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CURRENT RESEARCH IN GYNECOLOGIC CANCER
Prealbumin as a New Marker for Assessment of the Nutritional Status
in Patients with Gynecological Malignancies
Mahmoud El Sayed Hanafy Mellis
1
•Mohamed Moustafa Mohamed Rizk
2
•Noha Eid Hassan
1
•
Shimaa Abdellatif Mohamed
1
Received: 6 May 2018 / Accepted: 18 June 2018
Association of Gynecologic Oncologists of India 2018
Abstract
Background Gynecological malignancies contribute to 10–15% of cancers in women. Malnutrition is a common problem
in cancer patients and is a major cause of morbidity and mortality. As a result, it is important to assess every cancer
patient’s nutritional status using one or more of the methods that are developed for this purpose. Prealbumin (PAB) is a
potential useful marker because its serum concentrations are closely related to early changes in nutritional status.
Purpose To determine the role of PAB in assessment of malnutrition in gynecological cancer patients and to detect the
relationship between its serum level and occurrence of treatment-related side effects.
Methods We studied malnutrition prevalence and PAB serum concentrations in 100 gynecological cancer patients. The
scored patient-generated subjective global assessment (PG-SGA) was used as the reference method to determine malnu-
trition. Patients were followed up for development of complications after treatment.
Results According to the PG-SGA reference method, 53% of patients were classified as well nourished and 47% as
malnourished (40% moderate malnutrition and 7% severe malnutrition). PAB showed good correlation with the nutritional
status assessed by the PG-SGA with good sensitivity (91.49%).
Conclusion We conclude that PAB could represent a feasible and reliable tool in the evaluation of malnutrition in
gynecological cancer patients, especially in settings where it is difficult to obtain a more detailed and comprehensive
nutritional assessment.
Keywords Prealbumin Malnutrition Gynecological cancer
Introduction
Gynecological malignancies whether vulval, vaginal, cer-
vical, uterine, fallopian or ovarian cancers contribute to
10–15% of cancers in women with differing incidence and
prognosis that often depends on international geographical
location [1].
Malnutrition is a common and under-recognized prob-
lem in cancer patients, correlated to a number of physical,
psychological, and clinically relevant adverse effects;
including impaired tolerance to anticancer therapy, adverse
reactions, and reduced quality of life [2].
Definition
Malnutrition can be defined as a state of altered nutritional
status that is associated with increased risk of adverse
clinical events such as treatment-related complications or
death. Disease-related malnutrition occurs frequently in
patients with cancer and is a major cause of morbidity and
mortality [3]. The incidence of malnutrition in cancer
patients ranges between 40 and 80% [4], while the preva-
lence ranges from 50 to 80% [3] depending on tumor type,
tumor location, stage of disease, treatment received and the
type of nutritional assessment method used [5].
&Shimaa Abdellatif Mohamed
Shimaa.abdellatif@yahoo.com
1
Department of Obstetrics and Gynecology, Faculty of
Medicine, Alexandria University, Alexandria, Egypt
2
Department of Clinical Pathology, Faculty of Medicine,
Alexandria University, Alexandria, Egypt
123
Indian Journal of Gynecologic Oncology (2018) 16:43
https://doi.org/10.1007/s40944-018-0214-9(0123456789().,-volV)(0123456789().,-volV)
Diagnosis
There is no ‘‘gold standard’’ in diagnosis of malnutrition
and a variety of clinical and biological methods can be
used. However, if diagnosed, treatment of malnutrition is
proved to lead to better short- and long-term outcome [6].
As a result, it is important to assess every cancer
patient’s nutritional status using one or more of the meth-
ods that are developed for this purpose.
Nutritional Assessment
Biochemical Markers
•Measurement of serum proteins can provide indirect
information about the levels of visceral protein. Albu-
min and prealbumin are among such proteins. Preal-
bumin is a good marker of visceral protein status and is
affected earlier by acute variations in protein balance
[7,8].
•Other markers that have been studied include retinol-
binding protein (RBP), transferrin, total cholesterol and
indicators of inflammation such as C-reactive protein
(CRP) and total lymphocyte count (TLC) [8].
The ideal nutritional marker should readily respond to
changes in nutrient intake, be uninfluenced by other disease
processes, be measurable with equipment available in most
hospitals, and be relatively inexpensive to measure. The
marker must have a short biologic half-life, exist in a rel-
atively small pool, have a predictable catabolic rate, and a
rapid rate of synthesis that responds only to protein intake
[9].
The preferred marker for protein malnutrition is preal-
bumin. It is easily quantified on laboratory instruments
available in all hospitals and is less affected by liver dis-
ease than other serum proteins [9]. Prealbumin has one of
the highest ratios of essential to nonessential amino acids
of any protein in the body [10], making it a distinct marker
for protein synthesis.
Prealbumin
Also named transthyretin (TTR) for its function—trans-
porting thyroxine, triiodothyronine, and retinol.
