Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología
This proposal focuses on the identification of mutations and clinical and imaging biomarkers in patients with retinopathies associated with the ABCA4 gene, particularly Stargardt’s disease 1 (STGD1). In this disease, genetic testing is essential for early diagnosis, as it allows the detection of asymptomatic carriers and patients in early stages. However, it is also crucial to identify genetic variants and other factors that may influence phenotypic heterogeneity and explain the different forms of disease progression. To address this variability, it is necessary to deepen genetic knowledge and, at the same time, integrate these findings with the clinical data of each patient. The main objective of this project is to improve the diagnosis, prognosis, monitoring and prevention of ABCA4-associated retinopathies through the development and application of advanced diagnostic tools based on Artificial Intelligence (AI). These tools will combine genetic analysis (whole genome sequencing), multimodal imaging biomarkers and clinical data, ideally aligning with the IMPaCT program. Likewise, an innovative stratification of patients into subgroups according to the age of onset of the disease is proposed, which will facilitate the selection of key biomarkers for future clinical trials (CE) and optimize the design of personalized treatments, some already in advanced stages of research. Sex will also be evaluated as a possible modifying factor in the development and presentation of retinopathy, always considering the gender perspective.
The project will follow an ambispective, cross-sectional and observational cohort design, focused on patients diagnosed or suspected of STGD1 in Spain. Detailed clinical and demographic data will be collected, a complete ophthalmological examination will be performed, and multimodal images will be obtained that will be analyzed using AI. Advanced algorithms will be implemented to more accurately assess the evolution of the disease and the response to treatments. In addition, advanced genetic analyses will be carried out through the whole genome sequencing with the aim of fulfilling the purposes of the project. Finally, multimodal, clinical and genetic imaging data will be integrated to start developing AI-based predictive models, which could thus optimize early diagnosis, monitoring of clinical evolution and disease management.
Stargardt’s disease is the most common autosomal recessive retinal pathology. It causes diverse clinical manifestations both in its age of onset and in its clinical presentation. Molecular knowledge of the disease, including mutations with a worse visual prognosis, and variants of uncertain significance that are pathogenic, helps us to predict deleterious effects and the adequacy of patients to therapeutic alternatives.
OBJECTIVES (O)
O1. To assess the clinical course of Stargardt’s disease through the collection and longitudinal analysis of clinical and genetic data.
O2. Detect Identify variants of uncertain significance as pathogenic and mutations not previously identified by using advanced techniques (MLPA, Exome and Whole Genome) in patients who require it.
O3. Identify genetic mutations with a worse or better prognosis to predict disease progression and determine the pathogenic contribution of each of them.
O4. Incorporate imaging biomarkers to improve diagnostic accuracy and monitoring of disease progression.
O5. To identify both genetic and clinical phenotype modifiers that may influence the clinical variability of the disease.
O6. To generate a “dataset” of structured images (retinograms, FA, OCT and OCTA) to generate new biomarkers to develop and optimize algorithms based on AI-specific to the disease with which, in the future, it is intended to carry out a longitudinal extension study that allows the generation of progression labels and from them develop predictive models of disease progression.
O7. Train an AI algorithm with biomarkers (genetic/image) with this next-generation dataset, in order to be able to develop precision medicine tools for early diagnosis.
O8. Scientific communication and internationalization of project results
This comprehensive approach to the project ranges from genetic research to the clinical implementation of new technologies, promising a considerable improvement in the management of Stargardt’s disease. By integrating genetics, AI and imaging biomarkers, the aim is to move towards a personalized medicine model, where each patient receives a treatment adapted to their specific genetic and clinical profile, improving both prognosis and quality of life.