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A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup

Molecular Psychiatry volume 28, pages 1190–1200 (2023)Cite this article

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Abstract

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients’ subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

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Fig. 1: Diagnostic class distribution and immune profile of the clustering subgroups retrieved in the discovery sample.
Fig. 2: Diagnostic class distribution and immune profile of the clustering subgroups retrieved in the validation sample 1.
Fig. 3: Significant associations between FEP and HC cluster’s membership and structural brain MRI variables.

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Data availability

The original data used in this study are available online as Supplementary information. Supplementary information is available at MP’s website.

Code availability

The original code of the machine learning model is publicly available in GitHub (https://htmlpreview.github.io/?https://github.com/UniMiPsychiatry/Immunopsychiatry/blob/main/Analyses.html).

References

  1. Biedermann F, Fleischhacker WW. Psychotic disorders in DSM-5 and ICD-11. CNS Spectr. 2016;21:349–54.

    PubMed  Google Scholar 

  2. Blacker D, Tsuang MT. Contested boundaries of bipolar disorder and the limits of categorical diagnosis in psychiatry. Am J Psychiatry. 1992;149:1473–83.

    CAS  PubMed  Google Scholar 

  3. Fond G, Jamain S, Tamouza R, Arango C, Wolfgang Fleischhacker W, Glenthøj B, et al. The Promise of Biological Markers for Treatment Response in First-Episode Psychosis: A Systematic Review. Schizophr Bull. 2015;41:559–73.

    PubMed  PubMed Central  Google Scholar 

  4. Doherty JL, Owen MJ. Genomic insights into the overlap between psychiatric disorders: Implications for research and clinical practice. Genome Med. 2014;6:1–13.

    Google Scholar 

  5. Xia CH, Ma Z, Ciric R, Gu S, Betzel RF, Kaczkurkin AN, et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun. 2018;9:1. 2018;9:1–14

    Google Scholar 

  6. Voineskos AN, Jacobs GR, Ameis SH. Neuroimaging Heterogeneity in Psychosis: Neurobiological Underpinnings and Opportunities for Prognostic and Therapeutic Innovation. Biol Psychiatry. 2020;88:95.

    PubMed  Google Scholar 

  7. Ethridge LE, Hamm JP, Pearlson GD, Tamminga CA, Sweeney JA, Keshavan MS, et al. Event-related potential and time-frequency endophenotypes for schizophrenia and psychotic bipolar disorder. Biol Psychiatry. 2015;77:127–36.

    PubMed  Google Scholar 

  8. Kristian Hill S, Reilly JL, Keefe RSE, Gold JM, Bishop JR, Gershon ES, et al. Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. Am J Psychiatry. 2013;170:1275–84.

    PubMed  PubMed Central  Google Scholar 

  9. Khadka S, Meda SA, Stevens MC, Glahn DC, Calhoun VD, Sweeney JA, et al. Is aberrant functional connectivity a psychosis endophenotype? A resting state functional magnetic resonance imaging study. Biol Psychiatry. 2013;74:458–66.

    PubMed  PubMed Central  Google Scholar 

  10. Reilly JL, Frankovich K, Hill S, Gershon ES, Keefe RSE, Keshavan MS, et al. Elevated Antisaccade Error Rate as an Intermediate Phenotype for Psychosis Across Diagnostic Categories. Schizophr Bull. 2014;40:1011.

    PubMed  Google Scholar 

  11. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.

    PubMed  Google Scholar 

  12. Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The Heterogeneity problem: Approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23:584.

    PubMed  PubMed Central  Google Scholar 

  13. Insel TR, Cuthbert BN. Brain disorders? Precisely. Sci (1979) 2015;348:499–500.

    CAS  Google Scholar 

  14. Pigoni A, Dwyer D, Squarcina L, Borgwardt S, Crespo-Facorro B, Dazzan P, et al. Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study. Eur Neuropsychopharmacol. 2021;47:34–47.

    CAS  PubMed  Google Scholar 

  15. James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning. vol. 103. New York, NY: Springer New York; 2013.

  16. Chand GB, Dwyer DB, Erus G, Sotiras A, Varol E, Srinivasan D, et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 2020;143:1027–38.

    PubMed  PubMed Central  Google Scholar 

  17. Martinuzzi E, Barbosa S, Daoudlarian D, Bel Haj Ali W, Gilet C, Fillatre L, et al. Stratification and prediction of remission in first-episode psychosis patients: the OPTiMiSE cohort study. Transl Psychiatry. 2019;9:1–20.

