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Shen A, Chen C, Zhang Z, Zhou J, Lv Y, Wang J, Li J. Associations between socioeconomic status and rates of blood pressure changes among Chinese older adults: a longitudinal community-based cohort study. Public Health 2024; 232:121-127. [PMID: 38772200 DOI: 10.1016/j.puhe.2024.04.027] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/06/2024] [Accepted: 04/18/2024] [Indexed: 05/23/2024]
Abstract
OBJECTIVES The relationships between socioeconomic status (SES) and blood pressure changes among older adults in China remain unclear. This study aimed to examine the associations between SES and rates of blood pressure changes among Chinese older adults. STUDY DESIGN Community-based, prospective, longitudinal cohort study. METHODS This study included 13,541 participants aged ≥65 years from the Chinese Longitudinal Healthy Longevity Survey between 2002 and 2018. SES was assessed by educational level, occupation, household yearly per capita income, and financial support. The estimated annual changes (EACs) of blood pressure were computed as the difference in blood pressure levels between any two adjacent surveys divided by the time interval. Associations between SES and EACs of blood pressure were evaluated using generalised estimating equations. RESULTS Lower SES was significantly associated with greater annual increases of blood pressure among Chinese older adults. The effect of SES on EACs of blood pressure was more pronounced among non-hypertensive participants. Compared to EACs among non-hypertensive participants with high SES, multivariable-adjusted EACs among those with low SES increased by 0.57 mmHg (95% confidence interval [CI]: 0.16, 0.99), 0.32 mmHg (95% CI: 0.07, 0.57), and 0.40 mmHg (95% CI: 0.13, 0.66) for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. CONCLUSIONS This study revealed strong associations between SES and EACs of blood pressure among Chinese older adults, especially in the non-hypertensive population. Findings suggest that prevention strategies for hypertension should pay more attention to the older population with low SES.
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Affiliation(s)
- Anna Shen
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zenghang Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Qu Y, Sheng N, Ji S, Li Z, Wang J, Pan Y, Hu X, Zheng X, Li Y, Song H, Xie L, Zhang W, Cai J, Zhao F, Zhu Y, Cao Z, Lv Y, Dai J, Shi X. Dietary seafood as a potential modifier in the relationship between per- and polyfluoroalkyl substances (PFASs) burden and prediabetes/diabetes: Insights from a nationally representative cross-sectional study. J Hazard Mater 2024; 473:134645. [PMID: 38762989 DOI: 10.1016/j.jhazmat.2024.134645] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 04/28/2024] [Accepted: 05/16/2024] [Indexed: 05/21/2024]
Abstract
While seafood is recognized for its beneficial effects on glycemic control, concerns over elevated levels of per- and polyfluoroalkyl substances (PFASs) may deter individuals from its consumption. This study aims to elucidate the relationship between seafood intake, PFASs exposure, and the odds of diabetes. Drawing from the China National Human Biomonitoring data (2017-2018), we assessed the impact of PFASs on the prevalence of prediabetes and diabetes across 10851 adults, including 5253 individuals (48.1%) reporting seafood consumption. Notably, seafood consumers exhibited PFASs levels nearly double those of non-consumers. Multinomial logistic regression identified significant positive associations between serum PFASs concentrations and prediabetes (T3 vs. T1: ORPFOA: 1.64 [1.08-2.49], ORPFNA: 1.59 [1.19-2.13], ORPFDA: 1.56 [1.13-2.17], ORPFHxS: 1.58 [1.18-2.12], ORPFHpS: 1.73 [1.24-2.43], ORPFOS: 1.51 [1.15-1.96], OR6:2 Cl-PFESA: 1.58 [1.21-2.07]). Significant positive association were also found between PFHpS, PFOS, and diabetes. RCS curves indicated significant non-linear relationships between log-transformed PFOA, PFUnDA, PFOS, 6:2 Cl-PFESA, and FBG levels. Subgroup analyses revealed that seafood consumption significantly mitigated the associations between PFASs burdens and prediabetes/diabetes. These findings suggest a protective role of dietary seafood against the adverse effects of PFASs exposure on glycemic disorders, offering insights for dietary interventions aimed at mitigating diabetes risks associated with PFASs.
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Affiliation(s)
- Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Nan Sheng
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Jinghua Wang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yitao Pan
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaojian Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Haocan Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Linna Xie
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Wenli Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Jiayi Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Beijing 100021, Chaoyang, China.
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Lv Y, Zhang Y, Li X, Gao X, Ren Y, Deng L, Xu L, Zhou J, Wu B, Wei Y, Cui X, Xu Z, Guo Y, Qiu Y, Ye L, Chen C, Wang J, Li C, Luo Y, Yin Z, Mao C, Yu Q, Lu H, Kraus VB, Zeng Y, Tong S, Shi X. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study. Eur Heart J 2024:ehae206. [PMID: 38626306 DOI: 10.1093/eurheartj/ehae206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 02/17/2024] [Accepted: 03/19/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND AND AIMS Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China. METHODS A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality. RESULTS During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC. CONCLUSIONS Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC.
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Affiliation(s)
- Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Xiang Gao
- Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China
| | - Yongyong Ren
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Luojia Deng
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Lanjing Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Xingyao Cui
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Zinan Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Yufei Luo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, China
| | - Zhaoxue Yin
- Division of Non-Communicable Disease and Healthy Aging Management, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Hui Lu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Virginia Byers Kraus
- Department of Medicine, Duke Molecular Physiology Institute and Division of Rheumatology, Duke University School of Medicine, Durham, NC, USA
| | - Yi Zeng
- Center for Study of Healthy Aging and Development Studies, Peking University, Beijing, China
| | - Shilu Tong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
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Gao Y, Lv Y, Kong DQ, Li B. Effectiveness of cannulated screw fixation for femoral neck fracture assisted by three-dimensional printing navigation template in middle-aged and elderly patients. Eur Rev Med Pharmacol Sci 2024; 28:3208-3215. [PMID: 38708479 DOI: 10.26355/eurrev_202404_36049] [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: 05/07/2024]
Abstract
OBJECTIVE We sought to explore the effectiveness of cannulated screw fixation for femoral neck fractures in middle-aged and elderly patients assisted by a three-dimensional printing navigation template. PATIENTS AND METHODS A total of 98 middle-aged and elderly patients who underwent cannulated screw fixation for femoral neck fractures were retrospectively analyzed. They were allocated into two groups, each comprising 49 patients. Surgical indexes, hip function, and pain levels were compared between the two groups. RESULTS The study group, assisted by the three-dimensional printing navigation template, exhibited significantly reduced nail insertion, fewer instances of C-arm fluoroscopy, shorter operation time, quicker time to bone union, earlier initiation of walking exercise, shorter time to weight-bearing walking, and reduced hospital stay than those in the control group (all p<0.001). However, the study group also experienced higher blood loss compared to the control group (p<0.001). Postoperatively, at 3 months and 12 months, the study group demonstrated significantly higher scores compared to the control group (both p<0.001) and reported significantly lower pain scores than that in the other group at 1 week and 12 months post-surgery (both p<0.001). Furthermore, the study group experienced significantly fewer postoperative complications than the control group (p=0.029). CONCLUSIONS Cannulated screw fixation for femoral neck fractures assisted by a 3D printing navigation template is more effective and safer than traditional fixation methods. This approach represents a promising alternative for surgical management.
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Affiliation(s)
- Y Gao
- Department of Trauma Orthopedics, The Second Affiliated Hospital of Shandong First Medical University, Taian, China.
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Fan C, Jiang Z, Teng C, Song X, Li L, Shen W, Jiang Q, Huang D, Lv Y, Du L, Wang G, Hu Y, Man S, Zhang Z, Gao N, Wang F, Shi T, Xin T. Efficacy and safety of intrathecal pemetrexed for TKI-failed leptomeningeal metastases from EGFR+ NSCLC: an expanded, single-arm, phase II clinical trial. ESMO Open 2024; 9:102384. [PMID: 38377785 PMCID: PMC11076967 DOI: 10.1016/j.esmoop.2024.102384] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND This study aimed to evaluate the efficacy and safety of intrathecal pemetrexed (IP) for treating patients with leptomeningeal metastases (LM) from non-small-cell lung cancer (NSCLC) who progressed from epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in an expanded, prospective, single-arm, phase II clinical study (ChiCTR1800016615). PATIENTS AND METHODS Patients with confirmed NSCLC-LM who progressed from TKI received IP (50 mg, day 1/day 5 for 1 week, then every 3 weeks for four cycles, and then once monthly) until disease progression or intolerance. Objectives were to assess overall survival (OS), response rate, and safety. Measurable lesions were assessed by investigator according to RECIST version 1.1. LM were assessed according to the Response Assessment in Neuro-Oncology (RANO) criteria. RESULTS The study included 132 patients; 68% were female and median age was 52 years (31-74 years). The median OS was 12 months (95% confidence interval 10.4-13.6 months), RANO-assessed response rate was 80.3% (106/132), and the most common adverse event was myelosuppression (n = 42; 31.8%), which reversed after symptomatic treatment. The results of subgroup analysis showed that absence of brain parenchymal metastasis, good Eastern Cooperative Oncology Group score, good response to IP treatment, negative cytology after treatment, and patients without neck/back pain/difficult defecation had longer survival. Gender, age, previous intrathecal methotrexate/cytarabine, and whole-brain radiotherapy had no significant influence on OS. CONCLUSIONS This study further showed that IP is an effective and safe treatment method for the EGFR-TKI-failed NSCLC-LM, and should be recommended for these patients in clinical practice and guidelines.
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Affiliation(s)
- C Fan
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Z Jiang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - C Teng
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - X Song
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - L Li
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - W Shen
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Q Jiang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - D Huang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Y Lv
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - L Du
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - G Wang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Y Hu
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - S Man
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - Z Zhang
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin
| | - N Gao
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - F Wang
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - T Shi
- Department of Oncology, Heilongjiang Sengong General Hospital, Harbin, People's Republic of China
| | - T Xin
- Department of Oncology, Second Affiliated Hospital of Harbin Medical University, Harbin.
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Li Y, Lv Y, Li J, Ling P, Guo X, Zhang L, Ni J, Long Y. Dexamethasone relieves the inflammatory response caused by inguinal hernia meshes through miR-155. Hernia 2024:10.1007/s10029-024-02985-2. [PMID: 38492053 DOI: 10.1007/s10029-024-02985-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 02/06/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Inguinal hernia is a relatively common condition. Most patients with inguinal hernia require surgery. At present, mesh repair is one of the most effective methods to treat inguinal hernia, but insertion of the mesh can cause inflammation. Dexamethasone (DEX) can treat inflammation, but the mechanism by which DEX alleviates inflammation caused by inguinal hernia mesh placement remains unclear. METHOD We randomly divided rats into groups: negative control (NC), inguinal hernia (IH), polypropylene mesh (PM), DEX treatment, and miR-155 treatment groups. RT-qPCR was performed to determine the expression of miR-155. ELISA was implemented to determine the secretion of IL-1β, IL-6, and IL-18. Western blotting was used to detect caspase-1, JAK1, p-JAK1, STAT3, and p-STAT3 expression. A dual-luciferase reporter gene array identified a connection between miR-155 and JAK1. RESULTS The results revealed that the expression of miR-155, IL-1β, IL-6, and IL-18 was upregulated in the PM group. After DEX treatment, the secretion of miR-155, caspase-1, IL-1β, IL-6, and IL-18 decreased. Dual luciferase results confirmed that miR-155 induced the targeted downregulation of JAK1, while a miR-155 mimic reversed the therapeutic effect of DEX, and the expression levels of p-JAK1 and p-STAT3 increased. CONCLUSION DEX regulates the JAK1/STAT3 signaling pathway through miR-155 to relieve inflammation caused by inguinal hernia meshes.
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Affiliation(s)
- Y Li
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - Y Lv
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - J Li
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - P Ling
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - X Guo
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - L Zhang
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - J Ni
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China
| | - Y Long
- Department of General Surgery, The First People's Hospital of Yunnan Province, Xishan District, No. 157, Jinbi Road, Kunming, 650032, Yunnan, China.
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7
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Wei Y, Wang X, Sun Q, Shi W, Zhang W, Gao X, Li Y, Hao R, Dong X, Chen C, Cao K, Jiang W, Yang Z, Zhu Y, Lv Y, Xv D, Li J, Shi X. Associations of environmental cadmium exposure with kidney damage: Exploring mediating DNA methylation sites in Chinese adults. Environ Res 2024; 251:118667. [PMID: 38462081 DOI: 10.1016/j.envres.2024.118667] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/12/2024]
Abstract
Environmental exposure is widely recognized as the primary sources of Cadmium (Cd) in the human body, and exposure to Cd is associated with kidney damage in adults. Nevertheless, the role of DNA methylation in Cd-induced kidney damage remains unclear. This study aimed to investigate the epigenome-wide association of environmental Cd-related DNA methylation changes with kidney damage. We included 300 non-smoking adults from the China in 2019. DNA methylation profiles were measured with Illumina Infinium MethylationEPIC BeadChip array. Linear mixed-effect model was employed to estimate the effects of urinary Cd with DNA methylation. Differentially methylated positions (DMPs) associated with urinary Cd were then tested for the association with kidney damage indicators. The mediation analysis was further applied to explore the potential DNA methylation based mediators. The prediction model was developed using a logistic regression model, and used 1000 bootstrap resampling for the internal validation. We identified 27 Cd-related DMPs mapped to 20 genes after the adjustment of false-discovery-rate for multiple testing among non-smoking adults. 17 DMPs were found to be associated with both urinary Cd and kidney damage, and 14 of these DMPs were newly identified within the Chinese. Mediation analysis revealed that DNA methylation of cg26907612 and cg16848624 mediated the Cd-related reduced kidney damage. In addition, ten variables were selected using the LASSO regression analysis and were utilized to develop the prediction model. It found that the nomogram model predicted the risk of kidney damage caused by environmental Cd with a corrected C-index of 0.779. Our findings revealed novel DMPs associated with both environmental Cd exposure and kidney damage among non-smoking adults, and developed an easy-to-use nomogram-illustrated model using these novel DMPs. These findings could provide a theoretical basis for formulating prevention and control strategies for kidney damage from the perspective of environmental pollution and epigenetic regulation.
