Abstract: 224 words; Text: 3090 words; Figure legend: 108 words; 45 references; 2 Tables; 1 Figure
Clinical symptoms, mainly negative symptoms, mediate the influence of neurocognition and social cognition on functional outcome of schizophrenia
Chieh-Hsin Lin, M.D. a, b, Chieh-Liang Huang, M.D. c, Yue-Cune Chang, Ph.D. d, Po-Wei Chen, M.D. e, Chun-Yuan Lin, M.D. a,f,g , Guochuan E. Tsai, M.D., Ph.D h, Hsien-Yuan Lane, M.D., Ph.D. a,c,*
a Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
b Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
c Departments of Psychiatry, China Medical University Hospital, Taichung, Taiwan d Department of Mathematics, Tamkang University, Taipei, Taiwan
e Taichung Chin-Ho Hospital, Taichung, Taiwan
f Department of Psychiatry, Changhua Hospital, Changhua, Taiwan g National Changhua University of Education, Changhua, Taiwan
h Department of Psychiatry, Harbor-UCLA Medical Center, 1000 W. Carson Street, Torrance, CA 90509, U.S.A.
Running Title: Clinical symptoms mediate cognitive influence on functional outcome in schizophrenia
* Corresponding author at: Department of Psychiatry, China Medical University Hospital, No. 2, Yuh-Der Road, Taichung 404, Taiwan. Tel.: +886-921067260; fax: +886-4-2236-1230.
Email address: [email protected] (H.-Y. Lane).
Background: The functional outcome of schizophrenia is affected by multiple factors such as
cognitive function and clinical symptoms. The complex relationship among cognitive function (both neuro- and social-cognition), clinical symptoms, and functional outcome remains unclear. The current study employed structural equation modeling (SEM) to examine whether clinical symptoms mediate the relationship between cognitive function and functional outcome in a large cohort of patients with schizophrenia.
Method: Three hundred and two Han-Chinese patients with chronically stable schizophrenia
received evaluation of cognitive function (using the Measurement and Treatment Research to Improve Cognition in Schizophrenia [MATRICS] Consensus Cognitive Battery, including 7 domains covering neurocognition and social cognition), clinical symptoms (including positive, negative and depressive symptoms), and functional outcome as assessed by Global Assessment of Functioning Scale and Quality of Life Scale.
Results: SEM identified clinical symptoms as a mediator between cognitive function
(including all 7 domains of MATRICS) and functional outcome in schizophrenia. The relationship between cognitive function and functional outcome was significant in the basic model. In the mediation model, the link between cognitive function and functional outcome was mediated by clinical symptoms, mainly negative symptoms.
Conclusion: This study suggests that clinical symptoms, mainly negative symptoms, mediate
the influence of neurocognition and social cognition on functional outcome of schizophrenia. Future studies should explore the impact on other functional outcomes in different ethnicities and various illness phases.
Key words: schizophrenia; neurocognition; social cognition; functional outcome; clinical symptoms; structural equation modeling
Introduction
Schizophrenia is one of most disabling disorders worldwide that bring forth severe and persistent functional impairment. Identifying factors contributing to functional outcome becomes an important issue not only for drug development but also for psychiatric rehabilitation programming. Functional outcome, divided into domains of community outcome, social problem solving and psychosocial skill acquisition, is related to neurocognitive function (Green et al., 2000). Patients with schizophrenia are impaired in various cognitive functions, including both neuro- and social-cognition which are associated with functional outcome (Fett et al., 2011; Green et al., 2000; Horan et al., 2012). In addition, clinical symptoms, particularly the negative symptoms, are linked to functional outcome (Milev et al., 2005). The relationships between symptomatic domains and cognitive domains are also strong. Negative symptoms are found to have the strongest relationship with neuro-and social-cognition (Milev et al., 2005; Ventura et al., 2011); nevertheless, weak association is also noted between positive symptoms such as reality distortion (delusions and hallucinations) (Ventura et al., 2011) as well as depressive symptoms (Smith et al., 1999) with neuro- and social-cognition. Schizophrenic patients with greater improvement on core symptoms are more likely to reach clinical remission (Emsley et al., 2007).do we need this sentence?
