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Liu J, Fan Y, Song J, Song R, Li X, Liu L, Wei N, Yuan J, Yi W, Pan R, Jin X, Cheng J, Zhang X, Su H. Impaired thyroid hormone sensitivity exacerbates the effect of PM 2.5 and its components on dyslipidemia in schizophrenia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174055. [PMID: 38889814 DOI: 10.1016/j.scitotenv.2024.174055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 06/06/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Dyslipidemia in schizophrenia causes a serious loss of healthy life expectancy, making it imperative to explore key environmental risk factors. We aimed to assess the effect of PM2.5 and its constituents on dyslipidemia in schizophrenia, identify the critical hazardous components, and investigate the role of impaired thyroid hormones (THs) sensitivity in this association. METHODS We collected disease data on schizophrenia from the Anhui Mental Health Center from 2019 to 2022. Logistic regression was constructed to explore the effect of average annual exposure to PM2.5 and its components [black carbon (BC), organic matter (OM), sulfate (SO42-), ammonium (NH4+), and nitrate (NO3-)] on dyslipidemia, with subgroup analyses for age and gender. The degree of impaired THs sensitivity in participants was reflected by the Thyroid Feedback Quantile-based Index (TFQI), and its role in the association of PM2.5 components with dyslipidemia was explored. RESULTS A total of 5125 patients with schizophrenia were included in this study. Exposure to PM2.5 and its components (BC, OM, SO42-, NH4+, and NO3-) were associated with dyslipidemia with the odds ratios and 95 % confidence interval of 1.13 (1.04, 1.23), 1.16 (1.07, 1.26), 1.15 (1.06, 1.25), 1.11 (1.03, 1.20), 1.09 (1.00, 1.18), 1.12 (1.04, 1.20), respectively. Mixed exposure modeling indicated that BC played a major role in the effects of the mixture. More significant associations were observed in males and groups <45 years. In addition, we found that the effect of PM2.5 and its components on dyslipidemia was exacerbated as impaired THs sensitivity in the patients. CONCLUSIONS Exposure to PM2.5 and its components is associated with an increased risk of dyslipidemia in schizophrenia, which may be exacerbated by impaired THs sensitivity. Our results suggest a new perspective for the management of ambient particulate pollution and the protection of thyroid function in schizophrenia.
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Affiliation(s)
- Jintao Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Yinguang Fan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Rong Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xuanxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jiajun Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Xulai Zhang
- Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China.
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China; Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China.
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2
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He G, Jiang M, Tian S, He L, Bai X, Chen S, Li G, Wang C, Zhang Z, Wu Y, Su M, Li X, Guo X, Yang Y, Zhang X, Cui J, Xu W, Song L, Yang H, He W, Zhang Y, Li X, Gao X, Chen L. Clean air policy reduces the atherogenic lipid profile levels: Results from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) Study. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135394. [PMID: 39128148 DOI: 10.1016/j.jhazmat.2024.135394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/16/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024]
Abstract
Evidence of the associations between long-term exposure to PM2.5 and O3 and human blood lipid concentrations is abundant yet inconclusive. Whether clean air policies could improve lipid profiles remains unclear. In total, 2979312 participants from a Chinese nationwide prospective study were included. For cross-sectional analyses, linear mixed-effects models were utilized to assess the associations of pollutants with lipid profiles (TC, LDL-C, TG, HDL-C). For longitudinal analyses, a quasi-experimental design and difference-in-differences models were employed to investigate the impact of China's Clean Air Act. In the cross-sectional analyses, each IQR increase in PM2.5 was associated with 2.49 % (95 % CI: 2.36 %, 2.62 %), 2.51 % (95 % CI: 2.26 %, 2.75 %), 3.94 % (95 % CI: 3.65 %, 4.23 %), and 1.54 % (95 % CI: 1.38 %, 1.70 %) increases in TC, LDL-C, TG, and HDL-C, respectively. For each IQR increase in O3, TC, LDL-C, TG, and HDL-C changed by 1.06 % (95 % CI: 0.95 %, 1.17 %), 1.21 % (95 % CI: 1.01 %, 1.42 %), 1.78 % (95 % CI: 1.54 %, 2.02 %), and -0.63 % (95 % CI: -0.76 %, -0.49 %), respectively. Longitudinal analyses showed that the intervention group experienced greater TC, LDL-C, and HDL-C reductions (1.77 %, 4.26 %, and 7.70 %, respectively). Our findings suggest that clean air policies could improve lipid metabolism and should be implemented in countries with heavy air pollution burdens.
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Affiliation(s)
- Guangda He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meijie Jiang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Sifan Tian
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Linkang He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xueke Bai
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shi Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangyu Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chunqi Wang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zenglei Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Wu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingming Su
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangjie Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinxin Guo
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianlan Cui
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Xu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Song
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hao Yang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyan He
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Zhang
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xi Li
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
| | - Liang Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Du J, Shao B, Gao Y, Wei Z, Zhang Y, Li H, Li J, Li G. Relationship between exposure to fine particulate matter and cardiovascular risk factors and the modifying effect of socioeconomic status: a cross-sectional study in Beijing, China. Front Public Health 2024; 12:1398396. [PMID: 39100956 PMCID: PMC11294222 DOI: 10.3389/fpubh.2024.1398396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 07/08/2024] [Indexed: 08/06/2024] Open
Abstract
Accumulating research suggested that long-term exposure to fine particulate matter (PM2.5) is related to cardiovascular disease (CVD). However, evidence regarding the relationship between PM2.5 and CVD risk factors remains inconsistent. We hypothesized that this association may be partially modified by socioeconomic status (SES). To investigate the relationships and to test the modifying effect of SES, we included baseline data for 21,018 adults from September 2017 to May 2018. PM2.5 concentrations were determined by employing an amalgamation of linear measurements obtained from monitoring stations located near the participants' residential and workplace addresses. We assessed SES across several domains, including income, education, and occupation levels, as well as through a composite SES index. The results indicated that for every 10 μg/m3 increase in PM2.5 exposure, the risk of hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and hyperhomocysteinemia (HHcy) increased by 7.7% [Odds ratio (OR) = 1.077, 95% Confidence Interval (CI) = 1.011, 1.146], 19.6% (OR = 1.196, 95% CI = 1.091, 1.312), 4.2% (OR = 1.042, 95% CI = 1.002, 1.084), and 17.1% (OR = 1.171, 95% CI = 1.133, 1.209), respectively. Compared to the high SES group, those with low SES are more prone to hypercholesterolemia, hyperbetalipoproteinemia, diabetes, and HHcy. Notably, the disparities in SES appear significant in the relationship between PM2.5 exposure and hypercholesterolemia as well as hyperbetalipoproteinemia. But for diabetes and HHcy, the modification effect of SES on PM2.5 shows an inconsistent pattern. In conclusion, the results confirm the association between PM2.5 and cardiovascular risk factors and low SES significantly amplified the adverse PM2.5 effect on dyslipidemia. It is crucial to emphasize a need to improve the socioeconomic inequality among adults in Beijing and contribute to the understanding of the urgency in protecting the health of vulnerable groups.
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Affiliation(s)
- Jing Du
- Institute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Bing Shao
- Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yanlin Gao
- Institute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Zaihua Wei
- Institute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yu Zhang
- Hongzheng Medical Technology Co., Ltd., Tianjin, China
| | - Hong Li
- Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Jiang Li
- Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing, China
| | - Gang Li
- Institute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, Beijing, China
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4
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Wei X, Ho KF, Yu T, Lin C, Chang LY, Chen D, Tam T, Huang B, Lau AKH, Lao XQ. The joint effect of long-term exposure to multiple air pollutants on non-accidental and cause-specific mortality: A longitudinal cohort study. JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134507. [PMID: 38718510 DOI: 10.1016/j.jhazmat.2024.134507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/20/2024] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
The long-term joint impacts of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on mortality are inconclusive. To bridge this research gap, we included 283,568 adults from the Taiwan MJ cohort between 2005 and 2016 and linked with the mortality data until 31 May 2019. Participants' annual average exposures to PM2.5, NO2, and O3 were estimated using satellite-based spatial-temporal models. We applied elastic net-regularised Cox models to construct a weighted environmental risk score (WERS) for the joint effects of three pollutants on non-accidental, cardiovascular, and cancer mortality and evaluated the contribution of each pollutant. The three pollutants jointly raised non-accidental mortality risk with a WERS hazard ratio (HR) of 1.186 (95% CI: 1.118-1.259) per standard deviation increase in each pollutant and weights of 72.8%, 15.2%, and 12.0% for PM2.5, NO2, and O3, respectively. The WERS increased cardiovascular death risk [HR: 1.248 (1.042-1.496)], with PM2.5 as the first contributor and O3 as the second. The WERS also elevated the cancer death risk [HR: 1.173 (1.083-1.270)], where PM2.5 played the dominant role and NO2 ranked second. Coordinated control of these three pollutants can optimise the health benefits of air quality improvements.
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Affiliation(s)
- Xianglin Wei
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Kin Fai Ho
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Tsung Yu
- Department of Public Health, College of Medicine, National Cheng Kung University, Taiwan
| | - Changqing Lin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China
| | - Ly-Yun Chang
- Institute of Sociology, Academia Sinica, Taipei, Taiwan
| | - Dezhong Chen
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Tony Tam
- Department of Sociology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Bo Huang
- Department of Geography, The University of Hong Kong, Hong Kong Special Administrative Region of China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region of China
| | - Xiang Qian Lao
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong Special Administrative Region of China.
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Wu TQ, Han X, Liu CY, Zhao N, Ma J. A causal relationship between particulate matter 2.5 and obesity and its related indicators: a Mendelian randomization study of European ancestry. Front Public Health 2024; 12:1366838. [PMID: 38947357 PMCID: PMC11211571 DOI: 10.3389/fpubh.2024.1366838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024] Open
Abstract
Background In recent years, the prevalence of obesity has continued to increase as a global health concern. Numerous epidemiological studies have confirmed the long-term effects of exposure to ambient air pollutant particulate matter 2.5 (PM2.5) on obesity, but their relationship remains ambiguous. Methods Utilizing large-scale publicly available genome-wide association studies (GWAS), we conducted univariate and multivariate Mendelian randomization (MR) analyses to assess the causal effect of PM2.5 exposure on obesity and its related indicators. The primary outcome given for both univariate MR (UVMR) and multivariate MR (MVMR) is the estimation utilizing the inverse variance weighted (IVW) method. The weighted median, MR-Egger, and maximum likelihood techniques were employed for UVMR, while the MVMR-Lasso method was applied for MVMR in the supplementary analyses. In addition, we conducted a series of thorough sensitivity studies to determine the accuracy of our MR findings. Results The UVMR analysis demonstrated a significant association between PM2.5 exposure and an increased risk of obesity, as indicated by the IVW model (odds ratio [OR]: 6.427; 95% confidence interval [CI]: 1.881-21.968; P FDR = 0.005). Additionally, PM2.5 concentrations were positively associated with fat distribution metrics, including visceral adipose tissue (VAT) (OR: 1.861; 95% CI: 1.244-2.776; P FDR = 0.004), particularly pancreatic fat (OR: 3.499; 95% CI: 2.092-5.855; PFDR =1.28E-05), and abdominal subcutaneous adipose tissue (ASAT) volume (OR: 1.773; 95% CI: 1.106-2.841; P FDR = 0.019). Furthermore, PM2.5 exposure correlated positively with markers of glucose and lipid metabolism, specifically triglycerides (TG) (OR: 19.959; 95% CI: 1.269-3.022; P FDR = 0.004) and glycated hemoglobin (HbA1c) (OR: 2.462; 95% CI: 1.34-4.649; P FDR = 0.007). Finally, a significant negative association was observed between PM2.5 concentrations and levels of the novel obesity-related biomarker fibroblast growth factor 21 (FGF-21) (OR: 0.148; 95% CI: 0.025-0.89; P FDR = 0.037). After adjusting for confounding factors, including external smoke exposure, physical activity, educational attainment (EA), participation in sports clubs or gym leisure activities, and Townsend deprivation index at recruitment (TDI), the MVMR analysis revealed that PM2.5 levels maintained significant associations with pancreatic fat, HbA1c, and FGF-21. Conclusion Our MR study demonstrates conclusively that higher PM2.5 concentrations are associated with an increased risk of obesity-related indicators such as pancreatic fat content, HbA1c, and FGF-21. The potential mechanisms require additional investigation.
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Affiliation(s)
- Tian qiang Wu
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xinyu Han
- Department of First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Chun yan Liu
- Department of Endocrinology, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Na Zhao
- Department of Endocrinology, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jian Ma
- Department of Endocrinology, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
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6
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Han W, Zhang J, Xu Z, Yang T, Huang J, Beevers S, Kelly F, Li G. Could the association between ozone and arterial stiffness be modified by fish oil supplementation? ENVIRONMENTAL RESEARCH 2024; 249:118354. [PMID: 38325778 DOI: 10.1016/j.envres.2024.118354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/10/2023] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
BACKGROUND Arterial stiffness (AS) is an important predicting factor for cardiovascular disease. However, no epidemiological studies have ever explored the mediating role of biomarkers in the association between ozone and AS, nor weather fish oil modified such association. METHODS Study participants were drawn from the UK biobank, and a total of 95,699 middle-aged and older adults were included in this study. Ozone was obtained from Community Multiscale Air Quality (CMAQ) model matched to residential addresses, fish oil from self-reported intake, and arterial stiffness was based on device measurements. First, we applied a double robust approach to explore the association between ozone or fish oil intake and arterial stiffness, adjusting for potential confounders at the individual and regional levels. Then, how triglycerides, apolipoprotein B (Apo B)/apolipoprotein A (ApoA) and non-high-density lipoprotein cholesterol (Non-HDL-C) mediate the relationship between ozone and AS. Last, the modifying role of fish oil was further explored by stratified analysis. RESULTS The mean age of participants was 55 years; annual average ozone exposure was associated with ASI (beta:0.189 [95%CI: 0.146 to 0.233], P < 0.001), and compared to participants who did not consume fish oil, fish oil users had a lower ASI (beta: 0.061 [95%CI: -0.111 to -0.010], P = 0.016). The relationship between ozone exposure and AS was mediated by triglycerides, ApoB/ApoA, and Non-HDL-C with mediation proportions ranging from 10.90% to 18.30%. Stratified analysis showed lower estimates on the ozone-AS relationship in fish oil users (P = 0.011). CONCLUSION Ozone exposure was associated with higher levels of arterial stiffness, in contrast to fish oil consumption, which showed a protective association. The association between ozone exposure and arterial stiffness was partially mediated by some biomarkers. In the general population, fish oil consumption might provide protection against ozone-related AS.
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Affiliation(s)
- Wenxing Han
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Jin Zhang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Zhihu Xu
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Teng Yang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Institute for Global Health and Development, Peking University, Beijing, China.
| | - Sean Beevers
- Environmental Research group, school of public health, Imperial college London, London, UK.
| | - Frank Kelly
- Environmental Research group, school of public health, Imperial college London, London, UK.
| | - Guoxing Li
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Environmental Research group, school of public health, Imperial college London, London, UK.
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7
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Zhang C, Wang Y, Xie W, Zhang J, Tian T, Zhu Q, Fang X, Sui J, Pan D, Xia H, Wang S, Sun G, Dai Y. Sex differences and dietary patterns in the association of air pollutants and hypertension. BMC Public Health 2024; 24:1134. [PMID: 38654317 PMCID: PMC11040935 DOI: 10.1186/s12889-024-18620-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Hypertension is one of the major public health problems in China. Limited evidence exists regarding sex differences in the association between hypertension and air pollutants, as well as the impact of dietary factors on the relationship between air pollutants and hypertension. The aim of this study was to investigate the sex-specific effects of dietary patterns on the association between fine particulate matter (PM2.5), ozone(O3) and hypertension in adults residing in Jiangsu Province of China. METHODS A total of 3189 adults from the 2015 China Adult Chronic Disease and Nutrition Surveillance in Jiangsu Province were included in this study. PM2.5 and O3 concentrations were estimated using satellite space-time models and assigned to each participant. Dietary patterns were determined by reduced rank regression (RRR), and multivariate logistic regression was used to assess the associations of the obtained dietary patterns with air pollutants and hypertension risk. RESULTS After adjusting for confounding variables, we found that males were more sensitive to long-term exposure to PM2.5 (Odds ratio (OR) = 1.42 95%CI:1.08,1.87), and females were more sensitive to long-term exposure to O3 (OR = 1.61 95%CI:1.15,2.23). Traditional southern pattern identified through RRR exhibited a protective effect against hypertension in males (OR = 0.73 95%CI: 0.56,1.00). The results of the interaction between dietary pattern score and PM2.5 revealed that adherence to traditional southern pattern was significantly associated with a decreased risk of hypertension in males (P < 0.05), while no significant association was observed among females. CONCLUSIONS Our findings suggested that sex differences existed in the association between dietary patterns, air pollutants and hypertension. Furthermore, we found that adherence to traditional southern pattern may mitigate the risk of long-term PM2.5 exposure-induced hypertension in males.
