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Psychiatry Research 247 (2017) 6–11

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Cognitive dysfunction correlates with elevated serum S100B concentration in drug-free acutely relapsed patients with schizophrenia

MARK

Song Chena,b,c, Li Tiana,d, Nan Chena, Meihong Xiua, Zhiren Wanga, Guigang Yanga, ⁎ ⁎ Chuanyue Wangb,c, Fude Yanga, , Yunlong Tana, a

BeijingHuiLongGuan Hospital, Peking University, Beijing, China Beijing Key Laboratory of Mental Disorders, Department of Psychiatry, Beijing Anding Hospital, Capital Medical University, China c Center of Schizophrenia, Beijing Institute for Brain Disorders, Laboratory of Brain Disorders (Capital Medical University), Ministry of Science and Technology, China d Neuroscience Center, University of Helsinki, Helsinki, Finland b

A R T I C L E I N F O

A BS T RAC T

Keywords: Drug-naïve Drug-free Schizophrenia Cognition MATRICS Consensus Cognitive Battery (MCCB) S100B

S100B, a biomarker of glial dysfunction and blood-brain barrier (BBB) disruption, has been proposed to be involved in the pathophysiology of schizophrenia. In the present study, we aimed at exploring the association of serum S100B levels with cognitive deficits using MATRICS Consensus Cognitive Battery (MCCB) in schizophrenia, by excluding the impact of antipsychotics. Sixty-two unmedicated patients with schizophrenia during their acute phases were divided into a drug-naïve group (n=34) and a drug-free group (n=28). S100B serum concentrations were measured and MCCB was administered to all of the patients. Forty healthy controls donated their blood samples for S100B assessment. The results indicated that serum S100B was significantly elevated in the drug-naive/free acute-stage schizophrenic patients when compared to the healthy controls. In the drug-free group, the serum S100B level was an independent contributor to the global cognitive dysfunctions, particularly for the speed of processing, attention/vigilance, visual learning and reasoning/problem solving subscores. Nevertheless, no significant associations between S100B and MCCB composite score or any cognitive domain subscore were observed in the drug-naïve group. These findings support the hypothesis that glial dysfunction and associated marker protein S100B may contribute to the pathophysiologic development of neurocognitive deficits in the relapsed individuals with schizophrenia.

1. Introduction Cognitive impairment has been well validated in the patients with schizophrenia. The most pronounced cognitive deficits include selective and sustained attention, processing speed, verbal memory, working memory, executive function and social cognition (Barch and Ceaser, 2012; Green et al., 2004; Medalia and Choi, 2009). Although cognitive dysfunction as a core characteristic of schizophrenia has received extensive attention and research, the underlying neurobiological mechanisms remain mostly unknown. Recent conceptualization of involvement of neurodevelopment, neuroplasticity and neuroinflammation in the pathophysiology of cognitive impairments in schizophrenics provides an insight into exploring the disease mechanisms (Na et al., 2014; Skaper et al., 2014; Steullet et al., 2014). S100B, a calcium-binding protein, is predominantly produced by astrocytes, and is also expressed in other brain cells such as oligodendrocytes, microglial or even neurons (Steiner et al., 2007). Glial, especially



astrocytic, dysfunction appears to play an important role in the pathogenesis of schizophrenia (Bernstein et al., 2015; De Keyser et al., 2008; Hercher et al., 2014); therefore, astrocyte-derived S100B protein was considered as a neurobiological marker of astrocytic response in schizophrenics (Rothermundt et al., 2004). S100B can act in both autocrine and paracrine manners to regulate cell proliferation, differentiation and neuroprotection in the brain at pico- to nanomolar concentrations (Rothermundt et al., 2003). On the contrary, excessive S100B (at micro-molar concentrations) is neurotoxic and promotes neurodegeneration and apoptosis, by inducing the overexpression of inducible nitric oxide synthase and/or pro-inflammatory cytokines (Hu et al., 1996). In other words, increased S100B contributes to the imbalanced neuroinflammation and is also described as “the C-reactive protein of the brain” (Sen and Belli, 2007). In healthy aging adults (between the ages of 43 and 84 years), serum S100B was reported to be positively correlated with cognitive performance (Lam et al., 2013). However, in the individuals with

Corresponding authors at: Beijing HuiLongGuan Hospital, Peking University, Changping District, Beijing, China. E-mail addresses: [email protected] (F. Yang), [email protected] (Y. Tan).

http://dx.doi.org/10.1016/j.psychres.2016.09.029 Received 3 January 2016; Received in revised form 4 September 2016; Accepted 20 September 2016 Available online 22 September 2016 0165-1781/ © 2016 Published by Elsevier Ireland Ltd.

