Environmental and Molecular Mutagenesis 00:000^000 (2009)
Research Articles
Increased Consumption of Wheat Biofortified With
Selenium Does Not Modify Biomarkers of Cancer Risk,
Oxidative Stress, or Immune Function in Healthy
Australian Males
Jing Wu,1,2 Carolyn Salisbury,1 Robin Graham,2 Graham Lyons,2
and Michael Fenech1*
1
Nutritional Genomics and Genome Damage Diagnostics Laboratory,
CSIRO Human Nutrition, Food Science Australia, Adelaide, South Australia, Australia
2
Discipline of Plant and Food Science, School of Agriculture, Food and Wine,
University of Adelaide, Glen Osmond, South Australia, Australia
Increased intake of selenium (Se) may reduce the
risk of degenerative diseases including cancer but
excessive intake may be toxic. Wheat is a major
source of dietary Se in humans. However, the
effect of Se from wheat that is agronomically biofortified with Se on biomarkers of human health
status is unknown. This study aimed to investigate
whether improving Se status, by increased dietary
intake of Se-biofortified wheat, affects biomarkers
of cancer risk, cardiovascular disease risk, oxidative stress, and immune function in healthy South
Australian men. A 24-week placebo-controlled
double-blind intervention was performed in
healthy older men (n 5 62), with increased dose
of Se intake every 8 weeks. Wheat was provided
as 1, 2, and 3 puffed wheat biscuits, during
weeks 1–8, 9–16, and 17–24, respectively.
Blood was collected to measure a wide range of
disease risk biomarkers. Consumption of Se-biofortified wheat was found to increase plasma Se concentration from a baseline level of 122 to
192 lg/L following intake of three biscuits/day,
which provided 267 lg Se. Platelet glutathione
peroxidase, chromosome aberrations, and DNA
damage in lymphocytes measured using the cytokinesis-block micronucleus cytome assay and with
the Comet assay, plasma F2-isoprostanes, protein
carbonyls, plasma C-reactive protein, and leukocyte number were unaffected by the improved Se
status. Improvement of Se status by consumption
of Se-biofortified wheat did not substantially modify the selected biomarkers of degenerative disease risk and health status in this apparently selenium-replete cohort of healthy older men in South
Australia. Environ. Mol. Mutagen. 00:000–000,
2009. VC 2009 Wiley-Liss, Inc.
Key words: selenium; biofortified wheat; chromosome damage; DNA damage; oxidative stress;
immune function
INTRODUCTION
Moderate deficiency of selenium (Se) is associated with
various pathological conditions including infertility,
increased oxidative stress, immune dysfunction, cognitive
impairment, and increased risk for specific cancers such
as prostate cancer [Rayman, 2000, 2005; Gronberg, 2003;
Bjelakovic et al., 2004; Gromadzinska et al., 2008]. Several essential structural proteins and enzymes in the body
have improved function when seleno-amino acids such as
seleno-methionine (Se-met) and seleno-cysteine are incorporated into the protein instead of their sulphur-containing
amino acid analogs [Gromer et al., 2005; Hatfield et al.,
2006]. There is emerging evidence that increased intake
of organic Se may reduce the risk of certain degenerative diseases, but there is also concern that excessive Se
C 2009 Wiley-Liss, Inc.
V
intake may have unwanted toxic effects, such as selenosis [Yang and Zhou, 1994; Reid et al., 2004]. Within the
physiological concentration range of 3 to 430 lg Se/L,
Grant sponsors: National Center of Excellence for Functional Foods
(via the National Food Industry Strategy), HarvestPlus/The University of
Adelaide, The South Australian Grains Industry Trust Fund, Laucke
Flour Mills.
*Correspondence to: M. Fenech, CSIRO Human Nutrition, PO Box
10041 Gouger Street, Adelaide BC, SA 5000, Australia. E-mail:
michael.fenech@csiro.au
Received 29 October 2008; provisionally accepted 12 February 2009;
and in final form 13 February 2009
DOI 10.1002/em.20490
Published online in Wiley InterScience (www.interscience.wiley.com).
Environmental and Molecular Mutagenesis. DOI 10.1002/em
2
Wu et al.
Se, as Se-met, had no impact on baseline micronucleus
frequency or g-ray induced chromosome damage in
human lymphocytes in vitro; however, spontaneous frequencies of nucleoplasmic bridges and nuclear buds
declined significantly as the dose increased but higher
concentrations of Se-met caused strong inhibition of nuclear division and increased cytotoxicity [Wu et al.,
2009]. Dietary selenium supplementation in dogs
reduced DNA damage in prostate tissue as measured by
the alkaline Comet assay but was not associated with
glutathione peroxidase (GPx) activity in plasma; however, excessive intake of Se appeared to increase DNA
damage suggesting a U-shaped dose-response [Waters
et al., 2003, 2005]. In a study of men at high risk for
prostate cancer, DNA damage in lymphocytes measured
by Comet assay was shown to be inversely associated
with serum Se concentration for those with serum Se
less than 98 lg/L but not for those with higher concentrations [Karunasinghe et al., 2004]. Increased Se intake
has been associated with decreased risk for cardiovascular disease (CVD) but it is unknown whether this is due
to Se-mediated reduction in lipid peroxidation, inhibition
of inflammation, or an improved lipid profile in the
blood [Ravn-Haren et al., 2008]. There is also a need to
know that both CVD and cancer risk biomarkers are
affected favorably and thus verify that benefits, if present, occur across multiple conditions of degenerative disease.
In countries where wheat products are widely consumed, wheat is usually a major source of dietary Se. In
Australia, for example, it is estimated that most people
obtain around half their Se from wheat [Lyons et al.,
2004, 2005a]. The Se content of wheat can be increased
by agronomic biofortification. This involves fertilizing the
growing crop with an appropriate inorganic form of the
micronutrient, which the plant converts to several organic
Se forms, notably Se-met, which are more suitable for
human consumption [Lyons et al., 2003, 2005b]. The
effect of increased consumption of Se via Se-biofortified
wheat on genome damage and immune function has not
been tested previously.
