[Correlation between sleep status and frailty in adults aged 30-79 years in China].

Objective: To explore the correlation between sleep status and frailty in adults aged 30-79 years in China, and explore the potential effect modification of general and central obesity. Methods: Based on the baseline data of the China Kadoorie Biobank, we used multinomial logistic regression to analyze the correlation between long and short sleep duration, insomnia disorder, snoring, and unhealthy sleep score with risks of pre-frailty and frailty. Both overall and obesity-stratified analyses were performed. Result: Among the 512 724 participants, 2.3% had frailty and 40.1% had pre-frailty. There was a U-shaped relationship between sleep duration and frailty score. Short (OR=1.21, 95%CI: 1.19-1.23) or long sleep duration (OR=1.19, 95%CI: 1.17-1.21), insomnia disorder (OR=2.09, 95%CI: 2.02-2.17), and snoring (OR=1.61, 95%CI: 1.59-1.63) were all positively correlated with pre-frailty, and dose-response relationships were observed between unhealthy sleep score and pre-frailty (P for trend<0.001), with OR values of 1.46 (1.44-1.48), 1.97 (1.93-2.00) and 3.43 (3.21-3.67) respectively for those having unhealthy sleep score of 1 to 3. These sleep problems were also positively correlated with frailty. Compared with the overweight or obesity group, stronger relationships were observed between short sleep duration and frailty or pre-frailty and between insomnia disorder and pre-frailty, while the relationships between snoring and frailty and pre-frailty were weaker in the participants with normal weight (P for interaction <0.007 for all). We also observed similar effect modification by central obesity. Conclusion: Long or short sleep duration, insomnia disorder, snoring and higher unhealthy sleep scores were positively correlated with pre-frailty or frailty, general and central obesity status could modify the relationships.

[Associations of body mass index and waist circumference with hospitalization risk of kidney stones in Chinese adults].

Objective: To evaluate the associations of body mass index (BMI) and waist circumference with hospitalization risk of kidney stones in Chinese adults. Methods: This study used long-term follow-up data from China Kadoorie Biobank. After excluding participants with chronic kidney disease, cancer, and extreme BMI or waist circumference values at baseline, 502 096 participants were included in the present analysis. Cox proportional hazards regression models were used to estimate the associations of BMI and waist circumference with hospitalization risk of kidney stones. Results: During an average follow-up period of (10.7±2.2) years, 12 396 participants were hospitalized for the first time with kidney stones. Using the participants with BMI (kg/m2) of 20.5-22.4 as reference, the multivariable-adjusted HR (95%CI) for those with BMI of <18.5, 18.5-20.4, 22.5-23.9, 24.0-25.9, 26.0-27.9, 28.0-29.9, and ≥30.0 were 0.96 (0.87-1.05), 0.94 (0.88-1.00), 1.11 (1.05-1.17), 1.25 (1.18-1.32), 1.29 (1.21-1.37), 1.39 (1.28-1.50), and 1.54 (1.40-1.71), respectively. For every 1 kg/m2 increase in BMI, the HR of kidney stones hospitalization increased by 4% (HR=1.04, 95%CI: 1.04-1.05). Using the participants with waist circumference (cm) of 75.0-79.9 as reference, the multivariable-adjusted HR (95%CI) for those with waist circumference of <65.0, 65.0-69.9, 70.0-74.9, 80.0-84.9, 85.0-89.9, 90.0-94.9, and ≥95.0 were 0.82 (0.74-0.91), 0.85 (0.80-0.92), 0.95 (0.89-1.00), 1.16 (1.09-1.22), 1.22 (1.15-1.30), 1.28 (1.19-1.38), and 1.46 (1.35-1.58), respectively. For every 5 cm increase in waist circumference, the HR of kidney stones hospitalization increased by 9% (HR=1.09, 95%CI: 1.08-1.10). After mutually adjusting for BMI and waist circumference in the above models, the association between BMI and hospitalization risk of kidney stones disappeared. In contrast, the association between waist circumference and hospitalization risk of kidney stones remained unchanged. Conclusions: BMI and waist circumference were positively associated with hospitalization risk of kidney stones. The increased waist circumference was an independent risk factor for kidney stones among Chinese adults.

[Prevalence, patterns and long-term changes of multimorbidity in adults from 10 regions of China].

Objective: To describe the prevalence of multimorbidity and its secular trend, and to explore the common patterns of multimorbidity in Chinese adults. Methods: A total of 25 033 participants who attended the second resurvey of China Kadoorie Biobank (CKB) were included in the study. We used data collected both at baseline (2004-2008) and at resurvey (2013-2014). A total of 13 chronic conditions were included, defined by self-reported, physical examination, and blood sample testing. Multimorbidity was defined as co-existence of two or more chronic conditions. Patterns of multimorbidity were explored using hierarchical cluster analysis. Results: The mean age of participants was (51.5±10.1) years at baseline and (59.5±10.2) years at second resurvey. The prevalence of multimorbidity increased from 33.5% to 58.1% over (8.0±0.8) years of follow-up. The average number of chronic conditions per person increased from 1.15 to 1.82 and all participants increased 0.42 conditions per 5 years on average. Participants who were older, less educated or lived in urban areas had a higher prevalence of multimorbidity and a higher increase in the number of chronic conditions. The increase in the number of chronic conditions was also higher among smokers and heavy alcohol drinkers. The most common multimorbidity pattern in the present population consisted of obesity, hypertension, diabetes, stroke, and heart disease. Conclusions: The prevalence of multimorbidity in Chinese adults is increasing rapidly due to ageing population. Populations of different sociodemographic background and lifestyle habits may have different prevalence of multimorbidity and changes in rates over time.

