Content uploaded by Simin Nikbin Meydani
Author content
All content in this area was uploaded by Simin Nikbin Meydani on Feb 21, 2017
Content may be subject to copyright.
See corresponding editorial on page 545.
Substituting whole grains for refined grains in a 6-wk randomized trial
has a modest effect on gut microbiota and immune and inflammatory
markers of healthy adults
1–3
Sally M Vanegas,
4,5
Mohsen Meydani,
4
Junaidah B Barnett,
4
Barry Goldin,
5,6
Anne Kane,
6
Helen Rasmussen,
4
Carrie Brown,
4,10
Pajau Vangay,
7
Dan Knights,
8
Satya Jonnalagadda,
9,11
Katie Koecher,
9
J Philip Karl,
4,12
Michael Thomas,
4
Gregory Dolnikowski,
4
Lijun Li,
4
Edward Saltzman,
4,5
Dayong Wu,
4
* and Simin Nikbin Meydani
4
*
4
Jean Mayer USDA Human Nutrition Research Center on Aging,
5
Friedman School of Nutrition Science and Policy, and
6
School of Medicine, Tufts
University, Boston, MA;
7
Bioinformatics and Computational Biology and
8
Department of Computer Science and Engineering, University of Minnesota,
Minneapolis, MN; and
9
Bell Institute of Health and Nutrition, General Mills, Minneapolis, MN
ABSTRACT
Background: Observational studies suggest an inverse association
between whole-grain (WG) consumption and inflammation. How-
ever, evidence from interventional studies is limited, and few
studies have included measurements of cell-mediated immunity.
Objective: We assessed the effects of diets rich in WGs compared
with refined grains (RGs) on immune and inflammatory responses,
gut microbiota, and microbial products in healthy adults while
maintaining subject body weights.
Design: After a 2-wk provided-food run-in period of consuming a
Western-style diet, 49 men and 32 postmenopausal women [age
range: 40–65 y, body mass index (in kg/m
2
),35] were assigned to
consume 1 of 2 provided-food weight-maintenance diets for 6 wk.
Results: Compared with the RG group, the WG group had increased
plasma total alkyresorcinols (a measure of WG intake) (P,0.0001),
stool weight (P,0.0001), stool frequency (P= 0.02), and short-
chain fatty acid (SCFA) producer Lachnospira [false-discovery rate
(FDR)-corrected P= 0.25] but decreased pro-inflammatory Entero-
bacteriaceae (FDR-corrected P= 0.25). Changes in stool acetate
(P=0.02)andtotalSCFAs(P= 0.05) were higher in the WG
group than in the RG group. A positive association was shown
between Lachnospira and acetate (FDR-corrected P= 0.002) or
butyrate (FDR-corrected P= 0.005). We also showed that there was a
higher percentage of terminal effector memory T cells (P= 0.03)
and LPS-stimulated ex vivo production of tumor necrosis factor-a
(P= 0.04) in the WG group than in the RG group, which were
positively associated with plasma alkylresorcinol concentrations.
Conclusion: The short-term consumption of WGs in a weight-
maintenance diet increases stool weight and frequency and has
modest positive effects on gut microbiota, SCFAs, effector memory
T cells, and the acute innate immune response and no effect on
other markers of cell-mediated immunity or systemic and gut
inflammation. This trial was registered at clinicaltrials.gov as
NCT01902394. Am J Clin Nutr doi: 10.3945/ajcn.116.146928.
Keywords: gut microbiota, healthy adults, immune, inflammation,
whole grains
INTRODUCTION
Previous studies have shown that a dysregulated, prolonged
overproduction of inflammatory cytokines is associated with
cardiovascular diseases, type 2 diabetes, and certain cancers (1–3).
Some observational studies have suggested that diets that are
rich in whole grains (WGs)
13
are inversely associated with these
inflammation-related diseases and all-cause mortality (4–9).
These proposed benefits of consuming WGs may relate to the fact
that WGs are a rich source of vitamins, minerals, antioxidants,
dietary fiber, lignans, b-glucans, inulin, phytochemicals, phytos-
1
Supported by the Bell Institute of Health and Nutrition, General Mills
Inc., and by the USDA/Agricultural Research Service (ARS) (agreement 58-
1950-0-014). SMV was supported by a Stanley N Gershoff Scholarship from
the Friedman School of Science and Policy, by a National Research Service
Award from the National Institute of Diabetes and Digestive and Kidney
Diseases T32 Research Training Program in Nutrition and Chronic Disease
(grant 2T32DK062032-21), by an American Society for Nutrition 2012 Kraft
Foods Inc. predoctoral fellowship, and by the USDA/ARS (agreement 58-
1950-0-014). JPK was supported by the Science, Mathematics, and Research
Transformation Defense Education Program.
2
Any opinions, findings, conclusions, or recommendations expressed in this ar-
ticle are those of the authors and do not necessarily reflect the views of the USDA.
3
Supplemental Tables 1–8 are available from the “Online Supporting
Material” link in the online posting of the article and from the same link in
the online table of contents at http://ajcn.nutrition.org.
10
Present address: Boston University School of Public Health, 801 Massachu-
setts Avenue, Boston, MA 02118.
11
Present address: Kerry Inc., 3400 Millington Road, Beloit, WI 53511.
12
Present address: US Army Research Institute of Environmental Medi-
cine, 10 General Greene Avenue, Natick, MA 01760.
*To whom correspondence should be addressed. E-mail: dayong.wu@
tufts.edu (D Wu), simin.meydani@tufts.edu (SN Meydani).
Received October 6, 2016. Accepted for publication December 27, 2016.
doi: 10.3945/ajcn.116.146928.
13
Abbreviations used: Con A, concanavalin A; CRP, C-reactive protein; DTH,
delayed-type hypersensitivity; FDR, false-discovery rate; HNRCA, Human Nu-
trition Research Center on Aging; IFN-g, interferon-g; IS, inflammatory score;
LBP, LPS-binding protein; NK, natural killer; PBMC, peripheral blood mono-
nuclear cell; RG, refined grain; RPMI, Roswell Park Memorial Institute; SCFA,
short-chain fatty acid; vol:vol, volume:volume; WG, whole grain.
Am J Clin Nutr doi: 10.3945/ajcn.116.146928. Printed in USA. Ó2017 American Society for Nutrition 1of16
AJCN. First published ahead of print February 8, 2017 as doi: 10.3945/ajcn.116.146928.
Copyright (C) 2017 by the American Society for Nutrition
terols, phytin, and sphingolipids (10). However, these beneficial
WG constituents are substantially lost during processing to make
refined-grain (RG) flour including losses in ferulic acid (93%);
selenium (92%); antioxidant activity (89%); phenolic compounds
and magnesium (83%); flavonoids, zinc, and vitamin E (79%);
zeaxanthin (78%); fiber (58%); and lutein (51%) (11, 12).
WG consumption is also known to be associated with an increase
in the healthy gut microbiota phenotype, as indicated by their
richness and diversity (13–15), and in short-chain fatty acid (SCFA)
production. Both gut microbiota and SCFAs are considered im-
portant for immune function and gut health (16, 17). Emerging
in vivo evidence has suggested that immunologic dysregulation
may result from a dysbiosis of the gut microbiota (18–20). The
relations between WGs and microbiota, chronic disease and mi-
crobiota, and the corresponding immune and inflammatory indexes
indicate that WG has a potential to favorably alter microbiota and
to regulate immune and inflammatory responses (21, 22).
Several observational studies have shown an inverse associ-
ation between diets that are high in WGs and a decrease in
C-reactive protein (CRP) and the soluble TNF-areceptor TNF-R2
in diabetic women (23) and a decrease in CRP in healthy adults
(24–26), but discrepancy exists (27, 28). The limited interven-
tional trials have reported inconsistent findings of a decrease in
inflammatory markers IL-8 (29), IL-6 (30), and TNF-a(31) or
no change in IL-6 and CRP (32–35). In addition, none of the
studies evaluated cell-mediated immune responses, thereby
limiting the ability to assess the overall impact of WG on im-
mune and inflammatory responses. Several factors may explain
these discrepancies such as differences in participant BMI
ranges, sedentary lifestyles, and low WG habitual intake. Fur-
thermore, none of the studies completely controlled the diet of
participants. This is an important factor because day-to-day
variations in dietary components can potentially affect in-
flammation, thereby making it difficult to identify the true effect
of WGs. To this end, in the current study, we controlled the diet
by providing all meals to participants in both groups, which
served to minimize the interference from the variation in dietary
intake. Because weight loss could affect both immune and in-
flammatory markers, the diets that were used in our study were
designed to maintain participant weight throughout the in-
tervention period.
Thus, the objective of this 6-wk randomized controlled trial
was to assess the effect of WGs compared with RGs within the
context of a weight-maintenance diet on markers of systemic
inflammation, phenotype and functional aspects of the immune
system, gut microbiota and SCFAs, as well as stool weight, water
content, pH, and frequency of bowel movements in healthy adults
with BMI (in kg/m
2
) that ranged from normal to obese.
METHODS
Study design, recruitment, and enrollment
This randomized, controlled, parallel-design human trial was
conducted between May 2012 and September 2014 after being
approved by the Institutional Review Board at Tufts Medical
Center and Tufts University Health Sciences Campus (Institutional
Review Board 10110) and was registered at clinicaltrials.gov as
NCT01902394. Participants were recruited from the Jean Mayer
USDA Human Nutrition Research Center on Aging (HNRCA) at
TuftsUniversity Volunteer Recruitment Services database and with
advertisements posted in Tufts Medical Center clinics, local
newspapers, bulletin boards, media sources, and via the HNRCA
website. Interested individuals were first prescreened via an online
or telephone questionnaire. Qualified individuals were invited to an
onsite full-screening visit that was conducted by experienced
research study staff at the Metabolic Research Unit at the HNRCA.
The full screening included a health history and dietary habit
interviews, a fiber-screening questionnaire, and standard blood
tests. Participants were deemed eligible according to the following
these criteria: men and women aged 40–65 y (women must have
been .1 y postmenopausal or had both ovaries removed if pre-
menopausal); BMI from 20 to 35; creatinine concentration
#1.5 mg/dL; serum glutamic oxaloacetic transaminase, glutamic
pyruvic transaminase, and total bilirubin #2 times the upper limit
of the normal range; fasting glucose concentration ,125 mg/dL;
hematocrit $32%; white blood cell count $1.8 310
3
/mm
3
;
platelet count $100 310
3
/mm
3
; consent to be randomly as-
signed; and a report of the consumption of a low-fiber diet (men:
,7 g/1000 kcal; women: ,8 g/1000 kcal) for $2wkbefore
enrollment. Participants with higher fiber consumption were in-
vited to participate if they were willing to reduce habitual daily
fiber intake #2 wk before enrollment. In addition, participants
were included if they were willing to consume only the study
foods provided.
Additional exclusion criteria were based on participant weight
change, dietary commitment, supplement use, use of certain
medications, and health conditions. Participants were excluded
if they had a self-reported weight change .4 kg within the past
6-mo or if they were participating in a weight-loss program.
