evaluation of nutritional, inflammatory and fatty acid ... · 4.2.7 plasma fatty acids profile ......
TRANSCRIPT
Evaluation of Nutritional, Inflammatory and Fatty Acid Status in Patients with Gastric and Colorectal Cancers Receiving Chemotherapy
by
Denise Gabrielson
A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Nutritional Sciences
University of Toronto
© Copyright by Denise Gabrielson 2017
ii
Evaluation of Nutritional, Inflammatory and Fatty Acid Status in
Patients with Gastric and Colorectal Cancers Receiving
Chemotherapy
Denise Gabrielson
Master of Science
Department of Nutritional Sciences University of Toronto
2017
Abstract
Cancer-related malnutrition is a predominant problem for gastrointestinal cancer patients despite
nutrition interventions, possibly due to inflammation and altered fatty acid (FA) status. We
described changes in nutritional, inflammatory, and fatty acid status in gastric and colorectal
cancer patients undergoing first-line chemotherapy at 4 time points coinciding with
chemotherapy. Changes over time and factors relating to change in nutritional status according to
tumour presence were assessed using linear mixed effects models. There were significant
associations between time and tumour presence for weight (p < 0.001), and fat free mass (FFM)
measured by bioelectrical impedance analysis (BIA, p = 0.02), and skinfold anthropometry
(FSA, p = 0.04), with nutritional status indicators adversely affected by tumour presence. There
were positive associations between weight and total n-3 (β = 0.02, p < 0.01), FFM and IL-6
(BIA, β = 0.028, p = 0.02; FSA, β = 0.03, p = 0.02), and FFM and total n-6 (BIA, β = 0.003,
p=0.01). Changes in nutritional status during chemotherapy differed based on tumour presence
and were associated with increasing concentrations of cytokines and FA.
iii
Acknowledgements
Thank you to my supervisor, Dr. Pauline Darling, for fostering an interest in practice-based
nutrition research at a formative point in my dietetics training and career. Thank you for
embarking on this challenging project with me and for your dedication and time commitment to
this study.
Dr. Christine Brezden-Masley, thank you for your support throughout this project, particularly
with getting this project off the ground and with participant accrual. Also, thank you for being
such a strong advocate for the importance of nutrition in oncology.
Thank you to the other members of my committee: Dr. Mary Keith, and Dr. Richard Bazinet for
your support, feedback and expertise throughout this project.
This study and the completion of my MSc would not have been possible without the support of
many individuals at St. Michael’s Hospital. Julie Kruchowski, and Charmaine Mothersill, I
appreciate your support which allowed me to complete my MSc requirements while working
full-time. Thank you to Maureen Lee not only for your expertise with the cytokine analysis, but
for helping me become reoriented in the lab setting and for welcoming me into your lab space.
Thank you to Jenna Sykes for your statistical expertise and patience. My sincerest thanks to my
colleagues in the Medical Day Care Unit, my office family, and my ‘lab’, Arti Sharma Parpia
and Sabrina Janes, for offering support and words of encouragement when I needed it most. I am
also grateful to Kimberley Bradley for providing much needed guidance, insight and support
during the most challenging period of this journey.
Thank you to my family, and most of all my husband, Rob Maxwell, for your unwavering
support over the past several years. And William, thank you for your understanding and patience
for the many weekends when ‘mamma work’, and for not deleting my thesis.
Lastly, I would like to thank all the patients and families who participated in this project. I am
grateful for your dedication during such a challenging time.
This research was funded and supported by a grant from the
Canadian Foundation for Dietetic Research.
Table of Contents
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
List of Appendices ...........................................................................................................................x
List of Common Abbreviations ..................................................................................................... xi
Glossary ....................................................................................................................................... xiii
Introduction .................................................................................................................................1
Literature review .........................................................................................................................4
2.1 Gastrointestinal cancer .........................................................................................................4
2.1.1 Treatment in gastrointestinal cancer ........................................................................5
2.1.2 Standard medical nutrition therapy in oncology specific to gastrointestinal cancer and chemotherapy .........................................................................................6
2.2 Nutritional status and cancer ................................................................................................8
2.2.1 Nutritional status and cancer: screening and assessment .........................................8
2.2.1.1 Screening and assessment: body composition ...........................................9
2.2.2 Nutritional status and cancer: prevalence and identification of cancer-related malnutrition and cancer cachexia ...........................................................................11
2.3 Etiology and pathophysiology of cancer-related malnutrition and cachexia .....................14
2.3.1 Inflammation and cancer ........................................................................................14
2.3.1.1 Inflammation and gastrointestinal cancer: interleukin-6 and tumour necrosis factor alpha ................................................................................15
2.3.1.2 Inflammation and gastrointestinal cancer: C-reactive protein .................16
2.3.1.3 Inflammation and nutritional status in gastrointestinal cancer over time ..........................................................................................................16
2.3.2 Fatty acids in cancer ...............................................................................................17
2.3.2.1 Fatty acids: general background ..............................................................17
2.3.2.1.1Endogenous synthesis of long-chain polyunsaturated fatty acids .......... 18
v
2.3.2.1.2Long-chain polyunsaturated fatty acids and health ............................... 18
2.3.2.2 Role of fatty acids in cancer ....................................................................20
2.3.2.3 The influence of cancer on fatty acid status ............................................21
2.3.2.4 The influence of anti-cancer therapy on fatty acid status ........................22
2.3.3 The relationship between fatty acids and inflammation in cancer .........................23
2.3.3.1 Modulation of the inflammatory response with polyunsaturated fatty acids .........................................................................................................24
2.3.3.1.1n-3 supplementation in patients not receiving anti-cancer therapy ....... 25
2.3.3.1.2n-3 supplementation in patients receiving anti-cancer therapy ............. 27
2.4 Summary ............................................................................................................................29
Rationale and objectives ...........................................................................................................31
3.1 Rationale ............................................................................................................................31
3.2 Objectives ..........................................................................................................................33
Methods .....................................................................................................................................34
4.1 Study design and participants ............................................................................................34
4.2 Measurements ....................................................................................................................34
4.2.1 Chemotherapy ........................................................................................................34
4.2.2 Blood collection and processing ............................................................................34
4.2.3 Anthropometric data ..............................................................................................36
4.2.4 Nutritional status, body composition, and functional status ..................................36
4.2.5 Dietary intake .........................................................................................................37
4.2.6 Inflammatory markers ............................................................................................37
4.2.7 Plasma fatty acids profile .......................................................................................38
4.2.8 Other data ...............................................................................................................39
4.3 Statistical analysis ..............................................................................................................39
vi
Results .......................................................................................................................................40
5.1 Patient population ..............................................................................................................40
5.2 Patient characteristics prior to starting chemotherapy .......................................................40
5.3 Changes in nutritional, inflammatory and fatty acid status during chemotherapy – all patients ...............................................................................................................................44
5.3.1 Nutritional status ....................................................................................................44
5.3.2 Inflammatory status ...............................................................................................44
5.3.3 Fatty acid status ......................................................................................................44
5.4 Interrelationships between nutritional, inflammatory and fatty acid status over time – all patients ..........................................................................................................................48
5.4.1 Weight ....................................................................................................................48
5.4.2 Fat free mass as measured by BIA and FSA ..........................................................49
5.4.3 Nutritional risk .......................................................................................................50
5.5 The influence of tumour presence on changes in nutritional status prior to and during chemotherapy .....................................................................................................................50
5.5.1 Patient characteristics prior to starting chemotherapy ...........................................50
5.5.2 The influence of tumour presence on nutritional status .........................................51
5.5.3 The influence of tumour presence on inflammatory status ....................................54
5.5.4 The influence of tumour presence on fatty acid status ..........................................54
5.6 The influence of tumour presence on interrelationships between nutritional, inflammatory and fatty acid status over time .....................................................................56
Discussion .................................................................................................................................61
6.1 Changes in nutritional, inflammatory and fatty acid status during chemotherapy ............61
6.2 Interrelationships between nutritional, inflammatory and fatty acid status over time .......62
6.3 The influence of tumour presence on changes in nutritional, inflammatory and fatty acid status ...........................................................................................................................63
6.4 Strengths and limitations ....................................................................................................66
Conclusions ...............................................................................................................................70
vii
7.1 Future directions ................................................................................................................71
References ......................................................................................................................................72
Appendices ................................................................................................................................82
viii
List of Tables
Table 2-1. Chemotherapy protocols, duration, and common side effects ...................................... 7
Table 5-1. Baseline patient characteristics based on tumour presence ........................................ 42
Table 5-2. Markers of nutritional status over time – all patients ................................................. 45
Table 5-3. Markers of inflammation over time – all patients ...................................................... 46
Table 5-4. Markers of plasma phospholipid fatty acid status over time – all patients ................. 47
Table 5-5. Multivariate model for weight – all patients ............................................................... 48
Table 5-6. Multivariate model for FSA fat free mass – all patients ............................................. 49
Table 5-7. Multivariate model for BIA fat free mass – all patients ............................................. 49
Table 5-8. Multivariate model for PG-SGA score – all patients .................................................. 50
Table 5-9. Markers of nutritional and functional status over time – Resected ........................... 52
Table 5-9.1. Markers of nutritional and functional status over time – Non-resected ................. 53
Table 5-10. Markers of inflammation over time – Resected ....................................................... 55
Table 5-10.1. Markers of inflammation over time – Non-resected ............................................. 55
Table 5-11. Markers of plasma phospholipid fatty acid status over time – Resected ................. 57
Table 5-11.1. Markers of plasma phospholipid fatty acid status over time – Non-resected ....... 57
Table 5-12. Multivariate model for weight with tumour interaction ........................................... 58
Table 5-13. Multivariate model for FSA fat free mass with tumour interaction ......................... 59
Table 5-14. Multivariate model for BIA fat free mass with tumour interaction .......................... 59
Table 5-15. Multivariate model for PG-SGA score with tumour interaction .............................. 59
ix
List of Figures
Figure 2-1. Endogenous synthesis of long-chain polyunsaturated fatty acids in humans ........... 19
Figure 2-2. Summary of the potential relationships between nutritional status, inflammation and
fatty acid levels ............................................................................................................................ 30
Figure 4-1. Study schedule ........................................................................................................... 35
Figure 5-1. Flowchart of study participants ................................................................................. 41
Figure 5-2. Predicted markers of nutritional status by tumour presence ................................... 60
x
List of Appendices
Appendix 8.1 Summary of fish oil supplementation studies ....................................................... 83
Appendix 8.2 Consent form ......................................................................................................... 88
Appendix 8.3 Research poster ..................................................................................................... 93
Appendix 8.4 Sensitivity Analysis ............................................................................................... 94
xi
List of Common Abbreviations
AA Arachidonic Acid
AI Adequate Intake
ALA Alpha Linolenic Acid
AMA Arm Muscle Area
APR Acute Phase Response
BIA Bioelectrical Impedance Analysis
BMI Body Mass Index
CEA Carcinoembryonic Antigen
COX Cyclo-oxygenase
CRC Colorectal Cancer
CRP C-Reactive Protein
CT Computerized axial Tomography
DHA Docosahexaenoic Acid
DXA Dual-energy X-ray Absorptiometry
EFA Essential Fatty Acid
EPA Eicosapentaenoic Acid
FA Fatty Acid
FAME Fatty Acid Methyl Esters
FFM Fat Free Mass
GI Gastrointestinal
HETE Hydroxyeicosatentraenoic Acid
HGS Handgrip Strength
IL-6 Interleukin-6
LA Linoleic Acid
LBM Lean Body Mass
LME Linear Mixed Effect
LMF Lipid Mobilizing Factor
LOX Lipoxygenase
LT Leukotriene
xii
MAC Midarm Circumference
MNT Medical Nutrition Therapy
MRI Magnetic Resonance Imaging
MST Malnutrition Screening Tool
MUFA Monounsaturated Fatty Acid
NSCLC Non-Small Cell Lung Cancer
PG Prostaglandin
PG-SGA Patient Generated Subjective Global Assessment
PIF Proteolysis Inducing Factor
PUFA Polyunsaturated Fatty Acid
QOL Quality of Life
RD Registered Dietitian
SEE Standard Error of Estimation
SFA Saturated Fatty Acid
SPM Specialized Pro-resolving Lipid Mediators
TLC Thin-Layer Chromatography
TNF-α Tumour Necrosis Factor Alpha
TSF Triceps Skin Fold
TX Thromboxane
UBW Usual Body Weight
xiii
Glossary
Adjuvant chemotherapy: Chemotherapy given after the primary cancer treatment (i.e. after
surgery), to lower the risk of cancer recurrence (National Cancer Institute)
Anorexia: The loss of appetite or desire to eat.
Biotherapy: The use of living organisms, substances made from living organisms, or laboratory
made substances to treat disease. This includes monoclonal antibodies, protein-targeted
therapies, angiogenesis inhibitors, cytokines, and vaccines (National Cancer Institute, 2013).
Cachexia: In cancer, cachexia is defined as “a multifactorial syndrome characterised by ongoing
loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by
conventional nutritional support and leas to progressive functional impairment” (Fearon et al.,
2011).
Functional status: The ability to perform basic activities of daily living such as bathing,
dressing, transferring in and out of a bed or chair, toileting and eating, and instrumental activities
of daily living such as using the telephone, shopping, preparing food, housekeeping/laundry,
using transportation, managing medications, and managing finances (Brown et al., 2017).
Nutrition impact symptoms: Symptoms that impede nutritional intake, digestion, absorption
and utilization (Levin, 2013).
Palliative chemotherapy: Chemotherapy given to provide symptom control, improve quality of
life, and improve survival, in a non-curative setting (Roeland and LeBlanc, 2016).
Perioperative chemotherapy: Chemotherapy around the time of surgery or a combination of
pre- and post-operative chemotherapy.
Pre-operative chemotherapy: Chemotherapy given prior to surgery.
1
Introduction In 2016, it was estimated that 202,400 Canadians would develop cancer. It is the leading cause of
death in Canada with an estimated 216 deaths every day from the disease. Gastrointestinal (GI)
cancer, which may include cancer of the esophagus, stomach, pancreas, liver, colon, and rectum
was estimated to account for approximately 19% of all new cancer cases (Canadian Cancer
Society’s Advisory Committee on Cancer Statistics, 2016).
Patients with GI cancer often present with weight loss prior to starting chemotherapy and are at
risk for further weight loss during anticancer treatment. Reduced dietary intake, weight loss and
loss of lean body mass (LBM) contribute to poor nutritional status and/or cancer-related
cachexia. A compromised nutritional status prior to and during treatment has been associated
with reduced functional status, poor treatment tolerance, poor quality of life (QOL), and
ultimately shorter survival times (Andreyev et al., 1998; Deans et al., 2009; Dewys et al., 1980).
There are numerous factors involved in cancer-related malnutrition and cachexia, including
anorexia, treatment-related side effects, and alterations in intermediary and energy metabolism.
Standard nutrition interventions or medical nutritional therapy (MNT) provided by a registered
dietitian (RD) for patients receiving chemotherapy may involve managing nutrition impact
symptoms through diet education and other specialized diet interventions. Cancer-related
malnutrition and the associated weight loss and loss of LBM continues to be a predominant
problem for many patients with advanced cancer, despite traditional nutrition interventions such
as dietary counselling or the use of nutritional supplements (Tisdale 2002).
The inefficacy of standard nutrition therapy may be related to inflammation, more specifically,
the acute-phase response (APR). The APR is a common feature in patients with advanced cancer
and is associated with a poor prognosis. It is characterized by reprioritization of protein synthesis
for the production of acute-phase proteins such as serum C-reactive protein (CRP) (Barber et al.,
1999a; Stephens et al., 2008). In patients with gastroesophageal cancer, it was found that 83% of
patients present with weight loss at diagnosis and that an elevated serum CRP is an independent
predictor of the degree of weight loss (Deans et al., 2009). Cytokines are the predominant
regulators of the APR and interleukin-6 (IL-6) and tumour necrosis factor alpha (TNF-α) are
known to influence protein loss, anorexia, and to decrease gastric emptying and intestinal
2
motility (Stephens et al., 2008). In patients with locally advanced GI cancer and weight loss,
appetite was significantly lower in patients with an APR versus those without. Furthermore,
there was a significant reduction in survival in patients with an APR (O’Gorman et al., 1999).
This underscores the importance of considering the role of inflammation on nutritional decline in
cancer patients.
There is potential that the inflammatory response can be influenced by an individual’s fatty acid
(FA) status, given that eicosanoids are generated from 20-carbon polyunsaturated fatty acids
(PUFA) (Calder, 2006). n-6 FAs such as arachidonic acid (AA), and n-3 FA such as
eicosapentaenoic acid (EPA), give rise to inflammatory lipid mediators such as thromboxanes,
prostaglandins and leukotrienes (Colomer et al., 2007) and also give rise, along with
docosahexanoic acid (DHA) to compounds that help to resolve inflammation (Serhan and
Petasis, 2011). Eicosanoid production begins with the release of PUFAs from membrane
phospholipids. AA tends to produce more potent inflammatory eicosanoids compared to EPA.
Therefore, altering the composition of membrane phospholipids in favour of n-3 FA may help
attenuate the inflammatory response (Mocellin et al., 2016).
Altered FA levels such as elevated levels of AA have been demonstrated in patients with
advanced cancer and n6:n3 ratios have been inversely associated with body mass index (BMI)
(Pratt et al., 2002). FA alterations have also been found to differ by tumour type and the presence
or absence of weight loss, and the presence of inflammation (Zuijdgeest-Van Leeuwen et al.,
2002). Moreover, chemotherapy may be another possible cause of altered FA composition in
plasma phospholipids with one study showing very low levels of long chain PUFAs in three
patients following high dose chemotherapy (Pratt et al., 2002). These alterations in the FA
composition of plasma phospholipids could affect the extent and duration of the APR, and
subsequently nutritional status in cancer patients.
Some studies have demonstrated a potential beneficial effect of n-3 supplementation alone or as
part of an oral nutrition supplement on attenuating weight loss and altering markers of an APR.
These results have been inconsistent and have been primarily in patients with advanced
pancreatic cancer or in heterogenous cancer groups, presenting with weight loss, and generally
not receiving anti-cancer treatment such as chemotherapy. Consequently, there is a lack of
research on the occurrence and etiology of nutritional decline in gastric and colorectal cancer
3
(CRC) patients receiving first-line chemotherapy and inconclusive evidence for the role of n-3
supplements in this population.
Thus, the aim of this dissertation is to enhance knowledge of the potential mediators of the
decline in nutritional status in patients with gastric and CRC undergoing chemotherapy, with a
focus on inflammation and FA levels. This new data may contribute towards the development of
specialized nutrition interventions such as the use of n-3 supplementation in this population.
4
Literature review The purpose of this chapter is to provide an overview of GI cancer, chemotherapy treatment for
gastric cancer and CRC, the potential implications of the disease and treatment on nutritional
status, and the role of MNT in maintaining or improving nutritional status in GI cancer patients
receiving treatment. This chapter will also discuss factors affecting nutritional status in cancer,
the potential role of inflammation in the decline in nutritional status, and finally the potential of
FA in modulating inflammation and nutritional status.
2.1 Gastrointestinal cancer GI cancer, including gastric cancer and CRC accounts for approximately 22% and 17% of new
cancer cases in Canadian males and females, respectively (Canadian Cancer Society’s Advisory
Committee on Cancer Statistics, 2016). While GI cancer may refer to multiple sites within the GI
system, this dissertation focuses solely on gastric cancer and CRC.
Gastric cancer is the fifth most common cancer worldwide and the third most common cause of
death from cancer (World Cancer Research Fund International/American Institute for Cancer
Research, 2016). Risk factors for gastric cancer include smoking, infection with Helicobacter
Pylori, industrial chemical exposure, alcohol, consumption of foods preserved with salting,
consumption of processed meat, being overweight, and obesity (World Cancer Research Fund
International/American Institute for Cancer Research, 2016).
CRC is the third most common cancer worldwide, and the second most common cancer in
Canada (Canadian Cancer Society’s Advisory Committee on Cancer Statistics, 2016; World
Cancer Research Fund / American Institute for Cancer Research, 2011). It is linked to obesity, a
sedentary lifestyle, smoking, and consumption of red and processed meat, and alcohol intake.
Dietary fibre intake, and physical activity likely reduces the risk of developing CRC, and
consumption of milk, garlic, and calcium may also protect against CRC (World Cancer Research
Fund / American Institute for Cancer Research, 2011).
Survival in gastric cancer and CRC is impacted by the stage of disease at time of diagnosis. The
overall five-year survival for gastric cancer ranges from 67% for localized disease, and decreases
5
to 5% for distant or metastasized disease. In CRC, overall five-year survival ranges from >90%
for localized disease, down to 13.5% for metastasized disease (Howlader et al., 2015).
Individuals with GI cancer often present with symptoms that influence nutritional risk and may
adversely affect treatment outcomes. These symptoms include reflux, reduced appetite,
abdominal pain, nausea and vomiting, dysphagia, anemia and weight loss in individuals with
gastric cancer, and bleeding, obstruction and abdominal pain in individuals with CRC (Canadian
Cancer Society’s Steering Committee on Cancer Statistics, 2011).
2.1.1 Treatment in gastrointestinal cancer
Treatment for GI cancer may include chemotherapy, surgery, biotherapy, and radiation or a
combination of modalities. The type of treatment depends on the type and stage of cancer and the
intent of treatment, which may be curative, or palliative, the latter of which focuses on symptom
management in a non-curable setting. This study focuses on patients receiving chemotherapy.
Chemotherapy may be used pre-operatively, peri-operatively, or in a palliative setting. Pre-
operative chemotherapy is given with the intent of decreasing tumour burden prior to surgery.
Chemotherapy may also be used peri-operatively or as adjuvant therapy following surgical
resection. In a palliative setting for metastatic disease, chemotherapy may be used to extend
survival, control symptoms, and improve quality of life (QOL). Chemotherapy regimens
involving the use of 5-fluorouracil are commonly used in gastric cancer and CRC along with
other cytotoxic drugs. These cytotoxic drugs may be used with or without biotherapy, for
example the drugs Bevacizumab (Avastin®) or Trastuzumab (Herceptin®), which are monoclonal
antibodies that bind to specific growth factors and prevent the growth, progression or survival of
cancer cells.
