SUBSTRATE CONTRIBUTION TO ENDOGENOUS GLUCOSE PRODUCTION,
INSULIN RESISTANCE AND PROTEIN METABOLISM
IN NON-SMALL CELL LUNG CANCER CACHEXIA
Jacqueline M. MacAdams
Faculty of Agricultural and Environmental Science
School of Dietetics and Human Nutrition
McGill University, Montreal
June 2011
A thesis submitted to McGill University in partial fulfillment of the requirements
of the degree of Master of Science in Human Nutrition
© Jacqueline MacAdams 2011
TABLE OF CONTENTS
Abstract ................................................................................................................... iv
Résumé.....................................................................................................................v
Acknowledgements................................................................................................. vi
List of abbreviations .............................................................................................. viii
List of appendices .................................................................................................... x
List of figures ........................................................................................................... xi
List of tables ........................................................................................................... xii
Contributions of the authors ................................................................................ xiii
1 Introduction........................................................................................................ 1
2 Literature Review ............................................................................................... 2
2.1
Cachexia .................................................................................................... 2
2.2
Lung cancer in the Canadian context ....................................................... 3
2.3
Lung cancer etiology, diagnosis, treatment and prognosis ...................... 3
2.4
Cancer cachexia ........................................................................................ 5
2.5
Anorexia and decreased food intake ........................................................ 5
2.6
Energy expenditure .................................................................................. 8
2.7
Inflammation ............................................................................................ 9
2.8
Experimental models of cancer cachexia ............................................... 10
2.9
Metabolic alterations in cancer cachexia ............................................... 11
2.10 Lipid metabolism in cancer cachexia ...................................................... 11
2.11 Protein metabolism in cancer cachexia.................................................. 13
2.12 Acute phase protein synthesis ............................................................... 14
2.12.1 Albumin ...................................................................................... 15
2.12.2 Fibrinogen .................................................................................. 16
2.12.3 C-reactive protein ...................................................................... 17
2.13 Insulin resistance in cancer cachexia...................................................... 17
2.14 Glucose metabolism in cancer cachexia................................................. 20
i
2.15 Endogenous glucose production ............................................................ 21
2.16 Measurement of gluconeogenesis in human subjects........................... 23
2.17 The 2H2O method.................................................................................... 23
2.18 Lactate .................................................................................................... 26
2.19 Alanine and other glucogenic amino acids............................................. 27
2.20 Glycerol ................................................................................................... 28
2.21 The effect of diet on gluconeogenesis ................................................... 29
3 Rationale .......................................................................................................... 30
4 Objectives and hypotheses .............................................................................. 31
4.1 Objectives ................................................................................................. 31
4.2 Hypotheses ............................................................................................... 32
5 Manuscript ....................................................................................................... 33
5.1 Abstract .................................................................................................... 34
5.2 Introduction .............................................................................................. 35
5.3 Methods ................................................................................................... 37
5.3.1 Subjects ........................................................................................ 37
5.3.2 Body composition analysis ........................................................... 38
5.3.3 Diet ............................................................................................... 38
5.3.4 Deuterated water method and NMR spectroscopy analysis ....... 39
5.3.5 Endogenous glucose production.................................................. 40
5.3.6 Whole body protein kinetics ........................................................ 40
5.3.7 Hyperinsulinemic, euglycemic clamp protocol ............................ 41
5.3.8 Additional assays.......................................................................... 42
5.3.9 Statistical analyses ....................................................................... 43
5.4 Results ...................................................................................................... 43
5.5 Discussion ................................................................................................. 49
5.6 Acknowledgements .................................................................................. 55
6 Supplemental discussion .................................................................................. 73
7 Significance of the study and conclusion ......................................................... 76
ii
8 Supplemental methods .................................................................................... 78
8.1 Patient screening ...................................................................................... 78
8.2 Sample size calculation ............................................................................. 80
8.3 Gluconeogenesis....................................................................................... 80
8.4 Glucose kinetics ........................................................................................ 80
8.5 Calculations for leucine kinetics ............................................................... 81
8.6 Lactate analysis......................................................................................... 83
8.7 Analysis of CT scans .................................................................................. 84
8.8 Body composition, strength and overall status assessment .................... 84
8.9 Funding and ethics.................................................................................... 85
9 References ........................................................................................................ 86
Appendices .......................................................................................................... 100
iii
ABSTRACT
The loss of muscle mass and adipose tissue in cancer cachexia may be linked to
increased rates of gluconeogenesis (GNG) and altered whole-body protein
metabolism. This study measured the fractional contributions (%) of glycogen,
glycerol and phophoenolpyruvate (PEP) to endogenous glucose production (EGP)
using oral 2H2O in non-small cell lung cancer (NSCLC) patients and matched
healthy control subjects. Additionally,
13
C-leucine and 3H3-glucose tracers were
used to measure whole-body protein turnover and glucose kinetics respectively
during the fasting state and during a hyperinsulinemic euglycemic clamp. The
rate of EGP and the fractional substrate contributions were not different
between the NSCLC and control groups following a 17 hour fast. The majority of
EGP came from equal contributions of PEP and glycogen, whereas glycerol
contributed <10%. NSCLC patients were insulin resistant; their lesser clamp
glucose uptake was not correlated to GNG flux, but rates of protein oxidation
were, indicating less protein retention.
iv
RÉSUMÉ
La perte de tissus musculaire et adipeux associée à la cachexie du cancer pourrait
être reliée à des taux accrus de néoglucogénèse (GNG) et de turnover des
protéines corporelles. Cette étude a mesuré la contribution fractionnelle (%) du
glycogène, du glycérol et du phosphoénolpyruvate (PEP) à la production
endogène de glucose (EGP), à l’aide du 2H2O oral chez des patients avec cancer
du poumon (NSCLC) et des sujets témoins appariés. De plus, les cinétiques de
protéines et de glucose ont été quantifiées à l’aide des traceurs
3
13
C-leucine et
H3-glucose, à jeun et durant un clamp hyperinsulinique, euglycémique. Les taux
d’EGP et les contributions fractionnelles des substrats n’étaient pas différents
entres les groupes NSCLC et témoins suite à un jeûne de 17 heures. Le PEP et le
glycogène ont contribué également et majoritairement au EGP; le glycérol
contribuant pour <10%. Les patients avec NSCLC étaient résistants à l’insuline.
Leur taux inférieurs de captation du glucose n’étaient pas corrélés avec le flux
néoglucogénique, mais celui-ci était positivement relié aux taux d’oxydation des
protéines, indiquant une moindre rétention.
v
ACKNOWLEDGMENTS
I would like to acknowledge and thank the dedicated team at the McGill
Food Science and Nutrition Centre for their invaluable contribution to this
research.
Donato Brunetti and Marie Lamarche determined glucose
concentrations during the clamp studies. Marie Lamarche generated the glucose
kinetics data and prepared/ analyzed samples for C-peptide, insulin, free fatty
acids and glucagon with the assistance of Alexandre Morais. Ginette Sabourin
generated the HPLC-amino acid data and together we prepared the glucose
samples for NMR spectroscopy analysis. This analysis was performed by Drs.
Shawn Burgess and Santhosh Satapati at the University of Texas Southwestern
Medical Center at Dallas. Daniel White generated the leucine kinetics data and
determined the BCAA concentration during the clamp studies. Chantal Légaré
performed the oral glucose tolerance tests, collected samples during the clamp
studies and assisted the study participants while staying at the Clinical
Investigation Unit.
I would like to acknowledge the work of Aaron Winter who recruited and
studied eight control subjects and eight NSCLC patients. The samples and data
collected by Aaron have been included as a part of the current thesis. I would
like to thank the staff at the MGH Radiation-Oncology Clinic and RVH Oncology
Day Clinic for giving me the opportunity to recruit patients, in particular Drs. Neil
Kopec, Jonathan Wan, Vera Hirsh, Linda O’Fiara and Sergio Faria. I would like to
express my sincere gratitude to the individuals who generously donated their
time in participating in this study.
These individuals have made my first
experience in clinical research truly rewarding.
To my supervisory committee members including Dr. Linda Wykes and Dr.
Errol Marliss, I am very grateful for their valued feedback in the preparation and
editing of this thesis. In particular I would like to thank my supervisor Dr.
Stéphanie Chevalier for giving me the opportunity to be a part of this research,
for her support and positivity in the face of obstacles, for patience when editing
vi
my work and for her guidance and excellent mentorship.
Dr. Chevalier is
responsible for the design of the study, and for editing this thesis and
manuscript. Finally I would like to thank my family for their unfailing support
throughout my academic career.
vii
LIST OF ABBREVIATIONS
AA
amino acid
A1C
hemoglobin A1C
APP
acute phase protein
BCAA
branched chain amino acids
BIA
bioelectrical impedance analysis
BMI
body mass index
CC
cancer cachexia
CIU
clinical investigation unit
CRP
C-reactive protein
CT
computerized tomography
DXA
dual energy x-ray absorptiometry
EGP
endogenous glucose production
FFA
free fatty acid
FFM
fat-free mass
GNG
gluconeogenesis
2
deuterated water
H20
Hb
hemoglobin
HOMA –ir homeostasis model assessment for insulin resistance
IL
interleukin
IMAT
intramuscular adipose tissue
LBM
lean body mass
MGH
Montreal General Hospital
NMR
nuclear magnetic resonance
NSCLC
non-small cell lung cancer
OGTT
oral glucose tolerance test
PASE
Physical Activity Scale for the Elderly
PEP
phosphoenolpyruvate
PFT
pulmonary function test
PG-SGA
Patient Generated – Subjective Global Assessment
POMC
proopiomelanocortin
viii
REE
resting energy expenditure
RVH
Royal Victoria Hospital
SAT
subcutaneous adipose tissue
SMP
skeletal muscle protein
TAT
total adipose tissue
TNF-α
tumour necrosis factor-alpha
TNM
tumour node metastasis
VAT
visceral adipose tissue
WBP
whole body protein
ix
LIST OF APPENDICES
Appendix A: Supplemental figures and tables ................................................. 100
Appendix B: Diet recall and food frequency questionnaire sheets .................. 110
Appendix C: Patient Generated - Subjective Global Assessment ..................... 113
Appendix D: Physical Activity Scale for the Elderly (PASE) Questionnaire ....... 116
Appendix E: Patient consent form in English.................................................... 119
x
LIST OF FIGURES
Figure 1. Percent contribution of substrates to EGP .......................................... 61
Figure 2. Lactate concentration before and during the clamp ........................... 62
Figure 3. Change in whole-body leucine kinetic rates ........................................ 64
Figure 4. Correlation between change in leucine oxidation and GNG flux ........ 65
Figure 5. Change in rates of glucose kinetics ...................................................... 66
Figure 6. Correlation between fasting IL-6 concentration and %PEP ................. 69
Figure 7. Correlation between % body fat and %PEP ......................................... 72
xi
LIST OF TABLES
Table 1. Characteristics of control and NSCLC subjects...................................... 56
Table 2. Dietary intake data of control and NSCLC subjects at screening .......... 57
Table 3. Body composition data of control and NSCLC subjects ........................ 58
Table 4. Glucose production and substrate flux data of control and NSCLC
subjects ................................................................................................. 60
Table 5. Fasting plasma amino acid concentrations in control and NSCLC
subjects ................................................................................................. 63
Table 6. Biochemical data of the control and NSCLC subjects during the
fasting and hyperinsulinemic, euglycemic, isoaminoacidemic clamp
phases ................................................................................................... 67
Table 7. Correlations between markers of insulin resistance and the substrate
contributions to EGP and corresponding flux data .............................. 68
Table 8. Significant correlations between measures of body composition and
the substrate contributions to EGP and corresponding flux data ........ 70
xii
CONTRIBUTION OF THE AUTHORS
This thesis contains a manuscript entitled “Substrate contribution to
endogenous glucose production in non-small cell lung cancer cachexia”. As the
primary author I wrote the manuscript, performed all statistical analyses of the
data and prepared all figures and tables. Co-author Aaron Winter and I were
responsible for the screening and recruitment of subjects, medical chart review
and questionnaires, coordination of studies, patient attending during CIU stay,
measurements of body composition, data collection and compilation, and for
breath sample collection during the hyperinsulinemic clamp protocol. I was
responsible for analysis of CT scans for tissue volume measurements, analysis of
samples for lactate concentration and for preparing glucose samples for analysis
of positional deuterium enrichment. Co-author Dr. Shawn Burgess performed
NMR spectroscopy analysis of glucose samples at the University of Texas
Southwestern Medical Center in Dallas. Co-author and thesis supervisor Dr.
Stéphanie Chevalier designed the study, conducted the clamp protocol,
supervised laboratory analyses, and edited the manuscript. Thesis committee
members Dr. Errol Marliss and Dr. Linda Wykes reviewed the manuscript
contents and provided editorial feedback.
xiii
1 Introduction
The following thesis is a study of the metabolic alterations of glucose and
protein metabolism in non-small cell lung cancer (NSCLC). Lung cancer is the
leading cause of cancer mortality in North America and Europe, and over 50% of
patients with this disease will experience cancer cachexia (Canadian Cancer
Society, 2010; Jemal et al, 2010; Bruera, 1997). Cachexia is a wasting syndrome
characterized by involuntary loss of fat and muscle mass due to underlying
disease (Evans et al, 2008). Cancer cachexia is associated with poor response to
treatment, decreased quality of life and poor prognosis for the patient (Tisdale,
2009).
The loss of muscle and adipose tissue in cancer cachexia may result in
increased presentation of amino acids (AA) and glycerol to the liver for uptake
and use as substrates for gluconeogenesis (GNG). Previous studies in NSCLC
patients have assessed the contribution of individual substrates to GNG and the
rate of endogenous glucose production (EGP). The deuterated water method is
the most advanced technique for measurement of GNG to date (Nuttall et al,
2008) given that it accounts for all substrates contributing to EGP. This method
has yet to be applied in subjects with cancer cachexia. The rationale for this
study is to further the understanding of the metabolic alterations in glucose and
whole-body protein kinetics occurring in NSCLC patients with cachexia. A crosssectional, case-control study design was used to compare cachectic NSCLC
patients and healthy controls matched for age, gender, BMI and smoking history.
The objective of this research was to compare the relative contributions of
glycogenolysis and GNG to EGP in NSCLC patients and control subjects, and to
determine the effect of cancer on the presence and magnitude of insulin
resistance of both glucose and whole-body protein metabolism.
It was
hypothesized that NSCLC patients would demonstrate a greater contribution of
AAs and lactate to EGP via GNG through PEP.
1
The importance of this study is that to our knowledge it is the first to
employ the deuterated water method coupled with isotopic tracer infusions of
glucose and leucine during a hyperinsulinemic, euglycemic, isoaminoacidemic
clamp experiment. In this experiment we assessed the fractional contributions
of substrates to EGP, the rate of EGP, whole-body protein kinetics and quantified
the degree of insulin resistance within a single protocol. A well-characterized
group of NSCLC subjects was recruited and compared to a closely-matched group
of control subjects for the metabolic studies. All subjects were evaluated for
dietary intake, body composition, muscle strength, systemic inflammation and
performance status.
2 Literature Review
2.1 Cachexia
Cachexia is a multifactorial metabolic syndrome associated with
underlying illness. The word cachexia originates from the Greek words “kakos”
and “hexis” meaning bad condition (Tisdale, 2002). The main clinical feature of
this syndrome is a wasting of lean muscle and/or adipose tissue reserves. In
2008, the Cachexia Society proposed a consensus definition to clinically define
cachexia. Cachexia is an unintentional weight loss of ≥5% of edema-free body
weight within <12 months, with the presence of 3 out of 5 of the following
criteria: anorexia, fatigue, decreased muscle strength (lowest tertile), low fatfree mass index, abnormal biochemistry including increased C-reactive protein
(CRP) (> 5 mg/L), anemia [Hemoglobin (Hb) <120 g/L], or low serum albumin (<32
g/L) (Evans et al, 2008).
The severity of cachexia varies in relation to the progression of the
patient’s underlying disease state. Chronic diseases associated with cachexia
include chronic obstructive pulmonary disease, chronic heart and renal failure,
liver failure, acquired immunodeficiency syndrome, rheumatoid arthritis and
cancer (Morley et al, 2006; Plauth et al, 2002).
2
2.2 Lung cancer in the Canadian context
Cancer is the second highest cause of mortality in Canada after
cardiovascular diseases (Canadian Cancer Statistics, 2010). In 2010, prostate
cancer and breast cancer were the most commonly diagnosed malignancies in
men and women respectively, and lung cancer had the second highest agestandardized incidence rate (Canadian Cancer Statistics, 2010). Currently, lung
cancer is the leading cause of cancer mortality for both men and women in
Canada (Canadian Cancer Statistics, 2010).
For men, the age standardized
incidence and mortality rates in 2005 for lung cancer were 70.9 and 59.8 per
100,000 respectively, and for women, 47.5 and 35.9 per 100,000 respectively
(Canadian Cancer Statistics, 2010). The incidence of lung cancer in men has
reached a plateau since the 1980s but has continued to increase in women since
this time (Canadian Cancer Statistics, 2010). The gender difference in lung
cancer incidence is partially attributed to disparities in tobacco consumption
patterns. Cigarette smoking by women became popularized approximately 25
years after its popularization in men and as such, there is a lag in the incidence of
disease (Garcia et al, 2007).
2.3 Lung cancer etiology, diagnosis, treatment and prognosis
Exposure to tobacco smoke carcinogens found in cigarettes is the primary
risk factor for lung cancer development (Garcia et al, 2007). These carcinogens
have been shown to be associated with mutations in tumour suppressor genes
such as p53, or in protooncogenes such as ras leading to neoplasia (Sekido et al,
2003). Lung cancer is typically detected in the early stages by chest X-ray (Skeido
et al, 2003), and further staged by computerized tomography (CT) scan and/or
positron emission tomography scan to detect metastases (Hammerschmidt et al,
2009). The patient’s functional status is evaluated using a pulmonary function
test (PFT) and the pathologic confirmation of lung cancer is obtained through
sputum cytology or histological evaluation of a biopsy sample (Skeido et al, 2003;
Hammerschmidt et al, 2009).
3
Lung cancer is histologically categorized as small cell lung cancer or nonsmall cell lung cancer (NSCLC), with the latter accounting for up to 80% of lung
cancer cases (Hammerschmidt et al, 2009). Of the cases of NSCLC, 30-40% are
squamous cell carcinoma, 40% are adenocarcinoma and 10%-15% are large cell,
undifferentiated carcinoma (Hammerschmidt et al, 2009; Ries et al, 2007). The
International Association for the Study of Lung Cancer developed the tumour,
node, metastasis (TNM) lung cancer staging system based on the tumour size, its
spread to lymph nodes and the presence of metastases. According to the TNM
system, the T component classifies the size of the primary tumour and is graded
as T0 to T4. The N component indicates the presence of lymph node metastases
and is graded N0 to N3 depending on which lymph nodes are involved. The M
component indicates the presence (M1) or absence (M0) of distant metastatic
nodules (Jones et al, 2010). Based on the TNM classification, NSCLC is staged as
0, IA, IB, IIA, IIB, IIIA, IIIB or IV (Jones et al, 2010). The stage of the disease
determines the treatment options presented to the patient.
Lung cancer treatment modalities include surgery, radiotherapy,
chemotherapy and radio-chemotherapy (Hammerschmidt et al, 2009). Lung
cancer patients with chronic obstructive pulmonary disease and those with
advanced stages (IIIB or IV) tend to be inoperable, and as such they are treated
with radiotherapy or chemotherapy. Surgical candidates may be treated with
adjuvant radiation therapy or chemotherapy (Lu et al, 2008). Prognosis is
dependent on the stage of cancer, the tumour type, the presence of metastases,
and the age, degree of weight loss, performance status and comorbidities of the
patient (Chansky et al, 2009). Prognosis is inversely related with the age of the
patient and the stage of the disease (Hammerschmidt et al, 2009). In Canada,
the 5-year relative survival ratio for lung cancer for all ages and stages combined
is 13-17% according to 2002-2004 data (Canadian Cancer Statistics, 2009). In the
final stages of the disease, cancer cachexia is frequently present.
4
2.4 Cancer cachexia
Cachexia is observed in up to 50% of all cancer patients and is associated
with weight loss, reduced quality of life, decreased response to therapy and
reduced survival time (Tisdale, 2009). Cancer cachexia (CC) is more frequent in
patients with solid tumours and occurs in approximately 80% of patients with
upper gastrointestinal tract cancers and 60% of patients with lung cancer (Bruera
et al, 1997).
An international consensus definition for CC was recently
established. CC is defined as “a multifactorial syndrome characterized by an
ongoing loss of skeletal muscle mass (with or without loss of fat mass) that
cannot be fully reversed by conventional nutrition support and leads to
progressive functional impairment” (Fearon et al, 2011). CC is staged as: i)
precachexia: a weight loss ≤5%; ii) cachexia: weight loss >5% over the past 6
months, or BMI <20 and weight loss of >2%, or sarcopenia and weight loss >2%;
and, iii) refractory cachexia: procatabolic disease that is not responsive to
anticancer treatment and life expectancy <3 months (Fearon et al, 2011). Some
factors contributing to CC and its progression include: i) anorexia and decreased
food intake; ii) increased energy expenditure; iii) inflammation; and, iv) altered
lipid, protein and glucose metabolism.
2.5 Anorexia and decreased food intake
Anorexia, which is defined as the loss of appetite and the desire to eat, is
one factor that can contribute to the development of cancer cachexia (Laviano et
al, 2003). In contrast to starvation in which decreased energy intake results in
preferential depletion of adipose tissue reserves, cachexia-associated anorexia
involves depletion of both skeletal muscle and adipose tissue (Tisdale, 2002).
The prevalence of anorexia in individuals with cancer ranges from 15-40% of
patients upon diagnosis, and up to 80% of patients with certain types of cancer
in advanced stages (Sutton et al, 2003).
5
There are various causes for cancer-associated anorexia. Patients with
cancer frequently experience early satiety, food aversions and decreased dietary
intake due to the side effects of radiotherapy or chemotherapy. Nausea and
vomiting, dysphagia, dysgeusia and dysosmia are among the common symptoms
associated with chemotherapy treatment (Cameron et al, 2002; Laviano et al,
2003). Fatigue, depression, weakness and lack of energy may also contribute to
decreased food intake (Sutton et al, 2003). Several studies have examined the
potential pathophysiological mechanisms of cancer anorexia. Tumour-bearing
animal models provide evidence supporting the involvement of the
proopiomelanocortin (POMC) and neuropeptide Y / agouti-related protein
signalling pathways in the suppression of food intake in cancer-associated
anorexia (Laviano et al, 2003; Wisse et al, 2001). Normally, under conditions of
energy excess the activation of anorectic POMC neurons is increased leading to
less food intake (Laviano et al, 2008). In anorexia and cachexia, increased
hypothalamic expression of interleukin (IL)-1 and serotonin suppress the
inhibition of POMC neurons, resulting in prolonged anorectic effects (Laviano et
al, 2008).
The hormonal involvement of leptin and ghrelin has also been considered
in studies of lung cancer cachexia and anorexia (Aleman et al, 2002; Shimizu et
al, 2003). Ghrelin stimulates the release of growth hormone from the pituitary
gland and activates the neuropeptide Y neurons in the hypothalamic region
modulating food intake (Laviano et al, 2003). Fasting ghrelin levels have been
found to be elevated in NSCLC patients with cachexia and increased in those with
anorexia following chemotherapy.
This may represent a compensatory
mechanism of stimulating food intake (Shimizu et al, 2003). The activation of
neuropeptide Y by ghrelin may however be negated by IL-1 and serotonin which
inhibit neuropeptide Y, resulting in less stimulation of food intake (Laviano et al,
2008).
6
Assessment of dietary intake may be achieved using a 24-hr dietary recall,
food frequency questionnaires, food records, by direct observation or by food
weighing/plate waste. Inherent to each method are certain limitations. For
example, obtaining an accurate estimate of dietary intake for patients with
cancer may be impeded by poor memory when using a 24-hr recall (Bruera et al,
1986) and food frequency questionnaires. Also, food records or 24-hr recall data
from a single day may not be representative of habitual intake given the day to
day variability in food patterns. This is particularly true for individuals with
cancer whose energy and nutrient intakes may be highly variable (Hutton et al,
2006). Direct observation and food weighing may be more representative of
actual intakes, however these methods are more time consuming and labour
intensive.
Following the assessment of dietary intake, cancer-associated anorexia is
managed through dietary or therapeutic strategies in an attempt to attenuate
weight loss. Dietary strategies to increase caloric intake include the use of small,
frequent meals and/or snacks of energy dense foods, nutritional supplements
including omega-3 fatty acids or amino acids, and parenteral or enteral nutrition
support (Sutton et al, 2003). Therapeutic strategies for anorexia in patients with
cancer include the use of orexigenic agents (Bossola et al, 2007). Yadvusen et al
conducted a systematic literature review of prospective randomized controlled
trials examining the use of appetite stimulants in the treatment of cancerassociated anorexia (Yadvusen et al, 2005). It was concluded that there is strong
evidence to support the use of corticosteroids or progestogens including
megesterol acetate and medroxyprogesterone acetate (Yadvusen et al, 2005).
The use of other therapeutic agents such as melanocortin receptor antagonists,
ghrelin, and inhibitors of cytokine production and/or release are other potential
therapeutic strategies being studied (Bossola et al, 2007).
7
2.6 Energy expenditure
In addition to alterations in energy intake, energy expenditure is also
affected in patients with cancer. Total energy expenditure is a function of the
amount of energy expended at rest, the thermic effect of food and the energy
expended for physical activity (Shils et al, 2005). Energy expenditure can be
estimated using predictive equations such as the Harris-Benedict, Mifflin-St. Jeor
and Owen equations, or can be quantified by direct or indirect calorimetry
(Haugan et al, 2007). The advantage to using predictive equations is that they
can be easily applied in a clinical setting to determine caloric requirements of
hospitalized patients. Limitations to these equations occur when the patient’s
energy expenditure changes due to critical illness or injury, resulting in over- or
under-estimation of caloric requirements based on the predictive equation
(Haugan et al, 2007). Also, these equations do not account for body composition
and fat-free mass (FFM), the latter being the most important determinant of
energy expenditure. A more accurate measurement of energy expenditure is
obtained using calorimetry.
Resting energy expenditure (REE) is the amount of energy expended in
the resting, fasted state for the maintenance of metabolic processes including
cellular respiration and the function of vital organs (Shils et al, 2005). REE
accounts for approximately 70% of total energy expenditure in moderately active
individuals and the gold standard method of REE quantification is by direct
calorimetry. However for practical reasons, indirect calorimetry is more widely
used and is based on the rate of oxygen consumed and carbon dioxide produced
following a 12-hour fast (Haugan et al, 2007). Several factors influence REE
including age, gender, weight, and height. REE is often expressed as kcal/kg of
body weight per unit of time, but is most strongly correlated with FFM
(Cunningham, 1991). Illness, injury, infection and fever are states which can
result in elevated REE. Patients are said to be hypermetabolic when REE as
8
determined by indirect calorimetry exceeds the REE value determined using a
predictive equation (Haugan et al, 2007) or in reference to a control population.
REE has been found to increase in certain types of cancer (Sakurai et al,
1998). For example, in a study comparing 714 newly diagnosed cancer patients
(of which 134 had NSCLC), and 642 healthy controls matched for age, height,
weight and gender, it was found that patients with cancer had significantly
higher REE/kg FFM (Cao et al, 2010). When categorized by cancer type, patients
with NSCLC, esophageal, gastric, and pancreatic cancer had higher REE/kg FFM in
comparison to controls, whereas that of colorectal cancer patients was not
significantly different (Cao et al, 2010). Other studies have found increased
REE/kg FFM in lung cancer patients, which when combined with altered food
intake, may account for weight loss (Fredrix et al, 1990; Staal-van den Brekel et
al, 1995; Jatoi et al, 2001).
2.7 Inflammation
The presence of cancer results in elevated markers of inflammation which
are found systemically circulating in the host’s plasma and also in the tumour
micro-environment. The presence of a tumour induces the host’s secretion of
inflammatory cytokines. These cytokines trigger the synthesis and secretion of
acute phase proteins (APP) from the liver such as CRP, serum amyloid A protein
and fibrinogen.
Several studies have found increased levels of circulating
inflammatory cytokines including IL-6, IL-1, and tumour necrosis factor-alpha
(TNF-α) in patients with cancer and varying degrees of weight loss (Martignoni et
al, 2005; Mantovani et al, 2000; McKeown et al, 2004). However, not all human
studies have found an association between the degree of weight loss/anorexia
and plasma cytokine concentrations (Maltoni et al, 1997).
For example, a
systematic review by Heikkilä et al which included 18 studies of lung cancer,
found that IL-6 concentrations were either elevated or similar in lung cancer and
healthy control subjects (Heikkilä et al, 2008). The acute phase response APR
9
and the prognostic implications of markers of systemic inflammation in cancer
are discussed further in section 2.15.
At the level of the tumour microenvironment, inflammation is marked by
the presence of tumour-associated macrophages and dendritic cells, chemokines
and cytokines (Balkwill & Mantovani, 2001). Cytokines and chemokines may
facilitate growth, invasion and metastasis of the tumour (Balkwill & Mantovani,
2001 Mantovani et al, 2008). Cytokines including IL-6, IL-8, TNF-α, and IL-1β
have been found to be elevated in the microenvironment of solid tumours such
as lung, gastric, colorectal, prostate and breast tumours (Kowalewska et al,
2010).
Based on animal and in vitro studies, apart from the acute pahse
response the proposed consequences of tumour-host cytokine interactions are a
catabolic effect on skeletal muscle tissue and increased urinary nitrogen losses
(Skipworth et al, 2007).
Specifically, TNF-α has been associated with the
induction of proteolysis through the ubiquitin-proteasome proteolytic pathway
and with lesser lipid storage through suppression of lipoprotein lipase (Tisdale,
2002).
2.8 Experimental models of cancer cachexia
There are several animal models of cancer-associated cachexia, each with
inherent advantages and disadvantages to their use. One strategy for studying
tumour-host interactions is to implant cell lines of animal or human origin
subcutaneously into rodent hosts to induce tumour growth (Bennani-Baiti et al,
2010). Commonly implanted cell lines include the Lewis lung carcinoma, the
Yoshida ascites hepatoma or sarcoma, and the MAC 16 adenocarcinoma
(Baracos, 2001; Bennani-Baiti et al, 2010). Alternatively, xenografts of human
tissue may be implanted into immunosuppressed or immunodeficient animals,
or tumour growth may be induced by exposure to carcinogenic agents (BennaniBaiti et al, 2010).
10
The advantage of using animal models of cancer cachexia is that
confounding factors such as age, gender, environment and comorbidities can be
highly controlled (Bennani-Baiti et al, 2010). Also, the use of animal models in
studies examining food intake and anorexia in cancer allows for precise control
of diet (Emery, 1999). One limitation of using animal models of cancer cachexia
is that a single model is not able to fully encapsulate the complex multifactorial
interactions occurring in human cancer (Bennani-Baiti et al, 2010). While easy
assessment of tumour growth and changes in body composition is possible,
implanted cell-lines in animal models do not significantly metastasize and rapid
increases in tumour burden may be disproportionate to those in humans
(Bennani-Baiti et al, 2010; DeBoer, 2009). Additionally, animal models with
cancer are not typically treated with chemotherapy or radiotherapy and thus the
effect of these treatments on muscle wasting in cachexia is unclear (Baracos,
2001).
As such, essential insights into the metabolic alterations of cancer
cachexia require studies conducted in patients.
2.9 Metabolic alterations in cancer cachexia
While the coupling of increased energy expenditure and decreased intake
due to anorexia may contribute to weight loss, these changes cannot fully
account for the metabolic alterations occurring in cancer cachexia (Tisdale,
2002). The accelerated loss of fat and muscle tissues in cancer cachexia is a
consequence of metabolic alterations in lipid, protein and glucose metabolism, in
addition to inflammation, acute phase protein synthesis and insulin resistance.
Each of these aspects is examined in the following sections.
2.10 Lipid metabolism in cancer cachexia
Loss of adipose tissue is one of the key features of cancer cachexia. In
patients with advanced cancer, depletion of adipose tissue reserves as assessed
by CT scans has been found to be prognostic of survival, with accelerated losses
11
of adipose tissue occurring 6 months prior to death (Murphy et al, 2010).
Adipocyte atrophy may result from both increased rates of lipolysis and
decreased lipid storage (Bing et al, 2009).
Lipolysis is the hydrolysis of triglycerides stored in adipocytes to free fatty
acids (FFA) and glycerol.
It is mediated by the hormone sensitive lipase,
stimulated by epinephrine, glucagon, and adrenocorticotrophic hormone and
suppressed by insulin (Tisdale, 2009). The molecular mechanisms of adipose
tissue loss in cancer cachexia remain to be fully understood. In vitro analysis of
adipocytes from patients with cancer cachexia has shown that elevations in
hormone sensitive lipase mRNA and protein expression could account for
enhanced lipolysis in cachexia (Augustsson et al, 2007). Zinc-α2 glycoprotein,
also known as lipid mobilizing factor, is a tumour-derived protein that may
mediate adipose tissue atrophy, based on animal and in vitro experiments. In
vivo, plasma FFA and glycerol have been found to be elevated in patients with
gastrointestinal cancer in comparison to weight stable cancer patients (Rydén et
al, 2008; Augustsson et al, 2007). In patients with cancer and concomitant
weight loss, FFA and glycerol turnover have also been found to be increased
indicating enhanced lipolysis (Tisdale, 2002).
Lipid storage involves the hydrolysis of triglycerides from circulating
lipoproteins by lipoprotein lipase. Studies in animal models of cachexia have
found reduced lipoprotein lipase activity in adipose tissue, which may contribute
to decreased lipid storage (Obeid et al, 1993). In contrast, lipoprotein lipase
activity in tumour cells from NSCLC patients has been found to be two fold
greater in comparison to that in adjacent lung tissue cells, and is thought to
result in a greater influx of fatty acids and triglycerides to support tumour
growth (Trost et al, 2009).
12
2.11 Protein metabolism in cancer cachexia
Under normal non-growing conditions, the rates of protein synthesis and
degradation are balanced such that the body’s total protein mass is maintained
in equilibrium. Protein kinetics are influenced in part by the intake of food.
During the fasting state, the rate of proteolysis exceeds that of protein synthesis,
resulting in a net catabolic effect. Conversely, in the postprandial state the rate
of protein synthesis exceeds proteolysis such that there is a net anabolic effect
(Clague et al, 1983). Skeletal muscle comprises more than 40% of the body’s
protein, the remainder of which is found in the visceral protein pool which
includes solid tissue organs (heart, liver and kidneys), serum proteins, structural
proteins and cellular proteins (Gibson 2005). Protein kinetics can be studied in
vivo using amino acids labelled with stable isotopes to examine skeletal muscle
protein (SMP) and/or whole-body protein (WBP) metabolism. The principles of
protein tracer methodology are found in Section 8.5.
Animal models of cancer cachexia have shown that skeletal muscle
proteolysis was induced by inflammatory and tumour-derived catabolic factors
(Argiles et al, 2000; Baracos, 2001). However, conflicting results have been
found in human studies of muscle protein metabolism in cancer cachexia. One
study comparing the rate of protein catabolism measured by urinary excretion of
3-methylhistidine reported that muscle protein catabolism was increased in
patients with lung cancer in comparison to healthy control subjects (Heber et al,
1982).
In contrast, another study found that direct leg efflux of 3-
methylhistidine was unchanged in patients with cancer of varying types
(Lundholm et al, 1982).
Emery et al found that muscle protein synthesis was depressed in cancer
patients with recent weight loss in comparison to healthy controls, as assessed
by 13C labelled leucine during constant feeding (Emery et al, 1984). A limitation
of this study is that the patients were not matched for age. As such, the
decrease in muscle protein synthesis may be due in part to the patients with
13
cancer being significantly older than control subjects (Volpi et al, 2000). Protein
turnover rates as measured by continuous infusion of
14
C-lysine have also been
found to be increased in lung cancer patients in comparison to control subjects
(Heber et al, 1982). Thus, based on these findings and on experimental models
of cachexia, it appears that altered rates of muscle protein synthesis and
degradation may occur simultaneously in cancer patients, depending on the type
and severity of cancer (Tisdale, 2009).
In addition to SMP turnover, splanchnic and non-muscle organs
contribute to overall WBP kinetics. As in studies of muscle, those of WBP
turnover in cancer patients also yielded conflicting findings.
Of particular
relevance, in a study of isotopic leucine kinetics in lung cancer patients, Melville
et al found elevated whole-body turnover rates/kg LBM in cancer patients in the
fasting and fed states (Melville et al, 1990). Net protein balance was not
significantly different however between the cancer and control groups, given
that both synthesis and breakdown were elevated in the cancer subjects
(Melville et al, 1990). Increased WBP synthesis and turnover rates have also
been found in studies of cancer patients in the fasted state (Jeevanandam et al,
1984). In contrast, studies conducted on malnourished or cachectic cancer
patients have found that WBP breakdown was unchanged in comparison to
control subjects (Heslin et al, 1992; Emery et al, 1984; Dworzak et al, 1998).
Further investigation of both WBP and SMP metabolism in the fasting and fed
conditions in cancer cachexia is thus warranted.
2.12 Acute-phase protein synthesis
When tissue injury occurs due to acute infection, burns, chronic
inflammation, or malignancy, the synthesis of several proteins is altered to meet
the increased energy and protein demands associated with inflammation (Pepys
et al, 2003; Wang et al, 2009). This process is known as the acute phase protein
(APP) response.
During the APP response, transport, coagulation, and
14
complement proteins as well as proteinase inhibitors either increase or decrease
in concentration in the plasma to respond to tissue injury and inflammation
(Pepys et al, 2003). Those proteins that increase in concentration in the serum
during an APP response are termed positive APPs. Those that respond inversely
to disease progression and decrease in concentration are termed negative APPs
(Coventry et al, 2009).
As a result of the chronic inflammation associated with cancer, the APP
response results in elevated circulating concentrations of positive APPs such as
fibrinogen and CRP, and decreased concentrations of negative APPs such as
albumin (Scott et al, 2002). Quantitatively, albumin and fibrinogen are the most
significant proteins exported by the liver (McMillan et al, 1996). Changes in
albumin and fibrinogen concentrations during the APP response are the result of
altered rates of synthesis, degradation and transcapillary escape (Barber et al,
2000). Past studies on the APP response in cancer have suggested that APP
synthesis may accelerate loss of lean body tissue by diminishing the body’s
protein reserves, particularly in malnourished patients (Reeds et al, 1994; Barber
et al, 2000). The influence of cancer on the APPs albumin, fibrinogen and CRP
are discussed in the following sections.
2.12.1 Albumin
Albumin is a soluble protein produced by the liver, responsible for
maintaining colloid osmotic pressure and for transporting fatty acids, cortisol,
unconjugated bilirubin and thyroxine (Gibson 2005). Normal serum albumin
levels range from 35-55 g/L and its serum half-life is approximately 21 days (Shils
et al, 2006).
Elevated concentrations of serum albumin may occur with
dehydration. Conversely, serum concentrations may decrease as a result of
chronic protein energy malnutrition, edema, inflammatory diseases, hepatic or
renal disease and protein losing enteropathy. Therefore albumin is considered a
non-specific biomarker of nutritional status (Gibson 2005), and has been found
15
to be correlated with poor outcome in a variety of conditions including liver and
renal disease, AIDS and various cancers in which changes in albumin
concentration often serve as an indicator of disease progression (Margarson &
Soni, 1998).
Albumin synthesis is mediated by insulin and is increased in the presence
of amino acids (Volpi et al, 1996). In cases of insulin resistance or in the absence
of insulin (as in type 1 diabetes), albumin synthesis has been found to decrease,
even in conditions of hyperaminoacidemia (Volpi et al, 1996).
Although
hypoalbuminemia occurs during the APP response in cachectic cancer patients, it
has been found that albumin synthesis rates did not differ from those of healthy
control subjects in both the fasting and fed states (Fearon et al, 1998).
2.12.2 Fibrinogen
Fibrinogen is a plasma glycoprotein produced by the liver that is a
precursor to fibrin, a filamentous protein involved in coagulation. In contrast to
albumin, fibrinogen synthesis is not directly influenced by insulin; in cases of
insulin resistance (type 2 diabetes) or in the absence of insulin (type 1 diabetes),
stimulation of fibrinogen synthesis still occurs (Barazzoni et al, 2003; De Feo et
al, 1991).
Higher fibrinogen synthesis has been found in fasting cachectic
patients with pancreatic adenocarcinoma in comparison to healthy controls, as
assessed using the flooding dose method with 2H5-labeled phenylalanine as the
tracer (Preston et al, 1998). Similarly, fibrinogen synthesis rates were found to
be significantly elevated in cachectic cancer patients in comparison to healthy
controls in response to feeding (Barber et al, 2000). While increased fibrinogen
synthesis was found in cachectic patients in both the fasting and fed states, it
remains to be determined whether this up-regulation also occurs in the precachectic state in patients with lung cancer.
16
2.12.3 C-reactive protein
C-reactive protein is a positive APP that is a sensitive, nonspecific
biomarker of both acute and chronic inflammation. The normal range for serum
CRP levels in healthy individuals is 0 to 3.0 mg/L (Pepys et al, 2003) and its
plasma half-life is 19 h (Coventry et al, 2009). CRP gene transcription is upregulated in hepatocytes in response to circulating IL-6. IL-6 is secreted from
leukocytes at the site of inflammation or within the tumour microenvironment
(Wang et al, 2009). Following its synthesis, CRP is secreted from the liver and
binds to ligands found on damaged cell membranes, apoptotic cells, invading
microorganisms, or tumour cells. It then activates the complement system, thus
enhancing opsonisation (Pepys et al, 2003; Wang et al, 2009).
C-reactive protein is an important biomarker associated with prognosis in
lung cancer. In a study of 96 patients with resectable NSCLC, it was found that
those with pre-operative CRP ≤10 mg/L had a significantly longer median survival
in comparison to those with CRP levels >10 mg/L (O’Dowd et al, 2010). In a
retrospective study of 289 patients with stage IIIb or V NSCLC receiving palliative
first-line chemotherapy, Koch et al found that CRP and smoking status were
independently associated with prognostic factors for patient survival (Koch et al,
2009). Elevated CRP level at the time of diagnosis has also been found to be
associated with poorer chemotherapy response (Kasymjanova et al, 2010),
increased weight loss, decreased performance status, and reduced survival in
inoperable NSCLC (Scott et al, 2002).
2.13 Insulin resistance in cancer cachexia
Insulin is an anabolic peptide hormone produced by the pancreas that is
responsible for the cellular uptake of glucose by muscle and adipose tissue,
stimulation of protein synthesis, suppression of protein breakdown, indirect
stimulation of glycolysis, and the postprandial uptake of fatty acids for storage in
adipocytes and suppression of lipolysis. In addition, insulin is responsible for the
17
suppression of hepatic glucose production through gluconeogenesis and
glycogenolysis.
