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R E S E A R C H Open Access
Alterations in fecal microbiota composition
by probiotic supplementation in healthy
adults: a systematic review of randomized
controlled trials
Nadja B. Kristensen
*
, Thomas Bryrup, Kristine H. Allin, Trine Nielsen, Tue H. Hansen and Oluf Pedersen
Abstract
Background: The effects of probiotic supplementation on fecal microbiota composition in healthy adults have not
been well established. We aimed to provide a systematic review of the potential evidence for an effect of probiotic
supplementation on the composition of human fecal microbiota as assessed by high-throughput molecular approaches
in randomized controlled trials (RCTs) of healthy adults.
Methods: The survey of peer-reviewed papers was performed on 17 August 2015 by a literature search through
PubMed, SCOPUS, and ISI Web of Science. Additional papers were identified by checking references of relevant
papers. Search terms included healthy adult, probiotic, bifidobacterium, lactobacillus, gut microbiota, fecal microbiota,
intestinal microbiota, intervention, and (clinical) trial. RCTs of solely probiotic supplementation and placebo in healthy
adults that examined alteration in composition of overall fecal microbiota structure assessed by shotgun metagenomic
sequencing, 16S ribosomal RNA sequencing, or phylogenetic microarray methods were included. Independent collection
and quality assessment of studies were performed by two authors using predefined criteria including methodological
quality assessment of reports of the clinical trials based on revised tools from PRISMA/Cochrane and by the Jadad score.
Results: Seven RCTs investigating the effect of probiotic supplementation on fecal microbiota in healthy adults were
identified and included in the present systematic review. The quality of the studies was assessed as medium to high. Still,
no effects were observed on the fecal microbiota composition in terms of α-diversity, richness, or evenness in any of the
included studies when compared to placebo. Only one study found that probiotic supplementation significantly modified
theoverallstructureofthefecal bacterial community in terms of β-diversity when compared to placebo.
Conclusions: This systematic review of the pertinent literature demonstrates a lack of evidence for an impact of
probiotics on fecal microbiota composition in healthy adults. Future studies would benefit from pre-specifying
the primary outcome and transparently reporting the results including effect sizes, confidence intervals, and Pvalues as
well as providing a clear distinction of between-group and within-group comparisons.
Background
The human gut microbiota refers to the microbes that
reside inside the gut and partake in several functions
beneficial to the host including fermentation of other-
wise indigestible dietary fibers and other food items [1],
synthesis of vitamins and amino acids [2], prevention of
pathogen colonization [3], maturation and regulation of
the immune system [4], modulation of gastrointestinal
hormone release, and regulation of brain behavior
through bidirectional neuronal signaling as part of the gut-
brain axis [5]. The development of culture-indpendent,
high-throughput molecular techniques has enabled the
identification of previously unknown bacterial species,
thereby providing novel insights into the compositional
diversity and functional capacity of fecal microbiota. As
a result, studies have suggested that disorders such as
colorectal cancer, rheumatoid arthritis, type 2 diabetes,
and obesity are associated with disease-specific dysbiotic
* Correspondence: nadja@sund.ku.dk
The Novo Nordisk Foundation Center for Basic Metabolic Research, Section
of Metabolic Genetics, Faculty of Health and Medical Sciences, University of
Copenhagen, Universitetsparken 1, 2nd floor, Copenhagen Ø 2100, Denmark
© 2016 Kristensen et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kristensen et al. Genome Medicine (2016) 8:52
DOI 10.1186/s13073-016-0300-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
shifts in fecal microbiota [6–11]. Consequently, in recent
years the gut microbiota as a potential modifiable risk fac-
tor for disease development has received massive atten-
tion. One common approach applied to convey health
benefits by way of modifying the gut microbiota has been
the use of probiotic supplementation. Probiotics are de-
fined as live microorganisms that, when administered in
adequate amounts, confer a health benefit on the host in a
safe and efficacious manner [12]. Suggested mechanisms
by which probiotics may benefit the gut environment and
the health of the host include improvement of the intes-
tinal barrier function through effects on the epithelium
and mucus lining, production of anti-microbial substances,
competition with pathogenic bacteria, and regulation of
luminal acidity (reviewed in [13, 14]).
The therapeutic effect of probiotic supplementation
has been studied in a broad range of diseases, particu-
larly in regard to gastrointestinal and metabolic disor-
ders where results have supported the potential use of
probiotics as therapeutic agents (reviewed in [15, 16]).
Common to both sets of disorders is a multitude of readily
available, clinically relevant outcome measures (e.g. body
mass index, fat mass, insulin resistance, severity of gastro-
intestinal symptoms) by which to measure treatment ef-
fect. The effect of probiotics in disease-free individuals is,
however, not as easily assessed. Interpretation of an effect
on the composition of fecal microbiota in healthy indi-
viduals may be particularly complicated due to the lack
of an internationally accepted consensus definition of a
normal or a healthy fecal microbial community [17, 18].