Prealbumin is produced by the choroid plexus, by pan-
creatic islet cells in the embryonic yolk sac, and by ente-
rochromaffin cells in the gastrointestinal mucosa, but the
liver is quantitatively the most important source [11]. Liver
production is maintained until late in liver disease.
Similar to albumin, prealbumin (PAB) is also a negative
acute-phase protein produced by the liver. Thus, it is
affected by some of the same inflammatory states such as
infections and liver disease. However, there are a few key
differences between these two proteins. The half-life of
PAB is much shorter (2–3 days), and its total body pool is
considerably smaller than albumin. Both of these factors
theoretically allow it to be used as a more reliable indicator
of acute changes in a patient’s nutritional status. However,
PAB is degraded by the kidneys, and consequently, any
renal dysfunction causes an increase in its serum level [12].
There is solid proof that prealbumin is a good measure
of nutritional status among acute or chronic patients and
among critically ill; therefore, it has been widely used to
foresee outcomes such as duration of hospitalization,
development of infections, and even mortality [13]. Pre-
albumin has been also used for pre-surgical risk stratifi-
cation and in predicting post-surgical outcome [14,15].
Dietitians regularly use prealbumin levels in assessing
the nutritional needs of hospitalized patients especially the
critically ill. Prealbumin is currently recognized as a
faithful marker of malnutrition being introduced in practice
guidelines [16].
Serum concentration of prealbumin is influenced by
many factors, including recent dietary intake. It is
increased by severe renal failure, and by corticosteroids,
nonsteroidal anti-inflammatory agents and oral contracep-
tives [17]. It is decreased in liver disease, dialysis, hyper-
thyroidism, and significant hyperglycemia [17].
Anthropometric Measurements
Body Weight and Body Mass Index:
Anthropometric measurements such as patient’s height,
weight, and body mass index are considered relevant and
objective measures of a cancer patient’s nutritional status,
with few potential errors in measurement in a clinical
setting [18,19]. Body weight and weight history are
essential components of the initial nutritional assessment
due to the significant impact of weight loss and under-
weight on morbidity and mortality.
Weight loss must be assessed in relation to its duration
and whether it is unintentional or intended weight loss.
Unintentional weight loss can be expressed as a percentage
of usual body weight.
The Scored PG-SGA
The scored PG-SGA (patient-generated subjective global
assessment) is a validated nutritional assessment tool for
cancer patients [5].
The form has two sections: A medical history section
that is completed by the patient, and a physical assessment
section that is completed by nursing, medical, or dietetic
staff. The medical history section includes history of
weight change, dietary intake change, oncology nutrition
43 Page 2 of 10 Indian Journal of Gynecologic Oncology (2018) 16:43
123
impact symptoms like nausea, pain abdomen etc., that have
persisted for greater than 2 weeks, and functional capacity.
The healthcare professional section includes an evaluation
of metabolic demand, diagnosis, and comorbidities in
relation to nutrition requirements and elements of the
physical examination.
Features are subjectively graded according to severity
and combined into a global assessment in which patients
are classified as being well nourished (category-A), mod-
erately or at risk of being malnourished (category-B), or
severely malnourished (category-C). For each component
of the scored PG-SGA, a point (0–4) is awarded depending
on the impact of the component on nutritional status. The
total score is then summed, and this provides a guideline as
to the level of nutrition intervention required [20]. Higher
score indicates greater level of malnutrition. Score 0–1
means no intervention is required, 2–3 indicates the need of
patient education with symptomatic treatment, and score
4–8 requires intervention by a dietitian in conjunction with
physician as indicated by symptoms. A score C9 indicates
severe malnutrition in critical need for nutritional inter-
vention and symptom management [16]. The muscle status,
fat store, and fluid accumulation were assessed clinically.
Assessment of Muscle Mass and Subcutaneous
Fat
A decline in subcutaneous fat and overall body muscle
mass is a significant indicator of malnutrition [16,21]. As
part of the SGA nutritional tool, assessment of muscle mass
and subcutaneous fat are vital for detecting high-risk
patients for early intervention.
Evaluations of muscle mass and subcutaneous fat tissue
have been reported to be reliable, as have noninvasive tests
for assessing nutritional status [22].
Aim of the Work
The aim of the present study was to investigate serum
prealbumin level in patients with gynecological malig-
nancies and to determine its role in assessment of malnu-
trition and treatment-related complications.
Subjects
The study was conducted on 100 women recruited from El
Shatby Maternity University Hospital outpatients clinic.
Inclusion Criteria
Any patient diagnosed with gynecological malignancy
whether the treatment option will be surgery, chemother-
apy, or radiotherapy.
Exclusion Criteria
1. Patients with chronic liver disease.
2. Patients with chronic kidney disease.
3. Patients with thyroid dysfunction.
4. Patients with active inflammatory process.
Methods
Patients were selected from El-Shatby outpatient clinic,
Alexandria University.
All patients were subjected to the following:
1. Informed consent.
2. Detailed history taking (age, symptoms and onset,
medical history and surgical history).
