Unveiling the shared etiology between gastrointestinal disorders and valvular heart diseases through a genome-wide pleiotropy study.

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Introduction

Gastrointestinal disorders and valvular heart diseases (VHD) are among the most prevalent medical conditions globally, posing severe healthcare burdens and decreased quality of life for patients 1 , 2 . Gastrointestinal disorders have diverse clinical manifestations, reflecting different underlying pathophysiological processes, like chronic inflammation, impaired gut motility, and altered gastric acid secretion 3 . Similarly, VHD comprises a group of conditions affecting the normal functioning of the heart valves, leading to reduced blood flow and increased strain on the heart 4 . Both gastrointestinal disorders and VHD present multiple levels of complexity and heterogeneity, making them crucial areas of investigation in contemporary medicine.

Despite the distinct pathophysiological backgrounds of gastrointestinal disorders and VHD, they may share an underlying pathogenetic basis, as vascular diseases and inflammation are known to be involved in the development of these conditions 5 , 6 . Additionally, the burgeoning area of research supporting the gut-heart axis interconnects the gastrointestinal and cardiovascular systems, presenting complex and bidirectional communication 7 , 8 . This interaction involves various physiological processes, molecular mechanisms, and signaling pathways, which are influenced by the gut microbiome, diet, and metabolism, among other factors. Alterations in the gut-heart axis have been linked to the development and progression of several gastrointestinal and cardiovascular diseases 9 . Microbiome metabolites, hormonal regulation, and gastrointestinal inflammation can all impact vascular function and are closely linked to cardiac-related diseases 10 . Previous research has primarily focused on the relationship between gastrointestinal function and coronary artery disease 11 , heart failure 12 , and atherosclerosis 13 . However, while multiple studies highlighted the interplay between digestive tract and heart with a potential role for gut microbiome 7 , 14 , there have been few efforts in assessing the relationship between VHD and gastrointestinal disorders.

With advancements in genome-wide association studies (GWAS), there is growing evidence suggesting the role of genetic factors in the pathophysiology of both gastrointestinal disorders and VHD. GWAS have successfully identified several genetic loci associated with the risk for gastrointestinal disorders, further underscoring the importance of genetic factors in these diseases 15 – 18 . Additionally, previous research particularly highlighted the critical role of genetic variations in contributing to VHD susceptibility 19 , 20 . However, it is unclear on the extent of the pleiotropy between gastrointestinal disorders and VHD. Therefore, this study aims to explore the potential shared genetic pathways between gastrointestinal disorders and VHD, investigating their associations and potential mechanisms to illuminate new insights into their co-morbidity and inform more integrated treatment approaches.

In this study, we aimed to investigate the shared genetic architecture of VHD with respect to six gastrointestinal disorders (gastro-esophageal reflux disease [GORD], irritable bowel syndrome [IBS], peptic ulcer disease [PUD], inflammatory bowel disease [IBD], Crohn’s disease [CD], and ulcerative colitis [UC]) applying different analytic approaches (Fig. 1 ) to large-scale genome-wide datasets (Table 1 ). By elucidating potential links between these complex and heterogeneous conditions, we hope to generate knowledge that may facilitate breakthroughs in our understanding of their pathogenesis and treatment.

Methods

Data resources

The GWAS datasets investigated in the present study are summarized in Table 1 . GWAS of GORD, IBS, and PUD were previously conducted in 456,327 individuals from the UK Biobank 16 . IBD, CD, and UC GWAS were generated from a meta-analysis of multiple cohorts of European descent 18 . VHD GWAS data were obtained from the Finngen Project Release 8 (available at https://r8.finngen.fi/ , ID: I9_VHD) 21 . GWAS data on gut microbiota were generated from 18,340 subjects assessed for 211 taxa, including 131 genera, 35 families, 20 orders, 16 classes and 9 phyla 22 .

Genetic correlation analysis

Linkage Disequilibrium Score Regression (LDSC) 23 was utilized to estimate the genetic correlation between gastrointestinal disorders and VHD. LDSC genetic correlation analysis was conducted following the instructions available at https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation . Specifically, as recommended, we tested 1,217,311 variants present in the HapMap 3 reference panel and pre-computed linkage disequilibrium (LD) scores based on 1000 Genomes Project reference data on individuals of European ancestry 24 . Strict quality control was implemented for SNPs, including: (i) exclusion of non-biallelic SNPs and those with ambiguous allele coding; (ii) exclusion of SNPs without an rs label; (iii) removal of duplicate SNPs or those not included in the 1000 Genomes Project or with mismatched alleles; (iv) exclusion of SNPs located in the major histocompatibility complex (chr6: 28.5–33.5 Mb) due to their complex LD structure in LDSC analysis; (v) retention of SNPs with a minor allele frequency (MAF) > 0.01. As there was no sample overlap between gastrointestinal disorders and VHD (see description of “data resources” above), we estimated with LDSC genetic covariance intercept constrained to zero to maximize the statistical power of our genetic correlation analyses.

Pleiotropic analysis

PLACO (Pleiotropic analysis under composite null hypothesis) is a method to investigate variants with pleiotropic effect across multiple traits using genome-wide association statistics 25 . We calculated the square of Z-scores for each variant and removed SNPs with excessively high Z 2 (>80). Additionally, taking into account the potential correlation between gastrointestinal disorders and VHD, we estimated the correlation matrix of Z. We then used the horizontal α-cross joint test (IUT) method to test the non-multi-effect hypothesis. The final P value of the IUT test is the maximum of the P values testing H 0 and H 1 . We considered statistically significant pleiotropic effects surviving genome-wide multiple testing correction ( P < 5 × 10 −8 ).

Based on the PLACO results, we further mapped the identified loci to nearby genes and explored the common biological mechanisms of these multi-effect loci. We used Multi-marker Analysis of GenoMic Annotation approach 26 to genes underlying the SNP-level pleiotropic effects. Gene-based pleiotropic effects were defined considering a Bonferroni correction accounting for the number of genes tested ( P < 3 × 10 −6 ). FUMA tool 27 the Molecular Signature Database (MSigDB) 28 were used to investigate the gene-set enrichment underlying the gene-based pleiotropic effects.

Summary data-based Mendelian randomization

To follow up the pleiotropy analysis, we used the summary data-based Mendelian randomization (SMR) approach 29 . This method integrates genome-wide association statistics with expression quantitative trait loci (eQTL) data to identify genes whose expression levels are associated with the traits through shared causal genetic variants. In our analysis, we leveraged whole-blood eQTL datasets available from eQTLGen 30 and GTEx V8 31 . The Heterogeneity in Dependent Instruments (HEIDI) test was performed to distinguish pleiotropy from LD. We considered statistically significant SMR effects those surviving Bonferroni correction accounting for the number of tests performed ( P < 9.68 × 10 −5 ) with HEIDI P > 0.05.

Two-sample Mendelian randomization

We applied the two-sample Mendelian randomization (MR) approach that permits to estimate the effect of an exposure on an outcome of interest using genetic information 32 . Specifically, we investigated the bidirectional relationship between gastrointestinal disorders and VHD. PLINK 33 was used to define the genetic instruments considering variants association with the exposure of interest ( P < 5 × 10 −8 ) that were LD independent of each other. The r 2 threshold for instrumental variables was set at 0.001, and the window was set to 500 kb. To ensure the strength of instrumental variables, we calculated the r 2 and F-statistics for each instrumental variable 34 . The formula for F-statistics is as follows:

where r 2 represents the proportion of variance explained by instrumental variables, n represents the sample size, and k represents the number of SNPs. The main MR method used was the inverse variance weighted method (IVW), which requires that instrumental variables (IV) satisfy three assumptions: (i) IV should be related to exposure; (ii) IV should not be associated with confounding factors related to exposure and outcome; (iii) The effect of IV on the outcome is entirely achieved through exposure. Several sensitivity analyses were conducted. First, the Q test of IVW and MR-Egger can detect potential violations of assumptions by heterogeneity among various IVs 35 . Secondly, we applied MR-Egger to estimate the level of horizontal pleiotropy based on its intercept to ensure that genetic variation is independent of exposure and outcome 36 . Additional analyses using MR methods with different modeling assumptions and advantages (weighted median and mode) were performed to increase the stability and robustness of the results. The MR analysis was performed in R3.5.3 software, and MR analysis used the MendelianRandomization package 37 .

We also applied two-sample MR to investigate the possible causal effect of gut microbiota on both VHD and gastrointestinal disorders. Genome-wide association statistics were obtained from MiBioGen consortium initiative ( https://mibiogen.gcc.rug.nl/ ) 22 . This performed GWAS of 21 gut microbiota taxa, including 35 families, 20 orders, 16 classes, 9 phyla, and 131 genera assessed using 16S fecal microbiome gene typing in 18,340 individuals of European descent 22 . First, we used a threshold of P < 1 × 10 −5 to select significant SNPs for each gut microbiota (Supplementary Table 1 ). Then, we used the clumping procedure in PLINK software to exclude independent instrumental variables with r 2 < 0.001 33 , which was calculated from the European 1000 Genome Projects reference panel 24 . If the IV was missing from the result dataset, proxies with r 2 > 0.8 were added. Finally, to quantify the strength of IV, we also calculated the proportion of variance explained (PVE) and F-statistics (F statistics > 10) for each gut microbiota instrumental variable. Because this is an exploratory analysis, we did not apply a stringent multiple testing correction and considered associations with P < 0.01 as significant.

Validation in an independent VHD GWAS dataset

To assess the robustness of our findings, we performed an external validation using an independent large-scale GWAS dataset of VHD from a previously published meta-analysis 38 . We screened for the most significant SNPs in each previously identified pleiotropic region by extracting all SNPs located within the candidate loci boundaries based on chromosomal positions. For each region, SNPs were ranked by ascending P values, and the top SNP (smallest P ) was retained as the representative variant. We then evaluated whether these lead SNPs showed consistent effect direction and statistical significance in the independent VHD GWAS dataset.

Results

To assess the genome-wide pleiotropy between VHD and gastrointestinal disorders, we estimated genetic correlation using LDSC approach 23 . After considering Bonferroni multiple testing correction ( P < 0.008; Supplementary Table 2 ), VHD showed positive genetic correlation with GORD ( r g = 0.211, P = 5.41 × 10 −12 ), IBS ( r g = 0.227, P = 1.16 × 10 −11 ), PUD ( r g = 0.2104, P = 1.06 × 10 −6 ), IBD ( r g = 0.066, P = 0.006), and CD ( r g = 0.078, P = 0.001).

To follow up the genetic correlations observed, we used PLACO 25 approach to identify specific loci shared VHD to gastrointestinal disorders. This analysis uncovered 15 pleiotropic loci (Table 2 ). The Manhattan plot of the pleiotropic genes ( P < 3.00E-06) between gastrointestinal tract diseases and VHD can be seen in Fig. 2 . To strengthen the credibility of our findings, we validated the pleiotropic loci in an independent GWAS dataset of VHD. Among the loci previously identified, five SNPs surpassed the conventional genome-wide significance threshold ( P < 5 × 10 −8 ), and an additional 12 loci reached suggestive significance ( P < 1 × 10 −3 ) in the external VHD dataset. The direction of effect was consistent with the discovery dataset, supporting the reliability of the identified associations (Supplementary Table 3 ). Across the top signals, multiple pleiotropic variants clustered within the MHC region: rs431722 (UC-VHD pleiotropy P = 1.73 × 10 −18 ); rs1061808 (IBD-VHD pleiotropy P = 7.66 × 10 −13 ); rs2394976 (CD-VHD pleiotropy P = 9.99 × 10 −13 ); rs3131062 (GORD-VHD pleiotropy P = 5.25 × 10 −11 ); rs9281054 (IBS-VHD pleiotropy P = 7.83 × 10 −9 ). Although they were located in the same regions, these pleiotropic appear to be independent with partial LD observed only between rs3131062 and rs2394976 ( r 2 = 0.38) and between rs431722 and rs1061808 ( r 2 = 0.42). Overlapping signals of VHD pleiotropy with the gastrointestinal disorders investigated were observed also in several other genomic regions. To maximize the statistical power of our analysis, we conducted a gene-based analysis of the pleiotropic effects, identifying 64 loci surviving Bonferroni multiple testing correction ( P < 3 × 10 −6 ; Supplementary Table 4 ): 36 genes for VHD-IBD pleiotropy; 28 genes for VHD-CD pleiotropy; 23 genes for VHD-UC pleiotropy; 16 genes for VHD-UC pleiotropy; three genes for VHD-PUD pleiotropy; two genes for VHD-IBS pleiotropy. There was a consistent overlap of VHD pleiotropy genes across gastrointestinal disorders. For example, four genes within MHC region showed evidence of VHD pleiotropy with four gastrointestinal disorders: C6orf48 (VHD-pleiotropy: CD- P = 1 × 10 −8 ; GORD P = 4.83 × 10 −7 ; IBD P = 3.8 × 10 −8 ; UC P = 1.7 × 10 −8 ), MSH5 (VHD-pleiotropy: CD- P = 1.55 × 10 −10 ; GORD P = 4.40 × 10 −9 ; IBD P = 1.56 × 10 −9 ; IBS P = 1.79 × 10 −6 ), PRRC2A (VHD-pleiotropy: CD- P = 8.41 × 10 −7 ; GORD P = 1.48 × 10 −7 ; IBD P = 2.24 × 10 −6 ; IBS P = 2.28 × 10 −7 ), and PBX2 (VHD-pleiotropy: CD- P = 2.27 × 10 −6 ; GORD P = 1.76 × 10 −6 ; IBD P = 3.15 × 10 −7 ; UC P = 4.45 × 10 −13 ). The gene-set analysis identified that the pleiotropic genes identified were enriched for loci associated with a range of health outcomes (Supplementary Table 5 ). While the majority of the enrichments were shared across VHD pleiotropy with the gastrointestinal disorders investigated, we also identified mechanisms that appear to be VHD-related mechanism specific to each disorder (Table 3 ). Among them, the strongest ones were related to conotruncal heart defects ( P = 7.65 × 10 −5 ) for VHD-CD pleiotropic genes, atopic dermatitis ( P = 3.63 × 10 −6 ) for VHD-IBD pleiotropic genes, menopause age of onset for VHD-IBS pleiotropic genes ( P = 2.33 × 10 −5 ), Stevens-Johnson syndrome and toxic epidermal necrolysis ( P = 4.08 × 10 −11 ) for VHD-UC pleiotropic genes.

Our analysis using SMR approach 29 identified 16 Bonferroni-significant gene-disease associations mediated by whole-blood transcriptomic regulation (SMR P < 9.68 × 10 −5 , HEIDI P > 0.05; Supplementary Table 6A ). Although no locus showed Bonferroni-significant convergence between both VHD and gastrointestinal disorders, five genes ( CFDP1 , STAT3 , IMPDH2 , UBE2L3 , and STMN3 ) presenting Bonferroni-significant association with at least one of the gastrointestinal disorders tested (Supplementary Table 6A ) also had a nominally significant effect on VHD (SMR P < 0.05, HEIDI P > 0.05; Supplementary Table 6B ). AGER gene was the only Bonferroni-significant effect observed with respect to VHD (SMR beta=0.212, P = 4.82 × 10 −5 ; HEIDI P = 0.092). For this locus, HEIDI test indicated that its relationship with gastrointestinal disorders was driven by LD rather than pleiotropy (HEIDI P < 0.05; Supplementary Table 6C ).

While we observed locus-specific pleiotropy between gastrointestinal disorders and VHD, there was no evidence of cause-effect relationships (i.e., vertical pleiotropy) among them through the two-sample MR analyses performed ( P > 0.05; Supplementary Table 7 ). However, two-sample MR approach permitted us to observe that gut microbiota can affect both VHD and gastrointestinal disorders (Fig. 3 ; Supplementary Table 8 ). Among them, Butyricicoccus genus had a genetically inferred causal effect on VHD (beta = −0.133, P = 0.004) and GORD (beta = −0.1, P = 0.005). Considering a more stringent multiple testing correction ( P < 2.37 × 10 −4 ), we observed that Lachnospiraceae genus also affected GORD (beta = 0.91, P = 1.79 × 10 −4 ). This genus did not show any effect on the other illnesses investigated ( P > 0.15). Conversely, Ruminococcaceae genus showed Bonferroni significant effect on IBS (beta = −0.109, P = 2.06 × 10 −4 ) and nominally significant associations with CD (beta = −0.242, P = 0.001) and IBD (beta = −0.109, P = 0.039).

Discussion

In recent decades, multiple epidemiological studies highlighted the comorbidity between VHD comorbidities and gastrointestinal disorders 39 , 40 . Additionally, multiple research efforts confirmed the presence of a gut-heart axis, suggesting a complex and bidirectional communication between digestive tract and heart 7 . Although prior GWAS studies have established the role of genetic factors in the pathophysiology of both gastrointestinal disorders and VHD 15 , 19 , 20 , 41 , the scope and underlying genomic mechanisms of their interrelationship have remained elusive. To address this gap, our study employed genomic association analysis, offering novel insights into the phenotypic and genetic association underlying VHD comorbidity with gastrointestinal disorders. Specifically, we identified positive genetic correlations of VHD with GORD, IBS, PUD, IBD, and CD, suggesting that VHD pleiotropic mechanisms are widespread across gastrointestinal disorders.

In line with this hypothesis, our PLACO analysis identified 64 pleiotropic genes that survived Bonferroni multiple testing correction. These genes linked VHD to various gastrointestinal disorders. Intriguingly, several of these genes were localized within the MHC region, an area known for its crucial role in immune responses 42 . For instance, the gene MSH5 , implicated in both VHD and gastrointestinal disorders, plays a role in DNA repair and meiotic recombination 43 , 44 , suggesting that DNA instability might be a shared pathophysiological mechanism. Similarly, genes like PRRC2A and PBX2 45 , 46 , which are involved in cellular signaling and transcriptional regulation, could potentially affect both cardiac valvular structures and gastrointestinal mucosal integrity. In addition to genes in MHC regions, non-MHC genes like PHTF1 and RSBN1 may have distinct roles in cellular processes that might be pivotal in understanding the multifactorial nature of these diseases 47 , 48 . Similarly, PITX2 and LOC729065 , previously associated with gastrointestinal disorders, 49 , 50 could be key in deciphering the molecular interplay between cardiac and gastrointestinal physiology. Additionally, the identification of genes like NOD2 , a known actor in immune response and inflammatory processes, aligns with the proposition of shared inflammatory pathways between VHD and gastrointestinal disorders 51 . These findings point out that multiple molecular pathways contribute to the comorbidity between VHD and gastrointestinal disorders. This is also supported by the fact that several of the genes identified are also related to other outcomes related to different health domains. For instance, the enrichment of genes associated with conotruncal heart defects in VHD-CD pleiotropy could imply a shared developmental or structural etiology that may predispose individuals to both cardiovascular and gastrointestinal complications, emphasizing the importance of exploring congenital abnormalities. Similarly, the enrichment of genes related to atopic dermatitis in VHD-IBD pleiotropy could signify an underlying immune dysregulation affecting multiple organ systems. This is particularly pertinent given that both atopic dermatitis and IBD are conditions with known immune components 52 , 53 , as is VHD to some extent 54 . It is also intriguing that our enrichment analysis highlighted genes related to the age of onset for menopause in VHD-IBS pleiotropy. This could point to hormonal factors as additional mechanisms impacting both cardiovascular and gastrointestinal health 55 , 56 . In addition, the association with menopause age of onset for VHD-IBS pleiotropic genes raises intriguing possibilities regarding the role of hormonal changes and their potential impact on both gastrointestinal and cardiac physiology. Hormonal fluctuations and their influences on inflammatory responses, tissue integrity, and cellular signaling can be pivotal in understanding the shared pathophysiology between these disorders 57 . Lastly, the strong association with Stevens-Johnson syndrome and toxic epidermal necrolysis for VHD-UC pleiotropic genes underscores the importance of examining skin manifestations and severe mucocutaneous reactions as they may reflect systemic abnormalities and shared immunological pathways involved in both VHD and gastrointestinal disorders. Taken together, the enrichment findings add an additional layer of complexity to our understanding of the genetic underpinnings of these comorbid conditions, suggesting that the mechanisms linking VHD and gastrointestinal disorders may extend beyond the immediate disease pathways to involve broader biological systems and networks.

Integrating whole-blood transcriptomic information in our pleiotropy analysis using the SMR approach, we uncovered multiple loci that are likely associated with VHD and gastrointestinal diseases through genetically regulated transcriptomic changes in whole blood. However, we observed limited convergence which may be due to the need to investigate transcriptomic profiles of blood, heart, and gastrointestinal cells. Additionally, the SMR analysis highlighted that effects located in the MHC region such as the one observed for the AGER locus may appear to be shared between VHD and gastrointestinal diseases due to LD patterns rather than actual pleiotropic effects.

Beyond locus-specific pleiotropic mechanisms, the present study uncovered the potential role of gut microbiome in VHD comorbidity with gastrointestinal disorders, expanding the current knowledge regarding the role of gut microbiome on host health 58 , 59 . In particular, our findings highlighted potential causal effects of Butyricicoccus , Lachnospiraceae , and Ruminococcaceae genera, Butyricicoccus is known for its role in butyrate production, a short-chain fatty acid that has anti-inflammatory effects and is essential for maintaining gut integrity 60 . The genetically inferred protective effect of Butyricicoccus on both VHD and GORD supports that butyrate-producing bacteria may have cardioprotective roles 61 . Similarly, the Lachnospiraceae family, found to significantly affect GORD in our study, is recognized for its role in affecting gut health as well as possible microbiota signature in heart failure 62 , 63 . On the other hand, Ruminococcaceae showed potential effects on IBS, CD, and IBD. This genus is part of another family of butyrate producers 64 that have been implicated in altered gut motility and even systemic inflammation 65 .

