CARdiomyopathy in Type 2 DIAbetes Mellitus

Recruiting

• Conditions: Cardiomyopathy, Diabetic

• Registration Number: NCT04303364
• Lead Sponsor: Institut National de la Santé Et de la Recherche Médicale, France

Brief Summary

The objective of the CARDIATEAM clinical study is to assess the uniqueness of diabetic cardiomyopathy (DCM) relative to other forms of cardiomyopathy using unsupervised clustering approaches based on deep phenotyping (clinical, imaging and biological) information.

Detailed Description

CARDIATEAM study will address the uniqueness of DCM, and its progression towards heart failure (HF) with preserved ejection fraction (HFpEF) by recruiting a prospective CARDIATEAM cohort (n=1600 individuals) from existing cohorts using a defined set of selection criteria and will include type2-Diabetes mellitus (T2DM)and non-diabetic patients with a large spectrum of demographic, metabolic and cardiac clinical data. This will yield a wide range of T2DM - related phenotypes including common confounders such as BMI, smoking, age and blood pressure.

To clarify the phenotype of DCM and to differentiate it from the other forms of HF such as HFpEF or HCM, CARDIATEAM will perform unbiased clustering analysis from an in-depth phenotyping of these patients' populations

Study Timeline

• Study First Submitted: 2020-03-06
• Study First Submitted QC: 2020-03-10
• Study First Posted: 2020-03-11
• Study Start: 2020-10-02
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-01
• Last Update Posted: 2025-04-04
• Primary Completion: 2028-04-01
• Study Completion: 2028-04-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1600

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Assess the uniqueness of diabetic cardiomyopathy (DCM) relative to other forms of cardiomyopathy	3 years	Use of unsupervised clustering approaches based on deep phenotyping (clinical, imaging and biological) information

Secondary Outcome Measures

Name	Time	Method
Identify the best clinical, biological, imaging and multi-OMICs predictors belonging to each identified cluster	4 years	Specific focus on cluster(s) relating to a putative diabetic cardiomyopathy comparatively to other clusters \[diagnostic perspective\]
Assess prospective health outcomes (i.e. overall mortality, cardiovascular events and cardiac function) in the diabetic cardiomyopathy cluster identified	5 years	Compare them to those from the other clusters and pre-defined patient groups \[prognostic perspective\]
Explore the pathophysiological and potentially causal pathways characterizing diabetic cardiomyopathy	5 years	Better understand the underlying mechanisms responsible for establishment and progression of disease, based on OMICs data and causal inference modeling

Trial Locations

Locations (17)

Hopital Louis Pradel; 🇫🇷 Bron, France

Cardiology Outpatient Department at Hôpital Henri Mondor.; 🇫🇷 Créteil, France

Centre Hospitalier Universitaire Grenoble Alpes; 🇫🇷 Grenoble, France

department of diabetology and nutrition, APHM; 🇫🇷 Marseille, France

Hôpital CHU- Nantes; 🇫🇷 Nantes, France

Diabetology department, Cochin Institute; 🇫🇷 Paris, France

Diabetology departement, Lariboisière Hospital; 🇫🇷 Paris, France

University Hospital Aachen; 🇩🇪 Aachen, Germany

University Hospital Heidelberg; 🇩🇪 Heidelberg, Germany

Amsterdam UMC; 🇳🇱 Amsterdam, Netherlands

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