Insulin resistance occurs when tissues are unresponsive or
exhibit decreased sensitivity to insulin and is defined by lesser glucose uptake
(Godsland, 2009). In addition to the abnormal glucose uptake, insulin resistance
associated with type 2 diabetes, obesity and aging has been found to result in
the attenuation of protein anabolism in response to insulin (Pereira et al, 2008;
Chevalier et al, 2005; Chevalier et al, 2006b).
Insulin resistance can be estimated using a surrogate measure such as the
homeostasis model assessment of insulin resistance (HOMA-ir). HOMA-ir is
calculated by the product of fasting plasma insulin (µU/L) and glucose
concentrations (mmol/L), divided by a normalizing factor of 22.5 (Muniyappa et
al, 2008). Other methods of assessment including the minimal model analysis of
a frequently sampled intravenous glucose tolerance test and the oral glucose
tolerance test have been reviewed elsewhere (Muniyappa et al, 2008).
The gold-standard method for direct measurement of in vivo glucose
metabolism and insulin resistance (or inversely insulin sensitivity) is the
hyperinsulinemic, euglycemic clamp technique (DeFronzo et al, 1979).
This
involves continuous infusion of exogenous insulin and maintenance of glycemia
at a constant concentration by frequent measurement of glucose and
corresponding adjustment of exogenous glucose infusion rates. The infusion
rates necessarily reflect tissue insulin responsiveness. In comparison to HOMAir, clamp studies are technically more demanding and invasive.
Therefore
HOMA-ir is often used in epidemiological and clinical trials, while clamp studies
are more widely used in studies with fewer subjects (Wallace et al, 2004). The
correlation between these two measures has been found to vary between 0.58
and 0.88 (Wallace et al, 2004), with coefficients of variability being influenced by
the sensitivity of the insulin assay employed and the number of fasting samples
measured (Muniyappa et al, 2008). Also to be considered are the characteristics
of the population being assessed, including individuals with glucose intolerance
18
and/or diabetes. To obtain a stronger linear correlation with clamp glucose
sensitivity estimates, HOMA-ir may be log transformed in order to correct for
skewed fasting insulin values found in individuals with hyperinsulinemia
(Muniyappa et al, 2008; Katz et al, 2000). Studies on the validity HOMA-ir in the
assessment of insulin resistance specifically in cancer patients could not be found
in the literature.
Decreased insulin responsiveness can be associated with glucose
intolerance, which is one of the earliest recognized metabolic aberrations in
cancer patients (Tayek, 1992). In a study of normal and underweight head and
neck cancer patients, Tayek et al found that in response to an intravenous
glucose tolerance test, a 40-50% reduction in insulin secretion was observed in
cancer patients in comparison to controls during the first 2-5 min post glucose
infusion (Tayek et al, 1997). Total insulin secretion over 180 min was directly
correlated with BMI and was reduced in both underweight control and cancer
subjects, independent from the effects of cancer (Tayek et al, 1997). Insulin
sensitivity as assessed by infusion 6-3H-glucose was reduced by 32-44% in cancer
patients in comparison to similar-weight controls (Tayek et al, 1997). In patients
with lung cancer, previous hyperinsulinemic clamp studies have found that
insulin responsiveness was less, with mean glucose uptake rates 47% lower in
lung cancer patients in comparison to control subjects (Yoshikawa et al, 2001).
The mechanism by which insulin resistance develops in cancer has yet to
be fully elucidated. Chronic systemic inflammation and inflammatory cytokines
such as TNF-α and IL-6 are thought to contribute to decreased insulin sensitivity.
For example, TNF-α has been studied as a contributor to insulin resistance in
skeletal muscle through increased phosphorylation of insulin receptor substrate1, which regulates insulin signal transduction of glucose uptake by decreasing the
translocation of the glucose transporter GLUT-4 to the cell membrane
(Plomgaard et al, 2005).
Elevated serum IL-6 concentrations have been
associated with decreased glucose uptake during hyperinsulinemic euglycemic
19
clamp studies in lung, stomach, colon and esophageal cancer (Makino et al,
1998). Insulin resistance has also been found to be negatively correlated with
CRP concentrations in stomach, colorectal and lung cancer (Yoshikawa et al,
2001).
2.14 Glucose metabolism in cancer cachexia
Glucose kinetics can be studied in vivo in humans or animal models by
the isotopic dilution of infused labelled glucose. The total rate of glucose
appearance (Ra) reflects endogenous glucose production (EGP), plus infused
unlabelled glucose in the case of clamp studies. The rate of glucose disposal (Rd)
reflects glucose uptake by tissues. Both Ra and Rd are influenced by insulin
resistance. Natali et al measured EGP in 212 men and 132 women (age 18-85
yrs, BMI 15-55 kg/m2) using 3H-glucose. In the fasting state, EGP ranged from
209 to 1512 µmol/min, and LBM (as determined by Hume’s predictive equation)
accounted for differences in EGP due to sex, obesity and age (Natali et al, 2000).
When normalized for LBM, EGP ranged from 5.1 to 25.9 µmol/min/kg LBM in the
fasting state (Natali et al, 2000).
In patients with cancer, many studies have found glucose production and
turnover to be elevated in comparison to those of control subjects. A review of
18 studies conducted up to the early 1990s found that mean hepatic glucose
production was increased by 39% in weight-losing cancer patients and 25% in
weight stable cancer patients above that in control subjects (Tayek, 1992).
Another review found glucose production to be increased between 25-50%
(Sauerwein et al, 2001).
The variability may be due to differences in
methodology between studies and the fact that more than one type of cancer
was often included in the subject group.
Some studies have examined glucose production and turnover in a single
type of cancer. For example, using a constant infusion of [6-3H]-glucose Heber et
al found increased glucose turnover in the fasting state in lung cancer patients
20
(2.84 ± 0.16 mg/kg/min) compared to healthy controls (2.18 ± 0.06 mg/kg/min)
(Heber et al, 1982). Changes in EGP rates in this group of cancer patients did not
correlate with weight loss or age (Heber et al, 1982). While the majority of
studies have found elevated glucose production, this finding is not universal to
all studies of a single type of cancer. Using [6,6-2H2]-glucose, Lattermann et al
found glucose production was not significantly different between control
subjects (11.0 ± 1.6 µM/kg/min) and patients with bladder cancer (10.4 ± 1.3
µM/kg/min) following an overnight fast (Lattermann et al, 2003).
Using a
flooding dose of [1-14C]-glucose in weight-losing patients with colorectal
adenocarcinoma, Holroyde et al found elevated EGP in the cancer patients in
comparison to healthy control subjects (136.4 ± 9.0 compared to 101.0 ± 4.6
mg/kg/hr respectively) (Holroyde et al, 1984).
Finally, Shaw et al found
increased glucose turnover in the fasting state in advanced gastrointestinal
cancer patients (17.6 ± 1.4 µmol/min/kg) but similar rates in early
gastrointestinal cancer patients (13.3 ± 0.2 µmol/min/kg) and control subjects
(13.9 ± 0.3 µmol/min/kg) (Shaw et al, 1987).
2.15 Endogenous glucose production: glycogenolysis and gluconeogenesis
In the fasting state, blood glucose homeostasis is maintained through the
production of endogenous glucose from glycogenolysis and gluconeogenesis
(GNG). In the fed state, up to 700 g of glycogen or 15 g/kg is stored in the body,
primarily in the liver and skeletal muscles (Acheson et al, 1988).
Hepatic
glycogen concentration as determined by analysis of needle biopsy samples
revealed a significantly higher glycogen concentration in obese patients
compared to lean control subjects (515 ± 38 and 308 ± 58 mg/g protein
respectively) (Müller et al, 1993). Glycogenolysis is catalyzed by the enzyme
glycogen phosphorylase which releases glucose-1-phosphate from the nonreducing end of a glycogen chain (Nuttall et al, 2008). In the fasting state,
hepatic GNG and glycogenolysis are the main sources of circulating glucose and
21
their relative contributions are affected by the amount of glycogen stored
following dietary intake, previous exercise and fasting duration (Frayn, 2010).
Several
studies
have
examined
the
fractional
contribution
of
glycogenolysis to EGP under short and longer fasting conditions. An indirect
approach to quantifying the rate of hepatic glycogenolysis in humans is by
multiplying the change in liver 13C-labelled glycogen concentration by the change
in liver volume determined by magnetic resonance imaging (Rothman et al,
1991).
Alternatively, stable isotopes such as
13
C-acetate,
13
C-glucose,
13
C-
bicarbonate or 2H20 can be used to determine the ratio of glycogenolysis to GNG.
In the fasting state, glycogenolysis contributes 40-60% of glucose to EGP for fasts
between 10 and 20 hr (Nuttall et al, 2008). As the duration of fasting is
prolonged, the contribution of glycogenolysis declines and GNG becomes the
main source of EGP (Landau et al, 1995; Katz et al, 1998). Mice implanted with
cachexia-inducing MAC16 colonic adenocarcinoma have been found to have a
decrease in hepatic glycogen reserves that is directly proportional to weight loss
(Hirari et al, 1997). It remains to be determined whether these changes in
hepatic glycogen reserves also occur in cancer patients.
Endogenous glucose produced through GNG comes from glycerol, lactate,
and glucogenic AAs converted to TCA cycle intermediates.
Alanine and
glutamine are the two primary AAs exported from muscle and used for glucose
production (Frayn, 2010). The other glucogenic AAs are cysteine, glycine, serine,
threonine,
tryptophan,
asparagine,
aspartate,
phenylalanine,
tyrosine,
isoleucine, methionine, valine, arginine, glutamate, histidine and proline (Voet &
Voet, 2011). GNG occurs primarily in the liver and up to 20% occurs in the renal
cortex (Wolfe et al, 2008).
Hepatic GNG is activated by glucagon,
catecholamines and cortisol and is inhibited by insulin.
There are three
irreversible enzymatic reactions which are rate limiting steps in the formation of
glucose via GNG.
These reactions are the conversion of oxaloacetate to
phosphoenolpyruvate (PEP) by phosphoenolpyruvate carboxy kinase, the
22
conversion fructose-1,6-bisphosphate to fructose-6-phosphate by fructose-1,6bisphosphatase, and the conversion of glucose-6-phosphate to glucose by
glucose-6-phosphatase (Frayn, 2010).
2.16 Measurement of gluconeogenesis in human subjects
Prior to the use of isotopic tracers, splanchnic glucose production and
GNG were estimated by comparing differences in the concentrations of lactate,
pyruvate and alanine in samples of arterial, peripheral venous and hepatic vein
blood (Nuttall et al, 2008). A less invasive, more direct quantification of GNG in
humans can be achieved using an isotopic tracer of one of the glucogenic
precursors such as 13C-glycerol, 13C-lactate or 13C-pyruvate or a radioactive tracer
such as 14C-HCO3 or 14C-lactate (Nuttall et al, 2008).
The natural abundance of
alternative to
13
13
C is 1.11% (Wolfe et al, 2008).
As an
C labels, deuterium or tritium may be used to measure GNG.
Deuterium, which is denoted as 2H, is a stable isotope of hydrogen and has a
0.015% natural abundance (Wolfe, 1992). Tritium, which is denoted as 3H, is a
radioactive isotope with a half life of 12.3 yrs (Wolfe, 1992). Both deuterium and
tritium are commonly used in isotopic tracer studies.
In animal studies,
radioactive 3H20 has been used to quantify GNG. However, 3H20 cannot be
applied in human research because ingestion of a large quantity of tritiated
water would be required to obtain significant enrichment in glucose (Landau et
al, 1995). The use of deuterated water (2H20) provides an alternative approach
to measuring GNG in humans. This approach was introduced by the group of
Bernard Landau in the mid 1990s and is currently considered the most accurate
method of quantifying GNG (Nuttall et al, 2008).
2.17 The 2H20 method
The premise of the 2H20 method is that following the ingestion of
deuterated water, 2H is incorporated into molecules of endogenously produced
23
glucose. One of the assumptions of this method is that 2H20 mixes with body
water and a steady state of 2H20 enrichment is achieved (Landau et al, 1995).
Typically isotopic enrichment of 2H is maintained at 0.5% of body water.
Enrichment of glucose with deuterons occurs at all carbons, however enrichment
at carbons 2 (C2), 5 (C5) and 6S (C6S) (depicted in Figures 1a and 1b in Appendix
A) is used to calculate the relative contributions of glycogen, glycerol and PEP to
EGP. During GNG, PEP is converted to 2-phosphoglycerate by enolase with the
addition of water. During this step, when 2H20 has equilibrated with body water,
a deuteron is incorporated on C2 (Nuttall et al, 2008). A deuteron is also
incorporated on C2 during the isomerase reaction between dihydroxyacetone
phosphate and glycerol-3-phosphate.
In the newly synthesized molecule of glucose, C2 of the triose unit
becomes C6 in glucose. During GNG, fructose-6-phosphate is converted to
glucose-6-phosphate through the addition of a water molecule at C2 of glucose.
During this isomerase reaction, a deuteron may also be incorporated into the
glucose molecule. Therefore during GNG, deuterons are incorporated at C2 and
C5 (Wolfe et al, 2008).
In glycogenolysis, glycogen phosphorylase produces glucose-1-phosphate
molecules from the parent glycogen strand.
Glucose-1-phosphate is then
converted to glucose-6-phosphate which equilibrates with fructose-6-phosphate.
As in GNG, fructose-6-phosphate is converted to glucose-6-phosphate through
the addition of a water molecule at C2. At this point, a deuteron may therefore
also be incorporated into the glucose molecule.
Thus, deuterons become
incorporated at C2 during both glycogenolysis and GNG and enrichment at C2
becomes equal to that in body water (Chandramouli et al, 1997).
Enrichment at C6 in glucose begins when a deuteron is incorporated into
malate formed from fumarate via fumarase (Jones et al, 2001). Malate is
shuttled from the mitochondrion to the cytosol where it is converted to
oxaloacetate and subsequently to PEP (Nuttall et al, 2008). PEP is then used as a
24
substrate for GNG and results in enrichment at position C6 of glucose. During
these steps, enrichment of both the C6R and C6S enantiomers occurs at carbon 6
of glucose. The ratio of enrichment of 2H at C2, C5 and C6S on the glucose
molecule can then be measured by mass spectrometry or by nuclear magnetic
resonance (NMR) spectroscopy (Landau et al, 1997). NMR spectroscopy analysis
allows for the separation of C6R and C6S resonances (Jin et al, 2004). The
calculations for determining the fractional contributions from NMR spectroscopy
analysis are found in Appendix A, Figure 2. The fractional contribution of GNGPEP
to EGP is determined by calculating the ratio of C6S/C2.
The fractional
contribution of GNGGlycerol is determined by C5 - C6S /C2, and that of
glycogenolysis (GLY) to EGP, by 1 - (C5/C2) (Jin et al, 2004).
The main advantage of the 2H20 method is that it accounts for the
contribution of glucose produced from glycogen, glycerol and from TCA cycle
intermediates through PEP (Jones et al, 2001). When tracers of U-13C6 glucose or
3-13C lactate are used to measure EGP, they cannot differentiate between these
glucogenic substrates. When an isotopic tracer of
13
C or 3H is used in tandem
with the 2H20 method, in a single experiment it is possible to determine EGP as
well as individual fluxes of pyruvate, the TCA cycle and GNG (Jones et al, 2001).
In such an experiment, NMR analysis is preferable to GC-MS as the latter cannot
differentiate between 2H (m+1) and
13
C (m+1) (Burgess et al, 2003).
An
additional advantage is that deuterated water is less expensive in comparison to
the use of multiple isotopic infusates (Landau et al, 1999).
One of the practical limitations of the 2H20 method is that subjects may
experience vertigo following the ingestion of deuterated water (Landau et al,
1995). To minimize this side-effect the total amount of deuterated water may be
divided into smaller doses and ingested over a longer period of time. A second
limitation is that repeat measurements of GNG using the 2H20 method cannot be
made for several weeks given that 2H20 may be incorporated into glycogen
(Chandramoulli et al, 1997). When a repeat measurement is made, deuterons in
25
glycogen may result in an apparently elevated contribution of glycogenolysis to
GNG (Chandramoulli et al, 1997).
Several studies have examined the percent contribution of GNG to EGP
using the 2H20 method in various populations including healthy adults and those
with type 2 diabetes and/or obesity. In a study of lean and obese men and
women, Chevalier et al found that in lean subjects, the percent contribution of
glycogenolysis was 49% and the percent contribution from GNGglycerol and GNGPEP
were 10% and 41% respectively following a 17 h fast (Chevalier et al, 2006).
Obese subjects were found to have a significantly lower contribution of
glycogenolysis (39%) and higher contribution of GNGPEP (52%) (Chevalier et al,
2006). In the presence of insulin resistance such as in patients with type 2
diabetes, the percent contribution from GNG after an overnight fast has been
found to be elevated in comparison to non-obese control subjects matched for
BMI (64 ± 5 % vs. 47 ± 5% respectively) (Gastaldelli et al, 2000). In this study, a
group of non-diabetic obese subjects also had increased contribution of GNG
(62 ± 2 %) compared to lean, non-diabetic subjects and %GNG was
independently associated with BMI and fasting plasma glucose (Gastaldelli et al,
2000). GNG has been found to increase in a broad range of cancer types (Tayek,
1992) however the relative contribution of glycogenolysis and GNG from PEP,
glycerol and the TCA to EGP has yet to be determined in NSCLC patients using
the 2H20 method. Metabolic changes in the major gluconeogenic substrates
lactate, alanine and glycerol in cancer are discussed below.
2.18 Lactate
Lactate is a three carbon molecule produced under aerobic conditions
through the fermentation of pyruvate by lactate dehydrogenase. However in
tumour cells, pyruvate generated from glycolysis is reduced to lactate even
under highly aerobic conditions (Warburg et al, 1927).
Tumour cells
demonstrate increased glucose uptake, increased rates of glycolysis and
26
increased lactate production (Ferreira, 2010).
These metabolic changes
occurring in tumour cells were first observed by Otto Warburg in the 1920s and
together are known as the Warburg effect. Alterations in lactate metabolism in
cancer are relevant to GNG given its use as a gluconeogenic precursor. Enhanced
glycolysis in the tumour cell favours the conversion of pyruvate to lactate which
is exported into the blood stream. The liver then utilizes lactate as a substrate
for GNG via the Cori Cycle (Argiles et al, 1988). Tracer studies using U-14C lactate
conducted in patients with colorectal cancer have found that fasting venous
plasma lactate concentrations and plasma lactate production were significantly
higher compared to those in control subjects (Holroyde et al, 1979). Cersosimo
et al found that during a euglycemic clamp, weight losing pancreatic, gastric and
esophageal cancer patients had similar baseline peripheral lactate fluxes in
comparison to age and weight matched control subjects. Hyperinsulinemia
significantly augmented peripheral lactate efflux in the cancer group (Cersosimo
et al, 1991).
A review of 14 studies revealed that patients with various types of cancer
had elevated plasma lactate concentrations in 9 studies, whereas 5 studies
reported no difference in lactate concentrations compared to control subjects
(Tayek, 1992).
The mean fasting plasma lactate concentration from these
studies was 1.26 mmol/L, which was 40% higher than that observed in control
subjects (Tayek, 1992). The author of this review notes that those studies not
reporting a significant increase in lactate concentration in cancer patients may
have been due to small sample size and that tumour burden and hepatic
metastases may influence lactate production (Tayek, 1992).
2.19 Alanine and other glucogenic amino acids
In addition to lactate, alanine is also an important substrate for GNG.
During muscle protein breakdown, alanine and glutamine are the predominant
AAs exported from the muscle, released into circulation and taken up by the
27
liver. It has been proposed that muscle protein degradation associated with
cancer cachexia contributes to an increase in alanine utilization by the liver for
GNG (Argiles et al, 1988). Increased alanine conversion to glucose has been
found in studies of mixed cancer types and esophageal cancer (Waterhouse et al,
1979; Burt et al, 1982). In a study comparing GNG in weight stable and weight
losing lung cancer patients, Leij-Halfwerk et al found that those losing weight had
significantly higher whole body glucose turnover rates and a higher contribution
of alanine to GNG compared to weight stable and healthy control subjects (0.47
± 0.04 compared with 0.31 ± 0.04 and 0.29 ± 0.04 mmol/kg/h respectively) (LeijHalfwerk et al, 2000). Also, GNG from alanine was found to be positively
correlated with the degree of weight loss (Leij-Halfwerk et al, 2000). A limitation
of this study reported by the authors is that with the use of a 13C-alanine tracer,
GNG was underestimated due to the dilution of the tracer in intracellular
pyruvate and oxaloacetate pools (Leij-Halfwerk et al, 2000). Additionally, it is
unknown whether the relative contribution of GNG to EGP differs in comparison
to the contribution of glycogen. Given that alanine and lactate were the only
GNG substrates assessed, the contribution of all glucogenic precursors to GNG in
NSCLC patients remains to be determined.
2.20 Glycerol
As discussed in section 2.10, mobilization of triglycerides from adipose
tissue results in increased glycerol turnover in patients with cancer and
concomitant weight loss (Tisdale, 2002). Glycerol enters the gluconeogenic
pathway as dihydroxyacetone phosphate and is subsequently converted to
glucose. Prolonged fasting results in an increased contribution of glycerol to
GNG. Using a [1-13C]-glycerol tracer to measure the rate of glycerol appearance,
Baba et al found that in healthy subjects the contribution of glycerol to GNG
increased from 4.5% to 21.6% after 14 h and 62 h of fasting (Baba et al, 1995).
