Skip to main content
SpringerLink
Log in

Post-COVID Trajectory of Pentraxin 3 Plasma Levels Over 6 Months and Their Association with the Risk of Developing Post-Acute Depression and Anxiety

  • Original Research Article
  • Published:
CNS Drugs Aims and scope Submit manuscript

Abstract

Background and Objectives

Clinical manifestations of coronavirus disease 2019 (COVID-19) often persist after acute disease resolution. Underlying molecular mechanisms are unclear. The objective of this original article was to longitudinally measure plasma levels of markers of the innate immune response to investigate whether they associate with and predict post-COVID symptomatology.

Methods

Adult patients with previous severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during the first pandemic wave who underwent the 6-month multidisciplinary follow-up were included. Plasma levels of pentraxin 3 (PTX3), the complement components C3a and C5a, and chitinase-3 like-protein-1 (CHI3L1) were measured at hospital admission during acute disease (baseline) and at 1 and 6 months after hospital discharge. Associations with post-COVID-19 sequelae at 6 months were investigated using descriptive statistic and multiple regression models.

Results

Ninety-four COVID-19 patients were included. Baseline PTX3, C5a, C3a, and CHI3L1 did not predict post-COVID-19 sequelae. The extent of the reduction of PTX3 over time (delta PTX3) was associated with lower depressive and anxiety symptoms at 6 months (both p < 0.05). When entering sex, age, need for intensive care unit or non-invasive ventilation during hospital stay, psychiatric history, and baseline PTX3 as nuisance covariates into a generalized linear model (GLM), the difference between baseline and 6-month PTX3 levels (delta PTX3) significantly predicted depression (χ2 = 4.66, p = 0.031) and anxiety (χ2 = 4.68, p = 0.031) at 6 months. No differences in PTX3 levels or PTX3 delta were found in patients with or without persistent or new-onset other COVID-19 symptoms or signs at 6 months. Plasma levels of C3a, C5a, and CHI3L1 did not correlate with PTX3 levels at either time point and failed to associate with residual or de novo respiratory or systemic clinical manifestations of the disease at 6 months.

Conclusions

A lower reduction of plasma PTX3 after acute COVID-19 associates with the presence of depression and anxiety, suggesting an involvement of inflammation in post-COVID-19 psychopathology and a potential role of PTX3 as a biomarker.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Carfì A, Bernabei R, Landi F. Persistent symptoms in patients after acute COVID-19. JAMA. 2020;324(6):603–5. https://doi.org/10.1001/jama.2020.12603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601–15. https://doi.org/10.1038/s41591-021-01283-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Huang S, Zhou Z, Yang D, Zhao W, Zeng M, Xie X, et al. Persistent white matter changes in recovered COVID-19 patients at the 1-year follow-up. Brain. 2022;145(5):1830–8.

    Article  PubMed  Google Scholar 

  4. Venkatesan P. NICE guideline on long COVID. Lancet Respir Med. 2021;9(2):129. https://doi.org/10.1016/S2213-2600(21)00031-X.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nasserie T, Hittle M, Goodman SN. Assessment of the frequency and variety of persistent symptoms among patients With COVID-19: a systematic review. JAMA Netw Open. 2021;4(5): e2111417. https://doi.org/10.1001/jamanetworkopen.2021.11417.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Poletti S, Mazza MG, Calesella F, Vai B, Lorenzi C, Manfredi E, et al. Circulating inflammatory markers impact cognitive functions in bipolar depression. J Psychiatr Res. 2021;140:110–6. https://doi.org/10.1016/j.jpsychires.2021.05.071.

  7. Benedetti F, Poletti S, Vai B, Mazza MG, Lorenzi C, Brioschi S, et al. Higher baseline interleukin-1beta and TNF-alpha hamper antidepressant response in major depressive disorder. Eur Neuropsychopharmacol. 2021;42:35–44. https://doi.org/10.1016/j.euroneuro.2020.11.009.

