RESEARCH ARTICLE
Associations of Circulating Estrogens and Estrogen Metabolites
with Fecal and Oral Microbiome in Postmenopausal Women in
the Ghana Breast Health Study
Zeni Wu,a Ruth M. Pfeiffer,a Doratha A. Byrd,b Yunhu Wan,a Daniel Ansong,c Joe-Nat Clegg-Lamptey,d Beatrice Wiafe-Addai,e
Lawrence Edusei,d Ernest Adjei,c Nicholas Titiloye,c Florence Dedey,d Francis Aitpillah,c Joseph Oppong,c Verna Vanderpuye,d
Ernest Osei-Bonsu,c Casey L. Dagnall,a,f Kristine Jones,a,f Amy Hutchinson,a,f Belynda D. Hicks,a,f Thomas U. Ahearn,a Rob Knight,g
Richard Biritwum,h Joel Yarney,d Seth Wiafe,i Baffour Awuah,c Kofi Nyarko,h Montserrat Garcia-Closas,a Rashmi Sinha,a
Jonine D. Figueroa,a,j Louise A. Brinton,a Britton Trabert,k Emily Vogtmanna
aDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
bDepartment of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
cKomfo Anokye Teaching Hospital, Kumasi, Ghana
dKorle Bu Teaching Hospital, Accra, Ghana
ePeace and Love Hospital, Kumasi, Ghana
fCancer Genomics Research Laboratory, Frederick National Lab for Cancer Research, Frederick, Maryland, USA
gDepartment of Pediatrics, University of California San Diego, La Jolla, California, USA
hUniversity of Ghana, Accra, Ghana
iSchool of Public Health, Loma Linda University, Loma Linda, California, USA
jUsher Institute and CRUK Edinburgh Centre, University of Edinburgh, Edinburgh, United Kingdom
kDepartment of Obstetrics and Gynecology, University of Utah, and Huntsman Cancer Institute at the University of Utah, Salt Lake City, Utah, USA
Britton Trabert and Emily Vogtmann are co-senior authors. The author order was determined alphabetically.
ABSTRACT The human fecal and oral microbiome may play a role in the etiology of
breast cancer through modulation of endogenous estrogen metabolism. This study aimed
to investigate associations of circulating estrogens and estrogen metabolites with the fecal
and oral microbiome in postmenopausal African women. A total of 117 women with fecal
(N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing,
and estrogens and estrogen metabolites data measured by liquid chromatography tandem
mass spectrometry were included. The outcomes were measures of the microbiome and
the independent variables were the estrogens and estrogen metabolites. Estrogens and
estrogen metabolites were associated with the fecal microbial Shannon index (global
P , 0.01). In particular, higher levels of estrone (b = 0.36, P = 0.03), 2-hydroxyestradiol
(b = 0.30, P = 0.02), 4-methoxyestrone (b = 0.51, P = 0.01), and estriol (b = 0.36, P = 0.04)
were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone
(b = 20.57, P , 0.01) was inversely associated with the Shannon index as indicated by
linear regression. Conjugated 2-methoxyestrone was associated with oral microbial
unweighted UniFrac as indicated by MiRKAT (P , 0.01) and PERMANOVA, where conju-
gated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other Editor Sangeeta Khare, U. S. Food and Drug
estrogens or estrogen metabolites were associated with any other beta diversity meas- Administration
ures. The presence and abundance of multiple fecal and oral genera, such as fecal gen- This is a work of the U.S. Government and is
not subject to copyright protection in the
era from families Lachnospiraceae and Ruminococcaceae, were associated with several United States. Foreign copyrights may apply.
estrogens and estrogen metabolites as indicated by zero-inflated negative binomial Address correspondence to Emily Vogtmann,
regression. Overall, we found several associations of specific estrogens and estrogen emily.vogtmann@nih.gov.
metabolites and the fecal and oral microbiome. The authors declare no conflict of interest.
Received 24 April 2023
IMPORTANCE Several epidemiologic studies have found associations of urinary estro- Accepted 26 May 2023
gens and estrogen metabolites with the fecal microbiome. However, urinary estrogen Published 21 June 2023
concentrations are not strongly correlated with serum estrogens, a known risk factor for
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 1
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
breast cancer. To better understand whether the human fecal and oral microbiome were
associated with breast cancer risk via the regulation of estrogen metabolism, we con-
ducted this study to investigate the associations of circulating estrogens and estrogen
metabolites with the fecal and oral microbiome in postmenopausal African women. We
found several associations of parent estrogens and several estrogen metabolites with the
microbial communities, and multiple individual associations of estrogens and estrogen
metabolites with the presence and abundance of multiple fecal and oral genera, such as
fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen
metabolizing properties. Future large, longitudinal studies to investigate the dynamic
changes of the fecal and oral microbiome and estrogen relationship are needed.
KEYWORDS estrogens, fecal microbiome, oral microbiome, postmenopausal African
women
The human microbiome may play a role in the etiology of breast cancer. Severalcross-sectional studies have reported associations between the human microbiome
and breast cancer risk (1–3). One potential role of the human gut microbiome in breast
cancer development is the regulation of estrogen metabolism, especially in postmeno-
pausal women (4). Elevated circulating estrogens and estrogen metabolites are associ-
ated with a higher risk of postmenopausal breast cancer (5, 6). Estrogen metabolism
via hydroxylation or methylation can lead to variations in bioavailability (7). The parent
estrogens and estrogen metabolites can be also conjugated with sulfate to create a
reservoir in the body for future use, or conjugated with glucuronic acid to enable
excretion in bile and feces or excretion in urine (8). The circulating estrogens/estrogen
metabolites consist of both unconjugated and conjugated forms.
