The Microbiome-Gut-Brain axis regulates social cognition & craving in young binge drinkers

Abstract

Background: Binge drinking is the consumption of an excessive amount of alcohol in a short period of time. This pattern of consumption is highly prevalent during the crucial developmental period of adolescence. Recently, the severity of alcohol use disorders (AUDs) has been linked with microbiome alterations suggesting a role for the gut microbiome in its development. Furthermore, a strong link has emerged too between microbiome composition and socio-emotional functioning across different disorders including AUD. The aim of this study was to investigate the potential link (and its predictive value) between alcohol-related altered microbial profile, social cognition, impulsivity and craving.

Methods: Young people (N = 71) aged 18-25 reported their alcohol use and underwent a neuropsychological evaluation. Craving was measured at baseline and three months later. Diet was controlled for. Blood, saliva and hair samples were taken for inflammatory, kynurenine and cortisol analysis. Stool samples were provided for shotgun metagenomic sequencing and short-chain fatty acids (SCFAs) were measured.

Findings: Binge drinking was associated with distinct microbiome alterations and emotional recognition difficulties. Associations were found for several microbiome species with emotional processing and impulsivity. Craving showed a strong link with alterations in microbiome composition and neuroactive potential over time.

Interpretation: In conclusion, this research demonstrates alterations in the gut microbiome of young binge drinkers (BDs) and identifies early biomarkers of craving. Associations between emotional processing and microbiome composition further support the growing literature on the gut microbiome as a regulator of social cognition. These findings are of relevance for new gut-derived interventions directed at improving early alcohol-related alterations during the vulnerability period of adolescence.

Funding: C.C. and R.G-C. received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 754535. APC Microbiome Ireland is a research centre funded by Science Foundation Ireland (SFI), through the Irish Government's National Development Plan [grant no. SFI/12/RC/2273_P2]. J.F.C has research support from Cremo, Pharmavite, DuPont and Nutricia. He has spoken at meetings sponsored by food and pharmaceutical companies. G.C. has received honoraria from Janssen, Probi, and Apsen as an invited speaker; is in receipt of research funding from Pharmavite, Fonterra, Nestle and Reckitt; and is a paid consultant for Yakult, Zentiva and Heel pharmaceuticals. All the authors declare no competing interests.

Keywords: Adolescence; Binge drinking; Gut-brain axis; Microbiome; Social cognition.

Conflict of interest statement

Declaration of interests J.F.C has research support from Cremo, Pharmavite, DuPont and Nutricia. He has spoken at meetings sponsored by food and pharmaceutical companies. G.C. has received honoraria from Janssen, Probi, and Apsen as an invited speaker; is in receipt of research funding from Pharmavite, Fonterra, Nestle and Reckitt; and is a paid consultant for Yakult, Zentiva and Heel pharmaceuticals. All the authors declare no competing interests.

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

Study design and outline. Young binge and non-binge drinkers (N = 71) aged 18–25 were recruited. The study comprised three sessions, stool, blood and saliva samples were collected and cognitive performance was assessed. Craving levels were recorded again three months later (N = 56). Multiple regression models were used incorporating relevant covariables such as diet and correcting for multiple comparisons. Strict inclusion criteria were applied including not having an alcohol use disorder, other drug use or current medication. Findings show binge drinking is associated with a specific microbiome signature and suggest a role of the gut microbiome in regulating social cognition and craving. Biological samples provided differed (saliva N = 64; blood N = 56; hair N = 61).

Fig. 2

Inflammatory markers associations for binge drinking (BD) and craving.a.Alcohol consumption data. Raw drinking scores showing the high correlation between BD episodes (i.e., 60 g or more of pure alcohol on one occasion) in the last month (measured by the TLFB), with maximum drinks and BD in the past year (measured by AUDIT question 3 in which items 2–3 indicate monthly and weekly BD respectively). b. Binge drinking and inflammatory markers. BD correlated positively with higher stimulated inflammatory markers. c. Days since the last binge drinking episode and inflammatory markers. Significant negative correlations were found for number of days since last BD episode and stimulated inflammatory markers, i.e., the more recent the BD episode, the higher the inflammation. d. Craving and inflammatory markers. Higher craving (expectancy dimension) correlated with higher responsiveness of stimulated inflammatory markers.