Dietary exclusions included a vegetarian diet and not willing to
stop taking probiotics, multivitamins, and supplements including
fish oil or n–3 fatty acids and herbal supplements for 30 d before
and during study participation. Calcium and vitamin D supple-
ments were allowed. In addition to these criteria, participants
were excluded if they consumed .2 drinks alcohol/d or were
not willing to abstain from drinking alcohol during the study and
if they had food allergies or other issues with foods that would
preclude intake of study diets. The medication exclusion criteria
included the use of antibiotics within the past 3 mo, the use of
nonsteroidal anti-inflammatory medications or antihistamines,
or the inability to discontinue the use of these substances for
72 h before the first-day blood draw until 48 h after receipt of a
delayed-type hypersensitivity (DTH) skin-test implant, and the
use of immunosuppressive drugs. Exclusions for health condi-
tions included a diagnosis of autoimmune diseases, active cancer
or history of cancer within the past 5 y except for nonmelanoma
skin cancer, uncontrolled major illnesses such as cardiovascular
disease, and a history of inflammatory bowel disease or gas-
trointestinal disorders. All participants gave written informed
consent before participating in the study and received a stipend.
Sample-size calculations and random assignment
Sample-size calculations were based on available data for
DTH (36, 37), key inflammatory cytokines (IL-6 and TNF-a)
(38), selected gut microbiota (Bifidobacteria and Lactobacillus)
(38), and natural killer (NK) cell cytotoxicity (38). The largest
sample size needed was n= 37/group to detect a significant
difference at P,0.05 for DTH with 80% power. This sample
2of16 VANEGAS ET AL.
size was increased to n= 40/group to account for a 10% dropout
rate on the basis of studies at the center in this age group.
Participants were randomly assigned to the WG or RG group
with the use of block random assignment with stratification by
BMI (20–25, 25–30, and 30–35), age (40–55 and 55–65 y), sex,
and race (Caucasian, African American, Asian American, His-
panic, and other). The statistician, who had no contact with
participants and had no role in the data collection, assigned the
random-assignment coding for the WG and RG groups.
Study diets
All randomly assigned participants underwent a 2-wk run-in
phase in which they were provided with a Western-style diet
(high in saturated fats, red meats, simple carbohydrates, and
processed or refined foods and low in fresh fruit and vegetables,
WGs, seafood, and poultry). The purpose of the run-in period was
to minimize the effect of habitual diet intake before starting the
experimental diets. Total daily caloric intake of each participant
was initially calculated with the use of the Harris-Benedict
formula and was adjusted for physical activity when necessary
to maintain the current body weights of subjects. Participants
were instructed to maintain their current physical activity levels
throughout the study. All meals were based on the USDA Dietary
Guidelines 2010, which recommends that 50–55% of energy is
derived from carbohydrates, 15–20% of energy is derived from
protein, and 25–30% of energy is derived from fat (39). After
completion of the run-in phase, participants were assigned to the
following experimental diets: an RG diet (8 g/1000 kcal) and a
WG diet (16 g/1000 kcal), respectively (Figure 1). The targeted
fiber intake that was provided by the WG diet met the recom-
mended Dietary Guidelines for Americans (35 g/d), whereas the
fiber intake from the RG diet was slightly above the average
intake in adults.
The diets were similar in composition with the exception of the
source of grain. The WG group received all grains from WG
sources, and the RG group received all grains from RG-
containing foods. Otherwise, the diets were matched for servings
of fruit, vegetables, and protein (e.g., turkey meatloaf with 100%
whole-wheat bread crumbs with mixed vegetables or turkey
meatloaf with 100% white-bread crumbs with mixed vegetables).
Six 240-mL glasses of water or calorie-free drinks were rec-
ommended daily. The study dietitian developed 3-d menu cycles
at 3 caloric levels (2000, 2500, and 3000 kcal) that used com-
monly available ingredients and food items. Calories were ad-
justed (plus or minus) from these calorie amounts on the basis of
participants’ weight fluctuations.
Food was prepared with the use of standardized recipes by
trained staff at the Metabolic Research Unit of the HNRCA and
was picked up by participants 3 times/wk. Meals were packaged
with reheating instructions and a food checklist. To maintain
body weight within 61 kg of baseline weight, body weight was
measured 2–3 times/wk and calorie amounts were adjusted if
necessary. Participants were asked to complete the food
checklist, whereby they indicated the foods that were not con-
sumed each day, and to return the completed checklist on a
weekly basis. Subjects were also requested to report the con-
sumption of any additional food that was not provided by the
study. Although we discouraged the consumption of additional
food, we tried to accommodate the preferences of subjects. If
they were unhappy with a meal and would check off that they
did not eat that item, our dietitian would ask them what other
food on the menu they would like to eat more of and would try
to incorporate that food into the future menu with adjustment of
all nutrients to maintain the study requirements. Actual intake of
foods was determined by subtracting the amounts of foods that
were not consumed, as indicated on the food checklist, from the
amounts of foods that were provided to participants plus the
additional foods that were consumed. Participants’ actual in-
takes were analyzed with the use of the Nutrition Data System
for Research software (version 2011; Nutrition Coordinating
Center, University of Minnesota). Participants were also asked
to record the presence and severity of 6 gastrointestinal symp-
toms on a weekly basis. Compliance was assessed via the
measurement of plasma alkylresorcinol homologs (19:0, 21:0,
and 23:0), which are WG biomarkers that are present mainly in
the bran of WGs (40).
Sample collection for experimental analysis
Stool, 12-h fasting blood, and saliva samples were collected,
and DTH tests were conducted at baseline (at or near the end of
week 2 of the run-in period) and again at the end of dietary
intervention phase (at or near the end of week 8 of the study).
Participants were asked to stop the use of any anti-inflammatory
medicines, including aspirin and antihistamines, for 72 h before
blood collection and immunological testing.
Collection and processing of stool samples
Participants were provided with kits and instructions for stool
collection, storage, and delivery to the HNRCA. Stool samples
were collected in specimen containers and placed into plastic
bags, surrounded by frozen gel packs, and delivered to the center
in coolers. Participants were instructed to collect all stool samples
daily during a 72-h period, record the date and time of collection,
and deliver all stool samples within 24 h of production. The
number of bowel movements and weights of samples were
recorded by the study staff. From samples that were delivered
within 24 h after production, 3 aliquots of 4 g were collected, and
2 aliquots were immediately stored at 2808C in polypropylene
conical tubes for later analysis of SCFAs, cytokines, and IgA.
The third aliquot was homogenized by vigorous stirring with a
sterile spatula, and 5 3200-mg aliquots were placed in sterile
tubes (Eppendorf) and stored at 2808C for the later analysis of
the microbiota composition. All stool samples that were col-
lected within a 72-h window were pooled and used to determine
FIGURE 1 Schematic outline of the study protocol. DTH, delayed-type
hypersensitivity; RG, refined grain; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 3of16
aerobic and anaerobic bacteria, pH, and water contents. Stool
anaerobic and aerobic counts were determined by taking two
0.5-g stool samples, adding each sample to 4.5 mL phosphate-
buffered saline, and mixing vigorously. Each suspension was
sequentially diluted to 1:10 to achieve dilutions between 10
10
and 10
12
. Samples were plated onto petri dishes that contained
anaerobic blood agar and were incubated for 48 h at 378Cinan
anaerobic chamber or in a warm room under atmospheric con-
ditions for anaerobic and aerobic bacteria cultures, respectively.
CFUs on anaerobic and aerobic plates were counted, and the
results were expressed as CFUs per gram of stool. The stool
water content was determined by drying 2 g stool sample in an
oven for 12–16 h at 1408C. The fecal water weight was de-
termined by the difference between the predrying weight and
postdrying weight. The percentage of stool water content was
calculated as the result of fecal water being divided by the
predrying weight and multiplied by 100. To measure the stool
pH, 1 g stool was added to 1 mL distilled water, mixed thor-
oughly by vortex, and measured with a pH meter.
Saliva, blood, and plasma collection
Drinking straws were cut into 2-in pieces. Participants placed
one end of the straw into the mouth while placing the other end into
polypropylene vials. The allowance of saliva to pool in the mouth
and tilting the head forward caused the saliva to travel from the
mouth to the storage vials. Participants were asked to fill vials to a
designated line, and the duration of the process was recorded as
flow rates, which affected IgA concentrations (41). After collec-
tion, samples were stored at 2808C for a later analysis.
To account for day-to-day variations in ex vivo immunologic
function assays, venous blood was collected into either heparin-
treated (for culture) or EDTA-coated (for hematology and plasma
isolation) tubes on 2 consecutive days after a 12-h fast.
Compositional analysis of fecal microbiota by
high-throughput sequencing
DNA extraction from stool was done with the use of the
QIAamp DNA Stool Mini kit (Qiagen) with the following
modifications: Each 200 mg stool sample was thawed in a Qiagen
stool extraction buffer (Qiagen), containing 50 mg lysozyme and
13.5 U lysostaphin (Sigma Corp.), that was resuspended by
stirring the solution with a pipette tip, which was transferred to a
tube that contained 500 mg 0.1-mm zirconium and silica beads
(Biospec Products). The tubes were placed on a bead-beating
adapter on a Vortex Genie (both from MoBio) for 5 min of bead
beating at 48C. The tubes were heated to 378C for 10 min. After
the addition of 200 mg proteinase K (Qiagen) and 20 mg RNase
A (Sigma-Aldrich), the tubes were heated to 708C for 10 min,
which was followed by an additional 5 min of bead beating.
Tubes were centrifuged, and the supernatant fluid was added to a
tube that contained an InhibitEx tablet from the Qiagen kit
(Qiagen). The remaining procedure was conducted according to
the manufacturer’s protocol. Stool extractions were carried out
in batches of n= 15 with a no-stool extraction control included
in each batch to detect any potential cross-contamination.
Amplicons of the V4 region of the bacterial 16S ribosomal
DNA were generated with primers according to Caporaso et al.
(42). Each amplicon reaction was carried out in triplicate with a
template control; the success of the reaction (band with template;
no band with a negative control) was confirmed by running the
reaction products on an agarose gel. DNA concentrations of all
successful amplicon reactions were measured with the use of the
Quant-iT ds DNA Assay Kit, High Sensitivity (Invitrogen).
Amplicons were pooled in equimolar amounts, which was
followed by the purification of the pool on Angencourt AMpure
XP beads (Beckman Coulter) according to the manufacturer’s
protocol. Pools were submitted to the Tufts University Core
Facility for 250-bp paired-end sequencing on a MiSeq sequencer
(Illumina).
Marker-gene sequencing data were processed with the use of
the Quantitative Insights Into Microbial Ecology package (43).
Reads (results of nucleotide sequences) were clustered at 97%
identity and assigned taxonomy by open reference with the use of
the Greengenes reference database (greengenes.lbl.gov) (44) and
USEARCH program v6.1 (drive5.com) (45). Sequences from all
samples were subsampled at a sample-sequencing depth of
19,490 sequences to control for the differential sequencing effort.
Within-sample biodiversity was measured with the use of whole-
tree phylogenetic diversity. To quantify differences in microbial
compositions between participants over time, weighted and
unweighted UniFrac (a distance metric used for comparing bi-
ological communities) (46) distances between all pairs of sam-
ples was calculated.
