Group Title: Nutrition Journal 2009, 8:49
Title: Associations between dietary habits and body mass index with gut microbiota composition and fecal water genotoxicity: an observational study in African American and Caucasian American volunteers
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Title: Associations between dietary habits and body mass index with gut microbiota composition and fecal water genotoxicity: an observational study in African American and Caucasian American volunteers
Series Title: Nutrition Journal 2009, 8:49
Physical Description: Archival
Creator: Mai V
McCrary QM
Sinha R
Glei M
Publication Date: 40107
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Volume ID: VID00001
Source Institution: University of Florida
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Associations between dietary habits and body mass index with gut
microbiota composition and fecal water genotoxicity: an
observational study in African American and Caucasian American
Volker Mai*1, Quintece M McCrary2, Rashmi Sinha3 and Michael Glei4

Address: 'Microbiology and Cell Science, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA, 2Food Science and Technology,
University of Maryland Eastern Shore, Princess Anne, MD, USA, 3DCEG, National Cancer Institute, Bethesda, MD, USA and 4Institute for Nutrition,
Department for Nutritional Toxicology, Friedrich-Schiller-University, Jena, Germany
Email: Volker Mai*; Quintece M McCrary; Rashmi Sinha;
Michael Glei
* Corresponding author

Published: 21 October 2009 Received: 15 June 2009
Nutrition journal 2009, 8:49 doi: 10. 186/1475-2891 -8-49 Accepted: 21 October 2009
This article is available from:
2009 Mai et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: African Americans (AA) suffer from an increased incidence and mortality of
colorectal cancer (CRC). Environmental exposures including dietary habits likely contribute to a
high burden of CRC, however, data on the dietary habits of AA is sparse. Diet might change the
composition and the activities of the intestinal microbiota, in turn affecting fecal genotoxicity/
mutagenicity that is thought to be associated with carcinogenesis.
Methods: We assessed dietary habits by food frequency questionnaire and by food records in 52
AA and 46 CA residents of the Eastern Shore of MD. Fecal microbiota composition was
determined using 16S rRNA based methods and fecal genotoxicity measured using the Comet
Results: AA reported an increased intake of heterocyclic amines and a decreased dietary intake
of vitamins including vitamin D (p < 0.05) that correlated with differences in fecal microbiota
composition but not fecal genotoxicity. Intake of dietary fiber, calcium, total fat and heterocyclic
amines correlated with differences in microbiota composition. Total bacterial counts/g of stool and
raw counts of Bacteroides were increased in AA. In contrast to a previous study, BMI was not
associated with proportions of Bacteroides.
Conclusion: Dietary habits of African Americans, including increased HCA intake and decreased
vitamin D intake might at least partially contribute to CRC through modifications of gut microbiota
composition that result in changes of the intestinal milieu.

Introduction exposure of the colonic epithelium to mutagenic com-
Colorectal cancer (CRC) is thought to be strongly associ- pounds that may cause both initiation of cell transforma-
ated with environmental exposures including diet. Com- tion and tumor progression [1,2]. The objective of the
ponents ingested through the diet are a major source for current study was to determine if dietary habits, previ-

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ously shown to differ between African Americans (AA)
and Caucasian Americans (CA), effect the composition of
gut microbiota and result in differences in fecal water

AA suffer from an increased incidence and mortality of
CRC in comparison to CA (American Cancer Society: Sta-
tistics for 2008,
stt 0.asp?from=fast). Although the underlying causes for
these differences are not well established it is plausible
that differences in dietary habits in AA affect the colonic
environment by increasing exposure to mutagens directly
as well as indirectly through changes in the composition
of the metabolically active gut microbiota [3]. In a previ-
ous large study of associations between diet and CRC in
AA and CA in North Carolina differences between the two
racial groups were detected in the intake of micro- as well
as macronutrients [4-6]. This study also suggested that
associations between diet and CRC might differ between
the two racial groups. One observation in the above study
was an increased intake of heterocyclic amines (HCAs) by
AA. HCAs such as 2-amino-l-methyl-6-phenyl-imi-
dazo[4,5-b]pyridine (PhIP), and 2-amino-3,8-dimethyl-
imidazo[4,5-f]quinoxaline (MeIQx), and also
benzo[a]pyrene (Bap) a polycyclic aromatic hydrocarbon
(PAH), are possible humancarcinogens formed during
meat cooking. Large amounts of HCA are formed with
longer cooking times, internal temperatures of between
150 C and 200 C, and greater external charring [7], typi-
cally achieved with cooking methods such as barbeque.

