Application of untargeted plasma metabolomics and machine learning to construct a diagnostic model for hypertrophic...

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease. Recent metabolomics studies have revealed pathogenic mechanisms and provided new perspectives for diagnosis.

This study aimed to analyze plasma metabolic alterations and construct a preliminary diagnostic model for HCM based on untargeted metabolomics and machine learning (ML) algorithms, in order to explore potential pathogenic pathways and improve diagnostic accuracy during screening.

A total of 76 HCM patients and 35 normal participants were consecutively recruited from August, 2023 to December, 2023. Data were split into discovery and validation sets at a ratio of 7:3 and the feature combinations were selected using support vector machine (SVM) and random forest (RF). Stepwise multivariate linear regression analysis was performed to identify key metabolites associated with left ventricular wall thickness. Metabolic pathway analysis was performed using KEGG.

Totally 1481 metabolites were identified with 640 differential metabolites and 240 significant differential metabolites. Multivariate statistical analysis showed that metabolism results could effectively differentiate the two cohorts (OPLS-DA positive ion mode R2Y = 0.744, Q2 = 0.456; negative ion mode R2Y = 0.611, Q2 = 0.441). SVM and RF screened the same combination of features including 7-keto-8-aminopelargonic acid (KAPA), γ-linolenoyl ethanolamid, nitrilotriacetic acid, D-quinovose and N-acetyl-l-aspartic acid (NAA), which could effectively and accurately differentiate HCM patients from normal participants (in discovery and validation sets, the SVM model AUROC was 0.996 and 0.985 with accuracies of 96.1% and 97.1%, respectively; the RF model AUROC was 1.000 with accuracies of 94.8% and 100.0%, respectively). In metabolic pathway analysis, central carbon metabolism in cancer and protein digestion and absorption were significantly upregulated in HCM patients, which were connected by alanine, aspartate and glutamate metabolism. Stepwise multivariate linear regression analysis revealed that NAA was correlated with left ventricular mass index and RV₅+SV₁ (P < 0.05), which may be the central target of the connecting pathway.

Plasma metabolite diagnostic model including KAPA, γ-linolenoyl ethanolamid, nitrilotriacetic acid, D-quinovose and NAA can effectively and accurately screen HCM patients. Metabolomics combined with ML algorithm showed that alanine, aspartate and glutamate metabolism may be the pathogenic pathway leading to the occurrence of HCM with NAA as the central target.