Journal of Cancer Therapy, 2012, 3, 731-740 Published Online October 2012 (
Metabolic, Health and Lifestyle Profiling of Breast Cancer
Radiotherapy Patients and the Risk of Developing Fatigue
R. Hugh Dunstan1*, Diane L. Sparkes1, Christopher Wratten2, James W. Denham2, Johan Gottfries3,
Tim K. Roberts1, Margaret M. Macdonald1
1School of Environmental and Life Sciences, University of Newcastle, Callaghan, Australia; 2Radiation Oncology Department, Cal-
vary Mater Newcastle, Newcastle, Australia; 3Department of Chemistry and Molecular Biology, Gothenberg, Sweden.
Email: *
Received August 31st, 2012; revised September 28th, 2012; accepted October 10th, 2012
Background: Fatigue is commonly reported by cancer patients. In some instances it can persist after treatment is com-
pleted. In order to develop effective treatment strategies it is important to understand the mechanisms underlying the
development of fatigue and to be able to predict those that may be at greatest risk of experiencing fatigue during and
following treatment. The current paper examines predisposing factors for fatigue including altered fatty acid homeosta-
sis in a cohort of breast cancer radiotherapy patients. Methodology: Patients had undergone breast-conserving surgery
and adjuvant breast irradiation. Prior to radiotherapy the patients were free from significant fatigue. Levels of fatigue
were determined prior to and following radiotherapy using the Functional Assessment of Cancer Therapy fatigue sub-
scale. Plasma fatty acid levels, urinary and plasma amino acid levels, blood biochemistry factors and general health and
lifestyle characteristics were assessed. Results: Following radiotherapy, significant fatigue affected approximately one
third of the 26 patients and these subjects were then assigned to the fatigued cohort. Univariate analysis revealed that
higher levels of the fatty acids myristic acid and eicosadienoic acid were present for the fatigued cohort prior to radio-
therapy. Multivariate analysis also revealed that fatty acid homeostasis was altered between the fatigued and
non-fatigued groups at baseline. Orthogonal partial least squares discriminant analysis of the general health, lifestyle
and metabolic data revealed that the fatigued and non-fatigued patients could be clustered into two clearly separate
groups. Conclusions: The results supported the proposition that the fatigued patients had an underlying metabolic ho-
meostasis which may predispose them to the development of fatigue. Biochemical and general health profiling of breast
cancer patients has the potential to identify those at most risk of developing significant fatigue following radiotherapy.
Keywords: Fatigue; Fatty Acids; Radiotherapy; Breast Cancer
1. Introduction
With the development of improved methods for the
screening and treatment of breast cancer and subsequent
increased survival rates [1] there is a growing need to
consider quality of life after treatment is completed. Fa-
tigue is the most common symptom experienced by can-
cer patients. It can result in a reduction in quality of life
both during therapy [2] and in some cases persist long
after treatment has been completed [3,4]. Although its
etiology remains unknown a number of therapies have
been trialed for cancer-related fatigue including exercise
programs [5] and various psychosocial interventions [6].
Not all breast cancer patients receiving radiotherapy will
develop fatigue and to date it has not been possible to
predict which patients would develop significant fatigue
following treatment and which would remain fatigue-free.
It has been proposed that cancer-related fatigue is multi-
factorial in origin with biological, psychological and so-
cial factors contributing to its development [6]. Profiling
may provide an opportunity to further understand the
etiology of cancer-related fatigue and to potentially de-
velop a predictive method to identify those patients most
at risk of developing significant fatigue. The ability to
identify patients at risk of developing fatigue would al-
low clinicians to target therapies for fatigue at this sub-
group. Through patient profiling it may be possible to not
only prevent the development of fatigue but also to iden-
tify particular risk factors which are amenable to treat-
ment. The formulation of an amino acid nutritional sup-
plement aimed at addressing possible nutritional defi-
ciencies may prove to be one such treatment. The current
study aimed to determine whether alterations in underly-
ing fatty acid metabolism could be identified in breast
cancer radiotherapy patients who developed significant
*Corresponding author.
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Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
fatigue following treatment compared with those patients
who remained fatigue-free. The study also aimed to de-
termine whether patient profiling incorporating a range
of variables including metabolic and general health and
lifestyle characteristics, would allow for the differentia-
tion of fatigued and non-fatigued patient groups.
