EFFECT OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR POLYMORPHISM...
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EFFECT OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR POLYMORPHISM IN RELATION TO ALCOHOL METABOLISM AND OESTROGEN LEVELS, AND THEIR ASSOCIATION WITH BREAST CANCER Nicholas Lim Teck Yun Master’s thesis Public Health School of Medicine Faculty of Health Sciences University of Eastern Finland April 2017
Transcript of EFFECT OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR POLYMORPHISM...
EFFECT OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR
POLYMORPHISM IN RELATION TO ALCOHOL METABOLISM AND
OESTROGEN LEVELS, AND THEIR ASSOCIATION WITH BREAST
CANCER
Nicholas Lim Teck Yun
Master’s thesis
Public Health
School of Medicine
Faculty of Health Sciences
University of Eastern Finland
April 2017
UNIVERSITY OF EASTERN FINLAND, Faculty of Health Sciences.
Public health
LIM, NICHOLAS TY.: Effect of peroxisome proliferator-activated receptor polymorphism in
relation to alcohol metabolism and oestrogen levels, and their association with breast cancer.
Master’s thesis: 57 pages, 1 attachment (5 pages).
Supervisors: Professor Arto Mannermaa, PhD, Professor Tomi-Pekka Tuomainen, MD, PhD
April 2017
Keywords: Peroxisome proliferator-activated receptor, Alcohol metabolism, Oestrogen, Breast
Cancer
EFFECT OF PEROXISOME PROLIFERATOR-ACTIVATED RECEPTOR
POLYMORPHISM IN RELATION TO ALCOHOL METABOLISM AND OESTROGEN
LEVELS, AND THEIR ASSOCIATION WITH BREAST CANCER
Breast cancer is the commonest cancer in women with a high mortality rate. Despite screening
initiates, the number of morbidity and mortality remains high. Therefore, by discovering
modifiable epigenetic causes, the risk of breast cancer can be reduced by targeting external
factors depending on the genetic predisposition of a population.
This study aims to discover the relation of alcohol, PPAR, and oestrogen in association to breast
cancer risk.
Subjects were chosen from the Finnish population, from the Kuopio Breast Cancer Project
(KBCP). KBCP is a prospective population-based case control study done from 1990-1995.
Out of 1,919 participants, 520 were diagnosed with breast cancer which followed by data
collection regarding medical history, socioeconomic background, family history of breast
cancer, cigarette smoking, and alcohol use. Only patients who had the genotypes needed for
this thesis were selected (n=445). Controls were selected from the National Population Registry
living in the same area. The controls were matched based on long term residence in the area,
age, and genotype which ended up with 251 participants.
TagSNPs for PPAR and ADH genes were chosen using the previous studies and GWAS, and
extracted from ICOGS genotype data. PRS was used to estimate the risk effects associated with
40 common PPAR variants to breast cancer. It was also used to examine the collective PPAR
and ADH polymorphisms influence on each other in association to breast cancer. Oestrogen
serves as the effect modifier in this study using descriptive analysis.
Overall combination of the three PPAR variants brought a reduction in risk by 0.35. However,
there was no statistical significance. ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698
increases the risk of breast cancer and decreases the effect of PPARα, PPARδ and PPARγ
variants on breast cancer risk reduction. The variant rs4713854 with carriers of minor allele C
has nominal significance against common AA genotype in reducing breast cancer risk. The
frequency of breast cancer cases following oestrogen level are depending not on the length of
years but the amount of circulating oestrogen depending on menopausal status.
This finding suggest that ADH variants eliminates the protective effect of combined PPARα, δ
and γ polymorphisms against breast cancer risk. Oestrogen is a modifier of breast cancer risk,
however its extent as a predictor of breast cancer independent of PPAR and ADH is not known.
ACKNOWLEDGEMENTS
Foremost, I would like to thank my family: to my parents and sisters for their constant support
and encouragement throughout my years of study and my endeavour in writing this thesis. I am
also grateful for my significant other Vick, for the continuous motivation and devotion in every
step of the way.
I would like to express my sincere gratitude to my supervisors Professor Tomi-Pekka
Tuomainen and Professor Arto Mannermaa for their guidance and imparting knowledge on
genetics, cancer, and epidemiology. I am grateful to Dr. Henna Martiskainen for helping me
utilise Polygenic Risk Score analysis.
Finally, the unfailing support of my friends especially Elaine, Aisha, Godash, Aniza, Kevin and
their many care packages helped me persist in my work. This would not have been possible
without them.
This study was carried out between 2016-2017 at the unit of Clinical Pathology and Forensic
Medicine, Institute of Clinical Medicine, University of Eastern Finland.
ABBREVIATIONS
3βHSD 3β-Hydroxysteroid dehydrogenase
A Adenosine
ADH Alcohol dehydrogenase
AdipoR Adiponectin receptor
AI Aromatase inhibitor
ALDH Aldehyde dehydrogenase
ATM Ataxia-telangiectasia mutated
BRCA1 Breast cancer 1 gene
BRCA2 Breast cancer 2 gene
C Cytosine
CI Confidence interval
COGS Collaborative Oncological Gene‐Environment Study
COX-2 Cyclooxygenase 2
CYP Cytochrome
CYP450 Cytochrome P450
CYP2E1 Cytochrome P450 family 2 subfamily E member 1
DNA Deoxyribonucleic acid
E1 Oestrone
E2 Oestradiol
E3 Oestriol
EDC Endocrine disrupting compounds
EGFR Epidermal growth factor receptor
ER Oestrogen receptor
ERCC4 Excision repair cross-complementing 4
FNAC Fine-needle aspiration cytology
FSH Follicular stimulating hormone
G Guanine
GSTM1 Glutathione S-transferase Mu 1
GSTP1 Glutathione S-Transferase Pi 1
GWAS Genome-Wide Association Study
H202 Hydrogen peroxide
HER2 Human epidermal growth factor receptor 2
HRT Hormone replacement therapy
HSD17B1 Hydroxysteroid 17-beta dehydrogenase 1
iCOGS Illumina Custom Infinium genotyping array created by
Collaborative Oncology Gene-environment Study (COGS)
IGF1 Insulin-like growth factor 1
ISH In-situ hybridisation
KBCP Kuopio Breast Cancer Project
LD Linkage disequilibrium
LH Luteinising hormone
LHRH Luteinising hormone releasing hormone
MAPK Mitogen-activated protein kinase
MRI Magnetic resonance imaging
MTHFR Methylenetetrahydrofolate reductase
NBS1 Nibrin protein
OCP Oral contraceptive pill
OH Hydroxyl
OR Odds ratio
P53 Tumour protein p53
PCR Polymerase chain reaction
PI3K Phosphoinositide 3-kinase
PPAR Peroxisome proliferator-activated receptor
PPARα Peroxisome proliferator-activated receptor alpha
PPARδ Peroxisome proliferator-activated receptor delta
PPARγ Peroxisome proliferator-activated receptor gamma
PR Progesterone receptor
PRS Polygenic risk score
PTEN Phosphatase and tensin homolog
RFLP Restriction fragment length polymorphism
RNA Ribonucleic acid
ROS Reactive oxygen species
RTK Receptor tyrosine kinase
SNP Single nucleotide polymorphism
T Thymine
TMN Tumour Metastasis Nodes staging
TNF Tumour necrosis factor
VEGF Vascular endothelial growth factor
WAT White adipose tissue
XRCC X-ray Repair Cross-Complementing
Note: All genes are written in italic, and enzymes of the same name are written in normal
format.
