Integrative Genomic Analysis Reveals Insulin Resistance as a Key Mediator in the Causal Link between Metabolic Dysfunction–associated Steatohepatitis and Hypertension

Figures

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  Figure 1.. Methodological framework and data integration for the multitiered, two-step MR analysis. This schematic outlines the two-sample MR architecture employed to evaluate the causal relationship between MASH liability and hypertensive phenotypes, specifically investigating the mediating role of IR. (1) Data sources and instrument identification: genetic proxies for MASH liability were derived via a multitiered approach, leveraging discovery data from the Million Veteran Program (n = 218,595) and validation data from UKB (n = 438,089); mediator data for IR were harvested from a large-scale European meta-GWAS (MaGIC; n = 108,557); outcome statistics for essential hypertension and blood pressure traits—including systolic blood pressure, diastolic blood pressure, and pulse pressure—were obtained from the UKB European cohort (n = 735,757) to ensure population consistency. (2) Analytical methods and mediation pathways: the two-step MR framework defines “Path a” as the causal effect of MASH on IR and “Path b” as the independent effect of IR on blood pressure; UVMR and MVMR were used to isolate direct effects and calculate the mediated proportion through the product-of-coefficients method. (3)…

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  Figure 2.. MR analyses assessing the causal influence of MASH on hypertensive phenotypes. (A) Scatter plots illustrating the causal effect of genetic liability for MASH on various IR mediators, including HOMA-IR, fasting insulin, insulin sensitivity index, and Clamp-IR. Each data point represents an individual single-nucleotide polymorphism, with error bars indicating the standard error of the genetic association with the exposure and mediator. Regression lines represent different MR estimators: IVW (blue solid line), MR-Egger (red dashed line), weighted median (green solid line), and weighted mode (orange dashed line). (B) Scatter plots demonstrating the causal effect of the genetically predicted IR mediator on hypertensive outcomes: SBP, DBP, and PP. Consistent with panel A, the slopes of the regression lines indicate the magnitude of the causal effect across different MR models. (C) Forest plots summarizing the causal estimates (β coefficients in mm Hg) for SBP, DBP, and PP. Results are compared across four MR methods (IVW, MR-Egger, weighted median, and weighted mode) using two distinct genetic instrument sets: the “cALT proxy” discovery set (blue triangles) and the “imaging- and…

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  Figure 3.. Leave-one-out sensitivity analyses assessing the causal stability of the MASH–hypertension association. Leave-one-out plots demonstrate the robustness of the causal estimates for SBP, DBP, and PP across different genetic definitions of MASH. (A-C) Sensitivity analyses using the cALT elevation proxy as the exposure for SBP (A), DBP (B), and PP (C). Each row indicates the causal effect (point estimate and 95% confidence interval) calculated by the IVW method after systematically excluding the specific SNP listed on the y-axis. (D-F) Parallel analyses using the high-stringency imaging- and biopsy-confirmed MASH definition as the exposure for SBP (D), DBP (E), and PP (F). In all panels, the red marker at the bottom—“All (IVW estimate)”—represents the aggregate causal estimate including all available instruments. The vertical dashed line represents the null effect (0.0 mm Hg). The consistent stability of the effect sizes across all iterations indicates that the primary causal findings are not driven by any single influential or pleiotropic variant. cALT: chronic alanine aminotransferase; DBP: diastolic blood pressure; IVW: inverse variance–weighted; MASH: metabolic…