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Polymorphisms in genes involved in steroid hormone metabolism and mammographic density changes in women randomized to menopausal estrogen and progesterone therapy

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Polymorphisms in genes involved in steroid hormone metabolism and mammographic density changes in women randomized to menopausal estrogen and progesterone therapy

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Content POLYMORPHISMS IN GENES INVOLVED IN STEROID HORMONE
METABOLISM AND MAMMOGRAPHIC DENSITY CHANGES
IN WOMEN RANDOMIZED TO MENOPAUSAL
ESTROGEN AND PROGESTERONE THERAPY
By
Sarah Jane Lord
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)
May 2004
Copyright 2004 Sarah Jane Lord
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UMI Number: 1421779
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1 1
ACKNOWLEDGEMENTS
I would like to acknowledge the support of my supervisor, Associate Professor Giske
Ursin of the Department of Preventive Medicine, Keck School of Medicine,
University of Southern California, Los Angeles, for her expert guidance and
assistance and her inspiring enthusiasm.
I am indebted to Professor Howard Hodis of the Department of Preventive Medicine
and the Atherosclerosis Research Unit, Division of Cardiovascular Medicine, Keck
School of Medicine, University of Southern California, for his generous support of
this project. Professor Hodis, as the principal investigator of the Estrogen in the
Prevention of Atherosclerosis Trial and the Women’s Estrogen-Progestin Lipid-
Lowering Hormone Atherosclerosis Regression Trial, enabled the recruitment of
patients and access to clinical data from those trials. I am also very grateful to
Associate Professor Wendy J. Mack for facilitating this collaboration and providing
assistance with data collection and analysis, and Professor Malcolm C. Pike, who
provided insightful comments at all stages of this study.
I thank Assistant Professor David Van Den Berg who directed the laboratory studies.
I also thank Assistant Professor Sue A. Ingles and Assistant Professor Christopher A.
Haiman for sharing their expertise in genetic epidemiology, which helped to guide
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Ill
this research, Ms Wei Wang for performing laboratory studies and Associate
Professor Yuri R. Parisky for directing the mammographic screening studies.
Finally, this study would not have been have been possible without the dedicated
efforts of Martha Charlson, Christine Gesselman, Thelma Morales and Liny
Zurbrugg of the Atherosclerosis Research Unit who were responsible for patient
recruitment.
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TABLE OF CONTENTS
Acknowledgements........................................................................................................... ii
List of Tables......................................................................................................................v
Abstract.............................................................................................................................. vi
Introduction......................................................................................................................... 1
Methods.............................................................................................................................. 3
Results................................................................................................................................10
Discussion.........................................................................................................................20
Bibliography.....................................................................................................................24
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LIST OF TABLES
Table 1. Subject recruitment..............................................................................................6
Table 2. Participant and non-participant characteristics............................................... 10
Table 3. Baseline characteristics by study and treatment group...................................12
Table 4. Change in mammographic percent density (MFD) by
treatment assignment.........................................................................................14
Table 5. Least square mean change in MFD by genotype
and treatment arm, EFAT, adjusted................................................................. 16
Table 6. Least square mean change in MFD by genotype
and treatment arm, WELL-HART, adjusted................................................... 17
Table 7. Least square mean change in MFD by genotype
and treatment arm, EFAT and WELL-HART, adjusted................................ 19
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VI
ABSTRACT
This study investigates the genetic variants that determine increases in
mammographic percent density (MPD) in hormone users. We obtained DNA and
mammograms from 233 postmenopausal women who had participated in randomized
controlled trials with estrogen therapy (ET), estrogen and progestin therapy (EPT) or
placebo. The adjusted mean change in MPD on baseline and 12 month mammograms
was +4.6 % in the ET arm and +7.1% in the EPT arm, compared to +0.1% in the
placebo arm (p=0.0001). AKR1C4 and CYPIBI polymorphisms predicted MPD
change in the EPT treatment group (p<0.05), but the most consistent effect was
observed for AKR1C4, a gene involved in progesterone metabolism. In women using
EPT, those carrying one or two low activity AKR1C4 Val alleles showed a
significantly greater increase in MPD than women homozygous for the Leu allele
(p=0.0008). Therefore, this polymorphism may predict which women are
predisposed to MPD changes when exposed to EPT.
