Prebiotic diet changes neural correlates of food decision-making in overweight adults: a randomised controlled within-subject cross-over trial

Abstract

Objective: Animal studies suggest that prebiotic, plant-derived nutrients could improve homoeostatic and hedonic brain functions through improvements in microbiome-gut-brain communication. However, little is known if these results are applicable to humans. Therefore, we tested the effects of high-dosed prebiotic fibre on reward-related food decision-making in a randomised controlled within-subject cross-over study and assayed potential microbial and metabolic markers.

Design: 59 overweight young adults (19 females, 18-42 years, body mass index 25-30 kg/m²) underwent functional task MRI before and after 14 days of supplementary intake of 30 g/day of inulin (prebiotics) and equicaloric placebo, respectively. Short chain fatty acids (SCFA), gastrointestinal hormones, glucose/lipid and inflammatory markers were assayed in fasting blood. Gut microbiota and SCFA were measured in stool.

Results: Compared with placebo, participants showed decreased brain activation towards high-caloric wanted food stimuli in the ventral tegmental area and right orbitofrontal cortex after prebiotics (preregistered, family wise error-corrected p <0.05). While fasting blood levels remained largely unchanged, 16S-rRNA sequencing showed significant shifts in the microbiome towards increased occurrence of, among others, SCFA-producing Bifidobacteriaceae, and changes in >60 predicted functional signalling pathways after prebiotic intake. Changes in brain activation correlated with changes in Actinobacteria microbial abundance and associated activity previously linked with SCFA production, such as ABC transporter metabolism.

Conclusions: In this proof-of-concept study, a prebiotic intervention attenuated reward-related brain activation during food decision-making, paralleled by shifts in gut microbiota.

Trial registration number: NCT03829189.

Keywords: BRAIN IMAGING; BRAIN/GUT INTERACTION; DIETARY FIBRE; OBESITY; SHORT CHAIN FATTY ACIDS.

Figure 1

Study design. Within-subject cross-over dietary intervention design with two study arms and up to six measurement timepoints (upper panel, T0: screening; BL1/2: baseline 1/2, FU1/2: follow-up 1/2, T6: additional follow-up). Participants were randomly assigned to receive first prebiotics and second placebo (arm 1), or vice versa (arm 2), for 14 days each, separated by a 14-day wash-out period. Following the same timeline, at BL1, FU1, BL2 and FU2, participants provided stool samples and underwent fasting blood draw (1), anthropometric measurements (2), received a standard breakfast shake (3) and MRI assessments (4), followed by brief surveys (5), food remuneration (6) and further tests and questionnaires (7–8). Steps (9–11) indicate data processing and statistical analysis. Screens give fMRI wanting task paradigm scheme and timing. BL, baseline; FU, follow-up; fMRI, functional magnetic resonance imaging (MRI); LC-MS/MS, liquid chromatography–mass spectrometry; SPM, statistical parametric mapping, SwE, sandwich estimator, WGNCA, weighted graph network correlational analysis. Created with BioRender.com.

Figure 2

Consolidated Standards of Reporting Trials (CONSORT) flow diagram. Participants underwent a randomised controlled dietary intervention trial in a within-subject cross-over design. BMI, body mass index; fMRI, functional MRI.

Figure 3

Behavioural (A, B) and neural response (C, D) to food and art stimuli in overweight adults during decision-making. Participants responded to food with higher wanting scores compared with art (n_obs=32 111, n_subj=59) (A), showing highest mean values for moderately high caloric stimuli, and lowest mean values for art objects (n_obs=32 111, n_subj=59) (B). Food compared with art valuation elicited stronger brain activation particularly in subcortical areas of the reward network (n_subj=57) (C), while additional parametric modulation with wanting scores indicated a stronger brain activation in ventromedial prefrontal cortex and orbitofrontal cortex when comparing food versus art (n_subj=57) (D). Statistics were done with linear mixed effect modelling, up to 4 time points per participant×120 stimuli on wanting scores (main analysis) (A), (exploratory analysis) (B) and on voxel-wise blood-oxygen-level-dependent signal using the sandwich estimator toolbox with threshold-free cluster enhancement (TFCE) family wise-error correction (FWE) of multiple comparisons (C,D),(main analyses) (C,D). Colour bars depict parametric TFCE statistic (TFCE-t >50 for visualisation purposes) with wild-boot strapped p_FWE<0.05 marked in red outline.

Figure 4

Effects of prebiotic intervention on food decision-making. After the intervention, participants decreased wanting scores for food from caloric quartiles 1 and 4 as well as animals (exploratory analysis, n_subj=59, (A). At the neural level, brain activation decreased in the ventral tegmental area (VTA) and in two clusters in the orbitofrontal cortex (OFC) towards high-caloric, wanted food stimuli (main analysis, n_subj=57, (B, C). Statistics according to linear mixed effects modelling, up to 4 time points per participant×120 stimuli on wanting scores and on voxel-wise blood-oxygen-level-dependent signal using the sandwich estimator toolbox with threshold-free cluster enhancement (TFCE) family wise-error correction (FWE) of multiple comparisons. Colour bars depict parametric TFCE statistic with wild-boot strapped p_FWE<0.05 marked in red outline (upper right panel) and as enlargement (lower right panel).

Figure 5

Changes in secondary eating behaviour-related outcomes after prebiotic (red) compared with placebo condition (light blue). Hunger ratings during fMRI significantly decreased after prebiotics (A), while gender-standardised body fat mass (FM-stand, B), serum lipid markers low-density lipoprotein (LDL, C) and cholesterol (CHOL, D) significantly decreased after placebo (linear mixed effects modelling, all p<0.05, exploratory analyses). FMRI, functional magnetic resonance imaging.

Figure 6

Microbiota-related shifts after 2-week prebiotic intervention (exploratory analyses). Increases in stool frequency (A) and decreases in (B) microbiota richness, (C) evenness, (D) Shannon index, (E) beta diversity changes compared by dissimilarity gradients according to group and timepoint after prebiotics (pink) compared with placebo (blue), and (F) shifts in microbial family distribution. Asterisks in (A–D) indicating significant ANOVA results for null-full model comparisons (p<0.05). ANOVA, analysis of variance; ASV, amplicon sequencing variant; NMDS, non-metric multidimensional scaling.

Figure 7

Predicted functional shifts and their correlations with changes in microbiota genera and in reward-related brain activation after prebiotic intervention (exploratory analyses). (A) Dissimilarity of functional composition of microbiome preprebiotic to versus postprebiotic intervention based on NMDS stress test (p=0.001) and principal component analysis of relative abundance of predicted KEGG orthologues statistics, calculated by PERMANOVA (p_adj=0.002). (B) Change scores of Bifidobacterium abundance and arginine biosynthesis (ko00220), (C) Bifidobacterium abundance and cysteine and methionine metbolism (ko00270), (D) flavonoid biosynthesis (ko00941) and changes in reward-related brain response, (E) stilbenoid, gingerol biosynthesis (ko00945) and reward-related brain response. (B–E), all r>0.32, all p<0.05 according to Spearman’s correlation, line gives regression fit with 95% CI. KEGG, Kyoto Encyclopaedia of Genes and Genomes; NMDS, non-metric multidimensional scaling; PERMANOVA, permutational multivariate analysis of variance; VTA, ventral tegmental area.