Gut microbiota signatures of vulnerability to food addiction in mice and humans

Abstract

Objective
Food addiction is a multifactorial disorder characterised by a loss of control over food intake that may promote obesity and alter gut microbiota composition. We have investigated the potential involvement of the gut microbiota in the mechanisms underlying food addiction.

Design
We used the Yale Food Addiction Scale (YFAS) 2.0 criteria to classify extreme food addiction in mouse and human subpopulations to identify gut microbiota signatures associated with vulnerability to this disorder.

Results
Both animal and human cohorts showed important similarities in the gut microbiota signatures linked to food addiction. The signatures suggested possible non-beneficial effects of bacteria belonging to the Proteobacteria phylum and potential protective effects of Actinobacteria against the development of food addiction in both cohorts of humans and mice. A decreased relative abundance of the species Blautia wexlerae was observed in addicted humans and of Blautia genus in addicted mice. Administration of the non-digestible carbohydrates, lactulose and rhamnose, known to favour Blautia growth, led to increased relative abundance of Blautia in mice faeces in parallel with dramatic improvements in food addiction. A similar improvement was revealed after oral administration of Blautia wexlerae as a beneficial microbe.

Conclusion
By understanding the crosstalk between this behavioural alteration and gut microbiota, these findings constitute a step forward to future treatments for food addiction and related eating disorders.

Full text

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SamulėnaitėS, etal. Gut 2024;0:1–17. doi:10.1136/gutjnl-2023-331445
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Original research
Gut microbiota signatures of vulnerability to food
addiction in mice andhumans
Solveiga Samulėnaitė ,1,2 Alejandra García- Blanco,1
Jordi Mayneris- Perxachs ,3,4,5 Laura Domingo- Rodríguez,1
Judit Cabana- Domínguez ,6,7,8,9 Noèlia Fernàndez- Castillo ,6,7,8,9
Edurne Gago- García ,6,7,8,9 Laura Pineda- Cirera,6,7,8,9 Aurelijus Burokas ,2
Jose Espinosa- Carrasco,10 Silvia Arboleya,11,12 Jessica Latorre,3,4,5
Catherine Stanton ,11,13 Koji Hosomi,14 Jun Kunisawa ,14 Bru Cormand ,6,7,8,9
Jose Manuel Fernández- Real ,3,4,5,15 Rafael Maldonado ,1,16
Elena Martín- García1,16,17,18
To cite: SamulėnaitėS,
García- BlancoA, Mayneris-
PerxachsJ, etal. Gut Epub
ahead of print: [please
include Day Month Year].
doi:10.1136/
gutjnl-2023-331445
►Additional supplemental
material is published online
only. To view, please visit the
journal online (https:// doi. org/
10. 1136/ gutjnl- 2023- 331445).
For numbered affiliations see
end of article.
Correspondence to
DrRafael Maldonado,
Laboratory of
Neuropharmacology- Neurophar,
Pompeu Fabra University,
Barcelona 08002, Spain;
rafael. maldonado@ upf. edu and
DrJose Manuel Fernández-
Real, Endocrinology, Nutrition,
Eumetabolism & Health group,
Hospital Trueta of Girona,
CIBERobn and University of
Girona, Girona, Spain;
jmfreal@ idibgi. org
Dr Elena Martín- García;
elena. martin@ upf. edu
SSėnė, AG- B and JM- P
contributed equally.
RM and EM- G are joint senior
authors.
Received 1 November 2023
Accepted 1 April 2024
© Author(s) (or their
employer(s)) 2024. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published
by BMJ.
ABSTRACT
Objective Food addiction is a multifactorial disorder
characterised by a loss of control over food intake
that may promote obesity and alter gut microbiota
composition. We have investigated the potential
involvement of the gut microbiota in the mechanisms
underlying food addiction.
Design We used the Yale Food Addiction Scale (YFAS)
2.0 criteria to classify extreme food addiction in mouse
and human subpopulations to identify gut microbiota
signatures associated with vulnerability to this disorder.
Results Both animal and human cohorts showed
important similarities in the gut microbiota signatures
linked to food addiction. The signatures suggested
possible non- beneficial effects of bacteria belonging
to the Proteobacteria phylum and potential protective
effects of Actinobacteria against the development of
food addiction in both cohorts of humans and mice.
A decreased relative abundance of the species Blautia
wexlerae was observed in addicted humans and of
Blautia genus in addicted mice. Administration of the
non- digestible carbohydrates, lactulose and rhamnose,
known to favour Blautia growth, led to increased relative
abundance of Blautia in mice faeces in parallel with
dramatic improvements in food addiction. A similar
improvement was revealed after oral administration of
Blautia wexlerae as a beneficial microbe.
Conclusion By understanding the crosstalk between
this behavioural alteration and gut microbiota, these
findings constitute a step forward to future treatments
for food addiction and related eating disorders.
INTRODUCTION
The gut microbiota integrates a large variety of
microbes, including bacteria, fungi, archaea and
viruses that colonise the digestive tract. These
microorganisms offer numerous benefits by inter-
acting with the host and establishing a symbiotic
relationship.1 This symbiosis is not only essential
for peripheral physiological functions, but also
affects neurobiological processes. Multiple studies
have demonstrated the existence of a crosstalk
between several neurobiological processes and the
gut microbiota, including those related to mental
disorders, such as depression,2 anxiety, autism spec-
trum disorders and addiction.2–4 Interestingly, the
relationships between addiction and dysbiosis in
gut microbiota are gaining high relevance, mainly
in alcohol abuse.5 Ethanol consumption reduces
protective bacteria, increases intestinal permeability
WHAT IS ALREADY KNOWN ON THIS TOPIC
⇒Food addiction, characterised by loss of control
over food intake, may promote obesity and alter
gut microbiota content.
⇒Brain alterations related to behavioural
disorders have been reported to modify the
gut microbiome, and gut microbes alter the
neurobiology of brain regions involved in
behavioural control.
WHAT THIS STUDY ADDS
⇒We have identified specific gut microbiota
content associated 'in our human and animal
cohorts' with the differential vulnerability to
develop food addiction.
⇒We have also functionally validated in animals
the beneficial role of Blautia and rhamnose in
preventing the development of the behavioural
hallmarks of food addiction.
HOW THIS STUDY MIGHT AFFECT RESEARCH,
PRACTICE OR POLICY
⇒The elucidation of the specific microbiota
content associated with food addiction may
provide new biomarkers for this behavioural
disorder.
⇒The identification of these novel biological
mechanisms provides new advances toward
innovative interventions for food addiction and
related disorders using beneficial microbes and/
or dietary supplementation.
