Causal Inference of Metabolic Syndrome on Calcific Aortic Valve Stenosis: Linkage Disequilibrium Score Regression and Two-Sample, Two-Step Mendelian Randomization Analysis

Causal Inference of Metabolic Syndrome on Calcific Aortic Valve Stenosis: Linkage Disequilibrium Score Regression and Two-Sample, Two-Step Mendelian Randomization Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Inference of Metabolic Syndrome on Calcific Aortic Valve Stenosis: Linkage Disequilibrium Score Regression and Two-Sample, Two-Step Mendelian Randomization Analysis Shu Yang, Zhiqiang Bai, Xiangqing Ye, Sike Mo, Jianeng Chen, Minghuang Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7141693/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted 12 You are reading this latest preprint version Abstract 1.1 Background This study aims to clarify the causal relationship between metabolic syndrome (MetS) and calcific aortic valve stenosis (CAVS) through Linkage Disequilibrium score regression (LDSC) and two-sample and two-step Mendelian randomization (MR). 1.2 Methods We conducted LDSC analysis and two-sample and two-step MR with Bayesian weighted MR validation using genome-wide association studies (GWAS) summary statistics. 1.3 Result LDSC analysis identified MetS and CAVS (rg = 0.264, p = 4.61×10 –26 ). In the two-sample MR study, Mets (P = 1.39×10 –20 , OR = 1.82), waist circumference (WC, p = 5.50×10 –10 , OR = 1.51), triglycerides (TG, p = 2.24×10 − 9 , OR = 1.42), and systolic blood pressure (SBP, p = 1.83×10 − 6 , OR = 1.04) were positively correlated with CAVS. In contrast, high-density lipoprotein (HDL-C, p = 0.002, OR = 0.9), and diastolic blood pressure (DBP, p = 0.002, OR = 0.78) were negatively correlated with CAVS. Two-step MR analysis indicated that among 233 circulating metabolites, 19 risk factors and 3 protective factors mediated the impacts of MetS, WC and TG on CAVS. 1.4 Conclusion MetS and CAVS share a common genetic architecture, with central adiposity, dyslipidemia, and blood pressure exhibiting distinct causal pathways. Small very-low-density lipoprotein particles, apolipoprotein B, and remnant cholesterol are key mediators linking MetS, WC, TG and CAVS. Metabolic syndrome Aortic valve stenosis Linkage Disequilibrium Score Regression Mendelian randomization Metabolite Figures Figure 1 Figure 2 Figure 3 Introduction Calcific Aortic Valve Stenosis (CAVS), the predominant non-rheumatic valvulopathy in Western populations 1 , represents an active pathobiological process rather than simple age-related degeneration. Its progression shares remarkable parallels with atherosclerotic cardiovascular disease, mediated through common risk pathways including dyslipidemia, hypertension, smoking, and diabetes mellitus 2 . Despite advances in transcatheter interventions, surgical aortic valve replacement remains the definitive therapy for severe symptomatic cases 3 . Metabolic Syndrome (MetS), a cluster of metabolic abnormalities strongly associated with cardiovascular diseases, has emerged as a significant non-communicable health threat in contemporary society 4 . The National Cholesterol Education Program-Adult Treatment Panel III (NCEP: ATP III) established diagnostic criteria for MetS, which include at least three out of five components: central obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, and elevated fasting plasma glucose 5 . MetS components have shown epidemiological associations with CAVS in observational studies 6 – 10 . However, the inherent limitations of observational studies, particularly residual confounding, reverse causation, and measurement bias, preclude definitive causal inference in current research evidence. Linkage Disequilibrium Score Regression (LDSC) is a statistical method that leverages genome-wide association study (GWAS) summary data to analyze the genetic architecture of complex traits, estimate heritability, detect confounding factors, and assess genetic correlations between traits. The core principle of LDSC involves using a regression model based on linkage disequilibrium (LD) scores to dissect the polygenic architecture underlying significant GWAS signals 11 . A pivotal innovation of LDSC lies in its utilization of GWAS summary statistics—including effect size estimates, standard error measurements, and allele frequency distributions—which obviates the requirement for individual-level genotypic or phenotypic data. This methodological approach consequently mitigates data confidentiality constraints while substantially reducing computational infrastructure requirements. Moreover, LDSC is robust to sample overlap, making it a standardized tool for cross-cohort genetic correlation analyses. Through systematic quantification of pleiotropic genetic effects across phenotypic domains, LDSC enables elucidation of pathophysiological mechanisms underlying disease comorbidity—exemplified by the genome-wide covariance between MetS and CAVS—while generating polygenic risk profiles that strengthen causal inference frameworks in complex trait epidemiology 12 . Mendelian randomization (MR) is a causal inference method grounded in GWAS 13 . MR leverages single nucleotide polymorphisms (SNPs) associated with exposure as instrumental variables (IVs) to determine whether there is a causal relationship between exposure and outcome. Because genotypes are randomly assigned to offspring, common confounding factors have minimal impact on the relationship between genetic variation and outcomes, thereby minimizing the risk of reverse causality and confounding bias in both observational and experimental studies. This makes the causal chain highly reliable, placing MR second only to randomized controlled trials (RCTs) in the hierarchy of evidence in evidence-based medicine 14 . To enhance the reliability of causal inference, we introduce Bayesian Weighted Mendelian Randomization (BWMR), a statistical method that not only accounts for uncertainty in weak effects and weak pleiotropic effects but also adaptively detects outliers caused by strong pleiotropic effects. Through comprehensive simulations and real data analyses, BWMR has been shown to possess statistical efficiency and computational stability 15 . This multi-modal genetic epidemiology approach enables comprehensive evaluation of both shared genetic architecture and causal relationships between MetS components and CAVS pathogenesis. Materials and Methods 4.1 Source of data This study implemented a two-sample Mendelian randomization (TSMR) design to investigate causal relationships between metabolic syndrome (MetS) and its diagnostic constituents – specifically waist circumference (WC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), and type 2 diabetes mellitus (T2DM) – with calcific aortic valve stenosis (CAVS). The analysis utilized genome-wide association study (GWAS) summary statistics obtained from established public repositories, including the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) and the GWAS Catalog (www.ebi.ac.uk/gwas/), with complete phenotype specifications and corresponding identifier codes provided in Table 1 with corresponding phenotype identifiers. Given that this study exclusively used previously published data, ethical approval and informed consent were not required. 4.2 LDSC analysis We used Mets as the exposure variable and CAVS as the outcome variable to conduct a global genetic correlation analysis using cross-trait LDSC. The GWAS data for Mets and CAVS served as input data. Given that all individuals in the selected GWAS datasets were of European ancestry, we utilized the European population from the 1000 Genomes Project as reference data to calculate the linkage disequilibrium (LD) score, which reflects the genetic information content of SNPs. The core analytical model regressed GWAS χ² statistics against pre-computed LD scores using weighted least squares, with weights proportional to LD score precision, we obtained the genetic correlation (Rg) and intercept. Rg represents the genetic correlation coefficient between Mets and CAVS, while the intercept indicates potential sample overlap or pleiotropy. The LDSC analysis was conducted using the LDSCr package (version 0.1.0) in R software (version 4.4.3). 4.3 Selection and validation of Ivs The selection of single nucleotide polymorphisms (SNPs) as valid instrumental variables (IVs) for MR analysis was conducted in strict accordance with the three fundamental MR assumptions: (1) Strong association between genetic IVs and the target exposure; (2) Complete independence of IVs from potential confounders affecting the exposure-outcome relationship; (3) IVs influence the outcome solely through the exposure pathway, without alternative biological pathways 16 . Our SNP selection protocol incorporated the following rigorous criteria: (1) SNPs were required to demonstrate genome-wide significant associations (p < 5 × 10-8) with diagnostic components of MetS, ensuring compliance with the relevance assumption. (2) We performed clumping procedures (r² threshold < 0.001, clumping window = 10000 kb) to eliminate correlated SNPs, thereby maintaining genetic independence and mitigating bias from linkage disequilibrium. (3) Calculated instrument strength using the F-statistic (F = β²/SE²) for each SNP, with subsequent exclusion of variants demonstrating F-statistics < 10 17 . This stringent threshold minimizes potential pleiotropic effects and ensures sufficient statistical power. Following this multi-stage filtering process, we identified robust genetic instruments meeting all MR assumptions for subsequent causal inference analyses. 4.4 Two-sample Mendelian Randomization and Bayesian Weighted Mendelian Randomization To investigate the causal relationship between MetS and its related diagnostic components and CAVS, we conducted MR using IVs and outcomes. The primary analysis method was the inverse variance weighted (IVW) approach, supplemented by four additional methods: weighted mode, simple mode, weighted median, and MR-Egger 18 . The IVW method, serving as our cornerstone analytical approach, employs outcome variance reciprocals as weights while omitting intercept terms in regression models. This methodology has been widely recognized as a gold-standard estimation technique in MR studies due to its optimal balance between accuracy and robustness 19 . The MR-Egger method accounts for potential horizontal pleiotropy by estimating the intercept term, which can indicate the presence of pleiotropy 20 . The weighted median method mitigates the influence of outliers by using a weighted median estimator, enhancing the robustness of the results. In practice, the IVW method is generally more accurate than other methods. We prioritized IVW-derived estimates for primary conclusions given their generally superior statistical efficiency, while treating supplementary methods as validation tools to strengthen result credibility. Statistical significance was determined at P=0.05 threshold. Furthermore, BWMR was integrated into our two-sample MR framework, enhancing analytical stability through its probabilistic modeling approach that accounts for uncertainty in weak instrument bias and effect heterogeneity. This multi-layered analytical strategy was designed to ensure methodological rigor while mitigating limitations inherent to any single estimation approach, thereby producing more reliable causal inferences about the MetS-CAVS relationship. 