CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment

European Neuropsychopharmacology (]]]]) ], ]]]–]]]

www.elsevier.com/locate/euroneuro

CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment Sindre Rolstada,n, Joel Jakobssona, Carl Sellgrenb, Anniella Isgrena, Carl Johan Ekmanc, Maria Bjerkea, Kaj Blennowa, Henrik Zetterberga,d, Erik Pålssona, Mikael Landéna,b a

Institute of Neuroscience and Physiology, The Sahlgrenska Academy at The Gothenburg University, Gothenburg, Sweden b Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden c Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden d UCL Institute of Neurology, Queen Square, University College London, London, UK Received 21 January 2015; received in revised form 2 April 2015; accepted 24 April 2015

KEYWORDS

Abstract

Cerebrospinal fluid; Bipolar disorder; Neuropsychology; Inflammation

Persistent cognitive impairment in the euthymic state of bipolar disorder is increasingly recognized. Mounting evidence also suggests an association between neuroinflammation and cognitive dysfunction. The purpose of this study was to test if cerebrospinal fluid (CSF) markers of neuroinflammation could account for cognitive impairment in bipolar disorder. Hierarchical linear regression models were applied to account for performance in five cognitive domains using CSF neuroinflammatory biomarkers as predictors in patients with bipolar disorder type I and II (N =78). The associations between these biomarkers and cognition were further tested in healthy age- and sex-matched controls (N =86). In patients with bipolar disorder, the CSF biomarkers accounted for a significant proportion of the variance in executive functions (42.8%, p= o.0005) independently of age, medication, disease status, and bipolar subtype. The microglial marker YKL-40 had a high impact (beta= .99), and was the only biomarker that contributed individually. CSF biomarkers were not associated with cognitive performance in healthy controls. The CSF neuroinflammation biomarker YKL-40 is associated with executive performance in euthymic bipolar disorder, but not in healthy controls. & 2015 Elsevier B.V. and ECNP. All rights reserved.

n Corresponding author at: Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at The University of Gothenburg, Blå stråket 15, SE-413 45 Gothenburg, Sweden. Tel.: +46 703 162 916. E-mail address: [email protected] (S. Rolstad).

http://dx.doi.org/10.1016/j.euroneuro.2015.04.023 0924-977X/& 2015 Elsevier B.V. and ECNP. All rights reserved.

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

2

S. Rolstad et al.

1.

Introduction

Bipolar disorder (BD) is a lifelong psychiatric disease characterized by episodes of depression and mania or hypomania interspaced by periods of euthymia (Phillips and Kupfer, 2013). The worldwide prevalence of BD, depending on the definition, is estimated to about 1–3% (Merikangas et al., 2011). Costs and resource use are high given the chronicity of the disease (Bryant-Comstock et al., 2002); a recent Swedish resource use study calculated the average cost to 28 011 euro/year per patient (Ekman et al., 2013). Cognitive dysfunction is common in BD and a key predictor of functional outcomes (Bearden et al., 2011). Memory functions, attention/speed, and executive functions are compromised in euthymic BD (Bourne et al., 2013). Only slight differences in cognitive functioning have been found between the two main types of BD, i.e., type I (characterized by episodes of mania and depression) and II (characterized by episodes of hypomania and depression), with the exception of memory and semantic fluency (Bora et al., 2009). There is some evidence of cognitive deterioration during the course of illness (Robinson and Ferrier, 2006), but most cognitive functions appear to remain stable over time (Bourne et al., 2013). Meta-analyses of structural magnetic resonance imaging (MRI) studies report an increased volume of the lateral ventricles and globus pallidus, a decreased corpus callosum volume, and higher amount of deep white matter hyperintensities (Arnone et al., 2009). However, the neuroanatomical basis of cognitive deficit in BD is unclear and MRI needs to be complemented with other methods to elucidate the pathophysiology of cognitive dysfunction in BD (Savitz et al., 2005). Recent evidence indicates that peripheral inflammation and mild chronic neuroinflammation occur in BD (Berk et al., 2011), which might be a lead to explain the observed morphological and cognitive changes. A post-mortem study found elevated microglia marker levels in frontal cortices in patients with BD (Rao et al., 2010), suggesting that microglia, the tissue macrophages of the brain parenchyma, play a role in the pathophysiology of BD (Rao et al., 2010). Most other evidence of inflammation in BD, however, originates from studies assessing inflammatory markers in blood samples (Goldstein et al., 2009). Due to the relative impermeability of the blood–CSF barrier, concentrations of cytokines and other proteins in serum or plasma differ from the concentrations in cerebrospinal fluid (CSF) (Maier et al., 2005). Hence, peripheral measurements of cytokines do not necessarily reflect the immunological activity in the brain. In contrast, analysis of biomarkers for inflammation in Cerebrospinal fluid (CSF) may more accurately reflect brain immunological activity. A number of CSF inflammatory markers have been associated with microglial activation in neurological diseases. For example, increased concentrations of the secreted glycoprotein YKL-40, also known as CHI3LI (chitinase3-like protein), soluble CD14, and monocyte chemoattractant protein 1 (MCP-1) have been found in multiple sclerosis, Alzheimer’s disease (AD), and Parkinson’s disease (Lautner et al., 2011). Further, activated microglia release proteolytic enzymes – matrix metalloproteinase (MMPs) – that degrade proximal neurons and extracellular matrix (Lively and Schlichter, 2013). MMP activities are regulated by tissue inhibitors of metalloproteinase (TIMPs) (Brew and Nagase,

