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Zhang Z, Wu B, Qu YL, Li Y, Xu LJ, Lyu CX, Chen C, Wang J, Xue K, Wei Y, Zhou JH, Zheng XL, Qiu YD, Luo YF, Liu JX, Lyu YB, Shi XM. [Association of urinary cadmium level with body mass index and body circumferences among older adults over 65 years old in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2024; 58:227-234. [PMID: 38387955 DOI: 10.3760/cma.j.cn112150-20230912-00181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Objective: To investigate the association of urinary cadmium level with body mass index (BMI) and body circumferences among the older adults over 65 years old in 9 longevity areas of China. Methods: Subjects were older adults over 65 years old from the Healthy Aging and Biomarkers Cohort Study (HABCS) between 2017 and 2018 conducted in 9 longevity areas in China. A total of 1 968 older adults were included in this study. Information including socio-demographic characteristics, lifestyles, diet intake, and health status was collected by using questionnaires and physical examinations. Urine samples were collected to detect urinary cadmium and creatinine levels. Body circumferences included waist circumference, hip circumference and calf circumference. Subjects were divided into three groups (low:<0.77 μg/g·creatinine, middle:0.77-1.69 μg/g·creatinine, high:≥1.69 μg/g·creatinine) by tertiles of creatinine-adjusted urinary cadmium concentration. Multiple linear regression models were used to analyze the association of creatinine-adjusted urinary cadmium level with BMI and body circumferences. The dose-response relationship of creatinine-adjusted urinary cadmium concentration with BMI and body circumferences was analyzed by using restrictive cubic splines fitting multiple linear regression model. Results: The mean age of subjects was (83.34±11.14) years old. The median (Q1, Q3) concentration of creatinine-adjusted urinary cadmium was 1.13 (0.63, 2.09) μg/g·creatinine, and the BMI was (22.70±3.82) kg/m2. The mean values of waist circumference, hip circumference, and calf circumference were (85.42±10.68) cm, (92.67±8.90) cm, and (31.08±4.76) cm, respectively. After controlling confounding factors, the results of the multiple linear regression model showed that for each increment of 1 μg/g·creatinine in creatinine-adjusted urinary cadmium, the change of BMI, waist circumference, hip circumference, and calf circumference in the high-level group was -0.28 (-0.37, -0.19) kg/m2, -0.74 (-0.96, -0.52) cm, -0.78 (-0.96, -0.61) cm, and -0.20 (-0.30, -0.11) cm, respectively. The restrictive cubic splines curve showed a negative nonlinear association of creatinine-adjusted urinary cadmium with BMI (Pnonlinear<0.001) and negative linear associations of creatinine-adjusted urinary cadmium with waist circumference (Plinear<0.001), hip circumference (Plinear<0.001), and calf circumference (Plinear<0.001). Conclusion: Urinary cadmium level is significantly associated with decreased BMI, waist circumference, hip circumference and calf circumference among older adults over 65 years old in 9 longevity areas of China.
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Affiliation(s)
- Z Zhang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Li
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L J Xu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C X Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K Xue
- School of Public Health, Jilin University, Changchun 130012, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Luo
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J X Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zhong WF, Wang XM, Song WQ, Li C, Chen H, Chen ZT, Lyu YB, Li ZH, Shi XM, Mao C. [Association of lifestyle and apolipoprotein E gene with risk for cognitive frailty in elderly population in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2024; 45:41-47. [PMID: 38228523 DOI: 10.3760/cma.j.cn112338-20231027-00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Objective: To investigate the impact of lifestyle, apolipoprotein E (ApoE) gene, and their interaction on the risk for cognitive frailty in the elderly population in China. Methods: The study participants were from the Chinese Longitudinal Healthy Longevity Survey. The information about their lifestyles were collected by questionnaire survey, and a weighted lifestyle score was constructed based on β coefficients associated with specific lifestyles to assess the combined lifestyle. ApoE genotypes were assessed by rs429358 and rs7412 single nucleotide polymorphisms. Cognitive frailty was assessed based on cognitive function and physical frailty. Cox proportional hazards regression model was used to analyze the association of lifestyle and ApoE gene with the risk for cognitive frailty and evaluate the multiplicative and additive interactions between lifestyle and ApoE gene. Results: A total of 5 676 elderly persons, with median age [M (Q1, Q3)] of 76 (68, 85) years, were included, in whom 615 had cognitive frailty. The analysis by Cox proportional hazards regression model indicated that moderate and high levels of dietary diversity could reduce the risk for cognitive frailty by 18% [hazard ratio (HR)=0.82, 95%CI: 0.68-1.00] and 28% (HR=0.72, 95%CI: 0.57-0.91), respectively; moderate and high levels of physical activity could reduce the risk by 31% (HR=0.69, 95%CI: 0.56-0.85) and 23% (HR=0.77, 95%CI: 0.64-0.93), respectively. Healthy lifestyle was associated with a 40% reduced risk for cognitive frailty (HR=0.60, 95%CI: 0.46-0.78). ApoE ε4 allele was associated with a 26% increased risk for cognitive frailty (HR=1.26, 95%CI: 1.02-1.56). No multiplicative or additive interactions were found between lifestyle and ApoE gene. Conclusions: Dietary diversity and regular physical activity have protective effects against cognitive frailty in elderly population. Healthy lifestyle can reduce the risk for cognitive frailty in elderly population regardless of ApoE ε4 allele carriage status.
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Affiliation(s)
- W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Wang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - W Q Song
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - C Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - H Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Z T Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
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Wu B, Li Y, Xu LJ, Zhang Z, Zhou JH, Wei Y, Chen C, Wang J, Wu CZ, Li Z, Hu ZY, Long FY, Wu YD, Hu XH, Li KX, Li FY, Luo YF, Liu YC, Lyu YB, Shi XM. [Association of sleep duration and physical exercise with dyslipidemia in older adults aged 80 years and over in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2024; 45:48-55. [PMID: 38228524 DOI: 10.3760/cma.j.cn112338-20231007-00207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Objective: To explore the impact of sleep duration, physical exercise, and their interactions on the risk of dyslipidemia in older adults aged ≥80 (the oldest old) in China. Methods: The study subjects were the oldest old from four rounds of Healthy Aging and Biomarkers Cohort Study (2008-2009, 2011-2012, 2014 and 2017-2018). The information about their demographic characteristics, lifestyles, physical examination results and others were collected, and fasting venous blood samples were collected from them for blood lipid testing. Competing risk model was used to analyze the causal associations of sleep duration and physical exercise with the risk for dyslipidemia. Restricted cubic spline (RCS) function was used to explore the dose-response relationship between sleep duration and the risk for dyslipidemia. Additive and multiplicative interaction model were used to explore the interaction of sleep duration and physical exercise on the risk for dyslipidemia. Results: The average age of 1 809 subjects was (93.1±7.7) years, 65.1% of them were women. The average sleep duration of the subjects was (8.0±2.5) hours/day, 28.1% of them had sleep duration for less than 7 hours/day, and 27.2% had sleep for duration more than 9 hours/day at baseline survey. During the 9-year cumulative follow-up of 6 150.6 person years (follow-up of average 3.4 years for one person), there were 304 new cases of dyslipidemia, with an incidence density of 4 942.6/100 000 person years. The results of competitive risk model analysis showed that compared with those who slept for 7-9 hours/day, the risk for dyslipidemia in oldest old with sleep duration >9 hours/day increased by 22% (HR=1.22, 95%CI: 1.07-1.39). Compared with the oldest old having no physical exercise, the risk for dyslipidemia in the oldest old having physical exercise decreased by 33% (HR=0.67, 95%CI: 0.57-0.78). The RCS function showed a linear positive dose-response relationship between sleep duration and the risk for hyperlipidemia. The interaction analysis showed that physical exercise and sleep duration had an antagonistic effect on the risk for hyperlipidemia. Conclusion: Physical exercise could reduce the adverse effects of prolonged sleep on blood lipids in the oldest old.
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Affiliation(s)
- B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Li
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L J Xu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Z Zhang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Z Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Y Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Y Long
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X H Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K X Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Y Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Luo
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Shi XM. [Revision and prospect of "Standards for indoor air quality(GB/T 18883-2022)" in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:1725-1728. [PMID: 38008556 DOI: 10.3760/cma.j.cn112150-20230330-00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
The formulation and revision of the detection methods of indoor air quality standards is an important, rigorous and delicate endeavor. The standards for indoor air quality (GB/T 18883-2022) were issued by the State Administration of Market Regulation and the Standardization Administration on July 11, 2022, and implemented on February 1, 2023 by replacing indoor air quality standards (GB/T 18883-2002). The revised standard specifies hygienic requirements for physical, chemical, biological and radioactive indicators in indoor air and the corresponding test methods. This article interpreted the revision background, drafting principles, main indicators and methods, as well as the revision basis of the standards. Recommendations for the implementation of the standards are also proposed.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Human Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li YW, Li CC, Chen C, Li Z, Chen C, Fang JL, Li TT, Zhao F, Shi XM. [Study on formulation and revision of standard limits for inhalable particulate matter (PM 10) and fine particulate matter (PM 2.5) in "Standards for indoor air quality(GB/T 18883-2022)" in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:1761-1765. [PMID: 38008561 DOI: 10.3760/cma.j.cn112150-20230608-00451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
The pollution and health effects of indoor inhalable particulate matter (PM10) and fine particulate matter (PM2.5) are increasingly receiving public attention. The"Standards for indoor air quality (GB/T 18883-2022)"has revised the standard limit for PM10 and added the standard limit for PM2.5. This study analyzed and interpreted the relevant technical contents of the revision of the standard limits for two indicators, including the exposure status, health effects, and the basis for the determination of the limit value. It also proposed prospects for the future development and revision of standard limits for indoor particulate matters.
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Affiliation(s)
- Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J L Fang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T T Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zhou JH, Liu SX, Zhang Z, Ye LL, Wang J, Chen C, Cui J, Qiu YQ, Wu B, Lyu YB, Shi XM. [Distribution characteristics of body mass index among Chinese oldest-old aged 80 years and above]. Zhonghua Liu Xing Bing Xue Za Zhi 2023; 44:855-861. [PMID: 37380404 DOI: 10.3760/cma.j.cn112338-20230222-00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Objective: To investigate body mass index (BMI) level, identify the main type of nutritional problem, and describe the population distribution characteristics of BMI among Chinese people aged 80 years or above. Methods: The data of 9 481 oldest-old individuals were obtained from the 2017-2018 Chinese Longitudinal Healthy Longevity Survey. The Lambda-Mu-Sigma method, weighted estimates of BMI, and comparisons by BMI quintiles were used to describe the BMI level and distribution characteristics among the oldest-old. Results: The average age of the participants was (91.9±7.7) years, with P50 of the weighted BMI at 21.9 (95%CI: 21.8-22.0) kg/m2. The result of BMI level showed a decreasing trend with age, with a rapid decline before age 100, and then the trend became slower. There are about 30% of the oldest-old classified as undernutrition, but the prevalence of overnutrition is only about 10%. The population distribution characteristics by BMI quintiles showed the oldest-old with lower BMI levels were likely to have the following characteristics: sociodemographically, to be older, female, ethnic minority, unmarried/divorced/widowed, rural residents, illiterate, with inadequate living expenses, located in Central, South, or Southwest China; regarding lifestyles, lower BMI levels were observed for participants who were smoking, not exercising, lack of leisure activities, or with poor dietary diversity; considering functional status, participants with lower BMI levels were those who have poor chewing ability, disability in activities of daily living, cognitive impairment, hearing loss, visual impairment, or poor self-rated health status. The oldest-old with higher BMI levels were likely to have heart disease, hypertension, cerebrovascular disease, and diabetes. Conclusions: The overall BMI level was low among the Chinese oldest-old and it showed a downward trend with age. Currently, the main nutritional problem among the Chinese oldest-old was undernutrition rather than overweight or obesity. Management of healthy lifestyles, functional status, and diseases would be helpful to reduce the risk of undernutrition among the oldest-old.
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Affiliation(s)
- J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Z Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Cui
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Y Q Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Zhao C, Yao XY, Zhang L, Lyu J, Xu SQ, Fei J, Shi XM. [Research on the formulation and revision of standard limits for antimony,boron and vanadium in the "Standards for Drinking Water Quality (GB5749-2022)" in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:831-834. [PMID: 37357199 DOI: 10.3760/cma.j.cn112150-20221024-01028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
China is rich in antimony, boron, and vanadium mineral resources, which have been detected in environmental water bodies and drinking water. During the revision process of the "Standards for Drinking Water Quality (GB5749-2006)", research and evaluation are focused on three indicators: antimony, boron and vanadium. Vanadium is added and the limit value of boron is adjusted. This study reviews and discusses the technical contents related to the revision of the antimony, boron and vanadium, including the environmental presence levels, exposure status, health effects, and the revision of the standard limits of these three indicators. Suggestions are also made for the implementation of this standard.
