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

Orly Ben-Yacov, Anastasia Godneva, Michal Rein, Smadar Shilo, Maya Lotan-Pompan, Adina Weinberger, Eran Segal

Journal: Gut 2023;72(8):1486-1496

PMID: 37137684

Plain Language Summary

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Diet is a major contributor to cardiometabolic health and plays a fundamental role in the prevention, management and even reversal of many chronic diseases. The gut microbiota has a central role in human health and disease. Specifically, its role in cardiometabolic health has been studied extensively in recent years. The aim of this study was to evaluate the interplay between dietary modifications, microbiome composition and cardiometabolic health outcomes. This study was a randomised controlled trial of a 6-month dietary intervention comparing a personalised postprandial-targeting (PPT) diet versus Mediterranean (MED) diet in 200 adults with pre-diabetes. Results showed that: - PPT intervention induced greater changes in multiple dietary features compared with MED intervention. - PPT intervention increased microbiome diversity and richness and exerted specific microbiome species changes that associate with clinical outcomes. - Changes in specific gut microbiome species partially mediated the effects of dietary modifications on clinical outcomes. Authors conclude that the PPT diet prompted greater changes in gut microbiota composition, consistent with overall greater dietary modifications, as compared with the MED intervention.

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 , , 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.

© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Address: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.; School of Public Health, University of Haifa, Haifa, Israel.; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.; The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center, Petah Tikva, Israel.; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel [email protected].; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

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