It has been reported that the human gut microbiome modulates endogenous estro-
gen metabolism through bacterial beta-glucuronidase and beta-glucosidase enzyme
that deconjugate estrogens, leading to their reabsorption into circulation (4, 9). The
fecal Clostridia class, especially genera in the Ruminococcaceae family, contains beta-
glucuronidase enzyme activity and has been observed to have a higher relative abun-
dance in breast cancer cases (1, 10). Fecal Clostridia was also reported to be positively
associated with levels of urinary estrogens and their metabolites (11, 12). There is also
evidence that the oral microbiome is associated with breast cancer risk (13); however,
no studies have investigated associations between estrogens and their metabolites
and the oral microbiome.
To explore the interrelationships among estrogen metabolism and the human
microbiome, we investigated the associations of circulating estrogens and estrogen
metabolites with fecal and oral microbiome in postmenopausal women from the
Ghana Breast Health Study, a population-based case-control study of breast cancer
conducted in Accra and Kumasi, Ghana, West Africa (14).
RESULTS
As shown in Table 1, the mean age at blood draw was 57.9 (standard deviation 8.2)
years. Most participants were never smokers (99.1%), never consumed alcohol (61.5%),
never used oral contraceptives (86.3%), did not having a history of diabetes (88.0%),
and reported not using antibiotics within 30 days of blood draw (67.5%).
The associations between estrogens and estrogen metabolites with fecal microbial
alpha-diversity are presented in Table 2. Overall, estrogens and estrogen metabolites
were associated with the fecal microbial Shannon index (global P , 0.01) in the mutu-
ally adjusted model. In particular, concentrations of estrone (b = 0.36, P = 0.03), 2-
hydroxyestradiol (b = 0.30, P = 0.02), 4-methoxyestrone (b = 0.51, P = 0.01), and estriol
(b = 0.36, P = 0.04) were positively associated with the fecal Shannon index, while the
association with 16-alpha-hydroxyestrone was inverse (b=-0.57, P , 0.01). In the indi-
vidual hormone models, estrone (b = 0.22, P = 0.02), 4-methoxyestrone (b = 0.42,
P , 0.01), and estriol (b = 0.25, P = 0.04) were positively associated with the fecal
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 2
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
TABLE 1 Descriptive characteristics (N = 117)
Covariates N Percent
Age at blood draw Mean (6 SD) 57.96 8.2
Study site
KBTH 6 5.1
KATH 67 57.3
PLA 44 37.6
Year at blood draw
2014 27 23.1
2015 90 76.9
Time since menopause (yrs)
#2 17 14.5
3-5 15 12.8
6-10 28 23.9
.10 43 36.8
Missing 14 12.0
BMI (kg/m2)
Underweight (17–18.5) 6 5.1
Healthy wt (18.5–24.9) 43 36.8
Overweight (25.0–29.9) 35 29.9
Obese (30–67) 32 27.4
Unknown/Missing 1 0.9
Smoking status
Never 116 99.1
Unknown 1 0.9
Alcohol consumption
Yes 38 32.5
No 72 61.5
Unknown/Missing 7 6.0
Diabetes
Ever 13 11.1
Never 103 88.0
Unknown 1 0.9
Oral contraceptive use
Ever 16 13.7
Never 101 86.3
Antibiotic use
More than 30 days of blood draw 79 67.5
Within the last 30 days of blood draw 28 23.9
Unknown/Missing 10 8.5
microbial Shannon index, and additionally, positive associations were also found for es-
tradiol (b = 0.25, P = 0.01), 17-epiestriol (b = 0.28, P = 0.03), conjugated estrone (b =
0.20, P = 0.01), conjugated estradiol (b = 0.11, P = 0.02), and conjugated estriol (b =
0.21, P = 0.02). However, only 4-methoxyestrone had an FDR adjusted P-value less than
5%. No statistically significant associations were found for fecal microbial observed
ASVs and Faith’s PD in both mutually adjusted and individual hormone models. The
measured estrogens/estrogen metabolites were not associated with oral microbial
alpha-diversity with all global P-values and FDR adjusted P-values greater than 0.05
(Table S1).
In the beta-diversity analyses, conjugated 2-methoxyestrone was associated with
oral microbial unweighted UniFrac (Table S2), but estrogens or estrogen metabolites
were not associated with the remaining beta diversity matrices of the fecal and oral
microbiome as indicated by MiRKAT models. Consistent results were observed by
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 3
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 4
TABLE 2 Association between estrogens and estrogen metabolites with fecal microbial alpha diversity (N = 110)a
Mutually adjusted modelb Individual hormone modelc
Observed ASVs Faith's PD Shannon index
(global (global (global
p-value = 0.07) p-value = 0.22) p-value< 0.01) Observed ASVs Faith's PD Shannon index
Estrogens and estrogen metabolites b P value b P value b P value b P value b P value b P value
Parent Estrogens
Estrone 14.14 0.15 0.70 0.25 0.36 0.03 10.17 0.05 0.65 0.04 0.22 0.02
Unconjugated Estrone — — — — — — 6.33 0.40 0.39 0.39 0.19 0.15
Conjugated Estrone — — — — — — 10.31 0.02 0.67 0.01 0.20 0.01
Estradiol 1.72 0.86 20.05 0.94 0.15 0.35 10.42 0.05 0.63 0.05 0.25 0.01
Unconjugated Estradiol — — — — — — 6.08 0.28 0.41 0.24 0.16 0.10
Conjugated Estradiol — — — — — — 5.21 0.05 0.31 0.05 0.11 0.02
2-Hydroxylation Pathway Metabolites
2-Hydroxyestrone 23.42 0.84 0.42 0.70 20.49 0.10 17.52 0.07 1.23 0.03 0.30 0.08
2-Hydroxyestradiol 11.76 0.13 0.51 0.29 0.30 0.02 6.39 0.38 0.34 0.43 0.19 0.13
2-Hydroxyestrone-3-methyl ether 1.35 0.90 20.11 0.86 20.05 0.77 10.78 0.14 0.46 0.30 0.14 0.27
2-Methoxyestrone 21.28 0.90 20.12 0.84 0.15 0.35 6.67 0.33 0.43 0.30 0.19 0.11
