| Application: | 45676, The joint effects of genetic, lifestyle and environmental risk factors on common diseases and multimorbidity. |
| Title: | Associations of sleep duration and quality with incident cardiovascular disease, cancer, and mortality: a prospective cohort study of 407,500 UK biobank participants |
| Size: | 1301 B |
| Archived: | 19 Oct 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 4026
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Notes
ObjectiveFew studies have investigated the associations of sleep duration and sleep quality with incident cardiovascular diseases (CVDs), cancer, and mortality in the same large population. This study aimed at estimating the independent risk factors of long or short sleep durations and several typical characteristics of poor sleep quality for incident CVDs, cancer, and mortality.
Methods
In this prospective cohort study, 407 500 individuals were enrolled. Cox proportional hazards models were used to calculate the adjusted hazard ratios and 95% confidence intervals (HR, 95%CI) of associations of sleep duration and quality with incident CVDs, cancer, and mortality.
Results
Compared with the sleep duration of 7 h, sleep duration of <5 h and >9 h were both associated with higher risk of all-cause mortality (HR = 1.25, 95% CI: 1.16-1.34 and HR = 1.30, 95% CI: 1.22-1.38, respectively), CVD mortality (HR = 1.27, 95% CI: 1.09-1.49 and HR = 1.32, 95% CI: 1.16-1.50, respectively), and CVD incidence (HR = 1.23, 95% CI: 1.16-1.31 and HR = 1.08, 95% CI: 1.02-1.15, respectively). Additionally, long sleep duration (>9 h) was associated with a higher risk of cancer mortality (HR = 1.19, 95% CI: 1.10-1.30) and cancer incidence (HR = 1.08, 95% CI: 1.04-1.12). Moreover, CVD incidence was significantly associated with snoring, insomnia and narcolepsy, increasing the risk by 7%, 26%, and 20%, respectively.
Conclusion
Long sleep durations may substantially increase the risk of mortality and morbidity. Snoring, insomnia, and narcolepsy were independent risk factors for incident CVD.
Application 45676
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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|---|---|---|---|
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