The primary treatment for early stage gastric cancer is surgical resection alone or in combination
with perioperative chemoradiation or post-operative radiation/chemoradiation (Ajani et al., 2016;
Knight et al., 2013). The current standard for treatment of advanced gastric cancer (non-
resectable, locally advanced or metastatic adenocarcinoma), is first-line chemotherapy with
fluorouracil-based combination regimens. Common regimens include ECX, ECF, or FOLFOX
with the addition of Trastuzumab for HER-2 positive patients (Ajani et al., 2016; Mackenzie et
al., 2011). Another regimen under investigation as a first-line treatment in metastatic gastric or
gastroesophageal junction adenocarcinoma is IXO (Table 4-1).
6
Patients with resected CRC who are at a high risk for recurrence will typically undergo adjuvant
chemotherapy with FOLFOX, XELOX or Xeloda (Meyers et al., 2016). For cases in which there
are resectable metastases, for example the liver, patients may undergo surgical resection
followed by adjuvant chemotherapy with FOLFOX, XELOX, or Xeloda, or may undergo
neoadjuvant chemotherapy. In patients with metastatic CRC, the standard first-line treatment is
chemotherapy with FOLFIRI or FOLFOX with or without the use of Bevacizumab (Avastin®)
(Welch et al., 2010).
As previously mentioned, patients with GI cancer often present with symptoms related to the
presence of the tumour that may affect gastric motility, or may contribute to obstructive
symptoms such as nausea, vomiting, diarrhea, constipation or abdominal pain. These symptoms
can subsequently affect nutritional risk and nutritional status. This nutritional risk may be further
exacerbated from common side effects associated with chemotherapy, such as poor appetite,
mouth sores, nausea and vomiting, constipation and diarrhea. Factors affecting nutritional risk, or
nutrition impact symptoms, should be addressed through standard MNT.
2.1.2 Standard medical nutrition therapy in oncology specific to gastrointestinal cancer and chemotherapy
GI cancer patients are at risk for poor nutritional status from both the disease itself and due to
treatment, which may include surgery, radiation, chemotherapy, or a combination of modalities.
Patients often present with weight loss prior to starting chemotherapy and further weight loss
may occur due to side effects associated with antineoplastic therapy. Side effects may include
anorexia, nausea, vomiting, constipation, diarrhea, mucositis or stomatitis, dysgeusia, or taste
alterations, and fatigue. The Academy of Nutrition and Dietetics recommend that RDs, as part of
the interdisciplinary oncology team, provide nutrition care to adult oncology patients receiving
chemotherapy or radiation therapy (Thompson et al., 2017). The Nutrition Care Process is a
standardized method of administering nutrition care and involves nutrition assessment, diagnosis
of nutrition-related problems, evidence-based interventions, and monitoring and evaluation of
those interventions (Elliott, 2006). Within the Nutrition Care Process there are MNT protocols
which outline standardized steps in completing individualized nutrition assessments, the content
and frequency of care, and the measurement of outcomes to manage specific diseases (Elliott,
2006). Nutrition care provided by an RD has been associated with improved treatment outcomes
(Ravasco et al., 2012), QOL (Ravasco, 2005), reduced hospital admissions and length of stay
7
Table 2-1. Chemotherapy protocols, duration, and common side effects
Regimen Drugs Indications Frequency Common Side Effects with Nutritional Implications
Common Supportive Medications
ECF Epirubicin Cisplatin Fluorouracil
Neoadjuvant/Adjuvant Gastric Cancer Every 21 days Nausea, vomiting, stomatitis,
diarrhea, anorexia
Aprepitant* x 3 days; Ondansetron 8 mg BID x 1 day; Dexamethasone 8 md OD x 3 days**
ECX Epirubicin Cisplatin Xeloda
Palliative Advanced Gastric/Gastroesophageal Every 21 days Nausea, vomiting, stomatitis,
diarrhea, anorexia
Aprepitant* x 3 days; Ondansetron 8 mg BID x 1 day; Dexamethasone 8 mg OD x 3 days**
ToGA Cisplatin Xeloda Herceptin
Palliative Gastric Every 21 days Nausea, vomiting, diarrhea, mucositis, anorexia, abdominal pain
Aprepitant* x 3 days; Ondansetron 8 mg BID x 1 day; Dexamethasone 8 mg OD x 3 days**
IXO Irinotecan Xeloda Oxaliplatin
Palliative Metastatic Gastric/Gastroesophageal Every 21 days Nausea, diarrhea Ondansetron 8 mg BID x 3 days;
Dexamethasone 8 mg BID x 3 days
Xeloda Xeloda Palliative Advanced Colorectal Every 21 days Nausea, vomiting, diarrhea, mucositis, abdominal pain None
FOLFOX +/- Avastin
Folinic Acid Fluorouracil Oxaliplatin
Adjuvant/Palliative Advanced Colorectal Every 14 days Nausea, vomiting, diarrhea,
mucositis, abdominal pain Ondansetron 8 mg BID x 3 days; Dexamethasone 8 mg BID x 3 days
FOLFIRI +/- Avastin
Folinic Acid Fluorouracil Irinotecan +/- Avastin
Palliative Advanced/Metastatic Colorectal Every 14 days Nausea, vomiting, anorexia,
diarrhea, mucositis, abdominal pain Ondansetron 8 mg BID x 3 days; Dexamethasone 4 mg BID x 3 days
Cancer Care Ontario Drug Formulary * 125 mg on day 1, 80 mg on days 2 and 3. ** Aprepitant results in decreased clearance of dexamethasone by half, therefore dexamethasone dose equivalent to 16 mg daily.
8
(Odelli et al., 2005; Paccagnella et al., 2010), improved appetite (Ravasco, 2005), improved
treatment tolerance (Paccagnella et al., 2010; Ravasco, 2005), improved weight maintenance
(Poulsen et al., 2014) and increased energy and protein intake (Isenring et al., 2007; Poulsen et
al., 2014).
Standard of care and access to MNT by an RD may differ across cancer centres depending on
available resources. Standard practice may not always include an initial assessment and routine
follow-up by an RD, but instead may include provision of a nutrition and cancer booklet, general
education provided by nurses, and the offer of an RD assessment as required (Isenring et al.,
2007). In gastric cancer and CRC patients, the goals of MNT are to maintain or improve
nutritional status. This may include weight maintenance during treatment, adequate consumption
of calories and protein, prevention or treatment of vitamin and mineral deficiencies, maintaining
adequate hydration, completing planned treatment and avoiding treatment interruptions (i.e.
fewer delays in receiving chemotherapy), maintenance of functional status (i.e. the ability to
maintain normal activities of daily living), and adequate symptom management (Elliott and
Kiyomoto, 2010; Gill, 2013). Successful outcomes of these goals may be achieved through
patient and family education and counselling and the use of nutrition support as required (oral,
enteral, or parenteral). These outcomes of nutrition interventions as part of MNT may vary for
reasons related to the complex etiology and pathophysiology of cancer-related malnutrition as
discussed in section 2.3 below.
2.2 Nutritional status and cancer
The first step leading to the Nutrition Care Process and MNT involves the identification of
patients at risk for poor nutritional status and determines the type and depth of nutritional care
provided. Determining the most pertinent factors affecting nutritional risk is necessary for the
success of MNT.
2.2.1 Nutritional status and cancer: screening and assessment
Nutrition screening allows for the early identification of patients who are at risk for malnutrition
or who are already malnourished when starting treatment. It facilitates proactive management of
nutritional risk rather than a reactive approach to reversing malnutrition which can be more
challenging. The Academy of Nutrition and Dietetics recommends that malnutrition screening
9
should be completed at time of admission to oncology services and at each treatment visit in the
ambulatory setting (Levin, 2013).
There are several screening tools that have been validated in the oncology setting, with two
appropriate for an ambulatory care setting. These include the Malnutrition Screening Tool
(MST), and the Patient Generated Subjective Global Assessment (PG-SGA) (Levin, 2013).
While the MST relies solely on reported changes in weight and appetite, the PG-SGA offers a
more comprehensive picture of nutritional risk. The PG-SGA is modification of the Subjective
Global Assessment, which is considered the gold standard for nutritional assessment, but specific
to the nutritional status of oncology patients. The PG-SGA takes into account weight, intake,
symptoms affecting intake or nutrition impact factors, functional capacity, metabolic demand,
and physical assessment. Patients complete the first portion of the tool on changes in weight,
intake, nutrition impact symptoms and functional status, and a healthcare professional completes
the second portion on factors affecting metabolic demand, and physical assessment. Patients are
assigned a total PG-SGA score, which corresponds with nutritional triage recommendations and
allows for monitoring for improvement or deterioration of a patient’s nutritional status.
Additionally, patients are assigned a global categorical rating of A for well-nourished; B for
moderately malnourished or suspected malnutrition; or C for severely malnourished. This
validated tool can be used both to assess risk for malnutrition and to determine the actual
presence of malnutrition in oncology patients. The PG-SGA is the recommended tool to use as
part of a comprehensive nutrition assessment in oncology for the identification of malnutrition
(Thompson et al., 2017). An abridged or short-form version of the PG-SGA has also been
validated as a nutrition screening tool in the oncology outpatient setting (Gabrielson et al., 2013)
and this tool has been associated with clinical features of cancer cachexia such as elevated CRP,
lower hemoglobin, decreased BMI, fat mass, and handgrip strength (HGS) (Vigano et al., 2014).
HGS is a useful measure of muscle function, which may be impaired as a result of poor
nutritional status or malnutrition.
2.2.1.1 Screening and assessment: body composition
The determination of changes in body composition is important in the assessment of nutritional
status and detection of malnutrition in standard MNT. The usefulness of changes in body weight
in the assessment of nutritional status can be affected by hydration status, and fluid aberrations
10
such as ascites or edema (Mourtzakis et al., 2008). BMI does not distinguish between fat mass
and muscle mass, the loss of the latter having a greater impact on metabolic and functional status
as well as survival (Prado et al., 2009). Determination of body composition and quantifying
muscle mass may offer a more accurate picture of nutritional status. Skinfold anthropometry,
which involves measuring skinfold thickness using calipers, provides a measure of body fat,
while arm muscle area (AMA) can provide a measure of muscle mass, irrespective of hydration
status, edema, ascites, and tumour burden. Four-site skinfold anthropometry (FSA) measures
skinfold thickness at the biceps, triceps, subscapular and supra-iliac areas, from which total body
density and the relative proportions of fat to fat-free mass (FFM) can be estimated using linear
regression equations (Durnin and Womersley, 1973). AMA can estimate changes in muscle mass
from triceps skinfold thickness (TSF) and midarm circumference (MAC). The corrected AMA
addresses overestimation errors related to the midarm muscle compartment being noncircular
(5cm2), and bone area (2 to 5 cm2) (Heymsfield et al., 1982).
Dual energy X-ray absorptiometry (DXA), computerized axial tomography (CT) or magnetic
resonance imaging (MRI), are considered gold standard methods in assessing body composition.
DXA can estimate whole-body bone mineral, fat, and fat-free tissues with highly reproducible
results and a coefficient of variation of 2% for fat-free soft tissue and 0.8% for fat, though it is
not routinely done in an ambulatory cancer care setting (B. Heymsfield et al., 1997). CT or MRI
can differentiate between types of FFM, which includes lean tissues such as skeletal muscle,
organs, and can differentiate between types of adipose tissue. It can also account for tumour
burden which may contribute to the overall measure of FFM and would not be detected by
bioelectrical impedance analysis (BIA) or DXA (Mourtzakis et al., 2008). Regional CT imaging
is often readily available in a cancer setting as part of routine medical care, however access to
software and personnel needed for using this tool to assess FFM may not be routinely available.
BIA is a safe, inexpensive, and simple tool for a clinical setting and is useful for measuring body
composition in conjunction with anthropometry. It uses a low-level alternating current
administered through electrodes on the hand and the foot and impedance to electrical flow is
measured. Electrical flow travels through body water much more easily than through lipid and
bone. Using validated prediction formulas, fat-free body mass can be estimated from total body
water derived from BIA (B. Heymsfield et al., 1997). Relative to hydrodensitometry, BIA was
found to have a standard error of estimation (SEE) of estimating body fatness of 2.7% versus
11
3.9% using FSA (Lukaski et al., 1986). There are several limitations to the use of BIA in body
composition assessment. Aberrations in fluid status such as ascites, peripheral edema and
dehydration related to poor intake or GI losses may impact the accuracy of results of BIA
(Kushner et al., 1996). These factors and the importance of using an equation validated in the
same or similar population must be considered when using BIA in patients with cancer. The
ability of BIA to detect changes in body composition in an individual is also limited for small
changes, however BIA was shown to be accurate in detecting a change of > 5% in FFM in
healthy individuals with no effect on wasting from disease (HIV) on predictions (Kotler et al.,
1996).
2.2.2 Nutritional status and cancer: prevalence and identification of cancer-related malnutrition and cancer cachexia
Cancer-related malnutrition is an imbalance of energy, protein and other nutrients, arising from
decreased nutrient intake, increased or altered nutrient requirements, and leading to alterations in
metabolism, body composition and functional status. While there is no universally accepted
definition of malnutrition, accepted minimal criteria include: a low BMI (< 18.5 kg/m2) or
weight loss in combination with a low BMI; involuntary weight loss of ≥ 10% of usual body
weight in 6 months or ≥ 5% weight loss in 1 month; prolonged inadequate nutrition intake; loss
of body fat and/or muscle mass; reduced HGS; changes in functional status; changes in fluid
status; altered metabolic requirements; and altered eating behaviours (Cederholm et al., 2015;
White et al., 2012). Many studies investigating the prevalence of malnutrition in cancer have
defined malnutrition based on unintentional weight loss with or without consideration of dietary
intake data, while some have used more comprehensive tools such as the subjective global
assessment (SGA) or PG-SGA.
Unintentional weight loss prior to chemotherapy is frequent in patients with GI cancers,
occurring in greater than 50% of patients with CRC and greater than 77% in patients with non-
colorectal GI cancers (Dewys et al., 1980; Sánchez-Lara et al., 2013). Patients that present with
weight loss subsequently have higher rates of weight loss during treatment associated with
nutrition impact factors such as nausea, vomiting, and anorexia, and a greater decline in FFM
irrespective of tumour site or stage of disease (Buskermolen et al., 2012; Halpern-Silveira et al.,
2010; Sánchez-Lara et al., 2013). During chemotherapy, GI patients continue to have a high
prevalence of malnutrition with increased frequency in patients with unresected tumours, and in
12
those with gastric cancer (Attar et al., 2012). Weight loss and malnutrition in cancer have been
associated with poor QOL (Gavazzi et al., 2011), increased risk for treatment toxicities and
greater severity of toxicity (Andreyev et al., 1998), poor performance status (Andreyev et al.,
1998; Dewys et al., 1980), and shortened survival (Andreyev et al., 1998; Buskermolen et al.,
2012; Deans et al., 2009; Dewys et al., 1980).
Weight loss is a defining feature of cancer cachexia, which is characterized by progressive
weight loss with concurrent loss of skeletal muscle mass, and a variable degree of adipose loss.
Cancer cachexia differs from simple starvation in that it does not typically respond to standard
nutritional interventions such as dietary counselling and use of oral nutrition supplements
(Tisdale, 2002). An imbalance between energy intake and energy expenditure; metabolic
alterations in glucose metabolism; and increased lipolysis, and proteolysis may contribute to
further increases in energy expenditure and increased skeletal muscle catabolism which
ultimately contributes to weight loss. Reduced food intake from treatment-related side effects
can further exacerbate this weight loss. Similar to malnutrition, there has been a lack of a clear
definition and diagnostic criteria for cachexia in cancer (Fearon et al., 2011). This may be related
to the complex etiology of cancer cachexia. Previous diagnostic criteria have focused primarily
on involuntary weight loss of greater than 5% in a 6-month period or 10% in obese patients as a
potential indicator (Palesty and Dudrick, 2003). Other characteristics or sequelae used to
describe cancer cachexia have included weight loss, anorexia, weakness, muscle and fat loss,
depression and chronic nausea (Palesty and Dudrick, 2003; Trujillo, 2006). A more recent
proposed definition of cachexia is “a multifactorial syndrome characterised by an ongoing loss of
skeletal muscle mass (with or without the loss of muscle mass) that cannot be fully reversed by
conventional nutritional support and leads to progressive functional impairment (Fearon et al.,
2011)”.
In 2011, a formal consensus process was undertaken to provide a definition, classification and
diagnostic criteria for cancer cachexia. It has been described as continuum with three stages of
clinical relevance. The first stage is precachexia in which patients may show early signs such as
anorexia and involuntary weight loss of £5% (Fearon et al., 2011). Anorexia is common in
cancer patients and may be a result of the disease or chemotherapy. The tumour itself may
contribute to a decreased desire to eat though alterations in orexigenic and anorexigenic signals
through cytokines such as IL-6 or TNF-α. The tumour may also cause taste or sensory alteration,
13
changes in GI motility, or physical obstruction of the GI tract further contributing to reduced
dietary intake (Palesty and Dudrick, 2003). Chemotherapy may cause loss of appetite, mucositis,
nausea, vomiting, taste changes, GI dysmotility, and this can lead to food aversions and
inadequate nutrient intake (Fearon et al., 2011; Tisdale, 2002). Progression to the next stage,
cachexia, and associated physical changes may also occur in the absence of anorexia or without a
decrease in food intake (Tisdale, 2002). The cachexia stage of the continuum is defined by the
following criteria: 1) a weight loss of >5% over the past 6 months (in the absence of simple
starvation); 2) BMI <20 and any degree of ongoing weight loss > 2%; or 3) appendicular skeletal
muscle index consistent with sarcopenia, and any degree of weight loss >2%. The final stage of
the cachexia continuum is refractory cachexia which is defined by clinical characteristics such as
a low performance status or functional decline, a procatabolic state, lack of response to
anticancer treatment, and a prognosis of less than 3 months survival (Fearon et al., 2011).
This proposed definition considers the importance of low muscularity. Muscle wasting may
occur concomitant with adipose wasting however it can also occur in the presence of excess
adiposity. Sarcopenia has been defined as values less than 2 standard deviations below the sex-
specific mean for relative skeletal muscle mass index in healthy, young adults. This equates to
<7.26 kg/m2 for men, and <5.45 kg/m2 for women (Baracos, 2006). In a group of patients with
Stage IV CRC, 39% of patients were sarcopenic as measured using CT images (Thoresen et al.,
2013). In a heterogenous group of patients with advanced cancers, 98% of patients with cachexia
were found to be sarcopenic based on CT, with the majority of patients being classified as
normal weight or overweight/obese based on BMI classification (Sun et al., 2015). In this same
study, gastric cancer patients were found to have the highest prevalence of cancer cachexia
(76.5%) only second to pancreatic cancer. The prevalence of cancer cachexia was still high at
42% in patient with CRC (Sun et al., 2015).
While definitions and terminology may vary with respect to features of cancer-related
malnutrition and cachexia, it is recognized that a poor nutritional status is a prevailing issue in GI
cancer patients. Potential reasons will be discussed in the following sections.
14
2.3 Etiology and pathophysiology of cancer-related malnutrition and cachexia
Weight loss associated with diseases such as cancer, results partly from inadequate intake for
reasons outlined above. Inadequate intake may result from anorexia, treatment-related side
effects, and GI dysfunction. The provision of adequate nutrition should result in improved
nutritional status and the resolution of malnutrition. Weight loss however is not always mitigated
by the provision of adequate nutrition, particularly in a setting of cancer-related cachexia. The
lack of response to the provision of adequate nutrition may be due to the presence of
inflammation which can affect both metabolism and eating behavior (Laviano et al., 2015).
Decreased intake may arise not only from treatment-related effects (Grosvenor et al., 1989) but
also from the effect of inflammation on the hypothalamic control of food intake, and
anorexigenic and orexigenic pathways (Argilés et al., 2014). Metabolic changes associated with
inflammation include increased muscle proteolysis and increased lipolysis which are mediated
by inflammatory cytokines. Inflammatory cytokines are released by tumour cells and released by
activated immune cells. The loss of LBM attributed to cytokines is associated with not only
increased proteolysis or breakdown of muscle tissue, but also decreased protein synthesis or
reprioritization of protein synthesis to acute phase proteins. Tumour-specific products such as
lipid mobilizing factor (LMF), which stimulates lipolysis of white adipose tissue, and proteolysis
inducing factor (PIF), which increases protein degradation through the ubiquitin-proteasome
pathway, may further contribute to the breakdown of adipose and muscle. In gastric and
esophageal cancer patients, dietary intake, serum CRP concentrations, and advanced disease
were found to be independent predictors in determining extent of weight loss (Deans et al.,
2009). This supports a role of altered intake, disease, and inflammation in cancer-related
malnutrition. The role of inflammation in cancer and cancer-related malnutrition as well as the
potential for FA to modulate inflammation will be discussed in detail below.
2.3.1 Inflammation and cancer
While weight loss and cachexia may occur in the absence of systemic inflammation, the presence
of inflammation can have profound effects on dietary intake, energy balance, energy
expenditure, loss of LBM and adipose, and survival. Inflammation and the APR is the normal
host response to infection or injury and is usually of limited duration. Inflammation may arise
from anti-cancer treatment such as chemotherapy, and from the tumour itself creating a pro-
15
inflammatory and pro-tumorigenic environment. The pro-inflammatory environment supports
angiogenesis, tumour progression and metastatic spread (Grivennikov et al., 2010). Therapy-
induced inflammation may be of benefit in terms of mounting an anti-tumour response, and by
triggering cell death however in established tumours, the pro-tumour inflammation supporting
growth and progression is the most predominant type of inflammation (Mocellin et al., 2016).