The hypothesis of this study was that improving Se status of older South Australian men by increased intake of
Se-biofortified wheat has a beneficial impact on biomarkers
of risk for cancer, oxidative stress, and immune function.
To the best of our knowledge, the results of this study are
the most comprehensive assessment of the bioefficacy and
safety of Se-biofortified wheat performed so far.
MATERIALS AND METHODS
Wheat Used in the Intervention Trial
Wheat (cultivar Whylah) was biofortified with Se in October 2003 by
applying sodium selenate as a foliar spray at around flowering time on a
farm near Frances, in the South-East region of South Australia. The biofortified grain was analyzed by a fluorimetric method [Watkinson, 1966;
Koh and Benson, 1983] and found to contain 10 mg/kg Se. Control
wheat (cultivar Yitpi) was low in Se (0.07 mg/kg Se) but similar to the
biofortified wheat in protein and minerals (including Fe, Zn, Cu, Mn, S,
Ca, Mg) (data not shown).
Study Design
The study design was a double-blind placebo-controlled intervention
with a dose-response. The study was advertised in the local papers and
in electronic media from September to November 2004. The intervention
commenced in early February 2005 and was completed in early August
2005.
Respondents were initially screened for eligibility using the following
inclusion criteria: healthy males, aged 40–70 years, not supplementing
with selenium and not supplementing with above recommended daily
intake (RDI) levels of folate and/or vitamin B12 and/or vitamin C. The
following respondents were excluded from the study: (a) cancer patients
undergoing chemotherapy or radiotherapy, (b) those with sensitivity to
study foods, i.e., gluten/wheat intolerance, (c) those unable to comprehend or comply with the study protocol, and (d) those not available for
all sampling phases of the study.
A total of 179 men, living in or near Adelaide, South Australia, were eligible for the study and were screened for plasma Se level in peripheral
blood samples collected after an overnight fast. The 81 men with the lowest plasma Se concentration were then admitted into the trial and randomized to three dietary groups (N 5 27 per group) as shown in Figure 1. The
study was focused on men with lower Se status because it was expected
that any beneficial effects of improved Se status had a higher probability
of being observed in this group and because Se status is of particular relevance to men given its association with reduced prostate cancer risk.
The dietary groups were ‘‘CONTROL,’’ ‘‘BIOFORT,’’ and
‘‘PROFORT’’ depending on the wheat source of the biscuits they were
required to consume. Trial participants were required to consume one
biscuit per day for the first 8 weeks, then two biscuits daily for the next
8 weeks, then three biscuits daily for the final 8 weeks. The intention of
the study design was that each BIOFORT and PROFORT biscuit would
deliver 75 lg Se so that the daily amount of Se from the biscuits
would increase progressively from 75 lg, to 150 lg, to 225 lg during
each phase of the trial. The biscuits were developed by Laucke Flour
Mills (Strathalbyn, South Australia) and were made by soaking whole
grain wheat in water for 24 hr, then heating to expand the grain, and
compressing into a ‘‘puffed wheat’’ biscuit. As well as the biofortified
(BIOFORT) wheat biscuits and the low-Se control biscuits (CONTROL),
a process-fortified (PROFORT) ‘‘positive control’’ biscuit was developed
by adding pure Se-met (Eburon Organics, USA) to the water which was
absorbed by the grains. The PROFORT control was included to compare
the effect of Se provided by biofortification with that provided by process-fortification. The biscuits used in the trial were made in three separate batches before the trial commenced, their weight and Se concentration were monitored throughout the trial, and were found to remain constant during the trial period. The actual Se content per biscuit (mean
value (range) in lg) was 0.71 (0.64–0.75), 89.1 (86.4–94.5), and 101.9
(97.0–105.8) for CONTROL, BIOFORT, and PROFORT biscuits,
respectively. We had intended the Se concentration in PROFORT and
BIOFORT biscuits to be identical however due to a combination of
higher Se concentration and greater weight, the PROFORT biscuits contained 18% more total Se than the BIOFORT biscuits. The three types
of biscuits were completely indistinguishable from each other. None of
the staff directly involved in the trial, sample analyses or participants
had any knowledge of the selenium level or fortification process of the
various biscuit groups. The total estimated daily Se intakes of the trial
participants from the provided biscuits in each group are presented in
Figure 1. To assess compliance participants were required to keep a re-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
Se-Biofortified Wheat and Health Status Biomarkers
3
Fig. 1. Trial design. Asterisk indicates blood samples collected at the
beginning of the indicated week. CONTROL, BIOFORT, AND PROFORT groups consumed biscuits made from normal wheat, wheat biofortified with selenium, and wheat process fortified with selenomethoinine,
respectively. 1, 2, and 3 biscuits were consumed daily between weeks 0–
8, weeks 8–16, and weeks 16–24, respectively. The CONTROL, BIOFORT, and PROFORT biscuits contained 0.7, 89.1, and 101.9 lg Se
each, respectively. Estimated Se intakes per day (lg/day) at the different
stages of the trial are also indicated in the diagram.
cord of the number of biscuits they had eaten and return any biscuits
that were not consumed. Dietary intake of selenium before the intervention commenced was measured using a validated food frequency questionnaire [Baghurst and Record, 1984; Baghurst et al., 1992]. Fasted
blood samples were collected at the start (wk 0) and after 8 (wk 8), 16
(wk 16), and 24 (wk 24) weeks of the trial.
The study was approved by the Human Ethics Committee of Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Human Nutrition.
nucleus assay may be increased in those with low blood concentration of
folate and/or vitamin B12 [Fenech, 1998].
Main and Secondary Outcome Measures
Main outcome measures were (i) plasma Se as a biomarker of bioavailability, (ii) platelet GPx as a biomarker of seleno-enzyme activity,
and (iii) chromosome and DNA damage in lymphocytes measured using
the cytokinesis-block micronucleus cytome (CBMN Cyt) assay and the
Comet assay. The micronucleus frequency index in the CBMN Cyt assay
has been shown to be associated prospectively with cancer risk and the
micronucleus, nucleoplasmic bridge, and nuclear bud indices have been
shown to be strongly associated with lung cancer risk in smokers
[Bonassi et al., 2007; El-Zein et al., 2008].
