EBioMedicine 13 (2016) 190–200
Contents lists available at ScienceDirect
EBioMedicine
j ourna l homepage: www.eb iomed ic ine.comResearch PaperProbioticWith orWithout Fiber Controls Body Fat Mass, AssociatedWith
Serum Zonulin, in Overweight and Obese Adults—Randomized
Controlled Trial☆,☆☆Lotta K. Stenman a,⁎, Markus J. Lehtinen a, Nils Meland b, Jeffrey E. Christensen c, Nicolas Yeung a,
Markku T. Saarinen a, Michael Courtney c, Rémy Burcelin c, Marja-Leena Lähdeaho d, Jüri Linros e, Dan Apter f,
Mika Scheinin g, Hilde Kloster Smerud b, Aila Rissanen h, Sampo Lahtinen a
a Global Health and Nutrition Science, DuPont Nutrition & Health, FI-02460 Kantvik, Finland
b Smerud Medical Research, N-0212 Oslo, Norway
c Vaiomer S.A.S, FR-31670 Labège, France
d FinnMedi, FI-33520 Tampere, Finland
e Kerava Health Centre, FI-04200 Kerava, Finland
f VL-Medi, FI-00101 Helsinki, Finland
g Clinical Research Services Turku, FI-20520 Turku, Finland
h Obesity Research Unit, University of Helsinki, FI-00290 Helsinki, Finland☆ Conflict of interest statement: LKS, MJL, NY, MTS and
☆☆ The research was funded by DuPont Nutrition & Hea
⁎ Corresponding author at: DuPont Nutrition & Health
E-mail address: lotta.stenman@dupont.com (L.K. Sten
http://dx.doi.org/10.1016/j.ebiom.2016.10.036
2352-3964/© 2016 The Authors. Published by Elsevier B.Va b s t r a c ta r t i c l e i n f oArticle history:
Received 19 August 2016
Received in revised form 17 October 2016
Accepted 24 October 2016
Available online 26 October 2016Background: The gut microbiota is interlinked with obesity, but direct evidence of effects of its modulation on
body fat mass is still scarce. We investigated the possible effects of Bifidobacterium animalis ssp. lactis 420
(B420) and the dietary fiber Litesse®Ultra polydextrose (LU) on body fat mass and other obesity-related param-
eters.
Methods: 225 healthy volunteers (healthy, BMI 28–34.9) were randomized into four groups (1:1:1:1), using a
computer-generated sequence, for 6months of double-blind, parallel treatment: 1) Placebo,microcrystalline cel-
lulose, 12 g/d; 2) LU, 12 g/d; 3) B420, 1010 CFU/d in microcrystalline cellulose, 12 g/d; 4) LU + B420,
12 g + 1010 CFU/d. Body composition was monitored with dual-energy X-ray absorptiometry, and the primary
outcomewas relative change in body fatmass, comparing treatment groups to Placebo. Other outcomes included
anthropometric measurements, food intake and blood and fecal biomarkers. The study was registered in
Clinicaltrials.gov (NCT01978691).
Findings: There were marked differences in the results of the Intention-To-Treat (ITT; n=209) and Per Protocol
(PP; n=134) study populations. The PP analysis included only those participants who completed the interven-
tionwith N80%product compliance andno antibiotic use. In addition, three participantswere excluded fromDXA
analyses for PP due to a long delay between the end of intervention and the last DXA measurement. There were
no significant differences between groups in body fat mass in the ITT population. However, LU+ B420 and B420
seemed to improve weight management in the PP population. For relative change in body fat mass, LU + B420
showed a− 4.5% (−1.4 kg, P= 0.02, N= 37) difference to the Placebo group, whereas LU (+0.3%, P= 1.00,
N = 35) and B420 (−3.0%, P = 0.28, N = 24) alone had no effect (overall ANOVA P = 0.095, Placebo N =
35). A post-hoc factorial analysis was significant for B420 (−4.0%, P= 0.002 vs. Placebo). Changes in fat mass
were most pronounced in the abdominal region, and were reflected by similar changes in waist circumference.
B420 and LU + B420 also significantly reduced energy intake compared to Placebo. Changes in blood zonulin
levels and hsCRP were associated with corresponding changes in trunk fat mass in the LU + B420 group and
in the overall population. There were no differences between groups in the incidence of adverse events.
Discussion: This clinical trial demonstrates that a probiotic productwith orwithout dietary fiber controls body fat
mass. B420 and LU+ B420 also reduced waist circumference and food intake, whereas LU alone had no effect on
the measured outcomes.
© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).Keywords:
Clinical trial
Fiber
Obesity
Prebiotic
Probiotic
SynbioticSL are employees of DuPont Nutrition & Health.
lth.
, Sokeritehtaantie 20, FI-02460 Kantvik, Finland.
man).
. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
191L.K. Stenman et al. / EBioMedicine 13 (2016) 190–2001. IntroductionThe gut microbiota is associated with metabolic disorders such as
obesity and type 2 diabetes. Since the discovery of a link between gut
microbiota and obesity in 2006 (Turnbaugh et al., 2006; Ley et al.,
2006), increasing evidence has been presented to suggest a causal rela-
tionship between gut microbiota and metabolic disorders (Stenman
et al., 2015a).
One of the mechanisms postulated to explain this relationship is
the metabolic endotoxemia hypothesis, which links gut microbes to
low-grade inflammation and further to metabolic disorders
(Burcelin et al., 2009; Cani et al., 2007). In experimental animals
(Stenman et al., 2012; Brun et al., 2007) and also in humans
(Teixeira et al., 2012; Leber et al., 2012), obesity and metabolic dis-
orders have been associated with an impaired gut barrier, which
may lead to increased translocation of endotoxins (Kallio et al.,
2015; Stenman et al., 2014), especially in connection with a Western
diet (Pendyala et al., 2012). These highly inflammatory components
can ultimately lead to tissue inflammation and, consequently, meta-
bolic disorders (Burcelin et al., 2009).
Dietary recommendations have been put forward to try to prevent
metabolic disorders. In the Finnish Diabetes Prevention Study, carried
out in a large cohort of middle-aged participants with impaired glucose
tolerance, a simple lifestyle intervention reduced the risk of diabetes by
58% compared to control (Tuomilehto et al., 2001). Of those subjects
with excellent adherence to the intervention program, meeting four of
the five intervention goals, none developed type 2 diabetes. Still, in
the general population, the prevalence of metabolic disorders is rapidly
increasing, and supportive prevention options—such as those targeting
the gut microbiota—need to be developed to support current standards
of care.
Probiotics are livemicro-organisms that confer health benefits to the
host (Hill et al., 2014), whereas prebiotics are fibers that selectively im-
prove the growth of beneficial gut microbes (Gibson and Roberfroid,
1995). In experimental animals, we have previously shown that
Bifidobacterium animalis ssp. lactis 420 (B420) prevented weight gain,
improved insulin sensitivity, aswell as reduced endotoxemia and tissue
inflammation (Amar et al., 2011; Stenman et al., 2014, 2015b; Garidou
et al., 2015). The probiotic B420 has been given to humans in earlier
clinical trials, albeit at quite low doses (Klein et al., 2008; Roessler et
al., 2008; Kok et al., 1996), but its potential anti-obesity effects have
remained unexplored. The prebiotic employed in the current study,
Litesse® Ultra polydextrose (LU), is a randomly cross-linked polymer
of glucose, which remains undigested by the host and may increase
the number of Bifidobacteria in a colonic continuous culture system
(Probert et al., 2004). Its administration has been reported to induce sa-
tiation (Ibarra et al., 2015) and to ameliorate the glycemic response to a
glucose load (Jie et al., 2000), indicating potential benefits for weight
maintenance and metabolic health.
To date, there is no conclusive evidence for the ability of probiotics to
control body fat mass in humans. Despite a fair amount of promising
clinical findings (Sanchez et al., 2014; Kadooka et al., 2010, 2013;
Osterberg et al., 2015), randomized controlled trials demonstrating
beneficial probiotic effects in the primary statistical analysis of a
well-powered study conducted according to Good Clinical Practice
(GCP) are lacking. Only few studies on prebiotics have shown effects
on weight management (Parnell and Reimer, 2009; Li et al., 2010).
Furthermore, no clinical trials have explored probiotics and prebi-
otics alone and in combination to assess their potential synergistic
benefits for metabolic health. Therefore, we conducted a double-
blind, randomized, placebo-controlled, multi-center clinical trial
adhering to the GCP principles to investigate the effects of a probiotic
(B420) and a prebiotic (LU) on weight management and an exten-
sive panel of mechanistic parameters, including markers of low-
grade inflammation, adipose tissue metabolism, bacterial transloca-
tion and fecal short-chain fatty acids.2. Materials and Methods
This double-blind, randomized, parallel, placebo-controlled clinical
trial was conducted in four clinical research centers in southern Finland:
VL-Medi Oy (Helsinki), Clinical Research Services Turku (Turku),
FinnMedi Oy (Tampere) and Kerava Health Care Center (Kerava). All re-
search procedures performed in this trial were in strict accordance with
a pre-defined protocol, and adhered to international GCP guidelines and
theDeclaration of Helsinki. The studywas approved by theCoordinating
Ethics Committee of the Hospital District of Helsinki and Uusimaa and
all participants signed informed consent prior to participation. This
study is registered in ClinicalTrials.gov with the identifier
NCT01978691.
2.1. Participants
Eligible participants were 18–65 years old with a body mass index
(BMI) between 28.0–34.9 and a waist to hip ratio of ≥0.88 for males
and ≥0.83 for females.
