Journal of the Neurological Sciences 350 (2015) 46–50

Contents lists available at ScienceDirect

Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns

Serum NSE level and disability progression in multiple sclerosis☆ Marcus W. Koch a,b,⁎, Suzanne George a, Winona Wall a, V. Wee Yong a, Luanne M. Metz a a b

Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada

a r t i c l e

i n f o

Article history: Received 22 July 2014 Received in revised form 23 December 2014 Accepted 4 February 2015 Available online 11 February 2015 Keywords: Multiple sclerosis Biomarker Serum biomarker Progression Disability Progressive MS

a b s t r a c t Background: : Previous studies suggested that serum neuron specific enolase (NSE) may be a biomarker associated with progression in MS. Methods: : We measured serum NSE levels in 385 patients with multiple sclerosis (MS) (264 with relapsing– remitting (RR) MS, 86 with secondary progressive (SP) MS, and 35 with primary progressive (PP) MS), and compared levels between disease courses, between users and non-users of immunomodulatory treatment, and between patients with worsening or stable disability at one year follow-up (available in 161 patients). We also investigated the correlation between serum NSE and Expanded Disability Status Scale (EDSS) and MS Severity Score (MSSS) scores in the whole cohort and in subgroups, and built a multiple linear regression model to assess the influence of predictor variables on serum NSE. Results: : Age was the only independent predictor of serum NSE levels in the multiple linear regression model. In the subgroup of patients with PPMS, there was a moderate correlation between serum NSE and increasing MSSS (Pearson's r 0.35, p = 0.04) and EDSS (Spearman's rho 0.37, p = 0.03) scores. Conclusion: : Our data do not support the use of serum NSE as a prognostic biomarker in RRMS or SPMS. The correlations of serum NSE with EDSS and MSSS in the PPMS subgroup are interesting, but based on a small sample size and require replication in other cohorts. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease of the central nervous system (CNS) with unknown cause [1]. The disease course of MS varies widely between individual patients, and one of the most consistent findings in natural history studies of the disease is the wide variety in disease severity, or the time taken to landmark disability scores [2]. While some factors, such as the age at disease onset and relapsing–remitting versus primary progressive disease course are associated with disability accumulation, it remains very difficult to predict the prognosis of MS in an individual patient [2]. The search for sensitive biomarkers that predict the disease course is currently an active area of research [3]. Such biomarkers could be used to predict the overall prognosis for patient counseling and to select patients at highest risk for a severe disease course for more aggressive treatment. An additional important use for biomarkers is as outcome measures in phase 2 trials for new treatments for MS, which currently rely on imaging and clinical outcome measures alone.

☆ The authors declare that there is no conflict of interest. ⁎ Corresponding author at: Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. Tel.: +1 403 944 2509; fax: +1 403 270 7162. E-mail address: [email protected] (M.W. Koch).

http://dx.doi.org/10.1016/j.jns.2015.02.009 0022-510X/© 2015 Elsevier B.V. All rights reserved.

Despite much research, sensitive biomarkers predicting the disease course of MS are currently lacking. Several previous studies have investigated the protein biomarker Neuron Specific Enolase (NSE) in MS [4–7]. NSE is an important enzyme for glycolysis that is most commonly expressed in neurons, and it was hypothesized that levels of NSE in the cerebrospinal fluid (CSF) or serum could be used as a marker of neuronal degeneration [8]. The underlying reasoning here was that high levels of NSE would be associated with high levels of neuronal death and subsequent secretion of NSE into CSF or serum, or alternatively that low levels of NSE would be a reflection of an overall reduced pool of neurons in the CNS. Previous studies on NSE levels in MS were relatively small in size and most often lacked follow-up of clinical outcomes. Here we present our findings on the association of serum NSE levels and disability accumulation in a large cohort of patients with MS. 2. Methods 2.1. Study participants and clinical data Data for this study were collected in a large longitudinal study that enrolled patients from the Calgary MS clinic. The Calgary MS clinic is the main care centre for patients with MS in the southern part of the province of Alberta in Canada. Patients had a diagnosis of MS according to the Poser [9] or McDonald [10] diagnostic criteria. Expanded Disability Status Scale (EDSS) [11] scores and disease course were recorded by