Humans harbor in their guts a complex intestinal micro-
biota that varies in its composition between individuals.
Although recent studies have shed some light on the com-
plexity of the gut microbiota in a limited number of indi-
viduals, the extent of microbiota diversity and
associations with dietary habits are still poorly under-
stood [8]. Tremendous advances in our understanding of
the composition and activities of the gut microbiota have
recently been made, however, we still do not fully under-
stand the degree of complexity or the dynamics of the
human gut microbiota [9,10]. It is now well established
that the human gut microbiota is phylogenetically as well
as metabolically very diverse and makes important contri-
butions to the physiology of its human host [11,12]. Elo-
quent studies have shown communication between gut
microbiota and the human host, a requirement of gut
microbiota for appropriate priming and development of
the immune system and associations between gut micro-
biota and obesity mediated by the induction/suppression
of various human factors [13-17]. The host microbiota
can have profound effects on nutrient acquisition and
sequestering, immune priming and reactivity, as well as
direct effects on carbohydrate and other compound levels
in the systemic circulation [18,19]. The microbiota has

long been suspected to be associated with health as well
as with diseases including inflammatory bowel diseases
and CRC, recent data supports an association between
reduced microbiota diversity and Crohn's disease [20].

Fecal water, the water-soluble fraction obtained as the
supernatant after high speed centrifugation of total feces,
reflects the luminal content of both risk factors and pro-
tective factors [21]. Cytotoxicity/genotoxicity of fecal
water is a useful biomarker in studying the impact of envi-
ronmental factors on exposure of the gut to carcinogens
and the modification of this exposure by dietary habits
[22-24]. Cytotoxicity/genotoxicity of fecal water can be
assessed in vitro by exposing cultured human colon cells,
followed by assessment of cell viability and DNA damage
in single cells by the Comet assay [25]. Fecal water geno-
toxicity has previously been shown to be related to colon
carcinogenesis in animals [26]. In human studies, fecal
water genotoxicity was higher after a diet rich in meat as
compared to a diet enriched in fiber [27]. Supplementa-
tion with probiotic yoghurts has been shown to reduce
fecal water genotoxicity [28]. There also appears to be an
effect modification by smoking status as supplementation
with bread enriched with prebiotics and antioxidants
reduced the fecal water genotoxicity in non-smokers but
not in smokers, an effect which differed by the status of
GST M-polymorphisms [29].

In this study we investigated if differences in dietary habits
between the two groups are associated with cytotoxicity/
genotoxicity of fecal water and with fecal microbiota com-

Materials and methods
Study design
Target population
We included subjects that were at least 40 years of age, self
identified as being of AA or CA descent and able to pro-
vide informed consent and information regarding dietary
history as well as basic demographic data. We excluded
subjects that had a prior diagnosis of cancer (other than
skin cancer); suffered from inflammatory bowel disease;
had chronic diarrhea or acute diarrhea within the past 4
weeks; were hospitalized or used systemic antibiotics
within the past 4 weeks. A total of 98 subjects, 52 AA and
46 CA were enrolled. To evaluate associations between
obesity and microbiota composition all 14 lean (BMI
<25) and 14 randomly chosen obese subjects (BMI >30)
were selected. The study was approved by the Institutional
Review Boards at UMB and UMES.

Study logistics
Subjects were approached about the study through local
community groups. After enrollment, subjects received a
study kit that included all questionnaires as well as a stool

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Nutrition Journal 2009, 8:49


collection kit. After filling out the questionnaires subjects
kept a four day food record and collected the first stool
sample after its completion.