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2SamulėnaitėS, etal. Gut 2024;0:1–17. doi:10.1136/gutjnl-2023-331445
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and releases inflammatory factors, which finally contribute to
the psychopathology of alcoholism.6 Other substance use disor-
ders, such as opioid, cocaine or methamphetamine disorders,
have also been related to gut microbiota dysbiosis.7
Food addiction is a controversial concept still under debate,8
consisting of a complex multifactorial behavioural disorder
characterised by a loss of control over food intake that has
increased in prevalence in recent years.9 It is characterised by
the compulsive intake of palatable foods, which can produce
adaptive changes in the reward brain network. This behavioural
alteration is related to obesity and other eating disorders and
lacks effective treatment, leading to high socioeconomical costs
worldwide. Despite the early definition of this concept,10 the
fifth edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM- 5) does not include the concept of food addic-
tion.11 However, a widely accepted instrument currently used in
the clinic to evaluate food addiction is the Yale Food Addiction
Scale (YFAS), updated in 2016 to YFAS 2.0 to apply the DSM- 5
criteria for substance use disorder to food addiction.12 The YFAS
2.0 food addiction criteria can be summarised in three hallmarks
also used in rodent models to mimic this disorder: persistent
food seeking, high motivation to obtain food, and compulsivity-
like behaviour.13 14 In previous studies, we have validated the
animal model of food addiction in mice,15 and these behavioural
findings were replicated in other studies.13 14 We found that the
specific prelimbic to the nucleus accumbens brain circuit was
involved in the vulnerability to develop food addiction, and we
have also described specific epigenetic mechanisms involved in
this multifactorial disease.13 14
The study of the gut microbiota signatures related to food
addiction has gained attention in recent years. However, most
studies have been performed in rodents,16–18 and few human
studies have been reported.19 In spite of these findings, there is a
lack of translational studies that validate the functional relevance
of human findings in animal models, which would be required
to design more successful treatments.8 Environmental factors
and dietary patterns have a major influence on gut microbiota
composition, and the overconsumption of highly palatable food
may promote a gut microbiota dysbiosis that has been recently
proposed to participate in the loss of eating control.8 19 In agree-
ment, individuals with obesity, which may be promoted by food
addiction,20 showed altered gut microbiota with a reduced diver-
sity that facilitated energy absorption capacity and may affect
host brain function.21 However, the functional relevance of gut
microbiota in the loss of eating control and food addiction has
not yet been demonstrated.
In this study, we have obtained extreme subpopulations of
food addicted and non- addicted mice to identify the differ-
ential gut microbiota signatures associated with vulnera-
bility to addiction. Using parallel food addiction- like criteria,
we have applied the YFAS 2.0 score to classify a cohort of
patients to assess the possible gut microbiota signatures of this
behavioural disorder as potential biomarkers. We functionally
validated the role of Blautia, the genus most differentially
expressed in addicted mice and humans, by administering
the non- digestible carbohydrates,22 lactulose and rhamnose,
that increased Blautia abundance and prevented the develop-
ment of food addiction in mice. A similar result was observed
after oral administration of Blautia wexlerae as a beneficial
microbe. The strategy of a beneficial microbe and/or dietary
supplements to modulate gut microbiota is promising and can
be either extracted from non- digestible carbohydrate materials
or synthetically produced.23
METHODS
Detailed methods are provided in the online supplemental
material.
RESULTS
Characterisation of extreme subpopulations of addicted and
non-addicted mice
We used the genetically homogeneous inbreed C57Bl/6J strain
of mice that underwent an operant protocol of food addiction
during six sessions of fixed ratio (FR) 1 schedule of reinforce-
ment, followed by 92 daily sessions of FR5 (figure 1A). The
selected subset of mice belongs to a previous publication with
a large cohort of male JAX C57BL/6 J mice to evaluate miRNA
signatures associated with vulnerability to food addiction.14
In the late period of the food addiction protocol (figure 1A),
addicted mice performed higher persistence of response, moti-
vation and compulsivity than non- addicted mice, as expected
(figure 1B–D). In contrast, intake of pellets and body weight
were similar in addicted and non- addicted mice (figure 1E, F),
possibly due to the limited effort (FR5) required to obtain each
reward. Online supplemental material and online supplemental
table S1 provide a detailed description of the results of figure 1.
Gut microbiota profile of vulnerability to addiction in mice
We carried out the 16S rRNA gene amplicon sequencing of
caecum contents to study gut microbiota signatures associated
with food addiction- like behaviour using the cohorts of extreme
phenotypes described above. As expected, the murine caecal
microbiota was dominated by the phyla Firmicutes and Bacte-
roidetes, reaching almost 90% of the relative abundance, similar
in both addicted and non- addicted mice (figure 2A). See online
supplemental material for a detailed description of the results
of figure 2. Alpha diversity indexes were similar in both groups
(figure 2B, C), and there were no differences in beta diversity
(online supplemental figure S1). Importantly, analyses of the
data at the different taxonomic levels showed significant differ-
ences in the phyla, families and genera’s relative abundances of
several bacteria. Actinobacteria phylum (figure 2D), Coriobacte-
riaceae and Erysipelotrichaceae families (figure 2E), and Lach-
nospiraceae UCG- 001 and Enterohabdus genera (figure 2F) had
decreased relative abundances in addicted compared with non-
addicted mice. Other genera, such as Allobaculum and Blautia
(figure 2F) from Bacillota/Firmicutes phylum, showed a similar
decrease in addicted mice, altogether supporting the potential
beneficial profile of non- addicted gut microbiota signatures.
