Catechol-O-Methyltransferase Val158Met Polymorphism and Clinical Response to Antipsychotic Treatment in Schizophrenia and Schizo-Affective Disorder Patients: a Meta-Analysis

BACKGROUND: The catechol-O-methyltransferase (COMT) enzyme plays a crucial role in dopamine degradation, and the COMT Val158Met polymorphism (rs4680) is associated with significant differences in enzymatic activity and consequently dopamine concentrations in the prefrontal cortex. Multiple studies have analyzed the COMT Val158Met variant in relation to antipsychotic response. Here, we conducted a meta-analysis examining the relationship between COMT Val158Met and antipsychotic response. METHODS: Searches using PubMed, Web of Science, and PsycInfo databases (03/01/2015) yielded 23 studies investigating COMT Val158Met variation and antipsychotic response in schizophrenia and schizo-affective disorder. Responders/nonresponders were defined using each study's original criteria. If no binary response definition was used, authors were asked to define response according to at least 30% Positive and Negative Syndrome Scale score reduction (or equivalent in other scales). Analysis was conducted under a fixed-effects model. RESULTS: Ten studies met inclusion criteria for the meta-analysis. Five additional antipsychotic-treated samples were analyzed for Val158Met and response and included in the meta-analysis (ntotal=1416). Met/Met individuals were significantly more likely to respond than Val-carriers (P=.039, ORMet/Met=1.37, 95% CI: 1.02-1.85). Met/Met patients also experienced significantly greater improvement in positive symptoms relative to Val-carriers (P=.030, SMD=0.24, 95% CI: 0.024-0.46). Posthoc analyses on patients treated with atypical antipsychotics (n=1207) showed that Met/Met patients were significantly more likely to respond relative to Val-carriers (P=.0098, ORMet/Met=1.54, 95% CI: 1.11-2.14), while no difference was observed for typical-antipsychotic-treated patients (n=155) (P=.65). CONCLUSIONS: Our findings suggest that the COMT Val158Met polymorphism is associated with response to antipsychotics in schizophrenia and schizo-affective disorder patients. This effect may be more pronounced for atypical antipsychotics.C.C.Z. is supported by the Brain and Behavior Research Foundation, American Foundation for Suicide Prevention and Eli Lilly. D.F. is supported by the Vanier Canada Graduate Scholarship. D.J.M. has been or is supported by the Canadian Institute of Health Research (CIHR) Operating Grant: “Genetics of antipsychotic-induced metabolic syndrome,” Michael Smith New Investigator Salary Prize for Research in Schizophrenia, NARSAD Independent Investigator Award by the Brain & Behavior Research Foundation, and Early Researcher Award from Ministry of Research and Innovation of Ontario. E.H. is supported by the Canada Graduate Scholarship. H.Y.M. has grant support from Sumitomo Dainippon, Sunovion, Boehringer Ingelheim, Eli Lilly, Janssen, Reviva, Alkermes, Auspex, and FORUM. J.A.L. has received research funding from Alkermes, Biomarin, EnVivo/Forum, Genentech, and Novartis. J.L.K. is supported the CIHR grant “Strategies for gene discovery in schizophrenia: subphenotypes, deep sequencing and interaction.” J.R.B. is supported by NIH grant MH083888. A.K.T. is supported by a NARSAD Young Investigator Award. J.S. is supported by a Pfizer independent grant. P.M. receives salary from Clinica Universidad de Navarra and has received research grants from the Ministry of Education (Spain), the Government of Navarra (Spain), the Spanish Foundation of Psychiatry and Mental Health, and Astrazeneca. S.G. is supported by the Ningbo Medical Technology Project Fund (No. 2004050), the Natural Science Foundation of Ningbo (No. 2009A610186, No. 2013A610249), and the Zhejiang Provincial Medical and Health Project Fund (No. 2015127713). S.G.P. has received research support from Otsuka, Lundbeck, FORUM, and Alkermes

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers

Signal from Noise: Using Machine Learning to Distil Knowledge from Data in Biological Psychiatry

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares in medical research and biomedical scientists harnessing such methods to glean knowledge from data. This article provides a critical review of ML, covering common ML methods and historical trends of their use in psychiatry, and identifying areas of opportunity for future applications of ML in biological psychiatry. We also establish the ML in Psychiatry (MLPsych) Consortium, enumerate its objectives, and provide a set of standards (Guidelines for REporting ML Investigations in Neuropsychiatry [GREMLIN]) for designing and reporting studies that use ML. This review serves as a cautiously optimistic primer on ML for those on the precipice as they prepare to dive into the field, either as dedicated methodological practitioners or, at the very least, well-informed consumers

Catechol-O-Methyltransferase Val158Met Polymorphism and Clinical Response to Antipsychotic Treatment in Schizophrenia and Schizo-Affective Disorder Patients: a Meta-Analysis

Abstract Background: The catechol-O-methyltransferase (COMT) enzyme plays a crucial role in dopamine degradation, and the COMT Val158Met polymorphism (rs4680) is associated with significant differences in enzymatic activity and consequently dopamine concentrations in the prefrontal cortex. Multiple studies have analyzed the COMT Val158Met variant in relation to antipsychotic response. Here, we conducted a meta-analysis examining the relationship between COMT Val158Met and antipsychotic response. Methods: Searches using PubMed, Web of Science, and PsycInfo databases (03/01/2015) yielded 23 studies investigating COMT Val158Met variation and antipsychotic response in schizophrenia and schizo-affective disorder. Responders/nonresponders were defined using each study’s original criteria. If no binary response definition was used, authors were asked to define response according to at least 30% Positive and Negative Syndrome Scale score reduction (or equivalent in other scales). Analysis was conducted under a fixed-effects model. Results: Ten studies met inclusion criteria for the meta-analysis. Five additional antipsychotic-treated samples were analyzed for Val158Met and response and included in the meta-analysis (ntotal = 1416). Met/Met individuals were significantly more likely to respond than Val-carriers (P = .039, ORMet/Met = 1.37, 95% CI: 1.02–1.85). Met/Met patients also experienced significantly greater improvement in positive symptoms relative to Val-carriers (P = .030, SMD = 0.24, 95% CI: 0.024–0.46). Posthoc analyses on patients treated with atypical antipsychotics (n = 1207) showed that Met/Met patients were significantly more likely to respond relative to Val-carriers (P = .0098, ORMet/Met = 1.54, 95% CI: 1.11–2.14), while no difference was observed for typical-antipsychotic-treated patients (n = 155) (P = .65). Conclusions: Our findings suggest that the COMT Val158Met polymorphism is associated with response to antipsychotics in schizophrenia and schizo-affective disorder patients. This effect may be more pronounced for atypical antipsychotics