1
|
Zhao YM, Wang WH, Zhang W, Wang L, Li S, Wang JW, Liao LE, Yu GY, Sun Z, Qu YL, Gong Y, Lu Y, Wu T, Li YF, Wang Q, Zhao GH, Xiao Y, Ding PR, Zhang Z, Wu AW. [Long-term outcome of patients with rectal cancer who achieve complete or near complete clinical responses after neoadjuvant therapy: a multicenter registry study of data from the Chinese Watch and Wait Database]. Zhonghua Wei Chang Wai Ke Za Zhi 2024; 27:372-382. [PMID: 38644243 DOI: 10.3760/cma.j.cn441530-20240227-00074] [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: 04/23/2024]
Abstract
Objective: To report the long-term outcomes of Chinese rectal cancer patients after adopting a Watch and Wait (W&W) strategy following neoadjuvant therapy (NAT). Methods: This multicenter, cross-sectional study was based on real-world data. The study cohort comprised rectal cancer patients who had achieved complete or near complete clinical responses (cCRs, near-cCRs) after NAT and were thereafter managed by a W&W approach, as well as a few patients who had achieved good responses after NAT and had then undergone local excision for confirmation of pathological complete response. All participants had been followed up for ≥2 years. Patients with distant metastases at baseline or who opted for observation while living with the tumor were excluded. Data of eligible patients were retrospectively collected from the Chinese Wait-and-Watch Data Collaboration Group database. These included baseline characteristics, type of NAT, pre-treatment imaging results, evaluation of post-NAT efficacy, salvage measures, and treatment outcomes. We herein report the long-term outcomes of Chinese rectal cancer patients after NAT and W&W and the differences between the cCR and near-cCR groups. Results: Clinical data of 318 rectal cancer patients who had undergone W&W for over 2 years and been followed up were collected from eight medical centers (Peking University Cancer Hospital, Fudan University Shanghai Cancer Center, Sun Yat-sen University Cancer Center, Shanghai Changhai Hospital, Peking Union Medical College Hospital, Liaoning Cancer Hospital, the First Hospital of Jilin University, and Yunnan Cancer Hospital.) The participants comprised 221 men (69.4%) and 107 women (30.6%) of median age 60 (26-86) years. The median distance between tumor and anal verge was 3.4 (0-10.4) cm. Of these patients, 291 and 27 had achieved cCR or near-cCR, respectively, after NAT. The median duration of follow-up was 48.4 (10.2-110.3) months. The 5-year cumulative overall survival rate was 92.4% (95%CI: 86.8%-95.7%), 5-year cumulative disease-specific survival (CSS) rate 96.6% (95%CI: 92.2%-98.5%), 5-year cumulative organ-preserving disease-free survival rate 86.6% (95%CI: 81.0%-90.7%), and 5-year organ preservation rate 85.3% (95%CI: 80.3%-89.1%). The overall 5-year local recurrence and distant metastasis rates were 18.5% (95%CI: 14.9%-20.8%) and 8.2% (95%CI: 5.4%-12.5%), respectively. Most local recurrences (82.1%, 46/56) occurred within 2 years, and 91.0% (51/56) occurred within 3 years, the median time to recurrence being 11.7 (2.5-66.6) months. Most (91.1%, 51/56) local recurrences occurred within the intestinal lumen. Distant metastases developed in 23 patients; 60.9% (14/23) occurred within 2 years and 73.9% (17/23) within 3 years, the median time to distant metastasis being 21.9 (2.6-90.3) months. Common sites included lung (15/23, 65.2%), liver (6/23, 26.1%), and bone (7/23, 30.4%) The metastases involved single organs in 17 patients and multiple organs in six. There were no significant differences in overall, cumulative disease-specific, or organ-preserving disease-free survival or rate of metastases between the two groups (all P>0.05). The 5-year local recurrence rate was higher in the near-cCR than in the cCR group (41.6% vs. 16.4%, P<0.01), with a lower organ preservation rate (69.2% vs. 88.0%, P<0.001). The success rates of salvage after local recurrence and distant metastasis were 82.1% (46/56) and 13.0% (3/23), respectively. Conclusion: Rectal cancer patients who achieve cCR or near-cCR after NAT and undergo W&W have favorable oncological outcomes and a high rate of organ preservation. Local recurrence and distant metastasis during W&W follow certain patterns, with a relatively high salvage rate for local recurrence. Our findings highlight the importance of close follow-up and timely intervention during the W&W process.