  8. Mazza MG, Palladini M, Villa G, De Lorenzo R, Rovere Querini P, Benedetti F. Prevalence, trajectory over time, and risk factor of post-COVID-19 fatigue. J Psychiatr Res. 2022;155:112–9. https://doi.org/10.1016/j.jpsychires.2022.08.008.

  9. Manning K, Zvolensky MJ, Garey L, Long LJ, Gallagher MW. The explanatory role of fatigue severity in the relation between COVID-19 perceived stress and depression, anxiety, and panic severity. Cogn Behav Therapy. 2021:1-11. https://doi.org/10.1080/16506073.2021.1874503.

  10. Troyer EA, Kohn JN, Hong S. Are we facing a crashing wave of neuropsychiatric sequelae of COVID-19? Neuropsychiatric symptoms and potential immunologic mechanisms. Brain Behav Immun. 2020;87:34-9. https://doi.org/10.1016/j.bbi.2020.04.027.

  11. Passavanti M, Argentieri A, Barbieri DM, Lou B, Wijayaratna K, Foroutan Mirhosseini AS, et al. The psychological impact of COVID-19 and restrictive measures in the world. J Affect Disord. 2021;15(283):36–51. https://doi.org/10.1016/j.jad.2021.01.020.

    Article  CAS  Google Scholar 

  12. Kumar R, Aktay-Cetin O, Craddock V, Morales-Cano D, Kosanovic D, Cogolludo A, et al. Potential long-term effects of SARS-CoV-2 infection on the pulmonary vasculature: Multilayered cross-talks in the setting of coinfections and comorbidities. PLoS Pathog. 2023;19(1): e1011063. https://doi.org/10.1371/journal.ppat.1011063.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Maamar M, Artime A, Pariente E, Fierro P, Ruiz Y, Gutierrez S, et al. Post-COVID-19 syndrome, low-grade inflammation and inflammatory markers: a cross-sectional study. Curr Med Res Opin. 2022;38(6):901-9. https://doi.org/10.1080/03007995.2022.2042991.

  14. Saini G, Aneja R. Cancer as a prospective sequela of long COVID-19. BioEssays. 2021;43(6): e2000331. https://doi.org/10.1002/bies.202000331.

    Article  CAS  PubMed  Google Scholar 

  15. Mazza MG, Palladini M, Poletti S, Benedetti F. Post-COVID-19 depressive symptoms: epidemiology, pathophysiology, and pharmacological treatment. CNS Drugs. 2022;36(7):681–702. https://doi.org/10.1007/s40263-022-00931-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Porte R, Davoudian S, Asgari F, Parente R, Mantovani A, Garlanda C, et al. The long pentraxin PTX3 as a humoral innate immunity functional player and biomarker of infections and sepsis. Front Immunol. 2019;10:794. https://doi.org/10.3389/fimmu.2019.00794.

  17. Mantovani A, Garlanda C. Humoral innate immunity and acute-phase proteins. N Engl J Med. 2023;388(5):439–52. https://doi.org/10.1056/NEJMra2206346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hansen CB, Sandholdt H, Moller MEE, Perez-Alos L, Pedersen L, Bastrup Israelsen S, et al. Prediction of respiratory failure and mortality in COVID-19 patients using long pentraxin PTX3. J Innate Immun. 2022;14(5):493–501. https://doi.org/10.1159/000521612.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lapadula G, Leone R, Bernasconi DP, Biondi A, Rossi E, D'Angio M, et al. Long pentraxin 3 (PTX3) levels predict death, intubation and thrombotic events among hospitalized patients with COVID-19. Front Immunol. 2022;13:933960; https://doi.org/10.3389/fimmu.2022.933960.

  20. Stravalaci M, Pagani I, Paraboschi EM, Pedotti M, Doni A, Scavello F, et al. Recognition and inhibition of SARS-CoV-2 by humoral innate immunity pattern recognition molecules. Nat Immunol. 2022;23(2):275–86. https://doi.org/10.1038/s41590-021-01114-w.

    Article  CAS  PubMed  Google Scholar 

  21. Brunetta E, Folci M, Bottazzi B, De Santis M, Gritti G, Protti A, et al. Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19. Nat Immunol. 2021;22(1):19–24. https://doi.org/10.1038/s41590-020-00832-x.

    Article  PubMed  Google Scholar 

  22. Phetsouphanh C, Darley DR, Wilson DB, Howe A, Munier CML, Patel SK, et al. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat Immunol. 2022;23(2):210–6. https://doi.org/10.1038/s41590-021-01113-x.

    Article  CAS  PubMed  Google Scholar 

  23. De Lorenzo R, Conte C, Lanzani C, Benedetti F, Roveri L, Mazza MG, et al. Residual clinical damage after COVID-19: a retrospective and prospective observational cohort study. PLoS One. 2020;15(10): e0239570. https://doi.org/10.1371/journal.pone.0239570.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Farina N, Ramirez GA, De Lorenzo R, Di Filippo L, Conte C, Ciceri F, et al. COVID-19: Pharmacology and kinetics of viral clearance. Pharmacol Res. 2020;161:105114; https://doi.org/10.1016/j.phrs.2020.105114.

  25. Rovere Querini P, De Lorenzo R, Conte C, Brioni E, Lanzani C, Yacoub MR, et al. Post-COVID-19 follow-up clinic: depicting chronicity of a new disease. Acta Biomed. 2020;91(9-S):22-8. https://doi.org/10.23750/abm.v91i9-S.10146.

  26. Rovere-Querini P, Tresoldi C, Conte C, Ruggeri A, Ghezzi S, R DEL, et al. Biobanking for COVID-19 research. Panminerva Med. 2022;64(2):244-52. https://doi.org/10.23736/S0031-0808.20.04168-3

  27. De Lorenzo R, Magnaghi C, Cinel E, Vitali G, Martinenghi S, Mazza MG, et al. A Nomogram-based model to predict respiratory dysfunction at 6 months in non-critical COVID-19 survivors. Front Med (Lausanne). 2022;9:781410. https://doi.org/10.3389/fmed.2022.781410.

  28. R Del, Cinel E, Cilla M, Compagnone N, Ferrante M, Falbo E, et al. Physical and psychological sequelae at three months after acute illness in COVID-19 survivors. Panminerva Med. 2021. https://doi.org/10.23736/S0031-0808.21.04399-8.

  29. First MB. Diagnostic and statistical manual of mental disorders, 5th edition, and clinical utility. J Nerv Ment Dis. 2013;201(9):727-9. https://doi.org/10.1097/NMD.0b013e3182a2168a.

  30. Zung WW. A self-rating depression scale. Arch Gen Psychiatry. 1965;12(1):63–70. https://doi.org/10.1001/archpsyc.1965.01720310065008.

    Article  CAS  PubMed  Google Scholar 

  31. Beck AT, Steer RA. Internal consistencies of the original and revised Beck Depression Inventory. J Clin Psychol. 1984;40(6):1365–7. https://doi.org/10.1002/1097-4679(198411)40:6%3c1365::aid-jclp2270400615%3e3.0.co;2-d.

    Article  CAS  PubMed  Google Scholar 

  32. Creamer M, Bell R, Failla S. Psychometric properties of the impact of event scale—revised. Behav Res Ther. 2003;41(12):1489–96. https://doi.org/10.1016/j.brat.2003.07.010.

    Article  PubMed  Google Scholar 

  33. Vigneau F, Cormier S. The factor structure of the State-Trait Anxiety Inventory: an alternative view. J Pers Assess. 2008;90(3):280–5. https://doi.org/10.1080/00223890701885027.