Assessment of diet and medical history
Subjects completed the self-administered Block 98 food
frequency questionnaire (FFQ) that assessed their dietary
habits [30], and a specific questionnaire assessing con-
sumption of meat and its preparation (MMQ) [31]. Sub-
jects also kept a food record of all foods and drinks
consumed for four consecutive days. Medical history and
demographic information was assessed using a scan able
questionnaire specifically developed for this study.

Stool collection
After completing a four day food record subjects collected
a freshly voided stool sample. The stool sample was trans-
ported to the laboratory in a plastic bag containing an ice
pack within six hours. Upon arrival in the laboratory each
sample was immediately homogenized and fixed for FISH
analysis or frozen -80 C for all other analyses.

Analytical cohort
From the 98 subjects that completed the study protocol
we removed subjects with low quality dietary data, those
that skipped more than 10 questions on the FFQ and
those that reported a daily calorie intake of less than 400
or more than 5200 calories. 42 AA and 40 CA remained in
the FFQ/MMQ analysis and 39 AA and 38 AA in the Food
Record analysis. All 14 lean study subjects (BMI <25) and
14 randomly chosen obese subjects (BMI >30) were
included in the studies of the association between micro-
biota and obesity.

Microbiota analysis
DNA Extraction
DNA was isolated from 200 mg of stool for each sample
by using a modified QIAmp DNA Stool Mini kit (Qiagen,
Cat. No. 51504, Valencia, CA). Noted modifications were
the addition of 5 glass beads to the initial buffer solution
to allow better homogenization of the stool while vortex-
ing and the addition of 0.3 g zirconia beads (Biospec
Products Inc., Cat. No. 11079101z, Bartlesville, OK) fol-
lowed by 3 min of shaking in a Mini-BeadBeater (Glen
Mills Inc., Clifton, NJ), prior to the addition of the
Inhibit-EX tablet, to enhance the lysis of bacterial cell
walls. Purified DNA samples were eluted in a final volume
of 50 l and stored at -70 C until analyzed.

Denaturing Gradient Gel Electrophoresis (DGGE)
A 457-bp fragment from the V6 to V8 region of the bacte-
rial 16S rDNA gene was amplified with primers U968-GC
L1401 (5' GCG TGT GTA CAA GAC CC), as described by

Zoetendal et al. [32]. The GC clamp facilitates separation
by DGGE. DGGE was performed in an 8% (wt/vol) poly-
acrylamide gel with a denaturing gradient ranging from
40% to 50% at the top and bottom of the gel, respectively
(100% denaturing conditions were defined as 7 M urea
and 40% formamide). After electrophoresis (16 h, 65 V,
600C), the gels were stained with SYBER Green (Novex,
San Diego, CA) and scanned/analyzed with Quantity One
and Diversity Database software (Bio-Rad, Hercules, CA).

Fluorescent in situ hybridization (FISH)
Aliquots (0.5 ml) of homogenized feces were added to
PBS (4.5 ml), and the samples were prepared for FISH
analysis as described previously [33]. Briefly, the mixtures
were processed by vortexing them with 3-5 glass beads for
2 min, removing fecal debris by centrifugation at low
speed, fixing the bacteria-containing supernatant fluids
overnight in PBS containing 3% paraformaldehyde, and
storing aliquots of the fixed bacterial preparations at -
70 C until hybridization was performed. Hybridization
was performed by applying aliquots (10 1l) of appropri-
ate dilutions of the fixed bacterial preparations to gelatin-
coated microscopic slides, fixing the specimens to the
slides with 95% ethanol, and hybridizing with 10 ng of
the appropriate probe/pil, using the conditions described
previously [33-35]. The following probes were used: (i)
Bac303 for Bacteroides and Prevotella [36], (ii) Erec482 for
eubacteria, clostridia and ruminococci belonging to
Clostridium cluster XIVa [33], (iii) Bifl64 for bifidobacte-
ria [37], (iv) LAB158 for lactic acid bacteria [38]. After
briefly rinsing with distilled water, slides were air-dried
rapidly with compressed air and mounted with Vectash-
ield containing DAPI (4',6-diamidino-2-phenylindole)
(Vector Labs, Cat. No. H-1200, Burlingame, CA). DAPI
and Cy3/FITC positive cells were enumerated in a total of
12 fields at two different dilutions per sample at 100x
magnification using a Zeiss axioscope-40 epifluorescence-
equipped microscope (Zeiss, Jena, Germany). The propor-
tions of the two bacterial groups were calculated by divid-
ing the number positive for the specific probe by the total
bacteria as determined by DAPI. DAPI was used as it
allows for the enumeration of total bacteria without the
need for a separate hybridization.