CONTENTS
1 INTRODUCTION…………………………………………………………………………12
2 LITERATURE REVIEW…………………………………………………………………14
2.1 Breast Cancer……………………………………………………………………..…...14
2.1.1 Prevalence……………………………………………………………….……...14
2.1.2 Aetiology………………………………………………………………………..14
2.1.3 Clinical features……………………………………………………..…………..15
2.1.4 Diagnosis………………………………………………………….…………….15
2.1.5 Pathology…………………………………………………………….………….16
2.1.5.1 Histologic classification……………………………………….……...16
2.1.5.2 Histologic grading……………………………………….……………16
2.1.5.3 Biomarkers……………………………………………….…………...17
2.1.6 Treatment……………………………………………………………………....17
2.2 Alcohol………………………………………………………………………………..18
2.2.1 Ethanol metabolism………………………………………………………….....18
2.2.2 Enzymes involved in ethanol metabolism………………………………………19
2.2.2.1 ADH……………………………….……………………………….....19
2.2.2.2 ALDH2…………………………………...………………………..….19
2.2.2.3 CYP2E1…………………………………………………………….....20
2.2.4 Carcinogens from by-products of ethanol metabolism………………………….20
2.3 Oestrogen sources and levels…………………………………………….…………....20
2.3.1 Oestrogen in reproductive age women………………………………………......20
2.3.2 Oestrogen in post-menopausal women……………………………………….....21
2.3.3 Enzymes involved in oestrogen metabolism…………………………………….22
2.3.3.1 17βHSD..……………………………………………………………...22
2.3.3.2 CYP19A1……………………………………………………………..22
2.3.3.3 CYP17A1……………………………………………………………..22
2.3.3.4 3βHSD…………………………………………………………….…..22
2.3.4 Exogenous oestrogen………………………………………………………..….22
2.3.5 Oestrogen exposure and breast cancer risk…………………………..………….23
2.4 Genetic variation…………………………………………………………………..….24
2.4.1 Definition of genetic polymorphism………………………………………….....24
2.4.2 Tag SNP………………………………………………………………………....24
2.4.3 Population differences…………………………………………………..………25
2.4.4 Types of SNPs…………………………………………………………………..25
2.4.5 Importance…………………………………………………………………..…..25
2.5 Peroxisome proliferator-activated receptor (PPAR)……………………………...…..26
2.5.1 PPARα……………………………………………………………………….….26
2.5.2 PPARδ…………………………………………………………………………..26
2.5.3 PPARγ…………………………………………………………………………..26
2.5.4 PPAR and alcohol…………………………………………………………..…...27
2.5.5 PPAR and cancer…………………………………………………..……………27
2.6 Summary of the literature review…………………………………………………….27
3 AIMS OF THE STUDY……………………………………………………………………29
4 METHODOLOGY………………………………………………………………………...30
4.1 Study subjects and design………………………………………………………..…...30
4.2 Data collection and ethical considerations……………………………………………30
4.3 Genotyping of PPAR and ADH genes………………..................................................31
4.4 SNP selection and analysis of PPAR………………………………………………….31
4.5 Selection and analysis of ADH……………………….………………………..……..32
4.6 Oestrogen level……………………………………………………………………….33
4.7 Association analyses……………………………………………………………...…..33
5 RESULTS ………………………………………………………………………………….34
5.1 The effect of PPAR polymorphism on breast cancer risk………………………...…...34
5.1.1 The association of PPARα polymorphism to breast cancer risk………………....34
5.1.2 The association of PPARδ polymorphism to breast cancer risk………………….35
5.1.3 The association of PPARγ polymorphism to breast cancer risk…………...……..35
5.1.4 The effect of combined PPARα, PPARδ, and PPARγ polymorphisms on breast
cancer risk…………………………………………………………………………….36
5.2 The association of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 to breast
cancer risk…………..………………………………………………………………..……….37
5.3 The PRS of combined PPARα, δ, γ variants and ADH variants in association to breast
cancer risk…………………………………………………………………………...………..38
5.4 Predictive significance of oestrogen exposure throughout a woman’s lifetime on breast
cancer incidence………………………………..………………………………………..……39
6 DISCUSSION…………………………………………………………………………..….41
6.1 Summary of main findings……………………………………………………………41
6.2 Comparison with previous studies………………………………..…………………..41
6.2.1 PPARα on breast cancer risk……………………….……………………………41
6.2.2 PPARδ on breast cancer risk…………………………………………………….42
6.2.3 PPARγ on breast cancer risk……………………………………………….…….42
6.2.4 Predictive role of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 to
breast cancer risk………………………………………………………………..…….43
6.2.5 ADH influence on PPAR in association to breast cancer risk……………..……..43
6.2.6 Predictive role of oestrogen to breast cancer risk…………………….………….43
6.3 Strengths and limitations of the study………………………..……………...………..44
7 CONCLUSION………………………………………………………………….…………45
8 REFERENCES…………………………………………………………………………….46
9 APPENDICES………………………………….………………………………………….58
9.1 Table 4: Summary table of ORs for association between 40 PPAR polymorphisms and
breast cancer risk……………………………………………………..……………………….58
LIST OF TABLES
Table 1: Identified functional SNPs of PPAR genes (PPARα, PPARδ, PPARγ)……………....32
Table 2: Characteristics of the estimated oestrogen levels…………………………….………33
Table 3: Summary table of ORs for association between 40 PPAR polymorphisms and breast
cancer risk……………………………………………………………………………….……58
Table 4: Summary table of ORs for association between rs931635, rs1042026, rs698 and breast
cancer risk…………………………………………………………………………….………37
LIST OF FIGURES
Figure 1: Ethanol metabolism pathway (Seitz & Becker 2007)…………………….…………19
Figure 2: Synthesis of oestrogen (Pepe & Albrecht 2008)…………………………………….21
Figure 3: Oestradiol and FSH levels in serum of a woman over a lifetime (Shapiro 2001)…….21
Figure 4: Risk of PPARα variants of each case and control to developing breast cancer
according to PRS………………………………………………………………………...……34
Figure 5: Risk of PPARδ variants of each case and control to developing breast cancer according
to PRS…………………………………………………………………………………………35
Figure 6: Risk of PPARγ variants of each case and control to developing breast cancer according
to PRS………………………………………………………………………………...……….36
Figure 7: Risk of combined PPARα, δ and γ variants of each case and control to developing
breast cancer according to PRS………………………………………………………..………36
Figure 8: Risk of ADH1A rs931635, ADH1B rs1042026, ADH1C rs698 of each case and control
to breast cancer risk in the PRS………………………………………………………………..37
Figure 9: Combined risk of PPARα, δ and γ variants with ADH1A rs931635, ADH1B rs1042026,
ADH1C rs698 of each case and control to breast cancer risk in the PRS………………………38
Figure 10: Frequency of breast cancer cases according to length of exposure, age, and number
of pregnancies…………………………………………………………………………..…….39
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1 INTRODUCTION
Breast cancer is the commonest type of cancer among women both in the developed and
developing countries, and second most common cancer overall that affects women with a high
mortality rate. The recent number of new cases reported each year is an estimate of 1.7 million
and is expected to rise each following year (Ferlay et al. 2012). Breast cancer risks are due to a
combination of genetic mutation with external circumstances such as lifestyle and environment
(Martin & Weber 2000). It is a disease of complex variables from different molecular variations,
clinical signs and symptoms, exposure factors, predictive and response outcomes.
Yearly, breast cancer screening initiatives detect tumours at an earlier stage to prevent more
aggressive disease sequelae through modalities of self-examination programmes, routine blood
check-ups, mammography, and ultrasonography. Despite these efforts, early detection is an
insufficient method to decrease the burden of disease as the incidence time are often
unpredictable. Therefore, understanding the epigenetic of cancer could serve as an implication
for prevention, detection, curative, and survival analysis of the disease.
Genetic variations may either be sporadic or inherited, although sporadic variations occur very
rarely in the normal genome. These variations may occur at different positions along the length
of each gene which may lead to genetic variants that could result in breast cancer or decrease
one’s risk to developing cancer. Single Nucleotide Polymorphism (SNP) is a variation in a
single nucleotide at a specific position in the genome, and is seen in a significant number in a
population. It is the most common type of genetic variation, and has been suggested to underlie
differences in susceptibility to disease within a population SNPs are vital because it can be used
as potential diagnostic markers, and to assess prognostic values in breast cancer (Mahdi et al.
2013).
Merely having a causal mutation is not necessarily sufficient for the occurrence of disease,
hence the need to correlate the interactions of genetic variation with environmental factors. All
factors outside the body are classified as environmental factors. According to Hertz-Picciotto
et al. (2011), some of the known factors that increases risk of breast cancer are the use of
menopausal hormone therapy, consumption of alcohol, exposure to ionising radiation, and
being overweight after menopause among others.
The present study is to estimate the predictive value of varying polymorphisms of Peroxisome
Proliferator Activated Receptor (PPAR) gene in association with breast cancer risk using
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Polygenic Risk Score (PRS). As alcohol is a known inducing factor for breast cancer risk, there
is a need to further study the genetic variants involved in ethanol metabolism that can influence
the degree of PPAR pathway in the development of breast cancer. By-products of ethanol
metabolism are important, therefore genes involved in alcohol metabolism are used as a
determinant to estimate the effects of ethanol metabolism on PPAR and breast cancer.
Additionally, ADH genes are important in ethanol metabolism therefore used for this study.
PRS is useful in the reducing the possible confounders and is the basis of analysis for PPAR
and ADH genes. In addition, oestrogen levels evaluation as a weightage to the study will
determine if polymorphisms of PPAR and ADH are significant causal gene-environment
predictors in breast cancer risk.
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2 LITERATURE REVIEW
2.1 Breast Cancer
2.1.1. Prevalence
Breast cancer cases are reportedly 25% of all cancer cases and causes annually 15% of cancer-
related deaths among women. Half of the 1.7 million new cases reported in 2012 were recorded
from developed countries most noted from Northern America, Australia/New Zealand, and
Northern and Western Europe. Incidence were lowest from Africa and Asia though the rate has
also been increasing over the years (Ferlay et al. 2012, Torre et al. 2015).
2.1.2 Aetiology
Until this present day, the entirety of breast cancer causes is still not known and the knowledge
of each causative factor’s mechanism of action are merely surface facts that needs to be
investigated. No single causal nexus has been identified though interconnecting
pathoaetiologies were established (Martin 2013).
Exogenous factors include lifestyle habits, socioeconomic status, history of exposure to
radiation, geographical demographic, and medications especially hormonal therapy. The other
contributing factor are genes that increases the risk for breast cancer which could be divided in
three categories; Mendelian high penetrance, rare moderate-penetrance, and common low-
penetrance genes (Mavaddat et al. 2010, Collins & Politopoulos 2011). Breast Cancer 1
(BRCA1), Breast Cancer 2 (BRCA2), Tumour Protein p53 (p53), Phosphatase and Tensin
Homolog (PTEN), Ataxia-Telangiectasia Mutated (ATM), Nibrin (NBS1) have strong
association to breast cancer while minor gene-related penetrance are Cytochrome P450
(CYP450) genes, Glutathione S-Transferases Mu 1 (GSTM1), Gluthathione S-Transferases Pi
1 (GSTP1), alcohol and one-carbon metabolism genes (Alcohol Dehydrogenase 1C (ADH1C)
and Methylenetetrahydrofolate Reductase (MTHFR)), DNA repair genes (X-ray Repair Cross-
Complementing 1 (XRCC1), X-ray Repair Cross-Complementing 3 (XRCC3), Excision Repair
Cross Complementing 4 (ERCC4)). Progesterone Receptor (PR), Oestrogen Receptor (ER),
Tumour Necrosis Factor Alpha (TNFA) are genes encoding cell signalling molecules
(Dumitrescu & Cotarla 2005). External factors have been suggested to be more substantial
cause of breast cancer but little is known about the quantitative relation to cancer outcome
(Adami et al. 1995).
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Hormonal factors are possible major actors in the development of breast cancer, particularly
oestrogen. In cases of ER - positive cases, over-expression of the receptor occurs. Excessive
proliferation of mammary cells due to the binding of oestrogen to ER leads to disproportionate
Deoxyribonucleic acid (DNA) replication (Travis & Key 2003). Genotoxic effects arise from
oestrogen metabolism mediated by CYP450, damaging genetic information within a cell (Russo
& Russo 2004). Oestradiol also stimulates Receptor Tyrosine Kinase (RTK), Epidermal Growth
Factor Receptor (EGFR) and Insulin-like Growth Factor 1 (IGF-1) which in turn activates
Mitogen-Activated Protein Kinase (MAPK) and Phosphoinositide 3-Kinase (PI3K) pathways
increasing cell volume (Bi et al. 2000, Zhang et al. 2011, Christopoulos et al. 2015). The other
hormone moderated pathway involves PR, represents 3-5% of total breast cancer cases (Fuqua
et al. 2005). This therefore points out the importance of knowing the age of menarche and
menopause, and ingestion of external forms of hormonal treatment.
