(2024) 22:358
Herfindal et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03562-1

BMC Medicine

RESEARCH

Open Access

Effects of fructan and gluten on gut
microbiota in individuals with self‑reported
non‑celiac gluten/wheat sensitivity—arandomised controlled crossover trial
Anne Mari Herfindal1, Morten Nilsen1, Trude E. Aspholm1, Gry I. G. Schultz2, Jørgen Valeur3,4, Knut Rudi1,
Magne Thoresen5, Knut E. A. Lundin6,7, Christine Henriksen8 and Siv K. Bøhn1*

Abstract
Background Individuals with non-celiac gluten/wheat sensitivity (NCGWS) experience improvement in gastrointestinal symptoms following a gluten-free diet. Although previous results have indicated that fructo-oligosaccharides(FOS), a type of short-chain fructans, were more likely to induce symptoms than gluten in self-reported NCGWS
patients, the underlying mechanisms are unresolved.
Methods Our main objective was therefore to investigate whether FOS-fructans and gluten affect the composition and diversity of the faecal microbiota (16S rRNA gene sequencing), faecal metabolites of microbial fermentation(short-chain fatty acids [SCFA]; gas chromatography with flame ionization detector), and a faecal biomarker of gut
inflammation (neutrophil gelatinase-associated lipocalin, also known as lipocalin 2, NGAL/LCN2; ELISA). In the randomised double-blind placebo-controlled crossover study, 59 participants with self-reported NCGWS underwent
three different 7-day diet challenges with gluten (5.7 g/day), FOS-fructans (2.1 g/day), and placebo separately (three
periods, six challenge sequences).
Results The relative abundances of certain bacterial taxa were affected differently by the diet challenges. Afterthe FOS-fructan challenge, Fusicatenibacter increased, while Eubacterium (E.) coprostanoligenes group, Anaerotruncus,
and unknown Ruminococcaceae genera decreased. The gluten challenge was primarily characterized by increased
abundance of Eubacterium xylanophilum group. However, no differences were found for bacterial diversity (α-diversity),
overall bacterial community structure (β-diversity), faecal metabolites (SCFA), or NGAL/LCN2. Furthermore, gastrointestinal symptoms in response to FOS-fructans were generally not linked to substantial shifts in the gut bacterial
community. However, the reduction in E. coprostanoligenes group following the FOS-fructan challenge was associatedwith increased gastrointestinal pain. Finally, correlation analysis revealed that changes in gastrointestinal symptoms
following the FOS-fructan and gluten challenges were linked to varying bacterial abundances at baseline.
Conclusions In conclusion, while FOS-fructans induced more gastrointestinal symptoms than gluten
in the NCGWS patients, we did not find that substantial shifts in the composition nor function of the faecal microbiota
could explain these differences in the current study. However, our results indicate that individual variations in baseline*Correspondence:
Siv K. Bøhn
sivb@nmbu.no
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Herfindal et al. BMC Medicine(2024) 22:358

Page 2 of 19

bacterial composition/function may influence the gastrointestinal symptom response to both FOS-fructans and gluten. Additionally, the change in E. coprostanoligenes group, which was associated with increased symptoms, implies
that attention should be given to these bacteria in future trials investigating the impact of dietary treatments on gastrointestinal symptoms.
Trial registration Clinicaltrials.gov as NCT02464150.
Keywords Non-celiac gluten/wheat sensitivity (NCGWS), Gluten, FODMAP, Fructan, Fructo-oligosaccharides (FOS),
Gut microbiota, Gastrointestinal symptoms, Short-chain fatty acids (SCFA), Neutrophil gelatinase-associated lipocalin(NGAL), Lipocalin-2 (LCN2)

Background
Although the first cases of gluten sensitivity in theabsence of celiac disease (CeD) were reported 40 years
ago [1], the aetiology and underlying mechanisms
remain elusive, leading to ongoing debates regarding its
definition, diagnostic criteria, and the estimation of its
prevalence [2, 3]. Today, the condition is often labelled
non-celiac gluten/wheat sensitivity (NCGWS). Individuals afflicted by NCGWS report a diverse range of gastrointestinal (GI) and extraintestinal symptoms when
consuming gluten- and/or wheat-containing foods, without the diagnosis of CeD or wheat allergy (WA) [3, 4].
In contrast to CeD and WA, which are characterized
by a well-defined pathophysiology [5, 6], NCGWS lacks
clear diagnostic markers [2]. The clinical presentation of
NCGWS with respect to GI symptoms shares common
features with other disorders of gut–brain interaction,
notably irritable bowel syndrome (IBS) [7].
According to the Salerno criteria (2015) [4], the diagnosis of NCGWS is confirmed through the improvementof symptoms upon adherence to a gluten-/wheat-freediet, followed by the reappearance of symptoms during a double-blind placebo-controlled rechallenge withgluten. However, the results from rechallenge studies
have shown that the majority of self-reported NCGWS
patients did not display gluten-specific symptoms [8].
The challenges in replicating the patients’ alleged sensitivity to gluten have raised questions whether othercomponents of gluten-/wheat-containing foods could bea culprit for problems in NCGWS patients [8, 9]. These
components include α-amylase/trypsin inhibitors, agglutinin, and importantly, fructans [10].
Fructans are major carbohydrate components ofplant foods and are composed of fructose oligomers
or polymers [11]. Although several types of fructans
exist, inulin-type fructans are the most studied due totheir use as prebiotics, serving as substrates for beneficial gut microbes. For instance, inulin-type fructans
can stimulate the colonic growth of health-promoting
Bifidobacteria [12]. However, while generally considered beneficial for gut health, fructan oligosaccharides

belong to the broader category of compounds known as
fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) [13], which have been
extensively studied for their ability to cause GI symptoms in IBS patients [14].
Depending on their degree of polymerization (DP),i.e. the length of the fructose chain, the inulin-type
fructans are often categorized as inulins with DPs
between 2 and 60 or as fructo-oligosaccharides (FOS)
which exclusively consist of low-DP molecules (DP2–8) [15]. Low-DP fructans are particularly enriched incertain plant foods, such as wheat, onions, and garlic[13]. Since fructans are nondigestible in the small intestine, they are subject to fermentation by gut microbes,
particularly in the colon. Following fructan intake, theensuing production of gases by gut microbes, such ashydrogen and methane, and short-chain fatty acids(SCFA), in addition to osmotic effects, may cause
abdominal discomfort, bloating, and altered bowel habits [16]. However, susceptibility to fructan-induced GI
discomforts may depend on the initial state of the gut
microbiota, as shown in children with IBS [17].
Based on the results from a randomised double-blind
placebo-controlled crossover study, we previously concluded that FOS-fructans were more likely to induce
IBS-like GI symptoms than gluten in individuals selfreporting sensitivity to gluten-/wheat-containing foods[18] and the involvement of FODMAP, such as FOSfructans, as a contributing factor in NCGWS has also
been implicated by others [19]. Recent advances into how
the gut microbiota can affect the gut–brain axis point topossible microbial involvement in food-induced GI problems in various populations including NCGWS [20–22].
To our knowledge, the effects of FOS-fructan supplementation on the gut microbiota have not previously been investigated in self-reported NCGWS
populations, apart from those investigating restrictions
in total FODMAPs [23]. The aim of the current work
was therefore to explore whether FOS-fructan and gluten affect the faecal microbiota and related variablesof gut health differently and to investigate the possible

Herfindal et al. BMC Medicine(2024) 22:358

Page 3 of 19

Fig. 1 Timeline and data collection during the randomised double-blind placebo-controlled crossover study. FOS fructo-oligosaccharides, GSRS-IBS
gastrointestinal symptom rating scale IBS version, IBS irritable bowel syndrome

involvement of the faecal variables in the GI symptom
response to FOS-fructans and gluten in NCGWS.

Methods
Participants

The present study is part of a clinical trial conducted from
October 2014 to May 2016 at Oslo University Hospital,
Rikshospitalet, Oslo, Norway. The study population has
been described previously by Skodje et al. [18]. Briefly,
to be eligible for participation, adults (age 18–80 years)
were required to have experienced relief of GI symptoms
when adhering to a gluten-free diet (GFD). The exclusion
criteria were CeD or WA, pregnancy or lactation, use of
immunosuppressive agents, inflammatory bowel disease(IBD) or other gastrointestinal comorbidities, significant infection, childbearing potential without adequate
contraception, residing a considerable distance from thestudy site, or allergy to nuts or sesame seeds. IBS was not
an exclusion criterion. All participants were required tofollow a strict GFD for at least 6 months before the startof the study and to continue with GFD throughout the
study period.
Study intervention and design

The study followed a randomised double-blind placebocontrolled crossover design, as previously described [18](Fig. 1). The diet challenges consisted of three different
50-g muesli bars with added gluten, FOS-fructans, or no
additives (placebo). The ingredients for the three different

muesli bars are presented in Additional file 1: Table S1.
The FOS-fructan muesli bar was added 2.1 g FOSfructans (Orafti; Oligofructose, Beneo, Tienen, Belgium)
while the gluten bar was added 5.7 g gluten (Vital Wheat
Gluten; Manildra Group, Gladesville, New South Wales,
Australia) which was equivalent to the FOS-fructan andgluten content of four slices of wheat bread, respectively.
All three muesli bars were composed to be approximately energy equivalent (~ 220 kcal per 50 g), with asimilar balance between protein, carbohydrates, fat, andfibre [18]. All participants underwent three consecutive
7-day challenge periods consuming one muesli bar daily
with gluten, FOS-fructans, or placebo. Each 7-day challenge period was followed by a washout period of at least
1 week or until GI symptoms returned to baseline levels.
To address potential carry-over effects in the crossoverstudy, participants were randomised to follow one of six
diet challenge sequences (GFP, GPF, FGP, FPG, PGF, or
PFG, where G = gluten, F = FOS-fructan, and P = placebo)(Fig. 1). The randomisation sequences were prepared bya statistician with no clinical involvement in the study(web-based service at http://​rando​mizat​ion.​com/, second generator, balanced permutations, accessed September 26, 2014). The block size was equal to the trial size.
The allocation sequence was concealed for participants
and the involved researchers throughout the study (seedetails in Skodje et al. [18]). The researchers who were
responsible for assigning participants to the challengeHerfindal et al. BMC Medicine(2024) 22:358

sequences were not involved in generating the sequence
randomisation.
GI symptom data were recorded and stool samples
were collected at baseline and at follow-up after each of
the three diet challenges (four stool samples from eachparticipant in total). Other baseline assessments included
recording medical background, gastroscopy, blood tests,
7-day food recordings, and evaluation of diet adherence
to the GFD by trained dietitians [18]. Adherence during
the study was not re-evaluated, but the participants wereasked to keep their diet consistent with the baseline dietthroughout the study.
Outcomes

The present study reports on the secondary outcomes ofa clinical trial that was designed to investigate the effects
of gluten and FOS-fructan intake on GI symptoms inindividuals with self-reported NCGWS [18]. The resultsof the primary outcome of the clinical trial (i.e. GI symptoms) have been reported previously [18]. The main aimsof the present study were to compare the effects of thegluten, FOS-fructan, and placebo challenges on faecal
microbiota (β-diversity, α-diversity, and bacterial abundances), SCFA, and human neutrophil gelatinase-associated lipocalin, also known as lipocalin 2 (NGAL/LCN2).
Specifically, we investigated whether the diet challenges
induced different changes in the faecal outcomes. Moreover, we investigated whether baseline levels and changes
in the faecal outcomes were associated with GI symptom
responses following the different diet challenges.
Gastrointestinal symptoms