4.5 Two-step Mendelian Randomization analysis This study employed a two-step Mendelian randomization (MR) approach to investigate the mediating roles of circulating metabolites in the association between metabolic syndrome (MetS)-related components and calcific aortic valve stenosis (CAVS). Using 233 circulating metabolites as putative mediators, with genetic association data sourced from the GWAS Catalog (GWAS Catalog IDs: GCST90301941-GCST90302173, Table S 1), we conducted a comprehensive analysis comprising two sequential stages. In the first step, two-sample MR was performed to assess the genetic association between the metabolic traits and candidate mediators, yielding the exposure-mediator effect (β₁). In the second step, MR was used to simultaneously estimate the mediator-outcome effect (β₂) and the direct exposure-outcome effect (β₃). This enabled the calculation of the indirect mediation effect (β' = β₁β₂) and the mediation proportion (β₁β₂ / β₃). 4.6 Sensitivity analysis To validate the robustness of our MR estimates and address potential violations of core assumptions, we implemented three complementary sensitivity analyses. (1) Heterogeneity Assessment: We quantified heterogeneity across IVs using Cochran’s Q statistic, applied to both IVW and MR-Egger regression models. Significant heterogeneity (Q_pval < 0.05) prompted the use of random-effects IVW models to account for between-SNP variance, while fixed-effects IVW models were retained for homogeneous IVs sets (Q_pval ≥ 0.05) 21 . (2) Pleiotropy Evaluation: Directional pleiotropy was assessed through MR-Egger regression intercept analysis. A statistically significant intercept term (p < 0.05) indicated systematic horizontal pleiotropy, necessitating cautious interpretation of causal estimates 22 . This approach leverages the instrument strength independent of direct effects (InSIDE) assumption to detect bias from pleiotropic pathways. (3) Leave-One-Out Sensitivity Analysis: We systematically excluded individual SNPs and re-estimated effects to identify disproportionately influential variants. Results were visualized through sequential forest plots, enabling detection of IVs whose removal substantially altered effect magnitude or direction. All analyses were conducted in R v4.4.3 using the TwoSampleMR package for core MR operations. Visualization workflows employed forestplot for leave-one-out results and ggplot2 for effect estimate distributions. Analytical pipelines incorporated stringent quality control measures, including effect allele harmonization and palindromic SNP resolution. Results 5.1 Genetic correlation analysis Our LDSC analysis revealed a robust positive genetic correlation between MetS and CAVS (Rg = 0.265 ± 0.025, p = 4.6×10 -26 ), demonstrating substantial shared genetic architecture between these conditions. This statistically compelling association implies a potential directional relationship whereby elevated genetic susceptibility to MetS corresponds to increased genetic predisposition for CAVS. Both traits exhibited significant SNP-based heritability estimates (MetS: h 2 = 0.121; CAVS: h 2 = 0.011; p < 1×10 -30 ), confirming the polygenic nature of these disorders. Crucially, intercept estimates approximating unity (MetS: 0.97±0.036; CAVS: 1.037±0.008) excluded sample overlap as a confounding factor, validating the biological significance of this genetic relationship. These findings provide novel insights into the pleiotropic mechanisms underlying cardiometabolic pathophysiology and valvular degeneration. Complete genetic correlation estimates with quality control metrics are systematically documented in Table 2. 5.2 MR analysis and BWMR After rigorous screening of IVs adhering to MR core assumptions, we incorporated 525 MetS-associated SNPs, 298 WC-associated SNPs, 60 TG-associated SNPs, 351 HDL-C-associated SNPs, 20 SBP-associated SNPs, 171 DBP-associated SNPs, and 65 T2DM-associated SNPs. The forest plot and scatter plot of the MR analysis results are presented in Figure 1and Figure 2. IVW analysis revealed a robust causal relationship between MetS and CAVS risk (OR= 1.82 , p = 1.39×10 -20 ), consistently supported by weighted median (OR= 1.90, p = 3.24×10 -10 ), MR-Egger (OR = 1.72, p = 9.46×10 -4 ), and Bayesian-weighted MR (BWMR) methods (OR= 1.72, p = 9.31×10 -21 ). Component-specific analyses demonstrated that WC (IVW: OR = 1.51, p = 5.50×10 -10 ; BWMR: OR = 1.55, p = 1.32×10 -11 ) and TG (IVW: OR = 1.50, p = 2.24×10 -9 ; BWMR: OR = 1.43, p = 3.66×10 -10 ) significantly increased CAVS risk. Conversely, HDL-C showed a negative association (IVW: OR = 0.90, p = 0.023; BWMR: OR = 0.87, p = 0.001), suggesting potential protective effects. SBP exhibited a weak but stable positive association (IVW: OR = 1.04, p = 1.83×10 -6 ; BWMR: OR = 1.04, p = 7.36×10 -7 ). Notably, the DBP analysis generated inconsistent findings: IVW (OR = 0.78, p = 0.002) and BWMR (OR = 0.79, p = 0.003) indicated inverse associations, which contrasted with traditional epidemiological evidence supporting a positive link between DBP and cardiovascular risk. For T2DM, only marginal positive associations were observed in the IVW (OR = 1.06, p = 0.033) and BWMR (OR = 1.06, p = 0.021) models, while other methods yielded non-significant results, suggesting possible residual confounding in its causal relationship with CAVS. 5.3 Sensitivity analysis Heterogeneity and horizontal pleiotropy analyses are systematically presented in Table 3, with corresponding funnel plots visualized in Figure S 1. Significant heterogeneity was detected across all exposure-CAVS analyses (Cochran's Q p < 0.05; Global Test p < 0.05), necessitating random-effects inverse-variance weighted (IVW) models. Subsequent MR-PRESSO analysis revealed no outlier variants (outlier count = 0), while MR-Egger intercepts demonstrated negligible horizontal pleiotropy (intercept 0.05), confirming that observed heterogeneity neither originated from directional pleiotropy nor compromised effect estimate concordance. Leave-one-out sensitivity analyses demonstrated robust causal associations for MetS (Figure S 2), WC(Figure S 3), TG(Figure S 4), SBP (Figure S 6), and DBP (Figure S 7), with 100% of iterative estimates maintaining 95% confidence intervals (CIs) entirely on the same side of the null. For HDL-C (Figure S 5), 348 of 351 SNPs (99.1%) showed complete CI concordance, while 3 variants (rs75469215, rs116843064, rs964184) exhibited CI upper bounds marginally crossing the null - though point estimates remained directionally consistent with the primary analysis. Notably, T2DM (Figure S 8) analysis revealed two SNPs (rs57292959, rs76895963) with CI lower bounds slightly surpassing the null, despite concordant directional estimates. The borderline effect magnitude (odds ratio≈ 1) and methodological inconsistency warrant cautious interpretation of T2DM-CAVS causal relationships. 5.4 Two-step MR analysis This study employed a two-step MR analysis to evaluate the causal pathways through which MetS and its core components—including WC, HDL-C, TG, SBP, DBP, and T2DM—influence CAVS, mediated by circulating metabolites. In the first step, MetS and its components served as exposures, while 233 circulating metabolites were examined as potential mediators (primary results in Table S 2; sensitivity analyses in Table S 3). After false discovery rate (FDR) correction and application of predefined criteria (IVW P < 0.05; significant association in ≥2 of 4 supplementary MR methods with P 0.05), significant exposure-mediator associations were identified: MetS with 172 metabolites, WC with 137, TG with 158, and HDL-C with 145. No significant candidate mediating metabolites were found for SBP, DBP, or T2DM. In the second step, metabolites identified as significant mediators in the first step were treated as exposures, with CAVS as the outcome. Applying identical selection criteria, we identified 104 metabolites exhibiting significant causal effects on CAVS (sensitivity analyses in Table S 4). The number of metabolites demonstrating a significant mediating role in the causal pathway from each initial exposure to CAVS was as follows: 56 for MetS, 48 for WC, 63 for TG, and 46 for HDL-C (detailed in Figure 3 and Table S 5). No evidence of pleiotropy was detected in these analyses. However, Cochran's Q statistic indicated potential heterogeneity originating from the instrumental variables. Notably, 22 circulating metabolites served as mediators with consistent directions across all three pathways (MetS→CAVS, WC→CAVS, TG→CAVS) (Table S 6). Among them, 19 metabolites, including apolipoprotein B (ApoB), remnant cholesterol (Remnant-C), small very low density lipoprotein cholesterol (S - VLDL - C), and small very - low - density lipoprotein cholesteryl ester (S - VLDL - CE), consistently functioned as risk mediators. Three other metabolites (phospholipids to total lipids ratio in intermediate-density lipoprotein, total cholesterol to total lipids ratio in large HDL, and free cholesterol to total lipids ratio in large LDL) acted as protective mediators. Furthermore, the effect direction of certain metabolites (e.g., estimated fatty acid chain length, cholesteryl esters to total lipids ratio in medium VLDL, total cholesterol to total lipids ratio in small VLDL, cholesteryl esters to total lipids ratio in small VLDL, total cholesterol to total lipids ratio in very large VLDL, triglycerides to total lipids ratio in very large VLDL) varied across pathways, partially counteracting the risk effects of the primary components. The HDL-C→CAVS pathway identified 18 significant mediators. The mediation proportion exceeded 100% for three mediators (total cholesterol to total lipids ratio in medium HDL, 144%; triglycerides to total lipids ratio in large HDL, 108%; cholesteryl esters to total lipids ratio in medium HDL, 107%), a finding challenging to reconcile biologically and suggestive of unmeasured confounding pathways or effects. Nevertheless, the protective effects mediated by decreased ApoB (-76%) and increased IDL phospholipid proportion (54%) in this pathway remain noteworthy. Discussion This study identified a significant positive genetic correlation between metabolic syndrome (MetS) and calcific aortic valve stenosis (CAVS) through Linkage Disequilibrium Score Regression (LDSC) analysis (Rg = 0.26, p = 4.62×10 –26 ). These findings suggest potential shared genetic susceptibility loci between the two phenotypes, providing crucial genetic evidence for the comorbidity mechanism linking metabolic dysregulation and cardiovascular calcification. Notably, the heritability estimate for MetS (h 2 = 0.12) aligns with previous studies on metabolic traits including obesity and insulin resistance, reinforcing the pivotal role of genetic factors in its