R E S E A R C H Open Access
The influence of a short-term gluten-free
diet on the human gut microbiome
Marc Jan Bonder
, Ettje F. Tigchelaar
, Xianghang Cai
, Gosia Trynka
, Maria C. Cenit
, Barbara Hrdlickova
, Tommi Vatanen
, Dirk Gevers
, Cisca Wijmenga
, Yang Wang
and Alexandra Zhernakova
Background: A gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment
for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an
important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human
Methods: We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We
collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they
returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA
sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189
samples, six gut health-related biomarkers were measured.
Results: Inter-individual variation in the gut microbiota remained stable during this short-term GFD intervention.
A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family
Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p=2.81×10
other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism.
We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant
association to the change in diet. We observed strong relations between the predicted activity of pathways and
Conclusions: A GFD changes the gut microbiome composition and alters the activity of microbial pathways.
Keywords: Microbiome, Gluten-free diet, Biomarker, Observation study
Gluten is a major dietary component of wheat, barley, and
rye. In genetically susceptible individuals, the consumption
of gluten triggers the development of coeliac disease –an
autoimmune disorder commonly seen in populations of
European ancestry (with a frequency of approximately 1 %)
. In the absence of any medication, the only treatment is
a life-long gluten-free diet (GFD), which is effective and
well tolerated by the majority of patients. Non-coeliac glu-
ten sensitivity, another common disorder linked to the
consumption of gluten-containing food and resulting in a
range of symptoms of intestinal discomfort (such as diar-
rhea and abdominal pain), has also been shown to improve
on a GFD [2, 3]. More recently, a GFD is being considered
as a way to ameliorate symptoms in patients with irritable
bowel syndrome (IBS) .
However, beyond these medical indications, more and
more individuals are starting on a GFD to improve their
health and/or to control weight. The diet’spopularityhas
risen rapidly in the last few years, making it one of the most
popular diets worldwide, along with a low-carbohydrate
diet and a fat-free diet. The numbers of those adopting the
diet for non-medical reasons now surpass the numbers
of those who are addressing a permanent gluten-related
Several studies have reported the effect of a GFD on
the composition of the gut microbiome in coeliac disease
* Correspondence: firstname.lastname@example.org
Department of Genetics, University of Groningen, University Medical Centre
Groningen, Groningen, The Netherlands
Top Institute Food and Nutrition, Wageningen, The Netherlands
Full list of author information is available at the end of the article
© 2016 Bonder et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Bonder et al. Genome Medicine (2016) 8:45
patients [5–7]. In these studies, the microbiome compos-
ition in coeliac patients on a GFD was compared with un-
treated patients and healthy individuals. The most
consistent observation across these studies is the differ-
ence in the abundance and diversity of Lactobacillus and
Bifidobacterium in the treated and untreated coeliac dis-
ease patients. It should be noted that these studies were
relatively small (seven to 30 participants in each group).
Specifically, De Palma et al.  assessed the effect of a
one-month GFD on ten healthy individuals, but the study
was limited to the use of non-sequence based methods,
including FISH and qPCR. Their study described how Bifi-
were decreased during GFD, whereas Escherichia coli,
Enterobacteriaceae,andBifidobacterium angulatum were
increased. To the best of our knowledge, there has been
no comprehensive analysis of the effect of a GFD on the
entire gut microbiome composition using a next-generation
The effect of other diet interventions on the micro-
biome composition was recently studied using the 16S
rRNA sequencing method . In particular, it was shown
that a short-term animal-based diet led to an increased
abundance of bile-tolerant microorganisms (Alistipes,
Bilophila,andBacteroides) and a decreased abundance of
Firmicutes, which metabolize dietary plant polysaccha-
rides (Roseburia,Eubacterium rectale,andRuminococcus
In this work we assessed the effect of a GFD on gut
microbiota using the next-generation 16S rRNA sequen-
cing method. The analysis was performed in 189 samples,
representing up to nine time points for 21 individuals. We
investigated the diet-related changes both on the level of
taxonomic units as well as on the predicted bacterial path-
ways. Next to this, we assessed a set of selected bio-
markers to assess the gut health in relation to changes in
bacterial composition and their association to a GFD. Our
study offers insights into the interaction between the gut
microbiota and a GFD.
We enrolled 21 participants (nine men and twelve
women), without any known food intolerance and without
known gastrointestinal disorders, in our GFD study for
13 weeks (Fig. 1). After baseline measurements (T = 0), all
the participants started a GFD for four weeks (T = 1–4),
followed by a “wash-out”period of five weeks. Subse-
quently, data were collected when they returned to their
habitual diets (HD, gluten-containing) for a period of four
weeks (T = 5–8) (Fig. 1). Fecal samples were collected at all
time points. Blood was collected at baseline, at T = 2 and
The participants were aged between 16 and 61 years
(mean age, 36.3 years). Mean BMI was 24.0 and 28.6 %
(n= 6) of participants were smokers. The majority of
participants were European (n= 19), two participants
were South American, and one was Asian. Except for
one, none of the participants had taken an antibiotic
treatment for the year prior to the study start. In both diet
periods (GFD, HD), participants kept a detailed three-day
food record. All 21 participants completed the GFD
period; for 17 participants all data points were available.