As previously described [18], GI symptoms were measured using the IBS version of the Gastrointestinal Symptom Rating Scale (GSRS-IBS), a questionnaire developed
for self-evaluation of GI symptoms in patients with IBS[24]. The questionnaire consists of 13 items that cover
symptoms related to satiety (two items), abdominal pain
(two items), diarrhoea (four items), constipation (twoitems), and bloating (three items). All 13 items have a
Likert scale ranging from 1 (no discomfort at all) to 7
(very severe discomfort). Thus, the total GSRS-IBS score
ranges from 13 to 91. GSRS-IBS scores were retrospectively recorded to reflect the last 7 days.
Collection and preparation of stool samples

The collection and preparation of stool samples forthe different laboratory analyses were carried out asdescribed elsewhere [25]. Briefly, the stool samples
were collected in 13-mL tubes by the study participantsin their own home and stored in their freezers. On the
day of the clinic visits, the samples were transferred to
Oslo University Hospital under cold conditions (~4 °C),

Page 4 of 19

followed by storage at −80 °C. During transfer between
collaborating institutions, the samples were kept on dry
ice. Semi-frozen stool samples were divided into smalleraliquots which were subsequently weighed and logged.
Aliquots designated for DNA extraction (for assessmentof microbiota composition) were added Stool Transportand Recovery Buffer (cat. no. 03335208001, Roche) andvortexed. All aliquots were stored at −80 °C until further
processing.
Assessment of faecal microbiota composition

Gene sequencing of 16S rRNA has been described indetail previously [25]. A brief description follows. The faecal aliquots in Stool Transport and Recovery Buffer were
homogenized and subjected to mechanical lysis using the
FastPrep 96 (MP BioMedicals). Supernatants from processed samples were treated with protease, and DNA wasextracted using the Mag Midi LGC kit (cat. No. NAP40420,
LGC Genomics) on the KingFisher Flex DNA extraction
robot (Thermo Fisher Scientific). 16S rRNA genes (V3–V4
region) were amplified by PCR (prokaryote-targeting primers: forward 5’-CCT​ACG​
GGRBGCASCAG-3’, reverse
5’-GGA​CTA​CYVGGG​TAT​CTAAT-3’ [26]). After purification of amplicon PCR products using AMPure XP(Beckman-Coulter), PCR with index primers modified with
Illumina adapters (Additional file 1: Table S2) [27] was performed. The resulting products were quantified, normalized, and pooled. After being purified using AMPure XP
and diluted to 6 pM, the pooled library was sequenced with
the MiSeq Reagent Kit V3 (Illumina, cat. No. MS-102–
3003, Illumina) on the Illumina MiSeq. PhiX (20%, Illumina, cat. No. FC-110–3001) served as internal control.
Forward and reverse paired-end reads (300 bp) wereassembled, split into their respective samples, and quality-filtered using Quantitative Insights Into Microbial
Ecology (QIIME) [28]. The resulting dataset included in
total 7,790,257 sequences from the 227 faecal samples,ranging from 12,420 to 115,287 sequences per sample.
The mean (SD) sequencing depth was 34,318 (14,255)
sequences per sample. The closed-reference USEARCH
algorithm (version 8) [29, 30] was used against the SILVA
database (release 128) [31] to cluster the sequences into
taxonomically assigned operational taxonomic units(OTUs) with ≥ 97% identity. The OTU counts for eachsample were normalized by down-sampling (rarefying)
to an even library size of 10,000 sequences per sample
in QIIME (core_diversity_analyses.py). In total, 1096
OTUs were identified in the normalized dataset. The
OTUs were taxonomically binned into 13 phylum (p_)-,25 class (c_)-, 41 order (o_)-, 68 family (f_)-, and 237
genus (g_)-level taxa when excluding OTUs with no taxonomic assignment below kingdom level (i.e. OTUs classified as ‘unassigned’ or ‘unknown bacteria’). The taxon

Herfindal et al. BMC Medicine(2024) 22:358

abundances are presented as relative abundances (%)
where the lowest detectable abundance was 0.01% (1 outof 10,000 sequences).
Bacterial between-sample diversities (β-diversity)and within-sample diversities (α-diversity) were calculated based on the normalized OTU table(s) using thedefault QIIME script (core_diversity_analyses.py). Three
measures of β-diversity (binary Jaccard, Bray–Curtis,
and weighted UniFrac) and five indices of α-diversity(observed number of OTUs, Chao1, Shannon–Wiener,
Simpson, and phylogenetic diversity [PD] whole tree)
were calculated. Rarefaction curves for the α-diversity
indices are shown in Additional file 1: Fig. S1. While
β-diversity aims to quantify the overall dissimilarity (‘distance’) between two bacterial communities, α-diversity
describes the bacterial diversity within a given community. A higher value indicates less similarity and greater
diversity, respectively. The different α-diversity indices
are meant to capture the species richness (observed number of OTUs, Chao1), the combination of richness andevenness (Shannon–Wiener, Simpson), and phylogenetic
diversity (PD whole tree). Ordination of β-diversities
was performed using nonmetric multidimensional scaling. Additional details regarding the α- and β-diversity
analyses are provided in Additional file 1: Supplementary
method description [32, 33].
Faecal short‑chain fatty acid measurements

The method for quantification of faecal SCFA has been
described elsewhere [25]. Briefly, faecal aliquots were
homogenized in distilled water containing internal
standard (2-ethylbutyric acid) and sulfuric acid. The
homogenates were vacuum distilled [34, 35] and formic
acid was added. The distillates were analysed with gas
chromatography (Agilent 6850, Agilent) using an HPFFAP WAX capillary column (part No. 19091F-112E,
serial number USE400345H, Agilent J&W GC columns;
Agilent). Concentrations of acetic, propionic, butyric,iso-butyric, valeric, iso-valeric, caproic, and iso-caproic
acid were determined using internal standardization
employing flame ionization detection. All SCFA results
were adjusted for faecal wet weight and are expressed as
mmol/kg faeces and proportional levels (% of total SCFA
concentration). The SCFA caproic and iso-caproic acid
were not included in the statistical analyses due to low
detectability.
Faecal neutrophil gelatinase‑associated lipocalinmeasurements

Faecal NGAL/LCN2 was quantified by enzyme-linked
immunosorbent assay (ELISA) as described elsewhere [25].
Briefly, faecal aliquots were added Dulbecco’s phosphatebuffered saline (L0615, Biowest) with Tween20 (P1379,

Page 5 of 19

Sigma-Aldrich) and vigorously mixed using an MM2 mixer
mill (Retsch), followed by centrifugation. The supernatants
were diluted 1:50 in Dulbecco’s phosphate-buffered saline.
ELISA was performed using the Human lipocalin-2/NGAL
Duoset ELISA (DY1757, R&D Systems). Samples were analysed in duplicate. The results were adjusted for faecal wet
weight and are expressed as ng/g faeces.
Statistics

Sample size estimation was based on paired t-test of differences in the GSRS-IBS total score (the primary outcome of the clinical trial) between two challenges within
the same subject, as previously reported [18]. The total
level of significance was set to 0.05 (two-sided), and weused 0.02 for the pairwise comparisons (0.05/3, Bonferroni multiple comparison correction). With a power of
80% and anticipated drop-out of 30%, it was estimatedthat 66 participants were needed to detect a mean difference in the GSRS-IBS total score of 1.5 points (SD, 3.2).
All the statistical analyses were conducted in the R
programming environment (version 4.2.3) [33]. Additional details regarding the statistical analyses, including
specific use of packages and functions in R, can be foundin Additional file 1: Supplementary method description[33, 36–43]. A P value < 0.05 was considered statisticallysignificant. The Benjamini–Hochberg (BH) method wasin some cases (specified in the text) used to control thefalse discovery rate (FDR level 5%).
The responses to the gluten, FOS-fructan, and placebo challenges were evaluated for the faecal outcomes(α-diversity, taxa abundances, SCFA, and NGAL/LCN2)
using linear mixed models in which change values wereused as the response variable. The change values foreach of the three diet challenge periods were calculated
for each participant by subtracting the baseline value
from the follow-up value (after gluten, FOS-fructan, or
placebo challenge). Hence, each participant had threechange values, one for each diet challenge. Baseline values (continuous), period (categorical; period 1, 2, 3),
sequence (categorical; GFP, GPF, FGP, FPG, PGF, PFG),
and diet challenge (categorical; gluten, FOS-fructan, placebo) were used as fixed effects. A random intercept forparticipant was included. Interactions between the fixed
effects were not included in the models. Significant differences in the mean change across the diet challenges(P < 0.05) were followed by post hoc pairwise comparisons of the three diet challenges (FOS-fructan vs. gluten,
FOS-fructan vs. placebo, and gluten vs. placebo). The
three P values from pairwise comparisons for each faecal response were adjusted using the Bonferroni method.
For each faecal outcome, only participants with available
data from baseline and at least one of the challenge periods were included in the linear mixed model analysis. As

Herfindal et al. BMC Medicine(2024) 22:358

IBS status, age, gender, duration of gluten-free diet, bodymass index, HLA-DQ status nor thyroid disease had noeffect on the GI symptom response in the linear mixed
model [18], we did not include these variables as explanatory variables in the analyses in the current manuscript.
Furthermore, since positive wheat-specific IgE levels
were an exclusion criterion in the study, adjustment forwheat allergy status was not applicable.
Differences in the overall bacterial communities
between diet challenges based on the β-diversity measures were assessed using global permutation-based multivariate analysis of variance (PERMANOVA).
Spearman’s rank correlation coefficients (Rho) and corresponding P values were calculated to assess the associations between the changes in GI symptoms (GSRS-IBS
scores) and faecal outcomes (taxa abundances,α-diversity, SCFA, and NGAL/LCN2). For the faecal outcomes, both change and baseline values were used in theanalyses. Rho values between 0 and (−) 0.3 are describedas negligible, between (−) 0.3 and (−) 0.5 as low, between(−) 0.5 and (−) 0.7 as moderate, between (−) 0.7 and (−)
0.9 as high, and between (−) 0.9 and (−) 1 as very high[44]. Rho values for the correlations between GI symptoms and the faecal outcomes were visualized in correlograms with hierarchical clustering (Euclidean distances,complete linkage).
For statistical procedures involving the relative abundances of bacterial taxa, only taxa with a certain averageabundance and detectability were included. Specifically,
when performing linear mixed model analysis and correlation analysis using changes in relative abundances,a taxon was excluded if the median relative abundance
was less than 0.1% for at least one time point (baseline
or after a challenge) or if the taxon was detected in lessthan 50% of the samples from all time points (baseline
and after each challenge). When performing correlation
analysis using baseline abundances only, a taxon was
excluded if it was detected in less than 50% of the baseline samples. Furthermore, in cases where a taxon at agiven taxonomic level (e.g. family) included only one
lower-level taxon (e.g. family Bacteroidaceae and genus
Bacteroides), the results for the higher-level taxon are
not presented. Using the described filtering, 81 taxa were
included in the analyses involving change values, and
123 taxa were included in the analyses involving baseline
values only. The 0.1% abundance limit was considered asuitable threshold to filter the taxa for statistical analyses, since 0.1% in our dataset equals a read count of 10(10 out of 10,000 reads equals 0.1%). The 50% presence
limits were used to exclude taxa that were present in few
samples to assure that the data was more suitable for the
linear mixed model and correlation analyses.

