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Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
Published on: August 8, 2022
Novel biomarkers identifying hypertrophic cardiomyopathy and its obstructive variant based on targeted amino acid
Lanyan Guo1, Bo Wang2, Fuyang Zhang1
1Department of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
Insights
Plasma amino acid profiles differ in hypertrophic cardiomyopathy (HCM) patients. Specific amino acids and derivatives can help screen for HCM and distinguish obstructive from non-obstructive forms.
Area of Science:
- Cardiovascular Medicine
- Metabolomics
- Genetic Diseases
Background:
- Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart condition.
- Distinguishing obstructive (HOCM) from non-obstructive (HNCM) forms is crucial for management but challenging.
- HCM is linked to metabolic disturbances, particularly altered myocardial amino acid (AA) metabolism.
Purpose of the Study:
- To analyze plasma amino acid (AA) and derivative profiles in HCM patients.
- To identify potential biomarkers for HCM detection and subtyping.
- To develop screening models for HCM and its obstructive phenotype.
Main Methods:
- Targeted metabolomic analysis of plasma samples from 166 participants (57 HOCM, 52 HNCM, 57 controls) using HPLC-MS.
- Application of random forest, support vector machine, and logistic regression for biomarker identification.
- Development and validation of screening models to differentiate HCM from controls and HOCM from HNCM.
Main Results:
- Significant differences in serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels between HCM and controls.
- A four-biomarker panel (proline, glycine, cysteine, choline) effectively discriminated HCM from controls (AUC 0.79-0.83).
- A three-biomarker panel (arginine, proline, ornithine) differentiated HOCM from HNCM (AUC 0.82-0.83).
Conclusions:
- Distinct plasma amino acid (AA) and derivative profiles exist between HCM patients and healthy controls.
- Established screening models show potential for assisting in HCM diagnosis.
- These models may aid in differentiating between obstructive and non-obstructive HCM subtypes.
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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