AUTHOR=Xu Shihan , Liu Yanfei , Wang Qing , Liu Fenglan , Xu Fengqin , Liu Yue TITLE=Mendelian randomization study reveals a causal relationship between coronary artery disease and cognitive impairment JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1150432 DOI=10.3389/fcvm.2023.1150432 ISSN=2297-055X ABSTRACT=Background

Growing evidence suggests that Coronary artery disease (CAD) is associated with cognitive impairment. However, these results from observational studies was not entirely consistent, with some detecting no such association. And it is necessary to explore the causal relationship between CAD and cognitive impairment.

Objective

We aimed to explore the potential causal relationship between CAD and cognitive impairment by using bidirectional two-sample mendelian randomization (MR) analyses.

Methods

Instrument variants were extracted according to strict selection criteria. And we used publicly available summary-level GWAS data. Five different methods of MR [random-effect inverse-variance weighted (IVW), MR Egger, weighted median, weighted mode and Wald ratio] were used to explore the causal relationship between CAD and cognitive impairment.

Results

There was little evidence to support a causal effect of CAD on cognitive impairment in the forward MR analysis. In the reverse MR analyses, We detect causal effects of fluid intelligence score (IVW: β = −0.12, 95% CI of −0.18 to −0.06, P = 6.8 × 10−5), cognitive performance (IVW: β = −0.18, 95% CI of −0.28 to −0.08, P = 5.8 × 10−4) and dementia with lewy bodies (IVW: OR = 1.07, 95% CI of 1.04–1.10, P = 1.1 × 10−5) on CAD.

Conclusion

This MR analysis provides evidence of a causal association between cognitive impairment and CAD. Our findings highlight the importance of screening for coronary heart disease in patients of cognitive impairment, which might provide new insight into the prevention of CAD. Moreover, our study provides clues for risk factor identification and early prediction of CAD.