Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Abstract

Background

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are widely used antidiabetic agents with established cardiorenal benefits, yet their genetic causal association with venous thromboembolism (VTE) remains uncertain.

Methods

We employed a multifaceted approach to investigate the causal relationship between SGLT2 inhibition and VTE, as well as deep vein thrombosis (DVT) and pulmonary embolism (PE). Drug-target Mendelian randomization (MR) and summary-data-based MR (SMR) analyses were conducted using genetic variants proxying SGLT2 inhibition, derived from SLC5A2 expression quantitative trait loci and glycemic traits. Positive control analyses confirmed validity using type 2 diabetes outcomes. Meta-analyses were performed across multiple independent genome-wide association study datasets. Complementary pharmacovigilance analysis was conducted using the FDA Adverse Event Reporting System (FAERS) to assess disproportionality of SGLT2 inhibitor-related VTE reports.

Results

Genetically proxied SGLT2 inhibition was significantly associated with reduced risk of type 2 diabetes (P < 0.05), validating its robustness. Most MR analyses showed no consistent causal association with VTE, DVT, or PE across datasets, and meta-analyses yielded non-significant pooled estimates (all P > 0.05). SMR analyses revealed no association between SLC5A2 expression and VTE-related outcomes (all P_SMR > 0.05). FAERS disproportionality analysis also identified no safety signals for PE (reporting odds ratio [ROR] = 0.51; 95% CI: 0.42–0.64) or DVT (ROR = 0.54; 95% CI: 0.45–0.64).

Conclusions

This integrative study provides consistent evidence that SGLT2 inhibition is not causally associated with VTE, DVT, or PE, supporting its thrombotic safety. However, generalizability to non-European populations requires future validation in diverse cohorts.

Keywords

sodium-glucose cotransporter 2 inhibitors venous thromboembolism mendelian randomization FAERS deep vein thrombosis pulmonary embolism

1.Introduction

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is one of the major health burdens worldwide with life-threatening acute outcomes and chronic disabling sequelae.^1,2 In 2022, there were an estimated 1,182,065 total VTE hospital discharges for all-listed diagnoses in the United States, including 469,115 cases of PE and 712 950 cases of DVT, and many survivors suffer from debilitating long-term complications.³ The pathogenesis, rooted in Virchow’s triad of venous stasis, endothelial injury, and hypercoagulability. It often involves thrombus formation within venous valve pockets where local hypoxia and disturbed hemodynamics create a pro-thrombotic microenvironment.^4,5 Clinically, a wide array of risk factors, including advanced age, malignant tumor, major surgery, hospitalization, obesity, and diabetes, may contribute to disease susceptibility.⁶

Sodium-glucose cotransporter 2 (SGLT2) inhibitors represent a pivotal class of oral antidiabetic agents that lower blood glucose by inhibiting renal glucose reabsorption, thereby promoting glucosuria.⁷ Beyond their glycemic efficacy, large-scale cardiovascular outcome trials (CVOTs) have established robust cardiorenal protective benefits for SGLT2 inhibitors, leading to their expanded indications in patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD).^8,9 This broadening therapeutic landscape necessitates a comprehensive evaluation of their long-term safety profile. While certain adverse events like diabetic ketoacidosis and genital infections are recognized, the potential impact on other systems, including the coagulation cascade, remains an active area of investigation.

From a mechanistic standpoint, SGLT2 inhibitors may exert bidirectional effects on VTE risk.^10,11 On the one hand, the diuretic effect of SGLT2 inhibitors and their association with increased hemoglobin and hematocrit levels, known as a phenomenon termed SGLT2 inhibitor-induced erythrocytosis, have raised theoretical concerns regarding altered blood rheology and a potential increase in thrombotic risk.^11,12 Clinically, a nationwide registry study in patients with heart failure with reduced ejection fraction demonstrated that SGLT2 inhibitor treatment significantly increased erythrocytosis incidence but was not associated with higher 1-year VTE risk.⁹ Moreover, one meta‐analysis from regulatory submissions identified an increase in risk of thromboembolism events among SGLT2 inhibitor users, though the result is non‐significant.¹³ On the other hand, emerging evidence supports potential antithrombotic properties of SGLT2 inhibitors, including suppression of hepcidin-mediated iron sequestration, attenuation of systemic inflammation, improvement of endothelial function, and direct inhibition of platelet activation.^14-16 Correspondingly, some studies suggest SGLT2 inhibitors do not appear to be associated with VTE and may even have protective effects, while the dual SGLT1/2 inhibitor sotagliflozin may have a more significant antithrombotic effect than the selective SGLT2 inhibitor empagliflozin.^17,18 These discrepancies likely come from methodological limitations inherent in existing research, including confounding by indication in observational studies, insufficient statistical power in randomized controlled trials, and critical knowledge gaps regarding differential effects across SGLT2 inhibitor subtypes.