Stool SCFAs
Stool SCFAs were determined on the basis of the method de-
scribed by Tomcik et al. (47) with a slight modifications. Undiluted
stool samples were removed from storage at 2808C and freeze
dried for 5–7 d. Stool samples were ground to a fine powder with
the use of a mortar and pestle. Because the extraction yield of
SCFAs can be increased by decreasing the stool homogenate pH
(48), each stool sample (0.1 g) was suspended in 1% phosphoric
acid and homogenized by shaking for 30 min, which was fol-
lowed by centrifugation for 15 min at 7000 3gat 48C. The liquid
fraction was decanted and filtered through a 0.2-mm polysulfone
membrane (Sigma-Aldrich) and stored at 2808C for later ex-
traction. Fecal samples were spiked with internal standards
actetate-D4 and butyrate-D7 before being extracted by ethyl acetate;
samples were derivatized with 200 mL of 100 mmol penta-
flourobenzyl bromide acetone solution/L with 100 mLtrime-
thylamine (both from Sigma-Aldrich) as the catalyst (49) for
1hat608C. SCFAs were analyzed with the use of gas chro-
matography–mass spectrometry. A multipoint standard curve
was established for quantitative analyses of acetate, propionate,
and butyrate, respectively. Known SCFA concentrations were
linearly regressed to the ratio of the SCFA:inflammatory score
(IS) AUC. Concentrations were quantified with the use of the ratio
of each analyte to the internal standard in the equation from the
standard curve.
Stool cytokines
Stool cytokines were analyzed as previously described (50)
with a slight modification. Briefly, stool samples were stored at
2808C, were freeze dried for 5–7 d, and ground to a fine powder
with the use of a mortar and pestle. Stools were diluted in
phosphate-buffered saline at a 1:4 ratio and centrifuged at
4of16 VANEGAS ET AL.
20,000 3g. Samples were filtered through centrifuge tube filters
(Sigma-Aldrich) to remove particles; filtered samples were used
to quantify stool cytokine and IgA. Stool cytokines [interferon g
(IFN-g), TNF-a, IL-6, IL-7, and transforming growth factor b]
were measured with the use of a high-sensitivity ELISA, and
stool IgA was measured with the use of a regular ELISA
(eBioscience) according to the manufacturer’s instructions.
Measurement of DTH response
The DTH skin test was conducted as previously described by
Hamer et al. (7). The 3 recall skin antigens Tetanus toxoid
(Teta nu s t o x o i d USP; Aventis Pasteur), Candida albians (Candin;
Allermed Laboratories), and Tri cho phyton species (Trichophyton
mentagrophytes in conjunction with Trichophyton rubrum;
Hollister-Stier Laboratories) and a negative control (0.9% normal
saline) (Bound Tree Medical) were injected intradermally at
separate sites on the volar surface of the forearm. Induration of
the skin response was read 24 and 48 h later, and a positive re-
action was defined as a mean $5 mm. The number of positive
responses and a composite score of the responses to all antigens
were recorded.
Complete blood count and differential count
Whole blood that was collected in EDTA-coated tubes was used
to analyze complete blood counts and differential counts with
the use of an automated hematology analyzer (ABX Penta 60+;
ABX Diagnostics) according to the manufacturer’s instructions.
Samples were analyzed in duplicate by the Nutrition Evaluation
Laboratory at the HNRCA in the “complete blood count + 5
population differential count” mode. The mean was calculated
and used for the statistical analysis.
Lymphocyte subsets in peripheral blood
To determine the lymphocyte phenotype, whole blood surface
staining for different white blood cell markers was conducted with
the use of fluorescent-conjugated antibodies according to pre-
viously published methods (51). A 3- or 4-color flow cytometry
analysis was performed for a panel that consisted of anti-CD3 (total
T cells), CD4 (helper T cells), and CD8 (cytotoxic T cells) as well as
their naive and memory subpopulations (which were determined by
corresponding patterns in the expression of CD45RA, CD45RO,
and CD62L), CD19 (B cells), CD16 and CD56 (NK cells), and CD4
and CD25 (regulatory T cells). Antibodies and their corresponding
isotype controls were added according to the manufacturer’s
protocol. All samples were run on an Accuri C6 flow cytometer
(BD Biosciences), and acquired data were analyzed with the use of
Flowjo version 10 software (TreeStar).
Plasma alkylresorcinols, cytokines, and LPS-binding
protein
To assess adherence to the dietary regimen, plasma concen-
trations of the individual alkylresorcinol homologs 19:0, 21:0,
and 23:0 were measured with the use of gas chromatography–
mass spectrometry as previously described with minor modifications
FIGURE 2 Consolidated Standards of Reporting Trials diagram.
1
Other reasons included an unwillingness to eat all study foods, current participation in
other studies, and being over the BMI range at the start date but after signing consent.
2
Unwilling to eat all study foods. RG, refined grain; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 5of16
(52). The alkylresorcinol quantification was calculated by
introducing a multipoint standard curve for each alkylresorcinol
(19:0, 21:0, and 23:0). Concentrations were calculated with the
use of the ratio of each alkylresorcinol to the internal standard.
Plasma concentrations of TNF-a, IL-6, IL-8, IL-1b, and LPS-
binding protein (LBP) were analyzed with the use of electro-
chemiluminescence assays (Meso Scale Discovery) according to
the manufacturer’s instructions. The data acquired were ana-
lyzed with the use of the Meso Scale Discovery Workbench 3.0
Data Analysis Toolbox (Meso Scale Discovery). For a few
samples, because their IL-1bconcentrations were below the
detection limit, we set 50% of the detection limit as their esti-
mated values (53). Samples were analyzed in triplicates, and the
means were used for the statistical analysis. An overall IS was
computed from plasma concentrations of TNF-a, IL-6, IL-8, and
IL-1bas previously described (54). Briefly, individual cytokines
were ranked, standardized as zscores, and summed to compute a
score (IS) that was representative of the overall inflammation.
Lymphocyte proliferation
The ability of lymphocytes to proliferate was assessed by
quantifying the incorporation of [
3
H]-thymidine after stimulation
with phytohemagglutinin, concanavalin A (Con A), or antibodies
against CD3 (T cell receptor) and CD28 (T cell co-receptor) (anti-
CD3 and anti-CD28) with the use of a modified whole blood
culture protocol (55). Venous blood that was collected in 2 con-
secutive days was diluted to a final 1:10 ratio [volume:volume
(vol:vol)] with Roswell Park Memorial Institute (RPMI) 1640
media (Sigma-Aldrich), which was supplemented with penicillin
(100 U/mL), streptomycin (100 mg/mL), HEPES (25 mmol/L),
and glutamine (2 mmol/L). This complete RPMI-1640 media was
used in the whole study unless indicated otherwise. Diluted blood
in round-bottom 96-well cell culture plates was incubated in
triplicated wells per treatment of 72 h in the presence of phyto-
hemagglutinin at 2, 5, or 50 mg/mL, Con A at 5, 25, or 50 mg/mL,
or immobilized anti-CD3 (eBioscience) at 1, 5, or 10 mg/mL and
soluble anti-CD28 at 1 mg/mL. The culture condition for cell
function measurements in this study was an atmosphere of 378C,
5% CO
2,
and 95% humidity unless indicated otherwise. Cultures
were pulsed with 0.5 mCi [3H]-thymidine (Perkin Elmer) during
the last 4 h of incubation. Cells were harvested onto glass-fiber
mats (Wallac) with the use of a Perkin Elmer cell harvester
(Perkin Elmer). Cell proliferation was quantified as the amount of
[
3
H]-thymidine that was incorporated into the DNA, which was
determined with the use of liquid-scintillation counting with a
Micro Beta 2 MicroPlate counter (Perkin Elmer). Results are
expressed as mean counts per minute that were averaged from
both days of venous blood collection at baseline (week 2) and
6 wk postintervention (week 8).
Ex vivo cytokine production
The ability of immune cells to produce cytokines was assessed
under dynamic conditions by stimulation with LPS or anti-CD3
and anti-CD28 with the use of a modified whole blood culture
technique (56). Venous blood, which was collected on 2 consecutive
days, was diluted to a final ratio of 1:5 (vol:vol) or 1:3.6 (vol:vol)
with the RPMI-1640 media. To determine T cell cytokine IFN-g,
IL-2, and IL-4 production, diluted blood was stimulated with
TABLE 1
Baseline characteristics of participants
1
Characteristic RG group WG group
Subjects, n40 41
Sex, M/F, n25/15 24/17
Age, y 54 60.79 (41–65)
2
55 60.94 (40–65)
BMI, kg/m
2
26 60.47 (20–33) 26 60.47 (20–34)
Race, n
White 21 23
Black 9 9
Asian American 3 6
Other 7 3
Education, n(%)
Graduate of professional school 14 (37) 14 (34)
4 y of college 17 (45) 15 (37)
,4 y of college 6 (16) 9 (22)
Other 1 (3) 3 (7)
Marital status, n(%)
Married 16 (42) 12 (29)
Single 14 (37) 17 (41)
Divorced 6 (16) 8 (20)
Separated 1 (3) 3 (7)
Widowed 1 (3) 1 (2)
Occupation, n(%)
Service 6 (17) 12 (32)
Technical 6 (17) 1 (3)
Professional 6 (17) 5 (13)
Retired 3 (8) 3 (8)
Unemployed 2 (5) 2 (5)
Other 13 (36) 15 (39)
1
There were no statistical differences between groups on the basis of
Student’s ttest or Fisher’s exact test. RG, refined grain; WG, whole grain.
2
Mean 6SEM; range in parentheses (all such values).
TABLE 2
Alkylresorcinol concentrations of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
19:0, nmol/L 12.24 62.74 12.68 61.88 0.44 62.42 0.16 8.59 61.31 64.17 67.64 55.59 67.29 ,0.0001 55.15 67.76 ,0.0001
21:0, nmol/L 11.31 62.30 14.06 62.43 2.75 62.84 0.08 12.30 62.39 116.97 615.55 104.67 613.82 ,0.0001 101.90 614.27 ,0.0001
23:0, nmol/L 3.07 60.58 3.86 60.72 0.79 60.81 0.75 3.14 60.69 16.89 62.76 13.75 62.29 ,0.0001 12.96 62.46 ,0.0001
Total alkylresorcinol,
nmol/L
26.62 64.10 30.60 63.76 3.98 64.96 0.90 24.02 63.65 198.03 624.27 174.01 621.89 ,0.0001 170.00 622.70 ,0.0001
1
All values are means 6SEMs. n= 40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. RG, refined grain; WG, whole grain.
6of16 VANEGAS ET AL.
immobilized anti-CD3 (5 mg/mL) and soluble anti-CD28
(2 mg/mL) in round-bottom 96-well cell culture plates for 48 h.
To measure the production of inflammatory cytokines IFN-g,
TNF-a, IL-1b, IL-6, and IL-8, diluted blood was stimulated
with LPS (1 mg/mL) in flat-bottom 24-well cell culture plates for
24 h. Supernatant fluid was collected from these cultures for the
cytokine analysis.
