Author
Chen, J.Mize, T.
Wu, J.-S.
Hong, E.
Nimgaonkar, V.
Kendler, K.S.
Allen, D.
Oh, E.
Netski, A.
Chen, X.
Date
2020Journal
Schizophrenia Research and TreatmentPublisher
Hindawi LimitedType
Article
Metadata
Show full item recordAbstract
Schizophrenia is a complex disorder with many comorbid conditions. In this study, we used polygenic risk scores (PRSs) from schizophrenia and comorbid traits to explore consistent cluster structure in schizophrenia patients. With 10 comorbid traits, we found a stable 4-cluster structure in two datasets (MGS and SSCCS). When the same traits and parameters were applied for the patients in a clinical trial of antipsychotics, the CATIE study, a 5-cluster structure was observed. One of the 4 clusters found in the MGS and SSCCS was further split into two clusters in CATIE, while the other 3 clusters remained unchanged. For the 5 CATIE clusters, we evaluated their association with the changes of clinical symptoms, neurocognitive functions, and laboratory tests between the enrollment baseline and the end of Phase I trial. Class I was found responsive to treatment, with significant reduction for the total, positive, and negative symptoms (p=0.0001, 0.0099, and 0.0028, respectively), and improvement for cognitive functions (VIGILANCE, p=0.0099; PROCESSING SPEED, p=0.0006; WORKING MEMORY, p=0.0023; and REASONING, p=0.0015). Class II had modest reduction of positive symptoms (p=0.0492) and better PROCESSING SPEED (p=0.0071). Class IV had a specific reduction of negative symptoms (p=0.0111) and modest cognitive improvement for all tested domains. Interestingly, Class IV was also associated with decreased lymphocyte counts and increased neutrophil counts, an indication of ongoing inflammation or immune dysfunction. In contrast, Classes III and V showed no symptom reduction but a higher level of phosphorus. Overall, our results suggest that PRSs from schizophrenia and comorbid traits can be utilized to classify patients into subtypes with distinctive clinical features. This genetic susceptibility based subtyping may be useful to facilitate more effective treatment and outcome prediction. Copyright 2020 Jingchun Chen et al.Sponsors
This work was supported in part by grant MH101054 to XC from the National Institute of Mental Health, grant U54GM104944 pilot grant to JC from NIGMS-CTRIN, and grant P20 GM121325 to JC from NIGMS-COBRE.Identifier to cite or link to this item
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089306178&doi=10.1155%2f2020%2f1638403&partnerID=40&md5=3fcee752db1069ee3ba21beba7c67e24; http://hdl.handle.net/10713/13583ae974a485f413a2113503eed53cd6c53
10.1155/2020/1638403