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Published on: December 2, 2015
Ventricular cardiac magnetic resonance traits and schizophrenia risk: a UK Biobank and Mendelian randomization study
Jinfeng Yan1, Siqi Yu1, Yuan Gao2
1Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, Shanghai, China.
Background:
Genetic factors can influence variation in cardiac structure. Schizophrenia (SCZ) is also highly heritable. However, whether cardiac structural variation is related to SCZ risk remains unclear and insufficiently examined from a "heart-to-brain" perspective.
Methods:
Using data from 38,206 UK Biobank (UKB) participants, we assessed associations between 16 cardiac magnetic resonance imaging-derived ventricular traits and SCZ polygenic risk scores. Using ventricular cardiac magnetic resonance traits Genome-wide association study (GWAS) data from over 30,000 individuals and SCZ GWAS data from 127,906 individuals, two-sample Mendelian randomization (MR) was performed to evaluate potential causal associations. Reverse association analysis using data from UKB and reverse MR analysis were conducted to explore possible reverse effects.
Results:
Four ventricular traits showed significant associations with SCZ genetics, with end-systolic volume (ESV) of right ventricle (RV) exhibiting the strongest negative association. Forward MR provided evidence consistent with a potential causal association between genetically predicted higher ESV of RV and lower SCZ risk, with no strong indication of pleiotropy. All other traits in the forward MR analyses, as well as all traits in the reverse MR analyses, showed no statistically significant evidence of a potential causal association.
Conclusions:
Higher genetically predicted ESV of RV appears linked to reduced SCZ risk. These findings highlight ESV of RV as a ventricular characteristic with genetic relevance to SCZ. This study offers new insights into SCZ pathophysiology and opportunities for risk stratification and mechanistic investigation.
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