Published OnlineFirst June 27, 2013; DOI: 10.1158/1055-9965.EPI-13-0123
Cancer
Epidemiology,
Biomarkers
& Prevention
Research Article
Pleiotropy between Genetic Markers of Obesity and Risk of
Prostate Cancer
Todd L. Edwards1,2, Ayush Giri1, Saundra Motley1, Wynne Duong1, and Jay H. Fowke1,3
Abstract
Background: To address inconsistent findings of obesity and prostate cancer risk, we analyzed the
association between prostate cancer and genetic markers of obesity and metabolism.
Methods: Analyses included 176,520 single-nucleotide polymorphisms (SNP) associated with 23 metabolic
traits. We examined the association between SNPs and prostate cancer in 871 cases and 906 controls, including
427 high-grade cases with Gleason 7. Genetic risk scores (GRS) for body mass index (BMI) and waist-to-hip
ratio (WHR) were also created by summing alleles associated with increasing BMI or WHR.
Results: Prostate cancer was associated with five loci, including cyclin M2, with P values less than 1 104. In
addition, the WHR GRS was associated with high-grade prostate cancer versus controls [OR, 1.05; 95%
confidence interval (CI), 1.00–1.11; P ¼ 0.048] and high-grade prostate cancer versus low-grade prostate cancer
(OR, 1.07; 95% CI, 1.01–1.13; P ¼ 0.03). None of these findings exceeds the threshold for significance after
correction for multiple testing.
Conclusions: Variants in genes known to be associated with metabolism and obesity may be associated with
prostate cancer. We show evidence for pleiotropy between WHR GRS and prostate cancer grade. This finding is
consistent with the function of several WHR genes and previously described relationships with cancer traits.
Impact: Limitations in standard obesity measures suggest alternative characterizations of obesity may be
needed to understand the role of metabolic dysregulation in prostate cancer. The underlying genetics of WHR
or other Metabochip SNPs, while not statistically significant beyond multiple testing thresholds within our
sample size, support the metabolic hypothesis of prostate carcinogenesis and warrant further investigation in
independent samples. Cancer Epidemiol Biomarkers Prev; 22(9); 1538–46. 2013 AACR.
Introduction
Prostate cancer remains the leading cancer diagnosis,
and the second leading cause of cancer mortality, among
U.S. men (1). A strategy to prevent prostate cancer should
be a priority to minimize unnecessary treatment, yet a
modifiable risk factor useful in prostate cancer prevention
has not been defined. There is increasing evidence, however, that obesity plays a role in generating a state of
systemic inflammation and mediates steroid hormone
metabolism, insulin sensitivity, and cytokine and adipo-
Authors' Affiliations: 1Division of Epidemiology, Department of Medicine, 2Center for Human Genetics Research, Vanderbilt University; and
3
Vanderbilt-Ingram Cancer Center, Vanderbilt University School of
Medicine, Nashville, Tennessee
Note: Supplementary data for this article are available at Cancer Epidemiology Biomarkers and Prevention Online (http://cebp.aacrjournals.org/).
Current address for J.H. Fowke: Department of Medicine, Vanderbilt
University, Nashville, TN.
Corresponding Author: Jay H. Fowke, Institute of Medicine and Public
Health, 2525 West End Ave, 12th floor, Nashville, TN 37203. Phone: 615936-2903; Fax: 615-936-8291; E-mail: [email protected]
doi: 10.1158/1055-9965.EPI-13-0123
2013 American Association for Cancer Research.
1538
kine release (2). Obesity is associated with advanced prostate cancer and a potentially lethal phenotype (3–11), and
a recent meta-analysis reported a positive association
between body mass index (BMI) and advanced prostate
cancer risk [relative risk (RR) ¼ 1.12 (1.01–1.23)]. In contrast, however, obesity may have no association with
localized disease [RR ¼ 0.96 (0.89–1.03); ref. 12] or indeed
reduce risk. For example, the Prostate Cancer Prevention
Trial (PCPT) reported that a BMI > 35 was associated with
high-grade disease [RR ¼ 1.29 (1.01–1.67)] but also found
elevated BMI inversely associated with low-grade disease
[RR ¼ 0.82 (0.69–0.98)], consistent with a broader null or
protective association described between BMI and lowgrade prostate cancer (6, 12–14).