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INTRODUCTION
There is growing evidence that combined estrogen and progestin therapy (EPT)
increases the risk of breast cancer more than estrogen therapy (ET) alone
(Magnusson, Baron et al. 1999; Persson, Weiderpass et al. 1999; Colditz and Rosner
2000; Ross, Paganini-Hill et al. 2000; Schairer, Lubin et al. 2000; Writing Group for
the Women's Health Initiative 2002). One important question is whether we can
identify subgroups of women who are at a particularly greater risk of developing
breast cancer if they use EPT or ET. Mammographic percent density (MPD) is a
strong independent breast cancer risk factor (Oza and Boyd 1993; Boyd, Byng et al.
1995; Byrne, Schairer et al. 1995; Ursin, Ma et al. 2003), and increases when women
commence EPT. On average the change is 4-5% (Greendale, Reboussin et al. 2003),
however a sub-group of about 25% (range 10%-40%) of women starting EPT
undergo a substantial increase in MPD of > 10% or an upgrade in the four level
Wolfe classification (Berkowitz, Gatewood et al. 1990; Stomper, Van Voorhis et al.
1990; Laya, Gallagher et al. 1995; Marugg, van der Mooren et al. 1997; Persson,
Thurfjell et al. 1997; Greendale, Reboussin et al. 1999 ; Lundstrom, Wilczek et al.
1999; Lundstrom, Wilczek et al. 2001).
We do not know which factors modify change in MPD in ET or EPT users, but such
factors are important to identify, since they may also modify the increase in risk of
breast cancer in ET or EPT users. Obvious candidates include factors that affect
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metabolism of ET or EPT. We investigated whether known or suspected functional
variants in genes involved in hormone metabolism could predict changes in MPD in
women randomized to ET and EPT. We selected genes whose products are known
to modulate estrogen metabolism (catechol-O-methyltransferase (COMT),
cytochrome P450 IB l (CYPIBI) and UDP-glucuronosyltransferase lA l
(UGTlAl)), progesterone metabolism (aldo-keto reductase 1C4 (AKR1C4)) or
mode of action (progesterone receptor (PGR)).
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METHODS
Study subjects
Subjects were drawn from two randomized, double-blind, placebo-controlled studies
(Hodis, Mack et al. 2001; Hodis, Mack et al. 2003), conducted by the
Atherosclerosis Research Unit at the Keck School of Medicine of the University of
Southern California (USC).
The Estrogen in the Prevention of Atherosclerosis Trial (EFAT) was a clinical trial
conducted in postmenopausal women aged 45 years or older recruited from direct
advertising (Hodis, Mack et al. 2001). Eligible women had a serum estradiol level <
20 pg/mL and a fasting plasma low density lipoprotein cholesterol (LDL-C) >= 130
mg/dL. Exclusion criteria were: use of postmenopausal hormone therapy for more
than 10 years, or within the previous month of the first screening visit; history of
breast or gynecologic cancer; life threatening disease with prognosis less than 5
years; fasting triglyceride level >=400 mg/dL; high density lipoprotein level < 30
mg/dL; diastolic blood pressure >110 mmHg; current smoker; untreated thyroid
disease; renal insufficiency (serum creatinine > 2.5 mg/dL); fasting blood
glucose>200 mg/dL. The 222 subjects enrolled in this study were randomized to
receive either 1 mg/day of micronized 17 [beta]-estradiol (ET) or placebo over a
period of 2 years.
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The Women’s Estrogen-Progestin Lipid-Lowering Hormone Atherosclerosis
Regression Trial (WELL-HART) was conducted in postmenopausal women aged 50-
75 years with angiographically demonstrable coronary artery disease (Hodis, Mack
et al. 2003). Participants were recruited from five cardiac catheterization
laboratories in Los Angeles County that serve patients with diverse backgrounds.
Other criteria for inclusion and exclusion were as in EPAT except that smokers of <
15 cigarettes/day were not excluded from participation in WELL-HART. Two
hundred and twenty-six subjects were randomized to receive either 1 mg/day of
micronized 17 [beta]-estradiol with medroxyprogesterone acetate (MPA) 5 mg/day
for days 19-30 each month [EPT], ET or matching placebo over a period of 3 years
(Hodis, Mack et al. 2003).
In the current study, we included all subjects who had participated in EPAT or
WELL-HART for a minimum of 12 months, who had a current U.S. telephone
number and a mammogram within the 18 months prior to randomization that was at
least two months after any previous episodes of postmenopausal hormone use, and
who did not have breast implants or a history of breast cancer between
randomization and the follow-up mammogram. Potentially eligible women who were
willing to participate in this mammography density sub-study signed a written
informed consent form and provided a blood sample or, if they lived outside of the
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greater Los Angeles area, a buccal cell sample. The study protocol was approved by
the Institutional Review Board at USC.