Quantitative PCR (qPCR)
qPCR analysis was performed on a subset of 14 lean
(BMI<25) and 16 obese subjects (BMI>30) in duplicate
using a qPCR Core kit (Eurogentec, Cat. No. RT-SN10-
05NR, San Diego, CA) on a Stratagene MX3000P (La Jolla,
CA) in 12.5 ul reaction volumes consisting of 2 ul DNA
template (diluted 1:80 in water), 1 x Reaction Buffer, 200
tiM dNTP mix, 30 pM forward and reverse primers, 0.025
U/Il HotGoldStar Taq Polymerase, 1 x SYBR Green dye,
30 nM ROX passive reference dye (Stratagene, Cat. No.
600546, La Jolla, CA). The following primers and condi-

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Nutrition Journal 2009, 8:49


tions were used: 1) all eubacteria (V3 F: 5'-CCTACG-
56C, 3 mM MgCl); 2) Bacteroides-Prevotella-Porphy-
romonas group (Bac F: 5'-GGTGTCGGCITAAGTGCCAT-
3'; R: 5'-CGGA(C/T)GTAAGGGCCGTGC-3', 68C, 3 mM
MgCl2); Clostridium coccoides-Eubacterium rectale group
[39]. qPCR conditions were as follows: 10 min at 95C
followed by 40 cycles of 95 C for 30 s, annealing for 1
min (see primer specific temperature above), and exten-
sion at 720C for 30 s.

qPCR standard curves covering the range observed in the
samples were generated using a lab internal DNA standard
derived from a human stool sample. The proportions of
the two groups were calculated by dividing the number
positive for the specific primer set by the total number of
bacteria determined using the universal V3 primer set.

Fecal water analysis
Fecal samples were homogenized using a glass rod. For
preparation of the fecal water (FW), the feces were centri-
fuged (18500 rpm for 2 h at 40C) and the supernatants
were collected. After a further centrifugation step (13000
rpm for 10 min at 4 C) to obtain a clear solution, the liq-
uid phases were quantified, the pH-values were deter-
mined with a pH meter, and the samples were aliquoted
and stored at -80 C until use.

Human tumor cell line HT29
The human colon carcinoma cell line HT29, obtained
from the American Tissue Culture Collection (Rockville,
MD, USA), was used to test toxicity of the FWs [40]. Cells
were kept frozen in liquid Nitrogen until thawed and
grown at 370C in a (95%) humidified incubator (5%
CO2) in Dulbecco's Modified Eagle Medium (DMEM,
Gibco BRL, Eggenstein, Germany) supplemented with
10% fetal calf serum, penicillin (50 U/mL) and strepto-
mycin (50 utg/mL). Passages 22 to 29 (cytotoxicity and
genotoxicity studies) and 40-50 (challenge assay) were
used for the experiments.

Cytotoxicity assay
To determine the effective dose of FW the cell suspensions
were initially incubated with 5%, 10% or 20% of FW. All
following assays were performed using 20% concentra-
tion to reach a measurable DNA damage level. FW was
added to 100 tl cell suspension containing 4 x 105 HT29
cells. The suspensions were incubated for 30 min in a
shaking water bath at 370C and cytotoxicity was deter-
mined using the trypan blue exclusion assay.

Genotoxicity assay
DNA damage was measured in 1 x 105 cells suspended in
low melting point agarose on microscope slides with the
single cell microgel electrophoresis assay, also known as
the "Comet assay" [41] as previously described [42]. 50
images/slide were evaluated by measuring the percentage
of fluorescence in the tail.

Challenging treatment to evaluate the antigenotoxic potential
The Comet assay was also used to determine if a pretreat-
ment with fecal water leads to a modified genotoxic effect
of H202. Cells were pretreated with 20% fecal water (30
min, 370C) and subsequently incubated with 75 UiM
H202 for 5 min at 4 C. Viabilities were determined with
the trypan blue exclusion test and the remaining cells were
analyzed with the Comet assay as described above.

