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Effect of glutamate excitotoxicity on multiple sclerosis-related fatigue

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Effect of glutamate excitotoxicity on multiple sclerosis-related fatigue

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Content EFFECT OF GLUTAMATE EXCITOTOXICITY ON MULTIPLE SCLEROSIS-RELATED FATIGUE
by
Sriram Ramanan
A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
BIOSTATISTICS
May 2020
Copyright 2020 Sriram Ramanan
ii
Acknowledgements
This thesis is dedicated to
my parents, brother and grandmother,
who consistently push me to my limits;
Dr. David Pelletier and Dr. Steven Yong Cen lab, and
mentors who helped me learn and succeed in this program
Thank you all for helping to give me the life I love today.

iii
List of Tables
Table 1 Demographics By Gender ................................................................................................... 9
Table 2 Mixed-Effects Model Results For Fatigue ........................................................................ 10
Table 3 Mixed-Effects Model For Depression ............................................................................... 11
Table 4 Time Dependent Correlations .......................................................................................... 18
Table 5 Baseline Pearson Correlations ......................................................................................... 18
Table 6 Independent T-Tests Between Male and Female ............................................................ 19
iv
List of Figures
Figure 1 Glutamate N-acetylaspartate Synthesis in Axonal Mitochondria .................................... 3
Figure 2 Functional Assessment in Multiple Sclerosis (FAMS) version 4........................................ 6
Figure 3 Environmental Search and Literature Review Findings .................................................. 17
Figure 4 Depression Distribution for Males and Females............................................................. 20
Figure 5 Combined Fatigue Distribution for Males and Females ................................................. 20

v
ABSTRACT
Background: Multiple Sclerosis (MS) related fatigue affects one’s quality of life and has been
linked to brain volumetric loss. Glutamate (Glu) toxicity, including the Glutamate (Glu) and N-
acetylaspartate (NAA) ratio, plays a key role in brain volumetric loss in MS patients including
axonal loss, gray matter volume atrophy, and cognitive decline. MS-fatigue has been assessed
in previous studies but not studied with respect to glutamate toxicity. Therefore, we aimed to
evaluate the relationship of fatigue and glutamate toxicity using the Functional Assessment in
Multiple Sclerosis (FAMS) behavioral instrument.
Methods: A total of 375 MS patients (112 men, 263 women), with an average age (SD) of 41.5
(9.6) were included in this longitudinal analysis. FAMS and MRI spectroscopy (to measure brain
Glu and NAA) were collected annually; number of patient visits ranged from 1 to 5 years. Linear
mixed effects models were performed to assess repeated measures correlation between
fatigue and glutamate. Demographic factors including gender and age were tested as the effect
modifiers.
Results: Gender was a statistically significant effect modifier for the association between Glu in
normal appearing gray matter (NAGM) (interaction p=0.03), Glu/NAA normal appearing white
matter(NAWM) (interaction p=0.004) and fatigue. In particular, females showed a statistically
significant association of these brain metabolites with fatigue. Females’ GluNAGM, NAANAWM
and NAANAGM levels in the brain declined as fatigue scores increased compared to males.
Discussion: Our findings demonstrate that fatigue is related to Glu toxicity which causes
neuroaxonal loss in gray matter. These findings are more prominent in females than in males.
vi
Previous findings in brain volume show that gray matter volume atrophy is associated with Glu
toxicity; in conjunction to this thesis, gray matter volume atrophy is associated with fatigue.
vii
Table of Contents
Acknowledgements..........................................................................................................................ii
List of Tables ................................................................................................................................... iii
List of Figures .................................................................................................................................. iv
ABSTRACT .........................................................................................................................................v
BACKGROUND ................................................................................................................................. 1
Multiple Sclerosis ...................................................................................................................................... 1
Glutamate and N-acetylaspartate ............................................................................................................. 1
Multiple Sclerosis-related Fatigue............................................................................................................. 4
METHODS ........................................................................................................................................ 5
Subjects and Measurement of Glu and NAA metabolite Levels ............................................................... 5
Clinical Outcomes ...................................................................................................................................... 5
Statistical Analyses .................................................................................................................................... 7
RESULTS........................................................................................................................................... 9
Demographics of the Subjects................................................................................................................... 9
Fatigue and Glutamate Toxicity in Gray Matter ........................................................................................ 9
Depression and Glutamate Toxicity in Gray Matter ............................................................................... 11
DISCUSSION ................................................................................................................................... 12
Longitudinal results between Fatigue/Depression and Glu/NAA levels ................................................. 12
REFERENCES .................................................................................................................................. 14
APPENDIX ...................................................................................................................................... 17
Environmental Research and Literature Review ..................................................................................... 17
Time Dependent and Pearson Correlation ............................................................................................. 18
Independent T-Test ................................................................................................................................. 19