Page 6 of 19

Results
Participants, data collection, and baseline characteristics

The study population was described in detail by Skodje
et al. [18]. A graphical representation of participant/sample inclusion and exclusion can be found in Fig. 2. Initially,
232 individuals were subjected to eligibility assessment,
164 of whom were deemed ineligible either because they
did not meet the specified inclusion criteria or because
they fulfilled at least one exclusion criterion (see details
in Fig. 2). Consequently, the final cohort comprised 68
eligible participants who were randomised to the 6 dietchallenge sequences (11 to each challenge sequence, in
addition to 2 surplus subjects). Subsequently, nine participants were excluded: two due to redundancy in participation, one due to loss to follow-up, three due to withdrawal,
and three due to protocol violations. This culminated in a
subset of 59 participants for whom both GSRS-IBS dataand stool samples were available (challenge sequence
GFP: n = 10, GPF: n = 9, FGP: n = 10, FPG: n = 9, PGF:
n = 11, PFG: n = 10). However, stool samples/faecal outcomes were not obtained from all participants at every
designated time point. Specifically, the baseline stool sample was missing for one participant (sequence order PGF),
the samples for both gluten and placebo challenges were
missing for two participants (challenge sequence FGP and
FPG), one participant was missing the gluten challenge
sample (challenge sequence GPF), and one participant
was missing the samples from all diet challenges (challenge sequence FPG). Additionally, for the SCFA analysis,
the amount of stool material obtained from baseline wasinsufficient for two participants (challenge sequence GFP
and FGP). Finally, the NGAL/LCN2 results from the FOSfructan and placebo challenges for one participant were
excluded due to technical issues in the laboratory (challenge sequence FGP). Thus, in cases where the faecal outcome was missing from baseline, the participant had to be
excluded from all the statistical analyses, and participants
had to be excluded from the statistical analyses involving
changes in faecal outcomes in cases where outcomes were
missing from all diet challenges (Fig. 2).
Table 1 provides an overview of the baseline characteristics pertaining to the 58 participants whose faecal
outcomes were included in the subsequent statisticalanalyses. The demographic was dominated by females(90%), predominantly within the age range of 35 to
55 years, with a body mass index distribution primarily
categorized as normal or overweight. Prior to the intervention, participants had embraced a GFD for an average duration of approximately 2 years, driven by theirexperiences of GI symptoms such as bloating, diarrhoea,
constipation, and/or nausea while on a gluten-containing

Herfindal et al. BMC Medicine(2024) 22:358

Page 7 of 19

Table 1 Participant characteristics at baseline (n = 58)
Characteristic

Value

Female/male

52/6 (90/10)

Age (years)

44 ± 12

BMI (kg/m2)

25 ± 4

BMI category
Underweight (< 18.5)

1 (2)Normal weight (18.5–24.9)

31 (53)

Overweight (25.0–29.9)

20 (35)

Obese (> 30.0)

6 (10)

Symptoms before GFD
Gastrointestinal symptoms

58 (100)

Extraintestinal symptoms

42 (75)b

Duration on GFD (months)

22 [10, 49]

IBS by Rome III

17 (30)c

Allergy/intolerance

14 (25)cOther food exclusions

37 (64)

Study gastroscopy

47 (81)Marsh 042 (89)Marsh ­1a

5 (11)

GSRS-IBS total score30 [21, 37]

Total FODMAP intake (g/day)

10 [5, ­15]c

Total fructan intake (g/day)

2 [1, ­3]cValues are presented as the means ± SDs for normally distributed variables,
medians [25th,75th percentiles] for nonnormally distributed variables, or n (%)
for categorical variables. Additional baseline information has been reported by
Skodje et al. [18, 45]
BMI Body mass index, GFD Gluten-free diet, IBS Irritable bowel syndrome, GSRSIBS Gastrointestinal symptom rating scale IBS version, FODMAP Fermentable
oligosaccharides, disaccharides, monosaccharides, and polyolsaFig. 2 Flow chart of the population selection process and overview
of analysed samples/data. IBD Inflammatory bowel disease, NGAL
Human neutrophil gelatinase-associated lipocalin, SCFA Short-chain
fatty acids, WA Wheat allergy

diet. A notable majority (75%) had also experienced
extraintestinal symptoms, including manifestations suchas joint and muscle pain, and headaches, prior to theiradoption of GFD. Furthermore, it should be acknowledged that the participants’ baseline state was not devoidof GI symptoms, as evidenced by the average GSRS-IBS
total score of 30. Nevertheless, only 30% of the participants were formally diagnosed with IBS as per the Rome

> 25 intraepithelial lymphocytes per 100 epithelial cellsbn = 56 (two missing)cn = 57 (one missing)

III criteria. It is also worth noting that the average fructan
intake at baseline was 2 g per day, a quantity that doubled
during the subsequent FOS-fructan challenge period.
Additional baseline information has been reported by
Skodje et al. [18, 45]. None of the participants experienced severe adverse effects from the diet challenges.
Mean (SD) duration of the first and second washout periods were 13 (8.3) days and 16 (13) days, respectively.
No differences in overall faecal microbiota profiles(β‑diversity) across diet challenges

To evaluate potential differences in the overall bacterial
communities after the gluten, FOS-fructan, and placebo
challenges, we constructed ordination plots utilizing
nonmetric multidimensional scaling based on three distinct measures of between-sample diversity (β-diversity):binary Jaccard, Bray–Curtis, and weighted UniFrac.
The three β-diversity measures were selected based on
their capacity to capture various types of dissimilarities

Herfindal et al. BMC Medicine(2024) 22:358

Page 8 of 19

α-diversity across the gluten, FOS-fructan, and placebo
challenges were found for any of the α-diversity indices(P ≥ 0.11, Additional file 1: Table S3). Summary statistics
for the five α-diversity indices at baseline and after eachdiet challenge are given in Additional file 1: Table S4.
Differences in faecal bacterial abundances across dietchallenges

Fig. 3 Ordination plots from nonmetric multidimensional
scaling (NMDS) of Bray–Curtis distances. Each symbol represents
one follow-up sample from one participant after a given 7-day
challenge (gluten, FOS-fructan, or placebo), in total 169 samples.
Stress values indicate the goodness-of-fit of the NMDS. FOS
fructo-oligosaccharides

between bacterial community structures, including presence/absence, abundance, and phylogenetic relationships. However, regardless of β-diversity measures, visualinspection did not reveal any evident clustering basedon the diet challenges (Bray–Curtis in Fig. 3, binary Jaccard and weighted UniFrac in Additional file 1: Fig. S2B,
Fig. S3B), as confirmed by PERMANOVA (P ≥ 0.99). Thishigh degree of overlap of samples collected following thegluten, FOS-fructan, and placebo challenges suggests thatthe diet challenges involving gluten and FOS-fructans
did not give rise to substantial and discernible systematic alterations in the faecal microbiota compared to placebo. To corroborate this observation, we extended ouranalysis by constructing ordination plots encompassing
both baseline and challenge follow-up samples, as wellas pairwise comparisons. Notably, these additional plots(Additional file 1: Fig. S2, S3, and S4) also failed to reveal
distinctive clustering patterns among the samples, asconfirmed by PERMANOVA (P ≥ 0.92).
No differences in faecal bacterial α‑diversity across dietchallenges

To assess the potential impact of the gluten, FOSfructan, and placebo challenges on faecal bacterial richness and evenness, we employed five distinct indices ofwithin-sample diversity (α-diversity): observed numberof OTUs, Chao1, Shannon–Wiener, Simpson, and PD
whole tree. However, through linear mixed model analyses, no significant differences in the mean change in

Linear mixed model analyses were performed to assess
whether the gluten, FOS-fructan, and placebo challenges
induced different changes in faecal bacterial abundances(%) at various taxonomic levels (phylum, class, order,family, and genus). Of the 81 taxa included in the analysis(see method Sect. ‘Statistics’), significant differences in
the mean change across the diet challenges were foundfor 12 taxa (Table 2). The significant effects of diet challenge were followed by pairwise comparisons (i.e. FOSfructan vs. gluten, FOS-fructan vs. placebo, and gluten
vs. placebo) while applying Bonferroni corrections for
multiple testing (Table 2).
Following the FOS-fructan challenge, the mean change
for four taxa belonging to the Clostridiales order tended
to be different or was significantly different comparedto the gluten and placebo challenges. These genera
included Fusicatenibacter, Eubacterium coprostanoligenes
group (E. coprostanoligenes), Anaerotruncus, and unknown
Ruminococcaceae genera (Table 2). While the abundanceof Fusicatenibacter increased with the FOS-fructan challenge, the abundances of the genera E. coprostanoligenes,
Anaerotruncus, and unknown Ruminococcaceae decreased.
For these four taxa, no differences in the mean change were
found between the gluten and placebo challenges.
Following the gluten challenge, the mean changes
for four taxa were significantly different from the meanchanges for both the FOS-fructan and placebo challenges: the genera Eubacterium xylanophilum group,
Ruminococcus torques group, and Bilophila, as well as
the family Desulfovibrionaceae (primarily driven by thegenus Bilophila) (Table 2). The differences in the meanchange in the Eubacterium xylanophilum group between
the diet challenges were driven by increase in abundancewith the gluten challenge. For the Ruminococcus torques
group and Bilophila/Desulfovibrionaceae, the differencesin mean change were due to increased and decreased
abundance following the gluten and FOS-fructan/placebo challenges, respectively.
For the Clostridiales-families Christensenellaceae andFamily XIII, along with the genera Ruminococcaceae
NK4A214 group and Ruminococcaceae UCG-002, thechanges from baseline to follow-up tended to be or weresignificantly different between the gluten (increased
abundance) and FOS-fructan challenge (decreased abundance) (Table 2). However, no significant differences wereHerfindal et al. BMC Medicine(2024) 22:358

Page 9 of 19

Table 2 Changes in faecal bacterial abundances following the 7-day gluten, FOS-fructan, and placebo challenges (n = 57)
GlutenFOS-fructanPlacebo

Effect ofF vs. G
challenge

F vs. P

G vs. PEMM (CI)

EMM (CI)

EMM (CI)P ­valuea

P­valuebP­valueb

g_Fusicatenibacter

0.044 (−0.048, 0.14)

0.16 (0.072, 0.25)

0.029 (−0.063, 0.12)

0.02*

0.07

0.03*

> 0.99g_Eubacterium coprostanoligenes group−0.014 (−0.085, 0.058)−0.11 (−0.18, −0.039)−0.032 (−0.10, 0.039)

0.03*

0.04*

0.13

> 0.99P­valuebg_Anaerotruncus

0.020 (−0.028, 0.069)−0.047 (−0.094, −0.000)

0.030 (−0.018, 0.077)