Far fewer studies have addressed the relationships
between prostate cancer and metabolic dysregulation
related to excess visceral adiposity (12, 15). Interestingly,
the Health Professional Follow-up Study reported no
association between waist-to-hip ratio (WHR) and total
prostate cancer, but a marginally significant positive trend
with metastatic disease[RR(Q5 vs. Q1) ¼ 1.58 (0.87–2.89);
Ptrend ¼ 0.05); ref. 10]. In contrast, the PCPT reported waist
circumference (WC) was significantly associated with
increased risk of low-grade cancer (6). Interestingly, type
2 diabetes (T2D) may also be inversely associated with
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Pleiotropy in Obesity and Prostate Cancer
prostate cancer risk, although the separate roles of obesity
from T2D treatments such as metformin to control insulin
activity remain unclear (16, 17).
Further elucidation of the pathway(s) connecting obesity to prostate cancer is needed to understand whether
total and visceral obesity could advance and perhaps also
inhibit prostate cancer progression, or whether stage/
grade-specific associations are an artifact of differential
detection associated with obesity (18). Studies of blood
biomarkers of prostate cancer have had varying levels of
success, perhaps most notably the association with blood
insulin-like growth factor 1 (IGF1) and associated IGFbinding protein levels in advancing prostate cancer (19–
27). Further clues toward mechanism have been gained
from genome-wide association studies (GWAS), in which
a pleiotropic effect was identified for loci in JAZF1 and
HNF1B (formerly TCF2) across T2D and prostate cancer
(28, 29). These genes encode transcription factors involved
in cell-cycle regulation, whereas JAZF1 also downregulates fatty acid synthase (30) previously associated with
BMI and advanced prostate cancer (31).
The purpose of this investigation is to use a genetic
approach and extend the analysis of mechanisms linking
obesity to prostate cancer to consider the role of genetic
loci previously associated with 23 metabolic traits inducing BMI and estimates of visceral adiposity. Results may
identify novel genetic metabolic loci associated with prostate cancer, provide insight into candidate biologic
mechanisms linking obesity to prostate cancer, and define
obesity subgroups at greater risk for prostate cancer.
Methods
The Nashville Men’s Health Study (NMHS) is a multicentered, rapid-recruitment protocol to investigate the
clinical, genetic, and behavioral determinants of prostate
cancer detection, progression, and treatment outcomes.
All recruitment and data collection protocols were approved by International Review Boards (IRB) at Vanderbilt
University and the Tennessee Valley Veteran’s Administration (Nashville, TN). Men referred for prostate biopsy
to Vanderbilt University Medical Center, a large community urology practice in Nashville, and the Tennessee
Valley Veterans Administration Medical Center were
targeted for recruitment. Exclusion criteria included age
less than 40 years, a prior prostate cancer diagnosis, prior
prostate surgery, current androgen supplementation use,
or English insufficiency for informed consent. These urology clinics receive referrals from physicians throughout
metro Nashville, TN, and are the primary providers of
diagnostic services for urologic disease in the region.
Thus, all NMHS participants have healthcare access
sufficient to be diagnosed with prostate cancer. Recruitment occurred before the prostate biopsy procedure such
that biases associated with treatment or knowledge of
their disease status are prevented. Approximately 90% of
eligible men approached for recruitment consented to
participate.
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A blood sample collected at recruitment was refrigerated immediately after collection and hand-delivered
cold on that day to our DNA core lab at Vanderbilt
University for DNA extraction and processing. All body
size measures were obtained at the time of recruitment by
trained research staff. Weight (in kg; no shoes, hospital
gown) was measured on a calibrated scale, and height
(within 0.1 cm) was measured by stadiometer. Body
circumferences were measured using an anthropometric
tape measure with built-in tension meter (Gullick II) to
ensure an even tension was administered to the tape
across participants. Waist circumference was measured
at the plane across the iliac crest and usually represents
the narrowest part of the torso. Hip circumference was
measured at the maximum posterior extension of the
buttocks. Two measurements at each site are made in
rotational order, with a third measurement if the first 2
differed by more than 1 cm. WHR was calculated from the
average waist and hip circumference for each participant.
Participants also provided the time of their last food and
beverage intakes.
Diagnostic status was abstracted from medical charts. A
single pathologist reviewed more than 90% of biopsies.