Exclusion criteria were, briefly: diagnosed type 1 or type 2 diabetes
or cardiovascular disease, or use of related medication; use of laxatives,
fiber supplements or probiotics in the previous 6 weeks; inflammatory
disorders and use of immunomodulatory drugs; history of bariatric sur-
gery; use of anti-obesity drugs in the previous 3 months; recent (last
2months) or on-going antibiotic use; excessive use of vitaminD supple-
mentation; active or recent (last 3 months) participation in a weight
loss program or weight change of 3 kg during the past 3 months; preg-
nant or planning pregnancy within 6 months or breastfeeding women;
drug or alcohol abuse; and other reasons identified by the Investigator.
2.2. Intervention, Compliance and Stability
All study products were obtained from DuPont Nutrition and Health
(Madison,WI, USA) in sachets. The study armswere: 1) Placebo, micro-
crystalline cellulose 12 g/day; 2) B420, 1010 CFU/day in 12 g of micro-
crystalline cellulose (B420); 3) LU, 12 g/day; and 4) B420, 1010 CFU/
day in 12 g of LU (LU + B420). Microcrystalline cellulose was chosen
as placebo because it contains virtually no energy and is less fermented
by gut microbes than alternative dietary fibers. Participants were pro-
vided with commercially available fruit smoothie products and
instructed to mix the contents of one sachet into a 250 ml bottle of
smoothie (130 kcal) each daywhile otherwisemaintaining their regular
diet. The smoothies were provided to the subjects tomask subtle differ-
ences in the study products and to standardize the matrix in which the
products were consumed.
Following separate visits for screening and baseline assessments,
participants used the study product for six months, during which they
came for clinic visits at 2 months, 4 months and 6 months, with in-be-
tween telephone contacts to track compliance and potential adverse
events. After completing the intervention, participants came for one
more follow-up clinic visit onemonth after the end of the dietary inter-
vention (month 7).
Compliance was monitored with three different methods: 1) Partic-
ipants were asked to report product intake on a specific check-list, 2)
Participants were asked to return all used and unused sachets to the
site to count the number of opened sachets per the number of treatment
days, and 3) Fecal samples were analyzed for the presence of B420 with
qPCR from all participant who returned a fecal sample at the six-month
visit. Details of the qPCR detection procedures are reported in the Sup-
plementalMaterials andMethods. In the Intention-to-Treat (ITT) popu-
lation, the average calculated compliance (method 2) was 86% across
groups, while in the B420 and LU + B420 groups the bacterial strain
was detected in the feces of 82% of participants. The primers used for
qPCR were also able to detect certain other Bifidobacterium animalis
ssp. lactis strains such as HN019, Bl-04 and Bi-07, but not Bb12. Four
192 L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200participants (8%) in the Placebo group and seven (15%) in the LU group
tested positive in the qPCR assay (Table S1).
Product stability was monitored throughout the study. The mini-
mum target activity for B420 was planned at 1 × 1010 CFU/day. The
dose of B420 at the time of packaging was 1.4 × 1010 CFU/day in the
B420 group and 1.3 × 1010 CFU/day in the LU+ B420 group to account
for loss of stability during the study. Sachets returned from the study
participants were re-tested for probiotic cell count at the end of the
study with the following results: B420 1.1 × 1010 CFU/day, and
LU + B420 1.1 × 1010 CFU/day, demonstrating excellent stability of
the study probiotic. There were no contaminations in the Placebo and
LU products.
2.3. Body Composition-Related Outcomes
The pre-defined primary outcome of the present study was the rela-
tive change in body fat mass from baseline to end-of-treatment
(6 months). Body fat mass was measured with a dual-energy X-ray ab-
sorptiometry (DEXA) scan at qualified private medical centers. In addi-
tion, body fat mass and lean bodymass were recorded as total and from
individual regions of the body (android, gynoid, trunk, legs, arms).
Other obesity-related outcomes included body weight and waist and
hip circumference, which were measured with calibrated weighing
scales and tape measures, respectively.
2.4. Clinical Laboratory Outcomes
At the clinic visits (baseline, 2 months, 4 months, 6 months and fol-
low-up), bloodwas drawn in themorning from fasting participants and
sent from all sites to a certified central laboratory (United Medix Labo-
ratories, Finland) for analysis of serum high-sensitivity C-reactive pro-
tein (hsCRP), serum glucose, serum insulin, blood glycosylated
hemoglobin (HbA1c), serum lipids (total cholesterol, LDL, HDL and tri-
glycerides) and serum cortisol. The homeostatic model assessment of
insulin resistance (HOMA-IR) was calculated from fasting glucose and
insulin levels ([glucose in mmol/l] * [insulin in mU/l] / 22.5). Serum
liver markers (ASAT, ALAT, gamma-glutamyltransferase) were also an-
alyzed at the central laboratory to monitor product safety.
2.5. Exploratory Laboratory Outcomes
The following blood biomarkers were analyzedwith ELISA: interleu-
kin-6 (IL-6), tumor necrosis factor alpha (TNF-α), interleukin-1beta (IL-
1β), plasminogen activator inhibitor 1 (PAI-1), vascular cell adhesion
protein 1 (VCAM-1), intracellular adhesion molecule 1 (ICAM-1), E-
selectin, sCD14, leptin, monocyte chemoattractant protein 1 (MCP-1),
adiponectin, zonulin, angiopoietin-like protein 4 (angptl4), oxidized
LDL cholesterol (oxLDL) and ApoB-48. The employed assay kits and
their performance characteristics (coefficients of variation) are detailed
in Supplementary Materials and Methods.
For lipopolysaccharide (LPS) analyses, serum samples were diluted
1:100, heat-treated for 45 min at 60 °C, and assayed in triplicate using
the Limulus Amebocyte Lysate (LAL) kinetic chromogenic method
(Charles River). The quantitation limit was 48 EU/l.
2.6. Analysis of Acidic Fermentation Products in Fecal Samples
Fecal short-chain fatty acids (SCFA)were analyzedwith gas chroma-
tography, as detailed in the Supplemental Materials and Methods.
2.7. Food Intake and Exercise Measurements
Food intake was measured with a 5-day food diary at baseline,
2 months and 6months (end of intervention) as detailed in the Supple-
mental Materials and Methods.Habitual exercise was captured with a simple three-point question-
naire and calculated into metabolic equivalents (MET h/day), as previ-
ously described (Kujala et al., 1998).
2.8. Adverse Events
Investigators asked study participants about adverse events at every
study visit. All possible adverse eventswere recorded and coded accord-
ing to MedDRA. The adverse events were classified by the Investigator
according to severity (mild, moderate, severe) and possible relationship
to treatment (unrelated, unlikely, possible, probable, not assessable).
2.9. Sample Size Determination
A one-way analysis of covariance (ANCOVA)was used to analyze the
primary outcome, with baseline body fat mass as covariate. The mean
relative change from baseline to 6 months in body fat mass was as-
sumed to be 1% in the active treatment groups compared to the placebo
group, and the common standard deviationwas expected to be 2%.With
these assumptions, 43 participants per group (172 in total) was calcu-
lated to give above 80% power to reject a null hypothesis of no differ-
ence between the active treatments compared to placebo with a one-
wayANOVA, using a 5% level of significance. The power for the ANCOVA
model was expected to be similar or slightly higher than for an ANOVA
model. In order to compensate for an expected drop-out rate of approx-
imately 23.5%, a total of 225 participants were randomized into the
study.
2.10. Randomization and Blinding
The randomization scheme (1:1:1:1 allocation)was generated using
a computerized procedure into blocks of four randomization codes each.
Boxes of investigational product were labeled with the corresponding
randomization code and study centers were advised to always use the
smallest available randomization code and corresponding study prod-
uct. The randomization codewas generated by the contract research or-
ganization Smerud Medical Research Finland Ab Oy (Kirkkonummi,
Finland). The participants, the site personnel, the study monitor, the
statistician and sponsor's representatives were all blinded to the ran-
domization until the end of the intervention phase, when all data for
primary and secondary outcomes, adverse events and compliance had
been collected and validated. The study populations were defined
prior to breaking the treatment code. Exploratory outcomes were
assayed in a blinded fashion, but were not included in the blind data re-
view process.
2.11. Statistical Methods
The primary and pre-determined statistical analysis was anANCOVA
to compare changes from baseline to end-of-intervention. Baseline
values were used as covariates when they were significant in the
model. For the ITT population, the last observation was carried forward
for missing observations. All group-wise comparisons were performed
using Dunnett's adjustment for multiple testing. A factorial analysis
(two-way ANCOVA) was conducted post-hoc after unblinding, because
of the lower number of participants in the B420 group compared to the
other groups. There was no apparent reason for the higher number of
exclusions in the B420 group, since the reasons for exclusion were sim-
ilar across all groups.
Data for LPS, hsCRP and IL-6 were considered non-normally distrib-
uted. Data for hsCRP and IL-6 were analyzed with ANCOVA and
Dunnett's adjustment after log-transformation. Data for LPS were ana-
lyzed with the Kruskal-Wallis test, with Dwass, Steel, Critchlow-Fligner
analysis of between-group comparisons. Correlations between changes
from baseline to end-of-study for the different markers were calculated
with Spearman correlation.
