Research

Cardiometabolic diseases – including obesity, diabetes, and steatotic liver disease – are interconnected conditions that span metabolic, vascular, and hepatic systems. The liver is the central hub of this network, where metabolic and inflammatory pathways converge and influence systemic health. My research focuses on integrating digital, molecular, and experimental approaches to better understand these connections and to enable clinically more effective prediction and treatment of disease.

Despite major advances in biomarkers, imaging, and artificial intelligence, clinical translation often remains fragmented. My work seeks to close this gap by linking large-scale population data with mechanistic hepatology, uniting prediction, biology, and intervention into a single translational framework.

My work

My work sits at the intersection of digital health, systems medicine, and translational hepatology. I study how metabolic, vascular, and hepatic dysfunctions interact, viewing disease as a dynamic, multi-organ network rather than an isolated event. Through data science, biomarker research, and experimental models, I aim to transform prediction into prevention and biological discovery into therapeutic opportunity.

At the population level, I use large-scale biobank and clinical datasets to identify early markers of cardiometabolic and liver disease. These efforts focus on creating practical, scalable tools for early detection and equitable prevention. In parallel, I investigate molecular and immune pathways driving metabolic and alcohol-associated liver injury using humanized liver models, translating digital patterns into mechanistic insight.

This dual approach – from population to mechanism and back – allows my work to connect data-driven prediction with biological causality, ultimately informing how we diagnose, stratify, and treat patients.

Motivation and vision

Most cardiometabolic disorders develop silently for years before detection. I aim to make their identification earlier, simpler, and biologically informed, using layered frameworks where digital tools cast a wide net and molecular data refine precision, altering management as needed.

In the long term, I envision translational ecosystems that integrate digital epidemiology, biobanking, and experimental biology – accelerating the pathway from data to diagnostics, and from mechanisms to therapies. Beyond academia, I am committed to collaborations that convert scientific insight into scalable and clinically meaningful solutions, bridging medicine, biotechnology, and data science.

I welcome collaborations and student participation across disciplines – from computational health and molecular biology to clinical and regulatory science.
For inquiries: m.kokkorakis@umcg.nl.

Keywords

Cardiometabolic medicine; translational hepatology; digital health; systems medicine; biobanks; biomarkers; humanized liver models; lipid–immune interactions; metabolic inflammation; machine learning; precision prevention; therapeutic translation; real-world evidence.