An overview of the participants’characteristics can be
found in Additional file 1: Figure S1.
Written consent was obtained from all participants and
the study followed the sampling protocol of the LifeLines-
DEEP study , which was approved by the ethics
committee of the University Medical Centre Groningen,
document no. METC UMCG LLDEEP: M12.113965.
Gluten-free diet and dietary intake assessment
Methods to assess GFD adherence and dietary intake
have been described previously by Baranska et al.  In
short, before the start of the study, the participants were
given information on gluten-containing food products
by a dietician and they were instructed how to keep a
three-day food record. The food records were checked
for completeness and the macronutrient intake was cal-
culated. Days on which a participant had a daily energy
intake below 500 kcal or above 5000 kcal were excluded
from our analysis (n= 2). Of 21 participants, 15 (71 %)
completed the dietary assessments; three were excluded
from food intake analysis because of incomplete food re-
cords. We used the paired t-test to compare group
means between GFD and HD.
Blood sample collection
Participants’blood samples were collected after an over-
night fast by a trained physician assistant. We collected
two EDTA tubes of whole blood at baseline (T0) and dur-
ing the GFD period at time points T2 and T4; during the
HD period one EDTA tube was collected at time points
T6 and T8. Plasma was extracted from the whole blood
within 8 h of collection and stored at −80 °C for later
Fig. 1 Timeline of GFD study, including number of participants and
Bonder et al. Genome Medicine (2016) 8:45 Page 2 of 11
Fecal sample collection
Fecal samples were collected at home and immediately
stored at −20 °C. At the end of the 13-week study
period, all samples were stored at −80 °C. Aliquots were
made and DNA was isolated with the QIAamp DNA
Stool Mini Kit. Isolated DNA was sequenced at the
Beijing Genomics Institute (BGI).
We used 454 pyrosequencing to determine the bacterial
composition of the fecal samples. Hyper-variable region
V3 to V4 was selected using forward primer F515
(GTGCCAGCMGCCGCGG) and reverse primer: “E.
coli 907-924”(CCGTCAATTCMTTTRAGT) to exam-
ine the bacterial composition.
We used QIIME , v1.7.0, to process the raw data
files from the sequencer. The raw data files, sff files, were
processed with the defaults of QIIME v1.7.0, however we
did not trim the primers. Six out of 161 samples had fewer
than 3000 reads and were excluded from the analysis. The
average number of reads was 5862, with a maximum of
The operational taxonomic unit (OTU) formation was
performed using the QIIME reference optimal picking,
which uses UCLUST , version 1.2.22q, to perform the
clustering. As a reference database, we used a primer-
specific version of the full GreenGenes 13.5 database .
Using TaxMan , we created the primer-specific ref-
erence database, containing only reference entries that
matched our selected primers. During this process we
restricted the mismatches of the probes to the references
to a maximum of 25 %. The 16S regions that were cap-
tured by our primers, including the primer sequences,
were extracted from the full 16S sequences. For each of
the reference clusters, we determined the overlapping
part of the taxonomy of each of the reference reads in
the clusters and used this overlapping part as the taxo-
nomic label for the cluster. This is similar to the pro-
cesses described in other studies [9, 15–18].
OTUs had to be supported by at least 100 reads and
had to be identified in two samples; less abundant OTUs
were excluded from the analysis.
Estimation of gene abundance and pathway activity
After filtering the OTUs, we used PICRUSt  to esti-
mate the gene abundance and the PICRUSt output was
then used in HUMAnN  to calculate the bacterial
pathway activity. First, the reference database was clus-
tered based on 97 % similarity to the reference sequence
to better reflect the normal GreenGenes 97 % database
required for PICRUSt. Three out of 1166 OTUs did not
contain a representative sequence in the GreenGenes
97 % set and were therefore excluded from the analysis.