193L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200All datawere analyzedwith SAS version 9.3 using a significance level
of 0.05.3. Results
3.1. Recruitment and Participant Flow
All 225 participants were randomized at four centers in southern
Finland between December 2013 and October 2014, and the last partic-
ipant completed the last follow-up visit inMay 2015. Study participants
were randomized to four groups for a six-month intervention and a
one-month follow-up period: 1) Placebo, 12 g/day of microcrystalline
cellulose; 2) LU, 12 g/day; 3) B420, 1010 Colony Forming Units (CFU) /
day in 12 g microcrystalline cellulose; 4) LU + B420, 1010 CFU/day of
B420 in 12 g/day LU.
The study populations and reasons for exclusions are shown in Fig. 1.
Of the 225 randomized participants, the Investigator excluded one per-
son from the study due to very high cholesterol levels, before the partic-
ipant had taken any study product. Therefore, the total population
analyzed for adverse events included 224 participants. The ITT popula-
tion contained all 209 participants who were analyzed for any variable
after the baseline visit, whereas the Per Protocol (PP) population includ-
ed, as defined in the protocol, all 134 participants who completed the
study without major protocol violations and who had at least more
than one study visit completed. A Blind Data Review process prior to
unblinding the study detailed the major protocol violations as follows:
1) b80% of study product compliance or N7 consecutive days of non-
compliance; 2) Baseline or 6-month visit not completed; 3) ParticipantAssessed for eligiEnrollment
Placebo
Allocated to intervention (n= 57)
Randomized
PP analysis
LU
Allocated to intervention (n= 57)
Placebo ITT population (n= 56)
Reasons for exclusion:
¨ Not assessed for any 
variable after baseline 
(n= 1)
LU ITT population (n= 53)
Reasons for exclusion: 
¨ Not assessed for any 
variable after baseline 
(n= 4)
Placebo PP population (n= 36)
Reasons for exclusion:
¨ Study product non-
compliance (n= 7)
¨ Use of systemic 
antimicrobials (n= 11)
¨ End-of-intervention 
visit not completed (n= 
10)
LU PP population (n= 36)
Reasons for exclusion:
¨ Study product non-
compliance (n= 12)
¨ Use of systemic 
antimicrobials (n= 7)
¨ End-of-intervention visit 
not completed (n= 5)
¨ Use of high-dose vitamin 
supplements: (n= 2)
B4
Re
ITT analysis
Fig. 1. Participant flow. Before unblinding the study, participantswere divided into an Intention-
study protocol.randomized against inclusion and exclusion criteria (e.g. use of high-
dose vitamin D supplements); and 4) Use of systemic antibiotics during
the intervention period. In addition, three participants were excluded
from the PP population for body composition measurements for not
completing themeasurement within ten days after discontinuing treat-
ment. None of the participants were reallocated to another group for
analysis.
The demographics of the ITT and PP populations are shown in
Table 1 and were similar among the different treatment groups. Statis-
tical conclusions for the ITT and PP populations differed greatly, and
therefore drop-outs and protocol violations—which are common espe-
cially in long intervention studies such as this—were considered to
have a significant impact on the outcomes. Consequently, the present
report is mainly focused on the protocol-compliant PP population.
Main outcomes for the ITT population can be found in Table S2.3.2. Adverse Events and Safety Parameters
All symptoms occurring during the study were documented and
rated for severity and potential relationship with the study products.
There were no major differences in the distribution of adverse events
across groups. Over the course of 7 months, 199 of 224 study partici-
pants reported at least one adverse event, out of which 133were poten-
tially product-related. Adverse events are summarized in Table S3.
Gastrointestinal symptoms were generally mild, and the median dura-
tion of the cases of diarrhea and loose stools was three days. There
were no changes in measured safety parameters: blood pressure,
heart rate and liver enzymes (Table S3).bility (n= 263)
Excluded for not meeting entry criteria (n= 38)
(n= 225)
 (n= 134)
B420
Allocated to intervention (n= 55)
LU+B420
Allocated to intervention (n= 56)
B420 ITT population (n= 48)
Reasons for exclusion: 
¨ Not assessed for any 
variable after baseline 
(n= 7)
LU+B420 ITT population (n= 52)
Reasons for exclusion: 
¨ Not assessed for any 
variable after baseline 
(n= 4)
20 PP population (n= 25)
asons for exclusion:
¨ Study product non-
compliance (n= 14)
¨ Use of systemic 
antimicrobials (n= 8)
¨ End-of-intervention visit 
not completed (n= 8)
¨ Use of high-dose vitamin 
supplements: (n= 2)
LU+B420 PP population (n= 37)
Reasons for exclusion:
¨ Study product non-
compliance (n= 10)
¨ Use of systemic 
antimicrobials (n= 7)
¨ End-of-intervention visit 
not completed (n= 4)
¨ Use of high-dose vitamin 
supplements: (n= 1)
(n= 209)
to-Treat (ITT) population and a Per Protocol (PP) population according to adherence to the
Table 1
Baseline demographics of the Intention-to-Treat and Per Protocol populations.
Placebo LU B420 LU + B420 Total
Intention-to-Treat n 56 53 48 52 209
Gender n, Male/Female 12/44 12/41 9/39 9/43 42/167
Ethnicity n, Caucasian/Other 55/1 52/1 48/0 51/1 206/3
Age, years mean ± SD 49.9 ± 8.5 48.8 ± 10.5 50.6 ± 10.6 47.0 ± 11.1 49.1 ± 10.2
Weight, kg mean ± SD 88.5 ± 12.2 89.4 ± 9.1 88.7 ± 9.3 87.8 ± 11.0 88.6 ± 10.4
BMI, kg/m2 mean ± SD 31.2 ± 2.2 31.2 ± 1.6 31.5 ± 2.2 31.3 ± 2.0 31.3 ± 2.0
Waist circumference, cm mean ± SD 103.0 ± 8.4 103.4 ± 6.1 102.6 ± 6.9 102.5 ± 6.9 102.9 ± 7.1
Blood glucose, mmol/l mean ± SD 5.24 ± 0.50 5.20 ± 0.62 5.18 ± 0.43 5.19 ± 0.42 5.20 ± 0.50
Total Cholesterol, mmol/l mean ± SD 5.23 ± 0.87 5.22 ± 1.00 5.26 ± 1.08 5.50 ± 0.96 5.30 ± 0.97
LDL Cholesterol, mmol/l mean ± SD 3.10 ± 0.77 3.08 ± 0.82 3.25 ± 0.91 3.38 ± 0.89 3.20 ± 0.85
Triglycerides, mmol/l mean ± SD 1.22 ± 0.61 1.27 ± 0.53 1.27 ± 0.56 1.29 ± 0.61 1.26 ± 0.58
Per-Protocol n 36 36 25 37 134
Gender n, Male/Female 10/26 8/28 7/18 6/31 31/103
Ethnicity n, Caucasian/Other 36/0 35/1 25/0 37/0 133/1
Age, years mean ± SD 48.3 ± 8.6 48.6 ± 10.9 49.1 ± 11.9 47.1 ± 10.9 48.2 ± 10.4
Weight, kg mean ± SD 88.7 ± 12.5 89.7 ± 9.4 88.9 ± 10.3 87.7 ± 11.3 88.7 ± 10.9
BMI, kg/m2 mean ± SD 31.0 ± 2.2 31.2 ± 1.6 30.9 ± 1.9 31.2 ± 2.0 31.1 ± 1.9
Waist circumference, cm mean ± SD 102.1 ± 7.5 103.3 ± 6.6 103.3 ± 7.5 102.1 ± 7.3 102.7 ± 7.2
Blood glucose, mmol/l mean ± SD 5.21 ± 0.54 5.18 ± 0.68 5.16 ± 0.35 5.22 ± 0.42 5.20 ± 0.52
Total Cholesterol, mmol/l mean ± SD 5.16 ± 0.86 5.24 ± 1.01 5.14 ± 0.86 5.58 ± 0.90 5.29 ± 0.92
LDL Cholesterol, mmol/l mean ± SD 3.05 ± 0.75 3.12 ± 0.73 3.12 ± 0.66 3.39 ± 0.84 3.17 ± 0.76
Triglycerides, mmol/l mean ± SD 1.20 ± 0.67 1.25 ± 0.54 1.33 ± 0.58 1.36 ± 0.66 1.28 ± 0.62
194 L.K. Stenman et al. / EBioMedicine 13 (2016) 190–2003.3. B420 with or without LU Controls Body and Trunk Fat Mass, and Show
Synergistic Effects on Lean Body Mass
The primary outcome of this study was the relative change in body
fatmass from baseline to the end of the intervention period (6months).
Body composition was measured with DEXA every two months during
the intervention and at the follow-up after onemonth. In the ITT popu-
lation, therewere no statistically significant differences between the ac-
tive treatment groups and Placebo in the changes in total body fat mass
(Fig. 2A). In the PP population, however, the LU+B420 group showed a
significant reduction in the change in total body fat mass compared to
Placebo (Fig. 2B), resulting in an average difference of 1.4 kg in total
body fat between the groups. The effect seemed to persist throughout
the one-month wash-out period (Fig. 2C). The difference in body fat
mass wasmost noticeable in the trunk area and demonstrated a similar,
although non-significant pattern in the abdominal (i.e. android) area
(Fig. 2D–E). In other regions of the body LU + B420 had no effect. LU
and B420 (alone) were not statistically different from Placebo in any
of the above mentioned variables. For absolute values in all measured
areas of the body in the PP population, please refer to Table S4. Changes
in total body fat mass per the following subgroups are shown in Fig. S1:
men vs. women, low vs. high baseline fat intake, and those in the ITT
populationwhoused systemic antimicrobials during the study. Markers
of glucose metabolism are presented in Table S5, and cardiovascular
biomarkers in Table S6.