All cytokines, except IFN-g, were measured, and data were
analyzed with the use of Meso Scale Discovery system as de-
scribed previously for plasma cytokine analysis. Because it was
not possible to attain the dilution to measure IFN-galong with the
other cytokines, IFN-gwas measured with the use of a regular
ELISA with agents from BD Biosciences and a plate reader
(BioTek Instruments). In a few cases, IFN-g, IL-10, IL-4, and
IL-2 concentrations were below detection limits; therefore, one-
half of the detection-limit values were recorded as estimated
concentrations (57). Results from both days were averaged, and
mean values were used for the statistical analysis.
Isolation of peripheral blood mononuclear cells and NK
cell activity analysis
An NK cell activity assay was conducted with the use of a
modified flow-cytometry method on the basis of a previously
reported method (58). Briefly, peripheral blood mononuclear cells
(PBMCs) were isolated with the use of Ficoll-Histopaque gradient
centrifugation (Sigma-Aldrich) and suspended in RPMI-1640 and
5% fetal bovine serum at 2 310
6
cells/mL. PBMCs were in-
cubated in the presence of allophycocyanin-conjugated anti-CD56
and phycoerythrin-conjugated anti-CD16 monoclonal antibodies
(both from eBioscience) according to the manufacturer’s in-
structions. Human chronic myelogenous leukemia cells K562
(American Type Culture Collection CCL-243) were cultured in
RPMI-1640 media and 10% fetal bovine serum. K562 cells, at
1310
5
cells/mL, were incubated in the presence of 1 mMcar-
boxyflourescein succinimidyl ester. Effector cells (PBMCs) were
co-incubated with target cells (K562 cells) at effector:target ratios
of 100:1, 50:1, 25:1, and 12.5:1. The spontaneous death of target
cells was determined with the use of a culture without PBMCs.
All samples were run in triplicate. After 3 h of co-incubation, 5 mL
7-aminoactinomycin D (eBioscience) was added to each tube to
stain dead cells, which was incubated in the dark and on ice for an
additional 10 min. Samples were analyzed on an Accuri C6 flow
cytometer (BD Biosciences). The percentage of NK cells in
PBMCs was also determined with the use of flow cytometry. The
percentage of cytotoxic activity was calculated with the use of the
following equation:
Cytotoxicityð%Þ¼½dead target cellð%Þ2spontaneous deathð%Þ
½100 2spontaneous deathð%Þ 3100
ð1Þ
Salivary secretory IgA
Salivary secretory IgA was measured with the use of an indirect
competitive immunoassay kit according to the manufacturer’s
protocol (Salimetrics). Samples were read with the use of an
ELISA plate reader, and output was collected and analyzed with
TABLE 3
Stool characteristics of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Stool in 72 h, n3.62 60.24 3.46 60.26 20.15 60.20 0.33 3.78 60.30 4.34 60.32 0.56 60.25 0.03 0.76 60.32 0.02
Stool water content, % 72.11 61.11 72.79 61.13 0.86 61.44 0.39 71.35 61.46 74.57 61.19 3.23 61.53 0.01 2.36 62.11 0.15
Stool weight, g/d 105.05 68.57 95.83 68.57 29.22 68.07 0.26 116.60 611.25 178.68 612.79 62.07 69.96 ,0.0001 69.30 612.89 ,0.0001
Aerobic, CFUs 310
7
1.66 65.78 9.19 63.18 27.32 66.28 0.13 8.48 63.17 4.79 61.33 23.69 63.18 0.12 3.63 66.84 0.32
Anaerobic, CFUs 310
9
1.34 63.19 2.19 68.89 8.14 68.97 0.13 3.38 61.16 1.21 65.12 22.18 61.01 0.12 2.99 61.36 0.31
Stool pH 6.78 60.05 6.81 60.05 0.01 60.05 0.05 6.77 60.06 6.75 60.04 20.03 60.06 0.04 20.07 60.08 0.31
1
All values are means 6SEMs. n= 40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues were obtained with the use of an ANCOVA and
controlling for the baseline measure, BMI, age, and sex. RG, refined grain; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 7of16
the use of Gen5 software (BioTek Instruments). Samples were run
in duplicate, and mean values were calculated for each sample.
Blood lipid profile
Blood lipid profile outcomes (cholesterol, triglycerides, HDL,
LDL, and VLDL) were measured, and the full processing
methods were described elsewhere (M Meydani, M Thomas, JB
Barnett, SM Vanegas, O Chen, G Dolnikowski, S Jonnalagadda,
E Saltzman, S Roberts, SN Meydani, unpublished data, April
2016). Briefly, cholesterol, triglyceride, and HDL were measured
with the use of an enzymatic, calorimetric endpoint assay. LDL
was calculated if the triglyceride concentration was ,400 mg/dL,
or LDL was measured directly if the triglyceride concentra-
tion was $400 mg/dL. VLDL was calculated with the use of
Friedewald’s formula (59).
Statistical analysis
An analysis of the clinical variables was carried out with the use
of SAS software (release 9.2; SAS Institute Inc.). Data are pre-
sented as means 6SEMs. Both the change from baseline to
postintervention within each group (DRG or DWG) and the
difference of this change between groups (DWG minus DRG)
were reported. Normality tests were performed, and trans-
formations were done when needed before the statistical analysis.
The comparisons conducted include those between baseline and
postintervention in each diet group, between the 2 diet groups at
each time point, and between groups for the change from base-
line to postintervention. A general linear model was used to
analyze the data. Variables specified in the model were BMI, age,
sex, and baseline values of dependent variables because of their
influence on the microbiota and immune and inflammatory re-
sponses. A correlation analysis between outcomes that were
significantly (P,0.05) affected by the diet and alkylresorcinols
was corrected for measures at baseline, BMI, age, and sex. The
DTH response was the primary outcome, and other measures
were secondary outcomes. Within a category (such as immune or
fecal microbiota) and the same class (primary or secondary) of
outcomes, multiple comparisons were taken into account for
calculating the levels of significance (Pvalues). Results were
considered significant at P,0.05 or as a trend if P,0.1.
Differences in relative abundances of taxonomic groups that
were summarized at the levels of both the phylum and genus were
examined. Comparisons that were conducted included those
between baseline and postintervention in each diet group,
between the 2 diet groups at each time point, and between groups
for the change from baseline to postintervention. These com-
parisons were conducted with the use of a linear model in the
statistical package R(60) and controlling for age, BMI, and sex.
Pvalues were corrected for the total number of comparisons
with the use of a false-discovery rate (FDR) correction, whereby
significance was determined according to FDR-adjusted
Pvalues ,0.25. We chose the FDR of 0.25 for these analyses
because of the large number of tests.
a-Diversity values for each sample were described with the
use of the whole-tree phylogenetic diversity variable. a-Diversity
values were verified to be normally distributed with the use of the
Shapiro-Wilks test, and within- and between-group analyses were
carried out with the use of a paired ttest.
Weighted and unweighted UniFrac measures of between-sample
ecologic variation (bdiversity) were used to test the hypothesis
that participants were more similar to other subjects within the
same treatment group. This test was performed with the use of the
Adonis function in the vegan package in Rsoftware (60) similar to
that performed in the permutational multivariate ANOVA analysis.
A statistical analysis of associations between the relative
abundances of common taxa (those that were present in $10% of
samples) and clinical metadata covariates of interest were per-
formed with the use of a generalized linear regression in the
statistical package Rwhile controlling for age, BMI, and sex.
TABLE 4
Fecal water SCFA concentrations of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Acetate, mmol/L 20.73 61.11 19.3 60.92 21.43 60.96 0.03 21.63 61.17 22.34 61.26 0.71 60.98 0.19 2.13 61.36 0.02
Propionate, mmol/L 5.19 60.64 3.6 60.5 21.59 60.32 ,0.0001 5.37 60.71 3.46 60.46 21.91 60.25 ,0.0001 20.32 60.41 0.29
Butyrate, mmol/L 5.29 60.8 4.5 60.51 20.79 60.89 0.14 5.43 60.7 5.52 60.91 0.09 60.76 0.86 0.88 61.18 0.25
Total SCFAs, mmol/L 31.21 62.12 27.4 61.62 23.81 61.66 0.002 32.43 62.1 31.32 62.08 21.11 61.51 0.65 2.70 62.25 0.05
1
All values are means 6SEMs. n= 40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. RG, refined grain; SCFA, short-chain fatty acid; WG,
whole grain.
FIGURE 3 a-Diversity comparisons of gut microbiota in stool samples
that were collected at baseline and at the end of the intervention. Data are
presented as box-and-whisker plots that show the distribution of data in
quartiles. aDiversity did not significantly change from baseline after the
intervention in either diet group as observed with the use of phylogenic
diversity–richness metrics. The adiversity in stool samples that were col-
lected at baseline was lower in the WG group (n= 38) than in the RG group
(n= 39) (P= 0.01). RG, refined grain; WG, whole grain.
8of16 VANEGAS ET AL.
The significance of associations was corrected for the total
number of taxa comparisons with the use of a FDR correction.
In addition, correlation tests (with the use of Spearman corre-
lation) were conducted to control for outliers and were corrected
for the total number of comparisons with the use of the FDR.
Associations were identified as significant if they had FDR-
adjusted Pvalues ,0.25 for both generalized linear regression
and Spearman correlation results.
RESULTS
Recruitment, enrollment, and baseline comparisons
Initially, 1714 participants were prescreened via a telephone
or online screening questionnaire and were deemed eligible on
the basis of age, BMI, postmenopausal status, chronic illness,
medication use, and willingness to be randomly assigned. Of
these subjects, 319 men and women were screened at the
HNRCA for eligibility on the basis of laboratory exclusion
criteria. A total of 103 qualified volunteers were enrolled and
randomly assigned, and 81 of these individuals completed the
studywith40subjects(25menand15women)intheRGgroup
and41subjects(24menand17women)intheWGgroup
(Figure 2). Of 22 participants who dropped out, 13 individuals
dropped out during the run-in phase, and 9 individuals dropped
out after random assignment (n= 5 from the RG group, and n=
4 from the WG group) because of personal reasons that were
unrelated to the intervention including the need to take anti-
biotics, time constraints, family events, no shows, and a di-
verticulitis diagnosis by the primary care physician. Sample
sizes reported for different outcome measurements varied
slightly, which reflected missing samples that were due to the
reasons including below the detection limit, accidental loss of
samples during processing, and a technical failure in the
analysis. However, there were no samples that were purposely
excluded from the statistical analysis. There was no difference
in sex, age, BMI, race, education, marital status, or occupation
at baseline between groups (Ta b le 1) or in blood lipid profiles
(data not shown). Furthermore, there was no significant dif-
ference in the change in triglycerides, LDLs, HDLs, or VLDLs
between WG or RG groups (data not shown), but a signifi-
cantly smaller decrease (from baseline to postintervention)
in total cholesterol was shown in the WG group than in the
RG group (3.61 63.43 compared with 11.30 63.88 mg/dL;
P,0.05).
