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Fang J, Gao Y, Zhang M, Jiang Q, Chen C, Gao X, Liu Y, Dong H, Tang S, Li T, Shi X. Personal PM 2.5 Elemental Components, Decline of Lung Function, and the Role of DNA Methylation on Inflammation-Related Genes in Older Adults: Results and Implications of the BAPE Study. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15990-16000. [PMID: 36214782 DOI: 10.1021/acs.est.2c04972] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Epidemiological evidence of the effects of PM2.5 elements on lung function and DNA methylation is limited. We conducted a longitudinal panel study of 76 healthy older adults aged 60-69 years in Jinan, China, from September 2018 to January 2019. We periodically measured individual 72 h PM2.5 and element concentrations, lung function, and DNA methylation levels of eight inflammation-related genes. We used linear mixed-effect models to investigate the effects of exposure to personal PM2.5 elements on the lung function and DNA methylation. Mediation analysis was used to investigate the underlying effect mechanism. Negative changes in the ratio of forced expiratory volume in 1 s to forced vital capacity, ranging from -1.23% [95% confidence interval (CI): -2.11%, -0.35%] to -0.77% (95% CI: -1.49%, -0.04%), were significantly associated with interquartile range (IQR) increases in personal PM2.5 at different lag periods (7-12, 13-24, 25-48, 0-24, 0-48, and 0-72 h). Arsenic (As), nickel, rubidium (Rb), selenium, and vanadium were significantly associated with at least three lung function parameters, and IQR increases in these elements led to 0.12-5.66% reductions in these parameters. PM2.5 elements were significantly associated with DNA methylation levels. DNA methylation mediated 7.28-13.02% of the As- and Rb-related reduced lung function. The findings indicate that exposure to elements in personal PM2.5 contributes to reduced lung function through DNA methylation.
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Affiliation(s)
- Jianlong Fang
- China CDC Key Laboratory of Environment and Human Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Ying Gao
- China CDC Key Laboratory of Environment and Human Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Meiyun Zhang
- Chaoyang District Center for Disease Control and Prevention, Beijing 100021, China
| | - Qizheng Jiang
- China CDC Key Laboratory of Environment and Human 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 Human Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Xu Gao
- School of Public Health, Peking University, Beijing 100191, China
| | - Yuanyuan Liu
- China CDC Key Laboratory of Environment and Human Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Human 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 Human 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
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Human 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
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Human Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
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Liu M, Li Z, Lu F, Guo M, Tao L, Liu M, Liu Y, Deginet A, Hu Y, Li Y, Wu M, Luo Y, Wang X, Yang X, Gao B, Guo X, Liu X. Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 226:112794. [PMID: 34592518 DOI: 10.1016/j.ecoenv.2021.112794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. METHODS A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM2.5 and PM10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. RESULTS A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM2.5 corresponded to 1.91% (95% CI: 1.33-2.49%), 2.16% (95% CI: 1.08-3.25%), and 1.92% (95% CI: 1.46-2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0-8 days, lag 8 days, and lag 8 days. The effect size of PM2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM10 were 3.86% (95% CI: 2.48-5.27%), 3.73% (95% CI: 1.72-5.79%), and 3.92% (95% CI: 2.65-5.21%), respectively, at lag 0-13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. CONCLUSIONS Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM2.5 was higher in patients with T2D than those without T2D.
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Affiliation(s)
- Mengmeng Liu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China; National Institute for Data Science in Health and Medicine, Capital Medical University, China
| | - Zhiwei Li
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Feng Lu
- Beijing Municipal Health Commission Information Centre, Beijing 100034, China
| | - Moning Guo
- Beijing Municipal Health Commission Information Centre, Beijing 100034, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Mengyang Liu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yue Liu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Aklilu Deginet
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yaoyu Hu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yutong Li
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Mengqiu Wu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Xiaonan Wang
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Xinghua Yang
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Bo Gao
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China; National Institute for Data Science in Health and Medicine, Capital Medical University, China; Centre for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Australia.
| | - Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
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Chen X, Han Y, Chen W, Wang Y, Qiu X, Li W, Hu M, Wu Y, Wang Q, Zhang H, Zhu T. Respiratory Inflammation and Short-Term Ambient Air Pollution Exposures in Adult Beijing Residents with and without Prediabetes: A Panel Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:67004. [PMID: 32484751 PMCID: PMC7263737 DOI: 10.1289/ehp4906] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/07/2020] [Accepted: 05/14/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Accumulating evidence suggests that individuals with glucose metabolism disorders are susceptible to mortality associated with fine particles. However, the mechanisms remain largely unknown. OBJECTIVES We examined whether particle-associated respiratory inflammation differed between individuals with prediabetes and healthy control participants. METHODS Based on a panel study [A prospective Study COmparing the cardiometabolic and respiratory effects of air Pollution Exposure on healthy and prediabetic individuals (SCOPE)] conducted in Beijing between August 2013 and February 2015, fractional exhaled nitric oxide (FeNO) was measured from 112 participants at two to seven visits to indicate respiratory inflammation. Particulate pollutants-including particulate matter with an aerodynamic diameter of ≤2.5μm (PM2.5), black carbon (BC), ultrafine particles (UFPs), and accumulated-mode particles-were monitored continuously at a single central monitoring site. Linear mixed-effects models were used to estimate associations between ln-FeNO with pollutant concentrations at individual 1-h lags (up to 24 h) and with average concentrations at 8 and 24 h before the clinical visit. We evaluated glucose metabolism disorders as a potential modifier by comparing associations between participants with high vs. low average fasting blood glucose (FBG) and homeostasis model assessment insulin resistance (HOMA-IR) levels. RESULTS FeNO was positively associated with all pollutants, with the strongest associations for an interquartile range increase in 1-h lagged exposures (ranging from 21.3% for PM2.5 to 74.7% for BC). Associations differed significantly according to average HOMA-IR values when lagged 6-18 h for PM2.5, 15-19 h for BC, and 6-15 h for UFPs, with positive associations among those with HOMA-IR≥1.6 while associations were closer to the null or inverse among those with HOMA-IR<1.6. Associations between PM2.5 and FeNO were consistently higher among individuals with average FBG≥6.1 mmol/L vs. low FBG, with significant differences for multiple hourly lags. DISCUSSION Glucose metabolism disorders may aggravate respiratory inflammation following exposure to ambient particulate matter. https://doi.org/10.1289/EHP4906.
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Affiliation(s)
- Xi Chen
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Hebei Xiongan Green-Research Inspection and Certification Co., Ltd., Shenzhen Institute of Building Research Co., Ltd., Shenzhen, China
| | - Yiqun Han
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Wu Chen
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yanwen Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Xinghua Qiu
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, China
| | - Weiju Li
- Peking University Hospital, Peking University, Beijing, China
| | - Min Hu
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yusheng Wu
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Qi Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Hanxiyue Zhang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Tong Zhu
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, China
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Zhou Q, Wang Q, Chen B, Han Y, Cheng L, Shen Y, Hao P, Zhang Z. Factors influencing breath analysis results in patients with diabetes mellitus. J Breath Res 2019; 13:046012. [PMID: 31489846 DOI: 10.1088/1752-7163/ab285a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Breath analysis is used to detect the composition of exhaled gas. As a quick and non-invasive detection method, breath analysis provides deep insights into the progression of various kinds of diseases, especially those with metabolism disorders. Abundant information on volatile compounds in diabetic patients has been studied in numerous articles in the literature. However, exhaled gas in diabetic patients can be altered by various complications. So far, little attention has been paid to this alteration. In our paper, we found that under air pollution conditions, diabetic patients exhale more nitric oxide. Diabetic patients with heart failure exhale more acetone than those without heart failure. After 13C-labeled glucose intake, patients infected with Helicobacter pylori exhaled more 13C and less 18O than those without infection. Exhalation with chronic kidney disease changes volatile organic compounds on a large scale. Diabetic patients with ketoacidosis exhale more acetone than those without ketoacidosis. Some specific volatile organic compounds also emanate from diabetic feet. By monitoring breath frequency, diabetic patients with obstructive sleep apnea syndrome exhibit a unique breath pattern and rhythm as compared with other diabetic patients, and sleep apnea is prevalent among diabetic patients. In addition to clinical findings, we analyzed the underlying mechanisms at the levels of molecules, cells and whole bodies, and provided suggestions for further studies.