According to Michels et al. (2007), modifiable factors cause a significant increased risk of
developing the disease. Factors like diet, smoking, alcohol intake, physical activity, irradiation,
breastfeeding practices, and parity has been shown to play an important role. Diet and alcohol
are possible contributors of cancer causes due to different incidence rates among countries
depending on the food and drinking habits. In addition, some amount of genetic variation is
suggested to occur because of certain components in a diet and food, plus antioxidants from
some screened nutrients assisted DNA repair and showed antagonistic oestrogenic effect such
as vegetables and fruits. However, the amount of genetic variation cause by diet is unknown.
(Smith-Warner et al. 2001, Michels et al. 2007)
2.1.3 Clinical features
Signs and symptoms bringing attention to the disease are breast lump, localised pain, lumps
found in axillary region, engorgement of the breast, and in later presentations, inflammation of
the skin and tissues, ulceration, and discharge from nipple are seen (Ayoade et al. 2012). Some
accompanying features arise when metastasis occur in later stages to other organs causing bony
pain, neurological, respiratory, and gastrointestinal symptoms.
2.1.4 Diagnosis
Initial diagnosis of breast cancer involves a multidisciplinary team approach involving three
steps: a temporal sequence of palpation (physical examination), complemented by imaging
either by ultrasonography or mammography, and Fine-Needle Aspiration Cytology (FNAC)
which in combination is called triple approach or assessment (Martelli et al. 1990). Physical
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examination gives the lowest sensitivity of diagnosis, while imaging, and FNAC yields slightly
higher accuracy, however in combination of these three, the preciseness increases to over 95%
(Kaufman et al. 1994). Mammography is better in detecting small masses and a consensus
decided women age 40 years and above should be screened using this modality instead of
ultrasonography due to the changes in breast tissue density. A more invasive approach to FNAC
is opted in selective cases, the large core biopsy which decreases both the problem of possible
inadequate sampling via cytology but also provides a better preoperative diagnosis (Ciatto et
al. 1997). Magnetic Resonance Imaging (MRI) is an additional radiological tool used to
differentiate scarring from tumours, and to give detailed extent of cancer location and
metastasis.
2.1.5 Pathology
2.1.5.1 Histologic Classification
Structural components of breasts include lobules, ducts, and bloods vessels which makes up for
the mammary glands, and a stromal compartment of adipose and connective tissues.
Malignancies of the ducts and lobules are the commonest, while the remaining structural
anomalies amounts to approximately 25% of all cases. Based on the histopathology differences,
it aids in diagnosis, treatment choices, and prognosis of each patient (Weigelt et al. 2010).
The histopathologic classification is based on characteristics seen upon light microscopy of
biopsy specimens. The three most common histopathological types collectively represent
approximately three-quarters of breast cancers (Lakhani et al. 2012):
1) Invasive ductal carcinoma- 55% of breast cancer cases
2) Ductal carcinoma in situ- 13% of cases
3) Invasive lobular carcinoma- 5% of cases
2.1.5.2 Histologic grading
The idea of grading is to illustrate the aggressive potential of the tumour. Nottingham Histologic
Score system is one common method used internationally. Three factors are accounted for: The
amount of differentiation (gland formation), nuclear pleomorphism, and mitotic activity. Each
of these features are scored by pathologist from 1-3, and gives a combined score that ranges
from 3-5 (Grade 1), 6-7 (Grade 2), and 8-9 (Grade 3). Grade 1 expresses low grade, grade 2
means intermediate, moderately differentiated cells, and grade 3 exhibits high grade which has
the highest aggressive potential (Elston 1984, Rakha et al. 2010).
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2.1.5.3 Biomarkers
Biomarkers offer an insight to cancer prognostic as well as predictive values. PR and ER
expressions, and Human Epidermal Growth Factor Receptor 2 (HER2) mutations provide
assessment to breast cancer status (Allred et al. 1990). ER, and PR expressions are strong
predictive biomarkers but weak in assessing prognosis while (HER2) serves as both
substantially. These receptors are investigated through immunohistochemical staining with
additional In-Situ Hybridisation (ISH) method for HER2 (Wolff et al. 2007). However, in cases
of triple-negative breast cancers, staining of all three biomarkers are absent.
2.1.6 Treatment
Treatment for breast cancer depends on the type and stage of disease. Local therapies include
surgery, and radiation therapy for early stages of breast cancer though they are also used in
certain advanced cases in adjuvant with systemic treatments (Hack et al. 2015). The aim of
systemic treatments is that the administered medications tries to reach cancer cells throughout
the body, and comprises of chemotherapy, hormone therapy, and/or targeted therapy (National
Collaborating Centre for Cancer UK 2009).
Breast-conserving surgery or mastectomy are surgical procedures that are done, along with
sentinel lymph node biopsy or axillary lymph node dissection to assess spread to lymph nodes.
A further breast reconstruction surgery is offered in cases of mastectomy. In accelerated spread
with metastases, mastectomy is offered concurrently with radiation therapy (Agarwal et al.
2014). Two forms are used to destroy cancer cells, either internal, or external radiation.
Chemotherapy are given as neoadjuvant, adjuvant, or for advanced cases. A combination of
two or three chemo medications are usually used for synergistic effects with added targeted
drugs in cases of HER2+ (Vu et al. 2014).
In ER+, and PR+ breast cancer cases, cancer cells react to oestrogen, and/or progesterone
hormone resulting in excessive proliferation (Shoker et al. 1999, Diep et al. 2015). Hormone
therapy blocks those receptors in breast cancer cells, while some decreases oestrogen levels in
the body. Furthermore, in post-menopausal women, Aromatase Inhibitors (AI) stop oestrogen
production by impeding the enzyme aromatase in fat tissues (Fabian 2007). Hormone therapy
are typically recommended for approximately 5 years (Kennecke et al. 2006).
Targeted therapy aims to specific types of cancer cells. In HER2+ breast cancer, human
epidermal growth factor protein are suggested to be interrupted by monoclonal antibody or
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kinase inhibitor (Iqbal & Iqbal 2014). Targeted therapy is added with hormone therapy for
hormone receptor positive breast cancer to increase its effectiveness, as it slows cancer cells
growth by inhibiting specific proteins responsible for division of those cells (Dickson &
Schwartz 2009).
Ovarian ablation may be offered for premenopausal women with metastatic breast cancer. As
ovaries are the main source of oestrogen production, this method reduces oestrogen levels
substantially, and causes premature menopause. AI are then administrated to counter the
enzyme aromatase. Oophorectomy, use of Luteinising Hormone-Releasing Hormone (LHRH)
analogues, and administration of certain chemotherapy drugs that damages ovaries are
examples of ovarian ablation (Prowell & Davidson 2004).
2.2 Alcohol
2.2.1 Ethanol metabolism
Ethanol oxidation to acetaldehyde through: a) ADH enzyme (encoded by ADH1B and ADH1C
genes); b) microsomal enzymes Cytochrome P450 2E1 (CYP2E1); c) microbes in human
gastrointestinal tract. The relative contributions of different pathways are represented by the
thickness of the arrows in Figure 1. The oxidation of acetaldehyde to acetate is by Aldehyde
Dehydrogenase 2 (ALDH2). The highest rate of acetaldehyde oxidation is in people carrying
two active ALDH2*1 alleles, followed by those with one active ALDH2*1 and one inactive
ALDH2*2 allele, and last, those with two inactive ALDH2*2 alleles. Acetaldehyde, Reactive
Oxygen Species (ROS), and DNA-adducts are carcinogens formed during alcohol metabolism
are highlighted (Seitz & Becker 2007).
Alcohol metabolism takes places in the brain, pancreas, stomach, but substantially in the liver.
Figure 1 depicts the ethanol metabolism that occurs.
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Cytochrome ROS DNA- Adducts
P450 2E1
ADH1B*2
ALDH2* 1/2
ADH1B*1
Ethanol Acetaldehyde Acetate ADH1C*2 ALDH2* 1/1
ADH1C*1 ALDH2* 2/2
Microbes
Figure 1: Ethanol metabolism pathway. Modified from Seitz & Becker (2007).
2.2.2 Enzymes involved in ethanol metabolism
2.2.2.1 ADH
There are five classes of ADH, which are encoded by seven genes. However, in humans, class
1 hepatic form is the primary. This class 1 are encoded by ADH1A, ADH1B, and ADH1C. The
latter two shows differentiation through polymorphism. Different quantities of acetaldehyde
occur because of variants in alleles alter their activities. (Edenberg 2007)
The higher the quantities of acetaldehyde, the higher the risk of carcinoma occurrence. Some
ADH genes that codes for these enzymes have links to the behaviour of alcohol consumption.
Some increases the risk of alcohol dependency while others deter a person from alcohol
consumption due to its adverse effects as it changes the rate of enzyme activity in ethanol
metabolism (Kuo et al. 2008).
2.2.2.2 ALDH2
ALDH2 are encoded by ALDH2 gene in humans. It belongs to the ADH enzyme group and
serves as the second enzyme in the ethanol oxidation which converts acetaldehyde to acetate.
There are two major forms in the liver, the cytosolic and mitochondrial form. The normal
ALDH2 variant is ALDH2* 1/1 while two allelic variants exist: ALDH2* 1/2 and ALDH2* 2/2.
ALDH2* 1/2 offers partial protection against acetaldehyde while the latter, ALDH2* 2/2 has
low activity, resulting in acetaldehyde accumulation. (Peng & Yin 2009)
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2.2.2.3 CYP2E1
CYP2E1 enzymes have been suggested to clear toxin within the body and is encoded by the
gene of the same name. Moreover, variants of these genes are also identified to increase hepatic
cirrhosis occurrence. It is also one of the reasons responsible for chronic alcoholism though it
cannot account for all cases. (Cederbaum 2015)
Innumerable studies have suggested that acetaldehyde accumulation in the brain strengthen
drinking habits. Increased CYP2E1 expression have been linked to increment of acetaldehyde
and ROS accumulation. (Jin et al. 2013)
2.2.3 Carcinogens from by-products of ethanol metabolism
Acetaldehyde is the intermediate product of ethanol metabolism which binds to DNA leading
to formation of adducts. When DNA-adduct occurs, the damaged DNA forms a mutation that
disables the ability of complete replication of cells. Acetaldehyde also activates chromosomal
disfiguration, micronuclei, and sister chromatid exchanges (Mechilli et al. 2007).