Dietary intake and adherence
Body weight was maintained within 1 kg of baseline values
according to the study design. At baseline and at the end of the
intervention, mean 6SD body weights were 74.7 612.0
compared with 74.7 612.4 kg in the WG group and 75.4 612.0
compared with 74.9 611.7 kg in the RG group. There was no
difference in energy intake or total fat intake over the study
period both within each group and between groups (Supple-
mental Table 1). There was a negligible between-group dif-
ference in macronutrient intake with a WG-group compared
with RG-group increase by 3%, a decrease by 2%, and an in-
crease by 0.4% for carbohydrate, protein, and PUFAs, re-
spectively. We observed increased intake of cholesterol in the
RG group; this finding resulted in a small difference in cho-
lesterol intake when the change between WG and RG groups
was compared. Observed differences in micronutrient intake
reflected the nutritional composition of the WG food and the
manufacturer’s fortification of products that contained refined
cereals and white flour. Compared with the RG group, intakes of
iron, magnesium, zinc, and selenium were higher in the WG
group; however, intakes of vitamin D, thiamin, niacin, and folate
were lower.
The consumption of WGs, as expected, resulted in an increase
in total dietary fiber intake in the WG group than in the RG group.
Over the 6-wk intervention phase, the WG group had daily
reported consumption of 207 639 g WGs and 40 65gfiber
compared with 0 g WGs and 21 63 g fiber in the RG group.
There was a small increase in soluble fiber intake in RG and WG
groups at the follow-up time point compared with at baseline. In
contrast, insoluble fiber intake did not increase in the RG group
but doubled in the WG group. In this study, wheat was the
major contributor of WG intake; therefore, insoluble fiber was
the major source of dietary fiber. Incidences of self-reported
FIGURE 4 b-Diversity comparisons of gut microbiota in stool samples that were collected at baseline and after the intervention. Data are shown from
WG participants (n= 38) before the intervention (dark-blue dots) and after the intervention (light-blue dots) as well as from RG participants (n= 39) before the
intervention (dark-orange dots) and after the intervention (light-orange dots). A PC analysis of weighted (A) and unweighted (B) UniFrac distances.A
permutation-based ANOVA [Adonis function in the vegan package in Rsoftware (60); 99 permutations] with weighted UniFrac-distance metrics revealed that
the variation in the gut microbiota community structure could not be explained by the intervention group either at baseline (P= 0.39) or after the intervention
(P= 0.25). However, a similar analysis with the use of unweighted UniFrac metrics showed differences in the microbiota structure of WG and RG groups at
P= 0.05 and P= 0.07 for baseline and after the intervention, respectively. PC, principal coordinate; RG, refined grain; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 9of16
gastrointestinal symptoms did not differ between groups (Sup-
plemental Table 2).
Alkylresorcinols are phenolic lipids that are present in the bran
of wheat and rye, and thus, their plasma concentrations are suitable
biomarkers of WG wheat consumption. Plasma concentrations of
alkylresorcinol between groups were not different at baseline.
Increased WG consumption was accompanied by increases in total
alkylresorcinols and individual alkylresorcinols (19:0, 21:0, and
23:0) (all P,0.0001) in the WG group (Tab l e 2 ). Plasma al-
kylresorcinol concentrations of 19:0, 21:0, and 23:0 and total
alkylresorcinols in the WG group after the intervention were 6-,
9-, 5-, and 7-fold higher (all P,0.0001), respectively, than the
concentrations at baseline. The larger increase (9-fold) in plasma
concentrations of 21:0 relative to that of other alkylresorcinols in
the WG group reflected the fact that wheat was the main com-
ponent of the WG diet in the study. As expected, no changes in
plasma alkylresorcinol concentrations were shown in the RG
group after the intervention phase, which confirmed participant
adherence.
Stool weight, frequency, pH, water content, and SCFAs
We observed an increase in bowel-movement frequency (P= 0.03)
and stool weight (P=8.39310
28
) in the WG group (Tab l e 3 )with
no change in the RG group, which resulted in between-group dif-
ferences in bowel-movement frequency (P= 0.02) and stool weight
(P=1.77310
27
). Changes in stool acetate (DWG compared with
DRG mean 6SEM: 2.13 61.36 mmol/L; P= 0.02) and total
SCFA (2.70 62.25 mmol/L; P= 0.05) were larger in the WG group
than in the RG group mainly because of a decline in the RG group
(Tab l e 4 ). There was also a weak correlation between acetate and
19:0 (r=0.19,P= 0.02) and total alkylresorcinol (r=0.19,P=0.02),
which suggested that the difference in changes between the 2 groups
was a reflection of the difference in WG intake.
Fecal microbiota composition
Samples were sequenced in 2 batches (number of samples
per sequencing run: w100) to generate a good depth (number
of sequences per sample) of sequencing per sample. We ob-
tained an average of 189,971 sequences/sample and a total of
33,254,004 sequences that were assigned to barcodes. Reads
were clustered into 5341 total operational taxonomic units at a
97% sequence identity, used in a taxonomic analysis, and clas-
sified into 10 bacterial phyla and 118 genera. Community
complexities of WG and RG microbiota were compared ac-
cording to a(intraindividual) diversity and b(interindividual)
diversity. There were differences in adiversity at baseline;
however, these differences were no longer observed at the end of
the intervention (Figure 3). Although we observed a difference
in unweighted UniFrac at baseline (P= 0.05), there was no
difference in the weighted UniFrac analysis (Figure 4). This
result may have implied the presence of small populations of
bacteria, and when their abundance was taken into account, the
differences were no longer observed.
The bacterial composition was determined at the phyla and
genera levels; however, family-level comparisons were made
when genera-level identifications were not possible. There were
no differences between groups when the change (DWG com-
pared with DRG) of relative abundance at the phyla level was
TABLE 5
Relative abundance of bacteria phyla in stool samples of participants at baseline (week 2) and follow-up (week 8)
1
Phylum
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Firmicutes, % of total microbiota 51.41 63.00 48.61 62.74 22.80 62.87 0.97 43.29 62.57 43.57 62.33 0.28 62.70 0.94 3.08 62.75 0.94
Bacteroidetes, % of total microbiota 38.66 62.85 45.33 63.13 6.67 60.43 0.91 49.13 62.73 50.2 62.33 1.07 62.79 0.94 25.6 61.61 0.97
Proteobacteria, % of total microbiota 2.96 60.65 2.32 60.29 20.64 60.49 0.91 3.30 60.53 3.05 60.43 20.25 60.48 0.94 0.39 60.49 0.97
Actinobacteria, % of total microbiota 1.74 60.43 1.56 60.33 20.18 60.39 0.91 1.59 60.44 1.05 60.28 20.54 60.36 0.94 20.36 60.37 0.89
Tenericutes, % of total microbiota 1.45 60.54 0.73 60.26 20.72 60.42 0.91 0.73 60.31 0.71 60.28 20.02 60.29 0.94 0.70 60.43 0.89
Verrucomicrobia, % of total microbiota 0.45 60.11 0.71 60.28 0.26 60.27 0.91 0.26 60.1 0.39 60.14 0.13 60.12 0.94 20.13 60.20 0.94
Cyanobacteria, % of total microbiota 0.63 60.58 0.01 60.01 20.62 60.01 0.97 0.13 60.1 0.07 60.06 20.06 61.09 0.94 0.56 60.55 0.97
Fusobacteria, % of total microbiota 1.75 61.74 0.02 60.01 21.73 60.01 0.93 1.07 60.61 0.72 60.44 20.35 60.74 0.94 1.38 60.38 0.94
Lentisphaerae, % of total microbiota 0.02 60.01 0.03 60.01 0.01 60.02 0.91 0.07 60.07 0.02 60.01 20.05 60.04 0.94 20.06 60.03 0.89
1
All values are means 6SEMs. n= 40 in the RG group, and n= 39 in the WG group. There were no differences between groups at baseline. Pvalues were obtained with the use of a linear model;
covariates in the model included age, BMI, and sex with false-discovery rate correction for multiple testing. RG, refined grain; WG, whole grain.
10 of 16 VANEGAS ET AL.
compared (Table 5). At the family level, there was a significant
relative change toward a decrease in Enterobacteriaceae abun-
dance in the WG group than in the RG group (DWG compared
with DRG mean 6SEM: 20.07 60.11%; FDR-adjusted P=
0.25) (Table 6). At the genera level, there was a significant
relative change toward an increase in Lachnospira abundance in
the WG group than in the RG group (DWG compared with DRG
mean 6SEM: 1.04 60.33; FDR-adjusted P= 0.25). In addi-
tion, there was a trend of relative change toward an increase in
Roseburia abundance in the WG group than in the RG group
(DWG compared with DRG mean 6SEML: 1.32 60.33; FDR-
adjusted P= 0.30). Furthermore, we observed a positive corre-
lation of Lachnospira and Roseburia with both acetate and
butyrate at week 8 (FDR-adjusted P,0.25, Spearman P,0.25).
Salivary and stool IgA and stool cytokines
The WG intervention had no effect on the salivary IgA con-
centration or rate or on stool IgA (Supplemental Table 3)or
stool cytokine concentrations (Table 7).
DTH skin test
There was no within- or between-group difference in the total
number of positive antigens or total diameter of induration that
was measured at 48 h after implantation (Table 8). There was no
difference in DTH changes between RG and WG groups.
However, we observed an increase in the number of positive
antigens and total diameter of induration in response to Tri-
chophyton in both groups (Table 8), which might have been a
reflection of the repeated administration of the antigen.
White blood cell count differential, lymphocyte phenotype,
and lymphocyte proliferation
No between-group differences occurred in total numbers of
white blood cells, lymphocytes, monocytes, eosinophils, ba-
sophils, or neutrophils (Supplemental Table 4). WG intake did
not influence the percentages of T lymphocytes, CD4
+
Tcells
and subpopulations, CD8
+
T cells and subpopulations, B cells,
NK cells, NK T cells, or regulatory T cells (Tab l e 9 ). There
was a difference in the change of the percentage of total ter-
minal effector memory T cells (DWG compared with DRG
mean 6SEM: 2.68% 62.33%; P= 0.03). A partial correlation
analysis showed a modest positive correlation between termi-
nal effector memory cells and 19:0 (r=0.22,P= 0.04), 21:0
(r= 0.33, P= 0.001), and total alkylresorcinols (r=0.28,
P= 0.01), thereby suggesting that the increase in terminal
effector memory T cells was associated with an increase in
WG intake.
There was no within- or between-group difference in T cell
proliferation in response to the mitogens Con A, phytohemag-
glutinin, or anti-CD3 and anti-CD28 at any of the concentrations
used (Supplemental Table 5).
