CKDNapp

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

Project Lead

Helena U. Zacharias

Project partners

University Medicine Göttingen, University Clinic Freiburg, Helmholtz Center Munich

Funding

German Federal Ministry of Education and Research

Summary

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

Duration

2019-2024

Staff

Bence Olah

Publications

Publications in Journals

2023
[12]	Sood M, Suenkel U, von Thaler AK, Zacharias HU, Brockmann K, Eschweiler GW, Maetzler W, Berg D, Fröhlich H, Heinzel S. 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]	Shutta KH, Weighill D, Burkholz R, Guebila MB, DeMeo DL, Zacharias HU, Quackenbush J, Altenbuchinger M. DRAGON: determining regulatory associations using graphical models on multi-omic networks. Nucleic Acids Research. Oxford University Press; 2023;51(3):e15–e15. [BibTeX]
2022
[10]	Zacharias HU, Altenbuchinger M, Schultheiss UT, Raffler J, Kotsis F, Ghasemi S, Ali I, Kollerits B, Metzger M, Steinbrenner I, others. 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]	Zacharias HU, Kaleta C, Cossais Fçois, Schaeffer E, Berndt H, Best L, Dost T, Glüsing S, Groussin M, Poyet M, others. 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]	Winitzki D, Zacharias HU, Nadal J, Baid-Agrawal S, Schaeffner E, Schmid M, Busch M, Bergmann MM, Schultheiss U, Kotsis F, others. 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]	Schultheiss UT, Bächle H, Altenbuchinger M, Meiselbach H, Kosch R, Salloch S, Bratan T, Zacharias HU, Kotsis F. 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]	Schrod S, Schäfer A, Solbrig S, Lohmayer R, Gronwald W, Oefner PJ, Beißbarth T, Spang R, Zacharias HU, Altenbuchinger M. BITES: balanced individual treatment effect for survival data. Bioinformatics. Oxford University Press; 2022;38(Supplement_1):i60–i67. [BibTeX]
[5]	Lück N, Lohmayer R, Solbrig S, Schrod S, Wipfler T, Shutta KH, Guebila MB, Schäfer A, Beißbarth T, Zacharias HU, others. SpaCeNet: Spatial Cellular Networks from omics data. bioRxiv. Cold Spring Harbor Laboratory; 2022:2022–09. [BibTeX]
[4]	Altenbuchinger M, Berndt H, Kosch R, Lang I, Dönitz Jürgen, Oefner PJ, Gronwald W, Zacharias HU. Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data. Metabolites. MDPI; 2022;12(9):812. [BibTeX]
2021
[3]	Zacharias HU, Hertel J, Johar H, Pietzner M, Lukaschek K, Atasoy S, Kunze S, Völzke H, Nauck M, Friedrich N, others. 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]	Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic kidney disease cohort studies: A guide to metabolome analyses. Metabolites. MDPI; 2021;11(7):460. [BibTeX]
2020
[1]	Bonk S, Hertel J, Zacharias HU, Terock J, Janowitz D, Homuth G, Nauck M, Völzke H, Meyer zu Schwabedissen H, Van der Auwera S, others. Vitamin D moderates the interaction between 5-HTTLPR and childhood abuse in depressive disorders. Scientific reports. Springer; 2020;10(1):1–9. [BibTeX]