Objective
The project aims to improve patient care across the board, starting from those at risk of developing CVD to those with advanced disease. To allow a more personalised approach to patient care, biomarkers will be used to assign those diagnosed to clinically-meaningful subgroups – this will make it easier to pick up on which patients are in most urgent need of treatment. Moreover, tools based on artificial intelligence (AI) will make it possible to predict how individual patients will respond to different treatments. Part of the project will focus specifically on people with type 1 diabetes, who are at risk of developing CVD.
Background Rationale
One of the main causes of death globally, cardiovascular diseases (CVD) impact 85 million people in Europe alone, and the number of cases is growing as a result of bad lifestyle choices and an aging population. Many persons with cardiovascular disorders eventually develop chronic heart failure, which has a five-year mortality rate of 20 to 50%, despite advancements in treatment.
Numerous problems make it difficult to lower the number of people who suffer from and pass away from cardiovascular diseases. Among these are our inadequate knowledge of the ways in which risk factors (such diabetes and high blood pressure) affect the development of CVD and the course of the illness. The current "one size fits all" method of patient treatment presents another difficulty. We desperately need to comprehend CVD better, so we can deliver better treatments.
Description of Project
Cardiovascular diseases (CVD) are one of the leading causes of death worldwide; they affect 85 million people in Europe alone, and cases are rising due to unhealthy lifestyles and our ageing population. Despite advances in treatment, many people with cardiovascular diseases eventually develop chronic heart failure, which has a five-year mortality rate of 20 to 50 %.
Efforts to reduce the numbers of people affected by and dying from cardiovascular diseases are hampered by a number of issues. These include our incomplete understanding of how risk factors (such as diabetes and high blood pressure) influence who will develop CVD and how the disease progresses. Another challenge is the current ‘one size fits all’ approach to treating patients.
We urgently need a better understanding of CVD so that we can deliver more personalized treatments for patients, and that’s where iCARE4CVD comes in. The project aims to improve patient care across the board, starting from those at risk of developing CVD to those with advanced disease. To allow a more personalized approach to patient care, biomarkers will be used to assign those diagnosed to clinically-meaningful subgroups – this will make it easier to pick up on which patients are in most urgent need of treatment. Moreover, tools based on artificial intelligence (AI) will make it possible to predict how individual patients will respond to different treatments. Part of the project will focus specifically on people with type 1 diabetes, who are at risk of developing CVD.
The project will achieve this by gathering data on over 1 million patients from existing cohorts and providing anonymous access to the data via a blockchain-supported federated database. The project will develop AI-based models to identify different subgroups of patients and the best treatments for them. These models will be validated in large cohorts and a prospective intervention trial. Patients will be closely involved in iCARE4CVD to ensure that the project’s results meet their needs.
Ultimately, the results of iCARE4CVD will help to prevent and treat cases of CVDs, improving people’s lives and reducing the strain on healthcare systems.
Anticipated Outcome
In the end, iCARE4CVD's outcomes will improve people's lives and lessen the burden on healthcare systems by assisting in the prevention and treatment of CVD cases.
Relevance to T1D
Patients with type 1 diabetes (T1DM) continue to struggle to achieve specified glycaemic control targets. Additionally, cardiovascular events occur earlier and more frequently in patients with T1DM than in populations without diabetes. Women with T1DM have a higher risk of CV events, than women without diabetes. However, new treatments to reduce CV risk and improvement of glycaemic control for patients with T1DM are high unmet medical needs. In recent years large Cardiovascular and Renal Outcome Trials using Sodium-glucose cotransport-2 (SGLT2) inhibitors, GLP-1-Receptoragonists and a non-steroidal Mineralocorticoid Receptor Antagonist (MRA) have shown proven benefit for improving prognosis and organ protective outcomes. These clinical endpoint trials are milestones for improving and prolonging life of patients with T2DM. However, patients with T1DM were excluded from those cardiovascular outcome trials. Moreover, it remains unclear if all patients with type 1 diabetes would equally benefit from these intervention strategies.
Therefore, we want to use the unique opportunity of the iCARE4CVD consortium, to perform an openlabel proof of concept trial in patients with T1DM to clarify four medical needs:
● Explore whether a cardio-renal protective medical strategy (CRPS) proven in patients with type 2 diabetes can also modify CVD and/or progression of any diabetes specific complication in patients with T1DM.
● To investigate the safety of CRPS in T1DM under closed-loop treatment.
● To determine if all patients with T1DM respond equally to the intervention and if such response can be predicted by a biomarker profile.
This will be the first intervention study that evaluates the efficacy of a proven cardio-renal protective medical strategy (CRPS) for clinical outcomes in patients with T1DM and closed-loop systems. On the background of aims of iCARE4CVD, the hypothesis can be tested whether novel biomarker profiles are associated with prediction of treatment response.