Asthma increased the risk of atrial fibrillation: insights from clinical association to genetic and molecular evidence

Abstract

Background

Despite the high global incidence of asthma and atrial fibrillation (AF), the causal relationship between the two conditions remains unclear. Emerging observational evidence suggests a potential association; however, conflicting results and limited mechanistic insights necessitate a comprehensive evaluation. This study aims to determine the causal relationship between asthma and the risk of AF and to explore the underlying molecular mechanisms.

Methods

We performed a meta-analysis of observational studies to assess the association between asthma and AF risk, followed by a two-sample Mendelian randomization (MR) analysis to infer causality. Bioinformatics analyses using data from the Gene Expression Omnibus (GEO) were conducted to identify differentially expressed genes (DEGs) and enriched pathways. Potential therapeutic targets were validated through drug-gene interaction screening.

Results

The meta-analysis of eight observational studies involving 1,067,774 participants demonstrated that asthma was associated with a 21% increased risk of AF (odds ratio (OR) = 1.21, 95% confidence interval (CI): 1.07–1.36, p = 0.003). Two-sample MR analyses of European populations revealed that genetically predicted asthma was causally associated with increased AF risk (inverse-variance weighted (IVW) OR = 1.001, 95% CI: 1.000-1.002, p = 0.019), whereas no significant association was observed in East Asian cohorts. Transcriptomic analyses identified carboxypeptidase A3 (CPA3) as a shared upregulated gene in both asthma and AF, potentially involved in renin-angiotensin system dysregulation. Drug-gene interaction screening suggested osilodrostat and metyrapone as potential therapeutic candidates targeting CPA3.

Conclusions

Our study provides evidence that genetic predisposition to asthma is significantly associated with an increased risk of AF in the European population. Transcriptomic data analyses suggest that CPA3 and its interactions with therapeutic drugs could serve as potential biomarkers, offering new opportunities to improve prognosis in patients with coexisting asthma and AF.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12890-026-04274-3.

Introduction

Atrial fibrillation (AF), the most prevalent sustained cardiac arrhythmia, represents a substantial clinical and public health burden due to its strong associations with increased morbidity, mortality, and healthcare utilization [1, 2]. Population-based epidemiological studies have highlighted a striking 26% lifetime risk of developing AF starting at age 40 [3], with over half of cases potentially preventable through the management of modifiable risk factors such as obesity, diabetes, hypertension, dyslipidemia, coronary artery disease, and heart failure [4, 5]. Identifying additional modifiable risk factors remains a critical unmet need in AF prevention, as this would facilitate the development of more targeted primary and secondary prevention strategies.

Asthma, a common chronic respiratory disorder characterized by airway hyperresponsiveness and episodic inflammation leading to wheezing, cough, and dyspnea [6], has emerged as a potential candidate in cardiovascular risk profiling. Growing epidemiological evidence has implicated asthma as a risk modifier for conditions such as coronary artery disease, ischemic stroke, heart failure, and AF [7–10]. Landmark cohort studies have demonstrated significant associations: a longitudinal analysis of 50,000 individuals revealed a dose-dependent relationship between suboptimal asthma control and increased AF risk, while a multiethnic US cohort study with 13 years of follow-up found a 1.5-fold increased risk of incident AF in asthmatic patients (adjusted OR = 1.2, 95% CI: 1.109–1.298, p < 0.001) [11, 12]. Conversely, null findings have been reported in other large-scale analyses: Chamberlian et al. observed no significant association (OR = 0.84, 95% CI: 0.63–1.12) [13], and Kocoglu et al. found no independent association after comprehensive confounder adjustment (OR = 1.02, 95% CI: 0.97–1.07) [14]. These conflicting results underscore the need for more rigorous causal inference beyond traditional observational epidemiology.

The MR framework offers a robust solution by leveraging genetic variants as instrumental variables (IVs) to assess causal relationships [15, 16]. By capitalizing on the random segregation of parental alleles during gametogenesis, MR analysis mitigates biases from reverse causation and confounding factors such as environmental, behavioral, or socioeconomic influences. Recent advancements in large-scale genome-wide association studies (GWAS) and multi-omics technologies have enabled sophisticated MR designs to investigate complex trait relationships with enhanced statistical power [17–19]. MR has increasingly been applied to infer causality in cardiovascular diseases and to evaluate potential therapeutic targets, providing genetically anchored evidence that complements observational findings [20, 21].

In this study, we adopt a multi-methodological approach to clarify the asthma-AF relationship: first, by conducting a systematic meta-analysis of observational studies to synthesize current evidence on their clinical association; second, by employing two-sample MR to infer causal directionality using genetic instruments; and third, by exploring potential molecular pathways through integrative genomic analysis. Our objectives are to: (1) resolve inconsistencies in observational data through quantitative synthesis; (2) provide causal evidence using MR’s unique advantages in causal inference; and (3) identify biological mechanisms that may underpin the observed associations. These efforts aim to advance our understanding of AF etiology and inform clinical guidelines for asthma management as a potential strategy to reduce AF-related cardiovascular risk.

