Authors:
Shixiang Yu, Siyu Han, Mengya Shi, Makoto Harada, Jianhong Ge, Xuening, Xiang Cai, Margit Heier, Gabi Karstenmüller, Karsten Suhre, Christian Gieger, Wolfgang Koenig, Wolfgang Rathmann, Annette Peters and Rui Wang-Sattler
Metabolites 2024, 14, 258.
doi: https://doi.org/10.3390/ metabo14050258
Published: 30 April 2024
Abstract:
Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies,
given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning
approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside
clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up
F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19
clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI
cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model
to generate new incident cases, augmenting the dataset and improving feature representation. To
predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic
minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address
overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance
prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss
function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy
of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the
predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and
physical activity. This is the first study to construct a deep-learning approach for producing 7-year
MI predictions using the newly proposed loss function. Our findings demonstrate the promising
potential of our technique in identifying novel biomarkers for MI prediction.