When the APR is prolonged, it can have harmful effects on the host such as loss of LBM, poor
performance status and worse survival. The APR can lead to an increased demand for amino
acids to support hepatic protein synthesis of positive acute phase proteins, such as CRP, while
maintaining synthesis of negative acute phase proteins, such as albumin, prealbumin, and
transferrin (Stephens et al., 2008). This may adversely affect the supply of amino acids for the
synthesis of muscle (Tisdale, 2002) leading to muscle wasting. Inflammation is therefore an
important potential target for preventing cancer-related malnutrition and cachexia.
The APR is regulated primarily by cytokines, specifically IL-6, interleukin-1, TNF-α, and
interferon-γ, which have been implicated in mediating muscle protein loss, lipolysis, anorexia,
and suppression of food intake (Argilés et al., 2005; Palesty and Dudrick, 2003; Stephens et al.,
2008; Tisdale, 2002). Furthermore, PIF, in addition to its role in protein degradation, may
stimulate production of IL-6, IL-8 and CRP, contributing to an elevated APR (Deans and
Wigmore, 2005). While monocytes secrete IL-1, IL-6 and TNF-α, IL-6 has been shown to be the
strongest inducer of acute phase protein synthesis in the liver (Heinrich et al., 1990).
2.3.1.1 Inflammation and gastrointestinal cancer: interleukin-6 and tumour necrosis factor alpha
Increasing levels of IL-6 produced by tumours have been shown to correspond with the degree of
cachexia in mice bearing colon-26 adenocarcinoma with resolution or improvement of cachexia
following tumour resection or use of an IL-6 antagonist, respectively (Barton, 1997). Circulating
levels of IL-6 have been associated with low skeletal muscle index (Guthrie et al., 2013) and
elevated IL-6 and TNF-α have been correlated with tumour size in preoperative CRC patients
(Nikiteas, 2005; Shimazaki et al., 2013). Similarly, in gastric cancer patients, IL-6 was positively
correlated with CRP and tumour size, and was significantly higher in pre-operative gastric cancer
patients compared to healthy controls (Ikeguchi et al., 2009). In pancreatic cancer patients prior
to chemotherapy, elevated levels of IL-6 have been related to advanced age, the presence and
extent of liver metastases, high carcinoembryonic antigen (CEA), a tumour marker, high CRP,
16
and anemia (Miura et al., 2015). IL-6 has also been shown to be significantly higher in cases
with distant metastases versus without and in stage III-IV cases versus 0-II and showed an
inverse correlation with overall survival time (Shimazaki et al., 2013).
2.3.1.2 Inflammation and gastrointestinal cancer: C-reactive protein
As suggested above, CRP is correlated with IL-6 concentrations and is used as an indirect marker
of systemic inflammation and pro-inflammatory cytokine activity (Stephens et al., 2008). CRP is
an independent predictor of survival in patients with CRC and gastric cancer (Elahi et al., 2004;
McMillan et al., 2001, 2003; Read et al., 2006) and similar to IL-6, it has been found to be
negatively correlated with skeletal muscle index (Richards et al., 2012). In pancreatic cancer
patients receiving supportive treatment only (no chemotherapy or radiotherapy), CRP was
significantly correlated with performance status, percent weight loss, and LBM index. Rate of
weight loss and rise in CRP was also significantly greater with advancing disease than when
measured close to the time of diagnosis (Barber et al., 1999a). Elevated concentrations of
positive acute phase proteins including CRP are associated with increased total weight loss and
increased rates of weight loss at time of diagnosis in patients with gastroesophageal cancer
(Deans et al., 2009). CRP was found to be an independent predictor of weight loss along with
dietary intake and stage of disease (Deans et al., 2009). Gomes de Lima et al (2012),
demonstrated a high prevalence of elevated CRP in patients with GI cancer (73%). Furthermore,
patients with weight loss and malnutrition based on PG-SGA had significantly higher serum
CRP compared to well-nourished patients with GI cancer (Gomes de Lima and Maio, 2012). In
patients with CRC prior to starting chemotherapy or radiotherapy, an inflammatory state, as
defined by the Glasgow Prognostic Score (GPS), was significantly associated with nutritional
status defined by the subjective global assessment (SGA) (Maurício et al., 2013).
2.3.1.3 Inflammation and nutritional status in gastrointestinal cancer over time
The inflammatory response and the relationship with nutritional status may vary with changes in
tumour burden, disease progression or response to chemotherapy over time. Studies investigating
changes in parameters of nutritional status longitudinally have been limited in patients with GI
cancer. In patients with lung or GI cancer and a loss of body cell mass over a 12-week period, as
measured by total body potassium, there was a significant increase in CRP compared to baseline.
17
There was also a significant reduction in triceps skinfold (TSF) thickness, and also reductions in
weight, biceps skinfold thickness, and albumin, though these did not reach statistical significance
(McMillan et al., 1998). Additionally, there was a significant correlation between mean CRP
concentration and relative and absolute change in total body potassium over the 12-week follow-
up period (McMillan et al., 1998). This suggests that the rate of loss of overall body cell mass
and fat mass is related to the inflammatory response. Another study in 50 GI cancer patients who
had weight loss of >5% of their usual body weight (UBW) and receiving only supportive care
found that patients who continued to lose weight over a six to eight-week period had a significant
increase in CRP concentrations and a reduction in TSF and performance status compared with
baseline. These changes in nutritional status were not observed in weight stable patients or those
that had gained weight. Additionally, those patients who gained weight had significantly lower
levels of CRP at baseline versus those who continued to lose weight (O’Gorman et al., 1999).
2.3.2 Fatty acids in cancer
2.3.2.1 Fatty acids: general background
FA are composed of carbon chains with a methyl group at one end and a carboxyl group at the
other end. Saturated fatty acids (SFA) consist of only carbon-carbon single bonds whereas
unsaturated fatty acids contain one or more carbon-carbon double bonds. Essential fatty acids
(EFA) are those which cannot be synthesized de novo in humans and must be supplied in the
diet. These include the simplest n-3 FA, alpha-linolenic acid (ALA; 18:3n-3) and the simplest n-
6 FA, linoleic acid (LA; 18:2n-6). ALA can be found in many plant sources including soybeans,
seeds such as flax and chia, walnuts, and some leafy green vegetables (Calder, 2013; Kris-
Etherton and Innis, 2007), while LA is abundant in vegetable oils (corn, safflower, sunflower,
soybean), and animal meats (Kris-Etherton and Innis, 2007; Ratnayake and Galli, 2009). LA is
the main PUFA in most Western diets (Calder, 2013). Long-chain PUFAs include EPA (20:5n-
3), and DHA (22:6n-3) and are found primarily in cold-water fish such as mackerel, tuna, and
salmon (Kris-Etherton and Innis, 2007). Based on US dietary data surveys, total n-3 FA intake
for men and women has been estimated to range from approximately 1.3 to 1.8 g/d and 1.0 to 1.2
g/d, respectively. With respect to individual n-3 FA, the median intake of ALA is approximately
1.2 to 1.6 g/d for men and 0.9 to 1.1 g/d for women which is the basis for the recommendation
for adequate intake (AI) between 1.1 and 1.6 g/d. The median intakes of EPA range from 0.004
to 0.007 and 0.052 to 0.093 g/d, for men and women, respectively, while the median intake of
18
DHA ranges from 0.066 to 0.093 g/d for men and 0.052 to 0.069 g/d for women. Median n-6
PUFA intake, which is comprised mainly of LA, may range from 12 to 17 g/d for men and 9 to
11 g/d for women, leading to an AI of 17 g/d for men and 12 g/day for women, ages 19 to 50
years (Institute of Medicine (U.S.), 2005). In Canada, PUFA intake is estimated to be about 5.5%
of total energy intake in males and 5.7% in females, however no data is available on average
intakes of individual EFAs (Elmadfa and Kornsteiner, 2009).
2.3.2.1.1 Endogenous synthesis of long-chain polyunsaturated fatty acids
As mentioned, ALA and LA cannot be synthesized de-novo in humans, however ALA can be
metabolized by a series of desaturation and elongation reactions to EPA, DHA, and LA to the n-
6 FA AA (20:4n-6) (Figure 2-1). This synthesis of long-chain PUFAs, however, is inefficient in
humans and can be influenced by diet composition. Dietary EPA, DHA and LA for example, can
reduce the conversion of ALA to long-chain n-3 FA. LA specifically, and the conversion of LA
to AA, is in direct competition with the conversion of ALA to EPA. Both reactions rely on the
delta-6 desaturase enzyme pathway. While ALA is the preferred substrate for delta-6 desaturase,
LA is more predominant in the diet, thus the metabolism of n-6 FA takes precedence (Calder,
2013). Additionally, diets high in ALA may increase the rate of ALA oxidation resulting in
lower plasma concentration and a further reduction in the conversion to EPA and DHA
(Arterburn et al., 2006). While DHA may also act as a substrate for conversion to EPA through
β-oxidation, the conversion rate in humans is once again very low (Arterburn et al., 2006).
2.3.2.1.2 Long-chain polyunsaturated fatty acids and health
n-3 PUFAs have received much attention in the role of health and disease ranging from infant
development, to cardiovascular disease, mental health and cancer in adults. Health benefits are
thought to arise from the incorporation of n-3 FA into membrane phospholipids with subsequent
effects on the regulation of inflammation, platelet aggregation and vasoconstriction or dilation
(Riediger et al., 2009). Phospholipids are comprised of a glycerol backbone with two FA
attached at the sn-1 and sn-2 positions, and a phosphoric acid residue attached to either choline,
ethanolamine, serine, or inositol. The sn-1 position is typically occupied by a SFA or
monounsaturated fatty acid (MUFA), while the sn-2 position is occupied by a MUFA or PUFA
(Hodson et al., 2008). DHA, EPA, ALA, and AA all compete for the sn-2 position on membrane
phospholipids. Based on population studies showing the FA composition of various lipid
19
n-6 n-3
Linoleic acid (LA; 18:2) α-Linolenic acid (ALA; 18:3)
Δ6-desaturase
γ-Linolenic acid (18:3) Stearidonic acid (18:4)
Elongase
Dihomo-γ-linolenic acid (20:3) Eicosatetraenoic acid (20:4)
Δ5-desaturase
Arachidonic acid (AA; 20:4) Eicosapentaenoic acid (EPA; 20:5)
Elongase Elongase Δ6-desaturase β-oxidation
Docosahexaenoic acid (DHA; 22:6)
Figure 2-1. Endogenous synthesis of long-chain polyunsaturated fatty acids in humans
20
fractions, palmitic acid (16:0) and LA at 31.2 and 21.9 mol%, respectively, are the most
abundant FA in plasma phospholipids, followed by stearic acid (18:0), elaidic acid (18:1n-9), and
AA. Long chain n-3 PUFAs are present in much smaller amounts with content ranging between
0.1 to 6.0 mol% (Hodson et al., 2008). This FA composition of phospholipids, particularly, the
relative proportions of PUFAs can influence biological effects by affecting substrate availability
for enzymes involved in cell signaling, and subsequently affect processes such as inflammation
(Cockbain et al., 2012).
2.3.2.2 Role of fatty acids in cancer
In cancer, n-3 FA may exert benefits through several mechanisms. n-3 FA may suppress growth
factors such as vascular endothelial growth factor which in turn may suppress angiogenesis
required for rapidly growing tumours. n-3 FA may also inhibit nitric oxide production which is
also necessary for angiogenesis. Additionally, n-3 FA may have potential effects on improving
response to chemotherapy through up-regulation of cytotoxic transporters and supporting
oxidative stress processes (Arshad et al., 2011). Cell culture studies involving various human cell
lines, and animal studies relating to GI cancer have shown n-3 PUFA effects on inhibiting
proliferation and enhancing apoptosis through modulation of tumour suppressor pathways and
suppression of transcription factors necessary for cytokine production (Eltweri et al., 2017). In
humans, observational studies on CRC, have supported a dose-dependent reduction in CRC risk
with higher intakes of EPA, DHA and total n-3 PUFA (Kim et al., 2010) and a decreased risk of
colorectal adenomas with high serum n-3 PUFA levels (Pot et al., 2008). Conversely, these
studies showed an increased risk for CRC and for colorectal adenomas, with higher ratios of n-6
to n-3 intake, and with high serum n-6 PUFA, respectively (Kim et al., 2010; Pot et al., 2008). In
the treatment of CRC, in vitro studies involving treatment with EPA and DHA have shown an
association with reduced cellular proliferation and increased apoptosis (Cockbain et al., 2012).
Beneficial effects of n-3 PUFAs on development and progression of CRC have been attributed
not only to enhanced effects on proliferation and apoptosis as mentioned above, but also effects
of EPA on inhibiting the production of 2-series pro-inflammatory prostaglandins (PG) in favour
of less potent 3-series PGs, which may affect early carcinogenesis in CRC (Cockbain et al.,
2012).
21
2.3.2.3 The influence of cancer on fatty acid status
FA status refers to concentrations of FA in the blood or tissues. This section focuses on the
influence of cancer on concentrations of FA in the blood. Suboptimal concentrations of n-3 FA
and alterations in n-6 FA and SFA concentrations have been demonstrated in patients with
cancer. Additionally, differences in FA levels appear to vary based on tumour type, and stage of
disease. Zuijdgeest-van Leeuwen et al. (2002), investigated plasma n-3 FA concentrations in
untreated patients with pancreatic, lung, and esophageal cancer, in comparison to healthy
subjects. Plasma phospholipid levels of the n-3 FA, EPA, and DHA, were reduced in patients
with pancreatic cancer versus healthy controls, while the SFA and MUFAs palmitic and oleic
acid, respectively, were elevated. Similarly, EPA and DHA were decreased, though not
significantly in patients with lung cancer, while SFA were significantly higher. In contrast,
patients with esophageal cancer tended to have higher total n-3 FA concentrations, while total n-
6 FA were significantly reduced. The relationship between FA levels and weight loss may also
differ by tumour type. FA levels were shown to be significantly lower in lung cancer patients
with weight loss versus those without weight loss whereas FA did not differ in esophageal cancer
according to weight status (Zuijdgeest-Van Leeuwen et al., 2002). Thus, FA status differs in
patients with cancer compared to those without, and differ based on tumour type. Furthermore,
these alterations in FA levels may be associated with weight loss only in certain types of cancer.
Alterations in FA levels may also differ based on the extent or stage of disease. Like the above
studies, Macàšek et al. (2012) showed altered plasma FA profiles in hospitalized patients with
pancreatic cancer with increased total n-6, increased MUFA, and decreased total n-3 FA in
phosphlipids compared with healthy controls. With respect to specific FA, patients with
pancreatic cancer had significantly higher palmitic acid and AA, and decreased stearic, LA,
EPA, ALA, and DHA in phospholipids. These authors also found a negative relationship
between concentrations of ALA, EPA, and total n-3 FA with tumour staging in pancreatic cancer
suggesting FA alterations may be related to disease burden (Macášek et al., 2012). Another study
in bladder cancer patients, however, suggested that levels do not differ based on disease burden.
While both n-6 and n-3 FA were significantly lower in patients with bladder cancer versus
healthy controls, there were no significant differences in levels between patients with recurrent
disease versus those with no evidence of recurrence (McClinton et al., 1991).
22
Changes in FA metabolism in cancer may contribute to alterations in FA status. In a group of
untreated CRC patients, Baró et al (1998) observed significantly lower levels of the SFA,
palmitic and stearic and the MUFA, oleic acid, compared to healthy controls. They also observed
lower LA, and lower ALA, DPA, and DHA in plasma. In plasma phospholipids, levels of
palmitic and total SFA were significantly higher, while LA and total n-6 FA were significantly
lower in patients with cancer. There were no differences observed in individual n-3 FA or total
n-3 in plasma phospholipids between patients with cancer and healthy controls however n-3 FA
levels were lower in other plasma lipid fractions. Markers of nutritional status did not differ
between CRC patients and healthy controls, suggesting that differences in FA levels were related
to altered FA metabolism versus malnutrition (Baró et al., 1998). Another study demonstrated
that malnutrition may also be a factor affecting FA status, with significantly lower levels of LA
as a percentage of total FA in phospholipids observed in 12 untreated malnourished upper GI
cancer patients compared to healthy controls (Mosconi et al., 1989).
Thus, FA levels in cancer may vary based on tumour presence, and tumour stage or disease
burden. Additionally, the degree or direction of alterations in FA levels appears to vary based on
tumour type. Finally, alterations in FA levels may be related to changes in FA metabolism or
nutritional status in patients with cancer.
2.3.2.4 The influence of anti-cancer therapy on fatty acid status
The FA status of cancer patients undergoing anti-cancer therapy is not well described and data
specific to GI cancer patients, specifically gastric cancer and CRC, during chemotherapy is even
scarcer. Pratt et al. (2002) compared a heterogeneous group of patients with advanced cancer
with a group of burn patients and a group of breast cancer patients undergoing chemotherapy.
While groups were not statistically compared, the authors noted the lowest levels of total plasma
phospholipid FA and EFA in advanced cancer patients compared to the other groups. Most
plasma PL FA levels in patients receiving induction chemotherapy prior to stem cell transplant,
fell between levels observed in healthy subjects and advanced cancer patients. After high dose
chemotherapy post-stem cell transplant, long chain PUFA levels in plasma PL were severely
depleted or undetectable in the breast cancer patients, suggesting a possible effect of
chemotherapy on low levels of plasma PL FA (Pratt et al., 2002). Other studies describing FA
23
status, some in the gastrointestinal population have been part of n-3 supplement studies discussed
in chapter 2.3.3.1.2.
2.3.3 The relationship between fatty acids and inflammation in cancer
Plasma n-3 FA concentrations have been shown to be inversely related to CRP concentrations in
healthy individuals, with higher levels of inflammation being associated with lower levels of
total n-3 FA, EPA, and docosapentaenoic acid, which is a precursor to DHA (Micallef et al.,
2009). Circulating levels of PUFAs are also associated with concentrations of acute phase
proteins and proinflammatory cytokines. One Italian community-based population demonstrated
an association between lower AA, DHA, and EPA and higher IL-6, an association between lower
ALA and higher CRP concentrations, and an association between higher total n-3 FA and both
lower IL-6 and TNF-α (Ferrucci et al., 2006). In a sample of patients with lung, esophageal, or
pancreatic cancer, after controlling for tumour type, patients with CRP concentrations greater
than 10 mg/l had significantly reduced levels of total n-3 FA and DHA in plasma phospholipids
compared to patients with a CRP less than 10 mg/l (Zuijdgeest-Van Leeuwen et al., 2002).
The inflammatory response is mediated by eicosanoids, which are biologically active FA derived
from 20-carbon PUFAs. In addition to the modulation of the inflammatory response, eicosanoids
are also involved in cell growth and differentiation, immune function, platelet aggregation, and
angiogenesis (Berquin et al., 2008). During an acute inflammatory response, prostaglandins and
leukotrienes are produced from PUFAs through cyclooxygenase (COX), and lipoxygenase
(LOX) enzymatic pathways (Calder, 2003; Serhan and Petasis, 2011). Over time, PUFA-derived
pro-inflammatory lipid mediators increase over hours to days. Eventually, a lipid mediator class
switching occurs resulting in the production of pro-resolving mediators that promote resolution
of inflammation (Serhan and Petasis, 2011). The type of bioactive lipid metabolites produced by
COX and LOX enzymes (eicosanoids, lipoxins, maresins, and protectins), are determined by the
FA present in membrane phospholipids and either contribute to inflammation or to the resolution
of inflammation (Mocellin et al., 2016).
AA is involved in the initiation phase of the inflammatory response (Serhan and Petasis, 2011).
It is the most common eicosanoid precursor as it is highly prevalent in the membrane
phospholipids of immune cells (Calder, 2013). It is released from phospholipids by
phospholipase A2 enzymes in response to an inflammatory stimulus and acts a substrate for
24
COX, LOX, and cytochrome P450 enzymes. COX enzymes give rise to 2-series PGs and
thromboxanes (TX) which induce inflammatory reactions in damaged tissues. LOX enzymes
give rise to 4-series leukotrienes (LTs), and hydroxyeicosatentraenoic acids (HETE) which are
also pro-inflammatory eicosanoids (Calder, 2003, 2013; Coussens and Werb, 2002).
Additionally, AA gives rise to lipoxins, which are specialized pro-resolving lipid mediators
(SPMs) (Serhan and Petasis, 2011).
EPA competes with AA for LOX and COX enzymes, therefore a higher EPA content in
membrane phospholipids can lead to decreased production of AA-derived eicosanoids and
increased production of EPA-derived eicosanoids, specifically 3-series PG and TX, and 5-series
LT. EPA-derived eicosanoids are thought to be less biologically potent than those derived from
AA (Calder, 2003, 2013). Both EPA and DHA are also involved in the biosynthesis of anti-
inflammatory SPMs such as E-series resolvins derived from EPA and D-series resolvins,
neuroprotectins/protectins, and maresins, derived from DHA (Buckley et al., 2014). Thus,
altering the supply of EPA versus AA in relation to eicosanoid production has the potential to
modulate the inflammatory response and this will be discussed in detail below.
2.3.3.1 Modulation of the inflammatory response with polyunsaturated fatty acids
Several studies have demonstrated a decrease in the production of AA-derived eicosanoids with
high intakes of n-3 PUFAs and these will be discussed in sections 2.3.3.1.1 and 2.3.3.1.2. EPA
may exert its anti-inflammatory effects through replacing AA in phospholipid membranes
thereby reducing the amount of AA available for the formation of pro-inflammatory 4-series LT
and 2-series PG (Berquin et al., 2008; Calder, 2003). n-3 FA may also influence the
inflammatory response by inhibiting cytokine production such as TNF-α and IL-6 through the
inhibition of nuclear factor kappa-B (Calder, 2003). The influence of n-3 PUFAs on
inflammation is dependent on incorporation into the membrane phospholipids of host and tumour
cells. Successful incorporation may be dependent on dose and timing or extent of
supplementation with some studies suggesting an ideal dose of > 2g/day for anti-inflammatory
effects (Mocellin et al., 2016).
