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International Journal of Cardiology. Heart & Vasculature logoLink to International Journal of Cardiology. Heart & Vasculature
. 2024 Oct 16;55:101531. doi: 10.1016/j.ijcha.2024.101531

Risk of atrial fibrillation in patients with inflammatory bowel disease: A systematic review and meta-analysis

Yangyang Ke a,b,1, Wengen Zhu a,1,⁎, Wulamiding Kaisaier a,1, Yili Chen a
PMCID: PMC11795685  PMID: 39911609

Highlights

  • •

    The prevalence of AF in IBD patients is 6.23 %.

  • •

    The incidence of AF in IBD patients is 3.53 %.

  • •

    IBD patients have an elevated risk of AF compared to the general population.

  • •

    CD patients have a higher AF incidence compared to UC patients.

Keywords: Atrial fibrillation, Inflammatory bowel disease, Prevalence, Incidence, Meta

Abstract

Background

Several studies have reported the association between inflammatory bowel disease (IBD) and the risk of atrial fibrillation (AF). This systematic review and meta-analysis aimed to determine the prevalence and incidence of AF in the IBD population.

Methods

We conducted a systematic search of the PubMed and Embase databases for relevant studies published up to February 2024. We used the random-effects model to pool the prevalence and incidence rates of AF among IBD patients. The subgroup analyses were performed according to the IBD type.

Results

A total of twenty-five studies were included. The pooled prevalence of AF among IBD patients was 6.23 % (95 % confidence interval [CI]: 4.99 %−7.47 %). The incidence rate of AF among IBD patients was 3.53 % (95 % CI: 0.57 %−6.48 %). The risk of developing AF in IBD patients was 1.45 times higher than that in the general population (risk ratio [RR]: 1.45, 95 % CI: 1.21–1.73). When comparing specific IBD types to the general population, the RR was 1.35 (95 % CI: 1.11–1.64) for CD and 1.17 (95 % CI: 1.11–1.23) for UC.

Conclusions

Our findings suggest that IBD patients exhibit an increased risk of developing AF compared to the general population. CD patients have a higher AF incidence compared to UC patients.

1. Introduction

Inflammatory bowel disease (IBD) is characterized by chronic, recurrent inflammation of the gastrointestinal tract. Its extraintestinal manifestations may affect multiple systems and significantly impact the quality of life of the patient [1]. Epidemiological data reveal a rapid rise in the incidence and prevalence of IBD worldwide, particularly in Eastern Asia [2]. Atrial fibrillation (AF), the most common arrhythmia in clinical practice, has an estimated global prevalence of 50 million in 2020 [3], underscoring its significant public health impact. Several studies have shown that AF increases mortality, mean length of stay, mean total cost, and incidence of complications such as mechanical ventilation, sepsis, and acute respiratory failure in IBD hospitalized populations, suggesting a poor prognosis for IBD complicated by AF [4]. Therefore, the prevention and treatment of AF complications in IBD patients is crucial to improve prognosis of these patients.

Current studies have found that chronic inflammation inherent in IBD plays a role in the pathogenesis of AF [5], [6], [7], suggesting a potential epidemiological link between the two diseases. However, existing studies on the association between IBD and AF are limited. Therefore, our study conducted a comprehensive systematic review and meta-analysis, expanding the scope of existing literature, to assess the links between IBD and AF prevalence and incidence.

2. Methods

This review was conducted using standard methods in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [8].

2.1. Search strategy

We conducted a systematic search of the PubMed and Embase databases for articles published from their inception until February 2024. The MeSH terms 'atrial fibrillation' and 'inflammatory bowel disease' and its related keywords were used in the search strategies. In addition, we screened the reference lists of included studies for additional studies that met the inclusion criteria. The search strategies are shown in Supplementary Table 1.

