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Qiu YD, Guo YB, Zhang ZW, Ji SS, Zhou JH, Wu B, Chen C, Wei Y, Ding C, Wang J, Zheng XL, Zhong ZC, Ye LL, Chen GD, Lyu YB, Shi XM. [Association between cognitive impairment and main metals among oldest old aged 80 years and over in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:849-856. [PMID: 37357203 DOI: 10.3760/cma.j.cn112150-20230215-00111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Objective: To identify the main metals involved in cognitive impairment in the Chinese oldest old, and explore the association between these metal exposures and cognitive impairment. Methods: A cross-sectional study was conducted on 1 568 participants aged 80 years and older from Healthy Aging and Biomarkers Cohort Study (2017 to 2018). Fasting venous blood was collected to measure the levels of nine metals (selenium, lead, cadmium, arsenic, antimony, chromium, manganese, mercury, and nickel). The cognitive function of these participants was evaluated by using the Chinese version of the Mini-Mental State Examination (CMMSE). The random forest (RF) was applied to independently identify the main metals that affected cognitive impairment. The multivariate logistic regression model and restricted cubic splines (RCS) model were used to further verify the association of the main metals with cognitive impairment. Results: The age of 1 568 study subjects was (91.8±7.6) years old, including 912 females (58.2%) and 465 individuals (29.7%) with cognitive function impairment. Based on the RF model (the out-of-bag error rate was 22.9%), the importance ranking of variables was conducted and the feature screening of five times ten-fold cross-validation was carried out. It was found that selenium was the metal that affected cognitive function impairment, and the other eight metals were not included in the model. After adjusting for covariates, the multivariate logistic regression model showed that with every increase of 10 μg/L of blood selenium levels, the risk of cognitive impairment decreased (OR=0.921, 95%CI: 0.889-0.954). Compared with the lowest quartile(Q1) of blood selenium, the ORs (95%CI) of Q3 and Q4 blood selenium were 0.452 (0.304-0.669) and 0.419 (0.281-0.622) respectively. The RCS showed a linear dose-response relationship between blood selenium and cognitive impairment (Pnonlinear>0.05). Conclusion: Blood selenium is negatively associated with cognitive impairment in the Chinese oldest old.
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Affiliation(s)
- Y D Qiu
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Guo
- School of Public Health, Jilin University, Changchun 132000, China
| | - Z W Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 132000, China
| | - C Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Z C Zhong
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
| | - G D Chen
- School of Public Health, Zhejiang University, Hangzhou 310030, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- School of Public Health, Zhejiang University, Hangzhou 310030, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zheng XL, Wu B, Qu YL, Chen C, Wang J, Li Z, Qiu YD, Zhang Z, Li FY, Ye LL, Zhou JH, Wei Y, Ji SS, Lyu YB, Shi XM. [Association of plasma vitamin B 12 level with plasma uric acid level among the elderly over 65 years old in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:634-640. [PMID: 37165810 DOI: 10.3760/cma.j.cn112150-20221120-01134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Objective: To investigate the association of plasma vitamin B12 level with plasma uric acid level among the elderly over 65 in 9 longevity areas of China. Methods: The elderly over 65 years old with complete information on plasma vitamin B12 and plasma uric acid from Healthy Aging and Biomarkers Cohort Study (2017 to 2018) were recruited in this study. Information on socio-demographic characteristics, life styles, diet intake, and health status were collected by questionnaire and physical examination; and fasting venous blood was collected to detect the levels of plasma vitamin B12, uric acid and other indicators. Multiple linear regression models were used to analyze the association of plasma vitamin B12 level per interquartile range increase with plasma uric acid level. The association trend of plasma vitamin B12 level with plasma uric acid level was described by restrictive cubic splines fitting multiple linear regression model. Multiple logistic regression models were used to analyze the association of plasma vitamin B12 level stratified by quartiles with hyperuricemia. Results: A total of 2 471 participants were finally included in the study, the age was (84.88±19.76) years old, of which 1 291 (52.25%) were female. The M (Q1, Q3) level of plasma vitamin B12 was 294 (203, 440) pg/ml and the plasma uric acid level was (341.01±90.46) μmol/L. A total of 422 participants (17.08%) were defined with hyperuricemia. The results of multiple linear regression model showed that there was a positive association of plasma vitamin B12 level with plasma uric acid level after adjustment for covariates (P<0.05). An IQR increase in plasma vitamin B12 (237 pg/ml) was associated with a 6.36 (95%CI: 2.00-10.72) μmol/L increase in the plasma uric acid level. The restrictive cubic splines curve showed a positive linear association of log-transformed plasma vitamin B12 with uric acid level (P<0.001). Conclusion: There is a positive association of plasma vitamin B12 level with plasma uric acid level among the elderly over 65 years old in 9 longevity areas of China.