Multivariate regression analysis (SAS) was used to explore
associations between multiple exposure factors and
microbiota composition. Shannon Wiener index was used
to calculate microbiota diversity of the DGGE profiles.
Unpaired t-test and one-way ANOVA (Microsoft Excel ver-
sion 2003) were used to calculate means and variation
and for establishing two-sided significance levels (p <

Dietary analysis
After removal of subjects with low quality data 82 sub-
jects, 42 AA and 40 CA, were retained in the analytical
cohort for the FFQ based analysis of differences in dietary
habits between the two racial groups. Both groups were
similar in age and gender distribution. AA had a higher
mean weight and body mass index but lower income and
lower level of education (Table 1). AA consumed a diet

Table I: Study demographics in African American (AA) and
Caucasian American (CA) participants

Mean age (years)

Sex (male)

Mean weight

51.2 52.3


Mean BMI

Income <30,00US

College degree

48% 33%

BMI is body mass index (weight/height2 in kg/m2).

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lower in the percent of calories from fat and higher in per-
cent of calories from carbohydrates (Table 2). AA also had
a lower intake of supplemental vitamins including vita-
min D. Although total intake of HCA did not differ
between the groups, the intake of MeIQx and PhIP, two
HCA formed during meat preparation at high tempera-
ture, was increased in AA (p < 0.05).

Data from the four day food records mostly supported the
findings from the FFQ analysis, albeit fewer associations
reached statistical significance (data not shown). Specifi-
cally, the differences in percent calories from fat and car-
bohydrates as well as the decreased intake of vitamin D in
AA were confirmed with this secondary tool.

Fecal water analysis
FW analysis was performed in a subset of 21 AA and 22 CA
subjects for which we had a sufficient amount of fecal
sample. We successfully extracted an average of 18.6% FW
from the fecal samples. No difference in yield was
observed between the samples from AA and CA. The mean
pH of the FW was 6.9, the observed difference between AA
(6.7) and CA (7.1) was borderline significant (p = 0.05)
when both genders were combined and significant in
females alone (AA: 6.7, CA: 7.2; p < 0.05). To determine
the appropriate dose of fecal water for the genotoxicity
assay, the cell suspensions were incubated with 5%, 10%
or 20% FW. FW induced DNA damage in a dose depend-
ent manner (p = 0.052). However, even at the highest
dose of 20% FW, which was used in all subsequent stud-
ies, we observed only a weak increase in measurable DNA

We observed no differences in fecal water induced cyto-
toxicity or genotoxicity between the two racial groups as
both, the tail intensity and the viability of the HT 29 cells
showed no significant differences between the two groups
(Figure 1). When we analyzed the proportion of undam-
aged cells with a tail intensity <6% as an alternative meas-
ure of genotoxicity we detected only a small and
statistically insignificant difference between the two racial
groups that suggested a trend towards slightly reduced
genotoxicity in fecal waters from AA. Furthermore the
results showed a significantly increased genotoxicity in
FW from females that was not associated with cell viability
(Figure 1). BMI, age and diet including fat intake and die-
tary levels of heterocyclic amines were not associated with
either genotoxicity or viability (data not shown). The large
variation associated with the genotoxicity assay would not
have allowed us to detect any small or medium effects
with the limited number of subjects included in this
study. To evaluate potential protective effects of FW we
analyzed the effects of preincubation of HT29 cells with
fecal water before challenge with H202 and measurement
of its genotoxicity, using the remaining fecal waters from

Table 2: Differences in diet between AAs and CAs as determined
by FFQ

AA (42) CA (40) p-value

Total calories (kcal)

Protein (g)

Fat (g)

Carbohydrates (g)

% kcal from protein

% kcal from fat

% kcal from carbs

Dietary fiber (g)

Fruit (servings)


Dairy (servings)

Calcium (mg)


Calcium (mg)

Magnesium (mg)

Vitamin A (IU)

Vitamin C (mg)

Vitamin D (IU)