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Affiliation(s)
- Yuan Wei
- Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, Jilin, 130021, 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
| | - Xiaochen 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
| | - Qi 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
| | - Wanying Shi
- Department of Epidemiology and Health Statistics, and Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, China
| | - Wenli 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
| | - Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100083, China
| | - Yawei 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
| | - Ruiting Hao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xiaojie Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Chen 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
| | - Kangning 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
| | - Weilong Jiang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Zhengxiong Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Ying 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
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Dongqun Xv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Juan Li
- Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, Jilin, 130021, 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; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
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Ni W, Lv Y, Yuan X, Zhang Y, Zhang H, Zheng Y, Shi X, Xu J. Associations of low-density lipoprotein cholesterol with all-cause and cause-specific mortality in older adults in China. J Clin Endocrinol Metab 2024:dgae116. [PMID: 38436437 DOI: 10.1210/clinem/dgae116] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/30/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
CONTEXT Limited information was available on detailed associations of low-density lipoprotein cholesterol (LDL-C) with all-cause and cause-specific mortality in older adults. METHODS This prospective cohort study included a representative sample of 211,290 adults aged 65 or older, who participated in Shenzhen Healthy Aging Research 2018-2019. The vital status of the participants by 31 December, 2021 was determined. We estimated the hazard ratios (HR) with 95% confidence intervals for all-cause or cause-specific mortality using multivariable Cox proportional hazards models and Cox models with restricted cubic spline(RCS) . RESULTS The median follow-up time was 3.08 years. A total of 5,333 participants were confirmed to have died. Among them, 2,303 cardiovascular disease (CVD) deaths and 1,881 cancer deaths occurred. Compared to those with LDL-C of 100-129 mg/dL, the all-cause mortality risk was significantly higher for individuals with LDL-C level that was very low (< 70 mg/dL) or low (70-99 mg/dL). Compared with individuals with the reference LDL-C level, the multivariable-adjusted HR for CVD-specific mortality was 1.327 for those with very low LDL-C level (< 70 mg/dL), 1.437 for those with high LDL-C level (160 mg/dL ≦ LDL-C < 190mg/dL), 1.528 for those with very high LDL-C level (≥ 190 mg/dL). Low LDL-C level (70-99 mg/dL) and very low LDL-C level (< 70 mg/dL) were also associated with increased cancer mortality and other-cause mortality, respectively. The results from RCS curve showed similar results. CONCLUSION Considering the risk of all-causes mortality and cause-specific mortality, we recommended 100-159 mg/dL as the optimal range of LDL-C among older adults in China.
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Affiliation(s)
- Wenqing Ni
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
| | - Yuebin Lv
- National Institute of Environmental Health,Chinese Center for Disease Control and Prevention, Bejing, 100021, China
| | - Xueli Yuan
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
| | - Yan Zhang
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
| | - Hongmin Zhang
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
| | - Yijing Zheng
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
| | - Xiaoming Shi
- National Institute of Environmental Health,Chinese Center for Disease Control and Prevention, Bejing, 100021, China
| | - Jian Xu
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, 518020, China
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Li X, Chen C, Zhang Y, Wang J, Cui X, Xu L, Zhou J, Deng L, Zhang M, Lv Y, Yu Q, Shi X. Serum 25-Hydroxyvitamin D and Risk of Disability in Activities of Daily Living among the Oldest-Old: An Observational and Mendelian Randomization Study. J Nutr 2024; 154:1004-1013. [PMID: 38246357 DOI: 10.1016/j.tjnut.2024.01.016] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Vitamin D deficiency and disability are both prevalent among older adults. However, the association between them has rarely been investigated in the oldest-old subjects (aged ≥80 y), and the causality remains unclear. OBJECTIVE This study aimed to elucidate the causal effect of vitamin D on the incident risk of disability in activities of daily living (ADL) among Chinese oldest-old based on the 2012-2018 Chinese Healthy Ageing and Biomarkers Cohort Study. METHODS Serum 25-hydroxyvitamin D [25(OH)D] concentrations and ADL status at baseline and follow-up interviews were documented. Cox regression models were applied among 1427 oldest-old (mean age, 91.2 y) with normal baseline ADL status. One sample Mendelian randomization (MR) analyses were performed on a subset of 941 participants with qualified genetic data, using a 25(OH)D-associated genetic risk score as the genetic instrument. RESULTS During a median follow-up of 3.4 y, 231 participants developed disability in ADL. Serum 25(OH)D concentration was inversely associated with the risk of disability in ADL [per 10 nmol/L increase hazard ratio (HR) 0.85; 95% CI: 0.75, 0.96]. Consistent results from MR analyses showed that a 10 nmol/L increment in genetically predicted 25(OH)D concentration corresponded to a 20% reduced risk of ADL disability (HR 0.80; 95% CI: 0.68, 0.94). Nonlinear MR demonstrated a monotonic declining curve, with the HRs exhibiting a more pronounced reduction among individuals with 25(OH)D concentrations below 50 nmol/L. Subgroup analyses showed that the associations were more distinct among females and those with poorer health conditions. CONCLUSIONS Our study supports an inverse causal relationship between serum 25(OH)D concentration and the risk of disability in ADL among Chinese oldest-old. This protective effect was more distinct, especially for participants with vitamin D deficiency. Appropriate measures for improving vitamin D might help reduce the incidence of physical disability in this specific age group.
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Affiliation(s)
- Xinwei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China; China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Chen Chen
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Wang
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Xingyao Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China; China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Lanjing Xu
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jinhui Zhou
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Luojia Deng
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Min Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China; China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Yuebin Lv
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China.
| | - Xiaoming Shi
- China Centers for Disease Control and Prevention (CDC) Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese CDC, Beijing, China.
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10
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Ji S, Qu Y, Sun Q, Zhao F, Qiu Y, Li Z, Li Y, Song H, Zhang M, Zhang W, Fu H, Cai J, Zhang Z, Zhu Y, Cao Z, Lv Y, Shi X. Mediating Role of Liver Dysfunction in the Association between Arsenic Exposure and Diabetes in Chinese Adults: A Nationwide Cross-Sectional Study of China National Human Biomonitoring (CNHBM) 2017-2018. Environ Sci Technol 2024; 58:2693-2703. [PMID: 38285630 DOI: 10.1021/acs.est.3c08718] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Inconsistent results have been reported regarding the association between low-to-moderate arsenic (As) exposure and diabetes. The effect of liver dysfunction on As-induced diabetes remains unclear. The cross-sectional study included 10,574 adults from 2017-2018 China National Human Biomonitoring. Urinary total As (TAs) levels were analyzed as markers of As exposure. Generalized linear mixed models and restricted cubic splines models were used to examine the relationships among TAs levels, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations, and diabetes prevalence. Mediating analysis was performed to assess whether liver dysfunction mediated the association between TAs and diabetes. Overall, the OR (95% CI) of diabetes in participants in the second, third, and fourth quartiles of TAs were 1.08 (0.88, 1.33), 1.17 (0.94, 1.45), and 1.52 (1.22, 1.90), respectively, in the fully adjusted models compared with those in the lowest quartile. Serum ALT was positively associated with TAs and diabetes. Additionally, mediation analyses showed that ALT mediated 4.32% of the association between TAs and diabetes in the overall population and 8.86% in the population without alcohol consumption in the past year. This study suggested that alleviating the hepatotoxicity of As could have implications for both diabetes and liver disease.
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Affiliation(s)
- Saisai 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
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yingli 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
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Qi 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
| | - Feng 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
| | - Yidan Qiu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Zheng 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
| | - Yawei 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
| | - Haocan 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
| | - Miao 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
| | - Wenli 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
| | - Hui 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
| | - Jiayi 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
| | - Zhuona 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
| | - Ying 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
| | - Zhaojin 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
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, 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
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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11
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Xing Y, Li Z, Wang J, Qu Y, Hu Q, Ji S, Chang X, Zhao F, Lv Y, Pan Y, Shi X, Dai J. Associations between serum per- and polyfluoroalkyl substances and thyroid hormones in Chinese adults: A nationally representative cross-sectional study. Environ Int 2024; 184:108459. [PMID: 38320373 DOI: 10.1016/j.envint.2024.108459] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
Disruption of thyroid homeostasis has been indicated in human studies on the effects of per- and polyfluoroalkyl substances (PFASs). However, limited research exists on this topic within the general Chinese population. Based on a substantial and representative sample of the Chinese adult population, our study provides insight into how PFASs specifically affect thyroid homeostasis. The study included 10 853 participants, aged 18 years and above, sampled from nationally representative data provided by the China National Human Biomonitoring (CNHBM). Weighted multiple linear regression and restricted cubic spline (RCS) models were used to explore the associations between eight individual PFAS concentrations and total thyroxine (T4), total triiodothyronine (T3), and the T4/T3 ratio. Bayesian kernel machine regression (BKMR) and quantile-based g-computation (qgcomp) were employed to explore the joint and independent effects of PFASs on thyroid homeostasis. Both individual PFASs and PFAS mixtures exhibited a significant inverse association with serum T3 and T4 levels, and displayed a positive association with the T4/T3 ratio. Perfluoroundecanoic acid (PFUnDA) [-0.07 (95 % confidence interval (CI): -0.08, -0.05)] exhibited the largest change in T3 level. PFUnDA also exhibited a higher weight compared to other PFAS compounds in qgcomp models. Additionally, a critical exposure threshold for each PFAS was identified based on nonlinear dose-response associations; beyond these thresholds, the decreases in T3 and T4 levels plateaued. Specifically, for perfluoroheptane sulfonic acid (PFHpS) and 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), an initial decline in hormone levels was observed, followed by a slight increase when concentrations surpassed 0.7 ng/mL and 2.5 ng/mL, respectively. Sex-specific effects were more pronounced in females, and significant associations were observed predominantly in younger age groups. These insights contribute to our understanding of how PFAS compounds impact thyroid health and emphasize the need for further research and environmental management measures to address these complexities.
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Affiliation(s)
- Yanan Xing
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinghua Wang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qiongpu Hu
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaochen Chang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yitao Pan
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, 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, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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12
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Zeng Y, Chen H, Liu X, Song Z, Yao Y, Lei X, Lv X, Cheng L, Chen Z, Bai C, Yin Z, Lv Y, Lu J, Li J, Land KC, Yashin A, O'Rand AM, Sun L, Yang Z, Tao W, Gu J, Gottschalk W, Tan Q, Christensen K, Hesketh T, Tian XL, Yang H, Egidi V, Caselli G, Robine JM, Wang H, Shi X, Vaupel JW, Lutz MW, Nie C, Min J. Genetic associations with longevity are on average stronger in females than in males. Heliyon 2024; 10:e23691. [PMID: 38192771 PMCID: PMC10772631 DOI: 10.1016/j.heliyon.2023.e23691] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 11/30/2023] [Accepted: 12/09/2023] [Indexed: 01/10/2024] Open
Abstract
It is long observed that females tend to live longer than males in nearly every country. However, the underlying mechanism remains elusive. In this study, we discovered that genetic associations with longevity are on average stronger in females than in males through bio-demographic analyses of genome-wide association studies (GWAS) dataset of 2178 centenarians and 2299 middle-age controls of Chinese Longitudinal Healthy Longevity Study (CLHLS). This discovery is replicated across North and South regions of China, and is further confirmed by North-South discovery/replication analyses of different and independent datasets of Chinese healthy aging candidate genes with CLHLS participants who are not in CLHLS GWAS, including 2972 centenarians and 1992 middle-age controls. Our polygenic risk score analyses of eight exclusive groups of sex-specific genes, analyses of sex-specific and not-sex-specific individual genes, and Genome-wide Complex Trait Analysis using all SNPs all reconfirm that genetic associations with longevity are on average stronger in females than in males. Our discovery/replication analyses are based on genetic datasets of in total 5150 centenarians and compatible middle-age controls, which comprises the worldwide largest sample of centenarians. The present study's findings may partially explain the well-known male-female health-survival paradox and suggest that genetic variants may be associated with different reactions between males and females to the same vaccine, drug treatment and/or nutritional intervention. Thus, our findings provide evidence to steer away from traditional view that "one-size-fits-all" for clinical interventions, and to consider sex differences for improving healthcare efficiency. We suggest future investigations focusing on effects of interactions between sex-specific genetic variants and environment on longevity as well as biological function.
