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Zhou JH, Zhang YL, Li LF, Lu PL. [Correlation between prognostic nutritional index and pleural thickness with survival time of epithelial malignant pleural mesothelioma patients]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2024; 42:118-123. [PMID: 38403420 DOI: 10.3760/cma.j.cn121094-20230106-00011] [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: 02/27/2024]
Abstract
Objective: To explore the role of prognostic nutritional index (PNI) and pleural thickness in the prognostic evaluation of patients with epithelial malignant pleural mesothelioma (MPM) . Methods: In April 2022, a retrospective analysis was conducted on the data and laboratory data of 41 patients with epithelial MPM admitted to the cardiothoracic surgery department of Chuxiong Yi Autonomous Prefecture People's Hospital from January 2018 to May 2021. Univariate and multivariate analysis were used to evaluate the relationships between total survival time, clinical characteristics, PNI and pleural thickness in patients. Results: The 41 patients were mostly male (26 cases, 63.4%) , with a median age of 55 years old. The main clinical manifestations were chest pain (53.7%) , bloody pleural effusion (75.6%) , and chest pain combined with bloody pleural effusion (36.6%) . The median survival time of patients with different TNM stage, efficacy after 4 cycles of chemotherapy, PNI, maximum pleural thickness after chemotherapy (post max) , sum of post max in 3 zones after chemotherapy (post sum) were statistically different (χ(2)=3.89, 14.51, 15.33, 4.33, 12.05, P<0.05) . Compared with patients with high PNI and post sum<32.26 mm, MPM patients with low PNI and post sum≥32.26 mm have higher risk of death, and the differences were statistically significant (HR=1.52, 95%CI: 1.75-11.93, P=0.002; HR=1.70, 95%CI: 1.84-16.23, P=0.002) . Conclusion: PNI and post sum can be used to predict the prognosis of patients with epithelial MPM.
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Affiliation(s)
- J H Zhou
- Department of Cardiothoracic Surgery, Chuxiong Yi Autonomous Prefecture People's Hospital, Yunnan Province, Chuxiong 675000, China
| | - Y L Zhang
- Anesthesia Department 1, Chuxiong Yi Autonomous Prefecture People's Hospital, Yunnan Province, Chuxiong 675000, China
| | - L F Li
- Department of Cardiothoracic Surgery, Chuxiong Yi Autonomous Prefecture People's Hospital, Yunnan Province, Chuxiong 675000, China
| | - P L Lu
- Department of Cardiothoracic Surgery, Chuxiong Yi Autonomous Prefecture People's Hospital, Yunnan Province, Chuxiong 675000, China
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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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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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Zhu L, Guo ZL, Zhao DD, Sa RL, Zhao GY, Zhang Y, Qiu LR, Zhou JH, Li WJ, Guo H, Shen YY, Li XZ, Chen ZS, Chen G. [Efficacy and prognosis of infant kidney transplantation]. Zhonghua Yi Xue Za Zhi 2023; 103:3010-3016. [PMID: 37587680 DOI: 10.3760/cma.j.cn112137-20230306-00338] [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: 08/18/2023]
Abstract
Objective: To analyze the effect and prognosis of infant kidney transplantation. Methods: Clinical data of 37 cases of infant kidney transplantation under 3 years old in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from June 1, 2017 to July 31, 2022 were retrospectively collected. These 37 cases included 31 primary kidney transplantation and 6 secondary kidney transplantation. Kaplan-Meier method was used to draw the survival curve of the transplanted kidney and the recipient, and the prognosis and complications were analyzed. Median follow-up was 18 months (range: 6-66 months). Results: The recipients were 20 males and 17 females, with a median age of 16 months (range: 2 months, 26 days to 36 months) and a median weight of 8 kg (range: 3.2 to 14.0 kg). The youngest child was only 2 months, 26 days old, and weighed only 3.2 kg. The most common primary disease of recipients was congenital nephrotic syndrome (13 cases, 41.9%). Intra-abdominal transplantation occurred in 19 cases (51.3%) and intra-iliac fossa transplantation occurred in the remaining 18 cases (48.6%). Postoperative renal function recovery was delayed in 7 cases (18.9%), and thrombosis caused renal function loss in 5 cases (13.5%), of which 4 cases received second renal transplantation and were successful. During the follow-up period, there were 11 cases of acute rejection (29.7%) and 6 cases of CMV pneumonia (16.2%). The estimated glomerular filtration rate 1 year after transplantation was higher than that 1 month after surgery [(101.9±22.1) vs (71.1±25.6) ml/(min·1.73m2), P<0.001], and remained constant 2 years after transplantation. Both the 1-year and 2-year survival rates of the transplanted kidney were 85.3%, and both the 1-year and 2-year survival rates of the recipients were 96.8%. Conclusion: Although the implementation of infant kidney transplantation is difficult, it can still achieve relatively satisfactory efficacy and prognosis.
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Affiliation(s)
- L Zhu
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, China
| | - Z L Guo
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - D D Zhao
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, China
| | - R L Sa
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - G Y Zhao
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, China
| | - Y Zhang
- Department of Pediatrics, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - L R Qiu
- Department of Pediatrics, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - J H Zhou
- Department of Pediatrics, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - W J Li
- Department of Pharmacy, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - H Guo
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, China
| | - Y Y Shen
- Department of Renal Immunology Affiliated to Children's Hospital of Soochow University, Suzhou 215000, China
| | - X Z Li
- Department of Renal Immunology Affiliated to Children's Hospital of Soochow University, Suzhou 215000, China
| | - Z S Chen
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, China
| | - G Chen
- Institute of Organ Transplantation, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China The Key Laboratory of Organ Transplantation, the Ministry of Education, the Key Laboratory of Organ Transplantation, National Health Commission, the Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan 430030, 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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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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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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Liu JX, Wei Y, Zhou JH, Wang J, Song HC, Li XW, Xiang CZ, Xu YB, Ding C, Zhong ZY, Zhang Z, Luo YF, Zhao F, Chen C, Pi JB. [Association of hs-CRP with frailty and its components among the elderly over 65 years old in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:626-633. [PMID: 37165809 DOI: 10.3760/cma.j.cn112150-20221202-01171] [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 the levels of high sensitivity C-reactive protein (hs-CRP) with frailty and its components among the elderly over 65 years old in 9 longevity areas of China. Methods: Cross-sectional data from the Health Ageing and Biomarkers Cohort Study (HABCS, 2017-2018) were used and the elderly over 65 years old were included in this study. Through questionnaire interview and physical examination, the information including demographic characteristics, behavior, diet, daily activity, cognitive function, and health status was collected. The association between hs-CRP and frailty and its components in the participants was analyzed by multivariate logistic regression model and restrictive cubic spline. Results: A total of 2 453 participants were finally included, the age was (84.8±19.8) years old. The median hs-CRP level was 1.13 mg/L and the prevalence of frailty was 24.4%. Compared with the low-level group (hs-CRP<1.0 mg/L), the OR (95%CI) value of the high-level group (hs-CRP>3.0 mg/L) was 1.79 (1.35-2.36) mg/L. As for the components, the hs-CRP level was also positively associated with ADL disability, IADL disability, functional limitation and multimorbidity. After adjusting for confounding factors, compared with the low-level group, the OR (95%CI) values of the high-level group for the four components were 1.68 (1.25-2.27), 1.88 (1.42-2.50), 1.68 (1.31-2.14) and 1.39 (1.12-1.72), respectively. Conclusion: There is a positive association between the levels of hs-CRP and the risk of frailty among the elderly over 65 years old in 9 longevity areas of China. The higher hs-CRP level may increase the risk of frailty by elevating the risk of four physical functional disabilities, namely ADL disability, IADL disability, functional limitation and multimorbidity.