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Affiliation(s)
- C Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health/Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X Y Yao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health/Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health/Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health/Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S Q Xu
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
| | - J Fei
- Department of Environmental Health & Endemic Disease Control & Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health/Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Qiu YD, Guo YB, Zhang ZW, Ji SS, Zhou JH, Wu B, Chen C, Wei Y, Ding C, Wang J, Zheng XL, Zhong ZC, Ye LL, Chen GD, Lyu YB, Shi XM. [Association between cognitive impairment and main metals among oldest old aged 80 years and over in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:849-856. [PMID: 37357203 DOI: 10.3760/cma.j.cn112150-20230215-00111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Objective: To identify the main metals involved in cognitive impairment in the Chinese oldest old, and explore the association between these metal exposures and cognitive impairment. Methods: A cross-sectional study was conducted on 1 568 participants aged 80 years and older from Healthy Aging and Biomarkers Cohort Study (2017 to 2018). Fasting venous blood was collected to measure the levels of nine metals (selenium, lead, cadmium, arsenic, antimony, chromium, manganese, mercury, and nickel). The cognitive function of these participants was evaluated by using the Chinese version of the Mini-Mental State Examination (CMMSE). The random forest (RF) was applied to independently identify the main metals that affected cognitive impairment. The multivariate logistic regression model and restricted cubic splines (RCS) model were used to further verify the association of the main metals with cognitive impairment. Results: The age of 1 568 study subjects was (91.8±7.6) years old, including 912 females (58.2%) and 465 individuals (29.7%) with cognitive function impairment. Based on the RF model (the out-of-bag error rate was 22.9%), the importance ranking of variables was conducted and the feature screening of five times ten-fold cross-validation was carried out. It was found that selenium was the metal that affected cognitive function impairment, and the other eight metals were not included in the model. After adjusting for covariates, the multivariate logistic regression model showed that with every increase of 10 μg/L of blood selenium levels, the risk of cognitive impairment decreased (OR=0.921, 95%CI: 0.889-0.954). Compared with the lowest quartile(Q1) of blood selenium, the ORs (95%CI) of Q3 and Q4 blood selenium were 0.452 (0.304-0.669) and 0.419 (0.281-0.622) respectively. The RCS showed a linear dose-response relationship between blood selenium and cognitive impairment (Pnonlinear>0.05). Conclusion: Blood selenium is negatively associated with cognitive impairment in the Chinese oldest old.
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Affiliation(s)
- Y D Qiu
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Guo
- School of Public Health, Jilin University, Changchun 132000, China
| | - Z W Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 132000, China
| | - C Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Z C Zhong
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - G D Chen
- School of Public Health, Zhejiang University, Hangzhou 310030, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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9
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Shi XM. [Revision and prospect of the "Standards for Drinking Water Quality (GB5749-2022)" in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:801-805. [PMID: 37357194 DOI: 10.3760/cma.j.cn112150-20221027-01039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
The revision of the national standards for drinking water quality is an important, rigorous and delicate endeavor. The paper introduced the revision of this standard, emphasizing the revision principle, overall technical considerations, and revision contents. Recommendations were also proposed for the implementation of this standard.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention,Beijing 100021, China
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10
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Ding EM, Wang JN, Deng FC, Sun PJ, Li CF, Li CL, Wang Y, Fang JL, Tang S, Shi XM. [A panel study on the effect of atmospheric PM 2.5 exposure on the gut microbiome in healthy elderly people aged 60-69 years old]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:1-8. [PMID: 37198716 DOI: 10.3760/cma.j.cn112150-20230220-00133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Objective: To analyze the short-term effect of individual atmospheric PM2.5 exposure on the diversity, enterotype, and community structure of gut microbiome in healthy elderly people in Jinan, Shandong province. Methods: The present panel study recruited 76 healthy elderly people aged 60-69 years old in Dianliu Street, Lixia District, Jinan, Shandong Province, and followed them up five times from September 2018 to January 2019. The relevant information was collected by questionnaire, physical examination, precise monitoring of individual PM2.5 exposure, fecal sample collection and gut microbiome 16S rDNA sequencing. The Dirichlet multinomial mixtures (DMM) model was used to analyze the enterotype. Linear mixed effect model and generalized linear mixed effect model were used to analyze the effect of PM2.5 exposure on gut microbiome α diversity indices (Shannon, Simpson, Chao1, and ACE indices), enterotype and abundance of core species. Results: Each of the 76 subjects participated in at least two follow-up visits, resulting in a total of 352 person-visits. The age of 76 subjects was (65.0±2.8) years old with BMI (25.0±2.4) kg/m2. There were 38 males accounting for 50% of the subjects. People with an educational level of primary school or below accounted for 10.5% of the 76 subjects, and those with secondary school and junior college or above accounting for 71.1% and 18.4%. The individual PM2.5 exposure concentration of 76 subjects during the study period was (58.7±53.7) μg/m3. DMM model showed that the subjects could be divided into four enterotypes, which were mainly driven by Bacteroides, Faecalibacterium, Lachnospiraceae, Prevotellaceae, and Ruminococcaceae. Linear mixed effects model showed that different lag periods of PM2.5 exposure were significantly associated with a lower gut α diversity index (P<0.05 after correction). Further analysis showed that PM2.5 exposure was significantly associated with changes in the abundances of Firmicutes (Megamonas, Blautia, Streptococcus, etc.) and Bacteroidetes (Alistipes) (P<0.05 after correction). Conclusion: Short-term PM2.5 exposure is significantly associated with a decrease in gut microbiome diversity and changes in the abundance of several species of Firmicutes and Bacteroidetes in the elderly. It is necessary to further explore the underlying mechanisms between PM2.5 exposure and the gut microbiome, so as to provide a scientific basis for promoting the intestinal health of the elderly.
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Affiliation(s)
- E M Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China
| | - J N Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China
| | - F C Deng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China
| | - P J Sun
- School of Public Health, China Medical University, Shenyang 110122, China
| | - C F Li
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - C L Li
- School of Public Health, Cheeloo College of Medicine, Shandong University, Ji'nan 250012, China
| | - Y Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China
| | - J L Fang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China
| | - S Tang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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11
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Zheng XL, Wu B, Qu YL, Chen C, Wang J, Li Z, Qiu YD, Zhang Z, Li FY, Ye LL, Zhou JH, Wei Y, Ji SS, Lyu YB, Shi XM. [Association of plasma vitamin B 12 level with plasma uric acid level among the elderly over 65 years old in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:634-640. [PMID: 37165810 DOI: 10.3760/cma.j.cn112150-20221120-01134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Objective: To investigate the association of plasma vitamin B12 level with plasma uric acid level among the elderly over 65 in 9 longevity areas of China. Methods: The elderly over 65 years old with complete information on plasma vitamin B12 and plasma uric acid from Healthy Aging and Biomarkers Cohort Study (2017 to 2018) were recruited in this study. Information on socio-demographic characteristics, life styles, diet intake, and health status were collected by questionnaire and physical examination; and fasting venous blood was collected to detect the levels of plasma vitamin B12, uric acid and other indicators. Multiple linear regression models were used to analyze the association of plasma vitamin B12 level per interquartile range increase with plasma uric acid level. The association trend of plasma vitamin B12 level with plasma uric acid level was described by restrictive cubic splines fitting multiple linear regression model. Multiple logistic regression models were used to analyze the association of plasma vitamin B12 level stratified by quartiles with hyperuricemia. Results: A total of 2 471 participants were finally included in the study, the age was (84.88±19.76) years old, of which 1 291 (52.25%) were female. The M (Q1, Q3) level of plasma vitamin B12 was 294 (203, 440) pg/ml and the plasma uric acid level was (341.01±90.46) μmol/L. A total of 422 participants (17.08%) were defined with hyperuricemia. The results of multiple linear regression model showed that there was a positive association of plasma vitamin B12 level with plasma uric acid level after adjustment for covariates (P<0.05). An IQR increase in plasma vitamin B12 (237 pg/ml) was associated with a 6.36 (95%CI: 2.00-10.72) μmol/L increase in the plasma uric acid level. The restrictive cubic splines curve showed a positive linear association of log-transformed plasma vitamin B12 with uric acid level (P<0.001). Conclusion: There is a positive association of plasma vitamin B12 level with plasma uric acid level among the elderly over 65 years old in 9 longevity areas of China.
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Affiliation(s)
- X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Zhang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Y Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- Department of Environmental Epidemiology, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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12
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Zhong WF, Liang F, Wang XM, Chen PL, Song WQ, Nan Y, Xiang JX, Li ZH, Lyu YB, Shi XM, Mao C. [Association of sleep duration and risk of frailty among the elderly over 80 years old in China: a prospective cohort study]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:607-613. [PMID: 37165807 DOI: 10.3760/cma.j.cn112150-20221120-01130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Objective: To explore the association between sleep duration and the risk of frailty among the elderly over 80 years old in China. Methods: Using the data from five surveys of the China Elderly Health Influencing Factors Follow-up Survey (CLHLS) (2005, 2008-2009, 2011-2012, 2014, and 2017-2018), 7 024 elderly people aged 80 years and above were selected as the study subjects. Questionnaires and physical examinations were used to collect information on sleep time, general demographic characteristics, functional status, physical signs, and illness. The frailty state was evaluated based on a frailty index that included 39 variables. The Cox proportional risk regression model was used to analyze the correlation between sleep time and the risk of frailty occurrence. A restricted cubic spline function was used to analyze the dose-response relationship between sleep time and the risk of frailty occurrence. The likelihood ratio test was used to analyze the interaction between age, gender, sleep quality, cognitive impairment, and sleep duration. Results: The age M (Q1, Q3) of 7 024 subjects was 87 (82, 92) years old, with a total of 3 435 (48.9%) patients experiencing frailty. The results of restricted cubic spline function analysis showed that there was an approximate U-shaped relationship between sleep time and the risk of frailty. When sleep time was 6.5-8.5 hours, the elderly had the lowest risk of frailty; Multivariate Cox proportional risk regression model analysis showed that compared to 6.5-8.5 hours of sleep, long sleep duration (>8.5 hours) increased the risk of frailty by 13% (HR: 1.13; 95%CI: 1.04-1.22). Conclusion: There is a nonlinear association between sleep time and the risk of frailty in the elderly.
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Affiliation(s)
- W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - F Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Wang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - P L Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - W Q Song
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y Nan
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China School of Nursing, Southern Medical University, Guangzhou 510515, China
| | - J X Xiang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental and Health-related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, National Institute of Environmental and Health-related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
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13
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Ye JM, Zhou JH, Wang J, Ye LL, Li CF, Wu B, Qi L, Chen C, Cui J, Qiu YQ, Liu SX, Li FY, Luo YF, Lyu YB, Ye L, Shi XM. [Association of greenness, nitrogen dioxide with the prevalence of hypertension among the elderly over 65 years old in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:641-648. [PMID: 37165811 DOI: 10.3760/cma.j.cn112150-20230118-00044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Objective: To investigate the association of mixed exposure to greenness and nitrogen dioxide(NO2) and hypertension among the older adults aged 65 years and over in China. Methods: The study subjects were from the Chinese Longitudinal Healthy Longevity Survey from 2017 to 2018. A total of 15 423 older adults aged 65 years and over meeting the criteria were finally included in the study. A questionnaire survey was used to collect information on demographic characteristics, lifestyle habits and self-reported prevalence of hypertension. Blood pressure values were obtained through physical examination. The level of normalized difference vegetation index(NDVI) was measured by the Medium-resolution Imaging Spectral Radiator(MODIS) of the National Aeronautics and Space Administration(NASA). The concentration of NO2 was from China's surface air pollutant data set. Meteorological data was from NASA MERRA-2. The exposure to NDVI and NO2 for each study subject was calculated based on the area within a 1 km radius around their residence. The association between mixed exposure of NDVI and NO2 as well as their interaction and hypertension in older adults was analyzed by using the multivariate logistic regression model. The restrictive cubic spline(RCS) function was used to explore the exposure-response relationship between greenness and NO2 and the risk of hypertension in study subjects. Results: The mean age of 15 423 older adults were (85.6±11.6). Women accounted for 56.3%(8 685/15 423) and 55.6%(8 578/15 423) lived in urban areas. The mean time of residence was (60.9±28.5) years. 59.8% of participants were with hypertension. The mean NDVI level was 0.41±0.13, and the mean NO2 concentration was (32.18±10.36) μg/cm3. The results of multivariate logistic regression analysis showed that NDVI was inversely and linearly associated with the hypertension in older adults, with the OR(95%CI) value of 0.959(0.928-0.992). Compared with the T1 group of NDVI, the risk of hypertension was lower in the T3 group, with the OR(95%CI) value of 0.852(0.769-0.944), and the trend test was statistically significant(P<0.05). Compared with the T1 group of NO2, the risk of hypertension was higher in the T2 and T3 groups, with OR(95%CI) values of 1.160(1.055-1.275) and 1.244(1.111-1.393), and the trend test was statistically significant (P<0.05). The result of the RCS showed that NDVI was inversely and linearly associated with hypertension in older adults. NO2 was nonlinearly associated with hypertension in older adults. The interaction analysis showed that NDVI and NO2 had a negative multiplicative interaction on the risk of hypertension, with OR(95%CI) value of 0.995(0.992-0.997). Conclusion: Exposure to greenness and NO2 are associated with hypertension in older adults.
