholar 

  107. Xu T, Lu C, Feng L, Fan LX, Sun J, Fan B, Wang Q, Wang Y et al (2019) Liquid chromatography-mass spectrometry-based urinary metabolomics study on a rat model of simulated microgravity-induced depression. J Pharm Biomed Anal 165:31–40. https://doi.org/10.1016/j.jpba.2018.11.058

    Article  PubMed  CAS  Google Scholar 

  108. Song J, Ma W, Gu X, Zhao L, Jiang J, Xu Y, Zhang L, Zhou M et al (2019) Metabolomic signatures and microbial community profiling of depressive rat model induced by adrenocorticotrophic hormone. J Transl Med 17(1):224. https://doi.org/10.1186/s12967-019-1970-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Hashimoto K (2015) Inflammatory biomarkers as differential predictors of antidepressant response. Int J Mol Sci 16(4):7796–7801. https://doi.org/10.3390/ijms16047796

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Miller AH, Maletic V, Raison CL (2009) Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression. Biol Psychiat 65(9):732–741. https://doi.org/10.1016/j.biopsych.2008.11.029

    Article  PubMed  CAS  Google Scholar 

  111. Reininghaus B, Riedrich K, Dalkner N, Bengesser SA, Birner A, Platzer M, Hamm C, Gostner JM et al (2018) Changes in the tryptophan-kynurenine axis in association to therapeutic response in clinically depressed patients undergoing psychiatric rehabilitation. Psychoneuroendocrinology 94:25–30. https://doi.org/10.1016/j.psyneuen.2018.04.029

    Article  PubMed  CAS  Google Scholar 

  112. Valkanova V, Ebmeier KP, Allan CL (2013) CRP, IL-6 and depression: a systematic review and meta-analysis of longitudinal studies. J Affect Disord 150(3):736–744. https://doi.org/10.1016/j.jad.2013.06.004

    Article  PubMed  CAS  Google Scholar 

  113. Nedic Erjavec G, Sagud M, Nikolac Perkovic M, Svob Strac D, Konjevod M, Tudor L, Uzun S, Pivac N (2021) Depression: biological markers and treatment. Prog Neuropsychopharmacol Biol Psychiatry 105:110139. https://doi.org/10.1016/j.pnpbp.2020.110139

    Article  PubMed  CAS  Google Scholar 

  114. Chen JJ, Zhou CJ, Zheng P, Cheng K, Wang HY, Li J, Zeng L, Xie P (2017) Differential urinary metabolites related with the severity of major depressive disorder. Behav Brain Res 332:280–287. https://doi.org/10.1016/j.bbr.2017.06.012

    Article  PubMed  CAS  Google Scholar 

  115. Kaddurah-Daouk R, Yuan P, Boyle SH, Matson W, Wang Z, Zeng ZB, Zhu H, Dougherty GG et al (2012) Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile. Sci Rep 2:667. https://doi.org/10.1038/srep00667

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Kaddurah-Daouk R, Bogdanov MB, Wikoff WR, Zhu H, Boyle SH, Churchill E, Wang Z, Rush AJ et al (2013) Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo. Transl Psychiatry 3(1):e223. https://doi.org/10.1038/tp.2012.142

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Zhu H, Bogdanov MB, Boyle SH, Matson W, Sharma S, Matson S, Churchill E, Fiehn O et al (2013) Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder - possible role for methoxyindole pathway. PloS one 8(7):e68283. https://doi.org/10.1371/journal.pone.0068283

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Yang JJ, Wang N, Yang C, Shi JY, Yu HY, Hashimoto K (2015) Serum interleukin-6 is a predictive biomarker for ketamine’s antidepressant effect in treatment-resistant patients with major depression. Biol Psychiat 77(3):e19–e20. https://doi.org/10.1016/j.biopsych.2014.06.021

    Article  PubMed  CAS  Google Scholar 

  119. Canuso CM, Singh JB, Fedgchin M, Alphs L, Lane R, Lim P, Pinter C, Hough D et al (2018) Efficacy and safety of intranasal esketamine for the rapid reduction of symptoms of depression and suicidality in patients at imminent risk for suicide: results of a double-blind, randomized, placebo-controlled study. Am J Psychiatry 175(7):620–630. https://doi.org/10.1176/appi.ajp.2018.17060720

    Article  PubMed  Google Scholar 

  120. Mihaljevic S, Pavlovic M, Reiner K, Cacic M (2020) Therapeutic mechanisms of ketamine. Psychiatr Danub 32(3–4):325–333. https://doi.org/10.24869/psyd.2020.325

