Microbiota-Inflammation-Brain axis in heart failure: new food, biomarkerS and AI Approach for the prevention of undeRnutrition in Older
In agreement to the Joint Programming Initiative “A Healthy Diet for a Healthy Life”, the transnational and multidisciplinary AMBROSIA project fits with the areas of food and health tackling one of major social challenges in current healthcare, the undernutrition in a well-defined “older” population, or rather Heart Failure (HF) and Atrial Fibrillation patients (AF). In this patients’cohort, undernutrition, is one key factors leading through inflammation, to loss of function, disability and, ultimately, to death. In addition, AF and HF synergistically contribute to determine a frail status. Undernourished AF and HF patients enter a vicious cycle “of undernutrition, inflammation, cachexia”, determining a progressive cognitive decline and lean body mass regression. .Since the inflammation is closely related with the health of intestinal microbiota, shaping the gut microbiota composition with a probiotic-based food, specifically enriched with key nutrients, could be an efficacious and safe approach to break this cycle improving the cognitive functioning and skeletal muscle mass. AMBROSIA is based on a novel research, it was planned to leverage and blend the skills of all applicants, so that there is a partnership to achieve the main objectives and an even distribution of tasks in the various work packages. AMBROSIA aims to develop an innovative food product to prevent undernutrition in HF and AF older patients: a new chocolate AMBROSIA bar containing specific mix of probiotic strains and a cocktail of micro/macronutrients (fibers, protein hydrolysates, coenzyme Q10); further studies on the metabolism and absorption of novel hydrolysates will be performed involving the patient association. In addition, the realization of a proper-realistic strategy from development and delivery of the AMBROSIA bar to the HF and AF patients is included. The efficacy of “AMBROSIA” bar on undernutrition prevention and its impact on cognitive functioning and skeletal muscle mass of older HF and AF patients will be evaluated through a prospective monocentric interventional clinical study. Finally, statistical and machine learning methods will be developed for the identification of features from the “Microbiota-Inflammation-Brain axis” that are predictive for undernutrition (biomarkers) and/or related to AMBROSIA bar treatment outcome.
|IdISBa, Foundation Health Research Institute of the Balearic Islands, Lipids in Human Pathology group
|Section of Food adn Nutrition, School of Agriculture and Food Science, University College Dublin
|Northumbria University, NB144, Northumberland Building, Northumbria University
|Genevention GmbH, R&D
- The AMBROSIA chocolate bar has been designed and produced on a pilot scale, containing a specific mix of probiotics (Lactobacillus rhamnosus IMC 501 ® and Lactobacillus paracasei IMC 502), fibres and a cocktail of micro/macronutrients. The final composition of AMBROSIA is based on the nutritional requirements of the patients involved in the in vivo study, based on the total protein, essential amino acid profiles and the in vitro cardioprotective properties. The ingredients were balanced according to the production process of the chocolate bar to obtain a final AMBROSIA food product with optimum sensorial profile, the recommended dose for each active principle/component (probiotics, protein hydrolysates, inulin, coenzyme Q10) and a good shelf-life.
- AMBROSIA semantic knowledgebase has been developed and configurated on the basis of Genevention’s Semares TM platform. For this purpose, a data and metadata catalogue for AMBROSIA was established with all partners in several online meetings. Based on this catalogue, a harmonized (meta)data representation scheme for FAIR data management was derived and a corresponding semantic layer for the knowledgebase was developed. Furthermore, we developed a module for the streamlined insertion of clinical data into the knowledgebase, including the possibility to represent different clinical data categories and semi-automatically link clinical study data to corresponding experimental data.
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