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Can we predict cognitive decline after initial diagnosis of multiple sclerosis? Results from the German National early MS cohort (KKNMS)

  • Andreas Johnen
  • Paul-Christian Bürkner
  • Nils C. Landmeyer
  • Björn Ambrosius
  • Pasquale Calabrese
  • Jeremias Motte
  • Nicole Hessler
  • Gisela Antony
  • Inke R. König
  • Luisa Klotz
  • Muna-Miriam Hoshi
  • Lilian Aly
  • Sergiu Groppa
  • Felix Luessi
  • Friedemann Paul
  • Björn Tackenberg
  • Florian Then Bergh
  • Tania Kümpfel
  • Hayrettin Tumani
  • Martin Stangel
  • Frank Weber
  • Antonios Bayas
  • Brigitte Wildemann
  • Christoph Heesen
  • Uwe K. Zettl
  • Frauke Zipp
  • Bernhard Hemmer
  • Sven G. Meuth
  • Ralf Gold
  • Heinz Wiendl
  • Anke Salmen
  • German Competence Network Multiple Sclerosis (KKNMS)
Open Access
Original Communication

Abstract

Background

Cognitive impairment (CI) affects approximately one-third of the patients with early multiple sclerosis (MS) and clinically isolated syndrome (CIS). Little is known about factors predicting CI and progression after initial diagnosis.

Methods

Neuropsychological screening data from baseline and 1-year follow-up of a prospective multicenter cohort study (NationMS) involving 1123 patients with newly diagnosed MS or CIS were analyzed. Employing linear multilevel models, we investigated whether demographic, clinical and conventional MRI markers at baseline were predictive for CI and longitudinal cognitive changes.

Results

At baseline, 22% of patients had CI (impairment in ≥2 cognitive domains) with highest frequencies and severity in processing speed and executive functions. Demographics (fewer years of academic education, higher age, male sex), clinical (EDSS, depressive symptoms) but no conventional MRI characteristics were linked to baseline CI. At follow-up, only 14% of patients showed CI suggesting effects of retesting. Neither baseline characteristics nor initiation of treatment between baseline and follow-up was able to predict cognitive changes within the follow-up period of 1 year.

Conclusions

Identification of risk factors for short-term cognitive change in newly diagnosed MS or CIS is insufficient using only demographic, clinical and conventional MRI data. Change-sensitive, re-test reliable cognitive tests and more sophisticated predictors need to be employed in future clinical trials and cohort studies of early-stage MS to improve prediction.

Keywords

Multiple sclerosis Cognition Neuropsychology Cohort study Longitudinal 

Introduction

Cognitive impairment (CI) and associated neurobehavioral symptoms (e.g., fatigue, depression) are frequent and often highly debilitating in multiple sclerosis (MS) [1]. Particularly cognitive processing speed, executive functions such as working memory capacity as well as verbal and figural episodic memory show a disease-related decline with adverse effects on patient’s vocational status and quality of life [2, 3]. CI has been shown to be present in the earliest disease stages of MS as well as in clinically isolated syndrome (CIS) [4, 5]. Several studies suggest that CI can be present independent of physical disability and that its development and progression is most pronounced during the first years after disease onset [6, 7]. Despite its increasingly recognized clinical relevance for patients with early MS, little is known about risk factors that contribute to CI, its short-term course and a potential progression after initial diagnosis of MS [3, 8, 9]. Associations between clinical disease severity markers (e.g., EDSS, number of relapses, disease duration), conventional MRI parameters of disease burden (e.g., number and/or site of lesions, degree of atrophy) and both severity and profiles of CI have been reported in large cross-sectional cohort studies on a group level [8, 10, 11, 12]. However, these associations were less evident in patients with early disease stages [13]. A range of studies have also investigated longitudinally risk factors and prediction of long-term outcome of CI in patients with MS mainly based on clinical and MRI parameters [5, 6, 14, 15, 16]. Compatible with results from cross-sectional studies, baseline brain volume [14, 15] and to a lesser degree lesion metrics [6, 16] usually contribute to long-term prediction of CI but predictive abilities were generally low and inconsistent for short-term follow-up periods and early disease stages [5, 14]. Both cross-sectional and longitudinal studies, moreover, display a substantial heterogeneity regarding (i) assessments and definitions of CI, (ii) selection and measurement of predictor variables, (iii) homogeneity of sample characteristics (e.g., disease severity, intake of medication, etc.) and (iv) employed MRI techniques and length of follow-up periods. These methodological issues currently impede an integration and extrapolation of results onto individual cases with newly diagnosed MS [6, 8, 10, 11, 14, 15, 16, 17]. In turn, this gap in key-knowledge hinders incorporation of cognitive monitoring into standard clinical care which in turn hampers the development and evaluation of specific programs for the prevention and rehabilitation of CI in MS [1].

Here, we aimed to investigate whether CI and its short-term progression can be effectively predicted by a single marker or combinations of conventional demographic, clinical and MRI parameters that are readily available to clinicians at the time of diagnosing MS. We were further interested in the relative importance of these potential risk factors both for CI as well as for its longitudinal change. To this end, we analyzed cognitive screening data from the German National MS cohort (NationMS) of patients with initial diagnosis of either MS or CIS [18]. We assumed standard sociodemographic data, established clinical markers of MS disease burden and/or conventional MRI parameters at baseline to be predictive for CI. We further analyzed whether changes in cognitive test performance during the first year after diagnosis may be effectively predicted using these baseline parameters.

Materials and methods

NationMS cohort study

The German National MS cohort is a prospective longitudinal observational study comprising (a) detailed assessment of patients with first diagnosis of MS or CIS and (b) yearly follow-up assessment with a standardized protocol across 22 centers in Germany. It was approved by the ethics committee of Ruhr-University Bochum (Registration no. 3714-10), and consecutively, by all local committees of the participating centers. All patients provided written informed consent. Inclusion and exclusion criteria as well as assessment plans are laid out in detail elsewhere [18]. In short, inclusion required a recent diagnosis of either CIS or RRMS according to Barkhof [19] or 2005 McDonald [20] criteria, respectively; exclusion criteria implied previous intake of disease-modifying therapies (DMTs), other neurological or psychiatric conditions as well as progressive courses of MS. Assessment involved sociodemographic data, detailed neurological status, medication status regarding DMTs, standardized cranial MRI evaluation regarding signs of disease burden, collection of biomaterial as well as neuropsychological screenings and self-report questionnaires. Datasets from N = 1123 patients were included for baseline statistics. Data from N = 958 patients were available for follow-up assessment at an average of 12.13 (SD = 1.54) months after baseline.

Cognitive screening data

MUSIC: Multiple Sclerosis Inventory for Cognition

The MUSIC is a brief multiple-domain cognitive screening test geared towards rapid assessment of the most frequently impaired cognitive domains in MS [21]. It is widely used as a screening for CI in German-speaking countries and consists of six subtests, in the following order: (1) Word List Learning (number of words learned over two consecutive trials out of a list with 10 words), (2) Interference Word List Learning (number of words learned from a 10 word interference list), (3) Category Fluency Switch Condition (number of correctly associated words within 1 min from two continuously alternating semantic categories), (4) Modified Stroop Task (speed of correctly naming animal silhouettes either in a congruent or incongruent condition with printed animal names on them), (5) Word List Recall (number of correctly recalled words from the initially learned word list after a short delay). For easier inter-test and inter-subject comparisons, individual test scores were z standardized based on normative data from N = 158 German-speaking healthy young adults as laid out in detail elsewhere [21].

PASAT: Paced Auditory Serial Addition Test

The PASAT 3-s version is a widely used cognitive screening test in MS tapping into processing speed, divided attention and working memory. PASAT data were extracted from the Multiple Sclerosis Functional Composite (MSFC) [22]. Participants are asked to add numbers in a 1-back-like fashion during a continuous auditory presentation (one number presented every 3 s) and verbally state the correct sums continuously. Outcome measure is the number of correct calculations during a fixed time period. Administration was carried out in accordance with the manual including a preceding training trial and the use of a parallel version at follow-up. Analogous to the MUSIC data, individual PASAT test scores were z standardized, stratified for age and education based on normative data from a German sample of N = 241 healthy controls [23].

Across all cognitive tests (i.e., subtests of MUSIC and PASAT), a normative z score of − 1.645 was used as a cut-off for “impaired performance” as this value approximately represents the 5th percentile rank. Following the criterion put forth by Amato et al., impaired performance in two or more subtests was required to classify individual patients as having CI [6]. Additionally, an unweighted mean z score of all cognitive tests was calculated for each patient as a proxy for overall severity of CI.

Prediction parameters

A priori-considered predictors for CI and longitudinal change are depicted in Table 1. Besides general sociodemographic factors known to influence cognitive status, we examined a range of previously discussed disease-specific risk factors for CI in MS [9]. In total, we considered 17 predictor variables assessed at baseline pertaining to the domains demographics, clinical disease severity markers, MRI ratings of disease burden and self-reports on psychopathology (depressive symptoms and fatigue).

Table 1

Baseline predictors and sample characteristics (total N = 1123)

Predictor

Available N

Mean (SD)

Demographic characteristics

Age, years

1123

34.12 (9.67)

Sex, m:w

1123

348:775

Education, years

1103

14.41 (2.57)

Clinical characteristics

Time since symptom onset, years

1123

0.57 (0.61)

EDSS

1120

1.49 (0.99)

Total number of relapses

958

1.39 (0.62)

Type of disease, RRMS:CIS

1123

622:501

Type of first relapse, mono-:polysymptomatic

1122

831:291

Start of DMT after baseline, yes:no

958

782:176

MRI characteristics

Number of T2 lesions

1117

7.67 (2.21)

Periventricular lesions, yes:no

1123

1081:42

Juxtacortical lesions, yes:no

1123

871:252

Infratentorial lesions, yes:no

1123

666:457

Black holes, yes:no

822

489:333

Visible atrophy, yes:no

877

97:780

Psychopathological characteristics

Depressive symptoms, BDI-II total

1077a

7.61 (7.72):185

Fatigue, FSMC total

1073b

39.12 (18.21):390

CI cognitive impairment, EDSS Expanded Disability Status Scale, RRMS relapsing–remitting multiple sclerosis, CIS clinically isolated syndrome, DMT disease-modifying therapy, BDI Beck Depression Inventory, FSMC Fatigue Scale for Motor and Cognitive functions

aN = 103 (9.6%) of patients had scores ≥ 19, the clinical cut-off score suggested for indicating an at least mild depressive episode in patients with MS

bN = 390 (36.3%) of patients had scores ≥ 42, indicating at least mild fatigue

Statistics

SPSS 25 (IBM Corporation) was used for data preparation and R 3.3.0 (R Foundation, Vienna, Austria) for statistical computations. Descriptive statistics (means and SD as well as frequencies (%) of impaired cases) for baseline and follow-up cognitive data were computed. Change of CI from baseline to follow-up was evaluated using paired t tests. Linear multilevel models were applied to predict baseline cognitive test values as well as baseline to follow-up changes in cognitive test values and to control for possible dependency between observations gathered in the same participating center. All predictors were entered into the multiple regression model simultaneously so that co-variance between predictors was controlled for. Models were fitted adopting a Bayesian multilevel approach with the brms package [24] using the probabilistic programming language Stan. For all analyses, a 5% significance level was used and Bonferroni correction was applied within each regression model (that is over 18 regression coefficients per model). Prior to analyses, dichotomous variables (e.g., sex, presence of brain atrophy) were dummy-coded to include them into the regression models. Missing values in predictor variables were imputed by means of 20-fold multiple imputation by chained equations using the mice package [25]. The full analysis is available within the Open Science Framework (https://osf.io/wznca/).