Plasma cytokines and LBP, ex vivo production of cytokines,
and NK cell activity
WG intake had no effect on plasma concentrations of cytokines
or LBP (Supplemental Table 6). Although several inflammatory
markers are used to define chronic inflammation, it is increasingly
appreciated that the measurement of an integrated index, such
as an IS, that takes into account multiple cytokines may be a
better representation of systemic inflammation status. Thus, we
TABLE 6
Relative abundance of bacteria genera in stool samples of participants at baseline (week 2) and follow-up (week 8)
1
Taxon
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Lachnospira, % of total microbiota 2.16 60.37 1.31 60.26 20.85 60.31 0.99 1.71 60.26 1.90 60.41 0.19 60.34 1.00 1.04 60.33 0.25
Roseburia, % of total microbiota 1.88 60.29 1.48 60.19 20.40 60.24 0.99 1.89 60.38 2.81 60.46 0.92 60.42 1.00 1.32 60.33 0.30
Enterobacteriaceae, % of total
microbiota
0.05 60.02 0.09 60.04 0.04 60.03 0.99 0.33 60.17 0.30 60.20 20.03 60.19 1.00 20.07 60.11 0.25
1
All values are means 6SEMs. n= 40 in the RG group, and n= 39 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of a linear model; covariates in the model included age, BMI, and sex with false-discovery rate correction for multiple testing. RG,
refined grain; WG, whole grain.
TABLE 7
Stool water cytokine concentrations of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
IFN-g, pg/mL 0.37 60.19 0.21 60.06 20.16 60.16 0.63 0.14 60.03 0.24 60.14 0.10 60.15 0.61 0.27 60.22 0.97
IL-17, pg/mL 1.72 60.73 3.17 61.80 1.46 61.08 0.22 1.32 60.298 1.51 60.23 0.19 60.18 0.16 21.27 61.11 0.88
TNF-a, pg/mL 1.76 60.31 2.11 60.38 0.35 60.29 0.54 2.76 60.52 2.63 60.47 20.14 60.60 0.74 20.49 60.63 0.82
IL-6, pg/mL 0.34 60.13 0.29 60.08 20.05 60.12 0.09 0.603 60.23 0.43 60.15 20.17 60.17 0.14 20.12 60.20 0.89
TGF-b, pg/mL 0.02 60.01
b
0.08 60.06 0.06 60.06 0.83 0.097 60.04c 0.18 60.09 0.09 60.08 0.07 0.03 60.10 0.25
1
All values are means 6SEMs. n= 19–39 in the RG group, and n= 18–38 in the WG group. There were differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. IFN-g, interferon g; RG, refined grain; TGF-b,
transforming growth factor b; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 11 of 16
computed an IS in this study. There were no between-group
differences in the IS; however, there was a moderate inverse
correlation between the IS and total alkylresorcinol in the WG
group (Pearson’s r=20.40, P= 0.02).
There was a difference in the change from baseline to follow-up
between the 2 groups in LPS-stimulated TNF-aproduction (DWG
compared with DRG mean 6SEM: 2131 61399 pg/mL;
P=0.04)(Table 10), which was mainly due to a decrease in the
RG group. A partial correlation analysis showed a modest positive
correlation between TNF-aand 19:0 (r= 0.23, P= 0.03) or total
alkylresorcinols (r=0.23,P= 0.04), which suggested that the
change in LPS-induced production of TNF-awas associated with
an increase in WG intake.
There was no difference in the ex vivo production of cyto-
kines after stimulation with anti-CD3 and anti-CD28 at any of
the concentrations used (Supplemental Table 7). The WG in-
tervention had no effect on NK cell activity (Supplemental
Table 8 ).
DISCUSSION
This study was designed to determine the effects of the
consumption of diets that were matched for overall energy
contents and macronutrient compositions but that differed in
amounts of WGs on gut microbiota, their metabolites, and im-
mune and inflammatory responses in metabolically healthy
adults. As intended, body weight was maintained fairly constant
within 1 kg of baseline weights throughout the duration of the
study, and furthermore, the increase of WGs in the diet was well
tolerated because self-reported gastrointestinal side effects did
not differ between groups. In addition to increasing plasma
alkylresorcinol, the frequency of bowel movements, and stool
weight, the consumption of WGs had modest effects on the gut
microbiota composition, stool SCFA concentrations, and a few
variables of immune cell phenotype and function.
Plasma concentrations of alkylresorcinol as a biomarker of
WG intake can serve to monitor adherence because alkylre-
sorcinol are mainly found in the outer parts of WG wheat and rye,
TABLE 8
Delayed-type hypersensitivity of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Positive responses, n1.8 60.1 2.0 60.1 0.2 60.1 0.05 1.7 60.1 2.0 60.1 0.3 60.1 0.03 0.1 60.2 0.88
Total diameter, mm 27.4 62.5 31.0 62.9 3.6 63.2 0.10 24.7 62.3 31.0 62.2 5.9 62.6 0.10 2.31 64.1 0.90
Tetanus, mm 13.2 61.3 13.9 61.4 0.6 61.5 0.70 12.6 61.3 14.0 61.2 1.3 61.7 0.60 0.6 62.3 0.94
Candida, mm 10.8 61.3 11.6 61.4 0.8 61.3 0.60 11.4 61.5 13.3 61.4 1.8 61.3 0.20 1.0 61.9 0.51
Trichophyton, mm 3.6 61.1 5.8 61.4 2.1 60.9 0.04 1.6 60.8 3.8 61.1 2.2 60.8 0.03 0.1 61.2 0.93
1
All values are means 6SEMs. n= 38–40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. RG, refined grain; WG, whole grain.
TABLE 9
Immune cell phenotype of participants at baseline (week 2) and follow-up (week 8)
1
Cell type
RG group WG group
DWG 2
DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
Total T cells, % 66.03 61.78 63.36 62.89 22.67 63.47 0.71 62.42 62.14 66.60 61.43 4.18 62.21 0.30 6.86 64.11 0.33
Total effector
memory, %
18.92 61.71 16.92 61.42 22.00 61.69 0.11 21.12 61.55 21.80 62.01 0.68 61.62 0.45 2.68 62.33 0.03
CM, % 19.79 61.71 22.55 62.18 2.51 62.17 0.29 21.21 62.31 19.16 61.71 22.05 62.58 0.39 24.55 63.39 0.18
TEM, % 28.31 62.14 26.61 62.26 21.70 62.47 0.57 27.16 61.74 30.02 62.70 2.86 63.11 0.29 4.56 64.01 0.26
Naive, % 35.67 62.09 38.52 63.08 2.85 63.11 0.33 36.79 62.78 35.74 62.63 21.10 62.09 0.77 23.94 63.71 0.37
CD4, % 47.93 61.96 49.95 61.79 2.02 61.56 0.11 47.49 61.75 47.10 61.80 20.39 61.19 0.72 22.4 61.95 0.16
CD4 CM, % 19.96 63.56 16.53 63.35 23.64 63.52 0.58 15.16 63.19 15.32 63.39 0.17 64.02 0.62 3.81 65.36 0.96
CD4 EM, % 19.64 61.62 17.38 61.72 21.62 61.27 0.17 21.03 61.62 21.61 61.72 0.61 61.73 0.49 2.23 62.15 0.14
CD4 TEM, % 26.92 63.77 28.74 63.52 2.56 63.06 0.39 26.31 62.84 30.81 63.01 4.50 63.55 0.12 1.94 64.70 0.63
CD4 naive, % 34.15 62.36 38.93 62.71 4.78 62.53 0.08 37.60 62.26 35.39 62.21 22.21 62.10 0.54 26.99 63.28 0.10
CD8, % 19.24 61.43 19.15 61.37 0.18 61.12 0.59 17.77 61.06 16.93 60.97 20.84 60.73 0.18 21.02 61.33 0.19
CD8 CM, % 20.06 62.13 19.98 62.13 20.18 62.44 0.97 20.17 62.03 21.32 62.21 1.14 62.16 0.63 1.32 63.25 0.76
CD8 EM, % 19.63 61.65 20.15 61.80 0.74 61.77 0.87 18.99 61.93 20.58 62.16 1.59 62.52 0.58 0.86 63.11 0.79
CD8 TEM, % 30.07 62.83 29.46 62.89 20.99 62.65 0.80 31.02 62.63 29.18 62.85 21.84 63.28 0.41 20.85 64.24 0.70
CD8 naive, % 29.62 62.02 30.20 62.48 0.87 62.15 0.89 29.81 62.24 28.46 61.95 21.36 61.90 0.66 22.22 62.87 0.69
B cells, % 10.28 60.86 10.54 61.06 0.26 61.06 0.94 11.59 60.69 11.87 60.69 0.48 60.54 0.32 0.22 61.21 0.44
NK cells, % 6.43 60.77 6.29 60.63 20.14 60.66 0.20 5.66 60.67 5.93 60.78 0.19 60.55 0.47 0.33 60.86 0.70
NK T cells, % 1.11 60.92 0.38 60.14 20.73 60.92 0.77 0.38 60.14 0.59 60.36 0.21 60.39 0.51 0.94 61.00 0.79
CD4/CD25
+
, % 14.49 60.69 12.50 60.69 21.99 60.82 0.004 14.58 60.85 13.94 60.60 20.65 60.83 0.37 1.34 61.23 0.11
1
All values are means 6SEMs. n= 38–40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. CM, central memory; EM, effector memory; NK,
natural killer; RG, refined grain; TEM, terminal effector memory; WG, whole grain.
12 of 16 VANEGAS ET AL.
are not destroyed during food processing (61), and are well
absorbed in humans (62). Significantly increased plasma alkyl-
resorcinol in the WG group compared with in the RG group
indicated good adherence to the assigned diets, which was further
supported by the similar changes in stool output and frequency
because fiber is known to increase stool frequency and output
(63). The fiber from wheat is mostly insoluble, and insoluble
fibers are known to increase stool bulk, reduce transit time, and
make fecal elimination easier and quicker. In this way, insoluble
fiber can regulate bowel functions to promote the wellbeing of
healthy people and can function as a remedy to alleviate several
gastrointestinal diseases such as peptic ulcers and cancer (64, 65).
To determine the impact of WGs on immune and inflammatory
responses and gut microbiota, our study assessed the following: 1)
systemic and stool inflammatory cytokine concentrations and
plasma LBP concentrations, 2) phenotypic and functional immune
variables, and 3) gut microbiota and microbial products. We
showed no effect of WGs on plasma or stool inflammatory cytokine
concentrations or on LBP concentration in plasma. Our findings are
consistent with 4 (32–34, 66) of 5 [including Sofi et al. (29)]
published WG studies that only looked at inflammatory status but
not at the impact on the cell mediated immune response or gut
microbiota composition. The study of Sofi et al. (29) used a unique
type of Italian WG wheat with a special processing method, and
this WG flour is known to contain higher amounts of polyphenols
and flavonoids, which have anti-inflammatory effects (31).
We showed no effect of WGs on the majority of phenotypic or
functional immune variables. The increased DTH response in both
groups may have suggested a boosting effect of a repeated ex-
posure to the antigen, which may have masked the additional effect
of WGs even if the effect existed. In relation to this, we showed a
significant difference in the percentage of terminal effector
memory T cells between WG and RG groups, which may have
implied that there was an enhanced potential in the adaptive
immune response to a recall antigen. A similar finding was
reported by Ampatzoglou et al. (35) who showed a trend toward
increased CD4
+
terminal central memory cells in a WG group
than in a control group. In addition, we showed that the LPS-
stimulated TNF-aproduction was significantly better maintained
in the WG group that in the RG group. Because TNF-aserves as
an ex vivo surrogate marker for the inflammatory response after a
pathogen challenge, these data may have suggested that there
was a more-robust response to antigens. However, with consid-
eration of the lack of differences in other variables of immune and
inflammatory responses, the overall impact of an increase in the
consumption of WGs, in the absence of weight loss, on the re-
sistance to pathogens remains to be determined.
