Introduction

Arrhythmia is a disorder of the origin and/or conduction of cardiac activity resulting in an abnormal rate and/or rhythm of the heartbeat, which may result in sudden death from a sudden onset, or may continue to involve the heart and cause it to fail [1,2,3]. Arrhythmias can be categorized into bradyarrhythmias and tachyarrhythmias according to the heart rate, with bradyarrhythmias mainly characterized by bradycardia and atrioventricular block, and tachyarrhythmias mainly characterized by tachycardia, supraventricular tachycardia, ventricular fibrillation, and atrial fibrillation [4]. Atrial fibrillation causes hemodynamic abnormalities and increases the morbidity and mortality of thromboembolic events, while ventricular arrhythmias can lead to palpitations or blackouts and even sudden cardiac death, as all types of arrhythmias add to the global economic burden [5,6,7].

Gut flora is a group of microorganisms that are planted in the human intestinal tract and are interdependent with the human body over a long period of time, which is very large in number and is known as the "second genome" of the human body [1) [10, 21]. Data from SNPs with chained unbalanced aggregates were subsequently removed, with removal conditioned on LD (r2 < 0.001, distance = 10,000 kb). SNPs that did not belong to a specific bacterial trait were excluded. Serum metabolite data were downloaded from the GWAS data, and a total of 575 metabolite-associated exposures were collected, and SNPs were screened based on P < 1 × 10–5, r2 < 0.001, distance < 10 000 kb (Supplementary Table S2) [22].

Outcome sources

Data on atrial fibrillation were derived from a GWAS of susceptibility genes published in 2018 (PMID: 30,061,737), the study included 60,620 patients with atrial fibrillation, 970,216 healthy controls, and contained 33,519,037 SNPs [23]. Data for the remaining types of arrhythmic disease were obtained from studies in the UK Biobank and Finnish databases, with studies of supraventricular tachycardia comprising 1,306 patients, 461,704 healthy controls, and 9,851,867 SNPs. studies of bradycardia comprised 1,005 patients with supraventricular tachycardia, 462,005 healthy controls, and 9,851,867 SNPs. studies of bradycardia comprised 1,254 patients, 461,756 healthy controls, and 9,851,867 SNPs. The study of bradycardia contained 1,254 patients, 461,756 healthy controls, and 9,851,867 SNPs. The study of atrioventricular block contained 5,536 patients, 286,109 healthy controls, and 16,380,173 SNPs. The study of left bundle branch block consisted of 1,918 patients, 286,109 healthy controls, and 16,380,167 SNPs. The study of right bundle branch block consisted of 9,545 patients, 286,109 healthy controls, and 16,380,175 SNPs. The study of right bundle branch block consisted of 9,545 patients, 286,109 healthy controls, and 16,380,175 SNPs (Table 1). In this study, the diagnostic criteria for all types of arrhythmias adhere to the International Classification of Diseases, Tenth Revision (ICD-10).

Table 1 Source of GWAS data for all types of arrhythmias
Full size table

Statistical methods

In this study, the association between gut flora and various types of arrhythmias was analyzed by inverse variance weighting (IVW). The mean IVW values of SNP ratio estimates were derived by regressing SNP—gut flora on SNP—arrhythmia associations. The weighted median method (WME), MR-Egger regression test, Simple mode, and Weighted mode were used as supplements. Cochrane's Q was used to test the snp-related heterogeneity of each bacterial trait. In addition, sensitivity analyses were performed using the MR-Egger intercept test and leave-one-out analysis. p-values from the MR-Egger intercept test were used as an indicator of horizontal pleiotropy (p < 0.05 statistically significant). Leave-one-out analysis was used to identify potential pleiotropic effects from individual SNPs.

Results

Causal relationship between gut flora and various types of cardiac arrhythmias

Causal relationship between gut flora and atrial fibrillation

IVW analysis showed that family Family XIII id.1957 (OR = 0.87, 95% CI: 0.80-0.0.94, p = 0.0005) and phylum Lentisphaerae id.2238 (OR = 0.93, 95% CI: 0.89–0.97, p = 0.0007) and other 10 gut flora were negatively associated with the development of atrial fibrillation. genus Lachnospiraceae FCS020 group id.11314 (OR = 1.08, 95% CI: 1.01–1.15, p = 0.0209) and genus Streptococcus id.1853 (OR = 1.09, 95% CI: 1.01–1.18, p = 0.0299) and 4 other gut flora were positively associated with the development of atrial fibrillation (Supplementary Table S3) (Fig. 2A).