Other secondary bioefficacy biomarkers included (a) plasma F2-isoprostanes and protein carbonyls as indicators of oxidative stress, (b)
plasma C-reactive protein (CRP) as a biomarker of inflammation, and (c)
leukocyte number and subsets as biomarkers of immune function.
Plasma folate and vitamin B12 were also measured to assess whether
there were substantial dietary changes during the trial affecting these parameters which may have impacted on the DNA damage markers. Previous studies showed that chromosome damage measured using the micro-
Methods for Outcome Measures
Se concentrations in wheat, biscuits, and plasma were determined by
inductively coupled plasma mass spectrometry (ICPMS) (Agilent Technologies 7500c, Japan), following digestion with nitric acid and hydrochloric acid. Accuracy was assured by analysis of certified reference material (Seronorm Serum Lot JL4409). There was only 1.5% variation
from the expected value of the Seronorm reference material. Se speciation studies to determine concentration of Se-met and methionine selenoxide were performed using protease digestion and isotope dilution
HPLC coupled to ICP-MS [Kirby et al., 2006].
Platelet glutathione peroxidase activity was measured spectrophotometrically using the method of Misso et al. [1996] and a GPx assay kit
(Sigma catalogue # CGP-1).
Lymphocyte chromosome damage was measured using the CBMN
Cyt assay as described by Fenech [2007] using whole blood cultures of
fresh blood collected within 2 hr before commencing the assay. Frequency of binucleated cells with micronuclei (MN BNCs, a biomarker of
chromosome breakage or loss), nucleoplasmic bridges (NPB BNCs, a
biomarker of DNA misrepair or telomere end fusion), and nuclear buds
(NBUD BNCs, a biomarker of gene amplification) was measured in
1,000 BNCs. Ratios of mono-, bi-, and multinucleated cells were measured to determine the nuclear division index (NDI, a biomarker of mitogen and immune responsiveness). The slides were scored manually by a
single scorer (CS).
Lymphocyte DNA damage by the Comet assay was measured using
the alkaline method as described by Collins [2005] in both fresh and cry-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
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Wu et al.
TABLE I. P Values for Effect of Treatment and Time on Biomarkers Measured in the Trial
Biomarkers
Se status
Selenoprotein activity
DNA damage in lymphocytes
Comet assay
DNA damage in lymphocytes
CBMN Cyt assay
Immune function
Leucocyte counts
Oxidative stress and
inflammation
B vitamin status
Plasma Se
Platelet GPx activity
Fresh lymphocytes
Category 4
Fresh lymphocytes
Category 0–3
Frozen lymphocytes
Category 4
Frozen lymphocytes
Category 0–3
MN BNCs
NPB BNCs
NBUD BNCs
NDI
CD4:CD8 ratio
CD8 lymphocytes
CD4 lymphocytes
CD3 lymphocytes
Neutrophils
Lymphocytes
Total white cells
Protein carbonyls
F2-isoprostanes
C-creative protein
Plasma B12
Plasma folate
Effect of treatment
(P value)
Effect of time
(P value)
0.006
0.715
0.007
0.074
0.719
0.0001
0.146
0.011
0.214
0.061
0.193
0.825
0.868
0.600
0.591
0.146
0.435
0.422
0.456
0.080
0.106
0.876
0.174
0.621
0.391
0.334
0.487
0.207
0.622
0.074
0.514
0.592
0.707
0.001
0.180
0.199
0.043
0.228
0.234
0.004
0.421
0.091
0.917
0.039
GPx, glutathione peroxidase; CBMN Cyt assay, cytokinesis-block micronucleus cytome assay; MN BNCs,
binucleated cells with micronuclei; NPB BNCs, binucleated cells with nucleoplasmic bridge; NBUD BNCs,
binucleated cells with nuclear bud; NDI, nuclear division index. P values in bold are statistically significant.
opreserved isolated lymphocytes. We used both fresh and cryopreserved
lymphocytes because in the latter case we could analyze the cells from
each time-point and each individual within the same assay and thus minimize the effect of day-to-day assay variation on the results obtained. To
avoid confounding by apoptotic cells we scored Category 4 cells (with
most of the DNA in the comet tail) separately from those in Category 0–
3. The slides were scored manually by a single scorer (JW) as per criteria published elsewhere [Collins, 2002].
Plasma F2-isoprostanes were measured by gas chromatography mass
spectrometry using the method of Mori et al. [1999]. Protein carbonyls in
plasma were measured by ELISA using the method of Buss et al. [1997].
White blood cell counts, lymphocyte subsets, plasma CRP, plasma vitamin
B12, and folate were measured by the Institute of Medical and Veterinary
Sciences (Adelaide, South Australia) in their certified routine diagnostic
laboratory. All plasma measures were performed on cryopreserved samples
stored at 2808C after the intervention was completed.
Statistical Analysis
The sample size and study power estimates were based on the assumption that at least 20 subjects per group would complete the intervention
with high compliance and designed to detect a change of (a) 5% in
plasma Se concentration, (b) 32% change in the frequency of MN BNCs
in the CBMN-Cyt assay with 90% power and P < 0.05, and (c) to detect
a difference of 13 arbitrary units (AU) of DNA damage measured using
the Comet assay in lymphocytes between two groups with 80% power and
P < 0.05, two-tailed. These estimates were calculated using historical
standard deviation values of 5.8 lg/L (plasma Se assay) and 2.6 MN
BNCs per 1,000 BNCs (CBMN Cyt assay) for subjects within the age and
gender group relevant to this study. For the Comet assay, the sample size
required to detect a statistically significant difference was predetermined
based on the published data on 41 male healthy nonsmokers aged 40–55
yrs, in which the mean 6 1 SD of DNA damage (arbitrary unit, AU) in
lymphocytes by Comet assay was 82.3 6 14.1 [Piperakis et al., 2003].