Gut microbiota correlates with food addiction features in
mice
The possible correlations between gut microbiota signatures,
addiction- like criteria, and the phenotypic traits of the different
mice groups were further investigated (figures 3 and 4, online
supplemental figure S2, online supplemental figure S3). At the
genus level (figures 3 and 4), the Ruminococcaceae_NK4A214_
group and the Gastranaerophilales_uncultured organism genera
positively correlated with motivation in the addicted group,
whereas the Clostridiales_vadin BB60 group_uncultured,
Erysipelatoclostridium and Parabacteroides genera negatively
correlated with motivation in these addicted mice. Finally,
the genera Ruminococcaceae_UCG009, Lachnospiraceae_
FCS020_group, Peptococcus, Candidatus_Arthromitus, Rumini-
clostridium_6, Roseburia, Coprococcus_1 and Acetatifactor
positively correlated with persistence of response in the addicted
group. Online supplemental material has a detailed description
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Figure 1 Characterisation of extreme subpopulations of addicted and non- addicted mice. (A)Timeline of the procedure of operant behaviour
mouse model. Mice were trained during the first 6 days in operant behaviour sessions of 1 hour at a fixed ratio (FR) 1 schedule of reinforcement,
followed by 92 daily sessions of FR5. The addiction- like criteria (persistence of response, motivation and compulsivity) were evaluated in the late
period to categorise mice as addicted and non- addicted. (B–D).Behavioural tests for the three addiction- like criteria in the late period (individual
values with IQR) in the addicted and non- addicted groups. (B)Persistence of response (t test, ***p<0.001). (C)Motivation (Mann–Whitney U test,
***p<0.001). (D)Compulsivity (Mann–Whitney U test, ***p<0.001). (E)Pellet intake and (F)body weight for those mice classified as addicted (A)
and non- addicted (NA) (n=11 mice as A and n=13 as NA mice, trained with chocolate pellets). Statistical details are included in online supplemental
table S1. The selected subset of mice belongs to a previous publication with a large cohort of male JAX C57BL/6 J mice to evaluate miRNA signatures
associated with vulnerability to food addiction.14
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Figure 2 Gut microbiota profile of vulnerability to addiction in mice. (A)Pie chart at the phylum level shows a high proportion of the phyla
Firmicutes and Bacteroidetes, reaching almost 90% of the relative abundance in both groups of addicted and non- addicted mice. (B–C)Results of
Chao1 and Shannon alpha diversity indexes of addicted (A) and non- addicted (NA) mice. (D–F)Volcano plots representing the differential bacterial
abundance between addicted (A) and non- addicted (NA) mice after a long operant training protocol using the DESeq2 test. Differences were observed
in the relative abundances at the (D)phylum, (E)family and (F)genus levels. The volcano plot indicates −log 10 (p value) for bacteria (Y axis) plotted
against their respective log 2 (fold change) (X axis). The coloured dots represent significantly downregulated and upregulated bacteria between the
addicted and non- addicted groups, respectively (ie, Blautia is downregulated in addicted mice). Significantly different taxa (p<0.05) are coloured
according to the phylum. (G)Discriminant analysis effect size method (LEfSe) comparing addicted and non- addicted groups. Linear discriminant
analysis (LDA) was performed. Data are expressed as mean±SEM. DESeq2 test wa performed (n=11 non- addicted (NA) mice, n=13 addicted (A) mice).
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of the results of microbiota and food addiction signature correla-
tions at the family level (online supplemental figure S2 and S3).
Use of YFAS 2.0 to characterise addicted and non-addicted
humans
The possible gut microbiota signatures associated with food
addiction were investigated in a cohort of patients (n=88)
classified following the YFAS 2.0 questionnaire. The three
addiction- like criteria measured in our food addiction mouse
model recapitulates properly the principal features of the food
addiction human disease evaluated by the 35 item self- report
YFAS 2.0, as reported previously.14 As expected, the sum of the
YFAS 2.0 questions, under the criteria of persistence of response,
motivation and compulsivity, was much higher in participants
diagnosed with food addiction than in non- addicted subjects
(figure 5A–C). We also performed a principal component anal-
ysis (PCA) with the main variables (persistence of response,
motivation, compulsivity, tolerance, withdrawal, craving and
distress). The two principal components (PC) accounted for
75.8% and 7.1% of variations, respectively (figure 5D–G), and
two clusters of addicted and non- addicted groups were identified
(figure 5D). Interestingly, PC1 has strong loadings (>0.7) from
Figure 3 Caecal microbiota of non- addicted mice. Corrplot showing significant (p<0.05) Spearman correlations coefficients between microbiota
relative abundances at genus level and addiction criteria and phenotypic traits in non- addicted mice after a long operant training protocol (n=11 for
non- addicted mice).
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distress, withdrawal, tolerance and craving (figure 5E, F) but not
from the persistence of response, motivation or compulsivity
that had high loads in PC2 (figure 5E, G). Online supplemental
material has a detailed description of the results of figure 5D–G.
Cross-characterisation of mouse and human behaviours
Correlational analyses between addiction criteria in mice and
humans were also plotted together (figure 5H–J). The links
between correlations found in mice and humans further under-
lined the translational value of our behavioural results. In
non- addicted individuals, a positive correlation was found in
both humans and mice between the following addiction criteria:
compulsivity and persistence of response (figure 5H), compul-
sivity and motivation (figure 5I), and motivation and persistence
of response (figure 5J). We also performed a correlation matrix
to explore the nature of the association between each addiction
criterion and each phenotypic trait with the cohorts of mice and
humans, respectively (online supplemental figure S4). Online
supplemental material has a detailed description of the results of
figure 5 and online supplemental figure S4.
Figure 4 Caecal microbiota of addicted mice. Corrplot showing significant (p<0.05) Spearman correlations coefficients between microbiota relative
abundances at genus level and addiction criteria and phenotypic traits in addicted mice after a long operant training protocol (n=13 for addicted
mice).
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Figure 5 Gut microbiota signatures in humans. (A–C)Results of the three addiction- like criteria. (A)Persistence of response (Mann–Whitney U test,
***p<0.001), (B)motivation (Mann–Whitney U test, ***p<0.001), and (C)compulsivity (Mann–Whitney U test, ***p<0.001) (median and IQR),
comparing non- addicted (NA) and addicted (A) participants. n=88 for human participants (n=79 NA individuals, n=9 A individuals). (D–G)Principal
component analysis (PCA) of the three addiction criteria and the four phenotypic traits in humans. (D)Human subjects clustered by addicted or
non- addicted classification on the space yielded by two components of the PCA that account for the maximum data variance. (E)Criteria belonging
to each component, principal component (PC) 1 (75.8%) and PC2 (7.1%). (F, G)The order of factor loading of the different variables in PC1 and
PC2 is represented. The dashed horizontal line marked loadings >0.7, mainly contributing to the component. (H–J)Correlational analyses between
addiction criteria of persistence of response, motivation and compulsivity in addicted and non- addicted mice compared with addicted and non-
addicted individuals belonging to the human cohort were analysed together. (H)At the correlation between compulsivity and persistence of response,
a strong negative correlation for addicted individuals (both human and mice) and a moderate positive correlation for non- addicted individuals were
described. (I)At the correlation between compulsivity and motivation, a mild negative correlation for addicted individuals (mice) was found, while
positive correlations for non- addicted individuals (mice and humans) and addicted humans were described. (J)At the correlation between motivation
and persistence of response, a strong negative correlation for addicted individuals (humans) and mild for mice was found together with a moderate
positive correlation for non- addicted individuals (mice and humans) with a similar slope.
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Gut microbiota signatures in humans
Differential bacterial abundance using ANCOM- BC,
controlling for age, body mass index and sex were observed in
our human cohort (n=88) in the volcano plots (figure 6A–C).