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Affiliation(s)
- J M Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - C F Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Anhui Medical University, He Fei 230032, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - L Qi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Cui
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Y Q Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - S X Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - F Y Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, China Medical University, Shenyang 110013, China
| | - Y F Luo
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Anhui Medical University, He Fei 230032, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Ye
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li YW, Li Z, Song HC, Ding L, Ji SS, Zhang M, Qu YL, Sun Q, Zhu YD, Fu H, Cai JY, Li CF, Han YY, Zhang WL, Zhao F, Lyu YB, Shi XM. [Association between urinary arsenic level and serum testosterone in Chinese men aged 18 to 79 years]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:686-692. [PMID: 36977566 DOI: 10.3760/cma.j.cn112150-20221110-01095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Objective: To investigate the association between the urinary arsenic level and serum total testosterone in Chinese men aged 18 to 79 years. Methods: A total of 5 048 male participants aged 18 to 79 years were recruited from the China National Human Biomonitoring (CNHBM) from 2017 to 2018. Questionnaires and physical examinations were used to collect information on demographic characteristics, lifestyle, food intake frequency and health status. Venous blood and urine samples were collected to detect the level of serum total testosterone, urine arsenic and urine creatinine. Participants were divided into three groups (low, middle, and high) based on the tertiles of creatinine-adjusted urine arsenic concentration. Weighted multiple linear regression was fitted to analyze the association of urinary arsenic with serum total testosterone. Results: The weighted average age of 5 048 Chinese men was (46.72±0.40) years. Geometric mean concentration (95%CI) of urinary arsenic, creatinine-adjusted urine arsenic and serum testosterone was 22.46 (20.08, 25.12) μg/L, 19.36 (16.92, 22.15) μg/L and 18.13 (17.42, 18.85) nmol/L, respectively. After controlling for covariates, compared with the low-level urinary arsenic group, the testosterone level of the participants in the middle-level group and the high-level group decreased gradually. The percentile ratio (95%CI) was -5.17% (-13.14%, 3.54%) and -10.33% (-15.68%, -4.63). The subgroup analysis showed that the association between the urinary arsenic level and testosterone level was more obvious in the group with BMI<24 kg/m2 group (Pinteraction<0.05). Conclusion: There is a negative association between the urinary arsenic level and serum total testosterone in Chinese men aged 18-79 years.
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Affiliation(s)
- Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Fu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C F Li
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y Y Han
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Bu N, Wang SR, Gao YR, Zhao YH, Shi XM, Wang SH. [The role of Keap1/Nrf2/HO-1 signal pathway in liver injury induced by rare earth neodymium oxide in mice]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2023; 41:161-167. [PMID: 37006140 DOI: 10.3760/cma.j.cn121094-20211206-00600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
Objective: To investigate the role of Keap1/Nrf2/HO-1 signaling pathway in liver injury induced by neodymium oxide (Nd(2)O(3)) in mice. Methods: In March 2021, forty-eight SPF grade healthy male C57BL/6J mice were randomly divided into control group (0.9% NaCl), low dose group (62.5 mg/ml Nd(2)O(3)), medium dose group (125.0 mg/ml Nd(2)O(3)), and high dose group (250.0 mg/ml Nd(2)O(3)), each group consisted of 12 animals. The infected groups were treated with Nd(2)O(3) suspension by non-exposed tracheal drip and were killed 35 days after dust exposure. The liver weight of each group was weighed and the organ coefficient was calculated. The content of Nd(3+) in liver tissue was detected by inductively coupled plasma mass spectrometry (ICP-MS). HE staining and immunofluorescence was used to observe the changes of inflammation and nuclear entry. The mRNA expression levels of Keap1, Nrf2 and HO-1 in mice liver tissue were detected by qRT-PCR. Western blotting was used to detect the protein expression levels of Keap1 and HO-1. The contents of catalase (CAT), glutathione peroxidase (GSH-Px) and total superoxide dismutase (T-SOD) were detected by colorimetric method. The contents of interleukin 1β (IL-1β), interleukin 6 (IL-6) and tumor necrosis factor α (TNF-α) were determined by ELISA. The data was expressed in Mean±SD. Two-independent sample t-test was used for inter-group comparison, and one-way analysis of variance was used for multi-group comparison. Results: Compared with the control group, the liver organ coefficient of mice in medium and high dose groups were increased, and the Nd(3+) accumulation in liver of mice in all dose groups were significantly increased (P<0.05). Pathology showed that the structure of liver lobules in the high dose group was slightly disordered, the liver cells showed balloon-like lesions, the arrangement of liver cell cords was disordered, and the inflammatory exudation was obvious. Compared with the control group, the levels of IL-1β and IL-6 in liver tissue of mice in all dose groups were increased, and the levels of TNF-α in liver tissue of mice in high dose group were increased (P<0.05). Compared with the control group, the mRNA and protein expression levels of Keap1 in high dose group were significantly decreased, while the mRNA expression level of Nrf2, the mRNA and protein expression levels of HO-1 were significantly increased (P<0.05), and Nrf2 was successfully activated into the nucleus. Compared with the control group, the activities of CAT, GSH-Px and T-SOD in high dose group were significantly decreased (P<0.05) . Conclusion: A large amount of Nd(2)O(3) accumulates in the liver of male mice, which may lead to oxidative stress and inflammatory response through activation of Keap1/Nrf2/HO-1 signal pathway. It is suggested that Keap1/Nrf2/HO-1 signal pathway may be one of the mechanisms of Nd(2)O(3) expose-induced liver injury in mice.
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Affiliation(s)
- N Bu
- School of Public Health, Baotou Medical College, Baotou 014040, China
| | - S R Wang
- School of Public Health, Baotou Medical College, Baotou 014040, China
| | - Y R Gao
- School of Public Health, Baotou Medical College, Baotou 014040, China
| | - Y H Zhao
- School of Public Health, Baotou Medical College, Baotou 014040, China
| | - X M Shi
- School of Public Health, Baotou Medical College, Baotou 014040, China
| | - S H Wang
- School of Public Health, Baotou Medical College, Baotou 014040, China
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Song HY, Bu N, Gao YR, Zhao YH, Shi XM, Wang SH. [Effects of Nd(2)O(3) exposure of rare earth particles on C57 BL/6J male mice sex hormone secretion and CYP11A1/PLZF/STRA8 protein expression]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2022; 40:881-887. [PMID: 36646477 DOI: 10.3760/cma.j.cn121094-20210817-00401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Objective: To explore the effects of Nd(2)O(3) exposure to rare earth particles on the secretion of sex hormones, cytochrome P450 family member 11A1 (CYP11A1) , spermatogenesis markers promyelocytic leukemia zinc finger protein (PLZF) and retinoic acid stimulating gene 8 (STRA8) protein in C57 BL/6J male mice. Methods: In March 2021, Forty-eight male C57 BL/6J mice aged 6-8 weeks divided into control group and Nd(2)O(3) exposure low, medium and high dose groups (exposing doses of 62.5, 125.0, 250.0 mg/ml Nd(2)O(3)) , 12 per group. The mice in the Nd(2)O(3) groups were perfused with different doses of Nd(2)O(3) suspension by a one-time non-exposing tracheal instillation method, and the control group was perfused with an equal volume of normal saline, with a volume of 0.1 ml, to establish a mouse reproductive function injury model. After 28 days of exposure, the mice's body weight, testes and epididymis were weighed, and the organ coefficients were calculated; the two epididymis were taken to make a sperm suspension to determine the sperm count, survival rate, and deformity rate; inductively coupled plasma mass spectrometry (ICP-MS) method was used to detect the content of Nd in mouse testis tissue; HE staining was used to detect testicular tissue pathological changes and quantitative analysis; enzyme-linked immunosorbent assay (ELISA) method was used to detect serum luteinizing hormone (LH) and follicle stimulating hormone (FSH) and testosterone (T) content; western blot was used to detect the protein levels of CYP11A1, PLZF and STRA8 in testicular tissues. Results: Compared with the control group, with the increase of the exposure dose, the Nd content in the testis of the mice showed an increasing trend, the sperm survival rate and LH showed a decreasing trend, and the sperm deformity rate showed an increasing trend (P<0.05) ; Pathological showed that the number of sperm in the seminiferous tubules of the testicular tissue in the Nd(2)O(3) medium and high dose groups was significantly reduced, and the germinal epithelial disintegration, intraepithelial vacuolization, and exfoliation of spermatogenic cells and supporting cells occurred; The height of germinal epithelium was significantly reduced, and the percentage of damaged seminiferous tubules showed an increasing trend (P<0.05) ; FSH and T levels in serum in the middle and high dose groups of Nd(2)O(3), and CYP11A1, PLZF and STRA8 proteins in testicular tissues showed a downward trend with increasing dose (P<0.05) . Conclusion: The rare earth particulate Nd(2)O(3) may interfere with the expression of CYP11A1, PLZF and STRA8 protein, thereby causing the disorder of sex hormone secretion in the body, the maintenance of spermatogonia and the obstruction of the process of meiosis, causing reproductive function damage.
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Affiliation(s)
- H Y Song
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
| | - N Bu
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
| | - Y R Gao
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
| | - Y H Zhao
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
| | - X M Shi
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
| | - S H Wang
- Department of Occupational Health and Environmental Hygiene, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
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Li Z, Lyu YB, Zhao F, Sun Q, Qu YL, Ji SS, Qiu T, Li YW, Song SX, Zhang M, Liu YC, Cai JY, Song HC, Zheng XL, Wu B, Li DD, Liu Y, Zhu Y, Cao ZJ, Shi XM. [Association of lead exposure with stunting and underweight among children aged 3-5 years in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1597-1603. [PMID: 36372750 DOI: 10.3760/cma.j.cn112150-20211229-01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To evaluate the association of lead exposure with stunting and underweight among children aged 3-5 years in China. Methods: Data was collected from China National Human Biomonitoring (CNHBM) between January 2017 and December 2018. A total of 3 554 children aged 3-5 years were included. Demographic characteristic, lifestyle and nutritional status were collected through questionnaires. Height and weight were measured by standardized method. Stunting and underweight status were determined by calculating height for age Z-score and weight for age Z-score. Blood and urine samples were collected to detect the concentrations of blood lead, urinary lead and urinary creatinine. Children were stratified into 4 groups (Q1 to Q4) by quartiles of blood lead level and corrected urinary lead level, respectively. Complex sampling logistic regression models were applied to evaluate the association of the blood lead level, urinary lead level with stunting and underweight. Results: Among 3 554 children, the age was (4.09±1.06) years, of which 1 779 (80.64%) were female and 1 948 (55.84%) were urban residents. The prevalence of stunting and wasting was 7.34% and 2.96%, respectively. The M (Q1, Q3) for blood lead levels and urinary lead levels in children was 17.49 (12.80, 24.71) μg/L, 1.20 (0.61, 2.14) μg/g Cr, respectively. After adjusting for confounding factors, compared with the lowest blood lead concentration group Q1, the risk of stunting gradually increased in the Q3 and Q4 group (Ptrend=0.010), with OR (95%CI) values of 1.40 (0.80-2.46) and 1.80 (1.07-3.04), respectively. Compared with the lowest urinary lead concentration group Q1, the risk of stunting still increased in the Q3 and Q4 group (Ptrend=0.012), with OR (95%CI) values of 1.69 (1.01-2.84) and 1.79 (1.05-3.06), respectively. The correlation between the lead exposure and underweight was not statistically significant (P>0.05). Conclusion: Lead exposure is positively associated with the risk of stunting among children aged 3-5 years in China.
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Affiliation(s)
- Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Liu
- School of Public Health, Jilin University, Changchun 130012, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Qu YL, Zhao F, Ji SS, Hu XJ, Li Z, Zhang M, Li YW, Lu YF, Cai JY, Sun Q, Song HC, Li DD, Zheng XL, Wu B, Lyu YB, Zhu Y, Cao ZJ, Shi XM. [Mediation effect of inflammatory biomarkers on the association between blood lead levels and blood pressure changes in Chinese adults]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1591-1596. [PMID: 36372749 DOI: 10.3760/cma.j.cn112150-20211119-01067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the role of inflammatory biomarkers in the relationship between blood lead levels and blood pressure changes. Methods: A total of 9 910 people aged 18-79 years who participated in the China National Human Biomonitoring in 2017-2018 were included in this study. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information, and the data including height, weight and blood pressure were determined through physical examination. Blood and urinary samples were collected for the detection of blood lead and cadmium levels, urinary arsenic levels, white blood cells, neutrophils, lymphocytes, and hypersensitive C-reactive protein (hs-CRP). Weighted linear regression models were used to evaluate the associations between blood lead, inflammatory biomarkers and blood pressure. Mediation analysis was performed to investigate the role of inflammation in the relationship between blood lead levels and blood pressure changes. Results: The median (Q1, Q3) age of all participants was 45.4 (33.8, 58.4)years, including 4 984 males accounting for 50.3%. Multivariate logistic regression model analysis showed that after adjusting for age, gender, residence area, BMI, education level, smoking and drinking status, family history of hypertension, consumption frequency of rice, vegetables, and red meat, fasting blood glucose, total cholesterol, triglycerides, blood cadmium and urinary arsenic levels, there was a positive association between blood lead levels, inflammatory biomarkers and blood pressure (P<0.05). Each 2.71 μg/L (log-transformed) increase of the lead was associated with a 2.05 (95%CI: 0.58, 3.53) mmHg elevation in systolic blood pressure (SBP), 2.24 (95%CI: 1.34, 3.14) mmHg elevation in diastolic blood pressure (DBP), 0.25 (95%CI: 0.05, 0.46) mg/L elevation in hs-CRP, 0.16 (95%CI: 0.03, 0.29)×109/L elevation in white blood cells, and 0.11 (95%CI: 0.02, 0.21)×109/L elevation in lymphocytes, respectively. Mediation analysis showed that the levels of hs-CRP significantly mediated the association of blood lead with SBP, with a proportion about 3.88% (95%CI: 0.45%, 7.32%). The analysis also found that the levels of hs-CRP and neutrophils significantly mediated the association of blood lead with SBP, with a proportion about 4.10% (95%CI: 1.11%, 7.10%) and 2.42% (95%CI: 0.07%, 4.76%), respectively. Conclusion: This study suggests that inflammatory biomarkers could significantly mediate the association of blood lead levels and blood pressure changes.