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Affiliation(s)
- X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Zhang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Y Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L L Ye
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- Department of Environmental Epidemiology, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li YW, Li Z, Song HC, Ding L, Ji SS, Zhang M, Qu YL, Sun Q, Zhu YD, Fu H, Cai JY, Li CF, Han YY, Zhang WL, Zhao F, Lyu YB, Shi XM. [Association between urinary arsenic level and serum testosterone in Chinese men aged 18 to 79 years]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:686-692. [PMID: 36977566 DOI: 10.3760/cma.j.cn112150-20221110-01095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Objective: To investigate the association between the urinary arsenic level and serum total testosterone in Chinese men aged 18 to 79 years. Methods: A total of 5 048 male participants aged 18 to 79 years were recruited from the China National Human Biomonitoring (CNHBM) from 2017 to 2018. Questionnaires and physical examinations were used to collect information on demographic characteristics, lifestyle, food intake frequency and health status. Venous blood and urine samples were collected to detect the level of serum total testosterone, urine arsenic and urine creatinine. Participants were divided into three groups (low, middle, and high) based on the tertiles of creatinine-adjusted urine arsenic concentration. Weighted multiple linear regression was fitted to analyze the association of urinary arsenic with serum total testosterone. Results: The weighted average age of 5 048 Chinese men was (46.72±0.40) years. Geometric mean concentration (95%CI) of urinary arsenic, creatinine-adjusted urine arsenic and serum testosterone was 22.46 (20.08, 25.12) μg/L, 19.36 (16.92, 22.15) μg/L and 18.13 (17.42, 18.85) nmol/L, respectively. After controlling for covariates, compared with the low-level urinary arsenic group, the testosterone level of the participants in the middle-level group and the high-level group decreased gradually. The percentile ratio (95%CI) was -5.17% (-13.14%, 3.54%) and -10.33% (-15.68%, -4.63). The subgroup analysis showed that the association between the urinary arsenic level and testosterone level was more obvious in the group with BMI<24 kg/m2 group (Pinteraction<0.05). Conclusion: There is a negative association between the urinary arsenic level and serum total testosterone in Chinese men aged 18-79 years.