Unconjugated 2-Methoxyestrone — — — — — — 0.28 0.97 0.06 0.87 0.07 0.55
Conjugated 2-Methoxyestrone — — — — — — 1.55 0.68 0.13 0.57 0.06 0.34
2-Methoxyestradiol -7.52 0.47 -0.27 0.68 -0.11 0.52 26.49 0.42 20.26 0.58 20.07 0.64
Unconjugated 2-Methoxyestradiol — — — — — — 22.83 0.75 20.27 0.62 0.01 0.95
Conjugated 2-Methoxyestradiol — — to — — — 25.75 0.31 20.24 0.48 20.06 0.56
4-Hydroxylation Pathway Metabolites
4-Hydroxyestrone 25.58 0.59 20.26 0.69 20.24 0.17 9.62 0.13 0.50 0.19 0.16 0.15
4-Methoxyestrone 16.07 0.15 0.67 0.33 0.51 0.01 17.99 0.02 0.89 0.06 0.42 ,0.01*
4-Methoxyestradiol 0.67 0.95 20.30 0.66 0.14 0.45 28.07 0.35 20.74 0.16 20.09 0.55
16-alpha-Hydroxylation Pathway Metabolites
Estriol 14.70 0.16 0.94 0.15 0.36 0.04 12.69 0.05 0.81 0.04 0.25 0.03
Unconjugated Estriol — — — — — — 24.49 0.65 20.28 0.64 0.08 0.65
Conjugated Estriol — — — — — — 10.81 0.04 0.69 0.03 0.21 0.02
16-alpha-Hydroxyestrone 223.19 0.02 20.99 0.10 20.57 ,0.01 21.59 0.76 20.01 0.97 20.01 0.91
16-Ketoestradiol 8.26 0.44 0.13 0.85 0.19 0.28 5.89 0.36 0.28 0.47 0.11 0.35
16-Epiestriol 24.55 0.60 20.29 0.58 20.09 0.53 7.15 0.24 0.37 0.31 0.13 0.25
17-Epiestriol 15.53 0.09 0.98 0.09 0.15 0.33 18.46 0.01 0.96 0.02 0.28 0.03
aLinear regression model that includes all metabolites with additional adjustment for age and BMI; the missing BMI value of one woman was replaced by the median category; ASVs, amplicon sequence variants; Faith's PD, Faith’s
phylogenetic diversity.
bAs the concentration of the 5 conjugated metabolites are calculated as the difference between the combined estrogen measurement and the unconjugated estrogen measurement (e.g., conjugated estrone = estrone2
unconjugated estrone), the unconjugated and conjugated analytes were not included in the mutually adjusted model.
cThe False discovery rate (FDR) adjusted P value was calculated for the individual hormone models to adjust for multiple testing; *FDR adjusted P value#0.05; The P values present in the table are all normal P values.
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
FIG 1 Results from the zero-inflated negative binomial regression for the associations of estrogens and estrogen metabolites with fecal genera with an
FDR adjusted global P-value less than 0.05. The zero-inflated model indicates the associations for the genus-level presence, and the sequence-count model
indicates the associations for the number of reads for each genus. The color of the cells (orange to blue) indicates the strength of the association (model
estimates, b). The “*” in the cell indicates that the corresponding P-value is less than 0.05. The color of the “Global P value” column (white to gray)
indicates the scale of the global P-value and the exact number in the cell indicates the corresponding global P-value.
PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral micro-
bial variability in unweighted UniFrac (FDR adjusted P-value ,0.05) (Fig. S1).
Fig. 1 presents results for the associations of estrogens and estrogen metabolites with
fecal genera from the zero-inflated negative binomial regression analysis at an FDR of 5%
or less for the global P-value (results for all genera are included in Table S3). Overall, estro-
gens and estrogen metabolites were significantly associated with 41 fecal genera. Eighteen
out of these 41 genera belong to the order Clostridiales, including nine genera from the
family Lachnospiraceae and five genera from the family Ruminococcaceae. For the individual
associations of the estrogen or estrogen metabolites with these fecal genera, the sequence-
count (number of reads for each genus) contributed to more associations with estrogen
and estrogen metabolites than the zero-inflated part (presence of each genus).
Fig. 2 presents results for the associations of estrogens and estrogen metabolites
with oral genera from the zero-inflated negative binomial regression analysis at an FDR
of 5% or less for the global P-value (results for all genera are included in Table S4).
Estrogens and estrogen metabolites were positively or inversely associated with 27 oral
genera. Similar to the fecal microbiome results, the sequence-count also contributed to
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 5
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
FIG 2 Results from the zero-inflated negative binomial regression for the associations of estrogens and estrogen metabolites with oral genera with an FDR
adjusted global P-value less than 0.05. The zero-inflated model indicates the associations for the genus-level presence, and the sequence-count model
indicates the associations for the number of reads for each genus. The color of the cells (orange to blue) indicates the strength of the association (model
estimates, b). The “*” in the cell indicates that the corresponding P-value is less than 0.05. The color of the “Global P value” column (white to gray)
indicates the scale of the global P-value and the exact number in the cell indicates the corresponding global P-value.
more associations compared to the zero-inflated part of the model. Of the genus-level
associations, the counts for Christensenellaceae.R.7.group were associated with 12 out of
the 15 estrogens and estrogen metabolites (sequence-count b ranging from 21.40 to
1.78), while the presence of Moraxella was strongly associated with most estrogens and
estrogen metabolites (zero-inflated b ranging from21.35 to 1.27).