1
BACKGROUND
Multiple Sclerosis
Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous
system (CNS) that attacks components of the myelin sheath in neurons
1,2
. The disease often
starts with an attack that causes gradual brain, spinal cord and optic nerve atrophy that can last
from a few days to weeks; this is followed by remission that lasts from a few months to years.
This relapsing-remitting (RRMS) phase often lasts five to ten years. Over time, about 30% of
individuals with RRMS enter a secondary chronic-progressive state called secondary-progressive
MS (SPMS). This chronic-progressive state is often associated with the inability to walk.
Occasionally, however, clinical disability begins with this progressive phase, in which case the
disease is called primary-progressive MS (PPMS). This indicates that the earlier phase of
disease, characterized by distinct attacks followed by remission, may be mediated with an
autoimmune reaction
1
. The subsequent chronic phase of disease is due to degeneration of both
the myelin sheath, which is synthesized by oligodendroglial cells, and the underlying axon,
which emanates from the neuronal cell body some distance away
3
. MS also leads to spinal cord
atrophy that is most apparent among those with inability to walk and paralysis.
Glutamate and N-acetylaspartate
Glutamate (Glu) is the primary excitatory neurotransmitter in the nervous system and is
beneficial and essential to neuronal function
4,5
. As glutamate does not cross the blood brain
barrier, it is synthesized from glutamic acid, crucial for synthesis of proteins. Glu is formed
when glutamic acid drops a hydrogen from its side chain and is essential for normal
development and CNS function (Figure 1)
5
. In the central nervous system, Glu plays a major role
2
in cognition, learning, memory and fundamental brain processes. Glu also contributes to
formation of synapses, cell migration, differentiation and regulation of cell death. However,
excess Glu can cause chronic fatigue since it induces excitotoxicity and massive loss of brain
function
6
. Fatigue is caused due to various abnormal and pathological factors such as oxidative
stress, calcium overload, mitochondrial dysfunction, neuroinflammation and others. The axonal
space can experience toxicity when Glu concentrations exceed physiological concentrations in
the brain
4,6
.
In MS pathophysiology, evidence of Glu’s role includes: (a) elevations of phosphate-
activated glutaminase levels, which is the principal enzyme for Glu production, in macrophages
and microglial cells in white matter (WM) lesions; (b) deficiency of Glu transporters on
oligodendrocytes which are responsible for Glu uptake in MS WM; and (c) higher expressions of
Glu on injured axons
6–9
.
In addition, N-acetylaspartate (NAA) is an intermediary metabolite that is found in high
concentrations in the brain and serves as a neuronal marker
5,8–10
. The excess brain glutamate
effect on neuroaxonal integrity is measured by N-acetylaspartate (NAA), brain volume and
clinical outcomes
5,11
. Multivoxel spectroscopy at 3T was used to longitudinally estimate Glu and
NAA concentrations from large areas of normal-appearing white and gray matter (NAWM and
NAGM) over a period of 5 years; these measures were studied in a cohort of MS subjects.
12