2. Materials and Methods
2.1. Patient Characteristics and Measures
Study participants included twenty-six women (age, 55.6
± 7.7 years, mean ± SD) who underwent breast-con-
serving surgery for breast cancer followed by adjuvant
breast irradiation. Patients were assessed for levels of
fatigue using the FACT fatigue subscale [7]. Patients
were also assessed for anxiety and depression and nu-
merous general health and lifestyle characteristics. Blood
and urine samples were collected for blood biochemistry,
plasma fatty acid, plasma amino acid and urinary amino
acid analysis. With the exception of plasma fatty acids,
the methodology and results for these measures have
previously been presented for a subgroup of the current
patient cohort see [8]. It should be noted that as a large
number of subject characteristics were assessed, in some
instances missing data resulted in varying sample num-
bers. The study was a pilot study and the interpretation of
the results should be undertaken with this in mind. The
study protocol was approved by the Hunter Area Re-
search Ethics Committee and all subjects provided in-
formed consent.
2.2. Plasma Lipid Fatty Acid Analysis
Blood samples were collected from patients at baseline
(immediately prior to commencement of a radiotherapy
treatment regime), at 5 weeks and at 6 months after
commencement of radiotherapy. Thirty-five mL venous
blood samples were collected from each patient at the
Radiation Treatment Department, Calvary Mater New-
castle for blood biochemistry, plasma amino acid and
plasma fatty acid analysis. Each patient had fasted ap-
proximately 10 hours prior to sample collection. Lithium
heparin plasma separation tubes were used for the collec-
tion of blood samples for plasma fatty acids. Samples
were spun at 3000 rpm for 10 min, frozen within an hour
of collection and then stored at 80˚C. Samples were
transported to the University of Newcastle laboratory
where they were stored at 20˚C until processing. Fatty
acid composition of plasma lipids was then determined
via gas chromatography-mass spectrometry (GC-MS).
Prior to GC-MS detection, plasma lipids were converted
to fatty acid methyl esters (FAMEs) through a trans-
esterification method developed by Lepage and Roy [9].
2.3. Statistical Analysis
Univariate analyses and forward stepwise discriminant
analysis were performed using the Statsoft Statistica
(release 6.0) software. Fatty acid data were assessed us-
ing forward stepwise discriminant function analysis per-
formed on log transformed fatty acid concentration data.
Associations between altered blood biochemistry factors
and fatty acid concentration data were performed using
Spearman rank order correlation analysis. Mann-Whitney
U test, Chi square and Fisher’s exact probability were
also used where appropriate.
Orthogonal partial least squares discriminant analysis
(OPLS-DA) was conducted using SIMCA-P+ (12.0,
Umetrics Sweden) [10,11]. Plasma and urinary amino
acid data and blood biochemistry data were log trans-
formed prior to OPLS analysis. All data were pre-treated
by mean centering and unit variance scaling prior to
model generation. Optimal model complexity was pro-
duced according to the cross validation procedure [12].
All analyses were performed using seven cross validation
groups in which all data were left out of the modeling
once. As recommended, the assignment of cross-valida-
tion groups was implemented by SIMCA-P+. Data were
checked for outliers using Hotelling’s T2 and each indi-
vidual calculated orthogonal score distance to the mod-
eled X-data, and all observations were found to be well
within the 95% confidence interval.
3. Results and Discussion
Patient data were included in this analysis if they did not
report significant fatigue at the commencement of the
radiotherapy treatment regime. To determine this, a sub-
set of the FACT fatigue scale [7] consisting of 13 ques-
tions was used prior to commencement of radiotherapy
treatment to assess baseline levels of the fatigue in the
patients. In this study and previous research, significant
fatigue was defined as a score of <37 [13,14]. Patients
were included in the present study if they had a score of
37 at the baseline sampling point just prior to com-
mencement of radiotherapy. The patients were then sub-
sequently classified within the fatigued group (n = 9) if
they scored <37 at the 5-week and/or 6-month assess-
ments. The non-fatigued group (n = 17) scored 37 at
each follow-up assessment. Approximately one third of
the patient cohort developed significant fatigue following
radiotherapy (Table 1). As expected, significantly lower
mean fatigue scores for the fatigued group were seen at 5
weeks (mean ± SEM, fatigued 34.8 ± 2.7, non-fatigued
48.2 ± 1.1, P < 0.0001) and at the 6 month mark (fa-
tigued 36.0 ± 4.3, non-fatigued 51.5 ± 0.8, P < 0.0001).