In sensitivity analyses excluding women who had diabetes or used antibiotics
within the last 30 days before blood draw, the alpha-diversity associations were similar
to those for the overall population. The associations of Shannon index were stronger
among women who were menopausal for less than 10 years, whereas the associations
of observed ASVs were stronger among women who were menopausal for greater
than 10 years (Table S5).
DISCUSSION
In this study of the association of estrogen metabolism and the fecal and oral micro-
biome in postmenopausal African women in the Ghana Breast Health Study, we found that
parent estrogens and several estrogen metabolites, including 2-hydroxyestradiol, 4-methox-
yestrone, estriol, and 17-epiestriol, were positively associated with fecal microbial Shannon
index (alpha-diversity), while 16alpha-hydroxyestrone was inversely associated with this
same measure. In contrast, estrogens and estrogen metabolites were not associated with
oral microbial alpha-diversity. Estrogens and estrogen metabolites were not associated with
the fecal or oral microbial communities as measured by beta-diversity in general, although
conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac.
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 6
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
Estrogens and estrogen metabolites were associated with the presence and abundance of
multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and
Ruminococcaceae, which have estrogen metabolizing properties.
The associations between estrogens and the fecal microbiome have been reported
by previous cross-sectional studies. Similar to our results, one Korean study that
included 26 women aged 25 to 65 years found that women in a high estradiol group
had a greater average fecal microbial Shannon index and Simpson index than the me-
dium and low group, but no significant differences in the richness determined by
Chao1 (15), suggesting that estrogen levels may be related to both microbiome rich-
ness and evenness (as measured by Shannon index or Simpson index) but not micro-
biome richness alone (as measured by observed ASVs, Faith’s PD, or Chao1). In con-
trast, results of another study in Spain that included 15 women with polycystic ovary
syndrome, 16 nonhyperandrogenic female and 15 male controls, found an inverse
association between serum estradiol and the fecal microbial Shannon Index and Chao1
in a combined analysis of all participants (16). However, these inconsistent results are
not surprising since these studies were small and included participants with different
health conditions.
A few studies also investigated the associations between estrogen metabolites and
the fecal microbiome, but only considered urinary estrogen metabolites. It is difficult
to compare our results with these previous studies since estrogen metabolite concen-
trations have not been observed to be strongly correlated between urine and serum
(17). One study conducted within 25 men and 7 postmenopausal women in the United
States reported that levels of total urinary estrogens, and most individual estrogen
metabolites, including the 16-pathway estrogen metabolites, were positively associ-
ated with fecal microbial observed species and the Shannon index. This study also
found no association of estrogens and fecal microbial beta-diversity (11). In a cross-sec-
tional study within 60 healthy postmenopausal women enrolled in the Kaiser
Permanente of Colorado health plan in the United States, total urinary estrogens and
estrogen metabolites tended to nonstatistically significantly increase over quintiles of
alpha-diversity measures; the ratio of metabolites to parent estrogen and the ratio of
2- to 16-hydroxylation pathway metabolites showed a statistically significant increas-
ing trend across quintiles of Faith’s PD, but not for the other alpha-diversity measures
(12). Another cross-sectional study among 48 postmenopausal breast cancer cases and
48 matched controls also from Kaiser Permanente in Colorado found that Faith’s PD
was weakly correlated with total urinary estrogens and the estrogen metabolite to par-
ent estrogen ratio in controls (1, 10).
We found multiple associations of estrogen and estrogen metabolites with the
presence and abundance of multiple fecal and oral genera. Among the 100 fecal gen-
era that were included in our analysis, 41 were associated with circulating estrogens
and estrogen metabolites based on the FDR of 5% or less for the global P-value, with
almost half of these genera (18 out of 41) belonging to the order Clostridiales. In previ-
ous studies of urinary estrogen metabolism, the relative abundance of Clostridiales was
positively associated with urine estrogen and the ratio of metabolites to parent estro-
gens among postmenopausal women and men (11, 12). The order Clostridiales has
been found to be associated with breast cancer risk (1, 3), where cases had a higher rel-
ative abundance of the family Ruminococcaceae and a lower relative abundance of the
family Lachnospiraceae. In our study, Ruminococcaceae and Lachnospiraceae were the
families that contained the most fecal genera associated with estrogen metabolism,
although there was a mix of positive and inverse associations across estrogens and
estrogen metabolites and the genera. One of the mechanisms by which the fecal
microbiome may be related to breast cancer is through the beta-glucuronidase and
beta-glucosidase enzyme activity of some bacteria which enables them to deconjugate
conjugated estrogens, leading to their reabsorption into circulation (4, 9). In this study,
we found some genera previously noted to contain beta-glucuronidase and/or beta-
glucosidase (18), including bacteria belonging to the order Clostridiales such as the
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 7
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
genus Coprococcus within the Lachnospiraceae family and the genus Ruminococcus
within the Ruminococcaceae family, were associated estrogen metabolism. Also, there
were many other bacteria that have not been reported to contain genes encoding
beta-glucuronidase and/or beta-glucosidase also associated with estrogen metabolism
in our analyses, such as the genus Intestinibacter within the Clostridiales order or the
genus Sphingobium within the Sphingomonadales order. Further studies are needed to
replicate our findings and better understand the underlying mechanisms for the
observed associations.
The oral microbiome has been found to be associated with breast cancer in our previous
study, where alpha-diversity was inversely associated with breast cancer, and associations
were observed for beta-diversity and multiple taxa (13). However, we did not find associa-
tions between estrogens and estrogen metabolites with oral microbial alpha-diversity, and
only conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac,
suggesting that the association between the oral microbial composition and breast cancer
may not be related to estrogen metabolism. Some previous studies have suggested that
having a history of periodontal disease, a chronic inflammatory condition considered to be
caused by periodontal pathogens (19, 20), is associated with breast cancer risk (21, 22).