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Affiliation(s)
- Chen Zhang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
| | - Yuanyuan Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
| | - Wei Xie
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China
| | - Jingxian Zhang
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China
| | - Ting Tian
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China
| | - Qianrang Zhu
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China
| | - Xinyu Fang
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China
| | - Jing Sui
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
- Research Institute for Environment and Health, Nanjing University of Information Science and Technology, 211544, Nanjing, China
| | - Da Pan
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
| | - Hui Xia
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
| | - Shaokang Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China
| | - Guiju Sun
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China.
| | - Yue Dai
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, 210009, Nanjing, China.
- Institute of Food Safety and Assessment, Jiangsu Provincial Center for Disease Control and Prevention, 210009, Nanjing, China.
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8
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Anand K, Walia GK, Mandal S, Menon JS, Gupta R, Tandon N, Narayan KMV, Ali MK, Mohan V, Schwartz JD, Prabhakaran D. Longitudinal associations between ambient PM 2.5 exposure and lipid levels in two Indian cities. Environ Epidemiol 2024; 8:e295. [PMID: 38617424 PMCID: PMC11008625 DOI: 10.1097/ee9.0000000000000295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/10/2024] [Indexed: 04/16/2024] Open
Abstract
Background Exposure to ambient PM2.5 is known to affect lipid metabolism through systemic inflammation and oxidative stress. Evidence from developing countries, such as India with high levels of ambient PM2.5 and distinct lipid profiles, is sparse. Methods Longitudinal nonlinear mixed-effects analysis was conducted on >10,000 participants of Centre for cArdiometabolic Risk Reduction in South Asia (CARRS) cohort in Chennai and Delhi, India. We examined associations between 1-month and 1-year average ambient PM2.5 exposure derived from the spatiotemporal model and lipid levels (total cholesterol [TC], triglycerides [TRIG], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]) measured longitudinally, adjusting for residential and neighborhood-level confounders. Results The mean annual exposure in Chennai and Delhi was 40 and 102 μg/m3 respectively. Elevated ambient PM2.5 levels were associated with an increase in LDL-C and TC at levels up to 100 µg/m3 in both cities and beyond 125 µg/m3 in Delhi. TRIG levels in Chennai increased until 40 µg/m3 for both short- and long-term exposures, then stabilized or declined, while in Delhi, there was a consistent rise with increasing annual exposures. HDL-C showed an increase in both cities against monthly average exposure. HDL-C decreased slightly in Chennai with an increase in long-term exposure, whereas it decreased beyond 130 µg/m3 in Delhi. Conclusion These findings demonstrate diverse associations between a wide range of ambient PM2.5 and lipid levels in an understudied South Asian population. Further research is needed to establish causality and develop targeted interventions to mitigate the impact of air pollution on lipid metabolism and cardiovascular health.
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Affiliation(s)
- Kritika Anand
- Centre for Chronic Disease Control, New Delhi, India
| | | | | | - Jyothi S. Menon
- Centre for Chronic Disease Control, New Delhi, India
- Public Health Foundation of India, Gurugram, India
| | - Ruby Gupta
- Centre for Chronic Disease Control, New Delhi, India
- Public Health Foundation of India, Gurugram, India
| | - Nikhil Tandon
- All India Institute of Medical Sciences, New Delhi, India
| | - K. M. Venkat Narayan
- Emory Global Diabetes Research Center of the Woodruff Health Sciences Center, Atlanta, Georgia
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Mohammed K. Ali
- Emory Global Diabetes Research Center of the Woodruff Health Sciences Center, Atlanta, Georgia
- Rollins School of Public Health, Emory University, Atlanta, Georgia
| | | | - Joel D. Schwartz
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Dorairaj Prabhakaran
- Centre for Chronic Disease Control, New Delhi, India
- Public Health Foundation of India, Gurugram, India
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9
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Li Y, Wu J, Tang H, Jia X, Wang J, Meng C, Wang W, Liu S, Yuan H, Cai J, Wang J, Lu Y. Long-term PM 2.5 exposure and early-onset diabetes: Does BMI link this risk? THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 913:169791. [PMID: 38176550 DOI: 10.1016/j.scitotenv.2023.169791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/18/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Limited studies investigated the association between high-level fine particulate matter (PM2.5) pollution and early-onset diabetes, leaving the possible metabolic mechanisms unclear. We assessed the association of cumulative PM2.5 exposure with diabetes, including early-onset, in high-pollution areas of China and explored whether metabolic factors mediated this association. METHODS 124,204 participants (≥18 years) from 121 counties in Hunan province, China, were enrolled between 2005 and 2020, with follow-up until 2021. The ground-level air pollution concentrations at each participant's residence were calculated using a high-quality dataset in China. The independent association of PM2.5 with incident diabetes and early-onset diabetes was assessed by Cox proportional hazards models. Restricted cubic splines were utilized to establish the exposure-response relationships. The role of metabolism-related mediators was estimated by mediation analysis. RESULTS During a median follow-up of 8.47 (IQR, 6.65-9.82) years, there were 3650 patients with new-onset diabetes. Each 1 μg/m3 increase in the level of cumulative PM2.5 exposure was positively related to an increased incidence of diabetes (HR 1.177, 95 % CI 1.172-1.181) among individuals in the PM2.5 > 50 μg/m3 group after adjusting for multiple variables. The relationship of the PM2.5 dose-response curve for diabetes was non-linear. Significant associations between PM2.5 exposure and early-onset diabetes risk were observed, with this risk showing an increase with the earlier age of early diabetes onset. Males, young individuals (≤45 years), and those with a lower body mass index (BMI <24 kg/m2) appeared to be more susceptible to diabetes. Moreover, change in BMI significantly mediated 31.06 % of the PM2.5-diabetes relationship. CONCLUSIONS Long-term cumulative PM2.5 exposure increased the risk of early-onset diabetes, which is partially mediated by BMI. Sustained air pollution control measures, priority protection of vulnerable individuals, and effective management of BMI should be taken to reduce the burden of diabetes.
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Affiliation(s)
- Yalan Li
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jingjing Wu
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Haibo Tang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China; Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xinru Jia
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jie Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Changjiang Meng
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shiqi Liu
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hong Yuan
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jingjing Cai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jiangang Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Yao Lu
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China; Faculty of Life Sciences & Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK.
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Wang F, Chang L, Zhang X, Jia T, Wang Y, Wang Y, Liu G. Effects of Polycyclic Aromatic Hydrocarbon Exposure and Telomere Length and their Interaction on Blood Lipids in Coal Miners. J Occup Environ Med 2024; 66:111-117. [PMID: 37903596 DOI: 10.1097/jom.0000000000003002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
OBJECTIVE This study aimed to investigate the effects of polycyclic aromatic hydrocarbon (PAH) exposure and telomere length on lipids in coal miners. METHODS Basic personal information of 637 coal miners was collected by questionnaire survey. Logistic regression, the Bayesian kernel machine regression model, and weighted quantile sum regression were used to analyze the effects of PAH metabolites and telomere length and their interactions on blood lipids. RESULTS High exposure to 9-hydroxyphenanthrene (OR = 1.586, 95% CI: 1.011-2.487) and telomere shortening (OR = 1.413, 95% CI: 1.005-1.985) were associated with dyslipidemia. Weighted quantile sum results showed that 9-hydroxyphenanthrene accounted for the largest proportion of dyslipidemia (weight = 0.66). The interaction results showed that high 9-hydroxyphenanthrene exposure and short telomeres were risk factors for dyslipidemia in coal miners (OR = 2.085, 95% CI: 1.121-3.879). Conclusions: Our findings suggest that 9-hydroxyphenanthrene and shorter telomeres are risk factors for dyslipidemia, and their interaction increases the risk of dyslipidemia.
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Affiliation(s)
- Fang Wang
- From the Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan, China (F.W., L.C., X.Z., T.J., Y.W., Y.W.); Department of Epidemiology and Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China (F.W., L.C., X.Z., T.J., Y.W., Y.W.); and Xishan Coal and Electricity (Group) Co, Ltd, Occupational Disease Prevention and Control Center, Taiyuan, China (G.L.)
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11
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Valdés S, Doulatram-Gamgaram V, Maldonado-Araque C, García-Escobar E, García-Serrano S, Oualla-Bachiri W, García-Vivanco M, Garrido JL, Gil V, Martín-Llorente F, Calle-Pascual A, Castaño L, Delgado E, Menéndez E, Franch-Nadal J, Gaztambide S, Girbés J, Chaves FJ, Galán-García JL, Aguilera-Venegas G, Vallvé JC, Amigó N, Guardiola M, Ribalta J, Rojo-Martínez G. Association between exposure to air pollution and blood lipids in the general population of Spain. Eur J Clin Invest 2024; 54:e14101. [PMID: 37795744 DOI: 10.1111/eci.14101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/13/2023] [Accepted: 09/23/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND AND AIMS We aimed to assess the associations of exposure to air pollutants and standard and advanced lipoprotein measures, in a nationwide sample representative of the adult population of Spain. METHODS We included 4647 adults (>18 years), participants in the national, cross-sectional, population-based di@bet.es study, conducted in 2008-2010. Standard lipid measurements were analysed on an Architect C8000 Analyzer (Abbott Laboratories SA). Lipoprotein analysis was made by an advanced 1 H-NMR lipoprotein test (Liposcale®). Participants were assigned air pollution concentrations for particulate matter <10 μm (PM10 ), <2.5 μm (PM2.5 ) and nitrogen dioxide (NO2 ), corresponding to the health examination year, obtained by modelling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model). RESULTS In multivariate linear regression models, each IQR increase in PM10 , PM2.5 and NO2 was associated with 3.3%, 3.3% and 3% lower levels of HDL-c and 1.3%, 1.4% and 1.1% lower HDL particle (HDL-p) concentrations (p < .001 for all associations). In multivariate logistic regression, there was a significant association between PM10 , PM2.5 and NO2 concentrations and the odds of presenting low HDL-c (<40 mg/dL), low HDL-p ( CONCLUSIONS Our study shows an association between the exposure to air pollutants and blood lipids in the general population of Spain, suggesting a link to atherosclerosis.
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Affiliation(s)
- Sergio Valdés
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Viyey Doulatram-Gamgaram
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
| | - Cristina Maldonado-Araque
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Eva García-Escobar
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara García-Serrano
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Wasima Oualla-Bachiri
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta García-Vivanco
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) - División de Contaminación Atmosférica, Madrid, Spain
| | - Juan Luis Garrido
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) - División de Contaminación Atmosférica, Madrid, Spain
| | - Victoria Gil
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) - División de Contaminación Atmosférica, Madrid, Spain
| | - Fernando Martín-Llorente
- Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) - División de Contaminación Atmosférica, Madrid, Spain
| | - Alfonso Calle-Pascual
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition and Instituto de Investigación Sanitaria University Hospital S. Carlos (IdISSC), Department Medicine II, Universidad Complutense (UCM), Madrid, Spain
| | - Luis Castaño
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Hospital Universitario Cruces, BioCruces, UPV/EHU, Barakaldo, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Elías Delgado
- Department of Endocrinology and Nutrition, Hospital Universitario Central de Asturias/University of Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Edelmiro Menéndez
- Department of Endocrinology and Nutrition, Hospital Universitario Central de Asturias/University of Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Josep Franch-Nadal
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- EAP Raval Sud, Institut Català de la Salut, Red GEDAPS, Primary Care, Unitat de Suport a la Recerca (IDIAP - Fundació Jordi Gol), Barcelona, Spain
| | - Sonia Gaztambide
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Hospital Universitario Cruces - BioCruces Bizkaia - UPV-EHU, Baracaldo, Barcelona, Spain
| | - Joan Girbés
- Diabetes Unit, Hospital Arnau de Vilanova, Valencia, Spain
| | - F Javier Chaves
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Genomic Studies and Genetic Diagnosis Unit, Fundación de Investigación del Hospital Clínico de Valencia - INCLIVA, Valencia, Spain
| | | | | | - Joan Carles Vallvé
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain
| | - Núria Amigó
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Metabolomics Platform, Universitat Rovira i Virgili, IISRV, Reus, Spain
- Biosfer Teslab, Reus, Spain
| | - Montse Guardiola
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain
| | - Josep Ribalta
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
- Research Unit on Lipids and Atherosclerosis, Sant Joan University Hospital, Rovira i Virgili University, IISPV, Reus, Spain
| | - Gemma Rojo-Martínez
- Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga/Universidad de Málaga, Instituto de Investigación Biomedica de Málaga-IBIMA, Málaga, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
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Zhou Q, Li X, Zhang J, Duan Z, Mao S, Wei J, Han S, Niu Z. Long-term exposure to PM 1 is associated with increased prevalence of metabolic diseases: evidence from a nationwide study in 123 Chinese cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:549-563. [PMID: 38015390 DOI: 10.1007/s11356-023-31098-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023]
Abstract
Exposure to particulate matter (PM) has been linked to metabolic diseases. However, the effects of PM with an aerodynamic diameter ≤ 1.0 µm (PM1) on metabolic diseases remain unclear. This study is aimed at assessing the associations of PM1 with metabolic disease risk and quantifying the concentration-response (C-R) relationship of PM1 with metabolic disease risk. A national cross-sectional study was conducted, including 12,495 middle-aged and older adults in 123 Chinese cities. The two-year average concentration of PM1 was evaluated using satellite-based spatiotemporal models. Metabolic diseases, including abdominal obesity, diabetes, hypertension, dyslipidemia, and metabolic syndrome, were identified based on physical examination, blood standard biochemistry examination, and self-reported disease histories. Generalized linear models and C-R curves were used to evaluate the associations of PM1 with metabolic diseases. A total of 12,495 participants were included in this study, with a prevalence of 45.73% for abdominal obesity, 20.22% for diabetes, 42.46% for hypertension, 41.01% for dyslipidemia, and 33.78% for metabolic syndrome. The mean ± standard deviation age of participants was 58.79 ± 13.14 years. In addition to dyslipidemia, exposure to PM1 was associated with increased risks of abdominal obesity, diabetes, hypertension, and metabolic syndrome. Each 10 μg/m3 increase in PM1 concentrations was associated with 39% (odds ratio (OR) = 1.39, 95% confidence interval (CI) 1.33, 1.46) increase in abdominal obesity, 18% (OR = 1.18, 95%CI 1.12, 1.25) increase in diabetes, 11% (OR = 1.11, 95%CI 1.06, 1.16) increase in hypertension, and 25% (OR = 1.25, 95%CI 1.19, 1.31) in metabolic syndrome, respectively. C-R curves showed that the OR values of abdominal obesity, diabetes, hypertension, and metabolic syndrome were increased gradually with the increase of PM1 concentrations. Subgroup analysis indicated that exposure to PM1 was associated with increased metabolic disease risks among participants with different lifestyles and found that solid fuel users were more susceptible to PM1 than clean fuel users. This national cross-sectional study indicated that exposure to higher PM1 might increase abdominal obesity, diabetes, hypertension, and metabolic syndrome risk, and solid fuel use might accelerate the adverse effects of PM1 on metabolic syndrome risk. Further longitudinal cohort studies are warranted to establish a causal inference between PM1 exposure and metabolic disease risk.
