Lipidomics and Metabolic Biomarkers in Heart Disease
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 11
Special Issue Editors
Interests: forensic pathology; cardiovascular pathology; sudden cardiac death; forensic toxicology; ischaemic heart disease; metabolomics
Special Issue Information
Dear Colleagues,
Metabolic and lipidomic biomarker research is rapidly reshaping our understanding of heart disease, offering transformative opportunities for improved risk assessment, early diagnosis, and personalized patient management. Advances in lipidomics and metabolomics provide powerful tools for unraveling the complexity of cardiovascular health and disease, extending far beyond traditional metrics to illuminate a diverse range of molecular signatures and pathways involved in heart disease progression and outcomes.
This Special Issue invites original research articles, comprehensive reviews, and methodological papers focused on the role of lipidomic and metabolic biomarkers in all aspects of heart disease, including—but not limited to—coronary artery disease, heart failure, arrhythmias, acute ischemic events, and cardiometabolic risk. Of special interest are studies examining molecular and metabolic mechanisms underlying acute and chronic presentations and diagnoses, alongside innovative approaches to biomarker integration in clinical care and forensic investigations.
We welcome multidisciplinary submissions spanning experimental, translational, and clinical research, covering topics such as the following:
- Advanced analytical and computational platforms for lipidomic profiling in cardiovascular disease;
- Metabolomics-based strategies for biomarker discovery in heart disease;
- Integrative studies linking lipidomic, metabolomic, genetic, and proteomic data;
- Clinical and forensic investigations into metabolic predictors and disease mechanisms in acute and chronic heart conditions;
- Population studies and precision medicine approaches applied to cardiovascular risk management.
By gathering diverse research approaches, this Special Issue aims to foster collaboration across cardiovascular and allied scientific disciplines, promote new avenues for biomarker-driven risk prediction, and facilitate the integration of lipidomic and metabolic data into clinical and pathological practice for improved outcomes in heart disease.
Dr. Davide Radaelli
Dr. Matteo Scopetti
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- lipidomic
- metabolomic
- biomarkers
- cardiovascular disease
- risk stratification
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