Resolving the uncertainty surrounding the SGLT2 inhibitor-VTE relationship requires methodologies that can mitigate the confounding and biases inherent in conventional observational studies. Mendelian randomization (MR) analysis offers a powerful genetic epidemiological tool for causal inference by using genetic variants as instrumental variables.¹⁹ Specifically, drug-target MR analysis leverages genetic proxies for pharmacological inhibition to emulate the effect of long-term drug exposure on an outcome, akin to a randomized trial.^20,21 This approach has been successfully applied to investigate the effects of SGLT2 inhibition on various outcomes, including atrial fibrillation, heart failure, and intracerebral hemorrhage.^7,22,23 Concurrently, pharmacovigilance analyses of real-world data, such as the FDA Adverse Event Reporting System (FAERS), provide complementary evidence by detecting potential safety signals from spontaneous post-marketing reports.^8,24

2. Methods

2.1 Selection of Genetic Instruments for SGLT2 Inhibition

After consulting the Drugbank database, SLC5A2 gene was selected as the target gene (Table S1). As illustrated in Figure 1, the process of identifying genetic variants relevant to SGLT2 inhibition involved four distinct phases. The initial phase relied on publicly accessible data from the eQTLGen Consortium²⁵ to screen for genetic variants influencing the expression of SLC5A2. Moving to the second phase, we examined how each of these variants was correlated with glycated hemoglobin (HbA1c) levels, given that HbA1c serves as a key indicator of glycemic control.²⁶ The genome-wide association study (GWAS) summary statistics of HbA1c were derived from 344,182 diabetes-free individuals of European ancestry participating in the UK Biobank (Table S2). Only those variants exhibiting a robust association with HbA1c (P < 5 × 10^-5) proceeded to the next stage. The third phase entailed colocalization testing to determine whether the same causal variant underlies both SLC5A2 expression and HbA1c variation, with a posterior probability exceeding 60% interpreted as positive evidence for a shared signal.²⁶ Moreover, we applied LD-based clumping to the prioritized variants, referencing the 1,000 Genomes European panel and setting parameters at r² < 0.8 over a distance of 250 kb⁷. The strength of the genetic instruments was rigorously evaluated using the F-statistic; single nucleotide polymorphisms (SNPs) with an F-statistic below the conventional threshold of 10 were removed from the analysis to prevent weak instrument bias.²⁷

Figure 1.

The overall design of the present study

2.2 Outcome Sources

As for the positive control data sets, we extracted type 2 diabetes from the IEU Open GWAS database, with OpenGWAS IDs named “ebi-a-GCST90029024”, “finn-b-T2D″, and “ebi-a-GCST90093109”. GWAS summary statistics for VTE, DVT, and PE were also sourced from the IEU Open GWAS database. The dataset for VTE comprised ebi-a-GCST90038607, finn-b-I9_VTE, and ukb-d-I9_VTE. The dataset for DVT comprised ukb-d-I9_PHLETHROMBDVTLOW and finn-b-I9_PHLETHROMBDVTLOW. Moreover, finn-b-I9_PULMEMB, ebi-a-GCST90038614, ukb-b-18366 and ukb-d-I26 were selected to serve as the GWAS data set for PE. The details of each outcome can be found in Table S2. The present study is based on publicly available statistics. Ethical approval and informed consent have been waived off.

2.3 MR and Summary-Data-Based Mendelian Randomization (SMR) Analysis

To reveal the causal effects of SGLT2 inhibitor with VTE-related traits, we performed MR analysis utilizing the ‘TwoSampleMR’ R package (version 0.6.22). The associations between cis-eQTLs and the outcomes were calculated in the form of odds ratios (ORs) and corresponding CIs by applying the inverse-variance weighted (IVW) method as the primary analysis. MR-Egger, weighted median, and weighted mode methods were additionally conducted.²⁸ Moreover, positive control analyses were conducted to validate the genetic instruments for SGLT2 inhibitor, which was already approved by the FDA to control blood glucose and weight. Subsequently, we performed meta-analysis to pool the MR findings for VTE-, DVT-, and PE-related traits.²⁹ As for the SMR analysis, we assessed the effect of the expression level of SLC5A2 using cis-expression quantitative trait loci (eQTL) dataset (eQTLGen) on VTE-related traits (finn-b-I9_VTE, finn-b-I9_PHLETHROMBDVTLOW, and finn-b-I9_PULMEMB) managed by the FinnGen consortium as the outcome using the default parameters via SMR portal.