Higher Glu concentrations were related to increased rate of NAA decline, and higher Glu/NAA
ratio in NAWM was related to increased rate of decline of brain volume
12,13
. This evidence for a
relationship between glutamate and markers of MS disease progression led to the question
addressed in this thesis of MS-fatigue and its association with Glutamate toxicity.
3
Figure 1 Glutamate N-acetylaspartate Synthesis in Axonal Mitochondria

Note: Glutamine synthesized from astrocytes and enters the axons. Aspartate aminotransferase (AAT) converts
glutamate and oxaloacetate to alpha-ketoglutarate and aspartate, respectively, in order to circumvent the
tricarboxylic acid (TCA) cycle. Aspartate N-acetyltransferase (asp-N-AT) subsequently acetylates and converts
aspartate to NAA, which leaves the mitochondria. This reaction further drives the conversion of glutamate into
alpha-ketoglutarate so the TCA cycle continues adenosine triphosphate (ATP) production. NAA is subsequently
used as a marker outside the mitochondria for neurons
11
.
4
Multiple Sclerosis-related Fatigue
Fatigue is a common symptom in MS that can interfere in a person’s daily life. The cause
of MS-fatigue, clinical characteristics, and its relationship to other MS symptoms remain
unclear. There is increasing evidence that glutamate (Glu) plays a key role in the
pathophysiology of MS
14
. Increased extracellular Glu concentrations result in neuronal and glial
cell death via excitotoxic mechanisms
12
. MS-related fatigue is distinct from normal aging
fatigue; MS-fatigue can cause prolonged attacks on the body and severe decline in energy
15
.
Compared to healthy adults, MS patients report that fatigue interferes with meeting
responsibilities, prevents sustained physical functioning and comes on easily
14,16
.
MS-fatigue leads to a number of complications that adversely affect quality of life that
also have socio-economic implications. Patients with fatigue are more likely to retire early or
decrease their work hours. Outpatient visits, including rehabilitation services, are more
frequent among patients with fatigue compared to those without fatigue and mild levels of MS.
MS-fatigue can occur among patients of all MS subtypes, but it is of somewhat higher
prevalence in patients with SPMS than in those with RRMS and is more pronounced in
individuals with greater mobility impairment
14
. People with relapsing disease and accumulating
impairment also tend to be more fatigued than those relapsing but with stable disease
course
16
.
In this thesis, to assess fatigue and depression, a simple and valid instrument called
Functional Assessment of Multiple Sclerosis (FAMS) was being used to assess the quality of life
in people with MS
17,18
. Two sections with questions and subscales, ‘Thinking/Fatigue’ and
‘Emotional Well-Being’, were combined into variables ‘Fatigue’ and ‘Depression’, respectively.
5
We hypothesized that reduced fatigue and depression scores on FAMS are related to the
decrease in Glutamate levels in brain white matter (WM) and gray matter (GM) of MS
patients
18
.
METHODS
Subjects and Measurement of Glu and NAA metabolite Levels
The MS patient sample used in this analysis originated from a large, prospective,
phenotype-genotype biomarker study conducted at the University of California, San Francisco
(UCSF) Multiple Sclerosis Center between January 2005 and December 2010
12,17
. A total of 375
patients with CIS (56), RRMS (295), and SPMS (24) were followed longitudinally for five years.
FAMS data and Glu/NAA levels were collected at baseline and annual follow-up visits. Subjects
were excluded from the study if they had experienced a clinical relapse or used corticosteroids
one month prior, as this could affect the MRI and spectroscopy acquisition. Structural MRI
scans and TE-Averaged H-MRSI spectra were acquired over a single slice of thickness 1.5cm to
acquire Glu and NAA metabolite levels
12
. The spectroscopic data were acquired on the same 3T
GE Excite scanner as the structural MRI, immediately following the acquisition of the
anatomical images but prior to the administration of the contrast agent. The resulting coil
combination data were TE-averaged, and the metabolic concentration of Glu and NAA were
obtained using the linear combination model quantification algorithm
12
.
Clinical Outcomes
The outcomes of interest were Fatigue and Depression. Neurological evaluations were
performed annually using the instrument Functional Assessment in Multiple Sclerosis (FAMS)
version 4; neurological evaluations were administered within 2 weeks of the brain MRI
6
scans
12,18
. FAMS uses self-assessment questions based on patient ratings on aspects of quality
of life in the past 7 days.
Figure 2 Functional Assessment in Multiple Sclerosis (FAMS) version 4