3. Complete physical examination including measure-
ment of height and weight and calculation of the BMI
[23].
BMI ¼weight in kg
Height in mðÞ
2
4. Assessment of muscle mass and body fat percentage
using Beurer BG64 diagnostic scale.
5. Assessment of the nutritional status using the scored
PG-SGA (patient-generated subjective global
assessment).
The patient’s medical history components of the PG-
SGA include weight change, dietary intake symptoms
(such as nausea, vomiting and diarrhea that have persisted
for 2 weeks) and changes in functional capacity.
The weight section provides information about the
current body weight and the body weight 1 and 6 months
ago. The percentage of weight loss is calculated as follows:
[16]
%Weight change ¼Usual weight Present weight
Usual weight 100
The physical examination considers loss of subcuta-
neous fat, muscle wasting, ankle or sacral edema, and
ascites. Based on the overall assessment, the patient is
categorized into stage A, stage B, or stage C. A patient
staged with a global rating PG-SGA A is assumed to be
Indian Journal of Gynecologic Oncology (2018) 16:43 Page 3 of 10 43
123
well nourished, with a PG-SGA B moderately malnour-
ished and with a PG-SGA C severely malnourished [24].
6. Laboratory investigations:
Patients were subjected to the following laboratory
investigations:
1. Serum prealbumin level.
2. Kidney function tests (urea, creatinine and com-
plete urine analysis).
3. Liver function tests (SGOT, SGPT, Alb. and total
proteins).
4. Complete blood count (hemoglobin level, white
blood cell count and platelets).
5. CRP (c-reactive protein).
7. Follow-up of the patient as regards treatment-related
adverse effects such as long hospital stay, wound
complications, ICU (intensive care unit) admission,
toxicity from chemotherapy and mortality.
Results
The mean age of these patients was 52.67 ±11.90 years.
From the 100 studied cases, 43 (43%) patients are diag-
nosed as endometrial cancer, 39 (39%) ovarian cancer, 10
(10%) cervical cancer, 5 (5%) vulval cancer, and 3 (3%)
stump carcinoma.
Regarding the nutritional status, the studied patients are
divided into three groups according to PG-SGA (patient-
generated subjective global assessment).
Fifty-three patients (53%) were classified as well nour-
ished (PG-SGA A), 40 patients (40%) were moderately
malnourished (PG-SGA B), and only seven patients (7%)
were severely malnourished (PG-SGA C) (Table 1).
The mean nutritional score in the studied patients was
3.72 ±2.84.
Cases with ovarian cancer reported the highest incidence
of malnutrition, 28 ovarian cancer patients are malnour-
ished (stage B ?C) which is about 59.6% of all mal-
nourished cases (Table 2).
Table 3shows comparison between the well nourished
(stage A) and malnourished (stage B ?stage C) groups as
regards the demographic data.
There is no significant difference between the two
groups as regards the age (p = 0.371).
Group II (malnourished) had lower body weight, BMI,
muscle mass and body fat than group I (well nourished),
but did not reach statistical significance (p = 0.174, 0.559,
0.705, and 0.528 respectively).
As regards the serum level of prealbumin, 43 patients
(43%) had serum PAB in the normal range (15–35 mg/dl),
44 patients (44%) had serum PAB 11–14.9 mg/dl, 13
patients (13%) had serum PAB 5–10.9 mg/dl, and no
patient had serum PAB \5 mg/dl.
The mean serum PAB in the studied patients is
15.46 ±6.23 mg/dl (Table 4).
The mean serum PAB level in group I (well-nourished
group) was 17.44 ±6.20 mg/dl, while the mean serum
PAB in group II (the malnourished group) was
13.23 ±5.52 mg/dl. Using test for comparison of propor-
tions, there is a significant difference between the two
groups (U= 589.00, p=\0.001) (Table 5).
The association between serum PAB level and the
nutritional status is summarized in Table 6. According to
correlation coefficient, there is a positive correlation
between them (r
s
= –0.339, p= 0.001).
Table 7and Fig. 1show the ability of serum prealbumin
level to predict malnutrition in cases with gynecological
malignancy. The cut off point is B15.7 mg/dl. Serum PAB
can predict malnutrition in gynecological cancer with
91.49% sensitivity and 56.60% specificity. It has good
negative predictive value (88.2%) and acceptable positive
predictive value (65.2%).
Table 8shows the adverse effects reported in the study
and its relation to serum prealbumin level.
Nineteen patients (19%) had long hospital stay; the
mean serum PAB level in these patients was
11.0 ±3.28 mg/dl, and 81 patients (81%) did not report
this complication with mean PAB serum level
16.51 ±6.31 mg/dl and this result was statistically sig-
nificant (U= 318.50, p=\0.001).
Twenty-three patients (23%) had septic wound after
surgery; the mean serum PAB level in these patients was
11.53 ±2.75 mg/dl, and 77 patients (77%) did not report
this complication with mean PAB serum level
16.64 ±6.50 mg/dl and this result was statistically sig-
nificant (U= 385.50, p=\0.001).