QQ-plots on standard residues of all outcome measures were performed to test the normality of the data sets. Difference between distributions of smoking and alcohol status of three study groups were determined by Chi-square analysis. One-way ANOVA was used to compare
the difference of baseline characteristics between treatment groups, such
as age, BMI, plasma Se, as well as the changes of GPx value between
three treatment groups following the interventions after the baseline GPx
values were stratified into low, medium, and high tertile. The significance
of effect of treatment and time for each parameter was measured using
general linear model repeated measures mixed between-within subjects
ANOVA [Pallant, 2005] on the delta value of each follow-up time point
(i.e., baseline value subtracted from the actual value measured at that
time-point) with baseline values included as covariates to take account of
effect of the baseline value on the delta value. Cross correlation analysis
of relationships between all biomarkers (at the start or the end of the trial)
were conducted using partial correlation test, after controlling for any
effect of age. Differences with P value <0.05 were considered to be statistically significant. Statistical analyses were performed using the statistical
package SPSS for WINDOWS (version 16.0, SPSS Inc, Chicago).
RESULTS
Results for all biomarkers are summarised in Tables I–IX.
25, 24, and 24 participants started and 22, 19, and 21 com-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
Se-Biofortified Wheat and Health Status Biomarkers
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TABLE II. Cross Correlations Between Biomarkers at Baseline Analyzed Using Partial Correlation After Adjusting for Age
r Value
NBUD Trig
HDL
LDL
CRP
WCC
Lymp
Neut
CD3
CD4
CD8
Comet Comet
CD4:8 (frozen) (fresh)
Platelet Plasma
GPx
Se
NDI
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS 20.27*
NS
NS
MN
0.42** NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.34**
NS
NS
NPB
0.40** NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NBUD
—
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
0.25*
NS
NS
Protein
NS
NS
NS
NS
NS
0.26*
NS
NS
NS
NS
0.27*
NS
0.36**
NS
NS
carbonyl
Chol
0.28*
NS
0.87** NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Trig
— 20.47** NS
NS
0.31*
0.35** NS
NS 0.26*
NS
NS
NS
NS
NS
NS
HDL
—
NS 20.25* 20.34** 20.48** NS
NS
NS
NS
NS
NS
NS
NS
NS
CRP
—
0.31*
0.36** NS
NS
NS
NS
NS
NS
NS
NS
20.25*
WCC
—
0.68** 0.94** NS 0.41**
NS
0.26* 0.32*
NS
NS
NS
Lymp
—
0.40** 0.42** NS
NS
NS
NS
NS
NS
NS
Neut
—
NS 0.43** 20.29*
0.37** 0.42**
NS
NS
NS
CD3
— 0.31*
0.51** 20.35** NS
NS
20.35** 20.31*
CD4
— 20.62** 0.66** NS
NS
20.31* 20.27*
CD8
—
20.88** NS 20.29*
NS
NS
CD4:8
—
NS 20.4**
NS
NS
Comet
—
0.29*
NS
NS
(frozen)
NS, nonsignificant, r values 2-tailed; NDI, nuclear division index; MN, binucleated cells with micronuclei; NPB, binucleated cells with nucleoplasmic
bridge; NBUD, binucleated cells with nuclear bud; Chol, total plasma cholesterol; Trig, plasma triglyceride; HDL, high-density lipoprotein; LDL,
low-density lipoprotein; CRP, C-reactive protein; WCC, white cell count; Lymp, lymphocytes; Neut, neutrophils; CD3, CD3-positive lymphocytes;
CD4, CD4-positive lymphocytes; CD8, CD8-positive lymphocytes; CD4:8, ratio of CD4-positive: CD8-positive lymphocytes; GPx, glutathione peroxidase; NS, nonsignificant.
*P < 0.05; **P < 0.01.
TABLE III. Correlation Between Biomarkers at Week 24 Analyzed Using Partial Correlation After Adjusting for Age
r Value
MN
Protein
NBUD carbonyl
HDL
LDL
CRP
WCC
Lymp
Neut
CD4
CD8
CD4:8
Comet
(fresh)
Plasma
Se
NDI
20.29* NS
0.33**
NS
NS
0.29*
NS
NS
NS
NS
NS
NS
NS
NS
MN
—
0.34**
NS
NS
NS 20.31*
NS
NS
NS
NS
0.31*
NS
NS
NS
NPB
0.43**
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
NBUD
—
NS
NS
0.27*
NS
NS
NS
NS
NS
NS
NS
NS
NS
F2 iso-prostanes
NS
0.31*
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Protein carbonyl
—
NS
NS
NS
NS
NS
NS
NS
NS
NS
20.31*
NS
Chol
NS
0.92**
NS
NS
NS
NS
NS
NS
NS
NS
NS
Trig
20.48** NS
NS
0.27*
NS
NS
0.28*
NS
NS
NS
NS
HDL
—
NS 20.34** 20.36** 20.26* 20.36** NS
NS
NS
NS
NS
CRP
—
0.34** 0.28*
0.34** NS
NS
NS
NS
20.35**
WCC
—
0.70** 0.91** 0.34**
NS
0.35**
NS
NS
Lymp
—
0.44** NS
NS
NS
NS
NS
Neut
—
0.37**
NS
0.41**
NS
NS
CD3
0.44** 0.47**
NS
NS
NS
CD4
— 20.51** 0.68** 0.38**
NS
CD8
—
20.79** 20.27*
NS
NS, nonsignificant, r values 2-tailed; NDI, nuclear division index; MN, binucleated cells with micronuclei; NPB, binucleated cells with nucleoplasmic
bridge; NBUD, binucleated cells with nuclear bud; Chol, total plasma cholesterol; Trig, plasma triglyceride; HDL, high-density lipoprotein; LDL,
low-density lipoprotein; CRP, C-reactive protein; WCC, white cell count; Lymp, lymphocytes; Neut, neutrophils; CD3, CD3-positive lymphocytes;
CD4, CD4-positive lymphocytes; CD8, CD8-positive lymphocytes; CD4:8, ratio of CD4-positive: CD8-positive lymphocytes; NS, nonsignificant.
*P < 0.05; **P < 0.01.
pleted the intervention in the CONTROL, BIOFORT, and
PROFORT groups, respectively. Reasons for drop-out before
commencement of the trial were increased work commitments and overseas travel. Reasons for drop-out during the
trial included increased work commitments (N 5 3), overseas travel (N 5 1), voluntary withdrawal (N 5 2), increased
family responsibilities (N 5 2), noncompliance (N 5 2), and
one adverse event (N 5 1). Age (55.0 6 7.4 years, 56.1 6
Environmental and Molecular Mutagenesis. DOI 10.1002/em
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Wu et al.