The findings from our study have potential implications for clinical practice and could contribute to design other studies, including clinical trials, to improve the prevention and management of VHD in patients with gastrointestinal disorders. For instance, individuals identified as genetically predisposed to both gastrointestinal disorders and VHD could benefit from targeted screening programs, allowing for timely diagnosis and management of potential cardiovascular complications. Additionally, our research highlights the potential role of gut microbiota in influencing both gastrointestinal health and cardiovascular outcomes. This suggests that dietary interventions or probiotic therapies could be explored as preventive measures. By promoting a healthy gut microbiome, healthcare providers may be able to mitigate the risk of developing VHD while simultaneously improving gastrointestinal health. Integrating these insights into clinical practice encourages a multidisciplinary approach, wherein cardiologists and gastroenterologists collaborate to monitor and manage patients with comorbid conditions. This holistic care model not only enhances patient outcomes but also addresses the complex interplay between gastrointestinal and cardiovascular health, ultimately reducing the healthcare burden associated with these conditions.

While our study provides significant insights into the genetic and microbial factors influencing the comorbidity of VHD and gastrointestinal disorders, several limitations warrant consideration. Firstly, the cross-sectional nature of our data limits our ability to establish causal relationships. Although we employed Mendelian randomization to infer directionality, the observational design restricts our understanding of temporal dynamics between these conditions. Secondly, while our sample size is substantial, it may not fully encompass the genetic and microbial diversity present in different populations, potentially affecting the generalizability of our findings. Thirdly, our focus on pleiotropic effects of specific genes and microbial genera did not account for environmental factors such as diet and lifestyle, which can significantly influence both VHD and gastrointestinal disorders. Additionally, the lack of detailed clinical data on participants may limit our ability to control for confounding variables that could impact the observed associations. Lastly, while we identified key microbial genera, the precise functional mechanisms by which these genera exert their effects remain speculative and necessitate further experimental validation to confirm their roles in the comorbidity of these conditions.

In summary, our study sheds new light on the genetic and microbial factors influencing the comorbidity of VHD and gastrointestinal disorders. We identified positive genetic correlations and significant pleiotropic loci, as well as key microbial genera that contribute to these conditions. These findings provide new insights and evidence for future research and potential therapeutic interventions.

Supplementary information

Supplementary material

Acknowledgements

This work was supported by the Chinese Academy of Medical Science Innovation Fund for Medical Sciences (2016-I2M-1-009), and Natural Science Foundation of China (82192902). The authors thank the participants and the investigators involved in UK Biobank, FinnGen, and MiBioGen consortium for making their data available.

Author contributions

J.X., Z.L., L.W., F.P., and R.P. conceived, designed, and performed the analysis, and wrote the manuscript. H.J. and C.C. contributed to data acquisition. R.P. and J.X. reviewed data and the manuscript. W.Y., J.Y., R.P., and Y.Y. conceptualized the study, supervised the research, and revised the manuscript. All authors read and approved the final manuscript.

Data availability

All data generated or analyzed during this study are included in this published article and its Supplementary Information files. GWAS summary statistics used in this study are summarized in Table 1 and detailed in the "Data resources" subsection of the Methods section, with specific references provided therein. The analysis code used in this study has now been made publicly available on GitHub and can be accessed via the following link: https://github.com/JingXu0701/Gastro_CVD/blob/main/code_to_scxs.R .

Competing interests

R.P. received a research grant from Alkermes outside the scope of the present study and received an honorarium for his editorial work on the journal Complex Psychiatry. The other authors have no conflicts of interest to declare.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Jing Xu, Zeye Liu, Lianlian Wu, Fengbo Pei.

Contributor Information

Weixian Yang, Email: wxyang2009@sina.com.

Jiansong Yuan, Email: jsyuantg@163.com.

Renato Polimanti, Email: renato.polimanti@yale.edu.

Yuejin Yang, Email: yangyjfw@126.com.

Supplementary information

The online version contains supplementary material available at 10.1038/s44325-025-00054-w.

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Associated Data

Supplementary Materials

Supplementary material

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its Supplementary Information files. GWAS summary statistics used in this study are summarized in Table 1 and detailed in the "Data resources" subsection of the Methods section, with specific references provided therein. The analysis code used in this study has now been made publicly available on GitHub and can be accessed via the following link: https://github.com/JingXu0701/Gastro_CVD/blob/main/code_to_scxs.R .