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Affiliation(s)
- Qing Zhou
- Department of Endocrinology, Shandong Provincial Qianfoshan Hospital, the First Hospital Affiliated with Shandong First Medical University, Department of Cardiology, Shandong University Qilu Hospital, and School of Medicine of Shandong University, Jinan, 250012, Shandong, People's Republic of China
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Li F, Dong Y, Lu R, Yang B, Wang S, Xing G, Jiang Y. Susceptibility to the acute toxicity of acrylonitrile in streptozotocin-induced diabetic rats: protective effect of phenethyl isothiocyanate, a phytochemical CYP2E1 inhibitor. Drug Chem Toxicol 2019; 44:130-139. [PMID: 31258002 DOI: 10.1080/01480545.2019.1566354] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Diabetes mellitus is a significant global public health issue. The diabetic state not only precipitates chronic disease but also has the potential to change the toxicity of drugs and chemicals. Acrylonitrile (AN) is a potent neurotoxin widely used in industrial products. This study used a streptozotocin (STZ)-induced diabetic rat model to examine the role of cytochrome P450 2E1 (CYP2E1) in acute AN toxicity. The protective effect of phenethyl isothiocyanate (PEITC), a phytochemical inhibitor of CYP2E1, was also investigated. A higher incidence of convulsions and loss of the righting reflex, and decreased rates of survival, as well as elevated CYP2E1 activity, were observed in diabetic rats treated with AN when compared to those in non-diabetic rats, suggesting that diabetes confers susceptibility to the acute toxicity of AN. Pretreatment with PEITC (20-80 mg/kg) followed by AN injection alleviated the acute toxicity of AN in diabetic rats as evidenced by the decreased incidence of convulsions and loss of righting reflex, and increased rates of survival. PEITC pretreatment at 40 and 80 mg/kg decreased hepatic CYP2E1 activity in AN-exposed diabetic rats. PEITC pretreatment (20 mg/kg) increased the glutathione (GSH) content and glutathione S-transferase (GST) activity and further decreased ROS levels in AN-exposed diabetic rats. Collectively, STZ-induced diabetic rats were more sensitive to AN-induced acute toxicity mainly due to CYP2E1 induction, and PEITC pretreatment significantly alleviated the acute toxicity of AN in STZ-induced diabetic rats. PEITC might be considered as a potential effective chemo-preventive agent against AN-induced acute toxicity in individuals with an underlying diabetic condition.
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Affiliation(s)
- Fang Li
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Ying Dong
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Rongzhu Lu
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Bobo Yang
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Suhua Wang
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Guangwei Xing
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
| | - Yuanyue Jiang
- Department of Preventive Medicine and Public Health Laboratory Sciences, School of Medicine, Jiangsu University, Zhenjiang, PR China
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Park J, Oh S, Lee J. Effects of particulate matter on healthy human skin: a panel study using a smartphone application measuring daily skin condition. J Eur Acad Dermatol Venereol 2019; 33:1363-1368. [DOI: 10.1111/jdv.15517] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/29/2019] [Indexed: 12/26/2022]
Affiliation(s)
- J.H. Park
- Department of Dermatology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea
| | - S.J. Oh
- Department of Dermatology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea
| | - J.H. Lee
- Department of Dermatology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea
- Department of Medical Device Management & Research SAIHST Sungkyunkwan University Seoul Korea
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