2010). The concentrations of MMP and tissue inhibitors of metalloproteinases (TIMP) in CSF have been proposed to mirror the extent of inflammatory-mediated tissue remodeling in the CNS (Lorenzl et al., 2003). We recently assessed CSF markers of neuroinflammation (i.e., MCP-1, YKL-40, sCD14, TIMP-1, and TIMP-2) in patients with BD and healthy controls (Jakobsson et al., In press). We found increased CSF levels of MCP-1 and YKL-40 in persons with BD compared with healthy controls. These differences persisted after controlling for age, sex, smoking, body mass index, blood–brain barrier function, and acute-phase serum proteins. The patient-control differences in MCP-1 and YKL40 were independent of the association with serum levels. Inflammation has also been found to contribute to vascular cognitive impairment in patients with cardiovascular risk, where circulating levels of high-sensitivity C-reactive protein was associated with executive dysfunction independently of white matter lesions (Hoshi et al., 2010). Interestingly, mounting evidence suggests an association between neuroinflammation and cognitive dysfunction (Bjerke et al., 2011). Increased concentrations of neuroinflammation markers are found in neurodegenerative conditions such as AD (Eikelenboom et al., 2006). It is, however, undecided whether neuroinflammation play a role in cognitive dysfunction in BD. The aim here was therefore to study CSF biomarkers of neuroinflammation in relation to cognitive functioning. Given evidence of increased microglia marker levels in frontal cortices in patients with BD (Rao et al., 2010), we hypothesized that increased levels of CSF biomarkers of neuroinflammation would be negatively associated with executive function. Regression models with five aggregated cognitive domains were applied on a well-characterized sample of patients with BD using CSF biomarkers as predictors and covariates as appropriate. Models were repeated in healthy age- and sexmatched population controls to determine if observed associations were disease dependent.

2.

Experimental procedures

The St. Göran Bipolar Project is a naturalistic longitudinal study of persons with bipolar disorder and has been described in detail elsewhere (Ryden et al., 2009). In brief, patients were enrolled at the bipolar outpatient unit at the Northern Stockholm Psychiatric Clinic (Sweden). The inclusion criteria for the current study were Z 18 years of age, fulfilling the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) criteria for BD type I or II, and undergone a neuropsychological examination as well as lumbar puncture. Exclusion criteria were inability to complete the standard clinical assessment or incapability of providing informed consent. In total, 78 patients were included in the current study.

2.1.

Clinical assessment and diagnostic procedures

Patients were in a stable euthymic mood when procedures were carried out. In addition to a clinical assessment, euthymia was defined as a score below 14 on both the Montgomery–Åsberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS). A standardized interview protocol, the Affective Disorders Evaluation (ADE) (Sachs et al., 2003), was administered by a psychiatrist or a psychiatrist in training. The ADE directs the clinician through a systematic assessment of the patient’s past

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment history, current mental state, and diagnosis according to DSM-IV criteria as per the Structured Clinical Interview (SCID) for DSM-IV (Association, 2000). Psychiatric comorbidity was assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) (Sheehan et al., 1998). The lifetime severity of BD was assessed using the Clinical Global Impression (CGI). The self-report questionnaires the Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993) and the Drug Use Disorders Identification Test (DUDIT) (Berman et al., 2005) were used to screen for substance and alcohol abuse, as well as serum levels of carbohydrate-deficient transferrin. The diagnoses were based on the following sources of information: patient records, ADE, M.I.N.I, and interviews with next of kin where feasible. To reduce risk of inter-rater bias, a bestestimate diagnostic decision based on all information available at admission was made by a panel of experienced board certified psychiatrists specialized in BD.

2.2.

Control subjects

Age- and sex-matched, population-based controls were randomly selected by Statistics Sweden. As the expected response rate was 1:7, seven invitations were mailed out for each patient enrolled in the study. One out of seven returned the invitation. Responders were subjected to a preliminary telephone screening to rule out substance abuse, severe mental health-, and neurological problems. Seventy-five individuals were excluded at screening due to substance abuse or no longer willing to participate. Controls were included in the study if none of the following were observed during the assessment described below: severe mental health problems, neurological disease (excluding mild migraine), or substance abuse problems. Other exclusion criteria were untreated endocrine disorders, pregnancy, and BD in first-degree relatives or a family history of schizophrenia. The following did not warrant exclusion: minor depressive episodes, eating disorders, isolated episodes of panic disorder, alternatively obsessive–compulsive disorder that had remitted spontaneously or with brief psychotherapy. Patients and controls underwent identical examinations with the exception of the BD specific assessments. Only controls that had completed a neuropsychological examination and lumbar puncture were included in the current report (N=86).

2.3.

Ethics

The study was approved by the Regional Ethics Committee in Stockholm and conducted in accordance with the latest Helsinki

Table 1

3

Protocol. After complete description of the study, all enrolled patients and controls consented orally and in writing to participate in the study.

2.4.