2.2. Inclusion and exclusion criteria

We included studies that fulfilled the following criteria: i) Population: individuals with a diagnosis of IBD, encompassing either Crohn's disease (CD) or ulcerative colitis (UC). ii) Studies reporting on the prevalence or incidence rates of AF. The prevalence of AF was ascertained as the proportion of confirmed AF cases within the IBD patient cohort during the study's period. The incidence of AF was measured as the rate at which patients initially free from AF developed the AF condition over a defined time frame. iii) Study design: observational studies such as cross-sectional, case-control, and cohort studies, were considered eligible. We excluded case reports, review articles, editorials, letters, and case series that included five or fewer patients.

2.3. Study selection and data extraction

Two researches independently checked the titles and abstracts of retrieved records from the electronic databases. The duplicated studies were deleted, and studies that did not meet the inclusion criteria were also excluded. For the potentially relevant studies, we screened their full texts and selected the final included studies according to the predefined inclusion criteria. Disagreements were resolved through mutual discussion or consultation with senior researchers.

All data were extracted by two researchers. We extracted publication details such as study authors and years, country, enrollment period, study design type, follow-up duration, and details such as sample size, age, gender (female percentage), body mass index, number of AF cases, prevalent comorbidities (e.g., hypertension, diabetes mellitus, hyperthyroidism, dyslipidemia, obesity, smoking, cerebrovascular accident, heart failure) and effect estimates in the IBD and control groups.

2.4. Quality assessment

The quality of observational studies was assessed using the Newcastle-Ottawa Scale (NOS) tool, which was assessed based on 3 factors: method of subject selection, comparability of study groups, and outcomes of interest. An NOS of ≥6 points was considered moderate-to-high quality, otherwise low quality [9].

2.5. Statistical analysis

Statistical heterogeneity across the included studies was assessed using the I2 statistic, where an I2 value exceeding 50 % indicated substantial heterogeneity. For the prevalence analysis, we extracted the total sample size and the number of AF cases within the IBD population, which allowed us to calculate the prevalence estimates. In the incidence analysis, we identified the total number of individuals and the number of new AF cases in the IBD population to determine the incidence rates. Additionally, we extracted adjusted effect estimates, specifically the risk ratios (RRs) with its corresponding 95 % confidence intervals (CIs), from both the IBD patients and the general population. These data were pooled using the random-effects model.

The subgroup analyses were conducted for the CD and UC subgroups. Sensitivity analysis was conducted to assess the influence of each individual study on the overall results, employing a one-study-removed approach. The potential for publication bias was evaluated using Egger's test. All statistical analyses were conducted using Stata (version 16.1).

3. Results

Our initial search identified 767 records, including 92 from PubMed and 675 from Embase. Among them, 84 citations were duplicates, and 621 articles were excluded based on title and abstract screening. During the full-text check, additional 37 articles were eliminated due to inappropriate article type (n = 11) and unavailability of data (n = 26). Finally, a total of 25 studies were included in this meta-analysis [4], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33]. Five of these studies were used to analyze the incidence of AF in IBD, whereas 20 were used to analyze the prevalence of AF in IBD. The results of our search are detailed in the PRISMA flowchart (Fig. 1).

Fig. 1.

Fig. 1

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PRISMA Flowchart Depicting the Study Selection Process for This Meta-Analysis.

3.1. Baseline characteristics of patients

The baseline characteristics of the 25 studies that met the inclusion criteria are shown in Table 1. All studies were observational, with 4 prospective studies and 21 retrospective studies. The studies, published between 2009 and 2023, encompassed 18 from North America, 4 from Europe, and 3 from Asia. The meta-analysis included a total of 9,433,573 IBD patients and 759,267 matched controls, with an average age ranging from 30.4 to 56.4 years old and a female proportion varying from 29.7 % to 63.4 %. Eleven studies differentiated between CD and UC, with a total of 3,387,945 patients having CD and 2,232,689 patients having UC. Follow-up ranged from one year to 12.8 years. The total score of the included studies ranged from 3 to 9 as shown in Supplementary Table 2.

Table1.

