Novel biomarkers identifying hypertrophic cardiomyopathy and its obstructive variant based on targeted amino acid metabolomics

Abstract Background: Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM. Methods: Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography–mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. Results: The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75–0.91) in the training set and 0.79 (0.65–0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74–0.93) and 0.82 (0.66–0.98), respectively. Conclusions: The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.