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Affiliation(s)
- Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Fu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C F Li
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y Y Han
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Li Z, Lyu YB, Zhao F, Sun Q, Qu YL, Ji SS, Qiu T, Li YW, Song SX, Zhang M, Liu YC, Cai JY, Song HC, Zheng XL, Wu B, Li DD, Liu Y, Zhu Y, Cao ZJ, Shi XM. [Association of lead exposure with stunting and underweight among children aged 3-5 years in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1597-1603. [PMID: 36372750 DOI: 10.3760/cma.j.cn112150-20211229-01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To evaluate the association of lead exposure with stunting and underweight among children aged 3-5 years in China. Methods: Data was collected from China National Human Biomonitoring (CNHBM) between January 2017 and December 2018. A total of 3 554 children aged 3-5 years were included. Demographic characteristic, lifestyle and nutritional status were collected through questionnaires. Height and weight were measured by standardized method. Stunting and underweight status were determined by calculating height for age Z-score and weight for age Z-score. Blood and urine samples were collected to detect the concentrations of blood lead, urinary lead and urinary creatinine. Children were stratified into 4 groups (Q1 to Q4) by quartiles of blood lead level and corrected urinary lead level, respectively. Complex sampling logistic regression models were applied to evaluate the association of the blood lead level, urinary lead level with stunting and underweight. Results: Among 3 554 children, the age was (4.09±1.06) years, of which 1 779 (80.64%) were female and 1 948 (55.84%) were urban residents. The prevalence of stunting and wasting was 7.34% and 2.96%, respectively. The M (Q1, Q3) for blood lead levels and urinary lead levels in children was 17.49 (12.80, 24.71) μg/L, 1.20 (0.61, 2.14) μg/g Cr, respectively. After adjusting for confounding factors, compared with the lowest blood lead concentration group Q1, the risk of stunting gradually increased in the Q3 and Q4 group (Ptrend=0.010), with OR (95%CI) values of 1.40 (0.80-2.46) and 1.80 (1.07-3.04), respectively. Compared with the lowest urinary lead concentration group Q1, the risk of stunting still increased in the Q3 and Q4 group (Ptrend=0.012), with OR (95%CI) values of 1.69 (1.01-2.84) and 1.79 (1.05-3.06), respectively. The correlation between the lead exposure and underweight was not statistically significant (P>0.05). Conclusion: Lead exposure is positively associated with the risk of stunting among children aged 3-5 years in China.
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Affiliation(s)
- Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - T Qiu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Liu
- School of Public Health, Jilin University, Changchun 130012, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Qu YL, Zhao F, Ji SS, Hu XJ, Li Z, Zhang M, Li YW, Lu YF, Cai JY, Sun Q, Song HC, Li DD, Zheng XL, Wu B, Lyu YB, Zhu Y, Cao ZJ, Shi XM. [Mediation effect of inflammatory biomarkers on the association between blood lead levels and blood pressure changes in Chinese adults]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:1591-1596. [PMID: 36372749 DOI: 10.3760/cma.j.cn112150-20211119-01067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the role of inflammatory biomarkers in the relationship between blood lead levels and blood pressure changes. Methods: A total of 9 910 people aged 18-79 years who participated in the China National Human Biomonitoring in 2017-2018 were included in this study. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information, and the data including height, weight and blood pressure were determined through physical examination. Blood and urinary samples were collected for the detection of blood lead and cadmium levels, urinary arsenic levels, white blood cells, neutrophils, lymphocytes, and hypersensitive C-reactive protein (hs-CRP). Weighted linear regression models were used to evaluate the associations between blood lead, inflammatory biomarkers and blood pressure. Mediation analysis was performed to investigate the role of inflammation in the relationship between blood lead levels and blood pressure changes. Results: The median (Q1, Q3) age of all participants was 45.4 (33.8, 58.4)years, including 4 984 males accounting for 50.3%. Multivariate logistic regression model analysis showed that after adjusting for age, gender, residence area, BMI, education level, smoking and drinking status, family history of hypertension, consumption frequency of rice, vegetables, and red meat, fasting blood glucose, total cholesterol, triglycerides, blood cadmium and urinary arsenic levels, there was a positive association between blood lead levels, inflammatory biomarkers and blood pressure (P<0.05). Each 2.71 μg/L (log-transformed) increase of the lead was associated with a 2.05 (95%CI: 0.58, 3.53) mmHg elevation in systolic blood pressure (SBP), 2.24 (95%CI: 1.34, 3.14) mmHg elevation in diastolic blood pressure (DBP), 0.25 (95%CI: 0.05, 0.46) mg/L elevation in hs-CRP, 0.16 (95%CI: 0.03, 0.29)×109/L elevation in white blood cells, and 0.11 (95%CI: 0.02, 0.21)×109/L elevation in lymphocytes, respectively. Mediation analysis showed that the levels of hs-CRP significantly mediated the association of blood lead with SBP, with a proportion about 3.88% (95%CI: 0.45%, 7.32%). The analysis also found that the levels of hs-CRP and neutrophils significantly mediated the association of blood lead with SBP, with a proportion about 4.10% (95%CI: 1.11%, 7.10%) and 2.42% (95%CI: 0.07%, 4.76%), respectively. Conclusion: This study suggests that inflammatory biomarkers could significantly mediate the association of blood lead levels and blood pressure changes.