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patients with schizophrenia during their acute phases were screened. Of these subjects, 2 failed the screening, 6 did not complete the MCCB tests. Therefore, 62 patients divided into a drug-naïve group (n=34) and a drug-free group (n=28) were included in the analysis. The drugnaïve group consisted of 7 patients who had never been exposed to antipsychotic medications and 27 patients who had received less than two weeks of antipsychotics, while 28 medication-free patients were the ones with relapse because of drug discontinuance. Additionally, for normal controls, we did not administer MCCB tests.

cognitive decline-related diseases, such as circulatory arrest, stroke, traumatic brain injury, Alzheimer’s disease and mood disorders, studies have showed that S100B serum levels are significantly increased when compared to healthy controls (Schroeter et al., 2013; Sun and Feng, 2014; Yardan et al., 2011). Moreover, higher levels of S100B are correlated with lower total scores of cognitive performance in subcortical vascular dementia and comorbid brain abnormalities caused by rheumatoid arthritis (Hamed et al., 2012; Levada and Trailin, 2012). S100B may also play role in the pathophysiology of schizophrenia. Several studies have consistently demonstrated elevated levels of S100B in the peripheral blood or cerebrospinal fluid (CSF) of patients with schizophrenia (Aleksovska et al., 2014). Furthermore, persistently high S100B concentrations correlate with memory impairments in the patients with chronic schizophrenia (Pedersen et al., 2008). Risk variants in the S100B gene, including the A allele of rs9722, the G allele of rs1051169, and the AG haplotype, are also associated with elevated S100B level and poor performance on cognitive tasks in people with schizophrenia (Zhai et al., 2011). Despite these evidences, to our best knowledge, no study has yet examined the relationship between serum S100B level and cognition in schizophrenic patients using a comprehensive cognitive assessment battery. The MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery (MCCB) provides a reliable and valid assessment of cognition across all cognitive domains, including speed of processing, attention and vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition (Nuechterlein et al., 2008), and therefore was chosen in our current study. The reliability and validity of the Chinese version of MCCB has been confirmed (Zou et al., 2009). Given the influence of antipsychotics on S100B levels and cognitive functions (Zhang et al., 2010), acute schizophrenic patients who were drug-naïve or drug-free for at least one month were involved in this study. As mentioned above, we hypothesize that elevated serum S100B level is inversely related to cognitive ability as assessed by the MCCB in nonmedicated schizophrenia.

2.2. Blood sampling and serum S100B measurements All subjects fasted for at least 12 h prior to blood drawing between 7:00 a.m. and 9:00 a.m.. Blood samples were centrifuged to separate sera, and serum samples were stored at −80 °C until S100B analysis. Serum S100B levels were measured in duplicates in all subjects by sandwich ELISA using a commercially available kit (R & D systems, Beijing, China) as described previously (Qi et al., 2009). All samples were assayed by the same investigator, who was blind to the clinical situation. The sensitivity of the S100B assay was 0.1 ng/ml. Inter- and intra-assay variation coefficients were 6% and 4%, respectively. 2.3. The MCCB The MCCB was administered by trained staff under the supervision of a registered clinical neuropsychologist. The MCCB includes ten tests that assess seven cognitive domains: (1) Speed of processing: Trail Making Test, part A (TMT); Symbol Coding Subtest (SC); Category Fluency Test (CF); (2) Attention and vigilance: Continuous Performance Test—Identical Pairs (CPT-IP); (3) Working memory: Wechsler Memory Scale, spatial span (SS) and digit sequencing (DS) test; (4) Verbal learning: Hopkins Verbal Learning Test—Revised (HVLT-R); (5) Visual learning: Brief Visuos-patial Memory Test— Revised (BVMT-R); (6) Reasoning and problem solving: Neuropsychological Assessment Battery, mazes subtest (MAZES); (7) Social cognition: Mayer-Salovey-Caruso Emotional Intelligence Test, managing emotions subtest (ME). Raw scores were converted to normalized T-scores, and seven domain T-scores as well as a composite T-score were acquired using the MCCB scoring program. The MCCB was administered within 48 h after blood drawing.