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Affiliation(s)
- Qin Zhou
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, No. 98 XiWu Road, Xi'an, 710004, Shaanxi, China
| | - Xianfeng Li
- Department of Reproductive Service Technology, Urumqi Maternal and Child Health Hospital, No. 344 Jiefang South Road, Tianshan District, Urumqi, 830000, China
| | - Jinglong Zhang
- Department of Cardiovascular Surgery, Xijing Hospital, The Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China
| | - Zhizhou Duan
- Preventive Health Service, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, 152 Aiguo Road, Nanchang, Jiangxi, China
| | - Shuyuan Mao
- The Third Affiliated Hospital of Zhengzhou University, 7 Kangfu Road, Zhengzhou, 450000, Henan, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Shichao Han
- Department of Urology, Xijing Hospital, The Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China
| | - Zhiping Niu
- Department of Environmental Health, School of Public Health, Fudan University, 196 Xietu Road, Shanghai, 200032, China.
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Li Y, Guo B, Meng Q, Yin L, Chen L, Wang X, Jiang Y, Wei J, Wang J, Xia J, Wang Z, Duoji Z, Li X, Nima Q, Zhao X. Associations of long-term exposure to air pollution and physical activity with the risk of systemic inflammation-induced multimorbidity in Chinese adults: results from the China multi-ethnic cohort study (CMEC). BMC Public Health 2023; 23:2556. [PMID: 38129832 PMCID: PMC10734128 DOI: 10.1186/s12889-023-17518-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE Previous studies proved the effect of long-term exposure to air pollution or physical activity (PA) on the risk of systemic inflammation-induced multimorbidity (SIIM), while the evidence regarding their joint effects was rare, especially in low- and middle-income countries. Therefore, we aimed to examine the extent of interaction or joint relations of PA and air pollution with SIIM. METHODS This study included 72,172 participants from China Multi-Ethnic Cohort.The average concentrations of ambient particulate matter pollutants (PM1, PM2.5, and PM10) were estimated using satellite-based random forest models. Self-reported information on a range of physical activities related to occupation, housework, commuting, and leisure activities was collected by an interviewer-administered questionnaire. A total of 11 chronic inflammatory systemic diseases were assessed based on self-reported lifetime diagnosis or medical examinations. SIIM was defined as having ≥ 2 chronic diseases related to systemic inflammation. Logistic regression models were used to assess the complex associations of air pollution particulate matter and PA with SIIM. RESULTS We found positive associations between long-term air pollution particulates exposure and SIIM, with odds ratios (95%CI) of 1.07 (1.03 to 1.11), 1.18 (1.13 to 1.24), and 1.08 (1.05 to 1.12) per 10 µg/m3 increase in PM1, PM2.5, and PM10. No significant multiplicative interaction was found between ambient air pollutant exposure and PA on SIIM, whereas negative additive interaction was observed between long-term exposure to PM2.5 and PA on SIIM. The positive associations between low volume PA and SIIM were stronger among those exposed to high-level air pollution particulates. Compared with individuals engaged in high volume PA and exposed to low-level ambient air pollutants, those engaged in low volume PA and exposed to high-level ambient air pollutants had a higher risk of SIIM (OR = 1.49 in PM1 exposure, OR = 1.84 in PM2.5 exposure, OR = 1.19 in PM10 exposure). CONCLUSIONS Long-term (3 years average) exposure to PM1, PM2.5, and PM10 was associated with an increased risk of SIIM. The associations were modified by PA, highlighting PA's importance in reducing SIIM for all people, especially those living in high-level air pollution regions.
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Affiliation(s)
- Yajie Li
- Tibet Center for Disease Control and Prevention, 21 North linkuo Road, Lhasa, Tibet, China
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China
| | - Qiong Meng
- School of Public Health, Kunming Medical University, Kunming, China
| | - Li Yin
- Meteorological Medical Research Center, Panzhihua Central Hospital, 617067, Panzhihua, China
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, China
- Dali University, Dali, China
| | - Lin Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China
| | - Xing Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China
| | - Ye Jiang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | - Junhua Wang
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China
| | - Jinjie Xia
- Chengdu Center for Disease Control and Prevention, Chengdu, China
| | - Zihao Wang
- Chongqing Center for disease Control and prevention, Chongqing, China
| | | | - Xianzhi Li
- Meteorological Medical Research Center, Panzhihua Central Hospital, 617067, Panzhihua, China.
- Clinical Medical Research Center, Panzhihua Central Hospital, Panzhihua, China.
- Dali University, Dali, China.
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention, 21 North linkuo Road, Lhasa, Tibet, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
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Han X, Guo B, Wang L, Chen K, Zhou H, Huang S, Xu H, Pan X, Chen J, Gao X, Wang Z, Yang L, Laba C, Meng Q, Guo Y, Chen G, Hong F, Zhao X. The mediation role of blood lipids on the path from air pollution exposure to MAFLD: A longitudinal cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166347. [PMID: 37591384 DOI: 10.1016/j.scitotenv.2023.166347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/12/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND & AIMS Recent cross-sectional studies found that exposure to ambient air pollution (AP) was associated with an increased risk of metabolic dysfunction-associated fatty liver disease (MAFLD). The alternation of blood lipids may explain the association, but epidemiological evidence is lacking. We aimed to examine whether and to what extent the association between long-term exposure to AP and incident MAFLD is mediated by blood lipids and dyslipidemia in a prospective cohort. METHODS We included 6350 participants from the China Multi-Ethnic Cohort (CMEC, baseline 2018-2019, follow-up 2020-2021). Three-year average (2016-2018) of AP (PM1, PM2.5, PM10, NO2), blood lipids (TC, LDL-C, HDL-C, TG with their combinations) and incident MAFLD for each individual were assessed chronologically. Linear and logistic regression was used to assess the associations among AP, blood lipids, and MAFLD, and the potential mediation effects of blood lipids were evaluated using causal mediation analysis. RESULTS A total of 744 participants were newly diagnosed with MAFLD at follow-up. The odds ratios of MAFLD associated with a 10 μm increase in PM1, PM2.5, and NO2 were 1.35 (95 % CI: 1.14, 1.58), 1.34 (1.10, 1.65) and 1.28 (1.14, 1.44), respectively. Blood lipids are important mediators between AP and incident MAFLD. LDL-C (Proportion Mediated: 6.9 %), non-HDL (13.4 %), HDL-C (20.7 %), LDL/HDL (30.1 %), and dyslipidemia (6.5 %) significantly mediated the association between PM2.5 and MAFLD. For PM1, the indirect effects were similar to those for PM2.5, with a larger value for the direct effect, and the mediation proportion by blood lipids was less for NO2. CONCLUSION Blood lipids are important mediators between AP and MAFLD, and can explain 5 %-30 % of the association between AP and incident MAFLD, particularly cholesterol-related variables, indicating that AP could lead to MAFLD through the alternation of blood lipids. These findings provided mechanical evidence of AP leading to MAFLD in epidemiological studies.
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Affiliation(s)
- Xinyu Han
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lele Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kejun Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanwen Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shourui Huang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Institute for Disaster Management and Reconstruction, Sichuan University-The Hongkong Polytechnic University, Chengdu, Sichuan, China
| | - Xianmou Pan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jinyao Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xufang Gao
- Chengdu Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | - Zhenghong Wang
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - La Yang
- Tibet University, Lhasa, Tibet, China
| | - Ciren Laba
- Tibet Center for Disease Control and Prevention CN, Lhasa, Tibet, China
| | - Qiong Meng
- Department of Epidemiology and Health Statistics, School of public Health, Kunming Medical University, Kunming, Yunnan, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Gongbo Chen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Feng Hong
- School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Sun M, Li T, Sun Q, Ren X, Sun Z, Duan J. Associations of long-term particulate matter exposure with cardiometabolic diseases: A systematic review and meta-analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166010. [PMID: 37541522 DOI: 10.1016/j.scitotenv.2023.166010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/03/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND This review aimed to establish a holistic perspective of long-term PM exposure and cardiometabolic diseases, identify long-term PM-related cardiovascular and metabolic risk factors, and provide practical significance to preventative measures. METHOD A combination of computer and manual retrieval was used to search for keywords in PubMed (2903 records), Embase (2791 records), Web of Science (5488 records) and Cochrane Library (163 records). Finally, a total of 82 articles were considered in this meta-analysis. Stata 13.0 was accustomed to inspecting the studies' heterogeneity and calculating the combined effect value (RR) by selecting the matching models. The subgroup analysis, sensitivity analysis and publication bias tests were also performed. RESULTS Meta-analysis figured an association between PM and cardiometabolic diseases. PM2.5 (per 10 μg/m3 increase) boosted the risk of hypertension (RR = 1.14, 95 % CI: 1.09-1.19), coronary heart disease (CHD) (RR = 1.21, 95 % CI: 1.08-1.35), diabetes (RR = 1.16, 95 % CI: 1.11-1.21) and stroke (including ischemic stroke and hemorrhagic stroke). PM10 (per 10 μg/m3 increase) elevated the incidence of hypertension (RR = 1.11, 95 % CI: 1.07-1.16) and diabetes (RR = 1.26, 95 % CI: 1.08-1.47). PM1 (per 10 μg/m3 increase) exposure increased the risk of total dyslipidemia, yielding the RR of 1.10 (95 % CI: 1.01-1.18). Furthermore, the elderly, overweight and higher background pollutant level were potentially susceptible to related diseases. CONCLUSION There was a virtual connection between long-term exposure to PM and cardiometabolic diseases. PM2.5 or PM10 (per 10 μg/m3) increased the risk of hypertension, CHD, diabetes, stroke and dyslipidemia, causing cardiovascular "multimorbidity" in high-risk populations.
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Affiliation(s)
- Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Tianyu Li
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Qinglin Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Xiaoke Ren
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China.
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16
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Guo B, Huang S, Li S, Han X, Lin H, Li Y, Qin Z, Jiang X, Wang Z, Pan Y, Zhang J, Yin J, Zhao X. Long-term exposure to ambient PM2.5 and its constituents is associated with MAFLD. JHEP Rep 2023; 5:100912. [PMID: 37954486 PMCID: PMC10632732 DOI: 10.1016/j.jhepr.2023.100912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 11/14/2023] Open
Abstract
Background & Aims Existing evidence suggests that long-term exposure to ambient fine particulate pollution (PM2.5) may increase metabolic dysfunction-associated fatty liver disease (MAFLD) risk. However, there is still limited evidence on the association of PM2.5 constituents with MAFLD. Therefore, this study explores the associations between the five main chemical constituents of PM2.5 and MAFLD to provide more explicit information on the liver exposome. Methods A total of 76,727 participants derived from the China Multi-Ethnic Cohort, a large-scale epidemic survey in southwest China, were included in this study. Multiple linear regression models were used to estimate the pollutant-specific association with MAFLD. Weighted quantile sum regression was used to evaluate the joint effect of the pollutant-mixture on MAFLD and identify which constituents contribute most to it. Results Three-year exposure to PM2.5 constituents was associated with a higher MAFLD risk and more severe liver fibrosis. Odds ratios for MAFLD were 1.480, 1.426, 1.294, 1.561, 1.618, and 1.368 per standard deviation increase in PM2.5, black carbon, organic matter, ammonium, sulfate, and nitrate, respectively. Joint exposure to the five major chemical constituents was also positively associated with MAFLD (odds ratio 1.490, 95% CI 1.360-1.632). Nitrate contributed most to the joint effect of the pollutant-mixture. Further stratified analyses indicate that males, current smokers, and individuals with a high-fat diet might be more susceptible to ambient PM2.5 exposure than others. Conclusions Long-term exposure to PM2.5 and its five major chemical constituents may increase the risk of MAFLD. Nitrate might contribute most to MAFLD, which may provide new clues for liver health. Males, current smokers, and participants with high-fat diets were more susceptible to these associations. Impact and implications This large-scale epidemiologic study explored the associations between constituents of fine particulate pollution (PM2.5) and metabolic dysfunction-associated fatty liver disease (MAFLD), and further revealed which constituents play a more important role in increasing the risk of MAFLD. In contrast to previous studies that examined the effects of PM2.5 as a whole substance, this study carefully explored the health effects of the individual constituents of PM2.5. These findings could (1) help researchers to identify the specific particles responsible for hepatotoxicity, and (2) indicate possible directions for policymakers to efficiently control ambient air pollution, such as targeting the sources of nitrate pollution.
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Affiliation(s)
- Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shourui Huang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sicheng Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xinyu Han
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yajie Li
- Tibet Center for Disease Control and Prevention, Lhasa, China
| | - Zixiu Qin
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
| | - Xiaoman Jiang
- Chengdu Center for Disease Control and Prevention, Chengdu, China
| | - Zihao Wang
- Chongqing Municipal Center for Disease Control and Prevention, China
| | | | - Juying Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jianzhong Yin
- School of Public Health, Kunming Medical University, Kunming, Yunnan, China
- Baoshan College of Traditional Chinese Medicine, Baoshan, Yunnan, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - China Multi-Ethnic Cohort (CMEC) collaborative group
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Tibet Center for Disease Control and Prevention, Lhasa, China
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
- Chengdu Center for Disease Control and Prevention, Chengdu, China
- Chongqing Municipal Center for Disease Control and Prevention, China
- Tibet University, Lhasa, China
- School of Public Health, Kunming Medical University, Kunming, Yunnan, China
- Baoshan College of Traditional Chinese Medicine, Baoshan, Yunnan, China
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17
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Guo H, Wang M, Ye Y, Huang C, Wang S, Peng H, Wang X, Fan M, Hou T, Wu X, Huang X, Yan Y, Zheng K, Wu T, Li L. Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study. BIOLOGY 2023; 12:1470. [PMID: 38132296 PMCID: PMC10740487 DOI: 10.3390/biology12121470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/13/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.
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Affiliation(s)
- Huangda Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Mengying Wang
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China
| | - Ying Ye
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Chunlan Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xueheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Meng Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Tianjiao Hou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xiaoling Wu
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Xiaoming Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Yansheng Yan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Kuicheng Zheng
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Key Laboratory of Reproductive Health, Ministry of Health, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
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18
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Zhang Y, Shi J, Ma Y, Yu N, Zheng P, Chen Z, Wang T, Jia G. Association between Air Pollution and Lipid Profiles. TOXICS 2023; 11:894. [PMID: 37999546 PMCID: PMC10675150 DOI: 10.3390/toxics11110894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/30/2023] [Accepted: 10/28/2023] [Indexed: 11/25/2023]
Abstract
Dyslipidemia is a critical factor in the development of atherosclerosis and consequent cardiovascular disease. Numerous pieces of evidence demonstrate the association between air pollution and abnormal blood lipids. Although the results of epidemiological studies on the link between air pollution and blood lipids are unsettled due to different research methods and conditions, most of them corroborate the harmful effects of air pollution on blood lipids. Mechanism studies have revealed that air pollution may affect blood lipids via oxidative stress, inflammation, insulin resistance, mitochondrial dysfunction, and hypothalamic hormone and epigenetic changes. Moreover, there is a risk of metabolic diseases associated with air pollution, including fatty liver disease, diabetes mellitus, and obesity, which are often accompanied by dyslipidemia. Therefore, it is biologically plausible that air pollution affects blood lipids. The overall evidence supports that air pollution has a deleterious effect on blood lipid health. However, further research into susceptibility, indoor air pollution, and gaseous pollutants is required, and the issue of assessing the effects of mixtures of air pollutants remains an obstacle for the future.