Ackermans et al compared the quantification of GNG using [2-13C]-glycerol to the
28
2
H20 method in fasting healthy men and found that the rate of GNG was higher
when assessed by the latter method (Ackermans et al, 2001). To date, few
studies have assessed the contribution of glycerol to EGP using the 2H2O method.
Given that the mobilization of fat stores and weight loss is one of the
characteristics of cancer cachexia, measurement of the contribution of glycerol
to EGP is of particular relevance to determine whether cancer cachexia is
associated with an increase in glycerol flux and contribution to EGP through
GNG.
2.21 The effect of diet on gluconeogenesis
Several studies have examined the effect of diet on the contribution of
GNG to EGP using the 2H20 method. For example, the effect of calorie and
carbohydrate restriction on GNG and energy metabolism in healthy men and
women was examined in a study conducted by Browning et al. Subjects on a diet
of <20 g of carbohydrate per day had a greater contribution of GNG to glucose
production than did subjects on a low-calorie diet (Browning et al, 2008).
Likewise, Veldhorst et al used the 2H20 method and an infusion of [6,6-2H2]glucose to measure EGP, in healthy men consuming a diet that was either 30% of
calories from protein and 0% from carbohydrate, or a diet of 12%, 55%, and 33%
of energy from protein, carbohydrate, and fat, respectively (Veldhorst et al,
2009).
Fractional GNG was significantly increased in men consuming the
carbohydrate restricted diet (Veldhorst et al, 2009). These findings differ from
those of Bisschop et al who also used the 2H20 method and an infusion of [6,62
H2]-glucose to measure EGP. This group found that in healthy men consuming
eucaloric diets of 15% of calories from protein, with either high or low
carbohydrate content (85% vs. 2% of energy from CHO respectively), the low
carbohydrate diet had a minimal effect on the fasting rate of GNG. Rather, they
concluded that the carbohydrate content of the diet had a greater effect on the
29
modulation of the contribution of glycogenolysis to fasting glucose production
(Bisschop et al, 2000).
While these studies addressed the effect of varying carbohydrate and fat
intake on GNG and EGP, studies that specifically assess the effect of low dietary
protein intake on GNG and EGP using the 2H20 method could not be found in the
literature. Given that the composition of the diet has been shown to affect the
fractional contribution of GNG to glucose production, assessment of dietary
intake should be considered in the measurement of GNG, particularly in patients
with cancer who may have atypical intakes.
3 Rationale
Lung cancer is currently the leading cause of cancer mortality in Canada
and is highly associated with cachexia. The presence of cachexia can lead to
poorer response to treatment, decreased quality of life and increased mortality.
Inflammation, insulin resistance, the loss of lean muscle mass and mobilization of
adipose tissue stores are hallmarks of the cancer cachexia syndrome. Thus,
understanding the metabolic sequelae of this disease and the progression of
cachexia is of significant importance to improving the prognosis and quality of
life of lung cancer patients. The study of GNG and EGP is important in the
context of cancer cachexia, since it is unknown whether loss of muscle and
adipose tissue results in increased use of AAs and glycerol for GNG. Additionally,
studies have revealed mixed findings when EGP was measured in different
populations of cancer patients. Loss of lean body mass in cachectic NSCLC
patients is thought to result in the mobilization of AAs from muscle protein
reserves as a means of meeting the demands of acute-phase protein synthesis.
The presence of the tumour is thought to result in increased glucose use and
lactate production and delivery to the liver via the Cori Cycle.
To date no previous studies have examined the contribution of all
glucogenic AAs, glycerol, lactate and glycogen to EGP in NSCLC patients in the
30
fasting state. The current study is unique in that it employs the analysis of
deuterium enrichment of glucose through NMR spectroscopy, a technique
considered to be the most accurate method to date for quantifying of the
relative contributions of all glucogenic precursors to EGP (Nutall et al, 2008).
Also, this study simultaneously examines the role of insulin resistance on protein
and glucose kinetics in the fasting state and in conditions of hyperinsulinemia.
By examining the metabolic differences between control subjects and cachectic
NSCLC patients with respect to glucose kinetics, protein metabolism, insulin
sensitivity and GNG, the current study aimed to define and elucidate the role of
cancer and its intrinsic features on these metabolic processes.
4 Objectives and hypotheses
4.1 Objectives
The primary objective of this study is to assess the effect of non-small cell
lung cancer (NSCLC) on glucose and protein metabolism in patients with cancer
cachexia. Specifically the study aims to assess:
i)
the relative contributions of glycogenolysis and gluconeogenesis (GNG)
from phosphoenolpyruvate (PEP) and glycerol to endogenous glucose
production (EGP) in the fasting state;
ii)
the presence and magnitude of insulin resistance, and its effects on
whole-body protein metabolism;
iii)
the relationships among percent contributions of substrates to EGP,
insulin resistance and protein turnover.
By comparing NSCLC patients to control subjects matched for age,
gender, BMI and smoking history, this primary objective will integrate the
metabolic alterations of glucose, protein and lipid metabolism and assess their
relationships with insulin resistance and inflammation.
31
The secondary objective of the study is to compare NSCLC patients to
matched healthy controls in terms of their diet, physical activity level, energy
expenditure, body composition, and markers of inflammation.
Such an
assessment will garner a more comprehensive profile of the subjects studied, in
order to determine if correlations exist between these parameters and the
findings of the metabolic studies.
4.2 Hypotheses
It is hypothesized that NSCLC patients will demonstrate a greater
contribution of AAs and lactate to EGP via GNG through PEP.
Thus, in
comparison to healthy control subjects, NSCLC patients will have an increased
fractional contribution of PEP and glycerol to GNG and a lesser fractional
contribution of glycogenolysis for EGP. It is hypothesized that weight losing
NSCLC patients will have increased rates of protein catabolism compared to
healthy control subjects.
It is hypothesized that cachectic NSCLC patients will demonstrate a state
of insulin resistance due to the underlying inflammation associated with cancer.
During the clamp study, this will result in blunted anabolic responses to glucose
and AA infusion. Specifically, insulin resistance will be associated with decreased
glucose and AA uptake, blunted stimulation of whole-body protein synthesis and
less suppression of breakdown in NSCLC patients.
32
5 Manuscript
Substrate contribution to endogenous glucose production
in non-small cell lung cancer cachexia
Jacqueline MacAdams1, Aaron Winter1, Shawn C. Burgess2
and Stéphanie Chevalier1
1
McGill Nutrition and Food Science Centre, McGill University Health Centre,
Royal Victoria Hospital, Québec, Canada H3A 1A1; 2Department of Radiology,
University of Texas Southwestern Medical Center, Dallas, TX 75390
Address correspondence and reprint requests to Stéphanie Chevalier, McGill
Nutrition and Food Science Centre, McGill University Health Centre, Royal
Victoria Hospital, 687 Pine Ave. West, Montreal, Québec, Canada, H3A 1A1.
E-mail: [email protected]
-To be submitted to British Journal of Cancer-
33
5.1 Abstract
Background: Cancer is associated with metabolic alterations often leading to the
loss of adipose tissue and muscle. This may result in increased use of glycerol
and amino acids for gluconeogenesis (GNG) via phosphoenolpyruvate (PEP) and
the TCA cycle.
Methods: Whole-body protein and glucose kinetics were measured with L-[113
C]-leucine and D-[3-3H]-glucose tracers in the fasting state and during a
hyperinsulinemic, euglycemic, isoaminoacidemic clamp to determine insulin
resistance in 10 male NSCLC patients and 10 matched control subjects. The
fractional contributions of glycogenolysis (%GLY), glycerol (%GNGglycerol) and PEP
(%GNGPEP) to endogenous glucose production (EGP) were measured using oral
2
H20 and the positional deuterium enrichment of plasma glucose by NMR
spectroscopy.
Results: Following a 17 h fast, the rates of EGP, the fractional substrate
contributions to EGP and their respective fluxes were not different between
NSCLC (n=8) and control subjects (n=7). Glycogenolysis and GNG PEP contributed
equally to EGP. NSCLC patients had elevated markers of systemic inflammation
(CRP, IL-6) and insulin resistance with lower rates of glucose uptake in response
to hyperinsulinemia. The change in whole-body net protein balance was lower in
NSCLC patients and greater fasting total GNG flux was associated lesser protein
retention in response to insulin.
Conclusion: The presence of insulin resistance in NSCLC patients preceded any
further metabolic alterations in protein metabolism, EGP, GNG and
glycogenolysis that may occur in more advanced stages of cachexia.
Key words: non-small cell lung cancer, hepatic glucose production, insulin
resistance, glucose and protein metabolism, nuclear magnetic resonance
spectroscopy, cachexia
34
5.2 Introduction
Patients with cancer frequently exhibit involuntary loss of fat and muscle,
particularly in advanced stages of the disease.
Cachexia, a multi-factorial
syndrome of weight loss, inflammation and insulin resistance, results in poorer
response to treatment and exacerbated prognosis in cancer (Vigano et al, 2000).
Lung cancer is the leading cause of cancer mortality in North America and
Europe, and over 50% of patients with this disease will experience cachexia
(Canadian Cancer Society 2010; Jemal et al, 2010; Bruera et al, 1997).
The metabolic alterations underlying cancer cachexia influence
endogenous glucose production (EGP) by the liver as well as whole-body glucose
and protein kinetics. Decreased whole-body glucose uptake indicative of insulin
resistance has been reported using intravenous glucose tolerance tests (Tayek et
al, 1997) and in hyperinsulinemic euglycemic clamp studies of cancer patients
(Cersosimo et al, 1991; Yoshikawa et al, 2001). Increased fasting EGP and
turnover rates have been found in lung cancer (Heber et al, 1982) and studies of
mixed cancer types (Lundholm et al, 1982; Cersosimo et al, 1991) using isotopic
tracer methodology. However, studies with weight stable cancer patients (LeijHalfwerk et al, 2000) or those in early stages of the disease (Shaw et al, 1987)
have found rates of glucose production that were not significantly elevated
above those of healthy individuals. In studies of colorectal cancer, increased
recycling of lactate has been found (Long et al, 1991, Holroyde et al, 1984) and is
one of the proposed mechanisms for increased energy expenditure and lactate
provision for gluconeogenesis (GNG). In addition to lactate, muscle protein
degradation associated with cancer cachexia may contribute to an increase in
alanine utilization by the liver for GNG (Argiles et al, 1988). Increased alanine
turnover and a higher contribution of alanine to GNG have been found in weight
losing non-small cell lung cancer (NSCLC) patients in comparison to weight stable
and control subjects, as assessed by isotopic infusion (Leij-Halfwerk et al, 2000).
35
An alternative to assessing GNG by infusion of an isotopically labelled
gluconeogenic substrate is the oral 2H2O method. Using this method, analysis of
the positional enrichment of deuterium in plasma glucose by NMR spectroscopy
provides an assessment of the fractional contributions of glycogenolysis (%GLY),
glycerol (%GNGglycerol) and substrates cycling through phosphoenolpyruvate (PEP)
(%GNGPEP) to EGP. GNGPEP accounts for the contributions of lactate, alanine and
other gluconeogenic amino acids entering the TCA cycle (Chandramouli et al,
1997; Landau, 1999). The 2H2O technique can be used in tandem with isotopic
tracer infusion to quantify EGP and protein turnover rates in a single experiment.
Using this methodology, our group and others have found that in conditions of
insulin resistance including obesity (Chevalier et al, 2006a) and type 2 diabetes,
(Gastaldelli et al, 2000) there is an increase in gluconeogenic flux in the fasting
state. When whole-body protein turnover was simultaneously measured using
13
C-leucine infusion, GNG flux was found to be negatively correlated with whole-
body protein catabolism in obesity (Chevalier et al, 2006a). Studies of protein
turnover in fasting patients with cancer have been conflicting with unchanged
rates of protein breakdown in gastric cancer (Dworzak et al, 1998) and higher
breakdown in lung cancer (Melville et al, 1990; Heber et al, 1982).
To our knowledge, no studies of lung cancer patients have combined the
2
H2O method with isotopic tracers for whole-body glucose and protein kinetics to
provide an integrated assessment of the interplay between protein and glucose
metabolism, insulin resistance and the substrates for GNG. The aims of this
study were therefore twofold. Firstly, to determine the effect of NSCLC cachexia
on the relative contribution of substrates to EGP as assessed by NMR
spectroscopy. Secondly, to assess the relationships among insulin resistance,
inflammation, changes in body composition and glucose and whole-body protein
metabolism in a well-defined group of weight losing NSCLC patients with
advanced disease.
It was hypothesized that compared to healthy control
subjects NSCLC patients will have an increased fractional contribution of PEP to
36
GNG and a corresponding decrease in glycogenolysis for endogenous glucose
production in the fasting state. In addition, NSCLC patients will demonstrate
suppressed glucose uptake and blunted whole-body protein anabolism in
response to insulin.
5.3 Methods
5.3.1 Subjects
Ten men with non-small cell lung cancer were recruited from oncology
clinics at the MUHC-Montreal General and Royal Victoria Hospitals. Each was
screened by medical history, physical exam, laboratory investigation,
electrocardiogram and pulmonary function tests.
Inclusion criteria were as
follows: men aged 18-79 years with pathologically confirmed diagnosis of stage
IIIa/b or IV NSCLC, who were ineligible for surgery. Patients who had received
their last treatment of radio and/or chemotherapy >3 months were also eligible
for the study. Exclusion criteria were as follows: patients with diabetes or other
diseases known to affect glucose and protein metabolism, severe anemia (Hb <
100 g/L), recent acute weight loss (>10% in 3 months), BMI >27, pacemaker, or
diagnosis of small cell lung cancer, mesothelioma or primary tumours in other
organs. Also excluded were patients with metastases that significantly impair
organ functions, including liver metastases (as confirmed by computed
tomography scan), those with uncontrolled pain, those unable to refrain from
smoking for a day, or those taking medications including diuretics, steroids, βblockers, antianginals, antiarrythmics, anticoagulants and high-dose narcotic
analgesics.
Patients undergoing chemo or radiotherapy, or who were
participating in other clinical studies at the time of screening were also excluded.
Patients with NSCLC were compared to 10 healthy men without cancer matched
for age, smoking history and BMI who were recruited from advertisements. Each
subject was informed of the nature, purpose and possible risks of the study and
signed a consent form approved by the MUHC-RVH Ethics Board.
37
5.3.2 Body composition analysis
Dual energy x-ray absorptiometry (DXA) (Lunar Prodigy Advance, GE
Healthcare) was used to determine body composition with high precision. As a
means of comparison, other less precise methods of body composition
assessment were also performed including bioelectrical impedance analysis (BIA)
(RJL-101A; RJL Systems, Detroit, MI) and anthropometric measurements of
skinfolds [triceps, biceps, subscapular, suprailiac (Lange skinfold calipers, Beta
Technology Incorporated, Santa Cruz, CA)] and body circumferences (chest,
smallest waist, umbilical waist, hip, right thigh, right triceps and right calf).
Quantification of changes in muscle and adipose tissue were determined by
retrospective analysis of consecutive computerized tomography (CT) images
from the patients with NSCLC (n=9) using Slice-O-Matic software V4.3
(TomoVision, Montreal, QC, Canada) as described by others (Murphy et al, 2010;
Mourtzakis et al, 2008). Scans were analyzed at the third lumbar vertebrae (L3)
given that abdominal skeletal muscle and adipose tissue areas calculated at this
landmark are strongly correlated with whole body adipose and skeletal muscle
tissue volume (Shen et al, 2004). Appendicular skeletal muscle mass and whole
body lean and fat mass were also estimated by regression equations derived
from an advanced cancer patient cohort: appendicular skeletal muscle = 0.11 x
[skeletal muscle at L3 (cm2)] + 1.17; whole body fat mass (kg) = 0.042 x [total
adipose tissue at L3 (cm2)] + 11.2; whole body fat free mass (kg) = 0.30 x [skeletal
muscle at L3 (cm2)] + 6.06 (Mourtzakis et al, 2008).
5.3.3 Diet
Subjects were admitted to the Clinical Investigation Unit of the Royal
Victoria Hospital for 2 days. On the first day, subjects received an isoenergetic,
isoproteic diet of 30 g cereal (15 g All-Bran, 15 g Corn Flakes, Kellogg Canada Inc,
Mississauga, ON) and 200 ml of 2% milk for breakfast, and a liquid formula
(Ensure®, Abbott Laboratories, St. Laurent, QC) throughout the remainder of the
day. The total caloric content of the diet was determined based on diet history
38
(24-h recall and food frequency questionnaire) and resting metabolic rate
measured by indirect calorimetry (TrueOne 2400, Sandy Medics, Utah, USA) with
a 1.3-1.6 physical activity factor.
5.3.4 Deuterated water method and NMR spectroscopy analysis
Subjects began fasting at 17h30 and at 21h45 they ingested 5 g/kg body
water of 2H2O (99.9% 2H2O; CDN Isotopes, Pointe-Claire, QC, Canada) taken as 4
equal doses at 30 min intervals. Body water was calculated as 0.73 x lean body
mass (LBM), as determined by DXA. Subjects were provided water enriched at
0.50% of 2H2O ad libitum.
The following day, a 20 ml blood sample was collected at 10h30 (17 h
fast) in heparin-containing tubes for measurement of deuterium enrichment of
plasma glucose. Samples were centrifuged (15 min, 3000 rpm, 4 oC) and the
plasma supernatant was removed and frozen at -20oC until further analysis.
Plasma was deproteinized with 0.3 N Ba(OH)2 and 5% ZnSO4, centrifuged (15
min, 3000 rpm, 4oC) and passed through AG® 50W-X8 cation and AG® 1-X8 anion
exchange resins (Bio-Rad Laboratories, Inc., Hercules, CA ). The effluent was
lyophilyzed and purified glucose was converted to 1,2-diisopropylidene
glucofuranose derivative (monoacetone glucose) as previously described
(Chandramouli et al, 1997; Landau et al, 1995). Samples were analyzed by NMR
spectroscopy for isotopic enrichment at the H2, H5 and H6S on the backbone of
glucose (Chevalier et al, 2006a). Briefly, during in vivo glucose synthesis the
protons on the backbone of glucose become enriched with deuterium according
to the level of body water enrichment and the activity of the pathways
responsible for the exchange described (Chandramouli et al, 1997; Landau et al,
1995). While all 7 proton positions of glucose become enriched during in vivo
metabolism, enrichment at the H2, H5 and H6S are diagnostic of fluxes through
glycogenolysis and GNG (Chandramouli et al, 1997; Landau et al, 1995). The
positional enrichment through glycogenolysis (GLY) and through gluconeogenesis
via phosphoenolpyruvate (GNGPEP) and glycerol (GNGglycerol) were determined
39
from the following relationships: GLY = 1 - (H5/H2), GNGPEP = (H6S/H2) and
GNGglycerol = (H5 – H6S)/H2 (Burgess et al, 2003). The corresponding fluxes were
calculated by multiplying these relative contributions by the rate of endogenous
glucose production as previously described (Jin et al, 2004; Chevalier et al,
2006a).
5.3.5 Endogenous glucose production
At 8h00 after the overnight fast following 2H2O ingestion, intravenous
catheters were inserted in a dorsal hand vein for arterialized blood sampling and
in a contra-lateral antecubital vein for infusions. The subject’s hand was placed
in a heating box at 65°C to 70°C to arterialize the venous blood. Endogenous
glucose production was measured using D-[3-3H]-glucose with administration of
a priming dose (22 µCi [814 kBq]) and continuous infusion (0.22 µCi/min [8.14
kBq/min]) from 8h00 to 10h30. Following the start of glucose infusion, a total of
five 2 ml blood samples were collected at 90, 120, 130, 140 and 150 min.
Glucose kinetics were determined based on the single pool model at steady state
using a primed-constant infusion (Finegood et al, 1987; Wolfe et al, 2008). The
rate of glucose appearance (Ra) in the plasma/interstitial fluid pool reflects
endogenous glucose production and is determined based on the isotopic dilution
of D-[3-3H]-glucose within the pool. Samples were prepared as per (Finegood et
al, 1987) and analyzed for specific radioactivity (SA). Ra was calculated based on
the following equation (Wolfe et al, 2008):
Ra = tracer flow rate (ml/min) x tracer infusion conc. (uCi/ml) x 1000 mg / g
mean SA (uCi/g of glucose)
body weight (kg)
5.3.6 Whole body protein kinetics
Leucine kinetics were calculated according to the stochastic model
(Matthews et al, 1980), using plasma α-keto isocaproic acid (KIC) as an index of
the precursor pool enrichment (reciprocal model). An oral bolus of 0.1 mg/kg
bolus of NaH13CO2 (MassTrace Inc., Woburn,MA) was given to reach early steady
40
exhaled
13
13
CO2 enrichment, and a primed (0.5 mg/kg) constant infusion of L-[1-
C]-leucine was started at 0.008 mg/ kg · min in order to determine whole body
protein kinetics (Matthews et al, 1980). At baseline, 120, 130 and 150 min postinfusion, expired air samples were blown into a collection balloon and
transferred to 10 ml Vacutainer tubes for analysis of
13
CO2 enrichment.
After
120 min of tracer infusion, indirect calorimetry was performed for 20 min for
determination of the rate of CO2 production.
5.3.7 Hyperinsulinemic, euglycemic clamp protocol
The hyperinsulinemic, euglycemic, isoaminoacidemic clamp was 150 min
in duration, during which glucose and protein kinetics were measured as
described above. Following the fasting period (also 150 min in duration), a
primed infusion of biosynthetic regular human insulin (Humulin R; Eli Lilly
Canada, Toronto, ON, Canada) was started and maintained at a rate of 1.25
mU/kg FFM·min to achieve plasma insulin concentrations of 500 to 600 pmol/L.
