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

TL;DR

This paper presents a novel data integration framework for combining NMR metabolic fingerprints with clinical and demographic data in CKD research, adjusting for confounders and revealing significant associations missed by traditional methods.

Contribution

It introduces a mathematically grounded, algorithmic framework for integrative data analysis that inherently accounts for confounding variables, improving association discovery in omics studies.

Findings

Associations remain significant after confounder adjustment.

Routine univariate analysis can be biased by incomplete expert knowledge.

The integrated approach improves predictive performance on validation data.

Abstract

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.