Studies using n-3 supplementation provide further support for altered FA status in cancer, and a
potential role in modulating inflammation. By modulating inflammation through n-3 FA, there is
25
a potential to mitigate the negative impact of inflammation on nutritional status, however results
of n-3 supplement studies demonstrating this benefit have been mixed. Variable results may be
due variations in tumour type, baseline nutritional status, disease stage and trajectory (active
anticancer treatment versus palliation), and dosage and timing of supplementation. Results from
n-3 supplement studies in patients not receiving anti-cancer therapy such as chemotherapy will
be discussed separately from those in patients receiving anti-cancer therapy as the patient
populations may differ with respect to stage of disease and stage of cancer-related cachexia.
2.3.3.1.1 n-3 supplementation in patients not receiving anti-cancer therapy
Early studies have focused on supplementation with fish oil capsules or fish oil-enriched
nutritional supplements, in weight-losing patients with advanced cancer. Wigmore et al (1996)
demonstrated weight stabilization or weight gain with fish oil supplementation in patients with
unresectable pancreatic cancer though there was only a transient effect on reducing CRP. Fish oil
supplementation also led to significant increases in EPA levels, increased DHA, and decreased
AA after 1 month of supplementation (Wigmore et al., 1996). Barber et al (1999) investigated
the effects of a fish oil-enriched nutritional supplement versus supportive care only in weight-
losing patients with advanced pancreatic cancer. Patients receiving the supplement had a stable
APR while non-supplemented patients had increasing levels of CRP and decreasing levels of
albumin. Supplemented patients also gained weight while non-supplemented patients continued
to lose weight. This effect may have been related to either the provision of more adequate
nutrition through the nutrition supplement or possible attenuation of the APR (Barber et al.,
1999b). To determine possible mediators for these results, the authors investigated effects of a
fish oil-enriched supplement on potential mediators of cancer cachexia. Levels of pro-
inflammatory cytokines IL-6, TNF-α, and the tumour product, PIF were measured in 20 weight-
losing patients with pancreatic cancer over a 3-week period. IL-6 production was significantly
decreased, excretion of PIF was decreased and there was a median weight gain of 1.0 kg (Barber
et al., 2001). This supports a role of fish oil in influencing weight change through mediating the
APR.
Conversely, there was no change in the proportion of patients exhibiting an APR in a 12-week
study in 26 weight-losing patients with pancreatic cancer receiving EPA capsules. EPA
supplementation started at 1g per day and increased to a maintenance dose of 6 g per day by
26
week 4. While patients achieved weight stabilization suggesting a possible benefit in attenuating
weight loss, it is not clear whether this benefit is due to modulation of the inflammatory
response. Furthermore, only 14 of the 26 patients completed the study leading to a possible
overestimation of a beneficial effect of EPA in this advanced cancer group (Wigmore et al.,
2000). Additionally, in another study using an EPA-containing oral nutrition supplement in
weight-losing pancreatic cancer patients, results relating to nutritional status were less clear. In
this 8-week randomized double blind trial patients in both the experimental group and control
group experienced weight stabilization and stable lean body mass (LBM) during the study. This
was attributed to issues with compliance with both patient disclosure of use of n-3 supplements
in the control and experimental group, and adherence to the study protocol, which was evident
from plasma FA analysis. Despite these issues with compliance, correlation analysis showed a
significant association between supplement intake and increase in weight and LBM that was not
apparent in the control group. Additionally, in the experimental group, increased plasma EPA
levels were also associated with increased weight and LBM (Fearon et al., 2003). Markers of
inflammation were not measured in this study; therefore, no conclusions can be made whether
these associations may be related to the effects of FA on inflammation.
Further studies also support a relationship between plasma FA levels and weight or LBM, but
again did not look at markers of inflammation. In the study by Pratt et al (2002) investigating FA
composition in advanced cancer and the effects of 2-week fish oil supplementation versus
placebo, supplementation with fish oil led to significant increases in the n-3 FAs EPA and DHA
in plasma phospholipids, and a reduction in the n-6 FA LA, but not AA. Change in body weight
during 2-week period of supplementation was related to the increase in EPA content in plasma
phospholipids (Pratt et al., 2002). Taylor et al (2010) similarly found a positive correlation
between FA status and body weight in a group of 31 patients with a variety of metastatic cancers
presenting with weight loss, and not receiving anticancer treatment. A higher percentage of EPA
in total plasma PL was associated with a more positive median weight change. Supplementation
was also linked to improved appetite, and QOL (Taylor et al., 2010). A summary of studies
involving fish oil supplementation is provided in Appendix 8.1.
27
2.3.3.1.2 n-3 supplementation in patients receiving anti-cancer therapy
Only a few n-3 supplementation studies have focused on or included patients receiving active
anticancer treatment. Bruera et al (2003) conducted a 2-week randomized controlled trial with
fish oil capsules versus placebo in 60 weight-losing patients with advanced cancer in a palliative
care unit. Five patients received chemotherapy, and 4 received hormonal therapy. There were no
differences in appetite, nausea, well-being, caloric intake, nutritional status or functional status in
patients taking fish oil versus placebo (Bruera et al., 2003). Another study by Jatoi et al (2004)
which included chemotherapy and radiation treatment also did not support a role of EPA in
improving weight or appetite. The effects of an EPA-containing nutrition supplement alone, in
combination with the appetite stimulant megestrol acetate, and megestrol acetate alone, were
compared in patients with advanced cancer and cancer-associated wasting (prior weight loss and
poor caloric intake). The primary outcomes were weight, appetite, QOL and survival. The EPA-
containing supplement alone, or in combination with megestrol acetate, did not improve appetite
or weight more than just megestrol acetate alone (Jatoi et al., 2004).
Other studies in treatment patients have been more promising with one small pilot study
demonstrating beneficial effects of a nutrition intervention including an EPA-enriched oral
nutrition supplement, and weekly counselling by a dietitian in pancreatic and non-small cell lung
cancer (NSCLC) patients with cancer cachexia receiving chemotherapy. Seven patients
completed the 8-week study and demonstrated improvement in energy and protein intake, PG-
SGA score, performance status and QOL. There were also improvements in weight and LBM
though this did not reach statistical significance (Bauer and Capra, 2005). Another study looking
at fish oil supplementation versus standard of care (no intervention) in NSCLC patients receiving
first-line chemotherapy from start to completion of therapy found that patients receiving fish oil
maintained weight while patients who did not receive the intervention lost weight. Like studies
in non-chemotherapy patients, there was also a relationship between FA levels and LBM. More
specifically, patients with the greatest increase in plasma EPA had the greatest muscle gain as
measured by CT (Murphy et al., 2011a). In the same group of patients, there was also a better
response to chemotherapy after 2 weeks in the fish oil group compared to the standard of care
group as measured by clinical examination and imaging. Survival at one year was also greater in
the supplement group though this did not reach statistical significance (Murphy et al., 2011b).
28
Two studies looking at the effects of n-3 PUFA on nutritional and inflammatory status in CRC
suggest a benefit in maintaining or increasing weight and LBM. These studies have shown mixed
effects relating to inflammation giving rise to uncertainty as to how FA, nutrition and
inflammation are related. In a Phase II trial, Read et al (2007), investigated the effects of an
EPA-containing nutrition supplement on nutritional and inflammatory status, QOL, plasma
phospholipids and cytokine profile in patients on second-line chemotherapy (FOLFIRI) for CRC.
Second-line chemotherapy is given when patients have had disease progression or did not
tolerate their initial chemotherapy regimen. Unlike most other studies, not all patients were
weight-losing prior to starting the study and 48% of patients were considered well-nourished at
baseline. The EPA-enriched supplement in conjunction with counseling by a dietitian led to an
increase in weight and maintenance of LBM. While EPA and DHA increased, and AA decreased
with use of the supplement, there was no relationship between phospholipid long-chain PUFA
and lower levels of proinflammatory cytokines (Read et al., 2007). Similarly, in a group of well-
nourished CRC patients undergoing chemotherapy and supplemented with fish oil versus no
supplementation, there was a significant reduction in weight and BMI in the non-supplemented
group versus no change in the fish oil group. While CRP was reduced in the supplemented group
versus the non-supplemented group, this was not significant (Silva et al., 2012).
A more recent study in 2013, investigated the effects of a fish oil supplement on inflammatory
markers, nutritional status and plasma FA levels in 11 CRC patients undergoing chemotherapy.
Nine of the 11 patients underwent surgical resection in the preceding 4 months, and 2 patients
had metastatic disease. Six patients received 2 g of fish oil per day for 9 weeks of chemotherapy
and 5 patients acted as controls. No patients were classified as malnourished at baseline though
this was based solely on BMI classification tables from the World Health Organization. In terms
of inflammation, pro-inflammatory and anti-inflammatory cytokines did not change between the
start and end of the study and did not differ between groups. CRP however significantly
increased from baseline to week 9 in the control group and was significantly reduced in the
supplement group. CRP also differed between the control and supplement group by the end of
the study. In terms of fatty acid levels, as expected there was a significant increase in plasma
levels of EPA and DHA in the supplement group, and there was a significant reduction in AA
and the n6:n3 ratio. The control group did not have any significant changes in the proportion of
plasma FA though there was a significant increase in the n6:n3 ratio. Finally, with respect to
29
nutritional status, despite reduced inflammation and improved fatty acid status in the supplement
group, there were no significant changes in weight, BMI, body fat percentage and LBM. This
may suggest a beneficial effect of n-3 supplementation on preventing weight loss in CRC patient
during chemotherapy, however these nutritional markers also did not change in the control group
despite increasing inflammation and an increased n6:n3 ratio (Mocellin et al., 2013). While this
study considered both nutritional status and inflammation in the context of n-3 supplementation,
the lack of effect on nutritional status may be related to the small sample size. Additionally,
patients with resected disease were mixed with patients with metastatic disease and these patients
may differ in terms of nutritional risk factors related to tumour effects on weight loss and
catabolism.
2.4 Summary
In summary, patients with GI tumours may have a multitude of factors affecting nutritional risk
and nutritional status (Figure 2-2). The tumour itself may contribute to decreased nutrient intake
through GI dysfunction depending on the location of the tumour. The tumour may also
contribute to decreased nutritional status through tumour-specific products such as PIF and LMF
causing muscle and fat breakdown. Furthermore, the tumour may contribute to increased
inflammation to support tumour growth and progression and additionally, PIF may stimulate the
production of inflammatory cytokines. This inflammation contributes to poor nutritional status
through increased muscle breakdown and decreased muscle protein synthesis, and GI
dysfunction and anorexia further contributing to decreased nutrient intake. Patients undergoing
chemotherapy have the additional challenge of treatment-related side effects, which may
contribute to decreased nutrient intake through anorexia or through secondary symptoms such as
nausea, vomiting, diarrhea, and depression for example. There is also some evidence to suggest
that anticancer treatment itself contributes to inflammation, which again could lead to decreased
intake and poor nutritional status. FA levels have been shown to be altered in patients with GI
cancer. This may be related to the tumour, treatment, and may be exacerbated by decreased
nutrient intake. FA levels have the potential to modulate inflammation and subsequently the
effects of inflammation on nutritional status. Therefore, there is interest in improving our
understanding of this complex relationship between nutritional status, inflammation and FA
levels. Increased understanding of these relationships may facilitate the development of more
effective and timely nutrition intervention strategies in this population.
30
Figure 2-2. Summary of the potential relationships between nutritional status, inflammation and fatty acid levels
31
Rationale and objectives
3.1 Rationale Cancer-related malnutrition and cachexia are prevalent in patients with GI cancers with resulting
weight loss, poor functional status, poor QOL, poor treatment tolerance, and ultimately shorter
survival times (Andreyev et al., 1998; Dewys et al., 1980). There are numerous factors involved
in the etiology of cancer-related malnutrition and cachexia including decreased energy and
nutrient intake from anorexia, treatment-related side effects, and GI dysfunction and dysmotility
(Palesty and Dudrick, 2003). Additionally, effects of the tumour on intermediary and energy
metabolism may lead to the breakdown of fat and muscle.
Nutrition interventions aimed at stabilization of weight and ensuring adequate nutrition intake to
support maintenance of nutritional status have been associated with improved treatment
tolerance and outcomes (Ravasco, 2005; Ravasco et al., 2012), improved QOL (Ravasco, 2005),
and reduced hospital admissions (Paccagnella et al., 2010). Furthermore, stabilization of weight
during chemotherapy has been associated with improved survival (Andreyev et al., 1998).
Cancer-related malnutrition and cachexia however continues to be a predominant problem for
many patients despite standard nutrition interventions (Tisdale, 2002).
Inflammation, specifically the APR, may play a role in the continued and progressive decline in
nutritional status despite nutrition therapy. Inflammatory cytokines are the predominant
regulators of the APR and IL-6 and TNF-α are known to influence protein loss, anorexia,
decreased gastric emptying and intestinal motility (Argilés et al., 2005; Stephens et al., 2008).
FA may modulate the inflammatory response by altering eicosanoid production as suggested in
n-3 supplementation studies. This ability to modulate the inflammatory response and
subsequently nutritional status may be influenced by an individual’s FA levels, which could be
affected by dietary intake, disease burden and treatment. Even in healthy individuals, plasma n-3
FA concentrations have been shown to be inversely related to CRP concentrations with higher
levels of inflammation being associated with lower levels of total n-3 FA, EPA, and
docosapentaenoic acid (Micallef et al., 2009). In cancer patients, additional factors affecting
inflammation and FA levels, altered n-3 and n-6 FA levels could predispose a patient to
32
inflammation or limit the resolution of inflammation leading to poor nutritional status during
chemotherapy.
There is limited research describing the nutritional and inflammatory status in patients with
gastric cancer and CRC and most studies have focused on patients not receiving chemotherapy.
Additionally, many studies involving n-3 supplementation have been in patients with advanced
disease already presenting with progressive weight loss. Recalling that the success of MNT may
depend on the early identification and intervention for patients at high risk for nutritional decline,
there is an interest in examining the potential for modulation of inflammation earlier in the
disease trajectory. For example, there may be a potential benefit from n-3 supplementation prior
to or during anti-cancer treatment rather than in a palliative setting in which patients are more
likely to be in a state of refractory cachexia.
Knowledge of potential mediators of the decline in nutritional status that may occur in patients
with gastric cancer and CRC undergoing treatment is necessary for designing proactive
interventions that can prevent weight loss and associated complications. There may be a potential
benefit of n-3 supplementation in this population however there is uncertainty as to which
patients may be the most vulnerable (i.e. low levels of n-3 FA or high levels of inflammation) or
the most likely to benefit, and if there is an optimal time for potential supplementation (i.e. at the
beginning of chemotherapy). No other study to our knowledge has prospectively studied factors
predisposing patients with gastric cancer and CRC to nutritional decline during first-line
chemotherapy, with a focus on the interrelationships between nutritional status, inflammation
and FA levels. Furthermore, no study to our knowledge has compared patients with resected
disease to patients with non-resected disease to identify effects of tumour burden in patients
receiving chemotherapy.
The purpose of this study was to describe changes in nutritional, inflammatory and FA status
prior to and during chemotherapy, to describe changes in nutritional status in relation to levels of
inflammation and FA in patients with gastric cancer and CRC, and to identify factors associated
with nutritional depletion during treatment.
We hypothesized that in patients with gastric cancer and CRC, a decline in nutritional status
during the course of chemotherapy would be associated with increasing levels of inflammation
and decreasing levels of n-3 FA.
33
3.2 Objectives 1) To describe changes in nutritional status as measured by weight, PG-SGA score and
global rating, skinfold thickness, BIA, and dietary intake, as well as changes in
inflammation and n-3 FA status in patients with gastric cancer and CRC prior to and
during chemotherapy;
2) To investigate the interrelationships between changes in nutritional status, inflammation,
and FA levels in patients with gastric cancer and CRC receiving chemotherapy;
3) To compare nutritional outcomes in patients undergoing adjuvant chemotherapy (i.e.
following surgical resection of the tumour) with those undergoing palliative
chemotherapy (non-resectable/metastatic disease), as a control to account for the
influence of tumour burden.
34
Methods
4.1 Study design and participants This was a prospective, observational study of patients with newly diagnosed gastric cancer or
CRC attending the Medical Day Care Unit at St. Michael’s Hospital (Toronto, Canada) for first-
line adjuvant, neoadjuvant or palliative treatment with 5-fluorouracil-based chemotherapy.
Participants were recruited by consecutive sampling between January 2011 and June 2013.
Patients with physical/functional obstruction to the GI tract, undergoing concurrent treatment
with radiation, or those with a life expectancy < 3 months were excluded. Study participants
were assessed at 4 time points coinciding with scheduled clinic visits for chemotherapy.
Measurements were completed prior to the infusion of cycle 1 of chemotherapy (baseline), and
prior to administration of cycles 2, 3 and 4 of chemotherapy (Figure 4-1). During the study,
patients received standard medical nutrition therapy by the study RD which included dietary
interventions (education, diet modifications, use of oral nutrition supplement products) to
support maintenance of nutritional status during treatment. The study protocol was approved by
the St. Michael’s Hospital Research Ethics Board. Written informed consent was obtained from
each study participant (Appendix 8.2).
4.2 Measurements
4.2.1 Chemotherapy
All patients were receiving standard first-line 5-fluorouracil based chemotherapy for gastric
cancer or CRC as per provincial, national and international guidelines. ECF, ECX, ToGA, IXO
and Xeloda were administered every 3 weeks. FOLFOX +/- Avastin and FOLFIRI +/- Avastin
were administered every 2 weeks. Patients received supportive medications in standard dosages
(aprepitant, ondansetron and dexamethasone) in conjunction with chemotherapy (Table 2-1).
4.2.2 Blood collection and processing
Fasting blood samples were collected during the morning of regular clinic, prior to starting
chemotherapy (baseline) and prior to infusion of cycles 2, 3 and 4. Blood draws coincided with
routine pre-chemotherapy blood work to minimize participant burden. Samples were collected in
vacuum tubes containing EDTA for determination of plasma phospholipid FA profiles and
35
Figure 4-1. Study schedule
*Study duration dependent on chemotherapy regimen. See Table 4-1. **Blood work and measurements taken on day 1 of each cycle prior to infusion of chemotherapy. Dietary intake recorded prior to baseline and prior to each subsequent cycle of chemotherapy
Dietaryintake Dietaryintake Dietaryintake Dietaryintake
Bloodwork&measurements**
Bloodwork&measurements
Bloodwork&measurements
Bloodwork&measurements
6weeksor9weeks*
Cycle1 Cycle2 Cycle3 Cycle4
36
cytokines, and serum separating gel tubes for determination of CRP and albumin. Samples for
quantification of FA and cytokines were centrifuged at 1000 g for 15 minutes at a temperature of
4°C within 30 minutes of collection. Plasma aliquots were stored at -80°C until analysis.
4.2.3 Anthropometric data
Weight and height were measured at each visit using a digital platform scale and stadiometer.
Usual body weight and weight loss history was self-reported. Percent weight loss prior to
chemotherapy was calculated by subtracting current weight from reported pre-illness weight,
dividing the difference by the pre-illness weight, and multiplying by 100. Triceps, biceps,
supscapular and suprailiac skinfolds were measured to the nearest 0.1 mm using Lange Skinfold
Calipers (Beta Technology, Santa Cruz, California, USA). Mid upper arm circumference was
measured at the midpoint of the right arm between the acromion and the olecranon process using
a metric measuring tape. Anthropometric measurements were performed by the study RD
following standard techniques (Frisancho, 1990).
4.2.4 Nutritional status, body composition, and functional status
Nutritional risk and nutritional status were assessed at each visit using the Scored Patient-
Generated Subjective Global Assessment (PG-SGA©), which is a nutrition screening and
assessment tool validated in the oncology population. Nutritional risk was based on the PG-SGA
score with higher scores indicating higher nutritional risk and a greater need for nutrition
intervention. Nutritional status was assessed using the PG-SGA global rating (A=well-nourished,
B=moderate/suspected malnutrition, C=severely malnourished). The PG-SGA is described in
more detail in section 2.2.1. Height and weight were used to determine BMI (kg/m2). Body
composition was estimated using the sum of four skinfolds measured. Percent body fat was
obtained from an age- and sex-specific table with values based on the logarithmic transformation
of the sum of the four skinfolds using linear regression equations by Durnin and Womersley
(Durnin and Womersley, 1973). Fat mass was then determined by multiplying percent body fat
and current body weight. FFM was estimated by subtracting fat mass from current body weight.
Additionally, body composition was estimated with BIA using a single-frequency (50kHz)
tetrapolar technique (Quantum II Analyzer, RJL Systems, Detroit, USA). Measurements of
resistance and reactance were performed with patients in a supine position on the right side of the
body. Two electrodes were placed on the dorsum of the right hand and two were placed on the
37
dorsum of the right foot (Lukaski 1985). Measurements were repeated three times and the mean
of the measurements was used for analysis. Patients with ascites, peripheral edema, or receiving
IV fluids were excluded from BIA analysis. FFM from BIA was calculated using Kotler’s
equation (Kotler 1996). Percent body fat from BIA was estimated by using subtracting the FFM
value from current body weight to obtain a value for fat mass. Fat mass was then divided by the
current body weight and multiplied by 100 to obtain percent body fat. The trajectory of
nutritional status over time was additionally characterized using AMA. AMA was calculated
from MAC and TSF using the equations by Heymsfield et al (Heymsfield et al., 1982). Handgrip
strength was measured using a hydraulic hand dynamometer (Jamar, Lafayette Instrument,
Lafayette, Indiana, USA) according to the recommended standard procedures. Three maximal
values were recorded to the nearest 0.5 kg, and the mean of the 3 measurements was used for
analysis.
4.2.5 Dietary intake
Three-day food records were completed before baseline and prior to each subsequent study visit.
Patients were provided with oral and written instructions prior to the start of the study and were
provided with measuring utensils (measuring spoons and cups) to assist with accurate
quantification of food and beverages consumed. Food records were reviewed with patients at
each study visit for completeness. 24-hour diet recalls were completed for patients who did not
or were not able to complete a 3-day food record. Nutrient analysis was completed using The
Food Processor® SQL (ESHA Research, Version 10.12, Salem, Oregon, USA) with values from
the Canadian Nutrient File 2007b database. Patients were also asked to report use of any vitamin,
mineral, or natural health products. During the study, patients received MNT by an RD for any
nutrition impact symptoms identified on the PG-SGA tool, and food records were used to
optimize dietary intake to support maintenance of nutritional status.