Since merging the reference database at 97 % similarity
level led to merging of previously different clusters, for
the pathway analysis we chose to permute the cluster
representative names in the OTU-table 25 times; this
was to be sure that our OTU picking strategy would not
cause any problems in estimating the genes present in
each micro-organism. Next, we ran PICRUSt on the 25
permuted tables and calculated the average gene abun-
dance per sample. The average correlations between the
permutations within a sample was higher than 0.97
(Pearson r). Hence, we averaged the PICRUSt output,
which was then used to calculate the pathway activity in
Changes in the gut microbiome or in gene abundance due
To identify differentially abundant taxa, microbial bio-
markers, and differences in pathway activity between the
GFD and HD periods, we used QIIME and MaAsLin
. QIIME was used for the alpha-diversity analysis,
principal coordinate analysis (PCoA) over unifrac dis-
tances, and visualization. In the MaAsLin analysis we
corrected for ethnicity (defined as continent of birth)
and gender. MaAsLin was used to search for differen-
tially abundant taxonomic units to discriminate between
the GFD and HD time points. Additionally, we tested for
during transition from HD to GFD (T0–T4). MaAsLin
uses a boosted, additive, general linear model to discrim-
inate between groups of data.
In the MaAsLin analysis we did not test individual
OTUs, but focused on the most detailed taxonomic label
each OTU represented. Using the QIIMETOMAASLIN
 tool, we aggregated the OTUs if the taxonomic label
was identical and, if multiple OTUs represented a higher
order taxa, we added this higher order taxa to the ana-
lysis. In this process, we went from 1166 OTUs to 114
separate taxonomic units that were included in our ana-
lysis. Using the same tool, QIIMETOMAASLIN, we nor-
malized the microbial abundance using acrsin square
root transformation. This transformation leads to the
percentages being normally distributed.
In all our analyses we used the Q-value calculated using
the R  Q-value package  to correct for multiple
testing. The Q-value is the minimal false discovery rate at
which a test may be called significant. We used a Q-value
of 0.05 as a cutoff in our analyses.
Six biomarkers related to gut health were measured in
the “Dr. Stein & Colleagues”medical laboratory
(Maastricht, the Netherlands). These biomarkers in-
cluded: fecal calprotectin and a set of plasma cytokines as
Bonder et al. Genome Medicine (2016) 8:45 Page 3 of 11
markers for the immune system activation [25–27];
fecal human-β-defensin-2 as a marker for defense
against invading microbes [28, 29]; fecal chromogranin
A as a marker for neuro-endocrine system activation
[30–32]; fecal short-chain fatty acids (SCFA) secretion
as a marker for colonic metabolism ; and plasma
citrulline as a measure for enterocyte mass [34, 35].
The plasma citrulline level and the panel of cytokines
(IL-1β, IL-6, IL-8, IL-10, IL-12, and TNFα) were mea-
sured by high-performance liquid chromatography
(HPLC) and electro-chemiluminescence immunoassay
(ECLIA), respectively. In feces, we measured calpro-
tectin and human-β-defensin-2 levels by enzyme-
linked immunosorbent assay (ELISA), chromogranin
A level by radioimmunoassay (RIA), and the short-
chain fatty acids acetate, propionate, butyrate, valerate,
and caproate by gas chromatography–mass spectrom-
etry (GC-MS). All biomarker analyses were performed
non-parametrically, with tie handling, because of the
high number of samples with biomarker levels below
the detection limit. We used the Wilcoxon test to
compare the average biomarker levels between the diet
periods and the Spearman correlation to search for re-
lations between the microbiome or gene activity data
and the biomarker levels.
We first investigated if a GFD had a significant effect on
the daily intake of macronutrients by analyzing the GFD
and HD food records from participants (Additional file 2:
Table S1). Mean (SD) daily intakes of energy, protein, fat,
and carbohydrate during GFD and HD are shown in
Table 1. We observed slightly higher carbohydrate intake
and a slightly lower fat intake on GFD; however, none of
the differences in energy or macronutrient intake were
significantly different. We therefore concluded that dietary
macronutrient composition was not significantly changed
by following a GFD.
Microbial differences due to diet
In total we used 155 fecal samples, originating from 21
individuals, for the microbiota analysis and we observed
114 different taxonomic units. We first checked if GFD
influenced the number and proportion of bacteria in in-
dividual participants, for which we investigated differ-
ences in alpha diversity between the GFD and HD time
points using several alpha diversity measures (Observed
species, Shannon, Chao1, and Simpson indexes). We
found no differences in the alpha diversity in any of
these tests. Therefore, we concluded that a change in
diet did not influence the bacterial diversity within a
Next, we tested if there was any difference in the bacter-
ial diversity related to variation in diet between participants
(beta-diversity) by comparing the unweighted unifrac dis-
tance in sample groups. We observed a strong difference
when comparing different time points from a single indi-
vidual to all other individuals, regardless of diet type,
Wilcoxon pvalue <2.2 × 10
. When we compared the
diet-induced differences within the same individual, we
saw a small but significant change, Wilcoxon pvalue =
0.024, although the same diet time points were slightly
more alike (Additional file 3: Figure S2).