None of the 27 oral genera associated with estrogens and estrogen metabolites belong to
the red- or orange-complex of periodontal pathogens (two groups of bacteria strongly asso-
ciated with periodontal disease) (19), which also suggests that the possible association
between periodontal pathogens and breast cancer may be independent of alterations in
estrogen metabolism.
Our study has several important strengths. It is the first to investigate the associa-
tions between estrogen metabolism with the fecal and oral microbiome in an African
population. We obtained data on both the fecal and oral microbiome, and we used a
high-performance LC-MS/MS assay to evaluate individual estrogens and estrogen
metabolites with high reliability, sensitivity, and specificity. However, limitations of this
study should also be noted. First, this study is cross-sectional, therefore we were
unable to evaluate the dynamic change of the microbiome and estrogen interrelation-
ships or the causality of their associations (i.e., whether the microbiome plays a role in
estrogen metabolism, or microbial changes are a result of circulating estrogen levels).
Second, since 16S rRNA gene sequencing is unable to detect the microbial functional
genes, this study could not identify whether there are any microbial pathways involved
in estrogen metabolism. Finally, the sample size of our study is relatively small and
therefore limited our statistical power to identify small differences in associations.
In conclusion, we found positive associations of estrogens and estrogen metabolites,
including 2-hydroxyestradiol, 4-methoxyestrone, estriol, and 17-epiestriol, and inverse asso-
ciations of 16alpha-hydroxyestrone, with the fecal microbiome. Multiple associations with
individual genera for both fecal and oral microbiome were also identified. Larger studies
are needed to confirm our findings. Longitudinal studies to investigate the dynamic
changes of the fecal and oral microbiome and estrogen interrelationships are also needed.
MATERIALS ANDMETHODS
Study population selection. The Ghana Breast Health Study has been described in detail previously
(14). In brief, breast cancer cases or nonmalignant breast disease cases were identified from three hospi-
tals in Ghana: Korle Bu Teaching Hospital in Accra and Komfo Anoyke Teaching Hospital and Peace and
Love Hospital in Kumasi. Population controls were frequency matched by age group to cases and identi-
fied using a household census of randomly selected enumeration areas giving rise to the cases. Study
subjects had to have resided for at least 1 year in the study areas.
After providing informed consent, participants responded to a standardized interview-based ques-
tionnaire that focused on demographic characteristics and breast cancer risk factors, and provided sa-
liva, stool, and blood samples. Previously, 392 breast cancer cases, 100 nonmalignant breast disease
cases, and 433 controls with available fecal and oral samples were selected to study the associations of
the fecal or oral microbiome with breast cancer and nonmalignant breast disease (3, 13). In addition,
blood samples from 635 postmenopausal controls not using exogenous hormones at blood draw were
selected to measure the concentrations of the estrogens and their metabolites (23, 24).
In this study, 117 controls with both microbiome (fecal and oral) and circulating estrogen data were
included. To be consistent with the rarefaction rate of previous analyses (3, 13), we excluded individuals
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 8
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
whose oral samples had,20,000 reads (N = 3) or fecal samples with ,6,250 reads (N = 7), which yielded
samples sizes of 110 women and 114 women in the fecal and oral microbiome analyses, respectively.
Fecal and oral sample processing and bioinformatics. The methods for DNA extraction, PCR
amplification, and sequencing of the fecal and oral samples were described previously (3, 13). In brief,
DNA was extracted from the fecal samples using the MO-BIO PowerMag Soil DNA isolation kit and DNA
was extracted from the oral samples using the DSP DNA Virus Pathogen kit (Qiagen) in two separate lab-
oratories. The V4 region of the 16S rRNA gene was PCR amplified for both sample types. On the Illumina
MiSeq, 2 
 150 bp paired end sequencing was conducted for the fecal samples and 2 
 250 bp paired
end sequencing was conducted for the oral samples.
Sequencing data from both fecal and oral samples were processed using QIIME 2 version 2019.1 (25).
Sequence variants were generated using the DADA2 plugin. Taxonomy was assigned to the resulting ampli-
con sequence variants (ASVs) using SILVA classifier version v132 and relative abundances of taxa were esti-
mated (26). Based on rarefaction curves in the previous analyses (3, 13), we chose a rarefaction rate of 6,250
reads/sample for the fecal microbiome and 20,000 reads/sample for the oral microbiome. Alpha-diversity
measures (i.e., observed ASVs, Faith’s Phylogenetic Diversity [PD], and the Shannon index) and beta-diversity
measures (i.e., Bray-Curtis, Jaccard, weighted Unifrac, and unweighted Unifrac) were calculated from the rare-
fied data using QIIME 2 (25).
Serum estrogen assay. The methods for serum estrogen measurement have been described previ-
ously (27–29). Briefly, stable isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/
MS) was used to quantify 15 serum estrogens and estrogen metabolites, including estrone, estradiol,
2-hydroxylation pathway metabolites (2-hydroxyestrone, 2-hydroxyestradiol, 2-hydroxyestrone-3-methyl
ether, 2-methoxyestrone, and 2-methoxyestradiol); 4-hydroxylation pathway metabolites (4-hydroxyes-
trone, 4-methoxyestrone, and 4-methoxyestradiol); and 16-alpha-hydroxylation pathway metabolites (es-
triol, 16alpha-hydroxyestrone, 16-ketoestradiol, 16-epiestriol, and 17-epiestriol,). Five of the estrogens/
metabolites (estrone, estradiol, estriol, 2-methoxyestrone, and 2-methoxyestradiol) were also measured in
their unconjugated forms, and then the conjugated concentrations of these estrogens/metabolites were
calculated by subtracting the unconjugated from the combined concentration. Estrogens and estrogen
metabolites were expressed in picomoles per L. Quality control of the serum estrogen assay has been
reported elsewhere (23).
