Clinical & Experimental Cardiology
Open Access

Metabolomic Profiling in Acute Pericarditis for Differential Diagnosis Biomarkers

Peige Jalkanen^{* *}Correspondence: Peige Jalkanen, Department of Cardiology, University of Sao Paulo, Sao Paulo, Brazil, Email:

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Description

Acute pericarditis is an inflammatory condition of the pericardium, the fibroelastic sac surrounding the heart, which can manifest as chest pain, pericardial friction rub, and characteristic electrocardiographic changes. Despite its clinical significance, acute pericarditis remains a diagnostic challenge due to its overlapping presentation with other cardiovascular and systemic conditions, such as myocardial infarction, myocarditis, pulmonary embolism, and systemic inflammatory diseases. Traditional diagnostic tools, including electrocardiography, echocardiography, inflammatory markers such as C-reactive protein and erythrocyte sedimentation rate, and imaging, provide important information but often lack specificity or fail to detect subclinical forms. Consequently, there is growing interest in the application of metabolomic profiling as a tool for identifying novel biomarkers to enhance diagnostic precision and guide therapeutic strategies.

Metabolomics is the comprehensive study of low-molecularweight metabolites in biological systems. Unlike genomics or proteomics, which provide information about potential functional changes, metabolomics reflects the real-time biochemical state of an organism. Metabolite profiling allows for the detection of dynamic changes in metabolic pathways, providing insights into pathophysiological processes associated with disease. Advanced analytical platforms, including nuclear magnetic resonance spectroscopy, liquid chromatography-mass spectrometry, and gas chromatography-mass spectrometry, facilitate high-throughput detection and quantification of hundreds to thousands of metabolites simultaneously. In the context of cardiovascular disease, metabolomics has demonstrated potential in uncovering biomarkers for heart failure, atherosclerosis, myocardial infarction, and arrhythmias. However, its application in acute pericarditis remains relatively underexplored, despite the condition’s heterogeneous etiology, which includes viral, bacterial, autoimmune, post-infarction, and idiopathic forms. By identifying disease-specific metabolic fingerprints, metabolomics could not only differentiate pericarditis from mimicking conditions but also shed light on the underlying pathophysiology.

Acute pericarditis involves a cascade of inflammatory processes, oxidative stress, and cellular injury, all of which induce characteristic metabolic alterations. Several potential metabolic pathways may be perturbed. Inflammation induces alterations in amino acid levels, particularly tryptophan, glutamine, and arginine. Tryptophan catabolism via the kynurenine pathway is often upregulated in inflammatory conditions, which could serve as a biomarker for immune activation in pericarditis. Perturbations in glycolysis, the tricarboxylic acid cycle, and fatty acid β-oxidation have also been observed in systemic inflammation. Elevated lactate, altered acylcarnitines, and changes in TCA intermediates may reflect metabolic stress and myocardial-pericardial crosstalk. In addition, inflammatory cytokines can modify phospholipid and sphingolipid metabolism. Changes in lysophosphatidylcholine, sphingosine-1-phosphate, and ceramide species may indicate pericardial inflammation and could differentiate pericarditis from ischemic heart disease. Reactive oxygen species accumulation in inflamed pericardial tissue alters redox-sensitive metabolites such as glutathione, cysteine, and malondialdehyde, which might serve as adjunctive markers of inflammatory burden.

A critical clinical challenge in acute pericarditis is distinguishing it from conditions that mimic its presentation. Metabolomic profiling offers a novel approach to address this problem. While both pericarditis and myocardial infarction may present with chest pain and electrocardiographic changes, myocardial infarction is associated with more pronounced perturbations in cardiac energy metabolites such as creatine phosphate, succinate, and branched-chain amino acids due to myocardial necrosis. In contrast, pericarditis may exhibit elevated metabolites related to systemic inflammation without extensive myocardial energy depletion. Pulmonary embolism can induce hypoxia-driven metabolic changes, including increased lactate, pyruvate, and free fatty acids. Pericarditis, on the other hand, may show a distinct inflammatory lipid profile, including altered phospholipids and eicosanoids, without the hypoxia signature. Furthermore, viral pericarditis may be characterized by metabolites reflecting viral-host interactions, such as altered nucleotide and polyamine metabolism, while autoimmune pericarditis might involve specific amino acid and lipid changes associated with immune dysregulation. By integrating metabolomic signatures with clinical data, it is possible to generate a metabolic fingerprinting algorithm that can improve diagnostic accuracy, particularly in atypical presentations.

Despite its potential, translating metabolomic profiling into clinical practice faces several challenges. Metabolite levels can be affected by diet, circadian rhythms, medications, and comorbidities, making standardized collection, storage, and processing protocols essential for reproducible results. Metabolomics generates large datasets that require sophisticated bioinformatic analysis, and machine learning and multivariate statistical models are critical for identifying disease-specific metabolic patterns. Biomarkers identified in exploratory studies must also be validated in large, multi-center cohorts, and reproducibility across diverse populations is essential before clinical adoption. Additionally, advanced metabolomic techniques remain expensive and are currently limited to research settings, necessitating simplified, targeted assays for validated biomarkers to enable routine clinical use.

Conclusion

Metabolomic profiling holds considerable promise in the field of acute pericarditis. By capturing real-time biochemical alterations, it provides a novel avenue for identifying differential diagnostic biomarkers and understanding disease mechanisms. While challenges related to standardization, data analysis, and clinical validation remain, ongoing advances in analytical technology, bioinformatics, and systems biology are likely to accelerate the clinical translation of metabolomics. In the future, metabolomic fingerprinting could become an essential component of the diagnostic and therapeutic landscape in pericardial diseases, improving diagnostic accuracy, patient outcomes, and overall cardiovascular care.