Microbiota signatures, distinct between addicted and non-
addicted individuals, are represented by the differential
expression of bacteria species taxa coloured according to the
phylum (figure 6A). Multiple similitudes were found to be
overlapped between mice and humans. Interestingly, Blautia
wexlerae species were increased in individuals with a low
score in the YFAS scale in obese humans (figure 6B), in agree-
ment with the decreased Blautia genus abundance in addicted
mice (figure 2F). Additionally, there were no differences in
beta diversity (online supplemental figure S5). We explored
the influence of diet composition on the gut microbiota data
and did not observe any effect (online supplemental table S2).
Online supplemental material has a detailed description of the
results of figure 6A–G.
Functional validation with the non-digestible carbohydrates
lactulose and rhamnose in the mouse food addiction protocol
Both mice and human results suggest that some specific micro-
biota could be protective in preventing food addiction. The
strong similitudes found in the Blautia genus content in both
species underlines the potential beneficial effects of this partic-
ular gut microbiota. However, Blautia is a strictly anaerobic
bacteria, and its possible therapeutic use as a a beneficial microbe
to prevent food addiction would be difficult. Interestingly,
several well known prebiotics that could be used in humans have
been reported to enhance the abundance of the Blautia genus.
Therefore, we have investigated the possible protective effects
promoted by oral administration of lactulose and rhamnose
as non- digestible carbohydrates able to enhance Blautia genus
abundance in the gut.24 25
For this purpose, a total of 41 C57BL/6 J mice underwent an
operant protocol of 120 sessions (6- FR1 and 114- FR5 sessions
(online supplemental figures S6 and S7 and online supple-
mental table S3). Online supplemental material has a detailed
Figure 6 Volcano and scatter plots of bacterial abundance. (A–C)Volcano plots representing the differential bacterial abundance (pFDR<0.05)
using ANCOM- BC, controlling for age, body mass index and sex in humans for (A)all of the population (n=88), and (B)obese (n=36) and (C)non-
obese (n=52) individuals. Fold change (FC) associated with a unit change in the YFAS score and log10 Benjamini–Hochberg p values adjusted (pFDR)
are plotted for each taxon. Significantly different taxa are coloured according to the phylum. NS, non- significant. (D–G)Scatter plots of the partial
Pearson correlation between the centred log ratio (clr) levels of different species of the genus Blautia and the Yale Food Addiction Scale (YFAS) scores
in (D)the whole cohort (n=88) controlling for age, body mass index and sex, and in (E–G)patients with obesity (n=36), controlling for age and sex.
The residuals are plotted.
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description of the behavioural results of figure 7 and the PCA
and correlation matrix of figure 8. Importantly, motivation
for chocolate flavoured pellets was significantly reduced in the
rhamnose group compared with lactulose (Mann–Whitney U
test=31.50, p<0.05, figure 7C). Significantly, mice receiving
rhamnose showed decreased compulsivity in seeking palatable
food compared with control mice, revealing a beneficial effect
of this non- digestible carbohydrate to prevent food addiction
(Mann–Whitney’s U=49.50, p<0.05, figure 7D). Notably,
29.41% of control mice achieved 2–3 criteria and were consid-
ered addicted, whereas none of the mice receiving lactulose or
rhamnose achieved the addiction criteria (χ2=5, control vs lactu-
lose p<0.05 and χ2=5, control vs rhamnose p<0.05, figure 7E).
We next analysed the faecal microbiota composition of
mice from the functional validation experiment after oral
administration of lactulose and rhamnose. We found that
supplementation with rhamnose increased the levels of
several species from the SCFA producing family Lachno-
spiraceae, including several species from the genus Blautia,
such as Blautia faecis, Blautia sp, and Blautia_uc (online
supplemental figure S7A). Online supplemental material has
a detailed description of the microbiota composition results
shown in online supplemental figure S7.
Functional validation with the beneficial microbe
Blautia
wexlerae
in the mouse food addiction protocol
To further demonstrate that Blautia has a protective effect
against the development of food addiction, oral administration
of Blautia wexlerae in mice that underwent the long protocol of
food addiction (6- FR1 and 114- FR5 sessions) described in the
previous section was performed. We used the same regimen of
chronic Blautia oral administration 3 days per week at a concen-
tration (1×109 CFU) similar to what was described before to
prevent obesity and type 2 diabetes.26 For this purpose, a total
of 37 C57BL/6 J mice underwent an operant protocol of 120
sessions (figure 9). Online supplemental material has a detailed
description of the behavioural results shown in figure 9.
Importantly, Blautia treated mice showed similar persistence
of response, but significantly reduced motivation, and decreased
compulsivity for highly palatable food compared with control
mice, revealing a protective effect of Blautia to develop food
addiction (Mann–Whitney U test, p<0.05, figure 9B–D, online
supplemental table S4). Remarkably, 21.05% of control mice
reached 2–3 addiction criteria in the late period (98–114
sessions), whereas none of the mice receiving Blautia wexlerae
were classified as addicted (χ2=4.80, control vs Blautia p<0.05,
figure 9E). No significant differences between groups were
found in impulsivity, cognitive inflexibility, appetitive cue reac-
tivity and aversive cue reactivity (figure 9F–I). In addition, all
mice had similar food intake and body weight during the exper-
imental sequence (figure 9J, K).
PCA and correlation heatmap were performed to understand
further the correlation between Blautia administration and
behavioural phenotypes leading to the prevention of food addic-
tion. Online supplemental material has a detailed description of
the results shown in figure 10.
Blautia
correlates with phenotypic features in mice and
humans
Correlational analyses were performed between Blautia and the
phenotypic traits in humans and mice. In human studies, Blautia
correlated negatively with distress, and in mouse studies, Blautia
correlated negatively with the number of addiction criteria. We
have included this information in online supplemental figure S8
and the description of statistical analyses in online supplemental
material.
qPCR gene expression from the functional validation with
Blautia
treatment in the mouse food addiction protocol
No significant differences in gene expression were obtained
between mice treated with Blautia or vehicle when evaluating
four main targets of the dopaminergic system by qPCR in the
key regions of the reward system such as the medial prefrontal
cortex (mPFC) or the nucleus accumbens (NAc): Drd2 (dopa-
mine receptor type 2), Drd1 (dopamine receptor type 1), Th
(tyrosine hydroxylase) and Darpp32 (dopamine and cAMP regu-
lated neuronal phosphoprotein). Online supplemental figure S9
and online supplemental table S5 have more details.
DISCUSSION
We have identified a particular gut microbiota signature in mice
and humans with relevant changes in specific microbiota phyla,
families and genera when comparing addicted and non- addicted
phenotypes after a long operant training in the former, despite
similar pellet intake, identical diet, and experimental and housing
conditions, even considering that diet is the primary modulator
of gut microbiota.27 Remarkably, animals reaching food addic-
tion criteria showed lower abundances than non- addicted mice
in several important bacterial groups, including Enterorhabdus,
Lachnospiracceae, Allobaculum and Blautia genera, suggesting
potential protective effects. In contrast, the relative abundance
of Anaeroplasma genus and Gastranaerophilales families were
increased in addicted mice, suggesting non- beneficial effects.