Statistical analysis. Given their right-skewed distributions, the circulating estrogen measures were
log-transformed for analysis. Linear regression models were used to calculate the associations between
estrogens and estrogen metabolites as the predictor with fecal and oral microbial alpha-diversity as the
outcome using two approaches. First, parent estrogens and estrogen metabolites were all analyzed
jointly in the same model (i.e., mutually adjusted). Then, individual estrogen/estrogen metabolite levels
(including unconjugated and calculated conjugated concentrations) were further evaluated in separate
models. A false discovery rate (FDR) was calculated for the individual hormone models to adjust P-values
due to multiple testing (N = 25 tests).
For beta-diversity, the association between the estrogens and estrogen metabolites and overall
beta-diversity matrices was tested using Microbiome Regression-Based Kernel Association Test (MiRKAT)
(30). Permutational Multivariate Analysis of Variance (PERMANOVA) for the beta-diversity matrices was
used to calculate the distance-based coefficient of determination, R2, to quantify the percentage of mi-
crobial variability explained by estrogens and estrogen metabolites. The FDR adjusted P-value (N = 25
tests) was calculated for both MiRKAT and PERMANOVA.
For the taxonomic analyses, we restricted to genera present in 5% to 80% of the population. A
series of zero-inflated negative binomial regression models were used to examine associations of over-
all estrogens and estrogen metabolites with the presence/absence of (zero-inflated) and number of
reads (sequence-count) for specific genera. A global P-value and an FDR adjusted global P-value (N
fecal microbiome = 100 tests; N oral microbiome = 67 tests) were calculated for each model.
Individual estimates and the corresponding P-values for each estrogen or estrogen metabolite were
reported for the genera.
All models were adjusted for age (continuous) and body mass index (BMI, ,18.5,18.5 to 24.9, 24.9-
29.9, and $30 kg/m2). For one woman with missing BMI, the median category was used. Additional
adjustment for other potential confounding variables, including study site, year at blood draw, time
since menopause, alcohol consumption, diabetes, oral contraceptive use, and antibiotic use did not sub-
stantially change the estimates, and therefore only results from the minimally adjusted models are pre-
sented. In sensitivity analyses we restricted the study population to participants who never had diabetes,
did not use antibiotics within the last 30 days, were menopausal for ,10 years or $10 years. All statisti-
cal analyses were conducted using R version 3.6.2.
Ethics statement. All participants provided written informed consent. This study was approved by
the Special Studies Institutional Review Board of the National Cancer Institute (NCI; Rockville, MD, USA;
FWA number 00005897 and IORG number 00010), the Ghana Heath Service Ethical Review Committee
and Institutional Review Boards at the University of Ghana Noguchi Memorial Institute for Medical
Research (Accra, Ghana; FWA number 00001824 and IORG number 0000908), the Kwame Nkrumah
University of Science and Technology (Kumasi, Ghana), and the School of Medical Sciences at Komfo
Anokye Teaching Hospital (Kumasi, Ghana).
Data availability. The sequencing data are available on the Sequence Read Archive (NCBI SRA)
under BioProject ID PRJNA658160 for fecal microbiome data and BioProject ID PRJNA767189 for oral
microbiome data. Further data are available from Thomas U. Ahearn (thomas.ahearn@nih.gov) upon
request.
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 9
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
SUPPLEMENTAL MATERIAL
Supplemental material is available online only.
SUPPLEMENTAL FILE 1, PDF file, 0.1 MB.
SUPPLEMENTAL FILE 2, XLSX file, 0.1 MB.
ACKNOWLEDGMENTS
This work was supported by the Intramural Research Program in the Division of
Cancer Epidemiology and Genetics, the US National Institutes of Health (NIH),
National Cancer Institute (NCI). The success of this investigation would not have been
possible without exceptional teamwork and the diligence of the field staff who
oversaw the recruitment, interviews, and collection of data from study subjects.
Special thanks are due to the following individuals: Korle Bu Teaching Hospital, Accra
—Adu-Aryee, Obed Ekpedzor, Angela Kenu, Victoria Okyne, Naomi Oyoe Ohene Oti,
Evelyn Tay; Komfo Anoyke Teaching Hospital, Kumasi— Marion Alcpaloo, Bernard
Arhin, Emmanuel Asiamah, Isaac Boakye, Samuel Ka-chungu and; Peace and Love
Hospital, Kumasi—Samuel Amanama, Emma Abaidoo, Prince Agyapong, Thomas
Agyei-Ansong, Debora Boateng, Margaret Frempong, Bridget Nortey Mensah, Richard
Opoku, and Kofi Owusu Gyimah. The study was further enhanced by surgical expertise
provided by Lisa Newman of the University of Michigan and by pathological expertise
provided by Stephen Hewitt and Petra Lenz of the National Cancer Institute Maire A.
Duggan from the Cumming School of Medicine, University of Calgary, Canada. Study
management assistance was received from Ricardo Diaz, Shelley Niwa, Usha Singh,
Ann Truelove, and Michelle Brotzman at Westat, Inc. Appreciation is also expressed to
the many women who agreed to participate in the study and to provide information
and biospecimens in hopes of preventing and improving outcomes of breast cancer
in Ghana. This work utilized the computational resources of the NIH HPC Biowulf
cluster (http://hpc.nih.gov).
We have no conflicts of interest to declare.
This work was supported by the Intramural Research Program in the Division of
Cancer Epidemiology and Genetics, the US National Institutes of Health (NIH), National
Cancer Institute (NCI).