Because of the lower number of observations in the B420 group
(n = 24) compared to the other groups (n = 35–37), we decided to
conduct a post-hoc factorial analysis to evaluate the independent
effects of B420 and LU in the PP population. In the PP population,
there were significant differences between the B420 and Placebo
groups in their changes in total body fat mass (P = 0.002), trunk
fat mass (P = 0.0002) and android fat mass (P = 0.004), indicating
that the probiotic itself could be effective for controlling fat mass,
especially in the abdominal region. LU had no independent effects
on these parameters.
In addition, the results for lean body mass suggested synergy be-
tween the two investigational products, LU and B420. Although neither
of the products alone had an effect on lean body mass, their combina-
tion significantly increased lean body mass compared to Placebo (Fig.
2F), and the difference developed as a gradual increase from baseline,although there were no changes in the physical exercise habits in any
of the groups (Fig. S2).3.4. B420 With or Without LU Reduces Waist Circumference Compared to
Placebo
The differences in body fat mass outcomes were not completely
reflected in body weight (Fig. 2G). In fact, only the B420 group showed
any trend towards a reduction in body weight (P=0.15) in the PP pop-
ulation,whereas in the LU+B420 group, the reduction in body fatmass
was counterbalancedwith an increase in lean bodymass. In the factorial
analysis, there was a significant effect for B420 (P=0.03), but no effect
for LU (P = 0.80) compared to placebo in the PP population. The
LU + B420 group also showed a 2.7% (2.6 cm) reduction in waist cir-
cumference (P = 0.047) (Fig. 2H) and a trend (P = 0.079) towards a
1.3% (1.4 cm) smaller hip circumference compared to Placebo (Fig. 2I).
According to the factorial analysis, there was a significant 2.4% differ-
ence in the change in waist circumference between B420 and Placebo
(−2.4 cm compared to Placebo) (P= 0.004).3.5. Six-Month Supplementationwith B420 and LU+B420 Reduces Energy
Intake Compared to Placebo
Wemeasured dietary intake with 5-day food diaries at baseline and
at 2 and 6 months after study start. There were no differences between
the groups in baseline dietary intake (Table S7). B420 and LU+B420 re-
duced energy intake by approximately 300 kcal/day and 210 kcal/day
compared to Placebo (Table 2). This was associated with a reduced in-
take of dietary fat and fiber in the B420 group, but without differences
in the relative proportion of fat in the diet.
B420 also increased plasma cortisol concentrations (Fig. S3), which
has been suggested to increase energy expenditure and fat oxidation
(Brillon et al., 1995) and to suppress inflammation. Increased plasma
cortisol may also be related to an adverse stress response (Zunszain et
al., 2011), however, there was only one psychiatric-related adverse
event in the B420 group: amildworsening of depression,which also oc-
curred in one participant in the Placebo group. The investigator evaluat-
ed both cases as unrelated to the study product.
Pla
ce
bo LU
B4
20
LU
+
B4
20
-2
0
2
Ch
an
ge
in
bo
dy
w
ei
gh
t,
kg
Pla
ce
bo LU
B4
20
LU
+
B4
20
-6
-4
-2
0
2
4
6
Ch
an
ge
in
bo
dy
fa
tm
as
s,
%
a) b) c)
d) e) f)
P = 0.15
P = 0.76
P = 0.02
P = 0.28
P = 0.008
P = 0.08 P = 0.23P = 0.18 P = 0.012
P = 0.98
g) h) i)
P = 0.41
P = 0.15
P = 0.047
P = 0.26 P = 0.08
P = 0.79
Pla
ce
bo LU
B4
20
LU
+
B4
20
-6
-4
-2
0
2
4
6
Ch
an
ge
in
bo
dy
fa
tm
as
s,
%
0 2 4 6 +1
-5
0
5
10
Months into trial
Ch
an
ge
in
bo
dy
fa
tm
as
s,
%
Placebo
LU
B420
LU + B420
Pla
ce
bo LU
B4
20
LU
+
B4
20
-6
-4
-2
0
2
4
6
8
10
Ch
an
ge
in
tr
un
k
fa
tm
as
s,
%
Pla
ce
bo LU
B4
20
LU
+
B4
20
-10
-5
0
5
10
15
Ch
an
ge
in
an
dr
o
id
fa
tm
as
s,
%
Pla
ce
bo LU
B4
20
LU
+
B4
20
-4
-2
0
2
4
Ch
an
ge
in
le
an
bo
dy
m
as
s,
%
Pla
ce
bo LU
B4
20
LU
+
B4
20
-4
-3
-2
-1
0
1
2
3
Ch
an
ge
in
w
ai
st
ci
rc
um
fe
re
n
ce
,
%
Pla
ce
bo LU
B4
20
LU
+
B4
20
-4
-3
-2
-1
0
1
2
3
Ch
an
ge
in
hi
p
ci
rc
u
m
fe
re
n
ce
,
%
Fig. 2.Weight management outcomes. Change in total body fat mass from baseline to end of study (6 months of intervention) in the Intention-to-Treat (a) and Per Protocol (PP) (b)
populations. Evolution of body fat mass in the PP population during intervention and at follow-up (c). Changes in trunk fat mass (d), android fat mass (e), lean body mass (f), body
weight (g), waist circumference (h) and hip circumference (i) from baseline to end of study in the PP population. Body composition in panels a-f was measured with dual-energy X-
ray absorptiometry. Results are expressed as mean ± 95% CI. Results were analyzed with ANCOVA and Dunnett's pairwise comparisons corrected for multiple testing. In the ITT
population, the last observation was carried forward for the statistical analysis of participants who withdrew from the study. Solid line shows baseline; dotted line shows the level of
the Placebo group. N of participants included in the statistical analysis in the Intention-to-Treat population: Placebo n= 53, LU n= 51, B420 n= 47, LU + B420 n= 48. N for the PP
population: Placebo n= 35–36, LU n= 35–36, B420 n= 24–25, LU + B420 n= 37. Overall ANCOVA as follows: a) P= 0.46, b) P= 0.095, d) P= 0.036, e) P= 0.23, f) P= 0.30, g)
P= 0.13, h) P= 0.10, i) P= 0.31.
195L.K. Stenman et al. / EBioMedicine 13 (2016) 190–2003.6. Changes in Fat Mass Correlate with Circulating Zonulin and hsCRP
Levels of circulating zonulin, a potential marker of intestinal perme-
ability (Fasano et al., 2000; Moreno-Navarrete et al., 2012), seemed to re-
main consistently lower throughout the study in the B420 and LU+B420
groups compared to Placebo and LU (Fig. 3A), which was reflected as a
trend towards a difference between B420 and Placebo in the factorial
analysis (P= 0.063) (Table 3). Changes in zonulin were also statistically
significantly correlated with changes in trunk fat mass (r = 0.349,
P b 0.0001) (Fig. 3B), indicating a potential mechanistic link between
the two. This correlation was only evident in the LU + B420 group.Changes in hsCRP were somewhat similar to the changes in zonulin
(Fig. 3C). Although therewere no differences between groups in the pri-
mary analysis, a factorial analysis showed a tendency towards a reduc-
tion in hsCRP in B420 vs. Placebo (P = 0.073) (Table 3). Changes in
hsCRP were also significantly correlated with changes in trunk fat
mass (r = 0.217, P = 0.012) (Fig. 3D), but again only in the
LU + B420 group. Changes in hsCRP were also significantly correlated
with changes in zonulin (Spearman correlation r= 0.199, P= 0.021).
LU + B420 appeared to increase LPS level in plasma compared to
Placebo (Kruskal-Wallis P= 0.009), although the changes were small
and LPS level remained below or close to the detection limit in most
Table 2
Changes in food intake during the study, as assessed with 5-day food diaries (Per Protocol population).
Energy intake (kcal/day) Fat intake (g/day) Relative fat intake (% kcal) Fiber intake (g/day)
Absolute
Change from
baseline Absolute
Change from
baseline Absolute
Change from
baseline Absolute
Change from
baseline
Overall P P = 0.005 P = 0.008 P = 0.058 P = 0.055
Group Visit n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD n Mean ± SD
Placeboa Baseline 33 2240 ± 510 0 – 33 96 ± 32 0 – 33 38 ± 7 0 – 33 21 ± 6.6 0 –
Month 2 34 2270 ± 570 31 57 ± 500 34 90 ± 32 31 −4.9 ± 32 34 35 ± 7 31 −2.7 ± 6 34 20 ± 7.3 31 −1.3 ± 7
Month 6 36 2180 ± 460 33 −23 ± 600 36 92 ± 31 33 −2.2 ± 43 36 37 ± 7 33 −0.6 ± 8 36 20 ± 5.6 33 −1.2 ± 6
LU Baseline 33 2210 ± 650 0 – 33 92 ± 29 0 – 33 38 ± 5 0 – 33 22 ± 7.9 0 –
Month 2 35 2170 ± 640 33 −15 ± 430 35 93 ± 31 33 2.2 ± 23 35 39 ± 5 33 0.9 ± 6 35 20 ± 9.2 33 −1.5 ± 7
Month 6 34 2000 ± 520 33 −200 ± 510 34 80 ± 27 33 −12 ± 24 34 35 ± 4 33 −2.0 ± 6 34 21 ± 9.3 33 −1.1 ± 6
B420 Baseline 24 2200 ± 380 0 – 24 94 ± 22 0 – 24 38 ± 5 0 – 24 24 ± 6.6 0 –
Month 2 22 2130 ± 380 22 −120 ± 250 22 87 ± 26 22 −9.7 ± 18 22 36 ± 7 22 −2.4 ± 6 22 19 ± 5.7 22 −5.6 ± 5
Month 6 22 1900 ± 370 22 −320 ± 300⁎ 22 74 ± 22 22 −22 ± 19⁎ 22 35 ± 6 22 −4.0 ± 6 22 19 ± 6.8 22 −5.6 ± 6⁎
LU + B420 Baseline 35 2090 ± 640 0 – 35 87 ± 32 0 – 35 37 ± 6 0 – 35 20 ± 6.2 0 –
Month 2 34 1990 ± 440 33 −110 ± 530 34 79 ± 26 33 −8.6 ± 29 34 35 ± 7 33 −1.7 ± 7 34 16 ± 4.0 33 −4.2 ± 6
Month 6 31 1870 ± 440 29 −230 ± 640⁎ 31 74 ± 31 29 −10 ± 29 31 35 ± 6 29 −1.2 ± 5 31 16 ± 5.1 29 −3.9 ± 5
⁎ = Significant difference from Placebo, P b 0.05 (Dunnett's test, corrected for multiple comparisons). Only changes from baseline to month 6 were statistically compared between
groups.
a Women with energy intake b80% and men with energy intake b85% of basal metabolic rate excluded from dietary intake analyses. The energy content of the smoothie vehicle is
included in the results.