Methods and materials

Meta-analysis

Data source and search strategy

This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to conduct a systematic review and meta-analysis investigating the association between asthma and AF risk. Eligible studies were systematically retrieved from PubMed, the Cochrane Central Register of Controlled Trials, and EMBASE from database inception to May 31, 2025. The detailed search strategy and keywords are provided in Table S1. Additional eligible studies were identified through manual screening of the reference lists of included articles. No restrictions were imposed on language, publication date, or geographic region. The study protocol was prospectively registered in PROSPERO (CRD42024550007; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024550007).

Eligibility criteria

Studies were included if they examined the association between asthma and AF risk and reported effect estimates (OR, hazard ratio [HR], or relative risk [RR]) with corresponding 95% confidence intervals (CI). Inclusion criteria were defined according to the PICOS framework:

• Population: General population

• Exposure: Asthma diagnosis or management Comparison: Non-asthmatic individuals or the general healthy population

• Outcome: Incidence of AF or AF-related risk estimates (HR/OR/RR) Study Design: Observational studies (cohort, case-control, cross-sectional)

Exclusion criteria comprised:

1.  Basic experimental studies (e.g., in vitro/in vivo research)

2. Studies with incomplete or unavailable data

3. Non-original research (case reports, reviews, letters, comments)

4. Duplicate publications (only the most recent or comprehensive version was included) 

Study selection and data extraction

Two independent reviewers (ZY and ZYZ) conducted study selection and data extraction according to the predefined criteria. Discrepancies were resolved through discussion and consultation with a third reviewer (corresponding author). The extracted data included the first author, publication year, country, study design, data source, participant age, sample size, effect size, proportion of male participants, AF diagnostic methods, and covariates adjusted for in multivariable analyses.

Quality assessment

The Newcastle-Ottawa Scale (NOS) was applied to assess the methodological quality of the included studies [22]. Studies with NOS scores ≥ 6 were considered to be of moderate to high quality, whereas studies with scores < 6 were classified as low quality. Disagreements in scoring were resolved by consensus among three investigators (ZY, ZYZ, and SY).

Meta-analysis

Meta-analyses were conducted using Review Manager (RevMan) v5.3 (The Cochrane Collaboration). Based on the formulas of relative risk (RR = Ie/Iu, where Ie is the incidence rate of the exposed group, and Iu is the incidence rate of the non-exposed group), hazard ratio (HR= h1/h2, where h1 is hazard rate of the exposed group, and h2 is the hazard rate of the non-exposed group), and OR (OR= [p1/(1-p1)]/[p2/(1-p2)], the proportion of exposed cases in case group is p1, and the proportion of exposed controls in control group is p2), the calculation results of RR (Ie/Iu), HR (Ie/Iu), and OR (Ie/Iu) will be very close if the disease incidence rate is meager [23]. Because the incidence of AF is relatively low, the numerical estimates of RR, HR, and OR are expected to be comparable. Therefore, RR, HR, and OR were treated equivalently in the pooled analysis. Heterogeneity across studies was assessed using Cochran’s Q test and the I² statistic. An I² value < 50% indicated low heterogeneity, in which case a fixed-effects model was applied, whereas I² ≥50% indicated substantial heterogeneity and a random-effects model was used. Subgroup analyses were performed to explore potential sources of heterogeneity (e.g., study design, geographic region, and follow-up duration). Sensitivity analyses were conducted to evaluate the robustness of the pooled estimates by sequentially excluding individual studies. Publication bias was assessed using funnel plots, Egger’s test, and Begg’s test.

Mendelian randomization analysis

Following current MR methodological guidelines [24], two-sample MR analyses were performed using publicly available GWAS summary statistics to investigate the causal relationship between genetically predicted asthma and AF risk. GWAS datasets were obtained from the IEU Open GWAS Project. Asthma and AF cases were defined according to International Classification of Diseases (ICD-8/9) codes 493 and 427, respectively. Datasets with clearly defined and clinically validated phenotypes were prioritized over those relying solely on self-reported information.

When multiple GWAS datasets were available, the dataset with the largest sample size (particularly case numbers) was selected to maximize statistical power. To minimize bias due to population stratification, analyses were restricted to individuals of European or East Asian ancestry, and exposure and outcome datasets were matched by ancestry. Potential sample overlap between exposure and outcome GWAS datasets was also carefully evaluated to reduce bias toward inflated causal estimates.

