The Institute of Epidemiology mission is to understand the protective and harmful effects of genes, the environment and lifestyle on human health and disease using population-based epidemiology approaches.
Towards Precision Medicine in Diabetes: Identifying Subgroups of Diabetes and Associated Complications in a Population Based Setting
Diabetes is currently categorized in two main forms, type 1 and type 2 diabetes, with the latter being presented heterogeneously. A recent Swedish study have investigated this heterogeneous nature of Type 2 Diabetes proposing several clusters of patients with different characteristics (1). Another elegant investigation in the German Diabetes Study showed that distinct diabetes clusters were associated with specific risk patterns of diabetes-related complications (2). However, these results need to be further validated and generalized in a community based setting. This new substratification of people with diabetes embodies the potential to shape precision medicine so we could identify and target early treatment on patients that would benefit the most. Prediabetes, another condition characterized by increased levels of glucose which are not high enough to be classified as diabetes, is accounting for another high risk group population. 75% of prediabetes people will eventually develop diabetes, whereas the rest hold the potential to reversibility. Continues efforts to characterize differences of clusters in these individuals represent an efficient strategy to halt diabetes. Here, we propose to investigate and validate similar diabetes and prediabetes clusters in the KORA population based study. Our cohort benefits from comprehensive clinical phenotyping of around 4261 participants with a follow-up of 20 years. Based on these phenotypes, together with other data on biomarkers, genetic, epigenetic and imaging, we will characterize distinct clusters of diabetes cases employing machine learning and other novel statistical methodology. In a subcohort of the KORA study (3), participants have undergone a whole body MRI, data of which will allow to characterize clusters in relation to their MRI profiling of subclinical disease burden for example related to visceral fat, ectopic fat, vascular parameters etc. Another aim of the PhD project focuses on analysing how participants on different clusters migrate in-between other clusters during two follow-up visits (7 years apart each) and whether this cluster membership change over time is affected by varying medication. Lastly, identification of individuals with increased risk of diabetes related complications such as cardiovascular disease, chronic kidney disease, diabetic neuropathy, and fatty liver will be further investigated.
1. Ahlqvist E, Groop L. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endo. 2018;6(5):361-9.
2. Zaharia OP, Roden M, Grp GDS. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endo. 2019;7(9):684-94.
3. Bamberg F, Peters A. Subclinical Disease Burden as Assessed by Whole-Body MRI in Subjects With Prediabetes, Subjects With Diabetes, and Normal Control Subjects From the General Population: The KORA-MRI Study. Diabetes. 2017;66(1):158-69.