A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury

Abstract

Motivation: The discovery of new and unexpected biomarkers in cardiovascular disease is a highly data-driven process that requires the complementary power of modern metabolite profiling technologies, bioinformatics and biostatistics. Clinical biomarkers of early myocardial injury are lacking. A prospective biomarker cohort study was carried out to identify, categorize and profile kinetic patterns of early metabolic biomarkers of planned myocardial infarction (PMI) and spontaneous (SMI) myocardial infarction. We applied a targeted mass spectrometry (MS)-based metabolite profiling platform to serial blood samples drawn from carefully phenotyped patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy serving as a human model of PMI. Patients with SMI and patients undergoing catheterization without induction of myocardial infarction served as positive and negative controls to assess generalizability of markers identified in PMI.

Results: To identify metabolites of high predictive value in tandem mass spectrometry data, we introduced a new feature selection method for the categorization of metabolic signatures into three classes of weak, moderate and strong predictors, which can be easily applied to both paired and unpaired samples. Our paradigm outperformed standard null-hypothesis significance testing and other popular methods for feature selection in terms of the area under the receiver operating curve and the product of sensitivity and specificity. Our results emphasize that this new method was able to identify, classify and validate alterations of levels in multiple metabolites participating in pathways associated with myocardial injury as early as 10 min after PMI.

Availability: The algorithm as well as supplementary material is available for download at: www.umit.at/page.cfm?vpath=departments/technik/iebe/tools/bi

Fig. 1.

ROC curves and AUCs estimated for pBI versus paired statistical hypothesis testing. We used the cutoffs DA* = 0.2 (weak predictors), DA* = 0.4 and 0.6 (moderate and strong predictors, ROC curves not shown) to define the dependent variable for ROC analysis. The inverse P-values and absolute pBI scores were used in this analysis.

Fig. 2.

ROC curves and AUCs estimated for uBI versus unpaired statistical testing (P-value), IG and RF are depicted. TP²(TP^2*) is set to 0.4 (0.2) for defining the weak predictor class. TP² is the product of sensitivity and specificity. The inverse P-values and absolute uBI scores were used in this analysis.

Fig. 3.

Kinetic map of amino acids on PMI data at 10, 60 and 240 min after myocardial injury using the pBI scores. Red color increments indicate decreasing levels and blue indicates increasing levels.

Fig. 4.

Kinetic characteristic of hypoxanthine: categorization of pBI scores across the timepoints at 10, 60 and 240 min after PMI are exemplarily shown (height of bars). Values above bars denote median (IQR) of relative changes in levels versus baseline in percent.