4.2.6 Inflammatory markers
Plasma concentrations of high-sensitivity CRP were measured using an immunoturbidimetric
assay on a Beckman-Coulter LX-20 analyzer with a coefficient of variation of < 8%. Serum
concentrations above of CRP greater than 10 mg/L were considered to indicate the presence of
inflammation and an APR. Serum albumin concentrations were determined using the bromcresol
purple dye-binding technique on the SYNCHRON LX system. Plasma concentrations of the
38
cytokines IL-6 and TNF-a were measured in duplicate using enzyme-linked immunosorbent
assay (ELISA; Quantikine, R&D Systems, Minneapolis, USA). The detection limits of kits were
0.7 pg/mL and 5.5 pg/mL for IL-6 and TNF-a, respectively and coefficient of variation was <
8% for both assays. Analyses for CRP and serum albumin were performed in the core laboratory,
Department of Laboratory Medicine, St. Michael’s Hospital. Cytokine analysis was performed in
the laboratory of Dr. Philip Connelly, Keenan Research Centre for Biomedical Science, St.
Michael’s Hospital.
4.2.7 Plasma fatty acids profile
The FA profile of plasma phospholipids was quantified in the laboratory of Dr. Richard Bazinet
at the University of Toronto (Toronto, Canada). To assess FA, plasma total lipids were extracted
from plasma using chloroform/methanol (2:1, v/v) according to the Folch method (Folch et al.,
1957). Thin-layer chromatography (TLC) was used to separate the lipid classes. TLC plates were
activated by heating at 100°C for 1 hour. Total lipids were then loaded onto the plates and placed
in a tank with solvents. FA fractions were separated along with authentic standards in heptane–
diethyl ether–glacial acetic acid (60:40:2, v/v). Bands corresponding to the plasma lipid fractions
were visualized under UV light, after staining with 8-anilino-1-naphthalene sulphonic acid
(0·1 %, w/v). Heptadecanoic acid (C17:0) (Sigma, St. Louis, Missouri, USA) was added as an
internal standard to an aliquot of the plasma and the phospholipid band scraped. Total lipids were
extracted and FA were converted to fatty acid methyl esters (FAME) using 14% boron
triflouride-methanol at 100˚C for 1 hour (Sigma). FAME were analyzed by gas-liquid
chromatography using a capillary column (VF-23ms, 30 m × 0·25 mm inner diameter × 0·25 µm
film thickness; Agilent Technologies) and flame ionization detector, in a Varian-430 gas
chromatograph (Varian, Inc.). Samples were injected in splitless mode with the temperature of
the injector and detector ports set at 250°C. FAME were eluted using a temperature program set
initially at 50˚C for 2 min, increased at 20˚C/min and held at 170˚C for 1 minute and then
increased at 3˚C/min and held at 212˚C for 5 minutes. The carrier gas used was helium, set at a
constant flow rate of 0.7 ml/min. Peaks were identified by the retention times of FAME
standards (Nu-Chek-Prep, Elysian, Minnesota, USA) and FA concentrations (nmol/mL) were
calculated by proportional comparison of GC peak areas with that of the C17:0 internal standard
(Nishi et al., 2014). The inter and intra-assay coefficients of variation were < 4%. Total n-3
PUFAs represent the sum of alpha-linolenic acid, eicosatrienoic acid, eicosapentaenoic acid,
39
docosapentaenoic acid and docosahexaenoic acid. Total n-6 PUFAs represent the sum of linoleic
acid, gamma-linolenic acid, eicosadienoic acid, arachidonic acid, adrenic acid and
docosapentaenoic acid. Fatty acids were expressed as amounts (nmol/mL) and as a proportion
(%) of total PL.
4.2.8 Other data
Age, sex, diagnosis, stage of disease based on the American Joint Committee on Cancer TNM
staging system, location of metastases, prior surgery, chemotherapy regimen, and chemotherapy
dose reductions were recorded from patient medical records.
4.3 Statistical analysis Baseline descriptive analysis was performed using SPSS version 19.0. Results are reported as
median with range unless otherwise specified. Differences in baseline variables were assessed
using Fisher’s Exact Test for categorical variables, and the Mann-Whitney U Test for continuous
variables. The remaining analysis was completed using the open source software R, version
3.3.3. To determine changes in nutritional, inflammatory, and fatty acid status over time, a linear
mixed effect (LME) model with a random intercept was used. Model residuals were assessed for
normality and homogeneity of variance. Analyses were done with and without patients with self-
reported use of fish oil supplements (n=7) and patients with supplemental intakes of flaxseed oil
(n=1). To determine the association between covariates and outcomes of nutritional status, and to
determine if changes over time persisted after adjusting for known confounding factors, LME
model was used for each nutritional status outcome (weight, FFM from BIA and skinfold
anthropometry). The best LME for each nutritional status outcome was first chosen by running a
backwards selection algorithm. Variables that did not meet the criteria of having a Wald p-value
<0.05 were removed from the model until only significant variables remained. Visit was added to
each final model as a covariate to determine if the association over time remained significant
after adjusting for other known confounding factors. Variance inflation factors were calculated
for the final models to determine if multicollinearity was an issue. To determine if the
relationship between inflammation, fatty acid status and nutritional status differs depending on
tumour burden (resected versus non-resected tumour), the best LME model was used to adjust
for the interaction between tumour presence and visit. All p-values are two-sided and unadjusted.
Significance was considered at p<0.05.
40
Results
5.1 Patient population One-hundred and three patients were screened of which 34 did not meet inclusion criteria; 10
patients declined participation due to language barrier, feeling overwhelmed, or feeling that
participation would be too burdensome; and 15 patients were excluded due to the decision to
have treatment at another facility, alcoholism or inability to obtain consent prior to starting
chemotherapy. Of the 44 patients who agreed to participate, one expired prior to starting the
study, one started treatment at another facility and one did not start chemotherapy due to failure
to cope/poor performance status. Figure 5-1. provides details of patient accrual and study
completion according to whether patients had resected disease (undergoing adjuvant
chemotherapy post-surgery), or non-resected disease (undergoing neoadjuvant/palliative
chemotherapy).
5.2 Patient characteristics prior to starting chemotherapy Forty-one patients started the study. Baseline patient characteristics are shown in Table 5-2.
Twenty-three patients were males and 18 were females, with a mean age of 58.5 ± 11.3 years.
CRC was the most common diagnosis (68%). More than half the patients presented with Stage
IV disease. Most patients (58.5%) received FOLFOX with or without Avastin, or IXO; 22%
received FOLFIRI with or without Avastin; 14.6% received ECF, ECX or ToGA (Cisplatin,
Herceptin and Xeloda); and 4.9% received Xeloda alone (data not shown). Supportive
medications in standard dosages (aprepitant, ondansetron and dexamethasone) were provided in
conjunction with chemotherapy. The median duration of participation was 42 days, with a
minimum participation of 1 day (baseline visit only) and a maximum participation of 77 days
(additional time due to toxicity-related chemotherapy delays). Seven patients reported use of fish
oil supplements prior to starting chemotherapy.
41
Figure 5-1. Flowchart of study participants
*Other reasons: Alcoholism (n=1); Unable to obtain consent prior to treatment (n=10); No show (n=1); Planned treatment at another facility (n=3).
42
Table 5-1. Baseline patient characteristics based on tumour presence1 All patients (n=41)2 Resected (n=16)3 Non-resected (n=25)4 p
Age (y) 58.5 ± 11.3 55.3 ± 12.3 60.5 ± 10.3 0.217
Sex, n (%) 0.334
Male 23 (56) 7 (30) 16 (70) Female 18 (44) 9 (50) 9 (50)
Type of cancer, n (%) <0.01 Gastric 13 (32) 1 (8) 12 (92) Colorectal 28 (68) 15 (54) 13 (46)
Stage <0.001 II 2 (5) 2 (100) 0 (0) III 15 (37) 13 (87) 2 (13) IV 24 (58) 1 (4) 23 (96)
Fish oil supplement use, n (%) 5 7 (18) 2 (29) 5 (71) 0.685 Weight (kg) 71.6 (48.1-105.3) 65.7 (48.1-105.3) 71.7 (53.2-91.1) 0.831 % Weight loss prior to chemo 4.4 (0-21.6) 3.3 (0-21.6) 5.1 (0-21.6) 0.082 BMI (kg/m2) 24.8 (17.3-35.2) 26.6 (17.3-35.2) 24.6 (20.3-32.1) 0.350 PG-SGA global rating (A/B+C), n 18/20 10/5 8/15 0.096
PG-SGA score 7 (1-20) 6.0 (1-15) 8 (1-20) 0.173 Body composition
BIA Fat free mass (kg) 51.4 (36.9-75.5) 48.0 (36.9-75.5) 56.5 (37.5-68.9) 0.162 BIA % Body fat 24.9 (11.6-44.8) 28.3 (15.8-44.8) 22.0 (11.6-40.5) 0.038 FSA Fat free mass (kg) 51.6 (34.6-65.9) 48.5 (34.6-65.9) 53.8 (37.2-65.7) 0.314 FSA % Body fat 29.2 (12.6-42.6) 35.0 (20.8-41.9) 27.2 (12.6-42.6) 0.014 AMA (cm2) 39.2 (16.6-71.4) 36.4 (16.6-60.8) 39.9 (22.9-71.4) 0.378
Dietary intake Calories (kcal/day) 1771.1 (953.0-3295.8) 1819.7 (988.7-3078.4) 1601.4 (953.0-3295.8) 0.228 Protein (g/day) 78.1 (38.1-150.2) 85.7 (39.1-150.2) 74.4 (38.1- 136.2) 0.293 Carbohydrate (% of total energy) 52.4 (26.7-73.6) 54.9 (36.3-65.5) 48.7 (26.7-73.6) 0.439 Protein (% of total energy) 17.8 (10.5-31.6) 17.6 (11.3-22.2) 18.1 (10.5-31.6) 0.710 Fat (% of total energy) 30.3 (16.1-47.7) 29.9 (20.6-46.8) 30.8 (16.1-47.7) 0.643
Functional status
43
Table 5-1. (continued). All patients (n=41)2 Resected (n=16)3 Non-resected (n=25)4 p Handgrip strength (kg2) 68.3 (34.2-120.0) 58.3 (34.2-93.3) 76.7 (35.0-120.0) 0.091 Self-reported PG-SGA,n (%) 6 0.164
0 12 (32) 2 (17) 10 (83) 1 16 (42) 9 (56) 7 (44) 2 8 (21) 3 (38) 5 (63) 3 2 (5) 1 (50) 1 (50)
Serum albumin (g/L) 39.0 (17.0-48.0) 40.0 (34.0-48.0) 38.0 (17.0-45.0) 0.075 CRP (mg/L) 5.0 (0.5-144.2) 2.2 (0.5-33.6) 11.5 (0.7-144.2) <0.01 IL-6 (pg/mL) 4.3 (0.8-64.2) 3.4 (0.8-6.3) 5.2 (1.3-64.2) <0.01 TNF-a (pg/mL) 2.4 (0-17.1) 2.0 (0-6.7) 2.5 (0-17.1) 0.256 Plasma PL fatty acids (nmol/mL)
18:3 (n-3) (ALA) 5.8 (1.9-69.4) 6.7 (4.0-11.4) 4.6 (1.9-69.4) 0.030 18:2 (n-6) (LA) 509.5 (283.0-891.5) 597.7 (461.8-730.38) 451.6 (283.0-891.5) <0.01 20:4 (n-6) (AA) 238.4 (126.6-676.0) 283.2 (167.0-371.9) 227.8 (126.6-676.0) 0.100 20:5 (n-3) (EPA) 21.1 (7.6-138.6) 24.8 (9.8-36.4) 19.6 (7.6-138.6) 0.624 22:6 (n-3) (DHA) 68.0 (33.9-176.9) 66.6 (42.7-127.2) 69.4 (33.9-176.9) 0.729 Total n-37 191.2 (96.1-408.5) 233.4 (149.4-315.1) 171.2 (96.1-408.5) 0.133 Total n-68 783.7 (476.0-1580.5) 936.8 (651.8-1101.6) 696.4 (476.0-1580.5) <0.01 n-6/n-3 4.1 (2.0-3.2) 3.8 (3.0-5.3) 4.1 (2.0-5.2) 0.665 Total nmol/mL 2764.8 (1531.2-5313.4) 2940.6 (2203.7-3706.5) 2335.3 (1531.2-5313.4) 0.015
Means ± SD, frequencies, and median (range). Fisher’s Exact Test and Mann-Whitney U test used for categorical and continuous variables, respectively. Abbreviations: BMI, body mass index; PG-SGA, patient-generated subjective global assessment; BIA, bioelectrical impedance analysis; FSA, four-site skinfold anthropometry; AMA, arm muscle area; CRP, C-reactive protein; IL-6, interleukin-6; TNF-α, tumour necrosis factor α; PL, phospholipid; ALA, alpha-linolenic acid; LA, linoleic acid; AA, arachidonic acid; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid.
1 Resected = tumour resected (adjuvant therapy), Non-resected = tumour in situ (palliative or neoadjuvant/perioperative therapy) 2 Serum albumin, IL-6, Plasma PL fatty acids: n=39; PG-SGA, AMA, Handgrip strength, CRP, TNF-a: n=38; BIA: n=34; FSA: n=36; Dietary intake: n=37 3 PG-SGA, AMA, Dietary intake, Handgrip strength, Serum albumin, IL-6, CRP, TNF-a, Plasma PL fatty acids: n=15; FSA: n=14; BIA: n=13 4 Serum albumin, IL-6, Plasma PL fatty acids: n=24; PG-SGA, AMA, Handgrip strength, CRP, TNF-a: n=23; FSA, Dietary intake: n=22; BIA: n=22 5 Fish oil supplement use defined as patient-reported use of either ‘fish oil’ or ‘calamari oil’. 6 PG-SGA self-reported functional status based on ECOG (Eastern Cooperative Oncology Group) performance scale 7 Total n-3 PUFAs represent the sum of alpha-linolenic acid, eicosatrienoic acid, eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid. 8 Total n-6 PUFAs represent the sum of linoleic acid, gamma-linolenic acid, eicosadienoic acid, arachidonic acid, adrenic acid and docosapentaenoic acid.
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5.3 Changes in nutritional, inflammatory and fatty acid status during chemotherapy – all patients
5.3.1 Nutritional status
Prior to starting chemotherapy, 53% of patients presented with moderate or severe malnutrition
based on the PG-SGA and were classified in the B or C category. The median PG-SGA score
was 7 (range 1-20) suggesting nutritional risk requiring intervention by a dietitian. The most
frequent nutrition impact factors included early satiety (34%), no appetite (26%), and fatigue
(13%). Median energy and protein intake was 1771.1 kcal/d and 78.1 g/d, respectively. Median
carbohydrate, protein and fat intakes were within the acceptable macronutrient distribution
ranges for healthy populations. Over the course of the study, there was significant change in the
ratio of well-nourished to malnourished individuals with 53% of individuals at baseline
presenting with malnutrition based on the PG-SGA versus 21% by visit 4 (p<0.01, Table 5-2).
There was also a significant increase in energy intake over time with an average increase of 85
kilocalories per visit (p < 0.01).
5.3.2 Inflammatory status
CRP concentrations ranged widely from 0.5 to 144.2 mg/L prior to starting chemotherapy
suggesting a wide variability in the presence of an APR. Similarly, concentrations of the
cytokines, IL-6 and TNF-α, and serum albumin also varied widely at baseline. There were no
significant changes in markers of inflammation over time in all patients. Though CRP appeared
to be decreasing over time, this was not statistically significant (Table 5-3).
5.3.3 Fatty acid status
When considering the group as a whole, there was a significant increase in median
concentrations of LA, AA, EPA, DHA, total n-3, total n-6 and total plasma phospholipid FA
over the 4 study visits and a significant decrease in the n6 to n3 ratio over time (Table 5-4).
45
Table 5-2. Markers of nutritional status over time – All patients Baseline (Cycle 1)
n = 411 Cycle 2
n = 402 Cycle 3
n = 373 Cycle 4
n = 354 β p Weight (kg) 71.6 (48.1-105.3) 69.2 (46.0-105.0) 69.0 (47.9-104.0) 68.3 (49.4-103.5) -0.168 0.23
BMI (kg/m2) 24.9 (17.3-35.2) 24.2 (17.3-35.1) 24.7 (17.3-34.8) 24.6 (17.8-34.6) -0.063 0.22 PG-SGA global rating (A/B+C), n 18/20 22/17 28/7 27/7 -0.564 <0.001 PG-SGA score 7 (1-20) 6 (1-27) 5 (1-25) 4.5 (1-27) -0.481 0.086 Body composition
BIA Fat free mass (kg) 51.4 (36.9-75.5) 52.4 (37.9-75.5) 52.2 (38.0-75.0) 51.5 (38.2-74.5) 0.086 0.31
BIA % Body fat 24.9 (11.6-44.8) 22.7 (12.6-43.6) 22.9 (12.1-42.9) 23.0 (12.8-43.2) -0.106 0.23 FSA Fat free mass (kg) 51.6 (34.6-65.9) 49.7 (31.3-67.4) 49.8 (34.2-65.9) 48.4 (35.3-65.0) -0.039 0.69
FSA % Body fat 29.2 (12.6-42.6) 28.5 (15.6-43.3) 28.9 (15.6-41.9) 28.5 (15.6-42.6) 0.06 0.51 Arm muscle area (cm2) 39.1 (16.6-71.4) 38.3 (15.7-68.0) 38.2 (16.1-70.6) 39.7 (17.5-68.1) 0.033 0.9
Dietary intake Calories (kcal/day) 1771 (953-3296) 1711 (589.7-2974) 1987 (546.5-3096) 1903 (987-3838) 85.849 < 0.01 Protein (g/day) 78.1 (38.1-150.2) 69.8 (20.8-155.8) 84.7 (15.3-144.7) 81.8 (31.6-172.2) 2.382 0.068
Functional status Handgrip strength (kg2) 68.3 (34.2-120.0) 70.0 (33.3-128.3) 70.0 (38.3-118.3) 67.5 (31.7-120.0) 0.298 0.47 1 PG-SGA global rating and score, AMA, Handgrip strength, Self-reported functional status: n=38; BIA: n=34; FSA: n=36; Dietary intake: n=37 2 PG-SGA global rating and score, AMA, Dietary intake, Handgrip strength: n=39; BIA, FSA: n=38; Dietary intake: n=34 3 FSA, Handgrip strength: n=36; PG-SGA global rating and score, BIA: n=35; Dietary intake: n=33 4 PG-SGA global rating and score, BIA, AMA: n=34; FSA, Handgrip strength: n=33; Dietary intake: n=32
46
Table 5-3. Markers of inflammation over time – All patients
Baseline (Cycle 1) n = 391
Cycle 2 n = 392
Cycle 3 n = 373
Cycle 4 n = 354 β p
Serum albumin (g/L) 39.0 (17.0-48.0) 37.5 (27.0-44.0) 38.0 (16.0-42.0) 38.0 (14.0-44.0) -0.301 0.28 CRP (mg/L) 5.0 (0.5-144.2) 5.4 (0.3-76.0) 3.4 (0.4-33.3) 2.9 (0.3-163.6) -1.715 0.29 IL-6 (pg/mL) 4.3 (0.8-64.2) 4.5 (0.9-47.7) 4.5 (0.3-28.2) 3.4 (0.4-73.2) -0.565 0.45 TNF-a (pg/mL) 2.4 (0-17.1) 2.2 (0-16.5) 3.5 (0.7-13.4) 2.6 (0-17.7) -0.001 1 Median; range in brackets (all such values)
1 CRP, TNF-a: n=38 2 Serum albumin, TNF-a: n=38 3 CRP: n=36; Serum albumin: n=35; TNF-a: n=33 4 CRP: n=33; Serum albumin, TNF-a: n=32
47
Table 5-4. Markers of plasma phospholipid fatty acid status over time – All patients
Baseline (Cycle 1) n = 39
Cycle 2 n = 39
Cycle 3 n = 37
Cycle 4 n = 35 β p
18:3 (n-3) (ALA) 5.8 (1.9-69.4) 7.5 (2.7-15.0) 8.0 (4.1-19.5) 8.8 (2.6-22.9) 0.58 0.17 18:2 (n-6) (LA) 509.5 (283.0-891.5) 568.8 (274.1-973.3) 581.8 (360.3-1016.0) 672.4 (260.9-1039) 37.701 <0.001 20:4 (n-6) (AA) 238.4 (126.6-676.0) 244.2 (96.4-555.2) 260.4 (103.7-669.8) 273.9 (94.2-592.4) 8.303 0.018 20:5 (n-3) (EPA) 21.1 (7.6-138.6) 26.1 (6.8-154.0) 31.5 (8.2-95.7) 31.8 (11.0-84.9) 3.024 0.018 20:6 (n-3) (DHA) 68.1 (33.9-176.9) 79.7 (13.7-242.3) 81.7 (13.3-234.5) 84.3 (11.0-225.6) 2.946 0.017 Total n-31 191.2 (96.1-408.5) 215.8 (86.1-526.5) 254.0 (100.8-483.1) 260.0 (103.2-502.4) 19.495 <0.001 Total n-62 783.7 (475.9-1580) 836.1 (442.7-1374) 889.8 (507.1-1498) 996.2 (414.0-1418.0) 48.276 <0.001 n-6/n-3 4.1 (2.0-3.2) 3.6 (1.8-5.7) 3.6 (2.2-5.7) 3.5 (2.3-5.5) -0.103 <0.01 Total nmol/mL 2764.8 (1531.2-5313.4) 2959.0 (1635.0-4579.7) 3090.4 (1778.9-5478.0) 3334.1 (1548.6-5094.4) 171.34 <0.001 Median; range in brackets (all such values)
1 Total n-3 PUFAs represent the sum of alpha-linolenic acid, eicosatrienoic acid, eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid. 2 Total n-6 PUFAs represent the sum of linoleic acid, gamma-linolenic acid, eicosadienoic acid, arachidonic acid, adrenic acid and docosapentaenoic acid.
