Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging-Derived Phenotypes as Functional...

Area of Science:

• Neuroscience
• Genetics
• Immunology

Background:

• Autoimmune inflammatory diseases (AIDs) are genetically linked but their connection to brain functional networks remains unclear.
• Understanding these links is crucial for developing novel therapeutic strategies for AIDs.

Purpose of the Study:

• To investigate the causal relationships between genetically determined brain functional networks and the risk of developing 18 different autoimmune inflammatory diseases.
• To identify specific brain regions and network alterations associated with AIDs pathogenesis.

Main Methods:

• Utilized bidirectional two-sample Mendelian randomization (MR) analysis.
• Employed genome-wide association study (GWAS) data from 18 AIDs and 1,366 brain imaging-derived phenotypes (n = 8,428).
• Performed sensitivity analyses and double machine learning to ensure robustness of findings.

Main Results:

• Identified significant associations between brain activity and AIDs risk, including reduced left striatal activity with increased multiple sclerosis risk (OR=0.59).
• Found associations such as left uncinate fasciculus activity with elevated systemic lupus erythematosus risk (OR=3.72).
• Observed asymmetric cerebellar peduncle effects in cutaneous vasculitis and suppressed cerebellar area VIIIa in fibromyalgia.

Conclusions:

• Alterations in default mode, salience, and central executive networks are implicated in AIDs pathogenesis.
• Specific brain regions, including the striatum and cerebellar peduncles, represent potential therapeutic targets for autoimmune diseases.
• This study provides a genetic basis for the link between brain function and autoimmune inflammatory diseases.