Despite the exclusion of subjects reporting significant
fatigue prior to commencement of radiotherapy, a sig-
nificant difference was observed at baseline between the
Copyright © 2012 SciRes. JCT
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue 733
Table 1. Patient characteristics including age, body mass
index, menopausal status and radiation dosage for the en-
tire study cohort, the fatigued and non-fatigued groups.
Characteristic Entire
Number 26 9 17
Age (years, mean ± SD)*
Age range
55.6 ± 7.7
39.8 - 71.8
55.1 ± 7.1
39.8 - 63.2
55.8 ± 8.3
42.0 - 71.8
Body mass index
(mean ± SD)* 27.5 ± 5.2 28.7 ± 6.2 26.9 ± 4.6
Postmenopausal (n)* 12 5 7
Whole-breast radiation
dose (Gy) 50 50 50
Boost dose (Gy) (n)* 10 (9) 10 (3) 10 (6)
Laterality of primary
(percentage right breast)*
57.7% 44.4% 64.7%
Statistical tests: ANOVA, Chi square and Fisher’s exact probability were
used as appropriate. *None of the comparisons revealed statistically signifi-
cant differences between fatigued and non-fatigued study groups (P < 0.05).
mean fatigue scores of the two groups (mean ± SEM,
fatigued 46.2 ± 2.2, non-fatigued 51.7 ± 0.8, n = 9 and 17,
P < 0.01). Pre-treatment levels of fatigue have previously
been demonstrated to predict the levels of fatigue re-
ported following radiotherapy for cancer [15]. In the
current study, no significant differences were revealed
between the fatigued and non-fatigued patient character-
istics (age, BMI, menopausal status and radiation dosage
(Table 1).
With only one exception, systemic hormone therapy,
no significant differences were revealed between the fa-
tigued and non-fatigued groups in the many clinical or
general health and lifestyle characteristics assessed [8]
(for a list of measures undertaken, see [8]). In addition to
these factors, other confounding variables such as differ-
ing treatment interventions, types and stages of cancer
and the presence of fatigue prior to radiation therapy
were also controlled. Therefore it proved possible in the
current project to investigate the onset of fatigue follow-
ing radiotherapy treatment with a relatively homogenous
subject cohort.
Blood plasma amino acids and urinary amino acids
were previously assessed for a subset of the current pa-
tient cohort and these data have been summarized, see
[8]. The fatty acid levels were also assessed for the same
blood plasma samples and the results have been pre-
sented for the baseline, 5-week and 6-month sampling
points in Table 2.
In general, the fatigued group had higher levels of
saturated fatty acids (SFA), monounsaturated fatty acids
(MUFA), polyunsaturated fatty acids (PUFA) and total
fatty acids compared with the non-fatigued group at
baseline. However, the differences seen in these classes
of fatty acids did not reach levels of statistical signifi-
cance due to higher than anticipated variance. Two ex-
ceptions to this were myristic acid (C14:0) and eicosadi-
enoic acid (C20:2n-6) which were significantly elevated
for the fatigued group compared with the non-fatigued
group at baseline (Table 2).
The data for the baseline fatty acid data were analyzed
by forward stepwise discriminant function analysis which
indicated a significant difference in the fatty acid profiles
of the fatigued group compared with the non-fatigued
group (Wilks’ Lambda = 0.4, P < 0.002). The discrimi-
nant function model was able to accurately classify the
participants of the study into their appropriate clinical
groups with 75% of the fatigue group correctly classified
and 94% of the non-fatigued group (total accuracy,
Blood biochemistry analyses (electrolyte levels, kid-
ney, liver and thyroid function tests) were carried out for
each of the three time points. Univariate analysis (Mann-
Whitney U test) revealed that alkaline phosphatase was
reduced at baseline and after 5 weeks of radiotherapy for
the fatigued cohort in comparison to the fatigue-free
group. Anion gap was reduced at 5 weeks while total
protein levels were reduced at 6 months for the fatigued
cohort, see [8]. Fatty acid data were subjected to correla-
tion analyses with two of these significantly altered
blood biochemistry factors, total protein and alkaline
phosphatase. Correlation analyses were performed against
all fatty acids measured and for each sampling time (see
Figures 1 and 2). The r-values have been plotted for each
of the fatty acids which have been grouped according to
their fatty acid classes (SFA, MUFA and PUFA) to show
positive and negative associations. The correlations dis-
cussed were significant at the P < 0.05 level. It would be
expected that if metabolic homeostasis was not affected
in the fatigued group compared with the non-fatigued
group, then no differences in correlation patterns would
be evident. Figure 1(a) shows that in the non-fatigued
group all the fatty acids had positive associations with
the total plasma protein concentrations. A large number
of the fatty acids (C16:0, C18:0, C22:0, C16:1n-7,
C18:1n-7, C18:1n-9, C20:1n-9, C24:1n-9 and C20:4n-6),
and the fatty acid classes (total SFA, total MUFA and
total fatty acids) showed significant positive correlations
(P < 0.05) for the non-fatigued group. In contrast, the
fatigued group showed none of the above correlations but
did have C18:2n-6, total n-6 and total PUFA as signifi-
cant positive correlations with total plasma protein.
