Speaker
Description
Cardiovascular diseases (CVDs) represent a global health challenge, causing approximately 17.9 million deaths worldwide in 2019, including 158,359 deaths in Germany. Traditional risk stratification methods focusing on age, blood pressure, and cholesterol levels often result in inaccurate risk assessments. The goal of stratification is to enable more precise and individualized diagnoses, prognoses, and therapies. By identifying specific subgroups within a broader patient population, medical research can develop targeted and effective treatment strategies tailored to the unique needs of these subgroups in the context of personalized medicine.
The landscape of personalized medicine, particularly concerning CVDs, has advanced rapidly due to progress in genomics, data analysis, and artificial intelligence (AI). Incorporating non-traditional risk factors, such as genetic information, into clinical practice has the potential to enhance risk prediction accuracy and treatment stratification, significantly aiding in the prevention of complex diseases. For instance, coronary artery disease (CAD), a prevalent CVD, is influenced by multiple genetic variants (alleles) across the genome. The ApoE gene, which encodes the apolipoprotein E protein, plays a critical role in lipid metabolism and is linked to both neurodegenerative and cardiovascular diseases. The ApoE ε4 allele significantly increases the risk of Alzheimer's and CVDs, including CAD. Early identification of individuals with these genetic markers can lead to proactive cholesterol management, substantially reducing the risk of developing CAD.
Polygenic Risk Scores (PRS), which aggregate the effects of numerous small genetic variations, have shown promise for personalized risk prediction. PRS can be particularly useful for individuals with a family history or genetic predisposition to CVDs. Although PRS are still in the research phase for CVDs, growing studies demonstrate their utility in risk prediction, sparking interest in their clinical application. However, challenges related to data privacy and access to sensitive data persist. The transfer of genomic data from patients to physicians is heavily regulated by privacy laws such as the GDPR and the EU's AI Act.
This research proposes a patient-centered approach that ensures the confidentiality of individual genomic data. Genetic risk factors will be analyzed using AI to enhance risk prediction and treatment stratification, supporting personalized cardiovascular medicine. The goal is to implement personalized risk prediction and treatment stratification for CVDs through AI-based analysis of genetic risk factors, embedded in a patient-centered approach that guarantees data confidentiality.
The envisioned approach involves two modules. The patient module, a client-side software on the patient's smartphone, enables local storage of genomic data and local calculation of CVD risk without transmitting genomic data to cloud providers. Patients can select body organs and send risk analysis results to a chosen physician. The analysis identifies potential disease traits and calculates individual PRS, with results condensed by an AI algorithm. Medical results are not displayed to patients but are interpreted by physicians. The physician module allows physicians to access and visualize results, along with summaries of relevant scientific publications. A language model derived from existing open-source large language models will generate controlled summaries, aiding physicians in personalized diagnoses. This approach paves the way for tailored cardiovascular medicine, where data privacy and ethics play central roles. The resulting decentralized, AI-based platform for personalized genomic health services, exemplified by heart diseases, contrasts with traditional cloud-based methods by empowering patients with data control while enabling personalized medicine based on genomic data, expected to enhance patient trust in personalized medical services.