Terms such as ecological stability, idealized compos-
ition, or favorable functional profile have been suggested
as hallmarks of a healthy gut microbiota [17]. These are
all very unspecific concepts and the compositional and
functional characteristics of a healthy gut microbial
community remain to be defined. Furthermore, an ef-
fect of probiotics on the composition of the gut micro-
biota is an intermediate outcome only and should be
interpreted with caution in regard to implications for
the health of the host. Despite these limitations, several
probiotic interventions aiming to observe alterations in
fecal microbiota composition have been performed in
healthy adults [19–28]. Results from these studies have
the potential to provide insights into the underlying
mechanisms of probiotics and fecal microbiota. Cur-
rently, no systematic review has addressed the effects of
probiotics on fecal microbiota composition using high-
throughput metagenomic methods (i.e. phylogenetic
microarrays, 16S ribosomal RNA (rRNA) sequencing,
or shotgun metagenomic sequencing) in healthy adults.
In the context of a billion dollar market for probiotic
supplements [29] with products being marketed, in
part, toward healthy individuals by stating effects on
gastrointestinal health, alluding to the fecal microbial
community, an overview of the current evidence is
warranted.
The objective of the present systematic review was to
explore in healthy adults the current evidence for an
effect of probiotic supplementation compared to pla-
cebo on the composition of human fecal microbiota as
assessed by high-throughput molecular approaches in
randomized controlled trials (RCTs).
Methods
We undertook a systematic review of the possible effects
of probiotic intervention on the composition of fecal
microbiota in healthy adults. The available literature was
identified and examined as a systematic review and not a
meta-analysis due to the heterogeneity of the study designs
and methods. The results are reported in accordance with
the PRISMA statement guidelines (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) [30].
The study followed an a priori established protocol.
Eligibility criteria
The criteria for eligibility were healthy adults as study
population, probiotics and placebo as intervention, alter-
ation in composition of fecal microbiota assessed by
shotgun metagenomic sequencing, 16S rRNA sequen-
cing or phylogenetic microarray methods as the primary
outcome, and RCT as the study design with no criteria
on study duration. No limits were applied for language
or time. Studies not exploiting the randomized con-
trolled design and providing only within-group results
(i.e. results before and after the intervention in the pro-
biotic group only) were not included. Moreover, only
studies assessing the overall bacterial ecology were in-
cluded. Accordingly, studies investigating survival of the
probiotic strains only were considered ineligible. Studies
with interventions combining probiotics with other sup-
plements (e.g. prebiotics, antibiotics, medications) were
excluded. If studies had more than two arms, only the com-
parison of probiotics to placebo was considered. Studies
examining both healthy and unhealthy participants were
excluded.
Information sources, search strategy, and study selection
The identification of papers involved four sequential
processes performed by two independent reviewers
(NBK and TB). On 17 August 2015, a literature search
was conducted through multiple electronic databases
(PubMed, SCOPUS, and ISI Web of Science) to capture
as many relevant citations as possible. The search
phrase used was:
Healthy adult AND (probiotic OR bifidobac* OR
lactobac*) AND (gut microbio* OR f*cal microbio* OR
intestinal microbio*) AND (clinical trial OR interven-
tion OR trial).
Kristensen et al. Genome Medicine (2016) 8:52 Page 2 of 11
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In PubMed, “Species”was limited to include only
humans and “Article types”was limited to cover “Clinical
trial,”“Comparative study,”“Controlled clinical trial,”
“Journal article,”and “Randomized controlled trial.”
In ISI Web of Science, “Document types”was limited to
contain “Article,”“Clinical trial,”“Other,”and “Abstract.”
In Scopus, “Source type and document type”was limited
to comprise “Journals and article,”“Short survey,”and
“Erratum.”For “Subject area,”“Agricultural and biological
sciences,”“Nursing,”“Pharmacology,”“Toxicology and
pharmaceutics,”“Environmental science,”“Veterinary,”
“Chemistry,”and “Neuroscience”were excluded.
Full reports were obtained and screened for all titles
appearing to meet the inclusion criteria or in case of
any uncertainty. References in 31 full-text articles were
also assessed for inclusion in the present review.
Screening and eligibility assessment by title and ab-
stract resulted in 1373 citations (Fig. 1). The assessment
was performed independently in an unblinded standard-
ized manner by NBK and TB resulting in seven included
studies. Any disagreements between reviewers were re-
solved by consensus.