Many of the associations observed in the fatigue group
were negative but not significant at the baseline assess-
ment. These results indicated that different associations
were present between the plasma fatty acid composition
and plasma protein concentrations for the fatigued group
compared with the non-fatigued group.
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Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
Copyright © 2012 SciRes. JCT
Table 2. The concentrations of blood plasma fatty acids in the fatigued and non-fatigued groups at the commencement of
radiotherapy (baseline), completion of radiotherapy (5-week) and at 6-months after the baseline assessment.
Plasma fatty acid Baseline
nmol/mL, mean (SEM)
5 weeks
nmol/mL, mean (SEM)
6 months
nmol/mL, mean (SEM)
Fatigued Non-fatigued Fatigued Non-fatigued Fatigued Non-fatigued
Myristic acid (14:0) 338.9 (27.2)* 266.1 (22.9*) 289.3 (40.8) 319.3 (28.5) 332.9 (38.4) 294.8 (22.5)
Palmitic acid (16:0) 22324.0 (1625.8) 15837.1 (2470.6)18965.0 (2430.0)19777.0 (2664.3)18813.9 (4212.7) 14081 (2691.1)
Stearic acid (18:0) 3,932.8 (124.5) 3111.4 (364.3) 3541.8 (311.5) 3543.4 (345.0) 3361.3 (514.5) 2911.4 (401.8)
Arachidic acid (20:0) 107.4 (5.6) 92.1 (7.2) 100.1 (7.7) 105.1 (7.4) 104.5 (8.6) 92.2 (6.8)
Behenic acid (22:0) 306.6 (15.2) 268.3 (28.4) 251.8 (32.0) 281.4 (20.3 274.6 (23.1) 258.3 (23.0)
Lignoceric acid (24:0) 291.7 (20.5) 273.8 (29.4) 265.0 (15.7) 281.4 (21.2) 261.1 (24.0) 261.8 (25.5)
Total SFA 27301.3 (1742.8) 19848.7 (2877.7)23413.0 (2747.9)24307.6 (3019.1)23148.2 (4768.2) 17899.7 (3122.9)
Palmitoleic acid
(16:1n-7) 1537.6 (219.5) 1271.3 (156.4) 1218.9 (187.1) 1530.8 (164.6) 1448.8 (210.1) 1203.1 (153.2)
cis-Vaccenic acid
(18:1n-7) 1194.7 (103.9) 983.0 (116.9) 1004.7 (73.0) 1207.9 (143.4) 1069.6 (180.6) 872.2 (144.2)
Oleic acid (18:1n-9) 24637.8 (2161.1) 18940.5 (3276.8)20800.4 (2756.2)23442.8 (3589.5)22412.5 (5250.3) 16831.6 (3572.8)
Gondoic acid (20:1n-9) 143.7 (6.2) 146.6 (7.1) 147.5 (3.4) 152.3 (5.9) 150.9 (7.2) 143.3 (7.3)
Erucic acid (22:1n-9) 167.0 (25.2) 149.7 (20.5) 183.1 (23.9) 178.4 (25.2) 107.7 (17.4) 114.5 (16.4)
Nervonic acid
(24:1n-9) 1062.7 (56.5) 867.3 (116.7) 975.1 (92.0) 986.6 (99.2) 874.2 (109.7) 810.0 (109.2)
Total MUFA 28743.4 (2437.3) 22358.5 (3603.0)24329.6 (3018.6)27498.9 (3911.3)26063.7 (5683.6) 19974.6 (3947.4)
n – 6
Linoleic acid (18:2n-6) 20971.3 (1049.7) 15650.9 (2350.3)18581.2 (1358.9)17,802.6 (2432.1)15,698.8 (3233.7) 13369.1 (2337.5)
Arachidonic acid
(20:4n-6) 5913.7 (496.8) 4468.5 (631.1) 5255.1 (500.5) 5,416.9 (672.1) 4,705.6 (896.5) 3927.5 (605.5)
Eicosadienoic acid
(20:2n-6) 100.0 (3.2)** 77.6 (7.4)** 92.6 (4.7) 89.3 (6.7) 84.0 (7.1) 80.5 (7.1)
Total n-6 26,985.0 (1276.0) 20197.1 (2954.5)23865.9 (1721.7)23,308.7 (2996.5)20,488.3 (4016.8) 17377.1 (2917.7)
Eicosapentaenoic acid
(20:5n-3) 970.0 (145.3) 860.5 (158.1) 821.0 (117.8) 958.9 (159.5) 772.7 (141.5) 739.7 (89.0)
Docosahexaenoic acid
(22:6n-3) 1983.1 (240.8) 1478.2 (189.3) 1906.8 (227.9) 1,774.9 (187.3) 1,888.7 (375.0) 1564.0 (226.8)
Total n-3 2953.1 (373.9) 2338.7 (341.0) 2727.8 (331.0) 2,733.7 (332.5) 2,661.4 (510.5) 2303.7 (303.8)