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Affiliation(s)
- Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, 100871, China
- Center for the Study of Aging and Human Development, Medical School of Duke University, Durham, NC, USA, 27710
| | - Huashuai Chen
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, 100871, China
- Business School of Xiangtan University, Xiangtan, 411105, China
| | | | - Zijun Song
- The First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yao Yao
- Center for the Study of Aging and Human Development, Medical School of Duke University, Durham, NC, USA, 27710
| | - Xiaoyan Lei
- Center for the Study of Aging and Human Development, Medical School of Duke University, Durham, NC, USA, 27710
| | - Xiaozhen Lv
- French National Institute of Health and Medical Research (INSERM) and Ecole Pratique des Hautes Etudes (EPHE) FR, Italy
| | - Lingguo Cheng
- School of Business, Nanjing University, Nanjing, 210093, China
| | | | - Chen Bai
- Center for the Study of Aging and Human Development, Medical School of Duke University, Durham, NC, USA, 27710
| | - Zhaoxue Yin
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yuebin Lv
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Jiehua Lu
- Department of Sociology, Peking University, Beijing, 100871, China
| | - Jianxin Li
- Department of Sociology, Peking University, Beijing, 100871, China
| | - Kenneth C. Land
- Duke Population Research Institute's Center for Population Health and Aging, Duke University, Durham, NC, USA, 27710
| | - Anatoliy Yashin
- Duke Population Research Institute's Center for Population Health and Aging, Duke University, Durham, NC, USA, 27710
| | - Angela M. O'Rand
- Duke Population Research Institute's Center for Population Health and Aging, Duke University, Durham, NC, USA, 27710
| | - Liang Sun
- The MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, 100730, China
| | - Ze Yang
- The MOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, 100730, China
| | - Wei Tao
- School of Life Sciences, Peking University, Beijing, 100871, China
| | - Jun Gu
- School of Life Sciences, Peking University, Beijing, 100871, China
| | - William Gottschalk
- Department of Neurology, Medical Center, Duke University, Durham, NC, USA, 27710
| | - Qihua Tan
- University of Southern Denmark, Odense, DK-5000, Denmark
| | | | - Therese Hesketh
- Institute for Global Health, University College London, London, UK
- Institute for Global Health, School of Public Health, Zhejiang University, Hangzhou, 310058, China
| | - Xiao-Li Tian
- Human Aging Research Institute and School of Life Science, Nanchang University, Jiangxi, 330031, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou 310008, China310058
| | - Viviana Egidi
- Department of Statistical Sciences, University of Rome La Sapienza, Roma, 00161, Italy
| | - Graziella Caselli
- Department of Statistical Sciences, University of Rome La Sapienza, Roma, 00161, Italy
| | - Jean-Marie Robine
- French National Institute of Health and Medical Research (INSERM) and Ecole Pratique des Hautes Etudes (EPHE) FR, Italy
| | - Huali Wang
- The Third Affiliated Hospital of Health Science Center, Peking University, Italy
| | - Xiaoming Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | | | - Michael W. Lutz
- Department of Neurology, Medical Center, Duke University, Durham, NC, USA, 27710
| | - Chao Nie
- BGI-Shenzhen, Shenzhen, 518083, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China
| | - Junxia Min
- The First Affiliated Hospital, Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
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13
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Ji JS, Xia Y, Liu L, Zhou W, Chen R, Dong G, Hu Q, Jiang J, Kan H, Li T, Li Y, Liu Q, Liu Y, Long Y, Lv Y, Ma J, Ma Y, Pelin K, Shi X, Tong S, Xie Y, Xu L, Yuan C, Zeng H, Zhao B, Zheng G, Liang W, Chan M, Huang C. China's public health initiatives for climate change adaptation. Lancet Reg Health West Pac 2023; 40:100965. [PMID: 38116500 PMCID: PMC10730322 DOI: 10.1016/j.lanwpc.2023.100965] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/01/2023] [Accepted: 11/01/2023] [Indexed: 12/21/2023]
Abstract
China's health gains over the past decades face potential reversals if climate change adaptation is not prioritized. China's temperature rise surpasses the global average due to urban heat islands and ecological changes, and demands urgent actions to safeguard public health. Effective adaptation need to consider China's urbanization trends, underlying non-communicable diseases, an aging population, and future pandemic threats. Climate change adaptation initiatives and strategies include urban green space, healthy indoor environments, spatial planning for cities, advance location-specific early warning systems for extreme weather events, and a holistic approach for linking carbon neutrality to health co-benefits. Innovation and technology uptake is a crucial opportunity. China's successful climate adaptation can foster international collaboration regionally and beyond.
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Affiliation(s)
- John S. Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yanjie Xia
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Linxin Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Weiju Zhou
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National School of Public Health, Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Guanghui Dong
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Qinghua Hu
- Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National School of Public Health, Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Li
- Public Meteorological Service Centre, China Meteorological Administration, Beijing, China
| | - Qiyong Liu
- National Institute of Infectious Diseases at China, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanxiang Liu
- Public Meteorological Service Centre, China Meteorological Administration, Beijing, China
| | - Ying Long
- School of Architecture, Tsinghua University, Beijing, China
| | - Yuebin Lv
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jian Ma
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yue Ma
- School of Architecture, Tsinghua University, Beijing, China
| | - Kinay Pelin
- School of Climate Change and Adaptation, University of Prince Edward Island, Prince Edward Island, Canada
| | - Xiaoming Shi
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shilu Tong
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Queensland University of Technology, Brisbane, Australia
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Bin Zhao
- Department of Building Science, School of Architecture, Tsinghua University, Beijing, China
| | - Guangjie Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Margaret Chan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
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14
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Zhi Y, Liu L, Wang H, Chen X, Lv Y, Cui X, Chang H, Wang Y, Cui S. Prenatal exome sequencing analysis in fetuses with central nervous system anomalies. Ultrasound Obstet Gynecol 2023; 62:721-726. [PMID: 37204857 DOI: 10.1002/uog.26254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/28/2023] [Accepted: 05/07/2023] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To evaluate the utility of prenatal exome sequencing (pES) in fetuses with central nervous system (CNS) abnormalities. METHODS This was a retrospective cohort study of fetuses identified to have CNS abnormality on prenatal ultrasound and/or magnetic resonance imaging. All fetuses were first analyzed by chromosomal microarray analysis (CMA). Fetuses with a confirmed aneuploidy or causal pathogenic copy-number variant (CNV) on CMA did not undergo pES analysis and were excluded, while those with a negative CMA result were offered pES testing. RESULTS Of the 167 pregnancies included in the study, 42 (25.1%) were identified to have a pathogenic or likely pathogenic (P/LP) variant. The diagnostic rate was significantly higher in fetuses with a non-isolated CNS abnormality than in those with a single CNS abnormality (35.7% (20/56) vs 14.5% (8/55); P = 0.010). Moreover, when a fetus had three or more CNS abnormalities, the positive diagnostic rate increased to 42.9%. A total of 25/42 (59.5%) cases had de-novo mutations, while, in the remaining cases, mutations were inherited and carried a significant risk of recurrence. Families whose fetus carried a P/LP mutation were more likely to choose advanced pregnancy termination than those with a variant of uncertain significance, secondary/incidental finding or negative pES result (83.3% (25/30) vs 41.3% (38/92); P < 0.001). CONCLUSION pES improved the identification of genetic disorders in fetuses with CNS anomalies without a chromosomal abnormality or CNV identified on CMA, regardless of the number of CNS anomalies and presence of extracranial abnormality. We also demonstrated that pES findings can significantly impact parental decision-making. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- Y Zhi
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - L Liu
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - H Wang
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - X Chen
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Y Lv
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - X Cui
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - H Chang
- Scientific Research Office, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Y Wang
- Clinical Laboratory, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - S Cui
- Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
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Chen C, Li X, Wang J, Zhou J, Wei Y, Luo Y, Xu L, Liu Z, Lv Y, Shi X. Longitudinal Changes of Cognition and Frailty With All-Cause and Cause-Specific Mortality in Chinese Older Adults: An 11-Year Cohort Study. Innov Aging 2023; 7:igad114. [PMID: 38024331 PMCID: PMC10681360 DOI: 10.1093/geroni/igad114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Indexed: 12/01/2023] Open
Abstract
Background and Objectives Physical function deterioration is always accompanied by a cognitive decline in older adults. However, evidence is lacking for the long-term simultaneous changing patterns of cognition and physical frailty and their associations with mortality among older adults. Research Design and Methods This study included 8,231 adults aged ≥65 with a baseline and at least one follow-up assessment of both cognition and physical frailty from the 2007-2018 Chinese Longitudinal Healthy Longevity Survey. Physical frailty (FRAIL phenotype) and cognition (Mini-Mental State Examination) were applied. Group-based joint trajectory modeling was used to fit the joint trajectories of cognition and physical frailty. Cox proportional hazards model was used to evaluate the trajectory-mortality associations. Results Three distinct joint trajectories were identified: no joint progression (34.4%), moderate joint progression (47.0%), and rapid joint progression (18.6%). During a median follow-up of 8.3 years, the rapid joint progression group, compared to the no joint progression, had the highest risk for all-cause mortality (hazard ratio (HR), 3.37 [95% CI: 2.99-3.81]), cardiovascular (CVD) mortality (3.21 [2.08-4.96]) and non-CVD mortality (2.99 [2.28-3.92]), respectively. Joint trajectory was found to be more predictive of mortality as compared to baseline measures of cognition and/or frailty (C-statistic ranged from 0.774 to 0.798). Higher changing rates of cognition and frailty were observed among all-cause decedents compared to CVD and non-CVD decedents over a 45-year span (aged 65-110) before death. Discussion and Implications Our study suggested that subjects with the worst cognitive decline and severest physical frailty progression were at the highest risk for all-cause and cause-specific mortality. Our findings expand the limited prior knowledge on the dynamic course of cognition and frailty.
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Affiliation(s)
- Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yufei Luo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Lanjing Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zuyun Liu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
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Wang J, Chen C, Zhou J, Ye L, Li Y, Xu L, Xu Z, Li X, Wei Y, Liu J, Lv Y, Shi X. Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study. Lancet Healthy Longev 2023; 4:e535-e543. [PMID: 37804845 DOI: 10.1016/s2666-7568(23)00140-x] [Citation(s) in RCA: 2] [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: 05/05/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Lifestyle and longevity genes have different and important roles in the human lifespan; however, the association between a healthy lifestyle in late-life and life expectancy mediated by genetic risk is yet to be elucidated. We aimed to investigate the associations of healthy lifestyle in late-life and genetic risk with life expectancy among older adults. METHODS A weighted healthy lifestyle score was constructed from the following variables: current non-smoking, non-harmful alcohol consumption, regular physical activity, and a healthy diet. Participants were recruited from the Chinese Longitudinal Healthy Longevity Survey, a prospective community-based cohort study that took place between 1998 and 2018. Eligible participants were aged 65 years and older with available information on lifestyle factors at baseline, and then were categorised into unhealthy (bottom tertile of the weighted healthy lifestyle score), intermediate (middle tertile), and healthy (top tertile) lifestyle groups. A genetic risk score was constructed based on 11 lifespan loci among 9633 participants, divided by the median and classified into low and high genetic risk groups. Stratified Cox proportional hazard regression was used to estimate the interaction between genetic and lifestyle factors on all-cause mortality risk. FINDINGS Between Jan 13, 1998, and Dec 31, 2018, 36 164 adults aged 65 years and older were recruited, among whom a total of 27 462 deaths were documented during a median follow-up of 3·12 years (IQR 1·62-5·94) and included in the lifestyle association analysis. Compared with the unhealthy lifestyle category, participants in the healthy lifestyle group had a lower all-cause mortality risk (hazard ratio [HR] 0·56 [95% CI 0·54-0·57]; p<0·0001). The highest mortality risk was observed in individuals in the high genetic risk and unhealthy lifestyle group (HR 1·80 [95% CI 1·63-1·98]; p<0·0001). The absolute risk reduction was greater for participants in the high genetic risk group. A healthy lifestyle was associated with a gain of 3·84 years (95% CI 3·05-4·64) at the age of 65 years in the low genetic risk group, and 4·35 years (3·70-5·06) in the high genetic risk group. INTERPRETATION A healthy lifestyle, even in late-life, was associated with lower mortality risk and longer life expectancy among Chinese older adults, highlighting the importance of a healthy lifestyle in extending the lifespan, especially for individuals with high genetic risk. FUNDING National Natural Science Foundation of China. TRANSLATION For the Mandarin translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Lanjing Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Zinan Xu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, China
| | - Junxin Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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17
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Ye L, Zhou J, Tian Y, Cui J, Chen C, Wang J, Wang Y, Wei Y, Ye J, Li C, Chai X, Sun C, Li F, Wang J, Guo Y, Jaakkola JJK, Lv Y, Zhang J, Shi X. Associations of residential greenness and ambient air pollution with overweight and obesity in older adults. Obesity (Silver Spring) 2023; 31:2627-2637. [PMID: 37649157 DOI: 10.1002/oby.23856] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVE This study aimed to examine the impact of greenness and fine particulate matter <2.5 μm (PM2.5 ) on overweight/obesity among older adults in China. METHODS A total of 21,355 participants aged ≥65 years were included from the Chinese Longitudinal Healthy Longevity Survey between 2000 and 2018. Normalized difference vegetation index (NDVI) with a radius of 250 m and PM2.5 in a 1 × 1-km grid resolution were calculated around each participant's residence. Cox proportional hazards models were used to estimate the effects of NDVI and PM2.5 on overweight/obesity. Interaction and mediation analyses were conducted to explore combined effects. RESULTS The study observed 1895 incident cases of overweight/obesity over 109,566 person-years. For every 0.1-unit increase in NDVI the hazard ratio of overweight/obesity was 0.91 (95% CI: 0.88-0.95), and for every 10-μg/m3 increase in PM2.5 the hazard ratio was 1.11 (95% CI: 1.07-1.14). The effect of NDVI on overweight/obesity was partially mediated by PM2.5 , with a relative mediation proportion of 20.10% (95% CI: 1.63%-38.57%). CONCLUSIONS Greenness exposure appears to lower the risk of overweight/obesity in older adults in China, whereas PM2.5 , acting as a mediator, partly mediated this protective effect.
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Affiliation(s)
- Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanlin Tian
- Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Jia Cui
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yueqing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Jiaming Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Anhui Medical University, Hefei, China
| | - Xin Chai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chris Sun
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fangyu Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Anhui Medical University, Hefei, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Jouni J K Jaakkola
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Finnish Meteorological Institute, Helsinki, Finland
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Juan Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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Sun M, Niu W, Shi L, Lv Y, Fu B, Xia Y, Li H, Wang K, Li Y. Host response of Nicotiana benthamiana to the parasitism of five populations of root-lesion nematode, Pratylenchus coffeae, from China. J Helminthol 2023; 97:e73. [PMID: 37771040 DOI: 10.1017/s0022149x2300055x] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
In a recent survey of nematodes associated with tobacco in Shandong, China, the root-lesion nematode Pratylenchus coffeae was identified using a combination of morphology and molecular techniques. This nematode species is a serious parasite that damages a variety of plant species. The model plant benthi, Nicotiana benthamiana, is frequently used to study plant-disease interactions. However, it is not known whether this plant species is a host of P. coffeae. The objectives of this study were to evaluate the parasitism and pathogenicity of five populations of the root-lesion nematode P. coffeae on N. benthamiana.N. benthamiana seedlings with the same growth status were chosen and inoculated with 1,000 nematodes per pot. At 60 days after inoculation, the reproductive factors (Rf = final population densities (Pf)/initial population densities (Pi)) for P. coffeae in the rhizosphere of N. benthamiana were all more than 1, suggesting that N. benthamiana was a good host plant for P. coffeae.Nicotiana. benthamiana infected by P. coffeae showed weak growth, decreased tillering, high root reduction, and noticeable brown spots on the roots. Thus, we determined that the model plant N. benthamiana can be used to study plant-P. coffeae interactions.