MR analyses relied on three core assumptions:

1. Instrumental variables (IVs) are strongly associated with asthma (p < 5 × 10^− 8).

2. IVs influence AF exclusively through their association with asthma (no horizontal pleiotropy).

3. IVs are independent of confounding factors.

Instrumental variables selection and analytical methods

Instrumental variables (IVs) for asthma were selected from GWAS summary statistics using stringent criteria: genome-wide significance (p < 5 × 10^− 8), low linkage disequilibrium (LD; r² < 0.001), and an F-statistic > 10 to exclude weak instrument bias. The inverse-variance weighted (IVW) method was used as the primary analytical approach, while MR-Egger regression was applied to evaluate the robustness of the results. For exposures instrumented by at least three IVs, the IVW method under a multiplicative random-effects model was implemented as the primary statistical analysis; otherwise, a fixed-effects IVW model was applied. Heterogeneity among IVs was assessed using Cochran’s Q statistic, and directional pleiotropy was evaluated using the MR-Egger intercept test (p < 0.05 indicating potential pleiotropy) [25–27]. Horizontal pleiotropy was further examined using the MR-PRESSO global test [28]. Leave-one-out analyses were conducted to determine whether individual SNPs disproportionately influenced the overall causal estimates [29]. Considering smoking as an important confounder for both asthma and AF [30, 31], multivariable Mendelian randomization (MVMR) analyses were performed to adjust for the potential influence of smoking (GWAS Trait ID: ieu-b-4877). All statistical analyses were conducted using two-tailed tests in R software (version 4.2.0) with the TwoSampleMR (version 0.5.6) and MR-PRESSO (version 1.0) packages. In addition, the statistical power of the IVW method was estimated using the online tool available at https://shiny.cnsgenomics.com/mRnd/. The fully annotated analysis code is provided in the Supplementary R scripts.

Transcriptomic analyses

Differential gene expression and functional enrichment

Microarray datasets GSE43696 (88 asthmatic patients vs. 20 healthy controls) and GSE14975 (5 AF samples vs. 5 sinus rhythm samples) were obtained from the GEO database for DGE analysis. DEGs were identified using the limma R package with thresholds of log₂(fold change) > 1 and adjusted p < 0.05. To validate these findings, independent datasets were used, including the asthma dataset GSE158752 (50 asthmatic patients vs. 17 healthy controls) and the AF dataset GSE31821 (4 AF samples vs. 2 sinus rhythm samples). Differences between groups were assessed using a two-tailed unpaired Student’s t-test. Data are presented as mean ± standard error of the mean (SEM), and a two-sided p-value < 0.05 was considered statistically significant. Functional enrichment analysis of the shared DEGs was conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool [32], with an adjusted p-value ≤ 0.05 considered statistically significant. The fully annotated analysis code is provided in the Supplementary R scripts.

Exploring potential drug targets in asthma associated AF

AF-related genes were retrieved from the GeneCards database, and genes overlapping with asthma-related DEGs were considered potential therapeutic targets. The Drug–Gene Interaction Database (DGIdb) was then used to identify approved or repurposable drugs targeting these genes [33]. Only approved drugs with an absolute interaction score greater than 2 were selected for further analysis. Molecular docking analyses were performed using the CB-Dock2 web server. Protein crystal structures were obtained from the RCSB Protein Data Bank (PDB), and three-dimensional drug structures were retrieved from PubChem [34–36]. Optimal ligand–protein interactions were determined based on the most negative Vina docking score, indicating the highest predicted binding affinity.

Results

Meta-analysis of asthma and atrial fibrillation risk

Study selection

As illustrated in Fig. 1, a total of 747 records were identified from PubMed, the Cochrane Central Register of Controlled Trials, and Embase using the predefined search strategy. After removing duplicate records and screening titles and abstracts, 32 potentially relevant studies remained for further evaluation. Full-text assessment of these 32 studies led to the exclusion of 23 articles based on the predefined inclusion and exclusion criteria. Ultimately, eight studies (four case-control studies and four prospective cohort studies) were included in the final meta-analysis [7, 11–14, 37–39].

Fig. 1.

The flow diagram showing the process of literature search and selection

Study characteristics and quality assessment

The eight included studies were published between 2014 and 2021 and comprised a total of 1,067,774 adult participants aged 18 years or older. Geographically, six studies were conducted in Europe or North America, while two were conducted in Asia. All studies had sufficiently long follow-up periods to capture atrial fibrillation outcomes. The included studies encompassed both cohort and case-control designs. Notably, the study by Tattersall MC et al. reported adjusted hazard ratios separately for intermittent and persistent asthma. Detailed study characteristics are summarized in Table 1. According to the Newcastle-Ottawa Scale, all studies were rated as moderate methodological quality, with individual NOS scores presented in Table S2.

Table 1.

The characteristics of selected studies pooled in the meta-analysis

Author	Year	Country	Study type	Data source	Age (years)	Sample size	Asthma size	Control size	Male (%)	Follow-up (years)	Effect size	Dignosis method	Adjusted variables
Chan et al. [12]	2014	Taiwan, China	Case-control study	The Taiwan National Health Insurance database	≥ 18	17,514	7,439	10,075	47.02	7	1.2 (1.1–1.3)	Electrocardiography and Holter monitoring	Age, gender, history of diabetes, hypertension, congestive heart failure, coronary artery disease, chronic renal failure, medications.
MartínPérez et al. [37]	2016	United Kingdom	Case-control study	The Health Improvement Network database	20–89	4,489	1,489	3,000	55.05	2.67	1.23 (1.05–1.43)	Electronic medical records	Sex, age, time interval between start date and index date, and primary care practitioner visits in the year before the index date
Chamberlain et al. [13]	2017	USA	Case-control study	The Rochester Epidemiology Project	≥ 18	2,860	1,430	1,430	48.6	6.3	0.84 (0.63–1.11)	Electrocardiograms	Heart failure, coronary artery disease, chronic kidney disease, chronic obstructive pulmonary disease.
Cepelis et al. [7]	2018	Norway	Prospective cohort study	The Nord-Trøndelag Health Study	≥ 20	60,986	12,380	48,606	47.2	15.4	1.38 (1.18–1.61)	ICD-10 and medical records reviewed	Age, sex, BMI, smoking status, alcohol use, physical activity, education level, waist-to-hip ratio, diabetes mellitus.
Jani et al. [38]	2018	United Kingdom	Prospective cohort study	UK Biobank	37–73	500,892	-	-	41.4	7	1.46 (1.02–2.08)	Self-reported	Age, gender, socioeconomic, smoking and anti-coagulation status.
Carter et al. [14]	2019	United Kingdom	Case-control study	7 National Health Service Hospitals	≥ 18	362,544	60,424	302,120	36.7	12.3	1.02 (0.97–1.07)	ICD-10 and OPCS-4	age, sex, ethnic group, and the cardiovascular diseases, cardiac procedures, and common causes of death
Tattersall et al. [11]	2020	USA	Prospective cohort study	The Multi-Ethnic Study of Atherosclerosis	62.0 ± 10.2	6,615	-	-	47	12.9	intermittent 1.10 (0.82–1.41) Persistent 1.49 (1.03–2.14)	Electrocardiograms and ICD-9	age, race, sex, BMI, systolic blood pressure, smoking status, alcohol use, hypertension medication use, diabetes mellitus, education.
Choi et al. [39]	2021	Korea	Prospective cohort study	Korea National Health Insurance Services database	40–79	111,874	-	-	38.2	7.2	1.36 (1.32–1.4)	ICD-10	age, sex, history of smoking, drinking level, low income, diabetes mellitus, hypertension, dyslipidemia, inflammatory bowel disease, psoriasis.

Abbreviations: AF atrial fbrillation, ECG electrocardiogram, ICD International Clarification of Disease, OPCS Office of Population Censuses and Surveys Classification of Interventions and Procedures

Asthma is associated with an increased risk of atrial fibrillation

A pooled analysis of the eight studies was conducted to evaluate the association between asthma and AF risk. Given the substantial heterogeneity among studies (I² = 92%, p < 0.001), a random-effects model was applied. The meta-analysis demonstrated that asthma was associated with a significantly increased risk of AF, corresponding to a 21% higher risk among individuals with asthma (OR = 1.21, 95% CI: 1.07–1.36, p = 0.003; Fig. 2). To explore potential sources of heterogeneity, subgroup analyses were performed. Potential contributing factors included asthma duration, hormone therapy, sex, and mean age; however, subgroup analyses for these variables could not be conducted due to insufficient available data. Therefore, subgroup analyses were limited to study design and geographic region, which were consistently reported across all studies. When stratified by study design (Fig. 3), a significant positive association was observed in cohort studies (OR = 1.36, 95%CI: 1.32–1.40, p < 0.0001; I² = 0%, p = 0.66), whereas no statistically significant association was identified in case-control studies (OR = 1.09, 95%CI: 0.96–1.24, p = 0.18; I² = 83%, p = 0.0006). Stratification by geographic region demonstrated significant associations in both European/North American and Asian populations (all p < 0.05). However, considerable heterogeneity remained within both subgroups (Asia: I² = 88%, p = 0.003; Europe/US: I² = 76%, p = 0.0003).

Fig. 2.

Forest plot depicting the relationship between asthma and the risk of AF

Fig. 3.

Forest plots of the subgroup analysis on the association between asthma and AF. (A) Subgroup by geographic area. (B) Subgroup by study design

Sensitivity analysis and publication bias

Sensitivity analysis was performed to assess the influence of individual studies on the overall estimates. Exclusion of any single study did not substantially alter the pooled odds ratio (OR), which ranged from 1.18 (95%CI: 1.05–1.32, p = 0.005) to 1.28 (95%CI: 1.15–1.37, p < 0.001) (Figure S1, Table 2). Heterogeneity remained significant (I² = 78–93%, all p < 0.001), indicating that no single study had a dominant impact on the results, thereby confirming the robustness of our findings. Due to the relatively small sample size, publication bias was assessed using funnel plots, Begg’s test, and Egger’s test. The funnel plot demonstrated approximate symmetry (Figure S2), and both Egger’s (P >|t|= 0.563) and Begg’s (P >|z|= 0.754) tests indicated no significant publication bias.

Table 2.

Sensitivity analysis of meta-analysis

Study omitted	Effect estimates	95%CI	p value	Heterogeneity
				I2	p value
Chan, 2014 [12]	1.21	1.04–1.40	0.01	93%	< 0.001
MartínPérez, 2016 [37]	1.20	1.05–1.38	0.008	93%	< 0.001
Chamberlain, 2017 [13]	1.25	1.10–1.41	< 0.001	93%	< 0.001
Cepelis, 2018 [7]	1.18	1.03–1.35	0.01	93%	< 0.001
Carter, 2019 [14]	1.25	1.15–1.37	< 0.001	88%	0.003
Tattersall (intermittent Asthma), 2020 [11]	1.22	1.07–1.38	0.003	93%	< 0.001
Tattersall (persistent Asthma), 2020 [11]	1.19	1.05–1.35	0.008	93%	< 0.001
Choi, 2021 [39]	1.18	1.05–1.32	0.005	78%	< 0.001
Jani, 2018 [38]	1.19	1.05–1.35	0.008	93%	< 0.001

OR odds ratio, CI confidence interval

MR analysis of the causal association between asthma and risk of AF

Selection of SNPs

The MR analysis workflow is illustrated in Fig. 4. In total, multiple European GWAS datasets, comprising up to 954,276 individuals, were included. The primary asthma exposure dataset (ebi-a-GCST006862) included 127,669 participants (19,954 cases and 107,715 controls). Additionally, this MR study analyzed a total of 294,703 East Asian individuals (28,994 cases and 265,709 controls) (Table 3). Instrumental variables (IVs) significantly associated with asthma were selected from GWAS summary data using stringent criteria: p < 5 × 10⁻⁸; linkage disequilibrium [LD] r² < 0.001, clumping distance = 10,000 kb. For univariable MR analysis, 17 SNPs from the GWAS ebi-a-GCST006862, 8 SNPs from GWAS ieu-a-44, and 7 SNPs from GWAS ebi-a-GCST90018587 were included (Table S1). All SNPs had F-statistics > 10, confirming their strong predictive power for asthma.

Fig. 4.

Description of the study design in this mendelian randomization (MR) analysis. Exposure: asthma; Outcome: Atrial fibrillation; LD: linkage disequilibrium

Table 3.

Details of selected GWASs used in this study

Exposure/outcome	Population	Sample size	Cases	Controls	No. of SNPs	PMID	GWAS ID
Asthma	European	127,669	19,954	107,715	1,999,262	29,273,806	ebi-a-GCST006862
		26,475	10,365	16,110	546,183	20,860,503	ieu-a-44
	East Asian	175,948	13,015	162,933	12,452,735	34,594,039	ebi-a-GCST90018575
Atrial fibrillation	European	462,933	3,518	459,415	9,851,867	UKBiobank	ukb-b-11,550
		337,199	3,818	333,381	10,894,596	UKBiobank	ukb-a-536
	East Asian	118,755	15,979	102,776	10,719,646	28,416,818	ebi-a-GCST004296
Smoking	European	607,291	311,629	295,662	11,802,365	30,643,251	ieu-b-4877

SNPs Single nucleotide polymorphisms, GWAS Genome-Wide Association Studies

Genetic susceptibility to asthma and AF risk

In European cohorts, MR analyses revealed a significant causal association between asthma and AF (IVW OR = 1.001, 95%CI: 1.000–1.002.000.002, p = 0.019), with consistent results obtained using MR-Egger methods (Table 4; Fig. 5A-B). This association was further validated using independent GWAS datasets (Table 4; Fig. 5C-D). In contrast, no significant causal effect was detected in East Asian populations (IVW OR = 1.004, 95%CI: 0.943–1.069, p = 0.904; MR-Egger OR = 0.86, 95%CI: 0.699–1.057, p = 0.166; Table 4). Although the statistical power in both the European and East Asian populations was below 10%, we performed additional heterogeneity and pleiotropy tests (Table S4). These analyses suggested that neither heterogeneity nor pleiotropy were present.

Table 4.

The causal association between Asthma and Atrial fibrillation risk in the univariable MR analyses

Exposure	Outcome	No. of SNPs	Methods	β	OR (95% CI)	p	Power (%)
European
Asthma (ebi-a-GCST006862)	Atrial fibrillation (ukb-b-11550)	17	IVW	0.001	1.001 (1.000–1.002.000.002)	0.019	3
			MR-Egger	0.003	1.003 (1.0005–1.006)	0.035	/
Atrial fibrillation (ukb-b-11550)	Asthma (ebi-a-GCST006862)	11	IVW	0.768	2.155 (0.018–250.495.018.495)	0.752	100
			MR-Egger	0.317	1.373 (0.000–19565.000)	0.950	/
Asthma (ieu-a-44)	Atrial fibrillation (ukb-a-536)	8	IVW	0.002	1.002 (1.0002–1.003)	0.021	3
			MR-Egger	0.013	1.013 (1.004–1.022)	0.030	/
Atrial fibrillation (ukb-a-536)	Asthma (ieu-a-44)	7	IVW	−0.534	0.586 (0.001–299.561.001.561)	0.866	100
			MR-Egger	2.521	12.439 (0.000–1239666.000)	0.735	/
East Asian
Asthma (ebi-a-GCST90018575)	Atrial fibrillation (ebi-a-GCST004296)	26	IVW	0.004	1.004 (0.943–1.069)	0.904	3
			MR-Egger	−0.151	0.86 (0.699–1.057)	0.166	/
Atrial fibrillation (ebi-a-GCST004296)	Asthma (ebi-a-GCST90018575)	23	IVW	−0.002	0.998 (0.950–1.051)	0.942	3
			MR-Egger	0.035	1.036 (0.934–1.148)	0.511	/

Fig. 5.