0.01*

0.05*

0.02*

> 0.99g_Unknown Ruminococcaceae

0.025 (−0.10, 0.15)−0.16 (−0.28, −0.035)

0.037 (−0.091, 0.16)

0.02*

0.05

0.03*

> 0.99g_Eubacterium xylanophilum
group0.100 (0.037, 0.16)−0.033 (−0.095, 0.028)

0.000 (−0.062, 0.063)< 0.001*< 0.001* > 0.99

0.02*

g_Ruminococcus torques group0.072 (−0.003, 0.15)−0.063 (−0.14, 0.010)−0.067 (−0.14, 0.007)

0.005*

0.02*> 0.990.01*

f_Desulfovibrionaceae

0.025 (−0.018, 0.068)−0.037 (−0.078, 0.005)−0.040 (−0.082, 0.003)

0.01*

0.04*

> 0.99

0.03*
0.04*

g_Bilophila

0.017 (−0.024, 0.058)−0.043 (−0.084, −0.003)−0.035 (−0.076, 0.006)

0.009*

0.01*> 0.99f_Christensenellaceae

0.044 (−0.053, 0.14)−0.074 (−0.17, 0.021)

0.018 (−0.078, 0.11)

0.04*

0.05

0.19

> 0.99f_Family XIII

0.008 (−0.023, 0.038)−0.043 (−0.073, −0.013)−0.024 (−0.054, 0.006)

0.02*

0.02*

0.90

0.26

g_Ruminococcaceae NK4A214
group

0.061 (−0.025, 0.15)−0.061 (−0.14, 0.023)−0.007 (−0.092, 0.078)

0.04*

0.03*

0.73

0.45

g_Ruminococcaceae UCG-002

0.068 (−0.017, 0.15)−0.050 (−0.13,0.032)

0.015 (−0.069, 0.099)

0.05*

0.04*

0.50

0.80

Only taxa with significant differences across diet challenges are included in the table. Changes in bacterial relative abundances from baseline to diet challenge
follow-ups are presented as estimated marginal means (EMM) (95% confidence intervals (CI)) from linear mixed models with change values as the response variable
and baseline values, period, sequence, and diet challenge as explanatory variables. The change values for each diet challenge were calculated for each participant
by subtracting the baseline value from the follow-up value (i.e. positive value: increased, negative value: decreased). Follow-up and baseline values were sqrttransformed, i.e. a change value is the sqrt-transformed follow-up value minus the sqrt-transformed baseline value for a given participant/challenge. Among the 57
participants included in the analyses, two participants had abundance data only from the FOS-fructan challenge, and one participant was missing abundance datafrom the gluten challenge
FOS Fructo-oligosaccharides, F FOS-fructan, G Gluten, P Placeboa

P value for the main effect of diet challenge from linear the mixed model (not adjusted for the total number of taxa subjected to mixed model analysis)b

Bonferroni-adjusted P values from pairwise comparisons between diet challenges*

P < 0.05

observed between the placebo and gluten challenges or
between the placebo and FOS-fructan challenges forthese four taxa.
In addition to the 12 taxa described above, 22 taxa
displayed notable changes from baseline to follow-up
for at least one of the three diet challenges but did not
significantly differ across challenges (Additional file 1:
Table S5). These included increased abundance of thegenera Bifidobacterium, Anaerostipes, Faecalibacterium,and Sutterella, and decreased abundance of Subdoligranulum following the FOS-fructan challenge. For the gluten challenge, the abundances of the genera Streptococcus
and Phascolarctobacterium were found to be decreased
and increased, respectively.
When examining the average (median) relative abundances at the phylum- and family levels across all samples, the phyla Firmicutes (60%), Bacteroidetes (34%),
Proteobacteria (2.0%), and Actinobacteria (1.3%) exhibited the highest relative abundances, along with thefamilies Lachnospiraceae (28%), Ruminococcaceae (27%),
Bacteroidaceae (21%), and Rikenellaceae (4.1%). However,substantial variations were observed among the samples,as shown by the abundance distributions in Fig. 4. For
instance, the ratio of Firmicutes to Bacteroidetes (%:%)
ranged from 35:60 to 91:4, and the abundances of Proteobacteria and Actinobacteria ranged from < 0.1% to 26%
and 9%, respectively. The variation among samples at thegenus level is shown in Additional file 1: Fig. S5. Summary statistics for bacterial relative abundances at baseline and after each diet challenge are given in Additional
file 1: Table S6.
It should also be noted that if the statistical tests of themain effects of diet challenge were to be adjusted for thenumber of taxa subjected to linear mixed model analysis (81 taxa, see method Sect. ‘Statistics’) using the BH
method for multiple adjustments, the 12 taxa in Table 2
would not attain significance. Furthermore, to check
whether the 50% presence and 0.1% abundance limit
would lead to the exclusion of important taxa influenced
by the dietary challenges (see method Sect. ‘Statistics’),
we also performed a filtration based on a 25% presence
limit and a 0.05% abundance limit. Using these limits, 11
additional taxa were included in the mixed model analysis (92 taxa in total instead of the original 81). However,Herfindal et al. BMC Medicine(2024) 22:358

Page 10 of 19

Fig. 4 Faecal bacterial abundances (%) at baseline and following the 7-day gluten, FOS-fructan, and placebo challenges. A Bacterial phyla (alldetected) and B abundant families. Only participants with 16S sequencing data from all four time points are included in the figure (n = 54). One
bar represents one participant at a given time point. Participant bars for baseline and after each challenge are ordered according to the abundanceof Firmicutes (in A) and Lachnospiraceae (in B) at baseline. Taxa are ordered according to mean overall abundance. Families with a mean overall
abundance ≤ 1% are displayed as ‘Others’. FOS fructo-oligosaccharides

no differences across diet challenges were found for anyof these 11 additional taxa (data not shown).
No differences in faecal short‑chain fatty acids across dietchallenges

Faecal SCFA were examined to discern potential differential effects of the gluten, FOS-fructan, and placebo
challenges on bacterial fermentation. Utilizing linear
mixed model analyses, no differences in the mean change
in total SCFA concentration (mmol/kg faeces) or in
the concentrations or proportional levels (% of total) ofindividual SCFA (acetic, propionic, butyric, iso-butyric,valeric, and iso-valeric) were found across the diet challenges (Table 3). However, we observed that the faecal
SCFA concentrations generally increased from baseline
to follow-ups, regardless of diet challenge (see the positive estimated marginal means in Table 3). Summary statistics for SCFA at baseline and after each diet challenge
are given in Additional file 1: Table S7.

No differences in faecal neutrophil gelatinase‑associatedlipocalin across diet challenges

Faecal NGAL/LCN2 concentrations (ng/g faeces) were
quantified to explore potential changes in low-grade
intestinal inflammation induced by the gluten, FOSfructan, and placebo challenges. Using linear mixed
model analysis, the estimated marginal mean change(95% confidence interval) for the diet challenges were17 (−52, 87), 31 (−37, 99), and 25 (−45, 95), respectively,
with no significant differences across challenges (P = 0.96,
n = 57). Among the 57 participants included in the linear
mixed model analysis, two participants had only NGAL/LCN2 data from the FOS-fructan challenge, one participant had missing NGAL/LCN2 data from the gluten
challenge, and one participant had only NGAL/LCN2
data from the gluten challenge. Summary statistics for
NGAL/LCN2 at baseline and after each diet challenge are
given in Additional file 1: Table S8.

Herfindal et al. BMC Medicine(2024) 22:358

Page 11 of 19

Table 3 Changes in faecal SCFA following the 7-day gluten, FOS-fructan, and placebo challenges (n = 55)
GlutenFOS-fructanPlacebo

Effect ofchallenge

EMM (CI)

EMM (CI)

EMM (CI)P ­valuea

Total SCFA, conc

5.5 (−0.3, 11)

6.0 (0.4, 12)

4.8 (−0.9, 11)0.95

Acetic acid, conc

2.6 (−0.9, 6.0)

3.0 (−0.4, 6.4)

1.9 (−1.5, 5.4)

0.86

Butyric acid, conc

1.2 (−0.4, 2.7)

1.7 (0.2, 3.2)

1.3 (−0.2, 2.8)

0.86

Propionic acid, conc

1.4 (0.3, 2.5)

1.1 (0.1, 2.2)

1.3 (0.2, 2.4)

0.93

Valeric acid, conc

0.2 (0.0, 0.3)

0.1 (−0.1, 0.2)

0.1 (−0.0, 0.3)

0.67

Iso-valeric acid, conc

0.1 (−0.1, 0.4)

0.0 (−0.2, 0.3)

0.1 (−0.2, 0.4)

0.76

Iso-butyric acid, conc

0.1 (−0.1, 0.2)

0.0 (−0.1, 0.2)

0.1 (−0.1, 0.2)

0.94

Acetic acid, %−2.0 (−3.8, −0.3)−1.6 (−3.3, 0.1)

−2.2 (−4.0, −0.5)

0.84

Butyric acid, %1.1 (−0.1, 2.3)

1.7 (0.6, 2.9)

1.6 (0.4, 2.8)

0.69

Propionic acid, %0.8 (−0.0, 1.7)

0.4 (−0.4, 1.2)

0.8 (−0.0, 1.6)

0.57

Valeric acid, %0.2 (0.0, 0.4)

−0.0 (−0.2, 0.1)

0.1 (−0.1, 0.3)

0.09

Iso-valeric acid, %0.0 (−0.4, 0.4)

−0.3 (−0.7, 0.1)

−0.1 (−0.5, 0.3)

0.37

Iso-butyric acid, %−0.0 (−0.3, 0.2)

−0.2 (−0.4, 0.1)

−0.1 (−0.3, 0.1)

0.57

Changes in SCFA concentrations (‘conc’, mmol/kg faeces) and proportional levels (‘%’, of total SCFA concentration) from baseline to diet challenge follow-ups arepresented as estimated marginal means (EMM) (95% confidence intervals (CI)) from linear mixed models with change values as the response variable and baseline
values, period, sequence, and diet challenge as explanatory variables. The change values for each diet challenge were calculated for each participant by subtracting
the baseline value from the follow-up value (i.e. positive value: increased, negative value: decreased). Among the 55 participants included in the analyses, 2participants had SCFA data only from the FOS-fructan challenge, and 1 participant was missing SCFA data from the gluten challenge
FOS Fructo-oligosaccharides, SCFA Short-chain fatty acidsa
P value for the main effect of diet challenge from the linear mixed model. Since no significant differences were found across diet challenges (P ≥ 0.05), pairwise
comparisons between the three diet challenges were not performed