In the PCoA analysis over the unweighted unifrac dis-
tance (Fig. 2a), we also saw that the main driver of the
diversity is the inter-individual difference, with partici-
pants clustering together, both during and after the
dietary intervention. In the first ten principal coordi-
nates, which explain more than half of the total vari-
ation, we observed changes between the time points for
individual participants, although there was no single
component, or combination of components, capturing
in the first ten components.
We therefore concluded that a GFD has a significant
effect on the diversity between the groups, but that the
inter-individual effect on the variation of the micro-
biome is stronger than the effect of diet.
We further investigated changes in beta-diversity in
relation to the time points (Fig. 2b). When we plotted
PCo1 versus the time points, we observed a separation
into two groups. Since PCo1 describes the difference
in alpha-diversity between samples, we concluded that
this separation is based on richness. The richness
separates all but one participant into either a clear
high-richness or low-richness group (Fig. 2b). There is
a significant difference in richness between the two
groups, Wilcoxon pvalue = 0.0016, excluding the one
participant who seems to be an intermediate. How-
did not see any significant difference in stability, i.e. in
variation in richness, between the low- and high-
Table 1 Mean and standard deviation (SD) of energy, protein,
carbohydrates, and fat intake during the gluten-free diet (GFD)
and habitual diet (HD). g = grams, en% = energy %
GFD (n= 12) HD (n= 12)
Nutrient Mean SD Mean SD pvalue
Energy (kcal) 1709.5 344.0 1811.5 433.9 0.243
Protein (g) 73.1 18.4 78.1 18.2 0.401
Protein (en%) 17.1 17.2
Carbohydrates (g) 211.1 50.3 199.9 63.2 0.275
Carbohydrates (en%) 49.4 44.1
Fat (g) 63.7 18.1 72.5 24.3 0.109
Fat (en%) 33.6 36.0
Bonder et al. Genome Medicine (2016) 8:45 Page 4 of 11
Differentially abundant taxa
When comparing the HD and GFD time points, corrected
for age and ethnicity in MaAsLin, we observed eight sig-
nificant microbial changes (Fig. 3 and Table 2). The stron-
gest association was found to the family Veillonellaceae,of
which the abundance in the gut dropped significantly on a
, q = 0.003) (Fig. 3b and Additional
file 4: Figure S3). Other species that decreased on a GFD
included Ruminococcus bromii (p= 0.0003, q = 0.01) and
Roseburia faecis (p= 0.002, q = 0.03). While families Victi-
vallaceae (p= 0.0002, q = 0.01), Clostridiaceae (p= 0.0006,
q = 0.015), and Coriobacteriaceae (p= 0.003, q = 0.035),
order ML615J-28 (p= 0.001, q = 0.027), and genus Slackia
(p= 0.002, q = 0.01) increased in abundance on a GFD.
Next, we tested for trends during the diet change;
however, we did not observe a time-dependent change
in the microbiome composition. Since we observed two
different groups based on richness in the PCoA analysis,
we tested for different reactions to the change in diet in
the high-richness- and low-richness groups. However,
no significant associations were found in this analysis.
Since six out of the 28 participants smoked, we tested for
overlap between smoke-associated bacteria and diet-related
bacteria. We did not find any overlap; Additional file 5:
Imputation of bacterial function
Next to the taxonomic associations, we also aimed to
study differences in pathway composition in relation to
GFD. We applied PICRUSt and HUMAnN for pathway
annotation, as described in Methods. In total, 161 path-
ways and 100 modules were predicted, all of the pathways
and modules were found in at least 1 % of the samples.
We used MaAsLin to identify differences in the path-
way composition and conducted the same tests –GFD
versus HD and the time-series test –as for the microbial
Fig. 2 PCoA plot showing the differences in the samples. aSamples plotted on PCoA 1 and 2, percentage of explained variation is given in the
legends. Each color represents an individual, the larger and less opaque spheres are gluten-free diet time points, and the smaller spheres inthesamecolor
are habitual diet time points. bThe differences in the first component over the time points. There are two groups based on richness, i.e. high versus low,
one individual had samples in both groups. The sample belonging to both richness groups has a bolder color
Fig. 3 aCladogram showing the differentially abundant taxa. This plot shows the different levels of taxonomy. Gray indicates bacteria higher in
the habitual diet and red indicates those higher in the gluten-free diet. The different circles represent the different taxonomic levels. (From inside
to outside: Kingdom, Phylum, Class, Order, Family, Genus, and Species). bComparison of the abundance of Veillonellaceae* in the gluten-free diet
vs. habitual diet. In the plot, the aggregate “overall weeks”including correction is shown. * Veillonellaceae is placed in the order Clostridiales in
GreenGenes 13.5. However, according to the NCBI classification, it belongs to order Negativicutes
Bonder et al. Genome Medicine (2016) 8:45 Page 5 of 11
composition. The data were again corrected for age and
ethnicity. We observed that 19 KEGG pathways and two
KEGG modules (Table 3) were different in abundance
between GFD and HD. We did not observe associations
related to the transition from GFD to HD (T0–T4). Four
out of five top associations, all with a Q-value <0.0003,
are related to metabolism changes: tryptophan metabol-
ism, butyrate metabolism (Fig. 4a), fatty acid metabol-
ism, and seleno-compound metabolism.
