Comprehensive metabolomic characterization of atrial fibrillation

Metabolomics in Atrial Fibrillation: Unlocking Novel Biomarkers and Pathways for Diagnosis, Prognosis, and Personalized Treatment

BACKGROUND/OBJECTIVES

Atrial 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.

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation

The primary cellular substrates of atrial fibrillation (AF) and the mechanisms underlying AF onset remain poorly characterized and therefore, its risk assessment lacks precision. While the use of omics may enable discovery of novel AF risk factors and narrow down the cellular pathways involved in AF pathogenesis, the work is far from complete. Large-scale genome-wide association studies and transcriptomic analyses that allow an unbiased, non-candidate-gene-based delineation of molecular changes associated with AF in humans have identified at least 150 genetic loci associated with AF. However, only few of these loci have been thoroughly mechanistically dissected, indicating that much remains to be discovered for targeted diagnostics and therapeutics. Metabolomics and metagenomics, on the other hand, add to the understanding of AF downstream of the primary substrate and integrate the signalling of environmental and host factors, respectively. These two rapidly developing fields have already provided several correlates of prevalent and incident AF that require additional validation in external cohorts and experimental studies. In this review, we take a look at the recent developments in genetics, transcriptomics, metagenomics, and metabolomics and how they may aid in improving the discovery of AF risk factors and shed light into the molecular mechanisms leading to AF onset.

SGLT2 inhibition, circulating metabolites, and atrial fibrillation: a Mendelian randomization study

BACKGROUND

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have shown promise in reducing the risk of atrial fibrillation (AF). However, the results are controversial and the underlying metabolic mechanism remains unclear. Emerging evidence implied that SGLT2 inhibitors have extra beneficial metabolic effects on circulating metabolites beyond glucose control, which might play a role in reducing the risk of AF. Hence, our study aimed to investigate the effect of circulating metabolites mediating SGLT2 inhibition in AF by Mendelian randomization (MR).

METHODS

A two-sample and two-step MR study was conducted to evaluate the association of SGLT2 inhibition with AF and the mediation effects of circulating metabolites linking SGLT2 inhibition with AF. Genetic instruments for SGLT2 inhibition were identified as genetic variants, which were both associated with the expression of SLC5A2 gene and glycated hemoglobin level (HbA1c). Positive control analysis on type 2 diabetes mellitus (T2DM) was conducted to validate the selection of genetic instruments.

RESULTS

Genetically predicted SGLT2 inhibition (per 1 SD decrement in HbA1c) was associated with reduced risk of T2DM (odds ratio [OR] = 0.63 [95% CI 0.45, 0.88], P = 0.006) and AF (0.51 [0.27, 0.97], P = 0.039). Among 168 circulating metabolites, two metabolites were both associated with SGLT2 inhibition and AF. The effect of SGLT2 inhibition on AF through the total concentration of lipoprotein particles (0.88 [0.81, 0.96], P = 0.004) and the concentration of HDL particles (0.89 [0.82, 0.97], P = 0.005), with a mediated proportion of 8.03% (95% CI [1.20%, 14.34%], P = 0.010) and 7.59% ([1.09%, 13.34%], P = 0.011) of the total effect, respectively.

CONCLUSIONS

This study supported the association of SGLT2 inhibition with a reduced risk of AF. The total concentration of lipoprotein particles and particularly the concentration of HDL particles might mediate this association. Further mechanistic and clinical studies research are needed to understand the mediation effects of circulating metabolites especially blood lipids in the association between SGLT2 inhibition and AF.

Obesity-Related Atrial Fibrillation: Cardiac Manifestation of a Systemic Disease

Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased morbidity and mortality. The mechanisms underlying AF are complex and multifactorial. Although it is well known that obesity is a strong risk factor for AF, the mechanisms underlying obesity-related AF are not completely understood. Current evidence proposes that in addition to overall hemodynamic changes due to increased body weight, excess adiposity raises systemic inflammation and oxidative stress, which lead to adverse atrial remodeling. This remodeling includes atrial fibrosis, atrial dilation, decreased electrical conduction between atrial myocytes, and altered ionic currents, making atrial tissue more vulnerable to both the initiation and maintenance of AF. However, much remains to be learned about the mechanistic links between obesity and AF. This knowledge will power the development of novel diagnostic tools and treatment options that will help combat the rise of the global AF burden among the obesity epidemic.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.