CSF sampling and biomarker analyses

For ethical reasons, patients remained on their prescribed medication at the time of the sampling. The risk of diurnal fluctuations was reduced by consistently performing lumbar puncture at 0900–1000 AM after a night of fasting. A spinal needle was inserted into the L3/ L4 alternatively L4/L5 interstitium and a total volume of 12 ml of CSF was drawn, softly inverted to avoid gradient effects. The fluid was separated into 1.0–1.6 ml aliquots that were stored at 80 1C pending analysis. All samples were thawed and refrozen once prior to analysis. Commercial electrochemiluminescence ELISAs (Meso Scale Discovery, Rockville, MD) were used to analyze MCP-1 and TIMP-1 (Human MCP-1 Ultra-Sensitive Kit and Human TIMP-1 kit). Commercial colorimetric ELISAs (R&D systems, Inc., Minneapolis, MN) were used to determine sCD14, YKL-40, and TIMP-2 (Human sCD14 quantikine ELISA kit, Human chitinase-3 quantikine ELISA kit, and Human TIMP-2 quantikine ELISA kit). All biochemical analyzes were performed at the Clinical Neurochemistry Laboratory in Mölndal, Sweden by board-certified staff blinded to patient identity and diagnosis. All intra-assay coefficients of variation were o10%.

2.5.

Neuropsychological examination

The neuropsychological examination comprised five cognitive domains deemed important to characterize cognition in BD. Several cognitive functions were assessed within each domain to obtain a thorough estimate of the participants’ cognitive status. Two sessions were generally required for patients and one for controls. The individual test scores were z-transformed (M =0; SD=1) on the basis of controls´ performance and then combined into cognitive domains guided by their common measurement properties according to reference literature (Table 2) (Lezak et al., 2012).

2.6.

Statistical analysis

The primary outcome of the current study was the ability of the CSF biomarkers of neuroinflammation (YKL-40, sCD14, TIMP1, and MCP1) to account for cognitive performance. The models were repeated in age- and sex-matched controls. Preliminary analyses were conducted to ensure no violation of linearity, multicollinearity, normality, and homoscedasticity. Variables

Characteristics for patients with bipolar disorder and controls.

Females Age University degree Bipolar type I CGI YMRS MADRS Mood stabilizers Lithium Benzodiazepines Antidepressants Antipsychotics

Patients (N = 78) M/N (SD/%)

Controls (N = 86) M/N (SD/%)

p

47 38.2 31 48 4.4 1.3 4.4 62 48 16 28 12

40 (47%) 37.8 (13.3) 35 (41.4%)

.002 – –

(60.3%) (13.4) (44.3%) (61.5) (.8) (1.9) (6.1) (79.5) (61.5) (20.5) (38.9) (15.4)

Categorical variables and values are italicized. CGI =Clinical Global Impression, YMRS=Young Mania Rating Scale, MADRS=Montgomery–Åsberg Depression Rating Scale. Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

4

S. Rolstad et al.

were transformed as appropriate to normalize their distributions. Hierarchical linear regression was performed to assess the degree to which CSF biomarkers can account for the variance in the cognitive domain scores controlling for covariates. CSF biomarkers were entered as independent variables in all models. At step 1, the following covariates were entered into the model for patients: age, bipolar subtype, CGI total score, MADRS, YMRS, global assessment of functioning (GAF), use of mood stabilizers (lithium and anticonvulsants), antidepressants, antipsychotics, and benzodiazepines. At step 2, CSF biomarkers were entered into the model. We report the R2 change statistics that provides information regarding the unique variance accounted for by the independent variables. We also present the R2 and the adjusted regression

coefficient (Adj. R2) of the model, and the standardized regression coefficients (beta). Analysis of variance was applied for comparisons of continuous data with normal distributions and Mann-Whitney U test for data with skewed distributions. Chi-square was performed for comparisons of dichotomous data. In order to reduce alpha inflation, pvalues r.01 were considered significant. Mediation effects were assessed using bootstrapping for continuous mediators and logistic mediation analysis for dichotomous mediators (Mackinnon and Dwyer, 1993). All analyses were performed using SPSS version 22 (Armonk, NY: IBM Corp.)

Table 2 Cognitive domains and functions assessed in the St Göran bipolar project.

Characteristics of the patient and control groups are shown in Table 1. The proportion of males was higher in the control group (p= .002). Patient-control comparisons of cognitive tests (Palsson et al., 2013) and CSF microglia biomarkers concentrations have been presented previously (Jakobsson et al., In press). In brief, patients performed worse in all cognitive domains (p .01 in visuospatial functions to o.0005) (Table 3). All CSF inflammatory biomarkers concentrations were significantly higher in patients as compared with healthy controls (p =.01–o.0005) (Table 3).

Cognitive domain

Specific functions and neuropsychological tests (battery)

Speed and attention

Cognitive speed: Trail making Test number sequencing (D-KEFS); Digit Symbol (WAIS-III) Verbal episodic memory: Claeson-Dahl; Non-verbal episodic memory: Rey Complex Figure recall; Working memory: Digit Symbol (WAIS-III), Letter-Number Sequencing (WAIS-III) Spatial organization: Rey Complex Figure copy; Construction: Block Design (WAIS-III) Verbal abstraction: Similarities (WAIS-III); Verbal fluency: Verbal Fluency Test (D-KEFS) Inhibition: Color-Word interference (D-KEFS), Design Fluency (D-KEFS), Trail Making Test number-letter sequencing (D-KEFS), Tower Test (D-KEFS); Distractibility: Continuous Performance Test

Learning and memory

Visuospatial functions Verbal functions Executive functions

WAIS-III=Wechsler’s Adult Intelligence Scale version III, DKEFS=Delis–Kaplan executive function system.