Vitamin E (a-TE)

Heterocyclic amines

Total (ng/day)

MelQx (ng/day)

PhlP (ng/day)

Dimelqx (ng/day

Bap (ng/day)

69 0.6

82 0.6

248 214 0.19

13.8 15.1 0.09

35.2 39.2 0.01

52.4 46.2 <0.01

17.6 16.7 0.7

1.5 0.9

3.8 0.24

0.68 1.43 <0.01

608 774 0.07

108 418 <0.01

42 0.02

140 380 <0.01

77 166 0.02

46 157 <0.01

190 128 0.1

25 0.04

78 0.07

2.8 2.1 0.2

22 0.1

PhlP is 2-amino- -methyl-6-phenyl-imidazo[4,5-b]pyridine, MelQx is
2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline, Dimelqx is 2-amino-
3,4,8-trimethylimidazo[4,5-f]quinoxaline Bap is benzo[a]pyrene.

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m Tail Intensity

* Viabilty



80 .

60 5




male female male female NaCI H202



Figure I
DNA damage and viability of HT 29 cells after incubation with FW. AAs (n = 21), CAs (n = 22), males and females.
NaCI -- negative control; H202-- positive control.

33 subjects. Although H202 clearly induced DNA breaks
preincubation of the HT29 cells with fecal waters did not
affect the degree of DNA damage (data not shown).

Microbiota analysis
We first analyzed overall microbiota composition by
qualitative DGGE profiling, an efficient but crude method
for assessing microbiota diversity (a measure of species
richness and their distribution). We did not detect a differ-
ence in overall diversity with any of our measured varia-
bles (data not shown). Based on DGGE profiles, the mean
Shannon-Wiener diversity index in all samples, a measure
of overall microbiota diversity, was 2.3 with a SD of 0.2
and a range between 2.0 and 2.7. In contrast, quantitative
microbiota analysis by FISH revealed some differences
between the two racial groups. The total number ofbacte-
ria/g of stool was higher in AA when compared to CA (Fig-
ure 2). These differences, although statistically significant
on the decimal scale, were not significant after log trans-
formation. Although total number of Bacteroidetes that
hybridized to our probe were increased in AA (Figure 2)
the proportions of Bacteroidetes or Clostridia cluster XIVa
(Firmicutes) that were targeted with the respective probes
did not differ between AA and CA. We detected suggestive
associations between dietary intake and quantities of bac-
terial groups. Subjects that consumed high amounts of

calories from fat harbored fewer Clostridia. Consumption
of dietary fiber was positively associated with numbers of
LAB, which are thought to be beneficial to health. Grain
fiber and fiber from fruits and veggies but not fiber from
beans appeared associated with LAB (data not shown).
Subjects with higher intake of HCAs had higher amounts
of Clostridia cluster XIVa. All of these potential associa-
tions between exposure of interest and microbiota
reached statistical significance (p < 0.05) only in the ini-
tial analyses of raw data, however, after log transforma-
tion of bacterial counts to achieve a more normal
distribution none of the associations remained signifi-

When we analyzed the proportions of Bacteroidetes and
Clostridia cluster XIVa (Firmicutes) in all 14 lean study
subjects (BMI<25) compared to 14 matched obese sub-
jects (BMI>30), we failed to detect an association with
BMI by either FISH (Figure 3a). The apparent lack of asso-
ciations between obesity and the proportions of Bacter-
oidetes and Clostridia cluster XIVa in our study
population was confirmed using a second quantitative
approach, qPCR (Figure 3b).

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Nutrition Journal 2009, 8:49












Figure 2
Comparison of microbiota between AA and CA. Fixed stool samples were hybridized with probes directed against
Bacteroidetes (Bac 303), Clostridium Cluster XIVa (Erec482), Bifidobacteria (Bifl 64) and Lactic Acid bacteria (Lab 158) and
enumerated by FISH as described in Materials and Methods. Total bacterial counts were determined using DAPI.

