Periodic Reporting for period 5 - CARDIATEAM (CARdiomyopathy in type 2 DIAbetes mellitus)
Reporting period: 2023-03-01 to 2024-02-29
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.
By the end of period 5 (P5), CARDIATEAM consortium has successfully focused its recruitment efforts on two main groups of volunteers (no heart failure with and without type 2 diabetes [T2DM]) to enroll a total number of 894 patients. To ease the recruitment a fourth amendment to the protocol has been implemented in all sites, one Dutch centre (Amsterdam) has been reactivated and some centres (FIBHGM and Mondor’s hospitals & UNIVDUN) agreed to raise their foreseen numbers of recruited patients.
The required infrastructure to collect imaging data and the CARDIATEAM web-based database platform are fully operational with successful implementation of all data flows and quality assurance measures. The database has been structured to gather all clinical, imaging and omics data of the recruited patients.
After a successful quality check to LIH and a preliminary clustering analysis based on S.O.M methodology, biological samples have been selected for the first omics analyses early P5. Thanks to the synergic work of WP2, WP3 and WP4, we have performed two additional descriptive analyses on available data (N=550 included patients in November 2023 and N= 637 patients in February 2024) to document the distribution characteristics of patients compatible with eligibility criteria. These descriptive analyses comforted the one performed during RP4 on a total of 406 participants with T2DM and without T2DM patients, and paving the way for a first cluster analysis.
Upon receiving the samples from IBBL, UNITO and HMGU engaged in meticulous experimental preparations that included the selection of the most suitable kits and the randomization of samples to mitigate potential confounding factors. This attention combined with stringent quality control measures, ensured the reliability of the generated OMICs data.
To fulfil the objectives to perform an initial unsupervised analysis on available -omics data produced by WP5 and available in the CARDIATEAM database (WP4), a synergic work of WP4, WP5 and WP6 was organized with bi-monthly meetings. The initial -omics data from WP5 and available in this period were from genetics (SNP), methylomics, proteomics, targeted metabolomics and targeted lipidomics data generated from 164 patient samples. Work was performed in collaboration with the groups generating the data (WP5: UNITO, HMGU) to prepare the data for subsequent analysis (e.g. quality control, normalisation, outliers, imputation). Following the first clustering of patients with specific clinical variables (WP4), the first step was to investigate potential patterns in the different omics data, in an unsupervised way across methylomics, proteomics and targeted lipidomics and metabolomics. Multi-omics integration produced latent variables, or global spaces of common variation, which correlated to different levels to clinical variables, allowing to identify different potential signature for phenotypes related to diabetic cardiomyopathy. In the future we will use these signatures to investigate enriched pathways and potential mechanisms underlying the pathology.
Concerning animal models, WP7 partners have performed an multicentric experimental study according strict SOPs (approved during RP4) on identified two preclinical models of DCM to be exploited by the WP7 partners and CARDIATEAM consortium: 1) High Fat Diet murine models (exposure to high-fat diet, 60% fat + 7% sucrose, during 5 and 15 weeks), and 2) Goto-Kakizaki rats, a spontaneous model of non-obese T2DM obtained by repeated inbreeding from Wistar rats selected at the upper limit of normal distribution for glucose tolerance.
The required infrastructure to collect imaging data and the CARDIATEAM web-based database platform are fully operational with successful implementation of all data flows and quality assurance measures. The database has been structured to gather all clinical, imaging and omics data of the recruited patients.
After a successful quality check to LIH and a preliminary clustering analysis based on S.O.M methodology, biological samples have been selected for the first omics analyses early P5. Thanks to the synergic work of WP2, WP3 and WP4, we have performed two additional descriptive analyses on available data (N=550 included patients in November 2023 and N= 637 patients in February 2024) to document the distribution characteristics of patients compatible with eligibility criteria. These descriptive analyses comforted the one performed during RP4 on a total of 406 participants with T2DM and without T2DM patients, and paving the way for a first cluster analysis.
Upon receiving the samples from IBBL, UNITO and HMGU engaged in meticulous experimental preparations that included the selection of the most suitable kits and the randomization of samples to mitigate potential confounding factors. This attention combined with stringent quality control measures, ensured the reliability of the generated OMICs data.
To fulfil the objectives to perform an initial unsupervised analysis on available -omics data produced by WP5 and available in the CARDIATEAM database (WP4), a synergic work of WP4, WP5 and WP6 was organized with bi-monthly meetings. The initial -omics data from WP5 and available in this period were from genetics (SNP), methylomics, proteomics, targeted metabolomics and targeted lipidomics data generated from 164 patient samples. Work was performed in collaboration with the groups generating the data (WP5: UNITO, HMGU) to prepare the data for subsequent analysis (e.g. quality control, normalisation, outliers, imputation). Following the first clustering of patients with specific clinical variables (WP4), the first step was to investigate potential patterns in the different omics data, in an unsupervised way across methylomics, proteomics and targeted lipidomics and metabolomics. Multi-omics integration produced latent variables, or global spaces of common variation, which correlated to different levels to clinical variables, allowing to identify different potential signature for phenotypes related to diabetic cardiomyopathy. In the future we will use these signatures to investigate enriched pathways and potential mechanisms underlying the pathology.
Concerning animal models, WP7 partners have performed an multicentric experimental study according strict SOPs (approved during RP4) on identified two preclinical models of DCM to be exploited by the WP7 partners and CARDIATEAM consortium: 1) High Fat Diet murine models (exposure to high-fat diet, 60% fat + 7% sucrose, during 5 and 15 weeks), and 2) Goto-Kakizaki rats, a spontaneous model of non-obese T2DM obtained by repeated inbreeding from Wistar rats selected at the upper limit of normal distribution for glucose tolerance.
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.