Results

Frequencies of patients with and without CI are depicted in Fig. 1a for baseline and follow-up for each cognitive subtest/domain separately. At baseline, a total of 245 (22%) of patients were classified as having CI with the highest frequencies observed in the interference subscore of the Modified Stroop Task (N = 185; 17%) of the MUSIC followed by the PASAT (N = 135, 12%). Other subtests (e.g., verbal learning and memory) were substantially less frequently impaired. At follow-up, the general profile of relatively frequent impairments in processing speed and executive functions compared to other cognitive domains was similar to baseline. However, substantially less frequent impairments were observed across all tests at follow-up (overall CI in N = 120; 14%).

Fig. 1

a Frequencies of patients with overall CI (≥ 2 tests impaired compared to age- and education-corrected normative data) and of patients with impairments (z score <− 1.645) in single cognitive tests for baseline (BL) and follow-up (FU) assessments. b Mean normative z scores stratified for age and education for overall CI (mean z score of all tests) and for each cognitive test separately for baseline (BL, left) and follow-up (FU, right)

Regarding the severity of deficits, normative z scores of baseline cognitive tests and significances of changes from baseline to follow-up are presented in Fig. 1b and Table 2.

Table 2

Mean (SD) of unstandardized raw scores and mean normative z scores of cognitive tests for baseline, follow-up and longitudinal change

Cognitive test

N

Baseline

Follow-up

Longitudinal change

Raw mean (SD)

Mean z (SD)

N

Raw mean (SD)

Mean z (SD)

Raw change (SD)

p Bon

Word list learning (max 20)

1067

15 (3)

0.23 (1.26)

913

16 (3)

0.61 (1.18)

0.81 (2.70)

< 0.001

Interference list (max 10)

1067

6 (2)

0.42 (0.99)

913

6 (2)

0.49 (1.01)

0.13 (1.70)

0.028

Word list free recall (max 10)

1065

6 (2)

0.03 (1.23)

913

7 (2)

0.42 (1.21)

0.74 (2.16)

< 0.001

Category fluency switch

1067

15 (5)

− 0.06 (0.86)

913

15 (5)

0.08 (0.88)

0.77 (4.82)

< 0.001

Stroop naming speed seconds

1066

22 (5)

− 0.20 (1.55)

910

21 (5)

− 0.05 (1.56)

− 0.55 (4.51)

< 0.001

Stroop interference seconds

1066

28.6 (8.3)

− 0.40 (1.69)

910

27.6 (7.9)

− 0.20 (1.61)

− 1.12 (7.81)

< 0.001

Stroop inhibition quotient

1066

7 (6)

− 0.34 (1.42)

910

6 (5)

− 0.22 (1.20)

− 0.57 (6.70)

0.012

MUSIC total score (max 32)

1067

25 (5)

N/A

913

26 (4)

N/A

1.02 (3.87)

< 0.001

PASAT 3 s (max 60)

1038

46 (11)

− 0.20 (1.11)

876

49 (11)

0.10 (1.05)

2.75 (9.10)

< 0.001

Overall CI (mean z of all tests)

1038

N/A

− 0.06

921

N/A

0.16

0.21 (0.67)

< 0.001

MUSIC subscores as well as PASAT test scores and corresponding normative z scores for baseline and follow-up

MUSIC multiple sclerosis inventory for cognition, Max maximum raw score, CI cognitive impairment, N/A not applicable, PASAT paced auditory serial addition test, pBon Bonferroni-corrected p value for paired t test of baseline vs. follow-up mean scores

Additionally, spaghetti plots depicting individual cognitive changes from baseline to follow-up can be found in Supplementary Figure 1 for each subtest.

Compared to normative data, the sample’s average overall cognitive ability was not pathological with a mean of all cognitive tests of z = − 0.06 at baseline. Compatible with frequency data, processing speed (PASAT, z = − 0.20) and executive functions (modified Stroop Test interference seconds, z = − 0.40) were the domains with the lowest performances on average. At follow-up, patients performed significantly better on the mean cognitive z score (z = 0.16 p < 0.0001). Likewise, significant gains from baseline to follow-up were observed in the majority of subtests with the exception of the Stroop Inhibition Quotient and the Learning trial of the Interference word list for which no change occurred.

Results of the multilevel linear regression models are presented for the mean z score of all cognitive tests representing a proxy for overall CI. Regression coefficients of the model including all predictors for baseline CI are provided in Table 3.