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Affiliation(s)
- 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 School of Public Health, China Medical University, Shenyang 110001, 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 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
| | - 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 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 Z Xiang
- 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 110001, China
| | - Y B 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, China Medical University, Shenyang 110001, 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
| | - Z Y Zhong
- School of Public Health, China Medical University, Shenyang 110001, 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
| | - 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
| | - 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
| | - J B Pi
- School of Public Health, China Medical University, Shenyang 110001, China
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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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Ye LL, Zhou JH, Tian YL, Liu SX, Liu JX, Ye JM, Cui J, Chen C, Wang J, Wu YQ, Qiu Y, Wei B, Qiu YD, Zheng XL, Qi L, Lv YB, Zhang J. [Association of greenness exposure with waist circumference and central obesity in Chinese adults aged 65 years and over]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:86-92. [PMID: 36854442 DOI: 10.3760/cma.j.cn112150-20221117-01118] [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: 03/02/2023]
Abstract
Objective: To examine the association of greenness exposure with waist circumference (WC) and central obesity in older adults in China. Methods: Based on the cross-sectional data from the Chinese Longitudinal Healthy Longevity Survey in 2017-2018, 14 056 participants aged 65 years and over were included. Demographic characteristics, lifestyle, WC, and other information were collected through a questionnaire and physical examination. Based on the satellite monitoring data of moderate-resolution imaging spectroradiometer (MODIS) provided by NASA, the annual mean of normalized difference vegetation index (NDVI) within a radius of 1 000 meters was obtained as the measurement value of greenness exposure. Multivariate linear regression model, multivariate logistic regression model, and restricted cubic splines (RCS) model were used to analyze the association and dose-response relationship between greenness exposure and WC and central obesity in older adults in China. Results: A total of 14 056 participants were enrolled with a median age of 84.0 years [IQR: 75.0-94.0 years]. About 45.0% (6 330) of them were male and 48.6% (5 853) were illiterate. There were 10 964 (78.0%) participants from rural. The mean of WC was (84.4±10.8) cm. Central obesity accounted for 60.2% (8 465), and the NDVI range was (-0.06, 0.78). After adjusting for confounding factors, the multivariate linear regression model showed that the change value of WC in the urban group [β (95%CI):-0.49 (-0.93, -0.06)] was smaller than that in the rural [-0.78 (-0.98, -0.58)] for every 0.1 unit increase in NDVI (Pinteraction=0.022). Compared with the Q1 group in NDVI, WC of Q2 and Q3 groups in rural decreased, and the β (95%CI) values were-1.74 (-2.5, -0.98) and-2.78 (-3.55, -2.00), respectively. The multivariate logistic regression model showed that after adjusting for confounding factors, the risk of central obesity decreased for urban and rural older adults with an increase of 0.1 unit in NDVI, and the OR (95%CI) values were 0.87 (0.80, 0.95) and 0.86 (0.82, 0.89), respectively (Pinteraction=0.284). Compared with the Q1 group in NDVI, the risk of central obesity in the Q2 and Q3 groups in rural was lower, and the OR (95%CI) values were 0.68 (0.58, 0.80) and 0.57 (0.49, 0.68), respectively. The results of the multivariate regression model with RCS showed that there was a non-linear association of NDVI with WC (Pnonlinear=0.006) and central obesity (Pnonlinear=0.025). Conclusion: Greenness exposure is negatively associated with WC and central obesity in older adults in China.
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Affiliation(s)
- L L Ye
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, 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 L Tian
- Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, 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
| | - 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
| | - 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
| | - J Cui
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, 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
| | - 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
| | - Y Q 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 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
| | - B 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
| | - 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
| | - 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
| | - Y B Lv
- 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 Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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Ju AP, Zhou JH, Gu H, Ye LL, Chen C, Guo YB, Wang J, Zhang ZW, Qu YL, Liu Y, Liu L, Xue K, Zhao F, Lyu YB, Ye L, Shi X. [Association of body mass index and waist circumference with frailty among people aged 80 years and older in Chinese]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1584-1590. [PMID: 36372748 DOI: 10.3760/cma.j.cn112150-20211228-01196] [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 examine the association of body mass index (BMI) and waist circumference (WC) with frailty among oldest-old adults in China. Methods: A total of 7 987 people aged 80 years and older (oldest-old) who participated in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2017-2018 were included. Information on demographic characteristics, behavior pattern, diet, activities of daily living, cognitive function, health status, disease condition were collected by questionnaire and physical examination. Generalized linear mixed model and restricted cubic splines (RCS) were used to analyze the association of BMI and WC with frailty. Results: The mean age of all participants was 91.7 years, and their mean BMI and WC were (21.3±3.5) kg/m2 and (82.9±10.5) cm, respectively. The proportion of male was 42.3% (3 377/7 987), and the proportion of people with frailty was 33.7% (2 664/7 987). After controlling confounding factors, compared with T2 (19.1-22.1 kg/m2) of BMI, the OR (95%CI) of the female T1 (<19.1 kg/m2) and T3 (≥22.2 kg/m2) group was 1.39 (1.17-1.65) and 1.27 (1.07-1.52), respectively. Compared with T2 (77-85 cm) of WC, the OR (95%CI) of female T1 (<77 cm) and T3 (≥86 cm) group was 1.20 (1.01-1.42) and 1.10 (0.93-1.31), respectively. The results of multiple linear regression model with restrictive cubic spline showed that there was a non-linear association of BMI and WC with frailty in female. Conclusion: There is a U-shaped association of BMI and WC with frailty in female participants.
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Affiliation(s)
- 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 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
| | - 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
| | - 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 Science and Peking Union Medical College, Beijing 100730, 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 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 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 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 Editorial Department of Chinese Journal of Preventive Medicine, Chinese Medical Journal, Beijing 100052, 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 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
| | - L 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
| | - 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 School of Public Health, Jilin University, Changchun 130012, 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
| | - Lin Ye
- School of Public Health, Jilin University, Changchun 130012, China
| | - Xiaoming 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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Song J, Dong Y, Du CH, Zhang ZY, Shen MF, Zhang Y, Zhou JH, Li SZ. [Measurement of morphological features of Oncomelania hupensis shells in Yunnan Province]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2022; 34:341-351. [PMID: 36116923 DOI: 10.16250/j.32.1374.2022067] [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: 06/15/2023]
Abstract
OBJECTIVE To investigate the morphological variation of Oncomelania hupensis shells in Yunnan Province, so as to provide insights into the understanding of O. hupensis genetic evolution and control. METHODS According to the O. hupensis density, geographical location, altitude, water system and environmental type, 12 administrative villages were sampled from 10 schistosomiasis-endemic counties (districts) in 3 prefectures (cities) of Yunnan Province as snail collection sites. From December 2021 to January 2022, about 200 snails were collected from each collection site, among which thirty adult snails (6 to 7 spirals) were randomly selected from each site, and the 11 morphological indexes of snail shells were measured and subjected to cluster analysis and principal component analysis. RESULTS Of O. hupensis snails from 12 localities of Yunnan Province, the longest shell (7.33 mm) was detected in snails from Yongle Village, Eryuan County, with the shortest (4.68 mm) in Dongyuan Village, Gucheng District, and the largest angle of apex (59.47°) was measured in snails from Caizhuang Village, Midu County, with the smallest (41.40°) in Qiandian Village, Eryuan County. The mean coefficient of variation was 9.075% among O. hupensis snails from 12 localities of Yunnan Province, with the largest coefficient of variation seen in the thickness of the labra brim (29.809%). Among O. hupensis snails from 12 localities of Yunnan Province, the mean Euclidean distance was 2.26, with the shortest Euclidean distance seen between O. hupensis snails from Qiandian Village of Eryuan County and Wuxing Village of Dali City (0.26), and the largest found between O. hupensis snails from Caizhuang Village of Midu County and Cangling Village of Chuxiong County (8.17). Cluster analysis and principal component analysis classified O. hupensis snails from 12 localities of Yunnan Province into three categories, including the O. hupensis snail samples from Caizhuang Village of Midu County, O. hupensis snail samples from Cangling Village of Chuxiong County, and O. hupensis snail samples from Qiandian Village of Eryuan County, Wuxing Village of Dali City, Yangwu Village of Yongsheng County, Xiaoqiao Village of Xiangyun County, Yongle Village of Eryuan County, Xiaocen Village of Dali City, Anding Village of Nanjian County, Dongyuan Village of Gucheng District, Lianyi Village of Heqing County, and Dianzhong Village of Weishan County. The variations in these three categories of snail samples were mainly measured in the principal component 2 related to the angle of apex and the thickness of the labra brim. CONCLUSIONS The variations in the Euclidean distance and morphological features of shells of O. hupensis from 12 localities of Yunnan Province gradually rise with the decrease in the latitude of the collection sites. The angle of apex is an indicator for the growth of O. hupensis whorl.