The relationship among neuro- and social-cognition, clinical symptoms and functional outcome is complex and it is important to take all these factors into account simultaneously while investigating their interactions. Moreover, the relationship between two factors may be mediated by another factor. For example, Ventura et al. (2009) addressed that negative symptoms at least partially mediate the relationship between neurocognition and functional outcome. Sergi et al. (2006) demonstrated that social perception mediates the influence of early visual processing on functional status. Schmidt et al. (2011) found that social cognition
mediates an indirect relationship between neurocognition and functional outcome. Intrinsic motivation was also suggested to be a critical mechanism for explaining the relationship between neurocognition and psychosocial functioning (Nakagami et al., 2008). Traditional regression or correlation modeling may not be very satisfying in comparing the relative weights of each variable in an integral whole. A possible resolution is the structural equation model (SEM), which is powerful in testing a set of confirmatory factor analyses and regression equations simultaneously (Hoyle, 1995).
The MATRICS Consensus Cognitive Battery (MCCB) developed by the National Institute of Mental Health includes social cognition as one of the seven cognitive domains in schizophrenia (Green et al., 2004). MCCB, which serves as the standard measure for cognitive studies in schizophrenia, has excellent clinical relevance for real-world functioning and is more and more widely used by recent ongoing trials (Keefe et al., 2011). Although some researchers (van Hooren et al., 2008) have conceptualized social cognition as a construct separate from neurocognition, Vauth et al. (2004) used SEM to demonstrate that social cognition is closely correlated with neurocognition (correlation coefficient = 0.91) and neurocognition accounts for 83% of the variance in social cognition. However, it remains unclear when neuro- and social-cognition, clinical symptoms and functional outcome are included in a single model: whether social cognition and neurocognition are better considered as a single construct or as distinct two?
Though the mediation models among neurocognition, social cognition, and functional outcome have been demonstrated by SEM (Schmidt et al., 2011; Sergi et al., 2006), one unanswered issue concerns the relationship between neuro- and social-cognition and functional outcome when clinical symptoms are taken into account simultaneously. Furthermore, to date, SEM research using the MCCB for the assessment of neuro- and social-cognition as well as comprehensive assessment of clinical symptoms is lacking. Albeit with
limitation, the Global Assessment of Functioning (GAF) Scale of the DSM-IV was used as the sole assessment of functional outcome of schizophrenia in some studies (Schmidt et al., 2011). GAF, jointly with social and occupational functioning and quality of life, has been regarded as a valid tool for assessing functional outcome (Schennach-Wolff et al., 2009). In addition, quality of life as well as subjective wellbeing has also been recognized as an important measure of the outcome of schizophrenia patients (Narvaez et al., 2008; Schennach-Wolff et al., 2010), particularly when social cognition and clinical symptoms are among the predictive factors (Hofer et al., 2009). Patient satisfaction is related to their willingness to be engaged in treatment, subsequently related to symptomatic and functional outcome (Lambert and Naber, 2004). GAF scores are correlated with quality of life in schizophrenia patients (Woon et al., 2010). Subjective rating of quality of life and objective measure of global functioning has been used together for the assessment of longitudinal outcome of schizophrenia (Sim et al., 2006). Therefore, we used both GAF and quality of life to represent the functional outcome of schizophrenia in this study.
The present study aimed to use the SEM to test the relationship among neuro- and social-cognition assessed by the MCCB, clinical symptoms composed of positive, negative and depressive symptoms, and functional outcome represented by both GAF and quality of life in a large sample of patients with schizophrenia who had been clinically stable for 3 months or longer. We hypothesized that the relationship between neuro- and social-cognition and functional outcome would be mediated by clinical symptoms particularly the negative symptoms. We also tested whether a one-factor model that represents neurocognition and social cognition as a single construct or a two-factor model that represents the two domains as separate constructs would fit the overall data better.
Materials and Method
Hospital, a major medical center in Taiwan, and conducted in accordance with the Declaration of Helsinki.