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Affiliation(s)
- Yi Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Jiaqi Shi
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Ying Ma
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Nairui Yu
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Pai Zheng
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Zhangjian Chen
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Tiancheng Wang
- Department of Laboratory Medicine, Peking University Third Hospital, Beijing 100191, China;
| | - Guang Jia
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
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Patterson WB, Holzhausen E, Chalifour B, Goodrich J, Costello E, Lurmann F, Conti DV, Chen Z, Chatzi L, Alderete TL. Exposure to ambient air pollutants, serum miRNA networks, lipid metabolism, and non-alcoholic fatty liver disease in young adults. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 264:115486. [PMID: 37729806 PMCID: PMC10548742 DOI: 10.1016/j.ecoenv.2023.115486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND AND AIM Ambient air pollution (AAP) exposure has been associated with altered blood lipids and liver fat in young adults. MicroRNAs regulate gene expression and may mediate these relationships. This work investigated associations between AAP exposure, serum microRNA networks, lipid profiles, and non-alcoholic fatty liver disease (NAFLD) risk in young adults. METHODS Participants were 170 young adults (17-22 years) from the Meta-AIR cohort of the Children's Health Study (CHS). Residential AAP exposure (PM2.5, PM10, NO2, 8-hour maximum O3, redox-weighted oxidative capacity [Oxwt]) was spatially interpolated from monitoring stations via inverse-distance-squared weighting. Fasting serum lipids were assayed. Liver fat was imaged by MRI and NAFLD was defined by ≥ 5.5% hepatic fat fraction. Serum microRNAs were measured via NanoString and microRNA networks were constructed by weighted gene correlation network analysis. The first principal component of each network represented its expression profile. Multivariable mixed effects regression models adjusted for sociodemographic, behavioral, and clinical covariates; baseline CHS town code was a random effect. Effects estimates are scaled to one standard deviation of exposure. Mediation analysis explored microRNA profiles as potential mediators of exposure-outcome associations. DIANA-mirPATH identified overrepresented gene pathways targeted by miRNA networks. RESULTS Prior-month Oxwt was associated with NAFLD (OR=3.45; p = 0.003) and inversely associated with microRNA Network A (β = -0.016; p = 0.026). Prior-year NO2 was associated with non-HDL-cholesterol (β = 7.13; p = 0.01) and inversely associated with miRNA Network A (β = -0.019; p = 0.022). Network A expression was inversely associated with NAFLD (OR=0.35; p = 0.010) and non-HDL-C (β = -6.94 mg/dL; p = 0.035). Network A members miR-199a/b-3p and miR-130a, which both target fatty acid synthase, mediated 21% of the association between prior-month Oxwt exposure with NAFLD (p = 0.048) and 23.3% of the association between prior-year NO2 exposure and non-HDL-cholesterol (p = 0.026), respectively. CONCLUSIONS Exposure to AAP may contribute to adverse lipid profiles and NAFLD risk among young adults via altered expression of microRNA profiles.
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Affiliation(s)
- William B Patterson
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Elizabeth Holzhausen
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Bridget Chalifour
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Jesse Goodrich
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Elizabeth Costello
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | | | - David V Conti
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Lida Chatzi
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Tanya L Alderete
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.
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Li B, Wen F, Liu K, Xie Y, Zhang F, Li P, Sun Y, Qu A, Yang X, Zhang L. The mediation effect of lipids, blood pressure and BMI between air pollutant mixture and atherosclerotic cardiovascular disease: The CHCN-BTH cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 264:115491. [PMID: 37729805 DOI: 10.1016/j.ecoenv.2023.115491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND The combine effect of air pollutant mixture on atherosclerotic cardiovascular disease (ASCVD) remain undefined. This study aims to explore the association between long-term exposure of air pollutants and ASCVD, focusing on the mediating role of lipids, blood pressure and BMI. METHODS This study was based on the CHCN-BTH cohort study. The annual concentrations of air pollutants and PM2.5 constituents were sourced from in the Tracking Air Pollution in China (TAP) and ChinaHighAirPollutants (CHAP) datasets from 2014 to 2019. A Cox mixed-effects model was used to investigate the associations between long-term exposure of air pollutants and ASCVD. The combined impact of the air pollutant mixture was assessed using Quantile g-Computation. Stratified, sensitivity, and mediation analyses were conducted. RESULTS A total of 27,134 participants aged 18-80 were recruited in the present study. We found that each IQR increase of PM2.5, PM1, NO2, O3, BC, SO42-, and OM were significantly associated with the incidence of ASCVD, the hazard ratios (HRs) and 95 % confidence interval (CI) were 1.55 (1.35, 1.78), 1.46 (1.27, 1.67), 1.30 (1.21, 1.39), 1.66 (1.41,1.95), 2.14 (1.63, 2.83), 1.65 (1.25, 2.17) and 1.92(1.52, 2.45), respectively. The combined effect of air pollutant mixture on ASCVD was 1.79 (1.46, 2.20), PM2.5 contributed 83.3 % to this combined effect. Mediation effect models suggested that air pollutants and ASCVD might be mediated through SBP, DBP, HDL-C, LDL-C, hsCRP and BMI (mediation proportion range from 1.3 % to 26.1 %), Notably, HDL-C played mediation roles of 11.3 % (7.0 %, 18.4), 26.1 % (17.7 %, 38.1 %) and 25.4 % (15.4, 47.7 %) in the effects of long-term exposure to PM2.5, PM1 and OM on ASCVD, respectively. CONCLUSIONS Long-term, high-level air pollutant exposure was significantly associated with an elevated risk of ASCVD, particularly for PM2.5. Blood pressure, lipids and BMI, especially HDL-C, may mediate the effects of air pollutants exposure on ASCVD.
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Affiliation(s)
- Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Pandi Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yuan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Aibin Qu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaojun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
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Zhao Y, Shen G, Lin X, Zhang L, Fan F, Zhang Y, Li J. Identifying the Relationship between PM 2.5 and Hyperlipidemia Using Mendelian Randomization, RNA-seq Data and Model Mice Subjected to Air Pollution. TOXICS 2023; 11:823. [PMID: 37888673 PMCID: PMC10611378 DOI: 10.3390/toxics11100823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023]
Abstract
Air pollution is an important public health problem that endangers human health. However, the casual association and pathogenesis between particles < 2.5 μm (PM2.5) and hyperlipidemia remains incompletely unknown. Mendelian randomization (MR) and transcriptomic data analysis were performed, and an air pollution model using mice was constructed to investigate the association between PM2.5 and hyperlipidemia. MR analysis demonstrated that PM2.5 is associated with hyperlipidemia and the triglyceride (TG) level in the European population (IVW method of hyperlipidemia: OR: 1.0063, 95%CI: 1.0010-1.0118, p = 0.0210; IVW method of TG level: OR: 1.1004, 95%CI: 1.0067-1.2028, p = 0.0350). Mest, Adipoq, Ccl2, and Pcsk9 emerged in the differentially expressed genes of the liver and plasma of PM2.5 model mice, which might mediate atherosclerosis accelerated by PM2.5. The studied animal model shows that the Paigen Diet (PD)-fed male LDLR-/- mice had higher total cholesterol (TC), TG, and CM/VLDL cholesterol levels than the control group did after 10 times 5 mg/kg PM2.5 intranasal instillation once every three days. Our study revealed that PM2.5 had causality with hyperlipidemia, and PM2.5 might affect liver secretion, which could further regulate atherosclerosis. The lipid profile of PD-fed Familial Hypercholesterolemia (FH) model mice is more likely to be jeopardized by PM2.5 exposure.
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Affiliation(s)
- Yixue Zhao
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Geng Shen
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Xipeng Lin
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Long Zhang
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Fangfang Fan
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Yan Zhang
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Jianping Li
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Beijing 100191, China
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Pan M, Liu F, Zhang K, Chen Z, Tong J, Wang X, Zhou F, Xiang H. Independent and interactive associations between greenness and ambient pollutants on novel glycolipid metabolism biomarkers: A national repeated measurement study. ENVIRONMENTAL RESEARCH 2023; 233:116393. [PMID: 37308069 DOI: 10.1016/j.envres.2023.116393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/22/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
This study aims to investigate the independent and interactive effects of greenness and ambient pollutants on novel glycolipid metabolism biomarkers. A repeated national cohort study was conducted among 5085 adults from 150 counties/districts across China, with levels of novel glycolipid metabolism biomarkers of TyG index, TG/HDL-c, TC/HDL-c, and non-HDL-c measured. Exposure levels of greenness and ambient pollutants (including PM1, PM2.5, PM10, and NO2) for each participant were determined based on their residential location. Linear mixed-effect and interactive models were used to evaluate the independent and interactive effects between greenness and ambient pollutants on the four novel glycolipid metabolism biomarkers. In the main models, the changes [β (95% CIs)] of TyG index, TG/HDL-c, TC/HDL-c, and non-HDL-c were -0.021 (-0.036, -0.007), -0.120 (-0.175, -0.066), -0.092 (-0.122, -0.062), and -0.445 (-1.370, 0.480) for every 0.1 increase in NDVI, and were 0.004 (0.003, 0.005), 0.014 (0.009, 0.019), 0.009 (0.006, 0.011), and 0.067 (-0.019, 0.154) for every 1 μg/m3 increase in PM1. Results of interactive analyses demonstrated that individuals living in low-polluted areas could get greater benefits from greenness than those living in highly-polluted areas. Additionally, the results of mediation analyses revealed that PM2.5 mediated 14.40% of the association between greenness and the TyG index. Further research is needed to validate our findings.
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Affiliation(s)
- Mengnan Pan
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Feifei Liu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Ke Zhang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Zhongyang Chen
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Jiahui Tong
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Xiangxiang Wang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Feng Zhou
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China.
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Pan X, Hong F, Li S, Wu J, Xu H, Yang S, Chen K, Baima K, Nima Q, Meng Q, Xia J, Xu J, Guo B, Lin H, Xie L, Zhang J, Zhao X. Long-term exposure to ambient PM 2.5 constituents is associated with dyslipidemia in Chinese adults. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 263:115384. [PMID: 37603926 DOI: 10.1016/j.ecoenv.2023.115384] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/12/2023] [Accepted: 08/16/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) consists of various toxic constituents. However, the health effect of PM2.5 may differ depending on its constituents, but the joint effect of PM2.5 constituents remains incompletely understood. OBJECTIVE Our goal was to evaluate the joint effect of long-term PM2.5 constituent exposures on dyslipidemia and identify the most hazardous chemical constituent. METHODS This study included 67,015 participants from the China Multi-Ethnic Cohort study. The average yearly levels of PM2.5 constituents for all individuals at their residences were assessed through satellite remote sensing and chemical transport modeling. Dyslipidemia was defined as one or more following abnormal blood lipid concentrations: total cholesterol (TC) ≥ 6.22 mmol/L, triglycerides (TG) ≥ 2.26 mmol/L, high-density lipoprotein cholesterol (HDL-C) < 1.04 mmol/L, and low-density lipoprotein cholesterol (LDL-C) ≥ 4.14 mmol/L. The logistic regression model was utilized to examine the single effect of PM2.5 constituents on dyslipidemia, while the weighted quantile sum regression model for the joint effect. RESULTS The odds ratio with a 95 % confidence interval for dyslipidemia positively related to per-SD increase in the three-year average was 1.29 (1.20-1.38) for PM2.5 mass, 1.25 (1.17-1.34) for black carbon, 1.24 (1.16-1.33) for ammonium, 1.33 (1.24-1.43) for nitrate, 1.34 (1.25-1.44) for organic matter, 1.15 (1.08-1.23) for sulfate, 1.30 (1.22-1.38) for soil particles, and 1.12 (1.05-1.92) for sea salt. Stronger associations were observed in individuals < 65 years of age, males, and those with low physical activity. Joint exposure to PM2.5 constituents was positively related to dyslipidemia (OR: 1.09, 95 %CI: 1.05-1.14). Nitrate was identified as the constituent with the largest weight (weighted at 0.387). CONCLUSIONS Long-term exposure to PM2.5 constituents poses a significant risk to dyslipidemia and nitrate might be the most responsible for the risk. These findings indicate that reducing PM2.5 constituent exposures, especially nitrate, could be beneficial to alleviate the burden of disease attributed to PM2.5-related dyslipidemia.
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Affiliation(s)
- Xianmou Pan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Feng Hong
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China
| | - Sicheng Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jialong Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Institute for Disaster Management and Reconstruction, Sichuan University-The Hongkong Polytechnic University, Chengdu, Sichuan, China
| | - Shaokun Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kejun Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kangzhuo Baima
- School of Medicine, Tibet University, Lhasa, Tibet, China
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention, Lhasa, Tibet, China
| | - Qiong Meng
- Department of Epidemiology and Health Statistics, School of Public Health, Kunming Medical University, Kunming, Yunnan, China
| | - Jinjie Xia
- Chengdu Center for Disease Control & Prevention, China
| | - Jingru Xu
- Chongqing Municipal Center for Disease Control and Prevention, China
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Linshen Xie
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Juying Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Wang Q, Wang Z, Chen M, Mu W, Xu Z, Xue M. Causality of particulate matter on cardiovascular diseases and cardiovascular biomarkers. Front Public Health 2023; 11:1201479. [PMID: 37732088 PMCID: PMC10507646 DOI: 10.3389/fpubh.2023.1201479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/31/2023] [Indexed: 09/22/2023] Open
Abstract
Background Previous observational studies have shown that the prevalence of cardiovascular diseases (CVDs) is related to particulate matter (PM). However, given the methodological limitations of conventional observational research, it is difficult to identify causality conclusively. To explore the causality of PM on CVDs and cardiovascular biomarkers, we conducted a Mendelian randomization (MR) analysis. Method In this study, we obtained summary-level data for CVDs and cardiovascular biomarkers including atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), ischemic stroke (IS), stroke subtypes, body mass index (BMI), lipid traits, fasting glucose, fasting insulin, and blood pressure from several large genome-wide association studies (GWASs). Then we used two-sample MR to assess the causality of PM on CVDs and cardiovascular biomarkers, 16 single nucleotide polymorphisms (SNPs) for PM2.5 and 6 SNPs for PM10 were obtained from UK Biobank participants. Inverse variance weighting (IVW) analyses under the fixed effects model were used as the main analytical method to calculate MR Estimates, followed by multiple sensitivity analyses to confirm the robustness of the results. Results Our study revealed increases in PM2.5 concentration were significantly related to a higher risk of MI (odds ratio (OR), 2.578; 95% confidence interval (CI), 1.611-4.127; p = 7.920 × 10-5). Suggestive evidence was found between PM10 concentration and HF (OR, 2.015; 95% CI, 1.082-3.753; p = 0.027) and IS (OR, 2.279; 95% CI,1.099-4.723; p = 0.027). There was no evidence for an effect of PM concentration on other CVDs. Furthermore, PM2.5 concentration increases were significantly associated with increases in triglyceride (TG) (OR, 1.426; 95% CI, 1.133-1.795; p = 2.469 × 10-3) and decreases in high-density lipoprotein cholesterol (HDL-C) (OR, 0.779; 95% CI, 0.615-0.986; p = 0.038). The PM10 concentration increases were also closely related to the decreases in HDL-C (OR, 0.563; 95% CI, 0.366-0.865; p = 8.756 × 10-3). We observed no causal effect of PM on other cardiovascular biomarkers. Conclusion At the genetic level, our study suggested the causality of PM2.5 on MI, TG, as well HDL-C, and revealed the causality of PM10 on HF, IS, and HDL-C. Our findings indicated the need for continued improvements in air pollution abatement for CVDs prevention.
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Affiliation(s)
- Qiubo Wang
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhimiao Wang
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
| | - Mingyou Chen
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
| | - Wei Mu
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
| | - Zhenxing Xu
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
| | - Mei Xue
- Department of Cardiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Cardiac Electrophysiology and Arrhythmia, Jinan, China
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Chen WY, Fu YP, Tu H, Zhong W, Zhou L. The association between exposure to volatile organic compounds and serum lipids in the US adult population. Lipids Health Dis 2023; 22:129. [PMID: 37568143 PMCID: PMC10422774 DOI: 10.1186/s12944-023-01895-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND AND AIM Epidemiological evidence on the relationship between exposure to volatile organic compounds (VOCs), both single and mixed, and serum lipid levels is limited, and their relationship remains unclear. Our study aimed to investigate the associations of exposure to VOCs with serum lipid levels in the US adult population. METHODS AND RESULTS The study examined the association of 16 VOC levels (2-methylhippuric acid, 3- and 4-methylhippuric acid, N-acetyl-S-(2-carbamoylethyl)-L-cysteine, N-acetyl-S-(N-methylcarbamoyl)-L-cysteine, 2-aminothiazoline-4-carboxylic acid, N-acetyl-S-(benzyl)-L-cysteine, N-acetyl-S-(n-propyl)-L-cysteine, N-acetyl-S-(2-carboxyethyl)-L-cysteine, N-acetyl-S-(2-cyanoethyl)-L-cysteine, N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine, N-acetyl-S-(2-hydroxypropyl)-L-cysteine. N-Acetyl-S-(3-hydroxypropyl)-L-cysteine, mandelic acid, N-acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine, phenylglyoxylic acid and N-acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) with total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) using data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2015, and a total of 1410 adults were enrolled. The association was evaluated by Bayesian kernel machine regression (BKMR), multiple linear regression and weighted quantile sum (WQS) regression. In BKMR analysis, exposure to VOCs is positively correlated with levels of TC, TG, and LDL-C. However, statistical significance was observed only for the impact on TG. Our linear regression analysis and WQS regression generally support the BKMR results. Several VOCs were positively associated with serum lipid profiles (e.g., the ln-transformed level of mandelic acid (MA) displayed an increase in estimated changes of 7.01 (95% CIs: 2.78, 11.24) mg/dL for TC level), even after the effective number of tests for multiple testing (P < 0.05). CONCLUSIONS Exposure to VOCs was associated with serum lipids, and more studies are needed to confirm these findings.