Biomarkers in relation to diet changes
Biomarkers related to GFD versus HD
We measured four biomarkers in feces: calprotectin,
human-β-defensin-2, chromogranin A, and a set of five
short-chain fatty acids (acetate, propionate, butyrate, val-
erate, and caproate). In addition, we measured citrulline
levels and a panel of cytokines (IL-1β, IL-6, IL-8, IL-10,
IL-12, and TNFα) in blood. The Wilcoxon test was used
to test biomarker level differences between the average
Table 2 GFD-induced changes in taxonomic composition
Taxonomic unit Coefficient N.not.0/N pvalue Q-value
0.0424 155/155 2.81 × 10
p_Lentisphaerae|c_Lentisphaeria|o_Victivallales|f_Victivallaceae −0.0093 89/155 2.30 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Ruminococcaceae|g_Ruminococcus|s_bromii 0.0151 99/155 2.94 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Clostridiaceae −0.0121 150/155 5.69 × 10
p_Tenericutes|c_RF3|o_ML615J-28 −0.0095 82/155 1.30 × 10
p_Firmicutes|c_Clostridia|o_Clostridiales|f_Lachnospiraceae|g_Roseburia|s_faecis 0.0065 100/155 1.88 × 10
p_Actinobacteria|c_Coriobacteriia|o_Coriobacteriales|f_Coriobacteriaceae|g_Slackia −0.0044 43/155 2.14 × 10
p_Actinobacteria|c_Coriobacteriia|o_Coriobacteriales|f_Coriobacteriaceae −0.0137 155/155 2.67 × 10
A positive coefficient means more of the microbe was present during the habitual diet, while a negative coefficient means less of the microbe was present during the
habitual diet. All associations were to the kingdom bacteria, for readability the kingdom label is not presented.
Veillonellaceae is placed in the order Clostridiales in
GreenGenes 13.5. However, according to the NCBI classification, it belongs to order Negativicutes
Table 3 GFD-induced changes in pathway and module activity
Feature Coefficient N.not.0/N pvalue Q-value
KO00380: Tryptophan metabolism −0.0011 155/155 2.45 × 10
KO00650: Butyrate metabolism −0.0014 155/155 2.72 × 10
KO00071: Fatty acid metabolism −0.0011 155/155 4.74 × 10
KO00450: Selenocompound metabolism 0.0009 155/155 9.23 × 10
KO00630: Glyoxylate and dicarboxylate metabolism −0.0010 155/155 2.53 × 10
KO00520 Amino sugar and nucleotide sugar metabolism 0.0009 155/155 2.83 × 10
M00064: ADP-L-glycero-D-manno-heptose biosynthesis 0.0066 155/155 4.12 × 10
KO00643: Styrene degradation −0.0013 155/155 4.29 × 10
M00077: Chondroitin sulphate degradation Chondroitin sulphate degradation −0.0037 76/155 5.81 × 10
KO00760: Nicotinate and nicotinamide metabolism 0.0008 155/155 6.79 × 10
KO00620: Pyruvate metabolism −0.0012 155/155 0.002 0.023
KO00253: Tetracycline biosynthesis −0.0027 155/155 0.002 0.024
KO00471: D-Glutamine and D-glutamate metabolism 0.0012 155/155 0.002 0.024
KO04122: Sulphur relay system −0.0020 155/155 0.002 0.024
KO00633: Nitrotoluene degradation −0.0022 155/155 0.002 0.024
KO00072: Synthesis and degradation of ketone bodies −0.0020 155/155 0.003 0.028
KO00310: Lysine degradation −0.0007 155/155 0.003 0.031
KO00624: Polycyclic aromatic hydrocarbon degradation 0.0006 155/155 0.005 0.043
KO00561: Glycerolipid metabolism −0.0012 155/155 0.005 0.043
KO00680: Methane metabolism −0.0006 155/155 0.006 0.047
KO00550: Peptidoglycan biosynthesis 0.0011 155/155 0.007 0.047
A positive coefficient means more activity of the pathway/module during the habitual diet, while a negative coefficient means less activity of the pathway/module during
the habitual diet
Bonder et al. Genome Medicine (2016) 8:45 Page 6 of 11
values and the GFD and HD period values. We saw no
significant change in biomarker levels in relation to GFD
(Table 4A and B).
Correlations between fecal biomarkers and microbiome
We correlated the fecal biomarker levels to the micro-
biome composition as well as to the microbiome predicted
pathways and modules. After multiple testing correction,
we observed many statistically significant correlations
between the levels of biomarkers and microbiome/
pathway abundances; the absolute correlation, Spearman
Rho, was between 0.14 and 0.59. An expected observation
was the correlation of the butyrate pathway activity to the
butyrate biomarker, as we had previously observed a
significant correlation between the predicted butyrate
pathway activity and diet change (Table 3). When correlat-
ing the actual butyrate measurements with the predicted
activity of the butyrate metabolism, we observed a low but
significant correlation of −0.269 (p= 0.0009, q = 0.0012,
Additional file 6: Figure S4). However, there was no
Table 4 Median and 25 %/75 % quantiles of the measured biomarkers
Habitual diet Gluten-free diet Wilcoxon test pvalue
Citrullin (mol/L) 45.60 (38.15–51.50) 48.00 (36.35–56.85) 0.9328
IL 1 Beta (g/L) 1.60 (0.68–2.10) 1.23 (0.79–1.68) 0.8870
IL 6 (g/L) BDL (BDL–1.60) BDL (BDL–0.38) 0.1240
IL 8 (g/L) 6.04 (2.89–12.61) 5.41 (3.34–11.19) 0.9030
IL 10 (g/L) 0.83 (0.74–1.01) 0.83 (0.74–0.97) 0.9322
IL 12P70 (g/L) 1.53 (0.95–1.78) 1.53 (0.95–2.11) 0.2131
TNF Alpha (g/L) 0.56 (BDL–4.33) BDL (BDL–5.13) 0.9761
Chromogranin A (nmol/g) 10.85 (7.69–23.09) 11.44 (7.37–27.18) 0.8128
Beta Defensin 2 (ng/g) 24.90 (18.78–35.03) 26.10 (20.03–46.90) 0.5256
Calprotectin (g/g) 21.55 (BDL–42.88) 13.05 (BDL–31.28) 0.0528
Acetate (mol/g) 24.37 (17.35–34.34) 23.61 (18.58–35.12) 0.8651
Propionate (mol/g) 7.55 (4.24–10.98) 6.84 (4.67–9.07) 0.6986
Butyrate (mol/g) 6.86 (3.53–10.63) 6.48 (4.27–10.40) 0.8882
Valerat (mol/g) 1.09 (0.74–1.76) 1.24 (0.79–1.70) 0.6824
Caproat (mol/g) 0.28 (0.05–0.85) 0.21 (0.04–0.66) 0.2488
None of the differences were statistically significant. BDL = below detection limit
Fig. 4 Box plot of predicted activity of butyrate metabolism per diet period (a) and the butyrate levels (mol/g) per diet period (b). There was a
significant increase in activity in butyrate metabolism (q = 0.001877), but no change in butyrate level was observed
Bonder et al. Genome Medicine (2016) 8:45 Page 7 of 11
significant difference in butyrate levels in the two diet
periods (Fig. 4b and Table 4). Another interesting correl-
ation was found between the predicted pyruvate metab-
olism pathway and the levels of propionate (mol/g),
since propionate can be oxidized to pyruvate , for
which we observed a correlation of −0.54 (p=9.44×
, Additional file 7: Figure S5). A
complete list of the significant correlations between
the fecal biomarkers and the microbiome composi-
tions, the predicted KEGG pathway activity scores, and
predicted activity of KEGG modules can be found in
Additional file 8: Tables S3, Additional file 9: Table S4,
and Additional file 10: Table S5.
We investigated the role of a four-week GFD on micro-
biome composition in healthy individuals and identified
moderate but significant changes in their microbiome
compositions and even stronger effects on the imputed
activity levels of bacterial pathways.