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Affiliation(s)
- Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X J Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Wang JN, Li TT, Fang JL, Tang S, Zhang Y, Deng FC, Shen C, Shi WY, Liu YY, Chen C, Sun QH, Wang YW, Du YJ, Dong HR, Shi XM. [Associations between personal fine particulate matter and blood lipid profiles: A panel study in Chinese people aged 60-69 years]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:897-901. [PMID: 35899340 DOI: 10.3760/cma.j.cn112150-20220525-00527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To explore the association between short-term exposures to fine particulate matter (PM2.5) on blood lipids in the elderly. Methods: In this panel study, five repeated measurements were performed on 76 people aged 60-69 in Jinan city. Each participant had a PM2.5 monitor for 72 hours before each health examination, including a questionnaire survey, physical examination, and biological sample collection. Serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were examined, and non-HDL-C concentrations were calculated by subtracting HDL-C from TC. The generalized linear mixed-effects model was used to quantify the association of personal PM2.5 exposure at different lag with blood lipids and dyslipidemia. Results: The age of 70 participants was (65.0±2.8) years, of which 48.6% (34/70) were males. The BMI of participants was (25.0±2.5) kg/m2. Their TC, TG, LDL-C, HDL-C, and non-HDL-C concentrations were (5.75±1.32), (1.55±0.53), (3.27±0.94), (1.78±0.52), and (3.97±1.06) mmol/L, respectively. Generalized linear mixed-effects model showed that after adjusting for confounding factors, at lag 72 hours, each 10 μg/m3 increase in PM2.5 was associated with the percentage change in TC, LDL-C, HDL-C and non-HDL-C about 1.77% (95%CI: 1.22%-2.32%), 1.90% (95%CI: 1.18%-2.63%), 1.99% (95%CI: 1.37%-2.60%) and 1.74% (95%CI: 1.11%-2.37%), and the OR values (95%CI) of hypercholesterolemia, hypertriglyceridemia and hyperbetalipoproteinemia were 1.11 (1.01-1.22), 1.33 (1.03-1.71) and 1.15 (1.01-1.31), respectively. Conclusion: There is a significant association of short-term PM2.5 exposure with the concentration of blood lipids and the risk of dyslipidemia in the elderly.
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Affiliation(s)
- J N Wang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T T Li
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J L Fang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S Tang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F C Deng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Shen
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - W Y Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Y Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q H Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y J Du
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H R Dong
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Ji SS, Lyu YB, Zhao F, Qu YL, Li Z, Li YW, Song SX, Zhang WL, Liu YC, Cai JY, Song HC, Li DD, Wu B, Liu Y, Zheng XL, Hu JM, Zhu Y, Cao ZJ, Shi XM. [Association of blood lead and blood selenium with serum high-sensitivity C-reactive protein among Chinese adults aged 19 to 79 years]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:195-200. [PMID: 35184484 DOI: 10.3760/cma.j.cn112338-20210715-00555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association of blood lead and blood selenium with serum high-sensitivity C-reactive protein (hs-CRP) among Chinese adults aged 19 to 79 years. Methods: The participants were enrolled from the first wave of China National Human Biomonitoring (CNHBM) conducted from 2017 to 2018. 10 153 participants aged 19 to 79 years were included in this study. Fasting blood samples were obtained from participants. Lead and selenium in whole blood and hs-CRP in serum were measured. Individuals with hs-CRP levels above 3.0 mg/L were defined as elevated hs-CRP. Generalized linear mixed models and restricted cubic spline models were used to analyze the association of blood lead and blood selenium with elevated hs-CRP. Logistic regression models were used to analyze the multiplicative scale and additive scale interaction between blood lead and blood selenium on elevated hs-CRP. Results: The age of participants was (48.91±15.38) years, of which 5 054 (61.47%) were male. 1 181 (11.29%) participants were defined as elevated hs-CRP. After multivariable adjustment, results from generalized linear models showed that compared with participants with the lowest quartile of blood lead, the OR (95%CI) of elevated hs-CRP for participants with the second, third, and highest quartiles were 1.14 (0.94-1.37), 1.25 (1.04-1.52) and 1.38 (1.13-1.68), respectively. When compared with participants with the lowest quartile of blood selenium, the OR (95%CI) of elevated hs-CRP for participants with the second, third and highest quartiles were 0.86 (0.72-1.04), 0.91 (0.76-1.11), and 0.75 (0.61-0.92), respectively. Results from the interaction analysis showed no significant interaction between lead and selenium on elevated hs-CRP. Conclusion: Blood concentration of lead was positively associated with elevated serum hs-CRP, and blood concentration of selenium was inversely related to elevated hs-CRP, while blood lead and selenium did not present interaction on elevated hs-CRP.
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Affiliation(s)
- S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - J M Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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21
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Liu Y, Lyu YB, Wu B, Wei Y, Chen C, Zhou JH, Zhao F, Li XW, Wang J, Li Z, Li CC, Ji SS, Li YW, Guo YB, Ju AP, Xue K, Shi XM, Yu Q. [Association between urinary arsenic levels and anemia among older adults in nine longevity areas of China]. Zhonghua Yi Xue Za Zhi 2022; 102:101-107. [PMID: 35012297 DOI: 10.3760/cma.j.cn112137-20210706-01516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association between urinary arsenic levels and anemia among older adults in nine longevity areas of China. Methods: A total of 1 896 subjects aged 65 years and above who participated in the Healthy Aging and Biomarkers Cohort Study (HABCS) in 2017-2018 were included. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information from the subjects. Through physical examination, data including height, weight and blood pressure were determined and body mass index (BMI) was calculated. Blood and urine samples were collected for the detection of hemoglobin (Hb), blood glucose, blood lipids, plasma vitamin B12 and urinary arsenic concentrations. The urinary arsenic levels were divided into four groups according to the quartiles of urinary arsenic concentrations (μg/g creatinine): Q1 (<18.7), Q2 (18.7-34.5), Q3 (34.6-69.5) and Q4(≥69.6). Multivariate logistic regression model and restricted cubic spline fitting logistic regression model were used to analyze the association between urinary arsenic levels and anemia. Results: The age of the 1 896 subjects (M (Q1, Q3)) was 83 (74, 92) years, including 952 females (50.21%), and the concentration of Hb (M (Q1, Q3)) was 135 (124, 147)g/L. The prevalence of anemia was 24.89% (472 cases). The geometric mean and M (Q1, Q3) of urinary arsenic concentrations were 37.5 and 34.6 (18.7, 69.6)μg/g creatinine, respectively. Multivariate logistic regression model analysis showed that after adjusting for age, gender, BMI, education level, smoking and drinking status, residence, economic level, ethnicity, the status of vitamin B12 deficiency, consumption frequency of aquatic products and meat, the prevalence of hypertension, diabetes and dyslipidemia, urinary arsenic levels were positively associated with anemia (Taking group Q1 as a reference, OR (95%CI) values in Q2, Q3 and Q4 groups were 1.73 (1.20-2.50), 2.08 (1.43-3.02) and 1.52 (1.02-2.28), respectively). The results of restricted cubic spline fitting logistic regression analysis showed a non-linear association between urinary arsenic concentrations and anemia (P<0.001). Subgroup analysis showed there was a negative multiplicative interaction between the prevalence of chronic diseases and urinary arsenic levels with OR (95%CI) was 0.55 (0.30-0.99), while no multiplicative interaction was found between age, gender, residence, smoking status, drinking status and urinary arsenic levels (P>0.05). There was a positive association between urinary arsenic levels and anemia in participants who were absence of chronic diseases,male, living in rural, smoking and drinking with OR (95%CI) values of 3.62 (1.30-10.06),2.46 (1.34-4.52), 1.70 (1.03-2.80), 2.21 (1.01-4.82) and 2.79 (1.23-6.33), respectively. Conclusion: There is a positive association between urinary arsenic levels and anemia among older adults in nine longevity areas of China.
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Affiliation(s)
- Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X W Li
- School of Public Health, Jilin University, Changchun 130012, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - A P Ju
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K Xue
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Yu
- School of Public Health, Jilin University, Changchun 130012, China
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22
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Zhou JH, Lyu YB, Wei Y, Wang JN, Ye LL, Wu B, Liu Y, Qiu YD, Zheng XL, Guo YB, Ju AP, Xue K, Zhang XC, Zhao F, Qu YL, Chen C, Liu YC, Mao C, Shi XM. [Prediction of 6-year risk of activities of daily living disability in elderly aged 65 years and older in China]. Zhonghua Yi Xue Za Zhi 2022; 102:94-100. [PMID: 35012296 DOI: 10.3760/cma.j.cn112137-20210706-01512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To construct an easy-to-use risk prediction tool for 6-year risk of activities of daily living(ADL) disability among Chinese elderly aged 65 and above. Methods: A total of 34 349 elderly aged 65 and above were recruited from the Chinese Longitudinal Healthy Longevity Survey. Demographic characteristics, lifestyle and chronic diseases of the elderly were collected through face-to-face interviews. The functional status of the elderly was evaluated by the instrumental activities of daily living(IADL) scale. The mental health status of the elderly was evaluated by the Mini-Mental State Examination. The height, weight, blood pressure and other information of the subjects were obtained through physical examination and body mass index(BMI) was calculated. The ADL status was evaluated by Katz Scale at baseline and follow-up surveys. Taking ADL status as the dependent variable and the key predictors were selected from Lasso regression as the independent variables, a Cox proportional risk regression model was constructed and visualized by the nomogram tool. Area under the receiver operating characteristic curve(AUC) and calibration curve were used to evaluate the discrimination and calibration of the model. A total of 200 bootstrap resamples were used for internal validation of the model. Sensitivity analysis was used to evaluate the robustness of the model. Results: The M(Q1, Q3) of subjects' age as 86(75, 94) years old, of which 9 774(46.0%) were males. A total of 112 606 person-years were followed up, 4 578 cases of ADL disability occurred and the incidence density was 40.7/1 000 person-years. Cox proportional risk regression model analysis showed that older age, higher BMI, female, hypertension and history of cerebrovascular disease were associated with higher risk of ADL disability [HR(95%CI) were 1.06(1.05-1.06), 1.05(1.04-1.06), 1.17(1.10-1.25),1.07(1.01-1.13) and 1.41(1.23-1.62), respectively.]; Ethnic minorities, walking 1 km continuously, taking public transportation alone and doing housework almost every day were associated with lower risk of ADL disability [HR(95%CI): 0.71(0.62-0.80), 0.72(0.65-0.80), 0.74(0.68-0.82) and 0.69(0.64-0.74), respectively]. The AUC value of the model was 0.853, and the calibration curve showed that the predicted probability was highly consistent with the observed probability. After excluding non-intervening factors(age, sex and ethnicity), the AUC value of the model for predicting the risk of ADL disability was 0.779. The AUC values of 65-74 years old and 75 years old and above were 0.634 and 0.765, respectively. The AUC values of the model based on walking 1 km continuous and taking public transport alone in IADL and the model based on comprehensive score of IADL were 0.853 and 0.851, respectively. Conclusion: The risk prediction model of ADL disability established in this study has good performance and robustness.
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Affiliation(s)
- J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J N Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - A P Ju
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K Xue
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X C Zhang
- Division of Non-communicable Disease and Aging Health Management, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Sun Y, Lyu YB, Zhong WF, Zhou JH, Li ZH, Wei Y, Shen D, Wu B, Zhang XR, Chen PL, Shi XM, Mao C. [Association between sleep duration and activity of daily living in the elderly aged 65 years and older in China]. Zhonghua Yi Xue Za Zhi 2022; 102:108-113. [PMID: 35012298 DOI: 10.3760/cma.j.cn112137-20210705-01508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association between sleep duration and activity of daily living (ADL) in the elderly aged 65 years and older in China. Methods: A total of 11 247 subjects aged 65 and above were included in the Chinese Elderly Health Factors Tracking Survey from March 29, 2005 to April 8, 2019. Self-made questionnaire was used to collect the data of population sociological characteristics, health status and disease status. ADL status was assessed by basic activities of daily living. The association between sleep duration and ADL impairment was assessed by Cox proportional risk regression model. The dose-response relationship between sleep duration and ADL impairment was analyzed using restricted cubic spline function. Results: The age of the subjects was (79±10) years, including 5 793(51.5%) females. The incidence of ADL impairment was 33.3% (3 747/11 247). Subjects were divided into short, medium, and long sleep groups according to sleep duration of fewer than seven hours, seven to eight hours, or more than eight hours. The number of short, medium and long sleepers was 2 974 (26.4%), 4 922 (43.8%) and 3 351(29.8%), respectively. The intermediate sleep group had the lowest incidence of impaired ADL (4.98/100 person-years). Cox proportional risk regression model analysis showed that: taking the intermediate sleep group as reference, after adjustment of gender, age, marital status, educational level, place of residence, living with family, smoking, drinking, exercise, frequency of fruit consumption, vegetable intake frequency, sleep quality, factors such as hypertension, diabetes, heart disease and cerebrovascular disease, the long sleep time increased the risk of impaired ADL [HR (95%CI): 1.148 (1.062-1.241)]. Subgroup analysis showed a weak positive multiplicative interaction between sleep duration and age [HR (95%CI): 1.004 (1.000-1.009)], but no multiplicative interaction between sleep duration and sex [HR(95%CI): 0.948 (0.870-1.034)]. Longer sleep duration increased the risk of ADL impairment in women [HR (95%CI): 1.195 (1.074-1.329)], but not in men [HR (95%CI): 1.084 (0.966-1.217)]. Longer sleep duration increased the risk of ADL impairment in people aged 80 years and older [HR (95%CI): 1.185 (1.076-1.305)], but not in people younger than 80 years [HR (95%CI): 1.020 (0.890-1.169)]. There was a non-linear dose-response relationship between sleep duration and ADL damage (P=0.007), and the risk of ADL damage was lowest when sleep duration was 7.5 h. Conclusion: Sleep duration was positively correlated with the risk of ADL impairment in the elderly in a nonlinear dose-response relationship.