M.W. Koch et al. / Journal of the Neurological Sciences 350 (2015) 46–50

an MS neurologist at the Calgary MS clinic. Venous blood samples were drawn through venipuncture in the antecubital fossa at the MS clinic on the same day, and the serum was separated through centrifugation. The samples were inspected and hemolytic samples were discarded. The serum samples were stored at −80 °C until analysis. The Multiple Sclerosis Severity Score (MSSS) [12] is a general measure of the speed of disability accumulation, based on a very large patient cohort drawn from several natural history cohorts. An MSSS score of above 5.0 denotes higher than average speed of disability accumulation. We calculated MSSS scores at baseline according to the method proposed by Roxburgh and colleagues [12]. One year followup data were obtained from the MS clinic database. We defined clinic visits that fell between 11 and 13 months after the date of the serum sample as one year follow-up visits. The study was approved by the University of Calgary Research Ethics Board. Informed consent was received from all participants. 2.2. NSE measurement NSE levels were measured in the serum samples with a commercially available ELISA kit (Human Enolase 2/Neuron-specific Enolase Quantikine ELISA Kit, R&D systems, Minneapolis, Minnesota, United States of America) according to the manufacturer's instructions. 2.3. Statistical analysis The difference in NSE levels between patients with an EDSS score of below 6.0 and 6.0 and higher at baseline, between patients with increased and stable EDSS at one year follow-up, and between patients using and not using an immunomodulatory drug (IMD) were analyzed with Student's t test. We compared NSE levels between RRMS, SPMS and PPMS, and between five groups of patients grouped by change in their follow-up EDSS score (1.0 or more lower, 0.5 points lower, no change, 0.5 point increase, 1.0 point increase compared to baseline) with the Kruskal–Wallis test. If the Kruskal–Wallis test showed a significant difference between the groups overall, it was followed by Dunnett's post test for pairwise comparisons between subgroups. The correlation between serum NSE levels and EDSS and MSSS scores was analyzed with Spearman's rank correlation test and Pearson's correlation test, respectively. We built a multiple linear regression model to estimate the effect of predictor variables on serum NSE levels. In this model serum NSE was the dependent variable, and age, sex disease duration, disease course EDSS at baseline and IMD use were entered as independent predictor variables. Statistical significance was taken to be at the two-sided 0.05 level. All statistical analyses were performed with the R statistical software package for Windows, version 3.0.1 [13]. 3. Results 3.1. Patient characteristics Patient characteristics at baseline and at one year follow-up are shown in Table 1. We included 385 patients with MS, 302 women and 83 men. Of the 385 patients, 264 had relapsing–remitting MS (RRMS), 86 had secondary progressive MS (SPMS), and 35 had primary progressive MS (PPMS). The baseline EDSS score was 6.0 or higher in 120 patients, and lower than 6.0 in 265 patients. The baseline MSSS score was higher than 5.0 in 159 patients, and 5.0 or lower in 226 patients. Of the 350 patients with either RRMS or SPMS, 204 were using an IMD; 115 were using glatiramer acetate, 88 were using interferon beta, and one patient was using mitoxantrone. None of the patients with PPMS were using an IMD. One year follow-up data was available for 161 patients 129 women and 32 men. Of these patients, 127 had RRMS, 21 SPMS, and 13 had PPMS. The EDSS at one year follow-up was increased compared to

47

Table 1 Patient characteristics at baseline and at one year follow-up.

