Xu Rong, Chen Rumeng, Xu Shuling, Ding Yining, Zheng Tingjin, Ouyang Chaoqun, Ding Xiaoming, Chen Linlin, Zhang Wenzhou, Ge Chenjin, Li Sen
Department of Pharmacy, Quanzhou Medical College, 362011 Quanzhou, Fujian, China.
School of Life Sciences, Beijing University of Chinese Medicine, 102488 Beijing, China.
Rev Cardiovasc Med. 2024 Jul 3;25(7):245. doi: 10.31083/j.rcm2507245. eCollection 2024 Jul.
Although observational studies have reported several common biomarkers related to coronary artery disease (CAD) and cancer, there is a shortage of traditional epidemiological data to establish causative linkages. Thus, we conducted a comprehensive two-sample Mendelian randomization (MR) analysis to systematically investigate the causal associations of 109 traits with both CAD and cancer to identify their shared risk and protective factors.
The genetic association datasets pertaining to exposure and outcomes were reviewed using the most recent and public genome-wide association studies (GWAS). Inverse variance weighting (IVW), weighted median (WM), and MR-Egger strategies were implemented for the MR analyses. The heterogeneity and pleiotropy were measured utilizing leave-one-out sensitivity testing, MR-PRESSO outlier detection, and Cochran's Q test.
The IVW analyses revealed that genetic-predicted mean sphered cell volume (MSCV) is a protective factor for CAD, and weight is a risk factor. MSCV and weight also show similar effects on cancer. Furthermore, our study also identified a set of risk and protective factors unique to CAD and cancer, such as telomere length.
Our Mendelian randomization study sheds light on shared and unique risk and protective factors for CAD and cancer, offering valuable insights that could guide future research and the development of personalized strategies for preventing and treating these two significant health issues.
尽管观察性研究报告了几种与冠状动脉疾病(CAD)和癌症相关的常见生物标志物,但缺乏传统流行病学数据来建立因果联系。因此,我们进行了一项全面的两样本孟德尔随机化(MR)分析,以系统地研究109个性状与CAD和癌症的因果关联,以确定它们共同的风险和保护因素。
使用最新的公开全基因组关联研究(GWAS)对与暴露和结局相关的遗传关联数据集进行了审查。在MR分析中采用了逆方差加权(IVW)、加权中位数(WM)和MR-Egger策略。利用留一法敏感性检验、MR-PRESSO异常值检测和 Cochr an's Q检验来测量异质性和多效性。
IVW分析显示,遗传预测的平均球形细胞体积(MSCV)是CAD的保护因素,而体重是风险因素。MSCV和体重对癌症也有类似影响。此外,我们的研究还确定了一组CAD和癌症特有的风险和保护因素,如端粒长度。
我们的孟德尔随机化研究揭示了CAD和癌症共同及独特的风险和保护因素,提供了有价值的见解,可指导未来的研究以及预防和治疗这两个重大健康问题的个性化策略的制定。