ROS is found in normal physiological conditions, however excess formation potentially
damages DNA, Ribonucleic acid (RNA) involved in amino acids for formation of proteins, lipid
peroxidation, and causes oxidative deactivation of specific enzymes. ROS also forms a DNA-
adduct through the formation of malondialdehyde during lipid peroxidation (Ayala et al. 2014).
The mutation formed may potentially lead to carcinogenesis.
2.3 Oestrogen sources and levels
2.3.1 Oestrogen in reproductive age women
Cholesterol is biosynthesised into various forms of oestrogen through specific pathways. Three
major forms are Oestrone (E1), Oestradiol (E2), and Oestriol (E3). Oestrogen is mainly
synthesised in the ovaries, placenta, and corpus luteum, and to a lesser extend in the heart, brain,
skin, and liver (Cui et al. 2013). Figure 2 gives an overall view of the synthesis of two major
forms of oestrogens. E2 is the major product in a premenopausal woman through the
aromatisation of testosterone in the ovaries, while E3 is mainly produced in the placenta of a
pregnant woman. Besides maintenance of pregnancy, oestrogen is also important for germ cell
maturation, bone remodelling (Väänänen & Härkönen 1996), and the maturation of the nervous
system (McEwen & Alves 1999). E2 levels along with Follicular Stimulating Hormones (FSH)
are showed in Figure 3. E2 levels in average are high during the reproductive age group.
21
Cholesterol
P450scc
Pregnenolone P45017a 17-Hydroxypregnenolone P45017a Dehydroepiandrosterone (DHEA)
3βHSD 3βHSD 3βHSD
Progesterone P45017a 17-Hydroxyprogesterone P45017a Androstenedione aromatase Oestrone
17βHSD 17βHSD
Testosterone aromatase Oestradiol
Figure 2: Synthesis of oestrogen. Modified from Pepe & Albrecht (2008).
Figure 3: Oestradiol and FSH levels in the serum of a woman over a lifetime. Modified from
Shapiro (2001).
2.3.2 Oestrogen in post-menopausal women
Oestrogen in post-menopausal women undergoes the biosynthesis process mainly in the adipose
tissue and adrenals. E1 is the major type of oestrogen synthesised at this stage of life through
the aromatisation of androstenedione (Cui et al. 2013). E2 levels are low in post-menopausal
women but FSH levels are increased.
0
20
40
60
80
100
120
140
160
0 20 40 60 80
Ho
rmo
ne:
leve
ls
Chronological: years
Oestradiol and FSH levels in the serum of a woman over a lifetime
Oestradiol (pg/ml)
FSH (mIU/ml)
22
2.3.3 Enzymes involved in oestrogen metabolism
2.3.3.1 17βHSD
Hydroxysteroid 17-Beta Dehydrogenase 1 (HSD17β1/17βHSD) gene codes for the enzyme of
the same name. In turn, 17BHSD enzyme regulates the potency of androgen and oestrogen by
oxidising the C17 hydroxy group resulting in interconversion of E1 and E2, as well as
testosterone and androstenedione. Its expression markedly decreases after menopause (He et al.
2016).
2.3.3.2 CYP19A1
More commonly known as aromatase, this enzyme is coded by the gene CYP19, located at
chromosome 15q21.1. This enzyme is more active in the later stages of life during the post-
menopausal phase where it converts androstenedione to E1, and testosterone to E2. However,
in younger women, it is also responsible for the development of the female physical sexual
characteristics. Aromatase is found in gonads, placenta, adipose tissue, bones, skin, and even
the brain (Meinhardt & Mullis 2002).
2.3.3.3 CYP17A1
This enzyme is encoded by the gene of the same name, located at chromosome 10q24.32. It is
part of the cytochrome P450 superfamily. P45017a (CYP17A1) is an important enzyme, found
in steroidogenic tissues such as testes, adrenal gland, ovaries, cardiac, and fat (National Center
for Biotechnology Information, U.S. National Library of Medicine 2017)..
2.3.3.4 3βHSD
3β-Hydroxysteroid Dehydrogenase (3βHSD) is produced in the adrenal gland, encoded by
HSD3β1 and HSD3β2 genes, and is involved in the corticosteroid pathway that forms
progesterone, 17-hydroxyprogesterone, and androstenedione (Simard et al. 2005).
2.3.4 Exogenous oestrogen
Apart from naturally occurring oestrogen production in the body, external sources also
contribute to accumulated oestrogen levels. These are categorised into two groups. First are
xenoestrogens, which are oestrogen-like compounds, a group of Endocrine Disrupting
Compounds (EDC). In has been proposed that these compounds compete in binding to hormone
receptors and causes oestrogen dominance which increases oestrogen levels within the body
23
(Roy et al. 2009). Some sources of xenoestrogens are found in red food dyes, insecticides,
combined oral contraceptive pills, and food preservatives.
The second group of exogenous oestrogens is phytoestrogen. These are found in certain plant-
based diets such as soybeans and products, lentils, sesame seeds, rice, and carrots (Gupta et al.
2016). Besides binding with oestrogen receptors, it also binds to PPARs (Gencel et al. 2012).
Hormone Replacement Therapy (HRT) was originally used as a treatment of surgically
menopausal, postmenopausal, and in some cases perimenopausal women. It is an exogenous
oestrogen, though usually, it is administered in combination with the hormones progesterone
and progestin (Campagnoli et al. 2005).
The role of HRT as a risk factor for breast cancer is not understood completely, as a clinical
trial study concluded in 2002 found that there was increased incidence of breast cancer in older
women above the age of 60 years, from HRT usage (Rossouw et al. 2002). A subsequent follow-
up study that ended in 2004 however, had a contradicting finding, suggesting that oestrogen-
only treatment did not increase risk of breast cancer but a combination of oestrogen plus
progestin did significantly increase the risk (National Heart, Lung, and Blood Institute 2006).
This could put forward a theory that there are missing gaps in methodology approaches of HRT
analysis that lead to contradicting results.
2.3.5 Oestrogen exposure and breast cancer risk
Oestrogen levels differ throughout the lifespan of a person. In women, it is responsible for the
maturation of female reproductive system, and sexual characteristics. It has been hypothesised
that duration of oestrogen exposure is one of the causes of breast cancer risk. This is explained
in cases of early menarche and late menopausal age (Lecarpentier et al. 2015) which equates to
the longer the oestrogen exposure, the higher the risk of breast cancer.
Nulliparity has been suggested to have both protective against and increased risk of breast
cancer, as the surge in oestrogen during pregnancy stimulates epithelial cells differentiation
resulting in reduce cell numbers prone for malignant transformation (Lecarpentier et al. 2015).
Without this surge, low parity, or nulliparous women are at higher risk. However, the marked
increase of oestrogen during pregnancy puts forward a possibility that malignant transformation
occurs because breast cells divide in at a higher rate from the excess circulating oestrogen
(Travis & Key 2003).
24
Some dietary patterns like alcohol consumption, and obesity have been studied. Findings from
the studies propose that alcohol intake may increase breast cancer risk through various
pathways (Scoccianti et al. 2014), while obesity results in an increase aromatisation of
androstenedione because of more availability of adipose tissues (Travis & Key 2003).
Oestrogen increases cell division and proliferation, which in turn may increase risk of genetic
mutations because nuclear DNA containing regulatory genes are disrupted (Travis & Key
2003).
2.4 Genetic variation
2.4.1 Definition of genetic polymorphism
Genetic information is stored in DNA. The building blocks of DNAs are nucleotides, consisting
of sugar-phosphate backbone and a nucleobase. Nucleobases have two groups: the pyrimidines,
consisting of Thymine (T), and Guanine (G), and the purines, Adenosine (A), and Cytosine (C)
(Genetic Home Reference 2016). Every three-nucleotide sequence, called codons have
information to form an amino acid (Berg et al. 2002).
Polymorphism of genes are due to changes in these base pairing sequences through either
deletion, insertion, relocation, or substitution of base pairs (Griffiths et al. 2000). A SNP is
where one single nucleotide is substituted. This is the most common variation seen in humans
(Shen et al. 1999). SNP explains to a certain extent, the susceptibility of different populations
to variety of diseases.
Genetic variations are vital for the development of personalised medicine as these variations
can be used to determine how an individual responds to chemicals, pathogens, and even the
formation and likelihood to develop a disease (Rajkumar 2010, Verma 2012).
2.4.2 Tag SNP
According to Takeuchi et al. (2005), a representative SNP which lies in an area of high Linkage
Disequilibrium (LD) within a genome is called a tag SNP. LD is when the rate of associated
alleles varies from what is expected of if the loci were random and independent. A haplotype
is a set of SNPs on a chromosome that usually occurs together. As tag SNPs are representations
of a set of SNPs located in a particular region of the genome, it enables identifying genetic
variation without having to study each individual SNP in a chromosomal region (Takeuchi et
al. 2005).
25
2.4.3 Population differences
While some diseases are triggered by inherited genetic risks, and some are by external factors,
researching polymorphisms is important because diseases typically have both genetic and
environmental background (Wilson et al. 2002). More often, variations of LD and haplotypes
are prominently noticeable in different populations (Takeuchi et al. 2005). Tag SNPs are unique
in populations and population differences need to be considered when studying polymorphisms.
2.4.4 Types of SNPs
There are three regions SNPs fall under: coding sequence of genes, non-coding sequence of
genes, and intergenic regions. Coding SNPs lie within the coding sequences of genes are
divided in two groups: synonymous, and nonsynonymous (Lee et al. 2006). Nonsynonymous
SNPs change the amino acid sequence of proteins, and are further categorised into two types of
substitution. First, is missense mutation that is a point mutation of a single nucleotide change,
producing a different amino acid. Non-sense, the second type of nonsynonymous substitution,
causes a point mutation in a sequence of DNA resulting in a premature stop codon. This in turn,
produces incomplete and often non-functional protein (Griffiths et al. 2000). However, majority
of SNPs are synonymous SNPs which were previously considered “silent” because they do not
change amino acids but may still alter messenger RNA stability (Duan et al. 2003).