Total PUFA 29938.0 (1278.8) 22535.8 (3205.2)26593.7 (1805.3)26,042 (3192.9)23,149.7 (4434.4) 19680.8 (3162.1)
Total Fatty Acids 85982.8 (4451.5) 64743.0 (9500.2)74336.3 (7083.0)77,848.9 (9938.2)72,361.7 (14724.0) 57555.1 (10018.6)
Statistical test: Mann-Whitney U test, P < 0.04* and P < 0.02**. Values are the mean (SEM) and are expressed as fatty acid concentration (nmol/mL). n = 8 and
17 at baseline, 9 and 16 at 5 weeks and 9 and 17 at 6 months for the fatigued and non-fatigued groups respectively.
Following five weeks of radiation treatment all fatty
acid associations with total plasma protein were positive
for the non-fatigued group, although only those for C22:0,
C24:0, C24:1n-9, C18:2n-6, total n-6, total n-3, total
PUFA and total fatty acids were statistically significant
(P < 0.05) (Figure 1(b)). In comparison to the
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue 735
(a) Baseline
(b) 5 Weeks
(c) 6 Months
Figure 1. Correlations between total protei n (g/L) and plasma fatty acids (nmol/mL) at each of the clinical assessment times,
(a) Baseline; (b) 5 weeks and (c) 6 Months, for the fatigued and non-fatigued patient groups.
Copyright © 2012 SciRes. JCT
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
associations demonstrated at baseline, the pattern seen at
5 weeks represented a changing profile of associations
for the non-fatigued cohort following radiotherapy. In
contrast, the fatigued group showed predominantly nega-
tive and non-significant associations between plasma
fatty acid concentrations and total protein levels at 5
At the 6 month sampling point, with only one excep-
tion, the non-fatigued group displayed negative correla-
tions between the plasma fatty acids and total protein,
none of which were significant. However, all the fatty
acids for the fatigued group displayed positive associa-
tions. The fatty acids C18:0, C20:0, C22:0, C24:0,
C20:1n-9, C22:1n-9, C24:1n-9, C18:2n-6, total n-6 and
total PUFA all displayed strong, significant correlations
with total protein. It was clear that the correlations be-
tween total protein levels and plasma fatty acid concen-
trations changed over the course of the study for both
groups. The profiles of associations were different be-
tween the fatigued groups and non-fatigued groups at all
stages of assessment.
A second set of correlational analyses was based upon
alkaline phosphatase activities and plasma fatty acids. In
contrast to total protein, alkaline phosphatase was not
correlated with any of the fatty acids measured for the
non-fatigued or fatigued groups at baseline (Figure 2(a)).
At 5 weeks (Figure 2(b)), alkaline phosphatase was neg-
atively correlated with both C22:6n-3 and total n-3 fatty
acids for the non-fatigued group. The fatigued group
showed a single significant correlation between alkaline
phosphatase and C14:0. The 6-month assessment (Fig-
ure 2(c)) showed no associations between alkaline phos-
phatase and plasma fatty acids for the non-fatigued group
while the fatigued group comprised many strong positive
correlations including with C16:0, C16:1n-7, C18:2n-6,
C18:1n-9, C18:1n-7, C18:0, C20:4n-6, C22:6n-3, C24:
1n-9, total fatty acids, total MUFA, total PUFA, total n-3
and total n-6 (P < 0.05).