Subjects
Unrelated patients with schizophrenia were recruited from inpatient and day-care units of China Medical University Hospital and affiliated Taichung Chin-Ho Hospital. All were Han Chinese in Taiwan, with age between 18 and 65. The subjects gave their written informed consent for participating in this study after complete description and discussion. The patients were free from any Axis I or II psychiatric disorder including smoking, alcohol drinking or other substances abuse/dependence except schizophrenia, as determined by experienced research psychiatrists using the Structured Clinical Interview for DSM-IV (American Psychiatric Association, 1994). All patients had been clinically stable for at least three months. All were in good physical health, as determined by history taking, physical examination, electrocardiogram, and laboratory tests including fasting blood sugar, lipid profile, liver and renal function tests and electrolytes. The patients who were unable to accomplish the whole assessments of cognitive function, clinical symptoms, and functional outcome (see below) were excluded.
Measurements of cognitive function
Cognitive function was assessed using a battery of tests, which were the same as or the analogues of tests from the MATRICS Consensus Cognitive Battery (MCCB) recommended by the USA National Institute of Mental Health MATRICS committee, due to lack of Chinese versions of some tests (Green et al., 2004). This battery included 7 domains: 1) speed of processing, consisting of 3 tests: Category Fluency, Trail Making A (Reitan, 1958), and WAIS-III Digit Symbol-Coding (Wechsler, 1997a); 2) sustained attention by Continuous Performance Test (Chen et al., 1998); 3) working memory, verbal (backward digit span
(Silver et al., 2003)) and nonverbal (WMS-III, Spatial Span (Wechsler, 1997b)); 4) verbal learning and memory (WMS-III, word listing); 5) visual learning and memory (WMS-III, visual reproduction); 6) reasoning and problem solving (WISC-III, Maze (Wechsler, 1991)), and 7) social cognition, measured by Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) V2.0 (Mayer et al., 2003). The MSCEIT includes eight tasks in four branches: perceiving emotions, facilitating emotions, understanding emotions and managing emotions (Mayer et al., 2003). Social cognition was assessed by the “managing emotions” branch, which has been recommended by the MATRICS committee as the most appropriate measure of social cognition (Eack et al., 2010; Green et al., 2005). The validity/reliability (Ma et al., 2010) and feasibility (Lo et al., 2010) of the Chinese version have been satisfactory. Each domain score was standardized to a T score with a mean of 50 and a standard deviation of 10 for making every domain more comparative. Similar methods have been reported previously with excellent test-retest reliability and validity (Keefe et al., 2011). For the domain with more than one test, a composite T score was calculated by standardizing the average of each T score.
Clinical assessments
The Positive and Negative Syndrome Scale (PANSS)-Positive subscale (Kay et al., 1987), Scale for the Assessment of Negative Symptoms (SANS) (Andreasen, 1989), and 17-item Hamilton Depression Rating Scale (HAMD17) (Hamilton, 1960) were used to assess positive, negative and depressive symptoms. Clinical ratings were done by research psychiatrists who were well-trained and experienced in the rating scales. Inter-rater reliability was analyzed using the analysis of variance (ANOVA) test. Only raters with an intraclass correlation coefficient of 0.90 or higher during pre-study training were allowed to rate the study patients. In order to maintain high inter-rater reliability and to prevent rater drift, raters met at least
once a month for training and reliability retesting.
Functional outcome
Functional outcome was measured using the Global Assessment of Functioning (GAF) Scale of the DSM-IV and the Quality of Life (QLS) Scale. Previous studies demonstrate that the GAF is a valid tool of assessing global psychological, social and occupational functioning for clinically stable patients with schizophrenia (Startup et al., 2002). The QLS is a 21-item scale based on a semi-structured interview for assessing quality of life (Heinrichs et al., 1984). Of the 21 items on the QLS, 10 (social activity, social initiatives, social withdrawal, sense of purpose, motivation, curiosity, anhedonia, aimless inactivity, capacity for empathy, emotional interaction) were selected for the inpatient and day care settings (Lane et al., 2010; Lane et al., 2008).