Associations between changes in gastrointestinal
symptoms and faecal outcomes

The principal investigations concerning the faecal outcomes showed that the diet challenges with FOS-fructans
and gluten induced different changes in certain bacterial
taxa, but that no differences were found for the remaining outcomes (α-diversity, SCFA, NGAL/LCN2). We
next examined whether the 12 bacterial taxa that weredifferentially affected by the diet challenges (see results
Sect. ‘Differences in faecal bacterial abundances acrossdiet challenges’ and Table 2) were associated with thechanges in the IBS-like GI symptoms (assessed via GSRSIBS total, pain, bloating, constipation, diarrhoea, andsatiety dimension scores). To examine these associations,
we employed Spearman’s rank correlation analysis, andthe results are graphically depicted in correlograms featuring hierarchical complete linkage clustering (Fig. 5).
As shown in Fig. 5, correlation coefficients for the different GSRS-IBS dimensions clustered mostly according todiet challenges, i.e. exacerbation of GI symptoms following the FOS-fructan and gluten challenges was associatedwith different types of changes in bacterial abundances.
However, few significant associations were found (notadjusted for multiple testing), and all were considerednegligible or low in strength (−0.36 < Rho < 0.32).
For the three taxa that increased and decreased following the gluten and FOS-fructan challenge, respectively(Christensenellaceae, Ruminococcaceae UCG-002, andRuminococcaceae NK4A214 group), the changes in bacterial abundance were not associated with increased
GI symptoms. There was also no association between
increased GI symptoms and the FOS-related decrease in
Anaerotruncus, unknown Ruminococcaceae genera, or
Clostridiales Family XIII, or for the FOS-fructan-related
increase in Fusicatenibacter. However, the decreased
abundance of the E. coprostanoligenes induced by the
FOS-fructan challenge was associated with increased GI
symptoms, significantly for the GSRS-IBS pain dimension.
For the Eubacterium xylanophilum group and Ruminococcus torques group, which both increased following the gluten challenge, increased abundances were associated withexacerbation in GI symptoms, however not significantly.
Correlograms depicting the correlations betweenchanges in GI symptoms and the remaining faecal outcomes (bacterial taxa not described above, α-diversity,
SCFA, and NGAL/LCN2) can be found in Additional
file 1: Fig. S6. Interestingly, increased faecal concentrations of SCFA were associated with less exacerbationof the GSRS-IBS constipation dimension following the
FOS-fructan challenge (significantly for total SCFA,
butyric-, and propionic acid). If adjusted for multiple
testing using the BH method, all correlations depicted in
Additional file 1: Fig. S6 were considered nonsignificant.

Herfindal et al. BMC Medicine(2024) 22:358

Page 12 of 19

Fig. 5 Correlations between changes in faecal outcomes and changes in GI symptoms. Correlograms illustrating Spearman’s rank correlation
coefficients (Rho) between changes (‘∆’) in gastrointestinal symptoms (GSRS-IBS total, pain, bloating, constipation, diarrhoea, and satiety dimension
scores) and changes in faecal taxa abundances following the 7-day gluten, FOS-fructan, and placebo challenges. The change values for each dietchallenge were calculated for each participant by subtracting the baseline value from the follow-up value. The colour scale indicates the strengthand direction of the correlations (–0.36 < Rho < 0.32). Significant correlations (not adjusted for multiple testing) are marked with asterisks (*P < 0.05,**P < 0.01). A positive correlation indicates that increased taxon abundances are associated with increased GSRS-IBS scores, while a negative
correlation indicates that reduced taxon abundances are associated with increased GSRS-IBS scores. Only the taxa that had changed differently
across the diet challenges (see ‘Results’ Sect. ‘Differences in faecal bacterial abundances across diet challenges’ and Table 2) were includedin the analysis. The arrows indicate the direction of change that was observed for the taxon following the different diet challenges, in accordancewith results shown in Table 2 (upwards arrow indicates increased abundance, downwards arrow indicates decreased abundance). FOS
fructo-oligosaccharides, GSRS-IBS gastrointestinal symptom rating scale IBS version, IBS irritable bowel syndrome

Associations between baseline gut microbiota
and inflammation status, and changes in gastrointestinal
symptoms

As previously reported by Skodje et al. [18], it was evident
that the participants in the study did not exhibit uniform
GI symptom responses to the diet challenges. This led us
to postulate that variations in the faecal microbiota andrelated variables at baseline might underlie the diversity
in symptom responses. To explore this proposition, we

employed Spearman’s rank correlation to evaluate potential associations between faecal outcomes at baseline (i.e.
bacterial abundances, bacterial α-diversity, SCFA, andNGAL/LCN2) and changes in IBS-like GI symptoms(assessed via GSRS-IBS pain, bloating, constipation, diarrhoea, and satiety scores) following the diet challenges.
The results of this analysis are visually represented using
correlograms featuring hierarchical clustering (Fig. 6). It
should be noted that only correlations between NGAL/Herfindal et al. BMC Medicine(2024) 22:358

LCN2 and GI symptoms remained significant if adjusted
for multiple testing using the BH method.
Complete linkage clustering of the correlation coefficients between baseline bacterial abundances andchanges in GI symptoms revealed that the coefficientswere organized into diet challenge clusters to a certain
extent (Fig. 6A, −0.37 < Rho < 0.39; negligible or low instrength). Notably, the changes in GSRS-IBS total, bloating, pain, and diarrhoea scores following the FOS-fructanand gluten challenges were found to be linked to varying
bacterial abundances at baseline. Following the FOSfructan challenge, low abundances of several taxa wereassociated with increased GI symptoms (negative associations). For instance, exacerbation of diarrhoea symptoms was associated with low abundances of the order
Clostridiales (including the genera Coprococcus1 andLachnospiraceae FCS020 group and the Ruminococcaceae
family) and the genera Dialister and Bilophila. In contrast to these negative associations between taxa and GI
response to the FOS-fructan challenge, high abundancesof the order Bacteroidales (including the abundant genus
Odoribacter) and the Lachnospiraceae NK4A136 groupwere associated with increased symptoms. For the GSRSIBS bloating dimension, which was of particular interest[18], negative associations were found only for the genera Ruminiclostridium9 and Coprococcus2 and positive
associations were found only for the genera Odoribacterand Lachnospiraceae NK4A136 group following the FOSfructan challenge. Baseline abundances of several taxa
were also associated with symptom exacerbation following the gluten challenge. Among others, these included
the family Coriobacteriaceae, Clostridiales-genus Eubacterium hallii group, genus Streptococcus, and Erysipelotrichaceae UCG-003, for which high abundances wereassociated with increased GI symptoms. The association
patterns described above were mainly present for the
specified challenge. See Fig. 6A for details.
When investigating the correlations between
α-diversities at baseline and the GI symptom responses

Page 13 of 19(Fig. 6B), we found that increased total and satiety symptoms following the FOS-fructan challenge were associated with low α-diversity, particularly for the indices
Shannon, Simpson, and observed number of OTUs.
However, the correlation coefficients did not cluster
according to diet challenge, and all correlations were negligible or low in strength (−0.32 < Rho < 0.12).
Only baseline concentrations of the low-abundant
SCFA valeric, iso-valeric-, and iso-butyric acid were
found to be associated with changes in GI symptoms following the gluten and FOS-fructan challenges (Fig. 6C).
For the FOS-fructan challenge, low concentrations ofvaleric acid and proportional levels of iso-valeric acid
were associated with exacerbated bloating and satiety
symptoms, respectively. Following the gluten challenge,
low concentrations of iso-butyric acid were associatedwith increased constipation. The correlation coefficients
did not cluster according to diet challenges, and all correlations were negligible in strength (−0.28 < Rho < 0.27).
No significant associations were found between baseline concentrations of faecal NGAL/LCN2 and symptom responses to the gluten and FOS-fructan challenges(Fig. 6D, −0.05 < Rho < 0.26; negligible or low in strength).
However, high NGAL/LCN2 concentrations at baseline
were significantly associated with increased GSRS-IBS
total, satiety, bloating, and pain scores following the placebo challenge (0.34 < Rho < 0.38; low in strength).

Discussion
While low FODMAP diets have been shown to reduce
GI problems in individuals with NCGWS [23, 46], thecausal mechanisms remain unclear. Potential explanations include changes in the gut microbiota due
to reduced bacterial fermentation and reduction inosmotic active components which may alleviate intestinal distention associated with bloating and abdominal pain [20–22]. The current work adds knowledge as
to how the gut microbiota community and biomarkers
of gut health respond to changes in the intake of one(See figure on next page.)
Fig. 6 Correlations between faecal outcomes at baseline and changes in GI symptoms. Correlograms illustrating Spearman’s rank correlation
coefficients (Rho) between changes (‘∆’) in gastrointestinal symptoms (GSRS-IBS total, pain, bloating, constipation, diarrhoea, and satiety dimension
scores) following the 7-day gluten, FOS-fructan, and placebo challenges and baseline (‘Bas’) faecal A taxon abundances (−0.37 < Rho < 0.39)
B α-diversity (−0.32 < Rho < 0.12), C SCFA (–0.28 < Rho < 0.27), and D NGAL/LCN2 (ng/g faeces; –0.05 < Rho < 0.38). The change values for each dietchallenge were calculated for each participant by subtracting the baseline value from the follow-up value. The colour scales indicate the strengthand direction of the correlations. Significant correlations (not adjusted for multiple testing) are marked with asterisk (*P < 0.05, **P < 0.01).
A positive correlation indicates that higher baseline values of the faecal outcome are associated with increased GSRS-IBS scores, while a negative
correlation indicates that lower baseline values of the faecal outcome are associated with increased GSRS-IBS scores. A A taxon was excluded
from the correlation analysis if it was detected in less than 50% of the baseline samples. Only taxa with significant correlations are shownin the figure (not adjusted for multiple testing). C Both SCFA concentrations (‘conc’, mmol/kg faeces) and proportional levels (‘%’, of total SCFA
concentration) were used in the analysis. FOS fructo-oligosaccharides, GSRS-IBS gastrointestinal symptom rating scale IBS version, IBS irritable bowel
syndrome, NGAL/LCN2 neutrophil gelatinase-associated lipocalin/lipocalin 2, SCFA short-chain fatty acids

Herfindal et al. BMC Medicine(2024) 22:358

Page 14 of 19

Fig. 6 (See legend on previous page.)

specific FODMAP, namely FOS-fructans, compared
to gluten and placebo. The effects of FOS-fructans
on the gut microbiota have not previously been studied in self-reported NCGWS. Although we previously
showed that the 7-day FOS-fructan challenge was more

likely to induce IBS-like symptoms than gluten in thecurrent randomised double-blind placebo-controlled
crossover trial [18], we found no prominent differential impacts of gluten versus FOS-fructan on the faecal
microbial diversity, SCFA, or NGAL/LCN2. However,Herfindal et al. BMC Medicine(2024) 22:358

some interesting differences across the diet challenges
were found for certain bacterial taxa, which could be
relevant to the GI problems experienced by the participants. Additionally, associations were found between
the baseline microbial composition and responses to the
FOS-fructan and gluten challenges, potentially influencing individual symptom responses.
No significant differences were detected in bacterial richness and diversity (α-diversity) or in overall bacterial community structure (β-diversity) between the diet challenges,
suggesting no prominent impact on the microbiota composition. This finding aligns with previous studies using
short-term FOS-fructan supplementation [17, 47], gluten
supplementation [48], or low/high gluten diets [49, 50].
However, the use of low-dose FOS-fructan supplementation in our study leaves the possibility of more pronounced
effects resulting from the use of higher doses or longer
intervention periods. Indeed, long-term intervention with
FOS-fructan supplementation increased the α-diversity inhealthy individuals [51]. Conversely, in a separate study,
2 weeks of supplementation with inulin and FOS-fructans
separately led to decreased α-diversity in healthy individuals [52].
Although most bacterial taxa did not exhibit differential effects from the diet challenge, certain changes wereobserved at the family and genus levels. With respect to
the FOS-fructan challenge, the most evident changes
were found for the Firmicutes genera Fusicatenibacterand E. coprostanoligenes, which increased and decreased,
respectively.
The increase in Fusicatenibacter following FOSfructan intake may be attributed to enhanced bacterial
growth due to FOS-fructan availability. Fusicatenibacter, a SCFA-producing genus [53, 54], has shown negative associations with diseases such as Parkinson’s disease[55], colon cancer [56], non-alcoholic fatty liver disease[57], Clostridioides difficile infection [58], and chronic
kidney disease [59]. Fusicatenibacter has also beenshown to decrease with age [60]. Although few studies
have explored its role in IBS symptoms, one small clinical trial (18 participants) noted increased Fusicatenibacter abundance alongside constipation relief after faecal
microbiota transplantation [61]. Conversely, increased
Fusicatenibacter abundance after a high FODMAP diet
was indirectly associated with increased abdominal pain,
possibly mediated by an increase in the bacterial metabolite 3-indolepropionic acid (IPA) [62, 63]. Additionally,in overweight young adults, Fusicatenibacter was associated with unhealthy eating patterns [64]. In our trial,
we observed weak, nonsignificant correlations between
Fusicatenibacter and GI symptoms post-FOS-fructanchallenge, but a significant negative association wasnoted with diarrhoea symptoms post-placebo challenge,