TABLE IV. Frequencies of Binucleated Lymphocytes With Micronuclei (MN BNCs), Binucleated Lymphocytes With Nuclear
Plasmic Bridges (NPB BNCs), Binucleated Lymphoctyes With Nuclear Buds (NBUD BNCs), and Nuclear
Division Index (NDI)
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
7.9 6 4.0
9.8 6 5.8
8.0 6 6.2
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
2.5 6 1.3
2.6 6 2.1
1.9 6 1.6
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1.1 6 1.1
1.8 6 1.6
1.6 6 1.3
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1.81 6 0.18
1.84 6 0.17
1.91 6 0.19
8 wk
16 wk
MN BNCs per 1,000 cells
6.9 6 4.1
7.1 6 3.4
9.1 6 3.8
7.6 6 4.5
8.5 6 4.4
7.3 6 3.8
0.83 for treatment
NPB BNCs per 1,000 cells
2.3 6 1.4
1.0 6 1.4
2.6 6 1.6
1.4 6 1.7
2.4 6 2.5
1.3 6 1.4
0.87 for treatment
NBUD BNCs per 1,000 cells
1.3 6 2.0
0.8 6 1.1
1.7 6 1.8
0.8 6 1.1
1.3 6 1.6
0.7 6 1.0
0.60 for treatment
NDI
1.82 6 0.14
1.80 6 0.19
1.88 6 0.19
1.91 6 0.19
1.83 6 0.16
1.94 6 0.13
0.59 for treatment
24 wk
ANOVA Pa
6.8 6 4.3
8.2 6 5.3
8.1 6 3.7
0.62 for time
0.73 for treatment 3 time
0.7 6 0.9
0.8 6 1.2
1.3 6 1.1
0.07 for time
0.23 for treatment 3 time
0.6 6 1.1
0.8 6 1.1
0.6 6 1.0
0.51 for time
0.69 for treatment 3 time
1.89 6 0.13
1.93 6 0.23
1.97 6 0.17
0.59 for time
0.85 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P value in bold is statistically significant.
TABLE V. DNA Damage (arbituary unit, AU) in Fresh Lymphocytes as Measured by the Comet Assay
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
35.4 6 12.8
30.7 6 15.1
27.4 6 11.0
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
14.4 6 10.4
18.4 6 16.9
9.1 6 5.6
8 wk
16 wk
Comet DNA damage, Category 0–3 (AU)
46.9 6 14.6
45.0 6 12.5
43.7 6 14.5
51.7 6 12.8
42.6 6 11.7
51.7 6 15.5
0.15 for treatment
Comet DNA damage, category 4 (AU)
30.4 6 13.2
52.5 6 29.3
32.0 6 13.6
56.4 6 18.3
26.4 6 13.5
45.7 6 18.8
0.72 for treatment
24 wk
ANOVA Pa
32.4 6 9.4
25.8 6 9.3
28.5 6 9.9
0.01 for time
0.39 for treatment 3 time
18.6 6 10.4
13.3 6 9.1
17.0 6 12.0
<0.0001 for time
0.13 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P values in bold are statistically significant.
6.6 years, and 55.8 6 7.1 years) or body mass index (BMI,
27.0 6 3.5 m2/kg, 26.0 6 3.3 m2/kg, 27.2 6 3.9 m2/kg) of
men (data shown are mean 6 SD), the proportion of smokers (18.2, 10.5, and 28.6%) and alcohol consumers (86.4,
94.7, and 66.7%) was not significantly different between
CONTROL, BIOFORT, and PROFORT groups, respectively.
Baseline plasma Se concentrations in the three groups were
not significantly different and the mean value (6SD) for
CONTROL, BIOFORT, and PROFORT groups was
121.0 (68.8) lg/L, 122.3 (617.1) lg/L, and 122.3
(612.8) lg/L, respectively. The estimated Se intake was
not different between CONTROL, BIOFORT, and PROFORT groups with the mean (6SD) intake of 155.5
(639.6) lg/day, 143.3 (681.8) lg/day, and 174.2
(656.0) lg/day, respectively. Compliance rate was high
(>97%) in all groups.
There was no change in the plasma Se concentration in
the CONTROL group. However, in both the BIOFORT
and PROFORT groups there was a significant time and
dose-related increase in plasma Se (time effect P 5
0.007, treatment effect P 5 0.006), but the increment was
much greater for the BIOFORT group in which a maximum increase of 70 lg/L Se was achieved by the end of
the trial compared to an increase of 16 lg/L in the PROFORT group (Fig. 2). Despite the changes in plasma Se
concentration, there was no significant effect on platelet
Environmental and Molecular Mutagenesis. DOI 10.1002/em
Se-Biofortified Wheat and Health Status Biomarkers
7
TABLE VI. Plasma F2 Isoprostanes (pmol/L), Plasma Protein Carbonyl (nmol/mg protein) and Plasma C-Reactive
Protein (mg/L)
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1865 (459)
1774 (495)
1805 (332)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
0.046 (0.022)
0.043 (0.047)
0.057 (0.058)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
2.24 (1.93)
1.76 (1.07)
1.66 (1.72)
8 wk
16 wk
Plasma F2 isoprostanes (pmol/L)
1837 (428)
1930 (565)
1851 (517)
1852 (471)
1885 (438)
1834 (403)
0.621 for treatment
Plasma protein carbonyl (nmol/mg protein)
0.043 (0.030)
0.046 (0.037)
0.058 (0.058)
0.050 (0.031)
0.049 (0.026)
0.069 (0.055)
0.174 for treatment
Plasma C-reactive protein (mg/L)
1.85 (1.25)
1.86 (1.19)
1.57 (1.31)
2.12 (1.96)
1.71 (1.601)
1.70 (1.25)
0.391 for treatment
24 wk
1840 (430)
1880 (550)
1725 (388)
ANOVA Pa
0.421 for time
0.202 for treatment 3 time
0.024 (0.015)
0.036 (0.032)
0.030 (0.018)
0.004 for time
0.206 for treatment 3 time
2.59 (1.97)
1.61 (1.35)
2.00 (2.52)
0.091 for time
0.187 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P value in bold is statistically significant.
