ArticleHeliyon2025
Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Serum Lipidomic Profile Signature of Active Acromegaly and Relationships to Cardiovascular Disease.International journal of molecular sciences · 2026Article
- Multi-omics insights into the molecular basis of powdery mildew resistance and root metabolic variation in Astragalus membranaceus var. mongholicus.Scientific reports · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objects: Our aim was to identify changes in the metabolome in dilated cardiomyopathy (DCM) as well as to construct a metabolic diagnostic model for DCM. Methods: We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis. It was validated in combination with transcriptome sequencing data from public databases. Machine learning models were used for the diagnosis of DCM. Results: Using multiple analytical techniques, 130 metabolite alterations were identified in DCM compared to healthy controls. Perturbations in glycerophospholipid metabolism (GPL) were identified and validated as a characteristic metabolic pathway in DCM. Through the least absolute shrinkage and selection operator (LASSO), we identified the 7 most important GPL metabolites, including LysoPA (16:0/0:0), LysoPA (18:1(9Z)/0:0), PC (20:3(8Z,11Z,14Z)/20:1(11Z)), PC (20:0/14:0), LysoPC (16:0), PS(15:0/18:0), and PE(16:0/20:4 (5Z,8Z,11Z,14Z)). The machine learning models based on the seven metabolites all had good accuracy in distinguishing DCM [All area under the curve (AUC) > 0.900], and the artificial neural network (ANN) model performed the most consistently (AUC: 0.919 ± 0.075). Conclusions: This study demonstrates that GPL metabolism may play a contributing role in the pathophysiological mechanisms of DCM. The 7-GPL metabolite model may help for early diagnosis of DCM.
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