Data collection process
Independent data collection was performed by two authors
(NBK and TB). Corresponding authors of the following
studies were contacted in order to acquire missing infor-
mation on allocation concealment or other measures of
risk of bias: Lahti et al., Rampelli et al., Ferrario et al., Bjerg
et al., Hanifi et al., and Simon et al. [19, 21–24, 27]. Unpub-
lished information about blinding was obtained from Lahti
et al., Bjerg et al., and Hanifi et al. [19, 21, 22] and the rea-
son for excluding data from three participants from the
intervention group (missing fecal samples) was obtained
from Hanifi et al. [21].
Fig. 1 Flow chart of literature selection process [30]
Kristensen et al. Genome Medicine (2016) 8:52 Page 3 of 11
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Data items
Information extracted from each included RCT was: (1)
participant characteristics (including age and gender);
(2) intervention (including probiotic strain and dosage
as well as mode of administration); (3) design (including
study design and duration); and (4) outcome measure
(including the microbiomics and intervention effect on
overall fecal microbiota structure).
Quality assessment
The methodological quality assessment of reports of the
clinical trials was performed independently by NBK and
TB using a three-item instrument (the Jadad score) that
evaluates likelihood of bias in research reports [31].
The three items evaluated by a five-point scale are
quality of randomization, quality of blinding, and rea-
sons for withdrawal/drop-out (0 = worst, 5 = best). Risk
of bias was further assessed regarding concealment of
randomization, early termination of trial, blinding of
patients, healthcare providers, data collectors and out-
come assessors, reporting of drop-out or withdrawal,
selective outcome reporting, and other potential biases
[32, 33].
Summary measures
Intervention effects on the overall fecal microbiota struc-
ture, that is, richness, abundance, evenness, α-diversity or
compositional dissimilarity (β-diversity), were the primary
measures of treatment effects.
Results
Study selection
A total of 1368 citations were identified through the
search in PubMed, SCOPUS, and ISI Web of Science
and an additional five were identified through checking
the references of relevant papers. After the removal of
duplicates, 1287 citations were left. NBK and TB
screened the initial search results using abstracts and
1256 citations were excluded as irrelevant for one or
more of the following reasons: animal study, meta-
analysis/review, non-healthy or non-adult participants, no
probiotic intervention, or no assessment of fecal micro-
biota composition. The full papers of the remaining 31
citations and references therein were assessed to select
studies for inclusion using the abovementioned criteria,
resulting in the exclusion of 24 studies due to one or a
combination of the following reasons: no assessment of
fecal microbiota composition, assessment of single-strain
survival only, inclusion of non-healthy participants, non-
randomized controlled design, provision of only within-
group results, and combined intervention of probiotic
with prebiotics or other foods. Following the selection
process (Fig. 1), seven studies [19–24, 27] remained
(five of which were identified by checking the references
of relevant papers) and were included in the present sys-
tematic review.
Study characteristics
All seven studies were published in English language
journals between February 2013 and October 2015
(EPub June 2015). One study was performed in Finland
[22], two in Italy [23, 27], two in Denmark [19, 20], one
in the United States [21], and one in Germany [24]. An
overview of the study characteristics and main results
are presented in Table 1. The studies were designed as
RCTs, one of which used a cross-over design [27]. Six
studies were double-blinded, whereas one was single-
blinded [20]. Participants were all healthy adults (range,
19–88 years) with a proportion of female participants in
the range of 50–100 %. The total number of included in-
dividuals was in the range of 21–81. The intervention
received was probiotics belonging to the genus Lactoba-
cillus (n = 5) [19, 20, 22, 24, 27], Bifidobacterium and
Lactobacillus combined (n = 1) [23], or Bacillus (n = 1)
[21] which was provided in biscuits (n = 1) [23], milk-
based drinks (n = 1) [22], sachets (n = 1) [20], or capsules
(n = 4) [19, 21, 24, 27] administered at a dose of ~10
9
to
10
11
colony-forming units (CFU) for 21–42 days. Three
of the studies collected additional samples 1–3 weeks
after the intervention had ended [19, 21, 22]. Compli-
ance was assessed by pill count or screening for the pro-
biotic in fecal microbiota and evaluated as sufficient in
most of the studies. However, Rampelli et al. found only
a trend towards enrichment of the probiotic strain [23].
Habitual diet was assessed in two studies [20, 27]. In the
present review, the primary outcome of interest is alter-
ations in fecal microbiota composition, which was assessed
by either microarray hybridization (HITchip (n = 1) [22],
HTF-Microbi.Array (n = 1) [23]), or next-generation se-
quencing methods (16S rRNA sequencing on Ion Torrent
PGM (n = 1) [27], Illumina MiSeq platforms (n = 1) [24], or
454 pyrosequencing (n = 2) [19, 21]), or metagenomics on
a SOLiD 5500×l platform (n = 1) [20]. Of the studies using
a 16S rRNA-based approach, one did not report which
hypervariable region of the 16S rRNA gene was targeted
and no studies targeted the same set of regions. The data-
bases used for mapping the sequences were GreenGenes
version 13.5 (n = 1) [27], RDP (MultiClassifier 1.1 or not
specified) (n = 2) [19, 24], or both (versions not specified)
(n = 1) [21], while two did not report the database used.
