Although many cardiovascular risk factors for stroke have been reported before, it has not been investigated comprehensively for all major stroke types in large datasets. This study aimed to evaluate the associations of healthy behaviors, biological phenotypes and cardiovascular health (CVH) with long-term risks of strokes events, overall and stroke subtypes (ischemic stroke [IS], intracerebral hemorrhage [ICH], subarachnoid hemorrhage [SAH], and unspecified stroke).
Between 2006 and 2010, a total of 354,976 participants (age 40-70 years) in the UK Biobank free of stroke and coronary heart disease were examined and thereafter followed up to 2020. According to American Heart Association guideline, the global CVH included four behavioral (smoking, diet, physical activity, body mass index) and three biological (blood glucose, blood cholesterol, blood pressure) metrics. The behavioral, biological and global CVH score was the sum of four, three, and seven metrics, respectively, and then was categorized into poor, intermediate and ideal group. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of stroke events.
A total of 5804 incident stroke cases, including 3664 IS, 714 ICH and 453 SAH, were documented over a median follow-up of 11 years. The risk of stroke decreased significantly and linearly with both increasing behavioral CVH score and biological CVH score. Ideal behavioral CVH group was significantly associated with lower risks of all stroke subtypes, biological CVH was related to stroke events except for SAH. Additionally, the 1-point increment in global CVH score was associated with 11%,13%, 8% and 13% lower risks of stroke, IS, ICH and unspecified stroke, however, there was no significant dose-dependent association between global CVH and SAH.
Our findings suggest inverse linear associations of behavioral, biological and global CVH with long-term risks of stroke and stroke subtypes, except for SAH, highlighting the benefits of maintaining better CVH status as a primordial prevention strategy of stroke.
The National Natural Science Foundation of China (71,910,107,004, 91,746,205).
The joint effects of genetic, lifestyle and environmental risk factors on common diseases and multimorbidity.
Most of the diseases are caused by the interaction of genetic, environmental and lifestyle risk factors. According to the WHO, lifestyle can account for 60% of the health and longevity, genetic conditions 15%, environmental and social factors 17%, and medical conditions 8 %. More and more people are suffering from multiple non-communicable diseases(NCDs), such as diabetes, cancers, cardiovascular disease, and chronic obstructive pulmonary disease. However, there are few studies investigating the panoramic associations between genetic, environmental and lifestyle risk factors and common diseases which involved diabetes, site-specific cancers, cardiovascular diseases, mental diseases , chronic obstructive pulmonary disease(COPD), and Alzheimer's disease . The term "multimorbidity" in our study is referred to the coexistence of two or more common diseases in the same individual ? We will estimate the joint effect of the genetic, lifestyle and environmental risk factors on common diseases and multimorbidity. The polygenic risk scores for individuals will be calculated. The associations of genetic, environmental and lifestyle factors with the risk of common diseases will be tested using Cox proportional hazards. The prediction model for the risk of common diseases will be constructed using the machine learning methods, such as the random forest method. The proposed project will use existing data collected by UK Biobank and will take approximately 24 months to complete. Understanding genetic predisposition to disease and knowledge of lifestyle modifications is necessary for the public to make informed choices. To investigate the associations between the genetic, environmental and lifestyle risk factors and common diseases is of great importance for public health. The risk assessment tool for common diseases based on the combination of the genetic, environmental and lifestyle risk factors can provide decision -making supports for precise and individualized intervention.
|Lead investigator:||Professor Yaogang Wang|
|Lead institution:||Tianjin Medical University|
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