    Article  PubMed  Google Scholar 

  34. Lore NI, De Lorenzo R, Rancoita PMV, Cugnata F, Agresti A, Benedetti F, et al. CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study. Mol Med. 2021;27(1):129. https://doi.org/10.1186/s10020-021-00390-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. McCullagh P, Nelder JA. Generalized linear models. 2nd ed. New York: Chapman & Hall; 1989.

    Book  Google Scholar 

  36. Agresti A. An introduction to categorical data analysis. New York: Wiley; 1996.

    Google Scholar 

  37. Dobson AJ. An introduction to generalized linear models. New York: Chapman & Hall; 1990.

    Book  Google Scholar 

  38. Magrini E, Mantovani A, Garlanda C. The dual complexity of PTX3 in health and disease: a balancing act? Trends Mol Med. 2016;22(6):497–510. https://doi.org/10.1016/j.molmed.2016.04.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Staubli SM, Schafer J, Rosenthal R, Zeindler J, Oertli D, Nebiker CA. The role of CRP and Pentraxin 3 in the prediction of systemic inflammatory response syndrome and death in acute pancreatitis. Sci Rep. 2019;9(1):18340. https://doi.org/10.1038/s41598-019-54910-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Carvelli J, Demaria O, Vely F, Batista L, Chouaki Benmansour N, Fares J, et al. Association of COVID-19 inflammation with activation of the C5a–C5aR1 axis. Nature. 2020;588(7836):146–50. https://doi.org/10.1038/s41586-020-2600-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rajkovic I, Denes A, Allan SM, Pinteaux E. Emerging roles of the acute phase protein pentraxin-3 during central nervous system disorders. J Neuroimmunol. 2016;15(292):27–33. https://doi.org/10.1016/j.jneuroim.2015.12.007.

    Article  CAS  Google Scholar 

  42. Bourgeois MA, Denslow ND, Seino KS, Barber DS, Long MT. Gene expression analysis in the thalamus and cerebrum of horses experimentally infected with West Nile virus. PLoS One. 2011;6(10): e24371. https://doi.org/10.1371/journal.pone.0024371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhang Y, Hu H, Liu C, Wu J, Zhou S, Zhao T. Serum pentraxin 3 as a biomarker for prognosis of acute minor stroke due to large artery atherosclerosis. Brain Behav. 2021;11(1): e01956. https://doi.org/10.1002/brb3.1956.

    Article  PubMed  Google Scholar 

  44. Lee HW, Choi J, Suk K. Increases of pentraxin 3 plasma levels in patients with Parkinson’s disease. Mov Disord. 2011;26(13):2364–70. https://doi.org/10.1002/mds.23871.

    Article  PubMed  Google Scholar 

  45. Ryu WS, Kim CK, Kim BJ, Kim C, Lee SH, Yoon BW. Pentraxin 3: a novel and independent prognostic marker in ischemic stroke. Atherosclerosis. 2012;220(2):581–6. https://doi.org/10.1016/j.atherosclerosis.2011.11.036.

    Article  CAS  PubMed  Google Scholar 

  46. Sezer S, Ucar F, Ulusoy EK, Erdogan S, Bilen S, Zungun C, et al. Serum amyloid A, fetuin-A, and pentraxin-3 levels in patients with ischemic stroke: novel prognostic biomarkers? Turk J Med Sci. 2014;44(1):16–23. https://doi.org/10.3906/sag-1211-90.

    Article  CAS  PubMed  Google Scholar 

  47. Ye Q, Wang B, Mao J. The pathogenesis and treatment of the `Cytokine Storm’ in COVID-19. J Infect. 2020;80(6):607–13. https://doi.org/10.1016/j.jinf.2020.03.037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Coperchini F, Chiovato L, Croce L, Magri F, Rotondi M. The cytokine storm in COVID-19: an overview of the involvement of the chemokine/chemokine-receptor system. Cytokine Growth Factor Rev. 2020;53:25–32. https://doi.org/10.1016/j.cytogfr.2020.05.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cheng Y, Desse S, Martinez A, Worthen RJ, Jope RS, Beurel E. TNFalpha disrupts blood brain barrier integrity to maintain prolonged depressive-like behavior in mice. Brain Behav Immun. 2018;69:556–67. https://doi.org/10.1016/j.bbi.2018.02.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dantzer R. Neuroimmune Interactions: from the brain to the immune system and vice versa. Physiol Rev. 2018;98(1):477–504. https://doi.org/10.1152/physrev.00039.2016.