On a taxonomic level we identified eight bacteria that
change significantly in abundance on GFD: Veillonella-
ceae,Ruminococcus bromii, and Roseburia faecis de-
creased on GFD, and Victivallaceae,Clostridiaceae,
ML615J-28,Slackia, and Coriobacteriaceae increased on
GFD. The strongest effect was seen in the decrease of
Veillonellaceae during GFD, Gram-negative bacteria
known for lactate fermentation. This is the first time
that the Veillonellaceae family has been associated to a
dietary intervention, but it was recently shown to be de-
creased in autistic patients . Remarkably, the patients
in that study were more often on a GFD (9/10) than the
control group (5/10). Our findings suggest that GFD, ra-
ther than autism, can be the cause of a lower abundance
of Veillonellaceae in these patients, thus highlighting the
importance of including dietary information in analyses
of microbiota in relation to diseases. Veillonellaceae is
considered to be a pro-inflammatory family of bacteria; an
increase in Veillonellaceae abundance was consistently re-
ported in IBD, IBS, and cirrhosis patients [39–41]. It is
conceivable that a decrease in Veillonellaceae abundance
might be one of the mediators of the GFD’sbeneficialef-
fect observed in patients with IBS and gluten-related
Several of the associated bacteria have been previously
linked to diet changes and starch metabolism. In par-
ticular, Ruminococcus bromii is important for the deg-
radation of resistant starch in the human colon  and
is increased when on a resistant starch diet . It is
also known that degradation of cellulose by Ruminococ-
cus results in the production of SCFA and hydrogen gas
; a decrease in abundance of Ruminococcus and its
fermentation products might explain the beneficial effect
of a GFD that is experienced by some IBS patients as
previously reported by Aziz et al. . Both Ruminococ-
cus bromii and Roseburia faecis were recently reported
to be influenced by switching from a vegetarian to a
meat-containing diet . It is likely that changes in these
bacteria observed in relation to GFD are the conse-
quences of the different starch composition of a GFD
versus HD. Moreover, stool consistency could influence
the results of microbiome composition ; unfortu-
nately, data on stool composition were not collected in
The five bacteria for which we found an increased abun-
dance on GFD are less well characterized although the
Slackia genus, its family Coriobacteriaceae, and the family
Clostridiaceae have been previously linked to gastrointes-
tinal diseases in humans –inflammatory bowel disease,
celiac disease, and colorectal cancer [47–49]. The Victival-
laceae family and ML615J-28 order have not been previ-
ously associated to diet change or phenotypic change in
human. However, in general, it could be hypothesized that
these bacteria benefit from a change in available substrates
as a result from the change in diet, which could in turn re-
sult in altered metabolite production and related gastro-
In this study we found a stronger effect of diet on the
imputed KEGG pathways than on the taxonomic level. So,
although the changes in the overall microbiome were
moderate, there were more profound effects on the path-
way activities of the microbiome.
The strength of our study lies in our analysis of the
microbiome at multiple time points for the same indi-
viduals. We identified that the inter-individual variability
is the strongest determinant of sample variability, sug-
gesting that in healthy individuals the gut microbiome is
stable, even with short-term changes in the habitual diet.
We did not observe differences in the downstream effect
of GFD in relation to high or low richness, which con-
tradicts previous observations . The study by David
et al.  identified a profound effect of short-term diet
change from a vegetarian to an animal-based diet and
vice versa. This profound short-term diet effect was not
observed in our study when changing from a gluten-
containing to a gluten-free diet. Induced by the diet
change, David et al.  found significant differences in
macronutrient intake between meat-based and plant-
based diet, whereas macronutrient intake in this study
was not changed during the diets. These results suggest
that changing the main energy source (meat vs. plant)
has a more profound effect on the microbiome than
changing the carbohydrate source (gluten). Although
De Palma et al.  did observe a reduction in polysac-
charide intake for GFD in healthy individuals, we were
unable to reproduce their finding because we could not
distinguish between different classes of carbohydrates
in our dataset as the food composition data on GFD
Bonder et al. Genome Medicine (2016) 8:45 Page 8 of 11
foods lacked this information. Further, it is possible that
changes in nutritional intake other than those driven by
gluten exclusion might influence microbiome changes.
For our selection of blood and stool biomarkers, we ob-
served no significant associations with the diet change. All
the selected biomarkers are markers of inflammation or
metabolic changes and remained in the normal range in
all our participants, with a high proportion of the values
of blood inflammatory markers being below the detection
limit. Overall, we conclude that a GFD and its down-
stream effects on the microbiome do not cause major
inflammatory or metabolic changes in gut function in
healthy participants. However, the lower abundance of
Veillonellaceae, the pro-inflammatory bacterium linked to
Crohn’s disease and other gut disease phenotypes, sug-
gests a reduction in gut inflammatory state. This change
in bacterial composition might be linked with a beneficial
effect of GFD for patients with gut disorders such as
gluten-related disorders and/or IBS.
We have identified eight taxa and 21 bacterial pathways
associated with a change from a habitual diet to a GFD
in healthy individuals. We conclude that the effect of
gluten intake on the microbiota is less pronounced than
that seen for a shift from a meat-based diet to a vegetar-
ian diet (or vice versa). However, a GFD diet clearly in-
fluences the abundance of several species, in particular
those involved specifically in carbohydrate and starch
metabolism. Our study illustrates that variations in diet
could confound the results of microbiome analysis in re-
lation to disease phenotypes, so dietary variations should
be carefully considered and reported in such studies.
The short-term GFD did not influence the levels of in-
flammatory gut biomarkers in healthy individuals. Fur-
ther research is needed to assess the impact of a GFD
on inflammatory and metabolic changes in gut function
in individuals with gastrointestinal conditions such as
IBS and gluten-related disorders.