Fig. 2
figure 2

Causal relationship between gut flora and various types of cardiac arrhythmias. A Causal relationship between gut flora and atrial fibrillation. B Causal relationship between metabolites and supraventricular tachycardia. C Causal relationship between gut flora and tachycardia. D Causal relationship between gut flora and bradycardia. E Causal relationship between gut flora and atrioventricular block. F Causal relationship between gut flora and left bundle branch block

Full size image

Causal relationship between gut flora and supraventricular tachycardia

IVW analysis showed that Family XIII id.1957 (OR = 0.99, 95% CI: 1.00–1.00, p = 0.0099) and phylum Lentisphaerae id.2238 (OR = 0.99, 95% CI: 0.99–1.00, p = 0.01659) and other 8 gut flora were positively associated with the development of supraventricular tachycardia (Supplementary Table S4) (Fig. 2B).

Causal relationship between gut flora and tachycardia

IVW analysis showed that 8 species of gut flora including family Desulfovibrionaceae id.3169 (OR = 0.99, 95% CI: 0.99–1.00, p = 0.0040) and genus Ruminococcaceae UCG013 id.11370 (OR = 1.01, 95% CI:1.00- 1.01, p = 0.0193) were positively associated with the development of tachycardia (Supplementary Table S5) (Fig. 2C).

Causal relationship between gut flora and bradycardia

IVW analysis showed that 6 gut flora including genus Christensenellaceae R 7group id.11283 (OR = 0.99, 95% CI: 0.99–0.99, p = 0.0096) and genus Escherichia Shigella id.3504 (OR = 1.00, 95% CI. 1.00–1.00, p = 0.0210) were positively associated with the development of bradycardia (Supplementary Table S6) (Fig. 2D).

Causal relationship between gut flora and atrioventricular block

IVW analysis showed that 2 gut flora, including genus Lachnospira id.2004 (OR = 0.56, 95% CI: 0.38–0.81, p = 0.0024) and genus Clostridium sensustricto1 id.1873 (OR = 0.61, 95% CI: 0.40–0.93, p = 0.0210) (Supplementary Table S7) (Fig. 2E).

At the same time, we found that the causal relationship between gut flora and the organization of left bundle branch block and atrioventricular block obtained by our analysis was almost the same(Supplementary Table S8) (Fig. 2F).

Causal relationship between metabolites and various types of arrhythmias

Causal relationship between metabolites and atrial fibrillation

IVW analysis showed that 12 metabolites, including Tryptophan betaine (OR = 0.83, 95% CI: 0.76–0.90, p = 0.0001) and Uridine (OR = 0.58, 95% CI: 0.40–0.84, p = 0.0037) were negatively associated with the development of AF. 14 metabolites including Docosahexaenoate (DHA; 22:6n3) (OR = 1.33, 95% CI:1.04–1.70, p = 0.0252) and Carnitine (OR = 1.31, 95% CI:1.02–1.69, p = 0.0348), were positively associated with the onset of AF (Supplementary Table S9) (Fig. 3A).

Fig. 3
figure 3

Causal relationship between metabolites and atrial fibrillation and supraventricular tachycardia. A Causal relationship between metabolites and atrial fibrillation. B Causal relationship between metabolites and supraventricular tachycardia

Full size image

Causal relationship between metabolites and supraventricular tachycardia

IVW analysis showed that 3 metabolites, Isobutyrylcarnitine (OR = 0.99, 95% CI: 0.99–1.00, p = 0.0003) and Pipecolate (OR = 0.99, 95% CI: 0.99–1.00, p = 0.0275) were negatively associated with the development of supraventricular tachycardia. Triglycerides in large VLDL (OR = 1.33, 95% CI: 1.00–1.00, p = 0.0131) and total cholesterol in small LDL (OR = 1.00, 95% CI: 1.00–1.00, p = 0.0016) and 47 other metabolites were positively associated with the development of supraventricular tachycardia (Supplementary Table S10) (Fig. 3B).