2.4 FAERS Database Analysis

We conducted a retrospective pharmacovigilance study using data from the FDA Adverse Event Reporting System (FAERS). All reports from 2004Q1 to 2025Q4 were extracted and processed. The drug of interest was SGLT2 inhibitors, including canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, and ipragliflozin, which were recorded as primary suspect (PS). Adverse events were identified using the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms (PTs): “PULMONARY EMBOLISM” and “PULMONARY THROMBOSIS” for PTE and “DEEP VEIN THROMBOSIS” for DVT. We performed case/non-case analyses to estimate the association between SGLT2 inhibitors and PE/DVT. Adverse event reports for SGLT2 inhibitors were compared with all other drugs in the database. Disproportionality was assessed using the Reporting Odds Ratio (ROR) and the Information Component (IC) calculated as the previous published study.²⁴ For clinicians to better understand the above terms, a Supplementary Glossary (Table S3) was provided.

2.5 Statistical Analysis

For the MR analyses, the MR-PRESSO was used to detect outlier genetic variants. In addition, heterogeneity and horizontal pleiotropy were assessed using Cochrane’s Q test (P < 0.05 indicating significant heterogeneity) and the MR-Egger intercept test (P < 0.05 indicating evidence of pleiotropy), respectively. For the meta-analysis, we used estimated ORs and 95% CI. I² was used to evaluate heterogeneity across included studies, and if I² > 50%, it is considered significant heterogeneity. To decrease the influence of heterogeneity on the accuracy, all the analyses are conducted using random effect model. Probability values of < 0.05 were considered statistically significant. For SMR analysis, a significant association was defined as P< 0.05 with no evidence that the association is driven by linkage disequilibrium (P_HEIDI > 0.05). The HEIDI (Heterogeneity in Dependent Instruments) test is used in SMR analysis to distinguish whether the observed association between gene expression and the outcome is due to a shared causal variant (i.e., the gene itself) or to linkage disequilibrium (i.e., nearby but distinct genetic variants). A P_HEIDI > 0.05 indicates no evidence of confounding by linkage disequilibrium, supporting that the SLC5A2 gene expression, rather than a neighboring gene, is likely responsible for the observed effect. All SMR and HEIDI analyses were performed using the SMR portal (https://yanglab.westlake.edu.cn/smr-portal/). As for the disproportionality analysis of FAERS, a signal was considered statistically significant if the lower limit of the 95% confidence interval for ROR > 1, and the lower limit of the 95% credible interval for IC > 0. All the analyses except SMR analysis were done with R software (V4.4.1).

3. Results

3.1 Results of MR Analyses

We selected 6 significant cis-eQTL SNPs from the eQTLGen consortium as genetic instruments for the SLC5A2 gene expression. All instruments had F-statistics exceeding 10, indicating negligible weak instrument bias (Table S4). As Table 1 shows, genetically proxied SGLT2 inhibition was associated with a significantly reduced risk of type 2 diabetes (P < 0.05), confirming the well-established hypoglycemic effect of SGLT2 inhibition. The colocalization testing showed a posterior probability exceeding 60% representing positive evidence for a shared signal between SLC5A2 gene expression and HbA1c level. This protective causal association was consistently replicated across another 2 external GWAS validation datasets (all P < 0.05), validating the reliability of the selected genetic instruments and the MR analysis framework.

Table 1.

Positive Control Analyses of the Genetically Proxied SGLT2 Inhibition on Type 2 Diabetes

Exposure	Trait id	Trait names	Method	nsnp	b	Pval
SGLT2 inhibitor	ebi-a-GCST90029024	T2D	IVW	6	-0.01	2.67E-05
SGLT2 inhibitor	finn-b-T2D	T2D	IVW	6	-0.31	2.68E-05
SGLT2 inhibitor	ebi-a-GCST90093109	T2D	IVW	5	-0.29	1.68E-02

Genetically proxied SGLT2 inhibition showed no statistically significant causal association with the risk of VTE, DVT, or PE. The results are presented in Figure 2. For PE, analysis of the ebi-a-GCST90038614 dataset revealed a weak but statistically significant increased risk (OR = 1.004, 95% CI: 1.002–1.007, P = 0.001), whereas the finn-b-I9_PULMEMB, ukb-b-18366, and ukb-d-I26 datasets all yielded non-significant associations (ORs ranging from 0.795 to 1.000, all P > 0.05). In the VTE outcome, divergent results were also observed. The ebi-a-GCST90038607 dataset showed a significantly elevated risk (OR = 1.012, 95% CI: 1.008–1.016, P < 0.001), while the finn-b-I9_VTE dataset demonstrated a significant protective effect (OR = 0.658, 95% CI: 0.531–0.815, P < 0.001), and the ukb-d-I9_VTE dataset was non-significant (OR = 1.000, 95% CI: 0.997–1.004, P = 0.894). For lower-extremity deep vein thrombosis (DVT), the finn-b-I9_PHLETHROMBDVTLOW dataset identified a significant protective effect (OR = 0.642, 95% CI: 0.477–0.866, P = 0.004), while the ukb-d-I9_PHLETHROMBDVTLOW dataset was non-significant (OR = 1.000, 95% CI: 0.997–1.002, P = 0.733). No outlier genetic variants were detected via MR-PRESSO, and MR-Egger intercept tests indicated no horizontal pleiotropy across all analyses (all P > 0.05). Cochrane’s Q value assessment found no significant heterogeneity in IVW estimators for SGLT2 inhibition and VTE, DVT, or PE (all P > 0.05). The details of the above results can be found in Table S5-7.