Biopsy Gleason score was recorded for participants diagnosed with cancer to define tumor aggressiveness, with a
total Gleason score of 7 or more defined as high-grade
prostate cancer.
We targeted 1,920 NMHS participants for DNA plating and genetic analysis. All participants were recruited
between January 1, 2006 and March 30, 2011. At that time,
there were 941 eligible prostate cancer cases eligible for
DNA plating and analysis. Controls were selected among
those men without prostate cancer, high-grade prostatic
intraepithelial neoplasia, or atypia suspicious for prostate
cancer. From more than 3,000 eligible controls at the time,
we randomly selected 979 for DNA plating.
Genotyping was conducted using the Illumina Metabochip (ref. 32; Illumina Inc.), and genotypes were called
using GenomeStudio. The Metabochip is a genotyping
array designed by several consortia to thoroughly investigate candidate genes for metabolic phenomena. A total
of 257 genomic regions are assayed using 217,695 singlenucleotide polymorphisms (SNP) in gene regions implicated by previous studies of 23 traits, including obesity,
T2D, blood lipids, cardiovascular traits, fasting glucose,
and blood cell counts.
Quality control
Quality control (QC) procedures were conducted on
called genotypes using the PLINK software package (33).
Of the 941 prostate cancer cases and 979 randomly selected controls, genotyping was successfully completed on
899 prostate cancer cases and 941 controls. Samples were
removed for genotyping efficiency less than 95% (18 cases,
36 controls), and then SNPs were removed for genotyping
efficiency less than 98% (15,716 SNPs). The plink–sex
check option was used on common SNPs on the X chromosome with minor allele frequencies (MAF) greater than
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Edwards et al.
0.2, and no participants were found not to be genetically
male. Cryptic relatedness was also assessed with identityby-descent analysis of genotypes using linkage disequilibrium (LD)-pruned SNPs, and one member of any pair of
participants who were related at the level of first cousins
or more was removed, with preference for retaining cases
over controls. We removed 10 participants (1 case, 9
controls) for cryptic relatedness. If a pair of participants
was found to be monozygotic twins or inadvertent duplicates, both were removed because the identity of the
individuals could not be confirmed (9 cases, 10 controls).
Once participants and SNPs had been through QC, we
also ran tests of Hardy–Weinberg equilibrium (HWE) at
each SNP and dropped all SNPs with HWE P < 1 106
(4,712 SNPs). We had QC samples on each genotyping
plate, and we compared these sample genotypes with
duplicate genotypes across plates to assess genotyping
fidelity. On average, genotypes among QC samples were
99.4% concordant. We evaluated population substructure
using principle components analysis with the EIGENSOFT software package (34). We selected 38,980 LDpruned common SNPs with MAF > 0.05 from the data
and extracted prostate cancer s for plotting our participants against International HapMap Project participants
to assess population stratification in our sample (Supplementary Fig. S1). We also present quantile–quantile (QQ)
plots of observed P values against P values from the
uniform distribution for analyses with and without adjustment for ancestry, where the unadjusted analyses had a
lGC of 1.04, and the adjusted analyses had a lGC of 1.01
(Supplementary Figs. S2 and S3).
Statistical analysis
We used logistic regression in PLINK to evaluate additively encoded SNP genotypes for association with prostate cancer risk, adjusting for age and 10 principal components of ancestry. Separate analyses also adjusted for
prostate volume and BMI or WHR. Tests were run for all
SNPs with MAFs greater than 1%. We also stratified highand low-grade prostate cancer cases and compared each
stratum with controls. Additional analyses compared
high- with low-grade prostate cancer to identify genetic
risk factors associated with cancer grade.
Genomic risk scores (GRS) were created to assess the
cumulative effect of alleles associated with increasing BMI
or WHR on prostate cancer risk. SNPs that had been
previously reported from large consortia GWAS studies
in European populations for association with BMI (35)
and WHR (36) were identified in the Metabochip data.
GRSs were calculated indexing the count of trait-increasing alleles divided by the number of available genotypes
in each study participant. We also created a second GRS
for BMI or WHR that weighted these scores by the regression coefficient from the original reports for the BMI or
WHR increase per allele for each SNP. The GRS for BMI
included 24 of 32 SNPS from the original reports after QC,
whereas the GRS for WHR included 14 of 14 identified
SNPs from the original reports.