Causal relationship between metabolites and tachycardia

IVW analysis showed that 4 metabolites, 1-palmitoleoylglycerophosphocholine (OR = 0.99, 95% CI:0.99–1.00, p = 0.0217) and gamma-glutamylisoleucine (OR = 0.99, 95% CI:0.98–1.00, p = 0.0477) were negatively associated with the onset of tachycardia. N2-dimethylguanosine (OR = 1.01, 95% CI:1.00–1.00, p = 0.0376) and X-04499–3,4-dihydroxybutyrate (OR = 1.01, 95% CI: 1.00–1.02, p = 0.0127) and 19 other metabolites were positively associated with the development of tachycardia (Supplementary Table S11) (Fig. 4A).

Fig. 4
figure 4

Causal relationship between metabolites and tachycardia, bradycardia and atrioventricular block. A Causal relationship between metabolites and tachycardia. B Causal relationship between metabolites and bradycardia. C Causal relationship between metabolites and atrioventricular block

Full size image

Causal relationship between metabolites and bradycardia

IVW analysis showed that 4 metabolites, 2-stearoylglycerophosphocholine (OR = 0.99, 95% CI:0.99–1.00, p = 0.0051) and Serotonin (5HT) (OR = 0.99, 95% CI:0.99–1.00, p = 0.0072) were negatively associated with the onset of bradycardia. Erythronate (OR = 1.01, 95% CI: 1.00–1.01, p = 0.0438) and 1-arachidonoylglycerophosphoinositol (OR = 1.01, 95% CI: 1.00–1.01, p = 0.0471) and 15 other metabolites were positively associated with the development of tachycardia (Supplementary Table S12) (Fig. 4B).

Causal relationship between metabolites and atrioventricular block

IVW analysis showed that X-12230 (OR = 0.41, 95% CI: 0.18–0.93, p = 0.0338) was negatively associated with the onset of atrioventricular block, and no metabolite was positively associated with the onset of atrioventricular block (Supplementary Table S13) (Fig. 4C).

Sensitivity analysis

Heterogeneity test: the results of the Q-test showed no heterogeneity between the included SNPs (p > 0.05). Horizontal pleiotropy: The results of MR-Egger regression intercepts showed no horizontal pleiotropy in the associations of gut flora and serum metabolites with the associations with each type of arrhythmia. The absence of SNPs with large effects on effect estimates in the analyses of gut flora and serum metabolites with each type of arrhythmia suggests that a causal relationship exists and that the causal relationship is reasonably stable.

Discussion

There is already a lot of research supporting the theory of a "gut-heart" axis-centred relationship between gut microbes and heart health, which means that the gut flora can influence the host's metabolism, inflammation levels, and immune system, which ultimately affects the heart's health [29,30,31]. Another study found that the abundance of Bifidobacterium and Ruminococcaceae were inversely related to different markers of low-grade inflammation such as hsCRP and interleukin (IL)-6, and Ruminococcaceae ruminantium reduces the inflammatory response by modulating T cell numbers and producing short-chain fatty acids [32, 33]. Therefore, we hypothesised that intestinal flora such as Bifidobacterium and Ruminococcaceae may reduce the risk of AF by suppressing the inflammatory response. Our study identified Clostridium lachnospira and Clostridium sensustricto, bacteria known for their ability to break down carbohydrates. Chronic consumption of high-sugar carbohydrates may lead to insulin resistance, which is associated with the development of chronic inflammation, and therefore we hypothesise that these two bacteria may influence the risk of develo** left bundle branch block and atrioventricular block through this mechanism. Additionally, our study identified anaerobic bacteria like Lachnospira and Rikenellaceae, which may be linked to an increased risk of arrhythmia These anaerobic bacteria break down carbohydrates and produce short-chain fatty acids (e.g., acetic acid, propionic acid, and butyric acid) and alcohols (ethanol, isopropanol, and butanol), which affect the activity of immune cells and inflammatory responses, and the various alcohols they produce are metabolised by alcohol, causing cardiomyocyte damage and death, which ultimately leads to alterations in cardiac structure and function [34, 34, 38, 39]. Immunocytes such as macrophages are normally present in large numbers right in the heart, and the inflammatory response can regulate calcium homeostasis and connexins through pathways such as CXCR4 and TYROBP and cause changes in atrial electrophysiology and structural substrates, as well as affecting the resting membrane potential and action potential of cardiomyocytes [40,41,42]. Flora such as Bifidobacterium and Lactobacillus were identified in this study, which have been shown to be involved in modulating the immune response and may therefore influence the risk of arrhythmia development from this mechanism [43, 44].