48
5.4 Interrelationships between nutritional, inflammatory and fatty acid status over time – all patients
To examine the influence of inflammation and FA status on changes in nutritional status, a
multivariate analysis focusing on weight, FFM as measured by BIA, and FFM as measured by
FSA as outcomes of nutritional status was completed. These variables were chosen as weight and
FFM are commonly reported in the literature as markers of nutritional status. We also chose to
investigate nutritional risk as an outcome, as measured by the PG-SGA score, given that we were
interested in examining factors affecting nutritional risk. The variables included in each model
were age, sex, tumour stage, diagnosis, calorie intake and protein intake. CRP, IL-6, and TNF-α
were included in the models as markers of inflammatory status. Finally, plasma phospholipid
concentrations of EPA, DHA, AA, total n-3, and total n-6 were selected to examine the influence
of FA status based on the literature demonstrating a potential role for these FA in influencing
nutritional status by modulating inflammation.
5.4.1 Weight
Following backwards selection, the best model for weight included sex, plasma phospholipid
concentrations of DHA and total n-3 (Table 5-5). Females on average weighed 11.8 kg less than
males after adjusting for visit, DHA, and total n-3 FA. There was a positive association between
weight and total n-3 with a 0.02 kg increase for every nmol/mL change in total n-3 (p<0.01), and
a negative association between weight and DHA (β = -0.05, p < 0.01). After adjusting for these
variables (holding sex, DHA and n-3 FA constant), there was a significant decrease in weight
over time by an average of 0.36 kg per visit (p=0.019), when considering all patients together.
Table 5-5. Multivariate model for weight – all patients Variable β SE p
(Intercept) 75.64 2.67 Time (visit) -0.36 0.15 0.02 Sex1 -11.76 3.86 p < 0.01 Plasma DHA -0.05 0.02 p < 0.01 Plasma Total n-3 0.02 0.01 p < 0.001 Linear mixed effects model using a backward selection algorithm including age, sex, tumour stage, diagnosis, total calories/day, total protein/day, CRP, IL-6, TNF-α, and plasma concentrations of EPA, DHA, AA, total n-3 and total n-6.
1 Male = reference
49
5.4.2 Fat free mass as measured by BIA and FSA
The best model for FFM as measured by FSA included sex and IL-6 (Table 5-6). The model for
FFM as measured by BIA included sex, IL-6, TNF-α, plasma AA, and plasma total n-6 (Table 5-
7). With respect the inflammatory markers, there was a significant positive association between
FFM and IL-6 as measured by both BIA (β = 0.032, p < 0.01) and FSA (β = 0.04, p < 0.01), and a
significant positive association between FFM as measured by BIA and TNF-α (β = 0.076, p =
0.04). In terms of the influence of FA, there was a significant negative association between
plasma AA and FFM (BIA) with a 0.010 kg decrease in FFM for every nmol/mL increase in AA
(p < 0.01). Conversely, there was a positive association between plasma total n-6 and FFM (BIA,
β = 0.003, p = 0.02). Holding sex and IL-6 constant, there was no significant change in FFM as
measured by FSA over time (β = -0.01, p = 0.94). Similarly, for FFM as measured by BIA, after
controlling for sex, IL-6, TNF-α, and plasma phospholipid concentrations of AA and total n-6,
there was not enough evidence to suggest a change in FFM over time (β = 0.036, p = 0.68).
Table 5-6. Multivariate model for FSA fat free mass – all patients Variable β SE p
(Intercept) 55.38 1.36 Time (visit) -0.01 0.09 0.94 Sex -14.85 2.04 p < 0.001 Il-6 0.04 0.01 p < 0.01 Linear mixed effects model as described in Table 5-5.
1 Male = reference
Table 5-7. Multivariate model for BIA fat free mass – all patients Variable β SE p
(Intercept) 59.45 1.53 Time (visit) 0.04 0.09 0.68 Sex1 -16.58 2.15 p < 0.001 Il-6 0.03 0.01 p < 0.01 TNF-α 0.08 0.04 0.04 Plasma AA -0.01 0.004 p < 0.01 Plasma Total n-6 0.003 0.001 0.02 Linear mixed effects model as described in Table 5-5.
1 Male = reference
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5.4.3 Nutritional risk
The best model for nutritional risk as measured by the PG-SGA score included diagnosis, total
protein intake, IL-6 and plasma phospholipid concentrations of total n-3 FA (Table 5-8). CRC
patients on average had lower nutritional risk scores versus patients with gastric cancer (β =-
4.562, p<0.01). There was also a significant negative relationship between nutritional risk and
protein intake though this was not likely clinically significant with a 0.04 point decrease in
nutritional risk for every gram increase in protein intake (p = 0.02). With respect to inflammatory
markers, there was a significant positive relationship between nutritional risk and IL-6 (β = 0.08,
p = 0.03) concentrations. Lastly, there was a significant negative relationship between nutritional
risk and plasma phospholipid FA concentrations of total n-3 (β = -0.01, p = 0.04). After adjusting
for these variables however, there was no significant change in nutritional risk over time (β =
0.22, p = 0.59).
Table 5-8. Multivariate model for PG-SGA score – all patients Variable β SE p
(Intercept) 16.40 2.36 Time (visit) -0.16 0.29 0.58 Diagnosis1 -4.65 1.54 p < 0.01 Protein intake (g/day) -0.04 0.02 0.02 Il-6 0.08 0.04 0.03 Plasma Total n-3 -0.01 0,01 0.04 Linear mixed effects model as described in Table 5-5.
1 Gastric cancer = reference
5.5 The influence of tumour presence on changes in nutritional status prior to and during chemotherapy
5.5.1 Patient characteristics prior to starting chemotherapy
There was a significant difference in the proportion of patients with gastric cancer versus CRC in
the resected versus non-resected groups (p < 0.01, Table 5.1). Additionally, the non-resected
group had a significantly higher proportion of patients with advanced disease (p < 0.01). While
patients in the resected group had mostly Stage II or Stage III disease, 92% of patients within the
non-resected group had stage IV disease. In the non-resected group, 18 patients with Stage IV
51
disease were receiving palliative chemotherapy, and 7 patients were receiving peri-operative
chemotherapy. With respect to metastatic disease, 60% of patients with non-resected disease had
one site of metastasis and 32% of patients had 2 sites of metastases (data not shown).
5.5.2 The influence of tumour presence on nutritional status
Prior to starting chemotherapy, there was a significantly higher number of malnourished patients
in the non-resected group (14 versus 4, p=0.013, in the non-resected versus resected groups,
respectively). Patients in the non-resected group were also at higher nutritional risk based on the
PG-SGA score, with a higher score indicating a greater need for nutritional intervention (9.5
versus 5, p=0.019), non-resected versus resected, respectively. The difference in nutritional risk
was only apparent with the exclusion of patients reporting fish oil supplement use (data not
shown). Median weight loss prior to starting treatment did not differ significantly between the
two groups (3.3 versus 5.1%, p = 0.082, resected versus non-resected, respectively). There was
no significant difference in median energy or protein intake between the two groups (Table 5-1).
There was no significant difference in FFM as measured by BIA or FSA, however there were
significant differences in percent body fat. Percent body fat from BIA was significantly lower at
baseline in the non-resected group versus the resected group (median 22.3 versus 28.3, p=0.038,
respectively). Percent body fat derived from FSA was significantly lower at baseline in the non-
resected group versus the resected group (median 27.2 versus 35.0, p=0.014, Table 5-1).
Over the course of chemotherapy, there was a significant interaction between tumour presence
and time for weight, BMI, FFM (FSA) and AMA (p < 0.05), and tumour presence and changes
in FFM (BIA, p < 0.01), indicating that the change in these variables over time were dependent
on whether patients had resected or non-resected disease. In patients with resected disease,
weight and BMI were stable during chemotherapy, while these nutritional parameters
significantly decreased in patients with non-resected disease (weight: β = -0.511, p < 0.01; BMI:
β = -0.186, p < 0.01; Tables 5-9 and 5-9.1). In terms of body composition, patients with resected
disease had a significant increase in muscle mass (BIA: β = 0.306, p < 0.01; FSA: 0.26, p = 0.02;
AMA: β = 1.131, p = 0.02) over the course of chemotherapy, while patients with non-resected
disease experienced a decrease in muscle mass (AMA: β = -0.773, p < 0.01). Average calories
consumed per day significantly increased over time in both groups (resected: β = 78.95, p = 0.02;
52
Table 5-9. Markers of nutritional and functional status over time – Resected Baseline (Cycle 1)
n = 161 Cycle 2
n = 162 Cycle 3
n = 153 Cycle 4
n = 154 β p Weight (kg) 65.7 (48.1-105.3) 66.7 (47.9-105.0) 68.6 (47.9-104.0) 69.5 (49.4-103.5) 0.301 0.12 BMI (kg/m2) 26.6 (17.3-35.2) 26.9 (17.3-35.1) 28.3 (17.3-34.8) 27.9 (17.8-34.6) 0.103 0.15 PG-SGA global rating (A/B+C), n 10/5 13/3 14/1 13/2 -0.515 0.085 PG-SGA score 6.0 (1-15) 5.5 (1-24) 5.0 (1-14) 4.0 (2-12) -0.185 0.59 Body composition
BIA Fat free mass (kg) 48.0 (36.9-75.5) 50.4 (37.9-75.5) 50.4 (38.0-75.0) 50 (38.5-74.5) 0.306 <0.001 BIA % Body fat 28.3 (15.8-44.8) 26.8 (13.9-43.6) 28.9 (16.8-42.9) 28.0 (15.5-43.2) 0.04 0.7 FSA Fat free mass (kg) 48.5 (34.6-65.9) 48.8 (34.2-67.4) 49.3 (34.2-65.9) 48.5 (35.3-64.8) 0.26 0.021 FSA % Body fat 35.0 (20.8-41.9) 32.5 (22.9-43.3) 34.2 (20.8-41.5) 34.7 (22.9-41.2) 0.147 0.3 Arm muscle area (cm2) 36.4 (16.6-60.8) 38.8 (15.7-68.0) 38.8 (16.1-56.5) 41.1 (17.5-65.0) 1.131 0.016
Dietary intake Calories (kcal/day) 1820 (988.7-3078) 1713 (1295-2974) 2031 (1143-2837) 1969 (1159-3838) 78.95 0.022 Protein (g/day) 85.7 (39.1-150.2) 71.2 (48.4-155.8) 89 (26.9-144.7) 83 (31.6-172.2) 2.342 0.24
Functional status Handgrip strength (kg2) 58.3 (34.2-93.3) 61.7 (40.0-91.7) 63.3 (41.7-111.7) 60.8 (35.0-115.0) 1.227 0.081
Median (range), and frequencies, linear mixed effects models with random intercept. Abbreviations are as in Table 5-1. 1 PG-SGA, AMA, Dietary intake, Handgrip strength: n=15; FSA: n =14; BIA: n=13 2 FSA, Dietary intake: n=15 3 BIA, FSA, Dietary intake: n=14 4 FSA, Handgrip strength: n=14
53
Table 5-9.1. Markers of nutritional and functional status over time – Non-resected Baseline (Cycle 1)
n = 251 Cycle 2
n = 242 Cycle 3
n = 223 Cycle 4
n = 204 β p Weight (kg) 71.7 (53.2-91.1) 69.5 (46.0-87.8) 69.2 (54.4-88.2) 68.2 (50.4-84.9) -0.511 <0.01 BMI (kg/m2) 24.6 (20.3-32.1) 24.1 (19.6-31.1) 24.2 (20.0-31.2) 24.0 (19.9-28.7) -0.186 <0.001 PG-SGA global rating (A/B+C), n 8/15 9/14 14/6 14/5 -0.636 <0.01 PG-SGA score 8 (1-20) 7 (1-27) 4.5 (1-25) 5.0 (1-27) -0.684 0.1 Body composition
BIA Fat free mass (kg) 56.5 (37.5-68.9) 56.3 (38.8-68.0) 55.5 (38.8-67.7) 52.5 (38.2-66.4) -0.073 0.58 BIA % Body fat 22.0 (11.6-40.5) 20.6 (12.6-41.0) 20.9 (12.1-40.6) 20.5 (12.8-39.8) -0.21 0.11 FSA Fat free mass (kg) 53.8 (37.2-65.7) 52.1 (31.3-63.6) 50.9 (36.2-64.8) 48.0 (37.0-65.0) -0.246 0.079 FSA % Body fat 27.2 (12.6-42.6) 27.9 (15.6-41.2) 26.5 (15.6-41.9) 24.7 (15.6-42.6) -0.002 0.99 Arm muscle area (cm2) 39.9 (22.9-71.4) 37.0 (23.2-64.1) 37.3 (22.2-70.6) 36.0 (22.7-68.1) -0.773 <0.01
Dietary intake Calories (kcal/day) 1601 (953-3296) 1708 (589.7-2847) 1801 (546.5-3096) 1890 (987-3588) 91.144 0.027 Protein (g/day) 74.4 (38.1-136.2) 66.5 (20.8-146.4) 79.2 (15.3-140.7) 78.7 (43.6-153.3) 2.349 0.18
Functional status Handgrip strength (kg2) 76.7 (35.0-120.0) 73.3 (33.3-128.3) 71.7 (38.3-118.3) 68.3 (31.7-120.0) -0.351 0.47
Median (range), and frequencies, linear mixed effects models with random intercept. Abbreviations are as in Table 5-1. 1 PG-SGA global rating and score, AMA, Handgrip strength: n=23; BIA: n=21; FSA, Dietary intake: n=22 2 PG-SGA global rating and score, FSA, AMA, Handgrip strength, Self-reported functional status: n=23; BIA: n=22; Dietary intake: n=19 3 BIA, Handgrip strength: n=21, PG-SGA global rating and score: n= 20; Dietary intake: n=19 4 PG-SGA global rating and score, BIA, FSA, AMA, Handgrip strength: n=19; Dietary intake: n=17
54
non-resected: β = 91.1, p = 0.03). While the macronutrient distribution of the diet did not change
in the resected group, there was a significant increase in the proportion of carbohydrate in the
diet in the non-resected group (β = 2.32, p < 0.01). Finally, in the non-resected group, there was
a significant change in the ratio of well-nourished to malnourished patients based on the PG-
SGA global rating with a significant decrease in the number of malnourished patients over time
(p<0.01).
5.5.3 The influence of tumour presence on inflammatory status
Prior to chemotherapy, patients with non-resected disease had significantly higher median levels
of CRP versus those with resected disease (11.5 versus 2.2 mg/L, p < 0.01) suggesting the
presence of an APR (CRP > 10 mg/L) in the non-resected group (Table 5-1). Similarly, there was
also a significant difference between groups for IL-6 with a higher median level in the non-
resected group compared with the resected group (5.2 versus 3.4 pg/mL, respectively). There
were no significant differences in serum albumin or TNF-α between the groups (Table 5-1). Of
note, these results were not altered with the exclusion of patients reporting fish oil and flax oil
use.
Over the course of chemotherapy, there were no significant interactions between tumour
presence and time for markers of inflammation indicating that change in these variables over
time were not influenced by whether patients had resected versus non-resected disease.
Consistent with this finding, during chemotherapy, plasma concentrations of albumin, CRP, IL-6
and TNF-α did not change over time in either group (Tables 5-10 and 5-10.1). It is interesting to
note, however, that the ranges for inflammatory markers varied widely in the non-resected group.
While there was no significant change in median CRP over time, the maximum value was lower
at cycle 2 and cycle 3, and increased again at cycle 4, and the median dropped to < 10 mg/L by
visit 3 and visit 4. A similar trend in the range of values was observed with IL-6 (Table 5-10.1).
5.5.4 The influence of tumour presence on fatty acid status
Prior to chemotherapy, patients with non-resected disease had significantly lower levels of ALA
(4.6 versus 6.7 nmol/L, p=0.03) and LA (451.6 versus 597.7 nmol/L, p < 0.01). Total n-6 and
total plasma phospholipid FA levels were also significantly lower in patients with non-resected
versus resected disease. There were no significant differences in levels of AA, EPA,
55
Table 5-10. Markers of inflammation over time – Resected Baseline (Cycle 1)
n = 15 Cycle 2
n = 161 Cycle 3
n = 152 Cycle 4
n = 153 β p Serum albumin (g/L) 40.0 (34.0-48.0) 37.0 (36-44) 39.0 (35-42) 38.0 (35-42) -0.553 0.05 CRP (mg/L) 2.2 (0.5-33.6) 2.4 (0.3-21.9) 3.4 (0.4-31.5) 2.7 (0.3-23.5) -0.17 0.77 IL-6 (pg/mL) 3.4 (0.8-6.3) 3.8 (1.1-14.5) 2.6 (1.1-8.1) 3.1 (1.2-9.1) 0.055 0.83 TNF-a (pg/mL) 2.0 (0-6.7) 1.4 (0-5.5) 2.5 (0.7-13.4) 3.4 (0-7.1) 0.37 0.19 Median (range), linear mixed effects models with random intercept. Abbreviations are as in Table 5-1.
1 Serum albumin: n=15 2 Serum albumin: n=14, TNF-a: n=13 3 CRP: n=14; Serum albumin, TNF-a: n=13
Table 5-10.1. Markers of inflammation over time – Non-resected Baseline (Cycle 1)
n = 241 Cycle 2
n = 232 Cycle 3
n = 223 Cycle 4
n = 204 β p Serum albumin (g/L) 38.0 (17.0-45.0) 38.0 (27-42) 38.0 (16-42) 37.0 (14-44) -0.164 0.7 CRP (mg/L) 11.5 (0.7-144.2) 15.3 (0.4-76.0) 3.3 (0.4-33.3) 5.8 (0.3-163.6) -2.885 0.29 IL-6 (pg/mL) 5.2 (1.3-64.2) 7.9 (0.9-47.7) 6.0 (0.3-28.2) 7.2 (0.4-73.2) -0.943 0.45 TNF-a (pg/mL) 2.5 (0-17.1) 3.0 (0.4-16.5) 3.8 (0.7-10.9) 2.5 (0.4-17.7) -0.242 0.45 Median (range), linear mixed effects models with random intercept. Abbreviations are as in Table 5-1.
1 CRP, TNF-a: n=23 2 TNF-a: n=22 3 Serum albumin, CRP: n=21; TNF-a: n=20 4 Serum albumin, CRP, TNF-a: n=19
56
DHA, total n-3, and the n6 to n3 ratio between patients with resected versus non-resected disease
at baseline (Table 5-1). After exclusion of patients with reported fish oil or flax oil use, there was
also a significant difference in plasma phospholipid concentrations of AA and total n-3 with
lower levels in patients with non-resected disease (data not shown).
Similar to inflammation, there were no significant interactions between tumour presence and
time for markers of plasma PL fatty acid status over the course of chemotherapy indicating that
changes in fatty acid status did not depend on whether patients had resected versus non-resected
disease. Changes in plasma PL FA concentrations are summarized in Tables 5-11 and 5-11.1. FA
status appeared to improve in both groups though changes in specific FA differed. In the resected
group, there was a significant increase in plasma phospholipid concentrations of ALA (β = 1.10,
p < 0.01), LA (β = 40.8, p < 0.01), AA (β = 11.2, p = 0.03), total n-3 (β = 25.0, p < 0.01), total n-
6 (β = 55.5, p < 0.01), and total FA (β = 210.8, p < 0.01), while there was a significant decrease
in the n-6 to n-3 FA ratio (β = -0.158, p < 0.01). There were fewer significant improvements in
the non-resected group with significant increases in only LA (β = 35.3, p < 0.01), total n-3 (β =
15.43, p < 0.01), total n-6 (β = 43.03, p < 0.01), and total FA (β = 142.5, p < 0.01). Changes in
plasma phospholipid concentrations of EPA and DHA in the resected group were 3.6 and 3.5
nmol/mL per unit of time, respectively, however this did not reach statistical significance (p =
0.055 and 0.062, for EPA and DHA, respectively).
5.6 The influence of tumour presence on interrelationships between nutritional, inflammatory and fatty acid status over time
The multivariate models for weight, FFM as measured by both FSA and BIA, and nutritional risk
as measured by PG-SGA were adjusted for the interaction between tumour presence and visit
(Tables 5.12 to 5.15). Figure 5-2 shows the predicted weight, FFM (FSA and BIA) and
nutritional risk based on PG-SGA score using the average values for relevant covariates. After
adjusting for the variables sex, DHA and total n-3, there was a significant association between
time and tumour presence for weight (p < 0.001), indicating that the relationship over time
significantly differs depending on whether a patient has had their tumour resected or not (Table
5.12). Patients with resected disease show an increase in weight over time while those with non-
resected disease show a decrease in weight over time (Figure 5-2a).
57
Table 5-11. Markers of plasma phospholipid fatty acid status over time – Resected Plasma phospholipid fatty
acids (nmol/mL) Baseline (Cycle 1)
n = 15 Cycle 2
n = 16 Cycle 3
n = 15 Cycle 4
n = 15 β p 18:3 (n-3) (ALA) 6.7 (4.0-11.4) 7.9 (2.7-15.0) 9.7 (6.0-17.8) 9.0 (3.0-22.9) 1.104 <0.01 18:2 (n-6) (LA) 597.7 (461.8 – 730.4) 622.6 (274.1-886.1) 705.8 (519.1-887.1) 771.7 (489.8-1039) 40.782 <0.01 20:4 (n-6) (AA) 283.2 (167.0-371.9) 269.8 (120.9-379.1) 279.5 (175.8-423.9) 298.5 (222.3-437.9) 11.241 0.034 20:5 (n-3) (EPA) 24.8 (9.8-36.5) 26.9 (9.2-154.0) 32.0 (13.7-88.8) 31.8 (14.6-82.8) 3.859 0.055 22:6 (n-3) (DHA) 66.6 (42.7-127.1) 65.1 (25.2-242.3) 78.9 (49.7-234.5) 84.0 (39.8-209.0) 3.846 0.062 Total n-31 233.4 (149.4-315.1) 237.2 (126.1-526.5) 260.4 (133.7-483.1) 281.6 (215.1-502.4) 25.006 <0.01 Total n-62 936.8 (651.8-1102) 908.8 (442.7-1311) 1012.7 (761.5-1305) 1082 (794.3-1403.0) 55.522 <0.01 n-6/n-3 3.8 (3.0-5.3) 3.5 (1.8-5.7) 3.6 (2.6-5.7) 3.5 (2.6-4.6) -0.158 <0.01 Total nmol/mL 2941 (2204-3706) 3242 (2084.5-4479) 3302 (2134-4661) 3510 (2566-5094) 210.781 <0.01 Median (range), linear mixed effects models with random intercept. Abbreviations are as in Table 5-1.