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Cancer Epidemiol Biomarkers Prev; 22(9) September 2013
We fit logistic regression models in STATA (STATA
Inc.) with terms for either the BMI or WHR GRS and
adjustment for age, 10 principal components of ancestry,
and BMI or WHR, respectively. We also fit models adjusted for prostate volume. These analyses were conducted in
the entire study and also in cases stratified by grade, as
described for the analyses of individual SNPs. In addition,
we feature association results for the individual SNPs
from the WHR score (Supplementary Tables S1 and S2).
Results
Single SNP associations
Our final analysis included 871 prostate cancer cases
and 906 controls. Prostate cancer cases were significantly
older, more likely to be African American, and had smaller prostate volumes than controls but did not significantly differ with regard to BMI, WHR, family history of
prostate cancer, diabetes, or statin use (Table 1). Examination of genetic ancestry using principal components
revealed a majority of the participants to be of European
ancestry (Supplementary Fig. S1). Although BMI and
WHR were not associated with total prostate cancer, we
found marginally significant associations for WHR [OR,
1.20; 95% confidence interval (CI). 0.99–1.46 per 0.1 unit
increase, P ¼ 0.06] and BMI (OR, 1.15; 95% CI, 0.99–1.33;
per 5 unit increase, P ¼ 0.07) with high-grade prostate
cancer (n ¼ 427) in comparison to low-grade prostate
cancer (n ¼ 444).
In primary analysis using a logistic regression model
adjusted for age and 10 principal components, prostate
cancer was associated with 5 distinct loci at a P < 1 104
(Table 2). Noteworthy SNPs [chromosome (CHR): position] included CHR10:104745421 in CNNM2 (OR, 1.55;
95% CI, 1.25–1.92; P ¼ 3.6 105); CHR12:109231826 in
ATP2A2 and near ANAPC7 (OR, 0.40; 95% CI, 0.25–0.61;
P ¼ 2.3 105); rs13111540 near ELOVL6 (OR, 1.31; 95% CI,
1.14–1.50; P ¼ 7.8 105); and CHR6:160836326 in LPAL2
(OR, 1.34; 95% CI, 0.32–5.60; P ¼ 5.8 105].
In analyses stratified by cancer grade, we found that
SNPs in the genes ZNF57, PLCE1, CADM2, and CABYR
were associated high-grade prostate cancer with P < 104
compared with controls (Table 3). Compared with lowgrade prostate cancer, we also found a unique set of
SNPs associated with high-grade prostate cancer near
the genes ZNF536, CCT6B, ASB7, HSP90AA6P, and
ROBO1 & 2 had P < 104 (Table 3). Tests for interaction
between listed SNPS with either WHR or BMI were not
statistically significant (data not shown). Furthermore, the
SNPs rs9409226, rs3786418, rs7252787, and rs1355625 listed
in Table 3 were no longer significant at P < 1 104 after
controlling for WHR, prostate size, and genetic ancestry.
WHR and BMI GRSs
Overall associations between GRSs for BMI or WHR
with total prostate cancer were not statistically significant
compared with controls (Table 4). Additional adjustment
for prostate volume, age, ancestry, and obesity measures
did not substantially affect results.
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Pleiotropy in Obesity and Prostate Cancer
Table 1. Distribution of characteristics by
case–control status among participants from
the NMHS
Covariate
Continuous
Age, y
BMI, kg/m2
WHR
Natural log prostate
volume
Controls
(n ¼ 906)
Cases
(n ¼ 871)
Mean (SD)
64.34 (8.61)
28.98 (4.59)
1.02 (0.07)
3.83 (0.51)
Mean (SD)
66.23 (8.81)
28.73 (4.64)
1.02 (0.07)
3.61 (0.46)
Categorical
Ancestry
African American
European
East Asian
Hispanic
Other
Family history of PC
No
Yes
Statin use
No
Yes
Diabetes status
No
Yes
n (%)
n (%)
80 (8.83)
787 (86.87)
3 (0.03)
12 (1.32)
24 (2.65)
107 (12.28)
741 (85.07)
1 (0.01)
5 (0.57)
17 (1.95)
721 (79.58)
185 (20.42)
674 (77.38)
197 (22.62)
Pa
<0.001
0.255
0.538
<0.001
0.020
0.305
0.336
0.143
0.347
0.259
0.192
559 (61.70)
347 (38.30)
511 (58.67)
360 (41.33)
784 (86.53)
122 (13.47)
744 (85.42)
127 (14.58)
0.498
NOTE: Prostate volume data were missing for 30 controls
and 8 cases.
