Context Recent studies have suggested that a higher body mass index (BMI) and serum urate levels were associated with a lower risk of developing dementia. However, these reverse relationships remain controversial, and whether serum urate and BMI confound each other is not well established. Objectives
To investigate the independent associations of BMI and urate, as well as their interaction with the risk of developing dementia.
Design and Settings
We analyzed a cohort of 502 528 individuals derived from the UK Biobank that included people aged 37-73 years for whom BMI and urate were recorded between 2006 and 2010. Dementia was ascertained at follow-up using electronic health records.
During a median of 8.1 years of follow-up, a total of 2138 participants developed dementia. People who were underweight had an increased risk of dementia (hazard ratio [HR] = 1.91, 95% confidence interval [CI]: 1.24-2.97) compared with people of a healthy weight. However, the risk of dementia continued to fall as weight increased, as those who were overweight and obese were 19% (HR = 0.81, 95%: 0.73-0.90) and 22% (HR = 0.78, 95% CI: 0.68-0.88) were less likely to develop dementia than people of a healthy weight. People in the highest quintile of urate were also associated with a 25% (HR = 0.75, 95% CI: 0.64-0.87) reduction in the risk of developing dementia compared with those who were in the lowest quintile. There was a significant multiplicative interaction between BMI and urate in relation to dementia (P for interaction = 0.004), and obesity strengthens the protective effect of serum urate on the risk of dementia.
Both BMI and urate are independent predictors of dementia, and there are inverse monotonic and dose-response associations of BMI and urate with dementia.
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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