In order to maintain plasma glucose of 5.5 mmol/L, an infusion of 20% (wt/vol)
potato starch–derived glucose (dextrose anhydrous; Avebe, Foxhol, the
Netherlands) in water with added D-[3-3H]-glucose was infused at variable rates
in conjunction with the insulin.
Arterialized venous blood samples were
collected every 10 min and glucose infusion rates were adjusted based on
plasma glucose concentration as determined using the glucose oxidase method
(GM7 Micro-Stat; Analox Instruments USA, Lunenberg, MA). Clamp glucose
turnover was measured using D-[3-3H]-glucose according to the “hot GINF”
method as described by Finegood et al (Finegood et al, 1987). The plasma
branched-chain amino acid (BCAA) concentrations were also measured every 10
min by an enzymatic fluorometric assay (Chevalier et al, 2004) and baseline
concentrations of plasma individual amino acids were maintained by feedback
adjustment of the rate of infusion of a 10% amino acid solution (10%
TrophAmine® without electrolytes; B. Braun Medical, Irvine, CA). Correction for
dilution in the background enrichment of expired 13CO2 and recovery of 13C from
41
the bicarbonate pool was made according to factors determined previously in
lean subjects under our experimental conditions (Chevalier et al, 2004; Chevalier
et al, 2005). For the calculations of leucine oxidation, the retention factor used
for
13
CO2 produced by oxidation and not released from the bicarbonate pool is
0.67 in the fasting state and 0.78 during the isoaminoacidemic state (Chevalier et
al, 2004; Leijssen et al, 1996).
Indirect calorimetry was performed during the last 30 min of each of the
fasting and clamp phase. Enrichment of
13
C-leucine, tritiated glucose specific
activity, insulin and plasma AA concentrations were determined from arterialized
blood samples collected at baseline, every hour, and at 10 min intervals for the
last 30 min of the clamp. Fasting cortisol, free testosterone, TSH, and growth
hormone were measured from the baseline sample. Serum albumin, fibrinogen,
FFA, glycerol, lactate, glucagon, C-peptide, insulin, IGF-1, and IL-6 were measured
in two samples at the end of the clamp period.
5.3.8 Additional assays
Glucose specific activity was assayed as previously described (Finegood et
al, 1987; Sigal et al, 1994) using a scintillation counter (Beckman Coulter LS 6500,
USA). The [13C] enrichment of plasma α-keto isocaproic acid was analyzed by gas
chromatography–mass spectrometry (GCMS 5988A; Hewlett-Packard, Palo Alto,
CA)
after
derivatization
with
N-methyl-N-(tertbutyldimethylsilyl)
trifluoroacetamide (Regis Technologies, Morton Grove, IL) to yield a TBDMS
derivative of hydroxyisocaproic acid. Expired air samples were analyzed for
13
CO2 enrichment by isotope ratio mass spectrometry on a Micromass 903D
(Vacuum Generators, Winsforce, U.K.).
Serum
insulin,
glucagon
and
C-peptide
were
measured
by
radioimmunoassay (Linco, St. Charles, MO). Plasma amino acids of interest were
determined by reverse-phase high-performance liquid chromatography after
pre-column derivatization with o-phtalaldehyde. Blood lactate concentrations
were determined by enzymatic fluorometric assay as described by Olsen (Olsen,
42
1971). Plasma free fatty acids (FFA) were determined using the NEFAC test kit
(Wako Chemicals USA, Richmond, VA).
IGF-1 and IL-6 were measured by
sandwich ELISA using the Quantikine® Human IGF-1 and Human IL-6 kits (R&D
Systems, Minneapolis, MN). Plasma albumin, fibrinogen, total protein, CRP,
triglycerides, total cholesterol, urea, and creatinine were measured by the
Biochemical Laboratory of the Royal Victoria Hospital, Montreal.
5.3.9 Statistical analyses
Data were analyzed using SPSS version 18.0.0 and significance was set at
P<0.05. Independent t-tests and Mann-Whitney U test (for non-parametric data)
were used to compare subject characteristics, % contributions to EGP and
baseline comparisons between the two groups. One sample t-tests were used to
test for significant % changes in tissue area/ 100 d measured by CT scan.
Repeated measures ANOVA was used for within- and between-subject responses
from fasting to clamp phases.
Repeated measures analysis of covariance
(ANCOVA) was performed with insulin as a covariate, for kinetic responses to the
clamps that significantly correlated with changes in insulin. Correlations were
determined using Pearson’s and Spearman’s tests for correlation coefficients
depending on the normality of the data distribution, which was tested using the
Kolmogorov-Smirnov test.
5.4 Results
Subject characteristics
Age, gender, BMI and smoking history were matched between NSCLC and
control subjects by design (Table 1). Four NSCLC patients were studied prior to
starting treatment and 6 were studied after treatment. The latter 6 patients had
signs of cachexia or recurrent active disease at the time of study. Metastases to
the bone (n=2) or brain (n=2) were present in 4 subjects. All NSCLC subjects
were free of liver metastases at the time of study as confirmed by CT scan and
none had edema. All NSCLC patients demonstrated at least on criterion of
43
cachexia including weight loss ≥5% in the past 12 months (n=8) and/or CRP >5
mg/L (n=7). None of the patients was anemic (Hb <120 g/L) or had serum
albumin <32 g/L. REE was not significantly different between subject groups
(Table 1). When REE was compared to the predicted values calculated using the
Harris-Benedict equation (Harris & Benedict, 1918), the mean REE was 105% for
the NSCLC patients and 100% for the control subjects. Although some NSCLC
patients (n=6) were hypermetabolic according to predicted values, the mean %
predicted REE was not different between the NSCLC and control groups.
NSCLC subjects demonstrated evidence of systemic inflammation.
Circulating concentrations of positive acute-phase proteins CRP, ferritin and
fibrinogen were higher and of negative acute-phase protein albumin was lower
in the NSCLC than in the control subjects (Table 1). IL-6 was significantly higher
in the NSCLC patients. At screening, the NSCLC group had a lower intake of fat,
protein and kcal as assessed by 24-h dietary recall and food-frequency
questionnaire for protein and energy-dense foods (Table 2). When normalized
to LBM, these differences were no longer significant.
NSCLC and control subjects were similar in terms of body composition,
for most compartments (Table 3). The % body fat as assessed by DXA, BIA or the
sum of four skinfold thicknesses was not significantly different between NSCLC
and control subjects. FFM as estimated with BIA was found to be significantly
less in the NSCLC patients, but no significant differences were found when FFM
was measured with DXA. NSCLC patients had smaller mean suprailliac skinfold
thicknesses, right thigh circumference, smaller lean leg mass and appendicular
muscle mass index as determined by DXA (Table 3).
CT scans were obtained at two time points prior to and/or overlapping
the time of the study for 9 NSCLC patients and the cross sectional area of total
muscle, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT),
subcutaneous adipose tissue (SAT) and total adipose tissue (TAT) was
determined. Tissue losses or gains were calculated by the difference in surface
44
area between the most recent and the oldest scans. The number of days
between scans was determined and ranged from 76 to 565 d. The change in
cross sectional area for each tissue was therefore normalized for 100 d. Muscle
loss was found in 44% of the patients (n=4), and the mean % change in loss / 100
d was not statistically significant from zero. The greatest tissue losses occurred
in VAT which ranged from to -1.5 to -98.8 cm2/100 d. Losses in SAT ranged from
-4.9 to -56.1 cm2/100 d and 2 patients gained SAT. The change in IMAT ranged
from -10.3 to 8.1 cm2/100 d and 4 patients experienced gains in IMAT. The %
change / 100 d in VAT area was the only tissue area found to be significantly
different from zero (P<0.05).
Contributions to EGP, flux data, lactate and AA concentrations
Following an overnight 17 h fast, EGP or the rate of glucose appearance
as assessed by dilution of constantly infused D-[3-3H]-glucose, was not
significantly different between groups (Table 4). Data for positional enrichment
of glucose were available for 8 NSCLC and 7 control subjects. The percent
contribution of glycogen, PEP and glycerol to EGP did not differ between groups,
with the majority of EGP coming from equal contributions of glycogen and PEP,
and glycerol accounting for less than 10% (Figure 1). The flux rate of each
substrate was calculated by multiplying the percent contributions of glycogen,
glycerol and PEP by the rate of EGP. As seen in Table 4, fluxes were not different
between groups. Likewise, total GNG flux (EGP x % contribution GNG PEP +
GNGglycerol) in the NSCLC and control groups were not different (Table 4).
Lactate and plasma AA concentrations were examined as substrates
contributing to EGP through GNG. Fasting blood lactate concentrations did not
differ between groups (Figure 2) and were not correlated with the %
contributions to EGP and their corresponding flux data. A significant increase in
lactate concentrations was observed from the fasting state to hyperinsulinemic
clamp phase in both groups (Figure 2). Fasting AA concentrations were similar
45
between the two groups (Table 5).
Although the percent contributions of
substrates and their fluxes were not significantly different between groups, the
wide range of individual contribution data allowed for testing relationships with
other factors influencing EGP. The association between the concentration of
individual AA and the % contributions to EGP as well as their fluxes was therefore
examined. Aspartate was the only AA found to be positively correlated with
%glycerol (r = 0.621, P= 0.024), glycerol flux (r = 0.709, P = 0.007) and total GNG
flux (r = 0.599, P = 0.031).
Protein kinetics
None of the fasting whole-body protein kinetics, i.e. total flux, oxidation,
breakdown, synthesis and net balance, differed between groups (data not
shown). No correlations were found between the fasting protein kinetics rates
and % contributions to EGP or their corresponding flux data. Changes in the
rates of whole-body protein kinetics in response to the hyperinsulinemic clamp
are shown in Figure 3. The change in net balance (synthesis – breakdown),
reflecting the net anabolic response to insulin was significantly less in the NSCLC
group (P = 0.002) (Figure 3). The change in leucine oxidation rate in response to
the clamp (µmol/kg LBM·min) was positively correlated with PEP flux (r = 0.672,
P = 0.006) and total GNG flux (r = 0.757, P = 0.001) (Figure 4).
Insulin resistance
Fasting plasma glucose concentrations were not significantly different
between the control and the NSCLC subjects (Table 4). In the fasting state, EGP
rates were not different between the NSCLC and control subjects (Table 4), nor
were the changes in EGP in response to insulin (Figure 5). Fasting glucose
disposal rates (Rd), matched production rates and did not differ (not shown). In
contrast, the rate of glucose disposal was 37% lower in the NSCLC group (10.0 ±
2.4 mg/kg LBM·min) versus the control group (6.3 ± 0.1 mg/kg LBM·min) during
46
the hyperinsulinemic phase of the clamp (P = 0.001). As a result, the overall
change in glucose uptake from fasting to the hyperinsulinemic clamp was
significantly less in the NSCLC group (P = 0.001), indicating insulin resistance
(Figure 5).
Fasting insulin and glucagon concentrations were not different
between groups (Table 6). Plasma insulin and glucagon concentrations during
the clamp were not significantly different between NSCLC and control subjects,
but plasma insulin increased less in NSCLC, as indicated by the clamp-by-group
interaction (Table 6).
To assess the association between insulin resistance and the %
contributions to EGP and their corresponding flux data, correlations were
examined with fasting plasma glucose, insulin, glucagon, the homeostasis model
estimated for insulin resistance (HOMA-ir) and with clamp glucose Rd (Table 7).
No correlations were found between the change in Rd during the clamp and the
% contributions to EGP and their corresponding flux data.
Fasting plasma
glucose concentrations did not correlate with the % contributions or their flux
data. Plasma insulin and glucagon concentrations were positively correlated
with %GLY and negatively correlated with %PEP and PEP flux. However, these
correlations were driven by the insulin and glucagon concentrations of one
subject in particular.
Plasma glucagon concentrations were also negatively
correlated with total flux and positively correlated with glycogen flux. HOMA-ir
did not correlate with measures of glucose substrate % contributions nor for
their corresponding fluxes.
Inflammation
The inflammatory markers CRP, IL-6, fibrinogen and albumin showed
skewed data distributions with clustered low normal values in controls, but
normally distributed values in NSCLC. As such, the associations between these
inflammatory markers and the % substrate contributions to EGP and their fluxes
were examined separately between NSCLC and control groups. The %PEP was
47
positively correlated with IL-6 in the NSCLC patients but not in control subjects
(Figure 6). Consistently, albumin was negatively correlated with the %PEP only in
NSCLC subjects (r = -0.805, P = 0.016). No correlations were found between CRP
or fibrinogen and the % contributions or corresponding fluxes in either the
NSCLC or control groups.
Body composition
Although most measures of body composition and % contributions to
EGP did not differ between the control and NSCLC groups, on an individual basis
significant correlations between these factors were found.
Because of the
previously demonstrated association between body composition and the %
contributions to EGP and their fluxes, the following correlations were tested and
significant correlations (P<0.05) are presented in Table 8. Many measures of
body fatness were found to correlate positively with glycogen flux and negatively
with %PEP, including the % body fat as measured with BIA (Figure 7). Of note
%PEP was negatively correlated with total body fat (kg) when assessed by DXA
and BIA (Table 8). Glycogen flux was positively correlated with total body fat (kg)
using DXA (r=0.562, P=0.029), BIA (r=0.628, P=0.012) and most strongly from
skinfold thickness (r=0.679, P=0.005).
BMI was positively correlated with
%glycerol and glycerol flux. The association between the degree of insulin
resistance and the body composition assessed by CT scans was also evaluated.
This was done in order to test if tissue areas, in particular the loss of visceral
adipose tissue, would be associated with increased insulin sensitivity, as
determined by glucose uptake rates during the hyperinsulinemic clamp. No
correlations were found between glucose uptake and the cross sectional areas
from the CT scans closest to the clamp day, nor for the tissue area changes /100
d. Likewise, no correlations were found between the cross sectional areas or the
absolute and % change in tissue areas/100 d and measures of % contribution of
substrates to EGP or their corresponding fluxes.
48
5.5 Discussion
In this study, the % contributions of glycogen, glycerol and PEP to EGP
were measured using the 2H2O method following a 17 h fast in NSCLC patients
with mild or moderate cachexia. Proteolysis-derived AA and lipolysis-derived
glycerol were postulated to contribute to increased endogenous glucose
production from GNG through PEP in cancer cachexia. Previous assessments of
EGP and GNG have been performed in NSCLC patients. However, the current
study is the first to utilize the 2H2O method in NSCLC patients in conjunction with
L-[1-13C]-leucine and D-[3-3H]-glucose tracers to measure whole-body protein
turnover and glucose kinetics during the fasting state and during a
hyperinsulinemic euglycemic clamp to establish insulin resistance. The principal
findings of this study were: i) the rates of EGP between NSCLC patients and
control subjects did not differ in the fasting state or in response to insulin; ii) the
fractional contributions to EGP came equally from PEP and glycogenolysis and
were similar between study groups as assessed by analyzing the positional
enrichment of deuterium in glucose using NMR spectroscopy; and, iii) decreased
glucose uptake was found in NSCLC patients in response to insulin indicating
insulin resistance together with a blunted whole-body net protein balance
response and lesser protein retention associated with greater fasting total GNG
flux.
Contrary to our original hypothesis, although NSCLC patients were found
to be insulin resistant, they did not demonstrate an increase in the %
contribution of PEP to EGP as was found in previous studies of subjects with type
2 diabetes (Gastaldelli et al, 2000) and in obese individuals (Chevalier et al,
2006a). In the latter study, the mean age of the obese subjects was significantly
greater than that of the lean control subjects (Chevalier et al, 2006a). It is
therefore possible that an aging effect exists which may account for the higher %
PEP contribution, concordant with decreases in insulin sensitivity associated with
aging. In fact, when we compared the group of NSCLC patients in the current
49
study to a group of previously studied young healthy men, the younger men had
a 37% PEP contribution which was significantly lower (P<0.05) than the 48% PEP
contribution found in the NSCLC patients (MacAdams et al, 2011).
Plasma concentrations of substrates contributing to GNG through PEP
such as plasma gluconeogenic AAs and lactate were not significantly different
between NSCLC patients and control subjects and did not correlate with %
contributions to EGP and their fluxes. Previous studies have found increased
fluxes of alanine in mixed cancer (Waterhouse et al, 1979) and weight losing
NSCLC patients (Leij-Halfwerk et al, 2000), which resulted in increased
contributions of alanine to glucose production. Also, increased plasma lactate
and increased glucose production through the Cori cycle have been
demonstrated in colorectal cancer patients (Holroyde et al, 1984).
This
highlights a key difference among studies of GNG in vivo. Specifically, the 2H2O
method accounts for all of the substrates contributing to GNG through PEP
including glycerol, but does not provide information on the individual fluxes of
lactate and key gluconeogenic AAs such as alanine. The reverse is true for tracer
studies of individual substrates to EGP, which cannot account for all substrates to
EGP.
In contrast to earlier studies in weight losing subjects with lung cancer
(Heber et al, 1982; Leij-Halfwerk et al, 2000) and mixed cancers (Lundholm et al,
1982; Cersosimo et al, 1991), in the current study the rate of EGP in the fasting
state was not significantly higher in the NSCLC patients. Unaltered rates of EGP
have also been found in studies in bladder cancer (Latterman et al, 2003), early
gastrointestinal cancer (Shaw et al, 1987) and NSCLC patients without weight
loss (Leij-Halfwerk et al, 2000). The type and severity of cancer may therefore be
important factors to consider when comparing rates of EGP among studies.
In the current study the association between whole-body protein
turnover and gluconeogenic AAs contributing to EGP through PEP was also
examined. No correlations were found between fasting protein kinetics rates
50
and % contributions to EGP or their corresponding fluxes.
This could be
explained by the fact that protein breakdown rates were not found to be higher
in the NSCLC subjects, in contrast to earlier studies of lung cancer patients that
reported higher protein breakdown rates (Melville et al, 1990; Heber et al,
1982). Our findings however are in agreement with those of Dworzak et al who
also reported unchanged protein turnover rates in patients with gastric cancer
(Dworzak et al, 1998). In terms of whole-body protein kinetics during the clamp,
the change in whole body net protein balance reflecting the net anabolic
response to insulin was significantly less in the NSCLC group in comparison to
control subjects. Also of note, the positive correlation between total GNG flux
and rates of protein oxidation indicates that less protein is retained in response
to insulin in association with higher total GNG flux in the fasting state. This may
have implications for lesser protein retention upon feeding. Therefore studies
on the effects of protein supplementation in NSCLC patients are warranted in
order to determine whether the blunted anabolic response to insulin may be
overcome by higher intakes of protein. When the change in the rate of leucine
oxidation in the NSCLC subjects was compared to that in a group of younger men
studied by our group (n=11, mean age 27 yrs), the change in oxidation was
significantly higher in NSCLC subjects (MacAdams et al, 2011).
In response to hyperinsulinemia, the change in glucose uptake during the
clamp was less in NSCLC patients, which clearly indicates insulin resistance. This
finding is in agreement with previous reports of lowered glucose uptake in lung
cancer patients during a hyperinsulinemic euglycemic clamp study conducted by
Yoshikama et al (Yoshikama et al, 2001). It has been suggested that systemic
inflammation, particularly elevated IL-6 may contribute to the development of
insulin resistance in cancer (Greenburg et al, 1992; Makino et al, 1998). When
the relationship between IL-6 and insulin resistance was measured in a group of
esophageal, colon, gastric and lung cancer patients, it was found that those with
detectable
IL-6
concentrations
had
51
lower
glucose
uptake
during
a
hyperinsulinemic clamp (Makino et al, 1998). Such a correlation between IL-6
and glucose uptake was not however found in our group of NSCLC patients.
Fibrinogen was the only marker of inflammation found to be correlated with
glucose uptake.
The relative contributions of GNG and glycogenolysis to EGP have been
found to be related with body composition, particularly in the presence of insulin
resistance. Specifically, %GNG has been found to be independently associated
with BMI, and in the presence of type 2 diabetes the effect of obesity further
increases the % contribution of GNG to EGP (Gastadelli et al, 2000). A previous
study by our group in obese individuals with insulin resistance found positive
correlations between the % contribution of PEP and both BMI and % body fat as
assessed by BIA (Chevalier et al, 2006a). Surprisingly, the current study found no
correlation with BMI and % PEP and a negative correlation between % body fat
and % PEP, despite the presence of insulin resistance in the NSCLC patients. The
opposite direction of these correlations may be due to differences in the range
of % body fat; the previous study of obese vs. lean subjects offered a large %
body fat range (8-55%) compared with a much tighter one in the current study
(10-33%), given that groups were matched for BMI. However in the current
study, the two individuals with the lowest % body fat also had the highest % PEP
contributions. Thus it may be that insulin resistance found in the NSCLC group
differs from that seen in the obese group of the previous study. For example the
obese group had significantly higher fasting insulin concentrations and % PEP
contributions, and clamp glucose uptake was negatively correlated with %PEP
(Chevalier et al, 2006a). These findings and such a correlation was not seen in
present study. The findings that adiposity was negatively correlated with %PEP
and positively correlated with glycogen flux could be interpreted as a sparing
effect of increased body fat in cancer cachexia, decreasing the use of AA as a
substrate for glucose production. In order to confirm that the correlations found
52
in this study are not spurious, a larger sample size of NSCLC patients and control
subjects would be required.