Our analysis of differences in dietary habits between AA
and CA is consistent with previous reports that suggested
an increased intake of HCAs and a decreased intake of
micronutrients in AA [4-6]. Although our study was lim-
ited in size, the agreement of two independent dietary
assessment methods (FFQ and food record) increases con-
fidence in our observation. As both HCA intake and vita-
min D levels have been associated with carcinogenesis
[7,43] there appears to be an opportunity for dietary pre-
vention to reduce the burden of cancer in AA.

Exposure of HT29 cells to FW from subjects in this study
did induce DNA damage. However, although the two
groups differed in various exposures, including BMI and
dietary habits, we did not detect significant differences in
responses to FW from the two groups. Our study was lim-
ited in size and measurement of cytotoxicity and genoto-

xicity included a large degree of variation. The observation
that FW from females appear to exhibit higher genotoxic-
ity is not consistent with the hypothesis that FW genotox-
icity is associated with CRC risk, as US females have lower
CRC rates than males. Although we used twice as much
fecal water (20%) compared to earlier studies [27,29], we
observed only a weak increase in measurable DNA breaks.
Future studies of associations between environmental fac-
tors and FW genotoxicity should be designed in larger
human cohorts preferably with controlled exposures to
reduce variation.

The observation that total bacterial counts/g of stool and
numbers of Bacteroides counts were increased in AA is
suggestive rather than confirmatory. Soft large stools of
rural Africans have been considered as representing low
colorectal cancer risk in early observations of the effects of
dietary fiber intake [44]. A higher water content of stools

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Nutrition Journal 2009, 8:49









Bacteroidetes (Bac303) Clostridia CI. XIV (Erec482)







Bacteroidetes Clostridia CI. XIV

Figure 3
Proportions of bacteroidetes and Clostridia Cluster XIVa in obese vs. lean subjects as determined by a) FISH
and b) qPCR analysis.

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would reduce bacterial densities and thus harder stools
might contain larger densities of bacteria. We collected
only one spot stool sample from each subject and varia-
tion in microbiota composition over time appears signifi-
cant [45,46]. The same qualifications need to be made
when interpreting our findings that intake of fiber, HCA
and calories from fat affected microbiota composition.
Although we have shown earlier that dietary interventions
in mice as well as in humans can affect microbiota com-
position [45,47], similar data from observational studies
is sparse [3]. There has been significant recent interest in
potential associations between gut microbiota and obes-
ity, specifically a decrease proportion of bacteria belong-
ing to the Bacteroidetes among obese [15,48-50]. Our
data does not support that hypothesis as AA in our study
had a higher BMI that correlated with higher, not lower,
numbers of Bacteroidetes. A detailed quantitative study in
a subset of lean and obese subjects from this study also
failed to detect such an association. We used a different
methodology (FISH and qPCR) compared to the at best
semi-quantitative 16S rRNA based analysis frequently
used by other groups. Our probes and primers admittedly
do not cover Bacteroidetes species perfectly and due to dif-
ferences in methodology results can't be compared
directly to 16S rRNA based studies. However, recent work
from other groups also appears inconsistent with a pro-
posed association between lower proportions of Bacter-
oidetes and obesity [17,51]. The ongoing Human
Microbiota Roadmap Project is aimed at improving our
understanding of normal microbiota composition and its
potential associations with health and disease http_:/ Our observations are consist-
ent with the hypothesis that diet mediated differences in
gut microbiota contribute to the observed increased risk
of colorectal cancer in AA. Because diseases such as CRC
can change the intestinal environment with likely impacts
on microbiota composition, future studies with prospec-
tively collected stool samples will be required to link
microbiota composition with disease risk.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
VM designed the study, helped with study logistics, ana-
lyzed the data and wrote the manuscript. QM enrolled
subjects, collected samples and performed the microbiota
analyses. RS helped with the design of the dietary assess-
ment and contributed to the HCA analysis. MG helped wit
the study design, supervised the mutagenicity studies and
wrote respective parts of the manuscript. All authors read
and approved the final manuscript.

This work was supported by a pilot grant from the Maryland Special Popu-
lation Network. Work in the lab of Dr. Mai is supported by ACS grant

MRSGT CCE-107301. We thank B.L. Zobel (deceased 2008) for discussion
regarding study design and interpretation of the findings. We thank Dr. C.
Boushey for help with the food record data.

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