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Affiliation(s)
- Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X J Hu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Ji SS, Lyu YB, Zhao F, Qu YL, Li Z, Li YW, Song SX, Zhang WL, Liu YC, Cai JY, Song HC, Li DD, Wu B, Liu Y, Zheng XL, Hu JM, Zhu Y, Cao ZJ, Shi XM. [Association of blood lead and blood selenium with serum high-sensitivity C-reactive protein among Chinese adults aged 19 to 79 years]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:195-200. [PMID: 35184484 DOI: 10.3760/cma.j.cn112338-20210715-00555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association of blood lead and blood selenium with serum high-sensitivity C-reactive protein (hs-CRP) among Chinese adults aged 19 to 79 years. Methods: The participants were enrolled from the first wave of China National Human Biomonitoring (CNHBM) conducted from 2017 to 2018. 10 153 participants aged 19 to 79 years were included in this study. Fasting blood samples were obtained from participants. Lead and selenium in whole blood and hs-CRP in serum were measured. Individuals with hs-CRP levels above 3.0 mg/L were defined as elevated hs-CRP. Generalized linear mixed models and restricted cubic spline models were used to analyze the association of blood lead and blood selenium with elevated hs-CRP. Logistic regression models were used to analyze the multiplicative scale and additive scale interaction between blood lead and blood selenium on elevated hs-CRP. Results: The age of participants was (48.91±15.38) years, of which 5 054 (61.47%) were male. 1 181 (11.29%) participants were defined as elevated hs-CRP. After multivariable adjustment, results from generalized linear models showed that compared with participants with the lowest quartile of blood lead, the OR (95%CI) of elevated hs-CRP for participants with the second, third, and highest quartiles were 1.14 (0.94-1.37), 1.25 (1.04-1.52) and 1.38 (1.13-1.68), respectively. When compared with participants with the lowest quartile of blood selenium, the OR (95%CI) of elevated hs-CRP for participants with the second, third and highest quartiles were 0.86 (0.72-1.04), 0.91 (0.76-1.11), and 0.75 (0.61-0.92), respectively. Results from the interaction analysis showed no significant interaction between lead and selenium on elevated hs-CRP. Conclusion: Blood concentration of lead was positively associated with elevated serum hs-CRP, and blood concentration of selenium was inversely related to elevated hs-CRP, while blood lead and selenium did not present interaction on elevated hs-CRP.
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Affiliation(s)
- S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S X Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - D D Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China School of Public Health, Jilin University, Changchun 130012, China
| | - X L Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - J M Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Liu Y, Lyu YB, Wu B, Wei Y, Chen C, Zhou JH, Zhao F, Li XW, Wang J, Li Z, Li CC, Ji SS, Li YW, Guo YB, Ju AP, Xue K, Shi XM, Yu Q. [Association between urinary arsenic levels and anemia among older adults in nine longevity areas of China]. Zhonghua Yi Xue Za Zhi 2022; 102:101-107. [PMID: 35012297 DOI: 10.3760/cma.j.cn112137-20210706-01516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the association between urinary arsenic levels and anemia among older adults in nine longevity areas of China. Methods: A total of 1 896 subjects aged 65 years and above who participated in the Healthy Aging and Biomarkers Cohort Study (HABCS) in 2017-2018 were included. A self-made questionnaire was used to collect demographic characteristics, lifestyle and other information from the subjects. Through physical examination, data including height, weight and blood pressure were determined and body mass index (BMI) was calculated. Blood and urine samples were collected for the detection of hemoglobin (Hb), blood glucose, blood lipids, plasma vitamin B12 and urinary arsenic concentrations. The urinary arsenic levels were divided into four groups according to the quartiles of urinary arsenic concentrations (μg/g creatinine): Q1 (<18.7), Q2 (18.7-34.5), Q3 (34.6-69.5) and Q4(≥69.6). Multivariate logistic regression model and restricted cubic spline fitting logistic regression model were used to analyze the association between urinary arsenic levels and anemia. Results: The age of the 1 896 subjects (M (Q1, Q3)) was 83 (74, 92) years, including 952 females (50.21%), and the concentration of Hb (M (Q1, Q3)) was 