pathogenesis. However, CAVS exhibited relatively low heritability (h 2 = 0.011), which may reflect its multifactorial etiology: substantial contributions from environmental risk factors (e.g., advanced age, hypertension, chronic kidney disease) combined with phenotypic heterogeneity in diagnostic criteria (e.g., variability in imaging-based calcification score thresholds) might partially obscure the detectability of genetic variants. Nevertheless, the highly significant genetic correlation (p < 1×10 –25 ) implies that the genetic association between MetS and CAVS retains biological importance even within this low heritability context. Importantly, as LDSC cannot delineate causal directionality, we further incorporated Mendelian Randomization (MR) analysis to mitigate potential confounding from reverse causation or pleiotropic effects. Utilizing two-sample Mendelian randomization (MR) analysis, this study established an independent causal effect of waist circumference (WC), a cardinal phenotype of central obesity, on calcific aortic valve stenosis (CAVS), which aligns with observational evidence linking obesity to valvular calcification 23 . Mechanistically, central obesity may drive valvular calcification through multiple pathological pathways: (1) visceral adipose accumulation-induced dysregulated fatty acid metabolism enhances reactive oxygen species (ROS) production, exacerbating mitochondrial dysfunction; (2) oxidative stress microenvironments activate osteogenic transcription factors such as Runx2, promoting osteogenic transdifferentiation of vascular smooth muscle cells (VSMCs) 24 . At the lipid metabolism level, the independent causal effect of elevated triglyceride (TG) levels provides genetic support for the "lipotoxicity hypothesis": TG-rich lipoprotein remnants (e.g., VLDL) retained in valvular interstitium trigger oxidative stress cascades, accelerating calcium deposition via BMP-2/Smad signaling upregulation 25 – 27 . Notably, while observational studies associate higher high-density lipoprotein cholesterol (HDL-C) with reduced CAVS risk/progression, prior MR analyses failed to confirm causality. Our study detected a modest protective trend for HDL-C (OR = 0.87–0.90), consistent with limited evidence in aortic stenosis research. Mendelian randomization-based mediation analysis reveals key metabolite-mediated pathways through which HDL-C influences CAVS. Certain metabolites significantly mediate HDL-C's protective effect: ApoB contributes 76% of this protection through a mechanism whereby elevated HDL-C levels lead to reduced ApoB, consequently lowering CAVS risk, consistent with ApoB's established role in cardiovascular disease 28 . Simultaneously, triglycerides within specific lipoprotein subfractions (e.g., large HDL, small HDL, and very small VLDL) mediate substantial proportions of the protective effect (108%, 86%, and 99%, respectively), likely reflecting HDL-C's function in transporting triglycerides from peripheral tissues to the liver 29 . Conversely, another group of metabolites significantly counteracts HDL-C's protection: sphingomyelins and phosphatidylcholines exhibit highly negative mediation proportions (-137% and − 136%, respectively). While acknowledging the potential for unmeasured confounding, the magnitude of these negative values strongly suggests a risk effect for sphingomyelins. As core components of membrane lipid rafts, sphingomyelins (SM) are hydrolyzed by sphingomyelinase(SMase) into ceramide(Cer) and phosphocholine, these metabolites promote lipid deposition and retention, while their direct cytotoxicity drives fibrosis and calcification 30 . Furthermore, significant risk mediation attributed to docosahexaenoic acid (DHA)-related metabolites (-75% to -81%) challenges the prevailing view of its universal cardioprotection. This finding necessitates cautious interpretation, suggesting that DHA may exert adverse effects under specific conditions or that the association could be influenced by confounding factors. Regarding blood pressure parameters, SBP demonstrated a positive causal association with CAVS, whereas DBP showed no significant effect. This aligns with hypertension's established role as a CAVS risk factor: chronic mechanical stress induces endothelial dysfunction, promoting lipid deposition and inflammation, potentially influencing all stages of aortic stenosis progression 31 . Although most epidemiological studies link both SBP and DBP to cardiovascular risk, the Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA) specifically associated elevated SBP—not DBP—with increased arterial calcific stenosis risk 32 – 34 . Observational studies propose diabetes and hyperglycemia as risk factors for aortic/mitral valve calcification 35 . However, our MR analysis revealed a weak direct causal effect of type 2 diabetes mellitus (T2DM) on CAVS (IVW OR = 1.06, p = 0.033), with TG, SBP, and WC exhibiting stronger independent effects. This discrepancy suggests that observational studies may overestimate T2DM's direct impact due to confounding by collinearity with obesity, lipids, and blood pressure, whereas MR disentangles these factors. Given the complex nature of MetS, this study investigated whether circulating metabolites act as causal mediators regulating the relationship between MetS, its diagnostic components, and CAVS. Employing Mendelian randomization-based mediation analysis, we identified 22 lipid-related metabolites that simultaneously mediate the associations of MetS and its core diagnostic components (WC and TG) with CAVS. These findings broadly support the concept that lipoprotein particle composition and transport efficiency constitute a core biological pathway linking metabolic dysregulation to CAVS pathology. ApoB, recognized as a risk factor for both atherosclerosis and valvular calcification, promotes CAVS by directly facilitating lipid deposition within valvular interstitial cells, thereby activating oxidative stress and calcification signaling pathways. Notably, its substantial mediating proportion (up to 56%) in the TG exposure pathway underscores the central role of elevated triglycerides in driving CAVS, likely through increased synthesis of ApoB-containing particles. Remnant cholesterol (RC), representing the cholesterol content within triglyceride-rich lipoproteins (TRLs), possesses the ability to penetrate the arterial wall. However, due to their larger size, RC particles are prone to entrapment within the arterial intima, promoting cholesterol accumulation, foam cell formation, and exacerbating vascular inflammation and endothelial dysfunction 36 . Subclassification of TRLs further delineated specific mediating pathways underlying the TG exposure effect. Intermediate-density lipoprotein triglycerides (IDL-TG) and small low-density lipoprotein triglycerides (small LDL-TG) mediated 66% and 65% of the TG exposure effect, respectively, a significantly higher proportion than that mediated by HDL-TG. This reflects the greater penetration capacity and valvular retention potential of smaller, denser lipoprotein particles. Furthermore, the substantial mediating proportion observed for small very-low-density lipoprotein total cholesterol (78%) and very small VLDL particle concentration (69%) collectively confirms that hypertriglyceridemia drives CAVS pathogenesis primarily by elevating concentrations of dense TRL particles, not solely through alterations in lipid composition. Concurrently, monounsaturated fatty acids (MUFAs) exhibited significant mediating effects across these three pathways. Elevated levels of 16:1 and 18:1 MUFAs likely indicate enhanced activity of stearoyl-CoA desaturase-1 (SCD1), an enzyme whose function – converting saturated fatty acids to MUFAs – is directly implicated in insulin resistance and valvular lipotoxicity 37 . While MR analysis effectively reduces confounding bias, caution is warranted regarding potential pleiotropic pathways affecting genetic instruments. For instance, certain SNPs may influence both metabolic traits and inflammation-related pathways. Future investigations should incorporate cohort studies and meta-analyses to strengthen result credibility through triangulation. Furthermore, within this mediation MR framework, some mediator variables may represent measurement variants of the exposure itself (e.g., the high correlation between total TG and VLDL-TG). Clarifying their independent pathological significance is crucial, as direct inclusion in models risks collinearity, thereby interfering with causal estimates. Subsequent studies must separate the independent effects of mediators by incorporating both exposure and mediator variables, potentially utilizing multivariable MR or employing metabolite clustering and dimension reduction techniques. Additionally, as the current genetic data primarily derive from European ancestry populations, the generalizability of the findings to other racial/ethnic groups requires validation through prospective cross-population studies. Conclusion This study provides robust genetic evidence supporting a shared genetic basis and causal relationship between metabolic syndrome (MetS), its key components, and calcific aortic valve stenosis (CAVS). Linkage disequilibrium score regression (LDSC) revealed a significant positive genetic correlation (Rg = 0.265). Mendelian randomization (MR) analyses confirmed that genetically predicted MetS increases the risk of CAVS (OR ≈ 1.8), an effect primarily driven by the independent causal contributions of waist circumference (WC; OR ≈ 1.5–1.6) and triglycerides (TG; OR ≈ 1.4–1.5). Higher high-density lipoprotein cholesterol (HDL-C) levels demonstrated a modest protective effect (OR ≈ 0.87–0.90). Critically, two-step MR mediation analysis identified specific circulating lipid metabolites acting as mediators between MetS, WC, TG, and coronary artery calcium score. Components of small very-low-density lipoprotein (VLDL) particles (total cholesterol, cholesteryl esters, free cholesterol), apolipoprotein B (ApoB), and remnant cholesterol accounted for a substantial proportion of these causal effects. These findings indicate that dysregulated lipoprotein metabolism, particularly involving triglyceride-rich remnant particles, constitutes a key biological mechanism underlying the association between MetS and coronary artery calcification. Key mediating metabolites, such as small VLDL components and ApoB, represent candidate biomarkers for understanding the pathway from metabolic dysfunction to valvular calcification. Their identification offers potential targets for future therapeutic strategies aimed at interrupting the progression from metabolic dysfunction to aortic valve calcification. Future studies should validate these findings in diverse populations and explore the utility of targeting these metabolic pathways. Abbreviations MetS: metabolic syndrome CAVS: calcific aortic valve stenosis LDSC: Linkage Disequilibrium score regression MR: Mendelian randomization WC: waist circumference TG: triglycerides SBP: systolic blood pressure DBP: diastolic blood pressure HDL-C: high-density lipoprotein cholesterol T2DM: type 2 diabetes mellitus NCEP: ATP III: The National Cholesterol Education Program-Adult Treatment Panel III GAWS: genome-wide association study SNPs: single nucleotide polymorphisms IVs: instrumental variables RCTs: randomized controlled trials BWMR: Bayesian Weighted Mendelian Randomization TSMR: two-sample Mendelian randomization Rg: genetic correlation IVW: inverse variance weighted InSIDE: the instrument strength independent of direct effects ROS: reactive oxygen species VSMCs: vascular smooth muscle cells ApoB: apolipoprotein B Remnant-C/RC: remnant cholesterol VLDL: very - low - density lipoprotein S - VLDL - C : small very low density lipoprotein cholesterol S - VLDL – CE: small very - low - density lipoprotein cholesteryl ester SM: sphingomyelins SMase: sphingomyelinase Cer: ceramide DHA: docosahexaenoic acid TRLs: triglyceride-rich lipoproteins FUMAs: monounsaturated fatty acids SCD1: stearoyl-CoA desaturase-1 Declarations 8 Acknowledgements We acknowledge the invaluable contributions of all genome-wide association study (GWAS) consortia and cohort participants whose publicly shared data enabled this research. Additionally, we extend our heartfelt appreciation to the developers and technical support personnel of the R programming language and its associated packages employed in this study. 