Collapse
Affiliation(s)
- Y M Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing),Gastrointestinal Cancer Center, Unit III, Peking University Cancer Hospital & Institute, Beijing 100142,China
| | - W H Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - W Zhang
- Department of Colorectal Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, China
| | - L Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing),Gastrointestinal Cancer Center, Unit III, Peking University Cancer Hospital & Institute, Beijing 100142,China
| | - S Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - J W Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China
| | - L E Liao
- Department of Colorectal Surgery, Sun Yat - sen University Cancer Center, Guangzhou 510060, China
| | - G Y Yu
- Department of Colorectal Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, China
| | - Z Sun
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Y L Qu
- Department of General Surgery, Liaoning Cancer Hospital, Shenyang 110042, China
| | - Y Gong
- Department of Gastrocolorectal Surgery, the First Hospital of Jilin University, Changchun 130021,China
| | - Y Lu
- Department of General Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266555,China
| | - T Wu
- Department of Colorectal Surgery, Yunnan Cancer Hospital, Kunming 650118, China
| | - Y F Li
- Department of Colorectal Surgery, Yunnan Cancer Hospital, Kunming 650118, China
| | - Q Wang
- Department of Gastrocolorectal Surgery, the First Hospital of Jilin University, Changchun 130021,China
| | - G H Zhao
- Department of General Surgery, Liaoning Cancer Hospital, Shenyang 110042, China
| | - Y Xiao
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - P R Ding
- Department of Colorectal Surgery, Sun Yat - sen University Cancer Center, Guangzhou 510060, China
| | - Z Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China
| | - A W Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing),Gastrointestinal Cancer Center, Unit III, Peking University Cancer Hospital & Institute, Beijing 100142,China State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Unit III, Peking University Cancer Hospital & Institute, Beijing 100142, China
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Li YW, Li Z, Song HC, Ding L, Ji SS, Zhang M, Qu YL, Sun Q, Zhu YD, Fu H, Cai JY, Li CF, Han YY, Zhang WL, Zhao F, Lyu YB, Shi XM. [Association between urinary arsenic level and serum testosterone in Chinese men aged 18 to 79 years]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:686-692. [PMID: 36977566 DOI: 10.3760/cma.j.cn112150-20221110-01095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Objective: To investigate the association between the urinary arsenic level and serum total testosterone in Chinese men aged 18 to 79 years. Methods: A total of 5 048 male participants aged 18 to 79 years were recruited from the China National Human Biomonitoring (CNHBM) from 2017 to 2018. Questionnaires and physical examinations were used to collect information on demographic characteristics, lifestyle, food intake frequency and health status. Venous blood and urine samples were collected to detect the level of serum total testosterone, urine arsenic and urine creatinine. Participants were divided into three groups (low, middle, and high) based on the tertiles of creatinine-adjusted urine arsenic concentration. Weighted multiple linear regression was fitted to analyze the association of urinary arsenic with serum total testosterone. Results: The weighted average age of 5 048 Chinese men was (46.72±0.40) years. Geometric mean concentration (95%CI) of urinary arsenic, creatinine-adjusted urine arsenic and serum testosterone was 22.46 (20.08, 25.12) μg/L, 19.36 (16.92, 22.15) μg/L and 18.13 (17.42, 18.85) nmol/L, respectively. After controlling for covariates, compared with the low-level urinary arsenic group, the testosterone level of the participants in the middle-level group and the high-level group decreased gradually. The percentile ratio (95%CI) was -5.17% (-13.14%, 3.54%) and -10.33% (-15.68%, -4.63). The subgroup analysis showed that the association between the urinary arsenic level and testosterone level was more obvious in the group with BMI<24 kg/m2 group (Pinteraction<0.05). Conclusion: There is a negative association between the urinary arsenic level and serum total testosterone in Chinese men aged 18-79 years.
Collapse
Affiliation(s)
- Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Fu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C F Li
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y Y Han
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| |
Collapse
|
5
|
Li Z, Lyu YB, Zhao F, Sun Q, Qu YL, Ji SS, Qiu T, Li YW, Song SX, Zhang M, Liu YC, Cai JY, Song HC, Zheng XL, Wu B, Li DD, Liu Y, Zhu Y, Cao ZJ, Shi XM. [Association of lead exposure with stunting and underweight among children aged 3-5 years in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1597-1603. [PMID: 36372750 DOI: 10.3760/cma.j.cn112150-20211229-01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To evaluate the association of lead exposure with stunting and underweight among children aged 3-5 years in China. Methods: Data was collected from China National Human Biomonitoring (CNHBM) between January 2017 and December 2018. A total of 3 554 children aged 3-5 years were included. Demographic characteristic, lifestyle and nutritional status were collected through questionnaires. Height and weight were measured by standardized method. Stunting and underweight status were determined by calculating height for age Z-score and weight for age Z-score. Blood and urine samples were collected to detect the concentrations of blood lead, urinary lead and urinary creatinine. Children were stratified into 4 groups (Q1 to Q4) by quartiles of blood lead level and corrected urinary lead level, respectively. Complex sampling logistic regression models were applied to evaluate the association of the blood lead level, urinary lead level with stunting and underweight. Results: Among 3 554 children, the age was (4.09±1.06) years, of which 1 779 (80.64%) were female and 1 948 (55.84%) were urban residents. The prevalence of stunting and wasting was 7.34% and 2.96%, respectively. The M (Q1, Q3) for blood lead levels and urinary lead levels in children was 17.49 (12.80, 24.71) μg/L, 1.20 (0.61, 2.14) μg/g Cr, respectively. After adjusting for confounding factors, compared with the lowest blood lead concentration group Q1, the risk of stunting gradually increased in the Q3 and Q4 group (Ptrend=0.010), with OR (95%CI) values of 1.40 (0.80-2.46) and 1.80 (1.07-3.04), respectively. Compared with the lowest urinary lead concentration group Q1, the risk of stunting still increased in the Q3 and Q4 group (Ptrend=0.012), with OR (95%CI) values of 1.69 (1.01-2.84) and 1.79 (1.05-3.06), respectively. The correlation between the lead exposure and underweight was not statistically significant (P>0.05). Conclusion: Lead exposure is positively associated with the risk of stunting among children aged 3-5 years in China.
