Key result
A plasma biomarker panel discriminates HCM from normal controls with ~0.83 AUC.
Why the study?
HCM is an underdiagnosed genetic heart disease, and discriminating between its obstructive and non-obstructive subtypes remains challenging despite differing management and prognosis.
Can targeted amino acid metabolomics identify specific biomarkers to discriminate hypertrophic cardiomyopathy from normal controls, and its obstructive variant from the non-obstructive variant?
Observational (n=166)
No
Can targeted amino acid metabolomics identify specific biomarkers to discriminate hypertrophic cardiomyopathy from normal controls, and its obstructive variant from the non-obstructive variant?
Effect estimate: AUC 0.83 (95% CI 0.75-0.91)
Plasma amino acid profiling combined with machine learning algorithms can effectively discriminate hypertrophic cardiomyopathy from normal controls and differentiate its obstructive and non-obstructive subtypes.
Should not yet change HCM diagnostic practice; leaves open prospective validation of metabolomic panels.
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.
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Guo et al. (2022) conducted an observational in Hypertrophic cardiomyopathy (HCM) (n=166). Targeted amino acid metabolomics panel (Panel A: proline, glycine, cysteine, and choline) vs. Normal controls was evaluated on Discrimination of HCM patients from normal controls (Training set) (AUC 0.83, 95% CI 0.75-0.91). A plasma biomarker panel consisting of four amino acids and derivatives (proline, glycine, cysteine, and choline) discriminated patients with hypertrophic cardiomyopathy from normal controls with an AUC of 0.83.
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