196 L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200participants. There was no effect on soluble cluster of differentiation 14
(sCD14), a co-receptor of LPS (Table 3), underlining the lack of inflam-
matory response to the slightly increased LPS levels. There were also
no differences in IL-6 levels (Table 3). Concentrations of TNF-α and IL-
1βwere below the lower limit of quantitation in 95 and 97% of the sam-
ples, respectively.
The corresponding outcomes for the ITT population are shown in
Table S8. Adipose tissue-related biomarkers are shown in Table S9.-7000
Ch
an
ge
 in
 z
o
n
u
lin
C
a) b)
c)
-7000
Ch
an
ge
 
in
 h
sC
RP
Ch
0 2 4 6 +1
-7.5
-5.0
-2.5
0.0
2.5
5.0
7.5
10.0
12.5
Months into trial
Ch
an
ge
in
se
ru
m
zo
n
u
lin
,n
g/
m
l
Placebo
LU
B420
LU + B420
0 2 4 6 +1
-3
-2
-1
0
1
2
3
Months into trial
Ch
an
ge
in
hs
CR
P,
m
g/
l
Placebo
LU
B420
LU + B420
d)
Fig. 3. Correlation between serum zonulin, serum hsCRP and trunk fat mass. Evolution of zonu
mass as changes from baseline to end-of-intervention (6 months) in the Per Protocol populati
each time point. Placebo n= 35–36, LU n= 35–36, B420 n= 24–25, LU + B420 n= 36–37.3.7. Increased Production of Bacterial Metabolites in Feces
In the factorial analysis, B420 increased the concentration of fecal
propionic acid, butyric acid and valeric acid (P b 0.05) (Table 4, data
for ITT population shown in Table S10.), which indicated an increased
metabolism of non-digestible polysaccharides and suggests changes in
the composition of the gut microbiota. A tendency for similar changes
was noted for acetic acid, although the factorial analysis was not-30
-20
-10
0
10
20
30
40
-2000 3000
Placebo
LU
B420
LU + B420
r = 0.160, P = 0.37
Total Spearman: r = 0.342, P < 0.0001
hange in trunk fat mass
r = 0.281, P = 0.10
r = 0.297, P = 0.16
r = 0.424, P = 0.01
-10
-8
-6
-4
-2
0
2
4
6
8
10
-2000 3000
Placebo
LU
B420
LU + B420
r = 0.050, P = 0.77
r = 0.061, P = 0.73
r = 0.100, P = 0.64
r = 0.369, P = 0.02
Total Spearman: r = 0.214, P = 0.014
ange in trunk fat mass
lin (a) and hsCRP (c), and correlations between zonulin (b) and hsCRP (d) with trunk fat
on. Panels a) and c) display changes from baseline as mean ± 95% confidence intervals at
Table 3
Changes in markers of endotoxemia and low-grade inflammation (Per Protocol population).
Placebo LU B420 LU + B420
Overall P Factorial P B420 Factorial P LUOutcome Mean ± SD Mean ± SD Mean ± SD Mean ± SD
n 35–36 35–36 25 37
Zonulin (ng/ml) Baseline 56.5 ± 12.6 55.5 ± 9.1 58.4 ± 11.4 64.6 ± 14.2
Month 6 59.7 ± 10.9 58.4 ± 12.0 57.1 ± 8.3 63.4 ± 13.0
Δ (ng/ml) +3.5 ± 10.0 +2.8 ± 8.6 −1.2 ± 9.1 −1.2 ± 7.0 0.10 0.063 0.84
ApoB-48 (μg/ml) Baseline 11.0 ± 7.2 9.5 ± 6.9 11.1 ± 5.5 9.8 ± 4.9
Month 6 11.0 ± 6.1 9.1 ± 5.4 12.6 ± 7.5 10.4 ± 5.7
Δ (μg/ml) +0.07 ± 5.7 −0.44 ± 5.3 +1.53 ± 6.5 +0.56 ± 4.0 0.74a 0.26a 0.092a
hsCRP (mg/l) Baseline 1.73 ± 1.4 2.20 ± 2.3 2.78 ± 2.6 2.58 ± 2.6
Month 6 2.08 ± 1.3 2.43 ± 2.30 2.22 ± 2.6 2.56 ± 2.6
Δ (mg/l) +0.35 ± 1.7 +0.23 ± 1.8 −0.56 ± 2.0 −0.02 ± 2.0 0.13a 0.073a 0.84a
LPS (EU/l) Baseline 65 ± 115 120 ± 206 43 ± 61 39 ± 43
Month 6 39 ± 26 101 ± 365 51 ± 76 48 ± 42
Δ (EU/l) −26 ± 108 −13 ± 341 +7.1 ± 39 +9.1 ± 40⁎ 0.007b 0.094a 0.22a
sCD14 (μg/ml) Baseline 1.62 ± 0.39 1.82 ± 0.43 1.80 ± 0.37 1.77 ± 0.49
Month 6 1.80 ± 0.61 1.87 ± 0.49 1.79 ± 0.54 1.75 ± 0.43
Δ (μg/ml) +0.18 ± 0.6 +0.06 ± 0.5 −0.01 ± 0.5 −0.03 ± 0.5 0.43 0.24 0.78
IL-6 (pg/ml) Baseline 10.5 ± 19 12.8 ± 20 9.0 ± 10 17.5 ± 54
Month 6 9.2 ± 16 12.3 ± 13 11.2 ± 13 8.0 ± 10
Δ (pg/ml) −1.2 ± 10 −0.54 ± 15 +2.2 ± 13 −9.5 ± 47 0.72a 0.42a 0.64a
⁎ Significant difference from Placebo, P b 0.05 (Dunnett's test, corrected formultiple comparisons). Only relative changes from baseline tomonth 6were statistically compared between
groups.
a Analyses on log-transformed data.
b Non-parametric analyses.
197L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200statistically significant (P = 0.13). In the primary statistical analysis,
only valeric acid was significantly increased by B420; there were no
other statistically significant differences between the groups. Total
fecal SCFA concentrations were also increased by B420 in the factorial
analysis.
4. Discussion
Over the past decade, advances in science have established a link be-
tween gut microbes and obesity, raising interest in developing
probiotics and prebiotics for weight management. This study shows
consistent results towards an improvement in weight management
-related parameters in the probiotic and synbiotic groups, includingTable 4
Changes in bacterial metabolites in feces in the Per Protocol population.
Placebo LU B42
Mean ± SD (μmol/g) Mean ± SD (μmol/g) Me
n 36 35–36 24
Acetic acid Baseline 42.3 ± 16 43.0 ± 18 38.
Month 6 37.1 ± 14 37.6 ± 14 39.
Δ −5.20 ± 14 −5.76 ± 18 +1
Propionic acid Baseline 11.8 ± 5.1 12.6 ± 5.7 10.
Month 6 9.9 ± 4.0 10.6 ± 5.6 11.
Δ −1.91 ± 4.1 −2.00 ± 6.0 +0
Butyric acid Baseline 11.5 ± 6.5 10.8 ± 6.2 11.
Month 6 10.2 ± 6.0 9.2 ± 4.8 13.
Δ −1.33 ± 6.2 −1.58 ± 5.9 +1
Valeric acid Baseline 1.6 ± 0.95 1.5 ± 1.1 1.6
Month 6 1.3 ± 0.81 1.4 ± 1.0 1.8
Δ −0.31 ± 0.9 −0.08 ± 1.4 +0
Lactic acid Baseline 0.81 ± 0.89 0.67 ± 0.45 0.7
Month 6 0.65 ± 0.69 0.88 ± 0.86 1.4
Δ −0.16 ± 0.8 +0.22 ± 0.8 +0
Branched-chain fatty acidsb Baseline 3.86 ± 2.2 3.37 ± 2.2 3.7
Month 6 2.77 ± 1.1 3.02 ± 1.5 3.7
Δ −1.09 ± 2.2 −0.32 ± 2.7 +0
Total Baseline 71.9 ± 25 71.9 ± 28 66.
Month 6 61.9 ± 22 62.7 ± 23 71.