For each question in the self-assessment section, ‘Emotional Well-Being’ and
‘Thinking/fatigue’, a 5 point scale ranged from 0{not at all} to 4{very much} (Figure 2). The
corresponding sections were combined into one numeric variable each: Fatigue (under
Thinking/Fatigue nine questions, range 0-36) and Depression (under Emotional Well-Being
seven questions, range 0-28)
18
.
The primary predictors of interest were Glu in normal appearing white matter
(Glu NAWM), Glu in normal appearing gray matter (Glu NAGM), NAA in normal appearing white
matter (NAA NAWM), NAA in normal appearing gray matter (NAA NAGM), Glu/NAA NAWM and
Glu/NAA NAGM ratios . Covariates in both Fatigue and Depression models included age of onset,
7
age at entry of study, sex, and disease duration. Each predictor was modeled with an
interaction term with gender (in addition to their main effects) to allow an association with
longitudinal change in the outcomes to differ by gender.
Statistical Analyses
Summary statistics were computed on all outcomes and predictors, and distributions
were assessed with histograms. Skewed outcomes (fatigue, depression) were normalized with a
square-root or log transformation. Exploratory univariate associations between Glu, Fatigue,
Depression and demographic information were examined using Pearson correlation (Table 5) or
Independent T-Test (Table 6).
Adjusted associations were assessed using linear mixed models of the following
form: Y i=X iB + Z ib i +e i, where Y i represents a vector of values of the dependent measure of
interest for the i
th
participant, X i represents a matrix of p predictors (independent variables) for
the i
th
participant, B represents a vector of p fixed effect beta weight estimates for each
predictor in X i, Z i represents a matrix of q random effect predictors, b i represents a vector
of q random effect estimates, and e i represents a vector of the model fit error, representing the
discrepancy between the model prediction for each observation from the i
th
participant and the
actual value of that observation
19
.
In this thesis, Glu was treated as a random effect in testing the time dependent
association with fatigue and depression. Demographic information such as gender and age
were tested as fixed effects and as interactions with Glu to assess effect modification. Akaike
information criterion (AIC) and Bayesian information criterion (BIC) were used to compare and
8
select covariance structure from the following: variance components (CV), autoregressive-1
(Ar1) compound symmetry (CS) and unstructured (UN).
Studentized residuals were used to examine model assumptions and to detect outliers
and potentially influential data points
19
. A repeated measures correlation was used to compare
correlation estimates for the correlation of subject means. We used the correlation method to
determine the most appropriate correlation estimate for repeated measures data by using a
SAS macro %MMCorr_NormalApprox to convert beta coefficient to correlation coefficient for
illustrating strength of correlation
20–22
.
All reported mixed-effects models were fitted using restricted maximum likelihood
estimation (RMLE) and modeled with fixed-effects. All statistical analyses were performed using
SAS (v9.4; www.sas.com).
9
RESULTS
Demographics of the Subjects
Demographics and clinical data for 375 subjects in the five year annual follow-up
included in this study are presented in Table 1.
Table 1 Demographics By Gender
Demographic characteristics (n = 375)
Age at disease onset, mean SD, y
Male(n=112)
32.7(9.3)
Female(n=263)
34.0(9.1)
Age at study entry, mean SD, y
Male
40.7(9.8)
Female
42.7(9.5)
Disease duration, mean (SD), y
Male
1.9(1.1)
Female
2.1(1.2)
Clinical subtype no. (%)
CIS
56(14.9)
RRMS
295(78.7)
SPMS
24(6.4)
Fatigue, mean(SD)
Male
2.8(1.4)
Female
3.2(1.5)
Depression, mean(SD)
Male
1.4(0.8)
Female
1.5(0.9)
SD = Standard Deviation; CIS = clinically isolated syndrome;
RRMS = relapsing –remitting multiple sclerosis;
SPMS = secondary progressive multiple sclerosis;
PPMS = primary progressive multiple sclerosis
Fatigue and Glutamate Toxicity in Gray Matter
Pearson correlations between Fatigue and Glu/NAA levels at baseline were not
statistically significant (Table 5). Independent t-tests indicated that females’ glutamate levels
were statistically significantly lower than male MS patients, who experienced fatigue. Females
consistently showed lower Glu and NAA levels by 20% in the gray matter with p < 0.001 (Table