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Affiliation(s)
- M Sun
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - W Niu
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - L Shi
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - Y Lv
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - B Fu
- College of Tobacco Science, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - Y Xia
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan430072, P.R.China
| | - H Li
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - K Wang
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
| | - Y Li
- College of Plant Protection, Henan Agricultural University, Zhengzhou450046, P.R.China
- National Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou450046, P.R.China
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Zhou J, Chen C, Wang J, Liu S, Li X, Wei Y, Ye L, Ye J, Kraus VB, Lv Y, Shi X. Development and Validation of a Lifespan Prediction Model in Chinese Adults Aged 65 Years or Older. J Am Med Dir Assoc 2023; 24:1068-1073.e6. [PMID: 36965505 PMCID: PMC10335838 DOI: 10.1016/j.jamda.2023.02.016] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/01/2023] [Accepted: 02/15/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVES Previous studies investigated factors associated with mortality. Nevertheless, evidence is limited regarding the determinants of lifespan. We aimed to develop and validate a lifespan prediction model based on the most important predictors. DESIGN A prospective cohort study. SETTING AND PARTICIPANTS A total of 23,892 community-living adults aged 65 years or older with confirmed death records between 1998 and 2018 from 23 provinces in China. METHODS Information including demographic characteristics, lifestyle, functional health, and prevalence of diseases was collected. The risk prediction model was generated using multivariate linear regression, incorporating the most important predictors identified by the Lasso selection method. We used 1000 bootstrap resampling for the internal validation. The model performance was assessed by adjusted R2, root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficient (ICC). RESULTS Twenty-one predictors were included in the final lifespan prediction model. Older adults with longer lifespans were characterized by older age at baseline, female, minority race, living in rural areas, married, with healthier lifestyles and more leisure engagement, better functional status, and absence of diseases. The predicted lifespans were highly consistent with observed lifespans, with an adjusted R2 of 0.893. RMSE was 2.86 (95% CI 2.84-2.88) and MAE was 2.18 (95% CI 2.16-2.20) years. The ICC between observed and predicted lifespans was 0.971 (95% CI 0.971-0.971). CONCLUSIONS AND IMPLICATIONS The lifespan prediction model was validated with good performance, the web-based prediction tool can be easily applied in practical use as it relies on all easily accessible variables.
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Affiliation(s)
- Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Sixin Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaming Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Virginia Byers Kraus
- Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Jiang Y, Wu Y, Li S, Fu S, Lv Y, Lin H, Yao Y. Editorial: Aging-friendly environments and healthy aging. Front Med (Lausanne) 2023; 10:1211632. [PMID: 37396904 PMCID: PMC10311415 DOI: 10.3389/fmed.2023.1211632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/05/2023] [Indexed: 07/04/2023] Open
Affiliation(s)
- Yuling Jiang
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Yifei Wu
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Shaojie Li
- China Center for Health Development Studies, Peking University, Beijing, China
| | - Shihui Fu
- Department of Cardiology, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
- Department of Geriatric Cardiology, People's Liberation Army General Hospital, Beijing, China
| | - Yuebin Lv
- Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing, China
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Zhou J, Li X, Gao X, Wei Y, Ye L, Liu S, Ye J, Qiu Y, Zheng X, Chen C, Wang J, Kraus VB, Lv Y, Mao C, Shi X. Leisure Activities, Genetic Risk, and Frailty: Evidence from the Chinese Adults Aged 80 Years or Older. Gerontology 2023; 69:961-971. [PMID: 37075711 PMCID: PMC10791136 DOI: 10.1159/000530665] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 04/04/2023] [Indexed: 04/21/2023] Open
Abstract
INTRODUCTION About half of adults aged ≥80 years suffer from frailty. Exercise is considered effective in preventing frailty but may be inapplicable to adults aged ≥80 years due to physical limitations. As an alternative, we aimed to explore the association of leisure activities with frailty and identify potential interaction with established polygenic risk score (PRS) among adults aged ≥80 years. METHODS Analyses were performed in a prospective cohort study of 7,471 community-living older adults aged ≥80 years who were recruited between 2002 and 2014 from 23 provinces in China. Leisure activity was assessed using a seven-question leisure activity index and frailty was defined as a frailty index ≥0.25 using a validated 39-item health-related scale. The PRS was constructed using 59 single-nucleotide polymorphisms associated with frailty in a subsample of 2,541 older adults. Cox proportional hazards models were used to explore the associations of leisure activities, PRS with frailty. RESULTS The mean age of participants was 89.4 ± 6.6 years (range: 80-116). In total, 2,930 cases of frailty were identified during 42,216 person-years of follow-up. Each 1 unit increase in the leisure activity index was associated with 12% lower risk of frailty (hazard ratio: 0.88 [95% confidence interval, 0.85-0.91]). Participants with high genetic risk (PRS >2.47 × 10-4) suffered from 26% higher risk of frailty. Interaction between leisure activity and genetic risk was not observed. CONCLUSION Evidence is presented for the independent association of leisure activities and genetic risk with frailty. Engagement in leisure activities is suggested to be associated with lower risk of frailty across all levels of genetic risk among adults aged ≥80 years.
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Affiliation(s)
- Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Xiang Gao
- Department of Nutrition and Food Hygiene, School of Public Health, Fudan University, Shanghai, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sixin Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiaming Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Virginia Byers Kraus
- Duke Molecular Physiology Institute and Division of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 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, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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He L, Zheng Z, Li X, Cao X, Zhang J, Chen C, Lv Y, Wu C, Barry LC, Ying Z, Jiang X, Shi X, Liu Z. Association of spouse's health status with the onset of depressive symptoms in partner: Evidence from the China Health and Retirement Longitudinal Study. J Affect Disord 2023; 325:177-184. [PMID: 36603600 DOI: 10.1016/j.jad.2022.12.118] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/29/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND This study aimed to evaluate the associations between the multidimensional health status of one spouse and the onset of depressive symptoms in partner, and whether the associations differed by gender and residence. METHODS We analyzed data from 2401 females and their husbands (scenario 1), and 2830 males and their wives (scenario 2) who participated in the 2011/2012 and 2015 waves of China Health and Retirement Longitudinal Study. Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale. Multidimensional health indicators included mobility disability, activities of daily living disability, frailty, global cognition, depressive symptoms, comorbidity, and self-reported health. Principal component analysis was used to construct a composite health indicator reflecting overall health status that was then categorized into three groups (poor, moderate, and excellent). Logistic regression models were performed. RESULTS We observed strong associations of spouse's health status with the onset of depressive symptoms in partner. For instance, females whose husbands had poor overall health status reported more depressive symptoms than those having husbands with excellent overall health after four years (OR: 1.75; 95 % CI: 1.35, 2.26). These associations were statistically significant in rural females and urban males, but surprisingly disappeared in rural males and urban females. LIMITATIONS No exact timing of depressive symptoms onset. CONCLUSIONS In Chinese middle-aged and older adults, spouse's health status is associated with depressive symptoms in partner and the associations vary by gender and residence. The findings underscore the importance of considering partner's health status to manage one spouse's mental health.
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Affiliation(s)
- Liu He
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhoutao Zheng
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chen 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
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Jiangsu 215316, China
| | - Lisa C Barry
- Department of Psychiatry, UCONN Health, CT 06030-1410, USA
| | - Zhimin Ying
- Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China.
| | - Xiaoyan Jiang
- Key Laboratory of Arrhythmias, Ministry of Education, Department of Pathology and Pathophysiology, School of Medicine, Tongji University, Shanghai 200092, 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
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
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23
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Qu Y, Ji S, Sun Q, Zhao F, Li Z, Zhang M, Li Y, Zheng L, Song H, Zhang W, Gu H, Fu H, Zheng X, Cai J, Zhu Y, Cao Z, Lv Y, Shi X. Association of urinary nickel levels with diabetes and fasting blood glucose levels: A nationwide Chinese population-based study. Ecotoxicol Environ Saf 2023; 252:114601. [PMID: 36753970 DOI: 10.1016/j.ecoenv.2023.114601] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/19/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Some epidemiological studies support a relationship between nickel exposure and diabetes in the general population. To address this, we tested the association of nickel exposure with diabetes in 10,890 adults aged ≥ 18 years old from the China National Human Biomonitoring study conducted in 2017-2018. Urinary nickel concentrations and fasting blood glucose (FBG) were measured, and lifestyle and demographic data were collected. Weighted logistic and linear regressions were used to estimate the associations of urinary nickel levels with diabetes prevalence and FBG. Restricted cubic splines (RCS) were used to test for the dose-response relationship. The odd ratio (95% confidence interval [CI]) of diabetes for the highest versus lowest quartiles of urinary nickel concentrations was 1.74 (1.28, 2.36) in the multivariate model (p trend =0.001). Each one-unit increase in log-transformed urinary nickel concentrations was associated with a 0.36 (0.17, 0.55) mmol/L elevation in FBG. The RCS curves showed a monotonically increasing dose-response relationship of urinary nickel with diabetes as well as FBG levels, and then tended to flatten after about 4.75 μg/L of nickel exposure. The nickel-diabetes association was stronger in individuals with lower than those with higher rice consumption (OR: 2.39 vs. 1.72). Our study supports a positive association between nickel exposure and diabetes prevalence in Chinese adults, especially in individuals with lower rice consumption. Further large-scale prospective studies are needed to validate our findings.
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Affiliation(s)
- Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Qi Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Miao Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Lei Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Haocan Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Wenli Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Hui Fu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Jiayi Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China.
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Wang Y, Lv Y, Liu Y, Xie C. The effect of surgical repair of hiatal hernia (HH) on pulmonary function: a systematic review and meta-analysis. Hernia 2023:10.1007/s10029-023-02756-5. [PMID: 36826630 PMCID: PMC10374806 DOI: 10.1007/s10029-023-02756-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
PURPOSE Hiatal hernia is renowned for the symptom of reflux, and few physicians associate a hiatal hernia with pulmonary issues. It is widely acknowledged that a hiatal hernia can be treated with surgery. However, less is known about how the surgical procedure would benefit pulmonary function. Thus, the aim of this study was to determine whether surgical repair can improve pulmonary function in patients with hiatal hernias. METHODS We registered the protocol on the PROSPERO (International Prospective Register of Systematic Reviews) platform (no. CRD42022369949). We searched the PubMed, Embase, Cochrane Library, and ClinicalTrials.gov databases for cohort studies that reported on the pulmonary function of patients with hiatal hernias. The quality of each cohort study was evaluated using the Newcastle-Ottawa scale (NOS). We then calculated mean differences (MDs) with 95% confidence intervals for these continuous outcomes. Each study's consistency was appraised using the I2 statistic. The sensitivity analysis was performed using the trim-and-fill method. Publication bias was confirmed using the funnel plot visually and Egger regression test statistically. RESULTS A total of 262 patients from 5 cohorts were included in the meta-analysis. The quality evaluation revealed that, of these 5 papers, 3 received 8 NOS stars out of 9 stars, 1 received 9, and the other received 7, meaning all included cohort studies were of high quality. The results showed that surgical repair for a hiatal hernia significantly improved forced expiratory volume in 1 s (FEV1; weighted mean difference [WMD]:0.200; 95% CI 0.047-0.353; I2 = 71.6%; P = 0.010), forced vital capacity (FVC; WMD: 0.242; 95% CI 0.161-0.323; I2 = 7.1%; P = 0.000), and total lung capacity (TLC; WMD: 0.223; 95% CI 0.098-0.348; I2 = 0.0%; P = 0.000) but had little effect on residual volume (RV; WMD: -0.028; 95% CI -0.096 to 0.039; I2 = 8.7%; P = 0.411) and the diffusing capacity carbon monoxide (DLCO; WMD: 0.234; 95% CI -0.486 to 0.953; I2 = 0.0%; P = 0.524). CONCLUSION For individuals with hiatal hernias, surgical repair is an efficient technique to improve respiratory function as measured by FEV1, FVC, and TLC.
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Affiliation(s)
- Y Wang
- General Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China.
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Y Lv
- General Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Y Liu
- General Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - C Xie
- General Surgery Department, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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25
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Lv Y, Yang Z, Ye L, Jiang M, Zhou J, Guo Y, Qiu Y, Li X, Chen C, Ju A, Wang J, Li C, Li Y, Wang J, Zhang J, Ji JS, Li T, Baccarelli AA, Gao X, Shi X. Long-term fine particular exposure and incidence of frailty in older adults: findings from the Chinese Longitudinal Healthy Longevity Survey. Age Ageing 2023; 52:7036277. [PMID: 36794712 PMCID: PMC9933051 DOI: 10.1093/ageing/afad009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 10/31/2022] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND The association between fine particular matter (PM2.5) and frailty is less studied, and the national burden of PM2.5-related frailty in China is unknown. OBJECTIVE To explore the association between PM2.5 exposure and incident frailty in older adults, and estimate the corresponding disease burden. DESIGN Chinese Longitudinal Healthy Longevity Survey from 1998 to 2014. SETTING Twenty-three provinces in China. SUBJECTS A total of 25,047 participants aged ≥65-year-old. METHODS Cox proportional hazards models were performed to evaluate the association between PM2.5 and frailty in older adults. A method adapted from the Global Burden of Disease Study was used to calculate the PM2.5-related frailty disease burden. RESULTS A total of 5,733 incidents of frailty were observed during 107,814.8 person-years follow-up. A 10 μg/m3 increment of PM2.5 was associated with a 5.0% increase in the risk of frailty (Hazard Ratio = 1.05, 95% confidence interval = [1.03-1.07]). Monotonic, but non-linear exposure-response, relationships of PM2.5 with risk of frailty were observed, and slopes were steeper at concentrations >50 μg/m³. Considering the interaction between population ageing and mitigation of PM2.5, the PM2.5-related frailty cases were almost unchanged in 2010, 2020 and 2030, with estimations of 664,097, 730,858 and 665,169, respectively. CONCLUSIONS This nation-wide prospective cohort study showed a positive association between long-term PM2.5 exposure and frailty incidence. The estimated disease burden indicated that implementing clean air actions may prevent frailty and substantially offset the burden of population ageing worldwide.
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Affiliation(s)
- Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ze Yang
- Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, China,Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Meijie Jiang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinwei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Aipeng Ju
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yang Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Juan Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Andrea A Baccarelli
- Laboratory of Environmental Precision Health, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Xu Gao
- Author correspondence to: Xiaoming Shi, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention; #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China. Tel: (+86) 1050930101; Fax: (+86) 1058900247.
| | - Xiaoming Shi
- Author correspondence to: Xiaoming Shi, China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention; #7 Panjiayuan Nanli, Chaoyang, Beijing 100021, China. Tel: (+86) 1050930101; Fax: (+86) 1058900247.