Table 3

Regression coefficients for baseline mean cognitive test scores

Coefficient

b

SE

l95% CI

u95% CI

p

p Bon

Intercept

− 0.267

0.162

− 0.585

0.050

0.098

Demographic characteristics

Age

− 0.148

0.023

− 0.193

− 0.103

0.000

0.000

Sex (female vs. male)a

0.279

0.047

0.186

0.372

0.000

0.000

Education, years

0.188

0.023

0.144

0.233

0.000

0.000

Clinical characteristics

Years since symptom onset

− 0.012

0.026

− 0.063

0.039

0.637

1.000

EDSS

− 0.112

0.025

− 0.161

− 0.064

0.000

0.000

Total number of relapses

0.040

0.047

− 0.052

0.133

0.399

1.000

Type of disease (RRMS vs. CIS)a

0.064

0.051

− 0.036

0.166

0.214

1.000

Type of 1st relapse (poly- vs. monosymptomatic)a

− 0.046

0.051

− 0.145

0.053

0.368

1.000

Start of DMT after baselinea

0.079

0.061

− 0.041

0.200

0.196

1.000

MRI characteristics

Number of T2 lesions

− 0.045

0.022

− 0.088

− 0.002

0.040

0.681

Periventricular lesions presenta

0.018

0.116

− 0.208

0.247

0.877

1.000

Juxtacortical lesions presenta

− 0.051

0.054

− 0.156

0.055

0.345

1.000

Infratentorial lesions presenta

− 0.059

0.044

− 0.144

0.027

0.175

1.000

Black holes presenta

− 0.013

0.056

− 0.123

0.097

0.823

1.000

Visible atrophy presenta

− 0.094

0.075

− 0.241

0.049

0.209

1.000

Psychopathological characteristics

Depressive symptoms (BDI-II)

− 0.111

0.031

− 0.170

− 0.050

0.000

0.006

Fatigue (FSMC total)

0.006

0.031

− 0.056

0.067

0.853

1.000

Coefficients in bold indicate a significant influence on the outcome variable (mean z of cognitive test performance at baseline). Negative regression coefficients indicate that larger values in the predictor have a negative influence on the outcome variable and vice versa

b regression coefficient, SE standard error, l95% CI lower bound of the 95% credible interval, u95% CI upper bound of the 95% credible interval, p uncorrected two-sided p value, pBon Bonferroni corrected two-sided p value

aDichotomous variables that have been dummy-coded prior to analysis

The proportion of variance explained by this model was R2 = 0.27 when including the variance explained by the participating center and R2 = 0.21 without it. The predictors that remained significant after Bonferroni correction were age (“more CI in older patients”), years of education (“more CI in patients with fewer years of academic education”), EDSS score (“more CI in patients with higher EDSS”), BDI-II score (“more CI in patients with more self-reported depressive symptoms”), and sex (“more CI in males”). Other MS-specific clinical or MRI characteristics did not significantly contribute to the prediction of baseline CI. Regression coefficients of the model including all predictors for the baseline to follow-up changes in cognitive test scores are provided in Table 4.

Table 4

Regression coefficients for baseline to follow-up changes in mean cognitive test scores

Coefficient

b

SE

l95% CI

u95% CI

p

p Bon

Intercept

− 0.273

0.228

− 0.723

0.174

0.234

Demographic characteristics

Age

− 0.073

0.035

− 0.141

− 0.005

0.034

0.580

Sex (female vs. male)a

0.141

0.072

0.001

0.283

0.049

0.826

Education (years)

− 0.031

0.035

− 0.099

0.036

0.372

1.000

Clinical characteristics

Years since symptom onset

0.044

0.040

− 0.034

0.124

0.266

1.000

EDSS

0.086

0.037

0.013

0.157

0.022

0.370

Total number of relapses

− 0.045

0.066

− 0.176

0.085

0.497

1.000

Type of disease (RRMS vs. CIS)a

− 0.037

0.079

− 0.192

0.117

0.642

1.000

Type of 1st relapse (poly- vs. monosymptomatic)a

0.106

0.077

− 0.044

0.258

0.173

1.000

Start of DMT after baseline a

− 0.125

0.088

− 0.299

0.048

0.158

1.000

MRI characteristics

Number of T2 lesions

− 0.006

0.034

− 0.072

0.059

0.850

1.000

Periventricular lesions presenta

0.211

0.176

− 0.134

0.553

0.231

1.000

Juxtacortical lesions presenta

0.149

0.083

− 0.015

0.311

0.076

1.000

Infratentorial lesions presenta

0.072

0.068

− 0.061

0.204

0.294

1.000

Black holes presenta

− 0.114

0.087

− 0.286

0.055

0.188

1.000

Visible atrophy presenta

0.124

0.113

− 0.093

0.348

0.271

1.000

Psychopathological characteristics

Depressive symptoms (BDI-II)

0.080

0.051

− 0.020

0.181

0.118

1.000

Fatigue (FSMC total)

− 0.106

0.053

− 0.212

− 0.003

0.042

0.715

Coefficients in bold indicate a significant influence on the outcome variable (mean z of baseline to follow-up change of cognitive test performance). Negative regression coefficients indicate that a larger value in the predictor has a negative influence on the outcome variable and vice versa

b regression coefficient, SE standard error, l95% CI lower bound of the 95% credible interval, u95% CI upper bound of the 95% credible interval, p uncorrected two-sided p value, pBon Bonferroni corrected two-sided p value

aDichotomous variables that have been dummy coded prior to analysis

No predictor remained significant after Bonferroni correction indicating that longitudinal cognitive change could neither be effectively predicted by the considered baseline variables nor the additional variable of DMT initiation after baseline (yes vs. no). The proportion of variance explained was R2 = 0.06 when including the variance explained by participating center and R2 = 0.05 without it. Likewise, results for each separate cognitive subtest were non-significant regarding the prediction of cognitive change from baseline to 1-year follow-up. These and other additional analyses are provided as supplementary material on https://osf.io/wznca/.

Discussion

Despite the increasingly recognized burden of CI in MS, little is known about an increased individual risk for CI after initial diagnosis of MS, hampering research on early prevention and treatment. In the current study, we aimed to characterize CI and identify risk factors for its severity and short-term course in a large, clinically homogeneous cohort of patients with first diagnosis of MS or CIS. To this end, neuropsychological screening data from N = 1123 patients enrolled in the multicentric German National MS cohort study were analyzed. We used linear multilevel regression models to predict CI and the short-term progression of CI from conventional MRI characteristics and other clinical and demographic parameters that are usually accessible to clinicians at the time of diagnosis.

