ReviewJournal of clinical medicine2024
Metabolomics in Atrial Fibrillation: Unlocking Novel Biomarkers and Pathways for Diagnosis, Prognosis, and Personalized Treatment.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.Frontiers in genetics · 2026Pooled it
- Metabolic Signatures in Pericardial Fluid and Serum Are Associated with Long-Term Restenosis After Isolated Coronary Artery Bypass Grafting.Journal of clinical medicine · 2026Article
- The gut-heart axis in atrial fibrillation: Pathophysiology, evidence, and therapeutic potential.Heart rhythm O2 · 2026Review
- Metabolic Drivers of Valve Calcification and Atrial Remodeling in Calcific Aortic Stenosis.Biomolecules · 2026Review
- Acupuncture and Metabolic-Mitochondrial Remodeling in Atrial Fibrillation: A Systematic Review and Research Recommendations.International journal of general medicine · 2026Review
- Metabolic Remodeling and Mitochondrial Stress in Atrial Fibrillation: Mechanisms and Translational Targets.Reviews in cardiovascular medicine · 2025Review
- Atrial Fibrillation in Diabetes: Pathogenesis and Targeted Rhythm Control Strategies.Current issues in molecular biology · 2025Review
- Multi-omics approaches for biomarker discovery and precision diagnosis of prediabetes.Frontiers in endocrinology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesAtrial fibrillation (AF) is the most frequent arrhythmia in the adult population associated with a high rate of severe consequences leading to significant morbidity and mortality worldwide. Therefore, its prompt recognition is of high clinical importance. AF detection often remains challenging due to unspecific symptoms and a lack of reliable biomarkers for its prediction. Herein, novel bioanalytical methodologies, such as metabolomics, offer new opportunities for a better understanding of the underlying pathological mechanisms of cardiovascular diseases, including AF. The metabolome, considered a complete set of small molecules present in the organism, directly reflects the current phenotype of the studied system and is highly sensitive to any changes, including arrhythmia's onset. A growing body of evidence suggests that metabolite profiling has prognostic value in AF prediction, highlighting its potential role not only in early diagnosis but also in guiding therapeutic interventions. By identifying specific metabolites as a disease biomarker or recognising particular metabolomic pathways involved in the AF pathomechanisms, metabolomics could be of great clinical value for further clinical decision-making, risk stratification, and an individual personalised approach. The presented narrative review aims to summarise the current state of knowledge on metabolomics in AF with a special emphasis on its implications for clinical practice and personalised medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.