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Affiliation(s)
- Wen-Yu Chen
- Cardiovascular Medicine Department, Nanchang University Second Affiliated Hospital, Nanchang, Jiangxi 330006 China
| | - Yan-Peng Fu
- Second Clinical Medical College of Nanchang University, Nanchang, Jiangxi 330006 China
- Department of Otorhinolaryngology Head and Neck Surgery, Second Affiliated Hospital of Nanchang University, No.1 Minde Road, Nanchang, China
| | - Hui Tu
- Nusring Department, Nanchang University Second Affiliated Hospital, Nanchang, Jiangxi 330006 China
| | - Wen Zhong
- Cardiovascular Medicine Department, Nanchang University Second Affiliated Hospital, Nanchang, Jiangxi 330006 China
| | - Liang Zhou
- Cardiovascular Medicine Department, Nanchang University Second Affiliated Hospital, Nanchang, Jiangxi 330006 China
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Huang W, Zhou Y, Chen X, Zeng X, Knibbs LD, Zhang Y, Jalaludin B, Dharmage SC, Morawska L, Guo Y, Yang X, Zhang L, Shan A, Chen J, Wang T, Heinrich J, Gao M, Lin L, Xiao X, Zhou P, Yu Y, Tang N, Dong G. Individual and joint associations of long-term exposure to air pollutants and cardiopulmonary mortality: a 22-year cohort study in Northern China. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 36:100776. [PMID: 37547049 PMCID: PMC10398602 DOI: 10.1016/j.lanwpc.2023.100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 08/08/2023]
Abstract
Background Evidence on the associations between long-term exposure to multiple air pollutants and cardiopulmonary mortality is limited, especially for developing regions with higher pollutant levels. We aimed to characterise the individual and joint (multi-pollutant) associations of long-term exposure to air pollutants with cardiopulmonary mortality, and to identify air pollutant that primarily contributes to the mortality risk. Methods We followed 37,442 participants with a mean age of 43.5 years in four cities in northern China (Tianjin, Shenyang, Taiyuan, and Rizhao) from January 1998 to December 2019. Annual particulate matter (PM) with diameters ≤2.5 μm (PM2.5), ≤10 μm (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) were estimated using daily average values from satellite-derived machine learning models and monitoring stations. Time-varying Cox proportional hazards model was used to evaluate the individual association between air pollutants and mortality from non-accidental causes, cardiovascular diseases (CVDs), non-malignant respiratory diseases (RDs) and lung cancer, accounting for demographic and socioeconomic factors. Effect modifications by age, sex, income and education level were also examined. Quantile-based g-Computation integrated with time-to-event data was additionally applied to evaluate the co-effects and the relative weight of contributions for air pollutants. Findings During 785,807 person-years of follow-up, 5812 (15.5%) died from non-accidental causes, among which 2932 (7.8%) were from all CVDs, 479 (1.3%) from non-malignant RDs, and 552 (1.4%) from lung cancer. Long-term exposure to PM10 (mean [baseline]: 136.5 μg/m3), PM2.5 (mean [baseline]: 70.2 μg/m3), SO2 (mean [baseline]: 113.0 μg/m3) and NO2 (mean [baseline]: 39.2 μg/m3) were adversely and consistently associated with all mortality outcomes. A 10 μg/m3 increase in PM2.5 was associated with higher mortality from non-accidental causes (hazard ratio 1.20; 95% confidence interval 1.17-1.23), CVDs (1.23; 1.19-1.28), non-malignant RDs (1.37; 1.25-1.49) and lung cancer (1.14; 1.05-1.23). A monotonically increasing curve with linear or supra-linear shape with no evidence of a threshold was observed for the exposure-response relationship of mortality with individual or joint exposure to air pollutants. PM2.5 consistently contributed most to the elevated mortality risks related to air pollutant mixture, followed by SO2 or PM10. Interpretation There was a strong and positive association of long-term individual and joint exposure to PM10, PM2.5, SO2, and NO2 with mortalities from non-accidental causes, CVDs, non-malignant RDs and lung cancer in high-exposure settings, with PM2.5 potentially being the main contributor. The shapes of associations were consistent with a linear or supra-linear exposure-response relationship, with no lower threshold observed within the range of concentrations in this study. Funding National Key Research and Development Program of China, the China Scholarship Council, the National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province.
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Affiliation(s)
- Wenzhong Huang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yang Zhou
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Xi Chen
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Xiaowen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Luke D. Knibbs
- Faculty of Medicine and Health, School of Public Health, The University of Sydney, NSW 2006, Australia
- Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Yunting Zhang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia
- Ingham Institute for Applied Medial Research, Liverpool, NSW 2170, Australia
- School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Shyamali C. Dharmage
- Allergy and Lung Health Unit, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology, Brisbane, QLD 4001, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Xueli Yang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Liwen Zhang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Anqi Shan
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin 300070, China
- Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin 300070, China
| | - Jie Chen
- Department of Occupational and Environmental Health, School of Public Health, China Medical University, Shenyang 110122, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich 80336, Germany
| | - Meng Gao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China
| | - Lizi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiang Xiao
- Department of Geography, Hong Kong Baptist University, Hong Kong SAR, China
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Peien Zhou
- Department of Public Health & Primary Care, University of Cambridge, Cambridge CB2 1TN, United Kingdom
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Naijun Tang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Guanghui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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Lv S, Shi Y, Xue Y, Hu Y, Hu M, Li S, Xie W, Li Y, Ouyang Y, Li Z, Liu M, Wei J, Guo X, Liu X. Long-term effects of particulate matter on incident cardiovascular diseases in middle-aged and elder adults: The CHARLS cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 262:115181. [PMID: 37393817 DOI: 10.1016/j.ecoenv.2023.115181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/07/2023] [Accepted: 06/22/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND Although there is evidence of long-term effects of particulate matter (PM) on cardiovascular diseases (CVD), researches about long-term effects of PM1 on CVD are limited. We aimed to examine the long-term effects and magnitude of PM, especially PM1, on incident CVD in China. METHODS We included 6016 participants aged ≥ 45 years without CVD at baseline in 2011 from the China Health and Retirement Longitudinal Study. Personal PM (PM1, PM2.5, and PM10) concentrations were estimated using geocoded residential address. Generalized linear mixed models and SHapley Additive exPlanation were utilized to calculate the impacts and contributions of PM on CVD. Sensitivity analyses were used to check the robustness. RESULTS After a follow up of 4-year, 481 (7.99 %) participants developed CVD. Per 10 μg/m3 uptick in 1-year average concentrations of PM1, PM2.5 and PM10 was associated with a 1.20 [95 % confidence interval (CI): 1.05-1.37], 1.13 (95 % CI: 1.11-1.15), and 1.10 (95 % CI: 1.06-1.13) fold risk of incident CVD, respectively. The 2-year average concentrations of PM1, PM2.5 and PM10 were associated with incident CVD, corresponding to a 1.03 (95 % CI: 0.96-1.10), 1.11 (95 % CI: 1.02-1.21), and 1.09 (95 % CI: 1.03-1.15) fold risk, respectively. The SHapley Additive exPlanation values of PM1, PM2.5, and PM10 were 0.170, 0.153, and 0.053, respectively, corresponding to the first, second, and fifth among all air pollutants. Effects of PM1, PM2.5 and PM10 on CVD remained statistically significant in two-pollutant models. The elderly, males, smokers and alcohol drinkers tended to have slightly higher effects, while the differences were not statistically significant (all P-values > 0.05) between subgroups. CONCLUSION Long-term exposure to PM1, PM2.5, and PM10 was associated with an increased incidence of CVD. The smaller the particle size, the more important it was for incident CVD indicating that emphasis should be placed on small size of PM.
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Affiliation(s)
- Shiyun Lv
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Yadi Shi
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Yongxi Xue
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Yaoyu Hu
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Meiling Hu
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Shuting Li
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Wenhan Xie
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Yuan Li
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Yixin Ouyang
- School of Public Health, Capital Medical University, Beijing 100069, China
| | - Zhiwei Li
- School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - 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, Beijing 100069, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20742, USA
| | - 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, Beijing 100069, China.
| | - 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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Yang L, Zhu Y, Zhao B, Wan W, Shi S, Xuan C, Yu C, Mao W, Yan J. Long-term cardiometabolic effects of ambient ozone pollution in a large Chinese population. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 261:115115. [PMID: 37295302 DOI: 10.1016/j.ecoenv.2023.115115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
Limited studies investigated the effects of long-term ozone exposure on cardiometabolic health. We aimed to examine the association of long-term ozone exposure with a range of cardiometabolic diseases, as well as the subclinical indicators in Eastern China. The study included 202,042 adults living in 11 prefecture-level areas in Zhejiang Province between 2014 and 2021. Using a satellite-based model with a 1 × 1 km spatial resolution, we estimated residential 5-year average ozone exposures for each subject. Mixed-effects logistic and linear regression models were applied to explore the associations of ozone exposure with cardiometabolic diseases and subclinical indicators, respectively. We found that a 9% [95% confidence interval (95% CI): 7-12%] higher in odds of cardiometabolic disease per 10 μg/m3 increase in ozone exposure. Specifically, we also found higher prevalence of cardiovascular diseases (15%), stroke (19%), hypertension (7%), dyslipidemia (15%), and hypertriglyceridemia (9%) associated with ozone exposure. However, we did not find significant associations between ozone exposure and coronary heart disease, myocardial infarction, or diabetes mellitus. Long-term ozone exposures were also significantly associated with adverse changes in systolic blood pressure, diastolic blood pressure, total serum cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, glucose concentration, and body mass index. Our results showed that people with lower education levels, those over 50 years old, and those who were overweight or obese were more susceptible to the effects of ozone on cardiometabolic diseases. Our findings demonstrated the detrimental effects of long-term ozone exposure on cardiometabolic health, emphasizing the need for ozone control strategies to reduce the burden of cardiometabolic diseases.
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Affiliation(s)
- Li Yang
- Zhejiang Provincial Research Center for Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yixiang Zhu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Bowen Zhao
- The First Clinical Medical College of Zhejiang Chinese Medicine University, Hangzhou, Zhejiang, China
| | - Wenjing Wan
- The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Su Shi
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Cheng Xuan
- Chronic Disease Control Department, Zhuji Second People's Hospital, Zhuji, Zhejiang, China
| | - Caiyan Yu
- Chronic Disease Control Department, Zhuji Second People's Hospital, Zhuji, Zhejiang, China
| | - Wei Mao
- Zhejiang Provincial Research Center for Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Zhejiang Hospital, Hangzhou, Zhejiang, China.
| | - Jing Yan
- Zhejiang Provincial Research Center for Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Zhejiang Hospital, Hangzhou, Zhejiang, China.
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29
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Macchi C, Sirtori CR, Corsini A, Mannuccio Mannucci P, Ruscica M. Pollution from fine particulate matter and atherosclerosis: A narrative review. ENVIRONMENT INTERNATIONAL 2023; 175:107923. [PMID: 37119653 DOI: 10.1016/j.envint.2023.107923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/22/2023]
Abstract
According to the WHO, the entire global population is exposed to air pollution levels higher than recommended for health preservation. Air pollution is a complex mixture of nano- to micro-sized particles and gaseous components that poses a major global threat to public health. Among the most important air pollutants, causal associations have been established between particulate matter (PM), mainly < 2.5 μm, and cardiovascular diseases (CVD), i.e., hypertension, coronary artery disease, ischemic stroke, congestive heart failure, arrhythmias as well as total cardiovascular mortality. Aim of this narrative review is to describe and critically discuss the proatherogenic effects of PM2.5 that have been attributed to many direct or indirect effects comprising endothelial dysfunction, a chronic low-grade inflammatory state, increased production of reactive oxygen species, mitochondrial dysfunction and activation of metalloproteases, all leading to unstable arterial plaques. Higher concentrations of air pollutants are associated with the presence of vulnerable plaques and plaque ruptures witnessing coronary artery instability. Air pollution is often disregarded as a CVD risk factor, in spite of the fact that it is one of the main modifiable factors relevant for prevention and management of CVD. Thus, not only structural actions should be taken in order to mitigate emissions, but health professionals should also take care to counsel patients on the risks of air pollution.
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Affiliation(s)
- Chiara Macchi
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
| | - Cesare R Sirtori
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
| | - Alberto Corsini
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy
| | - Pier Mannuccio Mannucci
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Angelo Bianchi Bonomi Hemophilia and Thrombosis Center, Milan, Italy
| | - Massimiliano Ruscica
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università degli Studi di Milano, Milan, Italy; Department of Cardio-Thoracic-Vascular Diseases - Foundation IRCCS Cà Granda Ospedale Maggiore Policlinico, Italy.
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30
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Deng P, Tang H, Zhu L, Duan J, Li F, Li Y, Wang J, Wu J, Meng C, Wang W, Yang Y, Chen Z, Wang J, Yuan H, Huang Z, Cai J, Lu Y. Association of long-term ambient fine particulate matter (PM 2.5) and incident non-alcoholic fatty liver disease in Chinese adults. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 329:121666. [PMID: 37080516 DOI: 10.1016/j.envpol.2023.121666] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Air pollution is increasingly recognized as an important environmental risk factor for non-alcoholic fatty liver disease (NAFLD). However, epidemiologic evidence on long-term exposure to high air pollution concentrations with incident NAFLD is still very limited. Here, we constructed a population-based dynamic cohort involving 17,106 subjects who were enrolled between 2005 and 2013 and subsequently followed until 2017, combined with a high-resolution ambient fine particulate matter ≤2.5 μm (PM2.5) dataset, to investigate the association of long-term PM2.5 exposure (cumulative annual average levels ranged from 36.67 to 111.16 μg/m3) with NAFLD incidence (N = 4,640). We estimated the adjusted hazard ratio (HR) for incident NAFLD among those exposed to the highest quartile of PM2.5 was 2.04 [95% confidence interval (CI), 1.80-2.30] compared with individuals exposed to the lowest quartile of PM2.5. The dose-response relationships for PM2.5 are non-linear for NAFLD across the exposure distribution. Further stratified analyses revealed that lean (<23 kg/m2), younger (<40-year-old), and women individuals appeared more vulnerable to the harmful effects of PM2.5 exposure. Our study suggests a greater long-term high ambient PM2.5 exposure is associated with an increased risk of NAFLD in Chinese adults, particularly in specific groups, including lean, women, and younger people.
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Affiliation(s)
- Peizhi Deng
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; Department of Endoscopy and Laser, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510062, China
| | - Haibo Tang
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Liyong Zhu
- Department of Metabolic and Bariatric Surgery, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Jingwen Duan
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Fei Li
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Yalan Li
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Jie Wang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Jingjing Wu
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Changjiang Meng
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Wei Wang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Yiping Yang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Zhiheng Chen
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Jiangang Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Hong Yuan
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; Department of Cardiology, The Third Xiangya Hospital of Central South University Changsha, 410013, China
| | - Zhijun Huang
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; National-Local Joint Engineering Laboratory of Drug Clinical Evaluation Technology, Central South University, Changsha, 410013, China
| | - Jingjing Cai
- Department of Cardiology, The Third Xiangya Hospital of Central South University Changsha, 410013, China
| | - Yao Lu
- Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, 410013, China; Department of Cardiology, The Third Xiangya Hospital of Central South University Changsha, 410013, China; School of Life Course Sciences, King's College London, London, WC2R 2LS, United Kingdom.
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Li M, Edgell RC, Wei J, Li H, Qian ZM, Feng J, Tian F, Wang X, Xin Q, Cai M, Lin H. Air pollution and stroke hospitalization in the Beibu Gulf Region of China: A case-crossover analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114814. [PMID: 36965278 PMCID: PMC10107400 DOI: 10.1016/j.ecoenv.2023.114814] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The relationship between air pollution and stroke has been extensively studied, however, the evidence regarding the association between air pollution and hospitalization due to stroke and its subtypes in coastal areas of China is limited. OBJECTIVE To estimate the associations between air pollution and hospitalizations of stroke and its subtypes in the Beibu Gulf Region of China. METHODS We conducted a time-stratified case-crossover study in 15 cities in Beibu Gulf Region in China from 2013 to 2016. Exposures to PM1, PM2.5, PM10, SO2, NO2, O3, and CO on the case and control days were assessed at residential addresses using bilinear interpolation. Conditional logistic regressions were constructed to estimate city-specific associations adjusting for meteorological factors and public holidays. Meta-analysis was further conducted to pool all city-level estimates. RESULTS There were 271,394 case days and 922,305 control days. The odds ratios (ORs) for stroke hospitalizations associated with each interquartile range (IQR) increase in 2-day averages of SO2 (IQR: 10.8 µg/m3), NO2 (IQR: 11.2 µg/m3), and PM10 (IQR: 37 µg/m3) were 1.047 (95 % CI [confidence interval]: 1.015-1.080), 1.040 (95 % CI: 1.027-1.053), and 1.018 (95 % CI: 1.004-1.033), respectively. The associations with hospitalizations of ischemic stroke were significant for all seven pollutants, while the association with hemorrhagic stroke was significant only for CO. The associations of SO2, NO2, and O3 with stroke hospitalization were significantly stronger in the cool season. CONCLUSIONS Short-term increase in SO2, NO2, and PM10 might be important triggers of stroke hospitalization. All seven air pollutants were associated with ischemic stroke hospitalization, while only CO was associated with hemorrhagic stroke hospitalization. These results should be considered in public health policy.
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Affiliation(s)
- Meijun Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Randall C Edgell
- Department of Neurology, School of Medicine, Saint Louis University, 1008 South Spring, Saint Louis, MO 63110, USA
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park 20740, USA
| | - Haopeng Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, Saint Louis, MO 63104, USA
| | - Jin Feng
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaojie Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Qinghua Xin
- Shandong Academy of Occupational Health and Occupational Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 271016, China.
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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32
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Fan Z, Li Y, Wei J, Chen G, Wang R, Xu R, Liu T, Lv Z, Huang S, Sun H, Liu Y. Long-term exposure to fine particulate matter and site-specific cancer mortality: A difference-in-differences analysis in Jiangsu province, China. ENVIRONMENTAL RESEARCH 2023; 222:115405. [PMID: 36736553 DOI: 10.1016/j.envres.2023.115405] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/26/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Accumulating studies have reported that chronic exposure to ambient fine particulate matter (PM2.5) can lead to adverse effects on lung cancer mortality; however, such chronic effects are less clear for mortality from other site-specific cancers. OBJECTIVE To explore the causal effect of long-term PM2.5 exposure on mortality from all-site and a variety of site-specific cancers in Jiangsu province, China during 2015-2020 using a difference-in-differences analysis. METHODS For each of 53 county-based spatial units in Jiangsu province, we calculated annual death counts for all-site cancer and 23 site-specific cancers. Using a validated high-resolution PM2.5 grid dataset, long-term PM2.5 exposure of a spatial unit within a given year was evaluated as the average of population-weighted annual concentrations during recent 10 years. Conditional Poisson regression models were employed to evaluate exposure-response associations adjusting for spatial and temporal variables, seasonal temperatures, relative humidity, and gross domestic product (GDP). RESULTS During the study period, we identified 947,337 adult cancer deaths in Jiangsu province. Each 1 μg/m3 increment in PM2.5 exposure was significantly associated with a 2.7% increase in the risk of all-site cancer mortality. PM2.5-mortality associations were also observed in cancer of lip, oral cavity and pharynx, stomach, colorectum, pancreas, lung, bone and joints, ovary, prostate, and lymphoma (all adjusted P < 0.05), with the relative risks ranging from 1.028 (95% confidence interval [CI]: 1.011, 1.046) for stomach cancer to 1.201 (95% CI: 1.120, 1.308) for bone and joints cancers. Exposure-response curves showed that these associations were close to linearity, though most of them had increasing slopes at high exposure levels. Overall, women and subjects in low GDP regions were more vulnerable to PM2.5 exposures. CONCLUSIONS Long-term exposure to ambient PM2.5 contributes to a higher risk of mortality from multiple site-specific cancers.
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Affiliation(s)
- Zhaoyu Fan
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China
| | - Yingxin Li
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
| | - Gongbo Chen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Rui Wang
- Luohu District Chronic Disease Hospital, Shenzhen, Guangdong, 518020, China
| | - Ruijun Xu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China
| | - Tingting Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China
| | - Ziquan Lv
- Central Laboratory of Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, 518055, China
| | - Suli Huang
- Department of Environment and Health, Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, 518055, China
| | - Hong Sun
- Department of Environment and Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, 210009, China.
| | - Yuewei Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, 510080, China.
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Lu Y, Yin P, Wang J, Yang Y, Li F, Yuan H, Li S, Long Z, Zhou M. Light at night and cause-specific mortality risk in Mainland China: a nationwide observational study. BMC Med 2023; 21:95. [PMID: 36927443 PMCID: PMC10022237 DOI: 10.1186/s12916-023-02822-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/08/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND While epidemiological studies have found correlations between light at night (LAN) and health effects, none has so far investigated the impacts of LAN on population mortality yet. We aimed to estimate the relative risk for mortality from exposure to LAN in Mainland China. METHODS This time-stratified case-crossover nationwide study used NPP-VIIRS to obtain daily LAN data of Mainland China between 2015 and 2019. The daily mortality data were obtained from the Disease Surveillance Point System in China. Conditional Poisson regression models were applied to examine the relative risk (RR) for mortality along daily LAN in each county, then meta-analysis was performed to combine the county-specific estimates at the national or regional level. RESULTS A total of 579 counties with an average daily LAN of 4.39 (range: 1.02-35.46) were included in the main analysis. The overall RRs per 100 nW/cm2/sr increases in daily LAN were 1.08 (95%CI: 1.05-1.11) for all-cause mortality and 1.08 (95%CI: 1.05-1.11) for natural-cause mortality. A positive association between LAN and all natural cause-specific mortality was observed, of which the strongest effect was observed on mortality caused by neuron system disease (RR = 1.32, 95%CI: 1.14-1.52). The results were robust in both younger and old, as well as in males and females. The more pronounced effect of LAN was observed in median LAN-level regions. Combined with an exposure-response curve, our study suggests a non-linear association between LAN and mortality in China. CONCLUSIONS Our study shows LAN is associated with mortality in China, particularly for neuron system disease-related mortality. These findings have important implications for public health policy establishment to minimize the health consequences of light pollution.
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Affiliation(s)
- Yao Lu
- Clinical Research Center, the Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Xicheng District, Beijing, 100050, China
| | - Jie Wang
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Yiping Yang
- Clinical Research Center, the Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Fei Li
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Hong Yuan
- Clinical Research Center, the Third Xiangya Hospital, Central South University, Changsha, 410013, China
| | - Shenxin Li
- Department of Surveying and Remote Sensing Science, School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China
| | - Zheng Long
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Xicheng District, Beijing, 100050, China
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Xicheng District, Beijing, 100050, China.
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Hu M, Wei J, Hu Y, Guo X, Li Z, Liu Y, Li S, Xue Y, Li Y, Liu M, Wang L, Liu X. Long-term effect of submicronic particulate matter (PM 1) and intermodal particulate matter (PM 1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study. ENVIRONMENTAL RESEARCH 2023; 217:114860. [PMID: 36423667 DOI: 10.1016/j.envres.2022.114860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND There is insufficient evidence of associations between incident dyslipidemia with PM1 (submicronic particulate matter) and PM1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM1 and PM1-2.5 on incident dyslipidemia respectively. METHODS We studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia. RESULTS After a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 μg/m³ uptick in four-year average concentrations of PMs (PM1 and PM1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01-1.23)] and 1.23 (95% CI: 1.06-1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM1 on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98-1.32) vs. 1.04 (95% CI: 0.89-1.21)], the elderly [1.23 (95% CI: 1.04-1.45) vs. 1.03 (95% CI: 0.91-1.17)], people with less than primary school education [1.12 (95% CI: 0.94-1.33) vs. 1.08 (95% CI: 0.94-1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00-1.36) vs. 1.06 (95% CI: 0.93-1.21)], however, the difference was not statistically significant (Z = -0.82, P = 0.413; Z = -1.66, P = 0.097; Z = 0.32, P = 0.752; Z = -0.89, P = 0.372). CONCLUSIONS Long-term exposure to PM1 and PM1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.
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Affiliation(s)
- Meiling Hu
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA.
| | - Yaoyu Hu
- 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.
| | - Zhiwei Li
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Yuhong Liu
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Shuting Li
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Yongxi Xue
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Yuan Li
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Mengmeng Liu
- School of Public Health, Capital Medical University, Beijing, 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China.
| | - Lei Wang
- Department of Food and Nutritional Hygiene, School of Public Health, Capital Medical University, China.
| | - 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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Wang Q, Li X, Zhong W, Liu H, Feng C, Song C, Yu B, Fu Y, Lin X, Yin Y, Chen T, Reinhardt JD, Yang S. Residential greenness and dyslipidemia risk: Dose-response relations and mediation through BMI and air pollution. ENVIRONMENTAL RESEARCH 2023; 217:114810. [PMID: 36395867 DOI: 10.1016/j.envres.2022.114810] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/02/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Evidence on associations of residential greenness with dyslipidemia is limited, particularly regarding dose-response relations and mediation. OBJECTIVES To investigate associations between greenness and dyslipidemia, non-linear dose-response relationships and mediators. METHODS This cross-sectional study draws on the 2018 Fujian Behavior and Disease Surveillance (FBDS) cohort that used multi-stage stratified random sampling from the general population of Fujian Province, China. Participants with one or more abnormities in total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), or low-density lipoprotein cholesterol (LDL-C) were classified as having dyslipidemia. Residential greenness was operationalized as 3-year average of the normalized difference vegetation index (NDVI500m) and enhanced vegetation index (EVI500m). A doubly robust approach was used for effect quantification. Dose-response relations were studied with natural cubic splines. Mediation via physical activity (PA), body mass index (BMI), PM2.5, PM10, SO2, and NO2 was also examined. RESULTS Data from 43,183 participants were analyzed. Increases in NDVI500m and EVI500m residential greenness were associated with decreased dyslipidemia risk and improved blood lipids. Non-linear dose response relationships were discovered. Significant reduction of dyslipidemia risk was observed at levels of EVI500m > 0.48 and NDVI500m > 0.65. Joint mediation effects of PA, BMI, PM2.5, PM10, NO2, and SO2 on the associations of NDVI500m and EVI500m with dyslipidemia risk were 49.74% and 44.64%, respectively. CONCLUSIONS Increased residential exposure to greenness was associated with decreased risk of dyslipidemia. A non-linear dose-response relationship between greenness and dyslipidemia suggests that specific thresholds of greenness need to be reached in order to achieve effects. BMI, PM2.5, and PM10 partially mediated the association.
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Affiliation(s)
- Qinjian Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaoqing Li
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Wenling Zhong
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Hongyun Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chuanteng Feng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China
| | - Chao Song
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Bin Yu
- Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China
| | - Yao Fu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xi Lin
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Yanrong Yin
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Tiehui Chen
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Jan D Reinhardt
- Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China; Department of Rehabilitation Medicine, Jiangsu Province Hospital/Nanjing Medical University First Affiliated Hospital, Nanjing, China; Swiss Paraplegic Research, Nottwil, Switzerland; Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland.
| | - Shujuan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Health Management Center, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, China; International Institute of Spatial Lifecourse Epidemiology (ISLE), Wuhan University, Wuhan, China.
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Li JM, Yang HY, Wu SH, Dharmage SC, Jalaludin B, Knibbs LD, Bloom MS, Guo Y, Morawska L, Heinrich J, Steve Hung Lam Y, Lin LZ, Zeng XW, Yang BY, Chen GB, Liu RQ, Dong GH, Hu LW. The associations of particulate matter short-term exposure and serum lipids are modified by vitamin D status: A panel study of young healthy adults. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120686. [PMID: 36400145 DOI: 10.1016/j.envpol.2022.120686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 10/26/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
Particulate matter (PM) exposure is associated to the adverse change in blood lipids. Vitamin D is beneficial to lipid metabolism, but whether vitamin D levels modifies the impact of air pollutants on lipids is unclear. The purpose of the study was to investigate if vitamin D modifies the associations of PM and serum lipids in young healthy people. From December 2017 to January 2018, a panel study with five once weekly follow-ups was conducted on 88 healthy adults aged 21.09 (1.08) (mean (SD)) years on average in Guangzhou, China. We measured serum lipids, serum 25-hydroxyvitamin D (25(OH)D) concentrations (440 blood samples in total), mass concentrations of particulate matter with diameters ≤2.5 μm (PM2.5), ≤1.0 μm (PM1.0), and ≤0.5 μm (PM0.5), and number concentrations of particulate matter with diameters ≤0.2 μm (PN0.2) and ≤0.1 μm (PN0.1) at each follow-up. Linear mixed-effect models were applied to assess the interaction of vitamin D and size-fractionated PM short-term exposure on four lipid metrics. We found the interactions between 25(OH)D and size-fractionated PM exposure on blood lipids in different lags (lag 3 days and 4 days). An interquartile range increase in PM2.5, PM1.0, PM0.5 were significantly associated with increments of 12.30%, 12.99%, and 13.66% in triglycerides (TGs) at lag 4 days at vitamin D levels <15 ng/mL group, respectively. Similar results were found for PN0.2, PN0.1 and low-density lipoprotein cholesterol (LDL-C). All the associations between size-fractionated PM and blood lipids were found null statistically significant in vitamin D levels ≥15 ng/mL group.
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Affiliation(s)
- Jia-Min Li
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Han-Yu Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Si-Han Wu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Bin Jalaludin
- Centre for Research, Evidence Management and Surveillance, South Western Sydney Local Health District, Liverpool, NSW, 2037, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia; School of Public Health and Community Medicine Sydney, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, NSW, 2006, Australia
| | - Michael S Bloom
- Department of Global and Community Health, George Mason University, Fairfax, VA, 22030, USA
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Lidia Morawska
- Queensland University of Technology, International Laboratory for Air Quality & Health, Brisbane, QLD, Australia; Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336, Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstraße 1, 80336, Munich, Germany
| | - Yim Steve Hung Lam
- Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore
| | - Li-Zi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Gong-Bo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Ru-Qing Liu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
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Liu F, Wang X, Pan M, Zhang K, Zhou F, Tong J, Chen Z, Xiang H. Exposure to air pollution and prevalence of metabolic syndrome: A nationwide study in China from 2011 to 2015. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158596. [PMID: 36089046 DOI: 10.1016/j.scitotenv.2022.158596] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/13/2022] [Accepted: 09/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Evidence concerning the influence of air pollution on metabolic syndrome (MetS) is still limited. We aimed to investigate whether sustained exposure to air pollutants are associated with increased prevalence of MetS and its individual components. METHODS We conducted a cross-sectional study comprised of 14,097 individuals participated in the first or third survey of the CHARLS. The personal cumulative (3-year averaged) exposure concentrations of nitrogen dioxide (NO2), particulate matter (PM) with a diameter of 1.0 μm or less (PM1), PM with a diameter of 10 μm or less (PM10) and PM with a diameter of 2.5 μm or less (PM2.5) were estimated using a spatiotemporal random forest model at 0.1° × 0.1° spatial resolution based on residential address of each participant provided. We utilized logistic regression models to estimate the associations of the four air pollutants with the prevalence of MetS and its individual components, and performed interaction analyses to evaluate potential effect modifications by gender, health status, age and drinking status. RESULTS Sustained exposure to air pollutants is associated with increased prevalence of MetS. For every 10 μg/m3 increase in NO2, PM1, PM10 and PM2.5, the adjusted odds ratio (OR) of MetS was 2.276 (95 % CI: 2.148, 2.412), 1.207 (95 % CI: 1.155, 1.263), 1.027 (95 % CI: 1.006, 1.048) and 1.027 (95 % CI: 0.989, 1.066), respectively. For MetS components, we observed significant associations between NO2, PM1, PM10 and central obesity, high blood pressure, elevated fasting glucose and low high-density lipoprotein cholesterol. For example, the adjusted OR of low high-density lipoprotein cholesterol for every 10 μg/m3 increase in NO2 was 1.855 (95 % CI: 1.764, 1.952). We also identified that age could significantly modified the association between NO2 and prevalence of MetS. CONCLUSIONS Chinese adults sustained exposure to higher concentrations of air pollutants are associated with increased prevalence of MetS and its components.