    Google Scholar 

  18. Stefanik L, Erdman L, Ameis SH, Foussias G, Mulsant BH, Behdinan T, et al. Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls. Neuropsychopharmacology. 2018;43:1180.

    PubMed  Google Scholar 

  19. Wenzel J, Haas SS, Dwyer DB, Ruef A, Oeztuerk OF, Antonucci LA, et al. Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints? Neuropsychopharmacology. 2021;46:1475–83.

    PubMed  PubMed Central  Google Scholar 

  20. Zheng W, Cai D, bin, Yang XH, Ungvari GS, Ng CH, Müller N, et al. Adjunctive celecoxib for schizophrenia: A meta-analysis of randomized, double-blind, placebo-controlled trials. J Psychiatr Res. 2017;92:139–46.

    PubMed  Google Scholar 

  21. Xiang YQ, Zheng W, Wang S, bin, Yang XH, Cai D, bin, Ng CH, et al. Adjunctive minocycline for schizophrenia: A meta-analysis of randomized controlled trials. Eur Neuropsychopharmacol. 2017;27:8–18.

    CAS  PubMed  Google Scholar 

  22. Girgis RR, Ciarleglio A, Choo T, Haynes G, Bathon JM, Cremers S, et al. A Randomized, Double-Blind, Placebo-Controlled Clinical Trial of Tocilizumab, An Interleukin-6 Receptor Antibody, For Residual Symptoms in Schizophrenia. Neuropsychopharmacology. 2018;43:6. 2017;43:1317–23

    Google Scholar 

  23. Pinto JV, Moulin TC, Amaral OB. On the transdiagnostic nature of peripheral biomarkers in major psychiatric disorders: A systematic review. Neurosci Biobehav Rev. 2017;83:97–108.

    CAS  PubMed  Google Scholar 

  24. Lizano P, Lutz O, Xu Y, Rubin LH, Paskowitz L, Lee AM, et al. Multivariate relationships between peripheral inflammatory marker subtypes and cognitive and brain structural measures in psychosis. Mol Psychiatry. 2021;26:3430–43.

    CAS  PubMed  Google Scholar 

  25. Hoang D, Xu Y, Lutz O, Bannai D, Zeng V, Bishop JR, et al. Inflammatory Subtypes in Antipsychotic-Naïve First-Episode Schizophrenia are Associated with Altered Brain Morphology and Topological Organization. Brain Behav Immun. 2022;100:297–308.

    CAS  PubMed  Google Scholar 

  26. Enrico P, Delvecchio G, Turtulici N, Pigoni A, Villa FM, Perlini C, et al. Classification of Psychoses Based on Immunological Features: A Machine Learning Study in a Large Cohort of First-Episode and Chronic Patients. Schizophr Bull. 2021;47:1141–55.

    PubMed  PubMed Central  Google Scholar 

  27. Roomruangwong C, Noto C, Kanchanatawan B, Anderson G, Kubera M, Carvalho AF, et al. The Role of Aberrations in the Immune-Inflammatory Response System (IRS) and the Compensatory Immune-Regulatory Reflex System (CIRS) in Different Phenotypes of Schizophrenia: the IRS-CIRS Theory of Schizophrenia. Mol Neurobiol. 2020;57:778–97.

    CAS  PubMed  Google Scholar 

  28. Noto MN, Maes M, Nunes SOV, Ota VK, Rossaneis AC, Verri WA, et al. Activation of the immune-inflammatory response system and the compensatory immune-regulatory system in antipsychotic naive first episode psychosis. Eur Neuropsychopharmacol. 2019;29:416–31.

    CAS  PubMed  Google Scholar 

  29. Radhakrishnan R, Kaser M, Guloksuz S. The Link between the Immune System, Environment, and Psychosis. Schizophr Bull. 2017;43:693–7.

    PubMed  PubMed Central  Google Scholar 

  30. Ruggeri M, Bonetto C, Lasalvia A, de Girolamo G, Fioritti A, Rucci P, et al. A multi-element psychosocial intervention for early psychosis (GET UP PIANO TRIAL) conducted in a catchment area of 10 million inhabitants: study protocol for a pragmatic cluster randomized controlled trial. Trials. 2012;13:73.