Δ −10.0 ± 23 −9.5 ± 28 +4
⁎ = Significant difference from Placebo, P b 0.05 (Dunnett's test, corrected for multiple com
between groups.
a Non-parametric analyses.
b Sum of isobutyric acid, isovaleric acid and 2-methyl-butyric acid.total body fat mass, trunk fat mass, waist circumference, and energy in-
take in the Per Protocol population. Furthermore, our study demon-
strates synergy between a probiotic and a prebiotic fiber supplement
in promoting lean body mass accumulation. We also present results
on biomarkers associated with gut barrier function, reduced inflamma-
tory tone and intestinal microbiota metabolism that suggest a
potential mechanism of action for B420 and LU + B420 in reducing
body fat mass.
We have previously shown that B420 reduces body fat mass gain in
mice that are fed a high-fat diet (Stenman et al., 2014). In the current
study, only the LU + B420 group significantly reduced total body fat
mass compared to Placebo in the PP population, while there were no
differences in the ITT population. A post-hoc factorial analysis showed0 LU + B420
Overall P Factorial P B420 Factorial P LUan ± SD (μmol/g) Mean ± SD (μmol/g)
35–37
5 ± 21 43.7 ± 18
8 ± 14 41.3 ± 17
.34 ± 24 −2.60 ± 18 0.34 0.13 0.99
7 ± 5.2 12.6 ± 5.9
6 ± 4.6 12.3 ± 5.9
.92 ± 5.8 −0.31 ± 7.0 0.095 0.025 0.79
7 ± 8.6 11.7 ± 6.3
1 ± 9.4 10.3 ± 5.4
.35 ± 13 −1.21 ± 7.2 0.45 0.0497 0.095
± 1.1 1.9 ± 0.9
± 1.3 1.5 ± 0.9
.26 ± 1.4⁎ −0.34 ± 1.0 0.083 0.046 0.43
1 ± 1.04 0.65 ± 0.82
0 ± 2.69 0.60 ± 0.62
.68 ± 2.2 +0.02 ± 0.7 0.14a 0.85a 0.58a
0 ± 2.2 4.21 ± 2.0
6 ± 3.6 3.08 ± 1.8
.06 ± 3.6 −1.20 ± 1.9 0.11 0.13 0.52
9 ± 33 74.7 ± 28
5 ± 27 69.1 ± 26
.6 ± 41 −5.6 ± 31 0.19 0.049 0.68
parisons). Only relative changes from baseline to month 6 were statistically compared
198 L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200that B420 alone had a significant effect in both the ITT and PP popula-
tion. The differences between the treatment groups were consistent
across several outcomes, providing further support to the previous re-
sults obtained in experimental animals.
We conducted statistical analyses on both, ITT and PP populations,
with different conclusions. In the ITT populations we used for missing
observations the Last Observation Carried Forward method that is a
widely accepted approach, but can alter the conclusions of very long
studies, which are prone to drop-outs and protocol violations. The find-
ings of the PP population can be considered as being better representa-
tive of the effect of the investigated products, but they, in turn, are prone
to bias from participant drop-outs. In this study, our PP population in-
cluded 64% of the ITT population, mainly due to drop-outs, non-compli-
ance and use of systemic antimicrobials. This weakens the statistical
power in the PP population and warrants further studies to support
the present findings.
In the current study, the study participants in the Placebo group
gained approximately 1 kg (1.1%) of weight compared to baseline.
This weight gain was not due to increased energy intake from the
smoothie vehicle, as energy intake did not change in the Placebo
group (−23 kcal). On the contrary, the LU + B420 and B420 groups
showed considerable reductions in their energy intake (−230 and
−320 kcal/day, respectively), while body weight and body fat mass
did not differ from baseline, suggesting that B420 and LU + B420 nor-
malized energy balance by reducing energy intake to a level that main-
tains, rather than increases, body fat mass.
According to theHelsinki Health Study, steadyweight gain over time
is rather common among an average Finnish middle-aged population
(Loman et al., 2013), as approximately 30% gained at least 5 kg weight
during a 5–7-year follow-up period. Our overweight and obese study
population (BMI 28–34.5) may have been more prone to weight gain
than the average population in the Helsinki Health Study.
Four clinical studies involving healthy, overweight and/or obese par-
ticipants have investigated the effects of micro-organisms on weight
management or body fat mass (Kadooka et al., 2010, 2013; Minami et
al., 2015; Sanchez et al., 2014). The effect size seen in the current
study (6.7% difference in trunk fat between LU + B420 and Placebo) is
quite comparable to that seen in 12-week interventionswith Lactobacil-
lus gasseri SBT2055where abdominal fatwas reduced by approximately
4.5% compared to placebo (Kadooka et al., 2010, 2013). It is difficult to
compare the employed statistical methodology, however, because it re-
mains unclearwhichparticipantswere included in the analyses. In com-
parison, intake of Bifidobacterium breve B-3 reduced body fat mass by
0.6 kg compared to placebo within 12 weeks (Minami et al., 2015). It
should also be noted that the study participants in these three studies
were Japanese whereas the present study was conducted on aWestern
population.
In a Canadian study population, a combination of Lactobacillus
rhamnosus CGMCC1.3724 (LPR), inulin and oligofructose helped to fur-
ther reduce weight (2.7 kg) and total body fat mass (1.8%) during a
weight-loss and weight maintenance period, but only in women
(Sanchez et al., 2014). In the present study, we did not findmarked dif-
ferences between the observed effects in men and women.
In terms of effect size, it is difficult to compare probiotics to pharma-
ceuticals, because the latter have been tested for weight loss instead of
fat mass loss, and they are accompanied by a weight-loss program.
However, the reported changes in bodyweight at six months compared
to placebo have been: −3.5 kg for orlistat (Torgerson et al., 2004),
−5.5% for liraglutide (Pi-Sunyer et al., 2015), and−3.2% for locaserin
(O'neil et al., 2012), compared to −1.4 kg (−4.5%) with LU + B420
and −1.0 kg (−3.0%) with B420 in fat mass in the present study. It
should be noted that probiotics are taken orally once per day, while
orlistat is taken multiple times per day and liraglutide requires a daily
subcutaneous injection. Therefore, probiotics and synbiotics could be a
safe and convenient, non-pharmaceutical support for controlling body
fat mass, especially prophylactically.The results of this study showed that changes in circulating zonulin
levels correlated with changes in trunk fat mass, but only in the
LU + B420 group, indicating a potential mechanistic relationship be-
tween zonulin and trunk fat mass. Also, B420 tended to reduce levels
of circulating zonulin. Zonulin is the human analogue of the zonula oc-
cludens toxin produced by Vibrio cholera (Wang et al., 2000; Fasano et
al., 2000). It selectively increases intestinal permeability in jejunum
and ileum (Wang et al., 2000), and its release is induced by certain gut
microbes, especially pathogens (El Asmar et al., 2002), indicating that
zonulin could act as a mechanistic link between changes in the gut mi-
crobiota and gut barrier function.
Whenmeasured from circulation, zonulin has been linkedwith insu-
lin resistance (Moreno-Navarrete et al., 2012), type 2 diabetes (Zhang et
al., 2014), higher BMI (Moreno-Navarrete et al., 2012) and non-alcohol-
ic fatty liver disease (Pacifico et al., 2014). The link between circulating
zonulin and intestinal zonulin remains somewhat elusive, since circu-
lating zonulin might not originate only from the gut (Wang et al.,
2000). Nevertheless, decreased circulating zonulin is associated with a
decreased lactulose/mannitol ratio, an indicator of intestinal permeabil-
ity (Russo et al., 2012; Liu et al., 2013), aswell as fewer postoperative in-
fection complications (Liu et al., 2013). Furthermore, type 1 diabetic
patients and their relatives were found to have increased circulating
zonulin levels that correlated with intestinal permeability (Sapone et
al., 2006). According to these findings, it is plausible that the changes
in circulating zonulin, as seen in the present study, are related to an im-
provement in gut barrier function.
Improved gut barrier function is hypothesized to reduce metabolic
endotoxemia, especially the concentration of highly inflammatory LPS,
and reduce low-grade inflammation (Burcelin et al., 2009). Our earlier
studies have shown that B420 improves epithelial barrier function in
cell culture (Putaala et al., 2008) and reduces epithelial translocation
of E. coli as well as circulating LPS levels in mice (Amar et al., 2011;
Stenman et al., 2014). We have also recently shown that LU + B420
modulates the intestinal immune system in mice and changes the mi-
crobial DNA signature in adipose tissue, whichwere associatedwith im-
provements in glucose metabolism (Garidou et al., 2015).
In this present study we saw a subtle increase in plasma LPS levels
without activation of inflammatory responses. LPS levels were below
or close to the detection limit both at baseline and at the end of inter-
vention, questioning the clinical relevance of this LPS increase. Howev-
er, this finding could point to a different manifestation of metabolic
endotoxemia betweenmice and humans. The LAL methodology detects
total LPS level in a sample and does not specify which bacterial species
the LPS molecules originate from. It has been reported that the LPS
levels detected with the LAL method may considerably differ from
their cytokine-producing potency (Gutsmann et al., 2010). Thus, al-
though the level of total LPS was increased with LU + B420 treatment,
its quality and inflammatory potency may have also been changed by
the treatment. This hypothesis was supported by the tendency towards
reduced hsCRP levels.
