Metabolomics and random forests in patients with complex congenital heart disease

Miriam Michel; Kai Thorsten Laser; Karl-Otto Dubowy; Sabine Scholl-Bürgi; Erik Michel

doi:10.3389/fcvm.2022.994068

Metabolomics and random forests in patients with complex congenital heart disease

Front Cardiovasc Med. 2022 Oct 5:9:994068. doi: 10.3389/fcvm.2022.994068. eCollection 2022.

Authors

Miriam Michel¹, Kai Thorsten Laser², Karl-Otto Dubowy², Sabine Scholl-Bürgi³, Erik Michel⁴

Affiliations

¹ Division of Pediatrics III - Cardiology, Pulmonology, Allergology and Cystic Fibrosis, Department of Child and Adolescent Health, Medical University of Innsbruck, Innsbruck, Austria.
² Division Pediatrics I - Inherited Metabolic Disorders, Department of Child and Adolescent Health, Medical University of Innsbruck, Innsbruck, Austria.
³ Center of Pediatric Cardiology and Congenital Heart Disease, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University of Bochum, Bad Oeynhausen, Germany.
⁴ Clinic for Pediatrics, Medizin Campus Bodensee, Friedrichshafen, Germany.

Abstract

Introduction: It is increasingly common to simultaneously determine a large number of metabolites in order to assess the metabolic state of, or clarify biochemical pathways in, an organism ("metabolomics"). This approach is increasingly used in the investigation of the development of heart failure. Recently, the first reports with respect to a metabolomic approach for the assessment of patients with complex congenital heart disease have been published. Classical statistical analysis of such data is challenging.

Objective: This study aims to present an alternative to classical statistics with respect to identifying relevant metabolites in a classification task and numerically estimating their relative impact.

Methods: Data from two metabolomic studies on 20 patients with complex congenital heart disease and Fontan circulation and 20 controls were reanalysed using random forest (RF) methodology. Results were compared to those of classical statistics.

Results: RF analysis required no elaborate data pre-processing. The ranking of the variables with respect to classification impact (subject diseased, or not) was remarkably similar irrespective of the evaluation method used, leading to identical clinical interpretation.

Conclusion: In metabolomic classification in adult patients with complex congenital heart disease, RF analysis as a one-step method delivers the most adequate results with minimum effort. RF may serve as an adjunct to traditional statistics also in this small but crucial-to-monitor patient group.

Keywords: Fontan; congenital heart disease; metabolomics; random forest; statistics.