2. Methods 2.1. Participants For inclusion in the study, patients had to fulfill all of the following criteria: (1) Provision of informed consent prior to any study-specific procedure; (2) Male and female patients aged over 18 and inclusive; (3) Meet the Diagnostic and Statistical Manual of Mental Disorders, fourth Edition (DSM-IV) for schizophrenia by agreement of two senior psychiatrists, using the Structured Clinical Interview for DSM-IV (SCID); (4) Antipsychotic drugs were stopped at least one month prior to this study; (5) Education level was more than 3 years; (6) Physically healthy; (7) No history of neurological disorders or head trauma; (8) Patients were able to understand and comply with all study procedures, as judged by the investigator. Subjects with current DSM-IV diagnosis other than schizophrenia were excluded from the study. Clinical psychopathological symptoms were evaluated by the Positive and Negative Syndrome Scale (PANSS), which were measured independently by two psychiatrists on the day of blood sample collection. To ensure consistency and reliability of ratings across the study, both senior psychiatrists were trained before the study began. After training, a correlation coefficient > 0.8 was maintained for the PANSS total scores. Forty physical healthy controls were recruited from the local community. None of the control subjects had any mental disorder or was taking medications. This study was approved by the ethics committee of Beijing Huilongguan Hospital in accordance with the Declaration of Helsinki. All patients were recruited from Beijing Huilongguan Hospital, a city-run psychiatric hospital in Beijing, China. Seventy unmedicated

2.4. Statistical analysis Demographic variables of drug-naïve, drug-free and normal control groups were compared using analysis of variance (ANOVA) for continuous variables and Chi-Square test for categorical variables, and Fisher’s least significant difference (LSD) test was performed for post-hoc pairwise comparisons. Analysis of covariance (ANCOVA) was applied to compare the serum S100B levels among the three groups with the factors that showed to be significantly different in ANOVA as covariates, and post-hoc comparisons between groups were made using Bonferroni procedure. Comparisons between the two groups of schizophrenic patients were assessed using Student's T-test for parametric nominal data, Mann-Whitney U test for nonparametric nominal data, and Chi-Square test for categorical data. Relationships between variables were assessed with Pearson's product moment correlation coefficients. Stepwise multiple linear regression analysis using the composite or seven domains of MCCB scores as the dependent variables was made to investigate the impact factors including the S100B serum levels. Data analysis was performed using the IBM SPSS Statistics 21.0. All p-values were two-tailed and statistical significance was set at 0.05. 3. Results Clinical and demographic characteristics for drug-naïve and drug7

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Table 1 Characteristics of drug-naïve, drug-free schizophrenic patients and healthy controls.

Age (years)a Gender (male/female)b Duration of illness (months, median) Education (years, median) BMI (kg/m2) Smoker/non-smoker PANSS total P subscore N subscore G subscore S100B (ug/L)c

Drug-naïve group (n=34)

Drug-free group (n=28)

Healthy controls (n=40)

t/z/F/x2 Value

p-Value

35.00 ± 12.92*** 14/20 24.0 12 22.20 ± 3.23 3/31(8.8%) 86.32 ± 13.17 25.15 ± 5.08 20.56 ± 5.34 40.62 ± 6.60 0.24 ± 0.08***

35.68 ± 9.78*** 10/18 73.5 12 23.90 ± 4.23 9/19(32.1%) 79.68 ± 12.94 21.93 ± 6.38 22.14 ± 4.60 35.61 ± 6.26 0.21 ± 0.07***

25.68 ± 6.55 20/20 NA NA NA NA NA NA NA NA 0.11 ± 0.03

11.467 1.450 −3.694 −0.511 −1.792 5.349 1.993 2.211 −1.237 3.046 34.624

3.32×10−5 0.484 2.21×10−4 0.609 0.078 0.021 0.051 0.031 0.221 0.003 7.04×10−12

a

One-Way ANOVA. Pearson X2 test. c Analysis of covariance with age as covariate. *** Indicates the comparisons between drug-naïve/drug-free patients and healthy controls, and p < 0.001. b

free schizophrenic patients along with healthy controls are presented in Table 1. Among the three groups, gender distribution was not different (p > 0.05), but there was a significant difference in age (p < 0.001). Both patient groups were older than the controls (all p < 0.001). However, the two patient groups did not significantly differ in age. Drug-naïve and drug-free patients showed no differences in education, BMI, PANSS total score and negative symptom subscore (all p > 0.05). Drug-free patients had longer duration of illness (p < 0.001) and higher smoking rate (p < 0.05) when compared to drug-naïve patients, who had significantly higher positive symptoms (p < 0.05) and general psychopathology (p < 0.01) subscores than drug-free patients.