Of the 222 subjects randomized in EPAT, 150 (68%) women were assessed as
eligible for recruitment to the present study, 149 of these 150 were successfully
contacted and 146 (97%) consented and provided a blood (n=131) or buccal (n=15)
specimen. An appropriately timed set of mammograms was available for 127 of
these participants, representing 85% of subjects contacted (Table 1). Of the 226
subjects randomized in WELL-HART, 163 (72%) were eligible for recruitment, 155
were successfully contacted, 140 (89%) consented and provided a blood (n=134) or
buccal (n=6) specimen. Mammograms were available for 106 of these women ,
representing 68% of subjects contacted (Table 1).
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Table 1. Subject recruitment
EPAT WELL-HART Total
Original recruitment 222 226 448
Subject exclusions
Died 1 10 11
Loss to f/u, moved out of country 27 16 43
Other withdrawal from parent trial 42 34 76
Breast cancer 1 1 2
Breast implants 1 0 1
Not competent to sign consent 0 2 2
No. eligible subjects available 150 163 313
Subject recruitment
Phone disconnected/no answer 1 8 9
Refusal 3 15 18
No. consenting subjects 146 140 286
Mammogram eligibility
No baseline mammogram (within 18 mos) 0 4 4
Baseline mammogram within 2mo of EPT/ET use 3 13 16
Baseline mammogram after trial start 2 8 10
Subjects with ineligible followup scan 3 0 3
No. consenting subjects with eligible scans: 138 115 280
Mammogram retrieval
Films not at facility, tracking unsuccessful 9 8 17
Facility closed/unable to locate 1 1 2
DNA specimen collection
Specimen stored incorrectly 1 0 1
No. mammograms sets and DNA specimens
retrieved for analyses
127 106 233
Genetic assays - successful
COMT 126 106 232
CYPIBI 127 104 231
UGT lA l 126 105 231
AKR1C4 127 105 232
PRO 127 105 232
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The reasons why 72 women in EPAT and 63 women in WELL-HART were not
eligible for the current study were as follows: loss to follow-up (EPAT 27,WELL-
HART 16); death (EPAT 1, WELL-HART 10); withdrawal from original trial
(EPAT 42, WELL-HART 34); breast implants (EPAT 1); breast cancer diagnosed
during the trial (EPAT 1, WELL-HART 1); not competent to sign informed consent
(WELL-HART 2).
Reasons for non-participation among eligible subjects were: telephone contact
unsuccessful (EPAT 1, WELL-HART 8); patient refusal (EPAT 3, WELL-HART
15); and specimen stored incorrectly (EPAT 1). Problems encountered retrieving
mammograms were: baseline mammogram not eligible (EPAT 5, WELL-HART 25);
or mammogram not located at the facility where it was taken and further tracking
was unsuccessful (EPAT 10, WELL-HART 9). No follow-up mammogram was
available for 3 EPAT subjects.
DNA Samples and Genotyping
Genomic DNA was isolated from blood using a QIAamp 96 DNA Blood Kit
(Qiagen, Valencia, CA). DNA isolation from buccal cells in mouthwash was
performed using the Puregene DNA isolation method (Gentra Systems Inc.,
Minneapolis, MN). Genotyping for the following single nucleotide polymorphisms
was performed using the fluorogenic 5'-nuclease assay (Lee, Connell et al. 1993):
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COMT (Vall58Met), CYPlBl(Val432Leu), PGR(Val660Leu), UGTlAl
(<7/7+TA)and AKR1C4 (Leu31 IVal). No signal or an indeterminate signal was
recorded for 1-2 specimens (<1%) in assays for each polymorphism and these results
are recorded as missing and excluded from our analysis of that gene. Of the 5%
blinded quality control repeats, results matched that of their corresponding specimen
in all assays except one where one of the controls gave no signal.
Assessment o f Mammographic Percent Density Change
A baseline mammogram performed closest to, but before the subject’s randomization
date and a mammogram obtained one year after randomization (mean = 12.9 months;
range 9-24 months) were used to measure MPD change. All films were scanned at
150 dots per inch (dpi) using a Cobrascan CX-812T scanner (Radiographic Digital
Imaging Inc, Torrance, California) with Adobe Image software. Mammographic
density was determined using a computer-assisted validated method (Ursin, Astrahan
et al. 1998). One assessor reviewed all the mammograms for absolute density. MPD
was calculated as the absolute dense area divided by the breast area multiplied by
100. The correlation of repeated MPD readings performed on a subset of 14
mammograms at different times was 0.95.
