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DOI: 10.1055/a-1211-2826
‘Precision psychiatry’ needs to become part of ‘personalized psychiatry’
Die „Präzisionspsychiatrie“ muss Teil der „personalisierten Psychiatrie“ werden![](https://www.thieme-connect.de/media/fdn/202012/lookinside/thumbnails/10.1055a-1211-2826-1.jpg)
Abstract
‘Precision medicine’ is defined as ‘an emerging approach for treatment and prevention that takes into account each person’s variability in genes, environment, and lifestyle’. Sometimes the term ‘personalized medicine’ is also used, either as a synonym or in a broader sense. In psychiatry, the term ‘personalized’ applies to different levels of health-care provision, such as the service organization and the choice of treatment plans based on the characterization of the individual patient. This approach is already feasible but, currently, it is often hampered by the shortage of human and financial resources. Recently, the terminology of ‘precision medicine’ has been extended to psychiatry: the term ‘precision psychiatry’ refers to the full exploitation of recent scientific and technological advances to achieve a close match between individual biosignature and prevention / treatment strategies. This article provides an overview of recent advances in neuroimaging, multi-omics and computational neuroscience, which have contributed to foster our understanding of the neurobiology of major mental disorders, and led to the implementation of a precision medicine-oriented approach in psychiatry.
We argue that, while ‘precision psychiatry’ represents an important step to further advance the effectiveness of the ‘personalized psychiatry’, the distinction between the two terms is important to avoid dangerous neglect of the current potential of personalized care in psychiatry and to underscore the need for disseminating good existing practices aimed at organizing mental health services and providing care according to person’s psychopathological characteristics, illness trajectory, needs, environment and preferences.
In conclusion, ‘precision psychiatry’ will contribute to advance ‘personalized psychiatry’, but for the time being keeping the distinction between the two terms will contribute to fully exploit the current potential of personalized care.
Zusammenfassung
Die „Präzisionsmedizin“ wird definiert als „ein neuer Ansatz zur Behandlung und Prävention, welcher die Variabilität jedes Menschen in Bezug auf seine Gene, seine Umwelt und seinen Lebensstil berücksichtigt“. Manchmal wird auch der Begriff „personalisierte Medizin“ im weiteren Sinne oder als Synonym verwendet. In der Psychiatrie bezieht sich der Begriff „personalisiert“ auf verschiedene Ebenen der Gesundheitsversorgung, wie z. B. die Serviceorganisation und die Wahl der Behandlungspläne aufgrund des individuellen Patientenprofils. Dieser Ansatz ist bereits realisierbar, scheitert jedoch derzeit häufig aufgrund eines Mangels an personellen und finanziellen Ressourcen. In jüngster Zeit wurde die Terminologie der „Präzisionsmedizin“ auf die Psychiatrie ausgedehnt: Der Begriff „Präzisionspsychiatrie“ bezieht sich auf die vollständige Nutzung der jüngsten wissenschaftlichen und technologischen Fortschritte, um eine enge Übereinstimmung zwischen individueller Biosignatur und Präventions-/Behandlungsstrategien zu erreichen. Dieser Artikel bietet einen Überblick über die jüngsten Fortschritte in den Bereichen Neuroimaging, Multi-Omics und Computational Neuroscience, die unser Verständnis für die Neurobiologie schwerer psychischer Störungen gefördert und zur Einführung eines auf der Präzisionsmedizin ausgerichteten Ansatzes in der Psychiatrie geführt haben. Wir behaupten, dass die „Präzisionspsychiatrie“ zwar einen wichtigen Schritt darstellt, um die Wirksamkeit der „personalisierten Psychiatrie“ weiter voranzutreiben, die Abgrenzung beider Begriffe jedoch wichtig ist, um eine gefährliche Nachlässigkeit in Bezug auf das derzeitige Potenzial der personalisierten Versorgung in der Psychiatrie zu vermeiden und die Notwendigkeit der Verbreitung bewährter Praktiken hervorzuheben, die darauf abzielen, die psychiatrische Versorgung zu strukturieren und die Behandlung entsprechend der psychopathologischen Ausprägungen, des Krankheitsverlaufes, den Bedürfnissen, der Umwelt und den Präferenzen eines Menschen zu gewährleisten. Zusammenfassend lässt sich sagen, dass die „Präzisionspsychiatrie“ dazu beitragen wird, die „personalisierte Psychiatrie“ voranzutreiben. Jedoch wird die weitere Abgrenzung der beiden Begriffe voneinander vorerst dazu beitragen, das derzeitige Potenzial der personalisierten Pflege voll auszuschöpfen.
Key words
precision psychiatry - personalized psychiatry - biomarkers - multi-omics - computational neurosciencePublication History
Received: 14 May 2020
Accepted: 28 June 2020
Article published online:
31 August 2020
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Literatur
- 1 National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. The national academies collection: reports funded by national institutes of health. In Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington (DC): National Academies Press (US); 2011. doi:10.17226/13284
- 2 Collins FS, Varmus H. A new initiative on precision medicine. 2015; 372: 793-795. DOI: 10.1056/NEJMp1500523 N Engl J Med.
- 3 Rees E, Owen MJ. Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. Genome Med 2020; 12: 43
- 4 U.S. Food and Drug Administration. Paving the way for personalized medicine: FDA’s role in a new era of medical product development (October 2013). Online:. https://www.fdanews.com/ext/resources/files/10/10-28-13-Personalized-Medicine.pdf; last accessed: 04. 04.2020
- 5 Fernandes BS, Williams LM, Steiner J. et al. The new field of ‘precision psychiatry’. BMC Med 2017; 15: 80
- 6 Galderisi S, Rucci P, Kirkpatrick B. et al. Interplay among psychopathologic variables, personal resources, context-related factors, and real-life functioning in individuals with schizophrenia: a network analysis. JAMA Psychiatry 2018; 75: 396-404
- 7 Perna G, Grassi M, Caldirola D. et al. The revolution of personalized psychiatry: Will technology make it happen sooner? Psychol Med 2018; 48: 705-713
- 8 Spaniel F, Vohlídka P, Hrdlicka J. et al. ITAREPS: Information technology aided relapse prevention programme in schizophrenia. Schizophr Res 2008; 98: 312-317
- 9 Aledavood T, Torous J, Triana Hoyos AM. et al. Smartphone-based tracking of sleep in depression, anxiety, and psychotic disorders. Curr Psychiatry Rep 2019; 21: 49-49
- 10 Nasrallah HA. The dawn of precision psychiatry. Curr Psychiatr 2017; 16: 7-8,11
- 11 Lin E, Lin C-H, Lane H-Y. Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int J Mol Sci 2020; 21: 969
- 12 Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69: 89-95. DOI: 10.1067/mcp.2001.113989.
- 13 Boksa P. A way forward for research on biomarkers for psychiatric disorders. J Psychiatry Neurosci 2013; 38: 75-77
- 14 Cuthbert BN. The RDoC framework: Facilitating transition from ICD / DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 2014; 13: 28-35
- 15 Baune B. Personalized Psychiatry. 1st ed. Cambridge: Academic Press; 2019
- 16 Hoffmann A, Ziller M, Spengler D. Progress in iPSC-based modeling of psychiatric disorders. Int J Mol Sci 2019; 20: 4896
- 17 Galderisi S, Delisi L, Borgwardt S. Neuroimaging of schizophrenia and other primary psychotic disorders achievements and perspectives: achievements and perspectives. 1st ed. Berlin: SpringerNature; 2019. doi:10.1007/978-3-319-97307-4
- 18 Demjaha A, Murray RM, McGuire PK. et al. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am J Psychiatry 2012; 169: 1203-1210
- 19 Shih P-AB. Metabolomics biomarkers for precision psychiatry. Adv Exp Med Biol 2019; 1161: 101-113
- 20 Knowles EEM, Huynh K, Meikle PJ. et al. The lipidome in major depressive disorder: shared genetic influence for ether-phosphatidylcholines, a plasma-based phenotype related to inflammation, and disease risk. Eur Psychiatry: J Assoc Eur Psychiatrists 2017; 43: 44-50
- 21 Consoloni JL, Ibrahim EC, Lefebvre MN. et al. Serotonin transporter gene expression predicts the worsening of suicidal ideation and suicide attempts along a long-term follow-up of a major depressive episode. Eur Neuropsychopharmacol: J Eur Coll Neuropsychopharmacol 2018; 28: 401-414
- 22 Tadić A, Müller-Engling L, Schlicht KF. et al. Methylation of the promoter of brain-derived neurotrophic factor exon IV and antidepressant response in major depression. Mol Psychiatry 2014; 19: 281-283
- 23 English JA, Lopez LM, O’Gorman A. et al. Blood-Based protein changes in childhood are associated with increased risk for later psychotic disorder: evidence from a nested case-control study of the ALSPAC longitudinal birth cohort. Schizophr Bull 2018; 44: 297-306
- 24 Greden JF, Parikh SV, Rothschild AJ. et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res 2019; 111: 59-67
- 25 Zeier Z, Carpenter LL, Kalin NH. et al. Clinical implementation of pharmacogenetic decision support tools for antidepressant drug prescribing. Am J Psychiatry 2018; 175 (09) : 873-886
- 26 Robicsek O, Karry R, Petit I. et al. Abnormal neuronal differentiation and mitochondrial dysfunction in hair follicle-derived induced pluripotent stem cells of schizophrenia patients. Mol Psychiatry 2013; 18: 1067-1076
- 27 Nakazawa T, Kikuchi M, Ishikawa M. et al. Differential gene expression profiles in neurons generated from lymphoblastoid B-cell line-derived iPS cells from monozygotic twin cases with treatment-resistant schizophrenia and discordant responses to clozapine. Schizophr Res 2017; 181: 75-82
- 28 Yahata N, Kasai K, Kawato M. Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatry Clin Neurosci 2017; 71: 215-237
- 29 Krystal JH, Murray JD, Chekroud AM. et al. Computational psychiatry and the challenge of Schizophrenia. Schizophr Bull 2017; 43: 473-475
- 30 Chand GB, Dwyer DB, Erus G. et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain: J Neurol 2020; 143: 1027-1038