The study by Brahe et al. [20] used metagenomics and
mapped reads to a reference catalogue of 3.3 million bac-
terial genes [34].
Risk of bias
Seven studies were identified and evaluated as medium
to high quality by the Jadad score (3–5) as presented in
Table 2. The quality of the included studies is generally
Kristensen et al. Genome Medicine (2016) 8:52 Page 4 of 11
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Table 1 Characteristics of the studies reviewed
Study Participant
characteristics
Intervention Design Microbiomics Sample size
calculation
Probiotic effects on fecal
microbiota compared to placebo
Lahti et al.
2013 [22]
25/25 (72 %)
23–55 years
Finland
L. rhamnosus GG ATCC53103
(1.55 × 10
10
CFU) in 250 mL
milk-based fruit drink
Double-blinded, parallel,
two-armed, placebo (drink)
controlled (21 days)
16S rRNA (regions V1 and V6);
HITchip based characterization of
>1000 microbial species-like phylotypes
Post hoc ↔Composition of the fecal microbiota
↔Stability of the fecal microbiota
quantified by inter-individual and
intra-individual correlations
within and between time points
Rampelli
et al. 2013
[23]
32/32 (59 %)
71–88 years
Italy
B. longum Bar33 and
L. helveticus Bar13 (10
9
CFU)
in biscuit
Double-blinded, parallel,
two-armed, placebo (biscuit)
controlled (30 days)
16S rRNA (region unknown);
HTF-Microbi.Array based characterization
of 31 phylogenetically related groups
No ↔Relative abundance of 31
phylogenetically related groups
Ferrario
et al. 2014
[27]
22/34* (56 %)
23–55 years
Italy
L. paracasei DG
(>2.4 × 10
10
CFU) in capsules
Double-blinded, cross-over, placebo
(capsules) controlled (two 28-day
intervention periods with a
28-day wash-out)
16S rRNA (region V3) sequencing on Ion
Torrent platform
No ↔α-diversity
Modified βdiversity (with absolute
distances higher for the probiotic
than for the placebo treatments)
Bjerg et al.
2015 [19]
20/64* (50 %)
20–45 years
Denmark
L. casei W8® (10
10
CFU)
in capsules
Double-blinded, parallel, two-armed,
placebo (capsules) controlled
(28 days)
16S rRNA (regions V3 and V4) sequencing
on Roche 454 pyrosequencing platform
No ↔α- and β-diversity
Brahe et al.
2015 [20]
34/58*
(100 %)
40–70 years
Denmark
L. paracasei F19 (9.4 × 10
10
CFU)
or flaxseed mucilage (10 g)
in sachets
Single-blinded, parallel, three-armed,
placebo (sachets) controlled (42 days)
Quantitative metagenomics
on a SOLiD 5500×l platform
Yes ↔Bacterial gene count (richness)
and abundance of specific bacterial
genes compared to placebo
Hanifi et al.
2015 [21]
37/81* (52 %)
19–49 years
United States
Bacillus subtilis R0179
(0.1 × 10
9
, 1.0 × 10
9
, and
10 × 10
9
CFU, respectively)
in capsules
Double-blinded, parallel, four-armed,
placebo (capsules) controlled
(28 days)
16S rRNA (regions V1 to V3)
pyrosequencing
No ↔β-diversity and OTU based richness
Simon et al.
2015 [24]
21/21 (52 %)
40–65 years
Germany
L. reuteri SD5865 (2 × 10
10
viable cells)
in capsules
Double-blinded, parallel, two-armed,
placebo (capsules) controlled
(28 days)
16S rRNA (regions V5 and V6)
sequencing on Illumina MiSeq platform
Yes ↔α- and β-diversity
Participant characteristics are number of participants with microbiome data/number of participants included in the study. Participant characteristics (% women and age range of participants in years) are based upon
number of participants included in the study. CFU, colony-forming units. OTU, operational taxonomic unit. ↔indicates no difference between the probiotic group and placebo. ↑indicates an increase in the probiotic
group compared to placebo. ↓indicates a decrease in the probiotic group compared to placebo. Pvalues are unadjusted for multiple testing. *Performed next-generation sequencing on fecal samples from a subgroup
of the include d individuals. In the study by Ferrario et al., 22 participa nts (of 34) completed the stu dy. Bjerg et al. sel ected 10 (of 32) placebo-t reated and 10 (of 32) probiotic-treated participa nts with the high est
triacylglycerol concentration in the blood at week 0. The stu dy by Brahe et al . had a third int erventio n arm not relev ant for the present study where the numb er of partic ipants were approximately two-t hirds of
the total number of participants included in th e study. Hanifi et al. selected 20 (a ll) placebo-treated and 17 (of 20 ) probiotic-treate d participants from the group with the highest dose (10 × 10
9
CFU) of the
probiotic tr eatment
Kristensen et al. Genome Medicine (2016) 8:52 Page 5 of 11
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Table 2 Assessment of risk of bias in the studies reviewed
Study Concealment of
randomization
RCT stopped
early
Patients
blinded
Healthcare providers
blinded
Data collectors
blinded
Outcome assessors
blinded
Reporting drop-out
or withdrawal
Other
potential bias
Selective outcome
reporting
Jadad
score
Lahti et al.