Abbreviation: PC, prostate cancer.
a
P values for continuous variables were based on t test
assuming unequal variance. P values for categorical variables are based on Fisher exact test. Ancestry was evaluated
as yes/no for each classification.
Table 5 summarizes GRS analyses stratified by prostate
cancer aggressiveness. Increasing GRS for BMI was not
significantly associated with low-grade prostate cancer
(OR, 1.01; 95% CI, 0.97–1.05; P ¼ 0.78) or high-grade
prostate cancer prostate cancer (OR, 0.99; 95% CI, 0.95–
1.04; P ¼ 0.71). In contrast, each unit increase in WHR-GRS
was associated with a statistically significant increase in
high-grade prostate cancer odds (OR, 1.05; 95% CI, 1.00–
1.11; P ¼ 0.048) compared with controls. Results for WHRGRS were similar when we compared high-grade prostate
cancer with low-grade prostate cancer (OR, 1.07; 95% CI,
1.01–1.13; P ¼ 0.03). Further adjustment for prostate volume
did not affect results. Weighting GRS WHR scores for the
reported association between each allele and WHR generated somewhat stronger ORs but were no longer statistically significant. None of these findings exceed the threshold for significance after correction for multiple testing.
Of the 14 SNPs used to create the GRS score for WHR,
only rs4846567 near LYPLAL1 (OR, 1.23; 95% CI, 1.01–
1.49; P ¼ 0.038) was significantly associated with highgrade prostate cancer, whereas rs4823006 near ZNRF3KREMEN1 (OR, 0.85; 95% CI, 0.72–1.01; P ¼ 0.058) held
a marginally significant association (Supplementary
Table S2).
Discussion
Prior studies report that obese men are at greater risk for
advanced prostate cancer (37–39); however, standard
measurements of body size cannot determine or identify
novel pathways. We hypothesized that obesity genetics
could deconstruct the components of obesity and help
better understand those aspects of obesity relevant to
prostate cancer. The Metabochip genotyping platform
provides an efficient means to target in detail more than
200,000 genetic variants previously associated with
metabolism and obesity. Our approach represents the
first Metabochip study of prostate cancer and is based
on the premise that the pathways linking obesity to
prostate cancer may well extend beyond our existing
framework of candidate pathways linking obesity to
T2D or other chronic diseases. Our overall analysis
found 5 genetic variants associated with total prostate
cancer and 5 variants associated with high-grade
Table 2. Overall analysis representing SNPs from the Metabochip platform with P < 104 comparing
prostate cancer cases (n ¼ 871) versus controls (n ¼ 906) from the NMHS
SNP
Chr. Position
CHR10:104745421 10
CHR6:160836326
6
CHR11:8363462
11
RS13111540
4
CHR12:109231826 12
104745421
160836326
8363462
111408792
109231826
On gene/nearby genes
EAF
EAF
EA/RA controls cases OR
SE
P
N
CNNM2,a NT5C2, AS3MT
LPAL2,a SLC22A3, LPA
STK33, LMO1, TRIM66
ELOVL6, ENPEP, PITX2
ATP2A2,a IFT81, ANAPC7
A/G
C/T
C/T
A/G
T/C
0.11
0.73
0.08
0.07
0.23
3.59 105
5.80 105
6.76 105
7.79 105
8.11 105
1,751
1,777
1,776
1,776
1,747
0.39
0.42
0.26
0.38
0.04
0.40
0.47
0.20
0.46
0.02
1.55
1.34
0.72
1.31
0.39
NOTE: Results adjusted for age and 10 principal components.
Abbreviations: EA, effect allele; EAF, effect allele frequency; RA, referent allele.
a
SNP is on the gene.
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871
871
871
871
871
2.32 105
4.59 105
5.77 105
6.81 105
5.90 105
0.12
0.10
0.10
0.11
0.10
NOTE: Results adjusted for age and 10 principal components.
Abbreviations: Chr, chromosome; EA, effect allele; EAF, effect allele frequency; HG, high-grade; LG, low-grade; RA, referent allele.
a
SNP is on the gene.
