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<p>Ischemic stroke (IS) is a complex disease influenced by genetic and environmental factors, large artery atherosclerosis (LAS) has the highest proportion among the three subtypes of IS. Neuroimaging is an important examination for ischemic stroke. This study aims to systematically evaluate the causal relationships between various neuroimaging features and LAS risk using Mendelian randomization (MR). We performed a two-sample MR analysis using genetic data from large-scale genome-wide association studies. Exposure data included structural MRI, diffusion tensor imaging, and resting-state fMRI features. Outcome data were from GWAS on LAS. Inverse variance weighting was the primary analytical method, supplemented by sensitivity analyses. A total of 186 imaging features showed causal associations with LAS risk in genetic prediction. Increased volume in specific brain regions (left middle temporal gyrus, right cerebellum) was associated with higher risk in genetic prediction. Altered white matter microstructure in tracts like the pontine crossing tract and functional connectivity strength within the default mode and salience networks were also causally linked to LAS in genetic prediction. This MR study provides robust genetic evidence that the structural integrity of specific brain regions, white matter pathway efficiency, and functional network connectivity play potential causal roles in LAS pathogenesis. These neuroimaging features hold promise as novel biomarkers for predicting stroke risk and offer new insights into the neural circuit mechanisms of LAS.</p>

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A Causal Association Study Between Brain Imaging Features and the Risk of Large Artery Atherosclerosis: A Two-sample Mendelian Randomization Study

  • 期刊论文

  • 发表日期：

    2026年3月28日

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  • Weiran Li,
  • Jinjian Li,
  • Wei He,
  • Xu Wang,
  • Dexi Zhao

摘要

Ischemic stroke (IS) is a complex disease influenced by genetic and environmental factors, large artery atherosclerosis (LAS) has the highest proportion among the three subtypes of IS. Neuroimaging is an important examination for ischemic stroke. This study aims to systematically evaluate the causal relationships between various neuroimaging features and LAS risk using Mendelian randomization (MR). We performed a two-sample MR analysis using genetic data from large-scale genome-wide association studies. Exposure data included structural MRI, diffusion tensor imaging, and resting-state fMRI features. Outcome data were from GWAS on LAS. Inverse variance weighting was the primary analytical method, supplemented by sensitivity analyses. A total of 186 imaging features showed causal associations with LAS risk in genetic prediction. Increased volume in specific brain regions (left middle temporal gyrus, right cerebellum) was associated with higher risk in genetic prediction. Altered white matter microstructure in tracts like the pontine crossing tract and functional connectivity strength within the default mode and salience networks were also causally linked to LAS in genetic prediction. This MR study provides robust genetic evidence that the structural integrity of specific brain regions, white matter pathway efficiency, and functional network connectivity play potential causal roles in LAS pathogenesis. These neuroimaging features hold promise as novel biomarkers for predicting stroke risk and offer new insights into the neural circuit mechanisms of LAS.