Table 3

3.

Results

3.1. CSF neuroinflammatory markers and cognitive functioning Covariates alone accounted for 9.9% (Adj. R2 = .04) of the variance in executive performance. However, only age (beta = .33, p = .003) and use of benzodiazepines (beta = .23, p = .004) contributed significantly. When CSF biomarkers were added to the model, the total variance explained increased to 52.7% (Adj. R2 = .48), F (8, 78) = 8.76, po.0005. CSF biomarkers alone accounted for 42.8% of the variance in executive performance after controlling for age, medication, MADRS score, GAF, and bipolar subtype: R2 change = .42, F change (4, 78) = 14.25, po.0005. Of the CSF biomarkers, YKL-40 was the only predictor that significantly contributed to explaining the variance in executive performance (beta = .99, po.0005). The beta value indicated a very large effect;

Cognitive performance and CSF biomarker concentrations for patients with bipolar disorder and healthy controls.

Memory functions Executive functions Visuospatial functions Speed/attention Verbal functions YKL-40 TIMP1 MCP-1 sCD14

Patients Mean/Md (SD)

Controls Mean/Md (SD)

p

.25 .67 .35 .71 .45 82610.76 34.40 528.7 52834.09

.01 .35 .02 .01 .02 65383.58 32.13 455.7 43057.86

o.0005 .001 .01 o.0005 o.0005 .02 .03 o.0005 .01

(.48) (.72) (1.05) (1.27) (.81) (47643.6) (7.0) (152.8) (24001.6)

(.41) (.57) (.74) (.90) (.61) (47351.9) (6.9) (119.0) (28138.7)

Median values are italicized. Cognitive performance is based on z-transformed values on the basis of controls performance. Cerebrospinal fluid in pg/ml. YKL40 =secreted glycoprotein YKL-40, TIMP1=tissue inhibitors of metalloproteinase 1, MCP1=monocyte chemoattractant protein 1, sCD14=soluble cluster of differentiation 14.

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment an increase of 1 SD YKL-40 (SD = 44,197) reduced performance in the executive domain by.99 SD. The CSF biomarkers also accounted for a significant proportion of the variance observed in the speed and attention domain. When only covariates were entered into the model, age accounted for 19% (Adj. r2 = .18) of the observed variance (p= o.0005). Inclusion of CSF biomarkers accounted for an additional 6% (p= .01) but no individual CSF biomarker contributed significantly to the model.

3.2.

Further analyses

The model of executive functions was repeated excluding cases with residuals 73SD without affecting the results (data not shown). No mediation effects were found. We also performed a bootstrap procedure to test the robustness of the model of executive functions using 1000 replications and a confidence interval (CI) of 95%. The bootstrap procedure produced highly similar results for the estimated coefficients. YKL-40 was significantly associated with executive functions (p= .006, CI = 0.000015, 0.000003). Also, the regression model was repeated excluding 3 patients with potential inflammatory comorbidity (unspecified tumor). Removal of these 3 cases had no impact on the model (data not shown).

3.3.

Controls

To investigate if the associations between CSF biomarkers and cognitive performance are disease specific or general physiological phenomena, the models were repeated including controls only. CSF biomarkers did not significantly explain the variance in any cognitive domain for the controls. Higher age decreased visuospatial performance (beta = .29, p= .002) and being male increased verbal performance (beta = .38, po.0005).

4.

Discussion

The current study is the first report on the association between CSF biomarkers of neuroinflammation and cognition in bipolar disorder (BD). In line with our hypothesis, higher levels of CSF neuroinflammatory markers were negatively associated with executive functions. More specifically, increased CSF concentration of the microglial biomarker YKL-40 had a very large effect on executive functions. Speed and attentional functions were also affected by CSF neuroinflammatory markers but no individual CSF biomarker significantly accounted for the variance observed. The findings could not be replicated in the control sample suggesting that neuroinflammatory CSF biomarkers play a disease specific role in accounting for executive impairment in BD. YKL-40 belongs to the glycoside hydrolase 18 chitinase family that binds chitins of various lengths but lacks chitinase activity (Renkema et al., 1998). It is primarily secreted by activated macrophages, neutrophils, chondrocytes, tumor cells, and vascular smooth muscle cells. It has also been hypothesized that activation of astrocyte cells in response to inflammation produces high levels of YKL-40 (Canto et al., 2015). YKL-40 is a biomarker for glial activation, and increased CSF levels have been found in inflammatory CNS