135 (124, 147)g/L. The prevalence of anemia was 24.89% (472 cases). The geometric mean and M (Q1, Q3) of urinary arsenic concentrations were 37.5 and 34.6 (18.7, 69.6)μg/g creatinine, respectively. Multivariate logistic regression model analysis showed that after adjusting for age, gender, BMI, education level, smoking and drinking status, residence, economic level, ethnicity, the status of vitamin B12 deficiency, consumption frequency of aquatic products and meat, the prevalence of hypertension, diabetes and dyslipidemia, urinary arsenic levels were positively associated with anemia (Taking group Q1 as a reference, OR (95%CI) values in Q2, Q3 and Q4 groups were 1.73 (1.20-2.50), 2.08 (1.43-3.02) and 1.52 (1.02-2.28), respectively). The results of restricted cubic spline fitting logistic regression analysis showed a non-linear association between urinary arsenic concentrations and anemia (P<0.001). Subgroup analysis showed there was a negative multiplicative interaction between the prevalence of chronic diseases and urinary arsenic levels with OR (95%CI) was 0.55 (0.30-0.99), while no multiplicative interaction was found between age, gender, residence, smoking status, drinking status and urinary arsenic levels (P>0.05). There was a positive association between urinary arsenic levels and anemia in participants who were absence of chronic diseases,male, living in rural, smoking and drinking with OR (95%CI) values of 3.62 (1.30-10.06),2.46 (1.34-4.52), 1.70 (1.03-2.80), 2.21 (1.01-4.82) and 2.79 (1.23-6.33), respectively. Conclusion: There is a positive association between urinary arsenic levels and anemia among older adults in nine longevity areas of China.
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Affiliation(s)
- Y Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X W Li
- School of Public Health, Jilin University, Changchun 130012, China
| | - J Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - A P Ju
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - K Xue
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Yu
- School of Public Health, Jilin University, Changchun 130012, China
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Lyu YB, Zhao F, Qiu YD, Ding L, Qu YL, Xiong JH, Lu YF, Ji SS, Wu B, Hu XJ, Li Z, Zheng XL, Zhang WL, Liu JX, Li YW, Cai JY, Song HC, Zhu Y, Cao ZJ, Shi XM. [Association of cadmium internal exposure with chronic kidney disease in Chinese adults]. Zhonghua Yi Xue Za Zhi 2021; 101:1921-1928. [PMID: 34139825 DOI: 10.3760/cma.j.cn112137-20210425-00996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze the association of the cadmium internal exposure with chronic kidney disease (CKD) in Chinese adults aged 18 and older. Methods: A total of 9 821 adults aged 18-79 from the China National Human Biomonitoring (CNHBM) from 2017 to 2018 were included. Blood and urine cadmium exposure levels were measured by inductively coupled plasma mass spectrometry (ICP-MS), and urine cadmium levels were adjusted with urine creatinine; CKD were defined by estimated glomerular filtration (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI). Weights were considered due to complex sampling process for in statistical analysis. Logistic regression is used to analyze the association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine exposure levels with CKD, and restricted cube spline (RCS) was used to assess the exposure-response curve of blood cadmium, urine cadmium and urine cadmium adjusted with creatinine with CKD. Results: The weighted age was 44.75 and males accounted for 61.1%. The prevalence rate of CKD was 12.7%. The geometric mean values of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine were 0.96 μg/L, 0.61 μg/L, and 0.58 μg/g. After adjusting for confounding factors, the weighted logistic regression showed that the lowest quintile (Q1) was compared with the odds ratio (OR) of the highest quintile (Q5) of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine and the 95% confidence interval (CI) was 1.80 (1.02-3.20), 1.77 (0.94-3.31) and 1.94 (1.11-3.37) respectively. In the restricted cubic spline regression model, non-linear association of blood cadmium, urine cadmium, and urine cadmium adjusted with creatinine with CKD were observed after adjusting for related confounding factors (P<0.001, 0.018, 0.031 respectively). The risk of CKD increased with the increment of cadmium exposure without risk threshold, and the exposure response curve was steeper at low cadmium exposure. Conclusions: Among Chinese adults aged 18 and older, cadmium exposure is positively associated with the risk of chronic kidney disease.