Three patients (3%) had been admitted to ICU, and 97
patients (97%) did not report these complications. The
Table 1 Prevalence of malnutrition among the studied cases
according to PG-SGA (n= 100)
Classification according to PG-SGA No. %
Stage
A 53 53.0
B 40 40.0
C 7 7.0
Total score
Min.–Max. 1.0–15.0
Mean ±SD. 3.72 ±2.84
Median 2.0
43 Page 4 of 10 Indian Journal of Gynecologic Oncology (2018) 16:43
123
mean value of serum PAB level was lower in the first group
(8.67 ±3.75 mg/dl) than the other group
(15.67 ±6.18 mg/dl, but this did not reach statistical sig-
nificance (U= 35.00, p = 0.025).
Only one case (1%) developed burst abdomen, and she
had serum PAB level 14.0 mg/dl. Eight patients (8%)
developed severe toxicity after chemotherapy mainly in the
form of severe vomiting after each session. The mean PAB
level in these cases was 12.36 ±1.26 mg/dl which is lower
than cases did not report this complication
(15.73 ±6.42 mg/dl) but with no statistical significance
(U= 223.50, p = 0.066).
In the present study, no cases died whether the treatment
was surgery or palliative chemotherapy.
The association between serum PAB level and occur-
rence of complications after treatment is summarized in
Table 9. According to correlation coefficient, there is a
positive correlation between them (r
s
=-0.506,
p=\0.001).
Discussion
The study was conducted on 100 women diagnosed with
gynecological cancer.
Table 2 Prevalence of
malnutrition in different types
of gynecological malignancy
Site of malignancy Stage according to PG-SGA x
2
p
Stage A
(well nourished)
(n= 53)
Stage B ?C
(n= 47)
No. % No. %
Endometrial 32 60.4 11 23.4 13.893
*
\0.001
*
Ovarian 11 20.8 28 59.6 15.779
*
\0.001
*
Cervical 5 9.4 5 10.6 0.040 1.000
Vulval 3 5.7 2 4.3 0.104 1.000
Stump carcinoma 2 3.8 1 2.1 0.232 1.000
x
2
,p:x
2
and pvalues for Chi-square test for comparing between the two categories
MCp: pvalue for Monte Carlo for Chi-square test for comparing between the two categories
*Statistically significant at pB0.05
Table 3 Comparison between the well-nourished and malnourished
groups as regards the demographic data
Score according to PG-SGA tp
Stage A
(well nourished)
(n= 53)
Stage B ?C
(malnourished)
(n= 47)
Age (years)
Min.–Max. 18.0–75.0 19.0–76.0 0.899 0.371
Mean ±SD. 53.68 ±10.72 51.53 ±13.13
Median 55.0 51
Body weight (kg)
Min.–Max. 54.5–128.0 48.0–132.0 1.370 0.174
Mean ±SD. 94.01 ±14.84 89.63 ±17.15
Median 97.50 94.0
BMI (Kg/m
2
)
Min.–Max. 19.5–40.0 18.40–40.50 0.587 0.559
Mean ±SD. 27.12 ±4.12 26.61 ±4.50
Median 27.0 26.0
Muscle mass (%)
Min.–Max. 28.50–35.0 27.0–33.50 0.379 0.705
Mean ±SD. 30.28 ±1.43 30.18 ±1.35
Median 30.0 30.10
Body fat (%)
Min.–Max. 23.0–45.50 23.0–47.0 0.633 0.528
Mean ±SD. 36.51 ±4.63 35.92 ±4.68
Median 37.40 37.0
t,p:tand pvalues for student ttest
Table 4 Distribution of the studied cases according to serum preal-
bumin (n= 100)
Serum prealbumin (mg/dl) No. %
15–35 43 43.0
11–14.9 44 44.0
5–10.9 13 13.0
\5 0 0.0
Min.–Max. 5.0–35.0
Mean ±SD. 15.46 ±6.23
Median 14.0
Indian Journal of Gynecologic Oncology (2018) 16:43 Page 5 of 10 43
123
From the 100 studied cases, 43 patients (43%) were
diagnosed as endometrial cancer, 39 (39%) ovarian cancer,
10 (10%) cervical cancer, 5 (5%) vulval cancer, and 3 (3%)
stump carcinoma.
The studied cases were divided into two groups
according to the nutritional status depending on the scored
PG-SGA: 53 cases as group I (stage A, well nourished) and
47 cases as group II (stage B, moderately malnourished and
stage C, severely malnourished).
In the current study, the mean age of all the studied
cases was 52.67 ±11.90 years.
There was no significant difference between the two
groups (group I, well nourished and group II, malnour-
ished) as regards the age.
This was in accordance with a study done by Bozzetti
et al., who screened the nutritional status of 1,453 cancer
outpatients and they documented that age was not related to
the nutritional status [25].