5

disorders, such as multiple sclerosis, with normalization after immunosuppressive treatment (Malmestrom et al., 2014), and in bacterial meningitis (Ostergaard et al., 2002). Increased CSF levels have also been found in neurodegenerative disorders such as AD (Craig-Schapiro et al., 2010) and in acute ischemic stroke (Hjalmarsson et al., 2014). YKL-40 has also been found to be associated with disease severity in disorders characterized by tissue remodeling and chronic inflammation (Johansen et al., 1999). Recently, we found increased CSF concentration of YKL40 in persons with BD compared to controls (Jakobsson et al., In press). There are few reports on the topic of CSF YKL-40 and cognition (Comabella et al., 2010; Modvig et al., 2015). Comabella et al. reported that YKL-40 concentrations were increased in patients with clinically isolated syndrome (CIS) as compared with symptomatic patients who did not convert, and healthy controls. CSF YKL-40 concentrations were also associated with baseline brain MRI findings mirroring degree of brain inflammation and lesion burden. In a study of CIS-patients presenting with optic neuritis, YKL-40 was associated with long-term impairment in capacity and rate of information processing (Modvig et al., 2015). The physiological role of YKL-40 is, however, not entirely clear (Comabella et al., 2010). In neurodegenerative diseases, levels of astrocyte produced YKL-40 have been found to rise in response to deposition of amyloid β (Craig-Schapiro et al., 2010) and a higher amyloid burden has been associated with cognitive impairment in healthy elderly and patients with prodromal dementia (Rolstad et al., 2011, 2013). The ratio of amyloid β has been found to be altered in BD (Jakobsson et al., 2013) but there is no evidence of an increased amyloid burden in the disorder. As proinflammatory cytokines – such as tumor necrosis factor-α and interleukin-1β – has been found to be associated with YKL-40 (Recklies et al., 2005), it is possible that YKL-40 increase proinflammatory activity. Mood episodes have been linked to high serum proinflammatory markers (Huang and Lin, 2007), but we did not find that mood indicators modified the relation between CSF YKL-40 and executive functions. It could thus be speculated that the CSF inflammatory activity in BD, alternatively the impact of the underlying processes, is not limited to mood episodes. A number of studies suggest that memory and executive functions are the domains showing greatest impairment in persons with BD compared with controls (Robinson et al., 2006). Whereas we found executive functions to be significantly affected by neuroinflammatory markers, memory functioning was not. As executive functions normally account for a large proportion of the variance in memory (Robinson and Ferrier, 2006), the lack of association between memory and YKL-40 is surprising. A possible reason is that the episodic memory test administered in the current study, Claeson-Dahl, is too undemanding and may require less strategy (executive involvement) as compared with more commonly administered tests such as the California Verbal Learning Test. Even though the results from the current study harmonizes with the findings from the post-mortem study that reported increased levels of neuroinflammatory markers in the frontal cortices in patients with BD (Rao et al., 2010), it remains unclear why neuroinflammatory markers affect frontocortical cognitive functions. It could be speculated that neuroinflammation, at least partially, account for the structural, metabolic, and signaling aberrations previously observed in the frontal

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

6

S. Rolstad et al.

cortices of patients with BD (Arnone et al., 2009) and that this results in an executive dysfunction. The current study encompassed a well-characterized sample of patients with BD and included age- and sexmatched population based controls to address whether associations were disease specific. CSF sampling was performed on average 7 months after completion of the neuropsychological examination. Even though this adjournment is suboptimal, it is unlikely to have compromised the data as both examinations were completed in a euthymic state of the disease. Also, cognitive performance has frequently been reported to be stable in BD (Mora et al., 2013; Mur et al., 2008). As we neither have found any differences concerning cognitive performance between BD subtypes previously, nor have observed any significant differences in CSF biomarkers concentrations between BD subtypes (Jakobsson et al., In press; Palsson et al., 2013), BD subtypes were treated as covariates rather than as separate groups. Cognitive performance was analyzed using aggregated cognitive domains instead of individual neuropsychological tests. This approach has the benefit of reducing the potential inflation of alpha resulting from a larger battery of tests and states the underlying measuring entities rather than test-specific associations (Bearden et al., 2011). However, some of the cognitive tests included in the test battery load on more than one cognitive domain, e.g. the Continuous Performance Tests is not only a test of executive inhibition but also sustained attention. The relatively limited sample size did not allow for performing a confirmatory factor analysis of the cognitive domains. As patients were only investigated in a euthymic state, it cannot be ruled out that the observed associations are sequelae following mania or depression. However, the lack of association between MADRS and YMRS scores and cognitive domains speaks against the notion of sequelae. Whereas CSF YKL-40 is a promising marker for specific cognitive functions in BD its clinical utility at this time is unclear. It remains to be elucidated whether it can be used to predict changes in cognitive functioning during the course of illness. In conclusion, the CSF neuroinflammation biomarker YKL40 is associated with executive performance in euthymic bipolar disorder, but not in healthy controls.

Funding source This research study was supported by grants from the Swedish Medical Research Council (K2014-62X-14647-12-51 and K2010-61P-21568-01-4), the Swedish foundation for Strategic Research, the Swedish Brain foundation, and the Swedish Federal Government under the LUA/ALF agreement (ALF 20130032, ALFGBG-142041). The founding sources had no role in 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.

Contributors All authors have contributed significantly in analyzing and/or interpreting the data, and drafting and/or revising the manuscript

for intellectual content. All authors are responsible for the content of the manuscript.

Conflict of interest The authors have no conflict of interest to declare.