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Affiliation(s)
- J Song
- School of Public Health, Dali University, Dali, Yunnan 671000, China
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - Y Dong
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - C H Du
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - Z Y Zhang
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - M F Shen
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - Y Zhang
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - J H Zhou
- Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, Yunnan 671000, China
| | - S Z Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai 200025, China
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Ma FX, Ren P, Cao J, Bian YQ, Zhou JH, Zhao CY. [Clinical application of three-dimensional printed preformed titanium mesh combined with free latissimus dorsi muscle flap in the treatment of squamous cell carcinoma with skull defect in the vertex]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2022; 38:341-346. [PMID: 35462512 DOI: 10.3760/cma.j.cn501120-20201221-00538] [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 explore the clinical effects of three-dimensional printed preformed titanium mesh combined with latissimus dorsi muscle flap free transplantation in the treatment of wounds with skull defect after radical surgery of squamous cell carcinoma in the vertex. Methods: A retrospective observational study was conducted. From January 2010 to December 2019, 5 patients with squamous cell carcinoma in the vertex accompanied with skull invasion who met the inclusion criteria were admitted to the Department of Burns and Plastic Surgery of the Second Affiliated Hospital of Air Force Medical University, including four males and one female, aged 50 to 65 years. The original lesion areas ranged from 5 cm×4 cm to 15 cm×8 cm. The titanium mesh was prefabricated via three-dimensional technic based on the result the scope of skull resection predicted with computerized tomography three-dimensional reconstruction before surgery. During the first stage, the soft tissue defect area of scalp (8 cm×7 cm to 18 cm×11 cm) after tumor enlargement resection was repaired with the preformed titanium mesh, and the titanium mesh was covered with latissimus dorsi muscle flap, with area of 10 cm×9 cm to 20 cm×13 cm. The thoracodorsal artery/vein was anastomosed with the superficial temporal artery/vein on one side. The muscle ends in the donor site were sutured together or performed with transfixion, and then the skin on the back were covered back to the donor site. On the 10th day after the first-stage surgery, the second-stage surgery was performed. The thin intermediate thickness skin graft was taken from the anterolateral thigh to cover the latissimus dorsi muscle flap. The duration and intraoperative blood loss of first-stage surgery were recorded. The postoperative muscle flap survival after the first-stage surgery and skin graft survival after the second-stage surgery was observed. The occurrence of complications, head appearance, and recurrence of tumor were followed up. Results: The average first-stage surgery duration of patients was 12.1 h, and the intraoperative blood loss was not more than 1 200 mL. The muscle flaps in the first-stage surgery and the skin grafts in the second-stage surgery all survived well. During the follow-up of 6-18 months, no complications such as exposure of titanium mesh or infection occurred, with good shape in the recipient sites in the vertex, and no recurrence of tumor. Conclusions: Three-dimensional printed preformed titanium mesh combined with latissimus dorsi muscle flap free transplantation and intermediate thickness skin graft cover is an effective and reliable method for repairing the wound with skull defect after extended resection of squamous cell carcinoma in the vertex. This method can cover the wound effectively as well as promote both recipient and donor sites to obtain good function and appearance.
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Affiliation(s)
- F X Ma
- Department of Burns and Plastic Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
| | - P Ren
- Department of Burns and Plastic Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
| | - J Cao
- Department of Burns and Plastic Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
| | - Y Q Bian
- Department of Burns and Plastic Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
| | - J H Zhou
- Department of Neurosurgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
| | - C Y Zhao
- Department of Burns and Plastic Surgery, the Second Affiliated Hospital of Air Force Medical University, Xi'an 710038, China
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14
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 400] [Impact Index Per Article: 200.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Le Y, Wang YY, Peng QZ, Wang BS, Huang B, Zhou JH, Jia GJ, Zhou Y, Xue M. [Langerhans cell histiocytosis involving pituitary and thyroid gland: a case report]. Zhonghua Nei Ke Za Zhi 2022; 61:327-330. [PMID: 35263977 DOI: 10.3760/cma.j.cn112138-20210601-00388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Y Le
- Department of Endocrinology & Metabolism, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - Y Y Wang
- Department of Thyroid & Parathyroid Surgery, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - Q Z Peng
- Department of Pathology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - B S Wang
- Library of Department of Scientific Research, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - B Huang
- Department of Endocrinology & Metabolism, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - J H Zhou
- Department of Hematology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - G J Jia
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - Y Zhou
- Department of Endocrinology & Metabolism, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
| | - M Xue
- Department of Endocrinology & Metabolism, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; the First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, China
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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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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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19
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Zhou JH. [The prospective on molecular diagnostics of primary mediastinal B-cell lymphoma and its clinical implications]. Zhonghua Bing Li Xue Za Zhi 2022; 51:77-81. [PMID: 34979764 DOI: 10.3760/cma.j.cn112151-20210525-00380] [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: 06/14/2023]
Affiliation(s)
- J H Zhou
- Department of Pathology and Laboratory Medicine,Indiana University, School of Medicine,Indiana, Indianapolis, IN 46202, U S A
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Li Q, Chen SQ, Huang HZ, Liu LW, Chen WH, Zhou JH, Tan N, Liu J, Liu Y. Association between recovered acute kidney injury within 48hours and mortality in patients following coronary angiography: a cohort study. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
The association of recovered acute kidney injury (AKI) with mortality was controversial. Our study aims to investigate the impact of recovered AKI on mortality in patients following coronary angiography (CAG).
Methods
Our study retrospectively enrolled 3,970 patients with pre-operative serum p creatinine (Scr) and twice measurements within 48hours after procedure. Recovered AKI defined as the diagnosis of AKI (Scr >0.3 mg/dL or >50% from the baseline level) on day 1 when Scr failed to meet the criteria for AKI on the day 2. Maintained AKI was defined as AKI not meeting the definition for recovered AKI. The primary outcome was 1-year all-cause mortality. Multivariable logistic regression was used to assess the association between recovered AKI and 1-year mortality.
Results
Among 3,970 participants, 861 (21.7%) occurred AKI, of whom 128 (14.9%) was recovered AKI and 733 (85.1%) was maintained AKI. 312 (7.9%) patients died within 1-year after admission. After multivariable analysis, recovered AKI was not associated with higher 1-year mortality (adjusted odds ratio [aOR], 1.37; CI, 0.68–2.51) compared without AKI. Among AKI patients, Recovered AKI was associated with a 52% lower 1-year mortality compared with maintained AKI. Additionally, maintained AKI was significantly associated with higher 1-year mortality (aOR, 2.67; CI, 2.05–3.47).
Conclusions
Our data suggested that recovered AKI within 48h was a common subtype of AKI following CAG, without increasing mortality. More attention need to be paid to the patients suffering from maintained AKI following CAG.