The usual belief, however, is that elderly patients are
more prone to malnutrition in cancer because of preexisting
problems of dietary intake apart from the effects of aging
per se [26].
In the current work, the prevalence of malnutrition
among the studied cases with gynecological cancer was
about 47% as assessed by PG-SGA and most of malnour-
ished cases were ovarian cancer (59.6% of all malnour-
ished cases).
Similar results reported by Chantragawee et al. [27] who
studied 97 female patients with gynecological malignancy
to find out the relationship between malnutrition, location
of cancer, stage of disease, and patient characteristics like
comorbidities, body mass index (BMI), serum albumin,
Table 5 Comparison between
the two studied groups
according to serum PAB level
Serum prealbumin
(mg/dl)
Score according to PG-SGA Up
Stage A
(n= 53)
Stage B ?C
(n= 47)
Min.–Max. 10.0–35.0 5.0–33.0 589.00
*
\0.001
*
Mean ±SD. 17.44 ±6.20 13.23 ±5.52
Median 16.0 12.50
U,p:Uand pvalues for Mann–Whitney test
*Statistically significant at pB0.05
Table 6 Correlation between nutritional statues and serum prealbu-
min (n= 100)
Nutritional statues
r
s
p
Serum prealbumin (mg/dl) -0.339
*
0.001
*
r
s
: Spearman coefficient
*Statistically significant at pB0.05
Table 7 Agreement (sensitivity, specificity) for serum prealbumin to predict cases with malnutrition (Stage B ?C)
AUC p95% C.I Cut off Sensitivity Specificity PPV NPV
Serum prealbumin 0.764
*
\0.001
*
0.670–0.858 B15.7 91.49 56.60 65.2 88.2
AUC area under a curve, p value probability value, CI confidence intervals, NPV negative predictive value, PPV positive predictive value
*Statistically significant at pB0.05
Fig. 1 ROC curve for serum prealbumin (mg/dl) to predict cases with
malnutrition (Stage B ?C)
43 Page 6 of 10 Indian Journal of Gynecologic Oncology (2018) 16:43
123
and serum hemoglobin. They documented that 53.5% of
gynecologic cancer patients were diagnosed as malnutri-
tion according to scored PG-SGA and most of them
(79.3%) were diagnosed as ovarian cancer [27].
Lower incidence reported by Laky et al. from Australia
who assessed the nutritional status of 194 patients with
suspected or proven gynecologic cancer. They found that
only 24% of gynecologic cancer patients had malnourished
as detected by scored PG-SGA, but in agreement with our
study the prevalence of malnutrition was highest in ovarian
cancer patients (67%) [28].
The low prevalence of malnutrition in Australia may be
because of low prevalence of preexisting malnutrition and
also the early diagnosis of cancer cases [28].
However, Das et al. reported very high incidence
(88.33%) of malnutrition among gynecological cancer
patients in India [29]. They assessed sixty gynecological
cancer patients for their nutritional status using BMI, serum
albumin, hemoglobin, percentage weight lost in last
1 month, and scored PG-SGA.
The high prevalence of malnutrition in India compared
to the more developed nations may be because of high
prevalence of preexisting malnutrition and also the late
stages at diagnosis [29].
As regards body weight and BMI in the two groups:
Group II (malnourished) had mean body weight of
89.63 ±17.15 kg which is lower than group I (well
nourished) who had mean body weight of
94.01 ±14.84 kg, but this did not reach statistical
significance.
Group II (malnourished) had mean BMI of
26.61 ±4.50 kg/m
2
which is lower than group I (well
nourished) who had mean BMI of 27.12 ±4.12 kg/m
2
,but
this didn’t reach statistical significance.
Similar results reported by Laky et al. [28] who assessed
the nutritional status of 194 patients with suspected or
proven gynecologic cancer according to the SGA and the
scored PG-SGA, skinfold-thickness, and body density
measurements before the primary treatment. Their results
suggest that BMI and body weight failed to detect mal-
nutrition among gynecologic cancer patients when used
alone as nutritional variables.
That could be explained by the fact that accumulation of
ascites and the occurrence of large tumors in gynecologic
cancer patients may contribute to their weight and mask
weight loss [28].