9 Author contributions S. Yang: Data acquisition and cleaning, data management, methodology, primary analysis, result visualization, writing - original draft. Z. Bai, X. Ye, S. Mo, J. Chen, M. Zhang, D. Zhu: software implementation, Result organization, validation, writing-review & editing. M. Zhu: Conceptualization, software implementation, project administration, Supervision, writing-review & editing. 10.1 Ethical considerations Given that this study exclusively used previously published data, ethical approval and informed consent were not required. 10.2 Consent to participate and consent to participate The GWAS summary statistics utilized in this Mendelian randomization study were obtained from publicly available repositories. All original studies contributing to these datasets obtained informed consent from participants and received ethical approval from relevant institutional review boards. As this secondary analysis exclusively employed de-identified and aggregated data, no additional ethical approval or participant consent was required for this specific investigation. 10.3 Consent for publication This study exclusively employed anonymized and aggregated statistical data; therefore, no further ethical approval or participant consent for publication was required under the relevant regulatory frameworks. 10.4 Funding The author(s) disclosed the following financial support received for their research, authorship, and/or publication of this article: Zhejiang Provincial Natural Science Foundation, Provincial Natural Science Foundation, LY24H170002. References Yadgir S, Johnson CO, Aboyans V, et al. 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Jul 3 2018;138(1):e35-e47. doi:10.1161/cir.0000000000000574 Tables Table 1 Summary of the GWAS summary statistics Phenotype Samples Size Ancestry Author Data source CAVS 941863 European Thériault S et al GWAS Catalog ID: GCST90310293 MetS 1384348 European Park S et al GWAS Catalog ID: GCST90444487 T2DM 336074 European DeWan AT et al GWAS Catalog ID: GCST90302887 HDL-C 403943 European Richardson et al IEU ID:ieu-b-109 SBP 97656 European Howe LJ et al IEU ID:ieu-b-4818 DBP 317756 European Neale et al IEU ID:ukb-a-359 WC 462166 European Ben Elsworth et al IEU ID:ukb-b-9405 CAVS: Calcific aortic valve stenosis; MetS: Metabolic syndrome; T2DM: Type 2 diabetes; HDL-C: HDL cholesterol; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; WC: Waist circumference Table 2 MetS-CAVS LDSC Analysis Trait h 2 h 2 _p Intercept Rg Rg_p Rg_SE MetS 0.121 0 0.97 0.265 4.26×10 -26 0.025 CAVS 0.011 2.70×10 -33 1.037 h 2 : SNP-based Heritability; h 2 _p: Heritability p-value; Intercept: Intercept term from LDSC regression; Rg: Genetic correlation; Rg_p: Genetic Correlation p-value; Rg_SE: Genetic Correlation Standard Error Table 3 The results of the heterogeneity and horizontal pleiotropy test Exposure Heterogeneity test Pleiotropy test MR-Egger IVW Intercept p Q Q_p Q Q_p Mets 837.108 6.57×10 -17 837.322 8.01E-17 7.37×10 -4 0.751 WC 485.355 2.32×10 -11 486.245 2.51E-11 -2.31×10 -3 0.462 TG 185.164 3.34×10 -15 185.194 4.22E-15 3.62×10 -3 0.572 HDL-C 806.092 3.50×10 -38 806.477 4.78E-38 8.39×10 -4 0.683 SBP 36.939 5.34×10 -3 40.91 2.48E-03 -0.003 0.181 DBP 339.144 1.73×10 -13 339.801 2.09E-13 -3.23×10 -3 0.568 T2DM 106.588 4.99×10 -4 111.701 2.09E-04 0.01 0.087 MetS: Metabolic syndrome; T2DM: Type 2 diabetes; HDL-C: HDL cholesterol; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; WC: Waist circumference Additional Declarations No competing interests reported. Supplementary Files Supplementalmateriallegends.docx TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif FigureS6.tif FigureS7.tif FigureS8.tif Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 25 Dec, 2025 Reviews received at journal 24 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:27:23","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":3255968,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-7141693/v1/48d108d2b1408ab931f16ea9.tif"},{"id":89655952,"identity":"36537767-1cca-4d4d-b648-07656fa1b6a1","added_by":"auto","created_at":"2025-08-22 10:27:23","extension":"tif","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":1372752,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS8.tif","url":"https://assets-eu.researchsquare.com/files/rs-7141693/v1/3cc55f2d4c0013a3204e95dd.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Inference of Metabolic Syndrome on Calcific Aortic Valve Stenosis: Linkage Disequilibrium Score Regression and Two-Sample, Two-Step Mendelian Randomization Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCalcific Aortic Valve Stenosis (CAVS), the predominant non-rheumatic valvulopathy in Western populations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, represents an active pathobiological process rather than simple age-related degeneration. Its progression shares remarkable parallels with atherosclerotic cardiovascular disease, mediated through common risk pathways including dyslipidemia, hypertension, smoking, and diabetes mellitus\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite advances in transcatheter interventions, surgical aortic valve replacement remains the definitive therapy for severe symptomatic cases\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMetabolic Syndrome (MetS), a cluster of metabolic abnormalities strongly associated with cardiovascular diseases, has emerged as a significant non-communicable health threat in contemporary society \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The National Cholesterol Education Program-Adult Treatment Panel III (NCEP: ATP III) established diagnostic criteria for MetS, which include at least three out of five components: central obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, and elevated fasting plasma glucose\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. MetS components have shown epidemiological associations with CAVS in observational studies\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, the inherent limitations of observational studies, particularly residual confounding, reverse causation, and measurement bias, preclude definitive causal inference in current research evidence.\u003c/p\u003e\u003cp\u003eLinkage Disequilibrium Score Regression (LDSC) is a statistical method that leverages genome-wide association study (GWAS) summary data to analyze the genetic architecture of complex traits, estimate heritability, detect confounding factors, and assess genetic correlations between traits. The core principle of LDSC involves using a regression model based on linkage disequilibrium (LD) scores to dissect the polygenic architecture underlying significant GWAS signals\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A pivotal innovation of LDSC lies in its utilization of GWAS summary statistics\u0026mdash;including effect size estimates, standard error measurements, and allele frequency distributions\u0026mdash;which obviates the requirement for individual-level genotypic or phenotypic data. This methodological approach consequently mitigates data confidentiality constraints while substantially reducing computational infrastructure requirements. Moreover, LDSC is robust to sample overlap, making it a standardized tool for cross-cohort genetic correlation analyses. Through systematic quantification of pleiotropic genetic effects across phenotypic domains, LDSC enables elucidation of pathophysiological mechanisms underlying disease comorbidity\u0026mdash;exemplified by the genome-wide covariance between MetS and CAVS\u0026mdash;while generating polygenic risk profiles that strengthen causal inference frameworks in complex trait epidemiology\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) is a causal inference method grounded in GWAS\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. MR leverages single nucleotide polymorphisms (SNPs) associated with exposure as instrumental variables (IVs) to determine whether there is a causal relationship between exposure and outcome. Because genotypes are randomly assigned to offspring, common confounding factors have minimal impact on the relationship between genetic variation and outcomes, thereby minimizing the risk of reverse causality and confounding bias in both observational and experimental studies. This makes the causal chain highly reliable, placing MR second only to randomized controlled trials (RCTs) in the hierarchy of evidence in evidence-based medicine\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. To enhance the reliability of causal inference, we introduce Bayesian Weighted Mendelian Randomization (BWMR), a statistical method that not only accounts for uncertainty in weak effects and weak pleiotropic effects but also adaptively detects outliers caused by strong pleiotropic effects. Through comprehensive simulations and real data analyses, BWMR has been shown to possess statistical efficiency and computational stability\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This multi-modal genetic epidemiology approach enables comprehensive evaluation of both shared genetic architecture and causal relationships between MetS components and CAVS pathogenesis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003e4.1 Source of data\u003c/h2\u003e\n\u003cp\u003eThis study implemented a two-sample Mendelian randomization (TSMR) design to investigate causal relationships between metabolic syndrome (MetS) and its diagnostic constituents \u0026ndash; specifically waist circumference (WC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), and type 2 diabetes mellitus (T2DM) \u0026ndash; with calcific aortic valve stenosis (CAVS). The analysis utilized genome-wide association study (GWAS) summary statistics obtained from established public repositories, including the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) and the GWAS Catalog (www.ebi.ac.uk/gwas/), with complete phenotype specifications and corresponding identifier codes provided in Table 1 with corresponding phenotype identifiers. Given that this study exclusively used previously published data, ethical approval and informed consent were not required.