Non-coding region SNPs affect gene expression through gene splicing, messenger RNA
degradation, transcription factor binding, or the sequence of non-coding RNA (Hrdlickova et
al. 2014).
2.4.5 Importance
With understanding of genetic variations, newer methods to prevent, control, and treat various
diseases become more efficient, as modified intervention on a population or individual level
would be possible.
In biomedical research, gene mapping helps in identifying markers related to diseases or normal
traits. Studies like Genome-Wide Association Studies (GWAS) can quantify and differentiate
genomes that are inherited from sporadic mutations (Altshuler et al. 2008).
In pharmacogenetics, drug therapy on various metabolic pathways can be sharpened according
to the polymorphisms of specific enzymes which will result in better efficacy of treatment and
minimise medication adverse effects (National Institutes of Health US 2007).
26
Potential modifiable risk can be altered and possibly eliminated when polymorphisms are
identified early.
2.5 Peroxisome proliferator activated receptor (PPAR)
PPAR are a group of transcription factors of nuclear hormone receptor superfamily. They are
all expressed but not limited only in ovaries, and are important for multiple operations,
including cholesterol metabolism, glucose metabolism, angiogenesis, cell remodelling, and
apoptosis, among others. There are three groups of PPARs: PPAR Alpha (PPARα), Delta
PPARδ), and Gamma (PPARγ). (Komar 2005, Tyagi et al. 2011)
2.5.1 PPARα
PPARα is mainly synthesised in the liver and to some extend in the ovary, kidney, heart, muscle,
and small intestine. In many studies involving metabolic syndrome, PPARα has been shown to
have regulatory properties influencing glucose, and lipid metabolism. (Tachibana et al. 2008,
Tyagi et al. 2011). This factor is activated by polyunsaturated fatty acids, and anti-
dyslipidaemia medications. Its activation agonist Wy-14,643 has been used to treat obesity-
related insulin resistance in mice, by preventing adipocytes hypertrophy, decreasing the
expression of macrophage-specific genes in White Adipose Tissue (WAT), and increases the
amount of Adiponectin Receptor (AdipoR)-1 and AdipoR2 in WAT (Tsuchida et al. 2005).
2.5.2 PPARδ
The expression of PPARδ is ubiquitous, and plays a vital role in wound healing, lipid catabolism
in skeletal muscles, suppresses inflammation mediators, and increase insulin sensitivity (Peters
et al. 2008, Tachibana et al. 2008, Coll et al. 2009).
2.5.3 PPARγ
PPARγ activity is found mainly in the adipose tissue, and because of this, extensive studies
have been conducted in relation to metabolic syndrome because of the adipose tissue- mediated
insulin resistance. Over the past years, PPARγ has been found to mediate the insulin-sensitising
class drug, thiazolidinedione, also known as glitazones (Spiegelman 1998). PPARγ activation
causes upregulation of genes by binding with coactivators’ complexes, and competing with
other transcription factors, resulting in increased levels of adiponectin, decrease in insulin
resistance, inhibition of angiogenesis, and adipocyte differentiation (Semple et al. 2006,
Medina-Gomez et al. 2007).
27
2.5.4 PPAR and alcohol
According to some studies, ethanol metabolism and PPAR functions have a propinquity
regulation to one another. When alcohol consumption increases, the enzymes, ADH and ALDH
downregulates PPAR activation. This leads to oxidative stress, inflammation of cells, and
inhibition of fatty acid metabolism. However, the induction of PPARα by its agonist Wy-14,643
restores and increases the number of binding capacity of PPARα, resulting in some PPARα
target genes not downregulated by ethanol metabolism enzymes and reduces the effect of
acetaldehyde on PPARα. Due to some remaining function of PPARα, the negative effects of
acetaldehyde are reduced. PPARα agonist Wy-14,643 has also been shown to slightly reduce
the level of ALDH2 protein. (Crabb et al. 2001, Mello et al. 2009)
2.5.5 PPAR and cancer
Various studies noticed a linked between PPARα and PPARδ to increased cell proliferation,
which encourages tumourigenesis, while PPARγ is seen to inhibit cell proliferation and induces
apoptosis, therefore protective against cancer (Tachibana et al. 2008). PPARγ in breast tissues
decreases Vascular Endothelial Growth Factor (VEGF) and Cyclooxygenase-2 (COX-2) which
are responsible for inflammatory response. This in turn blocks the cell tumourigenesis
progression (Apostoli et al. 2015). PPARα exhibits oxidative stress features by increasing
Hydrogen Peroxide (H2O2) at intracellular level, which in turn increases DNA synthesis (Goel
et al. 1986). While PPARδ have been theorised to encourage tumourigenesis through cell
proliferation, it is also a potent anti-inflammatory enzyme and affects PPARγ activity by
competing with it for ligand transcripts binding (Shi et al. 2002). PPARδ also have protective
effect on breast tissues against metabolic conditions which are activated by adipose tissue
(Wang et al. 2016).
2.6 Summary of the literature review
Occurrence of breast cancer is increasing annually, with higher rates in advanced nations
comparing to developing countries. There are probabilities that genetic variants occur to some
extend by modifiable or external factors in addition to being passed down from within the
familial gene pools. This is theorised because different populations have different frequency of
genetic variants due to different lifestyle and exposures. More research is needed to understand
the causal mechanisms of different variables that results in the development of cancer.
Aggressive tumours often have poor response to treatment causing accumulated rise in
mortality incidences. By identifying biomarkers or specific genetic traits that are linked to risk
28
of breast cancer. Therefore, possible preventive measures can be taken to alter potential
modifiable factors such as ethanol consumption. These external factors include the role of
ethanol metabolic pathway, oestrogen levels throughout the lifetime, and PPAR genes
activation through its different pathways influencing the occurrence of breast cancer. By
identifying specific variants in a selected population, early detection and specific preventive
interventions can be offered.
29
3 AIMS OF THE STUDY
Preventing and controlling of DNA damage caused by potentially modifiable source are
important. One of such modifiable source is alcohol, and the result of variations in the alcohol
metabolism affecting certain receptors that leads to cancer. This study aims to discover the
relation of alcohol metabolism, PPAR, and oestrogen in association to breast cancer risk.
The specific aims of this study are:
1. To investigate the association of combined PPARα, δ and γ polymorphism on breast
cancer risk
2. To measure the combined effect of PPAR and ADH SNPs on breast cancer risk
3. To substantiate if oestrogen is a modifier on breast cancer risk
30
4 METHODOLOGY
4.1 Study design, setting, and subjects
Subjects were chosen from the Finnish population, from the Kuopio Breast Cancer Project
(KBCP). KBCP is a prospective population-based case control study done from 1990-1995.
The study was conducted on women with breast symptoms seeking treatment from Kuopio
University Hospital. Written informed consent were collected from subjects participating in
KBCP. Out of 1,919 participants, 520 were diagnosed with breast cancer which followed by
data collection regarding medical history, socioeconomic background, family history of breast
cancer, cigarette smoking, and alcohol use. Information on clinic-pathological features,
interventions, and follow-up were taken from hospital registries.
Only patients who had the genotypes needed for this study were selected (n=445). Controls
were selected from the National Population Registry living in the same area. The controls were
matched based on long term residence in the area, age, and genotype which ended up with 251
participants.
4.2 Data collection and ethical considerations
Questionnaires were administered by trained nurses during participants’ visit to the hospital but
participants were not obligated to answer all the questions provided and incomplete answers
were taken into account for the KBCP.
For this study, the focus was on three entities. First were the PPARα, PPARδ and PPARγ
variants of each subjects that were genotyped. Second, the estimated oestrogen levels based on
the duration of active oestrogen synthesis from the time of menarche to menopause so that the
oestrogen exposure throughout a lifetime can be quantified along with the parity index, lactating
status, and age when breast cancer was diagnosed. Lastly, the alcohol data based on the alcohol
consumption answered by participants and the ADH1A, ADH1B and ADH1C variations among
these subjects.
The KBCP has been approved by the Ethical Committee of the University of Eastern Finland.
This is a non-experimental retrospective nested case-control study, and all written informed
consent from participants were taken during the initial questionnaire distribution. KBCP has
also been approved by Kuopio University Hospital Board on Research Ethics.
31
4.3 Genotyping of PPAR and ADH genes
Genomic DNA was extracted from peripheral blood lymphocytes of participants using standard
methods. Genotyping of samples was carried out using an Illumina Custom Infinium
genotyping array (iCOGS), designed for the Collaborative Oncological Gene‐Environment
Study (COGS) (Ronnberg 2014) and consisting of 211,155 SNPs.
4.4 SNP selection and analysis of PPAR genes
TagSNPs for PPARα, PPARδ and PPARγ were chosen using the GWAS. TagSNPs for regions
chr22:46150521-46243756, chr6:35342558-35428191, and chr3:12287368-12471013 were
selected for the Central European population using the Tagger multimarker algorithm with r2
cut off at 0.8 and minor allele frequency cut off at 0.05.
40 functional SNPs of PPAR gene were then identified. Out of these 40, 15 SNPs were common
PPARα variants, 13 SNPs of PPARδ, and 12 SNPs of PPARγ shown in Table 1. PRS was used
to estimate the risk effects associated with 40 common PPAR variants to breast cancer because
individual loci may have an insignificant effect on breast cancer risk. It was calculated for each
individual participant using the following formula:
∑ 𝑎𝑖𝑛𝑖=1 log ORi where n is the number of loci included in the model, a is the number of disease
alleles at locus, i and OR is the corresponding per-allele odds ratio for breast cancer.
Subsequently, using P values and log10 odds ratios for each available variant of PPAR SNP in
each participant, and adding them together, to obtain the PRS of PPAR on breast cancer risk of
each case and control. The –log10 scale was decided on for PRS analysis because even with
very large sample sizes, the predictive value would be optimised while eliminating confounders
(Dudbridge 2016).
32
Table 1: Identified functional SNPs of PPAR genes (PPARα, PPARδ, PPARγ).
