Integrative OMICs Analyses of Cardiometabolic Dysfunction
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Integrative Metabolomics".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 176
Special Issue Editors
Interests: bioinformatics; machine learning; omics data integration; translational research; metabolic diseases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cardiometabolic dysfunction arises from complex interactions among genetic susceptibility, environmental exposures, lifestyle factors, and aging. It is characterized by the coexistence of clinical manifestations, including obesity, dyslipidaemia, hypertension, systolic and diastolic cardiac dysfunction, and diabetic cardiomyopathy, with underlying mechanisms such as insulin resistance, chronic inflammation, and fibrosis.
These comorbidities both result from and contribute to molecular alterations across multiple biological layers. A systems level understanding of these processes is essential for improving disease prevention, early detection, and precision intervention. The integration of OMICs technologies, including genomics, proteomics, and metabolomics, has transformed cardiometabolic research by enabling comprehensive interrogation of molecular pathways and regulatory networks. In particular, metabolomics provides a functional readout that reflects the cumulative effects of genetic regulation, protein activity, and environmental exposures.
Advances in bioinformatics methods, including classical machine-learning approaches (e.g., regression and classification), supervised (e.g., random forest and support vector machines) and unsupervised (e.g., clustering) learning, deep-learning techniques (e.g., autoencoders and generative adversarial networks), and explainable artificial intelligence, now enable robust integration of multi-level OMICs data with clinical phenotypes. These approaches facilitate the identification of dysregulated metabolites, enzymes, and signaling pathways, improve risk stratification, and uncover mechanistic links among metabolic stress, inflammation, fibrosis, and cardiovascular dysfunction.
This Special Issue aims to highlight original research and methodological developments applying integrative OMICs analyses to cardiometabolic dysfunction. Topics of interest include, but are not limited to, multi-OMICs data integration, biomarker discovery, systems metabolomics (MS- or NMR-based), and translational studies linking molecular signatures to clinical outcomes. Contributions that advance mechanistic insight, predictive modeling, and precision medicine in cardiometabolic health are particularly encouraged.
Dr. Rui Wang-Sattler
Dr. Makoto Harada
Guest Editors
Manuscript Submission Information
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Keywords
- cardiometabolic disease
- metabolic syndrome
- OMICs integration
- metabolomics
- biomarker discovery
- machine learning
- explainable AI
- metabolic stress
- inflammation
- fibrosis
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