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Statistical Analysis
We determined the least-square mean change in MPD for subjects in each treatment
group overall and by genotype for each trial independently, and with the two trials
combined. We adjusted for factors known or suspected to be associated with
changes in mammographic density: race (White, African American, Latina,
Asian/Pacific Islander); BMI (mg/kg ); age at baseline mammogram (years); MPD at
baseline and any past use of ET/EPT (ever/never). A multiplicative
genotype*treatment interaction term was also included in the model to test for
genotype differences in the treatment-related change in MPD.
The allelic frequencies for all genes were in Hardy-Weinberg equilibrium across
both trials, with the exception of CYPIBI, which showed statistically significant
variation by ethnicity. Since Hardy-Weinberg equilibrium was maintained within
each ethnic group, a systemic genotyping problem with this locus is unlikely.
The laboratory personnel and mammographic density assessor were blinded to
treatment and study assignment. The SAS statistical software package (SAS Institute
Inc., Cary, NC) was used for all statistical analyses.
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1 0
RESULTS
Subject characteristics
The distribution of baseline characteristics for the 233 subjects included in this study
was similar to those of the parent trials (Table 2), except that the WELL-HART
participants included in this study were more likely to have education beyond high-
school (52%) than WELL-HART participants not included (32%; p = 0.0003).
However, education level was not associated with mammographic density at baseline
or change in density. Within each trial, baseline characteristics including age, parity,
BMI, family history, education and genotype were similar across treatment groups,
except that within WELL-HART the racial distribution differed significantly by
treatment assignment (p = 0.01).
Table 2. Participant and non-participant characteristics
Characteristic EPAT
non-
WELL-HART
non
participants participants p' participants participants p’
N=127 N=95 N:=106 N=120
Mean age at randomization
(+/-sd.) 61.2 (7.0) 61.0 (7.0) 0.76 63.7 (6.6) 63.4 (6.4) 0.77
Race
White, non-hispanic 74 52 34 35
Black, non-hispanic 16 9 19 19
Hispanic 23 25 40 61
Asian or Pacific Islander 14 9 0.49 13 5 0.07
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Table 2. continued
Characteristic EPAT WELL-HART
non- participan non
participants participants p ' ts participants p*
Education
up to high school completed
trade or business school/some
17 19 51 81
college
received bachelors
63 44 36 33
degree/postgrad
missing
47 32 0.42 19
2
4 0.0003
Age at menarcbe (years)
<=11 31 23 21 26
12 35 29 23 24
13 26 22 28 28
14+ 35 21 0.80 34 42 0.91
Parity (live & stillbirths)
0 28 15 10 7
1 9 11 8 8
2 38 24 17 19
3 20 16 9 16
4 19 14 28 21
5+ 13 15 0.53 34 49 0.36
Family history
first degree 15 13 12 8
no first degree 112
Past use of postmenopausal hormones
82 0.68 94 112 0.22
Ever used PMH 78 61 0.65 49 66 0.19
BMI at trial start
<=25 36 22 16 22
26-29 34 22 29 33
30-33 32 33 27 35
34+ 25 18 0.47 34 30 0.65
p value using Chi square test for categorical variables, anova for continuous variables.
There were several differences between the two trials, reflecting the differences in
the inclusion criteria and recruitment strategies. WELL-HART subjects were
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1 2
statistically significantly older, less educated, more obese, of higher parity, less
likely to have used postmenopausal hormones and more racially diverse than EPAT
subjects (Table 3).