    Article  PubMed  CAS  Google Scholar 

  121. Baranyi A, Meinitzer A, Rothenhäusler HB, Amouzadeh-Ghadikolai O, Lewinski DV, Breitenecker RJ, Herrmann M (2018) Metabolomics approach in the investigation of depression biomarkers in pharmacologically induced immune-related depression. PLoS One 13(11):e0208238. https://doi.org/10.1371/journal.pone.0208238

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Uher R, Tansey KE, Dew T, Maier W, Mors O, Hauser J, Dernovsek MZ et al (2014) An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. Am J Psychiatry 171(12):1278–1286. https://doi.org/10.1176/appi.ajp.2014.14010094

    Article  PubMed  Google Scholar 

  123. Martino M, Rocchi G, Escelsior A, Fornaro M (2012) Immunomodulation mechanism of antidepressants: interactions between serotonin/norepinephrine balance and Th1/Th2 balance. Curr Neuropharmacol 10(2):97–123. https://doi.org/10.2174/157015912800604542

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Humer E, Probst T, Pieh C (2020) Metabolomics in psychiatric disorders: what we learn from animal models. Metabolites 10 (2). https://doi.org/10.3390/metabo10020072

  125. Deng FL, Pan JX, Zheng P, Xia JJ, Yin BM, Liang WW, Li YF, Wu J et al (2019) Metabonomics reveals peripheral and central short-chain fatty acid and amino acid dysfunction in a naturally occurring depressive model of macaques. Neuropsychiatr Dis Treat 15:1077–1088. https://doi.org/10.2147/ndt.s186071

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Murrough JW, Abdallah CG, Mathew SJ (2017) Targeting glutamate signalling in depression: progress and prospects. Nat Rev Drug Discovery 16(7):472–486. https://doi.org/10.1038/nrd.2017.16

    Article  PubMed  CAS  Google Scholar 

  127. Pittenger C, Duman RS (2008) Stress, depression, and neuroplasticity: a convergence of mechanisms. Neuropsychopharmacology 33(1):88–109. https://doi.org/10.1038/sj.npp.1301574

    Article  PubMed  CAS  Google Scholar 

  128. Dranovsky A, Hen R (2006) Hippocampal neurogenesis: regulation by stress and antidepressants. Biol Psychiat 59(12):1136–1143. https://doi.org/10.1016/j.biopsych.2006.03.082

    Article  PubMed  CAS  Google Scholar 

  129. Xi G, Zhang X, Zhang L, Sui Y, Hui J, Liu S, Wang Y, Li L et al (2011) Fluoxetine attenuates the inhibitory effect of glucocorticoid hormones on neurogenesis in vitro via a two-pore domain potassium channel, TREK-1. Psychopharmacology (Berl) 214(3):747–759. https://doi.org/10.1007/s00213-010-2077-3

    Article  PubMed  CAS  Google Scholar 

  130. Block W, Traber F, von Widdern O, Metten M, Schild H, Maier W, Zobel A, Jessen F (2009) Proton MR spectroscopy of the hippocampus at 3 T in patients with unipolar major depressive disorder: correlates and predictors of treatment response. Int J Neuropsychopharmacol 12(3):415–422. https://doi.org/10.1017/S1461145708009516

    Article  PubMed  CAS  Google Scholar 

  131. Bi S, Chen J, Behles RR, Hyun J, Kopin AS, Moran TH (2007) Differential body weight and feeding responses to high-fat diets in rats and mice lacking cholecystokinin 1 receptors. Am J Physiol Regul Integr Comp Physiol 293(1):R55-63. https://doi.org/10.1152/ajpregu.00002.2007

    Article  PubMed  CAS  Google Scholar 

  132. Yu M, Jia H, Zhou C, Yang Y, Zhao Y, Yang M, Zou Z (2017) Variations in gut microbiota and fecal metabolic phenotype associated with depression by 16S rRNA gene sequencing and LC/MS-based metabolomics. J Pharm Biomed Anal 138:231–239. https://doi.org/10.1016/j.jpba.2017.02.008

    Article  PubMed  CAS  Google Scholar 

  133. Verma H, Phian S, Lakra P, Kaur J, Subudhi S, Lal R, Rawat CD (2020) Human gut microbiota and mental health: advancements and challenges in microbe-based therapeutic interventions. Indian journal of microbiology 60(4):405–419. https://doi.org/10.1007/s12088-020-00898-z

    Article  PubMed  PubMed Central  Google Scholar 

  134. Abelaira HM, Réus GZ, Quevedo J (2013) Animal models as tools to study the pathophysiology of depression. Braz J Psychiatry 35(Suppl 2):S112-120. https://doi.org/10.1590/1516-4446-2013-1098