We did not see treatment-associated reductions in blood glucose,
HbA1c, insulin or HOMA-IR in the present study. Althoughwe recruited
overweight and obese participants, baseline laboratory values indicated
that the participants had normal fasting glucose (average 5.18–
5.24 mmol/l) and insulin (average 7.84–9.07 mU/l) levels, leaving very
little room for improvement. Previously, we have shown improvement
in glucose metabolism with both B420 alone (Stenman et al., 2014,
2015b) and in combination with LU (Garidou et al., 2015). The treat-
ments should be tested in populations with impaired glucose metabo-
lism or in patients with type 2 diabetes.
This randomized, double-blind, placebo-controlled clinical interven-
tion trial in overweight and obese adults presents preliminary clinical
evidence that the probiotic B420 with or without Litesse Ultra
polydextrose can reduce body fat mass, waist circumference, energy in-
take and body weight compared to placebo. Our findings are of special
interest to populations who struggle to control their weight in a
199L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200Western cultural environment. Additionally, B420 and LU seem to have
synergistic effects in increasing lean body mass. LU alone had no effect
on the parameters tested. Mechanistically, the reduction of body fat
mass might be related to circulating zonulin, a potential marker of gut
barrier function, and attenuated low-grade inflammation, which both
support previous findings from experimental animals. Further clinical
trials are warranted to confirm these effects in larger participant popu-
lations and to further elucidate the underlying mechanisms.
Funding Sources
This study was fully funded by DuPont Nutrition & Health. DuPont,
Smerud and the Principal Investigator were jointly responsible for
drafting the study protocol and reports as well as interpretation of the
data. The lead author of this manuscript was employed at DuPont.
Conflicts of Interest
This study was fully funded by DuPont Nutrition & Health. Each of
the authors or their respective organizations were financially compen-
sated by DuPont for their contribution in the study. In addition, M.S. is
a shareholder of Clinical Research Services Turku, and M.C. and R.B.
are founders and shareholders of Vaiomer.
Author Contributions
Conceptualization, R.B., S.L.; Methodology, L.K.S., M.J.L., N.M., H.K.S.,
A.R., and S.L.; Formal Analysis, N.M.; Investigation, L.K.S., M.J.L., N.M.,
J.E.C., N.Y., M.T.S., M-L.L., J.L., D.A., and M.S.; Resources, M.C., M-L.L., J.L.,
D.A., M.S., and S.L.; Writing—Original Draft, L.K.S.; Writing—Review &
Editing, M.J.L., N.M., N.Y., M.T.S., R.B., H.K.S., M.S. and S.L.; Visualization,
L.K.S. andN.M; Supervision, R.B., H.K.S, A.R., and S.L.; Project Administra-
tion, L.K.S.
Acknowledgements
Wewish to extend our gratitude to the study participants, investiga-
tors, monitors and study nurses who enabled this study. We are espe-
cially thankful to Yvonne Peltonen for her firm study management.
Also, we are thankful to Angela Paulsen and Tad Stewart from DuPont
Nutrition & Health for the production and quality control of the study
products, as well as to all who participated in sample analyses: Jaana
Larsson-Leskelä, Henri Ahokoski, Kirsi Stenström and Sari Pohjola. We
are also very grateful to Tiina Roine and Auli Väänänen for food diary
data extraction.
Appendix A. Supplementary Data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.ebiom.2016.10.036.
References
Amar, J., Chabo, C., Waget, A., Klopp, P., Vachoux, C., Bermudez-Humaran, L.G., Smirnova,
N., Berge, M., Sulpice, T., Lahtinen, S., Ouwehand, A., Langella, P., Rautonen, N.,
Sansonetti, P.J., Burcelin, R., 2011. Intestinal mucosal adherence and translocation of
commensal bacteria at the early onset of type 2 diabetes: molecular mechanisms
and probiotic treatment. EMBO Mol. Med. 3, 559–572.
Brillon, D.J., Zheng, B., Campbell, R.G., Matthews, D.E., 1995. Effect of cortisol on energy ex-
penditure and amino acid metabolism in humans. Am. J. Phys. 268, E501–E513.
Brun, P., Castagliuolo, I., Di Leo, V., Buda, A., Pinzani, M., Palu, G., Martines, D., 2007. In-
creased intestinal permeability in obese mice: new evidence in the pathogenesis of
nonalcoholic steatohepatitis. Am. J. Physiol. Gastrointest. Liver Physiol. 292,
G518–G525.
Burcelin, R., Luche, E., Serino, M., Amar, J., 2009. The gut microbiota ecology: a new oppor-
tunity for the treatment of metabolic diseases? Front. Biosci. (Landmark Ed.) 14,
5107–5117.
Cani, P.D., Amar, J., Iglesias, M.A., Poggi, M., Knauf, C., Bastelica, D., Neyrinck, A.M., Fava, F.,
Tuohy, K.M., Chabo, C., Waget, A., Delmee, E., Cousin, B., Sulpice, T., Chamontin, B.,Ferrieres, J., Tanti, J.F., Gibson, G.R., Casteilla, L., Delzenne, N.M., Alessi, M.C.,
Burcelin, R., 2007. Metabolic endotoxemia initiates obesity and insulin resistance. Di-
abetes 56, 1761–1772.
El Asmar, R., Panigrahi, P., Bamford, P., Berti, I., Not, T., Coppa, G.V., Catassi, C., Fasano, A.,
2002. Host-dependent zonulin secretion causes the impairment of the small intestine
barrier function after bacterial exposure. Gastroenterology 123, 1607–1615.
Fasano, A., Not, T., Wang, W., Uzzau, S., Berti, I., Tommasini, A., Goldblum, S.E., 2000.
Zonulin, a newly discovered modulator of intestinal permeability, and its expression
in coeliac disease. Lancet 355, 1518–1519.
Garidou, L., Pomie, C., Klopp, P., Waget, A., Charpentier, J., Aloulou, M., Giry, A., Serino,
M., Stenman, L., Lahtinen, S., Dray, C., Iacovoni, J.S., Courtney, M., Collet, X., Amar,
J., Servant, F., Lelouvier, B., Valet, P., Eberl, G., Fazilleau, N., Douin-Echinard, V.,
Heymes, C., Burcelin, R., 2015. The gut microbiota regulates intestinal CD4 T
cells expressing ROR-gamma-t and controls metabolic disease. Cell Metab. 22,
100–112.
Gibson, G.R., Roberfroid, M.B., 1995. Dietarymodulation of the human colonic microbiota:
introducing the concept of prebiotics. J. Nutr. 125, 1401–1412.
Gutsmann, T., Howe, J., Zahringer, U., Garidel, P., Schromm, A.B., Koch, M.H., Fujimoto, Y.,
Fukase, K., Moriyon, I., Martinez-De-Tejada, G., Brandenburg, K., 2010. Structural pre-
requisites for endotoxic activity in the Limulus test as compared to cytokine produc-
tion in mononuclear cells. Innate Immun. 16, 39–47.
Hill, C., Guarner, F., Reid, G., Gibson, G.R., Merenstein, D.J., Pot, B., Morelli, L., Canani, R.B.,
Flint, H.J., Salminen, S., Calder, P.C., Sanders, M.E., 2014. Expert consensus document.
The International Scientific Association for Probiotics and Prebiotics consensus state-
ment on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol.
Hepatol. 11, 506–514.
Ibarra, A., Astbury, N.M., Olli, K., Alhoniemi, E., Tiihonen, K., 2015. Effects of polydextrose
on different levels of energy intake. A systematic review and meta-analysis. Appetite
87, 30–37.
Jie, Z., Bang-Yao, L., Ming-Jie, X., Hai-Wei, L., Zu-Kang, Z., Ting-Song, W., Craig, S.A., 2000.
Studies on the effects of polydextrose intake on physiologic functions in Chinese peo-
ple. Am. J. Clin. Nutr. 72, 1503–1509.
Kadooka, Y., Sato, M., Imaizumi, K., Ogawa, A., Ikuyama, K., Akai, Y., Okano, M., Kagoshima,
M., Tsuchida, T., 2010. Regulation of abdominal adiposity by probiotics (Lactobacillus
gasseri SBT2055) in adults with obese tendencies in a randomized controlled trial.
Eur. J. Clin. Nutr. 64, 636–643.
Kadooka, Y., Sato, M., Ogawa, A., Miyoshi, M., Uenishi, H., Ogawa, H., Ikuyama, K.,
Kagoshima,M., Tsuchida, T., 2013. Effect of Lactobacillus gasseri SBT2055 in fermented
milk on abdominal adiposity in adults in a randomised controlled trial. Br. J. Nutr.
110, 1696–1703.
Kallio, K.A., Hatonen, K.A., Lehto, M., Salomaa, V., Mannisto, S., Pussinen, P.J., 2015.
Endotoxemia, nutrition, and cardiometabolic disorders. Acta Diabetol. 52, 395–404.
Klein, A., Friedrich, U., Vogelsang, H., Jahreis, G., 2008. Lactobacillus acidophilus 74-2 and
Bifidobacterium animalis subsp lactis DGCC 420 modulate unspecific cellular immune
response in healthy adults. Eur. J. Clin. Nutr. 62, 584–593.
Kok, R.G., De Waal, A., Schut, F., Welling, G.W., Weenk, G., Hellingwerf, K.J., 1996. Specific
detection and analysis of a probiotic Bifidobacterium strain in infant feces. Appl. En-
viron. Microbiol. 62, 3668–3672.
Kujala, U.M., Kaprio, J., Sarna, S., Koskenvuo, M., 1998. Relationship of leisure-time phys-
ical activity and mortality: the Finnish twin cohort. JAMA 279, 440–444.