    PubMed  PubMed Central  Google Scholar 

  31. Ruggeri M, Bonetto C, Lasalvia A, Fioritti A, de Girolamo G, Santonastaso P, et al. Feasibility and effectiveness of a multi-element psychosocial intervention for first-episode psychosis: Results from the cluster-randomized controlled GET UP PIANO trial in a catchment area of 10 million inhabitants. Schizophr Bull. 2015;41:1192–203.

    PubMed  PubMed Central  Google Scholar 

  32. Jablensky A, Sartorius N, Ernberg G, Anker M, Korten A, Cooper JE, et al. Schizophrenia: manifestations, incidence and course in different cultures A World Health Organization Ten-Country Study. Psychol Med Monogr Suppl. 1992;20:1–97.

    CAS  PubMed  Google Scholar 

  33. Kübler U. Structured Clinical Interview for DSM-IV (SCID). In: Gellman MD, Turner JR, editors. Encyclopedia of Behavioral Medicine, New York, NY: Springer New York; 2013. p. 1919–20.

  34. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–4.

  35. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001;25:402–8.

    CAS  PubMed  Google Scholar 

  36. Wing JK, Babor T, Brugha T, Burke J, Cooper JE, Giel R, et al. SCAN: Schedules fonr Clinical Assessment in Neuropsychiatry. Arch Gen Psychiatry. 1990;47:589–93.

    CAS  PubMed  Google Scholar 

  37. World Health Organization. The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. Genève, Switzerland: World Health Organization; 1992.

  38. Pillinger T, D’Ambrosio E, McCutcheon R, Howes OD. Is psychosis a multisystem disorder? A meta-review of central nervous system, immune, cardiometabolic, and endocrine alterations in first-episode psychosis and perspective on potential models. Mol Psychiatry. 2019;24:776–94.

    CAS  PubMed  Google Scholar 

  39. Şahin ŞK, Şimsek L, Aksoy PG, Elboga G, Altindag A, Keskek ŞÖ. Relationship between the Visceral Adiposity Index and neutrophil/lymphocyte ratio in patients with bipolar disorder. Minerva 2021;62:5–11.

    Google Scholar 

  40. Tonna M, Ossola P, Marchesi C, Bettini E, Lasalvia A, Bonetto C, et al. Dimensional structure of first episode psychosis. Early Inter Psychiatry. 2019;13:1431–8.

    Google Scholar 

  41. Cabrera B, Bioque M, Penadés R, González-Pinto A, Parellada M, Bobes J, et al. Cognition and psychopathology in first-episode psychosis: are they related to inflammation? Psychol Med. 2016;46:2133–44.

    CAS  PubMed  Google Scholar 

  42. Asarnow RF, MacCrimmon DJ. Attention/information processing, neuropsychological functioning, and thought disorder during the acute and partial recovery phases of schizophrenia: A longitudinal study. Psychiatry Res. 1982;7:309–19.

    CAS  PubMed  Google Scholar 

  43. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31:968–80.

    PubMed  Google Scholar 

  44. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001;17:520–5.

    CAS  PubMed  Google Scholar 

  45. Walther A, Nili H, Ejaz N, Alink A, Kriegeskorte N, Diedrichsen J. Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage 2016;137:188–200.

    PubMed  Google Scholar 

  46. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J Stat Softw. 2014;61:1–36.

    Google Scholar 

  47. Andreoletti G, Lanata CM, Trupin L, Paranjpe I, Jain TS, Nititham J, et al. Transcriptomic analysis of immune cells in a multi-ethnic cohort of systemic lupus erythematosus patients identifies ethnicity- and disease-specific expression signatures. Commun Biol. 2021;4:1–13.

    Google Scholar 

  48. Yehya N, Varisco BM, Thomas NJ, Wong HR, Christie JD, Feng R, et al. Peripheral blood transcriptomic sub-phenotypes of pediatric acute respiratory distress syndrome. Crit Care. 2020;24:1–10.

  49. Luo C, Pi X, Hu N, Wang X, Xiao Y, Li S, et al. Subtypes of schizophrenia identified by multi-omic measures associated with dysregulated immune function. Mol Psychiatry. 2021;26:6926–36.

    CAS  PubMed  Google Scholar 

  50. Clatworthy J, Buick D, Hankins M, Weinman J, Horne R. The use and reporting of cluster analysis in health psychology: A review. Br J Health Psychol. 2005;10:329–58.