REFERENCES
1. Goedert JJ, Jones G, Hua X, Xu X, Yu G, Flores R, Falk RT, Gail MH, Shi J, 8. Raftogianis R, Creveling C, Weinshilboum R, Weisz J. 2000. Estrogen me-
Ravel J, Feigelson HS. 2015. Investigation of the association between the tabolism by conjugation. J Natl Cancer Inst Monogr 2000:113–124.
fecal microbiota and breast cancer in postmenopausal women: a popula- https://doi.org/10.1093/oxfordjournals.jncimonographs.a024234.
tion-based case-control pilot study. J Natl Cancer Inst 107:djv147. https:// 9. Kwa M, Plottel CS, Blaser MJ, Adams S. 2016. The intestinal microbiome
doi.org/10.1093/jnci/djv147. and estrogen receptor-positive female breast cancer. J Natl Cancer Inst
2. Zhu J, Liao M, Yao Z, Liang W, Li Q, Liu J, Yang H, Ji Y, Wei W, Tan A, Liang 108:djw029. https://doi.org/10.1093/jnci/djw029.
S, Chen Y, Lin H, Zhu X, Huang S, Tian J, Tang R, Wang Q, Mo Z. 2018. 10. Goedert JJ, Hua X, Bielecka A, Okayasu I, Milne GL, Jones GS, Fujiwara M,
Breast cancer in postmenopausal women is associated with an altered Sinha R, Wan Y, Xu X, Ravel J, Shi J, Palm NW, Feigelson HS. 2018. Post-
gut metagenome. Microbiome 6:136. https://doi.org/10.1186/s40168-018 menopausal breast cancer and oestrogen associations with the IgA-
-0515-3. coated and IgA-noncoated faecal microbiota. Br J Cancer 118:471–479.
3. Byrd DA, Vogtmann E, Wu Z, Han Y, Wan Y, Clegg-Lamptey JN, Yarney J, https://doi.org/10.1038/bjc.2017.435.
Wiafe-Addai B, Wiafe S, Awuah B, Ansong D, Nyarko K, Hullings AG, Hua X, 11. Flores R, Shi J, Fuhrman B, Xu X, Veenstra TD, Gail MH, Gajer P, Ravel J,
Goedert JJ. 2012. Fecal microbial determinants of fecal and systemic
Ahearn T, Goedert JJ, Shi J, Knight R, Figueroa JD, Brinton LA, Garcia-Closas
estrogens and estrogen metabolites: a cross-sectional study. J Transl Med
M, Sinha R. 2021. Associations of fecal microbial profiles with breast cancer
10:253. https://doi.org/10.1186/1479-5876-10-253.
and non-malignant breast disease in the Ghana Breast Health Study. Int J 12. Fuhrman BJ, Feigelson HS, Flores R, Gail MH, Xu X, Ravel J, Goedert JJ.
Cancer 148:2712–2723. https://doi.org/10.1002/ijc.33473. 2014. Associations of the fecal microbiome with urinary estrogens and
4. Parida S, Sharma D. 2019. The microbiome-estrogen connection and estrogen metabolites in postmenopausal women. J Clin Endocrinol
breast cancer risk. Cells 8:1642. https://doi.org/10.3390/cells8121642. Metab 99:4632–4640. https://doi.org/10.1210/jc.2014-2222.
5. Clemons M, Goss P. 2001. Estrogen and the risk of breast cancer. N Engl J 13. Wu Z, Byrd DA, Wan Y, Ansong D, Clegg-Lamptey JN, Wiafe-Addai B,
Med 344:276–285. https://doi.org/10.1056/NEJM200101253440407. Edusei L, Adjei E, Titiloye N, Dedey F, Aitpillah F, Oppong J, Vanderpuye V,
6. Sampson JN, Falk RT, Schairer C, Moore SC, Fuhrman BJ, Dallal CM, Osei-Bonsu E, Dagnall CL, Jones K, Hutchinson A, Hicks BD, Ahearn TU, Shi
Bauer DC, Dorgan JF, Shu XO, Zheng W, Brinton LA, Gail MH, Ziegler J, Knight R, Biritwum R, Yarney J, Wiafe S, Awuah B, Nyarko K, Figueroa JD,
RG, Xu X, Hoover RN, Gierach GL. 2017. Association of estrogen metab- Sinha R, Garcia-Closas M, Brinton LA, Vogtmann E. 2022. The oral micro-
olism with breast cancer risk in different cohorts of postmenopausal biome and breast cancer and nonmalignant breast disease, and its rela-
women. Cancer Res 77:918–925. https://doi.org/10.1158/0008-5472 tionship with the fecal microbiome in the Ghana Breast Health Study. Int
.CAN-16-1717. J Cancer 151:1248–1260. https://doi.org/10.1002/ijc.34145.
7. Samavat H, Kurzer MS. 2015. Estrogen metabolism and breast cancer. 14. Brinton LA, Awuah B, Nat Clegg-Lamptey J, Wiafe-Addai B, Ansong D,
Cancer Lett 356:231–243. https://doi.org/10.1016/j.canlet.2014.04.018. Nyarko KM, Wiafe S, Yarney J, Biritwum R, Brotzman M, Adjei AA, Adjei E,
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 10
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.
Estrogen Metabolism and Fecal and Oral Microbiome Microbiology Spectrum
Aitpillah F, Edusei L, Dedey F, Nyante SJ, Oppong J, Osei-Bonsu E, Titiloye women: the Ghana Breast Health Study. Breast Cancer Res 24:9. https://
N, Vanderpuye V, Brew Abaidoo E, Arhin B, Boakye I, Frempong M, Ohene doi.org/10.1186/s13058-022-01500-8.
