Protein supplementation during an energy-restricted diet induces visceral fat loss and gut microbiota amino acid metabolism activation: a randomized trial

Abstract

Interactions between diet and gut microbiota are critical regulators of energy metabolism. The effects of fibre intake have been deeply studied but little is known about the impact of proteins. Here, we investigated the effects of high protein supplementation (Investigational Product, IP) in a double blind, randomised placebo-controled intervention study (NCT01755104) where 107 participants received the IP or an isocaloric normoproteic comparator (CP) alongside a mild caloric restriction. Gut microbiota profiles were explored in a patient subset (n = 53) using shotgun metagenomic sequencing. Visceral fat decreased in both groups (IP group: - 20.8 ± 23.2 cm2; CP group: - 14.5 ± 24.3 cm2) with a greater reduction (p < 0.05) with the IP supplementation in the Per Protocol population. Microbial diversity increased in individuals with a baseline low gene count (p < 0.05). The decrease in weight, fat mass and visceral fat mass significantly correlated with the increase in microbial diversity (p < 0.05). Protein supplementation had little effects on bacteria composition but major differences were seen at functional level. Protein supplementation stimulated bacterial amino acid metabolism (90% amino-acid synthesis functions enriched with IP versus 13% in CP group (p < 0.01)). Protein supplementation alongside a mild energy restriction induces visceral fat mass loss and an activation of gut microbiota amino-acid metabolism.Clinical trial registration: NCT01755104 (24/12/2012). https://ichgcp.net/clinical-trials-registry/NCT01755104?term=NCT01755104&draw=2&rank=1 .

Conflict of interest statement

SPC works as Chief Scientific Officer at YSOPIA Bioscience (Bordeaux, France). LR works as project manager at YSOPIA Bioscience (Bordeaux, France). KC is currently a member of the Scientific Advisory board of YSOPIA Bioscience but this activity is not related to the topic of this publication. All the other authors declare no conflict of interest.

© 2021. The Author(s).

Figures

Figure 1

Investigational Product improved fat mass, maintained lean mass and reduced inflammation at week 12 post intervention. Changes in visceral fat area as established via CT scan in the (A) FAS population, (B) FAS without outliers and (C) PP population. Changes in fat mass (D) and lean mass (E) as established via DEXA scan and in TNFα (F) in FAS without outliers. Mean (SEM) for at least n = 51. *p < 0.05 (GEE model) Key: Circle is Control Product; Square is Investigational Product.

Figure 2

Evolution of metagenomic richness 12 weeks after dietary intervention. (A) Evolution of gene count (number of genes) and metagenomic richness (number of MGS) depending on baseline gene richness status The fixed effect of time (the intervention) was analysed in a mixed linear model with patients as random effects. *p < 0.05, **p < 0.001. (B) Evolution of richness associations with the clinical evolution. Heatmap of standardized beta coefficient from linear regression. Model is adjusted for baseline values + baseline BMI and sex. For cholesterol and triglycerides, the model is also adjusted for baseline statin intake. (C) Evolution of weight, fat mass, visceral fat mass and gene count depending on richness response. Change from baseline is (T12–T0/)T0 * 100. Gained species are individuals who increased their MGS richness. Lost species are individuals who decreased their metagenomic (MGS) richness. P values of the effect of MGS richness change in a linear regression model adjusted for baseline value, sex and BMI. Points are mean, bars are SEM. Figure conceived using R version 3.3.2, R Core Team (2019), https://www.R-project.org/.

Figure 3

Effects of protein supplementation on gut microbiome composition (IP vs. CP). (A) Evolution (relative change at T12) of gene count and MGS richness (number of present species) in investigational product group (IP, red colours) and Comparator group (CP, blue colours) depending on baseline metagenomic richness. LGC: low gene count at baseline (light colours). HGC: high gene count at baseline. p value of the effect intervention interaction with time in LGC patients (a) and HGC patients (b); (B) Alluvial plot showing the evolution of enterotype between T0 and T12 in CP group (left panel) and IP group (right panel); (C) Untargeted analysis of the effects of IP on metagenomic species (MGS) abundance changes with the intervention. MGS shown are the ones with a significant interaction of time with intervention in a mixed linear model with patients as random effects adjusted for baseline age, sex and BMI (p < 0.05 without adjustment for multiple comparisons). Bars represent the cliff delta effect of time (T12 vs T0) on MGS abundance in each group. *p < 0.05; **p < 0.01; ***p < 0.001 No significant differences resist to adjustment for multiple comparisons. IP: investigational product (high protein); CP: comparator product. Figure conceived using R version 3.3.2, R Core Team (2019), https://www.R-project.org/.

Figure 4

Effects of protein supplementation on gut microbiota function. (A) Effects of IP on functional modules from KEGG and GMM databases. Shown modules are those which changed differently (FDR < 0.1) between the two groups (IP vs. CP) adjusted for baseline BMI, age and sex. Bars represent log(fold change) at T12 of modules abundances in each group (IP vs. CP). Modules in bold: amino-acid metabolism modules. *p < 0.05; **p < 0.01; ***p < 0.001. (B) Heatmap of spearman correlations between functional modules fold changes and bio-clinical relative changes after the intervention (T12-T0/T0); *p < 0.05; **q < 0.05 (adjusted for multiple comparison, FDR method). (C) Evolution of the amino acid metabolism (degradation and synthesis) functional modules (KEGG and GMM) with the intervention. Increase is defined by a mean fold change > 0 and decrease by a mean fold change < 0 for each module. The observed proportion of increased module was compared to a theoretical value of 0.5 with a binomial test. *p < 0.05; **p < 0.01; ***p < 0.001 (adjusted for multiple comparisons, FDR method). (D) Effects of IP on amino acid metabolism functional groups of metagenomic species. Shown functional groups are those which changed differently (p < 0.05) between the two groups (IP vs. CP) adjusted for baseline BMI, age and sex. Bars represent log(fold change) at T12 of modules abundances in each group (IP vs. CP). *p < 0.05; **p < 0.01; ***p < 0.001. IP: investigational product (high protein); CP: comparator product. Figure conceived using R version 3.3.2, R Core Team (2019), https://www.R-project.org/.