The entire dataset consisting of clinical symptoms, life
style characteristics, blood biochemistry, blood plasma
amino acids and fatty acids as well as urinary excretion
of amino acids were collated for each patient and sub-
jected to orthogonal partial least squares (OPLS) dis-
criminant analysis. Inspection of the OPLS scores re-
vealed that fatigued patients and the non-fatigued pa-
tients could be effectively separated and clustered into
two clearly defined groups (Figure 3). This finding sup-
ported the proposition that the fatigued patients had an
underlying metabolism which made them susceptible to
developing severe fatigue following radiotherapy. The
patients who were assigned to the fatigued group have
been coded in red and are clustered on the right hand side
with higher t1 scores. It is possible to see that the two
repeat visits at 5 weeks and 6 months, also occurred
within the same cluster compared with the non-fatigued
patients who were positioned on the left-hand side with
negative t1 scores.
The OPLS-DA revealed the existence of a number of
components contributing to the reported development of
significant fatigue following radiation treatment. A pri-
mary set of these differential variables have been pre-
sented in Table 3 and include biochemical, clinical and
general health and lifestyle measures.
The components contributing to the OPLS-DA sepa-
ration of fatigued and non-fatigued patients included
numerous biochemical measures, the identification of
which supported the univariate analyses previously per-
formed for this group of patients. An increase in the ex-
cretion of urinary amino acids was seen for the fatigued
group in comparison to the non-fatigued group whilst
alkaline phosphatase, total protein and anion gap demon-
strated significant differences between the two groups,
see [8].
An overall pattern of increased excretion of urinary
amino acids was supported by the OPLS-components
from the DA model. The presence of a differing fatty
acid homeostasis and alterations in blood biochemistry
factors including in alkaline phosphatase activity were
also supported by the P1 loadings. Amongst the general
health and lifestyle factors contributing to the OPLS-DA
separation were factors which have previously been as-
sociated with cancer related fatigue such as anxiety [16],
depression [17,18], systemic hormone therapy [19] and
the presence of comorbid diseases such as arthritis [18].
The identification of other contributing factors in the
development of fatigue in breast cancer patients such as
tea, coffee and red wine consumption and the laterality of
the primary tumor may warrant further investigation. It
has been suggested that due to incidental cardiac irradia-
tion, the risk of development of cardiovascular disease
(CVD) in breast cancer survivors may be influenced by
the laterality of the tumor. Amongst older women, an
increase in the risk for CVD has been demonstrated for
breast cancer survivors who had received radiotherapy
treatment for left-sided tumors [20]. In the current study,
both primary tumor laterality (left-side) and CVD were
principal factors contributing to the separation of the
fatigued and non-fatigued patients.
4. Conclusion
Alterations in metabolic homeostasis between the fa-
tigued and the non-fatigued groups prior to and following
radiotherapy were indicated by both univariate and cor-
relational analyses of the fatty acid data. Forward step-
wise discriminant function analyses at baseline also in-
dicated that alterations in fatty acid homeostasis were
present before radiotherapy treatment was begun for the
fatigued cohort. In concert with the results of amino acid
Copyright © 2012 SciRes. JCT
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue 737
(a) Baseline
(b) 5 Weeks
(c) 6 Months
Figure 2. Correlations between alkaline phosphatase (U/L) and plasma fatty acids (nmol/mL) at each of the clinical assess-
ment times, (a) Baseline; (b) 5 weeks and (c) 6 Months, for the fatigued and non-fatigued patient groups.
Copyright © 2012 SciRes. JCT
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
Table 3. Parameters contributing to the OPLS-DA separation of patient groups: Characteristics of patients who developed
fatigue following radiotherapy compared with the non-fatigued cohort.