    PubMed  Google Scholar 

  51. Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders. Biol Psychiatry. 2016;1:433.

    Google Scholar 

  52. Narita A, Nagai M, Mizuno S, Ogishima S, Tamiya G, Ueki M, et al. Clustering by phenotype and genome-wide association study in autism. Transl Psychiatry. 2020;10:1–12.

    Google Scholar 

  53. Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiological Opt. 2014;34:502–8.

    Google Scholar 

  54. Smail MA, Wu X, Henkel ND, Eby HM, Herman JP, McCullumsmith RE, et al. Similarities and dissimilarities between psychiatric cluster disorders. Mol Psychiatry. 2021;26:9. 2021;26:4853–63.

    Google Scholar 

  55. Rand WM. Objective Criteria for the Evaluation of Clustering Methods. J Am Stat Assoc. 1971;66:846.

    Google Scholar 

  56. Kindler J, Lim CK, Weickert CS, Boerrigter D, Galletly C, Liu D, et al. Dysregulation of kynurenine metabolism is related to proinflammatory cytokines, attention, and prefrontal cortex volume in schizophrenia. Mol Psychiatry. 2020;25:2860–72.

    PubMed  Google Scholar 

  57. Belikan P, Bühler U, Wolf C, Pramanik GK, Gollan R, Zipp F, et al. CCR7 on CD4 + T Cells Plays a Crucial Role in the Induction of Experimental Autoimmune Encephalomyelitis. J Immunol. 2018;200:2554–62.

    CAS  PubMed  Google Scholar 

  58. Cosma NC, Üsekes B, Otto LR, Gerike S, Heuser I, Regen F, et al. M1/M2 polarization in major depressive disorder: Disentangling state from trait effects in an individualized cell-culture-based approach. Brain Behav Immun. 2021;94:185–95.

    CAS  PubMed  Google Scholar 

  59. Schiweck C, Claes S, van Oudenhove L, Lafit G, Vaessen T, de Beeck GO, et al. Childhood trauma, suicide risk and inflammatory phenotypes of depression: insights from monocyte gene expression. Transl Psychiatry. 2020;10:1. 2020;10:1–12

    Google Scholar 

  60. Netea MG, Domínguez-Andrés J, Barreiro LB, Chavakis T, Divangahi M, Fuchs E, et al. Defining trained immunity and its role in health and disease. Nat Rev Immunol. 2020;20:6. 2020;20:375–88

    Google Scholar 

  61. Joosten SA, van Meijgaarden KE, Arend SM, Prins C, Oftung F, Korsvold GE, et al. Mycobacterial growth inhibition is associated with trained innate immunity. J Clin Invest. 2018;128:1837–51.

    PubMed  PubMed Central  Google Scholar 

  62. Hurlin PJ, Huang J. The MAX-interacting transcription factor network. Semin Cancer Biol. 2006;16:265–74.

    CAS  PubMed  Google Scholar 

  63. Dezfouli S, Bakke A, Huang J, Wynshaw-Boris A, Hurlin PJ. Inflammatory disease and lymphomagenesis caused by deletion of the Myc antagonist Mnt in T cells. Mol Cell Biol. 2006;26:2080–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. O’Donovan SM, Sullivan C, Koene R, Devine E, Hasselfeld K, Moody CL, et al. Cell-subtype-specific changes in adenosine pathways in schizophrenia. Neuropsychopharmacology 2018;43:1667–74.

    PubMed  PubMed Central  Google Scholar 

  65. Tarantino N, Leboyer M, Bouleau A, Hamdani N, Richard JR, Boukouaci W, et al. Natural killer cells in first-episode psychosis: an innate immune signature? Mol Psychiatry. 2021;26:5297–306.

    CAS  PubMed  Google Scholar 

  66. Trovão N, Prata J, VonDoellinger O, Santos S, Barbosa M, Coelho R. Peripheral Biomarkers for First-Episode Psychosis—Opportunities from the Neuroinflammatory Hypothesis of Schizophrenia. Psychiatry Investig. 2019;16:177–84.

    PubMed  PubMed Central  Google Scholar 

  67. Boerrigter D, Weickert TW, Lenroot R, O’Donnell M, Galletly C, Liu D, et al. Using blood cytokine measures to define high inflammatory biotype of schizophrenia and schizoaffective disorder. J Neuroinflammation. 2017;14:188.