Funding Acknowledgement
Type of funding sources: None. Association of AKI and mortalitySubgroups analysis
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Affiliation(s)
- Q Li
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - S Q Chen
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - H Z Huang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - L W Liu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - W H Chen
- Longyan First Affiliated Hospital of Fujian Medical University, Department of Cardiology, Longyan, China
| | - J H Zhou
- Guangdong Pharmaceutical University, Guangzhou, China
| | - N Tan
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - J Liu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
| | - Y Liu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Department of Cardiology, Guangzhou, China
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Qiao YS, Zhou JH, Jin BH, Wu YQ, Zhao B. LINC00483 is regulated by IGF2BP1 and participates in the progression of breast cancer. Eur Rev Med Pharmacol Sci 2021; 25:1379-1386. [PMID: 33629308 DOI: 10.26355/eurrev_202102_24846] [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: 11/12/2022]
Abstract
OBJECTIVE To explore the role of long intergenic non-coding ribonucleic acid 483 (LINC00483) in the development of breast cancer (BC) and its possible mechanism of action. PATIENTS AND METHODS LINC00483 expression level in BC tissues and cell lines was detected via quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR). The association between LINC00483 expression and survival rate of BC patients was analyzed using Kaplan-Meier survival analysis. The binding relation between LINC00483 and insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was verified via RNA immunoprecipitation (RIP) and RNA pull-down assays. The expression of IGF2BP1 in BC patients was determined using qRT-PCR. Moreover, the role of LINC00483 on the proliferative ability of BC cells was detected via cell counting kit-8 (CCK8) and 5-Ethynyl-2'-deoxyuridine (EdU) assays. Whether LINC00483 exerts its effects under the regulation of IGF2BP1 was verified via reversal assay. RESULTS The results of qRT-PCR showed that LINC00483 had a significantly high expression in BC tissues and corresponding cell lines, and it rose with the increase in tumor stage, which was higher in patients with metastasis. CCK8/EdU assay revealed that the proliferative ability of MDA-MB-231 and MCF-7 cell lines was enhanced by overexpression of LINC00483. It was confirmed by RIP and pull-down assays that IGF2BP1 could bind to LINC00483, and the expression of LINC00483 was significantly promoted after up-regulation of IGF2BP1. It was found via qRT-PCR that the expression of IGF2BP1 evidently rose in BC patients, which was positively related with the expression level of LINC00483. The results of reversal assay manifested that the function of LINC00483 on cell proliferation was regulated by IGF2BP1. CONCLUSIONS LINC00483 has a significantly higher expression in BC tissues than that in para-carcinoma tissues, and its effect of promoting proliferation of BC cells may be regulated by IGF2BP1.
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Affiliation(s)
- Y-S Qiao
- Second Department of Oncology, Chifeng Municipal Hospital, Chifeng, China.
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22
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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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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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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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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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Li XY, Liu YR, Zhou JH, Li W, Guo HH, Ma HP. Enhanced expression of circular RNA hsa_circ_000984 promotes cells proliferation and metastasis in non-small cell lung cancer by modulating Wnt/β-catenin pathway. Eur Rev Med Pharmacol Sci 2020; 23:3366-3374. [PMID: 31081091 DOI: 10.26355/eurrev_201904_17700] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In recent years, several circular RNAs (circRNAs) have been identified to play important roles in human cancers. However, the exact effects and molecular mechanisms of circRNAs in non-small cell lung cancer (NSCLC) progression are still unknown. In this study, we aimed to investigate the effect of circular hsa_circ_000984 on NSCLC. PATIENTS AND METHODS Quantitative real-time PCR was performed to detect the expression levels of hsa_circ_000984 in NSCLC tissues and cell lines. The associations between the expression of hsa_circ_000984 and clinicopathological parameters and patients' survival were analyzed. The cell growth was detected by CCK-8 assay and colony formation assay. Cell migration and invasion assays were used to study the changes in cell migration and invasion capacity. Western blot was performed to analyze the possible relationship between hsa_circ_000984 and the genes downstream of the Wnt/β-catenin pathway. RESULTS We found that hsa_circ_000984 was highly expressed in NSCLC tissues and cell lines and correlated with advanced TNM stage and lymph nodes metastasis. The clinical assays indicated that patients with high hsa_circ_000984 expression had shorter overall survival and disease-free survival. The functional investigations showed that the knockdown of hsa_circ_000984 suppressed NSCLC cells proliferation, migration, invasion, and EMT. Moreover, hsa_circ_000984 displayed its oncogenic roles by modulating the activation of Wnt/β-catenin pathway, which was demonstrated by measuring the expression levels of, β-catenin, c-myc, and cyclin D1. CONCLUSIONS Our findings may facilitate a better understanding of hsa_circ_000984, and it might be a potential prognostic biomarker and treatment target for NSCLC patients.
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Affiliation(s)
- X-Y Li
- Department of Respiratory Medicine, Department of Ear-Nose-Throat (ENT); Jining No. 1 People's Hospital, Jining Medical University, Jining, Shandong, China.
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Zhou JH, Tan QY. [The role of the traditional surgical procedures for gastroesophageal reflux disease cannot be ignored]. Zhonghua Wai Ke Za Zhi 2020; 58:672-676. [PMID: 32878412 DOI: 10.3760/cma.j.cn112139-20200224-00125] [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
Gastroesophageal reflux disease (GERD) is a common digestive disease with characteristics of a multitude of pathogenesis, a variety of clinical manifestations and a strong negative impact on physical and mental health of the patients. GERD is classified into non-erosive reflux disease and reflux esophagitis in terms of absence or presence of mucosal damage at endoscopic findings. Proton pump inhibitors (PPI) are widely used in the treatment of GERD, especially for patients with non-erosive reflux disease or mild reflux esophagitis. However, PPI do not affect pathophysiologic mechanisms of GERD or reduce the number of reflux events. When PPI fails to adequately control the symptoms of GERD as a result of gastroesophagel junction structural defects, the antireflux surgical procedures are indicated to create a mechanical barrier to reflux. The laparoscopic fundoplication remains the most commonly performed and is the current "gold-standard" anti-reflux procedure. The outcomes of the antireflux surgical procedures are superior to medical therapy for GERD in light of subjective symptoms, objective examinations, quality of life and patient satisfaction. As of now, enough attention has not been paid to the traditional surgical procedures of GERD in China. It is controversial about which is optimal among the three major types of procedures, selection should be tailored to classification, mechanism, age, mental status and esophageal motility. GERD is a chronic disease and either medical or surgical therapy may put the patient at different risk, therefore the patient's preferences should be considered adequately before choosing the treatment protocols.
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Affiliation(s)
- J H Zhou
- Department of Thoracic Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Q Y Tan
- Department of Thoracic Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
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Zhong ZJ, Xu JF, Li ZZ, Zhou WY, Chen XX, Zhou JH, Li ZY. Regulation of HBV replication and gene expression by miR-501-3p via targeting ZEB2 in hepatocellular carcinoma. Neoplasma 2020; 67:735-742. [PMID: 32386477 DOI: 10.4149/neo_2020_190625n549] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/18/2019] [Indexed: 11/08/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.
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Affiliation(s)
- Z J Zhong
- Department of Clinical Lab, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - J F Xu
- Department of Clinical Lab, The Zhuhai Hospital of Guangdong Province Traditional Chinese Medical Hospital, Zhuhai, China
| | - Z Z Li
- Department of Pathology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - W Y Zhou
- Department of Centeral Laboratory, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - X X Chen
- Department of Medical Record Management, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - J H Zhou
- Department of Clinical Lab, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Z Y Li
- Department of Pathology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
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Li ZY, Li ZZ, Zhou JH, Zhong ZJ, Wang XJ, Zhong L, Zhou WY. WITHDRAWN: LncRNA-LINC00261 suppresses the progression of NSCLC cells through upregulating miR-19a-mediated Kruppel-like factor 2 (KLF2). Neoplasma 2020:190706N600. [PMID: 32305053 DOI: 10.4149/neo_2020_190706n600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/13/2019] [Indexed: 11/08/2022]
Abstract
Ahead of Print article withdrawn by publisher.