Page 15 of 19

suggesting a potential role in alleviating IBS-like
symptoms.
The decrease in E. coprostanoligenes after increasing
the intake of FOS-fructans aligns with a study in young
undergraduate students showing that a high-fibre dietdecreased the abundance of E. coprostanoligenes [65].
Furthermore, a recent study by Nordin et al. showeda lower abundance of these bacteria with a high FODMAP diet than with low FODMAP [62]. Nordin et al.
also reported that lower E. coprostanoligenes was weakly
associated with increased frequency of abdominal pain[62], which suggests a potential role for E. coprostanoligenes in mitigating IBS-like symptoms. In line with thesefindings, the present study also found a significant negative correlation between changes in pain symptoms andE. coprostanoligenes following the FOS-fructan challenge (i.e. reduced abundance associated with increased
symptoms). We also found convincing negative correlations between E. coprostanoligenes and several GI symptoms during placebo and gluten challenge. In contrast to
Nordin et al. and our findings, one study in children with
autism found E. coprostanoligenes to be positively associated with high GSRS scores [66]. Apart from its cholesterol-reducing abilities, little is known about the role ofE. coprostanoligenes in health in general [67].
The abundances of some bacteria, including thosein the Eubacterium xylanophilum group, increased in
response to gluten intake compared to the placebo and
FOS-fructan challenge, a novel finding with uncertain
implications. However, an experimental trial demonstrated increased Eubacterium xylanophilum groupabundance when culturing human faeces with wheatbran [68], suggesting a possible association with gluten intake. For the other bacteria affected by the gluten
challenge compared to FOS-fructan and placebo (Ruminococcus torques group, Desulfovibrionaceae, Bilophila),the impact of gluten was less clear. The minor effects ofgluten on the gut microbiota composition are consistent
with results from previous studies [48, 62].
No differences were found in faecal SCFA concentrations or proportional levels across the gluten, FOSfructan, and placebo challenges. The lack of difference
between the FOS-fructan and placebo challenge may
be due to the low dose and short duration of the FOSfructan supplementation, consistent with findings fromstudies using higher doses but similar durations [52, 69,70]. Longer durations of fructan supplementation havebeen associated with increased SCFA concentrations[71–73], although one study reported reduced levels [74].
It is worth noting that the studies showing increased
SCFA used a mix of FOS-fructans and inulin, unlike ourstudy which used only FOS-fructans. With gluten challenge, earlier studies have yielded mixed results on faecal

Herfindal et al. BMC Medicine(2024) 22:358

SCFA [49, 50, 62, 75]. Despite no significant differences
between the diet challenges, we observed a generalincrease in SCFA concentrations from baseline to followup for all challenges, possibly influenced by the fibre-containing ingredients in the muesli bars.
Changes in GSRS-IBS total, bloating, pain, and diarrhoea scores following the FOS-fructan and gluten challenges were associated with certain bacterial abundances
at baseline. This finding may explain the variable GI
symptom responses previously reported by Skodje et al.
[18]. Additionally, lower faecal concentrations of the
SCFA valeric and iso-valeric acid were associated withgreater increases in GI symptoms following the FOSfructan challenge, supporting the hypothesis of a potential link between baseline microbial function and the
response to FOS-fructans.
Since there are no available biomarkers for NCGWS[76], the response of faecal NGAL/LCN2 to the different diet challenges was investigated due to its potential
role in low-grade intestinal inflammation [77–79], which
could be relevant in NCGWS [80–82]. However, in
accordance with the previously reported plasma cytokine
results [83], no differences in faecal NGAL/LCN2 were
found across the diet challenges. Nevertheless, the significant correlations found between baseline NGAL/LCN2 concentrations and changes in several GI symptoms following the placebo challenge might indicate thatthe inflammatory state of the gut is important for fluctuations in GI symptoms in general (regardless of dietchallenge), consistent with the tendency for higher faecal NGAL/LCN2 concentrations in IBS patients than in
healthy controls [84].
A key strength of the current trial is the strong crossover design with a placebo-controlled diet challenge. To
address potential carry-over effects, different challengesequences were employed, and Skodje et al. also reported
no influence of sequence order on the GI symptoms [18].
However, one challenge in interpreting the results is thatthe participants’ background diets were not controlled for
FOS-fructans or other types of FODMAP. To improve theprotocol, a low FODMAP GFD could have served as the
background diet throughout the challenge and wash-out
periods, particularly since it previously was shown that
FODMAP intake varies significantly between individuals
with IBS [85]. Furthermore, because the dietary recordings from baseline were not re-evaluated during the otherstudy periods, we cannot rule out that changes in macro-/
micronutrients might have impacted the changes in the
microbiota. However, since the participants were asked tokeep their diet consistent with the baseline diet throughout the study, we have hopefully avoided dietary changes
to influence the microbiota results. Also, as the muesli
bars were not analysed for ‘background’ FOS-fructan/

Page 16 of 19

FODMAP content, we cannot rule out that there might
be a low level of FOS-fructans present in the placebo andgluten bar. However, as the ingredients used in the muesli
bars are reported to contain very low levels of FODMAPs,
we argue that these levels will not be clinically relevant
compared to the amount of FOS-fructans added to the
FOS-fructan bars. Additionally, it is possible that NCGWS
is too wide of an umbrella term, forcing different patient
cohorts under the same definition [8], potentially limiting
the generalizability of our results. This was indicated by theevident heterogeneity in the current study population withrespect to which challenge that had the highest GI symptom score [18]. Thus, this heterogeneity can have contributed to heterogeneous microbiota responses, making
it challenging to detect uniform effects on the gut microbiota following the different diet challenges. Finally, it is
crucial to recognize that the gut microbiota is a complex
community influenced by cohabitants and cross-feeding
dynamics [86], meaning that the total impact of a dietaryintervention on microbial metabolic outcomes depends on
individual variability within the microbial ecosystems.

Conclusions
In the first randomised double-blind placebo-controlled
crossover study examining the impact of FOS-fructanand gluten on the faecal microbiota in self-reported
NCGWS patients adhering to a gluten-free background
diet, we found no noteworthy differences in how the dietchallenges impacted the gut microbiota composition andrelated variables of gut health. However, both gluten and
FOS-fructans induced changes in certain bacteria. In
particular, a reduction in E. coprostanoligenes was mostprominently associated with an increase in GI symptoms,suggesting that attention should be given to these bacteria in future trials investigating the impact of dietary
treatments on GI symptoms. Additionally, while the baseline composition of the microbiota seems to influence thevariation in the individual GI symptom responses to the
FOS-fructan challenge, the symptoms experienced when
consuming FOS-fructans does not seem to be explainedby large changes in the microbiota. This study also provides evidence suggesting that the GI symptoms induced
by FOS-fructan are not driven by inflammation.
Abbreviations
BH	Benjamini–Hochberg
CeD	Celiac diseaseDP	Degree of polymerization
ELISA	Enzyme-linked immunosorbent assay
FDR	False discovery rate
FODMAP	
Fermentable oligosaccharides, disaccharides, monosaccharides, and polyols
FOS	Fructo-oligosaccharides
GFD	Gluten-free dietGI	Gastrointestinal

Herfindal et al. BMC Medicine(2024) 22:358

GSRS-IBS	Gastrointestinal symptom rating scale IBS version
IBD	Inflammatory bowel disease
IBS	Irritable bowel syndrome
IPA	3-Indolepropionic acid
NCGWS	Non-celiac gluten/wheat sensitivity
NGAL/LCN2	Human neutrophil gelatinase-associated lipocalin/lipocalin 2
NMDS	Nonmetric multidimensional scaling
OTU	Operational taxonomic unit
PD	Phylogenetic diversity
PERMANOVA	Permutation-based multivariate ANOVAQIIME	Quantitative insights into microbial ecology
SCFA	Short-chain fatty acids
WA	Wheat allergy

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03562-1.
Additional file 1: Table S1. Content of muesli bars used for the 7-day
gluten, FOS-fructan, and placebo challenges in g per 100 g. Table S2.
Primers modified with Illumina adapters used for index PCR duringlibrary preparation for gene sequencing of 16S rRNA. Table S3. Changes
in faecal α-diversity following the 7-day gluten, FOS-fructan, and placebo
challenges. Table S4. Summary statistics for faecal bacterial α-diversity atbaseline and after the 7-day FOS-fructan, gluten, and placebo challenges.
Table S5. Changes in faecal bacterial abundances following the 7-day gluten, FOS-fructan, and placebo challenges. Table S6. Summary statistics for
faecal bacterial abundancesof at baseline and after the 7-day FOS-fructan,
gluten, and placebo challenges. Table S7. Summary statistics for faecal
SCFA at baseline and after the 7-day FOS-fructan, gluten, and placebo
challenges. Table S8. Summary statistics for faecal NGAL/LCN2 concentrationsat baseline and after the 7-day FOS-fructan, gluten, and placebo
challenges. Fig. S1. Rarefaction curves. Fig. S2. Ordination plots from nonmetric multidimensional scaling of binary Jaccard β-diversity distances.
Fig. S3. Ordination plots from non-metric multidimensional scaling of
Bray–Curtis β-diversity distances. Fig. S4. Ordination plots from non-metricmultidimensional scaling of weighted UniFrac β-diversity distances. Fig.
S5. Faecal relative abundancesof bacterial genera at baseline and after the
7-day gluten, FOS-fructan, and placebo challenges. Fig. S6. Correlations
between changes in faecal outcomes and changes in GI symptoms.
Acknowledgements
We acknowledge Gunn Helen Malmstrøm and Jennifer T. Fiennes (Lovisenberg Diaconal Hospital) for performing the analysis of the SCFA, Kjersti
Langballe Rolfsen and Ingunn Hillestad Minelle (master students, Universityof Oslo) for contributing to the collection of stool samples, and Inga Leena
Angell and Ida Ormaasen (Norwegian University of Life Sciences) for the technical support. Finally, we would like to thank all the participants in the study.
Authors’ contributions
AMH conducted the research (microbiota laboratory analysis, performed
statistical analyses), interpreted the data, and wrote the paper. MN conducted
the research (supervised the microbiota laboratory analysis) and interpreted
the data. TEA conducted the research (NGAL laboratory analysis). GS designed
the research (conceptualization). JV conducted the research (responsible for
SCFA analysis). KR provided essential materials (facilitating the microbiota
analysis). MT conducted the research (statistical analysis). KEAL designed theresearch (conceptualization, funding acquisition). CH designed the research(conceptualization). SKB designed the research (conceptualization, funding
acquisition), conducted the research (supervision), analysed and interpreted
the data, wrote the paper, and had the primary responsibility for the finalcontent. All authors read, commented, and approved the final manuscript.
Funding
This work was supported by the Norwegian research fund for celiac disease(SKB). The funding sources had no role in the conceptualization, design, datacollection, analysis, decision to publish, or preparation of the manuscript.