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Affiliation(s)
- Y Sun
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D Shen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X R Zhang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - P L Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
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24
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Shi XM. [Strengthening the research on the key factors and related mechanisms of elderly health]. Zhonghua Yi Xue Za Zhi 2022; 102:85-89. [PMID: 35012294 DOI: 10.3760/cma.j.cn112137-20211114-02535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The process of population aging is gradually deepening, and the problems of disability, cognitive impairment and frailty are widely prevalent among the elderly in China. The key to healthy aging is to optimize functional ability, which is not only related to healthy people; many older adults suffer from one or more diseases, which do not affect their functional ability. The health of the elderly is affected by multi-dimensional factors such as heredity, environment, lifestyle, social behavior and mental psychology. Previous studies, focusing on the outcomes of death, diseases, functional ability and intrinsic capacity decline, have revealed a series of distribution characteristics and influencing factors, and found that there are special epidemiological characteristics on health among the oldest-old and are different from that in the general elderly. Nevertheless, there are still insufficient research to identify the key factors and related mechanisms of elderly health. This issue focused on the functional ability, intrinsic capacity and disease outcomes of the elderly, carried out analysis and research by using the data of national representative cohort and key areas, and made a series of explorations on the important factors and possible mechanisms of the elderly health, so as to provide scientific evidence for coping with population aging and health in China.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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25
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Shi XM. [Research progress in geriatric epidemiology in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:1713-1721. [PMID: 34814606 DOI: 10.3760/cma.j.cn112338-20210723-00582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Geriatric epidemiology deals with the application of epidemiological techniques to study the distribution and prevention of health status, diseases, and risk factors among the elderly, to develop intervention measures for promoting healthy ageing. The ageing of the population is a gradually developed phenomenon that is accelerating in China, geriatric epidemiology is one of the important branches of epidemiology and the major pillars of geriatric preventive medicine that play an important role in disease prevention and health promotion. In this article, we reviewed the recent Chinese studies in geriatric epidemiology, and synthesized the distributions and determinants of health-related status in Chinese elderly, and finally highlighted the future trends and expectations of the geriatric epidemiological research.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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26
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Xu Q, Wang JH, Zhang LL, Wang XY, Li N, Jin CH, Wang X, Li XM, Shi XM, Wang L. [Research on the status and risk factors of screen exposure in children under three years of age]. Zhonghua Er Ke Za Zhi 2021; 59:841-846. [PMID: 34587680 DOI: 10.3760/cma.j.cn112140-20210322-00242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the time and characteristics of screen exposure, to analyze the risk factors affecting screen exposure in children aged 3 years and younger, and to provide scientific basis for the intervention of screen exposure in children. Methods: The data were collected by convenience sampling from 317 children for routine examination aged 0-36 months who visited the Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics from December 2019 to December 2020. Self-designed questionnaires of the screen exposure were completed by the parents. The basic information, home nurture environment and screen exposure conditions were investigated. Children <18 months of age who used electronic devices and 18-36 months of age who spent more than 1 h/d on electronic devices were defined as with screen exposure. The differences between <18 and 18-36 months of age were compared by rank sum test. Chi-square test and multivariate Logistic regression were used to analyze the association between screen exposure and potential influential factors. Results: Among 317 children, 209 were boys and 108 girls, aged (28±10) months. There were 117 patients aged <18 months and 200 patients aged 18-36 months. Screen exposure time was 0.3 (0, 1.0) h/d and 1.2 (0.6, 2.0) h/d in children aged <18 months and aged 18-36 months, respectively (Z=-6.770, P<0.01). The proportion of screen exposure was 25.6% (30/117) and 49.0% (98/200) in two age groups, respectively. Logistic regression analysis disclosed that not being the first child (OR=3.81, 95%CI: 1.13-12.77, P=0.030), caregivers spending >1 h/d on electronic devices in front of their children (OR=7.39, 95%CI: 2.24-24.46, P=0.001), caregivers believing that screen exposure can promote early childhood development (OR=4.14, 95%CI: 1.26-13.52, P=0.019) were risk factors for children's screen exposure in <18 months of age. Caregivers spending >1 h/d on electronic devices in front of their children (OR=3.29, 95%CI: 1.78-6.08, P<0.01) was risk factor for children's screen exposure in 18-36 months of age. Mothers with bachelor's or higher degree (OR=0.19, 95%CI: 0.05-0.66, P=0.009; OR=0.35, 95%CI: 0.19-0.66, P=0.001), no television in living room (OR=0.11, 95%CI: 0.03-0.42, P=0.001; OR=0.45, 95%CI: 0.21-0.98, P=0.045) were protective factors for children's screen exposure in children <18 months and 18-36 months of age. Conclusions: Excessive screen exposure is common among children aged 3 years and younger. The exposure time increases with age, and is affected by several factors.
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Affiliation(s)
- Q Xu
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - J H Wang
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - L L Zhang
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X Y Wang
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - N Li
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - C H Jin
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X Wang
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X M Li
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X M Shi
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - L Wang
- Department of Child Healthcare, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
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Shi XM, Gong Y, Hu XD, Zhai L. [The relationship between elevated antiphospholipid antibodies and thrombosis in hospitalized patients]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:1100-1104. [PMID: 34619928 DOI: 10.3760/cma.j.cn112150-20201028-01319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: Assess the relationship between elevated antiphospholipid antibodies and thrombosis in hospitalized patients. Methods: Case control study. A total of 385 patients (149 males and 236 females, aged from 1 to 105 years, with a median age of 37 years) who were hospitalized in Peking University First Hospital from January 2015 to December 2019 and tested positive for any one of the anti-phospholipid antibodies were included in the study. All subjects were divided into thrombotic group and non-thrombotic group according to whether thrombus was detected by imaging examination during hospitalization. In thrombosis group, there were 66 males and 36 females, aged from 3 to 105 years, with a median age of 58 years. In non-thrombosis group, there were 83 males and 200 females, aged from 1 to 94 years, with a median age of 31 years. Clinical data and laboratory data of patients were recorded. ACL-IgM/IgG and anti-β2GPI-IgM/IgG were detected by ELISA and LA was detected by dRVVT and SCT on automatic coagulation analyzer. The rates of age, gender, smoking, obesity, hypertension, hyperlipidemia, diabetes and the median level of antiphospholipid antibodies were compared between two groups. Logistic multivariate regression analysis was used to determine the risk factors for thrombotic events. The mid-to-high titer value of aCL was established by the χ2-trend test and verified by logistic regression. Results: The median age (58 years) and the rates of male (64.7%), smoking (16.7%), hypertension (63.7%) and diabetes (28.4%) in thrombus group were significantly higher than those in non-thrombus group (Z=7.685, χ²=38.077, 16.312, 37.769, 24.749 respectively; P<0.01). The positive rate of anti-β2GPI-IgG and dRVVT in thrombosis group (11.8% and 78.4%) was significantly higher than that in non-thrombosis group (5.3% and 60.1%), as well as the median level of dRVVT (1.29 RU/ml vs 1.23 RU/ml) (χ²=3.864 and 10.309, Z=3.539; P<0.05). The median level of aCL-IgM was higher in non-thrombosis group (2.3 MPL vs 2.0 MPL). The positive rate of aCL-IgG was slightly higher in thrombosis group (18.6% vs 10.6%). Logistic regression analysis showed that men, hypertension, diabetes, advanced age, elevated dRVVT, and elevated anti-β2GPI-IgG are risk factors for thrombosis. Taking 36 GPL as the medium-to-high titer value of aCL-IgG, the risk of thrombosis increased by 2.45 times. Conclusions: In the anti-phospholipid antibody profile, LA detected by dRVVT method, anti-β2GPI-IgG and aCL-IgG may be valuable laboratory indicators for inpatient thrombotic events. The mid-to-high titer value of aCL-IgG is set at 36 GPL to distinguish the risk of thrombosis.
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Affiliation(s)
- X M Shi
- Department of Clinical Laboratory,Peking University First Hospital, Beijing 100034, China
| | - Y Gong
- Department of Clinical Laboratory,Peking University First Hospital, Beijing 100034, China
| | - X D Hu
- Department of Clinical Laboratory,Peking University First Hospital, Beijing 100034, China
| | - L Zhai
- Department of Clinical Laboratory,Peking University First Hospital, Beijing 100034, China
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Wang L, Hao Y, Chen L, Zhang YW, Deng HZ, Ke XY, Wang JH, Li F, Hou Y, Xie XH, Xu Q, Wang X, Guan HY, Wang WJ, Shen JN, Li F, Qian Y, Zhang LL, Shi XM, Tian Y, Jin CH, Liu XL, Li TY. [Psychological and behavioral functioning of children and adolescents during long-term home-schooling]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:1059-1066. [PMID: 34619922 DOI: 10.3760/cma.j.cn112150-20210602-00533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To study the characteristics and risk factors of psychological and behavioral problems of children and adolescents of different ages and genders in long-term home-schooling during the coronavirus disease-2019 pandemic. Further, to provide scientific basis for more targeted psychological intervention and coping strategies in the future. Methods: A cross-sectional survey using an online questionnaire was conducted on students aged 6-16 years old in five representative cities of North (Beijing), East (Shanghai), West (Chongqing), South (Guangzhou) and Middle (Wuhan) in China. In this study, the social behavior and psychological abnormalities which was defined as the positive of any dimension were investigated in multiple dimensions during long-term home-schooling. The influencing factors of psycho-behavioral problems were analyzed by Logistic regression, and the confounding factors were corrected with graded multivariable adjustment. Results: A total of 6 906 valid questionnaires were collected including 3 592 boys and 3 314 girls, of whom 3 626 were children (6-11 years old) and 3 280 were adolescents (12-16 years old). The positive detection rate of psychosocial-behavioral problems were 13.0% (900/6 906) totally, 9.6% (344/3 592) in boys and 16.8% (556/3 314) in girls respectively, and 7.3%(142/1 946) in boys aged 6-11, 14.0%(235/1 680) in girls aged 6-11, 12.3%(202/1 646) in boys aged 12-16, 19.6%(321/1 634) in girls aged 12-16 respectively. There were significant differences between the psychological problems group and the non-psychological problems group in gender, parent-offspring conflict, number of close friends, family income change, sedentary time, homework time, screen exposure time, physical activity, dietary problems (χ²=78.851, 285.264, 52.839, 26.284, 22.778, 11.024, 10.688, 36.814, 70.982, all P<0.01). The most common symptoms in boys aged 6-11 years were compulsive activity, schizoid and depression, in girls aged 6-11 years were schizoid/compulsive activity, hyperactivity and social withdrawal, in boys aged 12-16 years were hyperactivity, compulsive activity and aggressive behavior, and in girls aged 12-16 years were schizoid, anxiety/compulsive activity and depression/withdrawal, respectively. After graded multivariable adjustment, besides the common risk factors, homework time and online study time were the risk factors of 6-11 years old groups [boys OR(95%CI): 1.750 (1.32-2.32), 1.214(1.00-1.47), girls: 1.579(1.25-1.99), 1.222(1.05-1.42), all P<0.05], videogames time were the risk factors of 12-16 years old groups [ boys: 2.237 (1.60-3.13), girls: 1.272 (1.00-1.61), all P<0.05]. Conclusions: Some children and adolescents may have psychological and behavioral problems during long-term home-schooling. The psychological and behavioral manifestations differed in age and gender subgroups, which deserve special attention in each subgroups. Schools, families and specialists should actively provide precise psychological support and comprehensive intervention strategies according to special features and risk factors.
