Cardiovascular risk factors modulate the effect of brain imaging-derived phenotypes on ischaemic stroke risk
Studies have shown that cardiovascular risk factors are closely related to the occurrence of stroke, especially ischaemic stroke, as they can lead to changes in brain structure and function. However, the role of cardiovascular risk factors-induced changes in brain structure and function in the development of ischaemic stroke has not been studied. The aim of this study is thus to explore the causal association among cardiovascular risk factors, brain phenotypes and ischaemic stroke by assessing Mendelian randomization. We used univariate Mendelian randomization to sequentially investigate the causal effects of the 12 most common cardiovascular risk factors on brain structure and 3935 brain imaging-derived phenotypes in the development of ischaemic stroke. We also examined the mediating effect of brain structure on blood pressure-induced ischaemic stroke using a multivariable Mendelian randomization test. We tested the reliability of our results using the Steiger test, heterogeneity test, horizontal pleiotropy test and leave-one-out method. We found that 8 of the 12 examined cardiovascular risk factors were associated with 538 brain imaging-derived phenotypes, and 9 of the 12 cardiovascular risk factors were associated with IS. The main cardiovascular risk factors associated with brain imaging-derived phenotypes and ischaemic stroke was blood pressure (systolic and diastolic), which can affect the occurrence of ischaemic stroke through 6 types of brain imaging-derived phenotypes. However, extrapolation of our findings to other ethnic groups is challenging, and the possibility of reverse causality cannot be completely ruled out. This study identifies the role of cardiovascular risk factors, especially blood pressure, in affecting brain structure and ischaemic stroke risk. The findings assist in early risk detection and enhance stroke prevention strategies, also hinting at non-vascular factors' involvement.
Authors
- Chunyan Zuo
- Changhe Shi (ORCID: https://orcid.org/0000-0002-4843-0072)
- Yuming Xu (ORCID: https://orcid.org/0000-0003-2689-9897)
- Meng‐jie Li
- Mengnan Guo (ORCID: https://orcid.org/0000-0003-4816-6408)
- Shuangjie Li (ORCID: https://orcid.org/0000-0001-6667-8330)
- Chenyuan Mao
- Chan Zhang (ORCID: https://orcid.org/0000-0003-4021-9165)
- Zhiyun Wang (ORCID: https://orcid.org/0009-0005-6387-8763)
- Yanmei Feng (ORCID: https://orcid.org/0000-0003-4180-6564)
- Xiaoyan Hao (ORCID: https://orcid.org/0000-0002-3896-5727)
- Yuanyuan Liang (ORCID: https://orcid.org/0000-0003-0360-286X)
- Chenwei Hao
- Bo Song (ORCID: https://orcid.org/0000-0002-2082-0765)
- Dongrui Ma
Institutions
- Academy of Medical Sciences (GB)
- Zhengzhou University (CN)
- Henan Academy of Sciences (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Brain Communications
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/braincomms/fcaf183
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00