Page 17 of 19

Availability of data and materials
The datasets analysed during the current study are not publicly available because
the participants did not give written consent for their data to be shared publicly
but may be available from the corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human patients were approvedby the Regional Committee for Medical and Health Research Ethics, South East
A on September 16 2014 (identification 2013/1237 REC South East A). Written
informed consent was obtained from all participants. The study was also
registered at clinicaltrials.gov with the registration number NCT02464150. The
manuscript was prepared according to the CONSORT statement (http://​www.​conso​rt-​state​ment.​org).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, P. O. Box 5003, N‑1432 Ås, Norway. 2 Healthy Life Centre,
Municipality of Nes, Norway. 3 Unger-Vetlesen Institute, Lovisenberg Diaconal
Hospital, Oslo, Norway. 4 Institute of Clinical Medicine, University of Oslo, Oslo,
Norway. 5 Department of Biostatistics, Faculty of Medicine, University of Oslo,
Oslo, Norway. 6 Disease Research Centre, Norwegian Coeliac, University of Oslo,
Oslo, Norway. 7 Department of Gastroenterology, Oslo University Hospital Rikshospitalet, Oslo, Norway. 8 Department of Nutrition, Institute of Basic Medical
Sciences, University of Oslo, Oslo, Norway.
Received: 15 May 2024 Accepted: 14 August 2024

References
1. Cooper BT, Holmes GKT, Ferguson R, Thompson RA, Allan RN, Cooke WT.
Gluten-sensitive diarrhea without evidence of celiac disease. Gastroenterology. 1980;79(5):801–6.
2. Cardenas-Torres FI, Cabrera-Chavez F, Figueroa-Salcido OG, Ontiveros
N. Non-celiac gluten sensitivity: an update. Medicina (Kaunas).
2021;57(6):526.
3. Henggeler JC, Veríssimo M, Ramos F. Non-coeliac gluten sensitivity: areview of the literature. Trends Food Sci Technol. 2017;66:84–92.
4. Catassi C, Elli L, Bonaz B, Bouma G, Carroccio A, Castillejo G, et al. Diagnosis of non-celiac gluten sensitivity (NCGS): the Salerno experts’ criteria.
Nutrients. 2015;7(6):4966–77.
5. Cianferoni A. Wheat allergy: diagnosis and management. J Asthma
Allergy. 2016;9:13–25.
6. Catassi C, Verdu EF, Bai JC, Lionetti E. Coeliac disease. Lancet.
2022;399(10344):2413–26.
7. Catassi C, Alaedini A, Bojarski C, Bonaz B, Bouma G, Carroccio A, et al. The
overlapping area of non-celiac gluten sensitivity (NCGS) and wheat-sensitive irritable bowel syndrome (IBS): an update. Nutrients. 2017;9(11):1268.
8. Molina-Infante J, Carroccio A. Suspected nonceliac gluten sensitivity confirmed in few patients after gluten challenge in double-blind, placebocontrolled trials. Clin Gastroenterol Hepatol. 2017;15(3):339–48.
9. Muir JG, Varney JE, Ajamian M, Gibson PR. Gluten-free and low-FODMAP
sourdoughs for patients with coeliac disease and irritable bowel syndrome: a clinical perspective. Int J Food Microbiol. 2019;290:237–46.
10. Mumolo MG, Rettura F, Melissari S, Costa F, Ricchiuti A, Ceccarelli L, et al. Is
gluten the only culprit for non-celiac gluten/wheat sensitivity? Nutrients.
2020;12(12):3785.
11. Shi Y, Si D, Zhang X, Chen D, Han Z. Plant fructans: recent advances inmetabolism, evolution aspects and applications for human health. Curr
Res Food Sci. 2023;7: 100595.

Herfindal et al. BMC Medicine(2024) 22:358

12. Meyer D, Stasse-Wolthuis M. The bifidogenic effect of inulin andoligofructose and its consequences for gut health. Eur J Clin Nutr.
2009;63(11):1277–89.
13. Barrett JS, Gibson PR. Clinical ramifications of malabsorption offructose and other short-chain carbohydrates. Pract Gastroenterol.
2007;31(8):51–65.
14. van Lanen AS, de Bree A, Greyling A. Efficacy of a low-FODMAP diet in
adult irritable bowel syndrome: a systematic review and meta-analysis.
Eur J Nutr. 2021;60(6):3505–22.
15. Roberfroid M, Gibson GR, Hoyles L, McCartney AL, Rastall R, RowlandI, et al. Prebiotic effects: metabolic and health benefits. Br J Nutr.
2010;104(Suppl 2):S1–63.
16. Staudacher HM, Whelan K. The low FODMAP diet: recent advancesin understanding its mechanisms and efficacy in IBS. Gut.
2017;66(8):1517–27.
17. Chumpitazi BP, Hoffman KL, Smith DP, McMeans AR, Musaad S, Versalovic J, et al. Fructan-sensitive children with irritable bowel syndrome
have distinct gut microbiome signatures. Aliment Pharmacol Ther.
2021;53(4):499–509.
18. Skodje GI, Sarna VK, Minelle IH, Rolfsen KL, Muir JG, Gibson PR, et al. Fructan,
rather than gluten, induces symptoms in patients with self-reported nonceliac gluten sensitivity. Gastroenterology. 2018;154(3):529–39.e2.
19. Priyanka P, Gayam S, Kupec JT. The role of a low fermentable oligosaccharides, disaccharides, monosaccharides, and polyol diet in nonceliac
gluten sensitivity. Gastroenterol Res Pract. 2018;2018:1561476.
20. De Palma G, Reed DE, Bercik P. Diet-microbial cross-talk underlying
increased visceral perception. Gut Microbes. 2023;15(1):2166780.
21. Guo R, Chen LH, Xing C, Liu T. Pain regulation by gut microbiota:
molecular mechanisms and therapeutic potential. Br J Anaesth.
2019;123(5):637–54.
22. Wu J, Masuy I, Biesiekierski JR, Fitzke HE, Parikh C, Schofield L, et al. Gutbrain axis dysfunction underlies FODMAP-induced symptom generationin irritable bowel syndrome. Aliment Pharmacol Ther. 2022;55(6):670–82.
23. Dieterich W, Schuppan D, Schink M, Schwappacher R, Wirtz S, Agaimy A,
et al. Influence of low FODMAP and gluten-free diets on disease activity
and intestinal microbiota in patients with non-celiac gluten sensitivity.
Clin Nutr. 2019;38(2):697–707.
24. Wiklund IK, Fullerton S, Hawkey CJ, Jones RH, Longstreth GF, Mayer EA,
et al. An irritable bowel syndrome-specific symptom questionnaire:development and validation. Scand J Gastroenterol. 2003;38(9):947–54.
25. Herfindal AM, van Megen F, Gilde MKO, Valeur J, Rudi K, Skodje GI, et al.
Effects of a low FODMAP diet on gut microbiota in individuals withtreated coeliac disease having persistent gastrointestinal symptoms - a
randomised controlled trial. Br J Nutr. 2023;130(12):2061–75.
26. Yu Y, Lee C, Kim J, Hwang S. Group-specific primer and probe sets todetect methanogenic communities using quantitative real-time polymerase chain reaction. Biotechnol Bioeng. 2005;89(6):670–9.
27. Avershina E, Lundgard K, Sekelja M, Dotterud C, Storro O, Oien T, et al.
Transition from infant- to adult-like gut microbiota. Environ Microbiol.
2016;18(7):2226–36.
28. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello
EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.
29. Edgar RC. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics. 2010;26(19):2460–1.
30. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8.
31. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glockner FO.
SILVA: a comprehensive online resource for quality checked and aligned
ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res.
2007;35(21):7188–96.
32. Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, et al.
vegan: community ecology package (2022, R package version 2.6–4).
https://​CRAN.R-​proje​ct.​org/​packa​ge=​vegan
33. R Core Team. R: A language and environment for statistical computing(Version 4.2.3). R Foundation for Statistical Computing, Vienna, Austria.
2023. URL https://​www.R-​proje​ct.​org/.
34. Zijlstra JB, Beukema J, Wolthers BG, Byrne BM, Groen A, Dankert J. Pretreatment methods prior to gaschromatographic analysis of volatile fatty
acids from faecal samples. Clin Chim Acta. 1977;78(2):243–50.