1.68
1.53
1.52
0.64
0.67
0.31
0.40
0.50
0.27
0.39
0.22
0.31
0.40
0.36
0.48
C/T
G/A
T/C
A/G
A/G
ZNF536, URI1, POP4, TSHZ3
CCT6B,a LIG3
ASB7, LRRK1, ALDH1A3
HSP90AA6P, GALNTL6, AADAT
ROBO1, ROBO2
35400475
30294607
99081396
171857089
78439962
N
P
SE
OR
EAF HG
EAF LG
EA/RA
On gene/nearby genes
Position
Chr
SNP
High-grade vs. low-grade
rs7252787
19
CHR17:30294607
17
rs10902581
15
rs11132859
4
rs1355625
3
1,333
1,333
1,325
1,333
1,330
5.08 105
5.21 105
5.28 105
5.69 105
8.50 105
0.09
0.13
0.25
0.12
0.11
0.69
1.72
0.37
1.66
1.52
0.36
0.13
0.04
0.17
0.25
0.45
0.09
0.06
0.11
0.18
G/T
A/C
T/G
C/G
C/T
2857475
121890597
95918676
85876066
19994876
19
9
10
3
18
ZNF57,a ZNF556, ZNF555, ZNF77
MIR147A, CDK5RAP2, DBC1
PLCE1,a TBC1D12, PIPSL
CADM2,a VGLL3
CABYR,a OSBPL1A, TTC39C, LAMA3
N
P
SE
OR
EAF cases
EAF controls
EA/RA
On gene/nearby genes
Position
Chr
SNP
High-grade vs. controls
rs3855680
rs9409226
CHR10:95918676
CHR3:85876066
rs3786418
Table 3. Stratified analysis representing SNPs from the Metabochip platform with P < 104 comparing high-grade prostate cancer cases (n ¼
427) versus controls (n ¼ 906) and high-grade prostate cancer cases (n ¼ 427) versus low-grade prostate cancer cases (n ¼ 444) from NMHS
Edwards et al.
Cancer Epidemiol Biomarkers Prev; 22(9) September 2013
prostate cancer, at P < 1 104. In a targeted analysis
investigating GRSs for BMI and WHR, we found evidence of pleiotropy between genetic markers of WHR
and high-grade prostate cancer grade after adjusting
for measured WHR.
The SNP most statistically associated with total prostate cancer was CHR10:104745421, an SNP on cyclin
M2 (CNNM2) involved in magnesium transport and
metabolism. Magnesium serves as an enzyme cofactor
involved with energy metabolism, insulin activity, and
glycemic control (40). Genetic variants in CNNM2 are
associated with blood magnesium levels (41), and blood
magnesium levels were recently associated with prostate cancer (42). ATP2A2 (or SERCA2) is overexpressed
in PC3 prostate cancer cell lines and is under consideration as a therapeutic target in prostate cancer treatment
(43). Similarly, ELOVL6 is involved in the rate-limiting
step toward elongating saturated and monounsaturated
fatty acids and may contribute to obesity-induced insulin resistance (44). Several additional genes, including
ZNF57, PLCE1, and CADM2, were identified in analysis
of high-grade prostate cancer. Although these SNPs
derive from prior studies leading to an a priori relationship with obesity and metabolism, no individual SNP
was statistically significantly associated with prostate
cancer after correction for multiple testing, and thus
these associations should be considered preliminary.
Increasing WHR has been previously shown to increase
risk of T2D (45, 46), coronary heart disease (47), and allcause mortality (48), independent of total body adiposity
estimated by BMI. Similar to a recent meta-analysis of
WHR and prostate cancer reporting only a modest nonsignificant association [RR ¼ 1.11 (0.95–1.3), for each 0.1
WHR unit], we found analysis of WHR provided little
insight to the obesity–prostate cancer relationship. In
contrast, we found the genetic determinants of WHR were
associated with the development of aggressive prostate
cancer independent of measured WHR.
The SNPs that comprise the WHR GRSs from the
Metabochip include the SNP rs1055144 in nuclear factor
(erythroid-derived2)-like3 (NFE2L3 or Nrf3). The NFE2L3
maps near the HOXA cluster, a family of transcription
factors essential to control and coordinate positional identity from anterior to posterior during development and
implicated in multiple tumors (49–55). Indeed, NFE2L3
expression may be greatest during embryonic development and is also highly expressed in cancer cell lines (56).