Characteristics of the included studies.

Study, Year Country Enrollment period Study design Groups Sample size, n Mean age, y Female, % BMI, kg/m2 AF, n Hypertension, n Diabetes, n Hyperthyroidism, n Dyslipidemia, n Obesity, n Smoking, n CVA, n HF, n Adjusted RR Follow-up, years
Wang et al.,2009[10] United States 1979–2003 Retrospective study UC 8182 49.6 53.1 NR 232 NR NR NR NR 105 NR 25 NR NR 11
Kathpalia et al.,2013[11] United States 2001.10.1–2011.10.1 Retrospective study IBD 447 NR NR NR 49 NR NR NR NR NR NR NR NR NR NR
Chung et al.,2015[12] China 2000–2010 Retrospective study IBD 11445 53.5 ± 19.5 45.6 NR 267 2979 1746 NR NR NR NR 1335 1577 NR NR
Pattanshetty et al., 2015[13] United States 2001.1–2010.12 Retrospective study IBD 142 56.4 ± 15.4 63.4 28.9 ± 11.8 16 38 12 NR NR NR 65 7 NR NR NR
Baek et al.,2016[14] South
Korea		 2005–2015 Retrospective study IBD 3143 36.5 ± 17.1 40.9 NR 35 261 147 NR 152 6 NR NR 20 1.02(0.71–1.46) 6.8 ± 4.5
Khan et al.,2018[15] United States 2002–2014 Retrospective study IBD 677678 NR 51.4 NR 46676 NR NR NR NR NR NR NR NR NR NR
UC 243889 NR 48.6 NR 21590 NR NR NR NR NR NR NR NR NR
CD 433789 NR 53 NR 24726 NR NR NR NR NR NR NR NR NR
Panchal et al.,2019[16] United States 2012–2014 Retrospective study IBD 249045 NR NR NR 9713 NR NR NR NR NR NR NR NR NR NR
Ghoneim et al.,2020[17] United States 1994.9–2019.9 Retrospective study IBD 261890 NR 60.2 NR 17700 10397 5290 NR NR NR NR 11200 NR NR NR
Mubasher et al.,2020[18] United States 2012–2014 Retrospective study IBD 847235 52.45 56.91 NR 72565 265940 138090 NR 171285 81040 253100 19690 66020 0.86(0.85–0.88) NR
Rivington et al.,2020[19] United States 1999–2019 Retrospective study IBD 304050 NR NR NR 22890 NR NR NR NR NR NR NR NR NR NR
Lodhi et al.,2021[20] United States 2010–2020 Retrospective study IBD 8066 NR 52.1 NR 677 3162 471 NR NR NR NR 337 876 NR 5.7
Kichloo et al.,2021[21] United States 2018 Retrospective study IBD 92055 46.1 51.44 NR 3900 21953 9003 NR 13348 9157 17012 499 3091 NR NR
Rahman et al.,2021[22] United States 2015.10–2017.12 Retrospective study IBD 714863 50.8 ± 19.5 55.2 NR 64599 223063 117242 NR 295157 80711 NR NR 50274 NR NR
Saggi et al.,2022[23] United States 2017 Retrospective study CD 30212 NR NR NR 1234 8871 5621 NR 5994 NR NR NR 3279 NR NR
Horta et al.,2022[24] United States 2010.6–2015.6 Retrospective study UC 2138 NR 51,2 NR 134 NR NR NR NR NR NR 102 NR NR NR
Keller et al.,2022[25] Germany 2005–2018 Retrospective study CD 333975 38 ± 14 56 NR 5835 43933 NR NR 8386 NR NR 212 4203 NR NR