Baseline n Sex: female/male Age (median, IQR) Disease duration (median, IQR) EDSS (median, IQR) MSSS (mean, SD) Serum NSE level [μg/L] (mean, SD) One year follow-up n Sex: female/male Age (median, IQR) Disease duration (median, IQR) EDSS (median, IQR) Number EDSS increased/stable

Overall

RRMS

SPMS

PPMS

385 302/83 47, 40–54 11, 6–19

264 213/51 45, 38–50 9, 5.75–16

3.0, 2.0–6.0 4.44, 2.58 2.83, 0.75

2.5, 1.5–3.5 3.47, 2.13 2.78, 0.7

86 69/17 53, 47–57.75 18, 12.25–24.75 6.5, 6.0–6.5

35 20/15 52, 47.5–59 11, 5–17.5 6.0, 4.0–6.5 6.95, 2.16 3.15, 0.98

161 129/32 48, 41–54 11, 7–18

127 107/20 46, 40–51

21 14/7 51, 46–56

11, 7–17

14, 8–20

3.0, 2.0–6.0 49/112

2.5, 2.0–4.25 39/88

6.5, 6.0–6.5

6.4, 2.17 2.87, 0.79

8/13

13 8/5 54, 49–64 8, 7–17 6.0, 4.0–6.5 2/11

IQR: interquartile range, SD: standard deviation.

baseline in 49 patients, and unchanged or lower in 112 patients. For further analyses, we divided patients into five groups according to the change in EDSS score at followup: 1.0 points or more lower (n = 10), 0.5 points lower (n = 18), unchanged (n = 84), 0.5 points higher (n = 24), and 1.0 points or higher (n = 25).

3.2. NSE levels The results of the group comparisons at baseline and one year follow-up are shown in Table 2. Serum NSE levels were increased in SPMS compared with the RRMS and in the PPMS compared with SPMS, with an overall significant difference between the groups (Kruskal–Wallis p = 0.04, Table 2). Dunnett's post hoc test for pairwise comparisons showed a significant difference between PPMS and RRMS Table 2 Comparison of serum NSE levels between different patient groups at baseline and one year follow-up. There is a significant difference in serum NSE levels between the disease courses of MS with levels increasing from RRMS to SPMS to PPMS. Patient group

n

Serum NSE level (SD) [μg/L]

p

Whole cohort RRMS patients SPMS patients PPMS patients Patients with RRMS or SPMS on IMD Patients with RRMS or SPMS not on IMD EDSS at baseline below 6.0 EDSS at baseline 6.0 or higher MSSS at baseline above 5.0 MSSS at baseline 5.0 or lower All patients with one year follow-up EDSS at one year follow-up increased EDSS at one year follow-up unchanged or lower EDSS at one year follow-up 1.0 or more lower EDSS at one year follow-up 0.5 lower EDSS at one year follow-up unchanged EDSS at one year follow-up 0.5 higher EDSS at one year follow-up 1.0 or more higher

385 264 86 35 204 146 265 120 159 226 161 49 112 10 18 84 24 25

2.83 (0.76) 2.78 (0.70) 2.87 (0.79) 3.15 (0.98) 2.91 (0.73) 2.72 (0.71) 2.79 (0.73) 2.92 (0.81) 2.8 (0.72) 2.89 (0.8) 2.8 (0.72) 2.78 (0.76) 2.84 (0.64) 2.48 (0.63) 2.75 (1.06) 2.82 (0.7) 2.84 (0.76) 2.83 (0.52)



IMD: immunomodulatory drug, SD: standard deviation. a Kruskal–Wallis test. b t-Test.

0.04a 0.02a 0.15b 0.26b – 0.61b

0.28a

48

M.W. Koch et al. / Journal of the Neurological Sciences 350 (2015) 46–50

patients (p = 0.03), but no differences between RRMS and SPMS (p = 0.55) or between SPMS and PPMS (p = 0.19). There was also a significant difference in NSE levels between patients using and not using an IMD (p = 0.02). There was no significant difference in serum NSE levels between patients with an EDSS score of below 6.0 and 6.0 or higher (p = 0.15), or with MSSS scores of higher than 5.0 and 5.0 or lower (p = 0.26). There was also no significant difference in NSE levels between patients with increased and unchanged or lower EDSS score at follow-up (p = 0.61), or between the five groups subdivided by change in EDSS score at follow-up (p = 0.28). The results of the multiple linear regression model are given in Table 3. Age was the only independent predictor variable in the model (p = 0.002, with an estimated 0.01 mmol/L increase in serum NSE level per year of age increase).