Table 2 Comparison of MCCB performances between drug-naïve and drug-free schizophrenic patients.

3.1. Comparisons of serum S100B among drug-naïve patients, drugfree patients and healthy controls There was significant difference in S100B levels among the three groups (p < 0.001). This difference remained significant after controlling for age (p < 0.001). Serum S100B concentrations in both patient groups were higher than in the healthy controls (p < 0.001). However, there was no significant difference in serum S100B concentrations between drug-naïve and drug-free patients. Correlation analysis showed that the age, gender, education, duration of illness, BMI and smoking status were not associated with S100B in both patient groups (all p > 0.05). Separate correlation analysis showed that serum S100B level was positively correlated with the PANSS positive symptom subscore (r=0.388, p=0.045) in the drugnaïve group, but were not associated with any other psychopathological parameters within either the drug-naïve or drug-free group of patients (all p > 0.05).

Cognition

Drug-naïve Group (n=34)

Drug-free Group (n=28)

t Value

p-Value

MCCB composite score Processing speed Attention/ vigilance Working memory Verbal learning Visual learning Reasoning/ problem solving Social cognition

40.26 ± 11.71

39.98 ± 12.35

0.091

0.928

43.08 ± 9.54

43.10 ± 10.82

−0.007

0.994

40.04 ± 9.40

35.47 ± 10.12

1.841

0.071

43.36 ± 9.43

43.52 ± 12.81

−0.056

0.956

40.81 ± 14.30 43.75 ± 9.97 46.04 ± 14.12

39.65 ± 11.88 45.05 ± 10.72 47.19 ± 12.88

0.341 −0.496 −0.334

0.734 0.622 0.740

44.57 ± 14.06

42.97 ± 13.23

0.459

0.648

did not show associations with the MCCB in this group (all p > 0.05).. However, for the drug-free patients, correlation analysis showed that S100B was negatively associated with the MCCB composite score (r=−0.657, p=2.66×10−4) (Fig. 1B), working memory (r=−0.494, p=0.01), reasoning/problem solving (r=−0.639, p=3.35×10−4), visual learning (r=−0.596, p=0.001) and attention/vigilance (r=−0.493, p=0.009). Furthermore, S100B had a trend toward significant negative correlation with the processing speed (r=−0.379, p=0.051) and verbal learning (r=−0.379, p=0.052). In the drug-free group, there were markedly inverse associations between the PANSS negative subscore and the MCCB composite score and six subscores (all p < 0.05), except for the reasoning/problem solving subscore (p > 0.05). On the other hand, no significant correlations were observed between the MCCB performance and the other demographic and psychopathological parameters (all p > 0.05), except that gender was associated with the speed of processing (r=−0.397, p=0.037). Therefore, considering the MCCB composite score and seven subscores as the dependent variables, and the negative subscore and S100B serum levels as the independent variables (gender was also considered as an independent variable when the speed of processing was regarded as a dependent variable), a stepwise multivariate linear regression analysis demonstrated that S100B was an independent contributor to the MCCB composite score (β=−0.537, t=−3.603, p=0.001), speed of processing (β=−0.524, t=−3.473, p=0.002), attention/vigilance (β=−0.493, t=−2.837, p=0.009), visual learning (β=−0.596, t=−3.709, p=0.001) and reasoning/problem solving (β=−0.639, t=−4.152, p=3.35×10−4). PANSS negative subscore was

3.2. MCCB assessment in drug-naïve and drug-free patients Statistics of MCCB composite T-score and seven domain T-scores of drug-naïve and drug-free schizophrenic patients are showed in Table 2. Overall, the means of composite MCCB scores in both patient groups were 1 SD below the Chinese norms (a score of 50, with a standard deviation of 10). However, there was no significant difference in any cognitive dimension between the two groups of patients with schizophrenia (all p > 0.05). 3.3. Correlation between serum S100B levels and cognitive performance In the drug-naïve group, no significant associations between S100B and MCCB composite score (Fig. 1A) or any cognitive domain subscore were observed after controlling for positive symptoms using partial correlation analysis (all p > 0.05). Furthermore, the positive symptoms 8

