Periodic Reporting for period 4 - CARDIATEAM (CARdiomyopathy in type 2 DIAbetes mellitus)
Période du rapport: 2022-03-01 au 2023-02-28
Type 2 Diabetes (T2DM) epidemic is associated with serious comorbidities such as heart failure, the second most common cardiovascular disease after ischaemic heart disease in T2DM. CARDIATEAM is aiming to differentiate diabetic cardiomyopathy (DCM) against other forms of heart failure, identify its causal mechanism, and evaluate its impact on mortality. Thus, the identification of a novel phenotypic signature of DCM should allow to:
• predict cardiac function decline in T2DM patients
• allow for early preventive strategies
• facilitate tailored therapies to slow disease progression
• develop disease modeling for translatable preclinical models
Seven dedicated work packages (WP) have been set-up to establish a strong collaborative network of clinical and basic researchers. Enrolling 1,600 patients in a prospective clinical study, CARDIATEAM will provide a unique and highly standardized set of data and allow differentiation of related forms of heart failure. Deep phenotyping and unbiased machine learning will allow in a yet unrivalled precision to identify clusters of connected pathology, identification of affected biochemical pathways and specific biomarkers. A central database integrating historical data and data from clinical and experimental sources will permit novel bioinformatics-assisted visualization and modelling of interactions between phenotype, genetic, immune and metabolic pathways.
Building on these results, CARDIATEAM will enable DCM modelling, identify crucial pathophysiologic mechanisms and ultimately develop tailored treatments and robust preclinical models.
• predict cardiac function decline in T2DM patients
• allow for early preventive strategies
• facilitate tailored therapies to slow disease progression
• develop disease modeling for translatable preclinical models
Seven dedicated work packages (WP) have been set-up to establish a strong collaborative network of clinical and basic researchers. Enrolling 1,600 patients in a prospective clinical study, CARDIATEAM will provide a unique and highly standardized set of data and allow differentiation of related forms of heart failure. Deep phenotyping and unbiased machine learning will allow in a yet unrivalled precision to identify clusters of connected pathology, identification of affected biochemical pathways and specific biomarkers. A central database integrating historical data and data from clinical and experimental sources will permit novel bioinformatics-assisted visualization and modelling of interactions between phenotype, genetic, immune and metabolic pathways.
Building on these results, CARDIATEAM will enable DCM modelling, identify crucial pathophysiologic mechanisms and ultimately develop tailored treatments and robust preclinical models.
The fourth period (P4) still suffered from the effects of the COVID-19 pandemic impacting the recruitment rate of the CARDIATEAM study. However, by the end of P4, WP2 has achieved in collaboration with WP3, WP4 and WP5, the implementation of an adapted study protocol in all participating countries. The accuracy/integrity of the collected data and a harmonious conduction of the study throughout the recruiting sites was ensured by the global monitoring .
The main achievement of P4 was the opening of all the 16 centers in all countries involved in the study. P4 ended with a total of 578 patients recruited and 1st and 2cd year follow-up have been performed for the first recruits.
The required infrastructure to collect imaging data in the CARDIATEAM cohort has been installed. The CARDIATEAM web-based database platform is operational and has been successfully validated. Participating centres are being trained and certified by transferring collected data to the imaging core labs and uploading the measurements to a central repository. All data flows are in place and quality assurance measures have been implemented. Hereto, a certification process has been put in place as well as continuous monitoring of the core-lab performance. As the rate of patient inclusion has increased in P4 (total recruitment has more than doubled during RP4 i.e.; RP3 n=250 while by the end of RP4 n=578) the data-flow to and from the web-platform are put to the test, and modifications and further improvements were implemented on the go, based on the feedback of imaging core-labs and imaging centers.
The database which will gather all clinical, imaging and omics data of the recruited patients has been set up and structured. We have developed an eCRF system to collect clinical data from recruited patients. This system contains functionality allowing the manual, expert review of registered data to avoid errors and missing data. Training sessions for clinical research assistants on the tool have been organised at each of the different sites. A data transfer protocol has been implemented between the main data processing partners to securely push validated records into the central database. Pipelines for processing of clinical data into the database have been established and web-based mining tools to query the content of the database to allow live monitoring of collected data have been developed.
Like in previous periods, sample collection kits have been distributed to the already recruiting centers the collected biological samples has been sent for a successful quality check to LIH, and some of them have been selected by WP4 for the omics analyses to be started early RP5. The unsupervised machine learning methodology required to analyze the future deep phenotypic data has been set-up, tested and used to analyze the 408 initial volunteers for a preliminary clustering analysis identifying four clusters of patients based on S.O.M methodology to start early in P5. The synergic work of WP2, WP3, WP4 and WP5 thus has led to the identification of patients with outstanding clinical, imaging and routine biological phenotyping that will be undergo multi-OMICs analysis in early RP5 .
