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:

Characterizing metabolomic and proteomic changes in depression: a systematic analysis

Molecular Psychiatry volume 30, pages 3120–3128 (2025)Cite this article

Subjects

Abstract

Despite the widespread use of metabolomics and proteomics to explore the molecular landscape of depression, there is a lack of consensus regarding dysregulated molecules with replicable evidence. Thus, this study aimed to identify robust metabolomic and proteomic features in depression by integrating evidence from large-scale studies. In this study, a knowledge base-mining approach was adopted to compile a list of dysregulated molecules derived from metabolomic and proteomic studies. A vote-counting approach was performed to identify consistently altered molecules in the blood and urine samples of patients with depression. A total of 2398 molecular entries were selected, comprising 857 unique metabolites and 468 unique proteins from 143 metabolomic and 23 proteomic studies in depression. The results of vote-counting analyses revealed that 11 metabolites in blood and 5 metabolites in urine exhibited consistent disturbances across studies. Circulating levels of glutamic acid and phosphatidylcholine (32:0) were elevated in depressive patients, whereas the levels of tryptophan, kynurenic acid, kynurenine, acetylcarnitine, serotonin, creatinine, inosine, phenylalanine, and valine were lower. Urinary levels of isobutyric acid, alanine, and nicotinic acid were higher, whereas the levels of N-methylnicotinamide and tyrosine were lower. Moreover, analysis of the proteomic dataset identified only one circulating protein, ceruloplasmin, that was consistently dysregulated. Convergence comparison prioritized tryptophan as the top-ranked circulating metabolite, followed by kynurenic acid, acetylcarnitine, creatinine, serotonin, and valine. Collectively, robust evidence of metabolomic changes was observed in patients with depression, pointing to a role as potential biomarkers. Further investigation of consensus proteomic features for depression is necessitated.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Study flowchart and data statistics.
Fig. 2: Vote-counting results for metabolomic changes in the blood of patients with depression.
Fig. 3: Vote-counting results for metabolomic changes in the urine of patients with depression.
Fig. 4: Vote-counting results for proteomic changes in the blood of patients with depression.
Fig. 5: Convergence comparison of metabolomic changes in the blood of patients with depression.

Similar content being viewed by others

Data availability

The datasets generated in the current study are available in Supplemental Datasets 1–4, or on the ProMENDA website (https://menda.cqmu.edu.cn).

References

  1. Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299–312.

    PubMed  Google Scholar 

  2. Arias-de la Torre J, Vilagut G, Ronaldson A, Serrano-Blanco A, Martín V, Peters M, et al. Prevalence and variability of current depressive disorder in 27 European countries: a population-based study. Lancet Public Health. 2021;6:e729–38.

    PubMed  PubMed Central  Google Scholar 

  3. Goodwin RD, Dierker LC, Wu M, Galea S, Hoven CW, Weinberger AH. Trends in U.S. depression prevalence from 2015 to 2020: the widening treatment gap. Am J Prev Med. 2022;63:726–33.

    PubMed  PubMed Central  Google Scholar 

  4. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3:171–8.

    PubMed  Google Scholar 

  5. Marx W, Penninx BWJH, Solmi M, Furukawa TA, Firth J, Carvalho AF, et al. Major depressive disorder. Nat Rev Dis Primers. 2023;9:44.

    PubMed  Google Scholar 

  6. Tian H, Hu Z, Xu J, Wang C. The molecular pathophysiology of depression and the new therapeutics. MedComm. 2022;3:e156.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Harmer CJ, Duman RS, Cowen PJ. How do antidepressants work? New perspectives for refining future treatment approaches. Lancet Psychiatry. 2017;4:409–18.

    PubMed  PubMed Central  Google Scholar 

  8. Villas Boas GR, Boerngen de Lacerda R, Paes MM, Gubert P, Almeida WLDC, Rescia VC, et al. Molecular aspects of depression: a review from neurobiology to treatment. Eur J Pharmacol. 2019;851:99–121.

    CAS  PubMed  Google Scholar 

  9. Suhre K, McCarthy MI, Schwenk JM. Genetics meets proteomics: perspectives for large population-based studies. Nat Rev Genet. 2021;22:19–37.

    CAS  PubMed  Google Scholar 

  10. Nedic Erjavec G, Konjevod M, Nikolac Perkovic M, Svob Strac D, Tudor L, Barbas C, et al. Short overview on metabolomic approach and redox changes in psychiatric disorders. Redox Biol. 2018;14:178–86.

