Introduction

Calcific aortic valve stenosis (CAVS) is the most prevalent valvular heart disease in developed countries, with an incidence of 2%-3% in individuals aged >65 years.^1-4 Surgical or transcatheter valve replacement remains the primary effective therapeutic option.^5,6 Pathologically, CAVS is increasingly recognized as an active process involving inflammation, oxidative stress, and metabolic dysregulation, akin to atherosclerosis.^7,8 Among these mechanisms, metabolic disturbances, particularly insulin resistance, have emerged as a critical contributor to CAVS pathogenesis.^9,10 Insulin resistance exacerbates systemic inflammation, oxidative stress, and endothelial dysfunction, all of which are implicated in aortic valve calcification.^11-14

The triglyceride-glucose (TyG) index, a novel and cost-effective marker of insulin resistance derived from fasting triglyceride and glucose levels, has gained attention for its clinical utility.^15-20 Unlike traditional measures such as the Homeostasis Model Assessment of Insulin Resistance, the TyG index offers greater accessibility and reflects both lipid and glucose dysregulation, key drivers of systemic inflammation and vascular calcification.^21-27 Recent studies have underscored its relevance in CAVS, with a case-control study demonstrating a significant association between the TyG index and the presence of aortic valve calcification (OR = 1.743, P < .05, 95% CI: 1.04-2.93).²⁸ Furthermore, retrospective cohort studies have demonstrated that a higher TyG index is linked to poor prognosis in patients with CAVS undergoing transcatheter aortic valve replacement [Hazard ratio (HR) = 5.41, P < .001, 95% CI: 4.01-7.32],²⁹ and increases all-cause mortality in severe aortic stenosis (HR = 1.622, P = .018, 95% CI: 1.09-2.42).³⁰ These findings collectively suggest a potential role of the TyG index in CAVS progression.

However, the causal relationship between the TyG index and CAVS remains unclear, necessitating robust analytical approaches such as Mendelian randomization (MR) to address potential confounding and reverse causality.^31,32 While previous MR studies have separately established robust causal associations between triglycerides, diabetes, and CAVS,^33-35 the potential causal association between the TyG index, a composite metabolic biomarker, and CAVS remains unexplored. To address this knowledge gap, bidirectional 2-sample MR and multiple MR were employed to elucidate the independent causal effect of the TyG index on CAVS.

Methods

Study Design

This study utilized a bidirectional MR framework to assess causal relationships in both directions: (1) the effect of the TyG index on the risk of CAVS and (2) the effect of CAVS on the TyG index. The MR analysis is grounded in 3 fundamental assumptions: (1) the selected instrumental variables (IVs) must exhibit strong associations with the TyG index, triglyceride levels, and glucose levels; (2) the IVs must be independent of potential confounders; and (3) the IVs should influence CAVS exclusively through the TyG index, triglycerides, and glucose levels, but not other pathways. A schematic overview of the study design is presented in Figure 1.

Two Sample Mendelian Randomization Analysis

The 2-sample MR was adopted to investigate the causal association between TyG index and CAVS. Summary statistics were obtained from publicly available genome-wide association study (GWAS) databases, including the Integrative Epidemiology Unit Open GWAS Project (IEU-GWAS), the United Kingdom Biobank (UK Biobank), and the Finnish Genetics (FinnGen) database. In this study, single nucleotide polymorphisms (SNPs) strongly associated with the TyG index, triglyceride, and glucose levels were selected as IVs. These SNPs are randomly allocated at the time of conception, ensuring the minimal influence of environmental factors.³⁶ Initially, the random-effects inverse variance weighted (IVW) method was applied to estimate the causal effect of TyG index on CAVS. To enhance the robustness of the outcomes, complementary approaches such as the MR Egger, weighted median, simple mode, and weighted mode methods were applied. Furthermore, heterogeneity and pleiotropy were assessed using the IVW method and MR-Egger intercept, while leave-one-out analysis was performed to evaluate the influence of individual variants. All study procedures adhered to the STROBE-MR guidelines.^37,38

Multiple Mendelian Randomization Analysis

To further address potential pleiotropy arising from confounding factors, multiple MR analyses were conducted, adjusting for body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), diabetes mellitus (DM), and hypertension (HTN). First, the causal effects of TyG index, triglyceride, and glucose levels on CAVS were evaluated through multiple MR analyses. Subsequently, the IVW method and the MR-Egger intercept were utilized to evaluate heterogeneity and pleiotropy. All results were visualized in forest plots for clarity and comparison.