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32
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Wang KM, Jiang SF, Zhang ZH, Ye QQ, Zhang YC, Zhou JH, Hong QK, Yu JM, Wang HY. Impact of static biocarriers on the microbial community, nitrogen removal and membrane fouling in submerged membrane bioreactor at different COD:N ratios. Bioresour Technol 2020; 301:122798. [PMID: 31981907 DOI: 10.1016/j.biortech.2020.122798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/08/2020] [Accepted: 01/10/2020] [Indexed: 06/10/2023]
Abstract
The polyvinyl formal (PVFM) biocarrier addition in a membrane bioreactor (MBR) was evaluated at high and low carbon/nitrogen (C/N) ratio of 20.0 and 6.7. Results indicated that static biocarrier addition could enrich nitrification and denitrification bacteria, dominating by Tauera, Amaricoccus and Nitrosospira at the genus level and slightly improved the total nitrogen removal even at a low C/N ratio. The bulk sludge characteristics (such as bigger particle size, lower SMP, lower SMP P/C) were also significantly changed in the hybrid MBR (HMBR), leading to a more sustainable membrane operation. The biocarrier addition also reduced the relative abundance of Sphingobacterials_unclassified, Ohtaekwangia and Rhodocyclaceae_unclassified at the genus level, indicating less membrane fouling in the HMBR. Consequently, HMBR with static PVFM addition could partially overcome the drawback of low C/N ratio for total nitrogen removal and membrane fouling control, providing a more resilient MBR to the undesirable environment such as low C/N ratio.
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Affiliation(s)
- K M Wang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - S F Jiang
- College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310014, China
| | - Z H Zhang
- College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310014, China
| | - Q Q Ye
- College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310014, China
| | - Y C Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - J H Zhou
- College of Civil Engineering and Architecture, Zhejiang University of Technology, Hangzhou 310014, China
| | - Q K Hong
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - J M Yu
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
| | - H Y Wang
- College of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
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Zhou JH, Xing D, Ma HM, Zhao Y, Zhao YH, Wei HQ. [Experimental study on the effect of olfactory training on olfactory function in mice with olfactory dysfunction]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2020; 55:154-158. [PMID: 32074755 DOI: 10.3760/cma.j.issn.1673-0860.2020.02.013] [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 observe the effect of olfactory training on mice with olfactory dysfunction induced by 3-methylindole (3-MI). Methods: Thirty-one male BALB/c mice were randomly divided into 3 groups by random digits table: control group (group A, n=10), olfactory dysfunction group (group B, n=10) and olfactory dysfunction+olfactory training group (group C, n=11). Mice in group B and group C were intraperitoneally injected with 150 mg/kg 3-MI to induce olfactory dysfunction model, while mice in group A were intraperitoneally injected with corn oil of the same volume. From the first day after injection, mice in group C were treated with 4 kinds of odors by inhalation, while mice in group B were treated with distilled water by inhalation, with 2 times/d, 30 min/time/kind of odor, and continuous training for 28 d. Group A was not treated. Buried food pellet tests were conducted before injection and at 7, 14, 21 and 28 days after injection, respectively. The olfactory epithelium was harvested for observation of the number of olfactory marker protein (OMP) and the thickness of olfactory epithelium on the 28th day after injection. SPSS 23.0 software was used for statistical analysis. Results: Before injection, all mice in each group had no olfactory dysfunction. At the 7th, 14th, 21st and 28th days after injection, the food finding time of mice in group C was shorter than that in group B, and the difference was statistically significant ((175.88±100.50) s vs (266.73±46.83) s, (132.00±84.62) s vs (264.10±48.50) s, (103.57±77.43) s vs (197.43±69.78) s, (67.79±32.54) s vs (176.63±61.06) s, all P<0.05), but food finding time of mice in group B and C was longer than that in group A (the food finding time of group A at the 7th, 14th, 21st and 28th days after injection was (27.13±5.36) s, (25.83±7.28) s, (23.13±2.72) s, (26.63±7.60) s, respectively, all P<0.05). At the 28th day after olfactory training, the number of OMP positive cells in group B and C were fewer than that in group A, and the difference was statistically significant ((108.00±28.19)/HP vs (288.22±84.06)/HP, (199.33±58.55)/HP vs (288.22±84.06)/HP, all P<0.05). The number of OMP positive cells in group C were higher than that in group B (P<0.05). The number of OMP positive cells had negative correlation with food finding time (r=-0.886, P<0.01). As for the thickness of the olfactory epithelium, the thickness of group B was thinner than that in group A and C, and the difference was statistically significant ((59.57±31.27) μm vs (114.55±40.70)μm vs (90.54±37.72) μm, all P<0.05). Conclusion: Olfactory training can accelerate the recovery of olfactory function in 3-MI-induced olfactory impaired mice.
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Affiliation(s)
- J H Zhou
- Department of Otorhinolaryngology, the First Affiliated Hospital of China Medical University, Shenyang 110001, 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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38
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Pan NP, Zhou WX, Tang J, Zhou JH, Li JQ. [Analysis of influencing factors of endometrial disease of patients with breast cancer after operation]. Zhonghua Fu Chan Ke Za Zhi 2020; 54:848-853. [PMID: 31874475 DOI: 10.3760/cma.j.issn.0529-567x.2019.12.009] [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 study influencing factors which cause the endometrial diseases in patients with breast cancer after operation. Methods: A retrospective study was performed on 212 breast cancer post-operation patients with endometrial diseases between June 2006 and January 2018 in Women's Hospital School of Medicine Zhejiang University to analyse the factors which influenced the endometrial diseases. Results: The abnormal uterine bleeding and endometrial thickness were related to the severity of endometrial disease in patients with breast cancer, and they were independent risk factors for breast cancer patients to have endometrial cancer (P<0.05) . When the diagnostic cut off value of endometrial thickness was ≥0.49 cm, the sensitivity and specificity to endometrial cancer were 78% and 25%, respectively. The average endometrial thickness was (0.56±0.39) cm in patients who were treated by selective estrogen receptor modulator (SERM) after gynecological surgery, which was significantly thicker than that of aromatase inhibitor (AI) group [ (0.33±0.23) cm] and no treatment group [ (0.44±0.28) cm, P<0.05]. The endometrial disease recurrent rate and reoperation rate in SERM group were (26.2%, 14.3%) slightly higher than that of AI group (9.5%, 4.8%) and no treatment group (21.6%, 4.9%), but there were not significant differences (all P>0.05). Conclusions: The clinical symptom of abnormal uterine bleeding and thickening endometrium are risk factors for breast cancer patients to have endometrial cancer. The endometrial thickness has high predictive value for breast cancer patients to diagnose endometrial cancer. The SERM treatment increases the endometrial thickness, recurrent rate and reoperation rate in post-operation patients.
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Affiliation(s)
- N P Pan
- Department of Obstetrics and Gynecology, Women's Hospital School of Medicine Zhejiang University, Hangzhou 310006, China
| | - W X Zhou
- Department of Obstetrics and Gynecology, Women's Hospital School of Medicine Zhejiang University, Hangzhou 310006, China
| | - J Tang
- Department of Obstetrics and Gynecology, Yuhang District Maternal and Child Health Hospital, Hangzhou 311100, China
| | - J H Zhou
- Department of Obstetrics and Gynecology, Women's Hospital School of Medicine Zhejiang University, Hangzhou 310006, China
| | - J Q Li
- Department of Obstetrics and Gynecology, Women's Hospital School of Medicine Zhejiang University, Hangzhou 310006, China
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Zhu YX, Zhou JH, Li GW, Zhou WY, Ou SS, Xiao XY. Dexmedetomidine protects liver cell line L-02 from oxygen-glucose deprivation-induced injury by down-regulation of microRNA-711. Eur Rev Med Pharmacol Sci 2019; 22:6507-6516. [PMID: 30338821 DOI: 10.26355/eurrev_201810_16065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In liver transplantation, long-time portal vein blocking causes the occurrence of ischemic liver injury. Dexmedetomidine, a widely admired anesthetic, has been reported as a protective agent on organs under ischemic condition. The objective of this study was to reveal the role and underlying mechanism of dexmedetomidine in ischemic liver injury. MATERIALS AND METHODS L-02 cells were treated with dexmedetomidine during 6 h of oxygen-glucose deprivation (OGD) exposure. The expression of microRNA-711 (miR-711) in cell was overexpressed by miRNA transfection. Then, the following parameters were observed: cell viability, apoptosis, the expression of apoptosis-related proteins, and the expression and the release of Interleukin 1 beta (IL-1β) and Tumor necrosis factor alpha (TNF-α). RESULTS Apoptosis and inflammation were induced following OGD exposure in L-02 cells, as cell viability was impaired, apoptotic cell rate was increased, caspase-3, and caspase-9 was cleaved, and the expression and release of TNF-α and IL-1β were increased. Dexmedetomidine attenuated OGD-induced apoptosis and inflammation, and dexmedetomidine down-regulated the expression of miR-711. Also, dexmedetomidine blocked the activation of p38 mitogen-activated protein kinase (p38MAPK) and Janus kinase (JAK)/signal transducer and activator of transcription protein (STAT) signaling upon OGD. Moreover, when miR-711 was overexpressed, dexmedetomidine did not protect L-02 cells against OGD, and did not block p38MAPK and JAK/STAT signaling pathways. CONCLUSIONS Dexmedetomidine ameliorated OGD-induced cell apoptosis and inflammation in L-02 cells, exerting protective activities in ischemic liver injury. The anti-OGD effects of dexmedetomidine might be realized by down-regulation of miR-711 and suppression of p38MAPK and JAK/STAT signaling pathways.