2013 [22]
Yes No Yes Yes Yes No Yes No No 4
Rampelli et al.
2013 [23]
Yes No Yes Unclear
a
Unclear
a
Unclear
a
Unclear Yes
c
Yes
c
3
Ferrario et al.
2014 [27]
Yes No Yes Unclear
a
Unclear
a
Unclear
a
Yes No No 3
Bjerg et al.
2015 [19]
Yes No Yes Yes Yes Yes Yes Yes
b
No 5
Brahe et al.
2015 [20]
Yes No Yes No No No Yes No No 4
Hanifi et al.
2015 [21]
Yes No Yes Yes Yes Yes Yes Yes
b
No 5
Simon et al.
2015 [24]
Yes No Yes Unclear
a
Unclear
a
Unclear
a
Yes No No 4
Based on PRISMA (and Cochrane)’s tools for assessing risk of bias. The Jadad score is a three-item instrument that evaluates likelihood of bias in terms of quality of randomization, quality of blinding, and reasons for
withdrawal/drop-out. It is compiled of the following quality items from the table: Concealment of randomization, Patients blinded, Healthcare providers blinded, Data collectors blinded, Outcome assessors blinded,
and Reporting drop-out or withdrawal
a
Double-blinded study but unclear whether healthcare providers, data collectors, and outcome assessors were all blinded
b
Performed next-gene ration seq uencing on fe cal samples from a subg roup of the in cluded indi viduals . Bjerg et al. se lected 10 (o f 32) placebo-treated and 10 (of 32) probiotic-treated participants with the highest
triacylglycerol concentrat ion in the bloo d at week 0. Ha nifi et al. selected 20 ( all) placeb o-treat ed and 17 (of 20 ) probiotic-treated participants from th e group wit h the highes t dose (10 × 10
9
colony-forming units)
of the probi otic treatment
c
No direct comparison between treatment groups was made for the age-related dysbiosis
Kristensen et al. Genome Medicine (2016) 8:52 Page 6 of 11
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high in regard to risk of bias and the methods of asses-
sing fecal microbiota configuration. However, blinding of
healthcare providers, data collectors, and outcome asses-
sors were either not performed or unclearly reported in
three of the seven included studies, which may have
caused performance and detection bias. Two studies only
investigated the effect of the probiotic treatment on a
subgroup of participants, which is also a potential source
of bias.
Results of individual studies
In terms of richness, evenness, or α-diversity measures,
no effects were observed on the fecal microbiota com-
position in any of the included studies when compared
to placebo and only in the study by Ferrario et al. [27]
was it found that probiotic treatment significantly modi-
fied the compositional dissimilarity (β-diversity).
In the study by Lahti et al. [22], the temporal stability
of fecal microbiota, quantified by the correlation of the
fecal microbiota profiles among three time points, did
not differ between the probiotic (L. rhamnosus GG
ATCC53103) and the placebo group.
In the study by Rampelli et al. [23], there was no effect
of probiotic supplementation (B. longum Bar33 and L.
helveticus Bar13) on the relative abundance of 31 phylo-
genetically related groups when compared to placebo. In
the same study, the effect of probiotic supplementation on
age-related dysbiosis was also evaluated. The probiotic
intervention reverted an age-related increase of Clostridium
cluster Xi, C. difficile,C. perfringens,Enterococcus Faecium,
and Campylobacter when comparing the probiotic and the
placebo group to a common reference of eight young,
healthy adults; but no direct comparison was made be-
tween treatment groups.
In the study by Ferrario et al. [27], the α-diversity re-
ported as Chao1 and Shannon coefficients and number
of detected genera did not change as a result of the pro-
biotic intervention (L. paracasei DG) when compared to
placebo. Yet, the β-diversity between the probiotic and
the placebo group was modified with absolute distances
significantly higher for the probiotic than for the placebo
treatments when assessed by principal coordinate ana-
lysis (PCoA) of weighted UniFrac distances. Accordingly,
the relative abundance of Proteobacteria (P= 0.006) and
Clostridiales genus Coprococcus (P= 0.009) were inc-
reased and the Clostridiales genus Blautia (P= 0.036)
was decreased in the probiotic group when compared
to placebo. Additionally, analyses of predicted functional
profiles revealed changes in eight Kyoto Encyclopedia of
Genes and Genomes modules related to bacterial path-
ways in membrane transport, amino acid metabolism,
energy metabolism, and metabolism of cofactors and vita-
mins (P<0.05).