    CAS  PubMed  Google Scholar 

  11. 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.

    CAS  PubMed  Google Scholar 

  12. Xu HB, Zhang RF, Luo D, Zhou Y, Wang Y, Fang L, et al. Comparative proteomic analysis of plasma from major depressive patients: identification of proteins associated with lipid metabolism and immunoregulation. Int J Neuropsychopharmacol. 2012;15:1413–25.

    CAS  PubMed  Google Scholar 

  13. Tkachev A, Stekolshchikova E, Vanyushkina A, Zhang H, Morozova A, Zozulya S, et al. Lipid alteration signature in the blood plasma of individuals with schizophrenia, depression, and bipolar disorder. JAMA Psychiatry. 2023;80:250–9.

    PubMed  PubMed Central  Google Scholar 

  14. Ogawa S, Koga N, Hattori K, Matsuo J, Ota M, Hori H, et al. Plasma amino acid profile in major depressive disorder: analyses in two independent case-control sample sets. J Psychiatr Res. 2018;96:23–32.

    PubMed  Google Scholar 

  15. Caspani G, Turecki G, Lam RW, Milev RV, Frey BN, MacQueen GM, et al. Metabolomic signatures associated with depression and predictors of antidepressant response in humans: a CAN-BIND-1 report. Commun Biol. 2021;4:903.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Silva-Costa LC, Smith BJ, Carregari VC, Souza GHMF, Vieira EM, Mendes-Silva AP, et al. Plasma proteomic signature of major depressive episode in the elderly. J Proteom. 2022;269:104713.

    CAS  Google Scholar 

  17. 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. Biol Psychiatry. 2020;87:409–18.

    CAS  PubMed  Google Scholar 

  18. Pu J, Liu Y, Zhang H, Tian L, Gui S, Yu Y, et al. An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder. Mol Psychiatry. 2021;26:4265–76.

    CAS  PubMed  Google Scholar 

  19. Comes AL, Papiol S, Mueller T, Geyer PE, Mann M, Schulze TG. Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future. Transl Psychiatry. 2018;8:160.

    PubMed  PubMed Central  Google Scholar 

  20. Guest PC, Guest FL, Martins-de Souza D. Making sense of blood-based proteomics and metabolomics in psychiatric research. Int J Neuropsychopharmacol. 2016;19:pyv138.

    PubMed  Google Scholar 

  21. Romeo B, Choucha W, Fossati P, Rotge JY. Meta-analysis of central and peripheral γ-aminobutyric acid levels in patients with unipolar and bipolar depression. J Psychiatry Neurosci. 2018;43:58–66.

    PubMed  Google Scholar 

  22. Arnone D, Saraykar S, Salem H, Teixeira AL, Dantzer R, Selvaraj S. Role of kynurenine pathway and its metabolites in mood disorders: a systematic review and meta-analysis of clinical studies. Neurosci Biobehav Rev. 2018;92:477–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Pan JX, Xia JJ, Deng FL, Liang WW, Wu J, Yin BM, et al. Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Transl Psychiatry. 2018;8:130.

    PubMed  PubMed Central  Google Scholar 

  24. Goveia J, Pircher A, Conradi LC, Kalucka J, Lagani V, Dewerchin M, et al. Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities. EMBO Mol Med. 2016;8:1134–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Pu J, Liu Y, Gui S, Tian L, Yu Y, Song X, et al. Metabolomic changes in animal models of depression: a systematic analysis. Mol Psychiatry. 2021;26:7328–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Pu J, Liu Y, Gui S, Tian L, Yu Y, Wang D, et al. Effects of pharmacological treatment on metabolomic alterations in animal models of depression. Transl Psychiatry. 2022;12:175.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Pu J, Yu Y, Liu Y, Tian L, Gui S, Zhong X, et al. MENDA: a comprehensive curated resource of metabolic characterization in depression. Brief Bioinform. 2020;21:1455–64.

    CAS  PubMed  Google Scholar 

  28. Pu J, Yu Y, Liu Y, Wang D, Gui S, Zhong X, et al. ProMENDA: an updated resource for proteomic and metabolomic characterization in depression. Transl Psychiatry. 2024;14:229.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res. 2024;52:W398–W406.

    PubMed  PubMed Central  Google Scholar 

  30. Fischer M, Hoffmann S. Synthesizing genome regulation data with vote-counting. Trends Genet. 2022;38:1208–16.

    CAS  PubMed  Google Scholar 

  31. Rikke BA, Wynes MW, Rozeboom LM, Barón AE, Hirsch FR. Independent validation test of the vote-counting strategy used to rank biomarkers from published studies. Biomark Med. 2015;9:751–61.

    CAS  PubMed  Google Scholar 

  32. Xue C, Li G, Zheng Q, Gu X, Shi Q, Su Y, et al. Tryptophan metabolism in health and disease. Cell Metab. 2023;35:1304–26.

    CAS  PubMed  Google Scholar 

  33. Moncrieff J, Cooper RE, Stockmann T, Amendola S, Hengartner MP, Horowitz MA. The serotonin theory of depression: a systematic umbrella review of the evidence. Mol Psychiatry. 2023;28:3243–56.

    PubMed  Google Scholar 

  34. Jauhar S, Cowen PJ, Browning M. Fifty years on: serotonin and depression. J Psychopharmacol. 2023;37:237–41.

    PubMed  PubMed Central  Google Scholar 

  35. Vécsei L, Szalárdy L, Fülöp F, Toldi J. Kynurenines in the CNS: recent advances and new questions. Nat Rev Drug Discov. 2013;12:64–82.

    PubMed  Google Scholar 

  36. Marx W, McGuinness AJ, Rocks T, Ruusunen A, Cleminson J, Walker AJ, et al. The kynurenine pathway in major depressive disorder, bipolar disorder, and schizophrenia: a meta-analysis of 101 studies. Mol Psychiatry. 2021;26:4158–78.

    CAS  PubMed  Google Scholar 

  37. Tóth F, Cseh EK, Vécsei L. Natural molecules and neuroprotection: kynurenic acid, pantethine and α-lipoic acid. Int J Mol Sci. 2021;22:403.

    PubMed  PubMed Central  Google Scholar 

  38. Roiser JP, McLean A, Ogilvie AD, Blackwell AD, Bamber DJ, Goodyer I, et al. The subjective and cognitive effects of acute phenylalanine and tyrosine depletion in patients recovered from depression. Neuropsychopharmacology. 2005;30:775–85.

    CAS  PubMed  Google Scholar 

  39. Huang T, Balasubramanian R, Yao Y, Clish CB, Shadyab AH, Liu B, et al. Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women. Mol Psychiatry. 2021;26:3315–27.

    CAS  PubMed  Google Scholar 

  40. Mitani H, Shirayama Y, Yamada T, Maeda K, Ashby CR Jr, Kawahara R. Correlation between plasma levels of glutamate, alanine and serine with severity of depression. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30:1155–8.

    CAS  PubMed  Google Scholar 

  41. Ho CSH, Tay GWN, Wee HN, Ching J. The utility of amino acid metabolites in the diagnosis of major depressive disorder and correlations with depression severity. Int J Mol Sci. 2023;24:2231.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Peng YF, Xiang Y, Wei YS. The significance of routine biochemical markers in patients with major depressive disorder. Sci Rep. 2016;6:34402.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu X, Zhao W, Hu F, Hao Q, Hou L, Sun X, et al. Comorbid anxiety and depression, depression, and anxiety in comparison in multi-ethnic community of west China: prevalence, metabolic profile, and related factors. J Affect Disord. 2022;298:381–7.

    PubMed  Google Scholar 

  44. Lee BJ. Association of depressive disorder with biochemical and anthropometric indices in adult men and women. Sci Rep. 2021;11:13596.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Lieu C, Anderson R. Serum creatinine: why lower may not be better. Crit Care Med. 2007;35:2458–9.

    PubMed  Google Scholar 

  46. Xu Y, Zeng L, Zou K, Shan S, Wang X, Xiong J, et al. Role of dietary factors in the prevention and treatment for depression: an umbrella review of meta-analyses of prospective studies. Transl Psychiatry. 2021;11:478.

    PubMed  PubMed Central  Google Scholar 

  47. Licht K, Hartl M, Amman F, Anrather D, Janisiw MP, Jantsch MF. Inosine induces context-dependent recoding and translational stalling. Nucleic Acids Res. 2019;47:3–14.