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between drug-naïve and drug-free patients with acute episode, while it has been shown that drug-naïve early-stage schizophrenic patients have significantly higher S100B concentrations than medicated chronic schizophrenic patients (Zhang et al., 2010). Differences in disparate stages of disease progression (acute relapse vs. chronic phase) and exposure to antipsychotics (drug discontinuance vs. medicated) may contribute to this inconsistent result. As expected, both drug-naïve and drug-free schizophrenia patients in acute phase scored lower than the Chinese norms in MCCB performance after excluding uncooperative participants. Given cognitive dysfunction associated with duration of illness (Kaneda et al., 2013), we had assumed that drug-free patients would manifest more severe cognitive impairment when compared to drug-naïve ones, but no significant difference in any domain of MCCB between the two groups was observed. Plausible explanations could be: (1) Higher rate of smoking in the drug-free group may be potentially beneficial to neurocognitive performance, because nicotine has been described to improve cognitive functions in schizophrenic patients (Hambsch et al., 2014; Harris et al., 2004; Myers et al., 2004; Smith et al., 2006); (2) Between the two patient groups, there was no notable difference in PANSS negative symptoms, which could definitely influence cognitive tests. On the other hand, since drug-free patients had received systemic therapies with antipsychotics earlier, the effect of antipsychotics on cognition is also possible (Husa et al., 2014; Keefe, 2014). Correlation analysis together with multivariate linear regression analysis indicated that serum S100B concentration was an independent contributor to the speed of processing, attention/vigilance, visual learning, reasoning/problem solving and global cognitive function as represented by the MCCB composite score in drug-free patients, which is similar with observations in several previous studies. For example, Anya et al. reported that chronic schizophrenic patients with high S100B levels were impaired in verbal memory performance as compared to chronic and first-episode patients with low S100B levels (Pedersen et al., 2008), and elevated S100B levels were associated with visuospatial disability of schizophrenia as described by Zhai et al. (Zhai et al., 2011). Furthermore, schizophrenic patients receiving recombinant human erythropoietin (rhEPO) treatment showed a significant improvement in cognitive performance (RBANS subtests and WCST64) and a decline in serum S100B levels (Ehrenreich et al., 2007). The close relationship between S100B and cognitive function is further supported by a preclinical study showing that S100B deficient mice had enhanced long-term potentiation in the hippocampal CA1 region and better spatial memory in the Morris water maze test, whereas perfusion of hippocampal slices with recombinant S100B protein reduced longterm potentiation in mutant slices (Nishiyama et al., 2002). These results support the hypothesis that high levels of S100B derived from neuroglia may play a role in the pathogenesis of schizophrenia-related cognitive impairment, however, the exact mechanisms responsible for this have not yet been elucidated. However, no significant correlations between S100B and cognition performance were detected in the drug-naïve group, which was in contrast with the findings in drug-free recurrent episode patients with schizophrenia. The exact reason for this discrepancy is still unknown, but illness duration might play a key role. Elevated S100B levels in drug-naïve patients may not decrease to the normal level even in their stable mental state after treatment (Qi et al., 2009; Zhang et al., 2010). Hence, the increased S100B in the drug-free group may represent a more chronic phenomenon that repeats over time in some patients, which could lead to gradual but irreversible alterations of neurocircuitry and neurochemistry in specific brain regions, contributing, for example, to the cognitive deficits. A recent study by Milleit et al. (Milleit et al., 2016) investigated the associations between S100B and structural white matter abnormalities in unmedicated schizophrenia patients (first and recurrent episode) and healthy controls. Using voxel based morphometry (VBM), they found that the first episode patients had a negative correlation of S100B concentration to the white matter

Fig. 1. Correlation analysis showed a significant negative relationship between S100B and MCCB composite T-score (r=−0.657, p=2.66×10−4) in drug-free patients with schizophrenia, but no significant correlation between S100B and MCCB composite score (r=0.036, p=0.843) in drug-naïve schizophrenic patients.

found to be an independent contributor to the MCCB composite score (β=−0.359, t=−2.413, p=0.024), working memory (β=−0.542, t=−3.163, p=0.004), verbal learning (β=−0.386, t=−2.092, p=0.047) and social cognition (β=−0.607, t=−3.815, p=0.001). Besides, female gender was an independent predictor of poor performance of the processing speed (β=−0.602, t=−3.992, p=0.001). 4. Discussion Our present results show that (1) serum S100B was significantly elevated in drug-naive/free acute-stage schizophrenic patients when compared to healthy controls, while there was no marked difference between the two groups of patients; (2) drug-naive/free acute-stage schizophrenic patients scored lower in MCCB performance than the Chinese norms, but the two patient groups did not significantly differ on all of the seven MCCB domains; (3) in the drug-naïve group, S100B was not associated with any cognitive process; (4) in the drug-free group, S100B was an independent contributor to the global cognitive dysfunctions, particularly for the speed of processing, attention/ vigilance, visual learning and reasoning/problem solving. Our finding that serum S100B levels were significantly higher in patients with acute phase of schizophrenia than that in healthy controls was well consistent with earlier studies (Rothermundt et al., 2001; Steiner et al., 2012; Zhang et al., 2010). Also, in the chronic schizophrenics under treatment with antipsychotics, S100B levels were reported to be higher (Zhang et al., 2010). Furthermore, the data here showed that there was no observable difference in S100B levels 9

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Contributors

values in the right superior temporal gyrus (STG), the right middle temporal lobe, and a large region of the right frontotemporal white matter that corresponds to the right superior longitudinal fasciculus. Nevertheless, recurrent episode patients had a positive association in all of the above regions. Therefore, this suggests S100B is involved in an ongoing and dynamic process that is related to structural changes of brain at different stages of schizophrenia (Milleit et al., 2016), which is a possible mechanism explaining the present results that positive correlation between S100B concentration and positive symptoms was found only in the drug-naïve group, whereas significant negative correlation between S100B and neurocognition performance was seen only in the drug-free group. On the other hand, since the increase in S100B in drug-naïve patients was related to positive symptoms which may unpredictably affect cognitive performance, this could lead to the absence of association between S100B and cognition in this group. Lastly, as all drug-free patients were previously under systematic pharmacological treatments, we cannot completely rule out the antipsychotics-mediated effects on the association between S100B and cognitive performance. The present study has certain limitations that need to be considered. First, although this data provides preliminary evidence for the role of serum S100B as a biomarker for neurocognitive dysfunction in drug-free acute relapse patients with schizophrenia, a conclusive assessment is limited by the small sample size. Second, for healthy controls, we did not implement MCCB assessment, hence the relationship between S100B profiles and cognitive functioning in healthy young adults could not be detected, which should be improved in future studies. Third, it is still uncertain whether peripheral S100B reflects similar changes in the CNS, since other sources of serum S100B could include adipocytes, melanocytes, chondrocytes and myocardium (Donato, 2001; Schafer and Heizmann, 1996; Zimmer et al., 1995). For example, several lines of evidence suggest that raised BMI, which is likely associated with increased number of adipocytes, may contribute to elevated S100B serum concentrations (O’Connell et al., 2013; Steiner et al., 2010). In contrast, another study showed that extracranial sources of S100B do not affect serum levels in neuropsychiatric disorders in intact subjects (without traumatic brain or bodily injury from accident or surgery) (Pham et al., 2010). Anyway, metabolic parameters are still important confounding factors since schizophrenia are associated with a higher risk of obesity and metabolic syndrome, and future studies should overcome this limitation. Finally, the crosssectional study design that was used unfortunately does not permit strong inferences of causality between S100B levels and cognitive performance. To our knowledge, this is the first study to report a significant association between serum S100B levels and a broad-spectrum measure of cognition in drug-free acutely relapsed individuals with schizophrenia. Interestingly, this relationship has not been observed in the drug-naïve group, which might be on account of the different neurobiological mechanisms of cognitive deficits at early stage of schizophrenia and impact of previous antipsychotic treatment. In conclusion, these preliminary, but encouraging data support the concept that glial marker protein S100B may be involved in the pathophysiologic development of neurocognitive deficits in schizophrenia. In addition, whether this association could be replicated in other psychotic disorders is worthy of further exploration and research.

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Role of funding source This study was funded by the Beijing Natural Science Foundation (7151005) and the Long-Yue Project Foundation of Beijing Huilongguan Hospital (No. 2014LYYQ-01). Competing interests The authors reported no conflicts of interest. 10

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