aedma et al.,2022[26] United States 2003–2017 Retrospective study IBD 3560,159 NR NR NR 280909 NR NR NR NR NR NR NR NR NR NR
UC 1324746 NR NR NR 131795 NR NR NR NR NR NR NR NR NR
CD 2235,413 NR NR NR 149114 NR NR NR NR NR NR NR NR NR
Dasu et al.,2022[27] United States 2016–2019 Retrospective study IBD 874285 NR NR NR 53642 NR NR NR NR NR NR NR 184851 NR NR
UC 563700 48.5 ± 2.1 56 NR 34386 NR NR NR NR NR NR NR 81737 NR
CD 310585 39.5 ± 1.42 37 NR 19256 NR NR NR NR NR NR NR 103114 NR
Mahfouz et al.,2022[4] United States 2016–2019 Retrospective study IBD 27165 50.66 50.43 NR 2045 9119 2146 NR NR NR NR NR 1319 NR NR
Soni et al.,2023[28] United States 2016–2019 Retrospective study IBD 1272260 NR 56.23 NR 135960 564345 225505 NR 304900 154750 320785 NR 142925 NR NR
Kristensen et al., 2014[29] Denmark 1996.1.1–2011.12.31 Prospective study Control 236275 43.2 ± 18.7 54.1 NR 4390 3085 1896 262 NR NR NR NR 866 6.8
IBD 24499 43.9 ± 19.1 53.9 NR 685 816 477 85 NR NR NR NR 249 1.26(1.16–1.36)
UC 17831 NR NR NR 544 NR NR NR NR NR NR NR NR 1.23(1.12–1.35)
CD 6668 NR NR NR 141 NR NR NR NR NR NR NR NR 1.42(1.19–1.70)
Choi et al.,2019[30] South Korea 2010.1.1–2014.12.31 Prospective study Control 113088 39.42 ± 16.4 39 NR 772 13732 5306 1248 7929 NR NR NR NR 4.87 ± 1.28
IBD 37696 39.4 ± 16.4 39 NR 348 4098 1483 750 2527 NR NR NR NR 1.36(1.20–1.55)
UC 25347 43.8 ± 15.6 43.6 NR 253 3503 1223 526 2166 NR NR NR NR 1.24(1.07–1.43)
CD 12349 30.4 ± 14.2 29.7 NR 95 595 260 224 361 NR NR NR NR 1.91(1.47–2.49)
Sun et al.,2023[31] Sweden 1969–2017 Prospective study Control 399904 NR NR NR 22248 NR NR NR NR 4133 NR 9542 2464 10
IBD 83877 NR NR NR 5497 NR NR NR NR 1126 NR 2934 1057 NR
UC 46856 43.7 ± 18.4 46.3 NR 3261 4051 2571 NR 1265 461 NR 1419 505 1.12(1.07–1.17)
CD 24954 40.7 ± 19.1 52.2 NR 1450 2366 1332 NR 641 398 NR 840 317 1.12(1.05–1.20)
Starkman et al.,2023[32] United States 2016.1.1–2017.12.31 Retrospective study Control 10000 NR NR NR 129 NR NR NR NR NR NR NR NR 1–6
IBD 9066 NR NR NR 348 NR NR NR NR NR NR NR NR 3.11(2.56–3.86)
Tilly et al.,2023[33] United Kingdom 2006–2022 Prospective study Control NR NR NR NR NR NR NR NR NR NR NR NR NR 12.8
IBD NR NR NR NR NR NR NR NR NR NR NR NR NR NR
UC NR NR NR NR NR NR NR NR NR NR NR NR NR 1.17(1.05–1.31)
CD NR NR NR NR NR NR NR NR NR NR NR NR NR 1.23(1.05–1.45)
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BMI, body mass index; AF, atrial fibrillation; CVA, cerebrovascular accident; HF, heart failure; RR, risk ratio; IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease; NR, not reported.