Figure 2.

Forest plot of the association between the genetic variant and venous thrombotic events (lower extremity deep vein thrombosis, venous thromboembolism, and pulmonary embolism) across multiple cohorts. The plot presents the number of cases (ncase) and controls (ncontrol), odds ratio (OR) with 95% confidence interval (95%CI), and corresponding P value for each cohort and the combined total effect. Heterogeneity statistics (I², τ², and heterogeneity P value) and the application of a random effects model for meta-analysis are also indicated for each thrombotic outcome

3.2 Meta-Analysis of MR Results and SMR Analyses

As displayed in Figure 2, in the meta-analysis of the above MR findings for VTE-related traits, we discovered significant inter-trait heterogeneity (all I²>50%). Thus, we selected the random-effects model for further estimations. The pooled ORs with 95% CIs further displayed the absence of a causal link between SGLT2 inhibition and VTE, DVT, or PE (all P > 0.05). During the SMR analyses, no significant association was observed between the SLC5A2 gene expression level and the risk of VTE, DVT, or PE (all P_SMR > 0.05) with no evidence of linkage disequilibrium confounding in all analyses (all P_HEIDI > 0.05). The details of the results can be found in Figure 3 and Table S8. The SMR analyses further supported the lack of a causal relationship between SGLT2 inhibition and VTE-related traits.

Figure 3.

Genomic locus on chromosome 16 associated with venous thrombotic events and dot plots for Summary-data-based Mendelian Randomization (SMR). (A) for venous thromboembolism (VTE), (B) for pulmonary embolism (PE) and (C) for deep vein thrombosis (DVT) of lower extremities

3.3 FAERS Signal Mining Analysis

After screening for adverse event reports where SGLT2 inhibitors were listed as the PS, a total of 35,118 reports were identified (Figure 4). Among these, there were 83 cases of PE and 53 cases of DVT. Disproportionality analysis yielded a Reporting Odds Ratio (ROR) of 0.51 (95% CI: 0.42–0.64) for PE and 0.54 (95% CI: 0.45–0.64) for DVT, both of which were statistically significant (p < 0.001). The Information Component (IC) values were -0.95 (IC025: -1.26) for PE and -0.89 (IC025: -1.13) for DVT. The above results indicated no significant signals of SGLT2 inhibitors for either PE or VTE.

Figure 4.

Medication-induced risk of SGLT2 inhibitors in patients with pulmonary embolism (PE) and deep vein thrombosis (DVT). (A) FDA Event Reporting System (FAERS) Data analysis workflow. (B) Quantitative analysis of the reporting odds ratio (ROR) with 25th (ROR025) and 75th (ROR075) percentiles, P value (pvalue), and information component (IC) with 25th percentile (IC025) for PE and DVT, with the number of reported cases for each trait indicated

4. Discussion

4.1 Main Findings of the Study

In this study, we employed a multifaceted approach combining drug-target MR, SMR, and real-world pharmacovigilance analysis using the FAERS database to investigate the causal relationship between SGLT2 inhibition and the risk of VTE, DVT, and PE. The positive control analysis confirmed that SGLT2 inhibition was significantly associated with reduced risk of type 2 diabetes, consistent with established clinical evidence. This validation supports the reliability of our genetic instruments and MR framework. Our findings suggest that genetically proxied SGLT2 inhibition is not causally associated with these thrombotic outcomes after meta-analysis, despite some heterogeneity across individual GWAS datasets. The absence of a consistent causal effect is further supported by the SMR analyses. The FAERS disproportionality analysis revealed SGLT2 inhibitors did not emerge as significant signals for PE or DVT, which is consistent with the MR findings and supports the safety profile of SGLT2 inhibitors with respect to thrombotic risk.

Before performing the meta-analysis, we observed notable divergence across individual MR datasets. For VTE, the ebi-a-GCST90038607 dataset suggested a significantly elevated risk (OR = 1.012, P < 0.001), while the finn-b-I9_VTE dataset demonstrated a significant protective effect (OR = 0.658, P < 0.001), and the ukb-d-I9_VTE dataset was not significant. Similar inconsistencies were seen for DVT and PE. Several factors may drive these discrepancies. First, differences in population demographics across GWAS consortia may influence allele frequencies and LD patterns. Second, the inconsistency in VTE case definitions and ascertainment methods (e.g., hospital inpatient records vs. registry-based validated diagnoses) may introduce heterogeneity. Third, the statistical power varies considerably across datasets; datasets with smaller sample sizes or lower event rates may produce unstable estimates. Fourth, differences in genetic architecture, including the specific SLC5A2 cis-eQTL variants captured by each GWAS, could affect the precision of the genetic instruments. To address this heterogeneity, we employed a random-effects model for all meta-analyses, which accounts for between-study variance and yields more conservative pooled estimates. The pooled ORs remained non-significant across all thrombotic outcomes (all P > 0.05), supporting the overall safety profile.

4.2 Where we Stand

Due to their metabolic and cardiovascular benefits, SGLT2 inhibitors have become a cornerstone therapy for patients with diabetes mellitus. However, concerns regarding thromboembolic risk have emerged. The mild diuresis induced by SGLT2 inhibitors may lead to volume depletion and increased blood viscosity, potentially elevating VTE risk.^11,17 Conversely, SGLT2 inhibitors may also reduce VTE risk given their roles of reducing inflammation.^15,16

FDA safety signals are derived from systematic analysis of spontaneous adverse event reports (e.g., FAERS). Disproportionality methods, such as the ROR or IC, compare observed versus expected reporting frequencies for a given drug–event pair. A signal is declared when the lower bound of the 95% CI for ROR exceeds 1 (or IC > 0), indicating a disproportionately higher reporting rate that may warrant further investigation. Correspondingly, thromboembolic events have been reflected in a potential safety signal identified by the U.S. Food and Drug Administration (FDA) between April–June 2015 (https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm484292.htm). During the past years, though there were several studies pharmacovigilance analysis of SGLT2 inhibitors, significant signals haven’t been reported from FAERS in terms of VTE risk.^30-33 Thus, pharmacovigilance specifically addressing VTE using the most recently published data is still lacking.

Several clinical studies have explored the efficacy and safety of SGLT2 inhibitors in terms of VTE, though there is a heterogeneity across the results of these studies without significant conclusions. For example, Patil et al³⁴ demonstrated the use of SGLT2 inhibitors owned a HR of 1.03 when compared with glucagon-like peptide-1 receptor agonist (GLP-1RA). Another two trials indicated the incidence of VTE was low and did not differ between SGLT2 inhibitors and placebo.^35,36 Moreover, several meta-analyses also focused on such issue. For example, the potential for SGLT2 inhibitors to confer additional protective effects against VTE than dipeptidyl peptidase-4 (DPP4) inhibitors has been suggested by prior epidemiological evidence based on a nationwide population-based study and meta-analysis.³⁷ A previous meta-analysis demonstrated no increased risk of VTE with SGLT2 inhibitors.¹⁷ In contrast to these prior studies, our investigation offers several methodological advances. Unlike observational studies susceptible to confounding (obesity, advanced diabetes, and cardiovascular comorbidities are themselves independent risk factors for VTE), our drug-target MR design provides unconfounded estimates of lifelong SGLT2 inhibition on VTE risk.

In a MR analysis, Huang et al³⁸ demonstrated SGLT2 inhibitors significantly reduced the risk of VTE using 13 proxy SNPs by the method of MR. Nevertheless, they only utilized one GWAS data set to draw the conclusion, which may cause bias. In contrast, our study employed a more robust framework by integrating multiple independent GWAS datasets, followed by meta-analyses to pool estimates across heterogeneous populations. Furthermore, we complemented MR with SMR analysis to validate findings at the SLC5A2 gene expression level and with real-world FAERS disproportionality analysis, both of which supported the absence of a causal association. Hence, our multi-dataset, triangulation-based framework represents a major methodological advance over the single-dataset MR analysis by Huang et al. Above all, most of the recent studies tend to show no significant association between SGLT2 inhibitors and VTE.

4.3 Where we Are Heading

Corresponding to the most previous studies, our study further proved the low risk of VTE induced by the SGLT2 inhibitors, which offers distinct methodological advantages over conventional clinical studies in evaluating the causal relationship between SGLT2 inhibition and VTE risk. Drug-target MR has emerged as a valuable tool for both identifying novel therapeutic targets and investigating drug-related complications, with proven utility across a range of diseases.²⁶ Traditional observational studies are inherently susceptible to confounding by indication³⁹—where the underlying reasons for prescribing SGLT2 inhibitors (e.g., obesity, advanced diabetes, cardiovascular comorbidities) are themselves independent risk factors for VTE.⁴⁰ In contrast, our MR approach utilizes genetic variants that randomly assort at conception to proxy SGLT2 inhibition, mimicking the randomization process of clinical trials and providing unconfounded estimates of lifelong drug exposure on disease outcomes.

