Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes

Abstract

Objective: To explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes.

Design: In a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics.

Results: PPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (from Bacteroidales, Lachnospiraceae, Oscillospirales orders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight.

Conclusions: Our findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes.

Trial registration number: NCT03222791.

Keywords: diabetes mellitus; diet; nutrition.

Figure 1

PPT intervention induces greater changes in multiple dietary features compared to MED intervention. (A) Study design scheme and total sample count by category. Artwork created with ‘BioRender.com’. (B) CIs of 6 months changes in nutrients consumption. For each nutrient on the y-axis, the upper and lower thin lines indicate CIs of change within the MED and PPT arms, respectively. CIs length is normalised by the mean change in the respective dietary feature across the cohort. The thick purple line indicates CI of between group change difference (PPT change minus MED change). Red and black lines denote significant (p<0.05) and non-significant changes within arms, respectively (one-sided t-test). Asterisks on the right side of the panel denote between-group significant differences (two-sided t-test, *p<0.05, **p<0.01, ***p<0.001. n.s, non significant). (C) As in B but for 6 months changes in food categories consumption (presented as per cent of total energy intake). (D, E) Principal component analysis (PCA) of dietary consumption by arm and study phase (baseline vs intervention) evaluated based on nutrients (D) or food categories (E). (F) Box plots showing the PPT adherence score by arm and study phase (baseline vs intervention). Bars on top denote significance level for difference within arms (***p<0.001. n.s, non significant). PPT, personalised postprandial-targeting.

Figure 2

PPT intervention increases microbiome diversity and richness and exerts specific microbiome species changes that are associated with clinical outcomes. (A) Boxplots showing microbiome diversity (Shannon's diversity index) by arm and time point (baseline vs 6 months). Asterisks on top denote significance level for difference within arms (one-sided t-test). n.s, non significant; *p<0.05, **p<0.01, ***p<0.001. (B) Same as in A but for microbiome richness (# of species). (C) Heatmap of species-level microbiome taxa significantly changed within arms compared to baseline (p<0.05, one-tailed t-test, FDR corrected). Red and blue cells denote enrichment and reduction, respectively. White cells denote no significant change. Asterisks next to species names denote significant differences between arms in the respective species (MW-test, *p<0.05, **p<0.01, ***p<0.001). Species are grouped based on taxonomy hierarchy, with family-level taxonomy represented by colours in the inner circle and in the legend in the centre. (D) Heatmap showing significant associations (p<0.05) between 6 months changes in microbiome species (those distinctly changed between arms) and 6 months changes in clinical readouts or PPT adherence score across the cohort. ALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body mass index; BMR, basal metabolic rate; BP, blood pressure; FDR, false discovery rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; OGTT, oral glucose tolerance test; PPT, personalised postprandial-targeting.

Figure 3

Changes in specific gut microbiome species partially mediate the effect of dietary changes on clinical outcomes. (A) Heatmap showing significant associations (p<0.05, FDR corrected) between 6 months changes in clinical readouts and 6 months changes in nutrient consumption or PPT adherence score across the cohort. (B) Heatmap showing significant associations (p<0.05, FDR corrected) between 6 months changes in clinical readouts and 6 months changes in food categories consumption across the cohort. (C) As in A but for microbiome species versus nutrients or PPT adherence score. (D) As in B but for microbiome species versus food categories. (E) Alluvial plot showing significant mediatory effects of microbiome species (middle) in the association between dietary changes (left) and clinical outcomes (right). (F, G) Two examples of mediation paths with assessment of the proportional mediatory effect of microbiome species. ACME, average causal mediation effect; ADE, average direct effect; AST, aspartate aminotransferase; BMI, body mass index; BMR, basal metabolic rate; BP, blood pressure; FDR, false discovery rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MUFA, monounsaturated fatty acids; PPT, personalised postprandial-targeting.

Figure 4

Machine learning models trained on dietary changes and baseline clinical data predict clinical outcomes. (A) Heatmap showing the interindividual variation in clinical response to similar dietary modifications across the cohort. Participants (columns) are ordered by the 6 months change in PPT adherence score (top row). Ranked changes in major dietary features and clinical outcomes (rows) are presented in the upper and lower panels, respectively. (B) Box-plots showing the prediction accuracy (R) of different clinical outcomes for models trained with different sets of input features using GBDT (LightGBM, see Methods). Error bars in each box plot are based on repeated full cross validation. (C) Top six features contributing to BMI prediction (‘Personalised+Diet’ model), using the SHAP analysis. (D) As in C but for the ΔBMR prediction model. (E) Scatter plot of the correlation between baseline levels of one bacterial species from the Lachnospiraceae family (‘KLE1615_Unknown’) and the difference (Δ) between measured and predicted change in BMR outcome. (F) As in E but presented as violin plots for three quantiles of difference (Δ) from prediction (participants with zero levels of this bacterial species at baseline were filtered out). ALT, alanine transaminase; BMI, body mass index; BMR, basal metabolic rate; BP, blood pressure; CGM, continuous glucose monitoring; FDR, false discovery rate; FPG, fasting plasma glucose; GBDT, gradient-boosted decision trees; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MUFA, monounsaturated fatty acids; PPT, personalised postprandial-targeting; PUFA, polyunsaturated fatty acid.