In the study by Bjerg et al. [19], the β-diversity was
not affected by the probiotic intervention (L. casei W8®)
compared to placebo when assessed by PCoA of species
and genus level Operational Taxonomic Unit (OTU)
based UniFrac distances. Furthermore, no difference in
α-diversity (Chao1 and Shannon index) or species rich-
ness was observed between the probiotic and the pla-
cebo group.
In the study by Brahe et al. [20], fecal microbiota was
assessed by shotgun-sequencing-based metagenomics.
The bacterial gene count (richness) did not change
within the probiotic group (L. paracasei F19) compared
to placebo. Alterations in the abundance of individual
bacterial genes (2493 genes assigned to two metage-
nomic species) were observed after the intervention in
the probiotic group. However, fewer alterations were ob-
served in the intervention group compared to the pla-
cebo group (7436 genes assigned to six metagenomic
species). Yet, no direct comparison between the groups
is explicitly stated.
In the study by Hanifi et al. [21], no difference in com-
positional dissimilarity (β-diversity) between the treat-
ment groups (Bacillus subtilis R0179 in different doses)
and placebo was shown when analyzed using PCoA
based on the UniFrac metric. Sequence reads binned to
multiple OTUs assigned to the genus Ruminococcus in-
creased in the probiotic group (with the highest dose
(10 × 10
9
CFU, Table 2) compared to placebo (P< 0.01).
In the study by Simon et al. [24], the overall compos-
ition of fecal microbiota was unaffected by probiotic
supplementation (L. reuteri SD5865) both in terms of α-
(Chao1, Shannon, and Simpson indices) and β-(Bray-
Curtis, Morisita-Horn, and weighted UniFrac) diversity.
Discussion
Overall, this systematic review demonstrates that there is
no convincing evidence for consistent effects of probiotics
on fecal microbiota composition in healthy adults.
No effects were observed on the fecal microbiota com-
position in terms of α-diversity, richness, or evenness in
any of the included studies when compared to placebo.
Only in the paper by Ferrario et al. [27] was it reported
that probiotic supplementation significantly modified the
overall structure of the fecal bacterial community in
terms of compositional dissimilarity (β-diversity) when
compared to placebo.
Study design and reporting of results
Overall, the reporting of the analyses and results was non-
transparent and difficult to assess with very few effect
sizes, confidence intervals, and Pvalues reported. This is
possibly due to the fact that fecal microbiomics is a rela-
tively new research area that currently relies heavily on
non-parametric statistics and lacks an internationally
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accepted standard approach of reporting results. Unfortu-
nately, this impedes the comparison of results in the
present review. As the only study, Ferrario et al. [27] used
a cross-over design, which may not be the ideal design to
assess the effects of a probiotic intervention due to the risk
of carry-over effects [35]. In the study the probiotic cell
count was decreased after the 4-week wash-out period
compared with baseline count, suggesting that wash-out
was effective. However, a carry-over effect at the outcome
level cannot be excluded. Only two studies provided a
priori sample size calculations [20, 24], of which two calcu-
lated statistical power based on alterations in fecal micro-
biota composition [20, 22]. Thus, several of the studies
may well have been underpowered, with an inherent
risk of unequal distribution of confounding factors. A
potential confounder in the studies reviewed is habitual
diet. Human studies have revealed that short-term and
long-term changes in diet (such as plant-based vs. animal-
based, amount of dietary fibers and fat) influence the fecal
microbiota composition and function [36–38]. Hence, the
enormous inter-individual variation in the dietary intake
and its effect on the fecal microbiota may mask the true
picture of the impact of a probiotic treatment. Only one
of the included studies monitored habitual diet with the
aim of accounting for differences in dietary habits, specif-
ically considering prebiotic fibers, during the intervention
period [27]. A major limitation of most included studies is
an unclear, inexplicit statement of the pre-specified pri-
mary outcome and delimitation of secondary outcomes.
Only one study [24] is explicitly labeled as a pilot trial,
reporting a multitude of outcomes, only in part addressing
multiple testing. Two studies do not address the issue of
multiple testing [23, 27], while others report multiple pri-
mary outcomes or make no distinction between pri-
mary and secondary outcomes [19–22]. Reporting of
the results is generally unclear, with between-group
comparisons on primary outcomes intermixed with re-
sults on secondary outcomes and within-group compar-
isons of differences between baseline and post-
intervention measures.
