Buranee Kanchanatawan1, Sira Sriswasdi2, Supaksorn Thika1, Drozdstoy Stoyanov3, Sunee Sirivichayakul4, André F Carvalho5, Michel Geffard6,7, Michael Maes1,3,8. 1. Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. 2. Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. 3. Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria. 4. Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. 5. Department of Clinical Medicine and Translational Psychiatry Research Group, Faculty of Medicine, Federal University of Ceará, Fortaleza, CE, Brazil. 6. Research Department, IDRPHT, Talence, France. 7. GEMAC, Saint Jean d'Illac, France. 8. IMPACT Strategic Research Center, Deakin University, Geelong, Australia.
Abstract
RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. METHODS: This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia. RESULTS: We found that using negative symptoms as discriminatory variables, schizophrenia patients may be divided into two distinct classes modelled by (A) impairments in IgA/IgM responses to noxious and generally more protective tryptophan catabolites, (B) impairments in episodic and semantic memory, paired associative learning and false memory creation, and (C) psychotic, excitation, hostility, mannerism, negative, and affective symptoms. The first cluster shows increased negative, psychotic, excitation, hostility, mannerism, depression and anxiety symptoms, and more neuroimmune and cognitive disorders and is therefore called "major neurocognitive psychosis" (MNP). The second cluster, called "simple neurocognitive psychosis" (SNP) is discriminated from normal controls by the same features although the impairments are less well developed than in MNP. The latter is additionally externally validated by lowered quality of life, body mass (reflecting a leptosome body type), and education (reflecting lower cognitive reserve). CONCLUSIONS: Previous distinctions including "type 1" (positive)/"type 2" (negative) and DSM-IV-TR (eg, paranoid) schizophrenia could not be validated using machine learning techniques. Previous names of the illness, including schizophrenia, are not very adequate because they do not describe the features of the illness, namely, interrelated neuroimmune, cognitive, and clinical features. Stable-phase schizophrenia consists of 2 relevant qualitatively distinct categories or nosological entities with SNP being a less well-developed phenotype, while MNP is the full blown phenotype or core illness. Major neurocognitive psychosis and SNP should be added to the DSM-5 and incorporated into the Research Domain Criteria project.
RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. METHODS: This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia. RESULTS: We found that using negative symptoms as discriminatory variables, schizophreniapatients may be divided into two distinct classes modelled by (A) impairments in IgA/IgM responses to noxious and generally more protective tryptophan catabolites, (B) impairments in episodic and semantic memory, paired associative learning and false memory creation, and (C) psychotic, excitation, hostility, mannerism, negative, and affective symptoms. The first cluster shows increased negative, psychotic, excitation, hostility, mannerism, depression and anxiety symptoms, and more neuroimmune and cognitive disorders and is therefore called "major neurocognitive psychosis" (MNP). The second cluster, called "simple neurocognitive psychosis" (SNP) is discriminated from normal controls by the same features although the impairments are less well developed than in MNP. The latter is additionally externally validated by lowered quality of life, body mass (reflecting a leptosome body type), and education (reflecting lower cognitive reserve). CONCLUSIONS: Previous distinctions including "type 1" (positive)/"type 2" (negative) and DSM-IV-TR (eg, paranoid) schizophrenia could not be validated using machine learning techniques. Previous names of the illness, including schizophrenia, are not very adequate because they do not describe the features of the illness, namely, interrelated neuroimmune, cognitive, and clinical features. Stable-phase schizophrenia consists of 2 relevant qualitatively distinct categories or nosological entities with SNP being a less well-developed phenotype, while MNP is the full blown phenotype or core illness. Major neurocognitive psychosis and SNP should be added to the DSM-5 and incorporated into the Research Domain Criteria project.