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Affiliation(s)
- L Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - Y Hao
- Department of Child Health Care, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - L Chen
- National Clinical Research Center for Child Health and Disorder, Children's Hospital of Chongqing Medical University, Chongqing 400014, China
| | - Y W Zhang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center Affiliated to Medical School of Shanghai Jiaotong University, Shanghai 200127, China
| | - H Z Deng
- Child Developmental & Behavioral Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - X Y Ke
- Child Mental Health Research Center, Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China
| | - J H Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - F Li
- Department of Developmental-Behavioral Pediatrics,Xinhua Hospital Affiliated to Medical School of Shanghai Jiaotong University, Shanghai 200092, China
| | - Y Hou
- Department of Biostatistics, Peking University, Beijing 100871, China
| | - X H Xie
- Department of Surgery, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - Q Xu
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - H Y Guan
- Department of Early Childhood Development, Capital Institute of Pediatrics, Beijing 100020, China
| | - W J Wang
- Teacher Development Center, Shanghai Pudong Institute of Education Development, Shanghai 200127, China
| | - J N Shen
- Institute of Primary Education, Chongqing Educational Science Research Academy, Chongqing 400015, China
| | - F Li
- Department of Pediatrics, Jiangjin Centre Hospital, Chongqing 402260, China
| | - Y Qian
- Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders, National Health Commission Key Laboratory of Mental Health (Peking University), Beijing 100191, China
| | - L L Zhang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X M Shi
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - Y Tian
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - C H Jin
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X L Liu
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - T Y Li
- National Clinical Research Center for Child Health and Disorder, Children's Hospital of Chongqing Medical University, Chongqing 400014, China
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Wang JH, Wang L, Xu Q, Hou Y, Wang WP, Wang XY, Zhang LL, Jin CH, Wang X, Li XM, Shi XM. [Characteristics of consonant among children with speech sound disorder]. Zhonghua Er Ke Za Zhi 2021; 59:478-483. [PMID: 34102821 DOI: 10.3760/cma.j.cn112140-20201025-00969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the characteristics of consonant among children with speech sound disorder (SSD) and to provide an empirical basis for the subsequent clinical evaluation and evidence-based intervention. Methods: In this retrospective research a total of 1 395 children diagnosed with SSD from the Language-Speech Clinic of the Department of Children Health Care, Children's Hospital, Capital Institute of Pediatrics from January 2007 to December 2018 were enrolled and underwent the phonological examination on the lexical level with picture naming, according to phoneme development in Chinese mandarin. The Chi-square trend test was applied to analyze the differences and trends of the proportion of consonant error subtypes in children of different age groups. The Chi-square test was conducted to compare the proportion of consonant error subtypes in different gender. Results: The 1 395 children diagnosed with SSD included 1 044 boys and 351 girls, with an age of (5.1±0.8) years. The occurrence of consonant errors in different locations of articulation was the highest for blade-alveolar /l/ (71.8%, 1 002/1 395) and the lowest for labial/b/(9.3%, 130/1 395). The occurrence of consonant errors of labial/p/f/, supra-dental/z/c/s/, blade-alveolar/t/l/, blade-palatal/ch/r/, velar/k/h/, and lingua-palatal/q/decreased with age (all P<0.05). The occurrence of consonant errors of labial/b/m/, supra-dental/z/c/, blade-alveolar/n/l/, blade-palatal/sh/, velar/h/, and lingua-palatal/x/were higher in boys than those in girls (10.3% (108/1 044) vs. 6.3% (22/351), 11.4% (119/1 044) vs. 6.0% (21/351), 64.8% (676/1 044) vs. 51.9% (182/351), 67.8% (708/1 044) vs. 59.8% (210/351), 16.7% (174/1 044) vs. 8.8% (31/351), 73.7% (769/1 044) vs. 66.1% (232/351), 58.0% (606/1 044) vs. 47.6% (167/351), 24.0% (251/1 044) vs. 14.2% (50/351), and 39.9% (417/1 044) vs. 27.6% (97/351); χ²=5.167, 8.533, 16.889, 7.447, 12.863, 7.412, 11.650, 14.900, and 17.099, all P<0.05). The error types of consonant were substitution, omission, and distortion. Omission was the main error type of blade-alveolar/l/(53.3%, 743/1 395), distortion was the main error type of velar/h/(11.8%, 165/1 395), and substitution was the main error type of all other consonants. Substitution with blade-palatal/ch/having the highest occurrence of error (60.2%, 840/1 395). Conclusions: The occurrence of the blade-alveolar/l/error is the highest in children with SSD, with boys demonstrating more serious articulation problems. The main error type of consonant is substitution, with blade-palatal/ch/having the highest occurrence of error. These results suggest the necessity of attending to preschoolers' articulation development. Phonological training targeting blade-alveolar/l/and blade-palatal/ch/should be carried out as early as possible.
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Affiliation(s)
- J H Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - L Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - Q Xu
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - Y Hou
- Department of Biostatistics, Peking University, Beijing 100871, China
| | - W P Wang
- Department of Epidemiology, Capital Institute of Pediatrics, Beijing 100020, China
| | - X Y Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - L L Zhang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - C H Jin
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X M Li
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
| | - X M Shi
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing 100020, China
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Lyu YB, Zhao F, Qiu YD, Ding L, Qu YL, Xiong JH, Lu YF, Ji SS, Wu B, Hu XJ, Li Z, Zheng XL, Zhang WL, Liu JX, Li YW, Cai JY, Song HC, Zhu Y, Cao ZJ, Shi XM. [Association of cadmium internal exposure with chronic kidney disease in Chinese adults]. Zhonghua Yi Xue Za Zhi 2021; 101:1921-1928. [PMID: 34139825 DOI: 10.3760/cma.j.cn112137-20210425-00996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze the association of the cadmium internal exposure with chronic kidney disease (CKD) in Chinese adults aged 18 and older. Methods: A total of 9 821 adults aged 18-79 from the China National Human Biomonitoring (CNHBM) from 2017 to 2018 were included. Blood and urine cadmium exposure levels were measured by inductively coupled plasma mass spectrometry (ICP-MS), and urine cadmium levels were adjusted with urine creatinine; CKD were defined by estimated glomerular filtration (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI). Weights were considered due to complex sampling process for in statistical analysis. Logistic regression is used to analyze the association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine exposure levels with CKD, and restricted cube spline (RCS) was used to assess the exposure-response curve of blood cadmium, urine cadmium and urine cadmium adjusted with creatinine with CKD. Results: The weighted age was 44.75 and males accounted for 61.1%. The prevalence rate of CKD was 12.7%. The geometric mean values of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine were 0.96 μg/L, 0.61 μg/L, and 0.58 μg/g. After adjusting for confounding factors, the weighted logistic regression showed that the lowest quintile (Q1) was compared with the odds ratio (OR) of the highest quintile (Q5) of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine and the 95% confidence interval (CI) was 1.80 (1.02-3.20), 1.77 (0.94-3.31) and 1.94 (1.11-3.37) respectively. In the restricted cubic spline regression model, non-linear association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine with CKD were observed after adjusting for related confounding factors (P<0.001, 0.018, 0.031 respectively). The risk of CKD increased with the increment of cadmium exposure without risk threshold, and the exposure response curve was steeper at low cadmium exposure. Conclusions: Among Chinese adults aged 18 and older, cadmium exposure is positively associated with the risk of chronic kidney disease.
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Affiliation(s)
- Y B Lyu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- School of Public Health, Zhejiang University, Hangzhou 310011, China
| | - L Ding
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Xiong
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y F Lu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - X J Hu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - W L Zhang
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J X Liu
- School of Public Health, China Medical University, Shenyang 110001, China
| | - Y W Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Wu B, Lyu YB, Zhou JH, Wei Y, Zhao F, Chen C, Li CC, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Song HC, Tan QY, Zhang MY, Cao ZJ, Shi XM. [A cohort study on plasma uric acid levels and the risk of type 2 diabetes mellitus among the oldest old in longevity areas of China]. Zhonghua Yi Xue Za Zhi 2021; 101:1171-1177. [PMID: 33902249 DOI: 10.3760/cma.j.cn112137-20201221-03409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the effect of plasma uric acid level on the incident risk of type 2 diabetes mellitus (T2DM) among the oldest old (those aged ≥80 years). Methods: Participants were recruited from the Healthy Aging and Biomarkers Cohort Study (HABCS), which conducted a baseline survey in 2008-2009 and follow-up of 3 times in 2011-2012, 2014, and 2017-2018, respectively. A total of 2 213 oldest old were enrolled in this study. The general demographic, socioeconomic, lifestyle and disease data of the oldest old were collected, and physical measurements were made for the oldest old. Fasting venous blood was collected for uric acid and blood glucose detection. Information on the incident and death of T2DM were collected through the follow-up. Cox proportional hazard regression model was used to explore the association of hyperuricemia and plasma uric acid level with the incidence of T2DM. Restricted cubic spline (RCS) function was used to explore the dose-response relationship of plasma uric acid levels with the risk of T2DM. Results: The age of participants was (93.2±7.6) years old, and 66.7% of the participants (1 475) were female. The plasma uric acid level at baseline was (289.1±88.0)μmol/L, and the prevalence of hyperuricemia was 13.3% (294 cases). During 9 years of cumulative follow-up of 7 471 person-years (average of 3.38 years for each), 122 new cases of T2DM occurred and the incidence density was 1 632.98/105 person year. Cox proportional hazards regression analysis showed that per 10μmol/L increase in plasma uric acid level, the risk of T2DM increased by 1.1% [HR (95%CI): 1.011 (1.004, 1.017)]. Compared with the participants with the lowest quintile of plasma uric acid (Q1), the risk of diabetes increased by 20.7 % among the oldest old with uric acid in the highest quintile (Q5) [HR (95%CI):1.207 (1.029, 1.416)]. The risk of T2DM was 19.2% higher in the hyperuricemia group than that in the oldest old with normal plasma uric acid [HR (95%CI): 1.192 (1.033, 1.377)]. RCS function showed that the risk of T2DM increased with the increase in plasma uric acid levels in a nonlinear dose-response relationship (P=0.016). Conclusion: The incident risk of T2DM increases with the elevates of plasma uric acid levels in the oldest old.
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Affiliation(s)
- B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Y Tan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Y Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Sun B, Cheng YB, Yao XY, Zhao KF, Zhou ZR, Shi XM. [Current situation, problems and suggestions of environmental health standardization]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:424-427. [PMID: 33730839 DOI: 10.3760/cma.j.cn112150-20200507-00693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this paper, the history and current situation of environmental health standardization in China are reviewed, and the experience and shortcomings in the process of environmental health standardization in China are analyzed, suggestions for the next step of environmental health standards are also put forward.
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Affiliation(s)
- B Sun
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Cheng
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X Y Yao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K F Zhao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z R Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Ji SS, Lyu YB, Qu YL, Chen C, Li CC, Zhou JH, Li Z, Zhang WL, Li YW, Liu YC, Zhao F, Zhu HJ, Shi XM. [Association of sleep duration with cognitive impairment among older adults aged 65 years and older in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:31-38. [PMID: 33355766 DOI: 10.3760/cma.j.cn112150-20200916-01208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Objective: The study is to examine association of sleep duration and cognitive impairment in the older adults aged 65 years and older in China. Methods: We analyzed data from 2017-2018 wave of Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 14 966 participants were included in the analysis. Data with respect to socioeconomic status, community involvement, behavior pattern, diet, life style, family structure, disease condition, mental health and cognitive function were collected. Cognitive function was measured with Mini-mental State Examination (MMSE). We conducted generalized linear mixed models to examine associations of sleep duration with cognitive impairment, and subgroup analyses of sex and age were conducted. Results: Among 14 966 participants, the percentage of participants aged 65 to 79 years, 80 to 89 years, 90 to 99 years and 100 years and older was 5 148 (4.40%), 3 777 (25.24%), 3 322 (22.20%) and 2 719 (18.16%), respectively. A total of 2 704 participants reported sleep duration of 5 h and less, and 3 883 reported 9 h and more, accounting for 18.94% and 27.19%, respectively. In total, 3 748 were defined with cognitive impairment, accounting for 25.04%. The results of generalized linear mixed models showed that both short (≤5 h) and long (≥ 9 h) sleep duration were associated with cognitive impairment compared with sleep duration of 7 h, with OR(95%CI) of 1.35(1.09-1.68) and 1.70(1.39-2.07), respectively. The association of sleep duration with cognitive impairment was more obvious in males and individuals aged 65 to 79 years old. Conclusion: Short or long sleep duration was responsible for increased risk of cognitive impairment in older Chinese.
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Affiliation(s)
- S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H J Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Liu D, Zhao F, Huang QM, Lyu YB, Zhong WF, Zhou JH, Li ZH, Qu YL, Liu L, Liu YC, Wang JN, Cao ZJ, Wu XB, Mao C, Shi XM. [Effects of oxygen saturation on all-cause mortality among the elderly over 65 years old in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:45-52. [PMID: 33355768 DOI: 10.3760/cma.j.cn112150-20200630-00952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Objective: To investigate the association between oxygen saturation (SpO2) and risk of 3-year all-cause mortality among Chinese older adults aged 65 or over. Methods: The participants were enrolled from Healthy Aging and Biomarkers Cohort Study in year of 2012 to 2014 in 9 longevity areas in China. In this prospective cohort study, 2 287 participants aged 65 or over were enrolled. Data on SpO2 and body measurements were collected at baseline in 2012, and data on survival outcome and time of mortality were collected at the follow-up in 2014. Participants were divided into two groups according to whether SpO2 was abnormal (SpO2<94% was defined as abnormal). Results: The 2 287 participants were (86.5±12.2) years old, 1 006 were males (44.0%), and 315 (13.8%) were abnormal in SpO2. During follow-up in 2014, 452 were died, 1 434 were survived, and 401 were lost to follow-up. The all-cause mortality rate was 19.8%, and the follow-up rate was 82.5%. The mortality rate of SpO2 in normal group was 21.1%, and that of abnormal group was 41.6% (P<0.001). After adjusting for confounding factors, compared to participants with normal SpO2, participants with abnormal SpO2 had increased risk of all-cause mortality with HR (95%CI) of 1.62 (1.31-2.02); HR (95 % CI) was 1.49 (0.98-2.26) for males and 1.71 (1.30-2.26) for females in abnormal SpO2 group, respectively; HR (95%CI) was 2.70 (0.98-7.44) for aged 65-79 years old, 1.22 (0.63-2.38) for aged 80-89 years old, and 1.72 (1.35-2.19) for aged over 90 years old in abnormal SpO2 group, respectively. Conclusion: Abnormal SpO2 was responsible for increased risk of 3-year all-cause mortality among Chinese elderly adults.
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Affiliation(s)
- D Liu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q M Huang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Liu
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - J N Wang
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X B Wu
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Cheng X, Li ZH, Lyu YB, Chen PL, Li FR, Zhong WF, Yang HL, Zhang XR, Shi XM, Mao C. [The relationship between resting heart rate and all-cause mortality among the Chinese oldest-old aged more than 80: a prospective cohort study]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:53-59. [PMID: 33355769 DOI: 10.3760/cma.j.cn112150-20200629-00944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the association between resting heart rate(RHR) and all-cause mortality among the Chinese oldest-old aged more than 80. Methods: Using a total of seven surveys or follow-ups data (1998, 2000, 2002, 2005, 2008, 2011 and 2014) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 17 886 elderly over 80 years old were selected as subjects, their resting heart rate were measured though baseline survey and the survival outcome and death time of the subjects were followed up. The subjects were divided into 6 groups according to their resting heart rate. Cox regression model was used to estimate the effect of resting heart rate on mortality risk. The interaction of age, gender and resting heart rate was also analyzed by likelihood ratio test. Results: The age of subjects M (P25, P75) was 92 (86, 100) years old, including 10 531 females (58.9%) and there were 13 598 participants died, the mortality rate was 195.5 per 1 000 person-years. Multivariate Cox regression analysis showed that compared to the control group (60-69 pbm/min), the hazard ratio of the elderly are 1.06 (95%CI: 1.02, 1.11), 1.09 (95%CI: 1.04, 1.15), 1.23 (95%CI: 1.14, 1.34), 1.25 (95%CI: 1.08, 1.44) in the group of RHR between 70-79, 80-89, 90-99 and ≥100 pbm/min and P values are all less than 0.05. Likelihood ratio test showed that RHR and age had an interaction effect. (P for interaction=0.011). Conclusion: The risk of all-cause death increased with the increase of resting heart rate and this relationship was stronger between the 80-89 years old people.
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Affiliation(s)
- X Cheng
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - P L Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - F R Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - H L Yang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X R Zhang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Populaation Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
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Zhang MY, Lyu YB, Zhou JH, Zhao F, Chen C, Tan QY, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Wu B, Cao ZJ, Yu Q, Shi XM. [Association of blood lead level with cognition impairment among elderly aged 65 years and older in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:66-71. [PMID: 33355770 DOI: 10.3760/cma.j.cn112150-20200728-01066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the association between blood lead concentrations and cognition impairment among Chinese older adults aged 65 or over. Method: Data was collected in 9 longevity areas from Heathy Aging and Biomarkers Cohort Study between 2017 and 2018. This study included 1 684 elderly aged 65 years and older. Information about demographic characteristics, socioeconomic factors, health status and cognitive function score of respondents were collected by questionnaire survey and physical examination. Venous blood of the subjects was collected to detect the blood lead concentration. Subjects were stratified into four groups (Q1-Q4) by quartile of blood lead concentration. Multivariate logistic regression model was used to analyze the association between blood lead concentration and cognitive impairment. The linear or non-linear association between blood lead concentration and cognitive impairment were described by restrictive cubic splines (RCS). Results: Among the 1 684 respondents, 843 (50.1%) were female and 191 (11.3%) suffered from cognition impairment. After adjusting for confounding factors, the OR value and 95%CI of cognition impairment was 1.05 (1.01-1.10) for every 10 μg/L increase in blood lead concentration in elderly; Compared with the elderly in Q1, the elderly with higher blood lead concentration had an increased risk of cognitive impairment. The OR value and 95%CI of Q2, Q3 and Q4 groups were 1.19 (0.69-2.05), 1.45 (0.84-2.51) and 1.92 (1.13-3.27), respectively. Conclusion: Higher blood lead concentration is associated with cognitive impairment among the elderly aged 65 years and older in 9 longevity areas in China.
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Affiliation(s)
- M Y Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Y Tan
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Yu
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Tan QY, Lyu YB, Zhou JH, Zhang MY, Chen C, Zhao F, Li CC, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Wu B, Cao ZJ, Zhao SH, Shi XM. [Association of blood oxidative stress level with hypertriglyceridemia in the elderly aged 65 years and older in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:18-24. [PMID: 33355764 DOI: 10.3760/cma.j.cn112150-20200728-01065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the association of blood oxidative stress level with hypertriglyceridemia in the elderly aged 65 years and older in China. Methods: A total of 2 393 participants aged 65 years and older were recruited in 9 longevity areas from Heathy Aging and Biomarkers Cohort Study, during 2017 to 2018. Information on demographics characteristic, life style and health status were collected by questionnaire and physical examination, and venous blood was collected to detect the levels of blood oxidative stress and hypertriglyceridemia. The linear or non-linear association between oxidative stress and hypertriglyceridemia was described by restrictive cubic splines (RCS) fitting multiple linear regression model. The generalized linear mixed effect model was conducted to assess the association between oxidative stress and hypertriglyceridemia. Results: A total of 2 393 participants, mean age was 84.6 years, the youngest was 65 and the oldest was 112, the male was 47.9%(1 145/2 393), the triglyceride level was (1.4±0.8) mmol/L. The hypertriglyceridemia detection rate was 9.99%(239/2 393). The results of multiple linear regression model with restrictive cubic spline fitting showed that MDA level was linear association with triglyceride level; SOD level was nonlinear association with triglyceride level. MDA level had significantly association with hypertriglyceridemia, and the corresponding OR value was 1.063 (95%CI: 1.046,1.081) with 1 nmol/ml increment of blood MDA; SOD level had significantly association with hypertriglyceridemia, and the corresponding OR value was 0.986(95%CI: 0.983,0.989) with 1 U/ml increment of blood SOD. Conclusion: Among the elderly aged 65 and older in 9 longevity areas in China, MDA and SOD levels were associated with the risk of hypertriglyceridemia.
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Affiliation(s)
- Q Y Tan
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Y Zhang
- School of Public Health, Jilin University, Changchun 130012, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- Beijing Municipal Health Commission Information Center, (Beijing Municipal Health Commission Policy Research Center), Beijing 100034, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S H Zhao
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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38
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Shi XM. [Strengthening the research on geriatric epidemiology and promoting the elderly health]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 55:E001. [PMID: 33355762 DOI: 10.3760/cma.j.cn112150-20201204-01424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Currently, with the aging population, further studies on elderly health is urgently needed, especially for the oldest old who are vulnerable and longest-lived. Geriatric epidemiology deals with the application of epidemiological techniques to the study of older adults, which requires the integration of known methods to circumvent the specific problems involved in this population and to grasp the epidemiological rules of aging. This special issue on geriatric epidemiology focused on chronic diseases, mental health, cognitive function, and mortality of older adults, identified a series of modifiable risk factors of healthy aging such as lifestyles, biomarkers, and internal exposure markers. Future research needs to focus not only on chronic diseases, disabilities and mental disorders among older adults, but also on the negative impact of infectious diseases on the healthy aging and longevity.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Wei Y, Zhou JH, Zhang ZW, Tan QY, Zhang MY, Li J, Shi XM, Lyu YB. [Application of restricted cube spline in cox regression model]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:1169-1173. [PMID: 32842720 DOI: 10.3760/cma.j.cn112150-20200804-01092] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Restricted cubic spline Cox proportional hazard regression model analysis is an important method of epidemiological multivariate survival analysis. By comparing the typical Cox regression model and the restricted cubic spline Cox regression model, this study expounds the limitations of the typical Cox regression model, and explains the basic principles and implementation process of the restricted cubic spline Cox proportional hazard regression model. When the follow-up data does not meet the application conditions of the typical Cox regression model, this method can be used to realize the correlation analysis between continuous exposure and outcomes.
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Affiliation(s)
- Y Wei
- School of Public Health, Jilin University, Changchun 130012, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z W Zhang
- Editorial Department of Chinese Journal of Preventive Medicine, Chinese Medical Journal, Beijing 100052, China
| | - Q Y Tan
- School of Public Health, Jilin University, Changchun 130012, China
| | - M Y Zhang
- School of Public Health, Jilin University, Changchun 130012, China
| | - J Li
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Shi XM. [The critical role of environmental health and disinfection in the prevention and control of COVID-19 pandemic]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:918-922. [PMID: 32388938 DOI: 10.3760/cma.j.cn112150-20200405-00516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In order to combat against COVID-19 pandemic, China has adopted a series of prevention and control measures such as case isolation, close contact tracking management, environmental health improvements, disinfection, and personal protection. At present, China has achieved remarkable results in the control of COVID-19. This article outlines the role of environmental health and disinfection in the prevention and control of COVID-19 and analyzes relevant policies and countermeasures, which has been proved effective and deserved for extensive implementation in this combat. Suggestions are also provided for the further development of this field.
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Affiliation(s)
- X M Shi
- China CDC Key Laboratory of Environment and Population Health National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Shi WY, Guo MH, Du P, Zhang Y, Wang JN, Li TT, Lyu YB, Zhou JH, Duan J, Kang Q, Shi XM. [Association of sleep with anxiety in the elderly aged 60 years and older in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:13-19. [PMID: 32062936 DOI: 10.3760/cma.j.issn.0254-6450.2020.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the relationship of sleep duration and sleep quality with anxiety in the elderly aged 60 years and older in China. Methods: The elderly aged 60 years and older were selected from the China Short-term Health Effects of Air Pollution Study conducted between July 18, 2017 and February 7, 2018. Multivariate logistic regression models were used to analyze the association of sleep duration and sleep quality with anxiety. Results: A total of 3 897 elderly aged 60 years and older were included in the study. The age of the elderly was (73.4±8.0) years old. Among the elderly surveyed, 6.5% were defined with anxiety, and 18.7% reported poor sleep quality. Multivariate logistic regression models showed shorter sleep duration was the risk factor for anxiety in the elderly that after adjusting for factors such as general demographics, socioeconomic factors, lifestyle, health status, social support and ambient fine particulates exposure. Compared with the elderly with 7 hours of sleep duration daily, the OR (95%CI) of anxiety for those with sleep duration ≤ 6 hours was 2.09 (1.49-2.93). Compared with those with good sleep quality, the OR (95%CI) of anxiety for those with poor sleep quality was 5.12 (3.88-6.77). We also found statistically significant correlations of the scores of subscales of Pittsburgh sleep quality index with anxiety, in which the effects of sleep disturbance, subjective sleep quality and daytime dysfunction scores were most obvious, the ORs (95%CI) were 4.63 (3.55-6.04), 2.75 (2.33-3.23) and 2.50 (2.19-2.86), respectively. Subgroup analysis showed that the association of sleep duration and sleep quality with anxiety was more obvious in males and in those aged <80 years. Conclusion: Shorter sleep duration and poor sleep quality are associated with anxiety in the elderly in China.
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Affiliation(s)
- W Y Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M H Guo
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - P Du
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J N Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T T Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Duan
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Q Kang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zhou JH, Wei Y, Lyu YB, Duan J, Kang Q, Wang JN, Shi WY, Yin ZX, Zhao F, Qu YL, Liu L, Liu YC, Cao ZJ, Shi XM. [Prediction of 6-year incidence risk of chronic kidney disease in the elderly aged 65 years and older in 8 longevity areas in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:42-47. [PMID: 32062941 DOI: 10.3760/cma.j.issn.0254-6450.2020.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To establish a prediction model for 6-year incidence risk of chronic kidney disease (CKD) in the elderly aged 65 years and older in China. Methods: In this prospective cohort study, we used the data of 3 742 participants collected during 2008/2009-2014 and during 2012-2017/2018 from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey. Two follow up surveys for renal function were successfully conducted for 1 055 participants without CKD in baseline survey. Lasso method was used for the selection of risk factors. The risk prediction model of CKD was established by using Cox proportional hazards regression models and visualized through nomogram tool. Bootstrap method (1 000 resample) was used for internal validation, and the performance of the model was assessed by C-index and calibration curve. Results: The mean age of participants was (80.8±11.4) years. In 4 797 person years of follow up, CKD was found in 262 participants (24.8%). Age, BMI, sex, education level, marital status, having retirement pension or insurance, hypertension prevalence, blood uric acid, blood urea nitrogen and total cholesterol levels and estimated glomerular filtration rate in baseline survey were used in the model to predict the 6-year incidence risk of CKD in the elderly. The corrected C-index was 0.766, the calibration curve showed good consistence between predicted probability and observed probability in high risk group, but relatively poor consistence in low risk group. Conclusion: The incidence risk prediction model of CKD established in this study has a good performance, and the nomogram can be used as visualization tool to predict the 6-year risk of CKD in the elderly aged 65 years and older in China.
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Affiliation(s)
- J H Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - Y B Lyu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Duan
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Q Kang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - J N Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W Y Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z X Yin
- Division of Non-communicable Disease and Aging Health Management, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - F Zhao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Chen Q, Zhao F, Huang QM, Lyu YB, Zhong WF, Zhou JH, Li ZH, Qu YL, Liu L, Liu YC, Wang JN, Cao ZJ, Wu XB, Shi XM, Mao C. [Effects of estimated glomerular filtration rate on all-cause mortality in the elderly aged 65 years and older in 8 longevity areas in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:36-41. [PMID: 32062940 DOI: 10.3760/cma.j.issn.0254-6450.2020.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the association between estimated glomerular filtration rate (eGFR) and all-cause mortality in the elderly aged 65 years and older in longevity areas in China. Methods: Data used in this study were obtained from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey, 1 802 elderly adults were collected in the study during 2012-2017/2018. In this study, the elderly were classified into 4 groups, moderate-to-severe group [<45 ml·min(-1)·(1.73 m(2))(-1)], mild-to-moderate group [45- ml·min(-1)·(1.73 m(2))(-1)], mild group [60- ml·min(-1)·(1.73 m(2))(-1)] and normal group [≥90 ml·min(-1)·(1.73 m(2))(-1)] according to their eGFR levels. Results: After 6 years of follow-up, 852 participants died, with a mortality rate of 47.3%. Multivariate Cox regression analysis showed that the levels of eGFR were negatively correlated with all-cause mortality risk in the elderly (the HR of elderly was 0.993 and the 95%CI was 0.989-0.997 for every unit of eGFR increased, P=0.001), while compared with the group with normal eGFR, the HRs (95%CI) of the elderly in the moderate-to-severe group, mild-to-moderate group, and mild group were 1.690 (1.224-2.332, P=0.001), 1.312 (0.978-1.758, P=0.070), 1.349 (1.047-1.737, P=0.020) respectively [trend test P<0.001]. Conclusion: The decrease in eGFR was associated with higher mortality risk among the elderly in longevity areas in China.
