THE CONTEXT

A few definitions to help you understand our project.

CARDIATEAM

The project in-depth.

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.

CARDIATEAM Figure 1

OUR OBJECTIVES

Assess the uniqueness of Diabetic Cardiomyopathy

001-one

Provide access to cohorts within CARDIATEAM consortium for 

  • enrolment into a new one with deeply phenotyped patients with broad spectrum of cardiometabolic disorders 
  • access to existing published data for retrospective epidemiological meta-analyses.
002-two

Explore by unbiased statistical clustering analysis the specific contribution of Type 2 Diabetes to the development of diabetic cardiomyopathy.

003-three

Explore the role of confounding factors that include ageing process and metabolic exposome in inducing diabetic cardiomyopathy.

004-four

Understand the physiological pathways responsible for establishment and progression of diabetic cardiomyopathy by translating bedside phenotypic data to bench preclinical models of diabetic cardiomyopathy.

005-five

Use diabetic cardiomyopathy as a representative comorbidity for the paradigm of Heart Failure and a targetable mechanism for proof-of-concept studies

006-six

Explore the role of existing and newly identified biomarkers for risk stratification of Type 2 Diabetes patients and their potential application for personalised preventive and therapeutic strategies.

HOW WILL WE ACHIEVE THEM?

  • Interrogation of the available key longitudinal cohorts and new prospectively recruited CARDIATEAM cohorts to better understand the progression of diabetic cardiomyopathy to Heart Failure
  • Combination of deep clinical phenotyping with state-of-the-art big-data processing techniques
  • Identification of new imaging and molecular biomarkers
  • Prototyping of preclinical experimental models and tailor system-biology methods to identify innovative therapeutic targets

CARDIATEAM at a glance

APPROACH & METHODOLOGY

CARDIATEAM is a 5-year program consisting of 6 scientific work packages (WPs) that will foster the dynamic of translational research from patient cohorts to preclinical models and the dissemination on the knowledge on DCM to physicians, patients, regulators & health agencies.

Indeed CARDIATEAM consortium is aware of the need of a homogeneous and deep phenotype among patients in order to optimize the identification of the cluster corresponding to DCM patients by unsupervised analysis.

 

CARDIATEAM will address the uniqueness of DCM and its progression towards HFpEF by:

  • the exploitation of published data from 13 existing cohorts (epidemiological meta-analysis) with deep cardiometabolic phenotype (imaging techniques) and longitudinal follow-up among whose participants will be invited for a deep “re-phenotyping” in a new CARDIATEAM cohort

  • a prospective CARDIATEAM cohort will be recruited from these existing 16 cohorts using a defined set of selection criteria and will include T2DM and non-diabetic patients with a large spectrum of demographic, metabolic and cardiac clinical data from the 16 cohorts described above. This will yield a wide range of T2DM- related phenotypes including common confounders such as BMI, smoking, age and blood pressure.

  • systems biology analysis after integration of clinical phenotype, imaging and multi-OMICs datasets

  • preclinical studies relying on in silico and cellular models, small and large animal models relevant to DCM3

EXPECTED RESULTS

  • Establishment of a cohort of 1600 patients and with a follow-up of 3 years, phenotyping the patients with echo, CMR, retinography and -omics
  • Application of unsupervised machine learning algorithms to improve cardiac phenotyping & identification of DCM (WP4)
  • Provide a sex- and age- based stratification approach of T2DM patients at risk of DCM (WP 2)
  • Identification of causal mechanisms and pathways responsible for DCM (WP 2 & WP3 & WP5 & WP7) 

  • Identification of new potential therapeutic targets for preventing or alleviating DCM (WP6) 

  • Application of disease modelling to develop DCM preclinical models (WP7) 

  • New taxonomy of DCM to be communicated to health agencies, practitioners and patients (WP1)

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821508. 
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