    Article  PubMed  Google Scholar 

  135. Wang W, Guo H, Zhang S-X, Li J, Cheng K, Bai S-J, Yang D-Y, Wang H-Y et al (2016) Targeted metabolomic pathway analysis and validation revealed glutamatergic disorder in the prefrontal cortex among the chronic social defeat stress mice model of depression. J Proteome Res 15(10):3784–3792. https://doi.org/10.1021/acs.jproteome.6b00577

    Article  PubMed  CAS  Google Scholar 

  136. Harro J (2019) Animal models of depression: pros and cons. Cell Tissue Res 377(1):5–20. https://doi.org/10.1007/s00441-018-2973-0

    Article  PubMed  Google Scholar 

  137. Katz RJ, Roth KA, Carroll BJ (1981) Acute and chronic stress effects on open field activity in the rat: implications for a model of depression. Neurosci Biobehav Rev 5(2):247–251. https://doi.org/10.1016/0149-7634(81)90005-1

    Article  PubMed  CAS  Google Scholar 

  138. Lucca G, Comim CM, Valvassori SS, Réus GZ, Vuolo F, Petronilho F, Dal-Pizzol F et al (2009) Effects of chronic mild stress on the oxidative parameters in the rat brain. Neurochem Int 54(5–6):358–362. https://doi.org/10.1016/j.neuint.2009.01.001

    Article  PubMed  CAS  Google Scholar 

  139. Réus GZ, Stringari RB, Ribeiro KF, Cipriano AL, Panizzutti BS, Stertz L, Lersch C, Kapczinski F et al (2011) Maternal deprivation induces depressive-like behaviour and alters neurotrophin levels in the rat brain. Neurochem Res 36(3):460–466. https://doi.org/10.1007/s11064-010-0364-3

    Article  PubMed  CAS  Google Scholar 

  140. Nestler EJ, Hyman SE (2010) Animal models of neuropsychiatric disorders. Nat Neurosci 13(10):1161–1169. https://doi.org/10.1038/nn.2647

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Ma W, Song J, Wang H, Shi F, Zhou N, Jiang J, Xu Y et al (2019) Chronic paradoxical sleep deprivation-induced depression­like behavior, energy metabolism and microbial changes in rats. Life Sci 225:88–97. https://doi.org/10.1016/j.lfs.2019.04.006

    Article  PubMed  CAS  Google Scholar 

  142. Hellweg R, Zueger M, Fink K, Hörtnagl H, Gass P (2007) Olfactory bulbectomy in mice leads to increased BDNF levels and decreased serotonin turnover in depression-related brain areas. Neurobiol Dis 25(1):1–7. https://doi.org/10.1016/j.nbd.2006.07.017

    Article  PubMed  CAS  Google Scholar 

  143. Nam H, Clinton SM, Jackson NL, Kerman IA (2014) Learned helplessness and social avoidance in the Wistar-Kyoto rat. Front Behav Neurosci 8:109. https://doi.org/10.3389/fnbeh.2014.00109

    Article  PubMed  PubMed Central  Google Scholar 

  144. You Z, Luo C, Zhang W, Chen Y, He J, Zhao Q, Zuo R, Wu Y (2011) Pro- and anti-inflammatory cytokines expression in rat’s brain and spleen exposed to chronic mild stress: involvement in depression. Behav Brain Res 225(1):135–141. https://doi.org/10.1016/j.bbr.2011.07.006

    Article  PubMed  CAS  Google Scholar 

  145. Ibarguen-Vargas Y, Surget A, Vourc’h P, Leman S, Andres CR, Gardier AM, Belzung C (2009) Deficit in BDNF does not increase vulnerability to stress but dampens antidepressant-like effects in the unpredictable chronic mild stress. Behav Brain Res 202(2):245–251. https://doi.org/10.1016/j.bbr.2009.03.040

    Article  PubMed  CAS  Google Scholar 

  146. Tsugawa H, Bamba T, Shinohara M, Nishiumi S, Yoshida M, Fukusaki E (2011) Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis. J Biosci Bioeng 112(3):292–298. https://doi.org/10.1016/j.jbiosc.2011.05.001

    Article  PubMed  CAS  Google Scholar 

  147. Ho CSH, Tay GWN, Wee HN, Ching J (2023) The utility of amino acid metabolites in the diagnosis of major depressive disorder and correlations with depression severity. Int J Mol Sci 24(3):2231. https://doi.org/10.3390/ijms24032231