\u003c/p\u003e\n\u003ch2\u003e4.2 LDSC analysis\u003c/h2\u003e\n\u003cp\u003eWe used Mets as the exposure variable and CAVS as the outcome variable to conduct a global genetic correlation analysis using cross-trait LDSC. The GWAS data for Mets and CAVS served as input data. Given that all individuals in the selected GWAS datasets were of European ancestry, we utilized the European population from the 1000 Genomes Project as reference data to calculate the linkage disequilibrium (LD) score, which reflects the genetic information content of SNPs. The core analytical model regressed GWAS \u0026chi;\u0026sup2; statistics against pre-computed LD scores using weighted least squares, with weights proportional to LD score precision, we obtained the genetic correlation (Rg) and intercept. Rg represents the genetic correlation coefficient between Mets and CAVS, while the intercept indicates potential sample overlap or pleiotropy. The LDSC analysis was conducted using the LDSCr package (version 0.1.0) in R software (version 4.4.3).\u003c/p\u003e\n\u003ch2\u003e4.3 Selection and validation of Ivs\u003c/h2\u003e\n\u003cp\u003eThe selection of single nucleotide polymorphisms (SNPs) as valid instrumental variables (IVs) for MR analysis was conducted in strict accordance with the three fundamental MR assumptions: (1) Strong association between genetic IVs and the target exposure; (2) Complete independence of IVs from potential confounders affecting the exposure-outcome relationship; (3) IVs influence the outcome solely through the exposure pathway, without alternative biological pathways\u003csup\u003e16\u003c/sup\u003e. Our SNP selection protocol incorporated the following rigorous criteria: (1) SNPs were required to demonstrate genome-wide significant associations (p \u0026lt; 5 \u0026times; 10-8) with diagnostic components of MetS, ensuring compliance with the relevance assumption. (2) We performed clumping procedures (r\u0026sup2; threshold \u0026lt; 0.001, clumping window = 10000 kb) to eliminate correlated SNPs, thereby maintaining genetic independence and mitigating bias from linkage disequilibrium. (3) Calculated instrument strength using the F-statistic (F = \u0026beta;\u0026sup2;/SE\u0026sup2;) for each SNP, with subsequent exclusion of variants demonstrating F-statistics \u0026lt; 10\u003csup\u003e17\u003c/sup\u003e. This stringent threshold minimizes potential pleiotropic effects and ensures sufficient statistical power. Following this multi-stage filtering process, we identified robust genetic instruments meeting all MR assumptions for subsequent causal inference analyses.\u003c/p\u003e\n\u003ch2\u003e4.4 Two-sample Mendelian Randomization and Bayesian Weighted Mendelian Randomization\u003c/h2\u003e\n\u003cp\u003eTo investigate the causal relationship between MetS and its related diagnostic components and CAVS, we conducted MR using IVs and outcomes. The primary analysis method was the inverse variance weighted (IVW) approach, supplemented by four additional methods: weighted mode, simple mode, weighted median, and MR-Egger\u003csup\u003e18\u003c/sup\u003e. The IVW method, serving as our cornerstone analytical approach, employs outcome variance reciprocals as weights while omitting intercept terms in regression models. This methodology has been widely recognized as a gold-standard estimation technique in MR studies due to its optimal balance between accuracy and robustness\u003csup\u003e19\u003c/sup\u003e. The MR-Egger method accounts for potential horizontal pleiotropy by estimating the intercept term, which can indicate the presence of pleiotropy\u003csup\u003e20\u003c/sup\u003e. The weighted median method mitigates the influence of outliers by using a weighted median estimator, enhancing the robustness of the results. In practice, the IVW method is generally more accurate than other methods. We prioritized IVW-derived estimates for primary conclusions given their generally superior statistical efficiency, while treating supplementary methods as validation tools to strengthen result credibility. Statistical significance was determined at P=0.05 threshold. Furthermore, BWMR was integrated into our two-sample MR framework, enhancing analytical stability through its probabilistic modeling approach that accounts for uncertainty in weak instrument bias and effect heterogeneity. This multi-layered analytical strategy was designed to ensure methodological rigor while mitigating limitations inherent to any single estimation approach, thereby producing more reliable causal inferences about the MetS-CAVS relationship.\u003c/p\u003e\n\u003ch2\u003e4.5 Two-step Mendelian Randomization analysis\u003c/h2\u003e\n\u003cp\u003eThis study employed a two-step Mendelian randomization (MR) approach to investigate the mediating roles of circulating metabolites in the association between metabolic syndrome (MetS)-related components and calcific aortic valve stenosis (CAVS). Using 233 circulating metabolites as putative mediators, with genetic association data sourced from the GWAS Catalog (GWAS Catalog IDs: GCST90301941-GCST90302173, Table S 1), we conducted a comprehensive analysis comprising two sequential stages. In the first step, two-sample MR was performed to assess the genetic association between the metabolic traits and candidate mediators, yielding the exposure-mediator effect (\u0026beta;₁). In the second step, MR was used to simultaneously estimate the mediator-outcome effect (\u0026beta;₂) and the direct exposure-outcome effect (\u0026beta;₃). This enabled the calculation of the indirect mediation effect (\u0026beta;\u0026apos; = \u0026beta;₁\u0026beta;₂) and the mediation proportion (\u0026beta;₁\u0026beta;₂ / \u0026beta;₃).\u003c/p\u003e\n\u003ch2\u003e4.6 Sensitivity analysis\u003c/h2\u003e\n\u003cp\u003eTo validate the robustness of our MR estimates and address potential violations of core assumptions, we implemented three complementary sensitivity analyses. (1) Heterogeneity Assessment: We quantified heterogeneity across IVs using Cochran\u0026rsquo;s Q statistic, applied to both IVW and MR-Egger regression models. Significant heterogeneity (Q_pval \u0026lt; 0.05) prompted the use of random-effects IVW models to account for between-SNP variance, while fixed-effects IVW models were retained for homogeneous IVs sets (Q_pval \u0026ge; 0.05)\u003csup\u003e21\u003c/sup\u003e. (2) Pleiotropy Evaluation: Directional pleiotropy was assessed through MR-Egger regression intercept analysis. A statistically significant intercept term (p \u0026lt; 0.05) indicated systematic horizontal pleiotropy, necessitating cautious interpretation of causal estimates\u003csup\u003e22\u003c/sup\u003e. This approach leverages the instrument strength independent of direct effects (InSIDE) assumption to detect bias from pleiotropic pathways. (3) Leave-One-Out Sensitivity Analysis: We systematically excluded individual SNPs and re-estimated effects to identify disproportionately influential variants. Results were visualized through sequential forest plots, enabling detection of IVs whose removal substantially altered effect magnitude or direction. All analyses were conducted in R v4.4.3 using the TwoSampleMR package for core MR operations. Visualization workflows employed forestplot for leave-one-out results and ggplot2 for effect estimate distributions. Analytical pipelines incorporated stringent quality control measures, including effect allele harmonization and palindromic SNP resolution.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e5.1 Genetic correlation analysis\u003c/h2\u003e\n\u003cp\u003eOur LDSC analysis revealed a robust positive genetic correlation between MetS and CAVS (Rg = 0.265 \u0026plusmn; 0.025, p = 4.6\u0026times;10\u003csup\u003e-26\u003c/sup\u003e), demonstrating substantial shared genetic architecture between these conditions. This statistically compelling association implies a potential directional relationship whereby elevated genetic susceptibility to MetS corresponds to increased genetic predisposition for CAVS. Both traits exhibited significant SNP-based heritability estimates (MetS: h\u003csup\u003e2 \u003c/sup\u003e= 0.121; CAVS: h\u003csup\u003e2 \u003c/sup\u003e= 0.011; p \u0026lt; 1\u0026times;10\u003csup\u003e-30\u003c/sup\u003e), confirming the polygenic nature of these disorders. Crucially, intercept estimates approximating unity (MetS: 0.97\u0026plusmn;0.036; CAVS: 1.037\u0026plusmn;0.008) excluded sample overlap as a confounding factor, validating the biological significance of this genetic relationship. These findings provide novel insights into the pleiotropic mechanisms underlying cardiometabolic pathophysiology and valvular degeneration. Complete genetic correlation estimates with quality control metrics are systematically documented in Table 2.\u003c/p\u003e\n\u003ch2\u003e5.2 MR analysis and BWMR\u003c/h2\u003e\n\u003cp\u003eAfter rigorous screening of IVs adhering to MR core assumptions, we incorporated 525 MetS-associated SNPs, 298 WC-associated SNPs, 60 TG-associated SNPs, 351 HDL-C-associated SNPs, 20 SBP-associated SNPs, 171 DBP-associated SNPs, and 65 T2DM-associated SNPs. The forest plot and scatter plot of the MR analysis results are presented in Figure 1and Figure 2.\u003c/p\u003e\n\u003cp\u003eIVW analysis revealed a robust causal relationship between MetS and CAVS risk (OR= 1.82 , p = 1.39\u0026times;10\u003csup\u003e-20\u003c/sup\u003e), consistently supported by weighted median (OR= 1.90, p = 3.24\u0026times;10\u003csup\u003e-10\u003c/sup\u003e), MR-Egger (OR = 1.72, p = 9.46\u0026times;10\u003csup\u003e-4\u003c/sup\u003e), and Bayesian-weighted MR (BWMR) methods (OR= 1.72, p = 9.31\u0026times;10\u003csup\u003e-21\u003c/sup\u003e). Component-specific analyses demonstrated that WC (IVW: OR = 1.51, p = 5.50\u0026times;10\u003csup\u003e-10\u003c/sup\u003e; BWMR: OR = 1.55, p = 1.32\u0026times;10\u003csup\u003e-11\u003c/sup\u003e) and TG (IVW: OR = 1.50, p = 2.24\u0026times;10\u003csup\u003e-9\u003c/sup\u003e; BWMR: OR = 1.43, p = 3.66\u0026times;10\u003csup\u003e-10\u003c/sup\u003e) significantly increased CAVS risk. Conversely, HDL-C showed a negative association (IVW: OR = 0.90, p = 0.023; BWMR: OR = 0.87, p = 0.001), suggesting potential protective effects. SBP exhibited a weak but stable positive association (IVW: OR = 1.04, p = 1.83\u0026times;10\u003csup\u003e-6\u003c/sup\u003e; BWMR: OR = 1.04, p = 7.36\u0026times;10\u003csup\u003e-7\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eNotably, the DBP analysis generated inconsistent findings: IVW (OR = 0.78, p = 0.002) and BWMR (OR = 0.79, p = 0.003) indicated inverse associations, which contrasted with traditional epidemiological evidence supporting a positive link between DBP and cardiovascular risk. For T2DM, only marginal positive associations were observed in the IVW (OR = 1.06, p = 0.033) and BWMR (OR = 1.06, p = 0.021) models, while other methods yielded non-significant results, suggesting possible residual confounding in its causal relationship with CAVS.