Date Sources and Single Nucleotide Polymorphisms Selection

Genetic variants associated with the TyG index were derived from a prior GWAS based on the UK Biobank cohort,³⁹ which included 273 368 individuals aged 40-69 years without diabetes or lipid metabolism disorders. The SNPs associated with the TyG index at genome-wide significance (P < 5 × 10⁻⁸) were identified using linear regression, adjusted for age, sex, and the top 5 genetic principal components to control population stratification. These SNPs were further pruned by linkage disequilibrium with r ²< .01, and those that were significantly associated with triglyceride or glucose were also excluded. In total, 192 initial SNPs were selected for TyG index (Supplementary Table 1).

To ensure effectiveness of the SNPs and avoid bias, linkage disequilibrium was defined with r ²< 0.001 for triglyceride and glucose levels. The Data Harmonization key steps were as follows: (1) SNPs matching and strand alignment: genetic instruments for the exposure and outcome were initially matched by their ID. The effect alleles for all SNPs were aligned to the forward strand to ensure a consistent reference framework across datasets; (2) harmonization of effect alleles: for each SNP, it was ensured that the effect allele reported in the outcome dataset corresponded to the same physical allele as the effect allele in the exposure dataset. This was achieved by flipping the sign of the beta coefficient for the outcome association when the effect alleles were complementary or mismatched, thereby aligning the direction of effect; (3) quality control and exclusion criteria: ① palindromic SNPs: all ambiguous palindromic SNPs were excluded to prevent errors caused by indeterminate strand orientation, ② allele frequency check: for non-palindromic SNPs, the effect allele frequencies were compared between the exposure and outcome samples. The SNPs with an absolute allele frequency difference > 0.08 were removed to minimize bias from potential population stratification or poor imputation quality, ③ incompatible SNPs: any SNPs with non-matching alleles (e.g., A/C in the exposure vs. G/T in the outcome) that could not be resolved through strand flipping were deemed incompatible and excluded from the analysis.

Genetic variants for triglycerides and glucose were sourced from IEU-GWAS (https://gwas.mrcieu.ac.uk/), comprising 389 562 and 314 916 participants, respectively. The CAVS outcome data were obtained from the FinnGen database (https://www.finngen.fi/en), which included 12 418 cases and 487 930 controls. Further details are provided in Table 1. All studies were reviewed and approved by local institutional review boards. Genetic variants associated with exposure were rigorously selected based on their strength of association and independence. To assess the strength of the IVs, the F-statistics was calculated. All IVs were selected for the MR analyses only if they exceeded the empirical threshold of F > 10.⁴⁰ Additionally, the LDlink database (https://ldlink.nih.gov/) was utilized to exclude SNPs that were significantly associated with potential confounders or other traits related to CAVS and eliminated all SNPs associated (P < 5 × 10⁻⁸) with the following traits. This step ensured that the selected IVs were specific to the exposures of interest (TyG index, triglycerides, and glucose) and not influenced by other metabolic factors such as BMI, LDL-C, DM, or HTN. These confounders were selected a priori based on their established associations in the existing epidemiological literature.^34,41-44

Details on all datasets downloaded and screened are displayed in Table 1. Finally, 312 and 109 SNPs for triglyceride and glucose, respectively, were selectively obtained. In addition, 34 SNPs for CAVS were sourced from the FinnGen database for further reverse causality analysis (Supplementary Tables 2 -4).

Statistical Analysis

All statistics were calculated using R software version 4.4.2 (The R Foundation for Statistical Computing, Vienna, Austria). The causal effect was deemed significant if the IVW P value was below the Bonferroni-corrected threshold (P < .05/3 ≈ .017), while P values in the range of .017 to .05 were considered suggestive.

Results

Two-Sample Mendelian Randomization Analysis

The 2-sample MR analysis based on the IVW method demonstrated a significant causal association between genetically predicted TyG index (n = 273 368 individuals) and CAVS (OR = 1.50, P = .007, 95% CI: 1.12-2.02) (Figure 2). Consistent findings were observed using the MR-Egger method (n = 500 348 individuals) (Figure 2). Furthermore, MR analysis supported a causal role of triglycerides in CAVS development (OR = 1.29, P < .001, 95% CI: 1.15-1.45). A similar significant causal relationship was identified between glucose and CAVS (OR = 1.21, P = .01, 95% CI: 1.05-1.40) (Figure 2).

