孟德尔随机化
医学
内科学
优势比
血压
糖尿病
心力衰竭
心脏病学
冲程(发动机)
置信区间
疾病
2型糖尿病
内分泌学
遗传学
基因型
遗传变异
基因
生物
机械工程
工程类
作者
Jonathan L. Ciofani,Daniel Han,U. Allahwala,Benjamin Woolf,Dipender Gill,Ravinay Bhindi
标识
DOI:10.1016/j.amjcard.2023.08.007
摘要
Elevated blood pressure, dyslipidemia, and impaired glycemic control are well-established cardiovascular risk factors in Europeans, but there are comparatively few studies focused on East Asian populations. This study evaluated the potential causal relations between traditional cardiovascular risk factors and disease risk in East Asians through a 2-sample Mendelian randomization approach. We collected summary statistics for blood pressure parameters, lipid subsets, and type 2 diabetes mellitus liability from large genome-wide association study meta-analyses conducted in East Asians and Europeans. These were paired with summary statistics for ischemic heart disease (IHD), ischemic stroke (IS), peripheral vascular disease, heart failure (HF) and atrial fibrillation (AF). We performed univariable Mendelian randomization analyses for each exposure-outcome pair, followed by multivariable analyses for the available lipid subsets. The genetically predicted risk factors associated with IHD and AF were similar between East Asians and Europeans. However, in East Asians only genetically predicted elevated blood pressure was significantly associated with IS (odds ratio 1.05, 95% confidence interval 1.04 to 1.06, p <0.0001) and HF (odds ratio 1.05, 95% confidence interval 1.04 to 1.06, p <0.0001), whereas nearly all genetically predicted risk factors were significantly associated with IS and HF in Europeans. In conclusion, this study provides supportive evidence for similar causal relations between traditional cardiovascular risk factors and IHD and AF in both East Asian and European ancestry populations. However, the identified risk factors for IS and HF differed between East Asians and Europeans, potentially highlighting distinct disease etiologies between these populations.
科研通智能强力驱动
Strongly Powered by AbleSci AI