Acknowledgement We are indebted to the staff at the St Görans Bipolar Affective Disorder unit, including coordinator Martina Wennberg, study nurses Agneta Carlswärd-Kjellin and Benita Gezelius, and data manager Haydeh Olofsson. Björn Hultman, Pascal Borgström, Timea Sparding, and Josefin Östlind are acknowledged for designing the cognitive test battery. We thank Yngve Hallström for performing lumbar punctures and Erik Joas, Kristoffer Bäckman and Mathias Kardell for statistical and database support. Our gratitude also extends to the participating patients and controls. Finally, BBMRI.se and KI Biobank at Karolinska Institutet are acknowledged for professional biobank service.

References Arnone, D., Cavanagh, J., Gerber, D., Lawrie, S.M., Ebmeier, K.P., McIntosh, A.M., 2009. Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis the journal of mental science. Br. J. Psychiatry 195 (3), 194–201. Association, A.P., 2000. Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TRs. fourth edition American Psychiatric Association. Bearden, C.E., Shih, V.H., Green, M.F., Gitlin, M., Sokolski, K.N., Levander, E., Marusak, S., Hammen, C., Sugar, C.A., Altshuler, L.L., 2011. The impact of neurocognitive impairment on occupational recovery of clinically stable patients with bipolar disorder: a prospective study. Bipolar Disord. 13 (4), 323–333. Berk, M., Kapczinski, F., Andreazza, A.C., Dean, O.M., Giorlando, F., Maes, M., Yucel, M., Gama, C.S., Dodd, S., Dean, B., Magalhaes, P.V., Amminger, P., McGorry, P., Malhi, G.S., 2011. Pathways underlying neuroprogression in bipolar disorder: focus on inflammation, oxidative stress and neurotrophic factors. Neurosci. Biobehav. Rev. 35 (3), 804–817. Berman, A.H., Bergman, H., Palmstierna, T., Schlyter, F., 2005. Evaluation of the Drug Use Disorders Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. Eur. Addict Res. 11 (1), 22–31. Bjerke, M., Zetterberg, H., Edman, A., Blennow, K., Wallin, A., Andreasson, U., 2011. Cerebrospinal fluid matrix metalloproteinases and tissue inhibitor of metalloproteinases in combination with subcortical and cortical biomarkers in vascular dementia and Alzheimer’s disease. J. Alzheimer’s Dis.: JAD 27 (3), 665–676. Bora, E., Yucel, M., Pantelis, C., 2009. Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J. Affect. Disord. 113 (1-2), 1–20. Bourne, C., Aydemir, O., Balanza-Martinez, V., Bora, E., Brissos, S., Cavanagh, J.T., Clark, L., Cubukcuoglu, Z., Dias, V.V., Dittmann, S., Ferrier, I.N., Fleck, D.E., Frangou, S., Gallagher, P., Jones, L., Kieseppa, T., Martinez-Aran, A., Melle, I., Moore, P.B., Mur, M., Pfennig, A., Raust, A., Senturk, V., Simonsen, C., Smith, D. J., Bio, D.S., Soeiro-de-Souza, M.G., Stoddart, S.D., Sundet, K., Szoke, A., Thompson, J.M., Torrent, C., Zalla, T., Craddock, N., Andreassen, O.A., Leboyer, M., Vieta, E., Bauer, M., Worhunsky, P.D., Tzagarakis, C., Rogers, R.D., Geddes, J.R., Goodwin, G.M., 2013. Neuropsychological testing of cognitive impairment in