Collapse
Affiliation(s)
- Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Liu
- School of Public Health, Jilin University, Changchun 130012, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| |
Collapse
|
6
|
Qu YL, Zhao F, Ji SS, Hu XJ, Li Z, Zhang M, Li YW, Lu YF, Cai JY, Sun Q, Song HC, Li DD, Zheng XL, Wu B, Lyu YB, Zhu Y, Cao ZJ, Shi XM. [Mediation effect of inflammatory biomarkers on the association between blood lead levels and blood pressure changes in Chinese adults]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1591-1596. [PMID: 36372749 DOI: 10.3760/cma.j.cn112150-20211119-01067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the role of inflammatory biomarkers in the relationship between blood lead levels and blood pressure changes. Methods: A total of 9 910 people aged 18-79 years who participated in the China National Human Biomonitoring in 2017-2018 were included in this study. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information, and the data including height, weight and blood pressure were determined through physical examination. Blood and urinary samples were collected for the detection of blood lead and cadmium levels, urinary arsenic levels, white blood cells, neutrophils, lymphocytes, and hypersensitive C-reactive protein (hs-CRP). Weighted linear regression models were used to evaluate the associations between blood lead, inflammatory biomarkers and blood pressure. Mediation analysis was performed to investigate the role of inflammation in the relationship between blood lead levels and blood pressure changes. Results: The median (Q1, Q3) age of all participants was 45.4 (33.8, 58.4)years, including 4 984 males accounting for 50.3%. Multivariate logistic regression model analysis showed that after adjusting for age, gender, residence area, BMI, education level, smoking and drinking status, family history of hypertension, consumption frequency of rice, vegetables, and red meat, fasting blood glucose, total cholesterol, triglycerides, blood cadmium and urinary arsenic levels, there was a positive association between blood lead levels, inflammatory biomarkers and blood pressure (P<0.05). Each 2.71 μg/L (log-transformed) increase of the lead was associated with a 2.05 (95%CI: 0.58, 3.53) mmHg elevation in systolic blood pressure (SBP), 2.24 (95%CI: 1.34, 3.14) mmHg elevation in diastolic blood pressure (DBP), 0.25 (95%CI: 0.05, 0.46) mg/L elevation in hs-CRP, 0.16 (95%CI: 0.03, 0.29)×109/L elevation in white blood cells, and 0.11 (95%CI: 0.02, 0.21)×109/L elevation in lymphocytes, respectively. Mediation analysis showed that the levels of hs-CRP significantly mediated the association of blood lead with SBP, with a proportion about 3.88% (95%CI: 0.45%, 7.32%). The analysis also found that the levels of hs-CRP and neutrophils significantly mediated the association of blood lead with SBP, with a proportion about 4.10% (95%CI: 1.11%, 7.10%) and 2.42% (95%CI: 0.07%, 4.76%), respectively. Conclusion: This study suggests that inflammatory biomarkers could significantly mediate the association of blood lead levels and blood pressure changes.
Collapse
Affiliation(s)
- Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X J Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Ji SS, Lyu YB, Zhao F, Qu YL, Li Z, Li YW, Song SX, Zhang WL, Liu YC, Cai JY, Song HC, Li DD, Wu B, Liu Y, Zheng XL, Hu JM, Zhu Y, Cao ZJ, Shi XM. [Association of blood lead and blood selenium with serum high-sensitivity C-reactive protein among Chinese adults aged 19 to 79 years]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:195-200. [PMID: 35184484 DOI: 10.3760/cma.j.cn112338-20210715-00555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association of blood lead and blood selenium with serum high-sensitivity C-reactive protein (hs-CRP) among Chinese adults aged 19 to 79 years. Methods: The participants were enrolled from the first wave of China National Human Biomonitoring (CNHBM) conducted from 2017 to 2018. 10 153 participants aged 19 to 79 years were included in this study. Fasting blood samples were obtained from participants. Lead and selenium in whole blood and hs-CRP in serum were measured. Individuals with hs-CRP levels above 3.0 mg/L were defined as elevated hs-CRP. Generalized linear mixed models and restricted cubic spline models were used to analyze the association of blood lead and blood selenium with elevated hs-CRP. Logistic regression models were used to analyze the multiplicative scale and additive scale interaction between blood lead and blood selenium on elevated hs-CRP. Results: The age of participants was (48.91±15.38) years, of which 5 054 (61.47%) were male. 1 181 (11.29%) participants were defined as elevated hs-CRP. After multivariable adjustment, results from generalized linear models showed that compared with participants with the lowest quartile of blood lead, the OR (95%CI) of elevated hs-CRP for participants with the second, third, and highest quartiles were 1.14 (0.94-1.37), 1.25 (1.04-1.52) and 1.38 (1.13-1.68), respectively. When compared with participants with the lowest quartile of blood selenium, the OR (95%CI) of elevated hs-CRP for participants with the second, third and highest quartiles were 0.86 (0.72-1.04), 0.91 (0.76-1.11), and 0.75 (0.61-0.92), respectively. Results from the interaction analysis showed no significant interaction between lead and selenium on elevated hs-CRP. Conclusion: Blood concentration of lead was positively associated with elevated serum hs-CRP, and blood concentration of selenium was inversely related to elevated hs-CRP, while blood lead and selenium did not present interaction on elevated hs-CRP.
Collapse
Affiliation(s)
- S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - J M Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Yang Q, Zhang JY, Zhang XC, Xia RC, Yu H, Qu YL, Wang ZW, Tan R, Zhang SH, Li CT, Gao YZ. Mitochondrial DNA Polymorphism in Zhejiang She Population Based on Next Generation Sequencing. Fa Yi Xue Za Zhi 2021; 37:358-365. [PMID: 34379905 DOI: 10.12116/j.issn.1004-5619.2020.501101] [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: 11/04/2020] [Indexed: 11/30/2022]
Abstract
Abstract Objective To study the genetic polymorphism of whole mitochondrial DNA (mtDNA) genomes in She population in Zhejiang and to explore the maternal genetic structure of the She population. Methods Whole mtDNA genomes of 231 unrelated individuals from She population in Zhejiang Province were sequenced. The number of mutations and population genetics parameters such as, the haplotype diversity (HD), discrimination power (DP), and random match probabilities (RMP) were analyzed. The mtDNA haplogroups of Zhejiang She population were classified, and the maternal genetic relationships between She and nine other Chinese populations were estimated. Results In 231 Zhejiang She samples, 8 507 mutations (702 types) were observed and the samples were classified into 94 haplogroups. The HD, DP and RMP values were 0.998 6, 0.994 2 and 0.005 8, respectively. The lowest genetic differentiation degree (Fst=0.006 89) was detected between Zhejiang She population and southern Han population. Principal component analysis (PCA) and median-joining network analysis showed that the genetic distance of Zhejiang She population with Guangxi Yao, Yunnan Dai and Southern Han populations was relatively close, but the population still had some unique genetic characteristics. Conclusion The whole mtDNA genomes are highly polymorphic in Zhejiang She population. The Zhejiang She population contains complex and diverse genetic components and has a relatively close maternal genetic relationship with Guangxi Yao, Yunnan Dai and Southern Han populations. Meanwhile, Zhejiang She population has kept its unique maternal genetic components.