The IVW method was used to test for heterogeneity, and the MR-Egger intercept to test pleiotropy. Although significant heterogeneity was observed (P < .001), no directional pleiotropy was detected in the associations between the TyG index and CAVS (P = .16) (Figure 2, Supplementary Table 5). This suggested that the IVs exert their effects through the intended pathway, supporting the validity of the causal inference. Similarly, no significant evidence of directional pleiotropy was detected for the associations of triglycerides (P = .285) or glucose (P = .178) with CAVS. The effects of TyG index on CAVS were illustrated in scatter plots (Figure 3), where each point represents a genetic variant, demonstrating the association between its effect on exposure and outcome. Sensitivity analysis employing a leave-one-out approach demonstrated that no individual SNP exerted a disproportionate influence on the causal association between the TyG index and CAVS (Supplementary Figure 2). Additional visual representations, including scatter plots for triglycerides and glucose, as well as funnel and forest plots, are shown in Supplementary Figures 1-3.

Multiple Mendelian Randomization Analysis

Univariable MR analysis supported a causal role of the TyG index in CAVS development (OR = 1.77, P < .001, 95% CI: 1.47-2.12). Similar causal relationships were observed for triglycerides (OR = 1.28, P < .001, 95% CI: 1.16-1.41) and glucose (OR = 1.20, P = .011, 95% CI: 1.04-1.38) (Figure 4). To further rule out the influence of confounding factors, multiple MR analysis was performed, adjusting for BMI, LDL-C, DM, and HTN. The multiple MR analysis confirmed a significant association between the TyG index and CAVS (OR = 1.64, P = .003, 95% CI: 1.18-2.28) (Figure 4).

Reverse 2-Sample Mendelian Randomization Analysis

The reverse 2 sample MR analysis based on the IVW method revealed no significant association between genetically predicted CAVS (n = 500 348 individuals) and triglycerides (OR = 1.01, P = .129, 95% CI: 1.00-1.03) (Figure 5). Similarly, MR-Egger analysis showed no association between CAVS and triglycerides (n = 389 562 individuals) (Supplementary Table 7). No significant relationship was observed between CAVS and glucose (OR=1.01, P = .188, 95% CI: 1.00-1.02) (Figure 5), indicating the absence of reverse causality.

No significant evidence of directional pleiotropy was detected in the association between CAVS and triglycerides (P = .431) (Figure 5). For glucose, there is no evidence of heterogeneity (P = .112) and pleiotropy (P = .922). The effects of CAVS on TyG index were illustrated in scatter plots (Figure 6). Sensitivity analyses, including leave-one-out, funnel, and forest plots, were presented in Supplementary Figures 4-6.

Discussion

To the best of knowledge, this is the first bidirectional MR study to comprehensively assess the causal relationship between the TyG index and CAVS. Conversely, no substantial causal effect of CAVS on the TyG index was observed. These results highlight that insulin resistance, as reflected by the TyG index, contributes to CAVS pathophysiology independently of established clinical and metabolic confounders.

Previous observational studies have suggested that the TyG index, a surrogate marker of insulin resistance, may contribute to CAVS through its pro-oxidant and pro-inflammatory properties. However, the evidence remains inconsistent and limited. For instance, a case-control study involving 361 patients with aortic valve calcification and 89 controls reported a significant predictive value of the TyG index for aortic valve calcification (OR = 1.743, P < .05, 95% CI: 1.04-2.93).²⁸ Similarly, Milad et al³⁴ identified a significant association between triglycerides and aortic stenosis risk in an MR study (OR = 1.52, P = .006, 95% CI: 1.12-2.03). The Copenhagen General Population Study, encompassing 108 559 individuals, further corroborated these findings, showing that higher triglyceride levels (>5 mmol/L) were both observationally and genetically linked to an increased risk of aortic valve stenosis (OR = 1.52, P < .001, 95% CI: 1.02-2.27).⁴⁵ Additionally, another MR study revealed that genetically predicted type 2 diabetes was associated with increased CAVS risk (OR = 1.15, P < .001, 95% CI: 1.10-1.21).⁴³ Conversely, a cross-sectional study of 183 elderly patients with CAVS reported a negative association between the TyG index and CAVS (OR = 0.43, P < .001, 95% CI: 0.28-0.68).⁴⁶ These discrepancies likely stem from confounding factors and reverse causality, which the MR approach effectively mitigates.