Health and lifestyle
Less likely to report menopausal status as perimenopausal
or status unknown
Less likely to be taking antihypertensive medication
Less likely to be taking lipid lowering medication at
Shorter distances travelled to treatment
Laterality of primary tumor less likely to be on right-side
Drinking less tea (cups/day)
Less likely to have undergone major surgery in previous 6
Drinking less coffee (cups/day)
Less likely to have a diagnosis of coronary vascular disease
Less likely to report marital status as married
Higher HADS Depression score
More hours worked per week
More likely to be taking medications (other than aspirin/NSAI,
corticosteroid, antihypertensive, antiangina, lipid lowering, anticoagulin,
More likely to have a history of previous major surgery
More likely to be a drinker of red wine
More likely to report marital status as single—never married
More likely to report a previous/current significant infectious disease
and recovery
More likely to be receiving systemic hormone therapy (tamoxifen or
More likely to be receiving systemic hormone therapy
Higher HADS Anxiety score
More likely to be a coffee drinker
Laterality of primary tumor more likely to be on left-side
More likely to report a diagnosis of arthritis
More likely to report diagnosis with other major disease/s
More likely to be working
More likely to report menopausal status as pre-menopausal
More likely to be a tea drinker
Biochemical measures
Reduced measures Increased measures
Plasma components:
Alkaline phosphatase (U/L)
Total protein (g/L)
Anion gap (mmol/L)
C20:5n-3 (%)
C20:1n-9 (%)
Calculated globulin (g/L)
C16:1n-7 (%)
C24:0 (%)
Aspartic acid
C14:0 (%)
C22:0 (%)
Thyroid stimulating hormone (mU/L)
C22:1n-9 (%)
C18:0 (%)
Plasma components:
Chloride (mmol/L)
C16:0 (%)
Urinary components (concentration):
Glutamic acid
Alpha-aminoadipic acid
Alpha-aminobutyric acid
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Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
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-6 -5-4 -3 -2 -10123456
R2X[1] = 0.039763 R2X[XSide Comp. 1] = 0.115912
Ellipse: Hotelling T2 (0.95)
Fatigued 5 Weeks
Fatigued 6 months
on-fatigued baseline
on-fatigued 5 weeks
on-fatigued 6 months
Figure 3. Orthogonal partial least squares discriminant analysis (OPLS-DA) scores scatter plot showing cluster separation of
the biochemical, health and li festyle data of fatigue d breast ca ncer radi otherapy patients from the non-fati gued brea st cancer
radiotherapy patients at all three time points.
analysis carried out for a subgroup of the same patient
cohort [8] these findings supported the hypothesis that
the fatigued patients possessed an underlying metabolic
homeostasis which could contribute to their susceptibility
for the development of significant fatigue following ra-
diotherapy. The results indicated that the profiling of
breast cancer radiotherapy patients, including biochemi-
cal, health and lifestyle characteristics, has the potential
to identify those women most at risk of developing sig-
nificant fatigue. The components included within the
OPLS-DA model indicated that a number of risk factors
for fatigue were present within the fatigued cohort in-
cluding an elevated loss of amino acids via urinary ex-
cretion. The results generated suggest that the use of
OPLS-DA modeling has the potential to be used as a
screening tool to predict the development of significant
fatigue in breast cancer radiotherapy patients who are
free from fatigue prior to treatment. A larger scale study
is required to test the predictive power of the model.
5. Acknowledgements
This study was funded by the Judith Mason and Harold
Stannett Williams Memorial Foundation. We also wish
to thank Dr. Jane Ludbrook, Dr. Peter O’Brien and Dr.
Mahesh Kumar for their involvement in the recruitment
of patients.
[1] A. Maxmen, “The Hard Facts,” Nature, Vol. 485, No.
7400, 2012, pp. S50-S51. doi:10.1038/485S50a
[2] P. Stone, A. Richardson, E. Ream, et al., “Cancer-Related
Fatigue: Inevitable, Unimportant and Untreatable? Re-
sults of a Multi-Centre Patient Survey,” Annals of On-
cology, Vol. 11, No. 8, 2000, pp. 971-975.
[3] P. Servaes, M. F. M. Gielissen, S. Verhagen, et al., “The
Course of Severe Fatigue in Disease-Free Breast Cancer
Patients: A Longitudinal Study,” Psycho-Oncology, Vol
16, No. 9, 2007, pp. 787-795. doi:10.1002/pon.1120
[4] A. C. G. Cavalli Kluthcovsky, A. A. Urbanetz, D. S. de
Carvalho, et al., “Fatigue after Treatment in Breast Can-
cer Survivors: Prevalence, Determinants and Impact on
Health-Related Quality of Life,” Support Care Cancer,
Vol. 20, No. 8, 2012, pp. 1901-1909.