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Wu B, Pan Y, Li Z, Wang J, Ji S, Zhao F, Chang X, Qu Y, Zhu Y, Xie L, Li Y, Zhang Z, Song H, Hu X, Qiu Y, Zheng X, Zhang W, Yang Y, Gu H, Li F, Cai J, Zhu Y, Cao Z, S Ji J, Lv Y, Dai J, Shi X. Serum per- and polyfluoroalkyl substances and abnormal lipid metabolism: A nationally representative cross-sectional study. Environ Int 2023; 172:107779. [PMID: 36746113 DOI: 10.1016/j.envint.2023.107779] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/27/2022] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The associations of legacy per- and polyfluoroalkyl substances (PFAS) with lipid metabolism are controversial, and there is little information about the impact of emerging PFAS (6:2 Cl-PFESA) on lipid metabolism in China. OBJECTIVES We aimed to explore the associations of legacy and emerging PFAS with lipid profiles and dyslipidemia in Chinese adults. METHODS We included 10,855 Chinese participants aged 18 years and above in the China National Human Biomonitoring. The associations of 8 PFAS with 5 lipid profiles and 4 dyslipidemia were investigated using weighted multiple linear regression or weighted logistic regression, and the dose-response associations were investigated using restricted cubic spline model. RESULTS Among the 8 PFAS, the concentration of PFOS was the highest, with a geometric mean of 5.15 ng/mL, followed by PFOA and 6:2 Cl-PFESA, which were 4.26 and 1.63 ng/mL, respectively. Legacy (PFOA, PFOS, PFUnDA) or emerging (6:2 Cl-PFESA) PFAS were associated with lipid profiles (TC, LDL-C, HDL-C, non HDL-C) and dyslipidemia (high LDL-C, high TC, low HDL-C), and their effects on TC were most obvious. TC concentration increased by 0.595 mmol/L in the highest quartile (Q4) of PFOS when compared with the lowest quartile (Q1), (95 % CI:0.396, 0.794). Restricted cubic spline models showed that PFAS are nonlinearly associated with TC, non HDL-C, LDL-C and HDL-C, and that the lipid concentrations tend to be stable when PFOS and PFOA were > 20 ng/mL well as when the 6:2 Cl-PFESA level was > 10 ng/mL. The positive associations between PFAS mixtures and lipid profiles were also significant. CONCLUSIONS Single and mixed exposure to PFAS were positively associated with lipid profiles, and China's unique legacy PFAS substitutes (6:2 Cl-PFESA) contributed less to lipid profiles than legacy PFAS. In the future, cohort studies will be needed to confirm our findings.
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Affiliation(s)
- Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yitao Pan
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinghua Wang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaochen Chang
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuanduo Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Linna Xie
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zheng Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haocan Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaojian Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Institute of Environmental Health, School of Public Health, and Bioelectromagnetics Laboratory, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wenli Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanwei Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Fangyu Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiayi Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Lin Q, Ding K, Zhao R, Wang H, Ren L, Wei Y, Ye Q, Cui Y, He G, Tang W, Feng Q, Zhu D, Chang W, Lv Y, Mao Y, Wang X, Liang L, Zhou G, Liang F, Xu J. 43O Preoperative chemotherapy prior to primary tumor resection for colorectal cancer patients with asymptomatic resectable primary lesion and synchronous unresectable liver-limited metastases (RECUT): A prospective, randomized, controlled, multicenter clinical trial. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
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28
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Qiu Y, Lv Y, Zhang M, Ji S, Wu B, Zhao F, Qu Y, Sun Q, Guo Y, Zhu Y, Lin X, Zheng X, Li Z, Fu H, Li Y, Song H, Wei Y, Ding L, Chen G, Zhu Y, Cao Z, Shi X. Cadmium exposure is associated with testosterone levels in men: A cross-sectional study from the China National Human Biomonitoring. Chemosphere 2022; 307:135786. [PMID: 35872064 DOI: 10.1016/j.chemosphere.2022.135786] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/21/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Sex hormone disorders can cause adverse health consequences. While experimental data suggests that cadmium (Cd) disrupts the endocrine system, little is known about the link between Cd exposure and sex hormones in men. METHODS We measured blood cadmium (B-Cd), urine cadmium (U-Cd), serum testosterone and serum estradiol in men aged ≥18 years old participating in the China National Human Biomonitoring program, from 2017 to 2018. Urine cadmium adjusted for creatinine (Ucr-Cd) and the serum testosterone to serum estradiol ratio (T/E2) were calculated. The association of Cd exposure to serum testosterone and T/E2 in men was analyzed with multiple linear regression models. RESULTS Among Chinese men ≥18 years old, the weighted geometric mean (95% CI) of B-Cd and Ucr-Cd levels were 1.23 (1.12-1.35) μg/L and 0.53 (0.47-0.59) μg/g, respectively. The geometric means (95% CI) of serum testosterone and T/E2 were 18.56 (17.92-19.22) nmol/L and 143.86 (137.24-150.80). After adjusting for all covariates, each doubling of B-Cd level was associated with a 5.04% increase in serum testosterone levels (β = 0.071; 95%CI: 0.057-0.086) and a 4.03% increase in T/E2 (β = 0.057; 95%CI: 0.040-0.075); similar findings were found in Ucr-Cd. CONCLUSIONS In Chinese men, Cd may be an endocrine disruptor, which is positively associated with serum testosterone and T/E2.
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Affiliation(s)
- Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Miao Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qi Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yuanduo Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiao Lin
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Fu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Haocan Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Liang Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Guangdi Chen
- Institute of Environmental Health, School of Public Health, and Bioelectromagnetics Laboratory, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 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, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China.
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Wu B, Qu Y, Lu Y, Ji S, Ding L, Li Z, Zhang M, Gu H, Sun Q, Ying B, Zhao F, Zheng X, Qiu Y, Zhang Z, Zhu Y, Cao Z, Lv Y, Shi X. Mercury may reduce the protective effect of sea fish consumption on serum triglycerides levels in Chinese adults: Evidence from China National Human Biomonitoring. Environ Pollut 2022; 311:119904. [PMID: 35961572 DOI: 10.1016/j.envpol.2022.119904] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/12/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Sea fish contain omega-3 polyunsaturated fatty acids (omega-3 PUFAs) which have been found to reduce triglyceride (TG) levels. However, sea fish may contain pollutants such as mercury which cause oxidative stress and increase TG levels. Therefore, the relationship between sea fish and TG remains unclear. We aimed to explore whether blood mercury (BHg) can affect the effect of sea fish consumption frequency on TG level among Chinese adults. A total of 10,780 participants were included in this study. BHg levels were measured using inductively coupled plasma mass spectrometry (ICP-MS). The associations of sea fish consumption frequency with BHg and TG levels as well as the association of BHg with TG levels were evaluated using multiple linear regression. Causal mediation analysis was used to evaluate the mediation effect of BHg levels on the association of sea fish consumption frequency with TG levels. The frequency of sea fish consumption showed a negative association with TG level. Compared with the participants who never ate sea fish, the TG level decreased by 0.193 mmol/L in those who ate sea fish once a week or more [β (95%CI): -0.193 (-0.370, -0.015)]. Significant positive associations were observed of BHg with TG levels. With one unit increase of log2-transformed BHg, the change of TG level was 0.030 mmol/L [0.030 (0.009, 0.051)]. The association between sea fish consumption and TG was mediated by log2-transformed BHg [total effect = -0.037 (-0.074, -0.001); indirect effect = 0.009 (0.004, 0.015)], and the proportion mediated by log2-transformed BHg was 24.25%. BHg may reduce the beneficial effect of sea fish consumption frequency on TG levels among Chinese adults. Overall, sea fish consumption has more benefits than harms to TG.
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Affiliation(s)
- Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Liang Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Miao Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qi Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bo Ying
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xulin Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zheng Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Xiong J, Lv Y, Wei Y, Liu Z, Li X, Zhou J, Liu Y, Zhao F, Chen C, Gu H, Wang J, Zheng X, Xue K, Qiu Y, Shen T, Shi X. Association of blood mercury exposure with depressive symptoms in the Chinese oldest old. Ecotoxicol Environ Saf 2022; 243:113976. [PMID: 35994910 DOI: 10.1016/j.ecoenv.2022.113976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 06/15/2023]
Abstract
Depressive symptoms have a significant impact on the quality-of-life among the oldest old (aged ≥ 80 years) in the population. Current research on the association of blood mercury with depressive symptoms has mainly targeted the general population. However, it is unclear whether this association is present in the oldest old. We used data from the Healthy Aging and Biomarker Cohort Study carried out in 2017-2018, with 1154 participants aged ≥ 80 years eligible for analysis. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to detect blood mercury (Hg) levels, while the CES-D10 depression scale was used to assess depressive symptoms. The association between blood mercury levels and depressive symptoms was investigated using log-binomial and Poisson regression models. We also used restricted cubic splines (RCS) to assess the linear or nonlinear association of blood mercury with depressive symptoms scores. The 1154 participants ranged in age from 80 to 120 years, while the geometric mean of blood mercury concentration was 1.01 μg/L. After adjustment for covariates, log-binomial and Poisson regression analyses revealed a statistically significant, positive association of blood mercury with depressive symptoms. In comparison to the first tertile, the adjusted relative risks of blood mercury and the presence of depressive symptoms in the second and third tertiles were 1.55 (1.20-1.99) and 1.45 (1.11-1.90), respectively. The RCS model showed a linear association between blood mercury level and depressive symptoms scores. In conclusion, among the oldest old, we demonstrated that blood mercury levels were positively associated with depressive symptoms. Further surveys, especially cohort studies and clinical trials are needed to confirm these results.
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Affiliation(s)
- Jiahui Xiong
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, 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
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yuan 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, Jilin 130021, China
| | - Zuyun Liu
- School of Public Health, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xinwei Li
- School of Public Health, Jilin University, Changchun, Jilin 130021, China
| | - Jinhui 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
| | - Yang 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, Jilin 130021, China
| | - Feng 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
| | - Chen 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
| | - Heng 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
| | - Jun 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
| | - Xulin 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; School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 210046, China
| | - Kai 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, Jilin 130021, China
| | - Yidan Qiu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; School of Public Health, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tong Shen
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, China.
| | - Xiaoming Shi
- Department of Occupational Health and Environment Health, School of Public Health, Anhui Medical University, Hefei, Anhui 230032, 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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Wei Y, Zhou J, Zhao F, Chen C, Wang J, Luo Y, Li C, Xiong J, Lv Y, Li J, Shi X. Association of blood lead exposure with frailty and its components among the Chinese oldest old. Ecotoxicol Environ Saf 2022; 242:113959. [PMID: 35999770 DOI: 10.1016/j.ecoenv.2022.113959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Lead (Pb) is a widespread environmental contaminant, associated with a higher risk of functional impairment that can lead to frailty in older adults. However, few studies focused on the association of Pb exposure with frailty among the oldest old (aged ≥ 80 years). In this study, we aimed to assess the associations of Pb with frailty and its components in the oldest old. The included individuals were the oldest old aged ≥ 80 years who participated in a 2017 cross-sectional survey of the Healthy Aging and Biomarkers Cohort Study. Frailty was ascertained by the frailty index, which was created based on health deficits. We used logistic regression models to estimate the association of blood Pb with frailty and its components. The geometric mean and median of blood Pb were 38.51 μg/L and 36.27 μg/L among the oldest old, respectively. Compared with the first quartile of blood Pb, participants in the fourth quartile had higher risk of frailty and its components, the ORs are 1.71 (1.22-2.41), 1.99 (1.35-2.94), 1.91 (1.25-2.93), 1.57 (1.13-2.17) and 1.43 (1.05-1.96), for frailty, ADL disability, IADL disability, functional limitations, and hearing loss in the oldest old, respectively. There was a significant interaction between blood Pb and frailty in different age groups. In conclusion, our findings provide preliminary evidence that higher blood Pb may increase the risk of frailty among the oldest old by increasing the risk of disability in four physical functions: disability in ADL, disability in IADL, functional limitations, and hearing loss.
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Affiliation(s)
- Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yufei Luo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Jiahui Xiong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Juan Li
- Department of Hygienic Inspection, School of Public Health, Jilin University, Changchun, Jilin, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
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Zeng X, Su Y, Tan A, Zou L, Zha W, Yi S, Lv Y, Kwok T. The association of coffee consumption with the risk of osteoporosis and fractures: a systematic review and meta-analysis. Osteoporos Int 2022; 33:1871-1893. [PMID: 35426508 DOI: 10.1007/s00198-022-06399-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/04/2022] [Indexed: 01/11/2023]
Abstract
UNLABELLED To elucidate the association of coffee and bone health would help fracture risk reduction via dietary intervention. Although those who had higher coffee consumption were less likely to have osteoporosis, the associations between coffee consumption and fracture risk need further investigations with better study designs. INTRODUCTION The associations between coffee consumption and the risk of osteoporosis and fracture remain inconclusive. We aimed to better quantify these associations by conducting meta-analyses of observational studies. METHODS Relevant studies were systematically searched on PubMed, Web of Science, Cochrane library, and Embase Database up to November 25, 2021. The odds ratio (OR) or relative risk (RR) with 95% confidence intervals (CI) was pooled and a dose-response analysis was performed. RESULTS Four studies with 7114 participants for osteoporosis and thirteen studies with 391,956 participants for fracture incidence were included in the meta-analyses. High versus low coffee consumption was associated with a lower risk of osteoporosis [pooled OR (95% CI): 0.79 (0.65-0.92)], while it was non-significantly associated with fracture incidence [pooled OR (95% CI): 0.86 (0.67-1.05) at hip and 0.89 (0.42-1.36) at non-hip]. A non-linear association between the level of coffee consumption and hip fracture incidence was shown (P = 0.004). The pooled RR (95% CI) of hip fracture risk in those who consumed 1, 2-3, 4, and ≥ 9 cups of coffee per day was 0.92 (0.87-0.97), 0.89 (0.83-0.95), 0.91 (0.85-0.98), and 1.10 (0.76-1.59), respectively. The significance in the association between coffee consumption and the hip fracture incidence decreased in those studies that had larger sample size, higher quality, and more adjustments. CONCLUSIONS A dose-dependent relationship may exist between coffee consumption and hip fracture incidence. The effect of high versus low coffee consumption was influenced by study designs. Further studies with dedicated designs are needed to confirm the independent effects of coffee consumption on bone health.
