Faith M Hanlon1, Ronald A Yeo2, Nicholas A Shaff3, Christopher J Wertz4, Andrew B Dodd5, Juan R Bustillo6, Shannon F Stromberg7, Denise S Lin8, Swala Abrams9, Jingyu Liu10, Andrew R Mayer11. 1. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address: fhanlon@mrn.org. 2. Department of Psychology, University of New Mexico, 2001 Redondo S Dr., Albuquerque, NM 87106, USA. Electronic address: ryeo@unm.edu. 3. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address: nshaff@mrn.org. 4. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address: cwertz@mrn.org. 5. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address: adodd@mrn.org. 6. Department of Psychiatry, University of New Mexico School of Medicine, MSC09 5030, 1 University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: JBustillo@salud.unm.edu. 7. Psychiatry and Behavioral Health Clinical Program, Presbyterian Healthcare System, 1325 Wyoming Blvd. NE, Albuquerque, NM 87112, USA. Electronic address: sstromber@phs.org. 8. Department of Psychiatry, University of New Mexico School of Medicine, MSC09 5030, 1 University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: delin@salud.unm.edu. 9. Department of Psychiatry, University of New Mexico School of Medicine, MSC09 5030, 1 University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: SAbrams@salud.unm.edu. 10. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address: jliu@mrn.org. 11. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA; Department of Psychology, University of New Mexico, 2001 Redondo S Dr., Albuquerque, NM 87106, USA; Department of Psychiatry, University of New Mexico School of Medicine, MSC09 5030, 1 University of New Mexico, Albuquerque, NM 87131, USA; Department of Neurology, University of New Mexico School of Medicine, MSC10 5620, 1 University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: amayer@mrn.org.
Abstract
BACKGROUND: Patients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]). METHODS: The current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden). RESULTS: Symptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition. CONCLUSIONS: Current results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.
BACKGROUND:Patients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]). METHODS: The current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden). RESULTS: Symptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition. CONCLUSIONS: Current results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.
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