A toolbox for monitoring and tailoring treatment of chronic kidney disease patients – a personalized systems medicine approach


Helena U. Zacharias

Project partners

University Medicine Göttingen, University Clinic Freiburg, Helmholtz Center Munich


German Federal Ministry of Education and Research


Chronic Kidney Disease (CKD) can arise from multiple causes. It is characterized by a variable course of diseases and a high burden of cardiovascular and metabolic comorbidities, complicating optimal treatment. To provide optimal, personalized medical care for each patient, physicians need to obtain a detailed, comprehensive picture of that patient’s state. For this purpose, he/she integrates different levels of data, e.g., clinical/demographic parameters, biomarkers, and drug information, with medical knowledge. Because CKD is a complex disease, this data integration process is extremely challenging.
In our junior consortium CKDNapp, we will (1) computationally model this complex CKD system based on comprehensive biomedical and omics data collected within the German Chronic Kidney Disease (GCKD) study, (2) enrich these models with novel omics data, (3) discover novel biomarkers, and (4) build a clinical decision support (CDS) software based on these models assisting physicians in personalized everyday CKD patient care.
Our CDS software, called CKDNapp (CKD Nephrologists’ app), will (i) predict adverse events and disease progression, (ii) refine diagnosis of CKD staging, (iii) return transparent reasoning for all predictions and recommendations, (iv) offer in silico modification of patient parameters by the physician, and (v) deliver comprehensive literature support. It will be available as an easy-to-use software for smartphones, tablets, and desktop computers.

Link: CKDNapp Homepage





Publications in Journals
[12], , , , , , , , , . Bayesian network modeling of risk and prodromal markers of Parkinson’s disease. Plos one. Public Library of Science San Francisco, CA USA; 2023;18(2):e0280609. [BibTeX]
[11], , , , , , , . DRAGON: determining regulatory associations using graphical models on multi-omic networks. Nucleic Acids Research. Oxford University Press; 2023;51(3):e15–e15. [BibTeX]
[10], , , , , , , , , , . A predictive model for progression of CKD to kidney failure based on routine laboratory tests. American Journal of Kidney Diseases. Elsevier; 2022;79(2):217–230. [BibTeX]
[9], , , , , , , , , , . Microbiome and Metabolome Insights into the Role of the Gastrointestinal–Brain Axis in Parkinson’s and Alzheimer’s Disease: Unveiling Potential Therapeutic Targets. Metabolites. MDPI; 2022;12(12):1222. [BibTeX]
[8], , , , , , , , , , . Educational Attainment Is Associated With Kidney and Cardiovascular Outcomes in the German CKD (GCKD) Cohort. Kidney International Reports. Elsevier; 2022;7(5):1004–1015. [BibTeX]
[7], , , , , , , , . MO474: Expectation and Acceptance of a Clinical Decision Support Software by Nephrologist End-Users: The Ckdnapp Survey. Nephrology Dialysis Transplantation. Oxford University Press; 2022;37(Supplement_3):gfac071–005. [BibTeX]
[6], , , , , , , , , . BITES: balanced individual treatment effect for survival data. Bioinformatics. Oxford University Press; 2022;38(Supplement_1):i60–i67. [BibTeX]
[5], , , , , , , , , , . SpaCeNet: Spatial Cellular Networks from omics data. bioRxiv. Cold Spring Harbor Laboratory; 2022:2022–09. [BibTeX]
[4], , , , , , , . Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data. Metabolites. MDPI; 2022;12(9):812. [BibTeX]
[3], , , , , , , , , , . A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression. Molecular psychiatry. Nature Publishing Group UK London; 2021;26(12):7372–7383. [BibTeX]
[2], , , , . Chronic kidney disease cohort studies: A guide to metabolome analyses. Metabolites. MDPI; 2021;11(7):460. [BibTeX]
[1], , , , , , , , , , . Vitamin D moderates the interaction between 5-HTTLPR and childhood abuse in depressive disorders. Scientific reports. Springer; 2020;10(1):1–9. [BibTeX]

Auswahl an Pressebeiträgen:
„KI im Gesundheitswesen: Mathematik kann Leben retten“, NDR Beitrag zum Magazin DAS!, 13.02.2023 Link
“Personalisierte Vorhersage eines terminalen Nierenversagens für Patienten mit chronischer Niereninsuffizienz”, NephroNews, Ausgabe 3/2022 Link
„Studie findet Biomarker für Depressionen“, CAU Pressemitteilung, 09.06.2021 Link
„Forscherin findet Biomarker für Depressionen- möglicher Ansatz für Therapie“, Berliner Zeitung, 30.06.2021 Link
„Der Ursache für Depression auf der Spur“, Kieler Nachrichten, 24.07.2021 Link
„Möglicher Biomarker für eine Depression“, Deutsches Ärzteblatt, 02.08.2021 Link
„Kommt der Bluttest für Depressionen?“, Bild, 12.08.2021 Link
„Studie findet Biomarker für Depression“, NDR Info (Radio), 6.7.2021.
„Drohendes Nierenversagen: Kieler Wissenschaftlerin veröffentlicht neuen Risikorechner“, CAU Pressemitteilung, 11.10.2021 Link
„Neuer Risikorechner prognostiziert Nierenversagen“, Deutsches Ärzteblatt, 03.11.2021 Link
„Apps in der Betreuung von CKD-Patienten: Die Chronic Kidney Disease Nephrologist’s App“, NephroNews, Ausgabe 5/21 Link
„Entwicklung eines Entscheidungsunterstützungssystems für die ärztliche Behandlung in der niedergelassenen Nephrologie“, ConnexiPlus, 2021 Link