This Mendelian randomization study aims to investigate causal associations between genetically predicted insomnia and 14 cardiovascular diseases (CVDs) as well as the potential mediator role of 17 cardiometabolic risk factors.
Methods and Results
Using genetic association estimates from large genome-wide association studies and UK Biobank, we performed a 2-sample Mendelian randomization analysis to estimate the associations of insomnia with 14 CVD conditions in the primary analysis. Then mediation analysis was conducted to explore the potential mediator role of 17 cardiometabolic risk factors using a network Mendelian randomization design. After correcting for multiple testing, genetically predicted insomnia was consistent significantly positively associated with 9 of 14 CVDs, those odds ratios ranged from 1.13 (95% CI, 1.08-1.18) for atrial fibrillation to 1.24 (95% CI, 1.16-1.32) for heart failure. Moreover, genetically predicted insomnia was consistently associated with higher body mass index, triglycerides, and lower high-density lipoprotein cholesterol, each of which may act as a mediator in the causal pathway from insomnia to several CVD outcomes. Additionally, we found very little evidence to support a causal link between insomnia with abdominal aortic aneurysm, thoracic aortic aneurysm, total cholesterol, low-density lipoprotein cholesterol, glycemic traits, renal function, and heart rate increase during exercise. Finally, we found no evidence of causal associations of genetically predicted body mass index, high-density lipoprotein cholesterol, or triglycerides on insomnia.
This study provides evidence that insomnia is associated with 9 of 14 CVD outcomes, some of which may be partially mediated by 1 or more of higher body mass index, triglycerides, and lower high-density lipoprotein cholesterol.
Physical measurement, blood biochemistry, lifestyle, environmental exposure: causality, gene-environment interaction in relation to metabolic diseases and cancer risk.
Cancer and metabolic diseases account for a large proportion of the global burden of disease. Epidemiological association studies reported a variety of environmental risk factors and risk genes for these diseases. However, limited studies have definite causal effects on metabolic disease or cancer, except for few widely recognized risk factors such as smoking for lung cancer and high-calorie diet for obesity. In fact, the effects of these exposures and genes on the diseases are very complicated including direct effects, indirect effects, mediating effects, gene-environment interactions, gene pleiotropy, effect modification and so on. Meanwhile, routine observational studies inevitably suffer from confounding or reverse causality. Besides, metabolic diseases and cancer may also share common risk factors with each other, however, the causal effect and pathogenic mechanism of these factors may be highly heterogeneous.
Therefore, for each potential risk factor, if the causal association with a specific disease was determined, we could intervene the exposure levels of risk factors promptly to reduce the morbidity or mortality of metabolic diseases and cancer. So we aimed to explore the potential causal effect of specific exposure or pathway (physical measurement, blood biochemistry, lifestyle, environment, genes, interactions) on chronic metabolic diseases or cancer and to provide evidence for intervention and prevention. Then we also expect to develop some novel causal inference methods and effective disease prediction methods.
We intend to perform our research for the duration of three years. The study may have certain practicality values for public health and further research if our results are supported. Study on both genes and environmental exposures would provide strong evidence to clarify the relationship between various exposures and diseases. Causal findings may contribute to identify novel biological pathways for disease prevention, diagnosis and treatment or provide suitable prediction indicators. This is of great significance to public health for cancer and metabolic diseases controlling. Additionally, we may also provide new analytical strategies and methods, which may have some application values for further research.
|Lead investigator:||Professor Fuzhong Xue|
|Lead institution:||Shandong University|
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