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4.2. Biomarker signatures for disease and drug response prediction


How our research benefits to society

Diabetes is a devastating disease, with numerous complications, due to its adverse effects on the micro- and macrovascular system. This is not only true in the general population, but particularly so in patient populations, where diabetes is often not only more frequent, but – likely due to impaired homeostatic capacity – often also gives rise to more severe complications.

  • We hypothesize that patient populations may serve as a good model for the general population, with stronger relations between biomarkers and higher numbers and in time more nearby end-points, leading to higher efficiency of proof-of-principle studies for biomarkers. Omic-technologies, like NMR-metabolimics provide large amounts of data of which the predictive value for development of diabetes and for complications of diabetes is still in the beginning of its evaluation. We are currently generating such data in large cohorts of the general population, patient cohorts and in intervention studies in patients with diabetes.

    The aim of this project is to evaluate omics biomarkers for:

    1. Prediction of the development of diabetes;
    2. For the development of complications of diabetes in general populations cohorts, patients population cohorts and intervention studies and to compare their performance;
    3. Prediction of drug response to commonly used and novel treatments.

    Collectively these studies should foster individualization of treatment in patients with diabetes.

  • To assess biomarkers in general population and patients cohorts and to compare predictive capacity for development of diabetes, complications of diabetes, and drug response. For the latter we use intervention studies performed in patients with diabetes.

  • Assessments of omic profiles (metabolomics, proteomic, peptidomic).

    The omic profiles in general population and patients with diabetes will be compared to assess if there are disease specific profiles associated with diease progression or whether common omic profiles exists independent of background population, type of disease, or severity of the disease. The population will be divided in test and validation cohorts to ensure external validation of the discovered profiles. The omic profiles will be assessed in alreadly collected samples. Different matrices are available and will be used (plasma, serum, urine).

    1. Omics predictive biomarkers for the development of diabetes in the general population
    2. Omics predictive biomarkers for the development of diabetes in patient populations
    3. Omics predictive biomarkers for the development of complications from diabetes
    4. Omics predictive biomarkers for treatment response in patients with diabetes
    5. Review on the utility and analysis of Omics Biomarkers in the field of diabetes
  • We will identify biomarkers that allow for early detection of diabetes, its complications and response to treatment. This will allow for better personalization of treatment.

  • This project links with various other ProminenT projects on biomarkers and response to treatment in diabetes within ProminenT (Heerspink). It is likely that ‘inflammatory’ biomarkers are linked to disease progression and as such links between the projects from Boots, Koppelman, Bischof are envisioned.

This project is part of