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Affiliation(s)
- Y B Lyu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Qiu
- School of Public Health, Zhejiang University, Hangzhou 310011, China
| | - L Ding
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Xiong
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y F Lu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - X J Hu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X L Zheng
- Global Health Center, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - W L Zhang
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J X Liu
- School of Public Health, China Medical University, Shenyang 110001, China
| | - Y W Li
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Zhu
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Key Laboratory of Environment and Population Health, Chinese Center for Disease Control and Prevention, Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Wu B, Lyu YB, Zhou JH, Wei Y, Zhao F, Chen C, Li CC, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Song HC, Tan QY, Zhang MY, Cao ZJ, Shi XM. [A cohort study on plasma uric acid levels and the risk of type 2 diabetes mellitus among the oldest old in longevity areas of China]. Zhonghua Yi Xue Za Zhi 2021; 101:1171-1177. [PMID: 33902249 DOI: 10.3760/cma.j.cn112137-20201221-03409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the effect of plasma uric acid level on the incident risk of type 2 diabetes mellitus (T2DM) among the oldest old (those aged ≥80 years). Methods: Participants were recruited from the Healthy Aging and Biomarkers Cohort Study (HABCS), which conducted a baseline survey in 2008-2009 and follow-up of 3 times in 2011-2012, 2014, and 2017-2018, respectively. A total of 2 213 oldest old were enrolled in this study. The general demographic, socioeconomic, lifestyle and disease data of the oldest old were collected, and physical measurements were made for the oldest old. Fasting venous blood was collected for uric acid and blood glucose detection. Information on the incident and death of T2DM were collected through the follow-up. Cox proportional hazard regression model was used to explore the association of hyperuricemia and plasma uric acid level with the incidence of T2DM. Restricted cubic spline (RCS) function was used to explore the dose-response relationship of plasma uric acid levels with the risk of T2DM. Results: The age of participants was (93.2±7.6) years old, and 66.7% of the participants (1 475) were female. The plasma uric acid level at baseline was (289.1±88.0)μmol/L, and the prevalence of hyperuricemia was 13.3% (294 cases). During 9 years of cumulative follow-up of 7 471 person-years (average of 3.38 years for each), 122 new cases of T2DM occurred and the incidence density was 1 632.98/105 person year. Cox proportional hazards regression analysis showed that per 10μmol/L increase in plasma uric acid level, the risk of T2DM increased by 1.1% [HR (95%CI): 1.011 (1.004, 1.017)]. Compared with the participants with the lowest quintile of plasma uric acid (Q1), the risk of diabetes increased by 20.7 % among the oldest old with uric acid in the highest quintile (Q5) [HR (95%CI):1.207 (1.029, 1.416)]. The risk of T2DM was 19.2% higher in the hyperuricemia group than that in the oldest old with normal plasma uric acid [HR (95%CI): 1.192 (1.033, 1.377)]. RCS function showed that the risk of T2DM increased with the increase in plasma uric acid levels in a nonlinear dose-response relationship (P=0.016). Conclusion: The incident risk of T2DM increases with the elevates of plasma uric acid levels in the oldest old.