10
6). However, the ratio of Glu/NAA in both GM and WM did not significantly differ between
males and females.
The linear mixed effect models showed gender was a statistically significant effect
modifier for the association between Glu NAWM (p=0.03), Glu/NAA NAWM (p=0.004) and NAA NAGM
(p<0.001). In particular, females showed a statistically significant positive association of
Glu NAWM, NAA NAWM and NAA NAGM levels in the brain with fatigue scores (Table 2). AR(1) was
selected as the final covariance structure based on AIC and BIC criteria. The residual plots
showed a pattern of random distribution of residual value by predicted value. The residuals had
a normal distribution. No strong outliers were observed in the residual plots.
Table 2 Mixed-Effects Model Results For Fatigue
FATIGUE Male Beta Female Beta
Male
(Correlation)
Female
(Correlation)
Interaction
p - value
Glu NAWM -0.47 ±0.38 0.59 ±0.3 0 ±0.07 0.14 ±0.05 0.03*
Glu NAGM -0.4 ±0.33 0.41 ±0.26 -0.11 ±0.07 0.17 ±0.05 0.05
NAA NAWM -0.95 ±0.54 1 ±0.41 -0.1 ±0.08 0.12 ±0.05 0.001*
NAA NAGM -1.11 ±0.51 1.35 ±0.39 -0.16 ±0.08 0.17 ±0.05 0.0001*
Glu/NAA NAWM -0.2 ±0.29 -0.23 ±0.23 0.08 ±0.07 0.02 ±0.05 0.9299
Glu/NAA NAGM -0.27 ±0.4 0.04 ±0.3 0.09 ±0.07 0.05 ±0.05 0.5371
Note: Beta represents change in fatigue score per 10 units increase of Glu with ± standard error.
*interaction term p-value <0.05
11
Depression and Glutamate Toxicity in Gray Matter
Pearson correlations with Depression and Glu/NAA levels at baseline were not
statistically significant (Table 5). Independent t-tests suggest that there was no statistically
significant difference in Glu and NAA levels between male and female MS patients who
experienced depression (Table 3). The ratio of Glu/NAA in both GM and WM also did not
significantly differ between males and females.
Time dependent correlations between Glu NAWM, Glu NAGM, NAA NAWM, NAA NAGM,
Glu/NAA NAWM and Glu/NAA NAGM and depression were not detected in mixed models (Table 3).
Gender was not found to be a statistically significant effect modifier for the association
between all predictors and depression in mixed models.
Table 3 Mixed-Effects Model For Depression
DEPRESSION Male Beta Female Beta
Male
(Correlation)
Female
(Correlation)
Interaction
p - value
Glu NAWM -0.44 ±0.33 0.02 ±0.22 -0.06 ±0.07 0.03 ±0.05 0.26
Glu NAGM -0.4 ±0.26 0.03 ±0.2 -0.15 ±0.07 0.09 ±0.05 0.20
NAA NAWM -0.73 ±0.43 -0.27 ±0.29 -0.09 ±0.07 -0.04 ±0.05 0.39
NAA NAGM -0.09 ±0.4 0 ±0.28 -0.04 ±0.07 0.05 ±0.05 0.86
Glu/NAA NAWM -0.33 ±0.24 -0.01 ±0.19 -0.13 ±0.06 0.04 ±0.05 0.30
Glu/NAA NAGM -0.02 ±0.33 0.14 ±0.23 0 ±0 0 ±0 0.68
Note: Beta represents change in depression score per 10 units increase of Glu with ± standard error.
12
DISCUSSION
Our findings demonstrate that neuroaxonal loss in gray matter, measured by Glu and
NAA, seems to be more prominent in females than in males. Reduced GM volume has been
previously associated with long-term disability and Glu toxicity
14,16
. In this work, Fatigue scores
were further correlated with brain atrophy together with axonal loss, particularly in females.
This is also consistent with previous findings in brain volume studies where reductions in GM
and WM volume are prominent in and continue to atrophy as the duration of the disease and
Glu toxicity increases
12,13
.
Longitudinal results between Fatigue/Depression and Glu/NAA levels
The differential correlation of fatigue and Glu NAWM levels differ by gender, particularly in
females. This is also consistent across MS clinical subtypes and perhaps more importantly
throughout the duration of the disease. There is substantial biologic plausibility for these
findings in GM and WM lesions within MS patients.
There are other studies that evaluated the association of fatigue scores and MRI
features in MS. Fatigue was significantly associated with brain parenchymal fraction,
longitudinally
14,16,23
. Despite the size of this cohort, the study could be redesigned to further
assess the brain volumes from baseline to evaluate the correlation between Glu/NAA levels and
brain volume.
Fatigue is a complex phenomenon in MS and it is unlikely that any single research
finding will change our understanding towards therapy. However, we can gather some
promising trends in the axonal loss and gender specific differences with MS-related Fatigue. An
13
improved understanding should emerge after this work between fatigue and gray matter
pathology in MS.