Heterogeneity
Although study participants in the included trials were
all healthy adults, the demographic makeup varied
widely among studies. Rampelli et al. [23] included only
elderly individuals, who may respond differently to pro-
biotics than young individuals and Brahe et al. [20] in-
cluded postmenopausal women only.
Considering that the impact on the fecal microbiota
may differ among strains of the same bacterial species
[39], despite close phylogenetic relationships, a potential
source of heterogeneity is the use of various probiotic
agents. Six studies used single-strain interventions with
probiotic products belonging to the genera Lactobacillus
[19, 20, 22, 24, 27] or Bacillus [21]. One study used a
double-strain probiotic mixture of bacteria belonging
to the genera Lactobacillus and Bifidobacterium [23].
Whereas the use of different probiotic agents makes it
difficult to draw any meta-analytical conclusions, the
choice between single-strain and multi-strain interven-
tion is probably of less importance. In most cases, inert
bacteria are administered and within a few hours are
entering a diverse ecosystem where they are numerically a
minority. So while additive or synergistic effects might be
observed in vitro, the opportunity for metabolically active
strains delivered in combination to result in similar effects
in vivo may not present itself.
None of the studies included in the present review
comment on the rationale behind their choice of dosage.
The International Scientific Association for Probiotics
and Prebiotics provides a list of dosages ranging from
1×10
8
to 1.8 × 10
12
CFU twice daily depending on strain
and disease, based on at least one well designed clinical
trial showing a beneficial effect for a health promoting
or therapeutic outcome [40]. However, the list covers
gastrointestinal disorders only and does not address fecal
microbiota in healthy participants. In general, different
dosages should be assessed to facilitate an interpretation
of the dose-response relationship of probiotic consump-
tion on relevant outcomes, rather than on safety and via-
bility alone. The information provided by such studies
would enable the identification of the dosage needed to
observe an impact on the relevant outcome and add to
the likelihood that an observed association is causal [41].
Hanifi et al. [21] examined and detected oral dose-
response relationships, but for tolerance and gastrointes-
tinal viability only. As of now, it is impossible to draw
any conclusions on the ideal dosage regarding effects on
the fecal microbiota composition. Likewise, the optimal
duration of intervention remains elusive.
Mode of administration may also contribute to the
observed lack of impact on fecal microbiota. Ingested
probiotics must survive hostile environments including
acidic, protease and bile-salt rich conditions during their
passage through the gastrointestinal tract [42, 43]. Cur-
rently, screening feces is the only way to assess whether the
probiotics have survived through the gastrointestinal tract.
Yet, the site of action may be proximal to the colon and it
is not necessarily possible to conclude on the degree of
colonization or even the amount of bacteria that produce
the effect [44]. In contrast to the findings in five of the in-
cluded studies, the study by Rampelli et al. [23] showed
only a trend towards enrichment of the probiotic strain,
perhaps due to the use of biscuits as the mode of adminis-
tration, yet, another reason may be low compliance. This
may add to the explanation why little effect of probiotics
was found in Rampelli et al. [23]. Compliance was evalu-
ated as sufficient in the remaining studies [19–22, 24, 27].
Kristensen et al. Genome Medicine (2016) 8:52 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Further adding to the heterogeneity between studies
is the application of different methods for assessing
fecal microbiota. Even though all studies applied high-
throughput metagenomics approaches, the resolution
and specificity levels varied tremendously and no studies
used the same methodological approach. Only one study
examined the fecal microbiota by an untargeted metage-
nomic approach using shotgun sequencing and thereby
providing information on microbial gene and derived
functional levels, free of bias introduced by amplification
of a specific genomic region as is the case in targeted 16S
rRNA sequencing and array-based analyses [45]. Com-
positional information can be achieved by mapping the
reads to a microbial gene reference catalogue [34], as
was done by Brahe et al. [20]. Still, only a fraction of se-
quencing reads can be mapped to the existing reference
catalogues. The targeted 16S rRNA approach provides
information at the taxonomic level in the form of abun-
dance and phylogenetic relationship, but the method has
pitfalls in PCR amplification steps [46] and cross-platform
comparison is not straightforward. Of the included stud-
ies, two use 454 pyrosequencing [19, 21], one uses Illu-
mina MiSeq [24], another uses Ion Torrent sequencing
[27], and two use phylogenetic microarrays [22, 23]. The
sequencing platforms differ in costs, coverage, and length
of reads with the Illumina platform becoming more widely
used [45]. Community profiles from HITChip correlate
well with pyrosequencing-based compositions (Pearson
correlations at phylum (average r = 0.94), class (0.93),
order (0.94) and family levels (0.77)) and the HTF
microbe.array has demonstrated good reproducibility by
cluster analysis of the phylogenetic fingerprint in samples
from the same participant [47, 48]. In general, using
phylogenic microarray approaches have the advantages of
being cost-efficient for compositional characterization;
however cross-hybridization may occur and only taxa that
are covered by the reference sequences can be detected
[47]. Another well-known source of bias in 16S rRNA-
based studies is the targeted hypervariable region of the
16S rRNA gene. The region used for analysis in the in-
cluded studies applying 16S rRNA-based methods varies
with one study using V1 and V6 [22], one study using V3
[27], one study using V3 and V4 [19], one study using V5
and V6 [24], and one study does not specify [23]. Several
studies have examined the effects of region choice when
evaluating fecal microbiota composition with no current
international consensus [47, 49–51].
