Periodic Reporting for period 1 - CARDIATEAM (CARdiomyopathy in type 2 DIAbetes mellitus)
Reporting period: 2019-03-01 to 2020-02-29
In 2045, 68M of Europeans of all ages, genders and socioeconomic classes will be type 2 diabetes mellitus (T2DM) (www.diabetesatlas.org). The T2DM burden is not only related to health expenditure (8% of total European health spending, 2019) but also on mortality that is related to cardiovascular diseases for up to 1/2 of T2DM-related deaths. While ischemic heart disease represents the major cause of death of T2DM patients, heart failure (HF) is the 2nd most common cardiovascular disease in T2DM patients. Yet scientific community lacks evidence to assess the specificity of diabetic cardiomyopathy (DCM) that prevents T2DM patients from receiving a tailored therapy.
Thus, CARDIATEAM consortium ambitions to clarify the DCM features in order to:
• differentiate it from other forms of HF,
• identify causal mechanism of DCM, and
• evaluate the impact of DCM on mortality.
In turn, diagnosis of DCM can be improved based on newly defined inclusion criteria. This identification of a novel phenotypic signature should directly benefit to T2DM patients as it will allow to:
• predict their cardiac function decline
• implement preventive lifestyle changes to slow down cardiac function decline
• define therapies mitigating DCM progression.
• further refine care by developing disease modeling for translatable preclinical models
Dedicated work packages have been set-up to structure a strong collaborative network of clinical and basic researchers to address key knowledge gaps in DCM.
The novel DCM phenotypic signature will be identified by working on two fronts:
- In clinic based on:
• existing data: An integrated central database will assimilate historical data and data from clinical and experimental sources to permit novel bioinformatics-assisted visualization and modelling of interactions between phenotype, genetic, immune and metabolic pathways.
• newly collected data: A prospective clinical CARDIATEAM study (1600 participants) will provide a unique and highly standardized set of data on T2DM and non-T2DM volunteers to differentiate DCM from related HF forms. The use of unbiased machine learning to analyze that date from in depth clinic phenotyping, biologic and imagery-markers will allow in a yet unrivalled precision to identify clusters of connected pathology, identification of affected biochemical pathways and specific biomarkers.
- In basic science: Building on these results, disease can be modeled, allowing to identify crucial pathophysiologic mechanisms and ultimately the development of improved, tailored treatments.
Thus, CARDIATEAM consortium ambitions to clarify the DCM features in order to:
• differentiate it from other forms of HF,
• identify causal mechanism of DCM, and
• evaluate the impact of DCM on mortality.
In turn, diagnosis of DCM can be improved based on newly defined inclusion criteria. This identification of a novel phenotypic signature should directly benefit to T2DM patients as it will allow to:
• predict their cardiac function decline
• implement preventive lifestyle changes to slow down cardiac function decline
• define therapies mitigating DCM progression.
• further refine care by developing disease modeling for translatable preclinical models
Dedicated work packages have been set-up to structure a strong collaborative network of clinical and basic researchers to address key knowledge gaps in DCM.
The novel DCM phenotypic signature will be identified by working on two fronts:
- In clinic based on:
• existing data: An integrated central database will assimilate historical data and data from clinical and experimental sources to permit novel bioinformatics-assisted visualization and modelling of interactions between phenotype, genetic, immune and metabolic pathways.
• newly collected data: A prospective clinical CARDIATEAM study (1600 participants) will provide a unique and highly standardized set of data on T2DM and non-T2DM volunteers to differentiate DCM from related HF forms. The use of unbiased machine learning to analyze that date from in depth clinic phenotyping, biologic and imagery-markers will allow in a yet unrivalled precision to identify clusters of connected pathology, identification of affected biochemical pathways and specific biomarkers.
- In basic science: Building on these results, disease can be modeled, allowing to identify crucial pathophysiologic mechanisms and ultimately the development of improved, tailored treatments.
Since its kick-off the project has achieved its milestones thanks to the set-up of all instruments for enabling a tight monitoring of all activities and progress (https://cardiateam.eu/went).
This past year has been focused on achieving all mandatory steps required to run the prospective clinical study:
- Protocol and related document refinement (including standard operating procedures, SOPs)
- Obtainment of all mandatory authorizations from ethical committee and regulatory agencies (obtained in France; pending in Netherlands and Germany; to be submitted in UK and Spain)
- Deployment of prescreening strategies to ensure timely and successful recruitment of patients
- Training of imaging-personal (SOPs and an internal webinar) in order to ensure reproducibility and comparability of the imagery data collected across European centers.
- Setting of a secured anonymous collection of patient data in hospitals (electronic case report form) and imaging data in a centralized database while ensuring high quality to this data collection (quality control).The overall dataflow has been tested and is deployed; further refinements are currently being made. A statistical analysis plan was worked out to define how the data should be evaluated later
- Setting of a centralized biobank based on SOPs to standardize amongst all clinical sites the collection, processing and storage of the blood and urine samples and ensure strict quality control.
In fine, thanks to the proficient collaboration of our consortium, the regulatory, ethical and logistical locks were removed before the ignition of CARDIATEAM prospective clinical study.
Unfortunately, COVID-19 pandemic precluded us from starting inclusion of patients
This first period was also dedicated to dress an overview of existing preclinical models of diabetic and metabolic disorders pertinent to the project. Yet 4 interesting models were thoroughly discussed for their pros and cons for modelling diabetic-related cardiac defects.
This past year has been focused on achieving all mandatory steps required to run the prospective clinical study:
- Protocol and related document refinement (including standard operating procedures, SOPs)
- Obtainment of all mandatory authorizations from ethical committee and regulatory agencies (obtained in France; pending in Netherlands and Germany; to be submitted in UK and Spain)
- Deployment of prescreening strategies to ensure timely and successful recruitment of patients
- Training of imaging-personal (SOPs and an internal webinar) in order to ensure reproducibility and comparability of the imagery data collected across European centers.
- Setting of a secured anonymous collection of patient data in hospitals (electronic case report form) and imaging data in a centralized database while ensuring high quality to this data collection (quality control).The overall dataflow has been tested and is deployed; further refinements are currently being made. A statistical analysis plan was worked out to define how the data should be evaluated later
- Setting of a centralized biobank based on SOPs to standardize amongst all clinical sites the collection, processing and storage of the blood and urine samples and ensure strict quality control.
In fine, thanks to the proficient collaboration of our consortium, the regulatory, ethical and logistical locks were removed before the ignition of CARDIATEAM prospective clinical study.
Unfortunately, COVID-19 pandemic precluded us from starting inclusion of patients
This first period was also dedicated to dress an overview of existing preclinical models of diabetic and metabolic disorders pertinent to the project. Yet 4 interesting models were thoroughly discussed for their pros and cons for modelling diabetic-related cardiac defects.
CARDIATEAM ambitions to go beyond the state-of-the-art since it will investigate mechanisms underlying DCM and prognosis, therefore CARDIATEAM will provide a paradigm shift in the causes of DCM.
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 Provide a sex- and age- based stratification approach of T2DM patients at risk of DCM Identification of causal mechanisms and pathways responsible for DCM (Identification of new potential therapeutic targets for preventing or alleviating DCM
• Application of disease modelling to develop DCM preclinical models New taxonomy of DCM to be communicated to health agencies, practitioners and patients
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.
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 for this disease condition by:
- the discovery and validation of biomarkers being predictive for risk and progression to DCM
- the development and selection of appropriate (animal) models of relevance for human DCM to profile and develop drug candidates for this medical indication.
- to allow stratification of DCM patients for clinical drug trials; this would allow smaller and focused clinical studies to evaluate clinical 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 Provide a sex- and age- based stratification approach of T2DM patients at risk of DCM Identification of causal mechanisms and pathways responsible for DCM (Identification of new potential therapeutic targets for preventing or alleviating DCM
• Application of disease modelling to develop DCM preclinical models New taxonomy of DCM to be communicated to health agencies, practitioners and patients
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.
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 for this disease condition by:
- the discovery and validation of biomarkers being predictive for risk and progression to DCM
- the development and selection of appropriate (animal) models of relevance for human DCM to profile and develop drug candidates for this medical indication.
- to allow stratification of DCM patients for clinical drug trials; this would allow smaller and focused clinical studies to evaluate clinical 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.
