DOUBLE DOSE

2021

Cardiomyopathies, caused by genetic mutations affecting cardiac muscle components, pose significant economic and societal burdens due to their hereditary nature and early onset. Despite known genetic defects, predicting disease progression remains challenging due to extreme clinical variability. Recent research indicates that cardiomyopathy mutations induce metabolic stress, exacerbated by factors like obesity, which can accelerate disease progression. The Double Dose hypothesis suggests that targeting metabolic stress may offer preventive or curative strategies for these conditions.

The Focus
The Double Dose Consortium aims to understand how cardiomyopathy-causing mutations lead to structural changes in cardiomyocytes. This interdisciplinary effort combines experts in preclinical research, clinical genetics, health technology assessment, and clinical care focused on cardiomyopathy in both children and adults.

The Research
The consortium combines experts in preclinical research, clinical genetics, health technology assessment and clinical researchers with a strong clinical focus on cardiomyopathy in children and adults. These experts investigate how obesity and muscle adiposity contribute to vascular and cardiac muscle dysfunction in mutation carriers through the analysis of clinical data, patient samples, and experimental models. They will also study the mechanisms underlying ultrastructural changes in cardiomyocytes caused by these mutations, leading to impaired metabolism, contraction, relaxation defects, and disrupted cellular communication within the heart.

Utilizing extensive patient cohorts and ongoing studies, the consortium aims to optimize care for cardiomyopathy patients by assessing the cost-effectiveness of diagnostics and clinical interventions. They plan to translate findings on metabolic alterations into clinical trials targeting treatments that reduce metabolic stress. The Double Dose program will establish biobanks containing serum, tissue, and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to provide mechanistic insights into cardiomyopathy pathophysiology and improve diagnosis and care.

Origin
This consortium was funded through the Impulse Grant program by the Dutch Heart Foundation, together with Stichting Hartedroom. The consortium is a continuation of the Dosis consortium, in which the interaction between mutation and external factors was investigated. They found that cardiomyopathy-mutations induce metabolic stress and that secondary metabolic stress, such as obesity accelerates disease progression.

 

Read More

Collaborators

Contact person:

Dr. J. van der Velden (Jolanda)

Principal investigators

Read more

STRAP

2020
The STRAP consortium aims to reduce the burden of heart disease by early detecting heart disease deterioration, benefiting patients, healthcare workers, and society. This initiative responds to acute needs observed in cardiology clinics, combined with the increasing availability of health tracking technologies. The project focuses on developing a new, AI-powered solution using cost-effective technology to maximize impact on healthcare costs. The Research STRAP is dedicated to developing a comprehensive data collection platform integrating off-the-shelf and cutting-edge self-tracking technologies. This platform empowers patients to measure vital signs at home, eliminating the need for frequent clinic visits and enabling longitudinal data collection on daily activities and emotions. The platform enhances self-tracking adherence through gamification strategies. The project involves developing and evaluating novel diagnostic and prognostic methods through two trials with target groups where notable improvements are achievable and highly impactful: Trial for Elderly Heart Patients: reducing re-hospitalization among elderly heart patients to minimize health deterioration and healthcare costs. Trial at Cardiac Outpatient Clinics: lower costs and enhance the quality of heart disease diagnosis for individuals attending cardiac outpatient clinics. The foundation of the trials is twofold. Establishing a Robust Dataset: creating an interconnected dataset to evaluate digitalized techniques' performance in relation to health records. This dataset incorporates electrocardiography data, stethoscope audio recordings, wrist-worn device activity levels, electronic nose sensor data, and self-reported information via IoT technologies, including parameters like water consumption, sleep patterns, real-time feelings, physiological responses, and overall patient well-being. Employing this diverse dataset, STRAP develops innovative analysis and early diagnosis methods to advance heart disease detection and monitoring. Through these efforts, STRAP aims to implement advanced technologies and data-driven approaches to significantly impact heart disease management. Origin This project was funded within the Big Data & Health Program. The focus of this public-private research program is the use of big data for the early detection and prevention of cardiovascular diseases. The program has been developed by NWO, ZonMw, the Dutch Heart Foundation, the Top Sectors Life Sciences & Health (LSH), ICT and Creative Industry, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center. Within this research program, the ambitions of the Dutch Heart Foundation, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center were aligned with the ambitions of Commit2Data for the Top Sectors ICT, LSH, and Creative Industry, as described in the 2018-2019 Kennis- en Innovatiecontracts between NWO and the Top Sectors.
Learn more

PERFECT-FIT

2020
Smoking tobacco and physical inactivity are key preventable risk factors of cardiovascular disease (CVD). Perfect Fit aims to prevent CVD, promote well-being, and reduce healthcare costs, particularly targeting disadvantaged populations where smoking and physical inactivity are prevalent. The Research The project develops tailored, evidence-based, near real-time computer coaching for quitting smoking and enhancing PA. For every individual, a personal model is designed which generates personalized recommendations based on high-quality existing and newly collected data, and adapts to changing circumstances/progress (similar to a TomTom navigation system), using machine learning techniques and incorporating domain-specific expert knowledge (e.g. health behaviour change strategies). Virtual coaches (VCs) communicate advice in a motivating way that fits individuals’ persuasive communication styles. Perfect Fit integrates big-data science, sensor technology, and personalized real-time feedback to support smoking cessation and promote adequate physical activity (in both gym settings and daily life). The key questions of this study are: Which adaptivity is needed to create a robust, safe, and effective interaction between individuals and machines? How can we develop advanced data science methods and embed this in current smoking cessation and PA coaching practice? How do measurement modalities, feedback and communication affect individuals’ smoking status and PA? Origin This project was funded within the Big Data & Health Program. The focus of this public-private research program is the use of big data for the early detection and prevention of cardiovascular diseases. The program has been developed by NWO, ZonMw, the Dutch Heart Foundation, the Top Sectors Life Sciences & Health (LSH), ICT and Creative Industry, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center. Within this research program, the ambitions of the Dutch Heart Foundation, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center were aligned with the ambitions of Commit2Data for the Top Sectors ICT, LSH, and Creative Industry, as described in the 2018-2019 Kennis- en Innovatiecontracts between NWO and the Top Sectors.
Learn more
1 2 3 19

Looking for
Another item?

Back to overview
Newsletter
© 2024 Oscar Prent Assurantiën BV 
© 2024 | DCVA
Design & development:
Design & Bouw door: