[Association between unhealthy lifestyles and hypertension, diabetes and dyslipidemia in old adults in China].

Objective: To analyze the individual and cumulative effects of unhealthy lifestyle on the prevalence of hypertension, diabetes and dyslipidemia in old adults in China, and find out the critical lifestyle in the network. Methods: Based on the baseline data of Yunnan Behavior and Disease Surveillance Cohort in 2021, a total of 16 763 older adults aged ≥60 years were included in our study. The unhealthy lifestyle factors including smoking, drinking, unhealthy eating habit, lower physical activity level, abnormal BMI and abnormal waist circumference. We calculated the unhealthy lifestyle score by using the cumulative exposures of each participant. Multiple logistic regression and mixed graphical models were used to describe the association between unhealthy lifestyle and the prevalence of hypertension, diabetes and dyslipidemia. Results: The prevalence of hypertension, diabetes and dyslipidemia were 57.0%, 11.5% and 37.0%, respectively. Most of the unhealthy lifestyles included in the study were risk factors for hypertension, diabetes and dyslipidemia, and the risks of disease increased with the increase of the unhealthy lifestyle score. The participants with the highest score (score: 6) had significantly higher prevalence of hypertension (OR=3.99, 95%CI: 1.81-8.80), diabetes (OR=4.64, 95%CI: 1.64-13.15) and dyslipidemia (OR=4.26, 95%CI: 2.08-8.73) compared with those with lowest score (score: 0). In the network constructed by mixed graphical model, abnormal waist circumference (bridge strength=0.81) and hypertension (bridge strength=0.55) were vital bridge nodes connecting unhealthy lifestyle and hypertension, diabetes and dyslipidemia. Conclusions: The unhealthy lifestyle score was associated with risks for hypertension, diabetes and dyslipidemia. Abnormal waist circumference was the key factor for chronic diseases in old adults.

[Association between unhealthy lifestyles and hyperuricemia in occupational population and modification effect of hypertension and dyslipidemia].

Objective: To understand the relationship between unhealthy lifestyle and hyperuricemia, as well as the modification effects of hypertension and dyslipidemia in occupational population and provide a theoretical basis for the prevention of hyperuricemia. Methods: A cross-sectional survey design was adopted, based on baseline data from the Southwest Occupational Population Cohort from China Railway Chengdu Group Co., Ltd., which included the population in 28 prefectures from Sichuan Province and Guizhou Province, and 33 districts (counties) from Chongqing Municipality between October and December 2021. This study collected the information about the demographics characteristics, lifestyles, and prevalence of chronic non-communicable diseases of the study subjects through questionnaire, physical measurement and laboratory biochemical test. The unhealthy lifestyle score was scored based on smoking, alcohol consumption, dietary patterns, physical activity, and low weight or overweight, with higher scores being associated with more unhealthy lifestyles. The multivariate logistic regression model was used to analyze the relationship between unhealthy lifestyle score, smoking, alcohol consumption, other factors and hyperuricemia, and the stratified analysis was used to explore the modification effect of hypertension and other diseases on the relationship between unhealthy lifestyle and hyperuricemia. Results: A total of 11 748 participants were included in this study, the prevalence of hyperuricemia was 34.4%. Multivariate logistic regression model showed that current/previous smoking, current/previous alcohol consumption and BMI abnormality were risk factors for hyperuricemia, and the unhealthy lifestyle score showed a "cumulative" effect on the risk for hyperuricemia, with higher score increasing the risk of hyperuricemia, and the OR increased from 1.64 (95%CI: 1.34-2.00) to 2.89 (95%CI: 2.39-3.50). Stratified analysis showed that unhealthy lifestyles had a greater impact on the risk for hyperuricemia in people with hypertension and dyslipidemia. Conclusions: The coexistence of multiple unhealthy lifestyles might increase the risk of hyperuricemia, and this effect was stronger in participants with hypertension and dyslipidemia. Timely correction of unhealthy lifestyles, and control of hypertension and dyslipidemia might reduce the risk for hyperuricemia.

[Progress in complex network theory-based studies on the associations between health-related behaviors and chronic non-communicable diseases].