Table 4 Results of the correlation analyses. There is a moderate positive correlation between serum NSE levels and MSSS and EDSS scores in the PPMS subgroup.

3.3. Correlation analyses

in patients with PPMS, serum NSE levels were positively correlated with both disability as measured with the EDSS and with the speed of disability accumulation, or disease severity as measured with the MSSS. The multiple linear regression model showed that age was the only independent predictor of serum NSE levels, with higher levels in older patients. A difference in age is the most likely explanation for our finding of higher NSE levels in patients with PPMS and SPMS compared to RRMS, as well as lower NSE levels in patients using IMD, since patients with progressive disease courses are older than patients with RRMS, and IMD are more often prescribed in younger patients. In our cohort, there was a highly significant difference in age between the RRMS, SPMS and PPMS groups and between patient using and not using IMD. Previous reports on the influence of age on serum and CSF levels of NSE are inconclusive; some studies found increasing levels with advancing age, [14,15] while others did not [16,17]. Our findings suggest that age could be an important confounding factor in studies on serum NSE levels, highlighting the importance of age matching groups in future studies on NSE in MS. The pathophysiology of MS is driven by both inflammation and neurodegeneration. Inflammatory demyelinating lesions are the hallmark of the early disease phase and the relapsing–remitting disease course, while diffuse inflammatory changes and neurodegeneration are more prominent in progressive MS, in particular in PPMS [18–20]. Our finding that serum NSE levels are correlated with disability and disease severity in the PPMS, but not in the other patient groups, suggests that serum NSE may be a marker of neuronal degeneration in MS. It is documented that serum NSE levels are elevated in other diseases that are associated with acute neuronal injury, such as traumatic brain injury and stroke [21,22]. While it is tempting to speculate that the correlation of serum NSE levels with disability accumulation in the PPMS group may be a reflection of ongoing neurodegeneration, it should be kept in mind that the PPMS group in our study was the smallest of the included patient groups, and that the observed correlations and differences in level

The results of the correlation analyses are shown in Table 4. There were no strong or significant correlations between serum NSE levels and EDSS and MSSS scores in the whole cohort. In the group of patients with PPMS, there was a moderate significant positive correlation between serum NSE levels and both MSSS (Pearson's correlation coefficient 0.35, p = 0.04, Fig. 1) and EDSS scores (Spearman's correlation coefficient 0.37, p = 0.03, Fig. 1). Repeating these correlation analyses as partial correlations corrected for age gave similar results (data not shown). In patients with EDSS scores of 6.0 or higher, there was a weak, borderline significant correlation between serum NSE levels and EDSS scores (Speaman's correlation coefficient 0.18, p = 0.05). There were no other significant or strong correlations (Table 4). 3.4. Additional analyses Since the multiple linear regression model identified age as the only independent predictor of serum NSE levels, we explored whether there were age differences between the different disease courses, and between patients using and not using IMD, since these were the only variables with significant differences in serum NSE levels. There was a highly significant age difference between RRMS, SPMS and PPMS, with an overall significant difference between the groups (Kruskal–Wallis p ≤ 0.0001). Dunnett's post hoc test for pairwise comparisons showed a significant difference between PPMS and RRMS patients and between SPMS and RRMS (both p b 0.0001), but no significant differences between SPMS and PPMS (p = 0.08). There was a highly significant age difference between patients using and not using IMD (p b 0001). 4. Discussion In this large cohort study, serum NSE levels were not meaningfully related to clinical outcomes in patients with RRMS or SPMS; however,

Patient group

n

EDSSa

p

MSSSb

p

Whole cohort RRMS SPMS PPMS EDSS at baseline below 6.0 EDSS at baseline 6.0 or higher MSSS at baseline above 5.0 MSSS at baseline 5.0 or lower

385 264 86 35 265 120 159 226

0.08 0.03 −0.11 0.37 −0.03 0.18 0.13 0.03

0.10 0.64 0.11 0.03 0.59 0.05 0.09 0.63

0.03 −0.07 −0.12 0.35 −0.06 0.05 0.04 −0.08

0.5 0.22 0.26 0.04 0.32 0.56 0.61 0.18

a b

Spearman correlation coefficients. Pearson correlation coefficients.