Proinflammatory cytokines such as TNF-a increase
NFE2L3 expression and NFE2L3 transcripts were found
in human lymphoma, breast cancer, and testicular carcinoma tissue samples (56). In this study, rs1055144 was not
significant for high-grade prostate cancer, although the
direction and magnitude of effect were similar.
An SNP in the WHR GRS that was also nominally
associated with high-grade prostate cancer was rs4846567
on chromosome 1q41 (P ¼ 0.038) in a gene desert approximately 250 kb from the gene lysophospholipase-1
(LYPLAL1). This SNP is also 100 kb from the SNP
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Pleiotropy in Obesity and Prostate Cancer
Table 4. Overall ORs comparing BMI GRSs and WHR GRSs with prostate cancer risk among participants
from NMHS
Obesity scores
Cases/controls
OR (95% CI)
P
BMI GRS
Unweighted BMI allele scorea
Weighted BMI allele scoreb
863/876
863/876
1.00 (0.97, 1.04)
1.07 (0.91, 1.25)
0.94
0.41
WHR GRS
Unweighted WHR allele scorec
Weighted WHR allele scored
863/876
863/876
1.01 (0.97, 1.06)
1.10 (0.91, 1.32)
0.52
0.32
NOTE: All models adjusted for age, BMI, or WHR, respectively, natural log of prostate volume and 10 principal components. Information
on prostate volume was missing for 30 controls and 8 cases; results for multivariable analyses are thus based on 876 controls and 863
cases.
a
Unweighted BMI allele score represents the summation of the number of BMI increasing alleles per individual.
b
Weighted BMI allele score represents the summation of the number of BMI increasing alleles per individual after weighting with each
SNP's respective beta coefficient for BMI increase.
c
Unweighted WHR allele score represents the summation of the number of WHR increasing alleles per individual.
d
Weighted WHR allele score represents the summation of the number of WHR increasing alleles per individual after weighting with each
SNP's respective beta coefficient for BMI increase; per 0.3 increase in score (weighted WHR score range: 0.19–1.49).
rs2605100 (r2 ¼ 0.64, D0 ¼ 0.83), which was previously
discovered to be a female-specific WHR SNP (57), and is
93 kb from rs11118316 (r2 ¼ 0.29, D0 ¼ 0.94) which is a
determinant of the ratio of visceral to subcutaneous adiposity (58). LYPLAL1 is thought to function as a triglyceride lipase and is upregulated in subcutaneous adipose
tissue in obese participants (59). Gene variants in
LYPLAL1 have been associated with triglyceride levels
in men (60). Other SNPs, independent of the WHR SNPs,
in the 1q41 region were recently reported to be associated
with colorectal cancer at genome-wide significance (61,
62). The minor allele at rs4823006 within the WHR-GRS
was marginally protective for high-grade prostate cancer
(P ¼ 0.058). This SNP is near zinc ring finger 3 and kremen
1 (ZNRF3-KREMEN1) and aside from an association with
centralized fat deposition involved in Wnt signaling and
may act as a tumor suppressor (63). Attempts to replicate
the WHR GRS with prostate cancer have been hampered
by a lack of existing large prostate studies with both
genetic data and measured waist–hip data.
Table 5. ORs comparing BMI GRS and WHR GRS with prostate cancer risk stratified by severity of prostate
cancer among participants from NMHS
Low-aggression PC vs.
controls (442/876)
High-aggression PC vs.
controls (421/876)
High-aggression PC vs. lowaggression PC (421/442)
OR (95% CI)
P
OR (95% CI)
P
OR (95% CI)
P
Un-weighted BMI allele score
Weighted BMI allele scoreb
1.01 (0.97–1.05)
1.07 (0.89–1.28)
0.78
0.48
0.99 (0.95–1.04)
1.04 (0.85–1.27)
0.71
0.68
0.99 (0.95–1.04)
1.00 (0.81–1.24)
0.78
0.99
WHR GRS
Unweighted WHR allele scorec
Weighted WHR allele scored
0.98 (0.94–1.03)
1.08 (0.88–1.34)
0.53
0.45
1.05 (1.00–1.11)
1.10 (0.88–1.39)
0.048
0.40
1.07 (1.01–1.13)
1.05 (0.81–1.36)
0.03
0.70
BMI-GRS
a
NOTE: All models adjusted for age, BMI, or WHR, respectively, natural log of prostate volume and 10 principal components. Information
on prostate volume was missing for 30 controls and 8 cases; results for multivariable analyses are based on 876 controls and 863 cases.
