Article Text

Original research
Gut microbiota signatures of vulnerability to food addiction in mice and humans
  1. Solveiga Samulėnaitė1,2,
  2. Alejandra García-Blanco1,
  3. Jordi Mayneris-Perxachs3,4,5,
  4. Laura Domingo-Rodríguez1,
  5. Judit Cabana-Domínguez6,7,8,9,
  6. Noèlia Fernàndez-Castillo6,7,8,9,
  7. Edurne Gago-García6,7,8,9,
  8. Laura Pineda-Cirera6,7,8,9,
  9. Aurelijus Burokas2,
  10. Jose Espinosa-Carrasco10,
  11. Silvia Arboleya11,12,
  12. Jessica Latorre3,4,5,
  13. Catherine Stanton11,13,
  14. Koji Hosomi14,
  15. Jun Kunisawa14,
  16. Bru Cormand6,7,8,9,
  17. Jose Manuel Fernández-Real3,4,5,15,
  18. Rafael Maldonado1,16,
  19. Elena Martín-García1,16,17,18
    1. 1Laboratory of Neuropharmacology-Neurophar, Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain
    2. 2Department of Biological Models, Institute of Biochemistry, Life Sciences Center, Vilnius University, Vilnius, Lithuania
    3. 3Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IdibGi), Girona, Spain
    4. 4CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
    5. 5Department of Diabetes, Endocrinology and Nutrition, Dr Josep Trueta University Hospital, Girona, Spain
    6. 6Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
    7. 7Centro de Investigación Biomédica en Red de Enfermedades Raras, (CIBERER), Madrid, Spain
    8. 8Institut de Biomedicina de la Universitat de Barcelona, (IBUB), Barcelona, Spain
    9. 9Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Barcelona, Spain
    10. 10Centre for Genomic Regulation (CRG), Barcelona, Spain
    11. 11APC Microbiome Institute, University College Cork, Cork, Ireland
    12. 12Department of Microbiology and Biochemistry of Dairy Products, Instituto de Productos Lácteos de Asturias, Consejo Superior de Investigaciones Científicas (IPLA-CSIC), Villaviciosa, Asturias, Spain
    13. 13Teagasc Food Research Centre, Moorepark, Fermoy, Co, Cork, Ireland
    14. 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
    15. 15Department of Medical Sciences, Faculty of Medicine, University of Girona, Girona, Spain
    16. 16Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
    17. 17Departament de Psicobiologia i Metodologia de les Ciències de la Salut, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain
    18. 18Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
    1. Correspondence to Dr Rafael Maldonado, Laboratory of Neuropharmacology-Neurophar, Pompeu Fabra University, Barcelona 08002, Spain; rafael.maldonado{at}upf.edu; Dr Jose Manuel Fernández-Real, Endocrinology, Nutrition, Eumetabolism & Health group, Hospital Trueta of Girona, CIBERobn and University of Girona, Girona, Spain; jmfreal{at}idibgi.org; Dr Elena Martín-García; elena.martin{at}upf.edu

    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.

    • NEUROPHARMACOLOGY
    • NUTRITION
    • OBESITY
    • PSYCHOLOGY

    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.

    http://creativecommons.org/licenses/by-nc/4.0/

    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/.

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    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.

    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 interacting 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 spectrum 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 and releases inflammatory factors, which finally contribute to the psychopathology of alcoholism.6 Other substance use disorders, 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 addiction.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 agreement, individuals with obesity, which may be promoted by food addiction,20 showed altered gut microbiota with a reduced diversity 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 differential gut microbiota signatures associated with vulnerability 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 development 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.

    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 reinforcement, 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, motivation 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.

    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

    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 Bacteroidetes, 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 differences in the phyla, families and genera’s relative abundances of several bacteria. Actinobacteria phylum (figure 2D), Coriobacteriaceae and Erysipelotrichaceae families (figure 2E), and Lachnospiraceae 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.

    Supplemental material

    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).

    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, Ruminiclostridium_6, Roseburia, Coprococcus_1 and Acetatifactor positively correlated with persistence of response in the addicted group. Online supplemental material has a detailed description of the results of microbiota and food addiction signature correlations at the family level (online supplemental figure S2 and S3).

    Supplemental material

    Supplemental material

    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).

    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).

    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 analysis (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 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.

    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.

    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 underlined 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), compulsivity 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.

    Supplemental material

    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 agreement 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.

    Supplemental material

    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.

    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 microbiota 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 particular 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 supplemental table S3). Online supplemental material has a detailed 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 considered addicted, whereas none of the mice receiving lactulose or rhamnose achieved the addiction criteria (χ2=5, control vs lactulose p<0.05 and χ2=5, control vs rhamnose p<0.05, figure 7E).

    Supplemental material

    Supplemental material

    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.

    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).

    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 Lachnospiraceae, 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 concentration (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.

    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.

    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 reactivity and aversive cue reactivity (figure 9F–I). In addition, all mice had similar food intake and body weight during the experimental 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 addiction. Online supplemental material has a detailed description of the results shown in figure 10.

    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).

    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.

    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 (dopamine receptor type 2), Drd1 (dopamine receptor type 1), Th (tyrosine hydroxylase) and Darpp32 (dopamine and cAMP regulated neuronal phosphoprotein). Online supplemental figure S9 and online supplemental table S5 have more details.

    Supplemental material

    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 addiction 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 communication 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 previously associated with addiction, previous studies revealed a beneficial effect on other psychiatric disorders. Negative correlations were also observed between Enterorhabdus and brain kynurenine levels of mice that underwent chronic mild stress, with increased kynurenine levels being a reflection of disturbances in tryptophan metabolism.30 In agreement with the results in mice, the species from the Actinobacteria phylum (Bifidobacterium dentium) were upregulated in our non-addicted individuals 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 Lachnospiraceae genus was modified in a forced alcohol drinking group of mice. However, the results were controversial, suggesting 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 Lachnospiraceae 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 antiobesity 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 expression 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 potential 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 Anaeroplasma genus (from Tenericutes phylum and Anaeroplasmataceae family) was increased in addicted mice. An earlier study demonstrated the antiobesity activity of thinned peach polyphenols, 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 Ruminococcaceae 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 Clostridiales 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 vulnerable 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 similarities 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 microbiota derived metabolites are critical regulators of host appetite.41 In our human cohort, we observed significant differences in the relative abundances of bacteria belonging to Actinobacteria, 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/Firmicutes 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 Proteobacteria 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 microbiota 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 associated with different eating patterns,19 which underlies the association 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 approximately 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 assessment.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 involvement in improving metabolic diseases and promoting nutritional advantages.51 In addition, Blautia has shown potential properties 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 anaerobic 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 beneficial 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 protective effect, avoiding the development of food addiction in mice with a predominant effect in preventing motivation and compulsivity. 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 reinforcing 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 hydroxylase), 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 neurochemical 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 questionnaire 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 rhamnose, 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 prognostic 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.

    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.

    Ethics statements

    Patient consent for publication

    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.

    Acknowledgments

    We thank M Linares, R Martín, D Real and F Porrón for their technical support. We thank T Gusinkaia for her bioinformatic support.

    References

    Supplementary materials

    Footnotes

    • RM and EM-G are joint senior authors.

    • SSėnė, AG-B and JM-P contributed equally.

    • 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, 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.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • 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.

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