    CAS  PubMed  Google Scholar 

  48. Niemann B, Haufs-Brusberg S, Puetz L, Feickert M, Jaeckstein MY, Hoffmann A, et al. Apoptotic brown adipocytes enhance energy expenditure via extracellular inosine. Nature. 2022;609:361–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Muto J, Lee H, Lee H, Uwaya A, Park J, Nakajima S, et al. Oral administration of inosine produces antidepressant-like effects in mice. Sci Rep. 2014;4:4199.

    PubMed  PubMed Central  Google Scholar 

  50. Liu X, Teng T, Li X, Fan L, Xiang Y, Jiang Y, et al. Impact of inosine on chronic unpredictable mild stress-induced depressive and anxiety-like behaviors with the alteration of gut microbiota. Front Cell Infect Microbiol. 2021;11:697640.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Yang J, Zhang Z, Xie Z, Bai L, Xiong P, Chen F, et al. Metformin modulates microbiota-derived inosine and ameliorates methamphetamine-induced anxiety and depression-like withdrawal symptoms in mice. Biomed Pharmacother. 2022;149:112837.

    CAS  PubMed  Google Scholar 

  52. Bongiovanni R, Newbould E, Jaskiw GE. Tyrosine depletion lowers dopamine synthesis and desipramine-induced prefrontal cortex catecholamine levels. Brain Res. 2008;1190:39–48.

    CAS  PubMed  Google Scholar 

  53. Tian S, Wu L, Zheng H, Zhong X, Liu M, Yu X, et al. Dietary niacin intake in relation to depression among adults: a population-based study. BMC Psychiatry. 2023;23:678.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhao L, Guo S, Yang J, Wang Q, Lu X. Association between niacin intake and depression: a nationwide cross-sectional study. J Affect Disord. 2023;340:347–54.

    CAS  PubMed  Google Scholar 

  55. Zhao J, Zhang Y, Liu Y, Tang WQ, Ji CH, Gu JH, et al. Antidepressant-like effects of 1-methylnicotinamide in a chronic unpredictable mild stress model of depression. Neurosci Lett. 2021;742:135535.

    CAS  PubMed  Google Scholar 

  56. Liu Z, Li C, Fan X, Kuang Y, Zhang X, Chen L, et al. Nicotinamide, a vitamin B3 ameliorates depressive behaviors independent of SIRT1 activity in mice. Mol Brain. 2020;13:162.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Chen Y, Xiao N, Chen Y, Chen X, Zhong C, Cheng Y, et al. Semen sojae praeparatum alters depression-like behaviors in chronic unpredictable mild stress rats via intestinal microbiota. Food Res Int. 2021;150:110808.

    PubMed  Google Scholar 

  58. Liu X, Zhong S, Yan L, Zhao H, Wang Y, Hu Y, et al. Correlations among mRNA expression levels of ATP7A, serum ceruloplasmin levels, and neuronal metabolism in unmedicated major depressive disorder. Int J Neuropsychopharmacol. 2020;23:642–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Perng W, Aslibekyan S. Find the needle in the haystack, then find it again: replication and validation in the ‘omics era. Metabolites. 2020;10:286.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Halvey P, Farutin V, Koppes L, Gunay NS, Pappas DA, Manning AM, et al. Variable blood processing procedures contribute to plasma proteomic variability. Clin Proteom. 2021;18:5.

    CAS  Google Scholar 

  61. Ashkarran AA, Gharibi H, Voke E, Landry MP, Saei AA, Mahmoudi M. Measurements of heterogeneity in proteomics analysis of the nanoparticle protein corona across core facilities. Nat Commun. 2022;13:6610.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Lin Y, Caldwell GW, Li Y, Lang W, Masucci J. Inter-laboratory reproducibility of an untargeted metabolomics GC-MS assay for analysis of human plasma. Sci Rep. 2020;10:10918.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Ioannidis JP, Khoury MJ. Improving validation practices in “omics” research. Science. 2011;334:1230–2.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Pu J, Liu X, Liu Y, Gui S, Zhong X, Tian L, et al. Sex-specific plasma metabolome signatures in major depressive disorder. Psychiatry Clin Neurosci. 2019;73:713–4.