Leber, B., Tripolt, N.J., Blattl, D., Eder, M., Wascher, T.C., Pieber, T.R., Stauber, R., Sourij, H.,
Oettl, K., Stadlbauer, V., 2012. The influence of probiotic supplementation on gut per-
meability in patients with metabolic syndrome: an open label, randomized pilot
study. Eur. J. Clin. Nutr. 66, 1110–1115.
Ley, R.E., Turnbaugh, P.J., Klein, S., Gordon, J.I., 2006. Microbial ecology: human gut mi-
crobes associated with obesity. Nature 444, 1022–1023.
Li, S., Guerin-Deremaux, L., Pochat, M., Wils, D., Reifer, C., Miller, L.E., 2010. NUTRIOSE di-
etary fiber supplementation improves insulin resistance and determinants of meta-
bolic syndrome in overweight men: a double-blind, randomized, placebo-
controlled study. Appl. Physiol. Nutr. Metab. 35, 773–782.
Liu, Z.H., Huang, M.J., Zhang, X.W., Wang, L., Huang, N.Q., Peng, H., Lan, P., Peng, J.S., Yang,
Z., Xia, Y., Liu,W.J., Yang, J., Qin, H.L., Wang, J.P., 2013. The effects of perioperative pro-
biotic treatment on serum zonulin concentration and subsequent postoperative in-
fectious complications after colorectal cancer surgery: a double-center and double-
blind randomized clinical trial. Am. J. Clin. Nutr. 97, 117–126.
Loman, T., Lallukka, T., Laaksonen, M., Rahkonen, O., Lahelma, E., 2013. Multiple socioeco-
nomic determinants of weight gain: the Helsinki health study. BMC Public Health 13,
259.
Minami, J., Kondo, S., Yanagisawa, N., Odamaki, T., Xiao, J.Z., Abe, F., Nakajima, S.,
Hamamoto, Y., Saitoh, S., Shimoda, T., 2015. Oral administration of Bifidobacterium
breve B-3 modifies metabolic functions in adults with obese tendencies in a
randomised controlled trial. J. Nutr. Sci. 4, e17.
Moreno-Navarrete, J.M., Sabater, M., Ortega, F., Ricart, W., Fernandez-Real, J.M., 2012. Cir-
culating zonulin, a marker of intestinal permeability, is increased in association with
obesity-associated insulin resistance. PLoS One 7, e37160.
O'neil, P.M., Smith, S.R., Weissman, N.J., Fidler, M.C., Sanchez, M., Zhang, J., Raether, B.,
Anderson, C.M., Shanahan, W.R., 2012. Randomized placebo-controlled clinical trial
of lorcaserin for weight loss in type 2 diabetes mellitus: the BLOOM-DM study. Obe-
sity (Silver Spring) 20, 1426–1436.
Osterberg, K.L., Boutagy, N.E., Mcmillan, R.P., Stevens, J.R., Frisard, M.I., Kavanaugh, J.W.,
Davy, B.M., Davy, K.P., Hulver, M.W., 2015. Probiotic supplementation attenuates in-
creases in body mass and fat mass during high-fat diet in healthy young adults. Obe-
sity (Silver Spring) 23, 2364–2370.
Pacifico, L., Bonci, E., Marandola, L., Romaggioli, S., Bascetta, S., Chiesa, C., 2014. Increased
circulating zonulin in children with biopsy-proven nonalcoholic fatty liver disease.
World J. Gastroenterol. 20, 17107–17114.
200 L.K. Stenman et al. / EBioMedicine 13 (2016) 190–200Parnell, J.A., Reimer, R.A., 2009. Weight loss during oligofructose supplementation is asso-
ciated with decreased ghrelin and increased peptide YY in overweight and obese
adults. Am. J. Clin. Nutr. 89, 1751–1759.
Pendyala, S., Walker, J.M., Holt, P.R., 2012. A high-fat diet is associated with endotoxemia
that originates from the gut. Gastroenterology 142 (1100-1.e2).
Pi-Sunyer, X., Astrup, A., Fujioka, K., Greenway, F., Halpern, A., KREMPF, M., Lau, D.C., Le
Roux, C.W., Violante Ortiz, R., Jensen, C.B., Wilding, J.P., Obesity, S. & Prediabetes,
NNSG, 2015. A randomized, controlled trial of 3.0 mg of Liraglutide in weight man-
agement. N. Engl. J. Med. 373, 11–22.
Probert, H.M., Apajalahti, J.H., Rautonen, N., Stowell, J., Gibson, G.R., 2004. Polydextrose,
lactitol, and fructo-oligosaccharide fermentation by colonic bacteria in a three-stage
continuous culture system. Appl. Environ. Microbiol. 70, 4505–4511.
Putaala, H., Salusjarvi, T., Nordstrom, M., Saarinen, M., Ouwehand, A.C., Bech Hansen, E.,
Rautonen, N., 2008. Effect of four probiotic strains and Escherichia coli O157:H7 on
tight junction integrity and cyclooxygenase expression. Res. Microbiol. 159, 692–698.
Roessler, A., Friedrich, U., Vogelsang, H., Bauer, A., Kaatz, M., Hipler, U.C., Schmidt, I.,
Jahreis, G., 2008. The immune system in healthy adults and patients with atopic der-
matitis seems to be affected differently by a probiotic intervention. Clin. Exp. Allergy
38, 93–102.
Russo, F., Linsalata, M., Clemente, C., Chiloiro, M., Orlando, A., Marconi, E., Chimienti, G.,
Riezzo, G., 2012. Inulin-enriched pasta improves intestinal permeability and modifies
the circulating levels of zonulin and glucagon-like peptide 2 in healthy young volun-
teers. Nutr. Res. 32, 940–946.
Sanchez, M., Darimont, C., Drapeau, V., Emady-Azar, S., Lepage, M., Rezzonico, E., Ngom-
Bru, C., Berger, B., Philippe, L., Ammon-Zuffrey, C., Leone, P., Chevrier, G., St-Amand,
E., Marette, A., Dore, J., Tremblay, A., 2014. Effect of Lactobacillus rhamnosus
CGMCC1.3724 supplementation on weight loss and maintenance in obese men and
women. Br. J. Nutr. 111, 1507–1519.
Sapone, A., De Magistris, L., Pietzak, M., Clemente, M.G., Tripathi, A., Cucca, F., Lampis, R.,
Kryszak, D., Carteni, M., Generoso, M., Iafusco, D., Prisco, F., Laghi, F., Riegler, G.,
Carratu, R., Counts, D., Fasano, A., 2006. Zonulin upregulation is associated with in-
creased gut permeability in subjects with type 1 diabetes and their relatives. Diabetes
55, 1443–1449.
Stenman, L.K., Holma, R., Korpela, R., 2012. High-fat-induced intestinal permeability dys-
function associated with altered fecal bile acids. World J. Gastroenterol. 18, 923–929.Stenman, L.K.,Waget, A., Garret, C., Klopp, P., Burcelin, R., Lahtinen, S., 2014. Potential pro-
biotic Bifidobacterium animalis ssp. lactis 420 prevents weight gain and glucose intol-
erance in diet-induced obese mice. Benefic. Microbes 5, 437–445.
Stenman, L.K., Burcelin, R., Lahtinen, S., 2015a. Establishing a causal link between gut mi-
crobes, body weight gain and glucose metabolism in humans—towards treatment
with probiotics. Benefic. Microbes 1–12 (E-pub ahead of print).
Stenman, L.K., Waget, A., Garret, C., Briand, F., Burcelin, R., Sulpice, T., Lahtinen, S., 2015b.
Probiotic B420 and prebiotic polydextrose improve efficacy of antidiabetic drugs in
mice. Diabetol. Metab. Syndr. 7, 75.
Teixeira, T.F., Souza, N.C., Chiarello, P.G., Franceschini, S.C., Bressan, J., Ferreira, C.L., Peluzio
Mdo, C., 2012. Intestinal permeability parameters in obese patients are correlated
with metabolic syndrome risk factors. Clin. Nutr. 31, 735–740.
Torgerson, J.S., Hauptman, J., Boldrin, M.N., Sjostrom, L., 2004. XENical in the prevention of
diabetes in obese subjects (XENDOS) study: a randomized study of orlistat as an ad-
junct to lifestyle changes for the prevention of type 2 diabetes in obese patients. Di-
abetes Care 27, 155–161.
Tuomilehto, J., Lindstrom, J., Eriksson, J.G., Valle, T.T., Hamalainen, H., Ilanne-Parikka, P.,
Keinanen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., Salminen, V.,
Uusitupa, M., Finnish Diabetes Prevention Study, G, 2001. Prevention of type 2 diabe-
tes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
N. Engl. J. Med. 344, 1343–1350.
Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., Magrini, V., Mardis, E.R., Gordon, J.I., 2006. An
obesity-associated gut microbiome with increased capacity for energy harvest. Na-
ture 444, 1027–1031.
Wang, W., Uzzau, S., Goldblum, S.E., Fasano, A., 2000. Human zonulin, a potential modu-
lator of intestinal tight junctions. J. Cell Sci. 113 (Pt 24), 4435–4440.
Zhang, D., Zhang, L., Zheng, Y., Yue, F., Russell, R.D., Zeng, Y., 2014. Circulating zonulin
levels in newly diagnosed Chinese type 2 diabetes patients. Diabetes Res. Clin.
Pract. 106, 312–318.
Zunszain, P.A., Anacker, C., Cattaneo, A., Carvalho, L.A., Pariante, C.M., 2011. Glucocorti-
coids, cytokines and brain abnormalities in depression. Prog. Neuro-
Psychopharmacol. Biol. Psychiatry 35, 722–729.