Table 3. Baseline characteristics by study and treatment group^_____________
Characteristic EPAT WELL-HART
Placebo ET Placebo BT EPT
N=57 N=70 N=38 N=34 N=34
___________________ Mean (SEM)__________ Mean (SEM)________
Age at baseline
mammogram (years) 62.3(1.0) 60.2(0.8) 0.10 64.5 (1.0) 61.8(1.2) 64.1 (1.1) 0.19 0.005
Years since
menopause at baselinel5.1 (1.4) 12.5(1.0) 0.12 18.2(1.6) 15.9(1.4) 19.5 (1.9) 0.23 0.001
Number of deliveries
(live & stillbirths) 2.4 (0.2) 2.3 (0.2) 0.75 4.4 (0.6) 3.6 (0.4) 4.1 (0.5) 0.54 0.0001
Race'*
White, non-latina 34(59.7) 40(57.1) 13(34.2) 7 (20.6) 14(41.2)
Black, non-latina 6 (10.5) 10 (14.3) 4 (10.5) 10 (29.4) 5 (14.7)
Latina 10(17.5) 13 (18.6) 20(52.6) 9(26.5) 11 (32.4)
Asian or Pacific
Islander 7(12.3) 7(10.0) 0.91 1(2.6) 8(23.5) 4(11.8) 0.01^ 0.0005
Education'*
High school graduate
or less 8(14.0) 9(12.9) 21 (55.3) 15 (44.1) 15 (44.1)
Trade or business
school/some college 29 (50.9) 33 (48.6) 11 (29.0) 12 (35.3) 13 (38.2)
Bachelors degree or
more 20(35.1) 27 (38.6) 0.92 6(15.8) 7(20.6) 6(17.7) 0.85 0.0001
Family history of
breast cancer (first
degree relative)'* 9(15.8) 6(8.6) 0.21 2(5.3) 7(20.6) 3(8.8) 0.13^0.91
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13
Table 3. continued
Characteristic
Ever used
postmenopausal
hormones'^
EPAT (N=127)
Placebo ET
Mean (SEM)
WELL-HART (N=106)
Placebo ET EPT
Mean (SEM)
Age at menarche
(years) 12.7 (0.2) 12.6 (0.2) 0.86 12.8 (0.3) 12.8 (0.3) 13.3 (0.3) 0.32 0.21
36(63.2) 42(60.0) 0.72 13(34.2) 15 (44.1) 21 (61.8) 0.06 0.02
BMI kg/m^ 28.7(0.7) 28.7(0.7) 0.96 30.8(0.9) 31.4(1.0) 30.2(1.1) 0.73 0.007
% mammographic
density at baseline 17.8(2.3) 22.3(1.9) 0.13 9.3 (2.2) 10.8(2.1) 13.3(2.3) 0.29 0.0001
Abbreviations: SEM = standard error of the mean, ET = 1 mg/day of micronized 17 [beta]-estradiol
EPT = 1 mg/day of micronized 17 [betaj-estradiol with medroxyprogesterone acetate (MPA) 5mg/day
for days 19-30 each month.
^ p-value for comparison of characteristics by treatment group, test for comparison of categorical
variables, ANOVA for comparison of means.
^ p-value for comparison of characteristics between trials (EPAT vs WELL-HART), test for
comparison of categorical variables, ANOVA for comparison of means,
number of women (%). ^ Fisher’s exact test.
Three of these factors, age, parity and BMI, are known to be independently and
inversely associated with mammographic density (Oza and Boyd 1993) and as
expected from these differences, the mean percent mammographic density at
baseline was significantly lower among WELL-HART subjects compared with
EPAT subjects (WELL-HART = 11.1% vs EPAT = 20.3% ; p = 0.0001).
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Change in mammographic percent density by treatment arm
On average, women assigned to placebo did not exhibit any change in MPD from
baseline (Table 4). Women assigned to ET in each trial showed a similar 4-5%
increase in mammographic density over placebo in each trial (EPAT p=0.0001,
WELL-HART p=0.03). Women assigned to EPT in WELL-HART exhibited the
greatest mean MPD (7.8%), this was statistically significantly greater than placebo (p
= 0.0001), but not from the ET groups (p = 0.15).
Table 4. Change in mammographic percent density (MPD) hy treatment
Change in percent mammographic density
Mean (SEM)' p value Adjusted Mean^ (SEM) p value
EPAT
Placebo (N=57) -0.7 (0.8) ref -0.8 (0.9) ref
ET (N=70) 3.4 (0.7) 0.003 3.9 (0.9) 0.0001
WELL-HART
Placebo (N=38) 1.0 (1.4) ref 0.4 (1.6) ref
BT (N=34) 4.8 (1.5) 0.06 5.4 (1.5) 0.03
EPT (N=34) 7.8 (1.5) 0.001 7.7 (1.6) 0.001
p homogeneity of treatment
effect between trials 0.75
ALL WOMEN
Placebo (N=95) -0.02 (0.8) ref 0.1 (0.8) ref
ET (N=104) 3.9 (0.7) 0.0003 4.6 (0.8) 0.0001
EPT (N=34)
. .. ,
7.8 (1.3)
2 . A J. .
0.0001 7.1 (1.4) 0.0001
since menopause, past use of HRT, BMI & study group.
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15
Overall, one subject (1%) assigned to placebo showed a 10% increase in MPD over
12 months, compared with 17% of subjects assigned to ET and 32% assigned to
EPT. Increases in MPD in the EPT arm were apparent within each ethnic group.