Abbreviation: PC, prostate cancer.
a
Unweighted BMI allele score represents the summation of the number of BMI increasing alleles per individual.
b
Weighted BMI allele score represents the summation of the number of BMI increasing alleles per individual after weighting with each
SNP's respective beta coefficient for BMI increase.
c
Un-weighted WHR allele score represents the summation of the number of WHR increasing alleles per individual.
d
Weighted WHR allele score represents the summation of the number of WHR increasing alleles per individual after weighting with each
SNP's respective beta coefficient for BMI increase; per 0.3 increase in score (weighted WHR score range: 0.19–1.49).
www.aacrjournals.org
Cancer Epidemiol Biomarkers Prev; 22(9) September 2013
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1543
Published OnlineFirst June 27, 2013; DOI: 10.1158/1055-9965.EPI-13-0123
Edwards et al.
Strengths of our study population included prostate
cancer cases with measured body size, a large sample of
high-grade prostate cancer cases, and exclusion of latent
prostate cancer from the control group. Indeed, there are
few prostate cancer studies with systematically collected
obesity measures, detailed pathology data, and DNA for
genetic analyses. Nevertheless, the power for this study
was low to detect effects at individual SNPs, where we
had 80% power to detect an OR of 1.92 at a SNP with an
MAF of 0.1 and an OR of 1.53 at a SNP with an MAF of 0.5
at an a of 1 107, and no SNP achieved this level of
evidence. That these observations occurred due to chance
is possible; however, our investigation of obesity genetics
with prostate cancer is based on a priori hypothesis developed over years of past research suggesting an association
between obesity and high-grade prostate cancer. This
study was able to evaluate a very fine map of variation
in known metabolic genes for an influence on prostate
cancer risk and grade, thus assessing pleiotropy between
genetic risk factors for known metabolic traits with prostate cancer risk and grade. A more targeted hypothesisdriven approach assessed pleiotropy between GRS for
WHR or BMI with prostate cancer, as BMI and WHR are
the common metrics used in most epidemiologic studies.
This analysis featured fewer tests, a relatively high-variance predictor to increase power, and distinct and easily
testable null hypotheses, compared with the traditional
agnostic scan of variation using individual SNPs. Our
results were consistent with the overall literature in finding an association restricted to high-grade prostate cancer.
We recommend a cautious interpretation, as our results
do not meet strict statistical significance thresholds for
multiple testing and provide these tentative results to
prostate cancer investigators for replication in independent prostate cancer studies with obesity, pathology, and
genetic data.
In conclusion, a genetic risk score to redefine WHR was
significantly associated with high-grade prostate cancer,
independent of measured WHR. While the literature
carefully documents these phenomena, this is the first
demonstration of pleiotropy of known WHR genes on
prostate cancer aggression in a genetic association study.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: T.L. Edwards, J.H. Fowke
Development of methodology: T.L. Edwards, J.H. Fowke
Acquisition of data (provided animals, acquired and managed patients,
provided facilities, etc.): S. Motley, W. Duong, J.H. Fowke
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): T.L. Edwards, A. Giri, J.H. Fowke
Writing, review and/or revision of the manuscript: T.L. Edwards, A. Giri,
J.H. Fowke
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): T.L. Edwards, W. Duong, J.H.
Fowke
Study supervision: T.L. Edwards, J.H. Fowke
Grant Support
The study is supported by grant R01 CA121060 from the National
Cancer Institute (principal investigator: J.H. Fowke) and UL1 RR024975
from NCRR/NIH. T.L. Edwards is supported by a Vanderbilt Clinical and
Translational Research Scholar Award (5KL2RR024977).
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received February 1, 2013; revised June 3, 2013; accepted June 20, 2013;
published OnlineFirst June 27, 2013.
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Pleiotropy between Genetic Markers of Obesity and Risk of
Prostate Cancer
Todd L. Edwards, Ayush Giri, Saundra Motley, et al.
Cancer Epidemiol Biomarkers Prev 2013;22:1538-1546. Published OnlineFirst June 27, 2013.
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