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Farshim P, Walton G, Chakrabarti B, Givens I, Saddy D, Kitchen I, Swann JR, Bailey A (2016) Maternal weaning modulates emotional behavior and regulates the gut-brain axis. Sci Rep 6:21958. https://doi.org/10.1038/srep21958

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  149. Stockmeier CA (2003) Involvement of serotonin in depression: evidence from postmortem and imaging studies of serotonin receptors and the serotonin transporter. J Psychiatr Res 37(5):357–373. https://doi.org/10.1016/s0022-3956(03)00050-5

    Article  PubMed  Google Scholar 

  150. Monteleone P, Martiadis V, Maj M (2011) Circadian rhythms and treatment implications in depression. Prog Neuropsychopharmacol Biol Psychiatry 35(7):1569–1574. https://doi.org/10.1016/j.pnpbp.2010.07.028

    Article  PubMed  CAS  Google Scholar 

  151. Monje FJ, Cabatic M, Divisch I, Kim E-J, Herkner KR, Binder BR, Pollak DD (2011) Constant darkness induces IL-6-dependent depression-like behavior through the NF-κB signaling pathway. J Neurosci 31(25):9075–9083. https://doi.org/10.1523/JNEUROSCI.1537-11.2011

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  152. Altamura C, Maes M, Dai J, Meltzer HY (1995) Plasma concentrations of excitatory amino acids, serine, glycine, taurine and histidine in major depression. Eur Neuropsychopharmacol 5(Suppl):71–75. https://doi.org/10.1016/0924-977x(95)00033-l

    Article  PubMed  CAS  Google Scholar 

  153. Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, Yang DY, Fan SH et al (2012) Plasma metabonomics as a novel diagnostic approach for major depressive disorder. J Proteome Res 11(3):1741–1748. https://doi.org/10.1021/pr2010082

    Article  PubMed  CAS  Google Scholar 

  154. Zhou Y, Tao X, Wang Z, Feng L, Wang L, Liu X, Pan R, Liao Y et al (2019) Hippocampus metabolic disturbance and autophagy deficiency in olfactory bulbectomized rats and the modulatory effect of fluoxetine. Int J Mol Sci 20(17):4282. https://doi.org/10.3390/ijms20174282

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  155. Zhou Y-F, Feng L, Liu X-M, Tao X, Wang L-S, Zhang M-D, Wang Z, Chen S-G et al (2019) Urinary metabolic disturbance in the olfactory bulbectomized rats and the modulatory effects of fluoxetine. Life Sci 234:116751. https://doi.org/10.1016/j.lfs.2019.116751

    Article  PubMed  CAS  Google Scholar 

  156. Zhao T, Liu T, Wang L, Xie K, Tang H, Tang M (2024) Dysfunction of neurotransmitter metabolism is associated with the severity of depression in first-diagnosed, drug-naïve depressed patients. J Affect Disord 349:332–341. https://doi.org/10.1016/j.jad.2024.01.023

    Article  PubMed  CAS  Google Scholar 

  157. Becker M, Pinhasov A, Ornoy A (2021) Animal models of depression: what can they teach us about the human disease? Diagnostics (Basel) 11(1):123. https://doi.org/10.3390/diagnostics11010123

    Article  PubMed  CAS  Google Scholar 

  158. Janowsky DS, Overstreet DH, Nurnberger JI (1994) Is cholinergic sensitivity a genetic marker for the affective disorders? Am J Med Genet 54(4):335–344. https://doi.org/10.1002/ajmg.1320540412

    Article  PubMed  CAS  Google Scholar 

  159. Yadid G, Nakash R, Deri I, Tamar G, Kinor N, Gispan I, Zangen A (2000) Elucidation of the neurobiology of depression: insights from a novel genetic animal model. Prog Neurobiol 62(4):353–378. https://doi.org/10.1016/s0301-0082(00)00018-6

    Article  PubMed  CAS  Google Scholar 

  160. Eskelund A, Budac DP, Sanchez C, Elfving B, Wegener G (2016) Female flinders sensitive line rats show estrous cycle-independent depression-like behavior and altered tryptophan metabolism. Neuroscience 329:337–348. https://doi.org/10.1016/j.neuroscience.2016.05.024

    Article  PubMed  CAS  Google Scholar 

  161. Gross M, Sheinin A, Nesher E, Tikhonov T, Baranes D, Pinhasov A, Michaelevski I (2015) Early onset of cognitive impairment is associated with altered synaptic plasticity and enhanced hippocampal GluA1 expression in a mouse model of depression. Neurobiol Aging 36(5):1938–1952. https://doi.org/10.1016/j.neurobiolaging.2015.02.015