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Affiliation(s)
- X Zeng
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China
| | - Y Su
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China.
| | - A Tan
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China
| | - L Zou
- Hunan Provincial Institute of Emergency Medicine, Hunan Provincial People's Hospital, Changsha, China
| | - W Zha
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China
| | - S Yi
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China
| | - Y Lv
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, 371 Tongzipo Road, Yuelu District, Changsha, 410000, China.
| | - T Kwok
- Department of Medicine & Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
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Lv Y, Zhang J, Liu Y. AB0066 EFFECT AND MECHANISM OF QINXITONG ON BIOLOGICAL BEHAVIOR OF SYNOVIAL FIBROBLASTS IN RHEUMATOID ARTHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundChinese medicine has been used widely for the treatment of RA for a long history in China. The Fifth Hospital of Xi’an has treated patients of RA with QinXiTong(QXT) for 40 years which is mainly based on the water extract from Caulis Sinomenii(CS). However, the molecular mechanism of its anti-rheumatism effect remains unclear.ObjectivesTo investigate the effects of Qinxitong on proliferation, apoptosis, migration and invasion of synovial fibroblasts in rheumatoid arthritis and its mechanism.MethodsTaking rheumatoid arthritis synovial fibroblast cell line MH7A as the research object. The cells were divided into four groups according to the different dosage of Qinxitong intervention, namely control group, low dose group (QXT-20), medium dose group (QXT-50) and high dose group (QXT-100). CCK-8 assay was used to detect cell proliferation, flow cytometry was used to detect cell apoptosis, Transwell assay was used to detect cell migration and invasion, and Western blotting was used to detect total Erk protein (t-Erk) and phosphorylated Erk protein (p-Erk) expression levels.ResultsCCK-8 assay showed that Qinxitong could inhibit the proliferation of MH7A cells, compared with the control group, cell proliferation in QXT-50 and QXT-100 groups was significantly reduced (p<0.05) after 24h, 48, and 72h intervention, the effect of QXT-100 was even more significant (p<0.001); flow cytometry showed that QXT could promote apoptosis of MH7A cells, compared with the control group, the apoptosis rate of QXT-50 and QXT-100 groups was significantly increased (p<0.05); Transwell assay showed that QXT could inhibit the migration and invasion of MH7A cells, compared with the control group, the QXT-50 and QXT-100 groups showed significantly higher inhibition of migration and invasion of MH7A cells (p<0.05); Western blotting showed that QXT could reduced p-Erk expression level, compared with the control group, the decrease of p-Erk expression level in QXT-50 and QXT-100 groups was statistically significant (p<0.001).ConclusionQinxitong can inhibit the proliferation, promote apoptosis, inhibit migration and invasion of rheumatoid arthritis synovial fibroblasts by regulating Erk signaling pathway, providing scientific basis for clinical application of Qixintong.Disclosure of InterestsNone declared
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Liu L, Yan LL, Lv Y, Zhang Y, Li T, Huang C, Kan H, Zhang J, Zeng Y, Shi X, Ji JS. Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey. BMC Public Health 2022; 22:885. [PMID: 35509051 PMCID: PMC9066955 DOI: 10.1186/s12889-022-13126-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 03/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We hypothesize higher air pollution and fewer greenness exposures jointly contribute to metabolic syndrome (MetS), as mechanisms on cardiometabolic mortality. METHODS We studied the samples in the Chinese Longitudinal Healthy Longevity Survey. We included 1755 participants in 2012, among which 1073 were followed up in 2014 and 561 in 2017. We used cross-sectional analysis for baseline data and the generalized estimating equations (GEE) model in a longitudinal analysis. We examined the independent and interactive effects of fine particulate matter (PM2.5) and Normalized Difference Vegetation Index (NDVI) on MetS. Adjustment covariates included biomarker measurement year, baseline age, sex, ethnicity, education, marriage, residence, exercise, smoking, alcohol drinking, and GDP per capita. RESULTS At baseline, the average age of participants was 85.6 (SD: 12.2; range: 65-112). Greenness was slightly higher in rural areas than urban areas (NDVI mean: 0.496 vs. 0.444; range: 0.151-0.698 vs. 0.133-0.644). Ambient air pollution was similar between rural and urban areas (PM2.5 mean: 49.0 vs. 49.1; range: 16.2-65.3 vs. 18.3-64.2). Both the cross-sectional and longitudinal analysis showed positive associations of PM2.5 with prevalent abdominal obesity (AO) and MetS, and a negative association of NDVI with prevalent AO. In the longitudinal data, the odds ratio (OR, 95% confidence interval-CI) of PM2.5 (per 10 μg/m3 increase) were 1.19 (1.12, 1.27), 1.16 (1.08, 1.24), and 1.14 (1.07, 1.21) for AO, MetS and reduced high-density lipoprotein cholesterol (HDL-C), respectively. NDVI (per 0.1 unit increase) was associated with lower AO prevalence [OR (95% CI): 0.79 (0.71, 0.88)], but not significantly associated with MetS [OR (95% CI): 0.93 (0.84, 1.04)]. PM2.5 and NDVI had a statistically significant interaction on AO prevalence (pinteraction: 0.025). The association between PM2.5 and MetS, AO, elevated fasting glucose and reduced HDL-C were only significant in rural areas, not in urban areas. The association between NDVI and AO was only significant in areas with low PM2.5, not under high PM2.5. CONCLUSIONS We found air pollution and greenness had independent and interactive effect on MetS components, which may ultimately manifest in pre-mature mortality. These study findings call for green space planning in urban areas and air pollution mitigation in rural areas.
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Affiliation(s)
- Linxin Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Lijing L Yan
- Global Heath Research Center, Duke Kunshan University, Kunshan, China.,School of Public Health, Wuhan University, Wuhan, China.,Institute for Global Health and Development, Peking University, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai, China
| | - Junfeng Zhang
- Nicholas School of the Environment and Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China.,Center for the Study of Aging and Human Development, Duke Medical School, Durham, NC, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China.
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Lv Y, Mao C, Gao X, Ji JS, Kraus VB, Yin Z, Yuan J, Chen H, Luo J, Zhou J, Li Z, Duan J, Zhu Q, Zeng Y, Wang W, Wang J, Shi X. The obesity paradox is mostly driven by decreased noncardiovascular disease mortality in the oldest old in China: a 20-year prospective cohort study. Nat Aging 2022; 2:389-396. [PMID: 37118064 DOI: 10.1038/s43587-022-00201-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 03/07/2022] [Indexed: 04/30/2023]
Abstract
National and international recommendations of healthy body mass index (BMI) are primarily based on evidence in young and middle-aged populations, with an insufficient representation of the oldest old (aged ≥80 years). Here, we report associations between BMI and mortality risk in 27,026 community-dwelling oldest old (mean age, 92.7 ± 7.5 years) in China from 1998 to 2018. Nonlinear curves showed reverse J-shaped associations of BMI with cardiovascular disease (CVD), non-CVD and all-cause mortality, with a monotonic decreased risk up to BMIs in the overweight and mild obesity range and flat hazard ratios thereafter. Compared to normal weight, overweight and obesity were significantly associated with decreased non-CVD and all-cause mortality, but not with CVD mortality. Similar associations were found for waist circumference. Our results lend support to the notion that optimal BMI in the oldest old may be around the overweight or mild obesity range and challenge the application of international and national guidelines on optimal BMI in this age group.
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Affiliation(s)
- Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Mao
- Division of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Xiang Gao
- Department of Nutrition and Food Hygiene, School of Public Health, Fudan University, Shanghai, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Environmental Sciences and Policy, Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - Virginia Byers Kraus
- Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Zhaoxue Yin
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinqiu Yuan
- Division of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Huashuai Chen
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jiesi Luo
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhihao Li
- Division of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China
| | - Jun Duan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qingyang Zhu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yi Zeng
- Center for the study of Aging and Human Development and the Geriatric Division of School of Medicine, Duke University, Durham, NC, USA
- Center for Study of Healthy Aging and Development Studies, Peking University, Beijing, China
| | - Wentao Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 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, China.
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Qu Y, Lv Y, Ji S, Ding L, Zhao F, Zhu Y, Zhang W, Hu X, Lu Y, Li Y, Zhang X, Zhang M, Yang Y, Li C, Zhang M, Li Z, Chen C, Zheng L, Gu H, Zhu H, Sun Q, Cai J, Song S, Ying B, Lin S, Cao Z, Liang D, Ji JS, Ryan PB, Barr DB, Shi X. Effect of exposures to mixtures of lead and various metals on hypertension, pre-hypertension, and blood pressure: A cross-sectional study from the China National Human Biomonitoring. Environ Pollut 2022; 299:118864. [PMID: 35063540 DOI: 10.1016/j.envpol.2022.118864] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 01/14/2022] [Accepted: 01/15/2022] [Indexed: 06/14/2023]
Abstract
We aimed to explore the effects of mixtures of lead and various metals on blood pressure (BP) and the odds of pre-hypertension (systolic blood pressure (SBP) 120-139 mmHg, and/or diastolic blood pressure (DBP) 80-89 mmHg) and hypertension (SBP/DBP ≥140/90 mmHg) among Chinese adults in a cross-sectional study. This study included 11,037 adults aged 18 years or older from the 2017-2018 China National Human Biomonitoring. Average BP and 13 metals (lead, antimony, arsenic, cadmium, mercury, thallium, chromium, cobalt, molybdenum, manganese, nickel, selenium, and tin) in blood and urine were measured and lifestyle and demographic data were collected. Weighted multiple linear regressions were used to estimate associations of metals with BP in both single and multiple metal models. Weighted quantile sum (WQS) regression was performed to assess the relationship between metal mixture levels and BP. In the single metal model, after adjusting for potential confounding factors, the blood lead levels in the highest quartile were associated with the greater odds of both pre-hypertension (odds ratio (OR): 1.56, 95% CI: 1.22-1.99) and hypertension (OR:1.75, 95% CI: 1.28-2.40) when compared with the lowest quartile. We also found that blood arsenic levels were associated with increased odds of pre-hypertension (OR:1.31, 95% CI:1.00-1.74), while urinary molybdenum levels were associated with lower odds of hypertension (OR:0.68, 95% CI:0.50-0.93). No significant associations were found for the other 10 metals. WQS regression analysis showed that metal mixture levels in blood were significantly associated with higher SBP (β = 1.56, P < 0.05) and DBP (β = 1.56, P < 0.05), with the largest contributor being lead (49.9% and 66.8%, respectively). The finding suggests that exposure to mixtures of metals as measured in blood were positively associated with BP, and that lead exposure may play a critical role in hypertension development.
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Affiliation(s)
- Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Liang Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Wenli Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Xiaojian Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Xu Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Mingyuan Zhang
- School of Public Health, Jilin University, 2699 Qianjin Street, Changchun, Jilin, 130012, China
| | - Yanwei Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Miao Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Zheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Lei Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Huijuan Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Qi Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Jiayi Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Shixun Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Bo Ying
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Shaobin Lin
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, 201 Dowman Drive, Atlanta, GA, 30322, United States
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, 30 Shuangqing Street, Haidian, Beijing, 100084, China; Environmental Research Center, Duke Kunshan University, 8 Duke Avenue, Kunshan, Jiangsu, 215316, China; Nicholas School of the Environment, Duke University, 2080 Duke University Road, Durham, NC, 27708, United States
| | - P Barry Ryan
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, 201 Dowman Drive, Atlanta, GA, 30322, United States
| | - Dana Boyd Barr
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, 201 Dowman Drive, Atlanta, GA, 30322, United States
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, 7 Panjiayuan Nanli, Chaoyang, Beijing, 100021, China.
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Li M, Hou X, Zheng L, Ma Y, Li D, Lv Y, Chen J, Zheng W, Shao Y, Mou Y, Chen L. Utilizing phenotypic characteristics of metastatic brain tumors to improve the probability of detecting circulating tumor DNA from cerebrospinal fluid in non-small-cell lung cancer patients: development and validation of a prediction model in a prospective cohort study. ESMO Open 2021; 7:100305. [PMID: 34922300 PMCID: PMC8685990 DOI: 10.1016/j.esmoop.2021.100305] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background Circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF) has become a promising surrogate for genomic profiling of central nervous system tumors. However, suboptimal ctDNA detection rates from CSF limit its clinical utility. Thus precise screening of suitable patients is needed to maximize the clinical benefit. Patients and methods Between February 2017 and December 2020, 66 newly diagnosed non-small-cell lung cancer (NSCLC) patients with brain parenchymal metastases were prospectively enrolled as a training cohort and 30 additional patients were enrolled as an external validation cohort. CSF samples and matched primary tumor tissues were collected before treatment and subjected to next-generation sequencing (NGS). The imageological characteristics of patients’ brain tumors were evaluated by radiologists using enhanced magnetic resonance imaging images. The clinical and imageological characteristics were evaluated by complete subsets regression, Akaike information criteria, and Bayesian information criteria methods to establish the prediction model. A nomogram was then built for CSF ctDNA detection prediction. Results The somatic mutation detection rate of genes covered by our targeted NGS panel was significantly lower in CSF ctDNA (59.09%) than tumor tissue (91.84%). The Tsize (diameter of the largest intracranial lesion) and LVDmin (minimum lesion–ventricle distance for all intracranial lesions) were significantly associated with positive CSF ctDNA detection, and thus, were selected to establish the prediction model, which achieved an area under the ROC curve (AUC) of 0.819 and an accuracy of 0.800. The model’s predictive ability was further validated in the independent external cohort (AUC of 0.772, accuracy of 0.767) and by internal cross-validation. The CSF ctDNA detection rate was significantly improved from 58.18% (32/55) to 81.81% (27/33) in patients after model selection (P = 0.022). Conclusions This study developed a regression model to predict the probability of detecting CSF ctDNA using the phenotypic characteristics of metastatic brain lesions in NSCLC patients, thus, maximizing the benefits of CSF liquid biopsies. Intracranial tumor size and distance to nearest ventricle were significantly correlated with positive CSF ctDNA detection. A prediction model incorporating Tsize and LVDmin was developed and validated to evaluate the odds of CSF ctDNA positivity. The CSF ctDNA detection rate was significantly improved in patients after model selection.
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Affiliation(s)
- M Li
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - X Hou
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - L Zheng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - Y Ma
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, P. R. China
| | - D Li
- Chemotherapy Department 2, Zhongshan City People's Hospital, Zhongshan, Guangdong, P. R. China
| | - Y Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - J Chen
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - W Zheng
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China
| | - Y Shao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, P. R. China
| | - Y Mou
- Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
| | - L Chen
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, P. R. China.
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Deng GC, Lv Y, Yan H, Sun DC, Qu TT, Pan YT, Han QL, Dai GH. Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer. BMC Cancer 2021; 21:1227. [PMID: 34781928 PMCID: PMC8594118 DOI: 10.1186/s12885-021-08943-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 10/28/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC. METHODS A total of 323 patients with advanced and metastatic PC were identified from the Chinese People's Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively. RESULTS In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19-9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19-9, baseline CEA, change in CA 19-9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62-0.71) and 0.74 (95% CI, 0.69-0.77), respectively. CONCLUSION These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.
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Affiliation(s)
- G C Deng
- School of Medicine, Nankai University, Tianjin, China
- Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Y Lv
- School of Medicine, Nankai University, Tianjin, China
| | - H Yan
- School of Medicine, Nankai University, Tianjin, China
| | - D C Sun
- School of Medicine, Nankai University, Tianjin, China
| | - T T Qu
- School of Medicine, Nankai University, Tianjin, China
- Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Y T Pan
- School of Medicine, Nankai University, Tianjin, China
| | - Q L Han
- School of Medicine, Nankai University, Tianjin, China.
| | - G H Dai
- School of Medicine, Nankai University, Tianjin, China.
- Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, China.
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Zhou J, Lv Y, Zhao F, Wei Y, Gao X, Chen C, Lu F, Liu Y, Li C, Wang J, Zhang X, Gu H, Yin Z, Cao Z, Kraus VB, Mao C, Shi X. Albumin-corrected fructosamine predicts all-cause and non-CVD mortality among the very elderly aged ≥ 80 years without diabetes. J Gerontol A Biol Sci Med Sci 2021; 77:1673-1682. [PMID: 34758092 DOI: 10.1093/gerona/glab339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Several guidelines have suggested alternative glycemic markers for hemoglobin A1c among older adults with limited life expectancy or multiple coexisting chronic illnesses. We evaluated associations between fructosamine, albumin-corrected fructosamine (AlbF) and fasting plasma glucose (FPG) and mortality in the diabetic and non-diabetic subpopulations, compared which marker better predicts mortality among participants aged 80 and above. METHODS Included were 2,238 subjects from the Healthy Ageing and Biomarkers Cohort Study (2012-2018) and 207 participants had diabetes at baseline. Multivariable Cox proportional hazards regression models investigated the associations of fructosamine, AlbF, FPG and all-cause, cardiovascular disease (CVD), and non-CVD mortality in the diabetic and non-diabetic subpopulations. Restricted cubic splines (RCS) explored potential non-linear relations. C-statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) evaluated the additive value of different glycemic markers to predict mortality. RESULTS Overall, 1,191 deaths were documented during 6,793 person-years of follow-up. In the linear model, per unit increases of fructosamine, AlbF and FPG were associated with higher risk of mortality in non-diabetic participants, with hazard ratios of 1.02 (1.00, 1.05), 1.27 (1.14, 1.42) and 1.04 (0.98, 1.11) for all-cause mortality, and 1.04 (1.00, 1.07), 1.38 (1.19, 1.59) and 1.10 (1.01, 1.19) for non-CVD mortality, respectively. Comparisons indicated AlbF better predicts all-cause and non-CVD mortality in non-diabetic participants with significant improvement in IDI and NRI. CONCLUSIONS Higher concentrations of fructosamine, AlbF, and FPG were associated with higher risk of all-cause or non-CVD mortality among very elderly where AlbF may constitute an alternative prospective glycemic predictor of mortality.
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Affiliation(s)
- Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Xiang Gao
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiaochang Zhang
- Division of Non-communicable Disease and Healthy Ageing Management, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaoxue Yin
- Division of Non-communicable Disease and Healthy Ageing Management, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Virginia Byers Kraus
- Duke Molecular Physiology Institute and Division of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Chen Mao
- Division of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, 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, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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Chen C, Li X, Lv Y, Yin Z, Zhao F, Liu Y, Li C, Ji S, Zhou J, Wei Y, Cao X, Wang J, Gu H, Lu F, Liu Z, Shi X. High Blood Uric Acid Is Associated With Reduced Risks of Mild Cognitive Impairment Among Older Adults in China: A 9-Year Prospective Cohort Study. Front Aging Neurosci 2021; 13:747686. [PMID: 34720995 PMCID: PMC8552040 DOI: 10.3389/fnagi.2021.747686] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: It remains unsolved that whether blood uric acid (UA) is a neuroprotective or neurotoxic agent. This study aimed to evaluate the longitudinal association of blood UA with mild cognitive impairment (MCI) among older adults in China. Methods: A total of 3,103 older adults (aged 65+ years) free of MCI at baseline were included from the Healthy Aging and Biomarkers Cohort Study (HABCS). Blood UA level was determined by the uricase colorimetry assay and analyzed as both continuous and categorical (by quartile) variables. Global cognition was assessed using the Mini-Mental State Examination four times between 2008 and 2017, with a score below 24 being considered as MCI. Cox proportional hazards models were used to examine the associations. Results: During a 9-year follow-up, 486 (15.7%) participants developed MCI. After adjustment for all covariates, higher UA had a dose-response association with a lower risk of MCI (all Pfor trend < 0.05). Participants in the highest UA quartile group had a reduced risk [hazard ratio (HR), 0.73; 95% (CI): 0.55–0.96] of MCI, compared with those in the lowest quartile group. The associations were still robust even when considering death as a competing risk. Subgroup analyses revealed that these associations were statistically significant in younger older adults (65–79 years) and those without hyperuricemia. Similar significant associations were observed when treating UA as a continuous variable. Conclusions: High blood UA level is associated with reduced risks of MCI among Chinese older adults, highlighting the potential of managing UA in daily life for maintaining late-life cognition.
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Affiliation(s)
- Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xueqin Li
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaoxue Yin
- Division of Non-communicable Disease and Healthy Ageing Management, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xingqi Cao
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaonan Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Lu
- Beijing Municipal Health Commission Information Center, Beijing Municipal Health Commission Policy Research Center, Beijing, China
| | - Zuyun Liu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.,Center for Clinical Big Data and Analytics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 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, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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Teng Z, Zhu Y, Teng Y, Long Q, Hao Q, Yu X, Yang L, Lv Y, Liu J, Zeng Y, Lu S. The analysis of osteosarcopenia as a risk factor for fractures, mortality, and falls. Osteoporos Int 2021; 32:2173-2183. [PMID: 33877382 DOI: 10.1007/s00198-021-05963-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/14/2021] [Indexed: 12/14/2022]
Abstract
UNLABELLED Osteosarcopenia is defined as the concomitant occurrence of sarcopenia and osteoporosis/osteopenia. This study aimed to clarify whether osteosarcopenia implies a greater risk of fractures, mortality, and falls and to draw attention to osteosarcopenia. INTRODUCTION Osteosarcopenia, which is characterized by the co-existence of osteoporosis/osteopenia and sarcopenia, is one of the most challenging geriatric syndromes. However, the association between osteosarcopenia and the risk of falls, fractures, disability, and mortality is controversial. METHODS We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials, from their inception to March 18, 2021, for cohort studies on the relationship between osteosarcopenia and fractures, falls, and mortality. Two reviewers independently extracted data and assessed study quality. A pooled analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) using fixed or random-effects models. RESULTS Eight cohort studies including 19,836 participants showed that osteosarcopenia significantly increased the risk of fracture (OR 2.46, 95% CI 1.83-3.30, Pheterogeneity = 0.006, I2 = 63.0%), three cohort studies involving 2601 participants indicated that osteosarcopenia significantly increased the risk of mortality (OR 1.66, 95% CI 1.23-2.26, Pheterogeneity = 0.214, I2 = 35.2%), and three cohort studies involving 3144 participants indicated that osteosarcopenia significantly increased the risk of falls (OR 1.62, 95% CI 1.28-2.04, Pheterogeneity = 0.219, I2 = 34.1%). No publication bias existed among the studies regarding the association between osteosarcopenia and fractures. The findings were robust according to the subgroup and sensitivity analyses. CONCLUSIONS This pooled analysis demonstrated that osteosarcopenia significantly increased the risk of fractures, falls, and mortality, thus highlighting its relevance in daily life. Therefore, we suggest that elderly persons should be aware of the risks associated with osteosarcopenia.
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Affiliation(s)
- Z Teng
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
- Yunnan Key Laboratory of Digital Orthopedics, The First People's Hospital of Yunnan Province, Kunming, China
- Graduate School of Kunming Medical University, Kunming, China
| | - Y Zhu
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
| | - Y Teng
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
| | - Q Long
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
| | - Q Hao
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
| | - X Yu
- Graduate School of Kunming Medical University, Kunming, China
| | - L Yang
- Graduate School of Kunming Medical University, Kunming, China
| | - Y Lv
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China
| | - J Liu
- Graduate School of Kunming Medical University, Kunming, China
| | - Y Zeng
- The Sixth Affiliated Hospital of Kunming Medical University, Kunming, Yuxi, China.
| | - S Lu
- Yunnan Key Laboratory of Digital Orthopedics, The First People's Hospital of Yunnan Province, Kunming, China.
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Lv Y, Wei Y, Zhou J, Xue K, Guo Y, Liu Y, Ju A, Wu B, Zhao F, Chen C, Xiong J, Li C, Gu H, Cao Z, Ji JS, Shi X. Human biomonitoring of toxic and essential metals in younger elderly, octogenarians, nonagenarians and centenarians: Analysis of the Healthy Ageing and Biomarkers Cohort Study (HABCS) in China. Environ Int 2021; 156:106717. [PMID: 34153888 DOI: 10.1016/j.envint.2021.106717] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/09/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Metals can be either toxic or essential to health, as they play different role in oxidative stress and metabolic homeostasis during the ageing process. Population-based biomonitoring have documented levels and ranges in concentrations among general population of 0-79 years of age. In people aged 80 and above, toxic metals and essential metals may have different risk profiles, and thus need to be better studied. OBJECTIVE Our aim is to investigate concentrations of toxic metals (arsenic, cadmium, lead and mercury) and essential metals (chromium, cobalt, molybdenum, manganese, nickel and selenium) and their role in diseases, nutritional status among younger elderly, octogenarians, nonagenarians and centenarians. METHODS A total of 932 younger elderly, 643 octogenarians, 540 nonagenarians, 386 centenarians were included from the cross-sectional Healthy Aging and Biomarkers Cohort Study in 2017-2018. Blood or urine biological substrates were collected from each participant to determine the concentrations of toxic metals and essential metals by inductively coupled plasma mass spectrometry. Random forest was constructed to rank the importance of toxic metals and essential metals in longevity. LASSO penalized regressions were performed to select the most significant metals associated with diseases and nutritional status, of which simultaneously included all metals and adjusted for the confounding factors. RESULTS Compared to women, we found higher biomarker concentrations in men for toxic metals (41.2 µg/L vs 34.4 µg/L for blood lead, 1.56 µg/L vs 1.19 µg/L for blood mercury) and lower concentration of essential metals (0.48 µg/L vs 0.58 µg/L for blood molybdenum, 10.0 µg/L vs 11.1 µg/L for blood manganese). These factors may contribute to gender difference observed in longevity, that women live longer than men. Blood lead and urine cadmium tended to increase with age (P <0.001); blood cobalt, molybdenum, manganese increased with age, blood selenium decreased with age while the prevalence of selenium deficiency was extremely low in centenarians. Among toxic metals and essential metals, LASSO penalized regression identified the most significant metals associated with chronic kidney disease was cadmium and arsenic; and it was manganese, cobalt, and selenium for diabetes; it was selenium, molybdenum, lead for anemia; it was mercury for underweight. In random forest model, the top four important metals in longevity were selenium, arsenic, lead and manganese both in men and women. CONCLUSIONS Generally, toxic metals levels were significantly higher while essential metals were relatively sufficient in Chinese centenarians. Toxic metals and essential metals played different role in diseases, nutritional status and longevity in the process of aging. Our research provided real world evidence of biomonitoring reference values to be used for the ongoing population health surveillance in longevity.
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Affiliation(s)
- Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Kai Xue
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yang Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Aipeng Ju
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiahui Xiong
- School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Heng Gu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu, China; Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Anhui Medical University, Hefei, Anhui, China.
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Wang K, Zhang H, Xu H, Lv Y, Shen X, Huang D, Zhang X. 1250P Differences of immune microenvironment among NSCLC patients with various KRAS mutation types. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Wei Y, Lv Y, Zhou J, Gao X, Duan J, Zhao C, Yin Z, Kang Q, Wu B, Chen C, Mao C, Li J, Shi X. Smoking cessation in late life is associated with increased risk of all-cause mortality amongst oldest old people: a community-based prospective cohort study. Age Ageing 2021; 50:1298-1305. [PMID: 33492360 DOI: 10.1093/ageing/afaa280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE we aimed to investigate the association of smoking cessation with risk of all-cause mortality amongst oldest old people (aged ≥ 80 years). DESIGN this was a prospective cohort study. SETTING the Chinese Longitudinal Healthy Longevity Survey, implemented in 23 provinces of China. PARTICIPANTS a total of 28,643 community-dwelling oldest old people (mean age, 92.9 ± 7.5 years) were included. METHODS in this community-based cohort study, Cox proportional hazards models were used to examine the association of smoking cessation with risk of all-cause mortality. RESULTS during 136,585 person-years of follow-up from baseline to 1 September 2014, compared with never smokers, hazard ratios and 95% confidence intervals for all-cause mortality were 1.06 (1.02-1.10) for current smokers, 1.23 (1.09-1.39) for transient quitters (≤1 consecutive years since smoking cessation), 1.22 (1.12-1.32) for recent quitters (2-6 consecutive years since smoking cessation) and 1.11 (1.02-1.22) for long-term quitters (>6 consecutive years since smoking cessation). Cox models with penalised splines revealed an increased risk of all-cause mortality after smoking cessation; the highest mortality risk was observed within 2-4 years after smoking cessation and the risk gradually decreased with duration of smoking cessation. We further conducted subgroup analyses and sensitivity analyses to reduce the impact of reverse causation. CONCLUSIONS smoking is harmful to health in all populations. Our study findings indicated smoking cessation in late life to be associated with increased risk of all-cause mortality amongst oldest old people who have smoked for a long time.