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Affiliation(s)
- Y-X Zhu
- Department of Anaesthesiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, 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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Ma HM, Zhao YQ, Zhao Y, Zhou JH, Zhang JR, Wei HQ. [Ganglioneuroma in poststyloid space removed under endoscope through transoral approach: a case report]. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2019; 33:468-469;473. [PMID: 31163562 DOI: 10.13201/j.issn.1001-1781.2019.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Indexed: 11/12/2022]
Abstract
Parapharyngeal space refers to the potential space under skull base between masticatory muscles and pharyngeal muscles, ranging from skull base at the top to hyoid bone at the bottom. The outer lateral wall consists of medial pterygoid muscle, deep parotid lobe and lower jawbone, lateral pharyngeal wall, medial pterygoid, deep lobe of parotid gland and mandible constitute the lateral wall, lateral pharyngeal wall forms medial wall, and prevertebral fascia constitutes the posterior wall, generally forming an inverted pyramid lacuna. Parapharyngeal space is divided into prestyloid space and poststyloid space by stylopharygeal fascia. Prestyloid space is relatively small and contains levator veli palatinetensor veli palatine, branches of maxillary artery, mandibular nerve and its branches. Poststyloid space is relatively large. It includes internal jugular vein, internal carotid artery, posterior cranial nerves, etc. Poststyloid space tumors are relatively rare. In this report, a case of ganglioneuroma wrapping right internal carotid artery is described, which is resected through oral approach.
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Zhang X, Zhou JH, Wang CY, Xu Y, Chen X. [Analysis of CO₂ laser and conventional laryngomicrosurgery treatments for vocal cord cyst: an evaluation of short-term voice acoustics outcomes]. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2019; 33:455-458. [PMID: 31163557 DOI: 10.13201/j.issn.1001-1781.2019.05.017] [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] [Received: 09/11/2018] [Indexed: 11/12/2022]
Abstract
Objective: To compare the short-term outcomes of CO₂ laser and conventional laryngeal microsurgery for vocal cord cyst. Method: Patients with vocal cord cyst were divided randomly into two groups. One group was treated with CO₂ laser (laser group) and the other underwent Micro-flap surgery(Micro-flap group). For the objective assessment, Amulti-dimensional voice program module for voice spectrum analysis was used. Result: In the laser group, there were no significant differences between the preoperative and 1 week postoperative parameters of Jitter, Shimmer and HNR(P>0.05). However,the parameters of G and VHI-10 were significantly different between the laser group and Micro-flap group(P<0.05). The objective data of the laser group pre-and post-surgery showed that the voice recovery of the laser group was significantly better than that of the Micro-flap group after 1 to 3 months of follow-up(P<0.05). But no significant differences of the parameters of G and VHI-10 was noted between the laser group and Micro-flap group(P>0.05). Conclusion: CO₂ laser laryngeal microsurgery for vocal cord cyst can significantly improve pronunciation quality.
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Affiliation(s)
- X Zhang
- Department of Otolaryngology, the Affiliated Huai' an No. 1 People' s Hospital of Nanjing Medical University, Huai' an, 223300, China
| | - J H Zhou
- Department of Otolaryngology, the Affiliated Huai' an No. 1 People' s Hospital of Nanjing Medical University, Huai' an, 223300, China
| | - C Y Wang
- Department of Otolaryngology, the Affiliated Huai' an No. 1 People' s Hospital of Nanjing Medical University, Huai' an, 223300, China
| | - Y Xu
- Department of Otolaryngology, the Affiliated Huai' an No. 1 People' s Hospital of Nanjing Medical University, Huai' an, 223300, China
| | - X Chen
- Department of Otolaryngology, the Affiliated Huai' an No. 1 People' s Hospital of Nanjing Medical University, Huai' an, 223300, China
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Ma P, Ma XX, Chang QY, Li LJ, Wang YN, Feng YP, Ma ZR, Zhou JH. The effects of nucleotide usage in key nucleotide positions +4 and -3 flanking start codon on translation levels mediated by IRES of hepatitis C virus. Acta Virol 2019; 62:441-446. [PMID: 30472875 DOI: 10.4149/av_2018_413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Internal ribosomal entry site (IRES) functions as a cis-acting RNA element, which drives an alternative and cap-independent translation initiation pathway. Currently, there are few studies on effects of nucleotide usages at key nucleotide positions +4 and -3 flanking start codon mediated by IRES of hepatitis C virus (HCV). Herein, we focus on the effect of nucleotide usages at -3 and +4 positions mediated by HCV IRES. The nucleotide contexts flanking AUG start codon employed by HCV IRES is firstly analyzed. We found that each position in the six nucleotide positions (-4 to +6) flanking start codon of HCV has a strong tendency to select the specific nucleotide. A set of bicistronic expression vectors containing CAT gene, HCV IRES and EGFP gene were constructed, including 16 different nucleotide combinations at position -3 and +4. Each set, in which nucleotide at the -3 and +4 position has been changed into different nucleotides, included 16 types of bicistronic expression vectors. It was found that the purine nucleotide at the position -3 or +4 obviously impacts on HCV IRES-related expression, and IRES-driven translation is potentially influenced by the Kozak rule. Our results suggest that optimization of nucleotides at positions -3 and +4 is a convenient and efficient way to enhance the level of IRES-mediated translation. Keywords: Cap-independent translation; internal ribosomal entry site; hepatitis C virus; bicistronic expression vector; translation efficiency.