1 Total n-3 PUFAs represent the sum of alpha-linolenic acid, eicosatrienoic acid, eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid. 2 Total n-6 PUFAs represent the sum of linoleic acid, gamma-linolenic acid, eicosadienoic acid, arachidonic acid, adrenic acid and docosapentaenoic acid.
Table 5-11.1. Markers of plasma phospholipid fatty acid status over time – Non-resected Plasma phospholipid fatty
acids (nmol/mL) Baseline (Cycle 1)
n = 24 Cycle 2
n = 23 Cycle 3
n = 22 Cycle 4
n = 20 β p 18:3 (n-3) (ALA) 4.6 (1.9-69.4) 7.5 (2.9-13.5) 7.7 (4.1-19.5) 8.7 (2.6-20.2) 0.21 0.76 18:2 (n-6) (LA) 451.6 (283.0-891.5) 503.5 (304.4-973.3) 554.2 (360.3-1016) 604.7 (260.9-944.8) 35.303 <0.01 20:4 (n-6) (AA) 227.9 (126.6-676.0) 231.4 (96.4-555.2) 220.7 (103.7-669.8) 228.7 (94.2-592.4) 6.387 0.18 20:5 (n-3) (EPA) 19.6 (7.6-138.6) 26.1 (6.8-89.7) 31.3 (8.2-95.7) 35.6 (11.0-84.9) 2.383 0.15 22:6 (n-3) (DHA) 69.4 (33.9-176.9) 86.5 (13.7-132.9) 87.3 (13.3-202.1) 85.1 (11.0-225.6) 2.291 0.14 Total n-31 171.2 (96.1-408.5) 199.9 (86.1-350.3) 223.6 (100.8-444.1) 243.9 (103.2-496.2) 15.434 <0.01 Total n-62 696.4 (475.9-1580) 766.1 (460.5-1374) 816.8 (507.1-1498) 874.7 (414.0-1418) 43.03 <0.01 n-6/n-3 4.1 (2.0-5.2) 3.8 (2.4-5.4) 3.6 (2.2-5.3) 3.5 (2.3-5.5) -0.062 0.23 Total nmol/mL 2335 (1531-5313) 2734 (1635-4579) 2979 (1779-5478) 3085 (1549-4965) 142.53 <0.01 Median (range), linear mixed effects models with random intercept. Abbreviations are as in Table 5-1.
1 Total n-3 PUFAs represent the sum of alpha-linolenic acid, eicosatrienoic acid, eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid. 2 Total n-6 PUFAs represent the sum of linoleic acid, gamma-linolenic acid, eicosadienoic acid, arachidonic acid, adrenic acid and docosapentaenoic acid.
58
Similarly, there was a significant interaction between time and tumour presence with FFM as
measured by FSA (Table 5.13), adjusting for sex and IL-6, with FFM decreasing over time in
those with non-resected disease (Figure 5-2b). Consistent with FFM measured by FSA, there was
a significant interaction between time and tumour presence for FFM measured by BIA after
adjusting for sex, IL-6, TNFα, plasma concentrations of AA, and total n-6 (Table 5.14). Again,
predicted FFM increases in the resected group and decreases in the non-resected group (Figure 5-
2c).
There was no evidence to suggest that change in nutritional risk over time as measured by the
PG-SGA score differed depending on whether the patient had their tumour resected or not (Table
5.15, Figure 5-2d).
A sensitivity analysis was also conducted with the removal of patients reporting fish oil or flax
oil use. The results of the multivariate analysis with adjustment for the interaction between
tumour presence and time remained unchanged for weight. The interaction between tumour
presence and time for FFM (BIA) became borderline significant, and the interaction between
tumour presence time for FFM (FSA) was no longer significant (Appendix 8.4).
Table 5-12. Multivariate model for weight with tumour interaction Variable β SE p
(Intercept) 77.78 3.85 Time (visit) 0.24 0.21 0.25 Tumour presence1 -2.45 4.02 0.55 Sex2 -12.63 3.91 p < 0.01 Plasma DHA -0.04 0.02 p < 0.01 Plasma Total n-3 0.02 0.01 p < 0.01 Time*Tumour presence -0.94 0.24 p < 0.001 Linear mixed effects model using a backward selection algorithm including age, sex, tumour stage, diagnosis, total calories/day, total protein/day, CRP, IL-6, TNF-α, and plasma concentrations of EPA, DHA, AA, total n-3 and total n-6, with an adjustment for the interaction between tumour presence and time (visit).
1 Resected = reference 2 Male = reference
59
Table 5-13. Multivariate model for FSA fat free mass with tumour interaction
Variable β SE p (Intercept) 55.43 2.04 Time (visit) 0.26 0.14 0.07 Tumour presence1 0.15 2.19 0.95 Sex2 -14.98 2.10 p < 0.001 Il-6 0.03 0.01 0.02 Time*Tumour presence -0.46 0.18 0.015 Linear mixed effects model as described in Table 5-12.
1 Resected = reference 2 Male = reference
Table 5-14. Multivariate model for BIA fat free mass with tumour interaction
Variable β SE p (Intercept) 60.31 2.20 Time (visit) 0.23 0.13 0.07 Tumour presence1 -0.93 2.28 0.69 Sex2 -16.93 2.22 p < 0.001 Il-6 0.03 0.01 0.02 TNF-α 0.06 0.04 0.07 Plasma AA -0.01 0.004 p < 0.01 Plasma Total n-6 0.003 0.001 0.01 Time*Tumour presence -0.34 0.16 0.04 Linear mixed effects model as described in Table 5-12.
1 Resected = reference 2 Male = reference
Table 5-15. Multivariate model for PG-SGA score with tumour interaction
Variable β SE p (Intercept) 18.29 2.97 Time (visit) 0.22 0.41 0.59 Tumour presence1 -0.75 2.06 0.72 Diagnosis2 -5.57 1.70 p < 0.01 Protein intake (g/day) -0.04 0.02 0.01 Il-6 0.08 0.04 0.04 Plasma Total n-3 -0.01 0,01 0.01 Time*Tumour presence -0.62 0.53 0,24 Linear mixed effects model as described in Table 5-12.
1 Resected = reference 2 Gastric cancer = reference
60
Figure 5-2. Predicted markers of nutritional status by tumour presence
Predicted markers of nutritional status by tumour presence for an average patient using the linear
mixed effects multivariate models (Tables 5.12 to 5.15). Predicted weight (a) for a male patient
with DHA = 85 nmol/mL and total n-3 = 250 nmol/mL; predicted FFM as measured by FSA (b)
for a male patient with an IL-6 = 9 pg/mL; predicted FFM as measured by BIA (c) for a male
patient with an IL-6 = 9 pg/mL, TNF-α = 3.6 pg/mL, AA = 264 nmol/mL, and total n-6 = 887
nmol/mL; and nutritional risk as measured by PG-SGA score (d) for a male patient with
colorectal cancer, IL-6 = 9 pg/mL, AA = 264 nmol/mL, and protein intake of 83 grams/day.
Abbreviations: DHA, docosahexaenoic acid; FFM, fat free mass; FSA, four-site skinfold
anthropometry; BIA, bioelectrical impedance analysis, AA, arachidonic acid; PG-SGA, patient-
generated subjective global assessment.
a) b)
c) d)
61
Discussion
6.1 Changes in nutritional, inflammatory and fatty acid status during chemotherapy
Patients with GI often present with poor nutritional status and are at risk for further decline in
nutritional status during treatment. Identification of factors affecting change in nutritional status
are important for developing interventions to address cancer-related weight loss and
malnutrition. In the present study, we described changes in nutritional, inflammatory, and FA
status over time in a cohort of patients with gastric cancer and CRC prior to and during first-line
chemotherapy. We also examined the relationship between nutritional status and changes in
inflammatory and FA status. Finally, we looked at the influence of tumour presence on changes
in nutritional, inflammatory and FA status over time and the impact of tumour presence on the
relationship of nutritional status with inflammatory and FA status indicators.
In examining all patients prior to chemotherapy, 53% of patients presented with moderate or
severe malnutrition based on a PG-SGA global rating of B or C. The median PG-SGA score was
7 (range 1-20) suggesting nutritional risk requiring intervention by a dietitian. These results are
consistent with a study examining a group of CRC patients prior to second-line chemotherapy in
which 52% of patients presented with malnutrition (PG-SGA B or C), and a mean PG-SGA score
of 7.5 ± 6.4 (Read et al., 2007). During chemotherapy, based on the univariate (unadjusted)
analysis, we found that most indices of nutritional status did not change over time apart from
nutritional status as measured by PG-SGA and caloric intake. Contrary to our expectations,
weight and FFM remained stable over time, and there was a significant improvement the ratio of
malnourished to well-nourished patients and a significant increase in caloric intake over time.
The improvement in nutritional status and caloric intake may be related changes in the impact of
tumour burden on nutritional status and/or to the effects of intensive nutrition intervention during
the study of which will be discussed further in sections 6.3 and 6.4.
CRP levels varied greatly prior to starting chemotherapy suggesting a wide variability in the
presence of an APR in this group of patients which has been similarly found in patients with
gastric and CRC at various time points in the disease trajectory (McMillan et al., 2001). There
were no significant changes in markers of inflammation over time. Previous studies have
demonstrated increasing CRP concentrations concurrent with loss of body cell mass (McMillan
62
et al., 1998), and weight loss (O’Gorman et al., 1999) over time, whereas in our group, weight
and FFM appeared stable when all patients were considered.
Unexpectedly, we observed an increase in concentrations of plasma phospholipid FA in all
patients during chemotherapy. Increasing PUFAs in plasma phospholipids was also observed in a
group of males with squamous esophageal cancer following chemotherapy and radiation with FA
profiles reaching levels of healthy controls (Zemanova et al., 2016). This is in contrast to the
findings of Pratt et al (2002) in which high dose chemotherapy was associated with depletion of
FA in a small group of breast cancer patients (Pratt et al., 2002). Potential explanations for this
finding will be discussed further in section 6.3.
6.2 Interrelationships between nutritional, inflammatory and fatty acid status over time
The multivariate analysis considering all patients demonstrated a positive association between
weight and total n-3 FA, holding sex, DHA, and time constant (Table 5-5). Several studies
involving n-3 supplementation either through fish oil or an enriched oral nutrition supplement
have shown an association between n-3 FA and weight stabilization or weight gain (Barber et al.,
1999b; Pratt et al., 2002; Read et al., 2007; Wigmore et al., 2000). We also observed a significant
negative association between plasma phospholipid concentrations of AA and FFM measured by
BIA. In the study by Read et al (2007), an EPA-enriched supplement was associated with an
increase in weight, maintenance of LBM concurrent with an increase in EPA, DHA and a
decrease in AA (Read et al., 2007).
Interestingly, our study found a significant positive relationship between FFM and plasma
concentrations of IL-6 and TNF-α. This is in contrast with our original hypothesis and with other
studies that have shown an association between decreased muscle mass and high levels of IL-6
(Guthrie et al., 2013; Miura et al., 2015). The measurement of FFM by BIA would not
discriminate between skeletal muscle and non-functional FFM such as tumour and liver mass. As
such, it is conceivable that a positive relationship between FFM and IL-6 could be influenced by
increasing tumour or liver mass (Mourtzakis et al., 2008). Higher levels of IL-6 have been
associated with greater disease burden (Guthrie et al., 2013; Ikeguchi et al., 2009; Miura et al.,
2015; Mouawad et al., 1996). We were unable to verify the response to chemotherapy and
changes in tumour size or disease burden in this study. This relationship however also held for
63
FFM as measured by FSA which unlike BIA, would not have been influenced by tumour mass.
Another possible explanation is a difference in the type of inflammation. While the negative
effects of cytokines on nutritional status are commonly reported in the literature, IL-6 and TNF-α
are also involved in the immune response and tumour cell death (Galli and Calder, 2009;
Meydani and Dinarello, 1993; Mocellin et al., 2016). This type of beneficial inflammation as a
normal host response is different in nature than inflammation that arises the tumour itself. In
established tumours, inflammation supports angiogenesis, tumour progression and metastatic
spread and is the more predominant type of inflammation (Grivennikov et al., 2010; Mocellin et
al., 2016). In our study, it is a possibility that as patients responded to treatment, decreased
tumour burden could also decrease the influence of the tumour-driven inflammation on muscle
breakdown. Thus, while cytokines are increasing in the context of a treatment-related APR,
reduced tumour burden overall leads to a net effect of muscle stability or repletion. This
hypothesis however, requires further study.
In examining nutritional risk as an outcome, we found that while there were no changes in
nutritional risk over time as measured by the PG-SGA score, nutritional risk was significantly
associated with diagnosis with a higher score among gastric cancer patients versus CRC patients.
It has been shown that there is a higher prevalence of weight loss prior to chemotherapy in non-
colorectal GI cancers and a high prevalence of malnutrition during treatment (Attar et al., 2012;
Dewys et al., 1980).
6.3 The influence of tumour presence on changes in nutritional, inflammatory and fatty acid status
Based on baseline characteristics of patients with respect to nutritional status, inflammation and
FA status and univariate analysis based on tumour presence, it is apparent that patients with non-
resected disease are distinct from patients with resected disease. Specifically, there did not
appear to be any significant changes in nutritional status over time in all patients when analyzed
together. There was however a significant interaction between tumour presence and time for
markers of nutritional status such as weight, BMI, FFM (BIA and FSA), AMA, and carbohydrate
intake meaning that the change in these variables over time was dependent on whether patients
had resected or non-resected disease. Of note, in the univariate analysis, there was no significant
64
interaction between tumour presence and time for markers of inflammatory status or FA status
suggesting that change in these variables over time are not dependent on tumour presence.
The most notable finding in this study was that change in nutritional status during chemotherapy
as measured by weight, was not significantly associated with changes in inflammation as
expected in our hypothesis. Change in nutritional status during chemotherapy appeared to be
driven by tumour presence based on the tumour presence and time interaction, with patients with
non-resected disease showing a decrease in weight over time, and those with resected disease
showing an increase in weight over time. Similarly changes in FFM over time, as measured by
BIA and FSA, also depended on tumour presence.
These results suggest that changes in nutritional status, specifically weight and FFM, in this
population of gastric cancer and CRC patients undergoing first-line chemotherapy, may be more
related to the influence of tumour versus the influence of changes in inflammation. Studies using
supplementation with fish oil alone or as part of a supplement have demonstrated improvement
in nutritional status as measured by weight or lean body mass without necessarily altering levels
of inflammation (Read et al., 2007; Silva et al., 2012; Wigmore et al., 1996, 2000). Most studies
have focused on patients further along in the disease trajectory, for example patients no longer
receiving anticancer therapy or patients who have already lost weight and may be in a refractory
cachexia stage. CRP increases and weight decreases with greater intensity as patients are nearer
to death compared to the time of diagnosis (Barber et al., 1999a). Thus, at later stages, it is likely
that inflammation has a greater impact on nutritional status. It is also possible that the influence
of n-3 FA on weight is not related to the modulation of inflammation in the absence of an
uncontrolled APR and may also differ depending on treatment response. This may explain both
the variable results in supplementation studies and the results in our observational study and
requires further study in this population. In a study by Stene et al (2015) half of patients with
advanced lung cancer had stable or increased muscle mass during palliative chemotherapy. The
majority of patients who experienced stable or increased muscle mass had achieved disease
control during chemotherapy as measured by CT scans before and after chemotherapy. It is also
worth noting that change in muscle mass during this study was not associated with baseline
inflammation as measured by CRP and albumin (Stene et al., 2015).
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Another notable and unexpected finding was the increase in FA over time in patients with both
resected and non-resected disease. Prior to chemotherapy, patients with non-resected disease had
lower plasma phospholipid concentrations of ALA, LA, total n-6 and total plasma phospholipid
FA compared to patients with resected disease at baseline. Lower plasma phospholipid
concentrations of PUFA have been found in both CRC and malnourished gastric cancer patients
(Baró et al., 1998; Mosconi et al., 1989). Furthermore, Murphy et al (2011) found lower total
phospholipid and FA alterations in NSCLC patients with advanced versus early disease (Murphy
et al., 2012). We did not analyze data according to advanced versus early disease, however there
was a significantly higher proportion of patients with stage IV disease in the non-resected group.
Examining change in FA over time, there is some evidence to suggest that change in FA profiles
during treatment are related to response to chemotherapy. A study in patients with non-
Hodgkin’s lymphoma undergoing chemotherapy found lower baseline levels of PUFA in patients
who did not complete chemotherapy versus those that completed chemotherapy and also found a
significant increase in PUFA at the end of chemotherapy in patients who completed
chemotherapy (Cvetković et al., 2013). Similarly, in NSCLC patients undergoing chemotherapy,
changes in plasma phospholipid FA differed between patients who completed chemotherapy and
those who did not due to disease progression or treatment-related toxicity. Patients with
progressive disease lost FA during treatment while patients who responded to chemotherapy with
reduced or stable disease maintained FA (Murphy et al., 2012). Proliferating tumour cells require
PUFA for the biosynthesis of membranes and signaling molecules which would limit the
incorporation of PUFA into lipoproteins (Currie et al., 2013). In our study, as the increase in
concentrations of plasma phospholipids FA appears more pronounced in the resected group
versus the non-resected group, it is possible that in the resected group, the absence of tumour
cells is enabling the repletion of FA status. Similarly, in the non-resected group, if patients are
responding to chemotherapy, there would be decreased tumour cell proliferation and use of
PUFA for membrane synthesis and cellular signaling, again allowing for repletion. While tumour
markers (CEA and CA 19-9) were measured at baseline which would reflect tumour burden, a
second set of data points coinciding with the end of the study were not available based on
standard of care for collection and analysis. Therefore, we were unable to determine response to
therapy. The study of non-Hodgkin’s lymphoma patients by Cvetković et al (2013) compared the
FA profile of patients based on response to chemotherapy and found higher proportions of n-3
PUFA in patients with remission versus those with stable disease and disease progression
66
(Cvetković et al., 2013). It cannot be ruled out that the increase in plasma phospholipid FA in our
study may be related to improved nutritional intake in addition to decreased disease burden.
Univariate analysis demonstrated an increase in caloric intake in both groups. Additionally, there
was a significant decrease in the number of malnourished patients over time in patients with non-
resected disease.
6.4 Strengths and limitations This prospective observational study investigated changes in nutritional, inflammatory and FA
status in a cohort of gastric cancer and CRC patients undergoing first-line 5-fluourouracil-based
chemotherapy. Strengths of this study included a focus solely on gastric and CRC patients
undergoing similar treatment regimens providing a homogenous study group; the prospective
nature and multiple time points allowing a comprehensive study of nutrition, inflammation and
FA status indicators which are unlikely to be static during anti-cancer treatment; and the
inclusion of multiple markers of nutritional status including dietary data, rather than a focus
solely on weight. There are also however several limitations to this study. Firstly, given the small
sample size, there is limited statistical power to detect changes and significant relationships
among multiple variables.
A second important limitation was the inability to control for the potential influence of a more
intensive approach to nutritional intervention on outcomes of this study, which may influence the
interpretation of the findings and generalizability of the study. In many clinics, the typical
standard of care is nutrition intervention by referral as deemed necessary by screening or through
the healthcare team. In our clinic, all patients starting chemotherapy undergo a baseline nutrition
assessment with MNT as required, however subsequent re-assessment is based on screening by
the healthcare team or by use of a validated nutrition screening tool. Patients are prioritized and
seen by level of nutritional risk and for suspected malnutrition. Patients in this study received
early and proactive MNT above and beyond the typical standard of care in many outpatient
oncology settings, including our own ambulatory clinic. All assessments and measurements were
completed by an RD and included 3-day food records, a comprehensive set of anthropometric
and body composition measurements, and a nutrition-focused physical exam; therefore, the RD
was equipped with the results from a comprehensive nutrition assessment at each visit allowing
early identification of factors that may adversely impact nutritional status. Accordingly, each
67
patient received early MNT to address nutrition impact factors and potential nutrient deficiencies
throughout the study period. Consistent with this, there was a significant improvement in
nutritional status over the course of chemotherapy with a significant decrease in the proportion of
patients classified as malnourished over time. Concurrently, there was also a significant increase
in caloric intake over time. A recent randomized trial in gastric cancer patients receiving post-
operative chemotherapy compared patients receiving general education at the beginning of
chemotherapy, with patients receiving intensive and individualized nutritional education
throughout the course of chemotherapy. Patients receiving the intensive nutrition intervention
had higher calorie and iron intake at the beginning of chemotherapy, and improved albumin,
serum protein, hemoglobin and weight during chemotherapy. Additionally, compliance to
chemotherapy was significantly higher in patients receiving intensive nutrition education and
withdrawal from treatment due to adverse effects was significantly lower (Xie et al., 2017).
There are a few considerations when interpreting the dietary intake data in this study. While we
used 3-day food records for dietary assessment which is a strength in this study, some patients
found the dietary assessment too burdensome which led to lower completion rates. Subsequently,
24-hour recalls were used in those patients who did not complete 3-day food records (15-27%).
