Linking Intestinal Bacteria and Host Metabolism to Tackle Type 2 Diabetes with Novel Food

Introduction

Poor nutritional regimes are main drivers of metabolic diseases and intestinal dysbiosis. Type 2 diabetes (T2D) prevalence is expected to reach 7% of the global population by 2023, and to curb its rise is an urgent public health need.

Importance of Intestinal Dysbiosis

Intestinal dysbiosis is a determinant factor in the progression of insulin resistance to T2D, shifting host metabolism through undefined mechanisms whose understanding would be crucial to allow personalized interventions. Advances have been limited by microbiome complexity and inter-individual variance.

Project Overview

DiBaN binds together the necessary combination of expertise to address this question, based on the concept that the initial driver of the nutritional effects is the metabolic shift that takes place in the intestinal bacteria, which is transduced to the host.

Technological Needs

In this context, technological breakthrough tools for novel food development, that ensure the promotion of a healthy microbiome-host metabolic interface, are an urgent need to prevent dysbiosis and T2D.

Development of Advanced Platforms

DiBaN will overcome current limitations in nutrient testing by developing advanced ex-vivo platforms that fully recapitulate the in vivo setting of dysbiosis and insulin resistance, which will be validated with in vivo omic data.

Insect Food Technology

To warrant the health-promoting effects of novel foods, we will test a new concept in the emerging field of insect food technology, which posits that adequate insect metabolic-intestinal health ensures the healthy properties of its derived products.

Biomarker Identification

The validation of this idea will also serve to identify biomarkers for the monitoring of the insects' health status.

Testing Model

A. domesticus will be the testing model of choice due to its excellent nutritional profile, which will be boosted by complementing the insect's diet with microalgae bioactive-rich extracts.

Integration of Data

All these data will be integrated for the design of a pilot artificial intelligence (AI)-based application for the prediction of personalized responses to nutritional interventions.