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Zhong XH, Ding J, Zhou JH, Yu ZH, Sun SZ, Bao Y, Mao JH, Yu L, Li ZH, Han ZM, Song HM, Jiang XY, Liu YL, Zhang BL, Xia ZK, Jin CH, Zhu GH, Wang M, Feng SP, Shen Y, Huang SM, Ma QS, Li HX, Wang XJ, Ichihara K, Yao C, Dong CY. [A multicenter study of reference intervals for 15 laboratory parameters in Chinese children]. Zhonghua Er Ke Za Zhi 2019; 56:835-845. [PMID: 30392208 DOI: 10.3760/cma.j.issn.0578-1310.2018.11.009] [Citation(s) in RCA: 4] [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
Objective: To establish comprehensive laboratory reference intervals for Chinese children. Methods: This was a cross-sectional multicenter study. From June 2013 to December 2014, eligible healthy children aged from 6-month to 17-year were enrolled from 20 medical centers with informed consent. They were assessed by physical examination, questionnaire survey and abdominal ultrasound for eligibility. Fasting blood samples were collected and delivered to central laboratory. Measurements of 15 clinical laboratory parameters were performed, including estradiol (E2), testosterone(T), luteinizing hormone(LH), follicle-stimulating hormone(FSH), alanine transaminase(ALT), serum creatinine(Scr), cystatin C, immunoglobulin A(IgA), immunoglobulin G(IgG), immunoglobulin M(IgM), complement (C3, C4), alkaline phosphatase(ALP), uric acid(UA) and creatine kinase(CK). Reference intervals were established according to central 95% confidence intervals for reference population, stratified by age and sex. Results: In total, 2 259 children were enrolled. Finally, 1 648 children were eligible for this study, including 830 boys and 818 girls, at a mean age of 7.4 years. Age- and sex- specific reference intervals have been established for the parameters. Reference intervals of sex hormones increased gradually with age. Concentrations of ALT, cystatin C, ALP and CK were higher in children under 2 years old. Serum levels of sex hormones, creatinine, immunoglobin, CK, ALP and urea increased rapidly in adolescence, with significant sex difference. In addition, reference intervals were variable depending on assay methods. Concentrations of ALT detected by reagents with pyridoxal 5'-phosphate(PLP) were higher than those detected by reagents without PLP. Compared with enzymatic method, Jaffe assay always got higher results of serum creatinine, especially in children younger than 9 years old. Conclusion: This study established age- and sex- specific reference intervals, for 15 clinical laboratory parameters based on defined healthy children.
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Affiliation(s)
- X H Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
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45
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Zhou JH, Zhu YY, Zhao Y, Zhao YH, Wei HQ. [Inflammatory pseudotumor in nasopharynx and retrostyloid space: a case report]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2019; 54:142-144. [PMID: 30776869 DOI: 10.3760/cma.j.issn.1673-0860.2019.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Affiliation(s)
- J H Zhou
- Department of Otorhinolaryngology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Y Y Zhu
- Department of Radiology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Y Zhao
- Department of Otorhinolaryngology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - Y H Zhao
- Department of Otorhinolaryngology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - H Q Wei
- Department of Otorhinolaryngology, First Affiliated Hospital of China Medical University, Shenyang 110001, China
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Zhou QZ, Guo XB, Zhang WS, Zhou JH, Yang C, Bian J, Chen MK, Guo WB, Wang P, Qi T, Wang CY, Yang JK, Liu CD. Expressions of miR-525-3p and its target gene SEMG1 in the spermatozoa of patients with asthenozoospermia. Andrology 2018; 7:220-227. [PMID: 30575326 PMCID: PMC6590180 DOI: 10.1111/andr.12573] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 09/20/2018] [Accepted: 11/07/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND Semenogelin 1 (SEMG1) is an important secretory protein in spermatozoa involved in the formation of a gel matrix encasing ejaculated spermatozoa. Previous studies show that the SEMG1 gene is highly expressed in spermatozoa from patients with asthenozoospermia (AZS); however, the underlying molecular mechanisms are not yet clear. OBJECTIVES To study the molecular mechanism of high expression of SEMG1 gene and its potential roles in AZS. MATERIALS AND METHODS Western blot and real-time PCR were used to detect the expression levels of SEMG1 protein and mRNA in the ejaculated spermatozoa from normozoospermic males and AZS patients. Bioinformatics analysis was used to predict miRNAs targeting for SEMG1 3'-untranslated region detection of the expression levels of all the candidate miRNAs in ejaculatory spermatozoa in AZS patients or normozoospermic volunteers. Luciferase reporter assays were performed to confirm it can directly bind to SEMG1. Correlation of miR-525-3p and SEMG1 mRNA expression with clinical sperm parameters were also analyzed. Finally, we conducted a follow-up study of reproductive history about all the subjects. RESULTS SEMG1 mRNA and protein level were significantly higher in AZS patients compared to that in normozoospermic volunteers (p < 0.001). Subsequently, microRNA-525-3p (miR-525-3p) which was predicted as a candidate regulator of SEMG1 was found lower expressed in ejaculatory spermatozoa in AZS patients (p = 0.0074). Luciferase experiment revealed that microRNA-525-3p could directly target SEMG1 3'-untranslated region and suppress its expression. Importantly, our retrospective follow-up study showed that both low miR-525-3p expression and high SEMG1 expression level was significantly associated with low progressive sperm motility, abnormal sperm morphology, and infertility. DISCUSSION AND CONCLUSION The elevated expression of SEMG1 and reduced expression of miR-525-3p are associated with AZS and male infertility. Our study provides a potential therapeutic target for the treatment of male infertility or for male contraception.
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Affiliation(s)
- Q-Z Zhou
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - X-B Guo
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - W-S Zhang
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - J-H Zhou
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - C Yang
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - J Bian
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - M-K Chen
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - W-B Guo
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - P Wang
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - T Qi
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - C-Y Wang
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - J-K Yang
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - C-D Liu
- Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
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Zhou JH, Wang D, Wang HR, Hou XL, Yu WD, Xu KX, Hu H. [Cytotoxic effects of γδT cells on bladder cancer cells and expression of MICA/B in bladder cancer]. Beijing Da Xue Xue Bao Yi Xue Ban 2018; 50:595-601. [PMID: 30122755] [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: 09/28/2022]
Abstract
OBJECTIVE To explore the role of γδT cells against bladder cancer and to detect the expression of stress proteins MICA/B recognized by γδT cells in bladder cancer. METHODS γδT cells from peripheral blood drawn from 6 bladder cancer patients with pamidronate stimulating were expanded. Flow cytometry was used to detect the purity and expansion folds of γδT cells, and the expression of CD107a on γδT cells after PMA/ionomycin stimulated. The cytotoxicity assay was carried out to test the cytotoxicity of γδT cells against human bladder cancer cell lines. The expression of MICA/B on bladder cancer cell lines and in bladder cancer tissues were detected through flow cytometry and immunohistochemistry respectively. RESULTS γδT cells from peripheral blood drawn from 6 bladder cancer patients were successfully expanded. The purity was 75%-94% and the expansion folds were 109-371 times. After being stimulated by PMA/ionomycin, the proportion of CD107a+ γδT cells increased significantly, reaching 40%-82%. γδT cells from the 6 bladder cancer patients showed obvious cytotoxic effects on 3 human bladder cancer cell lines which was enhanced as the effector: the target ratio increased. MICA/B were detected both in 3 bladder cancer cell lines and in 26 bladder cancer tissues. The staining score of MICA/B in invasive bladder cancer was slightly higher than that in non-invasive bladder cancer, and in advanced bladder cancer was higher than that in low grade bladder cancer, but the statistical analysis showed that the staining score of MICA/B was no significant correlation between the tissue and the tumor stages and grades. CONCLUSION γδT cells from the peripheral blood of the bladder cancer patients could be successfully expanded in vitro, and showed significant anti-bladder cancer effect. MICA/B were detected both in bladder cancer cell lines and in bladder cancer tissues. The statistical analysis showed that there was no significant correlation between the staining scores of MICA/B in the tissue and the tumor stages and grades.