In addition to DXA, BIA and anthropometric assessment of body
composition, this study also used CT scan analysis to measure adiposity and
muscle mass. Mourtzakis et al report coefficients of variability of 1.6% and 2.3%
for muscle and adipose tissue respectively using CT scan analysis and therefore
considered that changes between -2% and +2% to be maintenance of tissue
(Mourtzakis et al, 2008). Of the 9 patients examined in the current study, 4 had
a % change in muscle mass that was <1.6%, indicating that these patients
maintained their muscle tissue. The % change in muscle / 100 d was not
significantly different from zero indicating that at the time of study based on CT
scan assessment, significant muscle loss had not occurred. Confirmation of this
finding using repeated DXA scan assessment would have been beneficial
however this data was not available at the time of study. Given that the subjects
were only moderately cachectic at the time of study, further loss of muscle tissue
may occur in more advanced phases of the disease.
In another study using CT scan analysis in a cohort of colorectal and lung
cancer patients, Murphy et al found that on average patients lost 29% of total
adipose tissue 2 months prior to death, with accelerated losses beginning 7
months prior to death (Murphy et al, 2010). In our group of 9 NSCLC patients,
the average loss of total adipose tissue in 100 d was 11%, with the greatest and
only losses found to be significant were from visceral adipose tissue. It should be
noted however that unlike in the study of Murphy et al, our cohort of patients
were not assessed 2 months prior to death. The finding that the mean % weight
loss was lower in our cohort of NSCLC patients may be due to the fact that they
were not yet in the stage of cachexia in which rapid loss of adipose tissue occurs.
In order to determine whether changes in body composition such as
muscle and adipose tissue loss was associated with increased utilization of
muscle-derived AA and adipose tissue-derived glycerol as substrates for GNG,
53
the current study assessed correlations between % changes in tissue area and
the % contributions and their fluxes. No correlations were found between the
cross sectional areas or the % change in area / 100 d and measures of %
contribution of substrates to EGP or their corresponding fluxes. Also, it was
postulated that a loss of adipose tissue may result in increased insulin sensitivity.
This study did not find any correlations between the rates of glucose uptake
during clamp and the cross sectional areas from scans closest to the clamp day,
nor for the tissue area changes / 100 d. It is likely that the small sample size
limited the ability to determine correlations.
One strength of this study is that to our knowledge it was the first to use
the 2H2O method in NSCLC patients to quantify GNG and glycogenolysis. Dietary
intake on the day prior to the study was well controlled which may have helped
to reduce any variations in substrate contribution to EGP introduced by
significantly lowered intakes of carbohydrate (Bisschop et al, 2000; Browning et
al, 2008). This study found many correlations between the % contributions to
EGP, their fluxes and measures of inflammation, insulin resistance and body
composition.
Of note, there was a positive relationship between protein
oxidation in response to insulin and PEP flux and total GNG flux in the fasting
state, indicating less protein retention. Also, a positive association was observed
between %PEP and IL-6 concentrations in the NSCLC patients. A limitation of this
study is that these are associations and are not indicative of causal relationships.
Because of the cross-sectional design of this study, these data provide a
“metabolic snap-shot” of the subjects, and as such, it is not known exactly when
insulin resistance develops in NSCLC or the mechanism by which it occurs in
cancer. What is clear in this study is that insulin resistance in this group of NSCLC
patients preceded any possible future alterations in protein metabolism, EGP,
GNG and glycogenolysis that may arise in more advanced phases of cachexia.
In summary, using the 2H2O method the fractional contributions of PEP
and glycogenolysis to fasting glucose production were equal and did not differ
54
between NSCLC and control subjects. Total GNG flux was positively associated
with the change in protein oxidation indicating less protein retention in response
to insulin with greater fasting GNG flux. The % contribution of PEP was positively
associated with markers of inflammation in NSCLC and negatively correlated with
% body fat. A decrease in the net anabolic response to insulin and insulin
resistance preceded additional alterations in protein metabolism and EGP, GNG
and glycogenolysis that may occur in advanced NSCLC cachexia. Consideration of
changes in body composition, of dietary intake and cancer stage are warranted
in the study of cancer cachexia. Additional studies using the 2H2O method with
larger cohorts of NSCLC patients as well as prospective data and molecular
studies of the pathogenesis insulin resistance development are required in order
to fully understand the metabolic alterations occurring due to cancer.
5.6 Acknowledgements
SC developed the study design. JM and AW recruited patients and
collected and compiled data. JM analyzed the data and wrote the manuscript,
which was edited by SC.
We gratefully acknowledge Dr. Errol Marliss for
supervising the clinical care of the research participants, SB and Dr. Santhosh
Satapati for NMR spectroscopy analysis at the University of Texas Southwestern
Medical Center in Dallas, and Connie Nardolillo, Donato Brunetti, Daniel White,
Marie Lamarche, Ginette Sabourin, and Chantal Légaré for their help in data
collection and sample analysis.
This work was supported by the Canadian
Institutes of Health Research and Fonds de la recherche en santé du Québec.
The authors have no conflicts of interest to declare.
55
Table 1. Characteristics of control and non-small cell lung cancer (NSCLC)
subjects.
Control (n=10)
NSCLC (n=10)
Age (years)
Weight (kg)
Height (cm)
BMI (kg/m2)
Smoking history (pack-years)
Diagnosis (SCC/AC)
Stage (IIIA/IIIB/IV)
Weight Loss (%)
ECOG score (0/1/2)
63.3
69.7
173.7
23.1
21.6
Handgrip strength (kg) ǂ
PASE score
REE (kcal)
% REE (predicted from H-B)
C-reactive protein (mg/L)
Ferritin (µg/L)
Fibrinogen
Albumin (g/L)
Pre-albumin (g/L)
Total protein (g/L)
Hemoglobin (g/L)
IGF-1 (ng/mL)
Free testosterone (pmol/L) †
Growth hormone (µg/L)
IL-6 (pg/L)
±
±
±
±
±
-----
2.1
2.2
1.7
0.5
3.7
66.1 ±
64.6 ±
171.8 ±
22.0 ±
40.4 ±
1/9
2/3/5
7.8 ±
1/4/5
2.2
2.9
1.0
0.9
11.4
42.4
166.1
1466.4
99.6
±
±
±
±
1.5
18.3
47.5
1.8
38.5
97.2
1445.7
105.4
±
±
±
±
1.7
14.5
53.6
3.6
1.68
83.5
3.33
40.7
0.29
65.2
145.0
83.5
28.2
0.64
1.13
±
±
±
±
±
±
±
±
±
±
±
0.65
16.8
0.21
0.7
0.01
1.1
2.7
7.0
2.7
0.24
0.21
12.70
206.6
4.61
38.0
0.26
68.9
142.8
103.9
24.5
1.22
7.30
±
±
±
±
±
±
±
±
±
±
±
3.81
49.0
0.33
0.9
0.02
2.2
3.2
9.5
2.6
0.42
1.89
1.3
Data are mean ± SEM. SCC: Squamous cell carcinoma; AC: Adenocarcinoma;
ECOG: Eastern Cooperative Oncology Group questionnaire score; ǂ n = 8 in
NSCLC group; PASE: Physical Activity for the Elderly Score (n = 9 in control
group); REE: resting energy expenditure; H-B: Harris-Benedict equation; IGF-1:
insulin-like growth factor 1; †n = 8 per group. * p<0.05 vs. control in independent
t-tests. **p<0.001 vs. control in independent t-tests.
56
**
*
*
*
*
**
Table 2. Dietary intake data of control and non-small cell lung cancer (NSCLC)
subjects at screening.
Control (n=10)
NSCLC (n=10)
2342 ± 140
47 ± 3
1891 ± 76
41 ± 2
Carbohydrate
g/d
g / kg LBM
% kcal
275 ± 20
5.4 ± 0.4
48 ± 3
265 ± 13
5.7 ± 0.3
56 ± 2
Protein
g/d
g / kg LBM
% kcal
88 ± 4
1.7 ± 0.1
16 ± 1
71 ± 4
1.5 ± 0.1
15 ± 1
*
82 ± 8
1.7 ± 0.2
32 ± 3
59 ± 4
1.3 ± 0.1
28 ± 2
*
Energy
kcal / d
kcal / kg LBM
*
*
Fat
g/d
g / kg LBM
% kcal
Data are mean ± SEM. LBM: lean body mass, as determined by bioelectrical
impedance analysis. * p<0.05 vs. control in independent t-tests.
57
Table 3. Body composition data of control and non-small cell lung cancer (NSCLC)
subjects.
Control (n=10)
NSCLC (n=10)
LBM (kg)
FFM (kg)
51.8 ± 1.9
54.7 ± 2.1
47.9 ± 1.3
50.7 ± 1.4
Body fat (kg)
% body fat
16.9 ± 1.5
23.7 ± 1.8
15.2 ± 2.2
22.4 ± 2.6
Leg lean mass (kg)
Leg fat mass (kg)
18.0 ± 0.7
5.15 ± 0.39
15.4 ± 0.5
4.70 ± 0.68
Trunk lean mass (kg)
Trunk fat mass (kg)
24.1 ± 0.9
9.32 ± 0.88
23.3 ± 0.8
8.46 ± 1.30
Arm lean mass (kg)
Arm fat mass (kg)
6.09 ± 0.27
1.36 ± 0.10
5.54 ± 0.20
1.40 ± 0.24
AMMI (kg/m2)
7.95 ± 0.19
7.11 ± 0.28
Skinfold thicknesses
Right triceps (mm)
Right biceps (mm)
Subscapular (mm)
Suprailiac (mm)
% body fat
10.8
4.6
12.7
15.2
23.5
9.3
4.8
12.2
10.5
20.2
Body circumferences
Right triceps (cm)
Smallest waist (cm)
Hip (cm)
29.9 ± 0.6
88.7 ± 0.9
94.1 ± 1.3
28.9 ± 0.8
85.3 ± 2.9
91.1 ± 1.8
Right thigh (cm)
Right calf (cm)
53.2 ± 0.9
36.6 ± 0.6
47.8 ± 1.7
34.4 ± 1.1
16.1 ± 1.4
22.9 ± 1.6
53.7 ± 1.7
15.6 ± 1.8
23.5 ± 1.8
48.9 ± 1.3
Dual energy x-ray absorptiometry
Bioelectrical impedance analysis
Body fat (kg)
% body fat
FFM (kg)
58
±
±
±
±
±
1.0
0.2
0.8
1.0
0.9
±
±
±
±
±
1.1
1.1
1.6
1.4
2.0
**
*
*
*
*
Computerized tomography scans ǂ
Muscle area (cm2)
Intramuscular AT area (cm2)
Visceral AT area (cm2)
Subcutaneous AT area (cm2)
Total AT area (cm2)
Appendicular skeletal muscle (kg)
Whole body fat mass (kg)
Whole body fat free mass (kg)
145.6
17.3
98.1
112.8
228.2
17.2
19.5
49.8
% change in tissue area between scans / 100 d:
Muscle
Intramuscular AT
Visceral AT area
Subcutaneous AT
±
±
±
±
±
±
±
±
6.7
2.7
19.1
14.4
34.4
0.7
1.5
2.0
-0.81 ± 1.51
12.58 ± 13.20
-16.80 ± 7.07
-9.87 ± 4.83
Data are mean ± SEM. LBM: lean body mass; FFM: fat free mass; AMMI: appendicular
muscle mass index; AT: adipose tissue; ǂ n= 9 for NSCLC group and no scans were
analyzed for control subjects; * p<0.05 vs. control in independent t-tests. **p<0.01
vs. control in independent t-tests. ¥ p<0.05 in one-sample t-test.
59
¥
Table 4. Glucose production and substrate flux data of control and non-small cell
lung cancer (NSCLC) subjects.
Control (n=10)
Fasting plasma glucose (mmol/L)
EGP (mg/kg BW·min)
EGP (mg/min)
NSCLC (n=10)
5.66 ± 0.10
5.57 ± 0.13
2.23 ± 0.06
155.0 ± 4.4
2.33 ± 0.09
148.9 ± 5.0
Flux data (mg/kg·min) †
Glycogen
GNGGlycerol
GNGPEP
Total GNG
1.00
0.21
0.98
1.20
±
±
±
±
0.06
0.04
0.08
0.07
1.07
0.20
1.09
1.30
±
±
±
±
0.10
0.04
0.18
0.18
Flux data (mg/min) †
Glycogen
GNGGlycerol
GNGPEP
Total GNG
69.8
15.1
68.0
83.0
±
±
±
±
4.9
2.9
5.5
4.8
68.5
13.4
66.4
79.8
±
±
±
±
8.2
3.4
8.9
9.3
Data are mean ± SEM. EGP: endogenous glucose production following a 17 h
fast; BW: body weight; FFM: fat free mass; GNGPEP: fractional contribution of
phosphoenolpyruvate to gluconeogenesis; GNGGlycerol: fractional contribution of
glycerol to gluconeogenesis; Total GNG: EGP x (%GNGPEP + %GNGGlycerol).
† Flux data are for n = 7 control and n = 8 NSCLC subjects.
60
100%
% Contribution to EGP
80%
44.6 ± 3.3 %
60%
9.8 ± 1.9 %
44.8 ± 6.0 %
PEP
Glycerol
Glycogen
8.7 ± 2.0 %
40%
45.6 ± 2.2 %
46.5 ± 5.9 %
Control
NSCLC
20%
0%
Figure 1. Percent contribution (mean ± SEM) of phosphoenolpyruvate (PEP) and
glycerol through gluconeogenesis and glycogenolysis to endogenous glucose
production (EGP)
in 1.
non-small
cell lungofcancer
patients (NSCLC)
(n=8)
and control
Figure
Percent contribution
phosphoenolpyruvate
(PEP) and
glycerol
subjects (n=7) through
following
a
17
h
fast.
gluconeogenesis and glycogenolysis to endogenous glucose
production (EGP) in non-small cell lung cancer patients (NSCLC) (n=8) and
control subjects (n=7) following a 17-h fast.
61
Figure 2. Lactate concentration (mean ± SEM) before and during the clamp for
non-small cell lung cancer (NSCLC) and control subjects. a: significant from 14 hfasting; b: significant from 17 h-fasting by repeated measures ANOVA.
62
Table 5. Fasting plasma amino acid concentrations (µmol/L) in control and nonsmall cell lung cancer (NSCLC) subjects.
Control (n= 9)
Alanine
Glutamine
Arginine
Aspartate
Asparagine
Glycine
Histidine
Isoleucine
Methionine
Leucine
Lysine
Phenylalanine
Serine
Threonine
Tyrosine
Tryptophan
Valine
GNG AA
Total AA
239.7
589.2
98.8
19.8
48.5
197.9
87.8
54.8
23.7
132.0
186.8
79.6
104.7
128.6
71.5
48.5
216.6
1260.0
2589.9
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
13.3
15.9
4.7
2.5
3.3
11.1
4.7
2.0
1.0
5.7
13.9
3.5
8.3
8.6
5.2
2.4
6.2
24.7
52.8
NSCLC (n= 8)
217.6
566.9
97.0
35.8
48.0
200.1
90.5
58.5
22.0
120.0
171.6
75.9
127.4
114.9
75.1
44.3
191.8
1226.8
2534.9
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
10.3
18.8
4.0
10.4
2.5
20.0
5.7
3.6
1.1
7.6
9.2
4.3
17.2
9.0
7.4
2.2
11.8
51.2
92.4
Data are mean ± SEM. AA: amino acids; GNG AA: sum of gluconeogenic amino
acids (alanine, glutamine, glycine, threonine and serine).
63
Figure 3. Change in whole-body leucine kinetic rates (mean ± SEM) between the
fasting and hyperinsulinemic phases of the clamp study in control and non-small
cell lung cancer (NSCLC) subjects. * p<0.005 versus Control.
64
Figure 4. The correlation between the total GNG flux rate and the change in the
rate of leucine oxidation in response to insulin (r = 0.757, P = 0.001) in non-small
cell lung cancer (NSCLC) and control subjects.
65
Figure 5. Change in glucose kinetic rates (mean ± SEM) from fasting to
hyperinsulinemic clamp in control and non-small cell lung cancer (NSCLC)
subjects. LBM: lean body mass. * p<0.005 versus control subjects.
66
Table 6. Biochemical data of the control and non-small cell lung cancer (NSCLC)
subjects during the fasting and hyperinsulinemic, euglycemic, isoaminoacidemic
clamp phases.
Insulin (pmol/L)
Fasting
Clamp
C-peptide (pmol/L)
Fasting
Clamp
Glucagon (pmol/L)
Fasting
Clamp
Free fatty acids (µmol/L)
Fasting
Clamp
IGF-1 (ng/ml)
Fasting
Clamp
Total protein (g/L)
Fasting
Clamp
Albumin (g/L)
Fasting
Clamp
Fibrinogen (g/L)
Fasting
Clamp
IL-6 (pg/L)
Fasting
Clamp
Control
(n=10)
NSCLC
(n=10)
Clamp
Effect
(P value)
Clamp x
Group
(P value)
59.9 ± 5.7
549.3 ± 21.8
71.6 ± 6.2
487.5 ± 30.1
<0.001
0.046
508.8 ± 49.3
297.6 ± 54.3
663.8 ± 65.1
375.3 ± 47.8
<0.001
---
15.2 ± 1.3
14.0 ± 1.1
19.3 ± 1.9
15.5 ± 1.6
<0.001
0.032
465.7 ± 46.2
49.5 ± 9.8
565.6 ± 42.9
38.5 ± 5.4
<0.001
---
83.5 ± 7.0
84.1 ± 6.6
103.9 ± 9.5
99.8 ± 7.6
---
---
58.0 ± 0.9
52.0 ± 1.9
58.1 ± 1.1
54.9 ± 1.2
<0.001
---
35.3 ± 0.7
33.2 ± 0.6
32.8 ± 1.0
30.5 ± 1.0
*
*
<0.001
---
3.33 ± 0.21
3.13 ± 0.21
4.61 ± 0.33 *
4.40 ± 0.32 *
0.001
---
1.13 ± 0.21
1.67 ± 0.22
7.30 ± 1.89 *
8.20 ± 1.96 *
0.02
---
Data are mean ± SEM. * p<0.05 vs. control in independent t-tests. Clamp effect
and clamp by group interaction by repeated-measures ANOVA.
67
Table 7. Correlations between markers of insulin resistance and the substrate contributions to endogenous glucose production and
corresponding flux data.
% glycogen
r
P
Fasting concentration
Glucose (mmol/L) †
Insulin (pmol/L) †
Glucagon (pmol/L) †
HOMA - ir †
Δ Rd (mg/min)
% glycerol
R
P
% PEP
r
P
glycogen flux
r
P
glycerol flux
r
P
PEP flux
r
P
--0.547
0.613
--0.035
0.015
-------
-------
---0.588
-0.653
--0.021
0.008
----0.521
----0.047
-------
-------
-----0.539 0.038
-0.667 0.007
---
---
---
---
---
---
---
---
---
---
---
-0.136
0.643
-0.021
0.944
0.143
0.625
-0.084
0.776
-0.029
0.920
0.118
Total flux
r
P
-----0.586
----0.022
---
---
---
0.687
0.103
0.726
Flux data (mg/min). HOMA - ir: Homeostasis model assessment for insulin resistance; Δ Rd: Change in rate of glucose uptake
(hyperinsulinemic clamp - fasting) controlled for weight. †Only correlations found to be significant (P<0.05) using Pearson's or
Spearman's tests are presented. --- P>0.05 therefore no significant correlation.
68
Figure 6. Correlation between fasting IL-6 concentration and the % contribution
from phosphoenolpyruvate (%PEP) only in non-small cell lung cancer (NSCLC)
(r = 0.757, P = 0.030). There was no correlation in control subjects.
69
Table 8. Significant correlations between measures of body composition and the substrate contributions to endogenous glucose
production and their corresponding flux data. †
% glycogen
r
p
BMI (kg/m2)
---
---
% glycerol
r
p
r
% PEP
p
glycogen flux
r
p
glycerol flux
r
p
PEP flux
r
p
Total flux
r
p
0.589 0.021
---
---
---
---
0.625
0.013
---
---
---
---
---0.605
---0.656
-----0.525
-------
--0.017
--0.008
----0.044
-------
--0.629
--0.745
--0.591
0.613
0.562
-----
--0.012
--0.001
--0.020
0.015
0.029
-----
0.603
--------------0.531
---
0.017
--------------0.042
---
---------------------
---------------------
---------------------
---------------------
DXA
trunk LBM
trunk fat
arm LBM
arm fat
leg LBM
leg fat
body fat
% body fat
FFM index
AMM index
--0.560
--0.605
-------------
--0.522 0.046
0.03
----------0.017
-----------------------------------------
FFM
% body fat
body fat
-------
-------
-------
-------
---0.572
-0.573
--0.026
0.026
--0.628
0.659
--0.012
0.008
----0.515
----0.049
-------
-------
-------
-------
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
BIA
Circumferences
chest
waist
70
hip
triceps
calf
thigh
Skinfold
triceps
biceps
subscapular
suprailiac
total
% fat
--0.586
-----
--0.022
-----
---------
---------
---0.632
-----
--0.011
-----
--0.672
-----
--0.006
-----
0.516
----0.521
0.049
----0.047
---------
---------
---------
---------
0.710
--0.701
--0.794
0.694
0.003
--0.004
--0.000
0.004
-------------
-------------
-0.782
---0.719
-0.566
-0.818
-0.755
0.001
--0.002
0.028
0.000
0.001
0.692
--0.698
--0.711
0.679
0.004
--0.004
--0.003
0.005
-------------
-------------
-0.712
---0.741
-0.530
-0.792
-0.680
0.003
--0.002
0.042
0.000
0.005
-0.584
---0.626
---0.707
-0.560
0.022
--0.013
--0.003
0.030
---0.567
--0.028
-----
-----
-----
-----
-----
-----
-----
-----
--0.565
--0.028
--0.700
--0.004
REE
fasting
% predicted REE
†Only correlations found to be significant (P<0.05) using Pearson's or Spearman's tests are presented. Flux data (mg/min). PEP:
phosphoenolpyruvate; BMI: Body mass index; DXA: dual energy x-ray absorptiometry; LBM: lean body mass; FFM: fat free mass; FFM
index: fat free mass index (kg/m2); AMM index: Appendicular muscle mass index (kg/m2); BIA: bioelectrical impedance analysis; REE:
resting energy expenditure; % predicted REE: from Harris-Benedict equation. --- P>0.05 therefore no significant correlation.
