Bioinformatics-focused identification of metabolomic Markers in coronary microvascular disease

Abstract

Background

Coronary microvascular disease (CMVD), marked by dysfunction of the small coronary vessels, poses significant diagnostic challenges due to the complexity and high cost of current procedures like the index of microcirculatory resistance (IMR). This study aimed to identify metabolomic biomarkers from coronary artery samples to facilitate CMVD diagnosis using advanced bioinformatics techniques—specifically, random forest algorithms and generalized linear models (GLMs)—to develop more cost-effective blood-based diagnostics.

Methods

In this prospective study, 68 patients scheduled for coronary angiography and IMR assessment were enrolled. Plasma samples obtained from their coronary arteries were analyzed using untargeted metabolomics with liquid chromatography-mass spectrometry. Advanced bioinformatics methods were applied: random forest algorithms were utilized for feature selection to identify significant metabolites, and GLMs were constructed for predictive modeling. The diagnostic performance of the models was evaluated through receiver operating characteristic (ROC) curve analysis.

Results

The random forest analysis identified the top 10 metabolites that significantly contributed to the classification of CMVD. The GLM built using these metabolites demonstrated excellent diagnostic accuracy, achieving area under the ROC curve (AUC) values of 0.984 in the initial (discovery) cohort and 0.938 in the subsequent (validation) cohort. The use of mathematical modeling enhanced the robustness and interpretability of the biomarker selection process.

Conclusions

Advanced bioinformatics techniques, including random forest algorithms and GLMs, effectively identified key metabolites associated with CMVD. While the collection of coronary artery blood samples is invasive due to the necessity of coronary angiography, this method offers a more practical and cost-effective alternative to IMR measurement, potentially improving the diagnostic approach for CMVD.