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment euthymic bipolar disorder: an individual patient data metaanalysis. Acta Psychiatr. Scand. 128 (3), 149–162. Brew, K., Nagase, H., 2010. The tissue inhibitors of metalloproteinases (TIMPs): an ancient family with structural and functional diversity. Biochim. Biophys. Acta 1803 (1), 55–71. Bryant-Comstock, L., Stender, M., Devercelli, G., 2002. Health care utilization and costs among privately insured patients with bipolar I disorder. Bipolar Disord. 4 (6), 398–405. Canto, E., Tintore, M., Villar, L.M., Costa, C., Nurtdinov, R., Alvarez-Cermeno, J.C., Arrambide, G., Reverter, F., Deisenhammer, F., Hegen, H., Khademi, M., Olsson, T., Tumani, H., Rodriguez-Martin, E., Piehl, F., Bartos, A., Zimova, D., Kotoucova, J., Kuhle, J., Kappos, L., Garcia-Merino, J.A., Sanchez, A.J., Saiz, A., Blanco, Y., Hintzen, R., Jafari, N., Brassat, D., Lauda, F., Roesler, R., Rejdak, K., Papuc, E., de Andres, C., Rauch, S., Khalil, M., Enzinger, C., Galimberti, D., Scarpini, E., Teunissen, C., Sanchez, A., Rovira, A., Montalban, X., Comabella, M., 2015. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes a journal of neurology. Brain 138 (Pt 4), 918–931. Comabella, M., Fernandez, M., Martin, R., Rivera-Vallve, S., Borras, E., Chiva, C., Julia, E., Rovira, A., Canto, E., Alvarez-Cermeno, J.C., Villar, L.M., Tintore, M., Montalban, X., 2010. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis a journal of neurology. Brain 133 (Pt 4), 1082–1093. Craig-Schapiro, R., Perrin, R.J., Roe, C.M., Xiong, C., Carter, D., Cairns, N.J., Mintun, M.A., Peskind, E.R., Li, G., Galasko, D.R., Clark, C.M., Quinn, J.F., D’Angelo, G., Malone, J.P., Townsend, R.R., Morris, J.C., Fagan, A.M., Holtzman, D.M., 2010. YKL-40: a novel prognostic fluid biomarker for preclinical Alzheimer’s disease. Biol. Psychiatry 68 (10), 903–912. Eikelenboom, P., Veerhuis, R., Scheper, W., Rozemuller, A.J., van Gool, W.A., Hoozemans, J.J., 2006. The significance of neuroinflammation in understanding Alzheimer’s disease. J. Neural Transm. 113 (11), 1685–1695. Ekman, M., Granstrom, O., Omerov, S., Jacob, J., Landen, M., 2013. The societal cost of bipolar disorder in Sweden. Soc. Psychiatry Psychiatr. Epidemiol. Goldstein, B.I., Kemp, D.E., Soczynska, J.K., McIntyre, R.S., 2009. Inflammation and the phenomenology, pathophysiology, comorbidity, and treatment of bipolar disorder: a systematic review of the literature. J. Clin. Psychiatry 70 (8), 1078–1090. Hjalmarsson, C., Bjerke, M., Andersson, B., Blennow, K., Zetterberg, H., Aberg, N.D., Olsson, B., Eckerstrom, C., Bokemark, L., Wallin, A., 2014. Neuronal and glia-related biomarkers in cerebrospinal fluid of patients with acute ischemic stroke. J. Cent. Nerv. Syst. Dis. 6, 51–58. Hoshi, T., Yamagami, H., Furukado, S., Miwa, K., Tanaka, M., Sakaguchi, M., Sakoda, S., Kitagawa, K., 2010. Serum inflammatory proteins and frontal lobe dysfunction in patients with cardiovascular risk factors the official journal of the European Federation of Neurological Societies. Eur. J. Neurol. 17 (9), 1134–1140. Huang, T.L., Lin, F.C., 2007. High-sensitivity C-reactive protein levels in patients with major depressive disorder and bipolar mania. Prog. Neuropsychopharmacol. Biol. Psychiatry 31 (2), 370–372. Jakobsson, J., Bjerke, M., Sahebi, S., Isgren, A., Ekman, C., Sellgren, C., Olsson, B., Zetterberg, H., Blennow, K., Pålsson, E., Landén, M., 2015. J. Psychiatry Neurosci. 40 (2), 14018. Jakobsson, J., Zetterberg, H., Blennow, K., Johan Ekman, C., Johansson, A., Landén, M., 2013. Altered concentrations of amyloid precursor protein metabolites in the cerebrospinal fluid of patients with bipolar disorder official publication of the American College of Neuropsychopharmacology. Neuropsychopharmacology 38 (4), 664–672.