3.2. Prevalence and incidence of AF in patients with IBD

The prevalence analysis encompassed 9,278,435 patients across 20 studies [4], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], with 719,078 patients diagnosed with AF, resulting in a combined prevalence of 6.23 % (95 % CI: 4.99 %-7.47 %; P < 0.01; Fig. 2). The incidence analysis encompassed 155,138 patients across 4 studies [29], [30], [31], [32], with 6,878 patients diagnosed with AF, resulting in a combined incidence of 3.53 % (95 % CI: 0.57 %-6.48 %; P < 0.01; Supplementary Figure 1).

Fig. 2.

Fig. 2

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Prevalence of Atrial Fibrillation in the Inflammatory Bowel Disease Patients.

3.3. Comparison of AF incidence in IBD versus non-IBD patients

We extracted adjusted effect estimates for the IBD and control groups in five studies [29], [30], [31], [32], [33]. The pooled analysis revealed that the incidence of AF in IBD patients was 1.45 times greater than that in the control group (RR = 1.45; 95 % CI: 1.21–1.73; P < 0.01; Fig. 3). As shown in Fig. 4, the subgroup analysis indicated a 1.35-fold higher risk of AF in CD patients compared to that in the control group (RR = 1.35; 95 % CI: 1.11–1.64; P < 0.01), while UC patients exhibited a 1.17-fold higher risk of AF than that in the control group (RR = 1.17; 95 % CI: 1.11–1.23; P = 0.215), highlighting a higher AF incidence in CD patients than that in UC patients (P < 0.01).

Fig. 3.

Fig. 3

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Risk Ratio of Atrial Fibrillation in Patients with Inflammatory Bowel Disease Compared to the Control Group.

Fig. 4.

Fig. 4

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Comparative Risk Ratio of Atrial Fibrillation in Ulcerative Colitis and Crohn's Disease Patients Relative to a Control Group.

3.4. Sensitivity analysis

The overall prevalence of AF did not significantly change after one study at a time was removed (ranged from 5.97 % [95 %CI:4.78 %-7.16 %] to 6.51 % [95 %CI:5.24 %-7.78 %]). After excluding six studies published as abstracts, the prevalence was 5.87 % (95 %CI:3.79 %-7.95 %). There was also no significant change in the overall incidence of AF after one study at a time was removed (range 2.52 % [95 %CI: 0.95 %-4.09 %] to 4.40 % [95 %CI: 1.48 %-7.31 %]). After excluding one studies published as abstracts, the incidence was 3.42 % (95 %CI: −0.40 %-7.25 %). The pooled RR ranged from 1.22 (95 %CI:1.14–1.31) to 1.56 (95 %CI:1.17–2.09) after excluding one study at a time in the sensitivity analysis.

3.5. Publication bias

There was no evidence of publication bias for the overall prevalence (Egger's test: p = 0.55) and incidence (Egger's test: p = 0.112) of AF among IBD patients. In addition, the overall RR comparison (Egger's test: p = 0.093) between IBD and controls also suggested no significant publication bias.

4. Discussion

In the present meta-analysis, we fully evaluated the prevalence and incidence of AF among individuals with IBD. Our findings indicated that the estimated prevalence of AF within the IBD patient population was 6.23 %, and the estimated incidence was 3.53 %. Notably, the risk of developing AF was 1.45 times greater in IBD patients than that in the control group, underscoring the higher incidence rates of AF among those with IBD as compared to the general population. These results suggest that IBD might be a risk factor for the development of AF. Additionally, the subgroup analyses provided evidence that patients with CD exhibited a higher incidence for AF when compared to those with UC, potentially suggesting that CD may be particularly associated with an increased risk for AF.

Compared with the meta-analysis published by Goyal et al. [34], our current meta-analysis included a larger and more diverse cohort, providing a nuanced view of both the prevalence and incidence of AF in IBD patients. This approach not only mitigates potential biases but also offers a more accurate representation of the epidemiological relationship between IBD and AF risk. Our findings, which report an AF prevalence of 6.23 % and an incidence of 3.53 % among IBD patients, are further strengthened by a stratified analysis that highlights the distinct risk profiles of CD and UC.

