  Omics data facilitate the gain of novel insights into the pathophysiology of
diseases and, consequently, their diagnosis, treatment, and prevention. To that
end, it is necessary to integrate omics data with other data types such as
clinical, phenotypic, and demographic parameters of categorical or continuous
nature. Here, we exemplify this data integration issue for a study on chronic
kidney disease (CKD), where complex clinical and demographic parameters were
assessed together with one-dimensional (1D) 1H NMR metabolic fingerprints.
Routine analysis screens for associations of single metabolic features with
clinical parameters, which requires confounding variables typically chosen by
expert knowledge to be taken into account. This knowledge can be incomplete or
unavailable. The results of this article are manifold. 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 give several sanity checks. In particular, we show that the
discovered associations remain significant after variable adjustment based on
expert knowledge. In contrast, we illustrate that the discovery of associations
in routine analysis can be biased by incorrect or incomplete expert knowledge
in univariate screening approaches. Finally, we exemplify how our data
integration approach reveals important associations between CKD comorbidities
and metabolites. Moreover, we evaluate the predictive performance of the
estimated models on independent validation data and contrast the results with a
naive screening approach.