Frequency, severity and profile of CI

Adopting conventional criteria of overall CI, we found 22% of patients to be impaired at baseline, with largest deficits in subtests for processing speed and executive function and lowest impairments in verbal learning and memory. The result of a relatively larger impairment in attention and processing speed as compared to other cognitive domains is well in line with previous studies on the cognitive profile of patients with early MS [6, 8] and CIS [1, 3]. Overall frequency and mean severity of CI was lower in our sample than commonly reported: the majority of previous studies found approximately one-third of patients with CI in early MS or CIS [3, 6, 7], although reported frequencies range from < 15 to > 50% [5, 26]. One explanation for this discrepancy may be that the current sample is unique in terms of a homogeneous sample in a very early disease stage with a median disease duration of only 0.33 years [18]. Compensatory mechanisms such as cognitive reserve may attenuate direct measurability of CI specifically in young patients with low overall disease burden and high formal education resulting in lower frequencies [17]. Hence, patients with larger cognitive reserve capacity may be able to compensate for brain pathology despite suffering from clinically relevant CI [13]. An additional explanation for our finding of a lower prevalence of CI in patients with early MS and CIS may be that the employed screening tests are less sensitive to detect CI in these early disease stages that might extend beyond executive and speed-related domains. Reports on the prevalence of CI in MS depends on (a) the employed tests (e.g., screening tests only or extensive test batteries), (b) the formal definition of CI (e.g., one or two standard deviations below the norm; comparison to a control group), and (c) the composition of the sample (e.g., patients with progressive MS show a different degree of CI than patients with early MS or CIS [27]). Internationally accepted standards regarding screening for CI have been proposed in terms of the Brief International Cognitive Assessment in MS (BICAMS battery) and may allow a higher sensitivity to detect relevant CI in MS throughout the different disease stages [28]. For instance, the Symbol Digit Modalities Test (SDMT) has been shown to be a more reliable, and sensitive measure of cognitive processing speed than the PASAT employed in this study [29, 30]. More specific cognitive functions like calculation skills may as well influence individual PASAT results. Thus, while the Modified Stroop Task of the MUSIC was able to detect early deficits in processing speed and executive function, the single-trial ten-item list might be insufficient to reveal subtle memory changes that might unfold in a multiple-trial learning-paradigm.

Predictors of CI and its progression

We found baseline CI to be significantly associated with three general demographic characteristics: male sex, fewer years of education and higher age. These factors have previously been linked to lower (verbal-)cognitive test performance in healthy adults suggesting influences that are not specific to MS or CIS but may, nevertheless, be of clinical importance for the interpretation of MS patients’ test performances [31, 32]. Considering MS-specific clinical characteristics, only EDSS (a marker for mainly physical disease burden) and severity of depressive symptoms (BDI-II) were associated with severity of CI at baseline. These results are in line with previous evidence from large patient samples finding that higher EDSS and depressive symptoms negatively influence cognitive status [10, 27, 33]. Surprisingly, none of the conventional MRI (e.g., visual inspection of atrophy, number of T2 lesions) or other clinical predictors (e.g., type of disease CIS/RRMS, total number of relapses) that have previously been directly linked to CI and its long-term course contributed to prediction. This result may again cast doubts on the sensitivity of the employed screening tests to reliably detect CI in early disease stages. In the current sample, however, brain pathology and disease severity were also homogeneously low and relationships between CI and conventional markers for structural brain damage may be generally weak in early MS, even when using more sophisticated neuropsychological assessments. In a recent large cohort study, lack of association between brain pathology (as measured by voxel-based morphometry) and performance in the BICAMS test battery was termed a “clinico-radiological paradox” and attributed to both, stronger compensatory mechanisms (e.g., cognitive reserve) and a statistical restriction of range within a homogeneous sample of patients in early disease stages [13]. Despite the large sample size and the numerous considered clinical, demographic and conventional MRI baseline parameters as well as the variable of DMT initation after baseline, the longitudinal change of cognition over the course of 1 year could not be sufficiently predicted. One explanation may be that, for instance, for the considered MRI parameters and DMT initiation, the categorization was too broad (e.g., dichotomization DMT start yes vs. no, visible MRI atrophy yes vs. no). On the other hand, the follow-up interval of 1 year may be too short to detect clinically relevant changes. However, significant gains in cognitive performance were observed in the majority of patients and in most cognitive subtests. This strongly suggests that test performances in both, MUSIC and PASAT, were substantially influenced by practice effects, potentially masking clinically relevant longitudinal changes after 1 year. A recent review has estimated the average effect size of cognitive retesting in a 12-month interval to be as high as 0.25 while some standard neuropsychological tests reached effect sizes of 0.73 [34]. Likewise in patients with MS, carryover effects from one testing session to another is a frequent problem in longitudinal test designs and common to a range of neuropsychological tests including the PASAT and to lesser degrees also the SDMT [29, 35, 36]. Although alternate test versions matched for difficulty and modern regression-based normative data (including estimates for retesting effect-sizes) may attenuate the influence of practice effects, few standardized cognitive tests employed in testing patients with early MS provide these features. Moreover, despite the use of an alternate version in the PASAT in this study, patients on average performed significantly better at follow-up, highlighting a likely influence of familiarity that is not dependent on the particular stimuli. Practice effects may endure for approximately 1 year after a baseline assessment and are most pronounced between the first and second evaluations [37, 38]. This is, particularly, true for tests assessing memory, learning and executive functions while visuo-perceptive tasks are less prone to practice effects [39]. Hence, the difference of some cognitive tests in their resilience against practice effects has to be considered more rigorously when planning re-evaluation schedules. Moreover, additional cognitive testing (performed outside of the study or by patient self-assessment and training) needs to be controlled for.