Page 18 of 19

35. Hoverstad T, Fausa O, Bjorneklett A, Bohmer T. Short-chain fatty acids inthe normal human feces. Scand J Gastroenterol. 1984;19(3):375–81.
36. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests inlinear mixed effects models. J Stat Softw. 2017;82(13):1–26.
37. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models
using lme4. J Stat Softw. 2015;67(1):1–48.
38. Kassambara A. ggpubr: ’ggplot2’ based publication ready plots (2023, R
package version 0.6.0). https://​CRAN.R-​proje​ct.​org/​packa​ge=​ggpubr
39. Mangiafico S. rcompanion: functions to support extension education
program evaluation (2023, R package version 2.4.21). https://​CRAN.R-​
proje​ct.​org/​packa​ge=​rcomp​anion
40. Kassambara A. rstatix: pipe-friendly framework for basic statistical tests(2023, R package version 0.7.2). https://​CRAN.R-​proje​ct.​org/​packa​ge=​rstat​ix
41. Nieuwenhuis R, te Grotenhuis M, Pelzer B. Influence.ME: tools for
detecting influential data in mixed effects models. The R Journal.
2012;4(2):38–47.
42. Revelle W. psych: procedures for psychological, psychometric, and personality research (2023, R package version 2.3.6). https://​CRAN.R-​proje​ct.​
org/​packa​ge=​psych
43. Kolde R. pheatmap: pretty heatmaps (2019, R package version 1.0.12).
https://​CRAN.R-​proje​ct.​org/​packa​ge=​pheat​map
44. Mukaka MM. Statistics corner: a guide to appropriate use of correlation
coefficient in medical research. Malawi Med J. 2012;24(3):69–71.
45. Skodje GI, Minelle IH, Rolfsen KL, Iacovou M, Lundin KEA, Veierod MB,
Henriksen C. Dietary and symptom assessment in adults with selfreported non-coeliac gluten sensitivity. Clin Nutr ESPEN. 2019;31:88–94.
46. Biesiekierski JR, Peters SL, Newnham ED, Rosella O, Muir JG, Gibson PR. No
effects of gluten in patients with self-reported non-celiac gluten sensitivity after dietary reduction of fermentable, poorly absorbed, short-chain
carbohydrates. Gastroenterology. 2013;145(2):320-8.e3.
47. Liu F, Li P, Chen M, Luo Y, Prabhakar M, Zheng H, et al. Fructooligosaccharide (FOS) and galactooligosaccharide (GOS) increase bifidobacterium
but reduce butyrate producing bacteria with adverse glycemic metabolism in healthy young population. Sci Rep. 2017;7(1):11789.
48. Nobel YR, Rozenberg F, Park H, Freedberg DE, Blaser MJ, Green PHR, et al.
Lack of effect of gluten challenge on fecal microbiome in patients withceliac disease and non-celiac gluten sensitivity. Clin Transl Gastroenterol.
2021;12(12): e00441.
49. Bonder MJ, Tigchelaar EF, Cai X, Trynka G, Cenit MC, Hrdlickova B, et al. The
influence of a short-term gluten-free diet on the human gut microbiome.
Genome Med. 2016;8(1):45.
50. Hansen LBS, Roager HM, Sondertoft NB, Gobel RJ, Kristensen M, VallesColomer M, et al. A low-gluten diet induces changes in the intestinal
microbiome of healthy Danish adults. Nat Commun. 2018;9(1):4630.
51. Tandon D, Haque MM, Gote M, Jain M, Bhaduri A, Dubey AK, Mande SS.
A prospective randomized, double-blind, placebo-controlled, doseresponse relationship study to investigate efficacy of fructo-oligosaccharides (FOS) on human gut microflora. Sci Rep. 2019;9(1):5473.
52. Yin P, Du T, Yi S, Zhang C, Yu L, Tian F, et al. Response differences of gut
microbiota in oligofructose and inulin are determined by the initial gut
Bacteroides/Bifidobacterium ratios. Food Res Int. 2023;174(Pt 1): 113598.
53. Takada T, Kurakawa T, Tsuji H, Nomoto K. Fusicatenibacter saccharivoransgen. nov., sp. nov., isolated from human faeces. Int J Syst Evol Microbiol.
2013;63(Pt 10):3691–6.
54. O’Riordan KJ, Collins MK, Moloney GM, Knox EG, Aburto MR, Fulling C,
et al. Short chain fatty acids: microbial metabolites for gut-brain axis
signalling. Mol Cell Endocrinol. 2022;546: 111572.
55. Nishiwaki H, Ito M, Hamaguchi T, Maeda T, Kashihara K, Tsuboi Y, et al.
Short chain fatty acids-producing and mucin-degrading intestinal bacteria predict the progression of early Parkinson’s disease. NPJ Parkinsons Dis.
2022;8(1):65.
56. He T, Cheng X, Xing C. The gut microbial diversity of colon cancer
patients and the clinical significance. Bioengineered. 2021;12(1):7046–60.
57. Rodriguez-Diaz C, Taminiau B, Garcia-Garcia A, Cueto A, Robles-Diaz M,
Ortega-Alonso A, et al. Microbiota diversity in nonalcoholic fatty liver disease and in drug-induced liver injury. Pharmacol Res. 2022;182: 106348.
58. Crobach MJT, Ducarmon QR, Terveer EM, Harmanus C, Sanders I, Verduin KM, et al. The bacterial gut microbiota of adult patients infected,
colonized or noncolonized by Clostridioides difficile. Microorganisms.
2020;8(5):677.

Herfindal et al. BMC Medicine(2024) 22:358

59. Tran NTD, Chaidee A, Surapinit A, Yingklang M, Roytrakul S, Charoenlappanit S, et al. Strongyloides stercoralis infection reduces Fusicatenibacterand Anaerostipes in the gut and increases bacterial amino-acid metabolism in early-stage chronic kidney disease. Heliyon. 2023;9(9): e19859.
60. Yoshida N, Watanabe S, Yamasaki H, Sakuma H, Takeda AK, Yamashita T,
Hirata KI. Average gut flora in healthy Japanese subjects stratified by age
and body mass index. Biosci Microbiota Food Health. 2022;41(2):45–53.
61. Zhang X, Li N, Chen Q, Qin H. Fecal microbiota transplantation modulates
the gut flora favoring patients with functional constipation. Front Microbiol. 2021;12: 700718.
62. Nordin E, Hellstrom PM, Dicksved J, Pelve E, Landberg R, Brunius C. Effects
of FODMAPs and gluten on gut microbiota and their association with themetabolome in irritable bowel syndrome: a double-blind, randomized,
cross-over intervention study. Nutrients. 2023;15(13):3045.
63. Nordin E, Hellstrom PM, Vuong E, Ribbenstedt A, Brunius C, Landberg R.
IBS randomized study: FODMAPs alter bile acids, phenolic- and tryptophan metabolites, while gluten modifies lipids. Am J Physiol Regul Integr
Comp Physiol. 2023;325(3):R248–59.
64. Medawar E, Haange SB, Rolle-Kampczyk U, Engelmann B, Dietrich A,
Thieleking R, et al. Gut microbiota link dietary fiber intake and shortchain fatty acid metabolism with eating behavior. Transl Psychiatry.
2021;11(1):500.
65. Lin Z, Zhang X, Wu M, Ming Y, Wang X, Li H, et al. High-fiber diet andrope-skipping benefit cardiometabolic health and modulate gut
microbiota in young adults: a randomized controlled trial. Food Res Int.
2023;173(Pt 2): 113421.
66. Li N, Chen H, Cheng Y, Xu F, Ruan G, Ying S, et al. Fecal microbiota transplantation relieves gastrointestinal and autism symptoms by improving
the gut microbiota in an open-label study. Front Cell Infect Microbiol.
2021;11: 759435.
67. Juste C, Gerard P. Cholesterol-to-coprostanol conversion by the gut
microbiota: what we know, suspect, and ignore. Microorganisms.
2021;9(9):1881.
68. Duncan SH, Russell WR, Quartieri A, Rossi M, Parkhill J, Walker AW, Flint HJ.
Wheat bran promotes enrichment within the human colonic microbiotaof butyrate-producing bacteria that release ferulic acid. Environ Microbiol.
2016;18(7):2214–25.
69. Gibson GR, Beatty ER, Wang X, Cummings JH. Selective stimulation ofbifidobacteria in the human colon by oligofructose and inulin. Gastroenterology. 1995;108(4):975–82.
70. Hustoft TN, Hausken T, Ystad SO, Valeur J, Brokstad K, Hatlebakk JG, Lied
GA. Effects of varying dietary content of fermentable short-chain carbohydrates on symptoms, fecal microenvironment, and cytokine profiles
in patients with irritable bowel syndrome. Neurogastroenterol Motil.
2017;29(4): e12969.
71. Birkeland E, Gharagozlian S, Birkeland KI, Valeur J, Mage I, Rud I, Aas AM.
Prebiotic effect of inulin-type fructans on faecal microbiota and shortchain fatty acids in type 2 diabetes: a randomised controlled trial. Eur J
Nutr. 2020;59(7):3325–38.
72. Clarke ST, Green-Johnson JM, Brooks SP, Ramdath DD, Bercik P, Avila C,
et al. beta2-1 Fructan supplementation alters host immune responses ina manner consistent with increased exposure to microbial components:
results from a double-blinded, randomised, cross-over study in healthy
adults. Br J Nutr. 2016;115(10):1748–59.
73. Valcheva R, Koleva P, Martinez I, Walter J, Ganzle MG, Dieleman LA. Inulintype fructans improve active ulcerative colitis associated with microbiotachanges and increased short-chain fatty acids levels. Gut Microbes.
2019;10(3):334–57.
74. Salazar N, Dewulf EM, Neyrinck AM, Bindels LB, Cani PD, Mahillon J, et al.
Inulin-type fructans modulate intestinal Bifidobacterium species populations and decrease fecal short-chain fatty acids in obese women. Clin
Nutr. 2015;34(3):501–7.
75. Caminero A, Nistal E, Arias L, Vivas S, Comino I, Real A, et al. A gluten
metabolism study in healthy individuals shows the presence of faecal
glutenasic activity. Eur J Nutr. 2012;51(3):293–9.
76. Ierardi E, Losurdo G, Piscitelli D, Giorgio F, Amoruso A, Iannone A,
et al. Biological markers for non-celiac gluten sensitivity: a question
awaiting for a convincing answer. Gastroenterol Hepatol Bed Bench.
2018;11(3):203–8.

Page 19 of 19

77. Chakraborty S, Kaur S, Guha S, Batra SK. The multifaceted roles of neutrophil gelatinase associated lipocalin (NGAL) in inflammation and cancer.
Biochim Biophys Acta. 2012;1826(1):129–69.
78. Goetz DH, Holmes MA, Borregaard N, Bluhm ME, Raymond KN, Strong
RK. The neutrophil lipocalin NGAL is a bacteriostatic agent that interfereswith siderophore-mediated iron acquisition. Mol Cell. 2002;10(5):1033–43.
79. Abella V, Scotece M, Conde J, Gomez R, Lois A, Pino J, et al. The potentialof lipocalin-2/NGAL as biomarker for inflammatory and metabolic diseases. Biomarkers. 2015;20(8):565–71.
80. Carroccio A, Giannone G, Mansueto P, Soresi M, La Blasca F, Fayer F, et al.
Duodenal and rectal mucosa inflammation in patients with non-celiac
wheat sensitivity. Clin Gastroenterol Hepatol. 2019;17(4):682–90.e3.
81. Losurdo G, Piscitelli D, Pezzuto F, Fortarezza F, Covelli C, Marra A, et al.
T Helper lymphocyte and mast cell immunohistochemical pattern innonceliac gluten sensitivity. Gastroenterol Res Pract. 2017;2017:5023680.
82. Uhde M, Ajamian M, Caio G, De Giorgio R, Indart A, Green PH, et al.
Intestinal cell damage and systemic immune activation in individuals
reporting sensitivity to wheat in the absence of coeliac disease. Gut.
2016;65(12):1930–7.
83. Tye-Din JA, Skodje GI, Sarna VK, Dzuris JL, Russell AK, Goel G, et al.
Cytokine release after gluten ingestion differentiates coeliac disease
from self-reported gluten sensitivity. United European Gastroenterol J.
2020;8(1):108–18.
84. Akiho H, Ihara E, Nakamura K. Low-grade inflammation plays a pivotal
role in gastrointestinal dysfunction in irritable bowel syndrome. World J
Gastrointest Pathophysiol. 2010;1(3):97–105.
85. Nybacka S, Storsrud S, Liljebo T, Le Neve B, Tornblom H, Simren M,
Winkvist A. Within- and between-subject variation in dietary intake offermentable oligo-, di-, monosaccharides, and polyols among patientswith irritable bowel syndrome. Curr Dev Nutr. 2019;3(2):nzy101.
86. Smith NW, Shorten PR, Altermann EH, Roy NC, McNabb WC. Hydrogen cross-feeders of the human gastrointestinal tract. Gut Microbes.
2019;10(3):270–88.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.