Pramod Thomas1, Fanyin He2, Sati Mazumdar2, Joel Wood3, Triptish Bhatia4, Ruben C Gur5, Raquel E Gur5, Daniel Buysse6, Vishwajit L Nimgaonkar7, Smita N Deshpande8. 1. Department of Community medicine, Believers Church Medical College, Thiruvalla, Kerala, India. Electronic address: pramodbiostat@gmail.com. 2. Department of Biostatistics and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA. 3. Department of Psychiatry, University of Pittsburgh, School of Medicine, Pittsburgh, PA, USA. 4. Indo-US Projects, Department of Psychiatry, Centre of Excellence in Mental Health, Post Graduate Institute of Medical Education and Research-Dr. Ram Manohar Lohia Hospital, New Delhi, India. 5. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA. 6. Sleep and Chronobiology Center, Department of Psychiatry, Western Psychiatric Institute and Clinic, 3811 O'Hara St. University of Pittsburgh, School of Medicine, Pittsburgh PA, USA. 7. Department of Psychiatry, University of Pittsburgh, School of Medicine, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. 8. Department of Psychiatry, Centre of Excellence in Mental Health, Post Graduate Institute of Medical Education and Research- Dr. Ram Manohar Lohia Hospital, New Delhi, India.
Abstract
BACKGROUND: Impairment in cognitive variables and alterations in circadian function have been documented among patients with schizophrenia (SZ) and bipolar I disorder (BP1), but it is not known whether joint analysis of these variables can define clinically relevant sub-groups in either disorder. OBJECTIVES: To evaluate the pattern and relationship of cognitive and circadian function in SZ and BP1 patients with respect to diagnosis and indices of clinical severity. METHODS: Among patients with SZ and BP1, cognitive function was evaluated using the Penn Computerized Neurocognitive Battery and circadian function was assessed using the Composite Scale of Morningness/ Eveningness (CSM). Clinical severity was estimated using the Global Assessment of Function (GAF) scale, and age at onset of illness (AAO). The patients were compared with community based non-psychotic control individuals and non-psychotic first degree relatives of the SZ patients. The cluster distributions of cognitive function, circadian function and clinical severity were investigated and identified clusters compared across diagnostic groups. RESULTS: Across participants, the cognitive domains could be separated into two clusters. Cluster 1 included the majority of control individuals and non-psychotic relatives, while SZ patients predominated in Cluster 2. BP1 patients were distributed across both clusters. The clusters could be differentiated by GAF scores, but not AAO. CSM scores were not significantly correlated with individual cognitive domains or with the clusters. CONCLUSIONS: Clusters based on levels of cognitive function can discriminate SZ patients from control individuals, but not BP1 patients. CSM scores do not contribute to such discrimination.
BACKGROUND: Impairment in cognitive variables and alterations in circadian function have been documented among patients with schizophrenia (SZ) and bipolar I disorder (BP1), but it is not known whether joint analysis of these variables can define clinically relevant sub-groups in either disorder. OBJECTIVES: To evaluate the pattern and relationship of cognitive and circadian function in SZ and BP1patients with respect to diagnosis and indices of clinical severity. METHODS: Among patients with SZ and BP1, cognitive function was evaluated using the Penn Computerized Neurocognitive Battery and circadian function was assessed using the Composite Scale of Morningness/ Eveningness (CSM). Clinical severity was estimated using the Global Assessment of Function (GAF) scale, and age at onset of illness (AAO). The patients were compared with community based non-psychotic control individuals and non-psychotic first degree relatives of the SZpatients. The cluster distributions of cognitive function, circadian function and clinical severity were investigated and identified clusters compared across diagnostic groups. RESULTS: Across participants, the cognitive domains could be separated into two clusters. Cluster 1 included the majority of control individuals and non-psychotic relatives, while SZpatients predominated in Cluster 2. BP1patients were distributed across both clusters. The clusters could be differentiated by GAF scores, but not AAO. CSM scores were not significantly correlated with individual cognitive domains or with the clusters. CONCLUSIONS: Clusters based on levels of cognitive function can discriminate SZpatients from control individuals, but not BP1patients. CSM scores do not contribute to such discrimination.
Authors: Raquel E Gur; Vishwajit L Nimgaonkar; Laura Almasy; Monica E Calkins; J Daniel Ragland; Michael F Pogue-Geile; Stephen Kanes; John Blangero; Ruben C Gur Journal: Am J Psychiatry Date: 2007-05 Impact factor: 18.112
Authors: L Marsh; D Harris; K O Lim; M Beal; A L Hoff; K Minn; J G Csernansky; S DeMent; W O Faustman; E V Sullivan; A Pfefferbaum Journal: Arch Gen Psychiatry Date: 1997-12
Authors: Keith H Nuechterlein; Deanna M Barch; James M Gold; Terry E Goldberg; Michael F Green; Robert K Heaton Journal: Schizophr Res Date: 2004-12-15 Impact factor: 4.939
Authors: Margriet M Sitskoorn; André Aleman; Sjoerd J H Ebisch; Melanie C M Appels; René S Kahn Journal: Schizophr Res Date: 2004-12-01 Impact factor: 4.939