Conclusions

In patients first diagnosed with MS or CIS, demographic characteristics (male sex, higher age, lower education) as well as more severe depressive symptoms (BDI-II) and higher physical disability (EDSS) are significantly associated with severity of CI. In patients with these characteristics, neuropsychological monitoring and potentially cognitive rehabilitation should be considered. No other disease-specific clinical or conventional MRI parameters from clinical routine were significantly related to the presence of CI in this large cohort of patients in earliest disease stages. Moreover, longitudinal prediction of short-term cognitive change over the course of 1 year was insufficient despite the large number of patients and the inclusion of numerous conventional yet disease-specific and previously discussed predictor variables. These findings indicate that three branches of research are highly needed to increase our understanding of CI, its clinical relevance and its risk factors in early MS to blaze the trail for early interventions: (1) establishment and evidence-based proof of sensitive and change-sensitive cognitive outcome parameters providing free-to-use longitudinal normative data. (2) Evidence that these assessments are able to detect disease-specific and clinically relevant CI (i.e., by validation with patient-centered outcomes) from the earliest to advanced disease stages. (3) Improving the prediction of these measurements by the development of refined clinical scales and standardized automation of MRI parameters for use in clinical routine [40].

Notes

Acknowledgements

The authors and representatives of the KKNMS express their deep gratitude to all contributors of the study, especially the study nurses, for their motivated collaboration and recruitment efforts, all the patients and relatives for their participation and support, and the data monitoring and administrative personnel of the study. Other members of the KKNMS that acted as collaborators in this study: Seray Demir: Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Germany. Christoph Schröder: Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Germany. Lisa A. Voithenleitner: Dept. of Neurology, Klinikum rechts der Isar, Technical University of Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. Achim Berthele: Dept. of Neurology, Klinikum rechts der Isar, Technical University of Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. Sarah Haars: Dept. of Neurology, University of Leipzig, Germany. Sandra Nischwitz: Neurology, Max-Planck-Institute of Psychiatry, Munich, Germany. Matthias J. Knop: Neurology, Max-Planck-Institute of Psychiatry, Munich, Germany. Susanne Rothacher: Dept. of Neurology, Klinikum Augsburg, Germany. Jana Pöttgen: Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany. Clemens Warnke: Department of Neurology, Heinrich-Heine-University, Düsseldorf, Germany; Department of Neurology, University Hospital Köln, Cologne, Germany. Ralf A. Linker: Department of Neurology, University Hospital Erlangen, Germany. Ulf Ziemann: Department of Neurology and Stroke, and Hertie Institute for Clinical Brain Research, Eberhard-Karls-University Tübingen, Tübingen, Germany.

Funding

The German National MS cohort and KKNMS are supported by grants from the German Federal Ministry for Education and Research (BMBF), Grant no. 01GI0914 (Bochum), 01GI0916, 01GI1601G (Lübeck), and 01GI1601B (Marburg).