Oti N, Okyne V, Figueroa JD. 2017. Design considerations for identifying 24. Geczik AM, Falk RT, Xu X, Wiafe-Addai B, Yarney J, Awuah B, Biritwum R,
breast cancer risk factors in a population-based study in Africa. Int J Can- Vanderpuye V, Dedey F, Adjei E, Aitpillah F, Osei-Bonsu E, Oppong J,
cer 140:2667–2677. https://doi.org/10.1002/ijc.30688. Titiloye N, Edusei L, Nyarko K, Clegg-Lamptey JN, Wiafe S, Ansong D,
15. Shin JH, Park YH, Sim M, Kim SA, Joung H, Shin DM. 2019. Serum level of Ahearn TU, Figueroa J, Garcia-Closas M, Brinton LA, Trabert B. 2022. Rela-
sex steroid hormone is associated with diversity and profiles of human tion of circulating estrogens with hair relaxer and skin lightener use
gut microbiome. Res Microbiol 170:192–201. https://doi.org/10.1016/j among postmenopausal women in Ghana. J Expo Sci Environ Epidemiol
.resmic.2019.03.003. 33:301–310. https://doi.org/10.1038/s41370-021-00407-4.
16. Insenser M, Murri M, Del Campo R, Martinez-Garcia MA, Fernandez-Duran 25. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA,
E, Escobar-Morreale HF. 2018. Gut microbiota and the polycystic ovary Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K,
syndrome: influence of sex, sex hormones, and obesity. J Clin Endocrinol Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodriguez AM,
Metab 103:2552–2562. https://doi.org/10.1210/jc.2017-02799. Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall
17. Coburn SB, Stanczyk FZ, Falk RT, McGlynn KA, Brinton LA, Sampson J, DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM,
Bradwin G, Xu X, Trabert B. 2019. Comparability of serum, plasma, and uri- Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes
nary estrogen and estrogen metabolite measurements by sex and meno- S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L,
pausal status. Cancer Causes Control 30:75–86. https://doi.org/10.1007/ Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, et al. 2019.
s10552-018-1105-1. Reproducible, interactive, scalable and extensible microbiome data sci-
18. Dabek M, McCrae SI, Stevens VJ, Duncan SH, Louis P. 2008. Distribution of ence using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/
beta-glucosidase and beta-glucuronidase activity and of beta-glucuronidase s41587-019-0209-9.
gene gus in human colonic bacteria. FEMS Microbiol Ecol 66:487–495. 26. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J,
https://doi.org/10.1111/j.1574-6941.2008.00520.x. Glockner FO. 2013. The SILVA ribosomal RNA gene database project:
19. Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL. Jr, 1998. Microbial improved data processing and web-based tools. Nucleic Acids Res 41:
complexes in subgingival plaque. J Clin Periodontol 25:134–144. https:// D590–6. https://doi.org/10.1093/nar/gks1219.
doi.org/10.1111/j.1600-051x.1998.tb02419.x. 27. Xu X, Roman JM, Issaq HJ, Keefer LK, Veenstra TD, Ziegler RG. 2007. Quan-
20. Teles R, Teles F, Frias-Lopez J, Paster B, Haffajee A. 2013. Lessons learned titative measurement of endogenous estrogens and estrogen metabo-
and unlearned in periodontal microbiology. Periodontol 2000 62:95–162. lites in human serum by liquid chromatography-tandem mass spectrom-
https://doi.org/10.1111/prd.12010. etry. Anal Chem 79:7813–7821. https://doi.org/10.1021/ac070494j.
21. Chung SD, Tsai MC, Huang CC, Kao LT, Chen CH. 2016. A population- 28. Brinton LA, Trabert B, Anderson GL, Falk RT, Felix AS, Fuhrman BJ, Gass
based study on the associations between chronic periodontitis and the ML, Kuller LH, Pfeiffer RM, Rohan TE, Strickler HD, Xu X, Wentzensen N.
risk of cancer. Int J Clin Oncol 21:219–223. https://doi.org/10.1007/s10147 2016. Serum estrogens and estrogen metabolites and endometrial cancer
-015-0884-6. risk among postmenopausal women. Cancer Epidemiol Biomarkers Prev
22. Freudenheim JL, Genco RJ, LaMonte MJ, Millen AE, Hovey KM, Mai X, 25:1081–1089. https://doi.org/10.1158/1055-9965.EPI-16-0225.
Nwizu N, Andrews CA, Wactawski-Wende J. 2016. Periodontal Disease 29. Trabert B, Brinton LA, Anderson GL, Pfeiffer RM, Falk RT, Strickler HD,
and breast cancer: prospective cohort study of postmenopausal women. Sliesoraitis S, Kuller LH, Gass ML, Fuhrman BJ, Xu X, Wentzensen N. 2016. Cir-
Cancer Epidemiol Biomarkers Prev 25:43–50. https://doi.org/10.1158/ culating estrogens and postmenopausal ovarian cancer risk in the Women's
1055-9965.EPI-15-0750. Health Initiative Observational Study. Cancer Epidemiol Biomarkers Prev 25:
23. Geczik AM, Falk RT, Xu X, Ansong D, Yarney J, Wiafe-Addai B, Edusei L, 648–656. https://doi.org/10.1158/1055-9965.EPI-15-1272-T.
Dedey F, Vanderpuye V, Titiloye N, Adjei E, Aitpillah F, Osei-Bonsu E, 30. Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ,
Oppong J, Biritwum R, Nyarko K, Wiafe S, Awuah B, Clegg-Lamptey JN, Ringel Y, Li H, Wu MC. 2015. Testing in microbiome-profiling studies with
Ahearn TU, Figueroa J, Garcia-Closas M, Brinton LA, Trabert B. 2022. Meas- MiRKAT, the Microbiome Regression-Based Kernel Association Test. Am J
ured body size and serum estrogen metabolism in postmenopausal Hum Genet 96:797–807. https://doi.org/10.1016/j.ajhg.2015.04.003.
July/August 2023 Volume 11 Issue 4 10.1128/spectrum.01572-23 11
Downloaded from https://journals.asm.org/journal/spectrum on 01 September 2023 by 197.255.69.76.