    Article  PubMed  CAS  Google Scholar 

  162. Fabre V, Beaufour C, Evrard A, Rioux A, Hanoun N, Lesch KP, Murphy DL et al (2000) Altered expression and functions of serotonin 5-HT1A and 5-HT1B receptors in knock-out mice lacking the 5-HT transporter. Eur J Neurosci 12(7):2299–2310. https://doi.org/10.1046/j.1460-9568.2000.00126.x

    Article  PubMed  CAS  Google Scholar 

  163. Qneibi M, Bdir S, Bdair M, Aldwaik SA, Sandouka D, Heeh M, Idais TI (2024) AMPA receptor neurotransmission and therapeutic applications: a comprehensive review of their multifaceted modulation. Eur J Med Chem 266:116151. https://doi.org/10.1016/j.ejmech.2024.116151

    Article  PubMed  CAS  Google Scholar 

  164. Ma H, Li C, Wang J, Zhang X, Li M, Zhang R, Huang Z, Zhang Y (2021) Amygdala-hippocampal innervation modulates stress-induced depressive-like behaviors through AMPA receptors. Proc Natl Acad Sci U S A 118 (6). https://doi.org/10.1073/pnas.2019409118

  165. Hill MN, Gorzalka BB (2005) Is there a role for the endocannabinoid system in the etiology and treatment of melancholic depression? Behav Pharmacol 16(5–6):333–352. https://doi.org/10.1097/00008877-200509000-00006

    Article  PubMed  CAS  Google Scholar 

  166. Yang Y, Cui Y, Sang K, Dong Y, Ni Z, Ma S, Hu H (2018) Ketamine blocks bursting in the lateral habenula to rapidly relieve depression. Nature 554(7692):317–322. https://doi.org/10.1038/nature25509

    Article  PubMed  CAS  Google Scholar 

  167. Paul IA, Skolnick P (2003) Glutamate and depression: clinical and preclinical studies. Ann N Y Acad Sci 1003:250–272. https://doi.org/10.1196/annals.1300.016

    Article  PubMed  CAS  Google Scholar 

  168. Salsabil L, Shahriar M, Islam SMA, Bhuiyan MA, Qusar MS, Islam MR (2023) Higher serum nerve growth factor levels are associated with major depressive disorder pathophysiology: a case-control study. J Int Med Res 51(4):3000605231166222. https://doi.org/10.1177/03000605231166222

    Article  PubMed  CAS  Google Scholar 

  169. Boksa P (2013) A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci 38(2):75–77. https://doi.org/10.1503/jpn.130018

    Article  PubMed  PubMed Central  Google Scholar 

  170. Steffens DC, Wei J, Krishnan KR, Karoly ED, Mitchell MW, O’Connor CM, Kaddurah-Daouk R (2010) Metabolomic differences in heart failure patients with and without major depression. J Geriatr Psychiatry Neurol 23(2):138–146. https://doi.org/10.1177/0891988709358592

    Article  PubMed  PubMed Central  Google Scholar 

  171. Ding X, Yang S, Li W, Liu Y, Li Z, Zhang Y, Li L, Liu S (2014) The potential biomarker panels for identification of major depressive disorder (MDD) patients with and without early life stress (ELS) by metabonomic analysis. PLoS One 9(5):e97479. https://doi.org/10.1371/journal.pone.0097479

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Funding

The authors are thankful to the Department of Pharmaceuticals, Ministry of Chemical and Fertilizer, for providing funding support to AKD through an institutional grant. PS and VB are receiving an institutional fellowship.

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Author notes

  1. Pooja Singh and Boosani Vasundhara contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India

    Pooja Singh, Boosani Vasundhara, Nabanita Das & Ashok Kumar Datusalia

  2. Centre for Precision Medicine and Centre, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India

    Ruchika Sharma

  3. Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, 110017, India

    Anoop Kumar

  4. Department of Regulatory Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, 226002, India

    Ashok Kumar Datusalia

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  1. Pooja Singh
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V.B, P.S, and R.S: literature review, writing—original draft, and preparation; V.B, P.S, and R.S: preparation of illustrations and visualization; N.D. and A.K: writing—review and editing revision; and A.K.D: conception, supervision, and writing—final review and editing. All authors have approved the final version of the manuscript.

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Correspondence to Ashok Kumar Datusalia.

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Singh, P., Vasundhara, B., Das, N. et al. Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences. Mol Neurobiol 62, 718–741 (2025). https://doi.org/10.1007/s12035-024-04302-5

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