This differential microbiota profile is relevant considering their
relative abundance and the previous literature associating these
bacteria with the gut–brain axis.28 29 These associations were
correlational and suggested a probable bidirectional communi-
cation between the gut and the brain, where the gut can connect
with the brain through hundreds of metabolites, among others,
and the brain can link with the gut via autonomic nervous system
signalling that regulates gut bacterial abundance.8
Our results suggest that an increased relative abundance of
Enterorhabdus genus (Actinobacteria phylum) in the gut of non-
addicted mice can be beneficial, in agreement with the previous
literature. Although Enterorhabdus levels have not been previ-
ously associated with addiction, previous studies revealed a
beneficial effect on other psychiatric disorders. Negative correla-
tions were also observed between Enterorhabdus and brain
kynurenine levels of mice that underwent chronic mild stress,
with increased kynurenine levels being a reflection of distur-
bances in tryptophan metabolism.30 In agreement with the results
in mice, the species from the Actinobacteria phylum (Bifidobac-
terium dentium) were upregulated in our non- addicted individ-
uals with low YFAS 2.0 scores. The coincidence at the phylum
level in mice and humans can help to identify specific candidates
to predict loss of eating control across organisms, improving the
translatability of our work, mainly considering that only a 10%
overlap occurs at the species level between mouse and human
gut microbiota profiles.31
Our results in mice also showed a potential beneficial effect of
the Lachnospiraceae genus from the Bacillota/Firmicutes phylum
in food addiction that was coherent with the results obtained in
humans for several species of the phylum to which it belongs.
Previous studies showed that the abundance of the Lachnospir-
aceae genus was modified in a forced alcohol drinking group
of mice. However, the results were controversial, suggesting
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Figure 7 Characterisation of extreme subpopulations of addicted and non- addicted mice in the experiment with lactulose and rhamnose.
(A)Timeline of the procedure of operant behaviour mouse model. Mice were trained during the first 6 days in operant behaviour sessions of 1 hour at
a fixed ratio (FR) 1 schedule of reinforcement, followed by 114 daily sessions of FR5. The addiction- like criteria (persistence of response, motivation
and compulsivity) were evaluated in the late period (95- 114) to categorise mice into addicted and non- addicted. Mice received the non- digestible
carbohydrate lactulose, rhamnose or control in drinking water during the whole experimental sequence. Number of reinforcers during 1 hour
of operant training sessions maintained by chocolate flavoured pellets in the three groups (mean±SEM, repeated measures ANOVA, session ×
treatment effect ***p<0.001, post hoc Newman–Keuls, ˆp<0.05 control vs lactulose, &p<0.05 lactulose vs rhamnose, #p<0.05 control vs rhamnose).
(B–D)Behavioural tests for the three addiction- like criteria in the late period (individual values with IQR) in the addicted and non- addicted groups.
(B)Persistence of response. (C)Motivation (Mann–Whitney U test, *p<0.05). (D)Compulsivity (Mann–Whitney U test, *p<0.05). (E)Percentage of
mice classified as addicted and non- addicted in the lactulose, rhamnose and control groups. (F–I)Behavioural tests for the four phenotypic traits
associated with vulnerability to food addiction in the late period (individual values with IQR). (F)Impulsivity. (G)Cognitive inflexibility (Mann–
Whitney U test, ***p<0.001). (H)Appetitive cue reactivity. (I)Aversive cue reactivity. (J)Food intake. (K)Water intake. (L)Body weight. (M)(Blautia)/g
in mice faeces determined by pPCR (Mann–Whitney U test, *p<0.05). The sample size of mice in the lactulose and rhamnose groups was n=12, and
n=17 in the control group (total n=41). Statistical details are included in online supplemental table S3.
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Figure 8 Principal component analysis (PCA) revealed differential patterns of behavioural factor loadings in food addiction- like behaviour in mice
treated with lactulose and rhamnose. (A)Mice subjects clustered by addicted or non- addicted classification on the space yielded by two components
of the PCA that account for the maximum data variance (n=5 control addicted mice, n=12 control non- addicted mice, n=12 lactulose non- addicted
mice, n=12 rhamnose non- addicted mice). (B)Criteria belonging to each component, principal component (PC) 1 (37.7%) and PC2 (20.9%). (C,
D)Order of factor loading of the different variables in PC1 and PC2 is represented. The dashed horizontal line marked loadings >0.7, mainly
contributing to the component. (E)Heatmap correlation matrix of the three addiction criteria and the four phenotypic traits. Colours correspond to the
magnitude of Pearson correlations between each pair of variables and range from −1 (red) to+1 (blue). Significant Pearson’s correlations: *p<0.05,
**p<0.01, ***p<0.001 (n=36 mice).
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Figure 9 Characterisation of extreme subpopulations of addicted and non- addicted mice in the experiment with Blautia wexlerae supplementation.
(A, upper part)Timeline of the procedure of operant behaviour mouse model. Mice were trained during the first 6 days in operant behaviour sessions
of 1 hour at a fixed ratio (FR) 1 schedule of reinforcement, followed by 114 daily sessions of FR5. The addiction- like criteria (persistence of response,
motivation and compulsivity) were evaluated in the late period (98- 114) to categorise mice as addicted and non- addicted. Mice received the
beneficial microbe Blautia wexlerae or vehicle control, which were administered by oral route (gavage) during the whole experimental sequence.
Specifically, 250 µl of Blautia wexlerae were orally administered (intragavage) at a concentration of 1×109 CFU three times per week for the whole
experimental protocol, 1 hour before the self- administration session in the operant chambers. (A bottom part)Number of reinforcers during 1 hour
of operant training sessions maintained by chocolate flavoured pellets in the two groups (mean±SEM, repeated measures ANOVA, sessions,
***p<0.001). (B–D)Behavioural tests for the three addiction- like criteria in the late period (individual values with IQR) in the addicted and non-
addicted groups. (B)Persistence of response. (C)Motivation (Mann–Whitney U test, *p<0.05). (D)Compulsivity (Mann–Whitney U test,*p<0.05).
(E)Percentage of mice classified as addicted and non- addicted in the groups of Blautia wexlerae and control. (F–I)Behavioural tests for the four
phenotypic traits associated with vulnerability to food addiction in the late period (individual values with IQR). (F)Impulsivity. (G)Cognitive
inflexibility. (H)Appetitive cue reactivity. (I)Aversive cue reactivity. (J)Food intake. (K)Body weight. Sample size of mice in the Blautia wexlerae and
vehicle control groups was n=18–19 per group (total n=37). Statistical details are included in online supplemental table S4.