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Affiliation(s)
- Q Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - F Zhao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q M Huang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y B Lyu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W F Zhong
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - J H Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z H Li
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y L Qu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J N Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X B Wu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
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Kang Q, Lyu YB, Wei Y, Shi WY, Duan J, Zhou JH, Wang JN, Zhao F, Qu YL, Liu L, Liu YC, Cao ZJ, Yu Q, Shi XM. [Influencing factors for depressive symptoms in the elderly aged 65 years and older in 8 longevity areas in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:20-24. [PMID: 32062937 DOI: 10.3760/cma.j.issn.0254-6450.2020.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze influencing factors for depressive symptoms in the elderly aged 65 years and older in 8 longevity areas in China. Methods: We recruited 2 180 participants aged 65 years and older in 8 longevity areas from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey in 2017. Multivariate logistic regression analysis was performed to evaluate the relationships of socio-demographic characteristics, behavioral lifestyle, chronic disease prevalence, functional status, family and social support with depressive symptoms in the elderly. Results: The detection rate of depression symptoms was 15.0% in the elderly aged 65 years and older in 8 longevity areas of China, and the detection rate of depression symptoms was 11.5% in men and 18.5% in women. Multivariate logistic regression analysis results showed that the detection rate of depressive symptoms was lower in the elderly who had regular physical exercises (OR=0.44, 95%CI: 0.26-0.74), frequent fish intakes (OR=0.57, 95%CI: 0.39-0.83), recreational activities (OR=0.65, 95%CI: 0.44-0.96), social activities (OR=0.28, 95%CI: 0.11-0.73) and community services (OR=0.68, 95%CI: 0.50-0.93). The elderly who were lack of sleep (OR=2.04, 95%CI: 1.49-2.80), had visual impairment (OR=1.54, 95%CI: 1.08-2.18), had gastrointestinal ulcer (OR=2.97, 95%CI: 1.53-5.77), had arthritis (OR=2.63, 95%CI: 1.61-4.32), had higher family expenditure than income (OR=1.80, 95%CI: 1.17-2.78) and were in poor economic condition (OR=4.58, 95%CI: 2.48-8.47) had higher detection rate of depressive symptoms. Conclusion: The status of doing physical exercise, fish intake in diet, social activity participation, sleep quality or vision, and the prevalence of gastrointestinal ulcers and arthritis were associated with the detection rate of depressive symptoms in the elderly.
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Affiliation(s)
- Q Kang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - Y B Lyu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - W Y Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Duan
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei 230032, China
| | - J H Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J N Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130021, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Sun B, Cheng YB, Wang XL, Zhang W, Yao XY, Shi XM. [Suggestions on environmental and health work from Health Environment Promotion Campaigns]. Zhonghua Yu Fang Yi Xue Za Zhi 2019; 53:1193-1197. [PMID: 31795573 DOI: 10.3760/cma.j.issn.0253-9624.2019.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The Health Environment Promotion Campaigns (HEPCs) focus on the major environmental health issues and relevant factors of concern among the general public, and promote the achievement of the national health goal. Based on the summary and analysis of the background, key indicators, specific actions in different domains of the HEPCs, this paper proposes suggestions for scientifically implementing HEPCs from five aspects, namely, formulating implementation plans, establishing pilot areas, building comprehensive service platforms, improving the health literacy of residents and strengthening the development of protection technologies and standards.
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Affiliation(s)
- B Sun
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li HM, Yin HJ, Zhang ZH, Wang D, Shi XM, Liang JK, Hao HB, Li ZN. [Determination of samarium oxide and lanthanum oxide in the air of workplace by inductively coupled plasmamass spectrometry]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2019; 37:616-618. [PMID: 31495120 DOI: 10.3760/cma.j.issn.1001-9391.2019.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To establish a method for the determination ofsamarium oxide and lanthanum oxide by inductively coupled plasmamass spectrometryin the air of workplace. Methods: Samarium, lanthanum and their compounds in the air of workplace were collected through microporous filter. The samples were digested by nitricacid and perhydrol (V/V=4∶1) and detected by inductively coupled plasmamass spectrometry. Results: The linear range ofsamarium oxide and lanthanum oxide was 0-50.00 μg/L, Sm(2)O(3): y=0.0119x, r=0.9999; La(2)O(3): y=0.0617x, r=0.9998. The detection limits were less than 0.1 μg/L, and the minimum detection concentration were less than 1.52×10(-5) mg/m(3). The sampling efficiency were 100%, the recovery rates were 95.70%-102.01%, and the precision were 0.78%-1.58%. Conclusion: The indicators established in this study are conformed with the requirements of Chinese Occupational Standars of GBZ/T 210.4-2008, "The Guidelines for the Development of Occupational Hygiene StandarsMehods Part 4: Determination of Chemical Substances in the Air of Workplace".
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Affiliation(s)
- H M Li
- Baotou Medical College, Baotou 014040, China
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Liu BN, Shi XM, Zhong XH, Wang F, Ding J. [Analysis on diagnosis rate of chronic kidney disease in hospitalized pediatric patients]. Zhonghua Er Ke Za Zhi 2019; 57:669-673. [PMID: 31530351 DOI: 10.3760/cma.j.issn.0578-1310.2019.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze diagnosis rate of chronic kidney disease (CKD) in hospitalized pediatric patients in a single center and understand pediatricians' awareness of CKD. Methods: This was a cross-sectional study. Children who were admitted to the Division of Pediatric Nephrology, Peking University First Hospital from January 1, 2008 to December 31, 2017 and met the diagnostic criteria of CKD (kidney disease: improving global outcomes 2012 guideline) were recruited. A total of 4 472 cases were enrolled. Original CKD diagnosis was collected from the home page of medical records. Actual CKD diagnosis was validated and corrected by reviewing medical records and recalculating glomerular filtration rate. The diagnosis rate and influencing factors of pediatric CKD, the distribution and etiology of actual CKD were analyzed. The comparison between groups were performed with χ(2) test. Results: In 4 472 cases, there were 3 470 cases in actual CKD stage 1, among which only 24 cases were in original CKD stage 1. There were 543 cases in actual CKD stage 2-3, among which only 181 cases were in original CKD stage 2-3. Three hundred and one cases were in actual CKD stage 4-5, including 290 cases in original CKD stage 4-5. In addition, there were 43 cases with unknown CKD stage and 115 cases with acute kidney injury. Compared to original CKD diagnosis, the diagnosis rates of CKD stage 1-5 were 0.7% (24/3 470), 16.7% (58/348), 63.1% (123/195), 90.7% (78/86) and 98.6% (212/215), respectively. The proportions of actual CKD stage 1-5 were 80.4% (3 470/4 314), 8.1% (348/4 314), 4.5% (195/4 314), 2.0% (86/4 314) and 5.0% (215/4 314). The etiology of actual CKD included primary glomerular disease (62.2%, 2 686/4 314), secondary glomerular disease (19.7%, 849/4 314), hereditary kidney disease (9.1%, 391/4 314), congenital abnormalities of the kidney and urinary tract (CAKUT) (3.1%, 135/4 314), tubulointerstitial disease (2.2%, 94/4 314) and etiology uncertain (2.1%, 89/4 314). The leading cause of end stage renal disease was etiology uncertain (31.1%, 67/215), followed by hereditary kidney disease (24.2%, 52/215), CAKUT (16.3%, 35/215) and primary glomerular disease (16.3%, 35/215). Conclusions: Among actual CKD hospitalized pediatric patients, the diagnosis rate of CKD given by physicians at discharge was relatively low, especially patients in earlier CKD stages, which reflected serious lack of physicians' awareness of CKD.
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Affiliation(s)
- B N Liu
- Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
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48
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Shi XM, Liu BN, Zhong XH, Wang F, Ding J. [Epidemiology of chronic kidney disease in children]. Zhonghua Er Ke Za Zhi 2019; 57:721-724. [PMID: 31530363 DOI: 10.3760/cma.j.issn.0578-1310.2019.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- X M Shi
- Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
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Lyu YB, Zhou JH, Duan J, Wang JN, Shi WY, Yin ZX, Shi WH, Mao C, Shi XM. [Association of plasma albumin and hypersensitive C-reactive protein with 5-year all-cause mortality among Chinese older adults aged 65 and older from 8 longevity areas in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2019; 53:590-596. [PMID: 31177756 DOI: 10.3760/cma.j.issn.0253-9624.2019.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Objective: To investigate the relationship of plasma albumin and hypersensitive C-reactive protein (Hs-CRP) with 5-year all-cause mortality among Chinese older adults aged 65 and older. Method: Data was collected in 8 longevity areas of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) study conducted by Chinese Center for Disease Control and Prevention and Peking University at baseline survey in 2012 and 2014, the participants enrolled in 2012 was followed-up in 2014 and 2017, the participants enrolled in 2014 was followed-up in 2017 only. Finally, 3 118 older adults aged 65 and older with complete information on albumin, Hs-CRP and body mass index (BMI) were included in this study. Plasma samples of older adults were collected for the detection of albumin and Hs-CRP at baseline survey. Survival status and follow-up time was recorded for all participants. All older adults were divided into 4 groups according to the levels of plasma albumin and Hs-CRP, and Cox proportional hazard models were constructed to assess their influence on the risk of all-cause mortality. Results: Among 3 118 older adults included, the prevalence of hypoalbuminemia was 10.1% (316/3 118), and was 22.8% (711/3 118) for elevated Hs-CRP. During 10 132 person-years of follow-up, 1 212 participants died. Participants with hypoalbuminemia had increased risk of all-cause mortality, with an hazard ratio (HR) and 95% confidential interval (CI) of 1.18 (1.01-1.38), compared to participants with normal plasma albuminemia; participants with elevated Hs-CRP had increased risk of all-cause mortality, with an HR (95%CI) of 1.18 (1.04-1.35), compared to participants with normal plasma Hs-CRP. Participants with normal plasma albumin and elevated Hs-CRP, with hypoalbuminemia and normal Hs-CRP, with hypoalbuminemia and elevated Hs-CRP also had increased risk of all-cause mortality when compared to those with normal plasma albumin and normal Hs-CRP, the HR (95%CI) were 1.16 (1.01-1.34), 1.11 (0.91-1.37) and 1.43 (1.11-1.83), respectively. Conclusion: Hypoalbuminemia and elevated Hs-CRP were responsible for increased risk of 5-year all-cause mortality among Chinese older adults from 8 longevity areas.
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Affiliation(s)
- Y B Lyu
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Duan
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - J N Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W Y Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z X Yin
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - W H Shi
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - C Mao
- School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X M Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li YY, Chen SW, Zhao F, Zhang HM, Zhang WL, Qu YL, Liu YC, Gu H, Cai JY, Cao ZJ, Shi XM. [Association of arsenic with unexplained recurrent spontaneous abortion: a case-control study]. Zhonghua Yu Fang Yi Xue Za Zhi 2019; 53:470-474. [PMID: 31091603 DOI: 10.3760/cma.j.issn.0253-9624.2019.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the association of arsenic with unexplained recurrent spontaneous abortion (URSA). Methods: A case-control study was conducted to select URSA patients who were admitted to the Beijing Maternal and Child Health Care Hospital affiliated to Capital Medical University from April to October 2018 as a case group. Women who had a normal pregnancy in the Family Planning Department of the hospital but volunteered to have an abortion were selected as a control group. The case and control group were paired in a 1: 1 ratio. The inclusion criteria of the case group were patients with newly diagnosed recurrent spontaneous abortion who had clinically confirmed more than 2 spontaneous abortions and had 20 weeks prior to pregnancy, excluding patients with recurrent spontaneous abortion caused by abnormal blood coagulation (anti-phospholipid antibody positive), abnormal physiological anatomy (B-ultrasound), abnormal immune factors (anti-nuclear antibody positive, anti-cardiolipin antibody, etc.), genetic chromosomal abnormalities (karyotype analysis) and pathogenic microbial infection. The control group was matched according to the age of the case group (±3 years old) and the gestational age (±2 weeks) to exclude adverse pregnancy outcomes such as stillbirth, congenital malformation, premature delivery and low birth weight infants. A total of 192 subjects were included. Questionnaires were used to collect information of all subjects, and 12 ml of peripheral venous blood was collected to detect blood arsenic levels. Blood arsenic levels were divided into low concentration group (<1.00 μg/L), medium concentration group (1.00-1.50 μg/L) and high concentration group (>1.50 μg/L). The multivariate conditional logistic regression was performed to analyze the relationship between blood arsenic exposure and URSA and explore the influencing factors of blood Arsenic. Results: The geometric mean values of blood arsenic level in the cases group and control group were 1.68 (1.50-1.86) μg/L and 1.26 (1.17-1.37) μg/L, respectively. The blood arsenic level in the case group was significantly higher than that in the control group (P<0.05). The results of multivariate conditional logistic regression analysis showed that after adjusting for tobacco exposure during pregnancy, pre-pregnancy body mass index and the effects of residential decoration in past five years, the risk of URSA was higher in the high-concentration group compared with the low-concentration group (OR=2.56, 95%CI:1.06-6.24). Conclusion: Blood arsenic may increase the risk of URSA in women of childbearing age.
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Affiliation(s)
- Y Y Li
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - S W Chen
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - F Zhao
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - H M Zhang
- Department of Environment and Health, Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - W L Zhang
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Y L Qu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Y C Liu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - H Gu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - J Y Cai
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Z J Cao
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - X M Shi
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
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