Collapse
Affiliation(s)
- Q Yang
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - J Y Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Laboratory of Obstetrics and Gynecology, the Affiliated Luoyang Central Hospital, Zhengzhou University, Luoyang 471000, Henan Province, China
| | - X C Zhang
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - R C Xia
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - H Yu
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - Y L Qu
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - Z W Wang
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - R Tan
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - S H Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - C T Li
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - Y Z Gao
- Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, China
| |
Collapse
|
11
|
Xia RC, Zhang XC, Wang XX, Yang Q, Chen C, Yu H, Qu YL, Wang ZW, Shi Y, Xiang P, Zhang SH, Li CT. Identification of Cannabis Sativa L. Based on rbcL Sequence. Fa Yi Xue Za Zhi 2021; 37:187-191. [PMID: 34142479 DOI: 10.12116/j.issn.1004-5619.2020.501004] [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: 10/16/2020] [Indexed: 11/30/2022]
Abstract
Abstract Objective To assess the feasibility of the rbcL sequence of chloroplast DNA as a genetic marker to identify Cannabis sativa L. Methods The rbcL sequences in 62 Cannabis sativa L. samples, 10 Humulus lupulus samples and 10 Humulus scandens DNA samples were detected, and 96 rbcL sequences of the Cannabaceae family were downloaded from Genbank. Sequence alignment was performed by MEGA X software, the intraspecific and interspecific Kimura-2-Parameter (K2P) genetic distances were calculated, and the system clustering tree was constructed. Results The rbcL sequence length acquired by sequencing of Cannabis sativa L. and Humulus scandens were 617 bp and 649 bp, respectively, and two haplotypes of Cannabis sativa L. were observed in the samples. The BLAST similarity search results showed that the highest similarity between the sequences acquired by sequencing and Cannabis sativa L. rbcL sequences available from Genbank was 100%. The genetic distance analysis showed that the maximum intraspecific genetic distance (0.004 9) of Cannabis sativa L. was less than the minimum interspecific genetic distance (0.012 9). The results of median-joining network and system clustering tree analysis showed that Cannabis sativa L. and other members of the Cannabaceae family were located in different branches. Conclusion The rbcL sequence could be used as a DNA barcode for identifying Cannabis sativa L., and combined with comparative analysis of the rbcL sequence and system cluster analysis could be a reliable and effective detection method for Cannabis sativa L. identification in forensic investigation.
Collapse
Affiliation(s)
- R C Xia
- Department of Forensic Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou 325235, Zhejiang Province, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - X C Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - X X Wang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Q Yang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - C Chen
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,School of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China
| | - H Yu
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Y L Qu
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Z W Wang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.,Department of Forensic Medicine, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Y Shi
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - P Xiang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - S H Zhang
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| | - C T Li
- Department of Forensic Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou 325235, Zhejiang Province, China.,Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
| |
Collapse
|
12
|
Lyu YB, Zhao F, Qiu YD, Ding L, Qu YL, Xiong JH, Lu YF, Ji SS, Wu B, Hu XJ, Li Z, Zheng XL, Zhang WL, Liu JX, Li YW, Cai JY, Song HC, Zhu Y, Cao ZJ, Shi XM. [Association of cadmium internal exposure with chronic kidney disease in Chinese adults]. Zhonghua Yi Xue Za Zhi 2021; 101:1921-1928. [PMID: 34139825 DOI: 10.3760/cma.j.cn112137-20210425-00996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze the association of the cadmium internal exposure with chronic kidney disease (CKD) in Chinese adults aged 18 and older. Methods: A total of 9 821 adults aged 18-79 from the China National Human Biomonitoring (CNHBM) from 2017 to 2018 were included. Blood and urine cadmium exposure levels were measured by inductively coupled plasma mass spectrometry (ICP-MS), and urine cadmium levels were adjusted with urine creatinine; CKD were defined by estimated glomerular filtration (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI). Weights were considered due to complex sampling process for in statistical analysis. Logistic regression is used to analyze the association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine exposure levels with CKD, and restricted cube spline (RCS) was used to assess the exposure-response curve of blood cadmium, urine cadmium and urine cadmium adjusted with creatinine with CKD. Results: The weighted age was 44.75 and males accounted for 61.1%. The prevalence rate of CKD was 12.7%. The geometric mean values of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine were 0.96 μg/L, 0.61 μg/L, and 0.58 μg/g. After adjusting for confounding factors, the weighted logistic regression showed that the lowest quintile (Q1) was compared with the odds ratio (OR) of the highest quintile (Q5) of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine and the 95% confidence interval (CI) was 1.80 (1.02-3.20), 1.77 (0.94-3.31) and 1.94 (1.11-3.37) respectively. In the restricted cubic spline regression model, non-linear association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine with CKD were observed after adjusting for related confounding factors (P<0.001, 0.018, 0.031 respectively). The risk of CKD increased with the increment of cadmium exposure without risk threshold, and the exposure response curve was steeper at low cadmium exposure. Conclusions: Among Chinese adults aged 18 and older, cadmium exposure is positively associated with the risk of chronic kidney disease.