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Figure 10 Principal component analysis (PCA) revealed differential patterns of behavioural factor loadings in food addiction- like behaviour in
mice treated with Blautia wexlerae or control vehicle. (A)Mice subjects clustered by addicted or non- addicted classification on the space yielded
by two components of the PCA that account for the maximum data variance (n=4 control addicted mice, n=15 control non- addicted mice, n=18
Blautia wexlerea non- addicted mice). (B)Criteria belonging to each component, principal component (PC) 1 (33.2%) and PC2 (22.5%). (C, D)Order
of factor loading of the different variables in PC1 and PC2 is represented. The dashed horizontal line marked loadings >0.7, mainly contributing to
the component. (E)Heatmap correlation matrix of the three addiction criteria and the four phenotypic traits. Colours correspond to the magnitude
of Pearson correlations between each pair of variables and range from −1 (red) to +1 (blue). Significant Pearson’s correlations: *p<0.05, **p<0.01,
***p<0.001 (n=37 mice).
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non- beneficial or beneficial effects, depending on each species.32
Another study exploring alcohol use disorder vulnerability in
rats also showed opposite results in different genera of Lach-
nospiraceae family, with opposite correlations between dorsal
striatum dopamine D2 receptor (D2R) expression and relative
abundances, also suggesting non- beneficial or beneficial effects.6
The Allobaculum genus, which also belongs to the Bacillota/
Firmicutes phylum, was potentially protective in our mouse food
addiction model. Chronic alcohol exposure was reported to
increase Allobaculum spp. abundance in mice positively related
to alcohol preference, which suggests a non- beneficial effect of
this genus.33 In contrast, beneficial effects of the Allobaculum
genus were reported in an obesity mouse model with the anti-
obesity diet quercetin.34 Furthermore, Allobaculum positively
correlated with dorsal striatum D2R expression in a rat model
of alcohol addiction, suggesting a beneficial effect,6 in agreement
with the potential protective effect of this bacteria in our food
addiction model. Importantly, modifications in drd2 gene expres-
sion and other dopaminergic markers of the reward circuit have
been reported in our mouse model of food addiction.13
Blautia genus, belonging to the Lachnospiraceae family, was
downregulated in addicted mice and is an essential player in the
microbiota gut–brain axis. Striking similarities were observed in
Blautia content in mice and humans vulnerable to developing
food addiction, and these bacteria could have a relevant poten-
tial beneficial role in the regulation of brain function,26 35 as
discussed and validated later.
Our results in mice also suggested non- beneficial effects on
several bacteria groups, such as the Anaeroplasma genus and
Gastranaerophilales family. The relative abundance of Anaero-
plasma genus (from Tenericutes phylum and Anaeroplasmata-
ceae family) was increased in addicted mice. An earlier study
demonstrated the antiobesity activity of thinned peach polyphe-
nols, which had beneficial effects on gut microbiota by reducing
the Anaeroplasma abundance that positively correlated with
obesity.36 In agreement with the potential negative effects of
Gastranaerophilales, the beneficial activity of hawthorn seed
oil supplementation was reported to be related to the reduction
of this family.36 In our study, the Gastranaerophilales family
was specifically associated with the addiction- like criterion of
motivation.
The Clostridiales_vandin BB60 and Ruminococcaceae families,
both from the Bacillota/Firmicutes phylum, positively correlated
with the addiction criterion of persistence of response in addicted
mice and positively correlated between them, suggesting a joint
action of these clusters of bacteria. Alterations of Ruminococca-
ceae or Clostridiales bacterial abundances have been reported
in patients with autism spectrum disorders, schizophrenia and
social behavioural disorder,37 38 which further supports the
possibility that the gut–brain axis may affect the persistence of
response. In agreement, an increased abundance of Clostridi-
ales vadinBB660 has been reported in the maternal separation
model in rats,39 associating these bacteria with responsiveness
to stress.40 Furthermore, trends of increased Ruminococcaceae
and Clostridiales vadinBB660 families were revealed in vulner-
able rats to develop alcohol use disorder.6 All of these findings
together suggest that clusters of bacteria are addiction responsive
bacteria that may potentially affect host behaviours.
Our animal and human cohorts identified important similar-
ities in the behavioural characterisation of food addiction and
the gut microbiota signatures associated with this behaviour.
Microbiota is a regulator of the reward system,6 and gut micro-
biota derived metabolites are critical regulators of host appe-
tite.41 In our human cohort, we observed significant differences
in the relative abundances of bacteria belonging to Actinobac-
teria, Bacillota/Firmicutes and Proteobacteria phyla that were
in the same direction to what was found in the mice cohort,
demonstrating the high translational relevance of the results
obtained in animals and humans. Notably, Blautia species
were downregulated in addicted individuals, which could be
linked to beneficial effects in parallel with what was found in
mice at the genus level. Furthermore, the species Lactobacillus
kefiri (from the Lactobacillaceae family and Bacillota/Firmic-
utes phylum) was also reduced in humans diagnosed as food
addicted, suggesting a potential beneficial effect, similar to what
was described for mice at the family level. Although no effects
have been reported regarding the influence of this bacteria on
food addiction, previous studies have reported the beneficial
effects of dairy intake of Lactobacillus kefiri.42 43 Concerning
the Proteobacteria phylum, an increased relative abundance of
several species was found in individuals in our human cohort
with a high score in food addiction, in coherence with the results
in mice that suggested a non- beneficial effect of this phylum. In
previous studies, Proteobacteria was observed to be differentially
abundant among individuals diagnosed with schizophrenia, with
patients having an increased relative abundance of this phylum
compared with the healthy cohort.44 Proteobacteria species are
considered proinflammatory gut bacteria, and patients with
alcoholism and dysbiosis had higher abundances of Proteobac-
teria than subjects without alcoholism.45
From all of these similarities between mouse and human gut
microbiota associated with food addiction vulnerability, it is
important to underline the findings revealed on Blautia wexlerae
species and Blautia genus. Blautia wexlerae, Blautia schinkii and
Blautia gluceraseasa species were upregulated in non- addicted
human individuals, consistent with the increase of the Blautia
genus in non- addicted mice. These results suggest a beneficial
effect of these bacteria on food addiction. Previous studies
have identified other bacteria associated with food addiction,
precisely a protective effect of Bacteroides, Akkermansia and
Eubacterium genus and a risky association with megamonas in
human females.46 Other studies have observed that gut micro-
biota reduction in mice increases binge- like eating of palatable
food.47 In obese women with uncontrolled eating behaviour,
another study described peculiar gut microbiome clusters asso-
ciated with different eating patterns,19 which underlies the asso-
ciation of different endophenotypes with distinct microbiome
signatures. Importantly, the microbiota has been associated
with dysregulations of the dopaminergic reward system and the
hedonic food intake during obesity.16
To date, 20 species constitute the genus Blautia, including
Blautia coccoides, Blautia wexlerae, Blautia schinkii, Blautia
gluceraseasa and Blautia producta.48 Both Blautia genus and
Lachnospiraceae family that includes this genus belong to the
important Bacillota/Firmicutes phylum. Lachnospiraceae is the