[Comparison of Fried phenotype and frailty index and their associations with risk of mortality].

Objective: To compare the consistency of frailty status measured by Fried phenotype and frailty index composed of different numbers of deficits, and their prospective associations with risk of mortality. Methods: Data of 23 615 participants from the second resurvey of the China Kadoore Biobank (CKB) was used. Fried phenotype was constructed using five phenotypes, and frailty indexes (FI) were constructed using 28 and 40 deficits, respectively. We calculated the Weighted Kappa coefficient to compare the consistency of three measures in the classification of frailty status. Cox regression was performed to analyze the association of frailty status with risk of mortality. Results: The frailty prevalence calculated by Fried phenotype, FI-28, and FI-40 were 5.4%, 7.9%, and 4.0%, respectively. The Kappa coefficients of Fried phenotype with FI-28 and FI-40 were 0.357 and 0.408, respectively. The Kappa coefficients of FI-28 and FI-40 was 0.712. During an average of (3.9±0.5) years of follow-up, 755 participants died. When Fried phenotype was used, compared with the robust participants, the prefrail and frail participants had increased risk of mortality, the multivariable-adjusted HRs were 1.60 (95%CI: 1.32-1.94) and 2.90 (95%CI: 2.25-3.73), respectively. When FI-28 was used, the corresponding HRs were 1.71 (95%CI: 1.39-2.11) and 2.52 (95%CI:1.95-3.27) for prefrail and frail participants, and when FI-40 was used, the corresponding HRs were 1.98 (95%CI:1.60-2.44) and 3.71 (95%CI: 2.80-4.91). The association of frailty status with mortality differed in different age groups, with the association stronger in younger adults than in older adults. Conclusion: Fried phenotype and frailty index constituted with different numbers of deficits showed good consistency; which can be used to well predict the risk of mortality.

[Transitions on frailty status and related risk factors for its status worsening: finding from the Beijing MJ Health Screening Center].

Objective: To evaluate the transitions of frailty status and related factors influencing its worsening in middle-aged and elderly adults. Methods: Data was obtained from the Beijing MJ Health Screening Center. A total of 13 689 participants who attended health checkups at least twice during 2008-2019 and had more than three years' intervals during these two health checkups were included in the study. The frailty index comprising 28 variables was used to measure frailty status. Frailty was defined as frailty index ≥0.25, and prefrailty was defined as frailty index >0.10 and <0.25. Logistic regression analysis was performed to investigate the association of socio-demographic factors and lifestyle characteristics with the worsening of frailty status, stratified by frailty status at the first health checkup. Results: The mean age at the first and last health checkups were (42.3±9.2) and (47.9±9.3) years, respectively. The mean interval during these two health checkups was (5.7±1.9) years. At the first health checkup, the prevalence of frailty and prefrailty were 2.5% and 50.3%, respectively. While at the last health checkup, the prevalence of frailty and prefrailty rose to 3.9% and 55.4%. Of all participants, 67.3% remained in the same frailty state, 21.2% worsening, and 12.5% improving. In robust participants at the first health checkup, older age, female, low education level, smoking cessation, daily smoking, being general obesity measured by BMI or central obesity measured by WHR showed an increased the risk of worsening frailty status. However, in prefrail participants at the first health checkup, older age, female, general, or central obesity presented as risk factors for worsening frailty status. Conclusion: Modifiable factors such as low education level, smoking, and obesity may increase the risk of worsening frailty status.

[Progress in research of measurements of biological age].

Biological age (BA) can be used to measure the aging process of individuals and make up for the deficiency that chronological age cannot explain the discrepancy of health status among individuals at same chronological age. In recent years, multiple measurements of BA based on clinical or phenotypic, molecular biological, or compound indicators have emerged. In the paper, we summarize some common measurements of BA and compare their validities.

[Association between perceived built environment attributes and adults' leisure-time physical activity in four cities of China].

Objective: To explore the associations between perceived built environment attributes and adults' leisure-time physical activity in four cities of China. Methods: Multistage cluster random sampling method was used to select adults aged 25 to 64 in Hangzhou, Suzhou, Chengdu, and Qingdao. Data were collected from June 2017 to July 2018. The perception of the urban built environment was assessed by the neighborhood environment walkability scale-abbreviated (NEWS-A), and the physical activity was assessed by the International Physical Activity Questionnaire. Generalized linear mixed models were used to explore the relationship between the perceived built environment and leisure-time physical activities. Results: A total of 3 789 participants were included in the analysis. After adjusting for potential confounders, better access to public services (OR=1.34, 95%CI: 1.02-1.75) and higher aesthetic quality (OR=1.37, 95%CI: 1.09-1.73) were positively associated with the possibility of engaging in leisure-time physical activity in the past week. Similarly, these two attributes were positively associated with leisure-time walking. Higher scores on the perception of street connectivity were positively associated with leisure-time walking [exp(ß)=1.09, 95%CI: 1.00-1.19]. Higher residential density [exp(ß)=1.000 4, 95%CI:1.000 0-1.000 8], better access to physical activity destinations[exp(ß)=1.09, 95%CI: 1.00-1.19], and better aesthetics [exp(ß)=1.11, 95%CI:1.00-1.22] were associated with higher leisure-time physical activity. Similarly, these three attributes were positively associated with the possibility of meeting the WHO recommendations. Conclusion: Changing some urban built environment attributes may increase leisure-time physical activity.