Genetic determinants o f mammographic percent density change
There was no statistically significant association between genotype and baseline
MPD in either trial (Table 5 and 6). Overall there was also no evidence for an
association between MPD change and genotype in women randomized to ET (Table
7). However, in EPAT there was a statistically significant increase in MPD change
in women in the ET arm who possessed the COMT Met/Met genotype compared to
those with the VaW al genotype (p = 0.02, p-value for ET-genotype interaction =
0.12, Table 5), but no such association was observed in the WELL-HART ET arm
(Table 6).
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16
Table 5. Least square mean change in MPD by genotype and
4. ^ ________________ TT’T J A ' T ’ I
Genotype Placebo
Mean
(N)
SLM
ERT
Mean SLM
(N)
P^
COMT Vall58Met
-0.8 1.5 ref 2.4 1.5 ref
V aW al (18) (17)
-0.4 1.2 0.82 2.7 1.1 0.88
Val/Met (28) (34)
-1.4 2.0 0.79 7.1 1.5 0.02 0.13
Met/Met
(11) (18)
CYPIBI Val432Leu
0.2 1.6 ref 4.4 1.4 ref
Leu/Leu (16) (23)
-1.0 1.2 0.55 3.8 1.3 0.73
Leu/Val (33) (31)
-2.5 2.4 0.34 2.5 1.7 0.38 0.96
Val/Val (8) (16)
U G T lA l
-0.7 1.3 ref 4.9 1.1 ref
s/s < -6 (24) (32)
-1.1 1.3 0.85 2.9 1.3 0.23
s/1 (27) (28)
-1.4 2.6 0.81 1.5 2.3 0.16 0.68
1 /1 7+ (6) (9)
PRG Val660Leu
-0.5 1.0 ref 3.1 0.9 ref
Val/Val (41) (54)
0.7 1.8 0.56 6.6 1.7 0.06
Leu/Val (12) (16)
-6.4 3.1 0.07 0.37
Leu/Leu (4)
AKR 1C4 L311V
-1.2 1.0 ref 4.2 0.9 ref
Leu/Leu (46) (53)
0.5 2.1 0.46 2.6 1.7 0.37
Leu/Val (10) (17)
2.9 6.3 0.52 0.26
Val/Val
(1)
' Adjusted for race, age at baseline, years since menopause, BMI, MPD at baseline, past
use of HRT and study group. ^ ANCOVA p-value. ^ ANCOVA p-value for
ET/EFT*genotype interaction.
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17
Table 6. Least square mean change in MPD by genotype and treatment arm,
Genotype Placebo ET EPT
Mean Mean Mean
(N) SEM (N) SEM (N) SEM
P^ P^
COMT Vall58Met
-0.3 2.7 ref 6.6 2.7 ref 8.7 2.7 ref
Val/Val (12) (13) (12)
0.1 2.4 0.90 6.2 2.4 0.92 6.4 2.2 0.51
Val/Met (17) (14) (19)
0.2 3.3 0.90 1.6 3.5 0.29 10.4 5.4 0.78 0.75
Met/Met (9) (7) (3)
CYPIBI YaI432Leu
1.2 2.1 ref 2.1 2.5 ref 15.2 2.4 ref
Leu/Leu (20) (14) (13)
1.7 2.9 0.88 6.9 2.4 0.19 2.4 2.3 0.0002
Leu/Val (10) (13) (13)
0.9 3.3 0.93 9.6 3.6 0.11 8.3 3.4 0.09 0.007
Val/Val (7) (7) (7)
U G T lA l
-0.4 2.7 ref 6.6 2.2 ref 7.5 2.3 ref
s/s <=6 (13) (18) (18)
0.3 2.4 0.85 2.7 2.9 0.32 9.6 2.7 0.55
s/1 (17) (11) (12)
0.5 3.4 0.83 6.5 4.2 0.96 4.4 5.4 0.60 0.73
1 /1 1+ (8) (5) (3)
PRG VaI660Leu
0.4 2.0 ref 6.7 1.7 ref 8.7 1.8 ref
VaPVal (24) (27) (24)
-0.3 2.7 0.82 0.6 4.0 0.17 4.9 3.3 0.31
Leu/Val (13) (5) (8)
1.4 9.2 0.92 0.1 6.6 0.34 13.5 9.0 0.60 0.73
Leu/Leu
(1) (2) (1)
AKR 1C4 Leu311Val
0.6 4.7 4.6
Leu/Leu (27) 1.7 ref (26) 1.6 ref (25) 1.7 ref
1.2 7.7 16.6
Leu/Val
(11)
2.6 0.85 (8) 2.8 0.37 (7) 3.3 0.001
31.6
Val/Val
(1)
8.3 0.002 0.05
' Adjusted for race, age at baseline, years since menopause, BMI, MPD at baseline, past use of HRT
and study group. ^ ANCOVA p-value. ^ ANCOVA p-value for ET/EPT*genotype interaction.