In recent years, the research focus on health-related behavior and chronic non-communicable diseases has shifted from the analysis on independent effects of multiple causes on a single outcome to the evaluation the complex relationships between multiple causes and multiple effects. Complex network theory, an important branch of system science, considers the relationships among factors in a network and can reveal how health-related behaviors interact with chronic diseases through a series of complex network models and indicators. This paper summarizes the definition and development of complex network theory and its commonly used models, indicators, and case studies in the field of health-related behavior and chronic disease to promote the application of complex network theory in the field of health and provide reference and tools for future research of the relationship between health-related behavior and chronic disease.

[Association between work environment noise perception and cardiovascular diseases, depressive symptoms, and their comorbidity in occupational population].

Objective: To explore the association between occupational noise perception and cardiovascular disease (CVD), depression symptoms, as well as their comorbidity in occupational population and provide evidence for the prevention and control of physical and mental illnesses. Methods: A cross-sectional survey design was adopted, based on baseline data in population in 28 prefectures in Sichuan Province and Guizhou Province, and 33 districts (counties) in Chongqing municipality from Southwest Occupational Population Cohort from China Railway Chengdu Group Co., Ltd. during October to December 2021. A questionnaire survey was conducted to collect information about noise perception, depressive symptoms, and the history of CVD. Latent profile analysis model was used to determine identify noise perception type, and multinomial logistic regression analysis was conducted to explore the relationship between different occupational noise perception types and CVD, depression symptoms and their comorbidity. Results: A total of 30 509 participants were included, the mean age was (36.6±10.5) years, and men accounted for 82.0%. The direct perception of occupational noise, psychological effects and hearing/sleep impact of occupational noise increased the risk for CVD, depressive symptoms, and their comorbidity. By using latent profile analysis, occupational noise perception was classified into four levels: low, medium, high, and very high. As the level of noise perception increased, the association with CVD, depressive symptoms, and their comorbidity increased. In fact, very high level occupational noise perception were found to increase the risk for CVD, depressive symptoms, and their comorbidity by 2.14 (95%CI: 1.73-2.65) times, 8.80 (95%CI: 7.91-9.78) times, and 17.02 (95%CI: 12.78-22.66) times respectively compared with low-level occupational noise perception. Conclusions: Different types of occupational noise perception are associated with CVD and depression symptom, especially in the form of CVD complicated with depression symptom. Furthermore, the intensity of occupational noise in the work environment should be reduced to lower the risk for physical and mental health.

[Association between unhealthy lifestyles and diabetic dyslipidemia in occupational population and network analysis].

Objective: To understand the influence of unhealthy lifestyle on diabetic dyslipidemia and the key influencing factors in occupational population and provided scientific evidence for the prevention of diabetic dyslipidemia. Methods: Based on baseline data and follow-up data of Southwest Occupational Population Cohort from China Railway Chengdu Group Co., Ltd. during 2021. Diabetic dyslipidemia was defined as diabetes plus one or more forms of dyslipidemia, and unhealthy lifestyle factors included smoking, alcohol consumption, unhealthy dietary patterns, low physical activity, and abnormal BMI. Multivariate logistic regression model was used to analyze the relationship between unhealthy lifestyle scores and diabetic dyslipidemia, network analysis was used to find and explore the key lifestyles influencing glycolipid metabolism. Results: A total of 25 631 subjects were included. People with unhealthy lifestyle score 2 and 3 were 1.93 (95%CI: 1.31-2.86) times and 2.37 (95%CI: 1.60-3.50) times more likely to have diabetes with ≥1 forms of dyslipidemia than those with scores of 0; People with unhealthy lifestyle score 1, 2 and 3 were 1.98 (95%CI: 1.08-3.61) times, 2.87 (95%CI: 1.60-5.14) times and 3.95 (95%CI: 2.22-7.06) times more likely to have diabetes with ≥2 forms of dyslipidemia than those with score 0. Network analysis found that abnormal BMI and HDL-C were the "bridge nodes" that link unhealthy lifestyles with diabetic dyslipidemia. Conclusion: The higher the score of unhealthy lifestyle, the higher the risk for diabetic dyslipidemia, abnormal BMI and HDL-C are key factors influencing the association between unhealthy lifestyle and diabetic dyslipidemia.