Discovery and Characterization of Risk Factors for Chronic Kidney Disease Progression, Cardiovascular Diseases and Death

• Applicant: Dr. Peggy Sekula
• Subject Area: Nephrology ; Epidemiology and Medical Biometry/Statistics
• Term: from 2021 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460675747

Chronic kidney disease (CKD) represents a global public health burden due to its high prevalence and increasing incidence. During the course of disease, patients may suffer from progression and some may finally experience kidney failure (KF). In addition, patients are at high risk of morbidity and mortality, especially for acute kidney injury (AKI) and cardiovascular (CV) events. In order to address the public health burden of CKD, early recognition of patients at high risk for progression and adverse events is requested. Available risk prediction models to predict KF or CV outcomes in CKD patients are limited and further improvement is desirable by, for example, the incorporation of other prognostic biomarkers such as metabolites measured from blood or urine. In addition, the impact of non-fatal events such as AKI and non-fatal CV events (e.g. myocardial infarction) on the course of CKD is less understood. Currently, data of 5,217 CKD patients with moderately decreased kidney function at enrollment into the German Chronic Kidney Disease (GKCD) study are collected. Collected data cover information on clinical and demographic data as well as on self-reported health and medication data. In addition, blood and urine biosamples are collected, from which genetic data and baseline metabolite measurements (blood, urine) were already obtained. Moreover, prospective events on, for example, incident kidney and CV events are collected and adjudicated continuously. At present, follow-up information over the first six years since study entry is available.Utilizing the data available from the GCKD study, we thus aim to assess baseline metabolite levels and their ability to predict adverse outcomes in CKD patients as well as to model CKD progression in the presence of non-fatal events. The three specific objectives in this project are: (1) To evaluate associations of metabolite levels from plasma and urine with the prospective endpoints all-cause mortality, KF, and CV events. We expect to find known as well as novel associations. (2) To evaluate the prognostic value of selected endpoint-associated metabolites, while accounting for known prognostic factors. The performance will be compared in relation to the performance of clinical benchmark models. An external validation step using independent data is pursued. (3) Finally, we will explore the specific effects of non-fatal events such as MI or AKI on KF risk using the statistical framework of multi-state models.

• DFG Programme: Research Grants