    PubMed  Google Scholar 

  65. Chen JJ, Xie J, Li WW, Bai SJ, Wang W, Zheng P, et al. Age-specific urinary metabolite signatures and functions in patients with major depressive disorder. Aging. 2019;11:6626–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Chen JJ, Zhou CJ, Zheng P, Cheng K, Wang HY, Li J, et al. Differential urinary metabolites related with the severity of major depressive disorder. Behav Brain Res. 2017;332:280–7.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Joint project of Chongqing Municipal Science and Technology Bureau and Chongqing Health Commission (2023CCXM003), the Natural Science Foundation Project of China (82371526), the Natural Science Foundation of Chongqing (CSTB2024NSCQ-QCXMX0033 and CSTB2024NSCQ-MSX1027), the Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), and the Chongqing Medical Science and Technology Innovation Four Centers Construction Project - Chongqing Clinical Evaluation and Research Center for Cardiovascular and Cerebrovascular Drug and Devices (Materials) Project.

Author information

Authors and Affiliations

  1. Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Juncai Pu, Hailin Wu, Chi Liu, Xiaopeng Chen, Yue Chen, Xiang Chen, Renjie Qiao, Yanyi Jiang, Hanping Zhang, Yi Ren, Li Fan & Peng Xie

  2. NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Juncai Pu, Yiyun Liu, Hailin Wu, Chi Liu, Yin Chen, Wei Tang, Siwen Gui, Xiaogang Zhong, Dongfang Wang, Xiaopeng Chen, Yue Chen, Xiang Chen, Renjie Qiao, Yanyi Jiang, Hanping Zhang, Yi Ren, Li Fan, Haiyang Wang & Peng Xie

  3. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Yue Yu

  4. Jinfeng Laboratory, Chongqing, China

    Peng Xie

  5. Chongqing Institute for Brain and Intelligence, Chongqing, China

    Peng Xie

Authors
  1. Juncai Pu
    View author publications

    Search author on:PubMed Google Scholar

  2. Yiyun Liu
    View author publications

    Search author on:PubMed Google Scholar

  3. Hailin Wu
    View author publications

    Search author on:PubMed Google Scholar

  4. Chi Liu
    View author publications

    Search author on:PubMed Google Scholar

  5. Yin Chen
    View author publications

    Search author on:PubMed Google Scholar

  6. Wei Tang
    View author publications

    Search author on:PubMed Google Scholar

  7. Yue Yu
    View author publications

    Search author on:PubMed Google Scholar

  8. Siwen Gui
    View author publications

    Search author on:PubMed Google Scholar

  9. Xiaogang Zhong
    View author publications

    Search author on:PubMed Google Scholar

  10. Dongfang Wang
    View author publications

    Search author on:PubMed Google Scholar

  11. Xiaopeng Chen
    View author publications

    Search author on:PubMed Google Scholar

  12. Yue Chen
    View author publications

    Search author on:PubMed Google Scholar

  13. Xiang Chen
    View author publications

    Search author on:PubMed Google Scholar

  14. Renjie Qiao
    View author publications

    Search author on:PubMed Google Scholar

  15. Yanyi Jiang
    View author publications

    Search author on:PubMed Google Scholar

  16. Hanping Zhang
    View author publications

    Search author on:PubMed Google Scholar

  17. Yi Ren
    View author publications

    Search author on:PubMed Google Scholar

  18. Li Fan
    View author publications

    Search author on:PubMed Google Scholar

  19. Haiyang Wang
    View author publications

    Search author on:PubMed Google Scholar

  20. Peng Xie
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conception and design and interpretation of data: Juncai Pu and Peng Xie. Acquisition and analysis of data: Juncai Pu, Yiyun Liu, Yin Chen, Wei Tang, Yue Yu, Siwen Gui, Xiaogang Zhong, Dongfang Wang, Xiaopeng Chen, Yue Chen, Xiang Chen, Renjie Qiao, Yanyi Jiang, Hanping Zhang, Yi Ren, Li Fan and Haiyang Wang. Drafting display items: Juncai Pu, Hailin Wu, and Chi Liu. Drafting and revising the manuscript: Juncai Pu, Hailin Wu, Chi Liu and Peng Xie. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Peng Xie.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations. The study was approved by The Ethics Committee of Chongqing Medical University (IACUC-CQMU-2024-0115). The datasets generated in the current study were collected from publicly available literature or reports, therefore informed consent forms are not applicable.

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

Pu, J., Liu, Y., Wu, H. et al. Characterizing metabolomic and proteomic changes in depression: a systematic analysis. Mol Psychiatry 30, 3120–3128 (2025). https://doi.org/10.1038/s41380-025-02919-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-025-02919-z

Search

Advanced search

Quick links