Periodic Reporting for period 1 - DCM-NEXT (Enabling advances in diagnosis, patient stratification and treatment for dilated cardiomyopathy patients and families.)
Período documentado: 2023-10-01 hasta 2024-09-30
Project Overview: Understanding and Targeting Dilated Cardiomyopathy (DCM).
Dilated Cardiomyopathy (DCM) is a severe heart muscle disease characterized by the thinning and stretching of the heart's chambers, leading to impaired pumping of blood through the body. DCM presents a significant health challenge, affecting a substantial portion of the global population, with prevalence estimates reaching as high as 1 in 250 individuals. This disorder, often striking younger adults, may be associated with significant morbidity and mortality, primarily due to heart failure and sudden cardiac death. Despite its impact, key gaps in scientific knowledge exist, which hamper effective management of affected individuals. Firstly, our knowledge of what causes DCM and how genetics plays a role is incomplete, which limits the usefulness of genetic testing in clinical care. Additionally, treatment options for DCM are limited. Current therapies mainly focus on managing symptoms and are not specific to the disease. The only potential cure is a heart transplant, and there are no approved treatments that address the root causes of the condition.
Project Objectives:
The DCM-NEXT project aims at addressing critical gaps in DCM research and clinical care. The project seeks to achieve two primary objectives:
1)Genetic understanding: Enhance our understanding of the genetic factors underpinning DCM, bridging the gap between genetic causes and the clinical expression of the disease. The goal is to improve genetic testing to allow more accurate prediction of disease onset, progression, and the risk of major adverse cardiac events.
2)Therapeutic advancements: Accelerate the development of novel therapies by identifying and validating targets involved in the pathogenesis of DCM. The project aims to move beyond generic treatments that manage symptoms. By deciphering underlying molecular disease mechanisms, it will inform the development of therapies that target mechanisms of disease.
Dilated Cardiomyopathy (DCM) is a severe heart muscle disease characterized by the thinning and stretching of the heart's chambers, leading to impaired pumping of blood through the body. DCM presents a significant health challenge, affecting a substantial portion of the global population, with prevalence estimates reaching as high as 1 in 250 individuals. This disorder, often striking younger adults, may be associated with significant morbidity and mortality, primarily due to heart failure and sudden cardiac death. Despite its impact, key gaps in scientific knowledge exist, which hamper effective management of affected individuals. Firstly, our knowledge of what causes DCM and how genetics plays a role is incomplete, which limits the usefulness of genetic testing in clinical care. Additionally, treatment options for DCM are limited. Current therapies mainly focus on managing symptoms and are not specific to the disease. The only potential cure is a heart transplant, and there are no approved treatments that address the root causes of the condition.
Project Objectives:
The DCM-NEXT project aims at addressing critical gaps in DCM research and clinical care. The project seeks to achieve two primary objectives:
1)Genetic understanding: Enhance our understanding of the genetic factors underpinning DCM, bridging the gap between genetic causes and the clinical expression of the disease. The goal is to improve genetic testing to allow more accurate prediction of disease onset, progression, and the risk of major adverse cardiac events.
2)Therapeutic advancements: Accelerate the development of novel therapies by identifying and validating targets involved in the pathogenesis of DCM. The project aims to move beyond generic treatments that manage symptoms. By deciphering underlying molecular disease mechanisms, it will inform the development of therapies that target mechanisms of disease.
Clinical Database: DCM-NEXT leverages clinical data from 5 clinical sites, aggregating the largest collection of comprehensively characterized DCM patients globally. This scale allows for robust analyses, from discovery to validation, essential for achieving the goals of the project.
Phenomics: The project integrates imaging modalities like echocardiography and magnetic resonance imaging, alongside electrocardiography. Advanced artificial Intelligence (AI)-based approaches enhance the utility of these modalities, to enable studies aimed at improving accuracy of risk determination in individual patients.
Genomics: Through different genomic techniques including whole genome sequencing, the project aims to decipher the genetic factors that determine the risk of developing DCM and its severity. Insights gained will improve genetic testing and enable clinicians to better determine the risk in the individual patient, thereby improving patient care.
Data Integration and Patient Stratification Tools: AI-driven models are employed to analyze complex datasets comprehensively. The project aims to create innovative patient stratification tools for optimal, personalized disease management.
Transcriptomics: In-depth analysis of cellular and molecular profiles during DCM progression, aiming to uncover distinct mechanisms based on specific gene variants.
Target Identification: Integrated genetic, phenotypic, and transcriptomic data will be used for drug target identification.