Our approach captures the lifetime effect of SGLT2 inhibition, overcoming the limited follow-up (1–5 years) of clinical studies that may miss slowly accumulating thrombotic effects. Genetic variants reflect lifelong exposure to SGLT2 inhibition, enabling a more comprehensive assessment. We enhanced robustness through triangulation of multiple genetic methods, integrating standard MR, SMR, colocalization, and meta-analysis across independent GWAS datasets. SMR validated findings at the gene expression level, while colocalization ensured that shared causal variants drove the associations. Multiple outcome datasets allowed us to detect and quantify heterogeneity—an advantage over single observational studies or clinical trials.

4.4 Limitations

Several limitations should be considered. First, while drug-target MR proxies the lifelong effect of SGLT2 inhibition, this approach has inherent assumptions. Although we conducted comprehensive sensitivity analyses and colocalization, residual horizontal pleiotropy cannot be entirely excluded. Moreover, genetic instruments were derived from European populations. This is a significant limitation because genetic architecture (e.g., allele frequencies, linkage disequilibrium patterns), lifestyle factors, and VTE prevalence vary considerably across ethnic groups. Therefore, our findings may not be directly generalizable to non-European populations, which warrants future validation across diverse ancestral backgrounds. Second, MR estimates may not be directly comparable to shorter-term pharmacological effects in trials or real-world practice. Third, although SMR validated our findings at the gene expression level, it cannot distinguish causality from pleiotropy if the variant affects outcomes independently of SLC5A2 expression, despite the HEIDI test. Fourth, the FAERS pharmacovigilance analysis has inherent biases, including under-reporting, selective reporting, and lack of detailed clinical information (dosage, comorbidities, concomitant medications), precluding confounder adjustment or subgroup analyses. FAERS also cannot provide incidence rates—signals reflect disproportionality, not true risk. Finally, given that our MR analysis captures the lifetime effect of SGLT2 inhibition, and the FAERS analysis reflects post-marketing spontaneous reports, the possibility of a small effect size or rare thrombotic events cannot be definitively ruled out. Future large-scale prospective cohort studies or randomized controlled trials with extended follow-up periods and detailed adverse event adjudication are warranted to further validate our findings.

5. Conclusion

In conclusion, this integrative study provides consistent evidence that SGLT2 inhibition is not causally associated with VTE, DVT, or PE. These findings support the thrombotic safety of SGLT2 inhibitors and may inform clinical decision-making and regulatory assessments. However, generalizability to non-European populations still requires in future validation in diverse cohorts.

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material for Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis by Jing Liu, Zipei Ma, Piaoran Liu, Tiezhu Yao, Jing He, Tenghui Wang, Mengjia Li, Sihan Liu, Jingtao Ma, Zhenli Li in Clinical and Applied Thrombosis/Hemostasis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Supplemental Material

Supplemental Material - Explore the Causal Effect of SGLT2 Inhibition on Venous Thromboembolism Events: A Drug-Target Mendelian Randomization Study With Pharmacovigilance Analysis

Footnotes

Acknowledgments

Authors are thankful to study participants, hospital staff and doctors for their help in this study.

ORCID iD

Zhenli Li

Ethical Considerations

The present study is based on publicly available statistics. Ethical approval and informed consent have been waived off. Authors have approved the final version to be published.

Author Contributions

Zhenli Li and Jing Liu contributed to the conception, data analysis, and design of the study. Zhenli Li, Tenghui Wang, Mengjia Li, Zipei Ma, Piaoran Liu, Tiezhu Yao and Jingtao Ma drafted the work and reviewed the main manuscript. Zhenli Li made necessary analyses of this study. Zipei Ma, Piaoran Liu made great efforts for the revision.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Foundation of Hebei Provincial Department of Health (Grant Number 20260569).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings are all involved in the present manuscript.*

Use of Large Language Models,AI and Machine Learning Tools

The authors declare that AI technology was used exclusively for language refinement and did not contribute to the generation of scientific content, data analysis, or intellectual conclusions.

Supplemental Material

Supplemental material for this article is available online.

References

Marschang

Gerotziafas

Kozak

Cosmi

Catalano

Stanek

. Epidemiology of venous thromboembolism: implications for clinical practice. Pol Arch Intern Med. 2025;135(10):135. doi:10.20452/pamw.17105.

Bikdeli

Rashedi

Pfeferman

Barco

Piazza

Cushman

. Venous Thromboembolism: Global Burden of Disease Estimates Are Missing a Common Cardiovascular Condition. J Am Coll Cardiol. 2025;86(22):2135-2138. doi:10.1016/j.jacc.2025.08.078.

Palaniappan

Allen

Almarzooq

, et al. Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2026;153(9):e275-e906. doi:10.1161/CIR.0000000000001412.

