| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Neuroradiology |
1 From the Department of Radiology, New York University School of Medicine, 560 First Ave, New York, NY 10016 (O.G., D.M.M., B.S.Y.L., J.S.B., J.H., R.I.G.); and Department of Neurology, University of Pennsylvania Medical Center, Philadelphia (J.L., D.J., C.E.M.). Received July 26, 2001; revision requested September 6; final revision received March 13, 2002; accepted March 25. Supported by NIH grants NS33385, NS37739, and NS29029. Address correspondence to R.I.G. (e-mail: robert.grossman@med.nyu.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
MATERIALS AND METHODS: Whole-brain NAA (WBNAA) concentration was quantified in 49 patients with relapsing-remitting MS by using magnetic resonance (MR) imaging and proton MR spectroscopy. It was statistically analyzed by using Spearman rank correlation coefficients to test the intragroup relationship between WBNAA and Expanded Disability Status Scale (EDSS) score and Mann-Whitney analyses to test for differences between subgroups’ EDSS scores versus previously published WBNAA values for healthy subjects, disease duration, and age.
RESULTS: Analyses indicated three subgroups of WBNAA dynamics: Ten patients’ conditions were "stable," exhibiting an insignificant change of about 0% (0.02/14.37) per year of clinically definite disease duration (P = .54); 27 patients showed "moderate" decline, -2.8% (-0.34/12.18) per year (P < .01); and 12 patients experienced "rapid" decline, -27.9% (-3.39/12.14) per year (P < .01). No correlation was found between WBNAA deficit, EDSS score, and age.
CONCLUSION: Ascertaining an individual’s NAA concentration dynamics might enable early forecast of disease course, reflect disease severity and thus influence treatment decisions, and improve clinical trial efficiency by allowing selection of candidates on the basis of WBNAA dynamics in addition to clinical status.
© RSNA, 2002
Index terms: Magnetic resonance (MR), spectroscopy, 10.12145 • Sclerosis, multiple, 10.871
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
Several long-term therapies for MS have received Food and Drug Administration approval in the United States, for example, interferon beta-1
(Avonex; Biogen, Cambridge, Mass), interferon beta-1ß (Betaseron; Berlex Laboratories, Richmond, Calif), and copolymer-1 (Copaxone; Teva Pharmaceuticals USA, North Wales, Penn) (3–5). However, at an approximate annual cost of $15,000 per patient (6), spending in the United States alone exceeds $2.5 billion per year (7). Consequently, outside the United States, the cost-benefit ratio of the drugs is controversial, and treatment is not universally offered. In the United Kingdom, for example, interferon is administered to only 3% of patients (8).
Considering the early age of onset, disease duration, treatment cost, and the disease’s side effects and inconvenience, both patient and neurologist face three central questions: (a) What is the disease’s probable long-term course? (b) Is its activity severe enough to need therapeutic intervention? (c) What is the efficacy of therapy? Unfortunately, as far as we are aware, there are currently no reliable prognostic indices, as clinical and cognitive measures do not enable prediction of future course (9–11). Laboratory markers of disease progression, such as oligoclonal bands, have been only moderately useful and are invasive (12). Magnetic resonance (MR) imaging methods, although highly sensitive to lesions, even in individuals having their first clinical episode and not yet confirmed as having clinically definite MS (13,14), provide little prognostic information because of the variable course and pathologic heterogeneity of the disease (15,16).
It has been suggested that axonal damage followed by neuronal cell death from wallerian degeneration is the probable cause of permanent neurologic deficits in MS (17,18). This can be assessed directly with proton MR spectroscopic quantification of the amino-acid derivative N-acetylaspartate (NAA) (19,20), found almost exclusively in neurons and axons (21-23). Its decline in lesions has been detected with proton MR spectroscopy (19,24,25) and established directly with histopathologic findings (26,27). However, MS abnormalities are by nature diffuse, whereas MR imaging–depicted lesions are focal and rarely exceed 5% of total brain volume (28). Therefore, NAA assessment of the entire parenchyma is crucial to evaluate the full extent of the disease. Indeed, a proton MR spectroscopic method to quantify whole-brain NAA (WBNAA) concentration has shown that this concentration can be more than 20% lower in patients with relapsing-remitting MS than in their healthy contemporaries and declines 10 times faster with age (29,30).