In this study, we showed a modest effect of WGs on the
composition of microbiota and stool SCFA concentrations. These
observations were consistent with 2 (30, 31) of 3 (30, 31, 35)
previous studies that investigated the effects of WGs on gut
microbiota. We did not observe a difference in the bacterial di-
versity or phyla between groups, which was in agreement with the
other intervention trials that used WG wheat as the main source of
WGs (31, 67). In contrast, Mart´
ınez et al. (30) used WG barley and
brown rice as the main sources of WGs and reported increases in
gut microbial diversity and in the Firmicutes:Bacteroidetes ratio.
Similar to the results that were reported by Mart´
ınez et al.
(30) and Vitaglione et al. (31), we observed differences at the
genus level whereby there was an increase in the SCFA pro-
ducer Lachnospira and a decrease in proinflammatory Enter-
obacteriaceae. In variance to Mart´
ınez et al. (30), we observed a
difference in stool acetate between groups, which was mainly
driven by a decrease in the RG group. Note that the amount of
SCFAs that is present in the stool only accounts for w5–10% of
SCFAs produced (68); acetate is mainly produced in the colon
(69); and a lower pH favors the production of SCFA (70). In our
study, the RG group showed an increase in stool pH, whereas the
WG group showed a decrease in stool pH. Therefore, increased
SCFA production after WG consumption may be related to a
favorable environment such as lower pH in the colon. Of 3 studies
(30, 31, 35), Mart´
ınez et al. (30) and Vitaglione et al. (31)
reported a decrease in systemic inflammatory markers and
changes in the gut microbiota composition. The reason for the
inconsistent findings in these clinical trials is not completely un-
derstood; however, factors such as differences in the composition
of WG diets, not having completely controlled for other compo-
nents of the diets, the extent of adherence to the diets, and the
population studied may have contributed to the divergent findings.
The strength of the current study is that, to our knowledge, this is
the first WG intervention report that completely controlled the diet,
maintained weight, and kept other dietary components except fiber
constant. Furthermore, we included an analysis of plasma alkylre-
sorcinol to verify adherence. In doing so, we were able to assess the
effect of WGs on gut microbiota and immune and inflammatory
markers without the influence of confounding factors. Intervention
studies that have not included biomarkers of adherence have con-
sistently report mixed results (29, 32–34, 66) on inflammatory
markers, whereas studies that have included markers of WG
adherence and observed significant changes in gut microbiota
TABLE 10
LPS-induced cytokine production of participants at baseline (week 2) and follow-up (week 8)
1
Measure
RG group WG group
DWG 2DRG PBaseline Follow-up DRG PBaseline Follow-up DWG P
IFN-g, pg/mL 3015 6473 1888 6290 21127 6449 0.0004 3006 6619 2461 6567 2545 6243 0.07 582 6507 0.17
IL-10, pg/mL 743 668 809 674 66 660 0.07 724 656 747 655 23 636 0.43 242 669 0.46
TNF-a, pg/mL 15,319 61305 12,915 61109 22404 61103 0.005 16,276 61307 16,003 61408 2273 6868 0.97 2131 61399 0.04
IL-1b, pg/mL 1392 6106 1244 699 2148 691 0.02 1662 6147 1521 6131 2141 6105 0.44 6 6139 0.26
IL-6, pg/mL 3711 6318 3339 6264 2372 6202 0.05 3530 6255 3367 6240 2164 6191 0.45 209 6278 0.38
IL-8, pg/mL 54,247 65298 66,556 68977 12,310 65603 0.04 72,232 69921 76,619 610,901 4386 66115 0.27 27923 68304 0.46
1
All values are means 6SEMs. n= 40 in the RG group, and n= 41 in the WG group. There were no differences between groups at baseline. Pvalues
were obtained with the use of an ANCOVA and controlling for the baseline measure, BMI, age, and sex. IFN-g, interferon g; RG, refined grain; WG, whole grain.
WHOLE GRAINS, GUT MICROBIOTA, AND IMMUNE FUNCTION 13 of 16
composition have reported beneficial effects on inflammatory
markers (30, 31, 35). Thus, the modest effects that we observed on
immune and inflammatory markers and gut microbiota suggest that
more-pronounced changes in the gut microbiota, which may
require a prolonged intervention period, are needed to observe more-
dramatic effects on immune and inflammatory responses. Further-
more, the current study was conducted in healthy individuals who
were not likely to be immune compromised or to have high in-
flammatory status. It is possible that more-pronounced changes
would have been observed in participants who were preselected for
having high inflammatory status or chronic disease. Note that WG
foods contain more micronutrients and phenolic compounds that are
known to have various health benefits, including those on immune
and inflammatory responses, and we could not determine the con-
tribution of these components, as well as their interactions with fiber,
to the final effects in our clinical trial. Therefore, future intervention
studies should also consider the inclusion of a variety of grains
because grains vary in types of fiber and compositions of phyto-
chemicals and micronutrients. In particular, the WG in the current
study was predominantly from wheat, whereas oats contributed
,5%; however, oats are more prominent sources of soluble fiber,
which are known to beneficially alter risk factors for diseases (71).
Finally, genomic and epigenetic variations should be determined for
the varied responses to WG intake in individuals in terms of changes
in gut microbiota, inflammation status, and the immune response.
In conclusion, our study shows that 6 wk of WG consumption of
an isocaloric diet that does not result in weight loss is well tolerated
by healthy, middle-aged individuals; in addition to a significant
increase instool frequency and weight, we also show a very modest
effect on the gut microbiota composition, SCFAs, and certain
indicators of the immune response. Additional studies that use
other WGs with higher soluble fiber and/or phenolic compounds
are needed to determine the health benefits of WGs on healthy
individuals. In addition, studies that use WGs with and without
weight loss are needed for a better understanding of the impacts of
WGs on the gut microbiota and their associated health benefits.
We thank StephanieMarco for her assistance in the preparation of the man-
uscript and Weimin Guo for his help in the preparation of the figures and
tables.
The authors’ responsibilities were as follows—SMV, MM, JBB, BG, AK,
HR, SJ, KK, JPK, ES, DW, and SNM: designed the research; SMV, CB, PV,
and DK: performed the statistical analysis; SMV, BG, AK, PV, DK, DW, and
SNM: contributed to interpretation of the results; SMV, AK, HR, JPK, MT,
GD, and LL: conducted the research; SMV, DW, and SNM: wrote the man-
uscript and had primary responsibility for the final content of the manuscript;
and all authors: read and approved the final manuscript. Kerry Ingredients
did not contribute financially or intellectually to this research study. SJ was
employed by the General Mills Bell Institute of Health and Nutrition during
the study conception and conduct; and KK is an employee of the General
Mills Bell Institute of Health and Nutrition. The remaining authors reported
no conflicts of interest related to the study.
REFERENCES
1. Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of
tumor necrosis factor-alpha: direct role in obesity-linked insulin re-
sistance. Science 1993;259:87–91.
2. Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. J Clin
Invest 2005;115:1111–9.
3. Egger G. In search of a germ theory equivalent for chronic disease.
Prev Chronic Dis 2012;9:E95.
4. Jacobs DR Jr., Meyer HE, Solvoll K. Reduced mortality among whole
grain bread eaters in men and women in the Norwegian County Study.
Eur J Clin Nutr 2001;55:137–43.
5. Jensen MK, Koh-Banerjee P, Franz M, Sampson L, Gronbaek M,
Rimm EB. Whole grains, bran, and germ in relation to homocysteine
and markers of glycemic control, lipids, and inflammation 1. Am J Clin
Nutr 2006;83:275–83.
6. Jacobs DR Jr., Andersen LF, Blomhoff R. Whole-grain consumption is
associated with a reduced risk of noncardiovascular, noncancer death
attributed to inflammatory diseases in the Iowa Women’s Health Study.
Am J Clin Nutr 2007;85:1606–14.
7. Hamer DH, Sempertegui F, Estrella B, Tucker KL, Rodriguez A,
Egas J, Dallal GE, Selhub J, Griffiths JK, Meydani SN. Micronutrient
deficiencies are associated with impaired immune response and higher
burden of respiratory infections in elderly Ecuadorians. J Nutr 2009;
139:113–9.
8. Nettleton JA, Schulze MB, Jiang R, Jenny NS, Burke GL, Jacobs DR
Jr. A priori-defined dietary patterns and markers of cardiovascular
disease risk in the Multi-Ethnic Study of Atherosclerosis (MESA). Am
J Clin Nutr 2008;88:185–94.
9.SunQ,SpiegelmanD,vanDamRM,HolmesMD,MalikVS,
Willett WC, Hu FB. White rice, brown rice, and risk of type 2
diabetes in US men and women. Arch Intern Med 2010;170:961–9.
10. Slavin J, Jacobs D, Marquart L. Whole-grain consumption and chronic
disease: protective mechanisms. Nutr Cancer 1997;27:14–21.
11. Adom KK, Sorrells ME, Liu RH. Phytochemicals and antioxidant
activity of milled fractions of different wheat varieties. J Agric Food
Chem 2005;53:2297–306.
12. Fardet A. New hypotheses for the health-protective mechanisms of
whole-grain cereals: what is beyond fibre? Nutr Res Rev 2010;23:65–134.
13. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB,
Massart S, Collini S, Pieraccini G, Lionetti P. Impact of diet in shaping
gut microbiota revealed by a comparative study in children from Europe
and rural Africa. Proc Natl Acad Sci USA 2010;107:14691–6.
14. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA,
Bewtra M, Knights D, Walters WA, Knight R, et al. Linking long-
term dietary patterns with gut microbial enterotypes. Science 2011;334:
105–8.
15. Ou J, Carbonero F, Zoetendal EG, DeLany JP, Wang M, Newton K,
Gaskins HR, O’Keefe SJ. Diet, microbiota, and microbial metabolites
in colon cancer risk in rural Africans and African Americans. Am J
Clin Nutr 2013;98:111–20.
16. Benus RF, van der Werf TS, Welling GW, Judd PA, Taylor MA,
Harmsen HJ, Whelan K. Association between Faecalibacterium
prausnitzii and dietary fibre in colonic fermentation in healthy human
subjects. Br J Nutr 2010;104:693–700.
17. Bengmark S. Gut microbiota, immune development and function.
Pharmacol Res 2013;69:87–113.
18. Ivanov II, Atarashi K, Manel N, Brodie EL, Shima T, Karaoz U,
Wei D, Goldfarb KC, Santee CA, Lynch SV, et al. Induction of in-
testinal Th17 cells by segmented filamentous bacteria. Cell 2009;139:
485–98.
19. Ivanov II, Frutos Rde L, Manel N, Yoshinaga K, Rifkin DB, Sartor RB,
Finlay BB, Littman DR. Specific microbiota direct the differentiation
of IL-17-producing T-helper cells in the mucosa of the small intestine.