We investigated the link between the active stage and phenotype of IBD and the risk of AF, which was explored in two distinct studies. Kristensen et al.’s study [29] showed that the risk of AF escalated during periods of IBD activity, with a 2.63-fold increase (95 % CI: 2.26–3.06) during flares, a 2.06-fold increase (95 % CI: 1.67–2.55) during persistent activity, and no significant difference (0.97, 95 % CI: 0.88–1.08) during remission, compared to the control group. In the study of Sun et al. [31], compared to the control group, IBD patients with different Montreal classifications had different AF risks, with UC patients exhibiting a greater cumulative extent of disease showing a higher susceptibility to AF. In addition, the risk of AF in IBD patients with extraintestinal manifestations was 2.22 times higher than that of the control group. These findings suggest that the risk of AF is related to the degree of IBD activity, the phenotype of the disease, and the presence of extraintestinal manifestations. However, due to the small amount of data available for extraction, we were unable to pool these data for a meta-analysis.

We have examined the effects of drugs used in the treatment of IBD on the risk of AF. The findings of the Choi et al.’s study [30], which analyzed drugs for the treatment of IBD, revealed that patients with IBD who were treated with immunomodulators, systemic corticosteroids, and biologics had 1.45, 1.37, and 2.37 times the risk of AF compared to the control group, respectively. Patients receiving biologics exhibited the highest risk of AF. Kristensen et al [29]. Also found an increased risk of AF in a subgroup of IBD patients treated with biologics. These findings suggest that IBD drugs, especially the increasing use of biologics, may be one of the factors contributing to the increased risk of AF associated with IBD. However, these included studies did not compare the regimens for CD and UC, nor did they address the use of antiarrhythmic medications, underscoring the need for further investigation in subsequent research.

In addition to IBD, Tilly et al.'s research [33] has revealed a link between newly diagnosed AF and a spectrum of autoimmune conditions, including rheumatic fever without cardiac involvement, rheumatoid arthritis, polyarteritis nodosa, systemic lupus erythematosus (SLE), and systemic sclerosis. Additional studies have corroborated that SLE, rheumatoid arthritis, and psoriasis are associated with a heightened risk of AF [35], [36], [37]. This suggests that the pathway through which IBD raises the risk of AF is akin to its role as an inflammatory disorder. The underlying mechanism by which these diseases increase the risk of AF is likely due to chronic inflammation, which can lead to a prothrombotic state, endothelial dysfunction, and accelerated atherosclerosis—hallmarks that are also characteristic of IBD.

We observed from three out of five included studies that comorbidities such as hypertension, diabetes, and heart failure were more frequently reported in IBD patients than in the control group [29], [30], [31]. Consequently, we have emphasized the RR data adjusted for these comorbidities in our analysis. Even after this adjustment, four studies consistently showed a higher risk of AF in IBD patients [29], [30], [31], [33], suggesting that IBD is an independent risk factor of AF. Nevertheless, given that these comorbidities are also established risk factors for AF, they might interact with IBD in the pathogenesis of AF [38], [39], [40]. The current evidence is derived from observational studies, which inherently have limitations and needs further high-quality evidence.

IBD is characterized by chronic inflammation of the gastrointestinal tract, which can lead to systemic inflammation and potentially affect distant organs, including the heart. The relationship between IBD and AF is complex and multifactorial, with inflammation playing a vital role. Central to these processes are cytokines, including C-reactive protein (CRP), interleukin-6 (IL-6), IL-8, and tumor necrosis factor-alpha (TNF-α), along with immune cells and gut microbiota (Supplementary Figure 2). IBD patients often experience an inflammatory response triggered by genetic and environmental factors, which can compromise the integrity of the intestinal barrier. This disruption results in the release of inflammatory cytokines, such as TNF-α, CRP, IL-6, and IL-8, into the bloodstream. These cytokines then circulate systemically, reaching the heart where they can modulate the function of cardiomyocytes [41]. On the one hand, pro-inflammatory cytokines such as IL-6 and TNF can cause alterations in cardiomyocyte gap junctions by regulating the expression of cardiac connexins, resulting in delayed atrioventricular conduction [5], [42], [43]. At the same time, elevated inflammatory factors (e.g., CRP, IL-6) can also act directly on cardiomyocytes, resulting in abnormal calcium signal processing and shortened action potential and Ca2 transients [5], [44]. On the other hand, increased cytokines can also cause structural remodeling of the atrium. Liew et al [45]. Found that TNF activates the TGF-β signaling pathway and myofibroblasts, and increases the secretion of MMP-2 and MMP-9 that mediate atrial remodeling, leading to increased collagen synthesis and atrial fibrosis. These changes cause heterogeneous atrial conduction and atrial dilation, increasing susceptibility to AF.