As regards the muscle mass and body fat in the two
groups:
Table 8 Relation between
serum prealbumin and
occurrence of complication after
treatment
The complication recorded NSerum prealbumin (mg/dl) Up
Min.–Max. Mean ±SD. Median
Long hospital stay
No 81 8.50–35.0 16.51 ±6.31 15.0 318.50
*
\0.001
*
Yes 19 5.0–16.50 11.0 ±3.28 12.0
Septic wound
No 77 5.0–35.0 16.64 ±6.50 15.0 385.50
*
\0.001
*
Yes 23 5.0–16.50 11.53 ±2.75 12.0
ICU admission
No 97 5.0–35.0 15.67 ±6.18 14.0 35.00
*
0.025
*
Yes 3 5.0–12.50 8.67 ±3.75 8.50
Burst abdomen
No 99 5.0–35.0 15.48 ±6.26 14.0 47.00 0.931
Yes 1 14.0
Toxicity after chemotherapy
No 92 5.0–35.0 15.73 ±6.42 14.0 223.50 0.066
Yes 8 10.0–14.0 12.36 ±1.26 12.50
U,p:Uand pvalues for Mann–Whitney test for comparing between the three categories
*Statistically significant at pB0.05
Table 9 Correlation between serum PAB level and occurrence of
complication (n= 100)
Occurrence of complication
rp
Serum prealbumin (mg/dl) -0.506
*
\0.001
*
r: Pearson coefficient
*Statistically significant at pB0.05
Indian Journal of Gynecologic Oncology (2018) 16:43 Page 7 of 10 43
123
Group II (malnourished) had mean muscle mass of
30.18 ±1.35% which is slightly lower than group I (well
nourished) who had mean muscle mass of 30.28 ±1.43%
and this did not reach statistical significance.
Group II (malnourished) had mean body fat of
35.92 ±4.68% which is lower than group I (well nour-
ished) who had mean body fat of 36.51 ±4.63% and this
did not reach statistical significance.
This also was in accordance with Laky et al. [28] who
reported that neither FFM (Fat-free mass) nor FM (Fat
mass) was significantly lower in malnourished than in well-
nourished women.
A possible reason for the nonidentification of malnour-
ished patients via body density measurements may be that
many women were obese before disease development [28].
As regards serum prealbumin level, in the present study,
43 patients (43%) had serum PAB in the normal range
(15–35 mg/dl), 44 patients (44%) had serum PAB
11–14.9 mg/dl, 13 patients (13%) had serum PAB
5–10.9 mg/dl, and no patient had serum PAB \5 mg/dl.
The mean serum PAB in the studied patients was
15.46 ±6.23 mg/dl.
Ovarian cancer patients had lower serum prealbumin
level than other types of gynecological cancer, and this was
statistically significant.
Multiple studies have shown that prealbumin is
decreased in women with active ovarian cancer, even
before cachexia or other clinical signs of malnutrition are
evident [30]. The mechanism by which prealbumin is
decreased is not entirely clear [30].
The mean serum PAB in group II (the malnourished
group) was 13.23 ±5.52 mg/dl which was lower than that
of group I (well-nourished group) that measured
17.44 ±6.20 mg/dl, and this was statistically significant.
In the present study, there was a significant correlation
between the serum prealbumin level and the nutritional
status as assessed by PG-SGA.
Serum PAB can predict malnutrition in gynecological
cancer with 91.49% sensitivity.
Similar results reported by Devoto et al. [31] who
studied PEM (protein energy malnutrition) prevalence and
prealbumin serum concentrations in 108 hospitalized
patients. They report excellent correlation of prealbumin
with the DNA (detailed nutritional assessment), in patients
with and without increased CRP (_5 mg/L) in the hospi-
talized patients. Devoto et al. [31] interpret this correlation
as indicating that prealbumin is a good screening tool for
malnutrition and highly sensitive (83.1% sensitivity), in
both the presence and absence of SIRS (systemic inflam-
matory response syndrome).
Similarly, Nataloni et al. [32] investigated the role of
PAB in 45 consecutive head injury patients admitted to the
intensive care unit (ICU) and found good correlation
between serum PAB and the nutritional status. They found
that PAB was the most sensitive serum marker for the early
diagnosis of malnutrition and for assessing the appropri-
ateness of the nutritional therapy for malnourished patients
[32].
Contradictory results were reported by Boosalis et al.
[33] who determined visceral protein levels (albumin and
prealbumin) in patients in a medical ICU, head injury unit,
and burn unit. They concluded that hepatic proteins are not
indicators of nutritional status but rather indicators of
morbidity and mortality and recovery from acute and
chronic disease [33].
Our study may be one of the first studies that investi-
gated the serum prealbumin level and its correlation with
the nutritional status among gynecological cancer patients.
As regards occurrence of complications, in the present
study, 19 patients (19%) had long hospital stay, 23 patients
(23%) had septic wound after surgery, three patients (3%)
had been admitted to ICU, one case (1%) developed burst
abdomen, eight patients (8%) developed severe toxicity
after chemotherapy, and no cases died whether the treat-
ment was surgery or palliative chemotherapy.
There was a significant correlation between the serum
prealbumin level and occurrence of complications after
treatment.
This was in accordance with a study done by Geisler
et al. [7] on 108 ovarian cancer patients who underwent
nutritional assessment during their preoperative workup
and were followed for development of postoperative
complications. Geisler et al. reported that postoperative
complications increased with lower prealbumin levels in
the form of more blood loss, morbidity, and mortality.
Similar results obtained by Yu et al. [34] who recently
showed the impact of preoperative prealbumin level on the
outcome after cardiac surgery. They detected that patients
with low serum prealbumin level (\20 mg/dl) had an
increased risk of postoperative infections, and needed
longer mechanical ventilation.