14
REFERENCES
1. Nylander A, Hafler DA. Review series Multiple sclerosis. Journal of clinical Investigation.
2012;122(4):1180-1188. doi:10.1172/JCI58649.1180
2. Haines JD, Inglese M, Casaccia P. Axonal damage in multiple sclerosis. Mount Sinai
Journal of Medicine. 2011;78(2):231-243. doi:10.1002/msj.20246
3. Werner P, Pitt D, Raine CS. Multiple sclerosis: Altered glutamate homeostasis in lesions
correlates with oligodendrocyre and axonal damage. Annals of Neurology.
2001;50(2):169-180. doi:10.1002/ana.1077
4. Zhou Y, Danbolt NC. Glutamate as a neurotransmitter in the healthy brain. Journal of
Neural Transmission. 2014;121(8):799-817. doi:10.1007/s00702-014-1180-8
5. Clark JF, Doepke A, Filosa JA, et al. N-Acetylaspartate as a reservoir for glutamate.
Medical Hypotheses. 2006;67(3):506-512. doi:10.1016/j.mehy.2006.02.047
6. Matute C, Domercq M, Sánchez-Gómez MV. Glutamate-mediated glial injury:
Mechanisms and clinical importance. Glia. 2006;53(2):212-224. doi:10.1002/glia.20275
7. Pitt D, Nagelmeier IE, Wilson HC, Raine CS. Glutamate uptake by oligodendrocytes:
Implications for excitotoxicity in multiple sclerosis. Neurology. 2003;61(8):1113-1120.
doi:10.1212/01.WNL.0000090564.88719.37
8. Geurts JJG, Wolswijk G, Bö L, et al. Altered expression patterns of group I and II
metabotropic glutamate receptors in multiple sclerosis. Brain. 2003;126(8):1755-1766.
doi:10.1093/brain/awg179
15
9. Polacek H, Kantorova E, Hnilicova P, Grendar M, Zelenak K, Kurca E. Increased glutamate
and deep brain atrophy can predict the severity of multiple sclerosis. Biomedical Papers.
2019;163(1):45-53. doi:10.5507/bp.2018.036
10. Srinivasan R, Sailasuta N, Hurd R, Nelson S, Pelletier D. Evidence of elevated glutamate
in multiple sclerosis using magnetic resonance spectroscopy at 3 T. Brain.
2005;128(5):1016-1025. doi:10.1093/brain/awh467
11. Madhavarao CN, Arun P, Moffett JR, et al. Defective N-acetylaspartate catabolism
reduces brain acetate levels and myelin lipid synthesis in Canavan’s disease. Proceedings
of the National Academy of Sciences of the United States of America. 2005;102(14):5221-
5226. doi:10.1073/pnas.0409184102
12. Azevedo CJ, Kornak J, Chu P, et al. In vivo evidence of glutamate toxicity in multiple
sclerosis. Annals of Neurology. 2014;76(2):269-278. doi:10.1002/ana.24202
13. Azevedo CJ, Cen SY, Khadka S, et al. Thalamic atrophy in multiple sclerosis: A magnetic
resonance imaging marker of neurodegeneration throughout disease. Annals of
Neurology. 2018;83(2):223-234. doi:10.1002/ana.25150
14. Krupp LB, Serafin DJ, Christodoulou C. Multiple sclerosis-associated fatigue. Expert
Review of Neurotherapeutics. 2010;10(9):1437-1447. doi:10.1586/ern.10.99
15. Ma J, Zhang X. The relationship between season/latitude and multiple sclerosis.
Zhonghua nei ke za zhi. 2015;54(11):945-948.
16. Krupp LB, Larocca NG, Muir Nash J, Steinberg AD. The fatigue severity scale: Application
to patients with multiple sclerosis and systemic lupus erythematosus. Archives of
Neurology. 1989;46(10):1121-1123. doi:10.1001/archneur.1989.00520460115022
16
17. Mowry EM, Beheshtian A, Waubant E, et al. Quality of life in multiple sclerosis is
associated with lesion burden and brain volume measures. Neurology. 2009;72(20):1760-
1765. doi:10.1212/WNL.0b013e3181a609f8
18. Cella DF, Dineen K, Arnason B, et al. Validation of the functional assessment of multiple
sclerosis quality of life instrument. Neurology. 1996;47(1):129-139.
doi:10.1212/WNL.47.1.129
19. Rodriguez RN. SUGI 29 Statistics and Data Analysis SUGI 29 Statistics and Data Analysis.
Style (DeKalb, IL).:1-27.
20. Irimata K, Wakim P, Li X. Estimation of correlation coefficient in data with repeated
measures. SAS Global Forum. 2018;04(mL):Paper 2424-2018.
21. Roy A. Estimating correlation coefficient between two variables with repeated
observations using mixed effects model. Biometrical Journal. 2006;48(2):286-301.
doi:10.1002/bimj.200510192
22. Shen D, Lu Z. Paper 170-31 Computation of Correlation Coefficient and Its Confidence
Interval. Sugi 31. 2006:1-6.
23. ARM J, Ribbons K, Lechner-Scott J, Ramadan S. Evaluation of MS related central fatigue
using MR neuroimaging methods: Scoping review. Journal of the Neurological Sciences.
2019;400(February):52-71. doi:10.1016/j.jns.2019.03.007