Cell Host Microbe 2008;4:337–49.
20. Ostman S, Rask C, Wold AE, Hultkrantz S, Telemo E. Impaired reg-
ulatory T cell function in germ-free mice. Eur J Immunol 2006;36:
2336–46.
21. Segain JP, Raingeard de la Bletiere D, Bourreille A, Leray V,
Gervois N, Rosales C, Ferrier L, Bonnet C, Blottiere HM, Galmiche JP.
Butyrate inhibits inflammatory responses through NFkappaB in-
hibition: implications for Crohn’s disease. Gut 2000;47:397–403.
22. Maslowski KM, Vieira AT, Ng A, Kranich J, Sierro F, Yu D,
Schilter HC, Rolph MS, Mackay F, Artis D, et al. Regulation of in-
flammatory responses by gut microbiota and chemoattractant receptor
GPR43. Nature 2009;461:1282–6.
23. Qi L, van Dam RM, Liu S, Franz M, Mantzoros C, Hu FB. Whole-
grain, bran, and cereal fiber intakes and markers of systemic in-
flammation in diabetic women. Diabetes Care 2006;29:207–11.
24. Gaskins AJ, Mumford SL, Rovner AJ, Zhang C, Chen L, Wactawski-
Wende J, Perkins NJ, Schisterman EF. Whole grains are associated
with serum concentrations of high sensitivity C-reactive protein among
premenopausal women. J Nutr 2010;140:1669–76.
14 of 16 VANEGAS ET AL.
25. Masters RC, Liese AD, Haffner SM, Wagenknecht LE, Hanley AJ.
Whole and refined grain intakes are related to inflammatory protein
concentrations in human plasma. J Nutr 2010;140:587–94.
26. Montonen J, Boeing H, Fritsche A, Schleicher E, Joost HG,
Schulze MB, Steffen A, Pischon T. Consumption of red meat and
whole-grain bread in relation to biomarkers of obesity, inflammation,
glucose metabolism and oxidative stress. Eur J Nutr 2013;52:337–45.
27. Jensen MK, Koh-Banerjee P, Hu FB, Franz M, Sampson L, Gronbaek M,
Rimm EB. Intakes of whole grains, bran, and germ and the risk of
coronary heart disease in men. Am J Clin Nutr 2004;80:1492–9.
28. Lutsey PL, Jacobs DR Jr., Kori S, Mayer-Davis E, Shea S, Steffen LM,
Szklo M, Tracy R. Whole grain intake and its cross-sectional associ-
ation with obesity, insulin resistance, inflammation, diabetes and sub-
clinical CVD: the MESA Study. Br J Nutr 2007;98:397–405.
29. Sofi F, Ghiselli L, Cesari F, Gori AM, Mannini L, Casini A, Vazzana C,
Vecchio V, Gensini GF, Abbate R, et al. Effects of short-term con-
sumption of bread obtained by an old Italian grain variety on lipid,
inflammatory, and hemorheological variables: an intervention study. J
Med Food 2010;13:615–20.
30. Mart´
ınez I, Lattimer JM, Hubach KL, Case JA, Yang J, Weber CG,
Louk JA, Rose DJ, Kyureghian G, Peterson DA, et al. Gut microbiome
composition is linked to whole grain-induced immunological im-
provements. ISME J 2013;7:269–80.
31. Vitaglione P, Mennella I, Ferracane R, Rivellese AA, Giacco R,
Ercolini D, Gibbons SM, La Storia A, Gilbert JA, Jonnalagadda S,
et al. Whole-grain wheat consumption reduces inflammation in a
randomized controlled trial on overweight and obese subjects with
unhealthy dietary and lifestyle behaviors: role of polyphenols bound to
cereal dietary fiber. Am J Clin Nutr 2015;101:251–61.
32. Andersson A, Tengblad S, Karlstrom B, Kamal-Eldin A, Landberg R,
Basu S, Aman P, Vessby B. Whole-grain foods do not affect insulin
sensitivity or markers of lipid peroxidation and inflammation in
healthy, moderately overweight subjects. J Nutr 2007;137:1401–7.
33. Tighe P, Duthie G, Vaughan N, Brittenden J, Simpson WG, Duthie S,
Mutch W, Wahle K, Horgan G, Thies F. Effect of increased con-
sumption of whole-grain foods on blood pressure and other cardio-
vascular risk markers in healthy middle-aged persons: a randomized
controlled trial. Am J Clin Nutr 2010;92:733–40.
34. Brownlee IA, Moore C, Chatfield M, Richardson DP, Ashby P,
Kuznesof SA, Jebb SA, Seal CJ. Markers of cardiovascular risk are not
changed by increased whole-grain intake: the WHOLEheart study, a
randomised, controlled dietary intervention. Br J Nutr 2010;104:125–34.
35. Ampatzoglou A, Williams CL, Atwal KK, Maidens CM, Ross AB,
Thielecke F, Jonnalagadda SS, Kennedy OB, Yaqoob P. Effects of in-
creased wholegrain consumption on immune and inflammatory markers
in healthy low habitual wholegrain consumers. Eur J Nutr 2016;55:183–95.
36. Meydani SN, Meydani M, Blumberg JB, Leka LS, Siber G,
Loszewski R, Thompson C, Pedrosa MC, Diamond RD, Stollar BD.
Vitamin E supplementation and in vivo immune response in healthy
elderly subjects. A randomized controlled trial. JAMA 1997;277:
1380–6.
37. Cossack ZT. T-lymphocyte dysfunction in the elderly associated with
zinc deficiency and subnormal nucleoside phosphorylase activity: effect
of zinc supplementation. Eur J Cancer Clin Oncol 1989;25:973–6.
38. Vulevic J, Drakoularakou A, Yaqoob P, Tzortzis G, Gibson GR.
Modulation of the fecal microflora profile and immune function by a
novel trans-galactooligosaccharide mixture (B-GOS) in healthy elderly
volunteers. Am J Clin Nutr 2008;88:1438–46.
39. US Department of Agriculture, US Department of Health and Human
Services. Dietary Guidelines for Americans, 2010. 7th ed. Washington
(DC): US Government Posting Office; 2010.
40. McKeown NM, Marklund M, Ma J, Ross AB, Lichtenstein AH,
Livingston KA, Jacques PF, Rasmussen HM, Blumberg JB, Chen CY.
Comparison of plasma alkylresorcinols (AR) and urinary AR metab-
olites as biomarkers of compliance in a short-term, whole-grain in-
tervention study. Eur J Nutr 2016;55:1235–44.
41. Miletic ID, Schiffman SS, Miletic VD, Sattely-Miller EA. Salivary IgA
secretion rate in young and elderly persons. Physiol Behav 1996;60:
243–8.
42. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J,
Fierer N, Owens SM, Betley J, Fraser L, Bauer M, et al. Ultra-high-
throughput microbial community analysis on the Illumina HiSeq and
MiSeq platforms. ISME J 2012;6:1621–4.
43. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD,
Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, et al. QIIME
allows analysis of high-throughput community sequencing data. Nat
Methods 2010;7:335–6.
44. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ,
Probst A, Andersen GL, Knight R, Hugenholtz P. An improved
Greengenes taxonomy with explicit ranks for ecological and
evolutionary analyses of bacteria and archaea. ISME J 2012;6:
610–8.
45. Edgar RC. Search and clustering orders of magnitude faster than
BLAST. Bioinformatics 2010;26:2460–1.
46. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac:
an effective distance metric for microbial community comparison.
ISME J 2011;5:169–72.
47. Tomcik K, Ibarra RA, Sadhukhan S, Han Y, Tochtrop GP, Zhang GF.
Isotopomer enrichment assay for very short chain fatty acids and its
metabolic applications. Anal Biochem 2011;410:110–7.
48. Zhao G, Nyman M, Jonsson JA. Rapid determination of short-chain
fatty acids in colonic contents and faeces of humans and rats by
acidified water-extraction and direct-injection gas chromatography.
Biomed Chromatogr 2006;20:674–82.
49. Saraji M, Farajmand B. Application of si ngle-drop microextr action
combined with in-microvial derivatization for determination of acidic
herbicides in water samples by gas chromatography-mass spectrometry. J
Chromatogr A 2008;1178:17–23.
50. Nicholls S, Stephens S, Braegger CP, Walker-Smith JA, MacDonald TT.
Cytokines in stools of children with inflammatory bowel disease or in-
fective diarrhoea. J Clin Pathol 1993;46:757–60.
51. Davis C, Wu X, Li W, Fan H, Reddy M. Stability of im-
munophenotypic markers in fixed peripheral blood for extended anal-
ysis using flow cytometry. J Immunol Methods 2011;363:158–65.
52. Marklund M, McKeown NM, Blumberg JB, Chen CY. Hepatic bio-
transformation of alkylresorcinols is mediated via cytochrome P450
and beta-oxidation: a proof of concept study. Food Chem 2013;139:
925–30.
53. Hornung V, Guenthner-Biller M, Bourquin C, Ablasser A, Schlee M,
Uematsu S, Noronha A, Manoharan M, Akira S, de Fougerolles A,
et al. Sequence-specific potent induction of IFN-alpha by short in-
terfering RNA in plasmacytoid dendritic cells through TLR7. Nat Med
2005;11:263–70.
54. Cassidy A, Rogers G, Peterson JJ, Dwyer JT, Lin H, Jacques PF.
Higher dietary anthocyanin and flavonol intakes are associated with
anti-inflammatory effects in a population of US adults. Am J Clin Nutr
2015;102:172–81.
55. Bloemena E, Roos MT, Van Heijst JL, Vossen JM, Schellekens PT.
Whole-blood lymphocyte cultures. J Immunol Methods 1989;122:
161–7.
56. De Groote D, Zangerle PF, Gevaert Y, Fassotte MF, Beguin Y, Noizat-
Pirenne F, Pirenne J, Gathy R, Lopez M, Dehart I, et al. Direct stim-
ulation of cytokines (IL-1 beta, TNF-alpha, IL-6, IL-2, IFN-gamma
and GM-CSF) in whole blood. I. Comparison with isolated
PBMC stimulation. Cytokine 1992;4:239–48.
57. Hornung RW, Reed LD. Estimation of average concentration in the
presence of nondetectable values. Appl Occup Environ Hyg 1990;5:
46–51.
58. Kim GG, Donnenberg VS, Donnenberg AD, Gooding W, Whiteside TL.
A novel multiparametric flow cytometry-based cytotoxicity assay si-
multaneously immunophenotypes effector cells: comparisons to a 4 h
51Cr-release assay. J Immunol Methods 2007;325:51–66.
59. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concen-
tration of low-density lipoprotein cholesterol in plasma, without use of
the preparative ultracentrifuge. Clin Chem 1972;18:499–502.
60. Hornik K. The comprehensive R archive network. WIREs Comp Stat
2012;4:394–8.
61. Ross AB, Shepherd MJ, Schupphaus M, Sinclair V, Alfaro B, Kamal-
Eldin A, Aman P. Alkylresorcinols in cereals and cereal products. J
Agric Food Chem 2003;51:4111–8.
62. Ross AB, Kamal-Eldin A, Lundin EA, Zhang JX,