Subsequently, we co-analysed the MR results of serum metabolomics with those of intestinal flora and found similarities between the trends of some intestinal flora and those of serum metabolites, which may collectively affect the risk of arrhythmia development. The heart is controlled by the autonomic nervous system, and a variety of neurotransmitters affect the electrophysiological properties of cardiac cells, including the duration and conduction velocity of action potentials. The present study found that serum metabolites such as tryptophan, isoleucine and valine may be closely associated with the risk of AF. Branched-chain amino acids such as tryptophan are one of the raw materials for synthesising neurotransmitters and it has been demonstrated that there is a close correlation between the relationship between elevated branched-chain amino acids and cardiac arrhythmias, so our study further provides some theoretical support for this conclusion [45]. At the same time, metabolites such as tryptophan are intricately linked to inflammatory regulation and immune modulation, as part of the metabolism of tryptophan takes place in the gut [46]. In the results of previous MR gut flora analyses, Bifidobacteria, Anaerobacteria, and Odorobacteria were found to be potentially protective against certain types of cardiac arrhythmias, and all of these flora were shown to have a strong relationship with tryptophan metabolism [40]. In addition, our analyses revealed that uridine may be strongly associated with the risk of develo** AF. It has been suggested that uridine may modulate the inflammatory response by inhibiting inflammatory cell activity and other pathways, but whether it affects the risk of develo** AF through this pathway deserves further exploration [47, 48]. Lactate and valine are both common serum metabolites that were similarly found in this study to potentially influence the risk of develo** atrial fibrillation. It has been demonstrated that lactate accelerates vascular calcification and leads to mitochondrial dysfunction, and that valine exhibits antiarrhythmic effects in ischaemia–reperfusion experiments, mechanisms of action that may be closely related to cardiomyocyte apoptosis and inflammatory responses [49,50,51]. Meanwhile, combined with the results of our intestinal flora MR analyses, it was demonstrated that flora such as Bactericide and genus Odoribacter are strongly associated with lactate and valine, but the mechanism of action in disease remains unclear [52]. In addition, we have identified serum metabolites that may be associated with the risk of develo** supraventricular tachycardia, bradycardia, tachypnea, and atrioventricular block, but the effects may not be as closely related as those of atrial fibrillation.

Prospects and limitations of clinical applications

Based on the aforementioned research findings, the potential roles of gut microbiota and serum metabolites in arrhythmias reveal promising prospects for future clinical applications. Initially, alterations in specific gut microbiota and serum metabolites may serve as biomarkers for the early detection of arrhythmias. Furthermore, understanding how particular gut microbiota influence arrhythmias can aid in the development of personalized treatment plans. For instance, modifying one's diet to alter microbial composition, such as increasing probiotics, prebiotics, or certain types of fiber, might reduce the risk of atrial fibrillation if an individual's mouth and gut flora indicates a higher predisposition [53, 54]. Additionally, since gut microbiota can affect inflammation and immune responses through various mechanisms, they might present novel therapeutic targets for arrhythmia management. Despite this field offering many promising directions, current research is primarily still in the exploratory stage.

Limitations of this study include: Firstly, the research data encompasses only subjects of European descent, potentially limiting applicability to other populations. Secondly, although this is the first use of MR to investigate potential causal relationships between gut microbiota, metabolomics, and various types of arrhythmias, most conclusions are still theoretical. Further extensive research is required to establish specific causal links, evaluate intervention effectiveness, and consider inter-individual differences. Lastly, some gut microbiota and serum metabolites are still in the naming phase, with specific bacteria not yet definitively identified.

Conclusion

Gut flora and serum metabolites may influence the risk of develo** arrhythmias, but finding key gut flora and serum metabolites and exploring their specific mechanisms still requires extensive experimental validation.