17
APPENDIX
Environmental Research and Literature Review
To ensure that MS-fatigue was indeed different from normal fatigue and that no prior
studies explored the relationship between fatigue and glutamate using FAMS, we performed a
systematic electronic search of PubMed, Ovid Medline, Embase, Scopus and Google Scholar.
The following search terms were used: “fatigue”, “multiple sclerosis”, “magnetic resonance
imaging”, “brain atrophy”, “brain volume”, “structural MRI”, “Glutamate”, “toxicity”,
“functional MRI” and “magnetic resonance spectroscopy”.
Only original research publications published in English from 1980-2019 were included.
Publications were limited to those reporting data from human subjects, which included adult
MS patients with fatigue who had been diagnosed with Multiple Sclerosis (Figure 3).
Figure 3 Environmental Search and Literature Review Findings

18
Time Dependent and Pearson Correlation
Table 4 Time Dependent Correlations
Fatigue Male r p-value Female r p-value
Glu NAWM 0 ±0.07 0.96 0.14 ±0.05 0.01
Glu NAGM -0.11 ±0.07 0.12 0.17 ±0.05 0.001
NAA NAWM -0.1 ±0.08 0.21 0.12 ±0.05 0.03
NAA NAGM -0.16 ±0.08 0.04 0.17 ±0.05 0.002
Glu/NAA NAWM 0.08 ±0.07 0.20 0.02 ±0.05 0.73
Glu/NAA NAGM 0.09 ±0.07 0.20 0.05 ±0.05 0.26