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Affiliation(s)
- Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Hygienic Inspection, School of Public Health, Jilin University, Jilin, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiang Gao
- Nutritional Epidemiology Lab, Pennsylvania State University, University Park, PA, USA
| | - Jun Duan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chao Zhao
- Department of Hygienic Inspection, School of Public Health, Jilin University, Jilin, China
| | - Zhaoxue Yin
- Office of Non-communicable Disease and Ageing Health Management, Chinese Center for Disease Control and Prevention Beijing, China
| | - Qi Kang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Jilin, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chen Mao
- Division of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Juan Li
- Department of Hygienic Inspection, School of Public Health, Jilin University, Jilin, 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, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
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Zhang Z, Bai L, Guan M, Zhou X, Liang X, Lv Y, Yi H, Zhou H, Liu T, Gong P, Sun J, Zhang L. Potential probiotics Lactobacillus casei K11 combined with plant extracts reduce markers of type 2 diabetes mellitus in mice. J Appl Microbiol 2021; 131:1970-1982. [PMID: 33694236 DOI: 10.1111/jam.15061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/18/2020] [Accepted: 03/08/2021] [Indexed: 12/14/2022]
Abstract
AIMS Probiotics and plant extracts have been used to prevent the development of type 2 diabetes mellitus (T2DM). The study aimed to explore the effect of the interaction between potential probiotics and bitter gourd extract (BGE) or mulberry leaf extract (MLE) on T2DM. METHODS AND RESULTS Potential probiotics were tested for their gastrointestinal tract viability and growth situation combined with BGE and MLE in vitro. The diabetes model was constructed in C57BL/6 mice, and the potential effect and mechanism of regulating blood glucose were verified. Hematoxylin-eosin staining (HE), gas chromatography (GC), ELISA, and RT-PCR were also used for analysis. The results showed that Lactobacillus casei K11 had outstanding gastrointestinal tract viability and growth situation with plant extracts. Administration of L. casei K11 combined with BGE and MLE significantly reduced blood glucose levels and ameliorated insulin resistance in diabetic mice than the administration of Lactobacillus paracasei J5 combined with BGE and MLE. Moreover, in L. casei K11 combined with BGE and MLE groups, lipid metabolism, oxidative stress, and proinflammatory cytokine levels were regulated. Furthermore, the results indicated that L. casei K11 combined with BGE and MLE improved free fatty acid receptor 2 (FFAR2) upregulation, glucagon-like peptide-1 (GLP-1) secretion, and short-chain fatty acid (SCFA) levels. CONCLUSIONS These findings showed that L. casei K11 combined with BGE and MLE modified the SCFA-FFAR2-GLP-1 pathway to improve T2DM. SIGNIFICANCE AND IMPACT OF THE STUDY This study identified a new modality for evaluating interactions between potential probiotics and plant extracts. Our findings revealed that L. casei K11 combined with BGE and MLE significantly promoted the SCFA-FFAR2-GLP-1 pathway to inhibit T2DM.
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Affiliation(s)
- Z Zhang
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - L Bai
- College of Marine Life Sciences, Ocean University of China, Qingdao, Shandong, China
| | - M Guan
- Qingdao Central Hospital, Qingdao, Shandong, China
| | - X Zhou
- Qingdao Central Hospital, Qingdao, Shandong, China
| | - X Liang
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - Y Lv
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - H Yi
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - H Zhou
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - T Liu
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - P Gong
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
| | - J Sun
- Qingdao Central Hospital, Qingdao, Shandong, China
| | - L Zhang
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, China
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Tan Q, Lv Y, Zhao F, Zhou J, Yang Y, Liu Y, Zhang M, Lu F, Wei Y, Chen X, Zhang R, Chen C, Wu B, Zhang X, Li C, Huang H, Cai J, Cao Z, Yu D, Ji JS, Zhao S, Shi X. Association of low blood arsenic exposure with level of malondialdehyde among Chinese adults aged 65 and older. Sci Total Environ 2021; 758:143638. [PMID: 33288260 PMCID: PMC7897719 DOI: 10.1016/j.scitotenv.2020.143638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 04/13/2023]
Abstract
High environmental arsenic exposure can increase chronic oxidative stress in experimental studies and in occupational epidemiology studies. Many regulatory agencies have put forth arsenic exposure limits, it is still unclear that whether low environmental arsenic exposure was associated with adverse health outcome in general population. This study aimed to explore the association of low blood arsenic with malondialdehyde in community-dwelling older adults. We used a cross-sectional study of 2384 older adult individuals aged ≥65 years (mean age: 85 years) from the Healthy Aging and Biomarkers Cohort Study in 2017. The median blood arsenic level was 1.41 μg/L. High oxidative stress was categorized according to the 95th percentile of MDA levels (7.47 nmol/mL). Restricted cubic spline models showed that blood arsenic levels were positively associated with malondialdehyde levels (P < 0.01); and the risk of high oxidative stress was no longer significantly increased when blood arsenic level up to 8.74 μg/L. After adjusting for potential confounders, the odds ratios of high oxidative stress for the second, third, and fourth quartiles of blood arsenic were 2.35 (1.11-4.96), 3.87 (1.90-7.91), and 4.18 (2.00-8.72) (Ptrend < 0.01), compared with the first quartile. We concluded that even low arsenic exposure was associated with higher risk of oxidative stress, in a nonlinear dose-response.
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Affiliation(s)
- Qiyue Tan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yang Yang
- The University of Queensland Diamantina Institute, University of Queensland, Queensland, Australia
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mingyuan Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Feng Lu
- Beijing Municipal Health Commission Information Center, (Beijing Municipal Health Commission Policy Research Center), Beijing 100034, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Xin Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Jilin University, Changchun, Jilin, China
| | - Ruizhi Zhang
- School of Public Health, Jilin University, Changchun, Jilin, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiaochang Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hongyuan Huang
- School of Public Health, Jilin University, Changchun, Jilin, China
| | - Junfang Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Di Yu
- The University of Queensland Diamantina Institute, University of Queensland, Queensland, Australia
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu, China; Nicholas School of the Environment, Duke University, Durham, NC, USA
| | - Shuhua Zhao
- School of Public Health, Jilin University, Changchun, Jilin, 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, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Cao Z, Lin S, Zhao F, Lv Y, Qu Y, Hu X, Yu S, Song S, Lu Y, Yan H, Liu Y, Ding L, Zhu Y, Liu L, Zhang M, Wang T, Zhang W, Fu H, Jin Y, Cai J, Zhang X, Yan C, Ji S, Zhang Z, Dai J, Zhu H, Gao L, Yang Y, Li C, Zhou J, Ying B, Zheng L, Kang Q, Hu J, Zhao W, Zhang M, Yu X, Wu B, Zheng T, Liu Y, Barry Ryan P, Barr DB, Qu W, Zheng Y, Shi X. Cohort profile: China National Human Biomonitoring (CNHBM)-A nationally representative, prospective cohort in Chinese population. Environ Int 2021; 146:106252. [PMID: 33242729 PMCID: PMC7828642 DOI: 10.1016/j.envint.2020.106252] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/24/2020] [Accepted: 10/27/2020] [Indexed: 05/02/2023]
Abstract
OBJECTIVE Globally, developed countries such as the United States, Canada, Germany, Korea, have carried out long-term and systematic biomonitoring programs for environmental chemicals in their populations. The China National Human Biomonitoring (CNHBM) was to document the extent of human exposure to a wide array of environmental chemicals, to understand exposure profiles, magnitude and ongoing trends in exposure in the general Chinese population, and to establish a national biorepository. METHODS CNHBM adopted three-stage sampling method to obtain a nationally representative sample of the population. A total of 21,888 participants who were permanent residents in 31 provinces were designed to interviewed in this national biomonitoring (152 monitoring sites × 3 survey units × 2 sexes × 6 age groups × 4 persons = 21,888 persons) in 2017-2018. Unlike the US National Health and Nutrition Examination Survey, the CNHBM will follow the same participants in subsequent cycles allowing for dynamic, longitudinal data sets for epidemiologic follow-up. Each survey cycle of CNHBM will last 2 years and each subsequent cycle will occur 3 years after the prior cycle's completion. RESULTS In 2017-2018, the CNHBM created a large cohort of Chinese citizens that included districts/counties questionnaire, community questionnaire collecting information on villages/communities, individual questionnaire, household questionnaire, comprehensive medical examination, and collection of blood and urine samples for measurement of clinical and exposure biomarkers. A total of 21,746 participants were finally included in CNHBM, accounting for 99.4% of the designed sample size; and 152 PSUs questionnaires, 454 community questionnaires, 21,619 family questionnaires, 21,712 cases of medical examinations, 21,700 individual questionnaires, 21,701 blood samples and 21,704 urine samples were collected, respectively. Planned analyses of blood and urine samples were to measure both inorganic and organic chemicals, including 13 heavy metals and metalloids, 18 poly- and per-fluorinated alkyl substances, 12 phthalate metabolites, 9 polycyclic aromatic hydrocarbons metabolites, 4 environmental alkylated phenols, and 2 benzene metabolites. CONCLUSIONS CNHBM established the first nationally representative, prospective cohort in the Chinese population to understand the baseline and trend of internal exposure of environmental chemicals in general population, and to understand environmental toxicity.
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Affiliation(s)
- Zhaojin Cao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shaobin Lin
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yingli Qu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaojian Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shicheng Yu
- Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shixun Song
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Huifang Yan
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Liang Ding
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ying Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ling Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Miao Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Wenli Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Fu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yongjin Jin
- School of Statistics, Renmin University of China, Beijing, China
| | - Jiayi Cai
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xu Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chonghuai Yan
- The Children's Hospital, Fudan University, Shanghai, China
| | - Saisai Ji
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhuona Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiayin Dai
- Institute of Zoology, Chinese Academy Sciences, Beijing, China
| | - Huijuan Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lixue Gao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanwei Yang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bo Ying
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lei Zheng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qi Kang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Junming Hu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Weixia Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mingyuan Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaoyi Yu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Wu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tongzhang Zheng
- Department of Epidemiology, Brown University, Providence, RI, USA
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - P Barry Ryan
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Dana Boyd Barr
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Weidong Qu
- Department of Environment Health, School of Public Health, Fudan University, Shanghai, China
| | - Yuxin Zheng
- School of Public Health, Qingdao University, Qingdao, Shandong, 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, China.
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48
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Li R, Wang X, Sun Y, Lv Y, Dou X, Wang Q. Application of metagenomic next-generation sequencing in the diagnosis of imported malaria. Int J Infect Dis 2020. [DOI: 10.1016/j.ijid.2020.09.1115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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49
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Shen J, Duan H, Zhang B, Wang J, Ji JS, Wang J, Pan L, Wang X, Zhao K, Ying B, Tang S, Zhang J, Liang C, Sun H, Lv Y, Li Y, Li T, Li L, Liu H, Zhang L, Wang L, Shi X. Prevention and control of COVID-19 in public transportation: Experience from China. Environ Pollut 2020; 266:115291. [PMID: 32829124 PMCID: PMC7833563 DOI: 10.1016/j.envpol.2020.115291] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 07/13/2020] [Accepted: 07/17/2020] [Indexed: 05/09/2023]
Abstract
Due to continuous spread of coronavirus disease 2019 (COVID-19) worldwide, long-term effective prevention and control measures should be adopted for public transport facilities, as they are increasing in popularity and serve as the principal modes for travel of many people. The human infection risk could be extremely high due to length of exposure time window, transmission routes and structural characteristics during travel or work. This can result in the rapid spread of the infection. Based on the transmission characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the nature of public transport sites, we identified comprehensive countermeasures toward the prevention and control of COVID-19, including the strengthening of personnel management, personal protection, environmental cleaning and disinfection, and health education. Multi-pronged strategies can enhance safety of public transportation. The prevention and control of the disease during the use of public transportation will be particularly important when all countries in the world resume production. The aim of this study is to introduce experience of the prevention and control measures for public transportation in China to promote the global response to COVID-19.
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Affiliation(s)
- Jin Shen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Hongyang Duan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Baoying 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
| | - Jiaqi 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
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu, 215316, China; Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA
| | - Jiao 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
| | - Lijun Pan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xianliang 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
| | - Kangfeng 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
| | - Bo Ying
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Song Tang
- 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, Jiangsu, 211166, China
| | - Jian 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
| | - Chen Liang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Huihui 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
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yan 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
| | - Tao 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
| | - Li 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
| | - Hang 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
| | - Liubo 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
| | - Lin 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
| | - 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; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
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50
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Tang S, Mao Y, Jones RM, Tan Q, Ji JS, Li N, Shen J, Lv Y, Pan L, Ding P, Wang X, Wang Y, MacIntyre CR, Shi X. Aerosol transmission of SARS-CoV-2? Evidence, prevention and control. Environ Int 2020; 144:106039. [PMID: 32822927 PMCID: PMC7413047 DOI: 10.1016/j.envint.2020.106039] [Citation(s) in RCA: 306] [Impact Index Per Article: 76.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/30/2020] [Accepted: 08/03/2020] [Indexed: 05/09/2023]
Abstract
As public health teams respond to the pandemic of coronavirus disease 2019 (COVID-19), containment and understanding of the modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is of utmost importance for policy making. During this time, governmental agencies have been instructing the community on handwashing and physical distancing measures. However, there is no agreement on the role of aerosol transmission for SARS-CoV-2. To this end, we aimed to review the evidence of aerosol transmission of SARS-CoV-2. Several studies support that aerosol transmission of SARS-CoV-2 is plausible, and the plausibility score (weight of combined evidence) is 8 out of 9. Precautionary control strategies should consider aerosol transmission for effective mitigation of SARS-CoV-2.
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Affiliation(s)
- Song Tang
- 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, Jiangsu 211166, China
| | - Yixin Mao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Rachael M Jones
- Department of Family and Preventive Medicine, School of Medicine, University of Utah, Salt Lake City, UT 84108, USA
| | - Qiyue 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
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu 215316, China; Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - Na 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
| | - Jin Shen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Lijun Pan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Pei 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
| | - Xiaochen 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
| | - Youbin Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C Raina MacIntyre
- Kirby Institute, Faculty of Medicine, The University of New South Wales, Sydney, Australia; College of Public Service & Community Solutions and College of Health Solutions, Arizona State University, USA
| | - 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; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
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