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Affiliation(s)
- Feifei Liu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Xiangxiang Wang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Mengnan Pan
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Ke Zhang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Feng Zhou
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Jiahui Tong
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Zhongyang Chen
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China.
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Huang QS, Zhou LX, Yang LL, Jiang YX, Xiao H, Li DW, Zhou YM, Hu YG, Li N, Li YF, Ji AL, Luo P, Cai TJ. Association between ambient carbon monoxide levels and hospitalization costs of patients with myocardial infarction: Potential effect modification by ABO blood group. ENVIRONMENTAL RESEARCH 2023; 216:114516. [PMID: 36220442 DOI: 10.1016/j.envres.2022.114516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/29/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Previous researches have reported the association between air pollution and various diseases. However, few researches have investigated whether air pollutants are associated with the economic loss resulting from patients' hospitalization, especially the economic loss of hospitalization due to acute cardiovascular events. The purpose of our research was to explore the association between the levels of carbon monoxide (CO), taken as an index of pollution, and the hospitalization costs of myocardial infarction (MI), and the potential effect modification by the ABO blood group. A total of 3237 MI inpatients were included in this study. A multiple linear regression model was used to evaluate the association between ambient CO levels and hospitalization costs of MI patients. Moreover, we performed stratified analyses by age, gender, body mass index (BMI), season, hypertension, and ABO blood types. There was a positive association between the levels of CO in the air and the costs of hospitalization caused by MI. Furthermore, such association was stronger in males, BMI ≥25, <65 years, with hypertension, and non-O blood group. Interestingly, we found the association was particularly significant in patients with blood group B. Overall, our study first found that ambient CO levels could have an impact on the hospitalization costs for MI patients, and those with blood group B can be more sensitive.
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Affiliation(s)
- Qing-Song Huang
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Lai-Xin Zhou
- Medical Department, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Li-Li Yang
- Department of Information, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China
| | - Yue-Xu Jiang
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Hua Xiao
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Da-Wei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yu-Meng Zhou
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yue-Gu Hu
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Na Li
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ya-Fei Li
- Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Ai-Ling Ji
- Department of Preventive Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, China
| | - Peng Luo
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China.
| | - Tong-Jian Cai
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China; Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
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Li H, Ge M, Pei Z, He J, Wang C. Associations of environmental factors with total cholesterol level of middle-aged and elderly people in China. BMC Public Health 2022; 22:2423. [PMID: 36564736 PMCID: PMC9783789 DOI: 10.1186/s12889-022-14922-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Dyslipidemia is a key factor causing cardio cerebrovascular diseases, and the total cholesterol (TC) is an important lipid indicator among them. Studies have shown that environmental factors have a strong association with TC levels. Previous studies only focused on the seasonal variation of TC level and the short-term effects of some environmental factors on TC level over time, and few studies explored the geographical distribution of TC level and quantified the impact of environmental factors in space. METHODS Based on blood test data which was from China Health and Retirement Longitudinal Study (Charls) database, this study selected the TC level test data of middle-aged and elderly people in China in 2011 and 2015, and collected data from 665 meteorological stations and 1496 air pollutant monitoring stations in China. After pretreatment, the spatial distribution map of TC level was prepared and the regional statistics were made. GeoDetector and geographically weighted regression (GWR) were used to measure the relationship between environmental factors and TC level. RESULTS The TC level of middle-aged and elderly in China was higher in females than in males, and higher in urban areas than in rural areas, showing a clustered distribution. The high values were mainly in South China, Southwest China and North China. Temperature, humidity, PM10 and PM2.5 were significant environmental factors affecting TC level of middle-aged and elderly people. The impact of pollutants was more severe in northern China, and TC level in southern China was mainly affected by meteorological factors. CONCLUSIONS There were gender and urban-rural differences in TC levels among the middle-aged and elderly population in China, showing aggregation in geographical distribution. Meteorological factors and air pollutants may be very important control factors, and their influencing mechanism needs further study.
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Affiliation(s)
- Hao Li
- grid.412498.20000 0004 1759 8395Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang’an Street, Chang’an District, Xi’an, 710119 China
| | - Miao Ge
- grid.412498.20000 0004 1759 8395Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang’an Street, Chang’an District, Xi’an, 710119 China
| | - Zehua Pei
- grid.412498.20000 0004 1759 8395Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang’an Street, Chang’an District, Xi’an, 710119 China
| | - Jinwei He
- grid.440747.40000 0001 0473 0092Medical School, Yan’an University, 580 Shengdi Road, Yan’an, 716000 China
| | - Congxia Wang
- grid.43169.390000 0001 0599 1243Department of Cardiology, the Second Affiliated Hospital of Medical College, Xi’an Jiaotong University, No. 157, Xiwu Road, Xi’an, 710004 China
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Zuo H, Zheng T, Wu K, Yang T, Wang L, Nima Q, Bai H, Dong K, Fan Z, Huang S, Luo R, Wu J, Zhou J, Xu H, Zhang Y, Feng S, Zeng P, Xiao X, Guo B, Wei Y, Pei X, Zhao X. High-altitude exposure decreases bone mineral density and its relationship with gut microbiota: Results from the China multi-ethnic cohort (CMEC) study. ENVIRONMENTAL RESEARCH 2022; 215:114206. [PMID: 36058270 DOI: 10.1016/j.envres.2022.114206] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Geographic altitude is a potent environmental factor for human microbiota and bone mineral density. However, little evidence exists in population-based studies with altitude diversity ranges across more than 3000 m. This study assessed the associations between a wide range of altitudes and bone mineral density, as well as the potential mediating role of microbiota in this relationship. METHODS A total of 99,556 participants from the China Multi-Ethnic Cohort (CMEC) study were enrolled. The altitude of each participant was extracted from global Shuttle Radar Topography Mission (SRTM) 4 data. Bone mineral density was measured by calcaneus quantitative ultrasound index (QUI). Stool samples were collected for 16S rRNA gene sequencing (n = 1384). The metabolites of gut microbiota, seven kinds of short-chain fatty acids (SCFAs), were detected by gas chromatography-mass spectrometry (GC-MS, n = 128). After screening, 73,974 participants were selected for the "altitude-QUI" analysis and they were placed into the low-altitude (LA) and high-altitude (HA) groups. Additionally, a subgroup (n = 1384) was further selected for the "altitude-microbiota-QUI" analysis. Multivariate linear regression models and mediation analyses were conducted among participants. RESULTS A significant negative association between high-altitude and QUI was obtained (mean difference = -0.373 standard deviation [SD], 95% confidence interval [CI]: -0.389, -0.358, n = 73,974). The same negative association was also observed in the population with microbiota data (mean difference = -0.185 SD, 95%CI: -0.360, -0.010, n = 1384), and a significant mediating effect of Catenibacteriumon on the association between altitude and QUI (proportion mediated = 25.2%, P = 0.038) was also noticed. Additionally, the acetic acid, butyric acid, and total amount of seven SCFAs of the low-altitude group were significantly higher than that of the high-altitude group (P < 0.05). CONCLUSION High-altitude exposure may decrease bone mineral density in adults, thus increasing the risk of osteoporosis. The modulation of gut microbiota may be a potential strategy for alleviating the decrease of bone mineral density.
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Affiliation(s)
- Haojiang Zuo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Tianli Zheng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Kunpeng Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Tingting Yang
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China.
| | - Lingyao Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention, Lhasa City, Tibet Autonomous Region, 850000, China.
| | - Hua Bai
- College of Public Health, Kunming Medical University, Kunming, 650500, China.
| | - Ke Dong
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Ziwei Fan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Shourui Huang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Ruocheng Luo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Jialong Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Junmin Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Yingcong Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Shiyu Feng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Peibin Zeng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Xiong Xiao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
| | - Yonglan Wei
- Chengdu Center for Disease Control and Prevention, Chengdu, Sichuan, 610041, China.
| | - Xiaofang Pei
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China; Food Safety Monitoring and Risk Assessment Key Laboratory of Sichuan Province, 610041, Chengdu, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, 610041, Chengdu, China.
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Sun J, Peng S, Li Z, Liu F, Wu C, Lu Y, Xiang H. Association of Short-Term Exposure to PM 2.5 with Blood Lipids and the Modification Effects of Insulin Resistance: A Panel Study in Wuhan. TOXICS 2022; 10:663. [PMID: 36355954 PMCID: PMC9698404 DOI: 10.3390/toxics10110663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Results of previous studies about the acute effects of fine particulate matter (PM2.5) on blood lipids were inconsistent. This study aimed to quantify the short-term effects of PM2.5 on blood lipids and estimate the modifying role of insulin resistance, reflected by the homeostasis model assessment of insulin resistance (HOMA-IR). From September 2019 to January 2020, the study recruited 70 healthy adults from Wuhan University for a total of eight repeated data collections. At each visit, three consecutive days were monitored for personal exposure to PM2.5, and then a physical examination was carried out on the fourth day. The linear mixed-effect models were operated to investigate the impact of PM2.5 over diverse exposure windows on blood lipids. With the median of the HOMA-IR 1.820 as the cut-off point, participants were assigned to two groups for the interaction analyses. We found the overall mean level (standard deviation, SD) of PM2.5 was 38.34 (18.33) μg/m3. Additionally, with a 10 μg/m3 rise in PM2.5, the corresponding largest responses in triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), as well as high-density lipoprotein cholesterol (HDL-C), were −0.91% (95% confidence interval (CI): −1.63%, −0.18%), −0.33% (95% CI: −0.64%, −0.01%,), −0.94% (95% CI: −1.53%, −0.35%), and 0.67% (95% CI: 0.32%, 1.02%), respectively. The interaction analyses revealed that a significantly greater reduction in the four lipids corresponded to PM2.5 exposure when in the group with the lower HOMA-IR (<1.820). In conclusion, short-term PM2.5 exposure over specific time windows among healthy adults was associated with reduced TG, TC, as well as LDL-C levels, and elevated HDL-C. Additionally, the association of PM2.5−lipids may be modulated by insulin resistance.
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Affiliation(s)
- Jinhui Sun
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Shouxin Peng
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Zhaoyuan Li
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Feifei Liu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Chuangxin Wu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Yuanan Lu
- Environmental Health Laboratory, Department of Public Health Sciences, University of Hawaii at Manoa, 1960 East West Rd., Biomed Bldg D105, Honolulu, HI 96822, USA
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
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Duan JW, Li YL, Li SX, Yang YP, Li F, Li Y, Wang J, Deng PZ, Wu JJ, Wang W, Meng CJ, Miao RJ, Chen ZH, Zou B, Yuan H, Cai JJ, Lu Y. Association of Long-term Ambient Fine Particulate Matter (PM 2.5) and Incident CKD: A Prospective Cohort Study in China. Am J Kidney Dis 2022; 80:638-647.e1. [PMID: 35469967 DOI: 10.1053/j.ajkd.2022.03.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 03/05/2022] [Indexed: 02/02/2023]
Abstract
RATIONALE & OBJECTIVE Increasing evidence has linked ambient fine particulate matter (ie, particulate matter no larger than 2.5 μm [PM2.5]) to chronic kidney disease (CKD), but their association has not been fully elucidated, especially in regions with high levels of PM2.5 pollution. This study aimed to investigate the long-term association of high PM2.5 exposure with incident CKD in mainland China. STUDY DESIGN Prospective cohort study. SETTING & PARTICIPANTS 72,425 participants (age ≥18 years) without CKD were recruited from 121 counties in Hunan Province, China. EXPOSURE Annual mean PM2.5 concentration at the residence of each participant derived from a long-term, full-coverage, high-resolution (1 × 1 km2), high-quality dataset of ground-level air pollutants in China. OUTCOMES Incident CKD during the interval between the baseline examination of each participant (2005-2017) and the end of follow-up through 2018. ANALYTICAL APPROACH Cox proportional hazards models were used to estimate the independent association of PM2.5 with incident CKD and the joint association of PM2.5 with temperature or humidity on the development of PM2.5-related CKD. Restricted cubic splines were used to model exposure-response relationships. RESULTS Over a median follow-up of 3.79 (IQR, 2.03-5.48) years, a total of 2,188 participants with incident CKD were identified. PM2.5 exposure was associated with incident CKD with an adjusted hazard ratio of 1.71 (95% CI, 1.58-1.85) per 10-μg/m3 greater long-term exposure. Multiplicative interactions between PM2.5 and humidity or temperature on incident CKD were detected (all P < 0.001 for interaction), whereas an additive interaction was detected only for humidity (relative risk due to interaction, 3.59 [95% CI, 0.97-6.21]). LIMITATIONS Lack of information on participants' activity patterns such as time spent outdoors. CONCLUSIONS Greater long-term ambient PM2.5 pollution is associated with incident CKD in environments with high PM2.5 exposure. Ambient humidity has a potentially synergetic effect on the association of PM2.5 with the development of CKD. PLAIN-LANGUAGE SUMMARY Exposure to a form of air pollution known as fine particulate matter (ie, particulate matter ≤2.5 μm [PM2.5]) has been linked to an increased risk of chronic kidney disease (CKD), but little is known about how PM2.5 affects CKD in regions with extremely high levels of PM2.5 pollution. This longitudinal cohort study in China investigates the effect of PM2.5 on the incidence of CKD and whether temperature or humidity interact with PM2.5. Our findings suggest that long-term exposure to high levels of ambient PM2.5 significantly increased the risk of CKD in mainland China, especially in terms of cumulative average PM2.5. The associations of PM2.5 and incident CKD were greater in high-humidity environments. These findings support the recommendation that reducing PM2.5 pollution should be a priority to decrease the burden of associated health risks, including CKD.
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Affiliation(s)
- Jing-Wen Duan
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Ya-Lan Li
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Shen-Xin Li
- Department of Surveying and Remote Sensing Science, School of Geosciences and Info-physics, Central South University, Changsha, China
| | - Yi-Ping Yang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Fei Li
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yan Li
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jie Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Pei-Zhi Deng
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jing-Jing Wu
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Wei Wang
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Chang-Jiang Meng
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Ru-Jia Miao
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zhi-Heng Chen
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Bin Zou
- Department of Surveying and Remote Sensing Science, School of Geosciences and Info-physics, Central South University, Changsha, China
| | - Hong Yuan
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Jing-Jing Cai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Yao Lu
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center, The Third Xiangya Hospital, Central South University, Changsha, China; School of Life Course Sciences, King's College London, London, United Kingdom.
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Chen Z, Liu P, Xia X, Wang L, Li X. The underlying mechanism of PM2.5-induced ischemic stroke. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119827. [PMID: 35917837 DOI: 10.1016/j.envpol.2022.119827] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/04/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Under the background of global industrialization, PM2.5 has become the fourth-leading risk factor for ischemic stroke worldwide, according to the 2019 GBD estimates. This highlights the hazards of PM2.5 for ischemic stroke, but unfortunately, PM2.5 has not received the attention that matches its harmfulness. This article is the first to systematically describe the molecular biological mechanism of PM2.5-induced ischemic stroke, and also propose potential therapeutic and intervention strategies. We highlight the effect of PM2.5 on traditional cerebrovascular risk factors (hypertension, hyperglycemia, dyslipidemia, atrial fibrillation), which were easily overlooked in previous studies. Additionally, the effects of PM2.5 on platelet parameters, megakaryocytes activation, platelet methylation, and PM2.5-induced oxidative stress, local RAS activation, and miRNA alterations in endothelial cells have also been described. Finally, PM2.5-induced ischemic brain pathological injury and microglia-dominated neuroinflammation are discussed. Our ultimate goal is to raise the public awareness of the harm of PM2.5 to ischemic stroke, and to provide a certain level of health guidance for stroke-susceptible populations, as well as point out some interesting ideas and directions for future clinical and basic research.
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Affiliation(s)
- Zhuangzhuang Chen
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Peilin Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China.