Depression Male r p-value Female r p-value
Glu NAWM -0.06 ±0.07 0.41 0.03 ±0.05 0.57
Glu NAGM -0.15 ±0.07 0.03 0.09 ±0.05 0.09
NAA NAWM -0.09 ±0.07 0.22 -0.04 ±0.05 0.51
NAA NAGM -0.04 ±0.07 0.64 0.05 ±0.05 0.31
Glu/NAA NAWM -0.13 ±0.06 0.04 0.04 ±0.05 0.34
Glu/NAA NAGM 0.01 ±0.07 0.94 0.07 ±0.05 0.11

Table 5 Baseline Pearson Correlations
Pearson Correlation Coefficients Rho=0
N = 375
Fatigue r p
Glu NAWM 0.03 0.56
Glu NAGM 0.01 0.87
NAA NAWM 0.03 0.50
NAA NAGM 0.07 0.19
Glu/NAA NAWM -0.05 0.37
Glu/NAA NAGM 0.01 0.92

N = 322
Depression r p
Glu NAWM -0.06 0.28
Glu NAGM -0.02 0.71
NAA NAWM -0.04 0.51
NAA
NAGM
0.00 0.98
Glu/NAA
NAWM
-0.02 0.66
Glu/NAA NAGM -0.03 0.63

19
Independent T-Test
Table 6 Independent T-Tests Between Male and Female
Glu NAWM Gender Mean Std Dev
F 8.88 1.66
M 10.01 1.44

Glu NAGM Gender Mean Std Dev
F 10.42 1.88
M 11.64 1.98

NAA NAWM Gender Mean Std Dev
F 9.65 1.40
M 10.39 1.21

NAA NAGM Gender Mean Std Dev
F 8.64 1.51
M 9.19 1.55
All comparisons were p<0.001 statistically significant

20
Figure 4 Depression Distribution for Males and Females

Figure 5 Combined Fatigue Distribution for Males and Females

Abstract (if available)

Abstract Background: Multiple Sclerosis (MS) related fatigue affects one’s quality of life and has been linked to brain volumetric loss. Glutamate (Glu) toxicity, including the Glutamate (Glu) and N-acetylaspartate (NAA) ratio, plays a key role in brain volumetric loss in MS patients including axonal loss, gray matter volume atrophy, and cognitive decline. MS-fatigue has been assessed in previous studies but not studied with respect to glutamate toxicity. Therefore, we aimed to evaluate the relationship of fatigue and glutamate toxicity using the Functional Assessment in Multiple Sclerosis (FAMS) behavioral instrument. ❧ Methods: A total of 375 MS patients (112 men, 263 women), with an average age (SD) of 41.5 (9.6) were included in this longitudinal analysis. FAMS and MRI spectroscopy (to measure brain Glu and NAA) were collected annually

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Asset Metadata

Creator Ramanan, Sriram (author)

Core Title Effect of glutamate excitotoxicity on multiple sclerosis-related fatigue

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 05/04/2020

Defense Date 05/04/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Depression,fatigue,glutamate,MRI,multiple sclerosis,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Franklin, Meredith (committee chair), Cen, Steven (committee member), Mack, Wendy (committee member)

Creator Email ramanan.sriram@gmail.com,sriramr@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-296410

Unique identifier UC11665281

Identifier etd-RamananSri-8419.pdf (filename),usctheses-c89-296410 (legacy record id)

Legacy Identifier etd-RamananSri-8419.pdf

Dmrecord 296410

Document Type Thesis

Rights Ramanan, Sriram

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA