Summary
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. Intestinal dysbiosis is a determinant factor in the progression of insulin resistance to T2D, shifting host metabolism though undefined mechanisms whose understanding would be crucial to allow personalized interventions. Advances have been limited by microbiome complexity and inter-individual variance. 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. 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. 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, that will be validated with in vivo omic data. To warrantee the health promoting effects of novel foods we will test a new concept in the emerging field of insect food technology, that an adequate insect’s metabolic-intestinal health ensures the healthy properties of its derived products. The validation of this idea will also serve to identify biomarkers for the monitorization of the insect’s health status. A. domesticus will be the testing model of choice, due to its excellent nutritional profile, that will be boosted by complementing the insect’s diet with microalgae bioactive-rich extracts. 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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101162517 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2028 |
| Total budget - Public funding: | 3 999 222,50 Euro - 3 999 222,00 Euro |
Cordis data
Original description
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. Intestinal dysbiosis is a determinant factor in the progression of insulin resistance to T2D, shifting host metabolism though undefined mechanisms whose understanding would be crucial to allow personalized interventions. Advances have been limited by microbiome complexity and inter-individual variance. 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. 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. 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, that will be validated with in vivo omic data. To warrantee the health promoting effects of novel foods we will test a new concept in the emerging field of insect food technology, that an adequate insect’s metabolic-intestinal health ensures the healthy properties of its derived products. The validation of this idea will also serve to identify biomarkers for the monitorization of the insect’s health status. A. domesticus will be the testing model of choice, due to its excellent nutritional profile, that will be boosted by complementing the insect’s diet with microalgae bioactive-rich extracts. 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.Status
SIGNEDCall topic
HORIZON-EIC-2023-PATHFINDERCHALLENGES-01-03Update Date
23-12-2024
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