Evidence-informed cardiometabolic medicine
Evidence-informed cardiometabolic medicine
Diseases that are rooted in an unhealthy Western lifestyle, i.e., type 2 diabetes, cardiovascular disease, and metabolic liver disease, are the most pressing global health issues today. These diseases co-occur and interact, particularly in the context of obesity, leading to heart failure, stroke, and premature death. This thesis of Michail Kokkorakis responds to this growing burden by advancing tools that support earlier detection, personalized care, and treatment decisions, hallmarks of what is now emerging as cardiometabolic medicine.
The first part presents questionnaire-based models that identify individuals at risk for type 2 diabetes and coronary artery disease. These models were tested across multiple ethnic populations and showed high accuracy, offering an accessible and scalable option for preventive screening, especially in underserved regions.
The second part investigates whether adding data from scans or protein levels (proteomics) enhances risk prediction. Adding proteomics data into existing liver disease assessments indeed improves risk prediction for liver and secondary outcomes. Also, using brain imaging and proteomics, this research explores how obesity accelerates brain aging, uncovering sex-specific differences in neurodegeneration.
Lastly, real-world health records from over 3 million individuals were used to evaluate how commonly used diabetes medications differ in the risk of cognitive decline. These results address a significant gap in clinical guidelines and inform drug selection in patients with type 2 diabetes.
Collectively, these findings support the development of a firmly integrated form of evidence-informed care – cardiometabolic medicine – bringing together knowledge from endocrinology, cardiology, hepatology, and data science to tackle disease clusters with shared roots and overlapping trajectories.