Functional and Mechanistic Studies: Guided by insights from the above components, the project aims to investigate potential therapeutic targets and test interventions for their impact on cardiac structure and function.
Phenomics: The project integrates imaging modalities like echocardiography and magnetic resonance imaging, alongside electrocardiography. Advanced artificial Intelligence (AI)-based approaches enhance the utility of these modalities, to enable studies aimed at improving accuracy of risk determination in individual patients.
Genomics: Through different genomic techniques including whole genome sequencing, the project aims to decipher the genetic factors that determine the risk of developing DCM and its severity. Insights gained will improve genetic testing and enable clinicians to better determine the risk in the individual patient, thereby improving patient care.
Data Integration and Patient Stratification Tools: AI-driven models are employed to analyze complex datasets comprehensively. The project aims to create innovative patient stratification tools for optimal, personalized disease management.
Transcriptomics: In-depth analysis of cellular and molecular profiles during DCM progression, aiming to uncover distinct mechanisms based on specific gene variants.
Target Identification: Integrated genetic, phenotypic, and transcriptomic data will be used for drug target identification.
Functional and Mechanistic Studies: Guided by insights from the above components, the project aims to investigate potential therapeutic targets and test interventions for their impact on cardiac structure and function.
Potential Impacts:
DCM-NEXT aims not only to deepen our understanding of DCM but also to transform its diagnosis and treatment landscape, ultimately impacting millions of lives globally.
By focusing on improved risk prediction, DCM-NEXT addresses outstanding challenges in the domain of DCM and is expected to have considerable impact across various domains.
Societal Impact: The development of strategies for early detection and risk prediction are pivotal for enabling timely interventions to prevent or mitigate occurrence of heart failure and sudden cardiac death, and optimize follow-up regimens. DCM-NEXT will thus contribute to reducing morbidity and mortality. It will empower patients and relatives to make informed decisions, thereby reducing anxiety, stress and emotional burden, improving quality of life for patients.
Economic Impact: The project is expected to contribute to the sustainability of healthcare systems, leading to lower health care and patient and family costs. The improved personalized risk prediction strategies for affected individuals and their relatives, will enable tailored clinical monitoring and targeted implementation of diagnostics and interventions, leading to reduction of societal costs. Improved risk prediction will also enable targeted implementation of the emerging costly (gene) therapies. Economic benefits extend to the pharmaceutical and biotechnology industries, as the identification of new therapeutic targets could lead to the development of innovative drugs and technologies, driving growth within these sectors.
Scientific Impact: The development of multi-modal risk prediction models will drive innovation in diagnostic and monitoring technologies in cardiology and beyond. The identification of AI-enabled surrogate biomarkers of disease progression will inform future clinical trials such as those aimed at preventing development of disease in yet unaffected genotype-positive individuals. The identification of genetic risk factors will spur research into mechanisms of DCM and inform development of therapies.
DCM-NEXT aims not only to deepen our understanding of DCM but also to transform its diagnosis and treatment landscape, ultimately impacting millions of lives globally.
By focusing on improved risk prediction, DCM-NEXT addresses outstanding challenges in the domain of DCM and is expected to have considerable impact across various domains.
Societal Impact: The development of strategies for early detection and risk prediction are pivotal for enabling timely interventions to prevent or mitigate occurrence of heart failure and sudden cardiac death, and optimize follow-up regimens. DCM-NEXT will thus contribute to reducing morbidity and mortality. It will empower patients and relatives to make informed decisions, thereby reducing anxiety, stress and emotional burden, improving quality of life for patients.
Economic Impact: The project is expected to contribute to the sustainability of healthcare systems, leading to lower health care and patient and family costs. The improved personalized risk prediction strategies for affected individuals and their relatives, will enable tailored clinical monitoring and targeted implementation of diagnostics and interventions, leading to reduction of societal costs. Improved risk prediction will also enable targeted implementation of the emerging costly (gene) therapies. Economic benefits extend to the pharmaceutical and biotechnology industries, as the identification of new therapeutic targets could lead to the development of innovative drugs and technologies, driving growth within these sectors.
Scientific Impact: The development of multi-modal risk prediction models will drive innovation in diagnostic and monitoring technologies in cardiology and beyond. The identification of AI-enabled surrogate biomarkers of disease progression will inform future clinical trials such as those aimed at preventing development of disease in yet unaffected genotype-positive individuals. The identification of genetic risk factors will spur research into mechanisms of DCM and inform development of therapies.