The need for effective prognostic markers for MS, coupled with the direct link established between the disease, axonal damage, and NAA deficit, motivated the present study, the purpose of which was to quantify the rate of concentration decline of the neuronal marker NAA in the entire brain of patients with relapsing-remitting MS.
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
Y1) was defined as
|
|
|
|
for a nonselective, 1-msec, 180° inversion pulse on the phantom and subject, respectively, and reflect the momentary system’s sensitivity. To address the natural variations in brain size, QNAA was divided by that subject’s brain volume, obtained with MR imaging at 1.5 T (intermediate- and T2-weighted fast spin-echo imaging, with a repetition time msec/first echo time [TE] msec/second TE msec of 2,500/16/80, 256 x 256 matrix, 220-mm2 field of view, and 3-mm-thick sections). Images were processed by using the noncommercial 3DVIEWNIX package, which, on the basis of several manually preselected intensity points in the cerebrospinal fluid and gray and white matter, creates a brain mask (32). The subject’s brain volume is the sum of the pixels in this mask. The method has been shown to have better than 99% reproducibility (33).
Statistical Analysis
Authors of a previous study of nine female and four male control subjects, aged 16–52 years, estimated their mean WBNAA concentration to be 13.2 mmol/L ± 0.6 (SD) (30). Thus, by comparison, the ith patient’s mean rate of WBNAA decline per year of disease, 
ji, was estimated as:
|
Yji years of disease, as defined in Equation (1) for j = 1 or in Equation (2) for j = 2. It is important to point out that Equation (4) implicitly assumes that the WBNAA decline relative to "normal" (13.2 mmol/L ± 0.6) started either at, or at most, shortly before diagnosis for j = 1, or at the first symptom for j = 2.
The 49 patients were put into subgroups based on their ji value, as described subsequently. Least-squares regression analysis was performed to assess the relationship of WBNAA with Y1,2 and age in each group. Since the subgroups were constructed on the basis of estimated ji value, they could not be meaningfully cross compared with regard to their average rate of WBNAA decline. Furthermore, since only 11 patients were receiving immunomodulatory treatments of various type and length, either in absolute terms or as a percentage of disease duration, this factor was excluded from the analyses. Spearman rank correlation coefficients were produced to test the within-group relationship between WBNAA level and EDSS score. Mann-Whitney analyses were used to test for differences between the subgroups’ EDSS scores versus previously published WBNAA values for healthy subjects, disease duration, and age.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
1i).—For the 49 patients, 1i values for Equation (4) are plotted in Figure 1, a. Because a previous study of 13 control subjects (30) showed WBNAA level to be statistically constant with age, the condition of the 10 patients whose 1i values were 0 mmol/L/y or less is described as "stable" in Figure 1b. Since the reproducibility of WBNAA level was shown to have a 
value of 0.6 mmol/L (29), the condition of the 12 individuals with an 1i value greater than or equal to 3 per year (1.7 mmol/L/y) was deemed to be significantly different from stable, and these patients were described as undergoing "rapid" decline. The bulk of the cohort, 27 patients (in whom 0 
1i 1.7 mmol/L/y), was described as undergoing "moderate" decline, as shown in Figure 1b. Individual WBNAA levels and dynamics are compiled in Table 2.
|
|
Y1 values from Equation (1) are plotted in Figure 2. The symbolic labels for each patient were determined by their group assignment, described previously, and are shown in Figure 1, b. It is striking that although Figure 1 displays a nearly continuous distribution, Figure 2 readily exhibits three distinct subgroups, without any further assumptions or postprocessing.
|
Y1 in the subgroups. It showed that for the stable group, the linear relationship was
|
Y1 years, is defined by Equation 1. Similarly, the linear prediction equation for the moderate group was
|
|
To assess whether a linear model best predicts WBNAA behavior in each group, least-squares polynomial regression was also performed. The model function included terms up to cubic in disease duration. In every subgroup, however, neither cubic nor quadratic terms were found to be statistically significant (P > .19).
Expressing these annual changes as percentages of the intercept in Equations (5)–(7) yields 0% (0.02/14.37), -2.8% (-0.34/12.18), and -27.9% (-3.39/12.14) for the stable, moderate, and rapid subgroups, respectively. The regression lines of Equations (5)–(7), together with their respective 95% CIs, are also plotted in Figure 2. The median disease durations in the stable and moderate subgroups were not significantly different (P = .11) but were both longer (P < .01) than in the rapid subgroup.
WBNAA Correlation with Disease Duration from First Symptom
Annual WBNAA decline rates (2i).—The 2i value in Equation (4) for each patient is plotted in Figure 3, together with the cutoffs described in the annual WBNAA decline rates. Overall, the decline rates of five patients placed in the rapid group according to 1i value became moderate, also according to 2i value. No exchanges occurred between the moderate and stable subgroups.
|
Y2 levels from Equation (2) are shown in Figure 4, with the symbolic labels consistent with the patient’s position relative to the cutoffs in Figure 3. Least-squares regression analysis was used to characterize a linear relationship between WBNAA and Y2:
|
|
|
|
WBNAA Level Correlation with Patient Age
No significant correlation was found between age and WBNAA level for any subgroup.
WBNAA Level Correlation with EDSS Score
EDSS scores did not differ significantly between any of the subgroups (Table 1). The cohort’s WBNAA levels did not correlate with EDSS score (P > .05). Between-subgroup correlation was inappropriate, as the groupings resulted from a statistical construct.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
This study provides evidence for the existence of three differential strata of axonal dysfunction in 49 clinically similar patients with relapsing-remitting MS (average EDSS scores in Table 1), on the basis of the cross-sectional rate of whole-brain NAA level decline as a function of disease duration. This indicates that despite clinical similarity, these patients sustained disparate levels of axonal damage that accumulated at different rates. Specifically, a stable subgroup composed of 20% (10 patients) of the cohort exhibited constant WBNAA levels, similar to those in matched control subjects (30). A second subgroup composed of a majority (55% [27 patients]) of the patients sustained a moderate 0.34 mmol/L/y (2.7%) loss. Compared with them, the remaining 25% (12 patients) had a tenfold more rapid decline, and their median disease duration was significantly shorter than that in either of the other two subgroups.
No subgroup exhibited a correlation between WBNAA decline and age. Together with absolute WBNAA levels at or above normal, 13.2 mmol/L ± 0.6, this reflects neuronal maintenance in the stable subgroup. The rapid subgroup’s WBNAA levels did not correlate with age, either, but their low WBNAA levels suggest that they represent a different aggressive variant of MS with onset at any age, not a form of moderate disease that suddenly accelerated. Furthermore, results of this study indicate that the decline observed in two of the three subgroups (Figs 2, 4) and in a majority of patients is, on average, a continuous one-way process. Therefore, even if local or global, repair and recovery are possible, as previously suggested (20,36,37), they may be only partial and temporary. The long-term trend of MS is global average decline, as is expected with a chronic degenerative disease.
Since WBNAA measurement enables evaluation of the entire brain, deviations from normal reflect the current total axonal deficit, and its rate of decline is an index of disease aggression. In the current study, patients underwent WBNAA measurement only once; hence, the rates reported are cross sectional, not derived from serial follow-ups. Nevertheless, this does not detract from the general utility of these results, especially since current medical practice requires two separate clinical episodes to confirm MS.
The findings of the current study suggest that WBNAA level should be evaluated at presentation of the first neurologic symptom for three reasons. First, a deficit, as compared with the 13.2 mmol/L ± 0.6 average normal level, could indicate a developing abnormality (35). This is demonstrated in Figures 2 and 4, which depict below-normal WBNAA levels in all patients in the moderate and rapid subgroups. Second, if (or when) these individuals have another clinical episode at presentation, a second WBNAA-level evaluation at that time would establish their individual rate of axonal dysfunction, facilitating subgroup assignment at disease confirmation. Alternatively, since it is common for patients to go several years between first and second relapses, it may be appropriate to schedule a second WBNAA examination 1 year after the initial event; this is a common interval for patient consultations. Third, comparison of WBNAA level as a function of Y1 versus Y2 shows that the latter, which is a better estimate of true disease duration, results in assignment of fewer patients to the rapid subgroup. Considering the prognostic and treatment-staging consequences of belonging to that subgroup (38), described subsequently, it is imperative that this assignment be as accurate as possible.
The disparity between the patients’ clinical similarities and their WBNAA levels and dynamics reflects the brain’s ability to compensate for accumulating injury and to conceal its extent. This plasticity underlies the difficulty of using clinical criteria such as EDSS score to predict disease course. While it is a useful clinical measure of neurologic impairment, EDSS score consistently fails to reflect the full burden of the disease because of its weighting toward cerebellar and spinal cord deficits (9,15). In contrast, WBNAA yields the cerebral pathologic load directly, and its inferred rate of change in the moderate and rapid subgroups well exceeds the approximate 1% per year reported for global atrophy (39,40). Therefore, we hypothesize that the subgroup NAA dynamics presented herein predict the future maintenance of clinical function and, therefore, could establish long-term prognosis, treatment urgency, and more effective candidate selection criteria for clinical trials.
Prognosis
A major concern for patients with a new diagnosis is the future course of their disease. To the best of our knowledge, no clinical or paraclinical measure provides a definitive forecast. Scott et al (41) isolated six indices for age, symptoms, status at MR imaging, intervals between first and second attacks, frequency of episodes in the first 2 years, and completeness of recovery. Patients receiving a classification of "high risk" for four or more indices were found to have significantly greater disease progression and lower EDSS scores. However, they comprised only 24% of the cohort, which underscores the difficulty of assigning a prognosis. In contrast, WBNAA dynamics may provide a noninvasive prognostic measure for all patients. Specifically, patients in the stable subgroup may anticipate decades of little accumulation of cerebral abnormality and thus no need for therapeutic intervention. A majority of patients, those exhibiting moderate decline, may expect to follow the established model of MS progression, with its 10- and 20-year disability landmarks (2). Indeed, perhaps because of their clinically recognized course and duration, the 11 patients receiving medication during the course of the current study were all in that group. Finally, those in the rapid subgroup should perhaps be advised, despite a short disease duration, of the increased likelihood of decline in quality of life (2,35,38) and encouraged to engage in aggressive treatment to forestall it (42,43).
Staging Treatment
Criteria for MS treatment vary from country to country. Enrollment into therapeutic regimens is usually determined by clinical status, age, general health, acceptance of injection regime, and, increasingly, funding. This study suggests that clinically similar patients with relapsing-remitting MS accumulate axonal disease at significantly different rates. Therefore, their WBNAA dynamics may provide the sought noninvasive indication for staging treatment. Specifically, it may be beneficial to start medication from the most rapidly declining population and proceed to include more patients as far left on that axis toward stable as available resources will allow (42).
Stratification for Clinical Trials
It is ethically unacceptable to conduct a prospective treatment of unknown efficacy when proven ones are available. Therefore, since new drug trials must use the least number of patients for the shortest period, induction based on pathologic rather than clinical disease status could increase their efficiency. Different levels of abnormality and dynamics, shown herein to exist in the general population of patients with relapsing-remitting MS, could confound phase II and III clinical trials with type I and II statistical errors (44). Type I errors may be encountered when patients that might fit in the stable subgroup favorably bias an ineffective drug as effective. The more detrimental type II errors could be incurred when patients that might fit into the rapid subgroup erroneously cause rejection of an effective drug because of inadequate response. Considering the cost of pharmaceutical development, these could be expensive mistakes (44). Consequently, randomized recruitment based entirely on clinical metrics such as EDSS score will necessitate larger sample sizes and longer durations to achieve a given statistical power (43,45) than homogeneous moderate or rapid cohorts selected on the basis of WBNAA dynamics.
WBNAA measurement may, in addition, enable monitoring of the neuroprotective ability of a putative treatment, since it is possible to measure global NAA deficit at presentation and the rate of its loss serially over time. This would indicate the individual patients’ neuronal integrity at the start of, during, and at the end of drug studies. However, an intrinsic limitation of the WBNAA measurement approach is that it is insensitive to spinal cord disease. Thus, patients with predominantly spinal disease may be given an incorrect encouraging prognosis. The clinical effect of spinal lesions can be substantial, either through specific damage to eloquent areas or by causing wallerian degeneration upstream into the brain (18).
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
Author contributions: Guarantor of integrity of entire study, R.I.G.; study concepts, O.G., D.M.M., R.I.G.; study design, O.G., R.I.G.; literature research, O.G., D.M.M., R.I.G.; clinical studies, O.G., D.M.M., B.S.Y.L., J.L., J.H., D.J., C.E.M., R.I.G.; experimental studies, O.G., D.M.M., B.S.Y.L., J.L., R.I.G.; data acquisition, O.G., D.M.M., B.S.Y.L., J.L.; data analysis/interpretation, O.G., D.M.M., B.S.Y.L., J.H., J.S.B.; statistical analysis, J.S.B.; manuscript preparation, O.G., D.M.M., J.S.B.; manuscript definition of intellectual content, O.G., C.E.M., R.I.G.; manuscript editing, O.G., B.S.Y.L., C.E.M., R.I.G.; manuscript revision/review and final version approval, O.G., R.I.G.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSIONREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  D.J. Rigotti, M. Inglese, and O. Gonen Whole-Brain N-Acetylaspartate as a Surrogate Marker of Neuronal Damage in Diffuse Neurologic Disorders AJNR Am. J. Neuroradiol., November 1, 2007; 28(10): 1843 - 1849. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
O. Gonen, T.A. Oberndorfer, M. Inglese, J.S. Babb, J. Herbert, and R.I. Grossman Reproducibility of Three Whole-Brain N-Acetylaspartate Decline Cohorts in Relapsing-Remitting Multiple Sclerosis AJNR Am. J. Neuroradiol., February 1, 2007; 28(2): 267 - 271. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Ge Multiple Sclerosis: The Role of MR Imaging AJNR Am. J. Neuroradiol., June 1, 2006; 27(6): 1165 - 1176. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Rovaris, A. Gallo, A. Falini, B. Benedetti, P. Rossi, M. Comola, G. Scotti, G. Comi, and M. Filippi Axonal Injury and Overall Tissue Loss Are Not Related in Primary Progressive Multiple Sclerosis Arch Neurol, June 1, 2005; 62(6): 898 - 902. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. C. Tartaglia, S. Narayanan, S. J. Francis, A. C. Santos, N. De Stefano, Y. Lapierre, and D. L. Arnold The Relationship Between Diffuse Axonal Damage and Fatigue in Multiple Sclerosis Arch Neurol, February 1, 2004; 61(2): 201 - 207. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Adalsteinsson, A. Langer-Gould, R. J. Homer, A. Rao, E. V. Sullivan, C. A. Lima, A. Pfefferbaum, and S. W. Atlas Gray Matter N-Acetyl Aspartate Deficits in Secondary Progressive but Not Relapsing-Remitting Multiple Sclerosis AJNR Am. J. Neuroradiol., November 1, 2003; 24(10): 1941 - 1945. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| RADIOLOGY | RADIOGRAPHICS | RSNA JOURNALS ONLINE |
) distribution as a function of their average rate of WBNAA decline per year of clinically definite disease duration (
), moderate (
[all patients indicated with shaded boxes in this subgroup were receiving medication at the time of measurement]), and rapid (
) were determined according to the Results section. Note that individual WBNAA levels fall naturally into distinct subgroups. Solid lines = regressions for each subgroup (Eqq [5]-[7]; dashed lines = their 95% CIs.