Table 3 Summary of the multiple linear regression model. Age is the only independent predictor of serum NSE level. Coefficients

Estimate (95% CI)

Standard error

p

Agea Disease durationa IMD: no (reference) yes Sex: Female (reference) Male Disease course: RRMS (reference) SPMS PPMS

0.01 (0.005 to 0.24) 0.0008 (−0.009 to 0.01)

0.005 0.005

0.002 0.87

(Reference) −0.14 (−0.33 to 0.33)

(Reference) 0.09

0.1

(Reference) 0.04 (−0.14 to 0.22)

(Reference) 0.09

0.7

(Reference) −0.08 (−0.34 to 0.18) 0.14 (−0.19 to 0.47)

(Reference) 0.13 0.17

0.54 0.41

IMD: immunomodulatory drug, CI: confidence interval. a Per year increase.

M.W. Koch et al. / Journal of the Neurological Sciences 350 (2015) 46–50

49

Fig. 1. Correlation analyses showing a moderate significant positive correlation between serum NSE levels and EDSS and MSSS scores in patients with PPMS.

were weak to moderate. A possible source of error in all studies on NSE is the fact that NSE does not occur exclusively in neurons, but also in erythrocytes and platelets. We tried to avoid any confounding through hemolytic samples by inspecting the samples for signs of hemolysis. The literature on serum NSE levels in MS is dominated by small studies that are difficult to compare to one another. One early study on serum NSE levels in several neurological diseases showed levels within the normal range in 21 patients with MS, without differences between patients in a relapse or remission [4]. Another study reported no difference in serum and CSF NSE levels between patients with CIS (n = 20) and RRMS (n = 45) and normal control persons (n = 83) [5]. This is in contrast to a study comparing serum and CSF NSE levels between CIS patients and normal controls, which found lower serum and CSF NSE in patients with CIS [6]. Another study in 61 patients with MS found lower levels in patients with PPMS (n = 16) than in SPMS (n = 23) and RRMS (n = 25), a moderate negative correlation between serum NSE levels and EDSS and MSSS scores in the whole patient cohort, and lower NSE levels in patients with worsening disability over a five-year period, suggesting that lower NSE levels may be predictive of disease progression [7]. These two studies suggested that lower NSE levels in CIS and MS may be a reflection of decreased neuronal energy metabolism or a decreased pool of neurons due to ongoing neurodegeneration. These findings are in conflict with our results. Further studies are necessary to evaluate the possible association of serum NSE levels and disease progression in PPMS, and the possible value of serum NSE as a biomarker. Such studies should include MRI measures of brain atrophy. Since NSE can also be easily measured in CSF, it may be worthwhile to compare serum and CSF NSE levels with regard to their relation to disease progression in MS. Given the large number of RRMS and SPMS patients in our study, we are more confident in concluding that serum NSE is unlikely to be a useful prognostic marker in these disease subtypes. In summary, our data do not support the use of serum NSE as a sensitive prognostic biomarker in RRMS or SPMS. The observation that serum NSE levels and EDSS and MSSS scores are positively correlated in PPMS is interesting, and may suggest that the increased neuronal degeneration in PPMS may be measurable via serum NSE in peripheral blood. However, our findings in this subgroup are based on a relatively small number of only 35 patients. Future studies on serum NSE in PPMS patients should focus on PPMS, and compare other disability measures, such as cognitive performance, and imaging measures of brain atrophy.

Conflict of interest statement The authors declare that there is no conflict of interest.

