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Omics data facilitate the gain of novel insights into the pathophysiology ofdiseases and, consequently, their diagnosis, treatment, and prevention. To thatend, it is necessary to integrate omics data with other data types such asclinical, phenotypic, and demographic parameters of categorical or continuousnature. Here, we exemplify this data integration issue for a study on chronickidney disease (CKD), where complex clinical and demographic parameters wereassessed together with one-dimensional (1D) 1H NMR metabolic fingerprints.Routine analysis screens for associations of single metabolic features withclinical parameters, which requires confounding variables typically chosen byexpert knowledge to be taken into account. This knowledge can be incomplete orunavailable. The results of this article are manifold. We introduce a frameworkfor data integration that intrinsically adjusts for confounding variables. Wegive its mathematical and algorithmic foundation, provide a state-of-the-artimplementation, and give several sanity checks. In particular, we show that thediscovered associations remain significant after variable adjustment based onexpert knowledge. In contrast, we illustrate that the discovery of associationsin routine analysis can be biased by incorrect or incomplete expert knowledgein univariate screening approaches. Finally, we exemplify how our dataintegration approach reveals important associations between CKD comorbiditiesand metabolites. Moreover, we evaluate the predictive performance of theestimated models on independent validation data and contrast the results with anaive screening approach.

Fully integrative data analysis of NMR metabolic fingerprints with  comprehensive patient data: a case report based on the German Chronic Kidney  Disease (GCKD) study