PPARα PPARδ PPARγ
rs5767743 rs4713853 rs6782178
rs4253760 rs6901410 rs2972165
rs4253766 rs11751895 rs3112395
rs4253776 rs9658081 rs3963364
rs4253747 rs3777744 rs4135258
rs4253754 rs9658100 rs2938392
rs4253755 rs6457816 rs1175541
rs6007662 rs9658119 rs3105363
rs4253712 rs2016520 rs1152001
rs5767560 rs4713854 rs1152002
rs5766743 rs36018387 rs3103310
rs4253728 rs2076169 rs13099078
rs4253801 rs3734254
rs11704979
rs9626814
4.5 Selection and analysis of ADH
ADH SNPs were first chosen based on published literature and extracted from ICOGS genotype
data. Three notable SNPs were chosen based on genotype availability in KBCP. These variants
within rs931635 in the gene ADH1A, rs1042026 in gene ADH1B, and rs698 in gene ADH1C
were selected because of their known association with behaviour of higher alcohol consumption
and established links to breast cancer from previous studies (Edenberg et al. 2006, Birley et al.
2009, Toth et al. 2011). PRS was done for the combined three ADH variants. Later, the PRS of
PPAR and ADH were added up together to see PPAR and ADH polymorphisms influence on
each other in association to breast cancer.
33
4.6 Oestrogen level
Based on the answers by the participants in the questionnaire, the estimated length of oestrogen
exposure was calculated from age of menarche to menopause. In the analysis, the parity status,
history of lactation, length of menstruation in days, length of menstruation cycle in days, age at
time of diagnosis/questionnaire filled were added in. The characteristics of the oestrogen data
is provided in Table 2. This provides a view of the overall oestrogen exposure throughout the
lifetime, and identifies specific time of exposure where breast cancer risk is increased.
Oestrogen serves as the effect modifier in this study.
Table 2: Characteristics of the estimated oestrogen levels.
Characteristics Category
Age of menarche 10-19 years old
Age of menopause 29-60 years old
Total years of oestrogen exposure (from menarche to menopause) 15-49 years old
Length of menstruation (in days) 2-10 days
Length of menstrual cycle (in days) 14-45 days
No. of pregnancies 0-8 pregnancies
Age (at time of questionnaire) 23-91 years old
Lactating status 0 = no, 1 = yes
4.7 Association analyses
The association between the PRS of PPARα, δ and γ to breast cancer risk, and ADH to breast
cancer risk used logistic regression from SPSS version 23, by calculating the P trend for
heterogeneity, ORs and CIs depending on major and minor allele variants seen in each case,
and each control to determine the significance of the findings. Descriptive analysis was applied
for oestrogen levels according to all the answers given from the questionnaire and graphs
produced shows the oestrogen risk on breast cancer regardless of the PRS of PPARα, δ and γ
on breast cancer.
34
5 RESULTS
5.1 The effect of PPAR polymorphism on breast cancer risk
In the following PPAR PRS results, 0 is the denominator of risk score value, where any negative
value signifies decrease risk to developing breast cancer, and positive values indicate an
increased risk of breast cancer. Cases and controls are labelled as subjects 1 to 696 of the PPAR
variants analysed. The Odds Ratios (OR) and 95% Confidence Intervals (CI) calculated
according to each risk allele based on their homozygous references in those 40 PPAR
polymorphisms with breast cancer and controls among the 696 subjects are shown in Table 3
(Appendix 9.1). Minor alleles were calculated against common haplotype alleles to determine
if different alleles within a genotype have different significance value as a predictor in risk
score against breast cancer.
5.1.1 The association of PPARα polymorphism to breast cancer risk
Genotypes of 15 PPARα SNPs were analysed using PRS. As seen in Figure 4, there was an
association of PPARα with an average risk reduction of breast cancer by 0.18 but there was no
statistically significance in its reduction of breast cancer with all 15 SNPs of PPARα (P=0.181,
OR=0.242, 95% CI=0.151-1.309). Furthermore, there is no nominal significance between
minor alleles as seen in Table 3 (Appendix 9.1).
Figure 4: Risk of PPARα variants of each case and control to developing breast cancer
according to PRS.
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0 100 200 300 400 500 600 700 800
Ris
k sc
ore
val
ue
Subjects
PRS of 15 PPARα variants
35
5.1.2 The association of PPARδ polymorphism to breast cancer risk
The PRS ran using 13 SNPs of PPARδ shows that majority increases the incidence of breast
cancer. However, three variants did have protective effect against breast cancer. Figure 5 is the
PRS of PPARδ variants. There were three notable variants that had decreasing effect on breast
cancer risk: rs9658081, rs4713854 and rs36018387. Out of these, only one is statistical
significance with respect to the rs4713854 genotype where risk allele AC genotype analysis
showed P=0.04, OR=0.561, 95% CI=0.317-0.995 when compared to its reference AA
genotype. Although the three variants mentioned above brought an average reduction of 0.008,
and ten variants increase risk by 0.03, it was not statistically significant because P=0.09,
OR=0.823, CI=0.698-1.211. Therefore, there are no significant association of PPARδ
polymorphism to breast cancer risk reduction.
Figure 5: Risk of PPARδ variants of each case and control to developing breast cancer in the
PRS.
5.1.3 The association of PPARγ polymorphism to breast cancer risk
This PRS suggests that it decreases the risk of breast cancer using 12 SNPs of PPARγ. As seen
in Figure 6, there is an average risk reduction of breast cancer with all 12 SNPs by 0.03.
However, there are no nominal significant differences between minor alleles as shown in Table
4 (Appendix 9.1) and no statistically significant association of PPARγ polymorphism to breast
cancer reduction (P=0.920, OR=0.842, 95% CI=0.492-1.933).
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0 100 200 300 400 500 600 700 800
Ris
k sc
ore
val
ue
Subjects
PRS of 13 PPARδ variants
36
Figure 6: Risk of PPARγ variants of each case and control to developing breast cancer in the
PRS.
5.1.4 The effect of combined PPARα, PPARδ, and PPARγ polymorphisms on breast cancer
risk
The interaction between these three noted PPAR genes were calculated with PRS and
collectively showed in Figure 7, there was an association of PPAR polymorphisms in
decreasing the risk of breast cancer. The mean was 0.35 in reduction of risk. Although it was
not statistically significant, it may be considered as suggestive (P=0.06, OR=0.724, CI=0.462-
1.121).
Figure 7: Risk of combined PPARα, δ, and γ variants of each case and control to developing
breast cancer in the PRS.
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0 100 200 300 400 500 600 700 800
Ris
k sc
ore
val
ue
Subjects
PRS of 12 PPARγ variants
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0 100 200 300 400 500 600 700 800
Ris
k sc
ore
val
ue
Subjects
PRS of PPARα, δ, γ on breast cancer risk
37
5.2 The association of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 to breast
cancer risk
The following shows significant association between ADH1A rs931635, ADH1B rs1042026
and ADH1C rs698 by a mean of 0.4 to increase of breast cancer risk as seen in Figure 8. The
P=0.05, OR=0.674, 95% CI=0.457-0.985. However, there is no nominal difference between
minor alleles in association increased in breast cancer risk as reflected on Table 4.
Figure 8: Risk of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 of each case and
control to breast cancer risk in the PRS.
Table 4: Summary table of ORs for association between rs931635, rs1042026, rs698 and breast
cancer risk.
SNP Genotype Cases, n Controls, n P for trend OR CI (95%)
ADH1A
rs931635 GG 317 54
AG 248 24 0.771 0.555, 1.069
AA 48 5 0.190 0.674 0.362, 1.256
AG+AA 296 29 0.295 0.754 0.552, 1.031
ADH1B
rs1042026 AA 387 54
AG 192 26 0.753 0.533, 1.062
0
0.1
0.2
0.3
0.4
0.5
0.6
0 100 200 300 400 500 600 700 800
Ris
k sc
ore
val
ue
Subjects
PRS of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698
38
GG 34 3 0.258 0.989 0.495, 1.975
AG+GG 226 29 0.302 0.784 0.567, 1.085
ADH1C
rs698 GG 168 23
AG 315 39 0.902 0.626, 1.299
AA 130 21 0.855 0.920 0.591, 1.433
AG+AA 445 60 0.751 1.069 0.784, 1.456
5.3 The PRS of combined PPARα, δ, γ variants and ADH variants in association to breast
cancer risk
Figure 9 shows the combined PRS of PPARα, PPARδ, PPARγ with ADH1A rs931635, ADH1B
rs1042026 and ADH1C rs698 increases breast cancer risk by a mean of 0.1872. It indicates that
ADH gene decreases the protective effect of PPARs on breast cancer when comparing Figure
9 to Figure 7.
Figure 9: Combined risk of PPARα, δ, and γ variants with ADH1A rs931635, ADH1B rs1042026
and ADH1C rs698 of each case and control to developing breast cancer in the PRS.
-0.1
0
0.1
0.2
0.3
0.4
0.5
0 100 200 300 400 500 600 700 800
Rsi
k sc
ore
val
ue
Subjects
Polygenic risk score
39
5.4 Predictive significance of oestrogen exposure throughout a woman’s lifetime on breast
cancer incidence
Descriptive analysis was conducted from the oestrogen data from the KBCP cases and controls.
It represents the absolute numbers of the cases from the KBCP. First part of Figure 10: A), the
result shows that the frequency of breast cancer cases were highest between 35-40 years of
length of oestrogen exposure. The peak corresponds to the usual time of menopause occurrence
where oestrogen levels are highest. Subsequently, after menopause, oestrogen levels decrease
because oestrogen hormones circulating in the body are at smaller amount, which is reflected
by the declining frequency of breast cancer cases when length of exposure exceed 40 years.
Next, B) shows that when age of participants was categorised the peri-menopausal and
menopausal subjects (age groups between 45-65 years) in this study had a higher incidence of
breast cancer. Lastly, in C) and D) the trend of pregnancy numbers among cases and controls
are shown, indicating more women in the case-group does not have children versus those in the
control group.
A) B)
40
C)
D)
Figure 10: Frequency of breast cancer cases according to length of exposure, age, and number
of pregnancies.
A) Relation of length of oestrogen exposure (total number of years) with frequency of breast
cancer incidences out of the 696 subjects. B) Relation of age of studied subjects with frequency
of breast cancer incidences. Age was categorised (Age Cat) from 1.00 to 6.00. 1.00=34.9 years
and below, 2.00= 35-44.9 year olds, 3.00=45-54.9 year olds, 4.00=55-64.9 years, 5.00=65-74.9
years, and 6.00= 75 years and above. C) and D) Trend of parity index (number of pregnancies)
of cases and controls.