    Article  CAS  PubMed  Google Scholar 

  51. Najjar S, Pearlman DM, Alper K, Najjar A, Devinsky O. Neuroinflammation and psychiatric illness. J Neuroinflamm. 2013;10:43; https://doi.org/10.1186/1742-2094-10-43

  52. Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16(1):22–34. https://doi.org/10.1038/nri.2015.5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Benedetti F, Aggio V, Pratesi ML, Greco G, Furlan R. Neuroinflammation in bipolar depression. Front Psychiatry. 2020;11:71. https://doi.org/10.3389/fpsyt.2020.00071

  54. Polentarutti N, Bottazzi B, Di Santo E, Blasi E, Agnello D, Ghezzi P, et al. Inducible expression of the long pentraxin PTX3 in the central nervous system. J Neuroimmunol. 2000;106(1–2):87–94. https://doi.org/10.1016/s0165-5728(00)00214-9.

    Article  CAS  PubMed  Google Scholar 

  55. Goldsmith DR, Rapaport MH, Miller BJ. A meta-analysis of blood cytokine network alterations in psychiatric patients: comparisons between schizophrenia, bipolar disorder and depression. Mol Psychiatry. 2016;21(12):1696–709. https://doi.org/10.1038/mp.2016.3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang AK, Miller BJ. Meta-analysis of cerebrospinal fluid cytokine and tryptophan catabolite alterations in psychiatric patients: comparisons between schizophrenia, bipolar disorder, and depression. Schizophr Bull. 2018;44(1):75–83. https://doi.org/10.1093/schbul/sbx035.

    Article  PubMed  Google Scholar 

  57. Zhang Y, Wang J, Ye Y, Zou Y, Chen W, Wang Z, et al. Peripheral cytokine levels across psychiatric disorders: a systematic review and network meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;125:110740. https://doi.org/10.1016/j.pnpbp.2023.110740.

  58. Benedetti F, Dallaspezia S, Melloni EMT, Lorenzi C, Zanardi R, Barbini B, et al. Effective antidepressant chronotherapeutics (sleep deprivation and light therapy) normalize the IL-1beta:IL-1ra ratio in bipolar depression. Front Physiol. 2021;12:740686. https://doi.org/10.3389/fphys.2021.740686.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Benedetti F, Poletti S, Hoogenboezem TA, Mazza E, Ambree O, de Wit H, et al. Inflammatory cytokines influence measures of white matter integrity in Bipolar Disorder. J Affect Disord. 2016;15(202):1–9. https://doi.org/10.1016/j.jad.2016.05.047.

    Article  CAS  Google Scholar 

  60. Maes M, Song C, Yirmiya R. Targeting IL-1 in depression. Expert Opin Ther Targets. 2012;16(11):1097–112. https://doi.org/10.1517/14728222.2012.718331.

    Article  CAS  PubMed  Google Scholar 

  61. Mazza MG, De Lorenzo R, Conte C, Poletti S, Vai B, Bollettini I, et al. Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav Immun. 2020;89:594–600. https://doi.org/10.1016/j.bbi.2020.07.037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Mazza MG, Palladini M, De Lorenzo R, Magnaghi C, Poletti S, Furlan R, et al. Persistent psychopathology and neurocognitive impairment in COVID-19 survivors: effect of inflammatory biomarkers at three-month follow-up. Brain Behav Immun. 2021;94:138–47. https://doi.org/10.1016/j.bbi.2021.02.021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mazza MG, Palladini M, De Lorenzo R, Bravi B, Poletti S, Furlan R, et al. 1-year mental health outcomes in a cohort of COVID-19 survivors. J Psychiatr Res. 2021;22(145):118–24. https://doi.org/10.1016/j.jpsychires.2021.11.031.