Concerning animal models, WP7 partners have now finalised the overview of existing preclinical models of metabolic disorders pertinent to the project and published a first joint review article. Another article will be submitted early in RP5. Two models of interest have been selected for the use within the consortium and a joint protocol across 6 partner’s lab bases on mice issued from the same provider are being finalized early RP5.
The main achievement of P4 was the opening of all the 16 centers in all countries involved in the study. P4 ended with a total of 578 patients recruited and 1st and 2cd year follow-up have been performed for the first recruits.
The required infrastructure to collect imaging data in the CARDIATEAM cohort has been installed. The CARDIATEAM web-based database platform is operational and has been successfully validated. Participating centres are being trained and certified by transferring collected data to the imaging core labs and uploading the measurements to a central repository. All data flows are in place and quality assurance measures have been implemented. Hereto, a certification process has been put in place as well as continuous monitoring of the core-lab performance. As the rate of patient inclusion has increased in P4 (total recruitment has more than doubled during RP4 i.e.; RP3 n=250 while by the end of RP4 n=578) the data-flow to and from the web-platform are put to the test, and modifications and further improvements were implemented on the go, based on the feedback of imaging core-labs and imaging centers.
The database which will gather all clinical, imaging and omics data of the recruited patients has been set up and structured. We have developed an eCRF system to collect clinical data from recruited patients. This system contains functionality allowing the manual, expert review of registered data to avoid errors and missing data. Training sessions for clinical research assistants on the tool have been organised at each of the different sites. A data transfer protocol has been implemented between the main data processing partners to securely push validated records into the central database. Pipelines for processing of clinical data into the database have been established and web-based mining tools to query the content of the database to allow live monitoring of collected data have been developed.
Like in previous periods, sample collection kits have been distributed to the already recruiting centers the collected biological samples has been sent for a successful quality check to LIH, and some of them have been selected by WP4 for the omics analyses to be started early RP5. The unsupervised machine learning methodology required to analyze the future deep phenotypic data has been set-up, tested and used to analyze the 408 initial volunteers for a preliminary clustering analysis identifying four clusters of patients based on S.O.M methodology to start early in P5. The synergic work of WP2, WP3, WP4 and WP5 thus has led to the identification of patients with outstanding clinical, imaging and routine biological phenotyping that will be undergo multi-OMICs analysis in early RP5 .
Concerning animal models, WP7 partners have now finalised the overview of existing preclinical models of metabolic disorders pertinent to the project and published a first joint review article. Another article will be submitted early in RP5. Two models of interest have been selected for the use within the consortium and a joint protocol across 6 partner’s lab bases on mice issued from the same provider are being finalized early RP5.
CARDIATEAM will go beyond the state-of-the-art since it will investigate mechanisms underlying DCM and prognosis.
The expected results of CARDIATEAM will be
• establishment of a prospective cohort of 1,600 patients within 2 years and with a follow-up of 3 years, phenotyping the patients with echocardiography, 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)
The results of CARDIATEAM will 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.
The outcome and results of CARDIATEAM will have significant impact on the efficiency of R&D in the field of Diabetes & Cardiovascular Disease.
The deep molecular and phenotypic characterization of DCM with the discovery and validation of respective biomarkers will allow an improvement in developing therapeutic options by:
- discovery and validation of biomarkers being predictive for risk and progression to DCM as well as to monitor efficacy of treatment options.
- development appropriate (animal) models of relevance for human DCM to profile and develop drug candidates for this medical indication.
- stratification of patients with DCM for clinical drug trials; this would enable smaller and focused clinical studies to evaluate efficacy of drug candidates in this indication earlier, faster and cheaper to progress and introduce them into clinical practice.
SMEs involved in CARDIATEAM will strengthen their visibility in Europe. Finally, DCM cluster analysis will potentially allow the rational repositioning of existing treatments.
The expected results of CARDIATEAM will be
• establishment of a prospective cohort of 1,600 patients within 2 years and with a follow-up of 3 years, phenotyping the patients with echocardiography, 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)
The results of CARDIATEAM will 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.
The outcome and results of CARDIATEAM will have significant impact on the efficiency of R&D in the field of Diabetes & Cardiovascular Disease.
The deep molecular and phenotypic characterization of DCM with the discovery and validation of respective biomarkers will allow an improvement in developing therapeutic options by:
- discovery and validation of biomarkers being predictive for risk and progression to DCM as well as to monitor efficacy of treatment options.
- development appropriate (animal) models of relevance for human DCM to profile and develop drug candidates for this medical indication.
- stratification of patients with DCM for clinical drug trials; this would enable smaller and focused clinical studies to evaluate efficacy of drug candidates in this indication earlier, faster and cheaper to progress and introduce them into clinical practice.
SMEs involved in CARDIATEAM will strengthen their visibility in Europe. Finally, DCM cluster analysis will potentially allow the rational repositioning of existing treatments.