References [1] Noseworthy JH, Lucchinetti C, Rodriguez M, Weinshenker BG. Multiple sclerosis. N Engl J Med Sep 28 2000;343(13):938–52. [2] Renoux C. Natural history of multiple sclerosis: long-term prognostic factors. Neurol Clin May 2011;29(2):293–308. [3] Comabella M, Montalban X. Body fluid biomarkers in multiple sclerosis. Lancet Neurol Jan 2014;13(1):113–26. [4] Cunningham RT, Morrow JI, Johnston CF, Buchanan KD. Serum neurone-specific enolase concentrations in patients with neurological disorders. Clin Chim Acta Int J Clin Chem Oct 31 1994;230(2):117–24. [5] Sladkova V, Mareš J, Lubenova B, Zapletalova J, Stejskal D, Hlustik P, et al. Degenerative and inflammatory markers in the cerebrospinal fluid of multiple sclerosis patients with relapsing–remitting course of disease and after clinical isolated syndrome. Neurol Res May 2011;33(4):415–20. [6] Hein K, Kohler A, Diem R, Sattler M, Demmer I, Lange P, et al. Biological markers for axonal degeneration in CSF and blood of patients with the first event indicative for multiple sclerosis. Neurosci Lett May 2 2008;436(1):72–6. [7] Koch M, Mostert J, Heersema D, Teelken A, De Keyser J. Plasma S100beta and NSE levels and progression in multiple sclerosis. J Neurol Sci Jan 31 2007;252(2):154–8. [8] Marangos PJ, Paul SM. Brain levels of neuron-specific and nonneuronal enolase in Huntington's disease. J Neurochem Nov 1981;37(5):1338–40. [9] Poser CM, Paty DW, Scheinberg L, McDonald WI, Davis FA, Ebers GC, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol Mar 1983;13(3):227–31. [10] Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol 2011;69(2):292–302. [11] Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology Nov 1983;33(11):1444–52. [12] Roxburgh RHSR, Seaman SR, Masterman T, Hensiek AE, Sawcer SJ, Vukusic S, et al. Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology Apr 12 2005;64(7):1144–51. [13] R Development Core Team. R: a language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2013[Available from: http://www.R-project.org/]. [14] Van Engelen BG, Lamers KJ, Gabreels FJ, Wevers RA, van Geel WJ, Borm GF. Agerelated changes of neuron-specific enolase, S-100 protein, and myelin basic protein concentrations in cerebrospinal fluid. Clin Chem Jun 1992;38(6):813–6. [15] Nygaard O, Langbakk B, Romner B. Neuron-specific enolase concentrations in serum and cerebrospinal fluid in patients with no previous history of neurological disorder. Scand J Clin Lab Invest May 1998;58(3):183–6. [16] Schaf DV, Tort ABL, Fricke D, Schestatsky P, Portela LVC, Souza DO, et al. S100B and NSE serum levels in patients with Parkinson's disease. Parkinsonism Relat Disord Jan 2005;11(1):39–43. [17] Casmiro M, Maitan S, De Pasquale F, Cova V, Scarpa E, Vignatelli L. Cerebrospinal fluid and serum neuron-specific enolase concentrations in a normal population. Eur J Neurol Off J Eur Fed Neurol Soc May 2005;12(5):369–74.

50

M.W. Koch et al. / Journal of the Neurological Sciences 350 (2015) 46–50

[18] Lassmann H. Multiple sclerosis pathology: evolution of pathogenetic concepts. Brain Pathol Jul 2005;15(3):217–22. [19] Lucchinetti CF, Popescu BFG, Bunyan RF, Moll NM, Roemer SF, Lassmann H, et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med Dec 8 2011;365(23):2188–97. [20] Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol Nov 5 2012;8(11):647–56.

[21] Jauch EC, Lindsell C, Broderick J, Fagan SC, Tilley BC, Levine SR, et al. Association of serial biochemical markers with acute ischemic stroke: the National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator Stroke Study. Stroke J Cereb Circ Oct 2006;37(10):2508–13. [22] Chabok SY, Moghadam AD, Saneei Z, Amlashi FG, Leili EK, Amiri ZM. Neuron-specific enolase and S100BB as outcome predictors in severe diffuse axonal injury. J Trauma Acute Care Surg Jun 2012;72(6):1654–7.

Serum NSE level and disability progression in multiple sclerosis.

Previous studies suggested that serum neuron specific enolase (NSE) may be a biomarker associated with progression in MS...
255KB Sizes 0 Downloads 21 Views