    Article  Google Scholar 

  64. Poletti S, Paolini M, Mazza MG, Palladini M, Furlan R, Querini PR, et al. Lower levels of glutathione in the anterior cingulate cortex associate with depressive symptoms and white matter hyperintensities in COVID-19 survivors: Glutathione in COVID-19. Eur Neuropsychopharm. 2022.

  65. Haapasalo K, Meri S. Regulation of the complement system by pentraxins. Front Immunol. 2019;10:1750. https://doi.org/10.3389/fimmu.2019.01750.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Shelton RC, Liang S, Liang P, Chakrabarti A, Manier DH, Sulser F. Differential expression of pentraxin 3 in fibroblasts from patients with major depression. Neuropsychopharmacology. 2004;29(1):126–32. https://doi.org/10.1038/sj.npp.1300307.

    Article  CAS  PubMed  Google Scholar 

  67. Dickerson F, Katsafanas E, Schweinfurth LA, Savage CL, Stallings C, Origoni A, et al. Immune alterations in acute bipolar depression. Acta Psychiatr Scand. 2015;132(3):204–10. https://doi.org/10.1111/acps.12451.

    Article  CAS  PubMed  Google Scholar 

  68. Drexhage RC, Hoogenboezem TH, Versnel MA, Berghout A, Nolen WA, Drexhage HA. The activation of monocyte and T cell networks in patients with bipolar disorder. Brain Behav Immun. 2011;25(6):1206–13. https://doi.org/10.1016/j.bbi.2011.03.013.

    Article  CAS  PubMed  Google Scholar 

  69. Dickerson F, Stallings C, Origoni A, Katsafanas E, Schweinfurth LA, Savage CL, et al. Pentraxin 3 is reduced in bipolar disorder. Bipolar Disord. 2015;17(4):409–14. https://doi.org/10.1111/bdi.12281.

    Article  CAS  PubMed  Google Scholar 

  70. Miller LM, Jenny NS, Rawlings AM, Arnold AM, Fitzpatrick AL, Lopez OL, et al. Sex differences in the association between pentraxin 3 and cognitive decline: the cardiovascular health study. J Gerontol A Biol Sci Med Sci. 2020;75(8):1523–9. https://doi.org/10.1093/gerona/glz217.

    Article  CAS  PubMed  Google Scholar 

  71. Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133–46. https://doi.org/10.1038/s41579-022-00846-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Castanares-Zapatero D, Chalon P, Kohn L, Dauvrin M, Detollenaere J, Maertens de Noordhout C, et al. Pathophysiology and mechanism of long COVID: a comprehensive review. Ann Med. 2022;54(1):1473-87; https://doi.org/10.1080/07853890.2022.2076901.

  73. Benedetti F, Zanardi R, Mazza MG. Antidepressant psychopharmacology: is inflammation a future target? Int Clin Psychopharmacol. 2022;37(3):79–81. https://doi.org/10.1097/YIC.0000000000000403.

    Article  PubMed  Google Scholar 

  74. Kohler CA, Freitas TH, Stubbs B, Maes M, Solmi M, Veronese N, et al. Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and meta-analysis. Mol Neurobiol. 2018;55(5):4195–206. https://doi.org/10.1007/s12035-017-0632-1.

    Article  CAS  PubMed  Google Scholar 

  75. Hashimoto Y, Suzuki T, Hashimoto K. Mechanisms of action of fluvoxamine for COVID-19: a historical review. Mol Psychiatry. 2022;27(4):1898–907. https://doi.org/10.1038/s41380-021-01432-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Mazza MG, Palladini M, Villa G, Agnoletto E, Harrington Y, Vai B, et al. Prevalence of depression in SARS-CoV-2 infected patients: An umbrella review of meta-analyses. Gen Hosp Psychiatry. 2023;80:17–25. https://doi.org/10.1016/j.genhosppsych.2022.12.002.

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Rebecca De Lorenzo, Mario G. Mazza and Clara Sciorati have authors contributed equally to the manuscript.

Authors and Affiliations

  1. Vita-Salute San Raffaele University, Milan, Italy

    Rebecca De Lorenzo, Mariagrazia Palladini, Aurora Merolla, Fabio Ciceri, Francesco Benedetti, Patrizia Rovere-Querini & Angelo A. Manfredi

  2. Unit of Innate Immunity and Tissue Remodeling, Department of Internal Medicine, Division of Immunology, Transplantation and Infectious Diseases, IRCCS Ospedale San Raffaele, Milan, Italy

    Rebecca De Lorenzo, Clara Sciorati, Aurora Merolla & Patrizia Rovere-Querini

  3. Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, San Raffaele Turro, Via Stamira d’Ancona 20, 20127, Milan, Italy

    Mario G. Mazza, Mariagrazia Palladini & Francesco Benedetti

  4. IRCCS Humanitas Research Hospital, Rozzano, Italy

    Roberto Leone, Francesco Scavello, Barbara Bottazzi & Cecilia Garlanda

  5. Hematology and Bone Marrow Transplant Unit, IRCCS Ospedale San Raffaele, Milan, Italy

    Fabio Ciceri

  6. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy

    Cecilia Garlanda

  7. Unit of Autoimmunity and Vascular Inflammation, Division of Immunology, Transplantation and Infectious Diseases, IRCCS Ospedale San Raffaele, Milan, Italy

    Angelo A. Manfredi

Authors
  1. Rebecca De Lorenzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario G. Mazza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Clara Sciorati
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roberto Leone
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francesco Scavello
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mariagrazia Palladini
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aurora Merolla
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fabio Ciceri
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Barbara Bottazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Cecilia Garlanda
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Francesco Benedetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Patrizia Rovere-Querini
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Angelo A. Manfredi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario G. Mazza.

Ethics declarations

Funding

This work was supported by a philanthropic donation by Dolce & Gabbana fashion house (to BB and CG), by the Italian Ministry of Health (grant GR-2021-12374872-1 to MGM), by the Italian Ministry of Health for COVID-19 (grants COVID-2020-12371640 to CG and COVID-2020–12371617 to FC, FB, PR-Q, and AAM), by EU funding within the NextGenerationEU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT, to CG), and by a COVID-19 program project grant from the IRCCS San Raffaele Hospital (to FC, FB, PR-Q, and AAM).

Conflict of Interest

The authors declare no competing interests.

Ethics Approval

The COVID-BioB and the PT-COVID study protocols, compliant with the 1964 Declaration of Helsinki and its later amendments, were approved by the Hospital Ethics Committee.

Consent to Participate

All patients signed informed consent.

Consent for publication

Not applicable.

Availability of Data and Material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code Availability

Not applicable.

Author Contributions

MGM, RDL, and CS designed the study. MGM, RDL, MP, AM, and FB examined the patients and collected clinical and laboratory data. CS, RL, FS, BB, and CG analyzed the biomarkers. MGM and RDL performed the statistical analyses and wrote the first draft. All authors have read and approved the final submitted manuscript and agree to be accountable for the work.

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

De Lorenzo, R., Mazza, M.G., Sciorati, C. et al. Post-COVID Trajectory of Pentraxin 3 Plasma Levels Over 6 Months and Their Association with the Risk of Developing Post-Acute Depression and Anxiety. CNS Drugs (2024). https://doi.org/10.1007/s40263-024-01081-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40263-024-01081-4

Search

Navigation