71
Figure 7. The correlation between the percent contribution of
phosphoenolpyruvate (% PEP) to endogenous glucose production and percent
body fat as determined using BIA (r = -0.572, P = 0.026) of non-small cell lung
cancer (NSCLC) and control subjects.
72
6 Supplemental discussion
Returning to the overall hypotheses of this thesis, it was thought that
NSCLC patients would demonstrate a greater contribution of AAs and lactate to
EGP via GNG through PEP. Thus in comparison to healthy control subjects, it was
thought that NSCLC patients would have an increased fractional contribution of
PEP and glycerol to GNG and a lesser fractional contribution of glycogenolysis for
EGP.
Preliminary results for the first four subjects studied supported this
hypothesis; the percent contribution of PEP was greater than that in control
subjects. However, when the remaining samples were analyzed, two NSCLC and
two control subjects had PEP contributions that were <35% and one sample did
not produce any results. The low %PEP data could not be explained by apparent
methodological differences as none of the methods, i.e. preparatory steps and
NMR spectroscopy analyses, had changed between analyses. Furthermore, the
unexpectedly low %PEP in NSCLC patients could not be accounted for by clinical
or biochemical differences, at least from the array of parameters assessed.
One of the limitations of this study is that four of the samples for 2H2O
analysis failed to produce results when analyzed using NMR spectroscopy, thus
decreasing the sample size. In addition, one of the NSCLC patients did not
consume the 2H2O due to side effects including nausea.
The positional
enrichment of deuterium in glucose could therefore not be measured from
fasting plasma samples for this patient. As a result, data on the positional
enrichment of glucose were available for 8 NSCLC patients and 7 control
subjects. Based on the sample size calculation found in Section 8.2, this study
may not have been sufficiently powered to detect significant differences in
substrate contribution to EGP between the two study groups. These data are
currently under review to determine if the samples can be re-analyzed. Of note,
our collaborators at the University of Texas Southwestern Medical Center who
performed the NMR spectroscopy analysis were blinded to the identity of the
groups (NSCLC vs. control subjects).
73
In terms of the other hypotheses, it was thought that weight losing NSCLC
patients would have increased fasting rates of protein catabolism in comparison
to healthy control subjects. The data did not support this hypothesis. However,
the change in net protein balance from the fasting to hyperinsulinemic clamp
phases was significantly lower in the NSCLC patients. Net balance remained
positive, albeit lower in the NSCLC patients, thus they had a decreased anabolic
response to insulin. In essence, our data suggest that in this patient cohort,
muscle loss would not result from increased protein degradation in the fasted
state, but rather from a blunted anabolic response to insulin, at least in absence
of a protein intake that would increase circulating AAs.
The hypothesis that NSCLC patients would demonstrate a state of insulin
resistance due to the underlying inflammation associated with cancer was
partially supported. It was found that NSCLC patients demonstrated decreased
glucose uptake in response to insulin and had elevated markers of systemic
inflammation in comparison to control subjects. However a causal relationship
between markers of inflammation and insulin resistance could not be
established.
In this study, many correlations were assessed between the %
contribution of substrates to EGP and their fluxes with various measures of
insulin resistance and body composition.
Negative correlations were found
between the %PEP and PEP flux (mg/min) with glucagon and insulin. It should be
noted however, that the correlations may have been driven by the particularly
high fasting insulin concentration of one NSCLC patient. Further investigation of
these correlations with a larger cohort of subjects may therefore be warranted.
In this study body composition was assessed using BIA, DXA, skinfold
thicknesses and CT scan analysis. Using retrospective CT scans it was possible to
distinguish between subcutaneous, visceral, and intramuscular adipose tissue,
and to quantify changes in these compartments overtime by analyzing
consecutive scans. CT scan analysis provides information on the distribution of
74
adipose tissue which cannot be obtained from DXA scans or the other methods
of body composition assessment. Additionally, CT scans are routinely used in
staging and tracking disease progression in cancer patients and are therefore
readily available for research purposes, whereas DXA is not always available in
the hospital setting (Mourtzakis et al, 2008). Based on the regression equations
developed in a cohort of 31 NSCLC and colorectal cancer patients (Mourtzakis et
al, 2008), whole body FFM, appendicular skeletal muscle mass, and whole body
fat mass was determined using CT scan analysis. Based on the results of the
current study, in comparison to data from DXA appendicular skeletal muscle
mass was underestimated for all subjects using the above regression equations.
This is not surprising considering appendicular skeletal muscle mass is only
estimated using extrapolation of the cross-sectional muscle area at the third
lumbar vertebra.
DXA may therefore be more appropriate for examining
changes in appendicular muscle mass. Estimates of whole body fat free mass
using regression equations agreed with values determined using DXA, whereas
whole body fat mass was overestimated in several patients in comparison to
DXA. Interestingly, %body fat determined from BIA and skinfold thicknesses,
which are less precise techniques, (Table 3) showed similar results to those of
DXA. The advantage to using BIA, skinfold thicknesses and measurement of body
circumferences is that they can be easily applied in clinical settings in the
absence of CT scans and DXA.
In terms of the generalizability, it is unknown whether the insulin
resistance in lung cancer has the same etiology as insulin resistance in type 2
diabetes. Further research is required to fully elucidate the mechanism(s) by
which insulin resistance occurs in cancer and to determine whether it precedes
weight loss in cachexia. The results of this study cannot be generalized to cancer
patients with pre-existing insulin resistance due to type 2 diabetes or obesity,
and the effect of these conditions on the metabolic alterations in NSCLC is
unknown. Studies examining EGP in different types of cancers have found mixed
75
results including elevated or similar rates of EGP in cancer subjects. As such, the
results of the current study pertaining to the % contributions to EGP cannot be
generalized to other types of cancer and even varying degrees of severity within
the same type of cancer.
As shown in Appendix A, Figure 3, patient recruitment proved to be
challenging given the demanding nature of the protocol on the patients and the
stringent exclusion criteria for this study. These criteria were imposed in order
to obtain a well-characterized, homogenous group of patients without
underlying medical conditions that may introduce additional variability to the
data. Even within our group of relatively highly homogenous NSCLC patients,
variability was found in many of the variables examined. Thus it is difficult to
generalize the results of this study to patients with other types of cancer or at
different stages of disease progression and as previously mentioned, to patients
with metabolic disorders or taking medications that impact metabolism. Larger
prospective studies could address these issues, however these can be considered
highly invasive in light of the demand that consecutive metabolic studies would
impose on such gravely ill patients, and again recruitment may be problematic.
7 Significance of the study and conclusion
Given that lung cancer is currently the leading cause of cancer mortality
in Canada, understanding the metabolic sequelae of this disease is of significant
importance to improving the quality of life of lung cancer patients. By examining
the metabolic differences between cachectic NSCLC patients and control subjects
with respect to substrate contribution to endogenous glucose production, insulin
sensitivity, and protein metabolism, this study aimed to define and elucidate the
role of cancer and its intrinsic features on these metabolic responses.
To our knowledge, this study is the first to use deuterated water as a
tracer for the quantification of glycogenolysis and the substrate contributions to
gluconeogenesis in a well-defined group of cachectic NSCLC patients. The major
76
finding of this study is that the percent contributions of glycogen, glycerol and
phosphoenolpyruvate
to
endogenous
glucose
production
and
their
corresponding fluxes were similar between NSCLC patients and control subjects,
as were the rates of glucose production in the fasting state. It is recognized that
additional subjects may be have been required to achieve statistical power to
observe a significant difference in the contributions to EGP.
This study is significant for providing a better characterization of
metabolic alterations in cancer cachexia and especially the time-course of
events. Although NSCLC patients in the current study were in the advanced
stages of their disease, their condition was not so advanced as to prevent them
from completing this demanding full-day metabolic study. The degree of cancer
cachexia studied here was only mild to moderate with the patients’ reported
weight loss mainly due to visceral adipose tissue loss based on CT scan analyses.
Despite this, insulin resistance was already present in the NSCLC patients at the
time of study and therefore preceded any further metabolic alterations in
protein metabolism, EGP, GNG, glycogenolysis and muscle loss that may occur in
more advanced stages of cachexia. As such, our findings do not preclude that
metabolic alterations may arise later, with more severe deterioration of their
condition.
Finally, firm causal relationships cannot be inferred from the
associations found in this study between measures of body composition, insulin
resistance and the contributions to EGP and their fluxes.
Cancer cachexia is a complex metabolic syndrome, and as observed in
this study, is accompanied by systemic inflammation, insulin resistance and
changes in body composition. Further research both at the molecular and
whole-body levels is needed to fully understand the interaction between these
factors.
Following this elucidation, specific pharmacological or dietary
interventions could be designed to curb the loss of lean body mass and weight in
lung cancer patients. For example, provision of protein-rich meals warrants
further investigation as a nutritional strategy for the attenuation of muscle loss.
77
Dietary and pharmacologic interventions could serve to counter the progression
from mild weight loss to more severe states of cachexia, and as a result, improve
the prognosis and quality of life of the patient.
8 Supplemental methods
8.1 Patient screening
Screening of patients took place from April 17, 2010 to December 2, 2010
at the MGH radio-oncology lung cancer clinic, and the MGH and RVH oncology
day clinics. Patients attending these clinics were pre-screened for eligibility by
assessing information found in patient files and databases at the clinics. For
newly diagnosed cases of lung cancer, permission was obtained from the
resident physician to observe the initial consult with the patient to determine
their eligibility for the study. After obtaining permission from the attending
physician, those patients who met the inclusion criteria were approached and
informed of the study. If the patient expressed an interest in participating in the
study, an appointment was then established for a screening session at the RVH
Clinical Investigation Unit.
As shown in Appendix A, Figure 3, a total of 331 patients were prescreened. Listed in Appendix A, Table 1 are the reasons for exclusion and the
number and percentage of patients with a given exclusion factor who could not
be included in the study. The rationale for including men only is based on sex
differences in protein metabolism observed in previous studies (Chevalier et al,
2005a; Chevalier et al, 2005b) as well as the higher prevalence of lung cancer in
men.
Ten patients were diagnosed with other types of cancer including
mesothelioma (n=5), thymoma (n=3), laryngeal (n=1) and tracheal (n=1). Six
patients were excluded given their previous diagnosis with another condition or
illness including lupus (n=1), Crohn’s disease (n=1), Guillain Barré syndrome
(n=1), fibromyalgia (n=1), hepatitis B (n=1) and positive HIV status (n=1). Of the
78
eligible patients (n=10), 6 refused to participate in the study. Four patients
consented to the study and were further screened.
On the morning of the screening session, patients were instructed to
arrive at the RVH Clinical Investigation Unit (CIU) after fasting for 10 hours. The
subject was asked to remove his shoes and heavy clothing and his weight was
measured to the nearest 0.1 kg using a calibrated electronic scale (Scale-Tronix,
Mettler Toledo). The subject was then asked to stand against a wall mounted
stadiometer with shoulders back and head erect (Gibson 2005). The subject’s
height was then measured to the nearest 0.01 m. The subject’s BMI was
calculated by dividing his weight (kg) by height squared (m2). Samples of blood
(33 ml total) and urine (approximately 50 ml) were collected by the nurse or
technician. Samples were analyzed for complete blood count, full biochemistry,
A1C, TSH, CRP, serology for hepatitis and HIV, and urine analysis. Subjects then
underwent a chest x-ray and electrocardiogram.
Potential subjects were
subsequently interviewed for a diet history using a 24-h recall and a food
frequency questionnaire of protein and energy-dense foods (Appendix B). They
then underwent a complete physical examination by co-investigator Dr. E.B.
Marliss, MD. Control subjects who passed the initial screening session partook in
a second screening session which included an oral glucose tolerance test (OGTT)
and pulmonary function tests (PFT). In the healthy control subjects, 2 h following
the OGTT plasma glucose concentrations were on average of 6.07 ± 0.61 mmol/L.
Two patients with cancer did not pass the screening session at the RVH.
One patient’s Hb was 93 g/L and he was subsequently admitted to the MGH for
blood transfusion. The second patient’s screening CRP was 0.6 mg/L and he
reported that he had not lost weight. As such these two patients did not
participate in the study.
The remaining 2 patients with cancer who were
recruited passed the screening session and were able to complete the study.
These 2 patients were added to the group of 8 previously studied NSCLC
patients, for a total of 10 NSCLC patients. The 8 previous patients had been
79
recruited by a former student (AW) from the same oncology clinics at the
Montreal General Hospital and Royal Victoria Hospital, between February 2008
and March 2010. All 10 subjects underwent the same screening and study
protocols. Screening biochemical data are found in Appendix A, Table 2.
8.2 Sample size calculation
Sample size was calculated based on previous data obtained in our lab
using the same 2H2O and clamp methodology. A sample size of 9 subjects per
group was required to identify a 25% difference in the %GNGPEP contribution to
EGP, with a standard deviation of 0.07 (α = 0.05, β = 0.80).
8.3 Gluconeogenesis
Calculations
for
the
%
contributions
of
glycogenolysis,
and
gluconeogenesis from glycerol and PEP are found in Appendix A, Figure 2.
8.4 Glucose kinetics
The clamp protocol is outlined in Appendix A, Figure 4 in which D-[3-3H]glucose was used as a tracer for glucose kinetics. During the fasting state, the
rate of glucose appearance (Ra) was calculated based on the following equation,
in which SA is the specific activity (ratio of radioactive tracer/amount of
unlabeled tracee) as determined by a scintillation counter (Wolfe et al, 2008):
Ra = tracer flow rate (ml/min) x tracer infusion conc. (uCi/ml) x
1000 mg/g
mean SA (uCi/g of glucose)
body weight (kg)
During the fasting state, based on the single-pool model in steady-state
conditions, the rate of glucose uptake (Rd) is equal to Ra (Wolfe et al, 2008).
Under these conditions, when a plateau of enrichment in the body pool is
achieved, it is assumed that the D-[3-3H]-glucose tracer behaves identically to
the glucose tracee and that the tracer is lost from the body pool at the same rate
that it appears during constant infusion (Wolfe et al, 2008). As is seen in
80
Appendix A, Figure 4, following the fasting state, exogenous glucose is being
infused in order to maintain plasma glucose at 5.5 mM.
During a
hyperinsulinemic, euglycemic clamp when a significant quantity of exogenous
glucose is infused, changes in plasma enrichment occur and the rate of glucose
appearance may be less than the rate of infusion (Wolfe et al, 2008). In such a
case, the calculated Ra value would be negative, since according to the Steele
equation, Ra is determined by subtracting the rate of exogenous glucose infusion
from the calculated total glucose appearance (Steele, 1959; Finegood et al,
1987). To prevent this situation and to minimize changes in basal plasma SA,
D-[3-3H]-glucose tracer is added to the exogenous glucose infusate in a method
known as the “hot GINF” protocol (Finegood et al, 1987). Having employed this
protocol in the present study, Ra and Rd were calculated based on the following
equations (Finegood et al, 1987):
Ra = tracer flow rate (ml/min) x tracer infusion conc. (uCi/ml) x 1000 mg /g
mean SA (uCi/g of glucose)
body weight (kg)
+ exogenous glucose SA (uCi/g) x mean exogenous gluc. infusion rate(mg/kg·min)
mean SA (uCi/g)
- mean exogenous glucose infusion rate (mg/kg·min);
Rd = clamp Ra – exogenous glucose infusion rate.
8.5 Calculations for leucine kinetics
Whole-body protein turnover was calculated from leucine kinetics based
of the stochastic model (Matthews et al, 1980).
The underlying principal
assumption of this model is that under steady state conditions, the flux is equal
to the sum of all AA fluxes inward and outward from the AA pools. A diagram
representing the flow of AAs between the free-AA pool and the body protein
pool is found in Appendix A, Figure 5. AAs enter the free-AA pool when the 13Cleucine tracer is infused (i) or from unlabelled leucine intake (I). AAs also flow
81
from the body protein pool to the free-AA pool during protein breakdown (B).
Conversely, AAs leave the free-AA pool and enter the body protein pool during
protein synthesis (S). AAs also leave the free-AA pool when they are oxidized
(O). Expired air samples are collected and analyzed for 13C enrichment in CO2 by
isotope ratio-mass spectrometry. During the steady state, the flux or turnover
(Q) can be related to the other kinetics parameters based on the equation
Q = S + O = B + I (Golden & Waterlow, 1977).
L-[1-13C]-leucine was selected as the tracer for several reasons. Firstly,
leucine is an essential AA, therefore the rate of appearance into the plasma is
not affected by endogenous production and is assumed to be a function of
protein breakdown (Wolfe et al, 2008). Secondly, when the carboxyl-labelled L[1-13C]-leucine is irreversibly decarboxylated, the resulting
13
CO2 is expired,
therefore the tracer is not recycled into protein or lost to CO2 in the TCA cycle
(Matthews et al, 1980; Wolfe et al, 1992). Thirdly, leucine contributes 590
µmol/g of whole body protein (Waterlow et al, 1978) and is catabolised primarily
in the muscle rather than the liver, therefore providing an estimate turnover
from the body’s main protein reservoir (Wolfe et al, 1992). Finally, a rapid
equilibrium can be reached by priming the leucine and bicarbonate pools, and
this provides a more rapid determination of steady state whole body protein
synthesis and breakdown rates (Matthews et al, 1980; Wolfe et al, 2008).
During the AA infusion, kinetics account for the average rate of
exogenous leucine infusion (including tracer) to provide rates of endogenous
leucine appearance (Ra) and disposal (Rd).
oxidation, the retention factor used for
13
For the calculations of leucine
CO2 produced by oxidation and not
released from the bicarbonate pool is 0.67 in the fasting state and 0.78 during
the isoaminoacidemic state (Chevalier et al, 2004; Leijssen et al, 1996). Leucine
flux was calculated from the equation Q = i [(Ei/EKIC) – 1], where Q is leucine flux, i
is
13
C-leucine infusion rate, Ei is the enrichment of
plasma
13
13
C-leucine, and EKIC is the
C-α-KIC enrichment. α-KIC was used as an index of the precursor
82
intracellular pool enrichment given that it equilibrates rapidly with the plasma
pool and unlike plasma
13
C-leucine, does not underestimate Ra (Wolfe et al,
2008). α-KIC is produced during the transamination of leucine prior its complete
oxidation to CO2 and isovaleryl CoA (Wolfe et al, 2008).
Endogenous leucine rate of appearance (Leu Ra = whole-body protein
breakdown) was calculated as: Leu Ra = Q – I, where I is the infusion of unlabelled
leucine. Leucine nonoxidative rate of disposal (non-ox Rd = protein synthesis)
was calculated as: Non-ox Rd = Q – leucine oxidation. Leucine oxidation was
obtained from F13CO2/EKIC, where F13CO2 is (VCO2·13CO2 enrichment)/13CO2
recovery factor.
8.6 Lactate analysis
During the clamp, 0.5 ml blood samples were collected at five time
points: at baseline, at 140 and 150 min during the fasting phase, and at 140 and
150 min during the hyperinsulinemic phase. Samples were mixed with 6% PCA
for protein precipitation, centrifuged (1 min, 10000 G, 25:C), and the
supernatant was removed and kept at -80:C until analyzed.
Lactate
concentration was determined by the fluorometric assay as described by Olsen
(Olsen et al, 1971). Following centrifugation, 75 µl of sample was mixed with 1.4
ml of 1.1 M hydrazine hydrate buffer (Sigma-Aldrich, St. Louis, MO, pH 9.0), 37.5
µl of 120 mM ß-NAD (Roche Diagnostics, Indianapolis, IN) and 15 µl of 5 mg/ml Llactate dehydrogenase (Roche Diagnostics, Indianapolis, IN).
L-lactate
dehydrogenase catalyzes the reaction: L-lactate + NAD + H+ → Pyruvate +
NADH. Following the addition of the enzyme, fluorescence emitted from the
NADH was measured for 2 min at 25:C, by spectrofluorometry (Jasco FP-6200) at
excitation and emission wavelengths of 355 and 485 respectively. A standard
curve was generated from 0, 0.25, 0.50, 0.75 and 1.0 mM Na L-lactate in order to
quantify the concentration of lactate in the samples.
83
8.7 Analysis of CT scans
Quantification of changes in muscular and adipose tissue by analysis of
consecutive computerized tomography (CT) images has been validated as a
method of assessment of body composition changes in patients with cancer
(Murphy et al, 2010; Mourtzakis et al, 2008). Two CT scans were analyzed
retrospectively for each of 9 NSCLC patients using Slice-O-Matic software V4.3
(TomoVision, Montreal, QC, Canada).