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Affiliation(s)
- J H Zhou
- Department of Central Laboratory & Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing 100044, China
| | - D Wang
- Department of Central Laboratory & Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing 100044, China
| | - H R Wang
- Department of Urology, Peking University People's Hospital, Beijing 100044, China
| | - X L Hou
- Department of Central Laboratory & Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing 100044, China
| | - W D Yu
- Department of Central Laboratory & Institute of Clinical Molecular Biology, Peking University People's Hospital, Beijing 100044, China
| | - K X Xu
- Department of Urology, Peking University People's Hospital, Beijing 100044, China
| | - H Hu
- Department of Urology, Peking University People's Hospital, Beijing 100044, China
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Mou JJ, Qi MY, Li CS, Zheng HY, Chen M, Zhou JH, He JL, Xu WW, Xu ST, Xu XG. [Characterization analysis of gM, gL genes of varicella zoster virus in six provinces of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2018; 52:419-423. [PMID: 29614611 DOI: 10.3760/cma.j.issn.0253-9624.2018.04.016] [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 the genetic characterization of glycoprotein M(gM.),glycoprotein L(gL) of varicella zoster virus. Methods: According to the program of "Ministry of Science and Technology of China" , Based on the 12 suspected VZV patients monitored in Beijing (1 case), Shanghai (5 cases), Jilin (2 cases), Qinghai (1 case), Guangdong (2 case) and Sichuan (case) in 2007-2015. A total of 12 Vesicle fluid and throat swab samples were collected. Positive samples were identified by Agarose gel electrophoresis and two glycoprotein genes were amplified by polymerase chain reaction (PCR). Nucleotide sequences were determined and analyzed by PCR amplification of VZV positive specimens V-OKA-BK of the domestic varicella attenuated live vaccine and the Varilrix-1 of the imported attenuated live vaccine. Nucleotide sequences of VZV positive specimens, vaccine strains (V-OKA-BK, varilrix-1) and GenBank foreign wild strains (41 strains), parent strains (P-oka), vaccine strains (V-oka, Varilrix, Varivax) were compared using BioEdit and MEGA 5.0. Results: 12 specimens were VZV positive. Compared with the vaccine strains and the parent strains, the GM gene of 1 positive specimen had radical mutation at 86686 sites, which resulted in amino acid mutation, 5 positive specimens had base mutation at 87844 sites, and 30 strains of foreign wild strains had the same variation at 87 844 sites. 1 positive specimens of gL gene in 101245 sites had base mutation, and led to amino acid mutation, 6 positive specimens at 101624, 101625, 101626 sites had base of loss and the foreign wild strains in these 3 sites had the same variation. Compared with the vaccine strains, the nucleotide and amino acid homology of gM of 12 VZV positive specimens were 99.2%-100% and 98.2%-100%, respectively, and gL of those were 99.3%-100% and 98.6%-100%, respectively. Compared with 41 strains of foreign wild strains, homology of gM's nucleotides and amino acid were 99.3%-100% and 98.5%-100%, respectively; 99.1%-100% and 98.6%-100% for gL. The results of phylogenetic analysis showed that 7 VZV positive samples were on the same branch with 4 vaccine strains and p-oka strain. Based on gL, 12 VZV positive samples were on the same branch as the vaccine strains and p-oka strain. Conclusion: This study demonstrates that the genes of gM, gL are highly conserved and remain stable immunogen, which may be involved in the attenuation of VZV and need to be further researched.
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Affiliation(s)
- J J Mou
- Institute of Integrative Medicine of Dalian Medical University, Dalian 116044, China
| | - M Y Qi
- Institute of Integrative Medicine of Dalian Medical University, Dalian 116044, China
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Liu WH, Ma Y, Lu JY, Yan HC, Zhou JH, Liao XL, Zeng JH, Lin WQ, Wu D, Zhang ZB, Yang ZC, Chen ZQ, Chen JD, Li TG. [Willingness and influencing factors related to "centralized slaughtering, fresh poultry listing and marketing" strategy among the household chefs in Guangzhou]. Zhonghua Liu Xing Bing Xue Za Zhi 2018; 39:204-207. [PMID: 29495206 DOI: 10.3760/cma.j.issn.0254-6450.2018.02.013] [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: 06/08/2023]
Abstract
Objective: To study the willingness and influence factors related to "centralized slaughtering, fresh poultry listing and marketing" strategy, among the household chefs, and provide reference for government to adjust and optimize the strategy on avian influenza prevention. Methods: According to the geographical characteristics and regional functions, 6 'monitoring stations' were selected from 12 residential districts of Guangzhou, respectively. Another 21 meat markets which selling live poultry, were selected in each station and 5 household chefs of each market were invited to attend a face to face interview. Basic information, personal cognitive, willingness and influencing factors to the policy were under study. Univariate and multivariate logistic regression methods were used. Results: A total of 664 household chefs underwent the survey and results showed that the rate of support to the "centralized slaughtering, fresh poultry listing and marketing" strategy was 44.6% (296/664). Results from the multi-factor logistic regression showed that those household chefs who were males (OR=1.618, 95% CI: 1.156-2.264, P=0.005), having received higher education (OR=1.814, 95% CI: 1.296-2.539, P=0.001), or believing that the existence of live poultry stalls was related to the transmission of avian influenza (OR=1.918, 95% CI: 1.341-2.743, P<0.001) were factors at higher risk. These household chefs also intended to avoid the use of live poultry stalls (OR=1.666, 95%CI: 1.203-2.309, P=0.002) and accept the "centralized slaughtering, fresh poultry listing and marketing" strategy. Conclusion: Detailed study on this subject and, setting up pilot project in some areas as well as prioritizing the education programs for household chefs seemed helpful to the implementation of the 'freezing-fresh poultry' policy.
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Affiliation(s)
- W H Liu
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - Y Ma
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - J Y Lu
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - H C Yan
- Department of Disease Surveillance Guangzhou Military Area Command Center for Disease Control and Prevention, Guangzhou 510000, China
| | - J H Zhou
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - X L Liao
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - J H Zeng
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - W Q Lin
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - D Wu
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - Z B Zhang
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - Z C Yang
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - Z Q Chen
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - J D Chen
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
| | - T G Li
- Department of Infectious Disease Control and Prevention Guangzhou Center for Disease Control and Prevention, Guangzhou 510000, China
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Ren ST, Wang Y, Zhou JH, Long J, Wang CH, Li DB, Huang YZ. [Echocardiographic diagnosis of infracardiac total anomalous pulmonary venous connection]. Beijing Da Xue Xue Bao Yi Xue Ban 2017; 49:883-888. [PMID: 29045974] [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: 06/07/2023]
Abstract
OBJECTIVE To investigate the clinical usefulness of echocardiography in the diagnosis of infracardiac total anomalous pulmonary venous connection (ITAPVC) in neonates and infants. METHODS Retrospective analysis on 8 patients with ITAPVC was performed using echocardiography between April 2006 and December 2016. There were 4 boys and 4 girls with a mean age of 79.8 days (ranging from 15 to 195 days). A combined scanning via parasternal, subcostal and apical acoustic windows had been employed to diagnose ITAPVC and to trace the course and site of the anomalous pulmonary venous drainage, and to confirm the direction of the inter-atrial shunt and enlargement of right atrium and right ventricle. RESULTS Of the 8 patients who received echocardiography, ITAPVC was diagnosed in 7 patients. Mis-diagnosis by echocardiography was encountered in one patient. The diagnosis by echocardiography was compatible with the operative findings in 5 patients receiving surgery and with the results of multislice computed tomography in 6 patients. The diagnostic accuracy rate of ITAPVC was 87.5%. The indirect signs obtained from echocardiogram was coexistence of a small malformed, triangle-shaped left atrium and right to left shunting at atrial level with dilatation and tortuousness of portal vein or hepatic vein and abundant blood flow in liver. The direct signs was total pulmonary veins unconnected with left atrium, whose confluence joining into vertical vein drained right-inferiorly to portal vein or hepatic vein through diaphragm. Three parallel vessels including vertical vein, abdominal aorta and inferior vena cava arranged anteriorly, left-posteriorly and right-posteriorly with the opposite flow directions of inferior vena cava and the other two were found on sub-costal view. Sites of the drainage to the infra-diaphragm veins located portal vein in 8 patients. Stenosis of site of vertical vein connecting to portal vein or hepatic veins occurred in 3 patients. CONCLUSION Echocardiography has significant value in the diagnosis of pediatric ITAPVC and is capable of providing important structural and hemodynamic information for preoperative assessment of surgery. With multiple windows and multiple sections, ITAPVC could be diagnosed accurately by echocardiography. However, it is necessary to differentiate ITAPVC with intrahepatic portosystemic venous shunts or hepatic arteriovenous fistula.
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Affiliation(s)
- S T Ren
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - Y Wang
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - J H Zhou
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - J Long
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - C H Wang
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - D B Li
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
| | - Y Z Huang
- Department of Ultrasound, TEDA International Cardiovascular Hospital, Tianjin 300457, China
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