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Affiliation(s)
- B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Y Tan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Y Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
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Ji SS, Lyu YB, Qu YL, Chen C, Li CC, Zhou JH, Li Z, Zhang WL, Li YW, Liu YC, Zhao F, Zhu HJ, Shi XM. [Association of sleep duration with cognitive impairment among older adults aged 65 years and older in China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:31-38. [PMID: 33355766 DOI: 10.3760/cma.j.cn112150-20200916-01208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Objective: The study is to examine association of sleep duration and cognitive impairment in the older adults aged 65 years and older in China. Methods: We analyzed data from 2017-2018 wave of Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 14 966 participants were included in the analysis. Data with respect to socioeconomic status, community involvement, behavior pattern, diet, life style, family structure, disease condition, mental health and cognitive function were collected. Cognitive function was measured with Mini-mental State Examination (MMSE). We conducted generalized linear mixed models to examine associations of sleep duration with cognitive impairment, and subgroup analyses of sex and age were conducted. Results: Among 14 966 participants, the percentage of participants aged 65 to 79 years, 80 to 89 years, 90 to 99 years and 100 years and older was 5 148 (4.40%), 3 777 (25.24%), 3 322 (22.20%) and 2 719 (18.16%), respectively. A total of 2 704 participants reported sleep duration of 5 h and less, and 3 883 reported 9 h and more, accounting for 18.94% and 27.19%, respectively. In total, 3 748 were defined with cognitive impairment, accounting for 25.04%. The results of generalized linear mixed models showed that both short (≤5 h) and long (≥ 9 h) sleep duration were associated with cognitive impairment compared with sleep duration of 7 h, with OR(95%CI) of 1.35(1.09-1.68) and 1.70(1.39-2.07), respectively. The association of sleep duration with cognitive impairment was more obvious in males and individuals aged 65 to 79 years old. Conclusion: Short or long sleep duration was responsible for increased risk of cognitive impairment in older Chinese.
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Affiliation(s)
- S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H J Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zhang MY, Lyu YB, Zhou JH, Zhao F, Chen C, Tan QY, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Wu B, Cao ZJ, Yu Q, Shi XM. [Association of blood lead level with cognition impairment among elderly aged 65 years and older in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:66-71. [PMID: 33355770 DOI: 10.3760/cma.j.cn112150-20200728-01066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the association between blood lead concentrations and cognition impairment among Chinese older adults aged 65 or over. Method: Data was collected in 9 longevity areas from Heathy Aging and Biomarkers Cohort Study between 2017 and 2018. This study included 1 684 elderly aged 65 years and older. Information about demographic characteristics, socioeconomic factors, health status and cognitive function score of respondents were collected by questionnaire survey and physical examination. Venous blood of the subjects was collected to detect the blood lead concentration. Subjects were stratified into four groups (Q1-Q4) by quartile of blood lead concentration. Multivariate logistic regression model was used to analyze the association between blood lead concentration and cognitive impairment. The linear or non-linear association between blood lead concentration and cognitive impairment were described by restrictive cubic splines (RCS). Results: Among the 1 684 respondents, 843 (50.1%) were female and 191 (11.3%) suffered from cognition impairment. After adjusting for confounding factors, the OR value and 95%CI of cognition impairment was 1.05 (1.01-1.10) for every 10 μg/L increase in blood lead concentration in elderly; Compared with the elderly in Q1, the elderly with higher blood lead concentration had an increased risk of cognitive impairment. The OR value and 95%CI of Q2, Q3 and Q4 groups were 1.19 (0.69-2.05), 1.45 (0.84-2.51) and 1.92 (1.13-3.27), respectively. Conclusion: Higher blood lead concentration is associated with cognitive impairment among the elderly aged 65 years and older in 9 longevity areas in China.