Collapse
Affiliation(s)
- Y B Lyu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- School of Public Health, Zhejiang University, Hangzhou 310011, China
| | - L Ding
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Xiong
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y F Lu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - X J Hu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - W L Zhang
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J X Liu
- School of Public Health, China Medical University, Shenyang 110001, China
| | - Y W Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Qu YL, Cai JY, Chen X, Zheng L, Huang L, Yang JX, Ye X, Wang Q, Si GA, Cao ZJ. [Association of cadmium pollution with liver function of population in mineral polluted areas of Guangxi]. Zhonghua Yu Fang Yi Xue Za Zhi 2020; 54:839-843. [PMID: 32842312 DOI: 10.3760/cma.j.cn112150-20190801-00618] [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 cadmium exposure with liver function among adults in a non-ferrous metal mining area in Guangxi. Methods: A total of 310 residents aged 18 and above were recruited from 5 heavy metals polluted villages in a non-ferrous metal mining area in Guangxi from 2013 to 2014. The general demographic characteristics, blood cadmium levels and indicators of liver function index [Total bilirubin (TBIL), Glutamic oxaloacetic transaminase (AST), Alanine transaminase (ALT) and Glutamine transaminase (GGT)] were obtained by using questionnaire, physical examination and laboratory test. The blood cadmium levels were divided into quartiles as Q1-Q4 groups (using Q1 group as the reference).Multivariate logistic regression model was used to analyze the correlation between the blood cadmium level and functional liver index. Results: The age of subjects was (49.2±15.4) years, and 112 (36.1%) subjects were male residents. The prevalence of abnormal rates of TBIL,AST,ALT and GGT were 17.4% (54), 19.7% (61), 10.7% (33) and 11.9% (37), respectively. The geometric mean value of cadmium levels in adults was 3.72(95%CI: 3.43-4.02) μg/L. After adjusting for age, gender, body mass index (BMI), smoking, drinking, total cholesterol, hypertriglyceridemia and other factors, the risk of abnormal AST index in the highest concentration of blood cadmium group (Q4) was higher than that in the lowest concentration of blood cadmium group (Q1) (OR=2.92, 95%CI:1.07-7.98). Conclusion: The level of blood cadmium exposure is higher than the reference value of general population in China, and the elevated cadmium exposure is related to the increasing risk of AST abnormality.
Collapse
Affiliation(s)
- Y L Qu
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - J Y Cai
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - X Chen
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - L Zheng
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - L Huang
- Guangxi Center for Disease Prevention and Control, Nanning 530028, China
| | - J X Yang
- Jinchengjiang District Center for Disease Prevention and Control of Hechi City of Guangxi Province, Hechi 547000, China
| | - X Ye
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - Q Wang
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| | - G A Si
- Jinchengjiang District Center for Disease Prevention and Control of Hechi City of Guangxi Province, Hechi 547000, China
| | - Z J Cao
- National Institute of Environmental Health, Chinese Center For Disease Control And Prevention, Beijing 100050,China
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Li YY, Chen SW, Zhao F, Zhang HM, Zhang WL, Qu YL, Liu YC, Gu H, Cai JY, Cao ZJ, Shi XM. [Association of arsenic with unexplained recurrent spontaneous abortion: a case-control study]. Zhonghua Yu Fang Yi Xue Za Zhi 2019; 53:470-474. [PMID: 31091603 DOI: 10.3760/cma.j.issn.0253-9624.2019.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To explore the association of arsenic with unexplained recurrent spontaneous abortion (URSA). Methods: A case-control study was conducted to select URSA patients who were admitted to the Beijing Maternal and Child Health Care Hospital affiliated to Capital Medical University from April to October 2018 as a case group. Women who had a normal pregnancy in the Family Planning Department of the hospital but volunteered to have an abortion were selected as a control group. The case and control group were paired in a 1: 1 ratio. The inclusion criteria of the case group were patients with newly diagnosed recurrent spontaneous abortion who had clinically confirmed more than 2 spontaneous abortions and had 20 weeks prior to pregnancy, excluding patients with recurrent spontaneous abortion caused by abnormal blood coagulation (anti-phospholipid antibody positive), abnormal physiological anatomy (B-ultrasound), abnormal immune factors (anti-nuclear antibody positive, anti-cardiolipin antibody, etc.), genetic chromosomal abnormalities (karyotype analysis) and pathogenic microbial infection. The control group was matched according to the age of the case group (±3 years old) and the gestational age (±2 weeks) to exclude adverse pregnancy outcomes such as stillbirth, congenital malformation, premature delivery and low birth weight infants. A total of 192 subjects were included. Questionnaires were used to collect information of all subjects, and 12 ml of peripheral venous blood was collected to detect blood arsenic levels. Blood arsenic levels were divided into low concentration group (<1.00 μg/L), medium concentration group (1.00-1.50 μg/L) and high concentration group (>1.50 μg/L). The multivariate conditional logistic regression was performed to analyze the relationship between blood arsenic exposure and URSA and explore the influencing factors of blood Arsenic. Results: The geometric mean values of blood arsenic level in the cases group and control group were 1.68 (1.50-1.86) μg/L and 1.26 (1.17-1.37) μg/L, respectively. The blood arsenic level in the case group was significantly higher than that in the control group (P<0.05). The results of multivariate conditional logistic regression analysis showed that after adjusting for tobacco exposure during pregnancy, pre-pregnancy body mass index and the effects of residential decoration in past five years, the risk of URSA was higher in the high-concentration group compared with the low-concentration group (OR=2.56, 95%CI:1.06-6.24). Conclusion: Blood arsenic may increase the risk of URSA in women of childbearing age.
Collapse
Affiliation(s)
- Y Y Li
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - S W Chen
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - F Zhao
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - H M Zhang
- Department of Environment and Health, Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - W L Zhang
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Y L Qu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Y C Liu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - H Gu
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - J Y Cai
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Z J Cao
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - X M Shi
- National Institute of Environment Health Chinese Center for Disease Control and Prevention, Beijing 100050, China
| |
Collapse
|
23
|
Qu YL, Zhao F, Liu L, Song SX, Liu YC, Cai JY, Cao ZJ, Shi XM. [Cause and control of non-sampling error in China National Human Biomonitoring Program]. Zhonghua Yu Fang Yi Xue Za Zhi 2019; 53:107-111. [PMID: 30605972 DOI: 10.3760/cma.j.issn.0253-9624.2019.01.016] [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] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The size of the non-sampling error is directly related to the accuracy and reliability of the sampling survey result. This paper studied the non-sampling errors generated during the sampling process of the China National Human Biomonitoring Program(CNBP), mainly including the sampling frame error, non-response error and measurement error. The program reduced the influence of the non-sampling error on the quality of the survey effectively by scientifically designing the sampling scheme and questionnaire, strengthening investigator trainings and standardizing the data review, which could be used to provide reference for the control of non-sampling errors in public health monitoring projects in China.