TABLE VII. Plasma Low-Density Lipoprotein (LDL) Cholesterol, Plasma High-Density Lipoprotein (HDL) Cholesterol, Plasma
Triglycerides, and Plasma Total Cholesterol
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
3.62 (0.95)
3.32 (0.59)
3.14 (0.96)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1.29 (0.32)
1.42 (0.37)
1.21 (0.39)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1.58 (0.70)
1.38 (0.55)
2.19 (1.58)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
5.62 (0.98)
5.35 (0.67)
5.33 (1.05)
8 wk
16 wk
Plasma LDL cholesterol (mmol/L)
3.56 (0.99)
3.57 (1.02)
3.39 (0.64)
3.52 (0.66)
3.15 (0.78)
3.20 (0.76)
0.020 for treatment
Plasma HDL cholesterol (mmol/L)
1.28 (0.28)
1.26 (0.28)
1.41 (0.37)
1.43 (0.28)
1.17 (0.36)
1.22 (0.38)
0.779 for treatment
Plasma triglycerides (mmol/L)
1.75 (0.77)
1.70 (0.85)
1.49 (0.43)
1.31 (0.36)
2.44 (1.97)
2.11 (1.41)
0.400 for treatment
Plasma total cholesterol (mmol/L)
5.63 (1.12)
5.60 (1.11)
5.47 (0.78)
5.45 (0.69)
5.42 (0.95)
5.37 (0.69)
0.031 for treatment
24 wk
ANOVA Pa
3.43 (0.92)
3.42 (0.51)
3.20 (0.89)
0.712 for time
0.958 for treatment 3 time
1.30 (0.27)
1.46 (0.35)
1.20 (0.36)
0.146 for time
0.310 for treatment 3 time
1.54 (0.68)
1.54 (0.52)
1.82 (0.90)
0.864 for time
0.342 for treatment 3 time
5.42 (0.94)
5.57 (0.62)
5.23 (0.91)
0.119 for time
0.674 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P values in bold are statistically significant.
GPx activity (Fig. 3). A stratified analysis examining
whether GPx values were increased following consumption of BIOFORT wheat found no effect of baseline GPx
values on outcome (data not shown).
The CBMN Cyt assay results (Table IV) showed no
significant effect of time or treatment on frequency of
MN BNCs (Fig. 4A), and this lack of effect of treatment
was consistent in both those with an initially high and an
initially low MN-BNC frequency at baseline (data not
shown). Frequency of NPB BNCs and NBUD BNCs
tended to decrease and NDI tended to increase (nonsignificantly) with time but there was no difference between
treatments (Figs. 4B–4D).
The Comet assay results (Table V) for both Category
0–3 and Category 4 showed a significant effect of time (P
< 0.05) for fresh lymphocytes (Fig. 5) but not of treatment indicating a null effect of selenium supplementation
on DNA damage as measured by the Comet assay. The
Environmental and Molecular Mutagenesis. DOI 10.1002/em
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Wu et al.
TABLE VIII. White Cell Count, Lymphocyte Count, and Neutrophil Count
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
5.69 (1.67)
5.41 (1.19)
5.96 (1.47)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
1.751 (0.4763)
1.547 (0.2741)
2.009 (0.5862)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
3.43 (1.30)
3.38 (1.18)
3.47 (0.94)
8 wk
16 wk
White cell count (3109/L)
6.08 (1.25)
5.83 (1.74)
5.39 (1.25)
5.48 (1.20)
6.42 (1.72)
6.29 (1.68)
0.876 for treatment
Lymphocyte count (3109/L)
1.857 (0.5238)
1.841 (0.5741)
1.502 (1.906)
1.644 (0.4113)
2.069 (0.6145)
2.039 (0.6731)
0.106 for treatment
Neutrophil count (3109/L)
3.66 (0.80)
3.44 (1.26)
3.31 (1.03)
3.35 (1.10)
3.86 (1.18)
3.76 (1.10)
0.080 for treatment
24 wk
5.90 (1.26)
5.66 (1.75)
6.39 (2.14)
ANOVA Pa
0.234 for time
0.160 for treatment 3 time
1.759 (0.5427)
1.731 (0.5)
2.042 (0.6304)
0.228 for time
0.116 for treatment 3 time
3.60 (0.92)
3.60 (1.16)
3.86 (1.53)
0.043 for time
0.014 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P values in bold are statistically significant.
TABLE IX. Percentage of CD3-Positive Lymphocytes, Percentage of CD4-Positive Lymphocytes, and Percentage of
CD8-Positive Lymphocytes
Study group
Baseline
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
72.4 (5.8)
69.0 (8.4)
74.2 (6.1)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
47.5 (7.8)
46.5 (8.5)
49.4 (8.0)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
22.7 (8.9)
20.6 (8.5)
23.0 (8.2)
CONTROL N 5 22
BIOFORT N 5 19
PROFORT N 5 21
ANOVA Pa
2.50 (1.31)
2.86 (1.72)
2.52 (1.18)
8 wk
16 wk
Percentage of CD3-positive lymphocytes
72.2 (6.4)
71.2 (6.8)
70.1 (9.5)
68.9 (9.2)
75.0 (5.1)
73.6 (6.2)
0.456 for treatment
Percentage of CD4-positive lymphocytes
48.1 (8.0)
46.4 (7.4)
46.6 (10.2)
45.7 (9.6)
50.2 (7.6)
49.7 (7.6)
0.422 for treatment
Percentage of CD8-positive lymphocytes
22.0 (8.7)
22.6 (8.8)
21.2 (8.5)
20.2 (8.2)
23.3 (8.4)
22.4 (8.1)
0.435 for treatment
Ratio of CD4-positive: CD8-positive lymphocytes
2.64 (1.38)
2.49 (1.34)
2.74 (1.66)
2.79 (1.57)
2.52 (1.19)
2.72 (1.58)
0.146 for treatment
24 wk
ANOVA Pa
70.6 (7.1)
69.2 (8.8)
74.2 (5.5)
0.199 for time
0.692 for treatment 3 time
46.9 (6.8)
45.4 (9.1)
49.0 (7.4)
0.180 for time
0.487 for treatment 3 time
21.6 (6.8)
20.8 (8.0)
23.4 (7.9)
0.001 for time
0.032 for treatment 3 time
2.53 (1.38)
2.67 (1.50)
2.64 (1.82)
0.707 for time
0.387 for treatment 3 time
a
The statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA. Results shown are mean (SD). P values in bold are statistically significant.
time effect seemed to be attributable to an increase in
DNA damage at week 16 across all groups. Similar trends
were observed for the Comet assay results with cryopreserved lymphocytes (data not shown).