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Affiliation(s)
- M Y Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Y Tan
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Yu
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Tan QY, Lyu YB, Zhou JH, Zhang MY, Chen C, Zhao F, Li CC, Qu YL, Ji SS, Lu F, Liu YC, Gu H, Wu B, Cao ZJ, Zhao SH, Shi XM. [Association of blood oxidative stress level with hypertriglyceridemia in the elderly aged 65 years and older in 9 longevity areas of China]. Zhonghua Yu Fang Yi Xue Za Zhi 2021; 55:18-24. [PMID: 33355764 DOI: 10.3760/cma.j.cn112150-20200728-01065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the association of blood oxidative stress level with hypertriglyceridemia in the elderly aged 65 years and older in China. Methods: A total of 2 393 participants aged 65 years and older were recruited in 9 longevity areas from Heathy Aging and Biomarkers Cohort Study, during 2017 to 2018. Information on demographics characteristic, life style and health status were collected by questionnaire and physical examination, and venous blood was collected to detect the levels of blood oxidative stress and hypertriglyceridemia. The linear or non-linear association between oxidative stress and hypertriglyceridemia was described by restrictive cubic splines (RCS) fitting multiple linear regression model. The generalized linear mixed effect model was conducted to assess the association between oxidative stress and hypertriglyceridemia. Results: A total of 2 393 participants, mean age was 84.6 years, the youngest was 65 and the oldest was 112, the male was 47.9%(1 145/2 393), the triglyceride level was (1.4±0.8) mmol/L. The hypertriglyceridemia detection rate was 9.99%(239/2 393). The results of multiple linear regression model with restrictive cubic spline fitting showed that MDA level was linear association with triglyceride level; SOD level was nonlinear association with triglyceride level. MDA level had significantly association with hypertriglyceridemia, and the corresponding OR value was 1.063 (95%CI: 1.046,1.081) with 1 nmol/ml increment of blood MDA; SOD level had significantly association with hypertriglyceridemia, and the corresponding OR value was 0.986(95%CI: 0.983,0.989) with 1 U/ml increment of blood SOD. Conclusion: Among the elderly aged 65 and older in 9 longevity areas in China, MDA and SOD levels were associated with the risk of hypertriglyceridemia.
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Affiliation(s)
- Q Y Tan
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J H Zhou
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Y Zhang
- School of Public Health, Jilin University, Changchun 130012, China
| | - C Chen
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C C Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Lu
- Beijing Municipal Health Commission Information Center, (Beijing Municipal Health Commission Policy Research Center), Beijing 100034, China
| | - Y C Liu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Gu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - B Wu
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Z J Cao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S H Zhao
- School of Public Health, Jilin University, Changchun 130012, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Jee SH, Won SY, Yun JE, Lee JE, Park JS, Ji SS. Polymorphism p53 codon-72 and invasive cervical cancer: a meta-analysis. Int J Gynaecol Obstet 2003; 85:301-8. [PMID: 15145278 DOI: 10.1016/j.ijgo.2003.08.017] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2003] [Revised: 08/20/2003] [Accepted: 08/27/2003] [Indexed: 10/26/2022]
Abstract
OBJECTIVES Although some studies have reported that the arginine isoform on codon 72 of p53 increases the susceptibility to invasive cervical cancer, such data remain controversial. The objective of this study was to quantitatively summarize the evidence for such a relationship. METHODS Our data sources consisted of a MEDLINE search of the literature published before December 2002, bibliography review, and expert consultation. Thirty-seven studies met the inclusion criteria. Information on sample size, study design, Hardy-Weinberg equilibrium, and method of genotype determination was abstracted by two reviewers using a standardized protocol. The overall odds ratio (OR) of the p53 gene on invasive cervical cancer was estimated using the Mantel-Haenzel method. RESULTS The overall OR (95% confidence interval) for cervical cancer among those with the homozygous mutant (Arg/Arg) was 1.2 (1.1-1.3, P=0.001) compared with those with the heterozygous mutant (Arg/Pro). By a cellular type of cervical cancer, the overall OR among those with Arg/Arg was statistically significant in adenocarcinomas (1.7, 1.1-2.6, P=0.024), but not in squamous cell carcinomas (1.1, 0.9-1.2, P=0.960), compared with Pro/Pro. Compared with Arg/Pro, the OR among those with Arg/Arg was statistically significant in HPV types 16 (1,5, 1.2-2.0, P=0.002). CONCLUSIONS Overall, the p53 gene was associated with increased risk for invasive cervical cancer. However, the risk varied by country, cellular, and HPV type.
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Affiliation(s)
- S H Jee
- Department of Epidemiology and Health Promotion, Graduate School of Health Science and Management, Yonsei University, Seoul, South Korea.
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