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7 8
Nu
mb
er o
f ca
ses
Number of pregnancies
Trend of number of pregnancies among the cases
0
20
40
60
80
0 1 2 3 4 5 6 7 8
Nu
mb
er o
f co
ntr
ols
Number of pregnancies
Trend of number of pregnancies among the controls
41
6 DISCUSSION
6.1 Summary of main findings
PPARα and PPARγ variants decrease breast cancer risk, however there were not statistically
significant. Also, there were no significant differences between minor alleles in relation to the
risk reduction. PPARδ polymorphisms indicated a mixed picture in breast cancer risk as some
increases while some decreases the risk. Variants rs9658081, rs4713854 and rs36018387 are
seen to be protective against breast cancer, however no significant differences between alleles
were noted except in rs4713854, where carriers with minor allele C have nominal significance
against common AA genotype in reducing breast cancer risk. Overall combination of the three
PPAR variants brought a reduction in risk by 0.35.
ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 statistically significantly increases
the risk of breast cancer and decreases the effect of PPARα, PPARδ and PPARγ variants on
breast cancer risk reduction.
Oestrogen remains a factor in influencing breast cancer risk. It follows a trend where peri-
menopausal and menopausal women are within the highest group with breast cancer cases
because their circulating oestrogen are at the highest. Though oestrogen exposure is life-long,
the circulating E2 hormones decreases post-menopause, decreasing the risk of breast cancer.
6.2 Comparison with previous studies
6.2.1 PPARα on breast cancer risk
From previous studies, PPARα has been established through its various pathophysiology to
increase cell proliferation (Tachibana et al. 2008). Furthermore, PPARα polymorphism
rs4253760 study result had double the odds of breast cancer development in postmenopausal
women with fourteen different haplotypes (Golembesky et al. 2008).
In the present study, through PRS of 15 PPARα SNPs genotype, it is suggestive that PPARα
gene decreases the risk of breast cancer. This contradicts the findings from previous studies and
could be explained because of the methods in study analysis. Effect of single gene variant may
yield a different result in comparison to the epistasis effect between multiple variants which
draws a collective picture of the overall PPARα mechanism of action in correlation to breast
cancer risk.
42
The possible reason of PPARα in reducing breast cancer risk could possibly lie in its role in
coding the PPARα enzyme that is involved in lipid metabolism (Tyagi et al. 2011), because the
activation of the enzyme requires adipocyte tissues and polyunsaturated fatty acids. This might
explain the increase risk of breast cancer in certain population where obesity is endemic but
low in certain populations where obesity prevalence is low.
6.2.2 PPARδ on breast cancer risk
PPARδ appears to be the most important gene out of the PPAR family. It is known to inhibit
ligand-induced transcription activity of PPARα and PPARγ (Shi et al. 2002). Due to this, it
decreases the amount of PPARγ activation therefore indirectly increasing cancer risk. In
contrast, PPARδ is also a key to increase insulin sensitivity and decrease inflammation which
may explain the lower occurrence of breast cancer (Peters et al. 2008, Coll et al. 2009). From
the PRS analysis, PPARδ had mixed relation to breast cancer risk according to variants and
concurs with the findings from previous studies. Out of the thirteen SNPs analysed by PRS, ten
SNPs were seen in to increase breast cancer risk although it was not statistically significant,
while three SNPs decrease the risk of breast cancer. Based on previous study, three
polymorphisms have association to obesity. These are the variants rs2016520, rs3734254, and
rs9794 (Astarci & Banerjee 2010). It is possible that three polymorphisms; rs9658081,
rs4713854, and rs36018387 that were found to be protective against breast cancer risk in this
PRS analysis is not activated by fatty acid but is triggered by a different factor. However, the
lack of information of the percentage of body fat for the participants make this theory unclear.
The ten variants that increase the risk of breast cancer possibly follows the pathway where these
genetic variants are activated by adipose tissue seen in population that are high in obesity
prevalence (Astarci & Banerjee 2010).
6.2.3 PPARγ on breast cancer risk
The result of PPARγ PRS was consistent with previous studies suggesting PPARγ association
to be having a lowered risk of breast cancer. PPARγ has also been identified and utilised into
breast cancer treatment research. However, the role of PPARγ is selective depending on the
tissues it is expressed from. PPARγ expression is seen in a variety of cells, not exclusively only
in mammary epithelium. It is also found in adipocytes, and multiple tumour cells like colonic
cells (Dong 2013). In general, PPARγ expression decreases insulin resistance, cell proliferation,
and is involved in lipid metabolism. In cancerous environment, PPARγ expressions are
increased except in mammary glands where PPARγ deletion occurs intermediately (Nicol et al.
43
2004, Apostoli et al. 2015). This means that PPARγ’s tumour suppression or oncogenic effect
is specific to the cells involved and the PRS PPARγ result should not be a representative of all
cancers. This is also suggestive that some variants of PPARγ may either be seen in different cell
types or limited exclusively to selected cells. In case of mammary glands, adipocytes are
predominant. Among the 12 PPARγ polymorphisms investigated in this PRS study, four stand-
alone SNPs increase breast cancer risk although collectively with other SNPs, they have a
negative score value. These are rs4135258, rs2938392, rs1152001, and rs13099078. They were
however not statistically significant but this may be because of the small sample size of the
population in this study.
6.2.4 Predictive role of ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 to breast
cancer risk
ADH1A rs931635, ADH1B rs1042026 and ADH1C rs698 are involved in ethanol metabolism.
Therefore, these three variants were used to analyse the predictive role of ADH genes on breast
cancer risk. The finding is the same as previous studies suggesting that ADH increases the risk
of breast cancer (McCarty et al. 2012).
6.2.5 ADH influence on PPAR in association to breast cancer risk
In general, ADH genes downregulate PPAR activity. Due to this, the influence of PPARα,
PPARδ, and PPARγ on breast cancer risk is also reduced. While it concurs with previous
studies, there is lack of information about which allelic variants in PPARα that have a strong
activity in reducing negative effects of acetaldehyde during ethanol metabolism. This is
important because to some extent, PPARα has known propinquity regulation in ethanol
metabolism. (Mello et al. 2009)
6.2.6 Predictive role of oestrogen to breast cancer risk
This study reaffirmed findings from many previous studies. The longer the exposure of
oestrogen from menarche to menopause, the higher the risk of breast cancer. Subsequently a
declining frequency trend of breast cancer incidence after menopause happens because of the
decreased oestrogen levels (Travis & Key 2003). The usual ages of peri-menopausal and post-
menopausal women are between 45-65 years (Prior 1998, Gold 2011). However, the results in
this study are crude representations of the population and oestrogen levels. The results were not
adjusted according to the mean of the highest and lowest oestrogen exposure which would show
whether there was a linear relationship between oestrogen levels and breast cancer incidence.
44
6.3 Strengths and limitations of the study
By using PRS, potential confounders including other genes influencing the outcome are reduced
because PRS uses the number of disease alleles and corresponds to per-allele odds ratio to the
explicit gene studied in relation to the outcome. Therefore, the genotypes studied for the breast
cancer incidence in this cohort are specific as the number of cases and controls were known. It
explains the overall variant association to outcome as well as identifying nominal effect
differences of different allele combinations within a variant in relation to the outcome.
Additionally, PRS analysis combined effects of many variants of high- or low- risk genotypes
rather than single variants which results in a more significant effect on disease risk.
A major limitation of the study was some participants did not answer all the questions in the
questionnaire. This was particularly evident when possible confounders in alcohol could not be
identified as there were no available information about age, duration of alcohol consumption in
years, along with other indicators like smoking and obesity. Oestrogen levels were not adjusted
to highest and lowest because many participants did not recall the age of menarche and many
did not answer age of menopause. With this lack of data, a mean representative of an already
small sample size would not be a true representation of the study population.
Chance may also play significant role in this study due to the small sample size.
45
7 CONCLUSION
The evidence from this study suggests:
I Combined PPARα, δ and γ polymorphisms decrease breast cancer risk, however the
finding was not statistically significant.
II Combined effect of PPAR and ADH SNPs on breast cancer risk increases breast cancer
risk by a mean of 0.1872. ADH1A rs931635, ADH1B rs1042026, and ADH1C rs698
reduce PPARα, δ and γ polymorphisms’ protective effect on breast cancer risk.
III Oestrogen is a potent modifier of breast cancer risk, however its extent as a predictor of
breast cancer independent of PPAR and ADH is not known.