The causality of asthma on atrial fibrillation risk in Europeans. (A) Scatter plot of causal effect between asthma (ebi-a-GCST006862) and atrial fibrillation (ukb-b-11550). The slope of each line shows the estimated effect of per mendelian randomization method. (B) Forest plot of causal effect between asthma (ebi-a-GCST006862) and atrial fibrillation (ukb-b-11550). The red points demonstrate the integrated estimates using all SNPs together, using IVW method. Horizontal lines represent 95% confidence intervals. (C) Scatter plot of causal effect between asthma (ieu-a-44) and atrial fibrillation (ukb-a-536). The slope of each line denotes the estimated effect of per mendelian randomization method. (D) Funnel plot of causal effect between asthma (ieu-a-44) and atrial fibrillation (ukb-a-536). Horizontal lines represent 95% confidence intervals. IVW, Inverse-variance weighted; SNP, single nucleotide polymorphism

Consistent with the results of the MR Steiger test, reverse two-sample MR analysis showed no significant causal effect of genetically predicted AF on asthma risk (European: IVW OR = 2.155 (95%CI: 0.018–250.495.018.495), p = 0.752; East Asian: IVW OR = 0.998 (95%CI: 0.950–1.051), p = 0.942). These findings further support the directionality of our primary results. Multivariate MR, adjusting for smoking (trait-ID: ieu-b-4877), confirmed that the positive association between genetically predicted asthma and AF risk remained robust (OR = 1.002, 95%CI: 1.0006–1.003, p = 0.003). Furthermore, in the leave-one-out analysis, no individual SNPs were found to significantly influence the causal relationship between asthma and AF (Figure S3). Although one potentially influential SNP was identified, the direction of effect remained consistent, and no evidence of directional pleiotropy was detected. This further supports the robustness and reliability of the MR estimates.

Bioinformatics analysis

Identification and enrichment analysis of DEGs in asthma

After normalizing the microarray data (Fig. 6A), 23 upregulated and 15 downregulated genes were identified, with a log2 fold change (FC) > 1 and an adjusted p-value < 0.05. Volcano plots and heatmaps were used to visualize the DEGs (Fig. 6B-C). To explore the underlying biological mechanisms of these DEGs, functional enrichment analysis was conducted using the DAVID tool. As shown in Fig. 7A, the upregulated genes were primarily enriched in pathways such as pancreatic secretion, fructose and mannose metabolism, salivary secretion, influenza A, sphingolipid signaling, arginine biosynthesis, renin-angiotensin system, and folate biosynthesis. Additionally, the downregulated genes were enriched in pathways related to mannose-type O-glycan biosynthesis, IL-17 signaling, cholesterol metabolism, and hematopoietic cell lineage (Fig. 7B).

Fig. 6.

Differential expression of genes associated with asthma. A.Gene expression level of the GSE43696 after standardizing. B. Volcano map of differentially expressed genes between control and Asthma samples. C. Heatmap of differentially expressed genes between control and Asthma samples

Fig. 7.

Functional enrichment of analysis of DEGs. (A) KEGG pathway analysis of the up-regulated genes. (B) KEGG pathway analysis of the down-regulated genes. DEG, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes

Identification of potential target genes

Using GeneCards (https://www.genecards.org/), disease-associated genes for asthma and atrial fibrillation (AF) were identified. Intersection analysis between these DEGs and disease-related genes revealed CEA cell adhesion molecule 5 (CEACAM5), CPA3, and periostin (POSTN) as shared genes (Fig. 8A). Validation using an independent asthma cohort (GSE158752) and AF cohort (GSE31821) confirmed that the expression levels of these genes were significantly elevated in both asthma and AF patients. These expression patterns were consistent across diseases (Fig. 8B), suggesting their potential role in disease comorbidity and as therapeutic targets.

Fig. 8.

Identification of drug targets in asthma and AF. (A) Intersection of the asthma and AF associated genes and DEGs of the asthma and AF. (B) The expression analysis of the CEACAM5, CPA3, and POSTN genes in independent asthma cohort (GSE158752) and AF cohort (GSE31821)

Drug prediction and molecular docking

After removing drugs without structures from the PubChem database and eliminating homologous drugs, screening using the DGIdb database identified two potential approved drugs targeting CPA3, both with interaction scores > 2: Osilodrostat and Metyrapone (Table 5). Molecular docking confirmed the binding affinity between the target genes and the selected drugs. Three-dimensional and two-dimensional conformations, as well as Vina docking scores (with negative values indicating stability), are shown in Fig. 9A-B. For CPA3, docking scores ranged from − 6.4 (Metyrapone) to −5.9 (Osilodrostat) (Table 5). In summary, these drugs may represent promising therapeutic agents, potentially aiding in the development of new treatment strategies for asthma and AF. However, further investigation is necessary to assess their efficacy and safety.

Table 5.