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Li H, Ge M, Pei Z, He J, Wang C. Nonlinear associations between environmental factors and lipid levels in middle-aged and elderly population in China: A national cross-sectional study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155962. [PMID: 35588809 DOI: 10.1016/j.scitotenv.2022.155962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Blood lipid is an important factor affecting cardiovascular disease in middle-aged and elderly people. At present, the associations between environmental factors and blood lipid level in elderly people has been controversial, and the nonlinear effect of their relationship is lack of research. METHODS This study used data from a national cross-sectional survey of blood lipid levels in 13,354 subjects and data from environmental monitoring sites. Logistic regression was used to measure the relationship between the basic characteristics of the study population and blood lipid levels. After controlling the confounding factors, the nonlinear associations between environmental factors and blood lipid levels of middle-aged and elderly people in different geographical regions were studied by random forest model. RESULTS The risk of dyslipidemia is significantly higher in middle-aged women, obese people, elderly people, and urban people. Smoking and alcohol consumption increase the risk. The associations between environmental factors and lipid levels of middle-aged and elderly people are nonlinear, the correlation effect between air pollutants and blood lipid level is mainly shown in northern China, and the correlation between meteorological factors and blood lipid level is more obvious in southern China. CONCLUSIONS This study shows that the associations between environmental factors and lipid levels in middle-aged and elderly population are nonlinear and have regional differences. Therefore it should be considered in optimizing the allocation of public health resources and preventing and controlling environmental exposure of middle-aged and elderly population.
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Affiliation(s)
- Hao Li
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China
| | - Miao Ge
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China.
| | - Zehua Pei
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China
| | - Jinwei He
- Medical School, Yan'an University, 580 Shengdi Road, Yan'an 716000, China
| | - Congxia Wang
- Department of Cardiology, the Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, No. 157, Xiwu Road, Xi'an 710004, China
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Lei R, Wang Z, Wang X, Tian H, Wang B, Xue B, Xiao Y, Hu J, Zhang K. Effects of long-term exposure to PM 2.5 and chemical constituents on blood lipids in an essential hypertensive population: A multi-city study in China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 242:113867. [PMID: 35839530 DOI: 10.1016/j.ecoenv.2022.113867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Previous studies on the effects of fine particulate matter (PM2.5) and chemical constituents on lipid disorder among hypertension populations, particularly in China, are very limited. We aimed to examine the effects of long-term exposure to PM2.5 and chemical constituents on dyslipidemias in China. Finally, we included 34,841 participants with essential hypertension from 19 regions in China during 2010-2011. Data were modeled using the generalized additive mixed model. We found that PM2.5 and chemical constituents exposure were positively associated with the increased risk of dyslipidemias and increased levels of total cholesterol (TC) and triglyceride (TG). The odds ratio for hypercholesterolemia was 1.356 [95% confidence interval (CI): 1.246, 1.477] for PM2.5, and the strongest association with PM2.5 constituents was found for nitrate. Each 10 μg/m3 increase in PM2.5 showed a significant increase of TC by 2.60% (95% CI: 2.03, 3.17) and TG by 2.91% (95% CI: 1.60, 4.24), respectively. Meanwhile, an interquartile range increase in nitrate, ammonium and organic matter had stronger associations with TC and TG parameters than black carbon, sulfate, and mineral dust. Our findings may contribute to a better understanding of the chronic effects of PM2.5 and chemical constituents on lipid disorder in an essential hypertensive population.
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Affiliation(s)
- Ruoyi Lei
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Zengwu Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Xin Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Bo Wang
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Baode Xue
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Ya Xiao
- School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Jihong Hu
- School of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu 730000, China.
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, USA.
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Wang L, Chen G, Hou J, Wei D, Liu P, Nie L, Fan K, Wang J, Xu Q, Song Y, Wang M, Huo W, Jing T, Li W, Guo Y, Wang C, Mao Z. Ambient ozone exposure combined with residential greenness in relation to serum sex hormone levels in Chinese rural adults. ENVIRONMENTAL RESEARCH 2022; 210:112845. [PMID: 35134378 DOI: 10.1016/j.envres.2022.112845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/18/2022] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Long-term exposure to ambient ozone (O3) and residential greenness independently relate to altered hormones levels in urban settings and developed countries. However, independent and their joint associations with progestogen and androgen were sparsely studied in rural regions. MATERIALS AND METHODS A total of 6211 individuals were recruited in this study. Random forest model was applied to predict the daily average concentrations of O3 using the satellites data. Residential greenness was reflected by the normalized difference vegetation index (NDVI). Liquid chromatography-tandem mass spectrometry was used to measure serum progestogen and androgen concentrations. Gender and menopausal status modified associations of long-term exposure to O3 and residential greenness with hormones levels were analyzed by generalized linear models. RESULTS Long-term exposure to O3 was negatively related to 17-hydroxyprogesterone, testosterone, and androstenedione in both men and women (premenopausal and postmenopausal); the estimated β and 95% CI of ln-progesterone in response to per 10 μg/m3 increment in O3 concentration was -0.560 (-0.965, -0.155) in postmenopausal women. Association of long-term exposure to O3 with serum androgen levels in premenopausal and postmenopausal women were alleviated by residing in places with higher greenness. Additionally, a prominent effect of long-term exposure to O3 related to decreased serum progestogen and androgen levels was found in participants with middle- or high-level of physical activity or lower education level. CONCLUSIONS The results suggested that long-term exposure to high levels of O3 related to decreased serum androgen levels was attenuated by living in high greenness places in women regardless of menopause status. Future studies are needed to confirm the positive health effects of residential greenness on the potential detrimental effects due to exposure to O3.
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Affiliation(s)
- Lulu Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Gongbo Chen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, PR China
| | - Jian Hou
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Dandan Wei
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Pengling Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Luting Nie
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Keliang Fan
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Juan Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Qingqing Xu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yu Song
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Mian Wang
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Wenqian Huo
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Tao Jing
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Wenjie Li
- Department of Nutrition and Food Hygiene, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Zhenxing Mao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
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47
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Yan L, Pang Y, Wang Z, Luo H, Han Y, Ma S, Li L, Yuan J, Niu Y, Zhang R. Abnormal fasting blood glucose enhances the risk of long-term exposure to air pollution on dyslipidemia: A cross-sectional study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 237:113537. [PMID: 35468441 DOI: 10.1016/j.ecoenv.2022.113537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/16/2022] [Indexed: 06/14/2023]
Abstract
Both long-term exposure to air pollution and abnormal fasting blood glucose (FBG) are linked to dyslipidemia prevalence. However, the joint role of air pollution and FBG on dyslipidemia remains unknown clearly. In this study, we aimed to test whether abnormal FBG could enhance the risks of long-term exposure to air pollutants on dyslipidemia in general Chinese adult population. The present study recruited 8917 participants from 4 cities in Hebei province, China. Participants' individual exposure to air pollutants was evaluated by the Empirical Bayesian Kriging statistical model in ArcGIS10.2 geographic information system. Dyslipidemia was defined according to Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults. Subjects were grouped into normal, prediabetes, diabetes according to FBG level. Generalized linear models were applied to analyze the interaction of air pollutants and FBG on dyslipidemia prevalence. The prevalence of dyslipidemia was 43.83% in our investigation. After adjusting all covariates, we found the risk of four air pollutants (PM2.5, PM10, NO2, SO2) on dyslipidemia prevalence was stronger as higher FBG level, and the adjusted odd ratio of interaction (ORinter (95% CI)) between PM2.5, PM10, NO2, SO2 and FBG levels on dyslipidemia was 1.171 (1.162, 1.189), 1.119 (1.111, 1.127), 1.124 (1.115, 1.130), 1.107 (1.098, 1.115), respectively. Stratified analyses indicated the modifying effects of FBG on the association of air pollution with dyslipidemia were stronger among male, less than 65 years old, overweight/obesity (all Pinter<0.1). Our study concluded that high FBG levels strengthened the risk of long-term exposure to air pollution on dyslipidemia, especially more noticeable in male, less than 65 years old, overweight.
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Affiliation(s)
- Lina Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China
| | - Yaxian Pang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China
| | - Zhikun Wang
- Office of Academic Affairs, The First Affiliated Hospital of Hebei College of Traditional Chinese Medicine, Shijiazhuang 050017, PR China
| | - Haixia Luo
- Department of Cardiology, Shijiazhuang No.1 Hospital, Shijiazhuang 050011, PR China
| | - Yuquan Han
- Emergency Department, People's Hospital of Qingdao West Coast New Area, Shandong 266400, PR China
| | - Shitao Ma
- Department of Hospital Infection Control, The People's Hospital of Luanzhou, Luanzhou 063700, PR China
| | - Lipeng Li
- Department of Reproductive Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, PR China
| | - Jing Yuan
- Department of Biostatistics,Clinical Development Division of CSPC, Shijiazhuang 050035, PR China
| | - Yujie Niu
- Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China; Department occupational Health and Environmental Health, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China.
| | - Rong Zhang
- Department of Toxicology, School of Public Health, Hebei Medical University, Shijiazhuang 050017, PR China; Hebei Key Laboratory of Environment and Human Health, Shijiazhuang 050017, PR China.
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Wu H, Lu Z, Wei J, Zhang B, Liu X, Zhao M, Liu W, Guo X, Xi B. Effects of the COVID-19 Lockdown on Air Pollutant Levels and Associated Reductions in Ischemic Stroke Incidence in Shandong Province, China. Front Public Health 2022; 10:876615. [PMID: 35719628 PMCID: PMC9197688 DOI: 10.3389/fpubh.2022.876615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 04/19/2022] [Indexed: 12/04/2022] Open
Abstract
Background Local governments in China took restrictive measures after the outbreak of COVID-19 to control its spread, which unintentionally resulted in reduced anthropogenic emission sources of air pollutants. In this study, we intended to examine the effects of the COVID-19 lockdown policy on the concentration levels of particulate matter with aerodynamic diameters of ≤1 μm (PM1), ≤2.5 μm (PM2.5), and ≤10 μm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO) and the potential subsequent reductions in the incidence of ischemic and hemorrhagic stroke in Shandong Province, China. Methods A difference-in-difference model combining the daily incidence data for ischemic and hemorrhagic stroke and air pollutant data in 126 counties was used to estimate the effect of the COVID-19 lockdown on the air pollutant levels and ischemic and hemorrhagic stroke incident counts. The avoided ischemic stroke cases related to the changes in air pollutant exposure levels were further estimated using concentration-response functions from previous studies. Results The PM1, PM2.5, PM10, NO2, and CO levels significantly decreased by −30.2, −20.9, −13.5, −46.3, and −13.1%, respectively. The O3 level increased by 11.5% during the lockdown compared with that in the counterfactual lockdown phase of the past 2 years. There was a significant reduction in population-weighted ischemic stroke cases (−15,315, 95% confidence interval [CI]: −27,689, −2,942), representing a reduction of 27.6% (95% CI: −49.9%, −5.3%). The change in the number of hemorrhagic stroke cases was not statistically significant. The total avoided PM1-, PM2.5-, PM10-, NO2-, and CO–related ischemic stroke cases were 739 (95% CI: 641, 833), 509 (95% CI: 440, 575), 355 (95% CI: 304, 405), 1,132 (95% CI: 1,024, 1,240), and 289 (95% CI: 236, 340), respectively. Conclusion The COVID-19 lockdown indirectly reduced the concentration levels of PM1, PM2.5, PM10, NO2, and CO and subsequently reduced the associated ischemic stroke incidence. The health benefits due to the lockdown are temporary, and long-term measures should be implemented to increase air quality and related health benefits in the post-COVID-19 period.
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Affiliation(s)
- Han Wu
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zilong Lu
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States
| | - Bingyin Zhang
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, China
| | - Xue Liu
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Min Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wenhui Liu
- Information and Data Analysis Lab, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiaolei Guo
- Shandong Center for Disease Control and Prevention, and Academy of Preventive Medicine, Shandong University, Jinan, China
- Xiaolei Guo
| | - Bo Xi
- Department of Epidemiology, School of Public Health, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Bo Xi
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Zhang Z, Su Y, Jing R, Qi J, Qi X, Xie Z, Cui B. Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:37919-37929. [PMID: 35072876 DOI: 10.1007/s11356-021-18400-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
High levels of ambient fine particulate matter (PM2.5) might increase the risk of death due to cardiovascular diseases (CVDs). As a critical risk factor for CVDs, dyslipidemia can cause CVDs or exacerbate pre-existing ones. This study aimed to investigate whether a short-time exposure to PM2.5 leads to dyslipidemia (HyperTC, HyperLDL-C, HyperTG and HypoHDL-C) in adults. The serum lipid data were provided by the Sichuan Provincial People's Hospital Medical Examination Center. We included 309,654 subjects aged 18-79 between May 10, 2015, and May 10, 2017. An advanced distributed lag nonlinear model (DLNM) was applied to investigate the acute and lag effects of ambient PM2.5 on the risk of dyslipidemia. This study was also stratified by sex, age, BMI and season to examine potential effect modification. We observed that the associations between an interquartile increase in PM2.5 (43 μg/m3) and dyslipidemia were [relative risk (RR); 95% confidence interval (CI)]: 1.042 (1.013, 1.071) for HyperLDL-C and 1.027 (1.006, 1.049) for HyperTC at lag0 day. The lag effects were found at lag6 day for HyperLDL-C, in lag4-6 days for HyperTC and lag4-7 days for HyperTG. Short-term exposure to ambient PM2.5 was related to dyslipidemia and the effect modification was observed in the subgroup analysis. The female and normal-weight populations were more susceptible to the risks of PM2.5 on HyperLDL-C and HyperTC.
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Affiliation(s)
- Zizheng Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Su
- Clinical Laboratory, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Renjie Jing
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Qi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohui Qi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Xie
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
- Department of Dermatology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Cui
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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50
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Kim KN, Ha B, Seog W, Hwang IU. Long-term exposure to air pollution and the blood lipid levels of healthy young men. ENVIRONMENT INTERNATIONAL 2022; 161:107119. [PMID: 35123376 DOI: 10.1016/j.envint.2022.107119] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/11/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND There is insufficient evidence of an association between long-term exposure to air pollution and changes in blood lipid levels, and assessments may be influenced by residual confounding factors, such as socioeconomic status. OBJECTIVES To investigate the associations between long-term exposure to air pollution and blood lipid profiles while controlling for the risk of residual confounding factors. METHODS We conducted a study involving conscripted Korean soldiers to assess the associations between air pollution and blood lipid levels. The soldiers, who were randomly distributed among military units throughout the country, led homogenous lives and were subjected to health checkups 8-12 months post-enlistment. We analyzed data pertaining to those who enlisted and underwent health checkups in 2019 (n = 12,778) using linear mixed models. Additionally, we evaluated quantile-specific associations using quantile regression models. We also assessed interactions based on body mass index (BMI) at the time of enlistment (≥25.0 vs. < 25.0 kg/m2). RESULTS The linear mixed models revealed that a 10-µg/m3 increase in fine particulate matter ≤ 2.5 μm (PM2.5) decreased high-density lipoprotein cholesterol (HDL-C) levels by -0.66% (95% confidence interval [CI]: -1.21, -0.10), and a 10-ppb increase in nitrogen dioxide (NO2) increased total cholesterol (TC) levels by 1.04% (95% CI: 0.24, 1.84). In the quantile regression models, associations were also found at specific deciles. PM2.5 exposure contributed to higher TC, NO2 resulted in higher triglycerides and lower HDL-C, and ozone (O3) led to lower HDL-C. The association between O3 and TC differed according to BMI (p-value for interaction = 0.03); among those with a BMI ≥ 25.0 kg/m2, a 10-ppb increase in O3 increased TC by 1.09% (95% CI: 0.20, 1.09). DISCUSSION These results shed new light on the importance of controlling air pollution, which can contribute to abnormal blood lipid levels, an independent risk factor for cardiovascular disease.
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Affiliation(s)
- Kyoung-Nam Kim
- Department of Preventive Medicine and Public Health, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Beomman Ha
- The Republic of Korea Army Headquarter, Kyeryong, Republic of Korea
| | - Woong Seog
- The Armed Forces Capital Hospital, Seongnam, Republic of Korea
| | - Il-Ung Hwang
- Division of Public Health and Medical Care, Seoul National University Hospital, Seoul, Republic of Korea.
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