Mehta

Calcaterra

Bassareo

. COVID-19, thromboembolic risk, and Virchow's triad: Lesson from the past. Clin Cardiol. 2020;43(12):1362-1367. doi:10.1002/clc.23460.

Stone

Hangge

Albadawi

, et al. Deep vein thrombosis: pathogenesis, diagnosis, and medical management. Cardiovasc Diagn Ther. 2017;7(Suppl 3):S276-S284. doi:10.21037/cdt.2017.09.01.

Lee

Gallus

Jindal

Wang

. Incidence of Venous Thromboembolism in Asian Populations: A Systematic Review. Thromb Haemost. 2017;117(12):2243-2260. doi:10.1160/TH17-02-0134.

Sun

, et al. SGLT2 inhibition, circulating metabolites, and atrial fibrillation: a Mendelian randomization study. Cardiovasc Diabetol. 2023;22(1):278. doi:10.1186/s12933-023-02019-8.

Chen

Anijs

RJS

Verlaan

JPL

Scheres

LJJ

Klok

Cannegieter

. Novel Antidiabetic Drugs and Risk of Venous Thromboembolism: A Literature Review. Semin Thromb Hemost. 2025;51(7):756-767. doi:10.1055/a-2546-0353.

Mohamed

Andersen

Christensen

, et al. Erythrocytosis incidence and thromboembolic risk in heart failure with reduced ejection fraction treated with SGLT2 inhibitors: a nationwide register-based cohort study. Cardiovasc Diabetol. 2025;24(1):314. doi:10.1186/s12933-025-02871-w.

10.

Aoun

Jadoul

Anders

. Erythrocytosis and CKD: A Review. Am J Kidney Dis. 2024;84(4):495-506. doi:10.1053/j.ajkd.2024.02.015.

11.

Chen

Hsieh

TYJ

Sun

, et al. Erythrocytosis and thromboembolic risks associated with SGLT2 inhibitors in type 2 diabetes. Sci Rep. 2025;16(1):2000. doi:10.1038/s41598-025-31726-3.

12.

Ueda

Svanstrom

Melbye

, et al. Sodium glucose cotransporter 2 inhibitors and risk of serious adverse events: nationwide register based cohort study. BMJ. 2018;363:k4365. doi:10.1136/bmj.k4365.

13.

Foote

Blomster

, et al. Effects of sodium-glucose cotransporter-2 inhibitors on cardiovascular events, death, and major safety outcomes in adults with type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2016;4(5):411-419. doi:10.1016/s2213-8587(16)00052-8.

14.

Yaribeygi

Atkin

Jamialahmadi

Sahebkar

. A Review on the Effects of New Anti-Diabetic Drugs on Platelet Function. Endocr Metab Immune Disord Drug Targets. 2020;20(3):328-334. doi:10.2174/1871530319666191014110414.

15.

Stankovic

Miloradovic

Petrovic

Stoiljkovic

. Redefining SGLT2 inhibitors through cytoprotective mechanisms. Eur J Pharmacol. 2026;1016:178647. doi:10.1016/j.ejphar.2026.178647.

16.

Fonseca-Correa

Correa-Rotter

. Sodium-Glucose Cotransporter 2 Inhibitors Mechanisms of Action: A Review. Front Med (Lausanne). 2021;8:777861. doi:10.3389/fmed.2021.777861.

17.

Wang

Yang

Wang

Zhang

Tang

Feng

. Effects of sodium-glucose cotransporter 2 inhibitors on risk of venous thromboembolism in patients with type 2 diabetes: A systematic review and meta-analysis. Diabetes Metab Res Rev. 2020;36(1):e3174. doi:10.1002/dmrr.3174.

18.

Requena-Ibanez

Zafar

Ferrandez-Escarabajal

, et al. Rationale and Design of the SOTA-THROMBOSIS Trial (ATRU-VI): Antithrombotic Activities of Sotagliflozin Compared With Empagliflozin. J Cardiovasc Pharmacol. 2025;86(5):458-462. doi:10.1097/FJC.0000000000001747.

19.

Yang

Wan

, et al. The genetic causal relationship between type 2 diabetes, glycemic traits and venous thromboembolism, deep vein thrombosis, pulmonary embolism: a two-sample Mendelian randomization study. Thromb J. 2024;22(1):33. doi:10.1186/s12959-024-00600-z.

20.

Wang

Chen

Cheng

, et al. SGLT2 inhibitor, bone metabolism biomarkers, and osteoporosis risk: A Mendelian randomization study. Exp Gerontol. 2025;211:112935. doi:10.1016/j.exger.2025.112935.

21.

Song

, et al. SGLT2 inhibition, acylcarnitines and heart failure: a Mendelian randomization study. Open Heart. 2025;12(2):e003078. doi:10.1136/openhrt-2024-003078.

22.