Statistical analyses
We employed SEM (Arbuckle, 2006) to examine the relationships among cognitive function, clinical symptoms, and functional outcome. SEM encompasses diverse statistical techniques such as path analysis, confirmatory factor analysis, causal modeling with latent variables, analysis of variance, and multiple linear regression (Arbuckle, 2006). Factor loadings were used to specify the association between an unobservable construct (latent variable) and its theoretically related measures (indicator variables). Regression analyses were used to determine the relationships among the latent variables and were indexed by standardized path coefficients.
Raw data was checked for normality and outliers prior to the analyses. The latent variable cognitive function was indexed with seven indicator variables: speed of processing, sustained attention by Continuous Performance Test, verbal and nonverbal working memory, verbal learning and memory, visual learning and memory, reasoning and problem solving and
social cognition. The variable clinical symptoms had three indicator variables: negative symptoms by SANS, positive symptoms by PANSS-Positive subscale, and depressive symptoms by HAMD17. Functional outcome was a latent variable with two indicator variables: GAF and QLS.
We used two models to estimate potential mediation effects: a basic model positing a direct relationship between cognitive function and functional outcome, and a mediation model positing an indirect relationship between cognitive function and functional outcome through linkages with clinical symptoms. Model fit which represents how a SEM fits the sample data was assessed by five indices: the chi-square test (χ2), the goodness of fit index (GFI), the
Tucker-Lewis Index (TLI), the comparative fit index (CFI), and the root mean-squared error of approximation (RMSEA) (Arbuckle, 2006). A non-significant χ2, GFI greater than 0.90,
TLI and CFI greater than 0.95, and RMSEA less than 0.06 each indicates a good model fit between the data and the hypothesized model (Arbuckle, 2006; Hoyle, 1995).
For SEM analysis, a minimum sample of 100 has been recommended by some experts (Hoyle, 1995). Hair et al. (1998) suggested that the minimum sample size for SEM must be greater than the minimum ratio of at least 5 participants for each parameter to be estimated. Our mediation model comprised 35 estimated parameters which needed at least 175 participants. Another good rule-of-thumb recommended by Bentler and Chou (1987) is that at least 15 participants for each observed variable (15 × 12 observed variables = 180). Therefore, a sample size of 302 participants has greatly exceeded the minimal requirements. All statistical analyses were performed with Amos 5.0 software (SPSS Inc.) (Arbuckle, 2006).
Results
Three hundred and two eligible patients with schizophrenia were enrolled. The numbers, means, and standard deviations of all indicator variables are listed in Table 1. Two hundred and ninety participants had been taking antipsychotic medication continuously for at least 3
months (63.8% atypical neuroleptics, 21.7% typical neuroleptics, 14.5% mixed) as shown in Table 2. The remaining 12 were drug-free for > 3 months. Twenty patients had been taking antidepressants; 32 patients had been taking mood stabilizers; and 114 patients had been taking anticholinergic agents continuously for at least 3 months (Table 2). The zero-order correlations of the indicator variables are displayed in Table 3. The confirmatory factor analysis showed that all indicator variables were reliable and valid measures of their respective latent variables, supported by their significant moderate to high factor loadings (β = 0.43–0.94, P < .001).
The basic model depicted the direct relationship between cognitive performance and functional outcome (Figure 1). This model was statistically significant (standardized coefficient β = 0.67, P < .001) and provided a good fit for the data, as suggested by the non-significant chi-square and good indices of fit (χ2 = 28.84, df = 21, P = .12, GFI = 0.98, TLI =
0.98, CFI = 0.988, RMSEA = 0.035). The cognitive function accounted for 44% of the variance in the functional outcome.
The mediation model evaluated the strength of the indirect relationship while controlling for the direct effect of cognitive function on functional outcome (Figure 1). The direct path from cognitive function to functional outcome was no longer significant, as soon as the mediator was entered into the model (β = 0.21, P = .076). Instead, clinical symptoms encompassing positive, negative and depressive symptoms showed significant negative association with cognitive function (β = -0.57, P < .001) and was predictive of functional outcome itself (β = -0.81, P < .001). The mediation model also provided a very good fit for the data (χ2 = 51.96, df = 43, P = .164, GFI = 0.973, TLI = 0.986, CFI = 0.991, RMSEA =
0.026). This model explained 89% of the variance in the functional outcome. These data are consistent with a complete mediation effect through clinical symptoms.
