Research: Type 2 Diabetes


This part of our research focusses on type 2 diabetes. Type 2 diabetes is responsible for about 90% of all diabetes cases and is caused by a combination of insulin resistance and insufficient insulin secretion. In addition to genetic susceptibility, overweight and an inactive life style are major risk factors. Our studies use a combination of modern omics techniques, data science and bioinformatics to identify biomarkers for disease progression.

Patients with type 2 diabetes follow different trajectories of glycaemic progression once the disease has developed. Whilst the majority show no or limited glycaemic progression, about 10-30 percent are unable to achieve HbA1c levels below the glycaemic target despite intensive treatment. Whereas especially this group has an increased risk of diabetic complications. Early identification of the persons at risk may help to redirect our clinical resources to those who may benefit the most thereby facilitating value-based healthcare. At present there are however no good instruments to identify those at risk of progression at an early stage.

As indicated above clinical studies have not yet yielded strong predictors of glycaemic progression or development of diabetes complications.


Multiomics and big data science

In our group we are investigating the role and utility as biomarkers of various omic datatypes in diabetes, diabetes progression and complications. We apply a combination of multi-omics measurements, data science and functional studies. This research is performed in close collaboration with Professors JW Beulens and PM Elders from Amsterdam UMC, Location VUmc. We use data from the Hoorn studies and the Hoorn Diabetes Care System Cohort (DCS) for our studies. These cohort studies contain longitudinally collected clinical data and biomaterials of thousands of healthy and affected persons (

During the past years we have shown that various metabolites associate with diabetes, diabetes progression and complications such as Diabetic Kidney Disease (DKD) (BBMRI-NL). In addition, we participate in research to identify novel genetic predictors of drug response for commonly used diabetes drugs (Metformin, SU and DPP4-inhibitors). In two EU-funded projects (IMI-DIRECT and IMI-RHAPSODY) we investigated the utility of various omics measurements (Genomics, lipidomics, metabolomics and peptidomics) in risk stratification for type 2 diabetes development and progression. In these and other studies, we showed that five clusters of type 2 diabetes each have their own distinct molecular signatures (PMID: 34376475, 34110439, 36826505). Thereby, providing a better understanding of the heterogeneity of type 2 diabetes.

Furthermore, we identified biomarkers belonging to three different chemical classes that associate with rapid glycaemic progression, defined as the time to insulin use (PMID: 37137910). Using machine learning techniques we also showed that a set of biomarkers modestly improved prediction of rapid glycaemic progression on top of clinical biomarkers (PMID: 38374450).

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Non coding RNAs

Recently, we also started projects investigating the role of circulating small and long non-coding RNAs and mRNA in diabetes. In these studies we have identified a number of small and long non-coding RNAs that are associated with medication use, glycaemic progression and or DKD. Interestingly, the small ncRNAs found in the circulation are often highly expressed in only one or two tissues suggesting that they might be used in communication between cells and tissues.


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Looking for information on one of our topics, a new place to conduct your research or experienced research to join forces with?  Feel free to contact us.!

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