Version 1
: Received: 14 April 2020 / Approved: 15 April 2020 / Online: 15 April 2020 (08:19:08 CEST)
How to cite:
Al-Hakeim, H.K.; Mousa, R.F.; Al-Dujaili, A.H.; Maes, M. Pathway-Phenotypes of Non-responders and Partial Responders to Treatment With Antipsychotics in Schizophrenia: A Machine Learning Study. Preprints2020, 2020040231. https://doi.org/10.20944/preprints202004.0231.v1
Al-Hakeim, H.K.; Mousa, R.F.; Al-Dujaili, A.H.; Maes, M. Pathway-Phenotypes of Non-responders and Partial Responders to Treatment With Antipsychotics in Schizophrenia: A Machine Learning Study. Preprints 2020, 2020040231. https://doi.org/10.20944/preprints202004.0231.v1
Al-Hakeim, H.K.; Mousa, R.F.; Al-Dujaili, A.H.; Maes, M. Pathway-Phenotypes of Non-responders and Partial Responders to Treatment With Antipsychotics in Schizophrenia: A Machine Learning Study. Preprints2020, 2020040231. https://doi.org/10.20944/preprints202004.0231.v1
APA Style
Al-Hakeim, H.K., Mousa, R.F., Al-Dujaili, A.H., & Maes, M. (2020). Pathway-Phenotypes of Non-responders and Partial Responders to Treatment With Antipsychotics in Schizophrenia: A Machine Learning Study. Preprints. https://doi.org/10.20944/preprints202004.0231.v1
Chicago/Turabian Style
Al-Hakeim, H.K., Arafat Hussein Al-Dujaili and Michael Maes. 2020 "Pathway-Phenotypes of Non-responders and Partial Responders to Treatment With Antipsychotics in Schizophrenia: A Machine Learning Study" Preprints. https://doi.org/10.20944/preprints202004.0231.v1
Abstract
Objective: About a third of schizophrenia patients are treatment-resistant to antipsychotic therapy. No studies established the fingerprints or pathway-phenotypes of treatment-resistant schizophrenia. The present study aimed to delineate the pathway-phenotypes of non-responders (NRTT) and partial responders (PRTT) to treatment using machine learning. Methods: We recruited 115 schizophrenia patients and 43 healthy controls and measured schizophrenia symptom dimensions, neurocognitive tests, plasma CCL11, interleukin-(IL)-6, IL-10, Dickkopf protein 1 (DKK1), high mobility group box-1 protein (HMGB1), κ- and µ-opioid receptors (KOR and MOR, respectively), endomorphin-2 (EM-2), and β-endorphin. Results: Machine learning showed that the NRTT group is a qualitatively distinct class and is significantly discriminated from PRTT with an accuracy of 100% using a neuro-immune-opioid-cognitive (NIOC) pathway-phenotype with as main determinants list learning, controlled word association, and Tower of London test scores, CCL11, IL-6, and EM2. The top-5 symptom domains separating NRTT from PRTT were in descending order: psychomotor retardation, negative symptoms, psychosis, depression, and mannerism. Moreover, a NIOC pathway also discriminated PRTT from healthy controls with an accuracy of 100% while all PRTT and controls were authenticated as belonging to their respective classes. Conclusion: A non-response to treatment with antipsychotics is determined by increased severity of specific symptom profiles coupled with deficits in executive functions, and episodic and semantic memory, and aberrations in neuro-immune and opioid pathways. No patients showed complete remission after treatment indicating that non-remitting in PRTT is attributable to increased HMGB1 and residual deficits in attention, executive functions, and semantic memory.
Keywords
schizophrenia; neuroimmunomodulation; inflammation; biomarkers; major depression; treatment resistance
Subject
Medicine and Pharmacology, Psychiatry and Mental Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