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18
Two of the genes studied modified the MPD changes associated with randomization
to the WELL-HART EPT arm (Table 7). The strongest evidence for such an
interaction was with the AKR1C4 gentype. Both the Val/Val and Leu/Val genotypes
of the AKR1C4 gene were associated with a statistically significant increase in
density compared with the Leu/Leu genotype among women assigned to EPT (p <
0.001). However there were only 7 heterozygous women and one woman
homozygous for the Val allele. The AKR1C4 by treatment interaction was also
statistically significant (p = 0.001).
There was also a statistically significant association between the CYPIBI genotype
and MPD change among women taking EPT. The Leu/Leu genotype of CYPIBI
was associated with a statistically significantly greater MPD change in women
assigned to EPT compared with the Val/Val genotype (p = 0.02). However,
heterozygotes for this polymorphism showed the smallest increase in MPD. The
interaction between ET/EPT-CYPIBI was statistically significant (p = 0.0004).
There was no association between these polymorphisms and MPD changes in the ET
arm.
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19
Table 7. Least square mean change in MPD by genotype and treatment arm,
Genotype Placebo
Mean
(N)
SEM
ET
Mean SEM
(N)
EPT
Mean SEM
(N)
P^ P^
COMT Vall58M et
VaPVal -0.1 1.4 ref 3.7 1.4 ref 8.0 2.3 ref
(30) (30) (12)
Val/Met 0.6 1.2 0.73 4.2 1.1 0.74 6.0 1.9 0.49
(45) (48) (19)
Met/Met -0.2 1.8 0.95 6.0 1.6 0.27 9.5 4.4 0.76 0.80
(20) (25) (3)
CYPIBI Val432Leu
Leu/Leu 0.9 1.2 ref 3.6 1.2 ref 14.4 2.1 ref
(36) (37) (13)
Leu/Val 0.2 1.2 0.67 5.7 1.2 0.23 1.3 2.1 0.0001
(43) (44) (13)
Val/Val -0.7 1.9 0.48 4.7 1.6 0.60 6.7 2.9 0.02 0.0004
(15) (23) (7)
U G T lA l
s/s <7 0.4 1.3 ref 5.6 1.1 ref 6.9 1.9 ref
(37) (50) (18)
s/1 -0.3 1.2 0.67 3.2 1.3 0.14 8.6 2.3 0.54
(44) (39) (12)
1 /1 7+ 0.5 2.1 0.96 4.3 2.1 0.57 5.4 4.4 0.77 0.74
(14) (14) (3)
PRG Val660Leu
Val/Val 0.4 1.0 ref 4.5 0.9 ref 7.9 1.7 ref
(65) (81) (24)
Leu/Val 0.4 1.6 0.97 5.2 1.7 0.72 5.0 1.7 0.34
(25) (21) (8)
Leu/Leu -4.2 3.4 0.19 3.4 5.4 0.85 13.7 7.5 0.44 0.59
(5) (2) (1)
AKR1C4 L311V
Leu/Leu -0.2 0.9 ref 4.3 0.8 ref 3.8 1.6 ref
(73) (79) (25)
Leu/Val 1.4 1.6 0.38 5.4 1.5 0.48 16.6 2.8 0.0001
(21) (25) (7)
Val/Val 1.0 0.87 28.7 7.1 0.0008 0.001
(1) (0) (1)
' Adjusted for race, age at baseline, years since menopause, BMI, MPD at baseline, past use of
HRT and study group. ^ ANCOVA p-value for comparison of means ^ ANCOVA p-value for
ET/EPT*genotype interaction.
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2 0
DISCUSSION
In this study, we found that women randomized to ET and EPT had a statistically
significant mean increase in MPD over 12 months compared with women assigned
to placebo, with the women assigned to EPT having the greatest mean increase in
MPD. These findings are consistent with those in a recently published clinical trial
with EPT (Greendale, Reboussin et al. 2003). Interestingly, there was a similar effect
of ET in two diverse study populations despite significant differences in MPD at
baseline and other potential confounders (age, BMI, parity) between these two
populations. The reason for a lack of statistically significant difference between the
increase in MPD in the EPT arm and the ET arms may have been due to a small EPT
sample size, 34 subjects.
