Our research
Our research applies systems medicine approaches to tackle common complex diseases, with a historic focus on cardiometabolic disorders and how they drive the development of atherosclerotic cardiovascular disease (ACVD). We specialize in clinical biobanks to generate and analyze multi-modal "big data" to create reliable, tissue- and cell-level network models of human biology and disease.
Bridging the Knowledge Gap
A core mission for our research is to bridge the gap between our, for the most part, in-depth understanding of individual biological and disease pathways and the functioning of interacting organ systems. For this and the purpose to fulfill the promises of precision medicine, we are convinced network models are essential since they allow us to:
- More accurately predict disease risk (mainly through improved patient stratification)
- Improve diagnostic accuracy (through AI/machine learning analyses)
- Identify new, more effective therapeutic targets (mainly in the form of network key drivers).
- Monitor the molecular effects of treatments (through blood biomarkers of network activity)
To achieve this, we apply AI-based systems analyses to extensive clinical datasets of internal medicine conditions that combine detailed and deep patient information with imaging and comprehensive multi-omics data (genomics, epigenetics, transcriptomics, proteomics, and metabolomics).
The STARNET Biobank
The (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) Biobank are internationally recognized, invaluable resources for systems medicine research.
As a direct consequence of my early role as a thorax surgeon trainee at the Karolinska University Hospital, in a joint effort with the Tartu University Hospital in Estonia, we initiated the STARNET study that since 2007 has compiled samples from nearly 2,000 clinically well-characterized patients undergoing open-chest surgery, with (n=1,475) or without (n=452) ACVD.
STARNET’s unique value lies in its unparalleled depth of tissue sampling from the same individual:
- Blood: Including primary blood macrophages and foam cells.
- Arterial Wall: Specifically, the advanced atherosclerotic aortic wall and the non-diseased internal mammary artery.
- Major Metabolic Tissue Biopsies: Including the liver, skeletal muscle, and visceral and subcutaneous fat.
- Gut Microbiome: Since 2017, we have also been collecting feces samples for gut microbiome profiling.
- 5-year mortality and morbidity follow-up through patient records and registers
Systems Medicine Impact
Since 2013, we have generated comprehensive multi-omics datasets (including genomics, transcriptomics, and proteomics) across up to nine ACVD-relevant tissues, initially at the level of tissue biopsies but lately also at the level of single cells. This makes STARNET highly valued in the field of systems medicine—to some degree outperforming resources like GTEx (postmortal sampling and relative STARNET, phenotype-poor)—particularly for ACVD, metabolic and inflammatory conditions but generally for the entire research field of internal medicine. As such, STARNET has fostered many successful global collaborations, particularly in providing mechanistic insights to large clinical population-based studies like Genome-Wide Association Studies (GWAS) but also by providing clinical and functional contexts to animal and cell culture research.