\u003c/p\u003e\n\u003ch2\u003e5.3 Sensitivity analysis\u003c/h2\u003e\n\u003cp\u003eHeterogeneity and horizontal pleiotropy analyses are systematically presented in Table 3, with corresponding funnel plots visualized in Figure S 1. Significant heterogeneity was detected across all exposure-CAVS analyses (Cochran\u0026apos;s Q p \u0026lt; 0.05; Global Test p \u0026lt; 0.05), necessitating random-effects inverse-variance weighted (IVW) models. Subsequent MR-PRESSO analysis revealed no outlier variants (outlier count = 0), while MR-Egger intercepts demonstrated negligible horizontal pleiotropy (intercept \u0026lt; 0.001, p \u0026gt; 0.05), confirming that observed heterogeneity neither originated from directional pleiotropy nor compromised effect estimate concordance. Leave-one-out sensitivity analyses demonstrated robust causal associations for MetS (Figure S 2), WC(Figure S 3), TG(Figure S 4), SBP (Figure S 6), and DBP (Figure S 7), with 100% of iterative estimates maintaining 95% confidence intervals (CIs) entirely on the same side of the null. For HDL-C (Figure S 5), 348 of 351 SNPs (99.1%) showed complete CI concordance, while 3 variants (rs75469215, rs116843064, rs964184) exhibited CI upper bounds marginally crossing the null - though point estimates remained directionally consistent with the primary analysis. Notably, T2DM (Figure S 8) analysis revealed two SNPs (rs57292959, rs76895963) with CI lower bounds slightly surpassing the null, despite concordant directional estimates. The borderline effect magnitude (odds ratio\u0026asymp; 1) and methodological inconsistency warrant cautious interpretation of T2DM-CAVS causal relationships.\u003c/p\u003e\n\u003ch2\u003e5.4 Two-step MR analysis\u003c/h2\u003e\n\u003cp\u003eThis study employed a two-step MR analysis to evaluate the causal pathways through which MetS and its core components\u0026mdash;including WC, HDL-C, TG, SBP, DBP, and T2DM\u0026mdash;influence CAVS, mediated by circulating metabolites. In the first step, MetS and its components served as exposures, while 233 circulating metabolites were examined as potential mediators (primary results in Table S 2; sensitivity analyses in Table S 3). After false discovery rate (FDR) correction and application of predefined criteria (IVW P \u0026lt; 0.05; significant association in \u0026ge;2 of 4 supplementary MR methods with P \u0026lt; 0.05; MR-Egger intercept P \u0026gt; 0.05), significant exposure-mediator associations were identified: MetS with 172 metabolites, WC with 137, TG with 158, and HDL-C with 145. No significant candidate mediating metabolites were found for SBP, DBP, or T2DM.\u003c/p\u003e\n\u003cp\u003eIn the second step, metabolites identified as significant mediators in the first step were treated as exposures, with CAVS as the outcome. Applying identical selection criteria, we identified 104 metabolites exhibiting significant causal effects on CAVS (sensitivity analyses in Table S 4). The number of metabolites demonstrating a significant mediating role in the causal pathway from each initial exposure to CAVS was as follows: 56 for MetS, 48 for WC, 63 for TG, and 46 for HDL-C (detailed in Figure 3 and Table S 5). No evidence of pleiotropy was detected in these analyses. However, Cochran\u0026apos;s Q statistic indicated potential heterogeneity originating from the instrumental variables.\u003c/p\u003e\n\u003cp\u003eNotably, 22 circulating metabolites served as mediators with consistent directions across all three pathways (MetS\u0026rarr;CAVS, WC\u0026rarr;CAVS, TG\u0026rarr;CAVS) (Table S 6). Among them, 19 metabolites, including apolipoprotein B (ApoB), remnant cholesterol (Remnant-C), small very low density lipoprotein cholesterol (S - VLDL - C), and small very - low - density lipoprotein cholesteryl ester (S - VLDL - CE), consistently functioned as risk mediators. Three other metabolites (phospholipids to total lipids ratio in intermediate-density lipoprotein, total cholesterol to total lipids ratio in large HDL, and free cholesterol to total lipids ratio in large LDL) acted as protective mediators. Furthermore, the effect direction of certain metabolites (e.g., estimated fatty acid chain length, cholesteryl esters to total lipids ratio in medium VLDL, total cholesterol to total lipids ratio in small VLDL, cholesteryl esters to total lipids ratio in small VLDL, total cholesterol to total lipids ratio in very large VLDL, triglycerides to total lipids ratio in very large VLDL) varied across pathways, partially counteracting the risk effects of the primary components.\u003c/p\u003e\n\u003cp\u003eThe HDL-C\u0026rarr;CAVS pathway identified 18 significant mediators. The mediation proportion exceeded 100% for three mediators (total cholesterol to total lipids ratio in medium HDL, 144%; triglycerides to total lipids ratio in large HDL, 108%; cholesteryl esters to total lipids ratio in medium HDL, 107%), a finding challenging to reconcile biologically and suggestive of unmeasured confounding pathways or effects. Nevertheless, the protective effects mediated by decreased ApoB (-76%) and increased IDL phospholipid proportion (54%) in this pathway remain noteworthy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified a significant positive genetic correlation between metabolic syndrome (MetS) and calcific aortic valve stenosis (CAVS) through Linkage Disequilibrium Score Regression (LDSC) analysis (Rg = 0.26, p = 4.62×10\u003csup\u003e–26\u003c/sup\u003e). These findings suggest potential shared genetic susceptibility loci between the two phenotypes, providing crucial genetic evidence for the comorbidity mechanism linking metabolic dysregulation and cardiovascular calcification. Notably, the heritability estimate for MetS (h\u003csup\u003e2\u003c/sup\u003e = 0.12) aligns with previous studies on metabolic traits including obesity and insulin resistance, reinforcing the pivotal role of genetic factors in its pathogenesis. However, CAVS exhibited relatively low heritability (h\u003csup\u003e2\u003c/sup\u003e = 0.011), which may reflect its multifactorial etiology: substantial contributions from environmental risk factors (e.g., advanced age, hypertension, chronic kidney disease) combined with phenotypic heterogeneity in diagnostic criteria (e.g., variability in imaging-based calcification score thresholds) might partially obscure the detectability of genetic variants. Nevertheless, the highly significant genetic correlation (p \u0026lt; 1×10\u003csup\u003e–25\u003c/sup\u003e) implies that the genetic association between MetS and CAVS retains biological importance even within this low heritability context. Importantly, as LDSC cannot delineate causal directionality, we further incorporated Mendelian Randomization (MR) analysis to mitigate potential confounding from reverse causation or pleiotropic effects.\u003c/p\u003e\u003cp\u003eUtilizing two-sample Mendelian randomization (MR) analysis, this study established an independent causal effect of waist circumference (WC), a cardinal phenotype of central obesity, on calcific aortic valve stenosis (CAVS), which aligns with observational evidence linking obesity to valvular calcification\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Mechanistically, central obesity may drive valvular calcification through multiple pathological pathways: (1) visceral adipose accumulation-induced dysregulated fatty acid metabolism enhances reactive oxygen species (ROS) production, exacerbating mitochondrial dysfunction; (2) oxidative stress microenvironments activate osteogenic transcription factors such as Runx2, promoting osteogenic transdifferentiation of vascular smooth muscle cells (VSMCs)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAt the lipid metabolism level, the independent causal effect of elevated triglyceride (TG) levels provides genetic support for the \"lipotoxicity hypothesis\": TG-rich lipoprotein remnants (e.g., VLDL) retained in valvular interstitium trigger oxidative stress cascades, accelerating calcium deposition via BMP-2/Smad signaling upregulation\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNotably, while observational studies associate higher high-density lipoprotein cholesterol (HDL-C) with reduced CAVS risk/progression, prior MR analyses failed to confirm causality. Our study detected a modest protective trend for HDL-C (OR = 0.87–0.90), consistent with limited evidence in aortic stenosis research. Mendelian randomization-based mediation analysis reveals key metabolite-mediated pathways through which HDL-C influences CAVS. Certain metabolites significantly mediate HDL-C's protective effect: ApoB contributes 76% of this protection through a mechanism whereby elevated HDL-C levels lead to reduced ApoB, consequently lowering CAVS risk, consistent with ApoB's established role in cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Simultaneously, triglycerides within specific lipoprotein subfractions (e.g., large HDL, small HDL, and very small VLDL) mediate substantial proportions of the protective effect (108%, 86%, and 99%, respectively), likely reflecting HDL-C's function in transporting triglycerides from peripheral tissues to the liver\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Conversely, another group of metabolites significantly counteracts HDL-C's protection: sphingomyelins and phosphatidylcholines exhibit highly negative mediation proportions (-137% and − 136%, respectively). While acknowledging the potential for unmeasured confounding, the magnitude of these negative values strongly suggests a risk effect for sphingomyelins. As core components of membrane lipid rafts, sphingomyelins (SM) are hydrolyzed by sphingomyelinase(SMase) into ceramide(Cer) and phosphocholine, these metabolites promote lipid deposition and retention, while their direct cytotoxicity drives fibrosis and calcification\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, significant risk mediation attributed to docosahexaenoic acid (DHA)-related metabolites (-75% to -81%) challenges the prevailing view of its universal cardioprotection. This finding necessitates cautious interpretation, suggesting that DHA may exert adverse effects under specific conditions or that the association could be influenced by confounding factors.