Compliance with ethical standards

Conflicts of interest

Paul-Christian Bürkner, Nils C. Landmeyer, Nicole Hessler, Gisela Antony, Inke R. König, Lilian Aly, Sergiu Groppa and Pasquale Calabrese report no disclosures. Andreas Johnen received speaker’s honoraria and reimbursement of travel expenses from Actelion Pharmaceuticals unrelated to this work. Björn Ambrosius received travel grants from Novartis, not related to this work. He is now an employee of Celgene Corporation (not during the work of this project). Jeremias Motte received travel grants from Biogen idec, his research is funded by Klaus Tschira Foundation and Ruhr-University, Bochum (FoRUM-Program); none related to this work. Luisa Klotz received honoraria for lecturing and serving on advisory boards, as well as travel expenses for attending meetings and financial research support from Novartis, Biogen, Roche, Merck, Sanofi Genzyme, the BMBF and the Deutsche Forschungsgemeinschaft (DFG; German Research Society). Muna-Miriam Hoshi received travel expenses from Bayer Health Care and honoraria for an advisory board from Merck Serono GmbH. Felix Lüssi serves as an advisory board member for Roche Pharma and has received travel grants from Teva Pharma. Friedemann Paul serves on the scientific advisory board for Novartis; received speaker honoraria and travel funding from Bayer, Novartis, Biogen Idec, Teva, Sanofi-Aventis/Genzyme, Merck Serono, Alexion, Chugai, MedImmune, and Shire; is an academic editor for PLoS One; is an associate editor for Neurology® Neuroimmunology & Neuroinflammation; consulted for SanofiGenzyme, Biogen Idec, MedImmune, Shire, and Alexion; and received research support from Bayer, Novartis, Biogen Idec, Teva, Sanofi-Aventis/Genzyme, Alexion, Merck Serono, German Research Council, Werth Stiftung of the City of Cologne, German Ministry of Education and Research, Arthur Arnstein Stiftung Berlin, EU FP7 Framework Program, Arthur Arnstein Foundation Berlin, Guthy Jackson Charitable Foundation, and National Multiple Sclerosis of the USA; none related to this work. Björn Tackenberg received personal speaker honoraria and consultancy fees as a speaker and advisor from Bayer Healthcare, Biogen, CSL Behring, GRIFOLS, Merck Serono, Novartis, Octapharma, Roche, Sanofi Genzyme, TEVA und UCB Pharma. His University received unrestricted research grants from Biogen-idec, Novartis, TEVA, Bayer Healthcare, CSL-Behring, GRIFOLS, Octapharma, Sanofi Genzyme und UCB Pharma; none related to this work. Florian Then Bergh received travel support to attend scientific meetings, personal speaker honoraria, and consultancy fees as a speaker and advisor from Actelion, Bayer Healthcare, Biogen, Merck Serono, Novartis, Roche, Sanofi Genzyme, and TEVA. He received, through his institution, unrestricted research grants from Novartis, TEVA, Bayer Healthcare, and Actelion; none related to this work. He received funding from the DFG and, through TRM Leipzig, from the BMBF. Tania Kümpfel received travel expenses and personal compensations from Bayer Healthcare, Teva Pharma, Merck-Serono, Novartis, Sanofi-Aventis/Genzyme, Roche and Biogen as well as grant support from Bayer-Schering AG, Novartis and Chugai Pharma; none related to this work. Hayrettin Tumani received speaker honoraria from Bayer, Biogen, Fresenius, Genzyme, Merck, Novartis, Roche, Siemens, Teva; serves as section editor for the Journal of Neurology, Psychiatry, and Brain Research; and receives research support from Fresenius, Genzyme, Merck, and Novartis; none related to this work. Martin Stangel received honoraria for scientific lectures or consultancy from Bayer Healthcare, Biogen, Baxter/Baxalta, CSL Behring, Euroimmune, Grifols, Merck-Serono, Novartis, Roche, Sanofi-Aventis, and Teva. His institution received research support from Bayer Healthcare, Biogen Idec, Genzyme, Merck-Serono, Novartis, and Teva; none related to this work. Frank Weber received honoraria from Genzyme, Novartis TEVA and Biogen for speaking or for serving on a scientific advisory board, a travel grant for the attention of a scientific meeting from Merck-Serono and Novartis and grant support from Merck-Serono, Novartis and from the Federal Ministry of Education and Research (BMBF, Projects Biobanking and Omics in ControlMS as part of the Competence Network Multiple Sclerosis). Antonios Bayas received personal compensation from Merck Serono, Biogen, Bayer Vital, Novartis, TEVA, Roche and Sanofi/Genzyme and grants for congress trips and participation from Biogen, TEVA, Novartis, Sanofi/Genzyme, and Merck Serono; none related to this work. Brigitte Wildemann received grants from the German Ministry of Education and Research, Dietmar Hopp Foundation and Klaus Tschira Foundation, grants and personal fees from Biogen, Merck Serono, Sanofi Genzyme, Novartis pharmaceuticals, Teva Pharma, and personal fees from Bayer Healthcare; none related to this work. Christoph Heesen received research grants and speaker honoraria from Biogen, Genzyme, Roche, and Merck; none related to this work. Uwe K. Zettl received speaker fees from Aventis, Almirall, Biogen, Bayer, Merck, Novartis, Roche, and Teva. Frauke Zipp received funds for scientific consultation or research of DFG, BMBF, Novartis, Octapharma, Merck Serono, ONO Pharma, Biogen, Genzyme, and Sanofi Aventis within the past 3 years. Bernhard Hemmer served on scientific advisory boards for F. Hoffmann-La Roche Ltd, Novartis, Bayer AG, and Genentech; he has served as DMSC member for AllergyCare; he or his institution have received speaker honoraria from Biogen Idec, Teva Neuroscience, Merck Serono, Medimmune, Novartis, Desitin, and F. Hoffmann-La Roche Ltd; his institution has received research support from Chugai Pharmaceuticals; holds part of two patents; one for the detection of antibodies and T cells against KIR4.1 in a subpopulation of MS patients and one for genetic determinants of neutralizing antibodies to interferon during the last 3 years. Sven G. Meuth received honoraria for lecturing, travel expenses for attending meetings, and/or financial research support from Almirall, Bayer Health Care, Biogen, Diamed, Fresenius Medical Care, Genzyme, Merck Serono, Novartis, Novo Nordisk, ONO Pharma, Roche, Sanofi-Aventis and Teva. Ralf Gold serves on scientific advisory boards for Teva Pharmaceutical Industries Ltd., Biogen Idec, Bayer Schering Pharma, and Novartis; has received speaker honoraria from Biogen Idec, Teva Pharmaceutical Industries Ltd., Bayer Schering Pharma, and Novartis; serves as editor for Therapeutic Advances in Neurological Diseases and on the editorial boards of Experimental Neurology and the Journal of Neuroimmunology; and receives research support from Teva Pharmaceutical Industries Ltd., Biogen Idec, Bayer Schering Pharma, Genzyme, Merck Serono, and Novartis; none related to this work. Heinz Wiendl receives honoraria for acting as a member of Scientific Advisory Boards and as a consultant for Biogen, Evgen, MedDay Pharmaceuticals, Merck Serono, Novartis, Roche Pharma AG, Sanofi-Genzyme, as well as speaker honoraria and travel support from Alexion, Biogen, Cognomed, F. Hoffmann-La Roche Ltd., Gemeinnützige Hertie-Stiftung, Merck Serono, Novartis, Roche Pharma AG, Sanofi-Genzyme, TEVA, and WebMD Global. Prof. Wiendl is acting as a paid consultant for Abbvie, Actelion, Biogen, IGES, Novartis, Roche, Sanofi-Genzyme, and the Swiss Multiple Sclerosis Society. His research is funded by the BMBF, DFG, Else Kröner Fresenius Foundation, Fresenius Foundation, Hertie Foundation, NRW Ministry of Education and Research, Interdisciplinary Center for Clinical Studies (IZKF) Muenster and RE Children’s Foundation, Biogen GmbH, GlaxoSmithKline GmbH, and Roche Pharma AG, Sanofi-Genzyme. Anke Salmen received speaker honoraria and/or travel compensation for activities with Almirall Hermal GmbH, Biogen, Merck, Novartis, Roche, and Sanofi Genzyme; none related to this work. Collaborators: Seray Demir, Christoph Schröder, Susanne Rothacher, Lisa A. Voithenleitner and Jana Pöttgen report no disclosures. Achim Berthele reports personal fees from Bayer Healthcare, Biogen, Merck Serono, Mylan, Roche, and Sanofi Genzyme, and his institution received compensations for clinical trials from Alexion Pharmaceuticals, Biogen, Chugai, Novartis, Roche, Sanofi Genzyme, and Teva - all outside the submitted work. Sarah Haars received, through her institution, travel compensation to attend scientific meetings from Merck, Bayer, Novartis and Actelion, unrelated to this work. Sandra Nischwitz received honoraria for serving on scientific Adboards and as a speaker from Merck Serono, Novartis, Genzyme and Roche and grant support from Novartis. Matthias J. Knop received honoraria for serving on scientific Adboards and as a speaker from Biogen, Merck, Genzyme, Novartis and Roche. Clemens Warnke received honoraria and/or research funding from Bayer, Biogen, Novartis, and TEVA; none related to this work. Ralf A. Linker received Research Support and/or personal compensation for activities with Bayer Health Care, Biogen, Genzyme/Sanofi, Merck, Novartis Pharma, Roche, and TEVA Pharma; none related to this work. Ulf Ziemann received speaker honoraria and/or travel compensation from Biogen Idec GmbH, Bayer Vital GmbH, Bristol Myers Squibb GmbH, CorTec GmbH, Medtronic GmbH, and grants from Biogen Idec GmbH, Servier, and Janssen Pharmaceuticals NV; none related to this work.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