Zheng

Wang

, et al. Genetic insights into SGLT2 inhibition and atrial fibrillation: exploring the mediating role of inflammatory proteins. Cardiol J. 2025;32(6):635-642. doi:10.5603/cj.100340.

23.

Huang

Liu

Chen

. SGLT2 inhibition mitigates intracerebral hemorrhage risk by modulating inflammation. Sci Rep. 2025;15(1):17770. doi:10.1038/s41598-024-84118-4.

24.

Zhang

Wang

Zhao

Wang

Shang

. Evaluation of the real-world safety of eptifibatide in the treatment of ARDS: results of a disproportionality analysis of FAERS data. Front Med (Lausanne). 2026;13:1767036. doi:10.3389/fmed.2026.1767036.

25.

Võsa

Claringbould

Westra

, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300-1310. doi:10.1038/s41588-021-00913-z.

26.

Chen

Wang

. Unraveling the Genetic Links: The Impacts of Antidiabetic Drugs on Stroke Risk. Rev Cardiovasc Med. 2025;26(10):40051. doi:10.31083/rcm40051.

27.

Guo

. Explore the relationships between gut microbiota and cardiomyopathies with the potential mediating effect of cytokines: A univariable and multivariable Mendelian randomization study. Medicine (Baltimore). 2025;104(45):e45714. doi:10.1097/md.0000000000045714.

28.

Liu

Zhao

, et al. A Mendelian randomization study investigating the causal relationship between 35 blood and urine metabolite biomarkers and postmenopausal osteoporosis. Life Conflux. %12/%30. 2024;1(1):e111. doi:10.71321/ktnt2936.

29.

Sun

Liu

Dian

Zeng

Deng

Lei

. Association of glucagon-like peptide-1 receptor agonists with risk of cancers-evidence from a drug target Mendelian randomization and clinical trials. Int J Surg. 2024;110(8):4688-4694. doi:10.1097/js9.0000000000001514.

30.

Dong

Wang

Lan

Zeng

. A study on the pharmacovigilance of various SGLT-2 inhibitors. Front Med (Lausanne). 2024;11:1515847. doi:10.3389/fmed.2024.1515847.

31.

Dong

Guo

Cai

Guo

Liu

Cheng

. Post-marketing safety profile of Ertugliflozin: Insights from a real-world pharmacovigilance analysis based on the FAERS database. Eur J Pharmacol. 2025;1005:178077. doi:10.1016/j.ejphar.2025.178077.

32.

Liu

Wang

Huang

. The real-world safety profile of empagliflozin: a disproportionality analysis based on the FDA Adverse Event Reporting System (FAERS) database. BMC Pharmacol Toxicol. 2025;26(1):28. doi:10.1186/s40360-025-00861-y.

33.

Zhou

Guo

, et al. Safety of SGLT2 Inhibitors: A Pharmacovigilance Study from 2013 to 2021 Based on FAERS. Front Pharmacol. 2021;12:766125. doi:10.3389/fphar.2021.766125.

34.

Patil

Cook

Hobson

Kaur

Lee

. Evaluating the Safety of Sodium-Glucose Cotransporter-2 Inhibitors in a Nationwide Veterans Health Administration Observational Cohort Study. Am J Cardiol. 2023;201:281-293. doi:10.1016/j.amjcard.2023.06.016.

35.

Bhatt

Szarek

Steg

, et al. Sotagliflozin in Patients with Diabetes and Recent Worsening Heart Failure. N Engl J Med. 2021;384(2):117-128. doi:10.1056/NEJMoa2030183.

36.

Schulze

Bogoviku

Westphal

, et al. Effects of Early Empagliflozin Initiation on Diuresis and Kidney Function in Patients With Acute Decompensated Heart Failure (EMPAG-HF). Circulation. 2022;146(4):289-298. doi:10.1161/circulationaha.122.059038.

37.

Tsai

Lin

Yeh

, et al. Sodium-glucose co-transporter-2 inhibitors and the risk of venous thromboembolism: A nationwide population-based study and meta-analysis. Diabetes Metab Res Rev. 2024;40(2):e3739. doi:10.1002/dmrr.3739.

38.

Huang

Ruan

, et al. Comprehensive assessment of the causal effects and metabolite mediators of glucose-lowering drug targets on cardio-renal-liver-metabolic health. Metabolism. 2025;169:156276. doi:10.1016/j.metabol.2025.156276.

39.

Smith

Timpson

Ebrahim

. Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization. Ann Med. 2008;40(7):524-541. doi:10.1080/07853890802010709.

40.

Pastori

Cormaci

Marucci

, et al. A Comprehensive Review of Risk Factors for Venous Thromboembolism: From Epidemiology to Pathophysiology. Int J Mol Sci. 2023;24(4):3169. doi:10.3390/ijms24043169.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.01 MB

0.02 MB

0.01 MB

0.00 MB