The ANOVA P value results for the secondary outcome measures are summarized in Table I and results
detailed in Tables VI–IX. There was no significant effect
of time or treatment on F2-isoprostanes. In contrast,
plasma protein carbonyls exhibited a significant effect of
time (P 5 0.004) but not of treatment with a marked
reduction at week 24. CRP, leukocyte counts, and lymphocyte subsets were unaffected by the intervention
except for minor effects of time on neutrophil and CD8
lymphocyte count. There was no significant difference
between treatment groups with respect to plasma B12 and
plasma folate during the intervention, but there was a
marginal trend toward an increase in plasma folate concentration with time which achieved statistical signifi-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
Se-Biofortified Wheat and Health Status Biomarkers
9
tein (HDL, r 5 20.34, P 5 0.01) and HDL and triglyceride (r 5 20.48, P 5 0.01).
DISCUSSION
Fig. 2. Plasma Se concentration during the intervention trial. N 5 22,
19, 21 for CONTROL, BIOFORT, and PROFORT groups, respectively.
The statistical analysis was performed using delta value of result of each
follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA.
Results were mean 6 SEM (>).
Fig. 3. Platelet glutathione peroxidase (GPx) activity during the intervention period. N 5 22, 19, 21 for CONTROL, BIOFORT, and PROFORT
groups, respectively. The statistical analysis was performed using delta
value of result of each follow-up time point relative to baseline result and
baseline value was included as covariate using mixed between-within subjects ANOVA. Results were mean 6 SEM (>). NS, nonsignificant.
cance. BMI did not change significantly throughout the
intervention for all groups (data not shown).
A cross correlation analysis of the relationship between
plasma Se (at the start and the end of the trial), after controlling for age, and all biomarkers in this study was also
performed and found plasma Se was associated with CRP
(r 5 20.35, P 5 0.01) and CD3 and CD4 lymphocyte
count (r 5 20.31, 0.27, both P 5 0.05) but not other biomarkers (Tables II and III). Other relevant correlations
were those between NDI and MN (r 5 20.29, P 5
0.05), and NBUD with MN and NPB (r 5 0.42, 0.43,
both P 5 0.01) as well as CRP and high-density lipopro-
The results of this study show that organic Se in biofortified wheat is bioavailable in a time and dose-related
manner but has no impact on the health status biomarkers
in the cohort studied. Although we selected participants
with the lowest plasma Se concentrations in this cohort,
the mean plasma Se concentration at baseline was
122 lg/L which is considered, in other studies in other
countries, to be an adequate level to optimize function of
biomarkers of selenium status such as GPx and in terms
of minimizing prostate cancer risk attributable to selenium
deficiency [Combs, 2005; Rayman, 2005; Brinkman et al.,
2006; Gromadzinska et al., 2008]. Despite an increase of
plasma Se concentration in the BIOFORT group to a
maximum of 193 lg/L, there was no improvement in platelet GPx activity. Our results are consistent with those of
previous studies showing that GPx activity in platelets
and hemolysate is optimized at plasma Se concentration
greater than 100–120 lg/L [Misso et al., 1996;
Karunasinghe et al., 2006]. Similarly, there was no reduction in lymphocyte DNA damage measured using the
CBMN Cyt assay or the Comet assay that could be
explained by supplementation with the Se-biofortified
wheat. Our in vitro studies showed that the CBMN Cyt
assay is sensitive to both the cytoprotective/genome protective and cytotoxic/genotoxic effects of the organic
form of Se, Se-met, and for this reason, as well as its
comprehensive assessment of chromosome damage events
is an ideal tool to investigate potential beneficial and
adverse effects of food supplements and define simultaneously both bioefficacy and safety limits [Wu et al., in
press].
The similar trend with time, but not treatment, for a
reduction in plasma protein carbonyls suggests a reduction
in oxidative stress occurring during the intervention in all
groups in the aqueous phase which however was not
reflected in the lipid phase given that F2-isoprostanes
were unchanged during the intervention. However,
changes in plasma protein carbonyl were not correlated
with Comet or CBMN Cyt assay results (data not shown)
suggesting that any variation in oxidative stress was
below the threshold required to cause genome damage.
The observed variation with time in the Comet assay
could be due to unknown seasonal changes in exposure to
genotoxic environmental factors because DNA damage in
the Comet assay has been shown to vary significantly during the year and associated with increased exposure to
sunlight, environmental pollutants such as polycyclic aromatic hydrocarbons, changes in exercise level and diet
[Verschaeve et al., 2007]. The trends for reduction in
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Wu et al.