415_2018_9142_MOESM1_ESM.tiff (11.1 mb)
Suppl. Fig. 1: Spaghetti-plots indicating individual cognitive changes from baseline to follow-up for overall CI (mean z score of all tests) and for each cognitive test separately. (TIFF 11390 KB)

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Authors and Affiliations

  • Andreas Johnen
    • 1
  • Paul-Christian Bürkner
    • 2
  • Nils C. Landmeyer
    • 1
  • Björn Ambrosius
    • 3
  • Pasquale Calabrese
    • 4
  • Jeremias Motte
    • 3
  • Nicole Hessler
    • 5
  • Gisela Antony
    • 6
  • Inke R. König
    • 5
  • Luisa Klotz
    • 1
  • Muna-Miriam Hoshi
    • 7
    • 8
  • Lilian Aly
    • 7
    • 8
  • Sergiu Groppa
    • 9
  • Felix Luessi
    • 9
  • Friedemann Paul
    • 10
  • Björn Tackenberg
    • 11
  • Florian Then Bergh
    • 12
  • Tania Kümpfel
    • 13
  • Hayrettin Tumani
    • 14
    • 15
  • Martin Stangel
    • 16
  • Frank Weber
    • 17
    • 18
  • Antonios Bayas
    • 19
  • Brigitte Wildemann
    • 20
  • Christoph Heesen
    • 21
  • Uwe K. Zettl
    • 22
  • Frauke Zipp
    • 9
  • Bernhard Hemmer
    • 7
    • 8
  • Sven G. Meuth
    • 1
  • Ralf Gold
    • 3
  • Heinz Wiendl
    • 1
  • Anke Salmen
    • 3
    • 23
  • German Competence Network Multiple Sclerosis (KKNMS)
  1. 1.Department of NeurologyUniversity Hospital Münster, Westfälische-Wilhelms-University MünsterMünsterGermany
  2. 2.Department of Statistics, Faculty of PsychologyWestfälische-Wilhelms-UniversityMünsterGermany
  3. 3.Department of NeurologySt. Josef-Hospital, Ruhr-University BochumBochumGermany
  4. 4.Department of Neuropsychology and Behavioral NeurologyUniversity of BaselBaselSwitzerland
  5. 5.Institute of Medical Biometry and StatisticsUniversity of Lübeck, University Hospital Schleswig-HolsteinLübeckGermany
  6. 6.Central Information Office (CIO)Philipps-University MarburgMarburgGermany
  7. 7.Department of Neurology, Klinikum rechts der IsarTechnical University of MunichMunichGermany
  8. 8.Munich Cluster for Systems Neurology (SyNergy)MunichGermany
  9. 9.Department of Neurology and Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2)University Medical Center of the Johannes Gutenberg University MainzMainzGermany
  10. 10.NeuroCure Clinical Research Center and Experimental and Clinical Research CenterCharité, University Medicine Berlin and Max Delbrueck Center for Molecular MedicineBerlinGermany
  11. 11.Department of NeurologyPhilipps-University MarburgMarburgGermany
  12. 12.Department of NeurologyUniversity of LeipzigLeipzigGermany
  13. 13.Institute of Clinical NeuroimmunologyLudwig Maximilian University of MunichMunichGermany
  14. 14.Department of NeurologyUniversity of UlmUlmGermany
  15. 15.Clinic of Neurology DietenbronnSchwendiGermany
  16. 16.Department of NeurologyHannover Medical SchoolHannoverGermany
  17. 17.NeurologyMax-Planck-Institute of PsychiatryMunichGermany
  18. 18.Neurological ClinicSana Kliniken des Landkreises ChamChamGermany
  19. 19.Department of NeurologyKlinikum AugsburgAugsburgGermany
  20. 20.Department of NeurologyUniversity of HeidelbergHeidelbergGermany
  21. 21.Institut für Neuroimmunologie und Multiple SkleroseUniversitätsklinikum Hamburg-EppendorfHamburgGermany
  22. 22.Department of Neurology, Neuroimmunological SectionUniversity of RostockRostockGermany
  23. 23.Department of NeurologyInselspital Bern, Bern University Hospital and University of BernBernSwitzerland

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