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PRAESIIDIUM

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PHYSICS INFORMED MACHINE LEARNING BASED PREDICTION AND REVERSION OF IMPAIRED FAS...

PHYSICS INFORMED MACHINE LEARNING BASED PREDICTION AND REVERSION OF IMPAIRED FASTING GLUCOSE MANAGEMENT The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Gluc... ver más

31/12/2025

Líder desconocido

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto Líder desconocido

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-11-11

HORIZON-HLTH-2022-STAYHLTH-02-01: Personalised blueprint of chronic i... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

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Duración del proyecto: 37 meses Fecha Inicio: 2022-11-11
Fecha Fin: 2025-12-31

Líder del proyecto Líder desconocido

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Glucose (IFG) are intermediate glucose mishandling (i.e. intermediate conditions in the healthy-T2D transition) and are manifestations of the so-called prediabetes condition. Prediabetes itself is not an extensively studied condition compared to the overt T2D, but it is also a condition that can be reversed without the prescription usage to not proceed into T2D. The aim of our project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models (firstly developed during the FP7 MISSION-T2D project) that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process and immune system response. The prediction algorithm will be based on a physics-informed machine learning approach. A rich dataset of real-life data will be combined with a mathematical model to overcome the limits of a black-box ML approach, while reducing the computational time for simulating the solutions of a heavy mathematical models and improving its prediction performances.We will collect the necessary training data (e.g., diet questionnaire, physical activity, blood metabolites and microbiome) from already existing clinical studies (used as retrospective trials) which are representative of the real-life scenarios of a prediabetes/diabetes risk insurgence in adulthood (20-80y): family history, Metabolic Syndrome, Liver disease and obesity. A newly dedicated multicentric pilot prospective observational study will be also performed, during which we will also equip the participants with wearable sensors (e.g. glucose monitoring, bioimpedance, heart rate, accelerometer).

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