\u003c/p\u003e\u003cp\u003eRegarding blood pressure parameters, SBP demonstrated a positive causal association with CAVS, whereas DBP showed no significant effect. This aligns with hypertension's established role as a CAVS risk factor: chronic mechanical stress induces endothelial dysfunction, promoting lipid deposition and inflammation, potentially influencing all stages of aortic stenosis progression\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Although most epidemiological studies link both SBP and DBP to cardiovascular risk, the Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA) specifically associated elevated SBP—not DBP—with increased arterial calcific stenosis risk\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e–\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eObservational studies propose diabetes and hyperglycemia as risk factors for aortic/mitral valve calcification \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, our MR analysis revealed a weak direct causal effect of type 2 diabetes mellitus (T2DM) on CAVS (IVW OR = 1.06, p = 0.033), with TG, SBP, and WC exhibiting stronger independent effects. This discrepancy suggests that observational studies may overestimate T2DM's direct impact due to confounding by collinearity with obesity, lipids, and blood pressure, whereas MR disentangles these factors.\u003c/p\u003e\u003cp\u003eGiven the complex nature of MetS, this study investigated whether circulating metabolites act as causal mediators regulating the relationship between MetS, its diagnostic components, and CAVS. Employing Mendelian randomization-based mediation analysis, we identified 22 lipid-related metabolites that simultaneously mediate the associations of MetS and its core diagnostic components (WC and TG) with CAVS. These findings broadly support the concept that lipoprotein particle composition and transport efficiency constitute a core biological pathway linking metabolic dysregulation to CAVS pathology. ApoB, recognized as a risk factor for both atherosclerosis and valvular calcification, promotes CAVS by directly facilitating lipid deposition within valvular interstitial cells, thereby activating oxidative stress and calcification signaling pathways. Notably, its substantial mediating proportion (up to 56%) in the TG exposure pathway underscores the central role of elevated triglycerides in driving CAVS, likely through increased synthesis of ApoB-containing particles. Remnant cholesterol (RC), representing the cholesterol content within triglyceride-rich lipoproteins (TRLs), possesses the ability to penetrate the arterial wall. However, due to their larger size, RC particles are prone to entrapment within the arterial intima, promoting cholesterol accumulation, foam cell formation, and exacerbating vascular inflammation and endothelial dysfunction\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSubclassification of TRLs further delineated specific mediating pathways underlying the TG exposure effect. Intermediate-density lipoprotein triglycerides (IDL-TG) and small low-density lipoprotein triglycerides (small LDL-TG) mediated 66% and 65% of the TG exposure effect, respectively, a significantly higher proportion than that mediated by HDL-TG. This reflects the greater penetration capacity and valvular retention potential of smaller, denser lipoprotein particles. Furthermore, the substantial mediating proportion observed for small very-low-density lipoprotein total cholesterol (78%) and very small VLDL particle concentration (69%) collectively confirms that hypertriglyceridemia drives CAVS pathogenesis primarily by elevating concentrations of dense TRL particles, not solely through alterations in lipid composition.\u003c/p\u003e\u003cp\u003eConcurrently, monounsaturated fatty acids (MUFAs) exhibited significant mediating effects across these three pathways. Elevated levels of 16:1 and 18:1 MUFAs likely indicate enhanced activity of stearoyl-CoA desaturase-1 (SCD1), an enzyme whose function – converting saturated fatty acids to MUFAs – is directly implicated in insulin resistance and valvular lipotoxicity\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile MR analysis effectively reduces confounding bias, caution is warranted regarding potential pleiotropic pathways affecting genetic instruments. For instance, certain SNPs may influence both metabolic traits and inflammation-related pathways. Future investigations should incorporate cohort studies and meta-analyses to strengthen result credibility through triangulation. Furthermore, within this mediation MR framework, some mediator variables may represent measurement variants of the exposure itself (e.g., the high correlation between total TG and VLDL-TG). Clarifying their independent pathological significance is crucial, as direct inclusion in models risks collinearity, thereby interfering with causal estimates. Subsequent studies must separate the independent effects of mediators by incorporating both exposure and mediator variables, potentially utilizing multivariable MR or employing metabolite clustering and dimension reduction techniques. Additionally, as the current genetic data primarily derive from European ancestry populations, the generalizability of the findings to other racial/ethnic groups requires validation through prospective cross-population studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides robust genetic evidence supporting a shared genetic basis and causal relationship between metabolic syndrome (MetS), its key components, and calcific aortic valve stenosis (CAVS). Linkage disequilibrium score regression (LDSC) revealed a significant positive genetic correlation (Rg = 0.265). Mendelian randomization (MR) analyses confirmed that genetically predicted MetS increases the risk of CAVS (OR ≈ 1.8), an effect primarily driven by the independent causal contributions of waist circumference (WC; OR ≈ 1.5–1.6) and triglycerides (TG; OR ≈ 1.4–1.5). Higher high-density lipoprotein cholesterol (HDL-C) levels demonstrated a modest protective effect (OR ≈ 0.87–0.90).\u003c/p\u003e\u003cp\u003eCritically, two-step MR mediation analysis identified specific circulating lipid metabolites acting as mediators between MetS, WC, TG, and coronary artery calcium score. Components of small very-low-density lipoprotein (VLDL) particles (total cholesterol, cholesteryl esters, free cholesterol), apolipoprotein B (ApoB), and remnant cholesterol accounted for a substantial proportion of these causal effects. These findings indicate that dysregulated lipoprotein metabolism, particularly involving triglyceride-rich remnant particles, constitutes a key biological mechanism underlying the association between MetS and coronary artery calcification. Key mediating metabolites, such as small VLDL components and ApoB, represent candidate biomarkers for understanding the pathway from metabolic dysfunction to valvular calcification. Their identification offers potential targets for future therapeutic strategies aimed at interrupting the progression from metabolic dysfunction to aortic valve calcification. Future studies should validate these findings in diverse populations and explore the utility of targeting these metabolic pathways.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMetS: metabolic syndrome\u003c/p\u003e\n\u003cp\u003eCAVS: calcific aortic valve stenosis\u003c/p\u003e\n\u003cp\u003eLDSC: Linkage Disequilibrium score regression\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eWC: waist circumference\u003c/p\u003e\n\u003cp\u003eTG: triglycerides\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eHDL-C: high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eT2DM: type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eNCEP: ATP III: The National Cholesterol Education Program-Adult Treatment Panel III\u003c/p\u003e\n\u003cp\u003eGAWS: genome-wide association study\u003c/p\u003e\n\u003cp\u003eSNPs: single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eIVs: instrumental variables\u003c/p\u003e\n\u003cp\u003eRCTs: randomized controlled trials\u003c/p\u003e\n\u003cp\u003eBWMR: Bayesian Weighted Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eTSMR: two-sample Mendelian randomization\u003c/p\u003e\n\u003cp\u003eRg: genetic correlation\u003c/p\u003e\n\u003cp\u003eIVW: inverse variance weighted\u003c/p\u003e\n\u003cp\u003eInSIDE: the instrument strength independent of direct effects\u003c/p\u003e\n\u003cp\u003eROS: reactive oxygen species\u003c/p\u003e\n\u003cp\u003eVSMCs: vascular smooth muscle cells\u003c/p\u003e\n\u003cp\u003eApoB: apolipoprotein B\u003c/p\u003e\n\u003cp\u003eRemnant-C/RC: remnant cholesterol\u003c/p\u003e\n\u003cp\u003eVLDL: very - low - density lipoprotein\u003c/p\u003e\n\u003cp\u003eS - VLDL - C : small very low density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eS - VLDL \u0026ndash; CE: small very - low - density lipoprotein cholesteryl ester\u003c/p\u003e\n\u003cp\u003eSM: sphingomyelins\u003c/p\u003e\n\u003cp\u003eSMase: sphingomyelinase\u003c/p\u003e\n\u003cp\u003eCer: ceramide\u003c/p\u003e\n\u003cp\u003eDHA: docosahexaenoic acid\u003c/p\u003e\n\u003cp\u003eTRLs: triglyceride-rich lipoproteins\u003c/p\u003e\n\u003cp\u003eFUMAs: monounsaturated fatty acids\u003c/p\u003e\n\u003cp\u003eSCD1: stearoyl-CoA desaturase-1\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e8 Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe acknowledge the invaluable contributions of all genome-wide association study (GWAS) consortia and cohort participants whose publicly shared data enabled this research. Additionally, we extend our heartfelt appreciation to the developers and technical support personnel of the R programming language and its associated packages employed in this study.\u003c/p\u003e\n\u003cp\u003e9 Author contributions\u003c/p\u003e\n\u003cp\u003eS. Yang: Data acquisition and cleaning, data management, methodology, primary analysis, result visualization, writing - original draft. Z. Bai, X. Ye, S. Mo, J. Chen, M. Zhang, D. Zhu: software implementation, Result organization, validation, writing-review \u0026amp; editing. M. Zhu: Conceptualization, software implementation, project administration, Supervision, writing-review \u0026amp; editing.\u003c/p\u003e\n\n\u003cp\u003e10.1 Ethical considerations\u003c/p\u003e\n\u003cp\u003eGiven that this study exclusively used previously published data, ethical approval and informed consent were not required.\u003c/p\u003e\n\u003cp\u003e10.2 Consent to participate and consent to participate\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics utilized in this Mendelian randomization study were obtained from publicly available repositories. All original studies contributing to these datasets obtained informed consent from participants and received ethical approval from relevant institutional review boards. As this secondary analysis exclusively employed de-identified and aggregated data, no additional ethical approval or participant consent was required for this specific investigation.\u003c/p\u003e\n\u003cp\u003e10.3 Consent for publication\u003c/p\u003e\n\u003cp\u003eThis study exclusively employed anonymized and aggregated statistical data; therefore, no further ethical approval or participant consent for publication was required under the relevant regulatory frameworks.\u003c/p\u003e\n\u003cp\u003e10.4 Funding\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed the following financial support received for their research, authorship, and/or publication of this article: Zhejiang Provincial Natural Science Foundation, Provincial Natural Science Foundation, LY24H170002.