Fig. 4. DNA damage and cytotoxicity measured using the CBMN Cyt
assay. (A) Frequency of MN BNCs; (B) frequency of NPB BNCs; (C)
frequency of NBUD BNCs; (D) NDI. N 5 22, 19, 21 for CONTROL,
BIOFORT, and PROFORT groups, respectively. The statistical analysis
was performed using delta value of result of each follow-up time point
relative to baseline result and baseline value was included as covariate
using mixed between-within subjects ANOVA. Results were mean 6
SEM (>). NS, nonsignificant.
some of the chromosome aberration biomarkers (NPB
BNCs, NBUD BNCs) and protein carbonyls with time
may indicate a seasonal variation in these biomarkers
given that the intervention was of 6 months duration starting in late February/early March (mid/late summer in
Australia) and ending in late August/early September
(mid/late winter) that could be related to environmental
factors such as exposure to UV radiation and/or heat
stress as well as change in diet which is suggested by the
increase in plasma folate with time. The trend for reduced
NPB BNCs and NBUD BNCs with time may be
explained by the increased folate status because these biomarkers have been shown to be sensitive to folate concentration [Crott et al., 2001; Kimura et al., 2004]. However,
this hypothesis is negated by the fact that MN frequency,
which is also folate-sensitive, was unaffected by season
which agrees with observations from our laboratory in a
previous study [Fenech, 1998]. Se supplementation clearly
had no marked beneficial impact on leucocyte count or
ratios, suggesting that immune function was not substantially affected. This is also supported by the lack of a significant impact on NDI which is a measure of mitogenic
response of lymphocytes and thus considered a surrogate
marker of immune response.
We have considered the possibility of measuring oxidized DNA bases however we came to the conclusion that
the combination of the Comet and the CBMN Cyt assay
would be sufficient to indirectly also detect DNA base damage by oxidative stress. This is because reactive oxygen
species (ROS)-induced DNA base damage leads to the formation of abasic sites during base-excision repair which are
detectable in the alkaline Comet assay and MN and NPB in
the CBMN Cyt assay are efficiently induced by ROS such
as H2O2, superoxide, and activated neutrophils [Umegaki
and Fenech, 2000; Dotan et al., 2004; Muth et al., 2004;
Devaraj et al., 2008]. Furthermore, we felt that it was im-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
Se-Biofortified Wheat and Health Status Biomarkers
11
markers of this disease such as lipid profile and lipid oxidation. There was a weak negative correlation with CRP
at baseline and at the end of the trial but no impact of Se
supplementation on this biomarker of inflammation and
cardiovascular disease. Our results are in agreement with
those of Ravn-Haren et al. [2008] who also showed no
benefit of Se supplementation on conventional biomarkers
of cardiovascular disease. In another study in patients
with coronary artery disease there was no impact of Se
supplementation (as selenite) on endothelial function, biomarkers of inflammation, or oxidative stress even though
GPx activity was increased [Schnabel et al., 2008].
CONCLUSIONS
Fig. 5. DNA damage in fresh lymphocytes measured using the Comet
assay. (A) Results for cells showing category 0–3 DNA damage levels.
(B) Results for cells showing category 4 DNA damage. N 5 22, 19, 21
for CONTROL, BIOFORT, and PROFORT groups, respectively. The
statistical analysis was performed using delta value of result of each follow-up time point relative to baseline result and baseline value was
included as covariate using mixed between-within subjects ANOVA.
Results were mean 6 SEM (>). NS, nonsignificant.
portant to use any additional effort and resources to measure other biomarker of oxidative stress such as protein carbonyls and F2-isoprostanes because it is evident that measurement with DNA end-points alone is not sufficient to
obtain the full spectrum of ROS-induced damage to the organism [Dotan et al., 2004; Muth et al., 2004; Devaraj
et al., 2008]. We did not conduct the challenge assay with
ROS because our in vitro studies with human peripheral
blood lymphocytes with Se-met, the predominant organic
form of selenium, covering both physiological and supraphysiological concentrations, showed no impact on ionizing
radiation-induced DNA damage and chromosome aberrations measured using the Comet and the CBMN Cyt assay,
respectively [Wu et al., in press].
Despite the reported associations between increased Se
intake with decreased cardiovascular disease risk, our
study showed no beneficial impact on conventional bio-
The results from this study show that (a) supplementation with dietary Se in older men with already replete
plasma Se concentrations does not confer any additional
health benefits and (b) increasing plasma Se concentration
up to 193 lg/L using BIOFORT wheat does not appear to
have any obvious toxic effects, within the limits of the
study time-frame, the number of individuals in the trial,
and the range of biomarkers measured. Therefore, dietary intake of 105–315 lg of organic selenium from
BIOFORT wheat biscuits in addition to 158 lg from
other habitual dietary sources (calculated using a validated food frequency questionnaire) in South Australia
is unlikely to cause beneficial or adverse health outcomes in the short term based on the biomarkers of cancer and cardiovascular disease risk we used. These
observations are in accord with results of the SELECT
trial and the EPIC study [Allen et al., 2008; Lippman
et al., 2009], which showed no evidence of a link
between selenium status and prostate cancer risk in
well-nourished men. Given that there is some evidence
that the benefits and/or adverse effects of Se supplementation may depend on genotype variations due to common polymorphisms in key genes such as GPx, selenoprotein-P, and thioredoxin reductase-1 [Hu and
Diamond, 2003; Hu et al., 2005; Cai et al., 2006; Foster
et al., 2006], it will be necessary to find out in future
studies whether there are any specific genotypes or
higher susceptibility groups, including prostate cancer
patients, who are more likely to benefit, or be at risk of
toxic effects, from greater intake of wheat enriched in
Se by agronomic biofortification.
ACKNOWLEDGMENTS
The authors are grateful to Dr. Kath Cooper, and Martin and Kirsty Flower for assistance with production of
biofortified grain; Andrew Van der Sluys, Mark Laucke,
and David Hogan from Laucke Flour Mills for biscuit for-
Environmental and Molecular Mutagenesis. DOI 10.1002/em
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Wu et al.
mulation and production; Peter Babidge (South Australian
Research & Development Institute) for fluorimetric analyses of selenium; Teresa Fowles, Lyndon Palmer, and staff
at Waite Analytical Services for mass spectrometry and
atomic emission spectrometry; Jason Kirby (CSIRO Land
and Water, Adelaide) for performing the Se speciation
measurements; Professor Peter McLennan (University of
Wollongong) and Dr. Alice Owen (Monash University)
for protein carbonyl assays; Jonathan Hodgson and Kevin
Croft (University of Western Australia) for F2-isoprostane
assays; and Candita Sullivan for C-reactive protein assays.
The authors are grateful to Kylie Lange for her role as
biostatistician in providing advice on appropriate analyses
of data; the participants and the staff at the CSIRO’s
Clinical Trials Unit for recruitment of volunteers and
management of the trial.
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Accepted by—
H. Norppa