most abundant family of this phylum, accounting for approxi-
mately 50% of the total gut microbiota in humans.49 Blautia genus
is involved in host bile transformation28 that activates serotonin
synthesis. Previous studies with methamphetamine consumption
in humans showed contradictory results with increased Blautia
proportions in abusers, suggesting non- beneficial effects, but
also a positive association with abstraction in a cognitive assess-
ment.50 Other studies reported beneficial effects on obesity. Thus
breastfeeding is associated with a reduced risk of obesity and
comorbidities later in life, and a microbiota profile driven by
the genus Blautia was linked to beneficial metabolic effects.35
Blautia has also received particular attention for its involve-
ment in improving metabolic diseases and promoting nutritional
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advantages.51 In addition, Blautia has shown potential proper-
ties considering its ability to regulate host health and improve
metabolic syndrome, inhibiting the colonisation of pathogenic
bacteria in the intestine.48 However, its production requires
restrictive culture conditions due to its nature of strictly anaer-
obic bacteria, which makes it difficult for a potential use as a
beneficial microbe for human therapeutic purposes. Given the
role of Blautia in metabolic host regulation, using non- digestible
carbohydrates as substrates to promote Blautia proliferation
may represent a promising alternative to potentiate its beneficial
functions. Non- digestible carbohydrates, such as lactulose and
rhamnose, are promising candidates that have been shown to
increase the abundance of Blautia and cause beneficial effects on
the host.24 25 52 In the current study, we functionally validated the
potential protective effects of Blautia in the development of food
addiction by administering non- digestible carbohydrates, and
showing increased relative abundance of Blautia in mice faeces
in the case of rhamnose. Both non- digestible carbohydrates had
a protective effect since their administration completely avoided
the development of addictive- like behaviour in mice. This bene-
ficial impact was more pronounced in the case of rhamnose,
with more significant outcomes than lactulose. Furthermore, we
validated the specific involvement of Blautia in these responses
by orally administering Blautia wexlerae in mice as a beneficial
microbe. Oral Blautia administration also has a similar protec-
tive effect, avoiding the development of food addiction in mice
with a predominant effect in preventing motivation and compul-
sivity. In agreement, oral administration of Blautia wexlerae was
reported to ameliorate obesity and type 2 diabetes via metabolic
remodelling of the gut microbiota.26 The live biotherapeutic
strain, Blautia stercoris MRx0006, was recently demonstrated to
have beneficial behavioural effects in a mouse model of autism.53
Our study identified Blautia as a target of potential interest for
food addiction treatment and prevention. In agreement, a recent
study showed the association between Blautia and cocaine rein-
forcing properties in an intravenous cocaine self- administration
model in mice, although the relevance of this association was
not investigated, and machine learning approaches also suggest
a similar association with substance use disorder in humans.54 55
We also evaluated gene expression signatures related to food
addiction targeting the dopaminergic system in the mPFC and
NAc after Blautia administration, considering the alterations
in dopaminergic markers that we previously revealed in food
addicted mice.13 No significant differences in gene expression
were obtained between mice treated with Blautia or vehicle
when evaluating four main targets of the dopaminergic system
by qPCR in these two brain areas: Drd2 (dopamine receptor
type 2), Drd1 (dopamine receptor type 1), Th (tyrosine hydrox-
ylase), and Darpp32 (dopamine and cAMP regulated neuronal
phosphoprotein). These results suggest that the beneficial effects
of Blautia in the behavioural expression of food addiction were
not associated with major changes in these four markers of
dopamine activity in the mPFC and NAc. Multiple other neuro-
chemical mechanisms and pathways are involved in the loss of
eating control56–58 and the influence of the gut–brain axis in this
behaviour,59 opening new possibilities of research to elucidate
the precise mechanisms participating in the involvement of gut
microbiota in the loss of eating control.
We demonstrated a translational link between mice and humans
in gut microbiome composition associated with food addiction,
supporting a link between gut microbiota and vulnerability to
this behavioural disorder. The gut microbiome composition
found in our study was also associated with specific addiction
criteria, including motivation and persistence of response, in a
food addiction mouse model based on the human YFAS 2.0 ques-
tionnaire with marked similarities with findings in our human
cohort, substantiating the translational value of the model.
The functional relevance and beneficial effects of Blautia, the
most relevant similar findings revealed in mice and human gut
microbiota, was demonstrated by orally administering Blautia
wexlerae in mice, as well as by administering lactulose and rham-
nose, non- digestible carbohydrates that increased the relative
abundance of Blautia in mice faeces and that also prevented the
development of food addiction. These observations underlie the
possible role of the gut microbiome in predisposing individuals
to food addiction and offer a perspective on understanding the
aetiology of food addiction that remains essentially unknown.
Identification of non- beneficial bacteria can be helpful as prog-
nostic biomarkers for predicting vulnerability to food addiction
that may lead to a real improvement in clinical care. This novel
understanding of the role of gut microbiota in the development
of food addiction may open new approaches for developing
biomarkers and innovative therapies for food addiction and
related eating disorders.
Author affiliations
1Laboratory of Neuropharmacology- Neurophar, Department of Medicine and Life
Sciences, Pompeu Fabra University, Barcelona, Spain
2Department of Biological Models, Institute of Biochemistry, Life Sciences Center,
Vilnius University, Vilnius, Lithuania
3Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute
(IdibGi), Girona, Spain
4CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud
Carlos III, Madrid, Spain
5Department of Diabetes, Endocrinology and Nutrition, Dr Josep Trueta University
Hospital, Girona, Spain
6Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia,
Universitat de Barcelona, Barcelona, Spain
7Centro de Investigación Biomédica en Red de Enfermedades Raras, (CIBERER),
Madrid, Spain
8Institut de Biomedicina de la Universitat de Barcelona, (IBUB), Barcelona, Spain
9Institut de Recerca Sant Joan de Déu (IR- SJD), Esplugues de Llobregat, Barcelona,
Spain
10Centre for Genomic Regulation (CRG), Barcelona, Spain
11APC Microbiome Institute, University College Cork, Cork, Ireland
12Department of Microbiology and Biochemistry of Dairy Products, Instituto de
Productos Lácteos de Asturias, Consejo Superior de Investigaciones Científicas (IPLA-
CSIC), Villaviciosa, Asturias, Spain
13Teagasc Food Research Centre, Moorepark, Fermoy, Co, Cork, Ireland
14Laboratory of Vaccine Materials and Laboratory of Gut Environmental System,
Microbial Research Center for Health and Medicine, National Institutes of Biomedical
Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan. (NIBIOHN),
Ibaraki, Osaka, Japan
15Department of Medical Sciences, Faculty of Medicine, University of Girona, Girona,
Spain
16Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
17Departament de Psicobiologia i Metodologia de les Ciències de la Salut, Universitat
Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain
18Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
Acknowledgements We thank M Linares, R Martín, D Real and F Porrón for their
technical support. We thank T Gusinkaia for her bioinformatic support.
