Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study

Ching Jian, Marta Paulino Silvestre, Danielle Middleton, Katri Korpela, Elli Jalo, David Broderick, Willem Meindert de Vos, Mikael Fogelholm, Mike William Taylor, Anne Raben, Sally Poppitt, Anne Salonen, Ching Jian, Marta Paulino Silvestre, Danielle Middleton, Katri Korpela, Elli Jalo, David Broderick, Willem Meindert de Vos, Mikael Fogelholm, Mike William Taylor, Anne Raben, Sally Poppitt, Anne Salonen

Abstract

Background: Low-energy diets (LEDs) comprise commercially formulated food products that provide between 800 and 1200 kcal/day (3.3-5 MJ/day) to aid body weight loss. Recent small-scale studies suggest that LEDs are associated with marked changes in the gut microbiota that may modify the effect of the LED on host metabolism and weight loss. We investigated how the gut microbiota changed during 8 weeks of total meal replacement LED and determined their associations with host response in a sub-analysis of 211 overweight adults with pre-diabetes participating in the large multicentre PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) clinical trial.

Methods: Microbial community composition was analysed by Illumina sequencing of the hypervariable V3-V4 regions of the 16S ribosomal RNA (rRNA) gene. Butyrate production capacity was estimated by qPCR targeting the butyryl-CoA:acetate CoA-transferase gene. Bioinformatics and statistical analyses, such as comparison of alpha and beta diversity measures, correlative and differential abundances analysis, were undertaken on the 16S rRNA gene sequences of 211 paired (pre- and post-LED) samples as well as their integration with the clinical, biomedical and dietary datasets for predictive modelling.

Results: The overall composition of the gut microbiota changed markedly and consistently from pre- to post-LED (P = 0.001), along with increased richness and diversity (both P < 0.001). Following the intervention, the relative abundance of several genera previously associated with metabolic improvements (e.g., Akkermansia and Christensenellaceae R-7 group) was significantly increased (P < 0.001), while flagellated Pseudobutyrivibrio, acetogenic Blautia and Bifidobacterium spp. were decreased (all P < 0.001). Butyrate production capacity was reduced (P < 0.001). The changes in microbiota composition and predicted functions were significantly associated with body weight loss (P < 0.05). Baseline gut microbiota features were able to explain ~25% of variation in total body fat change (post-pre-LED).

Conclusions: The gut microbiota and individual taxa were significantly influenced by the LED intervention and correlated with changes in total body fat and body weight in individuals with overweight and pre-diabetes. Despite inter-individual variation, the baseline gut microbiota was a strong predictor of total body fat change during the energy restriction period.

Trial registration: The PREVIEW trial was prospectively registered at ClinicalTrials.gov ( NCT01777893 ) on January 29, 2013.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Overview of the study and variation of host variables. A Schematic overview of the study design. B Density plots showing inter-individual variation in host variables in response to the LED. The density plots display the distribution of the observed data (relative changes in host variables). The density function reflects the estimated underlying continuous probability from which the observed data have been sampled. BMI, body mass index; HOMA, homeostasis model for assessment of insulin resistance; FPG, fasting plasma glucose
Fig. 2
Fig. 2
Principal coordinate analysis (PCoA) of microbiota variation in pre-(blue dots) and post-(red dots) LED samples based on Bray-Curtis distances (A). Arrows link the baseline pre- and post-intervention sample of each individual, indicating direction of change. The blue and red dispersion ellipses represent standard deviations within the groups of pre- and post-intervention samples, respectively. The principal component (PC) scores of PC1 (B) and PC2 (C) are plotted by the sampling time points
Fig. 3
Fig. 3
The LED intervention reshapes the overall microbiota structure, alters relative abundances of individual bacterial taxa and predicted functions. Pre- and post-LED A richness, B diversity within samples (Shannon index), C average dissimilarities (beta diversity) estimated by Bray-Curtis distances between participants, and D Firmicutes to Bacteroidetes ratio. Differentially abundant E phyla, F genera (coloured by respective phyla) and G KEGG modules following the LED ranked by log-fold change are visualized by divergent bar plots. Only the 15 most abundant genera and KEGG modules are shown in F and G. Log2 fold change is calculated as post-LED/pre-LED; only significant results (FDR-P < 0.05) are plotted. The genera known to be able to produce butyrate are marked with an asterisk (*) in I
Fig. 4
Fig. 4
The LED intervention and body fat (%) reduction associated with reduced capacity for butyrate production in the gut microbiota. A qPCR quantification of the butyryl-CoA:acetate CoA-transferase gene in pre- and post-LED faecal samples. Data are expressed as 1/mean threshold cycle (Ct). B Relative changes (post–pre-LED) in body fat (%) (ΔBody fat %) significantly correlated with relative changes in butyrate production capacity (ΔButyryl CoA:acetate CoA transferase)
Fig. 5
Fig. 5
Correlation heatmaps for changes (post–pre-LED) in A bacterial genera and B KEGG functional modules. For readability of the figures, only prevalent bacterial genera (present in >30% of samples) or functional modules that had at least one significant association with changes in clinical measurements are shown. * FDR-P < 0.05; ** FDR-P < 0.01; *** FDR-P < 0.001
Fig. 6
Fig. 6
Amount of variation in changes of clinical indices explained by baseline gut microbiota. The bar graph shows the estimated R2 in Random Forest (RF) and stepwise regressions. The error bars show 95% confidence intervals from repeated cross-validation of the random forests to predict the delta (post–pre-LED) clinical indices. In both regression models, the adjusted model includes demographic variables (age, gender and ethnicity) in addition to microbiota features. The unadjusted model is included as a contrast to showcase the predictive power of gut microbiota features for specific clinical indices without conflating the information related to host clinical characteristics
Fig. 7
Fig. 7
Comparison of the association strengths between the true and predicted total body fat (%) change during the LED based on the four different models. The lines represent the fitted regression lines (Spearman’s rank correlation coefficients displayed at the upper left corner) and the corresponding shaded area represents the 95% confidence intervals for each model. GM, gut microbiota; host, host clinical characteristics including demographic characteristics, anthropometric and metabolic measurements as presented in Table 1

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