Wiesbaden, 02.05.2019

Personalized prevention of diabetes – relevance of subgroups

Professor Dr. Dr. h.c. mult. Martin Hrabĕ de Angelis
Member of the Board of the German Center for Diabetes Research (DZD)
Director of  the Institute of Experimental Genetics at Helmholtz Zentrum München
Chair of Experimental Genetics, Technische Universität München

Statement at the DGIM press conference, May 6, 2019

The number of people in Germany diagnosed with diabetes is increasing. Currently about 7 million people suffer from diabetes, and by 2040, the number of people with type 2 diabetes is projected to increase up to twelve million. [1]. The number of excess deaths associated with diabetes is high, one in five individuals in Germany dies of diabetes [2]. These statistics highlight the urgent need for new, effective prevention measures and innovative forms of treatment.

Type 2 diabetes is a disease that is characterized by very heterogeneous manifestations. Studies in Scandinavia show that patients with type 2 diabetes can be clustered into different subgroups that vary in the severity of the course of the disease [3, 4]. These subgroups were confirmed by the German Center for Diabetes Research (DZD) in analyses of the German Diabetes Study (GDS). Patients in certain subgroups have a high risk of diabetes complications. These findings also highlight the importance of targeted prevention of diabetes.

Different subgroups in prediabetes
Current DZD studies show that already in prediabetes there are different subgroups that react differently to lifestyle interventions [5]. Studies indicate that not every prediabetic has the same high risk of developing diabetes later on. Rather, there is a high-risk group: test persons who suffer from fatty liver disease with insulin resistance or insulin secretion disorders are very likely to develop manifest diabetes. In addition, there is increased risk of developing secondary diseases later on. Studies indicate that intensive lifestyle intervention involving increased exercise and accompanied  by sustained  counseling can help to delay or even prevent the onset of the metabolic disease. Prof. Dr. Andreas Fritsche (Institute for Diabetes Research and Metabolic Diseases of Helmholtz Zentrum München at the University of Tübingen, DZD) presented the results of the study on May 5th in his lecture "Results on high-risk groups from the PLIS prevention study" at the DGIM.

Digitalization enables research into the prediction and prevention of diabetes in a new dimension
The DZD is working to identify further subgroups of diabetes and prediabetes and to develop specific prevention approaches and therapies for these subgroups. To this end, we have, among other things, set up large multicenter studies. In addition, the DZD has a huge data trove consisting of cohorts, clinical studies, biosamples, preclinical models, investigations at various sites, results from omics analyses, genotyping and phenotyping. In the DZD-Connect project, we combine the research data from these heterogeneous sources and analyze them using innovative IT technologies to identify patterns – e.g. for subgroups of diabetes. In the next step, we aim to derive conclusions for diagnosis and therapy.

Digitalization opens up the possibility of researching the prediction and prevention of the widespread disease diabetes in a completely new dimension. By setting up a digital diabetes prevention center, DDCP for short, and involving large population groups, health and research data as well as innovative information technologies, the opportunity shall be taken to identify subgroups of diabetes in the population at an early stage and to develop targeted personalized prevention and therapy. [6]

1) Tönnies, T. , Röckl, S. , Hoyer, A. , Heidemann, C. , Baumert, J. , Du, Y. , Scheidt‐Nave, C. and Brinks, R.  Projected number of people with diagnosed Type 2 diabetes in Germany in 2040.
Diabet. Med., 2019
DOI:  https://doi.org/10.1111/dme.13902

2) Jacobs E, Hoyer A, Brinks R, Kuss O, Rathmann W.
Burden of Mortality Attributable to Diagnosed Diabetes: A Nationwide Analysis Based on Claims Data From 65 Million People in Germany.
Diabetes Care, 2017
DOI: 10.2337/dc17-0954

3) Emma Ahlqvist, PhD, Petter Storm, PhD, Annemari Käräjämäki, MD†, Mats Martinell, MD†, Mozhgan Dorkhan, PhD, Annelie Carlsson, PhD, Petter Vikman, PhD, Rashmi B Prasad, PhD, Dina Mansour Aly, MSc, Peter Almgren, MSc, Ylva Wessman, MSc, Nael Shaat, PhD, Peter Spégel, PhD, Prof Hindrik Mulder, PhD, Eero Lindholm, PhD, Prof Olle Melander, PhD, Ola Hansson, PhD, Ulf Malmqvist, PhD, Prof Åke Lernmark, PhD, Kaj Lahti, MD, Tom Forsén, PhD, Tiinamaija Tuomi, PhD, Anders H Rosengren, PhD, Prof, Leif Groop Prof
Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables

The Lancet Diabetes & Endocrinology, 2018
DOI: https://doi.org/10.1016/S2213-8587(18)30051-2

4) Stidsen JV, Henriksen JE, Olsen MH, Thomsen RW, Nielsen JS, Rungby J, Ulrichsen SP, Berencsi K, Kahlert JA, Friborg SG, Brandslund I, Nielsen AA, Christiansen JS, Sørensen HT, Olesen TB, Beck-Nielsen H.
Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool, Diabetes Metab Res Rev,. 2018
DOI: 10.1002/dmrr.3005

5) Böhm A, Hoffmann C, Irmler M, Schneeweiss P, Schnauder G, Sailer C, Schmid V, Hudemann J, Machann J, Schick F, Beckers J, Hrabě de Angelis M, Staiger H, Fritsche A, Stefan N, Nieß AM, Häring HU, Weigert C.
TGF-β Contributes to Impaired Exercise Response by Suppression of Mitochondrial Key Regulators in Skeletal Muscle
Diabetes, 2016
DOI: 10.2337/db15-1723

6) Jarasch A, Glaser A,·Häring·H, Roden M, · Schürmann·A,  Solimena·M, Theiss· F, Tschöp M, ·Wess·G,  Hrabe de Angelis M
Mit Big Data zur personalisierten Diabetesprävention
Diabetologe, 2018
DOI: https://doi.org/10.1007/s11428-018-0384-1

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