Contributors EM- G and RM conceived and designed the experimental approaches
in animal studies. JMF- R conceived and designed the experimental approaches in
humans. AB and LD- R performed the behavioral phenotype characterisation of the
first batch of mice with the supervision of EM- G and RM. AG- B, SS, JC- D, LP- C, NF- C
and LD- R performed statistical analyses and graphs with the supervision of EM- G
and RM. SA performed the DNA extractions, DNA library preparation for sequencing
and analyses supervised by CS. AB and SA made mice microbiota data analysis
and graphs. JL and JM- P performed the studies in humans with the supervision
of JMF- R. JE- C performed the bioinformatic analysis. SS performed the behavioral
experiment with non- digestible carbohydrates and qPCR for Blautia abundance with
the supervision of EM- G and RM. SS performed the experiment with the Blautia
beneficial microbe with the collaboration of AG- B and supervision of EM- G and RM.
JK and KH provided the Blautia beneficial microbe for the food addiction experiment
in mice. EG- G performed the qPCR experiment for gene expression in the mouse
brain with the supervision of NF- C and BC. EM- G and RM wrote the manuscript,
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and prepared the figures and tables with the support of SS and AG- B. BC, JC- D,
JMF- R and NFC provided a critical review of the manuscript with inputs from all the
other authors. RM and E.M.G. are guarantors. RM and EM- G are equally supervised
authors..
Funding This work was supported by La Caixa Health #LCF/PR/HR22/52420017,
the Spanish ’Ministerio de Ciencia, Innovación y Universidades (MICIU),
Agencia Estatal de Investigación (AEI)’ (PID2020- 120029GB- I00/MICIU/
AEI/10.13039/501100011033, RD21/0009/0019), the Spanish ’Instituto de Salud
Carlos III, RETICS- RTA’ (#RD12/0028/0023), the ’Generalitat de Catalunya, AGAUR’
(#2017 SGR- 669), ’La Marató de TV3’ #20221830, ’ICREA- Acadèmia’ (#2015) and
the Spanish ’Ministerio de Sanidad, Servicios Sociales e Igualdad, ’Plan Nacional
Sobre Drogas of the Spanish Ministry of Health’ (#PNSD- 2017I068) to RM, ’Plan
Nacional Sobre Drogas of the Spanish Ministry of Health’ (#PNSD- 2019I006, #PNSD-
2023I040) and Spanish Ministerio de Ciencia e Innovación (ERA- NET) PCI2021-
122073- 2A to EM- G. Spanish- MINECO (#SAF2017- 84060- R- AEI/FEDER- UE) to LD- R,
Spanish ’Ministerio de Ciencia, Innovación y Universidades’ (#RTI2018- 100968-
B- 100, #PID2021- 127776OB- I00), ’AGAUR- Generalitat de Catalunya’ (#2017- SGR-
738, #2021- SGR- 01093) and Spanish ’Ministerio de Sanidad, Servicios Sociales e
Igualdad, ’Plan Nacional Sobre Drogas of the Spanish Ministry of Health’ (#PNSD-
2017I050) to BC, ’Plan Nacional Sobre Drogas of the Spanish Ministry of Health’
(#PNSD- 2020I042) to NF- C and the European Regional Development Fund (project
No 01.2.2- LMT- K- 718- 03- 0099) under grant agreement with the Research Council
of Lithuania (LMTLT) to AB. The research leading to these results has also received
funding from the European Union H2020 Program [H2020/2014- 2020] under grant
agreements Nos 667302 (CoCA), 643051 (MiND) and 728018 (Eat2beNICE), ’La
Marató de TV3’ #20221831 and from the Catalan Government (ICREA Academia
Award 2021) to BC. JC- D was supported by the H2020 CoCA and Eat2beNICE
projects and EG- G by projects of the Spanish Ministries (#PNSD- 2020I042 and
#PID2021- 127776OB- I00). This work was partially supported by Instituto de Salud
Carlos III through the projects PI20/01090 and PI23/00575 to JM- P and PI18/01022
and PI21/01361 to JMF- R (co- funded by European Regional Development Fund. “A
way to make Europe”). It was also supported through the project CNS2023- 144218
funded by MCIN/AEI/10.13039/501100011033 and the European Union
NextGenerationEU/PRTR to JM- P. The work has also received funding from ”la
Caixa” Foundation under the grant agreement LCF/PR/HR22/52420017 and support
from Generalitat de Catalunya (ICREA Academia Award 2021 and 2021 SGR 01263)
to JMF- R. IDIBGI is a CERCA centre from the ’CERCA Programme/Generalitat de
Catalunya’.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in
the design, or conduct, or reporting, or dissemination plans of this research.
Patient consent for publication Not applicable.
Ethics approval The institutional review board- ethics committee and the
Committee for Clinical Research (CEIC) of Dr Josep Trueta University Hospital
(Girona, Spain) approved the study protocol, and informed written consent was
obtained from all participants. Participants gave informed consent to participate in
the study before taking part.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request. All
data are available in the main text or supplementary materials. Correspondence and
requests for materials should be addressed to José Manuel Fernández- Real, Rafael
Maldonado and Elena Martín- García.
Supplemental material This content has been supplied by the author(s). It
has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have
been peer- reviewed. Any opinions or recommendations discussed are solely those
of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and
responsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliability
of the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.
ORCID iDs
SolveigaSamulėnaitė http://orcid.org/0000-0001-7226-672X
JordiMayneris- Perxachs http://orcid.org/0000-0003-3788-3815
JuditCabana- Domínguez http://orcid.org/0000-0002-4732-7284
NoèliaFernàndez- Castillo http://orcid.org/0000-0001-9948-0312
EdurneGago- García http://orcid.org/0009-0002-6369-9982
AurelijusBurokas http://orcid.org/0000-0002-0364-3496
CatherineStanton http://orcid.org/0000-0002-6724-7011
JunKunisawa http://orcid.org/0000-0003-4901-1125
BruCormand http://orcid.org/0000-0001-5318-4382
Jose ManuelFernández- Real http://orcid.org/0000-0002-7442-9323
RafaelMaldonado http://orcid.org/0000-0002-4359-8773
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