Limited evidence suggests that immune cells can directly affect the action potential or electrophysiology of cardiomyocytes by forming direct contact. When coupled to spontaneously beating cardiomyocytes through a gap junction containing connexin-43, cardiac macrophages have a negative resting membrane potential and are synchronously depolarized with cardiomyocytes [7]. This suggests that cardiac macrophages can facilitate electrical conduction through the distal atrioventricular node through direct contact and may be one of the causes of inflammation-induced AF [7].

Gut microbes may also be involved in the mechanisms underlying the increased risk of AF associated with IBD. Recent studies have shown that gut microbial metabolites such as lipopolysaccharide (LPS), trimethylamine N-oxide (TMAO), and peptidoglycan (PAGln) can promote the occurrence of AF by exacerbating electrical and structural remodeling, oxidative stress, and autonomic nervous system dysregulation [46]. Due to the intestinal and blood barrier dysfunction in IBD, which is significantly permeable, bacterial products such as LPS can translocate into the systemic circulation [47], [48]. These detrimental gut microbial metabolites may serve as mediators of IBD-induced AF.

4.1. Limitations

There were several limitations to the findings of our study. Firstly, the number of studies included was relatively small, especially studies of incidence. Secondly, our meta-analysis included 25 observational studies, lacking randomized controlled trials, and some comprised conference abstracts yet to undergo formal peer review, potentially introducing bias. Thirdly, there was significant heterogeneity in the prevalence, incidence, and hazard ratio of AF in IBD patients. Although we performed sensitivity analyses, publication bias analyses, and subgroup analyses by types of IBD, the effects of confounding factors inherent in these included studies could not be completely excluded.

5. Conclusion

Current data suggest that patients with IBD are at an elevated risk for developing AF compared to the general population. The incidence is higher in patients with CD than in those with UC.

6. Authors’ contributions

Z-WG and C-YL designed this meta-analysis and made the inclusion and exclusion criteria. K-YY and W-K performed the literature search, study screenings, and data extraction. K-YY and W-K assessed the study quality assessment. K-YY conducted the statistical analyses, and all other authors contributed to the data interpretation and checked the data to ensure accuracy. K-YY finished the first draft, whereas Z-WG and C-YL revised the manuscript of this review. Z-WG edited the manuscript before submission to ensure standard English grammar.

Ethical approval

Not required.

CRediT authorship contribution statement

Yangyang Ke: Writing – original draft, Data curation. Wengen Zhu: Writing – review & editing. Wulamiding Kaisaier: Writing – original draft. Yili Chen: Writing – review & editing.

Funding

This study was funded by the National Natural Science Foundation of China (82370383); Guangdong Natural Science Foundation (2024A1515013289).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcha.2024.101531.

Contributor Information

Yangyang Ke, Email: kyy1605580@163.com.

Wengen Zhu, Email: zhuwg6@mail.sysu.edu.cn.

Wulamiding Kaisaier, Email: wulamid@mail2.sysu.edu.cn.

Yili Chen, Email: chenyil7@mail.sysu.edu.cn.

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (98.5KB, docx)

Data availability

Data will be made available on request.

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Supplementary Materials

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Data Availability Statement

Data will be made available on request.

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