Probiotics in health and disease
In a recent systematic review including 29 trials studying
healthy adults with undisturbed microbiota (using
non-high-throughput molecular techniques) only ~20 %
showed an effect of probiotic treatment on fecal micro-
biota. It is concluded that there is little, if any, evidence of
an effect of probiotic treatment in circumstances where
the microbiota is unperturbed by pathophysiological pro-
cesses or pharmaceutical treatment (antibiotics or chemo-
therapy), either concurrent with or prior to intervention.
However, where dysbiosis is present or where the micro-
biota is perturbed, there is some evidence for a restorative
or protective effect of certain strains of probiotics, both on
the fecal microbial community itself, but more import-
antly, also on host physiology, e.g. alleviation of gastro-
intestinal symptoms [18].
In the case of dysbiotic or perturbed microbial commu-
nities, any restorative or protective effect on the micro-
biota alone, without any measurable beneficial effect for
the host, would predominantly be of academic interest by
improving our understanding of the intestinal ecosystem.
In the case of undisturbed microbiota, any inference of
health benefit from changes to the microbiota alone would
be highly speculative without a direct linkage to relevant
host phenotypes. Ideally, hard endpoint data would deter-
mine the effects of probiotics in healthy individuals, but
considering the time perspective of generating such data
this may be long in coming. Until such studies are avail-
able, any statement on health benefits of probiotic supple-
mentation in healthy participants would rely on observed
effects on biomarkers or other intermediate outcomes.
Limitations
Limitations of this review include the search terms used
to identify relevant papers. In addition to probio*, we
specifically searched on bifido* and lacto*, but other
search terms such as Bacillus and Saccharomyces could
have been relevant. Publication bias is a well-known
challenge within the field of systematic reviews and
meta-analyses; however, the majority of the studies in-
cluded in the present review provide null findings, indi-
cating that this concern may be settled to some extent.
Language bias cannot be ruled out since our search was
exclusively based on English language dominated sources.
Conclusions
Based on our review of the available RCTs, we find there
is a lack of evidence to conclude whether or not there is
an effect of probiotics on fecal microbiota composition
in healthy adults, as assessed by high-throughput mo-
lecular techniques. A number of issues blur the conclu-
sions that can be drawn from the studies, including
small sample sizes with lack of statistical power, low
resolution-methods of assessing fecal microbiota com-
position, inter-individual variation in susceptibility to-
ward the probiotic, use of different probiotic strains
either in isolation or in combination, variations in dos-
age and administration mode of probiotics, duration of
intervention, or variation in the habitual diet of partici-
pants. Future research on the impact of probiotics on
Kristensen et al. Genome Medicine (2016) 8:52 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
fecal microbiota configuration and function should in-
volve statistically well-powered RCTs in well-phenotyped
individuals. Importantly, future studies would also benefit
from pre-specifying the primary outcome and transpar-
ently reporting the results including effect sizes, confi-
dence intervals, and Pvalues as well as providing a clear
distinction of within-group and between-group com-
parisons. For the purpose of demonstrating health ben-
efits of probiotic supplementation, effects should be
demonstrated on relevant host phenotypes, which is
non-trivial in healthy participants. Studies with micro-
biome features as the primary outcome should be reserved
for improving our understanding of biology in general and
our insight into microbial interactionsinvivoinparticular.
Abbreviations
CFU: Colony forming units; OTU: Operational taxonomic unit; PCoA: Principal
coordinate analysis; RCT: Randomized controlled trial; rRNA: Ribosomal RNA.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
NBK performed the literature search, quality assessment of the included
studies, and wrote the manuscript. TB performed the literature search and
quality assessment of the included studies. KA, TN, THH, and OP supervised
the literature search and quality assessment of the included studies and
revised the manuscript critically. All authors approved the final manuscript.
Funding
The Novo Nordisk Foundation Center for Basic Metabolic Research is an
independent research center at the Faculty of Health and Medical Sciences,
University of Copenhagen and is partially funded by an unrestricted grant
from the Novo Nordisk Foundation (http://www.metabol.ku.dk). The funders
played no role in study design, collection, analysis, interpretation of data,
writing of the report, or in the decision to submit the paper for publication.
Received: 15 January 2016 Accepted: 8 April 2016
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