Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging-Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases

孟德尔随机化和双重机器学习建模揭示脑成像衍生的表型是18种自身免疫性炎症疾病的功能性贡献因素

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Abstract

Autoimmune inflammatory diseases (AIDs) are genetically linked disorders with unclear causal links to brain functional networks. Using bidirectional two-sample Mendelian randomization (MR) on GWAS data from 18 AIDs and 1,366 brain imaging-derived phenotypes (n = 8,428), we identified significant associations, including reduced left striatal activity increasing multiple sclerosis risk (OR = 0.59), left uncinate fasciculus activity elevating systemic lupus erythematosus risk (OR = 3.72), and asymmetric cerebellar peduncle effects in cutaneous vasculitis (left: OR = 0.11; right: OR = 8.57) [exploratory finding with 24.8%-37.8% power]. Fibromyalgia suppressed cerebellar area VIIIa (β = -0.023). Sensitivity analyses, double machine learning, and >99% statistical power supported robustness. These findings suggest alterations in default mode, salience, and central executive networks contribute to AIDs pathogenesis, highlighting brain regions such as the striatum and cerebellar peduncles as potential therapeutic targets.

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