Food records may not capture the true variability in the diet between cycles of chemotherapy
with the poorest intake likely to be directly after administration of chemotherapy and the best
intake directly preceding the next cycle of chemotherapy. This would be even more pronounced
with a 24-hour diet recall which would presumably coincide with an individual’s best
intake/lowest impact of treatment-related symptoms, leading to an overestimation of dietary
intake. While generally less common than under-reporting, there are several other factors which
may lead to over-reporting of dietary intake including being male, being underweight or having a
normal/low BMI (Lutomski et al., 2011; Mattisson et al., 2005; Murakami and Livingstone,
2015).
A third limitation relates to the measurement of body composition in this study. Given that there
may be a clinically relevant deterioration in muscle mass irrespective of weight and BMI, the
inclusion of alternate measures of nutritional status such as BIA, FSA, and AMA, is a strength of
this study. It should be acknowledged however, that BIA cannot detect changes in FFM distinct
from changes in tumour size, is susceptible to alterations in fluid status, and may not have been
able to detect small changes in such a short period of time (Kotler et al., 1996; Kushner et al.,
68
1996). Measurement of body composition using CT imaging is one of the gold standards for
assessing body composition and can differentiate between types of FFM including skeletal
muscle, organs, types of adipose tissue, and tumour burden (Mourtzakis et al., 2008). While we
did have access to baseline CT imaging, we did not have a second set of measurements
coinciding with the end of the study period, so we would not have been able to examine changes
over time, nor did we have access to the required software for analysis. BIA may overestimate
total body water in underweight patients (< 95% ideal body weight) when using formulas that
have been derived in normal weight subjects (Simons et al., 1995). Given that the median BMI in
both resected and non-resected patients in this study was within a healthy range (26.6 and 24.6
kg/m2, respectively), the use of a formula derived in a normal-weight population was less likely
to be issue.
A fourth limitation was the inability to measure response to treatment during the study period.
While patients had tumour markers (CEA and CA 19-9) measured at baseline, and CT imaging
for staging, we did not have a second set of tumour markers or re-staging imaging coinciding
with the end of the study as per standard of care for collection and analysis. In a longitudinal
study by Prado et al (2013), muscle gain in advanced cancer patients, as assessed by CT images,
occurred in 15% of assessments and was related to disease status. Muscle gain was most likely to
occur in patients who were further from death and in periods of stable disease and with
optimized symptom management. The authors suggested the presence of an optimal window of
opportunity for successful nutrition therapy and reversal of cachexia (Prado et al., 2013). It is
possible that patients in this study were responsive to chemotherapy, and this combined with
intensive nutrition therapy for symptom management, took advantage of this anabolic potential
described by Prado et al. (2013). Unfortunately, we could not confirm this in the absence of
measured response to treatment.
Fifth, in terms of evaluating FA status, the lack of a healthy control group limited the ability to
make comparisons for whether these patients had reduced FA status before and during
chemotherapy. Additionally, without a healthy reference group, we were unable to determine
whether increases in concentrations of plasma phospholipid FA indicate an actual improvement
in FA status. We do know that FA concentrations increased in both groups and this may be
related to improved nutritional status.
69
Lastly, in this study, we also did not exclude patients reporting use of fish oil or flax seed oil.
There was no significant difference in the number of patients reporting fish or flax oil use
between the resected and non-resected group. Furthermore, we completed a sensitivity analysis
to see if exclusion of these patients would change our conclusions. While the relationship
between tumour presence and time for weight remained unchanged, the relationship between
tumour presence and time for FFM (BIA) became borderline insignificant. It is important to note
that this relationship was only borderline significant (p = 0.042) to begin with, thus the change in
significance may be due to a drop in the sample size with exclusion of these patients.
70
Conclusions Knowledge of potential mediators of the decline in nutritional status that may occur in patients
with GI cancers undergoing treatment is lacking but necessary to develop interventions to
mitigate weight loss and associated complications. The purpose of this study was to describe
changes in nutritional status in relation to levels of inflammation and FA in patients with gastric
cancer and CRC during chemotherapy, and to identify factors associated with nutritional
depletion during treatment. We hypothesized that in patients with gastric cancer and CRC, a
decline in nutritional status during chemotherapy would be associated with increasing levels of
inflammation and decreasing levels of n-3 FA. This hypothesis was not supported by the results
of this study though the findings contribute to the existing body of literature that has begun to
describe these relationships at varying stages of the disease trajectory.
In patients with gastric cancer and CRC receiving treatment with first-line chemotherapy, early
in their disease trajectory:
1. Weight and FFM of patients with resected disease increased over time whereas patients
with non-resected disease showed decreased weight and FFM over time, suggesting that
tumour presence has a significant influence on nutritional status during chemotherapy.
2. Markers of inflammation did not change significantly over time, suggesting that in the
early stages of first-line chemotherapy, changes in nutritional status are likely to be
influenced to a greater degree by tumour presence rather than changes in inflammation.
Further study using chemotherapy response data is needed to verify these findings.
3. Plasma phospholipid FA concentrations increase throughout initial treatment with first-
line chemotherapy.
4. The influence of intensive nutrition interventions on the observed changes in nutritional
and FA status may have played a role in this study, and deserves further investigation.
Overall, these study findings contribute to a greater understanding of the change and
interrelationships between nutritional, inflammatory and fatty acid status, early in the disease
trajectory.
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7.1 Future directions More longitudinal studies are needed to gain a better understanding of the relationship between
nutritional status, inflammatory, and FA status in GI cancer patients. These studies should
consider the heterogeneity that likely exists between those with resected versus non-resected
disease, different tumour types, stages of disease, and treatment modalities. It will be important
for future studies to determine 1) at what point in the disease and/or treatment trajectory, there
may be a switch in which there is a greater influence of inflammation on nutritional decline; and
2) when reversing inadequate intake may not be adequate in mitigating this nutritional decline
i.e. progression of cachexia to refractory cachexia. This could be achieved by following patients
for a longer period of time, considering chemotherapy response data and randomizing patients to
receive either typical standard nutritional care versus more intensive MNT. Furthermore, as
gastric cancer patients had significantly higher nutritional risk compared to CRC, future studies
may wish to look at these diagnoses separately. Lastly, our findings point to the need to consider
the presence of tumour as a main factor influencing nutritional outcomes in future studies
investigating nutritional intervention in GI cancers. This could also have implications in practice
in how GI cancer patients undergoing chemotherapy are screened and prioritized for receiving
timely MNT.
72
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Appendices
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Appendix 8.1 Summary of fish oil supplementation studies
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Table A8-1. Summary of studies of fish oil supplementation in patients not receiving anti-cancer therapy Study Design Population Intervention Outcomes Results
Wigmore et al. 1996
Open-label, single arm. Results compared with similar population in a previous study.
Patients with unresectable pancreatic cancer (n=18).
2 g/day fish oil capsules, increased weekly to a maximum dose of 16 g/day. Median maximum dose of 12 g fish oil/day = 2 g EPA/day.
Weight, MAMC, TSF, TBW measured by BIA, plasma PL FA, CRP.
Median weight gain of 0.3 kg/month compared to 2.9 kg/month weight loss prior to supplementation. No significant change in TBW, MAMC and TSF. Lower CRP 1 month after supplementation but increased again by 3 months. Significant increase in EPA, DHA and decrease in AA.
Barber et al. 1999b
Non-randomized control trial.
Weight-losing patients with advanced pancreatic cancer. 18 in control (C) and 18 in intervention (I) group. 6 healthy individuals for comparison.
2 cans of fish-oil enriched nutrition supplement/day (Total = 2.18 g EPA, 0.92 g DHA). Mean intake not reported.
Negative APP concentrations (albumin, prealbumin, transferrin), positive APP concentrations (CRP, α-1 antitrypsin, fibrinogen, α-1-acid glycoprotein, haptoglobin, cerulopasmin), weight.
Significantly higher concentrations of positive APP and lower negative APP in cancer patients vs. healthy controls. I: No change in APP except increase in transferrin. Median 1 kg weight gain. C: Increase in CRP and decrease in negative APP. Median 2.8 g weight loss.
Barber et al. 2001
Open-label, single arm.
Patients with unresectable pancreatic cancer with ongoing weight loss.
2 cans fish oil-enriched nutrition supplement/day (2.2 g EPA, 0.96 g DHA). Median intake of 1.9 cans/day.
IL-6, soluble TNF receptors, soluble IL-6 receptor, production of IL-β, IL-6, TNF. Hormones (Insulin, cortisol, leptin), PIF, weight.
Median weight gain of 1.0 kg x 3 weeks. Decreased IL-6 production, increased fasting insulin, decreased cortisol-insulin ratio. Decreased proportion of patients with detectable PIF.
Wigmore et al. 2000
Open-label single arm.
Patients with unresectable pancreatic cancer (n-26).
1 g/day for first week, 2 g/day for second week, 4 g/day for third week, 6 g/day thereafter via EPA capsules.
Weight, MAMC, TSF, APR, TBW, energy intake, plasma PL EPA, AA, WHO performance status, survival.
Decreased rate of weight loss, increased percentage of EPA in plasma PL, decreased AA. No change in APR.
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Table A8-1. Summary of studies of fish oil supplementation in patients not receiving anti-cancer therapy Study Design Population Intervention Outcomes Results
Pratt et al. 2002
Randomized, controlled, blinded.
Patients with advanced cancer: 13 in intervention (I), 10 in control (C) group. Burn injury (n=10). High-dose chemotherapy with stem cell transplant (n-3). Healthy subjects (n=6).
I: 18 fish oil capsules per day (180 g EPA, 120 mg DHA per capsule). C: Olive oil.
Fatty acid composition of neutrophils and plasma PL, nutritional status (BMI, total caloric intake, fat intake).
Decreased levels of plasma PL in advanced cancer patients lower than healthy subjects. Decreased PUFA following induction and high dose chemotherapy. After supplementation: I: Increased EPA and DHA in plasma PL. Change in body weight directly correlated with increased EPA content in plasma PL. C: No change in plasma PL composition.
Fearon et al. 2003
Randomized, controlled, double blinded
Patients with unresectable pancreatic cancer with >5% weight loss x 6 months. 95 in intervention (I) and 105 in control (C) group.
2 cans per day of oral supplement. I: n-3 FA and antioxidant enriched nutrition supplement (1.1 g EPA) C: Isocaloric isonitrogenous nutrition supplement. Mean intake of 1.4 cans/day.
Weight, TBW measured by BIA, dietary intake, plasma PL EPA, QOL.
I: Increase in total dietary intake (meals plus supplement). 26% reported some intake of supplement but minimal to no increase in plasma EPA. Significant positive correlation between daily supplement intake and weight and LBM, and between plasma EPA and weight and LBM. C: Increase in protein intake. 18% had high EPA levels at week 4 and/or 8. Stable weight and LBM in both groups.
Taylor et al. 2010
Open-label, single arm.
Patients with metastatic cancer (various tumour types) and weight loss.
1.5 g marine phospholipids per day (1.1 g EPA, 1.7g DHA). Average 94% of prescribed dose taken.
Weight, appetite, pain, BIA parameters, QOL, routine blood parameters including CRP, cytokines (IL-1, IL-6, TNF-α, lyso-PC, lipoprotein profiles, FA profiles of plasma PL, RBCs, MNL.
Increased HDL, IL-6, TNF-α. No change in BIA parameters. Decrease in AA as % total FA in RBC. Increase in DHA (% total FA) in plasma PL. Increase in DHA in RBC and MNL. Decrease in n6/n3 ratio in plasma PL and MNL. Positive correlation between EPA and weight in plasma PL and RBC. Improved QOL and appetite scores.
Abbreviations: EPA, eicosapentaenoic acid; MAMC, mid-arm muscle circumference; TSF, triceps skin fold; TBW, total body water; PL, phospholipid; FA: fatty acid; CRP, C-reactive protein; DHA, docosahexaenoic acid; AA, arachidonic acid; APP, acute phase proteins; PIF, proteolysis inducing factor; APR, acute phase response; BMI, body mass index; PUFA, polyunsaturated fatty acid; QOL, quality of life; LBM, lean body mass; BIA, bioelectrical impedance analysis; RBC, red blood cells; MNL, mononuclear leukocytes; HDL, high density lipoprotein.
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Table A8-2. Summary of studies of fish oil supplementation in patients receiving anti-cancer therapy Study Design Population Intervention Outcomes Results
Bruera et al. 2003
Randomized, double-blinded, controlled (x 2 weeks), followed by open-label (up to 90 days).
Patients with advanced cancer (locally recurrent or metastatic), various tumour types, with decreased appetite and weight loss. 30 in intervention (I), 30 in control (C) group. Chemotherapy and hormonal therapy permitted.
I: 6-18 capsules with 1000 mg fish oil (180 mg EPA, 120 mg DHA). C: 6-18 capsules with 1000 mg olive oil. Mean intake of 9.8 capsules/d (I), and 9.2 capsules/d (C).
Appetite, dietary intake, weight, BIA, MAC, TSF, subscapular skinfold, functional status, plasma PL FA profile
No change in symptoms, dietary intake, functional status. No correlation between fish oil dose and anthropometric variables.
Jatoi et al. 2004
Randomized, double-blinded, three study arms.
Patients with incurable cancer (except brain, breast, ovarian, prostate, or endometrial) with weight loss and poor dietary intake. 141 in EPA-treated (EPA), 140 in Megestrol acetate (MA) and 140 in combination (MA+EPA) group. Chemotherapy or radiation treatment permitted.
EPA: EPA nutrition supplement (1.09 g EPA, 0.46 g DHA) twice daily + placebo. 2. MA: Megestrol acetate (MA) 600 mg/d plus isocaloric, isonitrogenous supplement twice daily. Combination: MA + EPA nutrition supplement.
Weight (10% gain above baseline), appetite, survival, QOL, toxicity.
Great % of patients achieved 10% weight gain in MA vs. EPA group. Greater appetite stimulation in MA vs. EPA group when measured by FAACT tool. No difference in survival, QOL, and toxicity between the three treatment arms.
Bauer and Capra. 2005
Open label, single arm.
Pancreatic cancer and NSCLC patients with weight loss, receiving gemcitabine-based chemotherapy (n=8).
Weekly counselling by RD and at least one can per day of n-3 FA enriched nutrition supplement (1.1 g EPA). Mean intake of 1.06 g/d of EPA at week 4 and 1.36 g/d at week 8.
Dietary intake, body composition (LBM), nutritional status (PG-SGA score), performance status, QOL.
Increased total protein, energy, and fibre intake per day, PG-SGA score, performance status, and QOL. Change in nutritional status associated with QOL, performance status, and LBM.
Murphy et al. 2011a
Open-label, controlled.
Newly referred patients with NSCLC receiving first-line treatment with platinum-based doublet chemotherapy. 16 in intervention (I) and 24 in
I: 4 capsules per day (2.2 g EPA, 240 mg DHA) or 7.5 ml fish oil per day (2.2 g EPA, 500 mg DHA). C: No intervention.
Skeletal muscle, adipose tissue, weight, treatment response.
I: Weight maintenance, maintenance or gain in muscle mass (69%). C: Weight loss, maintenance of muscle mass (29%). No difference in adipose between groups. No difference in treatment response. Positive association between
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Table A8-2. Summary of studies of fish oil supplementation in patients receiving anti-cancer therapy Study Design Population Intervention Outcomes Results
control (C) group. plasma EPA and rate of muscle change. Murphy et al. 2011b
Open-label, controlled.
Advanced NSCLC patients (stage IIIB or IV) receiving first-line treatment with platinum-based doublet chemotherapy. 15 in intervention (I) and 31 in control (C) group.
I: 4 capsules per day (2.2 g EPA, 240 mg DHA) or 7.5 ml fish oil per day (2.2 g EPA, 500 mg DHA). C: No intervention. Mean intake of 2.1 g EPA per day.
Chemotherapy response, dose-limiting toxicity, survival.
Greater chemotherapy response in I vs. C group. EPA concentration significant independent predictor of chemotherapy response. Greater number of patients completing planned chemotherapy in I vs. C group. No difference in chemotherapy toxicity. Increased survival in I group but not significant.
Read et al. 2007
Open-label, single arm
23 patients with advanced colorectal cancer (Stage IV) on 2nd line chemotherapy with folinic acid, 5-fluorouracil, and irinotecan.
2 tetrapaks per day (240 ml, 1.09 g EPA, 0.46 g DHA each) nutrition supplement containing EPA + RD counselling. Mean intake of 1.7 tetrapaks per day.
Weight, body composition, CRP, QOL, dietary intake, plasma PL, cytokines.
Increased weight, maintenance of LBM. No change in QOL. Increased EPA, increase in CRP but returned to baseline by end of 9-week trial. Correlation between IL-6 and IL-10 and survival, and IL-12 and toxicity.
Silva et al. 2012
Randomized, controlled.
23 patients with colorectal cancer starting chemotherapy. 11 in intervention (I) and 12 in the control (C) group.
I: 4 capsules of fish oil supplement (600 mg EPA+DHA). C: No intervention. Mean intake not reported.
Weight, BMI, cytokines, CRP, albumin.
I: No change in weight and BMI. Decreased CRP, decreased CRP/albumin ratio. C: Decreased weight and BMI. No change in CRP.
Mocellin et al. 2013
Randomized, controlled.
11 patients with colorectal cancer starting chemotherapy. Chemotherapy drugs used alone or in combination: xeloda, oxaliplatin, 5-fluorouracil, leucovorin. 6 in intervention (I), and 5 in control (C) group.
I: Four capsules fish oil per day (90 mg EPA, 60 mg DHA per capsule), C: No intervention. Mean intake not reported.
Plasma TNF-α, IL-1β, IL-10, IL-17A, TNF-α/IL-10 and IL-1β/IL-10 ratios, serum albumin, CRP, CRP/albumin ratio, plasma EPA, DHA, AA, weight, BMI, body composition (skinfolds).
I: Decreased CRP, CRP/albumin ratio. Increased EPA, DHA. Decreased AA, and n6/n3 ratio. C: Increased CRP, CRP/albumin ratio, n6/n3 ratio. No change in weight, BMI, body composition in either group.
Abbreviations: EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid; BIA, bioelectrical impedance analysis; MAC, mid-arm circumference; TSF, triceps skin fold; PL, phospholipid; FA, fatty acid; QOL, quality of life; FAACT, function assessment of anorexia/cachexia therapy; NSCLC, non-small cell lung cancer; RD, Registered Dietitian; LBM, lean body mass; PG-SGA, patient-generated subjective global assessment; CRP, C-reactive protein; BMI, body mass index; AA, arachidonic acid.
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Appendix 8.2 Consent form
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Appendix 8.3 Research poster
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Appendix 8.4 Sensitivity Analysis In this appendix, we repeat multivariate analysis using linear mixed effects models as previously described with the exclusion of patients with self-reported use of fish or flax oil (n=8).
Table A8-3. Multivariate model for weight – all patients
Variable
β SE
p (Intercept) 74.91 3.13 Time (visit) -0.32 0.18 0.08 Sex1 -10.82 4.62 0.03 Plasma DHA -0.05 0.02 p < 0.01 Plasma Total n-3 0.02 0.01 p < 0.01
1 Male = reference
Table A8-4. Multivariate model for FSA fat free mass – all patients
Variable
β SE
p (Intercept) 54.16 1.58 Time (visit) 0.08 0.11 0.43 Sex1 -13.86 2.37 p < 0.001 Il-6 0.05 0.01 p < 0.01
1 Male = reference
Table A8-5. Multivariate model for BIA fat free mass – all patients
Variable
β SE
p (Intercept) 58.76 1.78 Time (visit) 0.07 0.09 0.40 Sex1 -15.67 2.62 p < 0.001 Il-6 0.04 0.01 p < 0.001 TNF-α 0.07 0.04 0.06 Plasma AA -0.01 0.004 p < 0.01 Plasma Total n-6 0.004 0.001 p < 0.01
1 Male = reference
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Table A8-6. Multivariate model for PG-SGA score – all patients
Variable
β SE
p (Intercept) 16.85 2.63 Time (visit) -0.06 0.31 0.86 Diagnosis1 -5.60 1.61 p < 0.01 Protein intake (g/day) -0.04 0.02 0.02 Il-6 0.08 0.04 0.03 Plasma Total n-3 -0.01 0.006 0.06
1 Gastric cancer = reference
Table A8-7. Multivariate model for weight with tumour interaction
Variable
β SE
p (Intercept) 78.18 4.23 Time (visit) 0.24 0.23 0.31 Sex1 -11.72 4.56 0.02 Tumour presence2 -4.56 4.61 0.33 Plasma DHA -0.05 -0.02 p < 0.01 Plasma Total n-3 0.02 0.01 p < 0.01 Time*Tumour presence -0.97 0.27 p < 0.001
1 Male = reference 2 Resected = reference
Table A8-8. Multivariate model for FSA fat free mass with tumour interaction
Variable
β SE
p (Intercept) 55.32 2.19 Time (visit) 0.25 0.15 0.09 Tumour presence1 -1.62 2.46 0.51 Sex2 -14.16 2.39 p < 0.001 Il-6 0.04 0.02 p < 0.01 Time*Tumour presence -0.34 0.21 0.11
1 Resected = reference 2 Male = reference
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Table A8-10. Multivariate model for PG-SGA score with tumour interaction
Variable
β SE
p (Intercept) 17.40 3.11 Visit 0.29 0.43 0.51 Tumour presence1 0.78 2.31 0.74 Diagnosis2 -5.85 1.83 p < 0.01 Protein intake (g/d) -0.04 0.02 0.02 Il-6 0.07 0.04 0.06 Plasma Total n-3 -0.01 0.006 0.04 Time*Tumour presence -0.64 0.58 0.27
1 Resected = reference 2 Gastric cancer = reference
Table A8-9. Multivariate model for BIA fat free mass with tumour interaction
Variable
β SE
p (Intercept) 60.40 2.43 Time (visit) 0.23 0.12 0.06 Tumour presence1 -2.22 2.66 0.41 Sex2 -16.17 2.63 p < 0.001 Il-6 0.04 0.01 p < 0.01 TNF-α 0.06 0.04 0.11 Plasma AA -0.013 0.004 p < 0.01 Plasma Total n-6 0.004 0.001 p < 0.01 Time*Tumour presence -0.312 0.168 0.07
1 Resected = reference 2 Male = reference