Ethics approval and consent to participate
This GFD study followed the sampling protocol of the
LifeLines-DEEP study, which was approved by the ethics
committee of the University Medical Centre Groningen
and conform the Declaration of Helsinki, document no.
METC UMCG LLDEEP: M12.113965. All participants
signed their informed consent prior to study enrolment.
Availability of data and materials
The supporting data are available to researchers in the
European Nucleotide Archive, under study accession
number PRJEB13219 (http://www.ebi.ac.uk/ena/data/
Additional file 1: Figure S1. Baseline characteristics of the GFD study
group. (TIF 1068 kb)
Additional file 2: Table S1. Results macronutrient intake per participant.
(XLSX 13 kb)
Additional file 3: Figure S2. Unweighted unifrac distances when
comparing inter-individual vs intra individual distances. In group 1 the
intra-individual differences are shown regardless of diet. Group 2 shows
the intra-sample differences are shown within the same diet. Group 3
shows the intra-individual differences are shown between the two diet
groups. In group 4 the inter-individual differences are shown regardless
of diet. Group 5 shows the inter-sample differences are shown within
the same diet. Group 6 shows the inter-individual differences are shown
between the two diet groups. The main difference is the intra- vs. inter-
individual difference. Also the same diet points in the samples are slightly
closer to each other. However, we do not see such a phenomenon for group
Additional file 4: Figure S3. Abundance of Veillonellaceae family in the
GFD participants. In all but four participants we see a clear trend of higher
levels of Veillonellaceae on the habitual diet. The rightmost samples do not
show this phenomenon. (TIF 4036 kb)
Additional file 5: Table S2. Relation of smoking and microbiome
composition. (XLSX 9 kb)
Additional file 6: Figure S4. Measured butyrate levels vs. the predicted
activity of butyrate metabolism. (TIF 6 kb)
Additional file 7: Figure S5. Measured propionate levels vs. the predicted
activity of pyruvate metabolism. (TIF 646 kb)
Additional file 8: Table S3. Correlation of bacteria and levels of fecal
biomarkers. (PDF 139 kb)
Additional file 9: Table S4. Correlation of predicted HUMAnN pathway
activity and levels of fecal biomarkers. (PDF 228 kb)
Additional file 10: Table S5. Correlation of predicted HUMAnN
module activity and levels of fecal biomarkers. (PDF 143 kb)
BGI: Beijing Genomics Institute; ECLIA: electro-chemiluminescence immunoassay;
EDTA: ethylenediaminetetraacetic acid; ELISA: enzyme-linked immunosorbent
essay; FISH: fluorescence in situ hybridization; GC-MS: gas chromatography–mass
spectrometry; GFD: gluten-free diet; HD: habitual diet; HPLC: high performance
liquid chromatography; IBS: irritable bowel syndrome; KEGG: Kyoto encyclopedia
of genes and genomes; OTU: operational taxonomic unit; PCoA: principal
coordinate analysis; qPCR: quantitative real-time polymerase chain reaction;
RIA: radioimmunoassay; SCFA: short chain fatty acids; SD: standard deviation.
The authors declare that they have no competing interests.
GT, AZ, and CW designed the study. GT, ET, BH, and MC were involved in
sample collection and DNA isolation. XC, HZ, and YW performed the data
generation. MJB, TV, DG, SZ, MC, and ET were involved in data processing,
analysis, and interpretation. MJB, SZ, and ET drafted the work. All authors have
critically revised this article and approved the final version to be published.
We thank all the participants for their collaboration as well as Jackie Senior
and Kate Mc Intyre for editing the manuscript. We thank Jackie Dekens, Zlatan
Mujagic and Daisy Jonkers for support with the biomarker analyses and Stein’s
lab for measuring the biomarkers. We thank Hermie Harmsen for the helpful
discussions during the project.
This study was funded by a European Research Council advanced grant (FP/
2007–2013/ERC grant 2012–322698) to CW, a grant from the Top Institute Food
and Nutrition Wageningen (GH001) to CW, and a Rosalind Franklin Fellowship
Bonder et al. Genome Medicine (2016) 8:45 Page 9 of 11
from the University of Groningen to AZ. GT is supported by the Wellcome Trust
Sanger Institute, Cambridge, UK (WT098051).
Department of Genetics, University of Groningen, University Medical Centre
Groningen, Groningen, The Netherlands.
Top Institute Food and Nutrition,
Wageningen, The Netherlands.
BGI, Shenzhen 518083, China.
Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Broad Institute of
MIT and Harvard, Cambridge, MA 02142, USA.
Department of Computer
Science, Aalto University School of Science, Espoo 02150, Finland.
Received: 29 October 2015 Accepted: 5 April 2016
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