[5] V. Mock, C. Frangakis, N. E. Davidson, et al., “Exercise
Manages Fatigue during Breast Cancer Treatment: A
Randomized Controlled Trial,” Psycho-Oncology, Vol. 14,
No. 6, 2005, pp. 464-477. doi:10.1002/pon.863
[6] K. M. Mustian, G. R. Morrow, J. K. Carroll, et al., “Inte-
grative Nonpharmacologic Behavioral Interventions for
the Management of Cancer-Related Fatigue,” Oncologist,
Vol. 12, Suppl. 1, 2007, pp. 52-67.
[7] S. B. Yellen, D. F. Cella, K. Webster, et al., “Measuring
Fatigue and Other Anemia-Related Symptoms with the
Functional Assessment of Cancer Therapy (FACT) Meas-
urement System,” Journal of Pain and Symptom Man-
agement, Vol. 13, No. 2, 1997, pp. 63-74.
[8] R. H. Dunstan, D. L. Sparkes, M. M. Macdonald, et al.,
“Altered Amino Acid Homeostasis and the Development
of Fatigue by Breast Cancer Radiotherapy Patients: A Pi-
lot Study,” Clinical Biochemistry, Vol. 44, No. 2-3, 2011,
pp. 208-215. doi:10.1016/j.clinbiochem.2010.10.002
[9] G. Lepage and C. C. Roy, “Direct Transesterification of
All Classes of Lipids in a One-Step Reaction,” The Jour-
nal of Lipid Research, Vol. 27, No. 1, 1986, pp. 114-120.
[10] J. Trygg and S. Wold, “Orthogonal Projections to Latent
Structures (O-PLS),” Journal of Chemometrics, Vol. 16,
No. 3, 2002, pp. 119-128. doi:10.1002/cem.695
Metabolic, Health and Lifestyle Profiling of Breast Cancer Radiotherapy Patients and the Risk of Developing Fatigue
[11] J. E. Jackson, “A User’s Guide to Principal Components,”
Wiley, New York, 1991. doi:10.1002/0471725331
[12] S. Wold, “Cross-Validatory Estimation of the Number of
Components in Factor and Principal Components Mod-
els,” Technometrics, Vol. 20, No. 4, 1978. pp. 397-405.
[13] C. Cleeland and S. Wang, “Measuring and Understanding
Fatigue,” Oncology, Vol. 13, No. 11A, 1999, pp. 91-97.
[14] C. Wratten, J. Kilmurray, S. Nash, et al., “Fatigue during
Breast Radiotherapy and Its Relationship to Biological
Factors,” International Journal of Radiation Oncology,
Biology and Physics, Vol. 59, No. 1, 2004, pp. 160-167.
[15] E. M. A. Smets, M. R. M. Visser, A. F. M. N. Wil-
lems-Groot, et al., “Fatigue and Radiotherapy: (A) Ex-
perience in Patients Undergoing Treatment,” British
Journal of Cancer, Vol. 78, No. 7, 1998, pp. 899-906.
[16] H. Geinitz, F. B. Zimmermann, R. Thamm, et al., “Fa-
tigue in Patients with Adjuvant Radiation Therapy for
Breast Cancer: Long-Term Follow-Up,” Journal of Can-
cer Research and Clinical Oncology, Vol. 130, No. 6,
2004, pp. 327-333. doi:10.1007/s00432-003-0540-9
[17] R. Morant, “Asthenia: An Important Symptom in Cancer
Patients,” Cancer Treatment Reviews, Vol. 22, Suppl. A,
1996, pp. 117-122. doi:10.1016/S0305-7372(96)90073-0
[18] J. E. Bower, P. A. Ganz, K. A. Desmond, et al., “Fatigue
in Breast Cancer Survivors: Occurrence, Correlates, and
Impact on Quality of Life,” Journal of Clinical Oncology,
Vol. 18, No. 4, 2000, pp. 743-753.
[19] S. Haghighat, M. E. Akbari, K. Holakouei, et al., “Factors
Predicting Fatigue in Breast Cancer Patients,” Support
Care Cancer, Vol. 11, No. 8, 2003. pp. 533-538.
[20] R. Haque, M. U. Yood, A. M. Geiger, et al., “Long-Term
Safety of Radiotherapy and Breast Cancer Laterality in
Older Survivors,” Cancer Epidemiology, Biomarkers &
Prevention, Vol. 20, No. 10, 2011, pp. 2120-2126.
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