    PubMed  PubMed Central  Google Scholar 

  68. Fillman SG, Weickert TW, Lenroot RK, Catts SV, Bruggemann JM, Catts VS, et al. Elevated peripheral cytokines characterize a subgroup of people with schizophrenia displaying poor verbal fluency and reduced Broca’s area volume. Mol Psychiatry. 2016;21:1090–8.

    CAS  PubMed  Google Scholar 

  69. MacDowell KS, García-Bueno B, Madrigal JL, Parellada M, Arango C, Micó JA, et al. Risperidone normalizes increased inflammatory parameters and restores anti-inflammatory pathways in a model of neuroinflammation. Int J Neuropsychopharmacol. 2013;16(Feb):121–35.

    CAS  PubMed  Google Scholar 

  70. Martín-Hernández D, Bris ÁG, MacDowell KS, García-Bueno B, Madrigal JL, Leza JC, et al. Modulation of the antioxidant nuclear factor (erythroid 2-derived)-like 2 pathway by antidepressants in rats. Neuropharmacology 2016;103(Apr):79–91.

    PubMed  Google Scholar 

  71. MacDowell KS, Munarriz-Cuezva E, Caso JR, Madrigal JL, Zabala A, Meana JJ, et al. Paliperidone reverts Toll-like receptor 3 signaling pathway activation and cognitive deficits in a maternal immune activation mouse model of schizophrenia. Neuropharmacology 2017;116(Apr):196–207.

    CAS  PubMed  Google Scholar 

  72. Pollak TA, Lennox BR, Müller S, Benros ME, Prüss H, Tebartz van Elst L, et al. Autoimmune psychosis: an international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin. Lancet Psychiatry. 2020;7:93–108.

    PubMed  Google Scholar 

  73. Sommer IE, van Westrhenen R, Begemann MJH, de Witte LD, Leucht S, Kahn RS. Efficacy of Anti-inflammatory Agents to Improve Symptoms in Patients With Schizophrenia: An Update. Schizophr Bull. 2014;40:181–91.

    PubMed  Google Scholar 

  74. Çakici N, van Beveren NJM, Judge-Hundal G, Koola MM, Sommer IEC. An update on the efficacy of anti-inflammatory agents for patients with schizophrenia: a meta-analysis. Psychol Med. 2019;49:2307.

    PubMed  PubMed Central  Google Scholar 

  75. Kahn RS, Sommer IE. The neurobiology and treatment of first-episode schizophrenia. Mol Psychiatry. 2015;20:84–97.

    CAS  PubMed  Google Scholar 

  76. Müller N, Myint AM, Krause D, Weidinger E, Schwarz MJ. Anti-inflammatory treatment in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2013;42:146–53.

    PubMed  Google Scholar 

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Funding

This study was partially supported by the Italian Ministry of Health (GR-2019-12369479 to AP) and by the Fondazione Cariverona (Promoting research to improve quality of care; Sotto-obiettivo A9 “Disabilità cognitiva e comportamentale nelle demenze e nelle psicosi” to PB and MR). The study was also partially funded by ERANET NEURON JTC2018 “Mental Disorders” UNMET project (Neuron-051) to PB. The APC costs were funded by GrantRicercaCorrente, Italian Ministry of Health.

Author information

Author notes

  1. These authors contributed equally: Paolo Enrico, Giuseppe Delvecchio.

  2. These authors jointly supervised this work: Roberto Furlan, Paolo Brambilla.

Authors and Affiliations

  1. Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy

    Paolo Enrico, Nunzio Turtulici, Rosario Aronica, Letizia Squarcina & Paolo Brambilla