Similarly, Zhang et al. [35] studied serum prealbumin
level in 105 patients with hemorrhagic stroke and docu-
mented significant correlations between serum prealbumin
levels and post-injury infection, gastrointestinal hemor-
rhage, and clinical outcome at discharge in patients with
hemorrhagic stroke.
As regards mortality, Codullo et al. [36] recently found
that serum prealbumin can be an independent predictor of
mortality in systemic sclerosis patients, especially in
patients without significant comorbidities.
In the literature, there were limited studies about the
correlation between serum prealbumin level and postop-
erative complications in gynecological cancer patients.
43 Page 8 of 10 Indian Journal of Gynecologic Oncology (2018) 16:43
123
Conclusion
In conclusion, our study results indicate that PAB is an
inexpensive, feasible, and reliable tool in the evaluation of
malnutrition affecting gynecological cancer patients, par-
ticularly in settings where it is difficult to perform a more
detailed and comprehensive nutritional assessment.
Recommendations
Further investigation with sequential measurements is
needed to clarify the complex relationship between PAB
and inflammation and clarify the role of PAB in monitoring
the efficacy of nutritional interventions.
Compliance with Ethical Standards
Conflict of interest All authors declare that they have no conflict of
interest.
Ethical Approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards.
Informed Consent Informed consent was obtained from all individual
participants included in the study.
References
1. Kehoe S. Treatments for gynecological cancers. Best Pract Res
Clin Obstet Gynaecol. 2006;20(6):985–1000.
2. Antoun S, Baracos V. Malnutrition in cancer patient: when to
have a specialized consultation? Bull Cancer. 2009;96:615–23.
3. Van Cutsem E, Arends J. The causes and consequences of cancer-
associated malnutrition. Eur J Oncol Nurs. 2005;9:S51–63.
4. Isenring E, Bauer J, Capra S. The scored Patient-generated
Subjective Global Assessment (PG-SGA) and its association with
quality of life in ambulatory patients receiving radiotherapy. Eur
J Clin Nutr. 2003;57:305–9.
5. Bauer J, Capra S, Ferguson M. Use of the scored Patient-Gen-
erated Subjective Global Assessment (PG-SGA) as a nutrition
assessment tool in patients with cancer. Eur J Clin Nutr.
2002;56:779–85.
6. Nechifor VA, Trufa
˘DI, Ciuta
˘CM, Graur LI, Potorac I, Graur M.
Perioperative nutritional status. RJDNMD. 2011;18(4):391–7.
7. Geisler JP, Linnemeier GC, Thomas AJ, Manahan KJ. Nutritional
assessment using prealbumin as an objective criterion to deter-
mine whom should not undergo primary radical cytoreductive
surgery for ovarian cancer. Gynecol Oncol. 2007;106:128–31.
8. Guerra LT, Rosa AR, Romani RF, Gurski RR, Schirmer CC,
Kruel CD. Serum transferrin and serum prealbumin as markers of
response to nutritional support in patients with esophageal cancer.
Nutr Hosp. 2009;24:241–2.
9. Spiekerman AM. Nutritional assessment (protein nutrition). Anal
Chem. 1995;67:R429–36.
10. Davies M. Nutritional screening and assessment in cancer-asso-
ciated malnutrition. EurJ Oncol Nurs. 2005;9(suppl 2):S64–73.
11. Tormey WP, O’Brien PA. Clinical associations of an increased
transthyretin band in routine serum and urine protein elec-
trophoresis. Ann Clin Biochem. 1993;30(pt 6):550–4.
12. Raguso CA, Dupertuis YM, Pichard C. The role of visceral
proteins in the nutritional assessment of intensive care unit
patients. Curr Opin Clin Nutr Metab Care. 2003;6:211–6.
13. Devakonda A, George L, Raoof S, Esan A, Saleh A, Bernstein
LH. Transthyretin as a marker to predict outcome in critically ill
patients. Clin Biochem. 2008;41:1126–30.
14. Moghazy AM, Adly OA, Abbas AH, Moati TA, Ali OS,
Mohamed BA. Assessment of the relation between pre-albumin
serum level and healing of skin grafted burn wounds. Burns.
2010;36:495–500.
15. Yang HT, Yim H, Cho YS, Kim D, Hur J, Kim JH, et al. Pre-
diction of clinical outcomes for massively-burned patients via
serum transthyretin levels in the early postburn period. J Trauma
Acute Care Surg. 2012;72(4):999–1005.
16. White JV, Guenter P, Jensen G, Malone A. Schofield M Con-
sensus statement: academy of Nutrition and Dietetics and
American Society for Parenteral and Enteral Nutrition: charac-
teristics recommended for the identification and documentation
of adult malnutrition (undernutrition). JPEN J Parenter Enteral
Nutr. 2012;36(3):275–83.
