ArticleMolecular biomedicine2026
Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning.
Article in Molecular biomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Integrative transcriptomic analysis and experimental validation identify GPR97 and PROK2 as novel genes upregulated in acute myocardial infarction.Functional & integrative genomics · 2026Article
- Acidosis-associated gene signature defines novel subtypes and dual-target therapeutic candidates in breast cancer.Journal of translational medicine · 2026Article
- Causal effects between blood metabolites and myocardial infarction: A 2-sample Mendelian randomization study.Medicine · 2026Article
- Single-cell mitophagy signature-based artificial intelligence model enhances prediction of prognosis and immunotherapy response in non-small-cell lung cancer.Respiratory research · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
Acute myocardial infarction (AMI) remains a leading cause of global cardiovascular morbidity and mortality. Limitations in current diagnostic methods hinder early detection and intervention, creating an urgent need for novel early diagnostic biomarkers. This study employed an integrated multi-omics approach, combining metabolomics, Mendelian randomization (MR), and transcriptomics data to identify potential AMI biomarkers. Plasma metabolomic profiling revealed 174 differentially abundant metabolites. Subsequent MR analysis pinpointed a key causal metabolite, L-arachidoyl carnitine (carnitine C20:0). Genes associated with this metabolite were retrieved from the GeneCards database and cross-referenced with differentially expressed genes from the GEO database, leading to the identification of 10 candidate biomarker genes: ACSL1, PYGL, DYSF, MGAM, SLC7A7, SULF2, KCNJ2, CYP1B1, NCF2, and SLC22A4. By constructing and evaluating 80 machine learning models, the Enet[alpha = 0.1] model was determined to have the optimal diagnostic performance. The diagnostic potential of these ten genes was further corroborated by logistic regression with tenfold cross-validation. Additionally, immune cell infiltration analysis using the CIBERSORT algorithm uncovered potential associations between the candidate genes and specific immune cell subpopulations. In conclusion, this sequential multi-omics investigation successfully identifies and validates 10 gene biomarkers related to AMI, offering new perspectives for early precision diagnosis and insights into the disease's pathogenesis, alongside potential therapeutic targets.
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