In this 2-sample MR study, genetic variants were utilized as IVs to establish a robust causal association between elevated TyG index (n = 273 368 individuals) and increased CAVS risk (OR = 1.50, P = .007, 95% CI: 1.12-2.02). Consistent results were observed for triglycerides and glucose levels, with complementary MR methods and sensitivity analyses confirming the robustness and reliability of these associations (P > .05). It was stated that while the primary sensitivity analyses did not detect directional pleiotropy, heterogeneity might still reflect pleiotropic effects. Reverse MR analyses further excluded the possibility of reverse causality (triglycerides: OR = 1.01, P = .129, 95% CI: 1.00-1.03; glucose: OR = 1.01, P = .188, 95% CI: 1.00-1.02). Multiple MR analysis, adjusted for potential confounders, reinforced the positive association between the TyG index and CAVS (OR = 1.64, P = .003, 95% CI: 1.18-2.28). These findings collectively highlight a consistent causal relationship and address critical limitations inherent in observational studies, thereby advancing the understanding of the potential pathogenesis of CAVS.

Several plausible mechanisms may explain the observed positive correlation between elevated TyG index and CAVS. First, systemic inflammation plays a pivotal role. Insulin resistance, as indicated by a higher TyG index, promotes systemic inflammation through the activation of pro-inflammatory pathways, such as nuclear factor-kappaB and the NLRP3 inflammasome.^47,48 These pathways are critical in driving aortic valve calcification by inducing osteogenic differentiation of valvular interstitial cells.^49,50 Second, oxidative stress is another key contributor. Insulin resistance is associated with increased oxidative stress, which exacerbates endothelial dysfunction and lipid deposition in the aortic valve.^51,52 Oxidative stress enhances the uptake of oxidized LDL by macrophages, leading to foam cell formation and subsequent calcification.⁵³ Third, the TyG index, as a composite marker of triglyceride and glucose dysregulation, is closely associated with lipid metabolism abnormalities. Elevated triglyceride levels promote lipoprotein deposition within the valve leaflets, a process that can initiate calcific remodeling and contribute to the pathogenesis of CAVS.^12,35,54

The TyG index may serve as an accessible and cost-effective biomarker for identifying individuals at high risk for CAVS. Early identification of at-risk populations could facilitate targeted preventive strategies. Further research is necessary to demonstrate the precise mechanistic pathways through which insulin resistance promotes valvular calcification, particularly the roles of inflammation, oxidative stress, and lipid metabolism.

Strengths and Limitations

This study is the first to investigate the causal relationship between the TyG index and CAVS, filling a significant gap in the literature. The bidirectional MR design provides a robust framework for assessing causality in both directions, effectively mitigating potential reverse causation. The use of genetic variants as IVs minimizes confounding and enhances causal inference. Nevertheless, several limitations should be acknowledged. First, the study population was exclusively of European ancestry, which may limit the generalizability of the findings to other ethnic groups. Genetic determinants of the TyG index and their impact on CAVS risk may vary across populations, underscoring the need for replication in more diverse cohorts. Second, while the MR approach reduces confounding, it relies on the assumption that the genetic instruments are valid, which may not hold in all cases. Although sensitivity analyses, including MR-Egger and IVW methods, were employed to address pleiotropy, residual pleiotropic effects cannot be entirely ruled out. Third, the limited GWAS data for the TyG index restricted its direct application in reverse MR analysis. Future studies with larger, more diverse populations are warranted to validate these findings.

Conclusion

In conclusion, the MR study demonstrates a causal association between higher TyG index and increased risk of CAVS, highlighting the important role of metabolic regulation in CAVS pathogenesis and prevention.

Supplementary Materials

Footnotes

Ethics Committee Approval: All data used in this study were obtained from publicly available sources and are in the public domain. Thus, no ethical approval or clinical trial registration was required.

Peer-review: Externally peer-reviewed.

Author Contributions: Conception – Y.H., X.S.; Design – X.S.; Supervision – Y.H.; Resource – Y.H., X.S.; Materials – X.S., Y.H.; Date Collection – X.S., X.X.; Analysis and interpretation – X.S., X.L.; Literature Search – X.S., X.L., X.X.; Writing – X.S., Y.H.; Critical Reviews – Y.H.

Acknowledgments: We gratefully acknowledge the contributions of researchers and study participants from publicly available databases, including the GWAS Catalog, UK Biobank, and FinnGen database.

Declaration of Interests: The authors declare no competing interests.

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