Our findings suggest that certain genes involved in hormone metabolism, in
particular AKR1C4 and possibly CYPIBI, may explain some of the observed
individual variation in changes in MPD experienced by women assigned to EPT.
AKR1C4 is involved in the metabolism of steroids in the liver. Its actions include
reduction of progesterone to 20-hydroxyprogesterone (Penning, Burczynski et al.
2000). Medroxyprogesterone acetate has a similar structure and metabolic pathway
as progesterone.. The Leu311Val polymorphism has been associated with a 3-5 fold
decrease in the catalytic activity of the enzyme (Kume, Iwasa et al. 1999).
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2 1
We observed a 24% carrier rate for the low activity Val allele. Subjects randomized
to EPT who were heterozygotes or homozygotes for this allele experienced
significantly greater increases in MPD compared with homozygotes for the wild-type
Leu/Leu allele. Our finding is strengthened by the apparent allelic dosage effect,
with the greatest increase in MPD in the one subject possessing two copies of the Val
allele.
The CYPIBI Val432Leu polymorphism predicted the change in MPD in women
randomized to EPT. Homozygotes of the Leu allele showed a statistically
significantly greater increase in density than VaW al homozygotes.However there
was no consistent dose effect with Leu alleles since heterozygotes had the lowest
density increase. The explanation for this finding is unclear. One case control study
in Asian women reported a 2.3-fold increase risk of breast cancer in Leu/Leu
homozygotes relative to Val/Val homozygotes (Zheng, Xie et al. 2000), while two
other studies showed no association in Caucasian women (Bailey, Roodi et al. 1998;
De Vivo, Hankinson et al. 2002). Another cross-sectional study showed no
association between CYPIBI genotype and mammographic density among women
using HRT (Haiman, Hankinson et al. 2003).
The COMT enzyme is responsible for the conjugation and inactivation of catechol
estrogen. A VallSSMet polymorphism has been associated with lower activity of this
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2 2
enzyme (Dawling, Roodi et al. 2001), and is associated with increased plasma levels
of 17-beta estradiol in postmenopausal women taking ET (Worda, Sator et al. 2003).
In a recent cross-sectional study we reported a statistically significant association
between the Met/Met allele and MPD among current users of HRT (Haiman,
Bernstein et al. 2002). In the current study, women assigned to ET in EPAT who
possessed this high risk variant showed a statistically significant increase in MPD
compared to ValA^al homozygotes, but this effect was not observed in the ET or
EPT arm of WELL-HART, suggesting that if such an effect exists, it is more modest
than those described for AKR1C4 and CYPIBI.
We found no evidence that the PGR or UGTl A l polymorphisms were associated
with MPD increase in women assigned to ET or EPT.
There are several limitations to our study. Study subjects represented 57% and 47%
of those originally randomized to EPAT and WELL-HART, respectively. A large
proportion of the participants in the parent trials had died or were lost to follow-up
since the completion of the original trial. The small sample size, particularly in the
EPT arm, limited our power to detect gene-environment interactions. However, it is
unlikely that this could have biased our results and caused the apparent associations
between genotype and change in MPD with treatment since the most likely effect of
this loss to follow-up would be a loss of statistical power.
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23
Another limitation is that women assigned to EPT were drawn from a select study
population with diagnosed cardiovascular disease and poor general health. Thus, it is
unclear to what extent our findings can be generalized to populations with better
health. However, it is unlikely that this has caused a bias in our findings since
similar MPD changes were observed in the ET arms of WELL-HART and the more
broadly representative healthy population of EPAT. Further, our finding of the
magnitude of the increase in MPD associated with EPT use was similar to that
recently reported from a trial of EPT use (Greendale, Reboussin et al. 2003).
The advantages of the randomized, double-blind, placebo-controlled design of both
parent trials most likely outweigh these limitations.
This is the first study to investigate genetic determinants of MPD changes in women
randomized to ET, EPT or placebo. MPD is a strong independent risk factor for
breast cancer. Although plausible, it is still unknown whether women with the
greatest increase in MPD in response to EPT are at higher risk for breast cancer
associated with EPT use than other women. Our findings suggest that the magnitude
of the increase in MPD may be greater in women with genetically-determined lower
activity of some enzymes that metabolize EPT, in particular the AICR1C4 enzyme,
which is involved in progesterone metabolism.
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24
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Asset Metadata

Creator Lord, Sarah Jane (author)

Core Title Polymorphisms in genes involved in steroid hormone metabolism and mammographic density changes in women randomized to menopausal estrogen and progesterone therapy

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag health sciences, public health,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-315641

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