46
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9 APPENDICES
9.1 Table 3: Summary table of ORs for association between 40 PPAR polymorphisms and
breast cancer risk
SNP Genotype Cases, n (%) Controls, n (%) P for trend OR CI (95%)
PPARα
rs5767743 AA 213 (47.9) 118 (47.0)
AG 192 (43.1) 113 (45.0) 1.062 0.769, 1.468
GG 40 (9.0) 20 (8.0) 0.840 0.903 0.504, 1.615
AG+GG 232 (52.1) 133 (53) 0.752 1.035 0.759, 1.411
rs4253760 AA 317 (71.2) 169 (67.3)
AC 116 (26.1) 77 (30.7) 1.249 0.886, 1.761
CC 11 (2.5) 5 (2.0) 0.413 0.855 0.292, 2.502
AC+CC 127 (28.6) 82 (32.7) 0.243 1.215 0.869, 1.698
rs4253766 GG 384 (86.3) 214 (85.3)
AG 58 (13.0) 37 (14.7) 1.145 0.734, 1.786
AA 3 (0.7) 0 0.218 0.000 0.000
AG+AA 61 (13.7) 37 (14.7) 0.084 1.088 0.700, 1.692
rs4253776 AA 381 (85.6) 212 (84.5)
AG 60 (13.5) 39 (15.5) 1.168 0.755, 1.808
GG 4 (0.9) 0 0.130 0.000 0.000
AG+GG 64 (14.4) 39 (15.5) 0.514 1.095 0.711, 1.687
Rs4253747 TT 301 (67.6) 182 (72.5)
AT 125 (28.1) 58 (23.1) 0.767 0.534, 1.102
AA 19 (4.3) 11 (4.4) 0.351 0.957 0.446, 2.058
AT+AA 144 (32.4) 69 (27.5) 0.442 0.792 0.564, 1.114
Rs4253754 GG 329 (73.9) 194 (77.3)
AG 108 (24.3) 52 (20.7) 0.817 0.561, 1.189
AA 8 (1.8) 5 (2.0) 0.558 1.060 0.342, 3.286
AG+AA 116 (26.1) 57 (22.7) 0.712 0.833 0.579, 1.198
Rs4253755 GG 388 (87.2) 215 (85.7)
AG 52 (11.7) 34 (13.5) 1.175 0.739, 1.866
59
AA 5 (1.1) 1 (0.4) 0.455 0.359 0.042, 3.095
AG+AA 57 (12.8) 35 (13.9) 0.492 1.103 0.701, 1.734
Rs6007662 AA 202 (45.4) 118 (47.0)
AG 200 (44.9) 109 (43.4) 0.933 0.674, 1.292
GG 43 (9.7) 24 (9.6) 0.916 0.955 0.552, 1.654
AG+GG 243 (54.6) 133 (53.0) 0.725 0.937 0.687, 1.278
Rs4253712 AA 306 (68.8) 184 (73.3)
AG 120 (27.0) 59 (23.5) 0.818 0.570, 1.174
GG 19 (4.3) 8 (3.2) 0.422 0.700 0.300, 1.632
AG+GG 139 (31.3) 67 (26.7) 0.295 0.802 0.568, 1.131
Rs5767560 AA 349 (78.4) 208 (82.9)
AT 84 (18.9) 41 (16.3) 0.824 0.546, 1.242
TT 10 (2.2) 2 (0.8) 0.211 0.338 0.073, 1.555
AT+TT 94 (21.1) 43 (17.1) 0.498 1.295 0.869, 1.932
Rs5766743 AA 261 (58.7) 164 (65.3)
AG 153 (34.4) 73 (29.1) 0.759 0.540, 1.067
GG 31 (7.0) 14 (5.6) 0.216 0.719 0.371, 1.391
AG+GG 184 (41.4) 87 (34.7) 0.650 0.752 0.546, 1.038
Rs4253728 GG 286 (64.3) 173 (38.9)
AG 123 (27.6) 59 (23.5) 0.780 0.544, 1.119
AA 21 (4.7) 7 (2.8) 0.176 0.542 0.226, 1.301
AG+AA 144 (32.3) 66 (26.3) 0.584 0.746 0.529, 1.052
Rs4253801 AA 442 (99.3) 251 (100)
AG 3 (0.7) 0 0.101 0.000 0.000
Rs11704979 GG 381 (85.6) 213 (84.9)
AG 59 (13.3) 36 (14.3) 1.087 0.695, 1.699
AA 3 (0.7) 0 0.244 0.000 0.000
AG+AA 62 (14.0) 36 (14.3) 0.498 1.034 0.664, 1.611
Rs9626814 GG 384 (86.3) 213 (84.9)
AG 58 (13.0) 38 (15.1) 1.181 0.759, 1.838
AA 3 (0.7) 0 0.199 0.000 0.000
AG+AA 61 (13.7) 38 (15.1) 0.487 1.123 0.725, 1.741
60
PPARδ
Rs4713853 AA 362 (81.3) 187 (74.5)
AG 40 (9.0) 22 (8.8) 0.979 0.568, 1.688
GG 1 (0.2) 2 (0.8) 0.558 3.559 0.321, 39.471
AG+GG 41 (9.2) 24 (9.6) 0.511 1.042 0.614, 1.769
Rs6901410 AA 426 (95.7) 245 (97.6)
AG 19 (4.3) 6 (2.4) 0.187 0.549 0.216, 1.393
Rs11751895 AA 434 (97.5) 245 (97.6)
AG 10 (2.2) 6 (2.4) 0.904 1.065 0.383, 2.967
Rs9658081 GG 444 (99.8) 250 (99.6)
AG 1 (0.2) 1 (0.4) 0.687 1.776 0.111, 28.517
Rs3777744 AA 389 (87.4) 219 (87.3)
AG 40 (9.0) 24 (9.6) 1.075 0.631, 1.829
GG 2 (0.4) 2 (0.8) 0.821 1.791 0.251, 12.802
AG+GG 42 (9.4) 26 (10.4) 0.712 1.109 0.662, 1.857
Rs9658100 AA 420 (94.4) 245 (97.6)
AC 19 (4.3) 6 (2.4) 0.187 0.549 0.216, 1.393
Rs6457816 AA 425 (95.5) 245 (97.6)
AG 20 (4.5) 6 (2.4) 0.146 0.520 0.206, 1.313
Rs9658119 AA 410 (92.1) 236 (94.0)
AC 35 (7.9) 15 (6.0) 0.348 0.745 0.398, 1.392
Rs2016520 AA 329 (73.9) 197 (78.5)
AG 110 (24.7) 51 (20.3) 0.774 0.532, 1.128
GG 6 (1.3) 3 (1.2) 0.399 0.835 0.207, 3.376
AG+GG 116 (26.0) 54 (21.5) 0.154 0.777 0.538, 1.123
Rs4713854 AA 394 (88.5) 234 (93.2)
AC 51 (11.5) 17 (6.8) 0.040 0.561 0.317, 0.995
Rs36018387 GG 320 (71.9) 182 (72.5)
AG 108 (24.3) 61 (24.3) 0.993 0.691, 1.427
AA 17 (3.8) 8 (3.2) 0.909 0.827 0.350, 1.955
AG+AA 125 (28.1) 69 (27.5) 0.661 0.971 0.687, 1.371
Rs2076169 AA 371 (83.4) 217 (86.5)
61
AG 72 (16.2) 33 (13.1) 0.784 0.502, 1.223
GG 2 (0.4) 1 (0.4) 0.552 0.855 0.077, 9.493
AG+GG 74 (16.6) 34 (13.5) 0.913 0.786 0.506, 1.219
Rs3734254 AA 347 (78.0) 207 (82.5)
AG 83 (18.7) 36 (14.3) 0.726 0.474, 1.111
GG 4 (0.9) 1 (0.4) 0.240 0.418 0.046, 3.766
AG+GG 87 (19.6) 37 (14.7) 0.448 0.711 0.467, 1.083
PPARγ
Rs6782178 GG 201 (45.2) 120 (47.8)
AG 203 (45.6) 96 (38.2) 0.792 0.568, 1.104
AA 41 (9.2) 35 (13.9) 0.064 1.430 0.863, 2.368
AG+AA 244 (54.8) 131 (52.1) 0.249 0.899 0.659, 1.226
Rs2972165 GG 332 (74.6) 200 (79.7)
AG 107 (24.0) 46 (18.3) 0.714 0.484, 1.051
AA 6 (1.3) 5 (2.0) 0.183 1.383 0.417, 4.592
AG+AA 113 (25.3) 51 (20.3) 0.492 0.749 0.515, 1.089
Rs3112395 GG 426 (95.7) 240 (95.6)
AG 18 (4.0) 10 (4.0) 0.986 0.448, 2.171
AA 1 (0.2) 1 (0.4) 0.922 1.775 0.111, 28.506
AG+AA 19 (4.2) 11 (4.4) 0.143 1.028 0.481, 2.196
Rs3963364 CC 329 (73.9) 179 (71.3)
AC 104 (23.4) 63 (25.1) 1.121 0.781, 1.609
AA 8 (1.8) 8 (3.2) 0.426 1.850 0.683, 5.012
AC+AA 112 (25.2) 71 (28.3) 0.469 1.173 0.828, 1.661
Rs4135258 GG 430 (96.6) 244 (97.2)
AG 13 (2.9) 7 (2.8) 0.949 0.374, 2.410
AA 2 (0.4) 0 0.406 0.000 0.000
AG+AA 15 (3.4) 7 (2.8) 0.551 0.822 0.331, 2.045
Rs2938392 AA 218 (49.0) 129 (51.4)
GG 116 (26.1) 65 (25.9) 0.947 0.652, 1.376
AG 111 (24.9) 57 (22.7) 0.770 0.868 0.590, 1.277
62
GG+AG 227 (51.0) 122 (48.6) 0.910 0.908 0.666, 1.238
Rs1175541 AA 203 (45.6) 102 (40.6)
CC 198 (44.5) 117 (46.6) 1.176 0.846, 1.636
AC 44 (9.9) 32 (12.7) 0.324 1.447 0.866, 2.419
CC+AC 242 (54.4) 149 (59.3) 0.217 1.225 0.896, 1.676
Rs3105363 AA 304 (68.3) 182 (72.5)
AG 129 (29.0) 24 (9.6) 0.829 0.583, 1.178
GG 12 (2.7) 5 (2.0) 0.484 0.696 0.241, 2.007
AG+GG 141 (31.7) 29 (11.6) 0.309 0.817 0.581, 1.150
Rs1152001 AA 217 (48.8) 122 (48.6)
AG 178 (40.0) 104 (41.4) 1.044 0.752, 1.449
GG 49 (11.0) 25 (10.0) 0.881 0.912 0.536, 1.549
AG+GG 227 (51.0) 129 (51.4) 0.494 1.015 0.745, 1.384
Rs1152002 AA 223 (50.1) 124 (49.4)
GG 138 (31.0) 77 (30.7) 1.003 0.704, 1.431
AG 84 (18.9) 50 (19.9) 0.945 1.070 0.708, 1.618
GG+AG 222 (49.9) 127 (50.6) 0.244 1.029 0.755, 1.402
Rs3103310 AA 297 (66.7) 179 (71.3)
AG 132 (29.7) 64 (25.5) 0.802 0.565, 1.139
GG 12 (2.7) 5 (2.0) 0.390 0.689 0.239, 1.988
AG+GG 144 (32.4) 69 (27.5) 0.098 0.792 0.564, 1.114
Rs13099078 CC 331 (74.4) 183 (72.9)
AC 103 (23.1) 60 (23.9) 1.054 0.731, 1.519
AA 11 (2.5) 8 (3.2) 0.827 1.315 0.520, 3.329
AC+AA 114 (25.6) 68 (27.1) 0.572 1.079 0.760, 1.532