Collapse
Affiliation(s)
- Y L Qu
- National Institute of Environmental Health, Chinese center For Disease Control And Prevention, Beijing 100050, China
| | | | | | | | | | | | | | | |
Collapse
|
24
|
Wang ZB, Xin HS, Wang MJ, Li ZY, Qu YL, Miao SJ, Zhang YG. Effects of Dietary Supplementation with Hainanmycin on Protein Degradation and Populations of Ammonia-producing Bacteria In vitro. Asian-Australas J Anim Sci 2013; 26:668-74. [PMID: 25049837 PMCID: PMC4093324 DOI: 10.5713/ajas.2012.12589] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Revised: 01/28/2013] [Accepted: 12/28/2012] [Indexed: 11/27/2022]
Abstract
An in vitro fermentation was conducted to determine the effects of hainanmycin on protein degradation and populations of ammonia-producing bacteria. The substrates (DM basis) for in vitro fermentation consisted of alfalfa hay (31.7%), Chinese wild rye grass hay (28.3%), ground corn grain (24.5%), soybean meal (15.5%) with a forage: concentrate of 60:40. Treatments were the control (no additive) and hainanmycin supplemented at 0.1 (H0.1), 1 (H1), 10 (H10), and 100 mg/kg (H100) of the substrates. After 24 h of fermentation, the highest addition level of hainanmycin decreased total VFA concentration and increased the final pH. The high addition level of hainanmycin (H1, H10, and H100) reduced (p<0.05) branched-chain VFA concentration, the molar proportion of acetate and butyrate, and ratio of acetate to propionate; and increased the molar proportion of propionate, except that for H1 the in molar proportion of acetate and isobutyrate was not changed (p>0.05). After 24 h of fermentation, H10 and H100 increased (p<0.05) concentrations of peptide nitrogen and AA nitrogen and proteinase activity, and decreased (p<0.05) NH3-N concentration and deaminase activity compared with control. Peptidase activitives were not affected by hainanmycin. Hainanmycin supplementation only inhibited the growth of Butyrivibrio fibrisolvens, which is one of the species of low deaminative activity. Hainanmycin supplementation also decreased (p<0.05) relative population sizes of hyper-ammonia-producing species, except for H0.1 on Clostridium aminophilum. It was concluded that dietary supplementation with hainanmycin could improve ruminal fermentation and modify protein degradation by changing population size of ammonia-producing bacteria in vitro; and the addition level of 10 mg/kg appeared to achieve the best results.
Collapse
Affiliation(s)
- Z B Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - H S Xin
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - M J Wang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - Z Y Li
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - Y L Qu
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - S J Miao
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - Y G Zhang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| |
Collapse
|
25
|
Li PH, Qu YL, Xu XJ, Zhu YW, Yu T, Chin KC, Mi J, Gao XY, Lim CT, Shen ZX, Wee ATS, Ji W, Sow CH. Synthesis of "cactus" top-decorated aligned carbon nanotubes and their third-order nonlinear optical properties. J Nanosci Nanotechnol 2006; 6:990-5. [PMID: 16736755 DOI: 10.1166/jnn.2006.167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
We report a new morphology of "cactus" top-decorated aligned carbon nanotubes grown by the PECVD method using pure C2H2 gas. Unlike most previous reports, no additional carrier gas is used for pretreatment. Carbon nanotubes can still grow and maintain the tubular structure underneath the "cactus" tops. It is proposed that the H atoms produced by the dissociation of C2H2 activate the catalyst nanoparticles. Scanning electron microscopy (SEM) shows that the top "cactus" morphology is composed of a large quantity of small nanosheets. Transmission electron microscopy (TEM) reveals the amorphous carbon nature of these "cactus" structures. The formation of these "cactus" structures is possibly due to covalent absorption and reconstruction of carbon atoms on the broken graphite layers of nanotubes produced by the strong ion bombardment under plasma. The third-order optical nonlinearities and nonlinear dynamics are also investigated. The third-order nonlinear susceptibility magnitude /chi(3)/ is found to be 2.2 x 10(-11) esu, and the relaxation process takes place in about 1.8 ps.
Collapse
Affiliation(s)
- P H Li
- Department of Physics, BLK S12, Faculty of Science, National University of Singapore
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Qu YL, Yang ZM, Xie HQ. [The influence of tissue engineered tendon on subgroup of T lymphocytes and its receptor in roman chickens]. Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi 2001; 15:113-7. [PMID: 11286160] [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: 02/19/2023]
Abstract
OBJECTIVE To investigate the influence of tissue engineered tendon on subgroup of T lymphocytes and its receptor in Roman chickens. METHODS The flexor digitorum profundus of the third toes of right feet in 75 Roman chickens were resected and made 2.5 cm defects as experimental model. They were randomly divided into five groups according to five repair methods: no operation (group A), autograft (group B), fresh allograft (group C), polymer combined with allogenous tendon cells (group D), derived tendon materials combined with allogenous tendon cells (group E). The proliferation and transformation of lymphocytes and contribution of CD4+, CD8+, CD28 and T cell receptor (TCR) were detected to study the immune response. RESULTS The CD4+, CD8+ and TCR of group D and E were increased slightly than that of group B after 7 days, while after 14 days, those data decreased gradually and no significant difference between tissue engineered tendon and autografts (P > 0.05), and there was significant difference between fresh allograft and tissue engineered tendon (P < 0.05). Lymphocytes transformation induced by conA also showed no significant difference between tissue engineered tendon and autografts (P > 0.05). CONCLUSION Tendon cells are hypoantigen cells, there are less secretion of soluble antigen or antigen chips dropped out from cells. Tissue engineered tendon has excellent biocompatibility.