For one patient, only one scan was
available for analysis, thus the change in time could not be determined. Scans
were analyzed at the third lumbar vertebrae (L3) given that this region has been
found to strongly correlate with whole body adipose tissue and muscle mass
(Shen et al, 2004). Tissues were discriminated by the following Hounsfield unit
thresholds: -29 to +150 for muscle (M), -190 to -30 for intramuscular adipose
tissue (IMAT), -190 to -30 for subcutaneous adipose tissue (SAT), and -150 to -50
for visceral adipose tissue (VAT) as per Murphy et al (Murphy et al, 2010). An
example of two analyzed CT scans is found in Appendix A, Figure 6. The crosssectional area (cm2) of each tissue was derived by multiplying the sum of the
tissue pixels by the pixel surface area (Murphy et al, 2010). The total adipose
tissue areas were estimated by the sum of SAT, VAT and IMAT cross-sectional
areas (Murphy et al, 2010). Appendicular skeletal muscle, whole body fat and fat
free mass were estimated by regression equations derived from a cohort of men
and women with advanced or metastatic NSCLC or colorectal cancer (n=31).
(Moutzakis et al, 2008). The equations are as follows:
Appendicular skeletal muscle = 0.11 x [skeletal muscle at L3 (cm2)] + 1.17;
Whole body fat mass (kg) = 0.042 x [total adipose tissue at L3 (cm 2)] + 11.2;
Whole body fat free mass (kg) = 0.30 x [skeletal muscle at L3 (cm2)] + 6.06.
8.8 Body composition, strength and overall status assessment
Percentage body fat was determined from the sum of the average of
three measurements of subscapular, suprailiac, right biceps and right triceps
skinfold thicknesses (Durnin et al, 1974). Circumferences of the chest, smallest
84
waist, umbilical waist, hip, right thigh, right triceps and right calf were also
measured. Following a 2 h fast on the first day of admission, the patient’s bone
mineral density and body composition was determined using DXA (Lunar Prodigy
Advance, GE Healthcare). The presence of sarcopenia was determined by muscle
mass index (kg/m2) based on published criteria as defined by Baumgartner et al
(Baumgartner et al, 1998). Muscle strength was assessed by handgrip strength
(Jamar® dynamometer), using the average of 3 maximal grips on the dominant
arm. Subjects with NSCLC completed the Patient Generated-Subjective Global
Assessment of lung cancer patients as an assessment of their global nutritional
and functional status (Appendix C). Physical activity was assessed using the
Physical Activity Scale for the Elderly (PASE) (Appendix D).
8.9 Funding and ethics
Financial support for this study was obtained from the CIHR grant (MOP93521), through the Frederick Banting and Charles Best Canada Graduate
Scholarships – Master’s Award and Fonds de la recherche en santé du Québec.
Additional sources of funding included the McGill Entrance Scholarship and
McGill Principal’s Graduate Fellowship. Initial approval of the study protocol
entitled “Muscle Protein Metabolism in Lung Cancer Cachexia (New Emerging
Team in Palliative Care: Cancer-Associated Cachexia-Anorexia Syndrome)” was
obtained on May 17, 2005, with annual reviews conducted since this date. Prior
to participating in the study, each potential subject was informed of the nature,
purpose and possible risks of the study and signed a consent form approved by
the Hospital’s Ethics Committee (Appendix E). The McGill University Ethics
Committee has determined that subjects receiving no direct therapeutic benefits
must be offered an honorarium to defray expenses such as transportation costs.
In addition to reimbursement for transportation expenses, subjects received $35
upon completion of the screening session and $250 upon completion of the
clamp study. Control subjects also eceived $75 upon completion of the OGTT
and PFT screening session.
85
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99
APPENDICES
Appendix A:
Supplemental figures and tables
100
PEP
-O
2-PG
2H
O
-O
2O
C
3-PG
-O
O
C
enolase
O
C
PG mutase
1,3-BPG
H
kinase
C O PO3
H C OH
C
H C OH
H C O PO3
H H
H
H C O PO3
phosphoglycerate
H C O PO3
ATP
G-3-P
G-3-P
H O
C
dehydrogenase
ADP
H C OH
NADH NAD + H+
H C O PO3
H
H C OH
H C O PO3
H
H
PEP Carboxykinase
GDP
CO2
GTP
OAA
COO-
H
C
O
C
H
OAA
COO-
Aspartate
COOH
O
C
H
COOcytosol
COO-
NADH NAD + H+
C
Fumarate
Malate
HO C
H
C
H
Malate
Dehydrogenase
H
COO-
2H
2O
fumarase
-OOC
H
C
C
H COOmitochondrion
Figure 1a. The incorporation of deuterons during gluconeogenesis. Deuterons
are indicated by red-coloured hydrogen atoms. Incorporation of deuterons at
carbon 2 (which eventually becomes carbon 6 in glucose) occurs when fumarate
is converted to malate inside the mitochondrial matrix during the TCA cycle.
Glucogenic amino acids that have entered the TCA cycle will be labelled at this
point. Malate is converted to oxaloacetate (OAA), which is then converted to
aspartate which is transported into the cytosol where OAA is re-generated.
Phophoenolpyruvate (PEP) is formed from OAA. Addition of deuterons occurs
when PEP is converted to 2-phosphoglycerate (2-PG). 2-PG is converted to 3phosphoglycerate (3-PG) and with the use of ATP, 1,3-bisphosphoglycerate (1,3BPG) is formed, which is converted to glyceraldehyde-3-phosphate (G-3-P).
(Landau, 1999; Nuttal et al, 2008; Wolfe & Chinkes, 2008).
101
Figure 1b. The incorporation of deuterons during gluconeogenesis. Deuterons
are indicated by red-coloured hydrogen atoms. Glycerol enters the
gluconeogenic pathway when it is converted to glycerol 3-phosphate (Glycerol-3P), followed by dihydroxyacetone phosphate (DHAP). The joining of DHAP and
glyceraldehyde-3-phosphate (G-3-P) by aldolase results in fructose 1,6bisphosphate (F-1,6-BP), which is hydrolyzed to fructose 6-phosphate (F-6-P).
Addition of deuterons at carbon 2 occurs when glucose 6-phosphate (G-6-P) is
formed from F-6-P. In the final step of GNG, glucose is formed from G-6-P by
glucose 6-phosphatase. (Landau, 1999; Nuttal et al, 2008; Wolfe & Chinkes,
2008).
102
Calculation of % contributions:
C5 / C2 = % total GNG
C6S / C2 = % GNG from PEP
1- (C5 / C2) = % from glycogen
(C5 - C6S) / C2 = % from glycerol
Figure 2. Positional enrichment of deuterons into plasma glucose for
determination of the percent contributions of glycogenolysis and
gluconeogenesis (GNG) from phophoenolpyruvate (PEP) and glycerol.
103
Patients
pre-screened
n = 331
Women
n = 150 (45.3%)
Exclusions
n = 171 (51.7%)
Refused to
participate
n = 6 (1.8%)
Failed screening
n =2 (0.6%)
Figure 3. Flow of patient recruitment by the thesis author. Patients were prescreened using medical records at the Montreal General and Royal Victoria
Hospital oncology clinics from April 17 to December 2, 2010. The percentages
were calculated based on the total number of pre-screened patients. The two
patients enrolled were added to a group of 8 patients previously recruited at the
same oncology clinics (from Feb 2008-March 2010) with similar success rates.
104
Table 1. Exclusion factors and the number and percentage of patients with a
given exclusion factor who could not be included in the study.
Exclusion Factor
Diabetes
Age >80 years
Medication
Current or recent treatment
Enrolled in another study
Missed appointment
BMI >27
Other cancer
Small cell lung cancer
Heart condition
Poor condition
Not stage III or IV NSCLC
Other illness
Liver metastases
Uncontrolled pain
No active disease
Distance or language barrier
Unknown stage
Previously studied
105
n = 171
25 (14.6%)
18 (10.5%)
14 (8.2%)
14 (8.2%)
12 (7.0%)
10 (5.9%)
10 (5.9%)
10 (5.9%)
9 (5.9%)
8 (4.7%)
7 (4.1%)
7 (4.1%)
6 (3.5%)
6 (3.5%)
4 (2.3%)
4 (2.3%)
3 (1.8%)
2 (1.2%)
2 (1.2%)
Table 2. Biochemical characteristics of control and non-small cell lung cancer
(NSCLC) subjects at screening.
Control (n=10)
NSCLC (n=10)
Creatinine (µmol/L)
Urea (mmol/L)
C-reactive protein (mg/L)
Pre-albumin (g/L)
Glucose fasting (mmol/L)
Hemoglobin A1C
HCO3 (mmol/L)
Cl (mmol/L)
69.7
4.95
1.68
0.29
5.03
0.05
27.4
103.8
±
±
±
±
±
±
±
±
2.1
0.27
0.59
0.01
0.11
0.00
0.5
0.8
79.6
3.85
12.70
0.26
5.10
0.05
25.8
102.5
±
±
±
±
±
±
±
±
4.6
0.42
3.81
0.02
0.13
0.00
0.6
1.3
K (mmol/L)
Na (mmol/L)
Lymphocytes (10-9/L)
Platelet (10-9/L)
Hematocrit
Hemoglobin (g/L)
Red blood cell (10-12/L)
Ferritin (µg/L)
WBC (10-9/L)
Triglycerides (nmol/L)
4.32
138.5
1.40
234.9
0.42
145.0
4.62
83.5
5.48
0.92
±
±
±
±
±
±
±
±
±
±
0.08
0.8
0.11
12.2
0.01
3.0
0.11
15.8
0.41
0.11
4.28
138.1
1.07
258.0
0.41
142.8
4.45
206.6
8.01
1.03
±
±
±
±
±
±
±
±
±
±
0.09
0.7
0.13
15.3
0.01
3.2
0.08
49.0
1.26
0.11
HDL cholesterol (mmol/L)
Cholesterol / HDL
LDL cholesterol (mmol/L)
Total cholesterol (mmol/L)
Uric acid (µmol/L)
Albumin (g/L)
Total protein (g/L)
Calcium total (mmol/L)
1.29
4.06
3.36
5.03
299.6
40.7
65.2
2.29
±
±
±
±
±
±
±
±
0.09
0.31
0.21
0.25
12.7
0.7
1.2
0.03
1.33
4.08
3.13
5.19
294.8
38.0
68.9
2.31
±
±
±
±
±
±
±
±
0.10
0.32
0.34
0.26
12.7
0.9
2.2
0.02
Calcium corrected (mmol/L) ǂ
Cortisol (nmol/L)
2.29 ± 0.02
297.7 ± 23.0
2.29 ± 0.02
365.9 ± 33.0
Testosterone (pmol/L)
Growth hormone (µg/L)
28.2 ± 2.6
0.64 ± 0.22
24.5 ± 2.6
1.22 ± 0.42
T4 (pmol/L)
T3 (pmol/L)
11.4 ± 0.3
4.88 ± 0.18
12.0 ± 0.5
4.75 ± 0.17
Thyroid stimulating hormone (mIU/L)
1.56 ± 0.18
1.48 ± 0.44
Data are mean ± SEM. ǂ n= 5 control, n=7 NSCLC. * p<0.05 vs. control in
independent t-tests.
106
*
*
*
*
Fasting Phase
8h00
Hyperinsulinemic Phase
Steady state
Steady state
10h00 -10h30
12h30-13h00
3
H-glucose
13
C-leucine
insulin
(1.25 mU/kg FFM /min)
20% glucose
10% TrophAmine®
20 min
20 min
20 min
indirect calorimetry
q 30 min
blood sampling
q 30 min
q 10 min
q 10 min
q 10 min
q 30 min
q 10 min
expired air sampling
Figure 4. Hyperinsulinemic euglycemic clamp protocol. During the 150 min
fasting phase, D-[3-3H]-glucose and L-[1-13C]-leucine tracers were infused for
measurement of glucose and whole-body protein kinetics respectively. During
the 150 min hyperinsulinemic, euglycemic, isoaminoacidemic phase insulin was
infused to reach postprandial insulin concentrations of 500 to 600 pmol/L. A
20% glucose solution was infused to maintain plasma glucose at 5.5 mmol/L with
D-[3-3H]-glucose for determination of insulin resistance. A 10% TrophAmine®
amino acid solution was infused to maintain baseline branched chain amino acid
concentrations. Steady state was defined as the final 30 min of each phase.
Blood and expired air samples were collected as indicated and indirect
calorimetry was performed on three occasions for 20 min.
107
Figure 5. The two-pool model for calculating whole body protein kinetics. 13Cleucine infused (i) enters the free amino acid pool and is transaminated by
branched-chain α-ketoacid dehydrogenase to 13C-Ketoisocaproate (13C-KIC)
which is subsequently oxidized (O) to 13CO2 and isovaleryl CoA. Free amino acids
enter the body protein pool during protein synthesis (S) and leave this pool
during breakdown (B). The amino acid flux (Q) or turnover rate is determined by
measurement of 13C-KIC enrichment in the plasma (Golden & Waterlow, 1977).
108
Muscle
Intramuscular adipose tissue
Visceral adipose tissue
Subcutaneous adipose tissue
Figure 6. Quantification of changes in muscular and adipose tissue by analysis of consecutive computerized tomography (CT)
scan images. Two CT scans were analyzed at the third lumbar vertebra retrospectively for 9 NSCLC patients using Slice-OMatic software V4.3 (TomoVision, Montreal, QC, Canada). The two images shown were taken 484 days apart, a period of
time which encompasses the day of the clamp study.
109
Appendix B:
Diet recall and food frequency questionnaire sheets
110
111
112
Appendix C:
Patient Generated - Subjective Global Assessment
113
114
115
Appendix D:
Physical Activity Scale for the Elderly (PASE) Questionnaire
116
117
118
Appendix E:
Patient consent form in English
119
Patient Information and Consent Form
For Patients with Lung Cancer
McGill University Health Centre
ROYAL VICTORIA HOSPITAL, DEPARTMENT OF MEDICINE
Title of the project:
Muscle Protein Metabolism in Lung Cancer
Cachexia (New Emerging Teams in Palliative
Care: Cancer-Associated Cachexia-Anorexia
Syndrome)
Protocol B: “Whole-body protein metabolism in
response to insulin and amino acids in non-small
cell lung cancer patients.”
Sponsor:
Canadian Institutes of Health Research (CIHR)
Investigators:
1665 (9 am to 5 pm)
Stéphanie Chevalier, PhD; telephone:(514) 843Errol B. Marliss, MD; telephone: (514) 843-1665
(9 am to 5 pm)
or through hospital locating at (514) 934-1934
ext. 35555 (any time)
Co-Investigators:
Christian Sirois, M.D.; telephone: (514) 934-1934
ext 44326
José A. Morais, M.D.
______________________________________________
Before you give your consent to be a research volunteer, please take time to read
carefully and consider the following information which describes the purpose and
procedure, the possible risks and benefits and other information about the
proposed research study. Please ask the study doctor or the research staff to
explain any words or information that you do not clearly understand.
Reason for the study
You have been invited by Drs. Stéphanie Chevalier and Errol B. Marliss from the
McGill Nutrition and Food Science Centre to take part in a study on the effects of
120
lung cancer on protein metabolism in response to insulin and amino acids (the
building blocks of proteins). Your participation in this study will involve a twoday stay in the hospital, one at the Clinical Investigation Unit and one at the
McGill Nutrition and Food Science Centre, both within the Royal Victoria
Hospital. By participating, you would enable these researchers to better
understand the influence of insulin and amino acids on protein synthesis and
breakdown in a meal-like situation and how this is affected by the presence of
lung cancer. This in turn, could lead to defining diet and/or medication strategies
to prevent muscle loss in patients like yourself.
1)
Information and Health Assessment Visit:
This first session involves coming to the research unit to meet with the research
staff for the following events: an interview with the research staff, discussion and
obtaining of your informed consent, a nursing health assessment, standard blood
and urine tests, and electrocardiogram (ECG), if these tests will not have been
done at the clinic. The study doctor (Marliss or Morais) will take a full medical
history and perform a physical exam.
2)
First day of stay, at the Clinical Investigation Unit (CIU):
You will come to the Clinical Investigation Unit of the Royal Victoria Hospital,
around 8:00 am, after an overnight fast.
You will be asked to breathe under a plastic canopy for 20 minutes, while
lying on a bed. This is to calculate what your body is using as a “fuel” for
energy. You will need to lie as still as you can on the bed, relaxed but not
sleeping, for the test to be accurate.
Your body fat and lean tissues will be measured by a safe, painless test,
using a very low electric current that lasts for a few seconds only. Your
body composition will also be measured by a scanning technique (dual Xray absorptiometry). For this test, that lasts for about 20 minutes, you will
lie down still on a mattress under the scanner. The amount of radiation
received from this test is less than that of exposure to a sunny day.
You will be offered bran cereal and milk for breakfast, but the rest of your
food will be a liquid formula diet that contains all the necessary nutrients
and that you must consume completely, in four (4) separate meals.
In the evening, you will drink a 150 mL (5 ounces) glass of water with
deuterium, as part of the water itself. Deuterium is a stable isotope (not
radioactive). Drinking deuterated water has caused slight, tolerable and
short-duration dizziness in some people. A urine sample and 9 mL of
blood will be collected at 4 pm, and again at 8 am and 11 am the next day.
You will sleep in a private room at the CIU.
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3)
Second day of stay, at the McGill Nutrition and Food Science Centre
The next day, protein metabolism studies will be performed and will last
approximately 8 hours.
On that morning, while you are fasting and resting comfortably in a bed,
two intravenous catheters will be placed, one in your arm and the other on
the back of your hand. Your hand will be placed in a warming box at 65oC
to make the blood in the vein similar to that of an artery. This catheter will
be used for repeated, painless blood samples to be taken. This is not a
painful or uncomfortable procedure.
The other catheter will be for infusion of radioactive glucose, insulin, 13Cleucine, 2H-phenylalanine (other stable isotopes), glucose (sugar) and a
mixture of amino acids (protein). The amount of radioactive material
received represents a radiation exposure similar to that received from a
standard X-ray of the chest. The dose is minimal and disappears totally
from the body (mainly in urine) within 2 ½ weeks of administration.
The mixture of amino acids and glucose will be infused along with the
insulin to keep your blood levels constant during 2½ hours and then,
infusion of amino acids will be raised to reach levels attained after eating a
meal, for another 2½ hours.
Blood samples of 1 mL will be taken every 5 minutes after the insulin has
started in order to verify your blood glucose and amino acid levels. Bigger
samples of 3-9 ml (2 tsps) will be taken periodically, for a maximum of 180
mL (6 ounces).
Three times during the day, you shall be asked to breathe under the plastic
canopy for 20 minutes.
Samples of your breath will be taken, about 20 times throughout the study
by simply blowing air into a special bag.
At the end of the study, you will be offered a complete meal.
Risks and Benefits: The risks involved in consuming the diet and in blood
sampling are considered to be minimal. You may feel warm when breathing
under the plastic canopy, but air conditioning will be on in the room to minimize
this effect. There may be slight pain or discomfort while doing blood sampling
with a slight risk for bruising and infection. The amount of blood drawn over the
entire study will not exceed half of that in an ordinary blood donation, therefore
the equivalent of a cup (250 mL). This amount will not cause symptoms after the
study nor interfere with any treatment that is planned for you.
Your participation in this study is voluntary. You may withdraw from the study at
any time without affecting your usual medical care. Although this protocol is not
expected to provide you any direct benefit, it is hoped that the information
obtained will lead to the advancement of scientific knowledge in the field of
nutrition and cancer. You will be offered dietary counselling by a registered
dietitian during your stay, if you wish to learn about and improve your nutritional
status.
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Confidentiality of Records
The results of this study will be treated in complete confidence. The urine, blood
and breath samples will be meticulously labeled and safely stored for a maximum
of 5 years, for analyses at the McGill Nutrition Centre. All records obtained from
your sample analysis as well as related hospital and office documents will be kept
in a secure and private research office. The results of this research may be
presented at meetings or in publications but your identity will not be disclosed.
Your name will not appear in any publication or report produced from this study.
As part of normal research practice it is important that information related to the
study is checked for accuracy. It may be necessary for regulatory agencies such as
Health Canada, the Canadian Institutes of Health Research or members of the
McGill University Health Centre Research Institute, or Research Ethics Board to
review the information obtained from your medical records. In such
circumstances, confidentiality will be maintained at all times.
Any questions you may have about the study procedures, or the study results will
be answered promptly by contacting the study doctors, Dr. Stéphanie Chevalier
and Dr. Errol B. Marliss at 514-843-1665, from 9 am to 5 pm., or by pager 514406-1746 at any time. You may also contact the study research nurse, Chantal
Légaré, during daytime at 514-934-1934 local 32817, who is available to respond
to your questions and concerns regarding this study.
Should you have any question regarding your rights as a research subject, and
wish to discuss them with someone not associated with the study, you may
contact the Ombudsman of the McGill University Health Centre at (514) 9341934 local 35655.
Compensation
Funds you will receive for participating in this research study will compensate for
losses and/or inconveniences that are related to your participation. Receipt of
funds is not the reason why you volunteered.
Liability
By signing this consent you do not waive any of your legal rights.
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Study Title: Muscle Protein Metabolism in Lung Cancer Cachexia (New
Emerging Teams in Palliative Care: Cancer Associated
Cachexia-Anorexia Syndrome)
Protocol B: “Whole-body protein metabolism in response to insulin and
amino acids in non-small cell lung cancer patients.”
Subject’s Declaration of Consent
I,
, have read the above
description
with
a
member
(s)
of
the
research
team,
______________________________________________

I fully understand the procedures, advantages, and disadvantages of the
study which has been explained to me.

I freely and voluntarily consent to participate in this project.

I understand that I may seek more information and that I am free to
withdraw at any time if I desire, and that it will not compromise my
medical care.

I understand that my personal information will be kept confidential.
Dated at Montreal: (month )
mm
(day) ______
dd
( year ) 20 ________
yy
PARTICIPANT: _____________________________________________
Signature
INVESTIGATOR: _____________________________________________
Signature
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