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.

Keywords: NEUROPHARMACOLOGY; NUTRITION; OBESITY; PSYCHOLOGY.

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.

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).

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).

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.

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.

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).

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.

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).