Meeting Banner
Abstract #2671

Cardiac metabolism assessed by MR Spectroscopy to classify the diabetic and obese heart: a Random Forest and Bayesian network study

Ina Hanninger1, Eylem Levelt2,3, Jennifer J Rayner2, Christopher T Rodgers2,4, Stefan Neubauer2, Vicente Grau1, Oliver J Rider2, and Ladislav Valkovic2,5
1Oxford Institute of Biomedical Engineering, Oxford, United Kingdom, 2Radcliffe Department of Medicine, University of Oxford Centre for Clinical Magnetic Resonance Research, Oxford, United Kingdom, 3University of Leeds, Leeds, United Kingdom, 4Wolfson Brain Imaging Centre, Cambridge, United Kingdom, 5Slovak Academy of Sciences, Institute of Measurement Science, Bratislava, Slovakia

In this study, Random Forest classification was used on data from 197 subjects to discriminate between non-diabetic, diabetic, and obese patients using 31P-MRS and 1H-MRS measurements of cardiac energetics, along with MRI measures of cardiac function. Achieving 91.67%, 73.08% and 88.89% test accuracies, SHAP feature importances indicate a higher predictive impact of metabolic metrics for classifying the diabetic heart compared to global function metrics. Bayesian networks generated through structure learning of the data further suggests a potential causal association of increased visceral fat, increased LVMass resulting in decreased PCr/ATP, and increased cardiac lipid levels attributed to these disease states.

This abstract and the presentation materials are available to members only; a login is required.

Join Here