Collapse
Affiliation(s)
- Y L Qu
- Department of Orthopedic Surgery, First University Hospital, West China University of Medical Sciences, Chengdu Sichuan, P, R, China 610041.
| | | | | |
Collapse
|
27
|
Qu YL, Sugiyama K, Ohnuki T, Hattori K, Watanabe K, Nagatomo T. Comparison of binding affinities of omega-conotoxin and amlodipine to N-type Ca2+ channels in rat brain. Zhongguo Yao Li Xue Bao 1998; 19:97-100. [PMID: 10374627] [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: 02/12/2023]
Abstract
AIM To compare the binding affinities of omega-conotoxin (CTX) and amlodipine to N-type Ca2+ channels in rat brains. METHODS Whole rat brains were homogenized in HEPES buffer 50 mmol.L-1 (pH 7.4) and centrifuged at 40,000 x g to obtain the membrane-entriched fraction. 125I-omega-conotoxin (125I-omega-CTX) was used as a radioligand. Using radioligand binding assay Kd and Bmax values of the radioligand were determined by Scatchard analysis. The IC50 value for each drug was obtained from displacement experiments. RESULTS No differences in Bmax values of 125I-omega-CTX binding sites between frozen and fresh tissues were observed. Values of Kd and Bmax of N-type Ca2+ channels were 0.02 +/- 0.01 nmol.L-1 and 1029 +/- 108 pmol/g protein, respectively. The pKi values of omega-CTX and amlodipine were 9.57 and less than 4, respectively. The pKi values of propranolol, prazosin, atropine, and histamine were very low. CONCLUSION The binding affinity of the L-type Ca(2+)-antagonist amlodipine to N-type Ca2+ channels in the rat brain was very low.
Collapse
Affiliation(s)
- Y L Qu
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | | | |
Collapse
|
28
|
Watanabe K, Ochiai Y, Washizuka T, Inomata T, Miyakita Y, Shiba M, Izumi T, Shibata A, Qu YL, Nagatomo T. Clinical evaluation of serum amlodipine level in patients with angina pectoris. Gen Pharmacol 1996; 27:205-9. [PMID: 8919632 DOI: 10.1016/0306-3623(95)02022-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Serum amlodipine levels were determined in 18 patients with vasospastic angina. Patients were divided into two groups: Group A (n = 9) received amlodipine 5 mg by single daily administration, and Group B (n = 9) received 10 mg given by single daily administration for the first 3 days, then 5 mg from the 4th day on. The serum amlodipine concentration in Group A took 7 days to reach a steady state of around 8 ng/ml. The level in Group B was 8.9 ng/ml at 3 days. From these results, the optimal dosage of amlodipine in the treatment of angina pectoris is 10 mg for the initial 3 days followed by 5 mg thereafter.
Collapse
Affiliation(s)
- K Watanabe
- Division of Cardiology, Tsubame Rosai Hospital, Niigata, Japan
| | | | | | | | | | | | | | | | | | | |
Collapse
|
29
|
Sugiyama K, Qu YL, Maruyama K, Hattori K, Watanabe K, Nagatomo T. Slow dissociation of long-acting Ca2+ antagonist amlodipine from 3H-PN200-110 binding sites in membranes of rat hearts and brains. Biol Pharm Bull 1996; 19:195-8. [PMID: 8850304 DOI: 10.1248/bpb.19.195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The dissociation rate of amlodipine ((+/-)-3-ethyl 5-methyl 2-[(2-aminoethoxy)methyl]-4-(o-chlorophenyl)-1,4-dihydro-6-methyl- 3,5- pyridinedicarboxylate benzenesulfonate) from rat heart and brain membranes preincubated with drugs and washed out with buffer was assessed by radioligand binding assay using 3H-PN200-110 as a radioligand. The remaining KCl-induced contraction in rat aortic strips washed out after treatment with this drug and the pKi (inhibition constant) values of the drug were compared with those of nisoldipine, nifedipine, manidipine and benidipine. The inhibition of 3H-PN200-110 binding induced by nifedipine was reversed by washing, whereas that induced by amlodipine, manidipine, and benidipine was not readily reversed under these conditions. When rat aortic strips were pretreated with Ca2+ antagonists, the rank order of the inhibition of contractions induced by 50 mM KCl was manidipine = benidipine > amlodipine > nisoldipine > nifedipine, even though Ca2+ antagonists were not present in the extracellular medium. The pKi values of amlodipine in the heart and brain were 6.86 and 7.41, respectively, and these values were lower than those of the other Ca2+ antagonists. There was a good correlation between the potency of the inhibition of 3H-PN200-110 binding by drugs after the washout of membranes and the inhibition exerted by the drugs in contractions induced by 50 mM KCl after the washout of tissues, although this residual inhibition was not correlated with pKi values. Thus, these results suggest that amlodipine has a very slow rate of dissociation from 3H-PN200-110 binding sites, as do manidipine and benidipine, and this property may explain its long-lasting antihypertensive effect.
Collapse
Affiliation(s)
- K Sugiyama
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | | | |
Collapse
|
30
|
Qu YL, Sugiyama K, Hattori K, Yamamoto A, Watanabe K, Nagatomo T. Slow association of positively charged Ca2+ channel antagonist amlodipine to dihydropyridine receptor sites in rat brain membranes. Gen Pharmacol 1996; 27:137-40. [PMID: 8742511 DOI: 10.1016/0306-3623(95)00085-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
1. No significant differences were observed in Kd and Bmax values between pH 7.2 (0.16 +/- 0.01 nM and 155.36 +/- 16.07 fmol/mg protein) and pH 10.0 (0.15 +/- 0.01 nM and 158.63 +/- 13.80 fmol/mg protein) in rat brain membranes. 2. The IC50 ratios at 0- and 270-min preincubations of amlodipine and manidipine at pH 7.2 were 23.09 and 10.25, respectively, whereas these ratios for these two drugs at pH 10.0 were 2.63 and 1.34, respectively. 3. In contrast, on treatment with nisoldipine, benidipine, SM-6586 and nifedipine, no significant differences were observed in the IC50 ratios between 0- and 270-min preincubations at pH 7.2 and 10.0.