Selected Drugs targeting key genes and molecular docking

Gene	PDB ID	Drug	PubChem ID	Regulatory approval	Interaction score	Vina score
CPA3	2PCU	Osilodrostat	44,139,752	Approved	4.38	−5.9
		Metyrapone	4174	Approved	2.63	−6.4

Fig. 9.

The results of molecular docking. A-B. The docking modes of CPA3 with Osilodrostat, and Metyrapone

Discussion

Asthma has increasingly been implicated as a potential contributor to AF in observational studies. However, the relationship between asthma and AF remains incompletely understood, and the underlying biological mechanisms have yet to be fully elucidated. In the present study, we employed an integrated analytical framework combining meta-analysis, MR, transcriptomic profiling, and exploratory drug-gene interaction analysis to comprehensively investigate the relationship between asthma and AF. Our findings provide converging evidence supporting a causal association between asthma and an increased risk of AF and suggest the involvement of a biologically plausible RAS underlying this relationship. These findings contribute to a deeper understanding of the systemic nature of asthma, its cardiovascular implications, and potential therapeutic strategies for AF prevention.

Our meta-analysis demonstrated that asthma was associated with a modest but statistically significant increase in AF risk, consistent with the findings reported by Nogueira-Garcia B [40]. Subgroup analysis by geographic region suggested a higher AF risk in Asian populations, which may partly reflect differences in asthma prevalence and environmental exposures between Eastern and Western populations [41]. However, this subgroup analysis included only two observational studies from Asian populations, which limits the statistical power and precision of the estimates. Notably, substantial heterogeneity was observed in the overall meta-analysis, primarily driven by variability among case-control studies. In contrast, cohort studies demonstrated highly consistent effect estimates with minimal heterogeneity. This pattern suggests that methodological differences-including exposure ascertainment, temporal relationships, and confounding control-may account for much of the observed variability. Case-control studies are inherently more susceptible to selection and recall bias, whereas cohort designs preserve temporal sequence and may therefore provide more reliable risk estimates [42]. Consequently, greater weight should be placed on cohort-based findings when interpreting the epidemiological association between asthma and AF.

An important consideration in interpreting the MR results is the magnitude of the estimated effect size. The observed ORs (e.g., OR: 1.001–1.003) may appear numerically small; however, these estimates are expressed per unit increase in genetically predicted liability to asthma rather than per clinical exposure or time-dependent risk [43]. Therefore, they are not directly comparable to effect sizes derived from observational studies, such as hazard ratios. Within the MR framework, the scale of the exposure is determined by the genetic instrument and reflects lifelong susceptibility rather than short-term or clinically defined exposure [44]. As such, even small effect estimates may represent meaningful causal contributions at the population level. Accordingly, the MR findings in this study should be primarily interpreted in terms of the direction and robustness of the causal association rather than the absolute magnitude of the odds ratio. The consistent direction of effect across multiple MR methods, together with the absence of significant horizontal pleiotropy, supports a causal contribution of asthma to the risk of atrial fibrillation, despite the modest numerical effect size.

Although previous MR studies have suggested that genetically predicted asthma may increase AF risk [45], our study substantially expands upon these findings in several important respects. First, we performed multivariable MR (MVMR) analyses to evaluate whether the causal relationship between asthma and AF was independent of potential confounders. Notably, the association remained significant after adjusting for smoking, indicating that the observed causal effect was unlikely to be attributable to smoking-related confounding. Second, our trans-ethnic MR analyses revealed distinct causal patterns across ancestries. In European populations, genetically predicted asthma was associated with an increased risk of AF, whereas no significant association was observed in East Asian populations. Importantly, null findings in underpowered MR analyses should not be interpreted as definitive evidence of absence, particularly given substantial inter-population differences in genetic architecture and environmental exposures. A paradigmatic example is the markedly high prevalence of aldehyde dehydrogenase 2 deficiency in East Asian populations, which impairs ethanol metabolism and amplifies alcohol-related cardiotoxicity [46]. Such evidence underscores the importance of ancestry-specific risk stratification and cautions against the direct extrapolation of cardiovascular prevention strategies across genetically distinct populations.

Given the observed detrimental association between asthma and AF, we further explored potential molecular mechanisms using transcriptomic analyses. A total of 38 DEGs identified in asthma were primarily enriched in inflammation- and endocrine-related pathways, including arginine biosynthesis, the RAS, and IL-17 signaling. These findings are consistent with previous studies demonstrating that the interaction between angiotensin II and its receptor on ILC2s plays a critical role in asthma pathogenesis [47]. The RAS is a key neuroendocrine and paracrine regulatory system involved in numerous cardiovascular, pulmonary, and renal processes [48]. Both experimental and clinical studies have highlighted the role of RAS activation in the pathogenesis of AF. For instance, Goette and colleagues demonstrated that increased local production of angiotensin II-mediated by the activation of angiotensin-converting enzyme (ACE), chymase, and angiotensinogen-contributes to atrial remodeling and the development of AF [49]. In addition, a real-world observational study reported that RAS inhibitors were associated with improved prognosis in AF patients with chronic obstructive pulmonary disease, particularly angiotensin-converting enzyme inhibitors [50]. Collectively, these findings suggest that dysregulation of the RAS may represent a shared mechanistic pathway linking asthma and AF, and that RAS inhibition may offer potential therapeutic benefits.