  2. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany

    Paolo Enrico

  3. Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy

    Giuseppe Delvecchio, Alessandro Pigoni, Maria G. Rossetti & Paolo Brambilla

  4. Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy

    Alessandro Pigoni

  5. Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy

    Filippo M. Villa

  6. Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy

    Cinzia Perlini

  7. USD Clinical Psychology, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy

    Cinzia Perlini

  8. Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy

    Marcella Bellani, Antonio Lasalvia, Chiara Bonetto & Mirella Ruggeri

  9. UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy

    Marcella Bellani & Mirella Ruggeri

  10. Department of Mental Health, AULSS 6 Euganea, Padua, Italy

    Paolo Scocco & Angelo Veronese

  11. Department of Health Sciences, San Paolo University Hospital, University of Milan, Milano, Milan, Italy

    Armando D’Agostino

  12. Department of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell’Alto Adige, Bolzano, Italy

    Stefano Torresani

  13. Department of Psychiatry, AUSL Emilia, Reggio Emilia, Italy

    Massimiliano Imbesi

  14. Department of Psychiatry, AUSL Romagna, Ravenna, Italy

    Francesca Bellini

  15. Faculty of Psychology, eCampus University, Novedrate, Como, Italy

    Luisella Bocchio-Chiavetto

  16. Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy

    Luisella Bocchio-Chiavetto & Massimo Gennarelli

  17. Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy

    Massimo Gennarelli

  18. Department of Medicine (DAME), University of Udine, Udine, Italy

    Matteo Balestrieri

  19. Centro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy

    Gualtiero I. Colombo

  20. Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy

    Annamaria Finardi & Roberto Furlan

  21. PIANO (Psychosis: Early Intervention and Assessment of Needs and Outcome), Verona, Italy

    Mirella Ruggeri, Maria Elena Bertani, Sarah Bissoli, Chiara Bonetto, Doriana Cristofalo, Katia De Santi, Antonio Lasalvia, Silvia Lunardi, Valentina Negretto, Sara Poli, Sarah Tosato, Maria Grazia Zamboni & Mario Ballarin

  22. Agenzia Sanitaria e Sociale Regionale, Regione Emilia-Romagna, Bologna and Brescia, Bologna, Italy

    Giovanni De Girolamo, Angelo Fioritti, Giovanni Neri, Francesca Pileggi & Paola Rucci

  23. IRCCS Centro S.Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Massimo Gennarelli, Luisella Bocchio Chiavetto, Catia Scasselatti & Roberta Zanardini

  24. Azienda Ospedaliera Universitaria Integrata, Verona, Regione Veneto, Italy

    Paolo Brambilla, Marcella Bellani, Alessandra Bertoldo, Veronica Marinelli, Valentina Negretto, Cinzia Perlini & Gianluca Rambaldelli

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  1. Paolo Enrico
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  2. Giuseppe Delvecchio
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  3. Nunzio Turtulici
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  4. Rosario Aronica
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  5. Alessandro Pigoni
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  7. Filippo M. Villa
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  13. Paolo Scocco
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  14. Armando D’Agostino
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  15. Stefano Torresani
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Consortia

GET UP Group

  • PIANO

    • Mirella Ruggeri
    • , Maria Elena Bertani
    • , Sarah Bissoli
    • , Chiara Bonetto
    • , Doriana Cristofalo
    • , Katia De Santi
    • , Antonio Lasalvia
    • , Silvia Lunardi
    • , Valentina Negretto
    • , Sara Poli
    • , Sarah Tosato
    • , Maria Grazia Zamboni
    •  & Mario Ballarin

  • TRUMPET

    • Giovanni De Girolamo
    • , Angelo Fioritti
    • , Giovanni Neri
    • , Francesca Pileggi
    •  & Paola Rucci

  • GUITAR

    • Massimo Gennarelli
    • , Luisella Bocchio Chiavetto
    • , Catia Scasselatti
    •  & Roberta Zanardini

  • CONTRABASS

    • Paolo Brambilla
    • , Marcella Bellani
    • , Alessandra Bertoldo
    • , Veronica Marinelli
    • , Valentina Negretto
    • , Cinzia Perlini
    •  & Gianluca Rambaldelli

Contributions

PB, PE and GD designed this particular study. MR, AL, MG, and PB designed the GET UP project. PB designed the First and Prevent projects and with MB the Mandrake project. PE and GD prepared the first version of the manuscript. NT carried out the data analysis. GIC, AF, and RF carried out the immune transcriptome analysis. MB, PB, CP, MGR, AP, AL, MB, MGR, LB-C, and MR coordinated subject recruitment and sample collection. MB, CB, MR, AL, and PB coordinated data management. CP, PS, AD, ST, MI, FB, and AV were involved in patient enrolment and assessment. All authors revised and approved the final version of the manuscript.

Corresponding author

Correspondence to Paolo Brambilla.

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Enrico, P., Delvecchio, G., Turtulici, N. et al. A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup. Mol Psychiatry 28, 1190–1200 (2023). https://doi.org/10.1038/s41380-022-01911-1

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  • DOI: https://doi.org/10.1038/s41380-022-01911-1

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