PuSH - Publication Server of Helmholtz Zentrum München

Altenbuchinger, M.* ; Zacharias, H.U. ; Solbrig, S.* ; Schäfer, A.* ; Büyüközkan, M. ; Schultheiß, U.T.* ; Kotsis, F.* ; Köttgen, A.* ; Spang, R.* ; Oefner, P.J.* ; Krumsiek, J. ; Gronwald, W.*

A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study.

Sci. Rep. 9:13954 (2019)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional H-1 nuclear magnetic resonance metabolic variables. Routine analysis screens for associations of single metabolic features with clinical parameters while accounting for confounders typically chosen by expert knowledge. This knowledge can be incomplete or unavailable. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and evaluate its performance by sanity checks and predictive performance assessment on independent test data. Particularly, we show that discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that associations discovered in routine univariate screening approaches can be biased by incorrect or incomplete expert knowledge. Our data integration approach reveals important associations between CKD comorbidities and metabolites, including novel associations of the plasma metabolite trimethylamine-N-oxide with cardiac arrhythmia and infarction in CKD stage 3 patients.
Additional Metrics?
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Keywords Risk-factors; Systems; Profile; Cohort; Gout; Gckd
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Quellenangaben Volume: 9, Issue: 1, Pages: , Article Number: 13954 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed