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CARdiomyopathy in type 2 DIAbetes mellitus

Project description

Big data and biobanks support improved diagnosis and treatment for heart disease in diabetics

The prevalence of type 2 diabetes has reached a pandemic scale with patients at an elevated risk for developing cardiomyopathy, a heart muscle disease that makes it difficult for the heart to pump blood, and heart failure is a leading cause of death. While diabetic cardiomyopathy (DCM) has been recognised as a distinct clinical condition, it is not certain if DCM does in fact have different development and disease progression compared to conventional cardiomyopathy. The CARDIATEAM initiative intends to answer this question by comparing diabetes and heart failure patients and healthy controls. Finding biomarkers of DCM while simultaneously developing big-data processing techniques to identify risk groups based on phenotypic differences should foster early diagnosis and treatment for better outcomes.

Objective

A rapidly evolving epidemic in Type 2 Diabetes (T2DM) is afflicting all ages, sexes and socioeconomic classes which includes serious comorbidities such as heart diseases. While ischaemic heart disease represents the major cause of death of T2DM patients, heart failure (80% of Heart Failure with preserved Ejection Fraction) is the second most common cardiovascular disease in T2DM patients. The aim of CARDIATEAM is to determine whether T2DM represents a central mechanism contributing to the pathogenesis and progression of a specific cardiomyopathy, called “diabetic cardiomyopathy” (DCM), assessing whether DCM is unique and distinct from the other forms of heart failure.
To achieve this aim CARDIATEAM will build up a deeply phenotyped cohort, including an innovative imaging protocol, based on privileged access within the CARDIATEAM to already existing highly pertinent cohorts of diabetes and heart failure patients and control groups. Central biobanking of the cohort samples will allow detailed omics analysis that will feed together with the phenotype and imaging data into the central CARDIATEAM database. The data gathered will enable unsupervised machine-learning for clustering this heterogeneous population on phenotypic differences beyond diabetes. State-of-the-art big-data processing techniques and disease modeling will allow for controlling for common confounders such as BMI, smoking, age and blood pressure and will finally lead to the identification of new imaging and molecular biomarkers as well as understanding the taxonomy of the development and progression of DCM. Tailored preclinical models will be developed to explore the identified pathways revealing new therapeutic targets.
The results of CARDIATEAM will be able to impact clinical care with the stratification of patients into risk groups of developing DCM, earlier diagnosis of DCM and an improvement of therapy thanks to better assessment of underlying pathophysiology and identification of new biomarkers.

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Coordinator

INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE
Net EU contribution
€ 1 053 229,18
Address
Rue de tolbiac 101
75654 Paris
France

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Region
Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
Links
Other funding
€ 1 870 011,12

Participants (31)