17. Myron Johnson A, Merlini G, Sheldon J, Ichihara K, Scientific
Division Committee on Plasma Proteins (C-PP), International
Federation of Clinical Chemistry and Laboratory Medicine
(IFCC). Clinical indications for plasma protein assays: trans-
thyretin (prealbumin) in inflammation and malnutrition. Clin
Chem Lab Med. 2007;45:419e26.
18. Bollag D, Genton L, Pichard C. Assessment of nutritional status.
Ann Med Interne (Paris). 2000;151(7):575–83.
19. Gregg JR, Cookson MS, Phillips S, Salem S, Chang SS, Clark
PE, et al. Effect of preoperative nutritional deficiency on mor-
tality after radical cystectomy for bladder cancer. J Urol.
2011;185(1):90–6.
20. McCallum PD. Patient-Generated Subjective Global Assessment.
In: McCallum PD, Polisena CG, editors. The clinical guide to
oncology nutrition. Chicago: Am Diet Assoc; 2000. p. 11–23.
21. Mulasi U, Kuchnia AJ, Cole AJ, Earthman CP. Bio impedance at
the bedside: current applications, limitations, and opportunities.
Nutr Clin Pract. 2015;30:180–93.
22. Nicolo M, Compher CW, Still C, Huseini M, Dayton S, Jensen
GL. Feasibility of accessing data in hospitalized patients to
support diagnosis of malnutrition by the Academy-A.S.P.E.N.
malnutrition consensus recommended clinical characteristics.
JPEN J Parenter Enteral Nutr. 2014;38:954–9.
23. World Health Organization (WHO). Global Database on Body
Mass Index. BMI Classification. 2006. http://apps.who.int/bmi/
index.jsp?introPage=intro_3.html. Accessed in March 2018.
24. Desbrow B, Bauer J, Blum C, Kandasamy A, McDonald A,
Montgomery K. Assessment of nutritional status in hemodialysis
patients using patient-generated subjective global assessment.
J Ren Nutr. 2005;15:211–6.
25. Bozzetti F, Mariani L, Lo Vullo S, Amerio ML, Biffi R, Cac-
cialanza G, et al. SCRINIO Working Group The nutritional risk
in oncology: a study of 1,453 cancer outpatients. Support Care
Cancer. 2012;20:1919–28.
26. Vandewoude MF. Nutritional assessment in oncogeriatrics.
Tijdschr Gerontol Geriatr. 2010;41:214–20.
27. Chantragawee C, Achariyapota V. Utilization of a scored patient-
generated subjective global assessment in detecting a malnour-
ished status in gynecologic cancer patients. Asian Pac J Cancer
Prev. 2016;17(9):4401–4.
Indian Journal of Gynecologic Oncology (2018) 16:43 Page 9 of 10 43
123
28. Laky B, Janda M, Cleghorn G, Obermair A. Comparison of
different nutritional assessments and body-composition mea-
surements in detecting malnutrition among gynecologic cancer
patients. Am J Clin Nutr. 2008;87:1678–85.
29. Das U, Patel S, Dave K, Bhansali R. Assessment of nutritional
status of gynecological cancer cases in India and comparison of
subjective and objective nutrition assessment parameters. South
Asian J Cancer. 2014;3(1):38–42.
30. Ronco DA, Manahan KJ, Geisler JP. Ovarian cancer risk
assessment: a tool for preoperative assessment. Eur J Obstet
Gynecol Reprod Biol. 2011;158(2):325–9.
31. Devoto G, Gallo F, Marchello C, Racchi O, Garbarini R, Bonassi
S, et al. Prealbumin serum levels as a useful tool in the assess-
ment of malnutrition in hospitalized patients. Clin Chem.
2006;52:2281–5.
32. Nataloni S, Gentilli P, Marini B, Guidi A, Marconi P, Busco F,
et al. Nutritional assessment in head injured patients through the
study of rapid turnover visceral proteins. Clin Nutr.
1999;18:247–51.
33. Boosalis MG, Ott L, Levine AS, Slag MF, Morley JE, Young B,
et al. Relationship of visceral proteins to nutritional status in
chronic and acute stress. Crit Care Med. 1989;17:741–7.
34. Yu PJ, Cassiere HA, Dellis SL, Manetta F, Kohn N, Hartman AR.
Impact of preoperative prealbumin on outcomes after cardiac
surgery. J Parenter Enter Nutr. 2015;39:870e4.
35. Zhang SQ, Peng B, Stary CM, Jian ZH, Xiong XX, Chen QX.
Serum prealbumin as an effective prognostic indicator for
determining clinical status and prognosis in patients with hem-
orrhagic stroke. Neural Regen Res. 2017;12(7):1097–102.
36. Codullo V, Cereda E, Klersy C, Cavazzana I, Alpini C, Bonardi
C, et al. Serum prealbumin is an independent predictor of mor-
tality in systemic sclerosis outpatients. Rheumatol.
2016;55:315e9.
43 Page 10 of 10 Indian Journal of Gynecologic Oncology (2018) 16:43
123