related, constructs (Schmidt et al., 2011; Sergi et al., 2007). To examine the separateness of social cognition from neurocognition in our sample, we compared a one-factor model that represented social cognition and neurocognition as a single latent variable with a two-factor model that represented social cognition and neurocognition as separate latent variables. For the one-factor model, the confirmatory factor analysis showed that all of the seven indicator variables of the neuro- and social-cognition had significant moderate-to-high loadings on the latent variable of cognitive function (β = 0.46–0.77, P < .001). For the two-factor model, the sex indicator variables of neurocognition had significant moderate-to-high loadings on the latent variable of neurocognition (β = 0.49–0.77, P < .001), and the indicator variable of social cognition also had significant high loading on the latent variable of social cognition (β = 0.71, P < .001). Nevertheless, the attempt to connect the latent variables of neurocognition and social cognition to represent a latent variable of cognitive function was unsuccessful. We then examined the correlation between cognitive function and functional outcome with the two-factor model in which social cognition and neurocognition were regarded as separate latent variables. This attempt was still unsuccessful and rejected. We further examined the separateness of social cognition from neurocognition in the mediation model of which clinical symptoms were the mediator. The result showed that separating social cognition from neurocognition as two distinct latent variables in the mediation model was also unsuccessful and rejected (the model fit parameters were not shown since the parameters were incorrect when the model was unsuccessful).
Discussion
Using SEM, which is more powerful than multiple regression in analyzing a set of interactive factors simultaneously (Hoyle, 1995), we tested the mediation effect of clinical symptoms on the relationship between neuro-/social cognition and functional outcome in a large sample of
patients with chronically stable schizophrenia. The direct relationship between neuro-/social cognition and functional outcome, which was significant in the basic model, became insignificant in the mediation model where clinical symptoms were entered in the model as the mediator, suggesting that clinical symptoms mediated the relationship between neuro-/social cognition and functional outcome in schizophrenia. Through examining the separateness of social cognition from neurocognition, the present study also demonstrated that social cognition and neurocognition were better regarded as a single construct when predicting the functional outcome mediated by clinical symptoms.
In line with previous findings (Rabinowitz et al., 2012; Tomotake, 2011), our study found that negative symptoms were significantly related to neuro- and social-cognition and contributed most to the functional outcome represented by GAF and QOL; nevertheless, positive and depressive symptoms also contributed small but significant portions to the functional outcome. Among them, positive symptoms had stronger correlation with GAF, and depressive symptoms had stronger correlation with QOL (Table 2). Earlier studies also suggested that positive symptoms were predictive of GAF score (Craig et al., 1999), and depressive symptoms predictive of QOL score (Tomotake, 2011). The results indicate that clinical symptoms are important predictors for outcome in schizophrenia.
Our mediation model explained 89% of the variance in functional outcome, while our basic model explained only 44%. A study of social cognition explained 79.7% of the variance in social function (Addington et al., 2010). A model of which emotion perception is the predictor variable explained 49% of the variance in functional outcome (Green et al., 2012).
Another study showed that social perception accounted for 18% of the variance in functional status (Sergi et al., 2006). Although the dependent variables of the aforementioned studies are different from the present study, it is believed that models including more mediators explain more variability in the observed construct. The variance explained in our basic model falls
within the range found by the other three studies abovementioned which looked at the role of different aspects of social cognition in predicting different aspects of functional outcome, but is higher than that of another study which showed that neurocognition accounted for 14% of the variance in functional outcome (Schmidt et al., 2011). A possible explanation of the higher percentage of variance in our mediation model is the inclusion of a broad span of cognitive and symptomatic domains which are commonly observed in schizophrenia. Furthermore, the marked difference in variance explained through the addition of clinical symptoms in the mediation model (from 44% with only cognitive domains to 89% when adding symptomatic domains) supports the strong influence of psychopathology on functional outcome. Inclusion of additional mediators such as motivation and hope may lead to a better fit of the model, because studies have indicated that motivation is associated with neurocognition as well as functional outcome (Gard et al., 2009; Nakagami et al., 2010; Schmidt et al., 2011) and hope is the catalyst of the recovery process (Andresen et al., 2003). From the result of our mediation model, it is reasonable to hypothesize that negative symptoms impair neuro- and social-cognition possibly through lowered motivation to attend the tasks and in turn make an impact on functioning, or negative symptoms decrease the motivation to participate in social activities which directly influence functional outcome.