Furthermore, our transcriptomic analyses identified CPA3, a gene related to RAS signaling, as a shared DEG between asthma and AF datasets. CPA3, a mast cell-derived enzyme, has been increasingly recognized as an important regulator of inflammatory responses and has been implicated in a variety of inflammatory and cardiovascular diseases [51]. Predominantly secreted by mast cells, CPA3 modulates local inflammatory responses and is closely associated with allergic reactions and airway remodeling in asthma [52]. Overexpression of CPA3 has been shown to exacerbate airway inflammation and mucus hypersecretion in asthmatic epithelial cells through activation of the JAK/STAT6 signaling pathway [53]. Interestingly, mast cells have also been reported to contribute to AF development through platelet-derived growth factor A (PDGF-A)-mediated cellular proliferation and collagen deposition, suggesting that modulation of the mast cell/PDGF-A axis may represent a potential upstream strategy for AF prevention [54]. In our study, CPA3 expression was significantly elevated in both asthma and AF tissues compared with control samples, suggesting that CPA3 may play a pivotal role in the comorbidity between asthma and AF.

Emerging evidence supports the concept that RAS inhibition may be beneficial for both the primary and secondary prevention of AF [55]. However, considering the potential adverse effect of airway hyperreactivity, the use of conventional RAS inhibitors should be carefully monitored in patients with coexisting asthma and AF. In the present study, we attempted to identify potential non-classical RAS-related therapeutic agents targeting CPA3 using the DGIdb database. Osilodrostat is a potent oral inhibitor of 11β-hydroxylase that can effectively control refractory hypertension by modulating steroidogenesis and the renin-angiotensin system [56]. Metyrapone is another 11β-hydroxylase inhibitor commonly used in the management of Cushing’s syndrome to reduce hypercortisolemia [57]. Interestingly, metyrapone has also been reported to improve airway inflammation and remodeling in asthma, suggesting a potential therapeutic role in respiratory disease [58]. These findings raise the possibility that non-classical RAS-modulating agents may represent promising therapeutic candidates for patients with coexisting asthma and AF. Nevertheless, further experimental and clinical studies are required to validate their efficacy and safety.

Despite the strengths of our integrative analytical approach, several limitations should be acknowledged. First, although multiple MR methods were applied, the statistical power of the analyses remained limited. Encouragingly, a series of pleiotropy and sensitivity analyses yielded consistent results, and no significant heterogeneity was detected, supporting the robustness of our findings. Second, our results support a causal association between asthma and AF in European populations; however, the limited statistical power of the East Asian MR analysis and the small number of Asian studies included in the observational meta-analysis prevent definitive conclusions regarding ancestry-specific effects. Future large-scale studies in diverse populations are needed to clarify whether these associations differ across ethnic groups. Third, although CPA3 was identified as a key shared gene, its precise role in the pathogenesis of asthma-AF comorbidity has not yet been fully elucidated. Rigorous experimental validation is necessary to confirm the predicted drug–gene interactions and to clarify their biological mechanisms. Finally, translation of these findings into clinical practice will require well-designed clinical trials to evaluate both efficacy and safety.

Conclusions

In conclusion, our study demonstrates a significant association between asthma and AF risk, particularly in European populations. These findings provide novel insights into the potential molecular mechanisms underlying this comorbidity, highlighting the potential role of CPA3 and the renin-angiotensin system. A comprehensive understanding of the shared pathogenic mechanisms between asthma and AF may facilitate the development of targeted prevention strategies and therapeutic interventions aimed at reducing cardiovascular complications in patients with asthma.

Authors’ contributions

Yan Zheng and Yang Shen conceptualised and initiated the study. Yanzhen Zhu, Huitian Liao, Zhenhong Jiang, Wenfeng He and Jin Ouyang conducted the data search and collection for the meta-analysis. Hong Deng, Jianping Hu, and Jiayi Feng conducted the data search and collection for the GWAS analysis. Yan Zheng and Yang Shen performed the Meta-analysis, MR analysis, and bioinformatic analysis and drafted the manuscript. All the authors critically reviewed the manuscript prior to submission.

Source of Funding

This study was supported by the National Natural Science Foundation of China (No.82360048), the Project of the Jiangxi Provincial Health Commission (No.202210499), the Science and Technology Research Project of the Education Department of Jiangxi Province (No.GJJ2200226), Jiangxi Province Natural Science Foundation youth project (No.2023BAB216081) and Jiangxi Province Key Laboratory of Molecular Medicine (No.2024SSY06231).

Data availability

Data analyzed by this study can be obtained from IEU OpenGWAS Project (https://gwas.mrcieu.ac.uk/), and GEO (http://www.ncbi.nlm.nih.gov/geo/) (Under accession: GSE43696, GSE14975, GSE158752, and GSE31821).

Declarations

Ethics approval and consent to participate

All clinical, GWAS and microarray datasets used in this study were publicly available, and ethical approval was obtained from all original studies.

Competing interests

The authors declare no competing interests.