Collapse
Affiliation(s)
- Y L Qu
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | | | |
Collapse
|
31
|
Qu YL, Takamizawa C, Sugiyama K, Maruyama K, Hattori K, Watanabe K, Nagatomo T. Residual inhibition in density of [3H]isradipine binding sites in rat brain membrane pretreated with amlodipine. Zhongguo Yao Li Xue Bao 1995; 16:289-293. [PMID: 7668092] [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: 05/21/2023]
Abstract
AIM To test changes in the density of [3H] isradipine binding sites in rat brain membrane pretreated with amlodipine and to compare with those of nifedipine and (+) SM-6586 (methyl 1, 4-dihydro-2, 6-dimethyl-3-(3-(N-benzyl-N-methylaminomethyl)-1,2,4- oxadiazolyl-5-yl)-4-(3-nitrophenyl) pyridine-5-carboxylate). METHODS The membrane-enriched fractions were prepared from rat brain. The brain membranes were preincubated with nifedipine (10 nmol L-1), amlodipine (1 mumol L-1) and SM-6586 (1 nmol L-1) or with no antagonists added for 45 min, and washing and centrifugation were performed 3 times. They were assayed with [3H]isradipine in incubation media. The Kd and Bmax values of the membrane fractions pretreated with the drugs were determined by Scatchard analysis. RESULTS The blockage of the [3H]isradipine binding sites induced by nifedipine was reversed by washing, enabling the low values of the specific binding sites to be observed. The blockages by amlodipine and SM-6586, on the other hand, were not readily reversed. No significant difference was found, however, between in the Kd walues of these drugs. CONCLUSION Amlodipine and SM-6586 are Ca2+ antagonists which dissociate slowly from the Ca2+ channel in membranes.
Collapse
Affiliation(s)
- Y L Qu
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | | | | | |
Collapse
|
32
|
Qu YL, Sugiyama K, Nagatomo T, Maniwa T, Miyagishi A. Calcium channel blocking properties of SM-6586 in rat heart and brain as assessed by radioligand binding assay. Jpn J Pharmacol 1993; 63:165-9. [PMID: 8283826 DOI: 10.1254/jjp.63.165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The interaction of SM-6586 (methyl 1,4-dihydro-2,6-dimethyl-3-[3-(N-benzyl-N-methyl-aminomethyl)-1,2,4- oxadiazolyl-5-yl]-4-(3-nitrophenyl)pyridine-5-carboxylate) with the specific binding of 3H-PN200-110 to rat heart and brain membranes was characterized and compared with those of other Ca2+ antagonists. The blockade of 3H-PN200-110 binding sites induced by nifedipine, nitrendipine and nimodipine was reversed by washing, whereas the blockade by SM-6586 was not readily reversed under these conditions. No significant difference was found in irreversibility between SM-6586 enantiomers. When rat aortic strips were pretreated with SM-6586, the contractions induced by 50 mM KCl were inhibits even though SM-6586 was not present in the extracellular medium. This residual inhibitory effect was much stronger than that of nicardipine. The inhibition of KCl-induced contractions by nifedipine and nitrendipine was easily reversed by washing. Thus, we suggest that (+)SM-6586 is a novel 1,4-dihydropyridine derivative having a very slow rate of dissociation from the binding site. This property may explain its long-lasting antihypertensive effect.
Collapse
Affiliation(s)
- Y L Qu
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | |
Collapse
|
33
|
Zhao CX, Peng YY, Qu YL. EPR studies on electron transfer reactions between O-benzoyl-N-alkylhydroxylamines and perfluoroacyl peroxides. Generation of n- and sec-alkyl perfluoroacyl nitroxides and sec- and tert-alkyl perfluoroalkyl nitroxides. Res Chem Intermed 1992. [DOI: 10.1163/156856792x00191] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
34
|
Kinami J, Qu YL, Tsuchihashi H, Nagatomo T, Maniwa T, Miyagishi A. Assessment of Ca(2+)-antagonistic effect of SM-6586 and its isomers, novel 1,4-dihydropyridine derivatives, by radioligand binding assay. Jpn J Pharmacol 1992; 58:75-8. [PMID: 1640663 DOI: 10.1254/jjp.58.75] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The Ca(2+)-antagonistic effects of the 1,4-dihydropyridine derivative (+/-)SM-6586 and its optical isomers were compared with those of its two derivatives ((+/-)SM-7297 and (+/-)SM-7548) and other Ca(2+)-antagonists using a radioligand binding assay. The Ca(2+)-antagonistic effects of the optical isomers of SM-6586 were in the order of (+) greater than (+/-) greater than (-)SM-6586 in both rat brain and heart. The pKi value of (+)SM-6586 was comparable to those of nimodipine, nicardipine, nifedipine and nitrendipine. The pA2 value for (+)SM-6586 was the highest among the SM-6586 isomers, thus suggesting that (+)SM-6586 has a potent Ca(2+)-antagonistic effect.
Collapse
Affiliation(s)
- J Kinami
- Department of Pharmacology, Niigata College of Pharmacy, Japan
| | | | | | | | | | | |
Collapse
|
35
|
Qu YL. [Study on the technology of anti-inflammatory injections using the method of orthogonal experiment]. Zhong Yao Tong Bao 1988; 13:30-1, 62-3. [PMID: 3197208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|