Fig. 3

Neuropsychological performance in social cognition and binge drinking(BD).a. Emotional functioning and binge drinking: raw data divided by tertiles. Higher BD was associated to poor emotion recognition for sadness and disgust (lower correct responses) in the Emotional Recognition Task (ERT). The Tertiles visualization depicts raw data. Statistical models with regression coefficients are described in text and full models can be found in Supplementary Material. b. Affective bias and binge drinking: raw data divided by tertiles. Higher BD was associated to slower reaction time (measured as higher latencies for correct responses) in the Affective Go/No-go (AGN). The Tertiles visualization depicts raw data. Statistical models with regression coefficients are described in text and Supplementary Material.

Fig. 4

Microbiome alterations in young binge drinkers.a. Alpha diversity by drinking tertiles. Alpha diversity was computed using the iNEXT library. Alpha diversity was not associated with BD variables. b. Beta diversity by drinking tertiles. Beta diversity was computed in terms of Aitchison distance, or Euclidean distance between clr-transformed data. Beta diversity changes were linked to maximum drinks per session (PERMANOVA [p = 0.007, R2 = 0.023]), depicted here as a principal component analysis (PCA) graph. c. Gut microbiome composition and binge drinking. Regression models (lm() function) of CLR-transformed species counts were related to drinking variables, adjusting for BMI, dietary intake and the number of days since the last BD episode. False Discovery Rate (FDR) using sequential modified Bonferroni correction for multiple testing was applied. Compositional results (revealed alterations in some species of the genus Alistipes (reductions) and Veillonella (increases) linked to higher number of drinks per occasion. The effect (β) is represented in red (increased) or blue (reduced) with higher colour intensity representing a bigger effect. Opaque points represent effects that pass FDR. d. Recency of binge dsrinking episodes and gut microbiome composition. A recent BD episode (the covariable days since last BD episode) was associated with further widespread microbiome alterations such as Bacteroides spp., Alistipes spp., Blautia wexlerae, Ruminococcus lactaris, Coprococcus euctactus among others. e. Gut microbiome composition by drinking tertiles. Compositional results are shown by drinking tertiles (low drinkers, BDs and high BDs) based on maximum drinks, in order to facilitate visualization of regression models depicted in c. The Tertiles visualization depicts raw data. Statistical models with regression coefficients are described in text (and in c and f) and full models can be found in Supplementary Material. f. Gut-metabolic modules and binge drinking. Gut-metabolic modules (R Gomixer tool) showed reduced histidine and maltose degradation linked to higher BD. g. Short-chain fatty acids (SCFAs) and binge drinking. Higher BD (higher number of drinks per session) was associated with lower isovalerate.

Fig. 5

Gut microbiome and craving. Regression models (lm() function) of CLR-transformed species counts were related to craving variables, adjusting for BMI, dietary intake and the number of days since the last BD episode. False Discovery Rate (FDR) using sequential modified Bonferroni correction for multiple testing was applied. The effect (β) is represented in red (increased) or blue (reduced) with higher colour intensity representing a bigger effect. Opaque points represent effects that pass FDR. Craving was associated with reductions in the Ruthenibacterium lactiformas species, both at baseline and at follow-up (3 months later). Gut-brain modules (the potential of the microbiome to produce chemicals with neuroactive potential) was calculated with R Gomixer tool. A number of gut-brain modules showed associations with craving, such as reduced butyrate and inositol synthesis and increased acetate, glutamate and tryptophan synthesis. Associations were found for craving at baseline and at follow-up (3 months later). Baseline craving dimensions showing significant associations were expectancy and purposefulness (first and second column respectively) and at follow-up obsessed and regulated dimensions (third and fourth column).

Fig. 6

Microbiome associations with social cognition and impulsivity.a. Gut microbiome composition and emotional functioning and impulsivity. Regression models (lm () function) of CLR-transformed species counts were related to social cognition difficulties and impulsivity, adjusting for BMI, dietary intake and the number of days since the last BD episode. False Discovery Rate (FDR) using sequential modified Bonferroni correction for multiple testing was applied. The effect (β) is represented in red (increased) or blue (reduced) with higher colour intensity representing a bigger effect. Opaque points represent effects that pass FDR. Emotion recognition showed a strong association with microbiome composition. Particularly, a decrease in Clostridium spp., Flavonifractor plautii, Eggerthella lenta and an increase of Coprococcus spp. was associated with poor recognition of sadness. Higher motor impulsivity was associated with several species, e.g., reduction of Collinsella, increased Roseburia and Parabacteroidetes. b and c. Gut microbiome and cognitive performance and impulsivity by drinking tertiles. Previous results are visualized by tertiles depending on emotion recognition performance and impulsivity, respectively. Tertiles represent raw data as a complementary visualization strategy.