Peter Henriksen

Postdoc Bioinformatics, PhD, MSc Big Data,
Systems Medicine Research


Phone: +45 29 43 48 61

Research area

My main area of research is to conduct advanced data analysis on studies related to diabetic kidney disease, but also on studies related to diabetes in general and Alzheimer’s disease.

Diabetic nephropathy

Diabetic kidney disease, also known as diabetic nephropathy (NP), is a common complication associated with diabetes and it can be fatal for the patient and costly for society as patients require expensive dialysis treatment. The PROTON (Personalising Treatment of Diabetic Nephropathy) project aims to carry out a detailed characterisation of persons with type 1 diabetes to learn more about NP in general and NP progression in particular.

Other diabetes studies

There are more than 300,000 persons with diabetes in Denmark alone and many of these experience severe complications. One such complication is nerve damage which can lead to further complications such as food ulcers and ultimately amputation. A major cause of diabetic nerve damage is events of hypoglycemia (low blood sugar levels), but little is known about how repeated exposure to hypoglycemia change human biology. Analysis of the metabolites that change upon hypoglycemia in persons, with and without hypoglycemic awareness, might therefore cast new light on this complication.

Current research

Analysing data from the PROTON project

I am currently analysing more than a thousand features generated by flow cytometry, ELISA multiplexing, mass spectrometry metabolomics and more than fifty clinical features. To this end I am using my own specialised code for preparing data along with classical statistical tests and models as well as common machine learning methods to elucidate aspects of the human biology which can be linked to diabetic nephropathy.

Analysing data from the HypoBrain study

In the HypoBrain study, people with and without hypoglycemic awareness are exposed to controlled hypoglycemia and the effects are analysed my measuring metabolites in the blood. So far a data analysis plan has been generated which entails generating a mixed effects statistical model for each of the identified metabolites.

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