7

Johansen, J.S., Stoltenberg, M., Hansen, M., Florescu, A., HorslevPetersen, K., Lorenzen, I., Price, P.A., 1999. Serum YKL-40 concentrations in patients with rheumatoid arthritis: relation to disease activity. Rheumatology 38 (7), 618–626. Lautner, R., Mattsson, N., Scholl, M., Augutis, K., Blennow, K., Olsson, B., Zetterberg, H., 2011. Biomarkers for microglial activation in Alzheimer’s disease. Int. J. Alzheimer’s Dis. 2011, 939426. Lezak, M., Howieson, D., Bigler, E., Tranel, D., 2012. Neuropsychological Assessment. Oxford University Press, USA. Lively, S., Schlichter, L.C., 2013. The microglial activation state regulates migration and roles of matrix-dissolving enzymes for invasion. J. Neuroinflamm. 10, 75. Lorenzl, S., Albers, D.S., LeWitt, P.A., Chirichigno, J.W., Hilgenberg, S.L., Cudkowicz, M.E., Beal, M.F., 2003. Tissue inhibitors of matrix metalloproteinases are elevated in cerebrospinal fluid of neurodegenerative diseases. J. Neurol. Sci. 207 (1-2), 71–76. Mackinnon, D.P., Dwyer, J.H., 1993. Estimating mediated effects in prevention studies. Eval. Rev. 17 (2), 144–158. Maier, B., Laurer, H.L., Rose, S., Buurman, W.A., Marzi, I., 2005. Physiological levels of pro- and anti-inflammatory mediators in cerebrospinal fluid and plasma: a normative study. J. Neurotrauma 22 (7), 822–835. Malmestrom, C., Axelsson, M., Lycke, J., Zetterberg, H., Blennow, K., Olsson, B., 2014. CSF levels of YKL-40 are increased in MS and replaces with immunosuppressive treatment. J. Neuroimmunol. 269 (1-2), 87–89. Merikangas, K.R., Jin, R., He, J.P., Kessler, R.C., Lee, S., Sampson, N.A., Viana, M.C., Andrade, L.H., Hu, C., Karam, E.G., Ladea, M., Medina-Mora, M.E., Ono, Y., Posada-Villa, J., Sagar, R., Wells, J.E., Zarkov, Z., 2011. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch. Gen. Psychiatry 68 (3), 241–251. Modvig, S., Degn, M., Roed, H., Sorensen, T., Larsson, H., Langkilde, A., Frederiksen, J., Sellebjerg, F., 2015. Cerebrospinal fluid levels of chitinase 3-like 1 and neurofilament light chain predict multiple sclerosis development and disability after optic neuritis. Multiple Scler. Mora, E., Portella, M.J., Forcada, I., Vieta, E., Mur, M., 2013. Persistence of cognitive impairment and its negative impact on psychosocial functioning in lithium-treated, euthymic bipolar patients: a 6-year follow-up study. Psychol. Med. 43 (6), 1187–1196. Mur, M., Portella, M.J., Martinez-Aran, A., Pifarre, J., Vieta, E., 2008. Long-term stability of cognitive impairment in bipolar disorder: a 2-year follow-up study of lithium-treated euthymic bipolar patients. J. Clin. Psychiatry 69 (5), 712–719. Ostergaard, C., Johansen, J.S., Benfield, T., Price, P.A., Lundgren, J.D., 2002. YKL-40 is elevated in cerebrospinal fluid from patients with purulent meningitis. Clin. Diagn. Lab. Immunol. 9 (3), 598–604. Palsson, E., Figueras, C., Johansson, A.G., Ekman, C.J., Hultman, B., Ostlind, J., Landen, M., 2013. Neurocognitive function in bipolar disorder: a comparison between bipolar I and II disorder and matched controls. BMC Psychiatry 13, 165. Phillips, M.L., Kupfer, D.J., 2013. Bipolar disorder diagnosis: challenges and future directions. Lancet 381 (9878), 1663–1671. Rao, J.S., Harry, G.J., Rapoport, S.I., Kim, H.W., 2010. Increased excitotoxicity and neuroinflammatory markers in postmortem frontal cortex from bipolar disorder patients. Mol. Psychiatry 15 (4), 384–392. Recklies, A.D., Ling, H., White, C., Bernier, S.M., 2005. Inflammatory cytokines induce production of CHI3L1 by articular chondrocytes. J. Biol. Chem. 280 (50), 41213–41221. Renkema, G.H., Boot, R.G., Au, F.L., Donker-Koopman, W.E., Strijland, A., Muijsers, A.O., Hrebicek, M., Aerts, J.M., 1998. Chitotriosidase, a chitinase, and the 39-kDa human cartilage glycoprotein, a chitin-binding lectin, are homologues of family

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023

8

S. Rolstad et al.

18 glycosyl hydrolases secreted by human macrophages. Eur. J. Biochem./FEBS 251 (1-2), 504–509. Robinson, L.J., Ferrier, I.N., 2006. Evolution of cognitive impairment in bipolar disorder: a systematic review of cross-sectional evidence. Bipolar Disord. 8 (2), 103–116. Robinson, L.J., Thompson, J.M., Gallagher, P., Goswami, U., Young, A.H., Ferrier, I.N., Moore, P.B., 2006. A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J. Affect. Disord. 93 (1-3), 105–115. Rolstad, S., Berg, A.I., Bjerke, M., Blennow, K., Johansson, B., Zetterberg, H., Wallin, A., 2011. Amyloid-beta(4)(2) is associated with cognitive impairment in healthy elderly and subjective cognitive impairment. J. Alzheimer’s Dis.: JAD 26 (1), 135–142. Rolstad, S., Berg, A.I., Bjerke, M., Johansson, B., Zetterberg, H., Wallin, A., 2013. Cerebrospinal fluid biomarkers mirror rate of cognitive decline. J. Alzheimer’s Dis.: JAD 34 (4), 949–956. Ryden, E., Thase, M.E., Straht, D., Aberg-Wistedt, A., Bejerot, S., Landen, M., 2009. A history of childhood attention-deficit hyperactivity disorder (ADHD) impacts clinical outcome in adult bipolar patients regardless of current ADHD. Acta Psychiatr. Scand. 120 (3), 239–246.

Sachs, G.S., Thase, M.E., Otto, M.W., Bauer, M., Miklowitz, D., Wisniewski, S.R., Lavori, P., Lebowitz, B., Rudorfer, M., Frank, E., Nierenberg, A.A., Fava, M., Bowden, C., Ketter, T., Marangell, L., Calabrese, J., Kupfer, D., Rosenbaum, J.F., 2003. Rationale, design, and methods of the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biol. Psychiatry 53 (11), 1028–1042. Saunders, J.B., Aasland, O.G., Babor, T.F., de la Fuente, J.R., Grant, M., 1993. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption —II. Addiction 88 (6), 791–804. Savitz, J., Solms, M., Ramesar, R., 2005. Neuropsychological dysfunction in bipolar affective disorder: a critical opinion. Bipolar Disord. 7 (3), 216–235. Sheehan, D.V., Lecrubier, Y., Sheehan, K.H., Amorim, P., Janavs, J., Weiller, E., Hergueta, T., Baker, R., Dunbar, G.C., 1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59 (Suppl. 20), 22–33 quiz 34–57.

Please cite this article as: Rolstad, S., et al., CSF neuroinflammatory biomarkers in bipolar disorder are associated with cognitive impairment. European Neuropsychopharmacology (2015), http://dx.doi.org/10.1016/j.euroneuro.2015.04.023