Whether social cognition and neurocognition are distinct domains in schizophrenia remains controversial (Sergi et al., 2007; van Hooren et al., 2008). The current study found that the one-factor model which represented social cognition and neurocognition as a single construct fit our data better than the two-factor model in which social cognition and neurocognition were separated as distinct constructs. Of note, in the present study, clinical symptoms acted as a single mediator of the relationship between neuro- and social-cognition and functional outcome. As shown in the study by Ventura et al. (2011), when considering negative symptoms alone, the relationship to social cognition was as strong as to
neurocognition, suggesting that negative symptoms played an important role in determining social cognition. For this reason, the two-factor constructs might be too complicated to achieve a successful model under the mediation of clinical symptoms. The results also support the concept proposed by the MATRICS that social cognition and neurocognition are an integral whole when measuring cognitive function of schizophrenia. Nevertheless, our assessment of social cognition was limited to the measure of managing emotion which may not reflect the complete picture of social cognition.
In both our basic and mediation models, social cognition had the least, but still significant, contribution to the cognitive function latent variable (standardized path coefficients = 0.45-0.46, R2 = 0.21, P < .001) when compared with other neurocognitive
domains (see figure 1). However, cultural or racial factors seem to affect the expression of social cognition from a socio-psychological perspective. Approaches to measurement of social cognition may need to be adapted to the specific cultures and ethnicities (Mehta et al., 2011). Whether our finding on Han Chinese in Taiwan can be applied to other ethnicities needs further investigation.
The present study is limited by that our functional outcome measurement did not cover social function, which was found to be linked with social cognition (Addington et al., 2010). We excluded the patients who could not accomplish the whole assessments, consequently reduced the generalizability. We also excluded smokers because smoking has been demonstrated to have an impact on a variety of neurocognitive function, such as visual attention, cognitive impulsivity, visuospatial memory, etc. (Wagner et al., 2012; Zhang et al., 2012). Thus, our finding may be unable to be extrapolate to patients who smoke. In addition, psychopathology is correlated with both subjective and objective measures of functional outcome; for example, depressive symptoms can result in viewing one’s quality of life worse than it is. Therefore, schizophrenia patients with different psychopathology profiles (such as a
more severe depressive status than that in the current study) may have unique SEM models for functional outcomes. Another limitation is its cross-sectional design which may not necessarily represent the longitudinal relationships among neuro-/social-cognition, clinical symptoms and functional outcome.
Despite these limitations, this study conducted in a large sample with the same ethnicity demonstrates clearly that clinical symptoms mediate the cognitive function-functional outcome relationship, providing a preliminary perspective in understanding the complex relationship between cognitive function and functional outcome. Our findings which indicate a critical role of clinical symptoms in the functional outcome of schizophrenia support the importance of symptomatic remission for functioning and quality of life (Brissos et al., 2011). Furthermore, treating symptoms more aggressively with antipsychotics or other approaches such as cognitive rehabilitation to reach clinical remission might be able to improve the functioning and quality of life of patients with schizophrenia. Future studies with prospective design are required for elucidating the impact of symptomatic remission on long-term functioning and quality of life of schizophrenia patients.
Acknowledgements
This work was funded by the National Science Council, Taiwan (NSC-97-2314-B-039-006-MY3, NSC-98-2627-B-039-001, and NSC 99-3114-B-182A-003), National Health Research Institutes, Taiwan (NHRI-EX-101-9904NI), Taiwan Department of Health Clinical Trial and Research Center of Excellence (DOH101-TD-B-111-004), and China Medical University Hospital, Taiwan (DMR-98-093 and DMR-98-095).
Role of Funding Source
The aforementioned institutes had no further role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit
the paper for publication.
Conflict of interest
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