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYadgir S, Johnson CO, Aboyans V, et al. 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Diabetes and elevated plasma glucose in heart valve calcification and disease: the Copenhagen General Population Study. \u003cem\u003eEur J Prev Cardiol\u003c/em\u003e. Feb 26 2025;doi:10.1093/eurjpc/zwaf106\u003c/li\u003e\n\u003cli\u003eDing X, Tian J, Chang X, Liu J, Wang G. Association between remnant cholesterol and the risk of cardiovascular-kidney-metabolic syndrome progression: insights from the China Health and Retirement Longitudinal Study. \u003cem\u003eEur J Prev Cardiol\u003c/em\u003e. Apr 24 2025;doi:10.1093/eurjpc/zwaf248\u003c/li\u003e\n\u003cli\u003eRimm EB, Appel LJ, Chiuve SE, et al. Seafood Long-Chain n-3 Polyunsaturated Fatty Acids and Cardiovascular Disease: A Science Advisory From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. Jul 3 2018;138(1):e35-e47. doi:10.1161/cir.0000000000000574\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable\u0026nbsp;1\u0026nbsp;Summary of the GWAS summary statistics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSamples Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAncestry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCAVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e941863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eTh\u0026eacute;riault S et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eGWAS Catalog ID: GCST90310293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMetS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1384348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePark S et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eGWAS Catalog ID: GCST90444487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e336074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDeWan AT et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eGWAS Catalog ID: GCST90302887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e403943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRichardson et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIEU ID:ieu-b-109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e97656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHowe LJ et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIEU ID:ieu-b-4818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e317756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNeale et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIEU ID:ukb-a-359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e462166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBen Elsworth et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 203px;\"\u003e\n \u003cp\u003eIEU ID:ukb-b-9405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCAVS: Calcific aortic valve stenosis; MetS: Metabolic syndrome; T2DM: Type 2 diabetes; HDL-C: HDL cholesterol; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; WC: Waist circumference\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2\u0026nbsp; MetS-CAVS LDSC Analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e_p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eRg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eRg_p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eRg_SE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eMetS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.26\u0026times;10\u003csup\u003e-26\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eCAVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.70\u0026times;10\u003csup\u003e-33\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eh\u003csup\u003e2\u003c/sup\u003e: SNP-based Heritability;\u0026nbsp;h\u003csup\u003e2\u003c/sup\u003e_p: Heritability p-value;\u0026nbsp;Intercept:\u003cem\u003e\u0026nbsp;\u003c/em\u003eIntercept term from LDSC regression; Rg:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGenetic correlation; Rg_p: Genetic Correlation p-value; Rg_SE: Genetic Correlation Standard Error\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3\u0026nbsp;The results of the heterogeneity and horizontal pleiotropy test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 67px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 328px;\"\u003e\n \u003cp\u003eHeterogeneity test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePleiotropy test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 157px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eQ_p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eQ_p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eMets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e837.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e6.57\u0026times;10\u003csup\u003e-17\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e837.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e8.01E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e7.37\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e485.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.32\u0026times;10\u003csup\u003e-11\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e486.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.51E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-2.31\u0026times;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e185.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.34\u0026times;10\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e185.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.22E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.62\u0026times;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e806.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3.50\u0026times;10\u003csup\u003e-38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e806.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.78E-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e8.39\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e36.939\u003c/p\u003e\n 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\u003cp\u003e1.73\u0026times;10\u003csup\u003e-13\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e339.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.09E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-3.23\u0026times;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e106.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4.99\u0026times;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e111.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2.09E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMetS: Metabolic syndrome; T2DM: Type 2 diabetes; HDL-C: HDL cholesterol; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; WC: Waist circumference\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, Aortic valve stenosis, Linkage Disequilibrium Score Regression, Mendelian randomization, Metabolite","lastPublishedDoi":"10.21203/rs.3.rs-7141693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7141693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003e1.1 Background\u003c/h2\u003e\u003cp\u003eThis study aims to clarify the causal relationship between metabolic syndrome (MetS) and calcific aortic valve stenosis (CAVS) through Linkage Disequilibrium score regression (LDSC) and two-sample and two-step Mendelian randomization (MR).\u003c/p\u003e\u003ch2\u003e1.2 Methods\u003c/h2\u003e\u003cp\u003eWe conducted LDSC analysis and two-sample and two-step MR with Bayesian weighted MR validation using genome-wide association studies (GWAS) summary statistics.\u003c/p\u003e\u003ch2\u003e1.3 Result\u003c/h2\u003e\u003cp\u003eLDSC analysis identified MetS and CAVS (rg\u0026thinsp;=\u0026thinsp;0.264, p\u0026thinsp;=\u0026thinsp;4.61\u0026times;10\u003csup\u003e\u0026ndash;26\u003c/sup\u003e). In the two-sample MR study, Mets (P\u0026thinsp;=\u0026thinsp;1.39\u0026times;10\u003csup\u003e\u0026ndash;20\u003c/sup\u003e, OR\u0026thinsp;=\u0026thinsp;1.82), waist circumference (WC, p\u0026thinsp;=\u0026thinsp;5.50\u0026times;10\u003csup\u003e\u0026ndash;10\u003c/sup\u003e, OR\u0026thinsp;=\u0026thinsp;1.51), triglycerides (TG, p\u0026thinsp;=\u0026thinsp;2.24\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e, OR\u0026thinsp;=\u0026thinsp;1.42), and systolic blood pressure (SBP, p\u0026thinsp;=\u0026thinsp;1.83\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, OR\u0026thinsp;=\u0026thinsp;1.04) were positively correlated with CAVS. In contrast, high-density lipoprotein (HDL-C, p\u0026thinsp;=\u0026thinsp;0.002, OR\u0026thinsp;=\u0026thinsp;0.9), and diastolic blood pressure (DBP, p\u0026thinsp;=\u0026thinsp;0.002, OR\u0026thinsp;=\u0026thinsp;0.78) were negatively correlated with CAVS. Two-step MR analysis indicated that among 233 circulating metabolites, 19 risk factors and 3 protective factors mediated the impacts of MetS, WC and TG on CAVS.\u003c/p\u003e\u003ch2\u003e1.4 Conclusion\u003c/h2\u003e\u003cp\u003eMetS and CAVS share a common genetic architecture, with central adiposity, dyslipidemia, and blood pressure exhibiting distinct causal pathways. Small very-low-density lipoprotein particles, apolipoprotein B, and remnant cholesterol are key mediators linking MetS, WC, TG and CAVS.\u003c/p\u003e","manuscriptTitle":"Causal Inference of Metabolic Syndrome on Calcific Aortic Valve Stenosis: Linkage Disequilibrium Score Regression and Two-Sample, Two-Step Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 10:27:17","doi":"10.21203/rs.3.rs-7141693/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-25T20:35:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T12:24:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T14:47:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T22:50:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172315971131332466511857919406039689945","date":"2025-09-10T08:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141393666529435916462742593160312862695","date":"2025-09-03T09:46:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237545287242797123613256138488316014731","date":"2025-09-01T08:09:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234867445170569075133354415328720399258","date":"2025-08-29T11:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T13:16:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T13:49:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T13:47:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2025-07-16T15:29:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90e8b241-841e-4eba-bb81-db23b9bf2181","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:10:14+00:00","versionOfRecord":{"articleIdentity":"rs-7141693","link":"https://doi.org/10.1186/s13019-026-03952-x","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2026-03-19 15:58:51","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-08-22 10:27:17","video":"","vorDoi":"10.1186/s13019-026-03952-x","vorDoiUrl":"https://doi.org/10.1186/s13019-026-03952-x","workflowStages":[]},"version":"v1","identity":"rs-7141693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7141693","identity":"rs-7141693","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}